Download three essays on financial intermediation and growth

THREE ESSAYS ON FINANCIAL INTERMEDIATION
AND GROWTH
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree of the Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Ranajoy Ray Chaudhuri, M.A.
*****
Graduate Program in Economics
The Ohio State University
2012
Dissertation Committee:
Professor Paul Evans, Advisor
Professor Huston McCulloch
Professor Robert de Jong
© Copyright by
Ranajoy Ray Chaudhuri
2012
Abstract
My dissertation explores the impact of financial development, as well as regulatory
changes in the financial sector, on economic growth. Recent literature on growth has often
focused on the importance of financial intermediation and institutional quality. Advocates of
financial development say that the development of the banking sector and stock markets increase
the financing available to firms, raising productivity. The “institutions hypothesis” proponents
suggest that institutions jointly determine the growth rate and the policy choice, while policies
themselves bear no causal connection to growth. Such hypothesis is difficult to test empirically
because the change in institutional quality is, with a few historic exceptions, very slow. For the
most part, therefore, a country’s economic performance can end up being attributed to a random
cause. Using a cross-country data set and numerous financial indicators, institutional quality
variables and growth measures, I find that this is not true of financial development. Financial
variables have a significant effect on growth that is distinct from that of institutions like private
property and rule of law.
I also consider this issue in the context of the fifty U.S. states. States differ with respect
to financial indicators like the number of banks, assets, equity, loans and deposits.
They also
vary in terms of their regulatory environments. States like Delaware, Texas and Nevada have
very high scores for economic freedom; Mississippi, New Mexico and West Virginia have very
low ones. The results again underscore the importance of financial deepening in order to achieve
economic growth.
Taking up from this point, the final essay studies the impact of U.S. banking deregulation
on growth. Many states relaxed restrictions on intra-state bank branching beginning in the early
1960s, both by allowing bank holding companies to convert subsidiaries into branches and by
permitting statewide de novo branching. This increased competition in the banking sector forced
banks to become more efficient. The existing literature suggests that one of the channels through
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which this worked was bank lending. Different industries have varying degrees of dependence on
external financing, and industries that have greater dependence should grow faster in the postderegulation period. Using a panel data set, I find this not to be the case for the U.S.; industries
that borrow less from banks actually grew at a faster rate after deregulation. This could reflect
commercial banks losing market share to other sources of external financing, the general decline
in the U.S. manufacturing sector and the terms of trade moving in favor of agriculture. I also
consider the effect of deregulation on various banking indicators and find the strongest impact to
be on the number of commercial banks operating in the state. Contrary to existing research, these
regulatory changes slowed down growth in the number of bank branches and offices, as well as
other measures of bank performance like assets, equity, loans and deposits. This suggests that the
gains from deregulation are short-lived, and also indicate unprofitable smaller banks shuttering
their operations and the emergence of credit unions and other alternatives to commercial banks.
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Acknowledgements
This dissertation would not have been complete without the intellectual support of my
advisor Professor Paul Evans. His patience and encouragement helped me overcome many
obstacles during my life as a graduate student, for which I am deeply indebted. I am also grateful
to Professor Huston McCulloch, Professor Robert de Jong and Professor Joseph Kaboski for their
time, advice and extremely valuable comments on my drafts.
My papers are much improved from the conversations I had with Professor Pok-Sang
Lam, Professor William Dupor, Professor Masao Ogaki and participants at The Ohio State
University Macroeconomics Luncheons and Cleveland State University Social Science Group
Meetings. I want to express my gratitude to Ross Levine and Sara Zervos for their help with
obtaining some of the data used in my work.
Additionally, I would like to especially thank Professor Hajime Miyazaki, Professor
Lucia Dunn and Professor Donald Haurin for their invaluable help and warm words of
encouragement during our interactions these past years. I would not be here today if it wasn’t for
the personal support of Professor Miyazaki.
John-David Slaughter and Yong Yu came to my rescue countless times when I had office
computing issues. Talking to Jo Ducey, Richard Corley, Ana Ramirez, Michelle Chapman, Janet
Myers and Christine Hill provided a welcome break from work. Paul Poast taught me much of
what I know about being an effective teacher, and I consider myself lucky for having had that
opportunity during my time at Ohio State.
My family has been a constant source of encouragement during my years of working on
this dissertation. My amazingly supportive partner Stephen has always been by my side, and my
mother, sister, brother-in-law, nieces and Stephen’s family all helped me tremendously in their
own ways. Friends I should thank are too numerous to list here, but the ones who stand out are
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Mauricio Ramirez Grajeda, Subhra Saha and Ranjan Shrestha. My graduate life would have been
very different and much less memorable without them in it.
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Vita
November 12, 1975 ………………………………… Born – Calcutta, India
1997 ………………………………………………… B.A. Economics,
Jadavpur University, India
1999 ………………………………………………… M.A. International Trade & Development,
Jawaharlal Nehru University, India
2002 ………………………………………………… M.A. Economics,
The Ohio State University, U.S.A.
2002 - present ………………………………………. Graduate Teaching Associate &
Graduate Research Associate,
The Ohio State University, U.S.A.
Instructor,
St. Mary’s College of Maryland, U.S.A.
Instructor,
Princeton University, U.S.A.
Field of Study
Major Field: Economics
vi
Table of Contents
Abstract ………………………………………………………………………………………
ii
Acknowledgements …………………………………………………………………………..
iv
Vita …………………………………………………………………………………………...
vi
List of Tables …………………………………………………………………………………
xi
List of Figures ………………………………………………………………………………..
xiii
Chapters:
1. INTRODUCTION ………………………………………………………………………..
1
1.1 Growth Theories ……………………………………………………………………..
1
1.2 The Empirics of Growth ……………………………………………………………..
2
1.3 The Integration Hypothesis …………………………………………………………..
3
1.4 The Geography Hypothesis …………………………………………………………..
6
1.5 The Institutions Hypothesis …………………………………………………………..
9
1.6 The Financial Hypothesis …………………………………………………………….
12
1.7 My Area of Focus …………………………………………………………………….
14
2. FINANCIAL VERSUS INSTITUTIONAL DEVELOPMENT: IMPACT ON ECONOMIC
GROWTH …………………………………………………………………………………
15
2.1 Introduction …………………………………………………………………………...
15
2.1.1 Literature on Financial Intermediation and Growth ……………………………
15
2.1.2 Literature on Institutional Quality and Growth ………………………………...
19
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2.2 Data and Variables Used ……………………………………………………………... 23
2.2.1 Data Sources …………………………………………………………………..
23
2.2.2 Variables ………………………………………………………………………
23
2.3 Descriptive Statistics …………………………………………………………………
26
2.4 Methodology and Results ………………………………………………………........
28
2.4.1 Results from the Benchmark Model …………………………………………..
28
2.4.2 2SLS Results …………………………………………………………………..
35
2.5 Conclusion ……………………………………………………………………………
38
3. DOES ECONOMIC FREEDOM MEAN FREEDOM TO GROW? ……………………..
40
3.1 Introduction …………………………………………………………………………..
40
3.1.1 Literature on Economic Growth of the U.S. States …………………………..
40
3.1.2 Literature on Institutional Quality of the U.S. States …………………………
46
3.2 Data and Variables Used ……………………………………………………………..
52
3.2.1 Data Sources …………………………………………………………………..
52
3.2.2 Variables Used ………………………………………………………………...
53
3.3 Methodology and Results ……………………………………………………………..
56
3.3.1 Results from the Benchmark Model …………………………………………..
56
3.3.2 Principal Component Analysis ………………………………………………..
62
3.3.3 2SLS Results ………………………………………………………………….
63
3.4 Conclusion ……………………………………………………………………………
65
4. HOW BANKING DEREGULATION AFFECTS GROWTH: EVIDENCE FROM A PANEL
OF U.S. STATES …………………………………………………………………………
67
4.1 Introduction …………………………………………………………………………..
67
4.1.1 Chronology of U.S. Banking Regulations ……………………………………
67
4.1.2 Literature on Banking Deregulation and Growth ……………………………..
72
4.2 Data and Variables Used ……………………………………………………………...
79
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4.2.1 Data Sources …………………………………………………………………..
79
4.2.2 Variables Used ………………………………………………………………...
79
4.3 Methodology and Results ……………………………………………………………..
82
4.3.1 OLS Regressions: State Personal Income …………………………………….
82
4.3.2 OLS Regressions: Gross Domestic Product by State ………………………… 86
4.3.3 OLS Regressions: Commercial Banking Indicators ………………………….. 89
4.3.4 Fixed Effects Regressions: External Financing ……………………………….. 92
4.3.5 Fixed Effects Regressions: Commercial Banking Indicators …………………. 98
4.4 Conclusion ……………………………………………………………………………. 101
5. CONCLUDING REMARKS ……………………………………………………………… 102
References …………………………………………………………………………………….. 104
Appendices:
A. APPENDIX FOR CHAPTER 2 …………………………………………………………... 118
Appendix A.1 Countries in the Sample ………………………………………………….. 118
Appendix A.2 Components of Kaufman, Kraay and Zoido-Lobaton’s
Governance Indices ………………………………………………………………………. 119
Appendix A.3 Sources of Kaufman, Kraay and Zoido-Lobaton’s
Governance Indices ………………………………………………………………………. 124
Appendix A.4 Institutional Quality ……………………………………………………… 125
Appendix A.5 Instrumental Variables …………………………………………………… 127
B. APPENDIX FOR CHAPTER 3 ………………………………………………………….. 129
Appendix B.1 Components of Fraser Institute’s Economic Freedom
Index …………………………………………………………………………………….. 129
Appendix B.2 Economic Freedom Index ……………………………………………….. 130
Appendix B.3 Instrumental Variables …………………………………………............... 132
Appendix B.4 Components of Sullivan’s Diversity Index ……………………………… 135
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Appendix B.5 Jurisdictions currently covered by Section 5 of the Voting
Rights Act (1965) ……………………………………………………………………….. 136
C. APPENDIX FOR CHAPTER 4 …………………………………………………………. 140
Appendix C.1 Years of Relaxing Out-of-State Bank Entry and Intrastate
Branching Restrictions …………………………………………………………………... 140
Appendix C.2 Degree of Dependence on External Financing by Sector ……………….. 142
x
List of Tables
Table
Page
2.1 Summary Statistics ………………………………………………………………………
26
2.2 Correlations ………………………………………………………………………………
28
2.3 Regressions on Bank Credit & Institutional Quality …………………………………….
29
2.4 Regressions on Bank Credit, Turnover & Institutional Quality …………………………
30
2.5 Regressions on Bank Credit, Value Traded & Institutional Quality ……………….........
31
2.6 Regressions on Bank Credit, Market Capitalization, Value Traded & Institutional
Quality …………………………………………………………………………………..
32
2.7 Regressions on Value Traded, Volatility & Institutional Quality ……………………….
33
2.8 Joint Significance of Financial Variables ………………………………………………..
34
2.9 Comparisons between Specifications (1) and (2) for Regression of Output Growth
on Bank Credit & Turnover …………………………………………………………… .
36
2.10 Comparisons between Specifications (1) and (2) for Regression of Output Growth
on Bank Credit & Market Capitalization ………………………………………………
37
3.1 Regressions on Bank Assets & Economic Freedom ……………………………………..
57
3.2 Regressions on Bank Loans & Economic Freedom ……………………………………..
58
3.3 Regressions on Equity Capital, Loan-to-Equity Ratio & Economic Freedom …………..
58
3.4 Regressions on Total Deposits & Economic Freedom …………………………………..
59
3.5 Regressions on Number of Branches & Economic Freedom ……………………………. 60
3.6 Regressions on Failures and Assistance Transactions & Economic Freedom …………... 61
3.7 Joint Significance of Financial Variables ………………………………………………… 61
3.8 Principal Components Analysis ………………………………………………………….
xi
62
3.9 Regressions on the First Principal Component …………………………………………..
63
3.10 Instrumental Variable Results – Financial Variables …………………………………… 64
4.1 Industry Fixed Effects ……………………………………………………………………. 94
4.2 Industry Fixed Effects with Lagged Dependent Variables (GSP) ……………………….. 95
4.3 Industry Fixed Effects with Lagged Dependent Variables (SPI) ………………………… 95
4.4 External Financing & Growth Effects ……………………………………………………. 96
4.5 Level Effects ……………………………………………………………………………… 97
4.6 Commercial Banking Indicator Fixed Effects ……………………………………………. 99
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List of Figures
Figure
Page
4.1 Number of U.S. Commercial Banks, Branches & Offices ..…………………………….
72
4.2 Tobacco Products (SPI) …………………………………………………………………
83
4.3 Food & Kindred Products (SPI) ………………………………………………………...
84
4.4 Electronic & Other Equipment (SPI) ……………………………………………………
85
4.5 Industrial Machinery & Equipment (SPI) ……………………………………………….
85
4.6 Tobacco Products (GSP) ………………………………………………………………...
87
4.7 Food & Kindred Products (GSP) ………………………………………………………..
87
4.8 Electronic & Other Equipment (GSP) …………………………………………………..
88
4.9 Industrial Machinery & Equipment (GSP) ……………………………………………...
89
4.10 Number of Commercial Banks ………………………………………………………...
90
4.11 Number of Branches per Bank …………………………………………………………
90
4.12 Bank Assets …………………………………………………………………………….
91
4.13 Bank Equity …………………………………………………………………………….
92
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CHAPTER 1
Introduction
1.1 Growth Theories
The two main approaches to economic growth on the theoretical front are the neoclassical
and endogenous growth theories. Neoclassical models have diminishing returns to the set of
reproducible factors of production.
Solow (1956), in a classic article, used a standard
neoclassical production function with decreasing returns to capital. The savings rate and rate of
population growth were assumed to be exogenous, and they together explained the steady state
level of per capita income. Savings and population growth rates vary across countries, causing
different countries to converge to different steady states. GDP per capita and capital stock per
worker grow as long as capital dilution is less than new investment, and decrease when capital
dilution is more than new investment. Countries that save more converge to a higher steady state,
while countries that have a higher rate of population growth converge to a lower one. The same
year, the Australian economist Swan (1956) independently published an article with very similar
findings. Other notable contributors to the field of neoclassical growth are Cass (1965) and
Koopmans (1965).
Endogenous growth models, on the other hand, are characterized by nondecreasing
returns to the set of reproducible factors of production. Unlike physical capital, knowledge and
human capital are characterized by increasing returns. Hence there is no steady state level of
income; countries that save more grow faster indefinitely and even those with similar preferences
and technology do not converge. Their advantage over the neoclassical model is that the rate of
technological progress is not determined by exogenous parameters, but is a product of economic
activity. The field is anchored on the works of Romer (1986, 1987) and Lucas (1988). Romer
assumes knowledge to be an input in production with increasing marginal productivity due to
specialization. Technical change is endogenous and growth rates can be increasing over time
1
with small perturbations amplified by the actions of private agents. Large countries may also
always grow faster than small countries. He also offers long run empirical support in favor of his
model. Lucas considers three models, one with physical capital accumulation and technological
change, another with human capital accumulation through schooling and a third with human
capital accumulation through learning-by-doing. He focuses on the positive externalities of
human capital in addition to its internal effects on the individual or his immediate family, and
calculates an elasticity of output with respect to the external effects of human capital of 0.4, citing
ideas exchanged in the financial district, garment district, diamond district, Columbia University
and New York University as examples within the city of New York. In the presence of external
effects, the wage rate of workers at a given skill level will rise with the wealth of the country.
1.2 The Empirics of Growth
Two of the groundbreaking works on the empirics of growth are by Barro (1991) and
Mankiw, Romer and Weil (1992). For 98 countries in the period 1960-1985, Barro finds that the
growth rate of real GDP per capita is positively related to initial human capital and negatively
related to initial real GDP per capita. Countries with more human capital have lower population
growth rates and higher physical investment to GDP ratio, so only poor countries with high levels
of human capital per person catch up with rich countries. Growth is inversely related to a proxy
for market distortions and the government consumption to GDP ratio, insignificantly related to
the share of public investment and positively related to measures of political stability. Being a
socialist country and being located in sub-Saharan Africa also reduces growth. Mankiw, Romer
and Weil find that an augmented Solow model that includes both physical and human capital fits
cross-country data very accurately. Saving and population growth explain more than half of the
variability in GDP per capita, and the augmented model explains around 80%. They also find
that holding population growth and capital accumulation constant, countries approximately
converge as predicted by the Solow model. The textbook model implies that countries reach
halfway to convergence in 17 years, whereas the augmented model suggests that it is 35 years.
The Solow model also predicts that the returns to physical and human capital are higher in poor
countries, and the authors find this to be largely true for profit rates and returns to schooling.
Possible explanations are the higher risk of expropriation of foreign capital by the government
and larger returns to schooling in poor countries due to a workforce with limited human capital.
2
Young (1995) focuses on the remarkable growth experiences of Hong Kong, Singapore,
South Korea and Taiwan, and finds that labor participation rates, educational levels and
investment rates increased rapidly in all four countries. There was also a huge transfer of labor
from other sectors into manufacturing, decline in birth rates and increase in female labor force
participation, resulting in the manufacturing sector’s massive growth. Once these factor input
increases are taken into account, total factor productivity growth rates in these countries are not
extraordinary but close to the historical rates for the OECD and Latin American countries.
Empirical literature has shifted away from production function accounting since then,
focusing more on the question of why some countries grow faster than others. My research is
based on the empirics of the neoclassical model. According to the model, labor, physical capital,
human capital and the level of technology are the factors that explain economic growth.
However, they often collectively fail to explain a significant portion of the growth process.
Growth economists have focused on several other factors that influence growth. Significant
among them are trade, geography, institutions and finance.
1.3 The Integration Hypothesis
The integration approach focuses on the effect of international trade on economic growth;
the examples that are often cited are the growth experiences of resource-poor countries such as
post-war Japan, followed by East Asian tigers of Korea, Taiwan, Hong Kong and Singapore.
Kuznets (1988) focuses on the high investment ratios and domestic savings rates, low Ginis,
small public sectors, government-aided credit allocation and foreign loan guarantees, competitive
labor markets with little unionization and export expansion. The East Asian super-exporters
traded the most and grew the fastest, followed by the Southeast Asian countries, whereas south
Asian countries lagged behind. Krueger (1990) finds that the shift in the successful East Asian
countries away from import controls resulted in a jump in growth rates, the marginal productivity
of capital and total factor productivity. By moving away from protectionism, these countries
benefited from realizing their comparative advantages. The incremental capital-output ratio fell
accordingly, leading to higher growth rates even with constant savings rates. Since the markets
for their goods expanded, they also benefited from economies of scale and managed to avoid
balance of payment difficulties. The costs of remaining government interventions such as credit
3
rationing and regulations governing international financial transactions go up as the economy
grows, providing an impetus for further liberalization.
Sachs and Warner (1995) focus on the timing and impact of trade liberalization on
subsequent growth or avoidance of balance-of-payment crises. Trade liberalization increases the
linkages between the liberalizing country and the global economy, and also forces the
government to undertake price liberalization, deregulation and social safety net reforms due to
international competition. Trade increases specialization, allocates resources efficiently, helps
diffuse knowledge through trade and increases domestic competition due to international
competition. Hence trade liberalization also explains the observation that many developing
countries are failing to catch up with developed countries; open economies tend to converge,
while closed economies do not.
Frankel and Romer (1999) construct measures of the geographic components of
countries’ trade and use them to obtain instrumental variable estimates of the effect of trade on
income. In contrast to conventional gravity models for bilateral trade, they use geographic
characteristics like the size of countries, their distance from one another, whether they share a
border and whether they are landlocked that have a direct effect on trade volume. They find no
evidence that countries whose income is high for other reasons engage in more trade, ruling out
reverse causality, and find that trade has a moderately statistically significant but quantitatively
large positive effect on income. Increasing the trade-to-GDP ratio by 1% raises GDP per capita
by 2.5%. They also find that GDP per capita goes up with size, reflecting more within-country
trade. Exploring the productivity effects of integration after controlling for institutional quality
and geography, Alcala and Ciccone (2004) find that international trade has economically
significant and statistically robust effects on productivity. They use real openness (exports plus
imports relative to purchasing power parity-based GDP), which they argue is theoretically a better
measure than the nominal measures used in the literature because trade might increase
productivity in manufacturing more than in non-tradable services, driving up the relative price of
services and reducing openness. The elasticity of productivity with respect to real openness is
around 1.2 with a standard error of around 0.35. With the elasticity of productivity with respect
to population size being around 0.25 with a standard error of around 0.1, they also find
significantly positive aggregate scale effects, reinforcing the results of Frankel and Romer. Trade
4
and population size are significant determinants of total factor productivity, though not of capitaloutput ratio or the average level of human capital.
Product variety is another channel through which trade can influence growth. Broda and
Weinstein (2006) show that unmeasured growth in product variety from U.S. imports has been an
important source of gains from trade over the past three decades. Consumers have low elasticities
of substitution across similar goods produced in different countries. The number of imported
product varieties has increased by a factor of four, the aggregate price index that takes into
account product variety has fallen by 1.2% per year relative to the conventionally measured
import price index over the last thirty years and welfare gains from the growth in the variety of
imports alone are 2.8% of the GDP.
Vamvakidis (2002) looks at the historical evidence on openness to international trade and
the growth rate of GDP per capita from 1870 to the present, and find no evidence for a positive
relationship between the two prior to 1970. In fact, he finds a negative correlation for the period
1920-1940; countries with higher tariff rates grew faster in that period. Most countries had high
tariffs during the inter-war period and the benefits of having lower tariffs were negligible. Trade
protection can also raise income if there is unemployment, especially in countries with fixed
exchange rate regimes, which described much of the world in the 1930s. The implication is that
the trade policy of a country cannot be set in isolation from the policies of the rest of the world.
The result could also be due to some additional variable correlated with both trade and growth;
for instance, resource abundant countries tend to grow more slowly for a variety of reasons, and
they also tend to have higher export to GDP ratios.
On the international financial front, Tornell and Velasco (1992) analyze a tragedy of the
commons game where all groups can access a common capital stock. Common examples are
cattle grazing in a pasture or vessels fishing in a lake, which typically lead to overconsumption
and underinvestment. Poor countries often don’t have well-defined or well-enforced property
rights. If the citizens of these countries have access to safe bank accounts in wealthier countries,
they engage in capital flight. However, the occurrence of capital flight does not imply that capital
account liberalization hurts growth and welfare.
5
1.4 The Geography Hypothesis
The geography approach has several noted proponents, whose main view is that the
physical geography of the country, with its attendant attributes like latitude, climate and
landlockedness, has a direct impact on growth through its impact on transport costs, disease
burdens and agricultural productivity. Of the thirty richest countries in the world (based on 1995
PPP-adjusted GDP per capita), the only two that are tropical are the city-states of Hong Kong
(China) and Singapore; four are subtropical and the rest temperate. Almost all the landlocked
countries are poor; the exceptions are the landlocked nations of western and central Europe (such
as Austria, Switzerland, Luxembourg and the Czech Republic) which are closely integrated with
the neighboring economies and Botswana, which has large deposits of diamonds.
Gallup (1998) finds that tropical agricultural productivity is 30% to 50% lower than
temperate agricultural productivity after controlling for labor, fertilizer, irrigation, transport
equipments and other factor inputs. Sachs (2001), using GIS mapping, suggests that production
technology in the tropics has lagged behind that in temperate zones in the two critical areas of
agriculture and health, and this has caused the former to lag behind in development. Agricultural
technology developed in temperate zones is unsuitable for the South, and poor public health has
slowed down the demographic transition from high fertility and mortality rates to low fertility and
mortality rates. Construction and energy use technologies are also climate-specific. Geopolitical
imbalances and demographic transition have further amplified these factors. This pattern is also
evident within large countries that encompass several climatic zones: sub-tropical southern U.S.
lags behind the temperate north, Brazil’s tropical northeast lags behind the temperate southeast
and sub-tropical southeastern China lags behind temperate northeastern China.
Diamond (1997) is of the opinion that the plants and animals domesticated, technology
developed and ideas stumbled upon in one part of the world will only be appropriate for adoption
in other parts located in the same ecological zone; hence diffusion will be east-west along
latitudes and not north-south along longitudes. Eurasia hence benefited from its vast east-west
orientation, giving it a fundamental long term advantage, whereas Africa was handicapped by its
north-south orientation. The Sahara desert also acted as a physical barrier. The new lands of the
Americas, Australia and New Zealand were separated from Eurasia by large bodies of water.
Moreover, Eurasia happened to be the homeland of most of the plant and animal species
6
possessing traits that made them easily domesticable, with the notable exceptions of corn and
llamas. Disease-causing pathogens that developed in densely populated pockets of Eurasia,
which Europeans were immune to, caused epidemics in the Americas that wiped out much of the
native population and cemented European dominance.
Focusing on the impact of climate on growth, Masters and McMillan (2001) try to
explain why temperate countries have tended to converge to high levels of income while tropical
countries have converged towards various lower income levels, the outliers being oil exporters,
city states or communist countries. For explanation, they look at the incidence of winter frost,
which kills most pests, pathogens and parasites every winter in the temperate countries. As a
result, these countries benefit in terms of improved agricultural and livestock productivity and
human health, as well as deeper and richer topsoil. Using five incidents of frost per year as the
break point to group countries into tropical and temperate zones, they find that growth increases
sharply at low levels of frost, and then remains unaffected by higher levels of frost (up to 25 days
per month). This effect is amplified by the fact that temperate countries transitioned away from
agriculture into other sectors where productivity converges across countries, whereas tropical
countries are more dependent on gains from specialization and trade.
Krugman and Venables (1995) show that a decrease in transport costs from high levels to
medium levels can hurt a high cost region by disproportionately benefitting a medium cost
region. Hence it is not obvious that a fall in transport costs will level the playing field between
the coast and the hinterland. Rappaport and Sachs (2003) find that U.S. economic activity is
overwhelmingly concentrated in its Atlantic, Pacific and Great Lakes shores, reflecting the
coast’s large contribution to productivity (access to ports and shipping routes) and quality of life
(scenic beauty, recreation and moderate weather). An interesting perspective is that the nature of
geographical advantage changes with technological progress. Landes (1998) argues that in early
civilizations, geographical advantage came from agricultural productivity. Hence all the earliest
seats of civilization were centered in fertile river valleys like the Nile, Tigris, Euphrates, Indus,
Yellow and Yangtze, all of which supported a high density of people. The lack of suitable
transport and communication technologies made most interregional and international trade too
costly. But as technology in these fields developed and trade became increasingly important,
these areas became handicapped by their remoteness and the Middle East and the eastern
Mediterranean prospered as they were the hubs of overland and coastal trade. With the advent of
7
ocean transport in the sixteenth century, geographical advantage shifted to the North Atlantic. In
a similar vein, northern Europe didn’t begin to be heavily settled and to develop in earnest until
the discovery of appropriate tools to fell its expansive forests.
Sachs and Warner (1999) find that higher natural resource exports (as a percentage of
GDP) in 1970 by countries are negatively related to their subsequent growth over 1970-1990.
Agriculture, minerals and fuel are taken to be resource-based exports.
This has recurred
throughout history; Netherlands eclipsed Spain with all its wealth from the New World in the
seventeenth century, Japan eclipsed Russia after the Second World War and the Asian Tigers
eclipsed China and India in the latter decades of the twentieth century. Some economists point to
the lack of positive externalities coming from the primary sector, in contrast to manufacturing.
Moreover, manufacturing leads to a more complex division of labor and a higher standard of
living. A wealth of natural resources might also induce deindustrialization (the so-called Dutch
disease) through its effect on employment. Transport costs have fallen, enabling Japan and Korea
to become world-class steel producers despite having no iron ore deposits to speak of. Terms of
trade have also moved against primary commodities and in favor of manufactures. Natural
resources generate high economic rents, most of which accrue to the government, and this creates
special-interest groups that impede innovation. In addition to increasing instances of corruption,
rents are deadweight losses that lower steady state income and economic growth along the path to
the steady state. The volatility of natural resource prices leads to uncertainty for the exporting
countries, which can reduce factor accumulation by raising the option value of waiting. Indeed,
Malaysia and Mauritius are the only two countries in their sample that grew at rates greater than
2%.
Bloom, Canning and Sevilla (2003) come to a different conclusion by rejecting the
hypothesis that intrinsic geography determines the income levels of countries in favor of a
poverty trap model with high and low level equilibria. Once a threshold is reached as a result of a
big push, increasing returns to scale propel the economy to the higher equilibrium. Improvements
in health result in falling mortality rates; couples opt to have fewer children, but invest more in
their human capital. This enables the economy to escape the Malthusian poverty trap, where
growing population keeps income per capita at subsistence levels. Geography matters only to the
extent that in the low level equilibrium, incomes are higher for cool coastal countries with high
rainfall evenly spread throughout the year. Most economists now agree that geography only has
8
an indirect impact on the growth process, primarily through its impact on institutional quality.
Easterly and Levine (2003) test the endowments, institutions and policy views against each other
and find that endowments of tropics, germs and crops affect development only through
institutions, and policies also have no effect once institutions are controlled for. Moreover,
geographically favored countries can fumble. North Korea is an obvious example: it is in the
temperate zone, has a long coastline and is adjacent to countries that have grown rapidly in the
past or are growing fast at present.
1.5 The Institutions Hypothesis
Some growth economists have focused on the impact of institutional quality on growth.
They argue that institutional quality determines both policy choices and the growth rate, but the
latter two bear no causal connections. Factors like physical capital, human capital and the level
of technology are proximate causes of growth. In the words of North and Thomas (1973), “the
factors we have listed (innovation, economies of scale, education, capital accumulation, etc.) are
not causes of growth; they are growth”. The fundamental cause of different countries having
very different growth experiences is the difference in their institutions. North (1990) says that
“institutions are the rules of the game in a society or, more formally, are the humanly devised
constraints that shape human interaction”.
One of the first papers in this area was Hall and Jones (1999). In order to explain why
output per worker varied so much across countries, the authors find that physical and human
capital only explain a part of the story. The variation in the level of the Solow residual is huge
across countries, and this suggests that productivity differences are largely influenced by “social
infrastructure”, which is an all-encompassing term covering the institutions and government
policies that have an effect on skill acquisition, capital accumulation, R&D and output. Different
countries have adopted different social infrastructures. This is endogenously determined, and is
partly determined by the extent to which the economy has been influenced by Western Europe.
Acemoglu, Johnson and Robinson (2001b) look at ex-colonies to see why institutional
qualities differ so much from one country to another.
Europeans adopted very different
colonization policies in different colonies. Colonies where Europeans had lower mortality rates
were places where the Europeans tended to settle, and their political and judicial institutions were
9
more likely to be modeled after those of the colonizing country. This happened with the British
in the U.S., Canada, Australia and New Zealand. As noted by the authors, the pilgrims decided to
settle on the eastern seaboard of America rather than Guyana because of the latter’s high
mortality rate. On the other hand, extractive institutions intended for transferring wealth to the
colonizer were more likely to be set up in colonies where Europeans faced high mortality rates.
This happened with the French in West Africa and with the Belgians in Congo. Institutions have
a tendency to persist; hence these countries continue to score low on measures of institutional
quality even after their independence. Once the effect of institutional quality is controlled for,
countries in Africa or countries that are close to the equator do not have lower incomes.
A related paper by Acemoglu, Johnson and Robinson (2002) explores the fact that most
areas where rich civilizations flourished 500 years ago (like Meso-America, India and Southeast
Asia) are poorer today, whereas areas that were poor then (like North America, Australia and
New Zealand) have prospered. The authors are of the view that in the then prosperous and
densely populated areas, Europeans took over the existing tax and tribute systems and introduced
extractive institutions to force the local population to work in mines and plantations. In contrast,
the colonizers settled in large numbers in the sparsely populated areas, created institutions of
private property and encouraged industry and commerce. European colonialism, thereby, lead to
an “institutional reversal” in these economies.
Compiling a database of 81 islands in the Atlantic, Pacific and Indian Oceans that were
colonized during the Age of Discovery, Feyrer and Sacerdote (2009) find that the duration for
which they were European colonies has a strong positive influence on current GDP per capita.
Longer involvement with the Europeans left these islands with a better structured government;
every 100 year period of colonization results in a 42% increase in GDP per capita. They also find
that the length of colonization after 1700 has a greater effect than that of Pre-Enlightenment
colonization (in the 16 th and 17th centuries), and American, British, French and Dutch rule are
more beneficial than Spanish and especially Portuguese rule. The adverse impacts of the latter
continue to be manifested in poor institutions to this day.
Acemoglu, Johnson, Robinson and Thaicharoen (2003) consider the post-war
macroeconomic experiences for countries, and find that countries that inherited more extractive
institutions from their colonial past were more likely to experience greater economic volatility,
10
more economic crises, poor macroeconomic performance (like high inflation rates, large budget
deficits and misaligned exchange rates) and slower growth rates. Once they control for the effect
of institutions, the authors find only a minor impact of macroeconomic policies on volatility and
crises, suggesting that the distortionary macroeconomic policies are probably the symptoms of
underlying institutional problems.
Easterly and Levine (2002) find that neither geographical factors like latitude,
landlockedness, minerals, crops and the prevalence of germs nor economic policies have any
direct effect on growth once they control for their effects on the quality of institutions. Rodrik,
Subramanian and Trebbi (2004) take a broader approach, where they look at trade in addition to
geography as causal variables for economic growth. Once again geography has a strong effect on
the quality of institutions but weak direct effects on growth. Trade too improves the institutional
quality but has insignificant direct growth effects, often entering the equation with the wrong
sign. Hence institutional quality “trumps” all other causal variables.
Sala-i-Martin (1997) sifts through the empirical growth literature and comes up with
sixty-two variables that are used to explain growth rates. He chooses initial per capita income,
initial life expectancy and initial primary school enrollment rate as the three fixed variables, and
combined the remaining variables in groups of three to test which variables are significant across
specifications. He finds that twenty-two of them to be significant; among them are regional
variables for sub-Saharan Africa and Latin America, political variables related to rule of law, civil
liberties, revolutions and coups and war, religious variables, market distortions, equipment
investment and non-equipment investment, primary sector production, openness, degree of
capitalism and being a former Spanish colony.
An interesting paper advocating the institutions hypothesis is Barro and McCleary
(2006), which looks at the impact of church attendance and religious beliefs on economic growth.
They find that economic growth is enhanced by religious beliefs, especially in heaven and hell,
but is diminished by church attendance. Religious beliefs enhance characteristics like honesty,
work ethic, thrift and openness to strangers that increase productivity in the workplace; church
attendance implies time spent away from work, though it builds up social capital. The presence
of a state religion and religious pluralism have significantly positive effects on church attendance,
while having a Communist regime and urbanization have significantly negative effects. The
11
results hold across religions, and are unrelated to the reverse channel of secularization hypothesis
in sociology, which states that economic development causes individuals to become less religious
and organized religion to play a smaller role in political decision-making and social mores.
1.6 The Financial Hypothesis
There is an abundance of literature on the impact of financial development on the
economic growth of a country. Not all economists agree on this issue. The debate originated
from the differing opinions of Robinson (1952), who believed that the causality ran from
economic growth to financial development as countries with good growth prospects were the
ones that were more likely to develop their financial sector, and Schumpeter (1912), who said that
the causality was from financial development to growth, as a country stymied by inadequate
financial capital could not grow.
A lot of papers have been written on both the theory and the empirics of this issue since
then. Most of the recent ones seem to agree with Schumpeter. One of the papers that have tried
to theoretically model this interlinkage is Bencivenga and Smith (1991). They construct an
endogenous growth model with multiple assets, where agents face random future liquidity needs
and need to accumulate capital as well as an unproductive asset. The introduction of financial
intermediaries in this model shifts the accumulation towards capital, thus promoting growth. In a
couple of papers, Galetovic (1994a, 1994b) shows that the type and quality of financial services
are important for growth. Both short-term and long-term loans have growth effects, and low
quality financial services or very high transaction costs can cause an economy to stop growing.
Moreover, financial innovations might have opposing static and dynamic effects from the point of
view of efficiency. Financial intermediaries with some market power do have the ability to weed
out low-quality firms, which in turn raises the growth rate, but if credit markets are perfectly
competitive, then all firms can obtain credit regardless of their type. Tressel (2003) develops a
model where an informal lending sector is necessary in the initial stages of development when
most agents are too poor to offer any collateral. The banking system, which has a much superior
ability for mobilizing savings, develops more gradually.
The model has two steady-state
equilibria. In one the economy stops growing and the banking sector witnesses only limited
development, while in the other the economy is on a sustained growth path and the informal
sector gradually vanishes.
12
One of the most cited papers on the empirical front is King and Levine (1993). The
findings of this paper agree with Schumpeter. For a sample of 77 countries, indicators of
financial development like the size of the formal financial sector relative to GDP, the size of
commercial banks relative to the central bank, the fraction of credit allocated to the private sector
and the size of the credit issued to private firms relative to GDP are significantly correlated with
growth and each of these can significantly predict subsequent values of the growth indicators.
Jayaratne and Strahan (1996) look at the impact of bank branching deregulations during the
Reagan administration on economic growth in the U. S. states and find that growth rates
increased substantially following the reforms. Furthermore, the cause of this increase was not an
increase in the quantity of bank lending but rather the quality of bank lending, as measured by
non-performing loans, the fraction of loans written off and the fraction of loans classified as
“insider loans”.
Levine and Zervos (1998) consider four different dependent variables: economic growth,
capital stock growth, growth in productivity and the private savings rate and find that even after
controlling for the standard variables, the extent of development of the banking sector and the
stock market both have significant positive impacts on the first three dependent variables. Rajan
and Zingales (1998) focus on a specific factor that might cause increased financial intermediation
to raise the rate of growth. They argue that financial development makes external finance less
costly and different industries have different degrees of dependence on external finance. Hence
industries like drugs & pharmaceuticals and plastic products that use a lot of external capital
should grow faster than industries like tobacco and pottery that use the least. Their cross-country
data indeed supports this view.
In the same vein, Levine (2001) finds that liberalizing restrictions on international capital
flows accelerates economic growth. Cross-border financial flows increase liquidity in the equity
market, which boosts total factor productivity growth. Competition from foreign banks also
forces domestic banks to become more efficient, also raising productivity.
The approach of Benhabib and Spiegel (2000) is to look at whether financial
development has growth effects due to its impact on factor accumulation rates or due to its impact
on total factor productivity. They find both to be true, but the factors correlated with the former
differ from the ones correlated with the latter. In case of the former, for physical capital the ratio
13
of banking to total assets and the interactive initial income and income inequality variables enter
significantly with their predicted signs after country fixed effects are included; for human capital
these are replaced by the ratio of bank assets to total assets and the ratio of liquid liabilities to
income. In case of the latter, the ratio of private sector financial assets to GDP is the only robust
variable.
Aghion, Howitt and Mayer-Foulkes (2005) introduce imperfect creditor protection in a
multi-country Schumpeterian growth model. Their theory predicts that any country with more
than some critical level of financial development will converge to the growth rate of the world
technology frontier, and that all other countries will have a strictly lower growth rate. They also
present empirical evidence in support of this result in the form of a cross-country growth
regression with a significant and sizeable negative coefficient on initial per capita GDP (relative
to the U.S.) interacted with financial intermediation. Controls for schooling, geography, health,
policy, politics and institutions do not affect its significance, nor show any independent effect.
1.7 My Area of Focus
I primarily focus on the Financial Hypothesis of economic development and explore how
the development of the banking sector and stock markets influence growth. Additionally, I look
at the role of institutions in economic development. The first essay explores these issues in a
cross-country setting, while the second essay explores them in the context of the fifty U.S. states.
To further analyze the Financial Hypothesis, the final essay examines the impact of deregulatory
changes in the U.S. banking industry on economic growth in depth.
14
CHAPTER 2
Financial versus Institutional Development:
Impact on Economic Growth
2.1 Introduction
2.1.1 Literature on Financial Intermediation and Growth
There is an abundance of literature on the impact of financial development on the
economic growth of a country. Not all economists agree on this issue. The debate originated
from the differing opinions of Robinson (1952), who believed that the causality ran from
economic growth to financial development as countries with good growth prospects were the
ones that were more likely to develop their financial sector, and Schumpeter (1912), who said that
the causality was from financial development to growth, as a country stymied by inadequate
financial capital could not grow.
A lot of papers have been written on both the theory and the empirics of this issue since
then. Most of the recent ones seem to agree with Schumpeter. One of the papers that have tried
to theoretically model this interlinkage is Bencivenga and Smith (1991). They construct an
endogenous growth model with multiple assets, where agents face random future liquidity needs
and need to accumulate capital as well as an unproductive asset. The introduction of financial
intermediaries in this model shifts the accumulation towards capital, thus promoting growth. In a
couple of papers, Galetovic (1994a, 1994b) shows that the type and quality of financial services
are important for growth. Both short-term and long-term loans have growth effects, and low
quality financial services or very high transaction costs can cause an economy to stop growing.
Moreover, financial innovations might have opposing static and dynamic effects from the point of
15
view of efficiency. Financial intermediaries with some market power do have the ability to weed
out low-quality firms, which in turn raises the growth rate, but if credit markets are perfectly
competitive, then all firms can obtain credit regardless of their type. Tressel (2003) develops a
model where an informal lending sector is necessary in the initial stages of development when
most agents are too poor to offer any collateral. The banking system, which has a much superior
ability for mobilizing savings, develops more gradually.
The model has two steady-state
equilibria. In one the economy stops growing and the banking sector witnesses only limited
development, while in the other the economy is on a sustained growth path and the informal
sector gradually vanishes.
One of the most cited papers on the empirical front is King and Levine (1993). The
findings of this paper agree with Schumpeter. For a sample of 77 countries, indicators of
financial development like the size of the formal financial sector relative to GDP, the size of
commercial banks relative to the central bank, the fraction of credit allocated to the private sector
and the size of the credit issued to private firms relative to GDP are significantly correlated with
growth and each of these can significantly predict subsequent values of the growth indicators.
Jayaratne and Strahan (1996) look at the impact of bank branching deregulations during the
Reagan administration on economic growth in the U. S. states and find that growth rates
increased substantially following the reforms. Furthermore, the cause of this increase was not an
increase in the quantity of bank lending but rather the quality of bank lending, as measured by
non-performing loans, the fraction of loans written off and the fraction of loans classified as
“insider loans”.
Levine and Zervos (1998) consider four different dependent variables: economic growth,
capital stock growth, growth in productivity and the private savings rate and find that even after
controlling for the standard variables, the extent of development of the banking sector and the
stock market both have significant positive impacts on the first three dependent variables. Rajan
and Zingales (1998) focus on a specific factor that might cause increased financial intermediation
to raise the rate of growth. They argue that financial development makes external finance less
costly and different industries have different degrees of dependence on external finance. Hence
industries like drugs & pharmaceuticals and plastic products that use a lot of external capital
should grow faster than industries like tobacco and pottery that use the least. Their cross-country
data indeed supports this view.
16
In the same vein, Levine (2001) finds that liberalizing restrictions on international capital
flows accelerates economic growth. Cross-border financial flows increase liquidity in the equity
market, which boosts total factor productivity growth. Competition from foreign banks also
forces domestic banks to become more efficient, also raising productivity.
The approach of Benhabib and Spiegel (2000) is to look at whether financial
development has growth effects due to its impact on factor accumulation rates or due to its impact
on total factor productivity. They find both to be true, but the factors correlated with the former
differ from the ones correlated with the latter. In case of the former, for physical capital the ratio
of banking to total assets and the interactive initial income and income inequality variables enter
significantly with their predicted signs after country fixed effects are included; for human capital
these are replaced by the ratio of bank assets to total assets and the ratio of liquid liabilities to
income. In case of the latter, the ratio of private sector financial assets to GDP is the only robust
variable.
Looking at the experience of U.S., U.K., Canada, Norway and Sweden over 1870-1929,
Rousseau and Wachtel (1998) use vector error correction models to establish the relationship
between financial development, monetary base and real GDP per capita. They then use Granger
causality tests to show a sizable effect of financial development on real output prior to the Great
Depression, with negligible feedback effects. Christopoulos and Tsionas (2004) consider a
cointegration framework and find a single equilibrium relationship between financial
development, growth and other ancillary variables, and also find unidirectional causality from
financial development to growth.
Aghion, Howitt and Mayer-Foulkes (2005) introduce imperfect creditor protection in a
multi-country Schumpeterian growth model. Their theory predicts that any country with more
than some critical level of financial development will converge to the growth rate of the world
technology frontier, and that all other countries will have a strictly lower growth rate. They also
present empirical evidence in support of this result in the form of a cross-country growth
regression with a significant and sizeable negative coefficient on initial per capita GDP (relative
to the U.S.) interacted with financial intermediation. Controls for schooling, geography, health,
policy, politics and institutions do not affect its significance, nor show any independent effect.
17
Controlling for the degree of country development, regional dummies, creditor protection
index, and incidences of banking and currency crises, Shen and Lee (2006) find that while stock
market development has a positive impact on growth, banking development has an unfavorable
impact. A possible explanation they offer is the relationship between banking development and
economic growth to be weakly inverse U-shaped.
High income levels and good creditor
protection mitigate the adverse effects of the banking sector on the growth process.
There is also a body of empirical work arguing the absence of any clear causal
connection between financial development and growth. For a sample of 95 countries, Ram
(1999) has shown that given the high amount of structural heterogeneity observed in crosscountry data, financial intermediation probably has a negligible impact on growth. Arestis,
Demetriades and Luintel (2001) argue that banks and stock markets are substitute sources for
corporate finance. When a firm issues new equity, its borrowing needs from banks decline.
Using quarterly time series data for five developed economies, they find that banks have a more
powerful effect on growth than stock markets, since the contribution of stock markets might have
been exaggerated by cross-country growth regressions. The presence of endogeneity can weaken
the effect of stock market variables, and employing time series data might give a better picture.
However, their restricted sample size implies that their results are not relevant for emerging
economies for which this debate might be the most important.
Levine (2002) looks into the relative merits of bank-based (for instance in Germany and
Japan) and market-based (for instance in U.S. and U.K.) financial systems, and although he finds
that overall financial development is robustly related to growth, neither approach is necessarily
better.
While acknowledging that banks might be more efficient at mobilizing savings,
identifying good investment opportunities and exercising corporate control, Levine also points
out several potential channels through which banking development can have a negative impact on
economic growth. Powerful banks can stymie innovation by protecting favored firms from
competition. They can also collude with firm managers against other creditors, leading to
corporate governance issues. Finally, excessive prudence on the part of bank loan officials can
deprive firms of capital necessary to carry out R&D activities. Khan and Senhadji (2003) find
banking sector indicators to be statistically insignificant in their cross-country panel data analysis.
They point out three different reasons for this finding, which are the possible non-linearity of the
relationship, growth in a particular country being much more volatile than banking development
18
and the standard financial indicators not being sophisticated enough to capture the changing
financial structure of countries.
There also exists the possibility that the finance-growth nexus is influenced by other
country-specific, regional or macroeconomic factors. Apart from some sub-Saharan countries,
Latin American countries (with a few exceptions) have grown at painfully slow rates over much
of the second half of the twentieth century. De Gregorio and Guidotti (1995) show a negative
impact of financial development on economic growth for a sample of twelve Latin American
countries over the 1950 – 1985 period. Using GMM dynamic panel techniques with data from 74
countries, Rioja and Valev (2004) show that finance can affect output through different channels
for different countries. While in low income countries (per capita incomes of less than $742)
financial development affected growth primarily through capital accumulation, in middle income
(per capita incomes between $742 and $2,490) and high income (per capita incomes above
$2,490) countries it was mostly through productivity growth. Using the regression tree technique
and the sample of Levine and Zervos (1998), Minier (2003) finds growth and financial
intermediation to be correlated for the 31 countries with high levels of market capitalization
(countries with average annual values of shares listed on domestic exchanges to GDP ratios of
0.03784 or more), but not for the remaining 11.
2.1.2 Literature on Institutional Quality and Growth
More recently, growth economists have started looking at the impact of institutional
quality on growth. They argue that institutional quality determines both policy choices and the
growth rate, but the latter two bear no causal connections. The main drawback of this line of
research is that no policy prescriptions emerge from it. Everything that happens to a country is
simply attributed to a black box termed “institutions”, with no clear-cut approaches for improving
these institutions if they are not performing as we would like them to.
One of the first papers in this area was Hall and Jones (1999). In order to explain why
output per worker varied so much across countries, the authors find that physical and human
capital only explain a part of the story. The variation in the level of the Solow residual is huge
across countries, and this suggests that productivity differences are largely influenced by “social
infrastructure”, which is an all-encompassing term covering the institutions and government
19
policies that have an effect on skill acquisition, capital accumulation, R&D and output. Different
countries have adopted different social infrastructures. This is endogenously determined, and is
partly determined by the extent to which the economy has been influenced by Western Europe.
Acemoglu, Johnson and Robinson (2001b) look at ex-colonies to see why institutional
qualities differ so much from one country to another.
Europeans adopted very different
colonization policies in different colonies. Colonies where Europeans had lower mortality rates
were places where the Europeans tended to settle, and their political and judicial institutions were
more likely to be modeled after those of the colonizing country. This happened with the British
in the U.S., Canada, Australia and New Zealand. As noted by the authors, the pilgrims decided to
settle on the eastern seaboard of America rather than Guyana because of the latter’s high
mortality rate. On the other hand, extractive institutions intended for transferring wealth to the
colonizer were more likely to be set up in colonies where Europeans faced high mortality rates.
This happened with the French in West Africa and with the Belgians in Congo. Institutions have
a tendency to persist; hence these countries continue to score low on measures of institutional
quality even after their independence. Once the effect of institutional quality is controlled for,
countries in Africa or countries that are close to the equator do not have lower incomes.
A related paper by Acemoglu, Johnson and Robinson (2002) explores the fact that most
areas where rich civilizations flourished 500 years ago (like Meso-America, India and Southeast
Asia) are poorer today, whereas areas that were poor then (like North America, Australia and
New Zealand) have prospered. The authors are of the view that in the then prosperous and
densely populated areas, Europeans took over the existing tax and tribute systems and introduced
extractive institutions to force the local population to work in mines and plantations. In contrast,
the colonizers settled in large numbers in the sparsely populated areas, created institutions of
private property and encouraged industry and commerce. European colonialism, thereby, lead to
an “institutional reversal” in these economies.
Compiling a database of 81 islands in the Atlantic, Pacific and Indian Oceans that were
colonized during the Age of Discovery, Feyrer and Sacerdote (2009) find that the duration for
which they were European colonies has a strong positive influence on current GDP per capita.
Longer involvement with the Europeans left these islands with a better structured government;
every 100 year period of colonization results in a 42% increase in GDP per capita. They also find
20
that the length of colonization after 1700 has a greater effect than that of Pre-Enlightenment
colonization (in the 16 th and 17th centuries), and American, British, French and Dutch rule are
more beneficial than Spanish and especially Portuguese rule. The adverse impacts of the latter
continue to be manifested in poor institutions to this day.
Acemoglu, Johnson, Robinson and Thaicharoen (2003) consider the post-war
macroeconomic experiences for countries, and find that countries that inherited more extractive
institutions from their colonial past were more likely to experience greater economic volatility,
more economic crises, poor macroeconomic performance (like high inflation rates, large budget
deficits and misaligned exchange rates) and slower growth rates. Once they control for the effect
of institutions, the authors find only a minor impact of macroeconomic policies on volatility and
crises, suggesting that the distortionary macroeconomic policies are probably the symptoms of
underlying institutional problems.
Easterly and Levine (2002) find that neither geographical factors like latitude,
landlockedness, minerals, crops and the prevalence of germs nor economic policies have any
direct effect on growth once they control for their effects on the quality of institutions. Rodrik,
Subramanian and Trebbi (2004) take a broader approach, where they look at trade in addition to
geography as causal variables for economic growth. Once again geography has a strong effect on
the quality of institutions but weak direct effects on growth. Trade too improves the institutional
quality but has insignificant direct growth effects, often entering the equation with the wrong
sign. Hence institutional quality “trumps” all other causal variables.
This strand of literature is different from the viewpoint of Gallup, Sachs and Mellinger
(1999), who control for economic policies and institutions and investigate three ways in which
geography can directly matter for growth: transport costs, disease burdens and agricultural
productivity. They find that geography is a factor in the choice of economic policy itself, and
predict that most of the population increase in the next 30 years is going to be in the
geographically disadvantaged regions.
Sachs (2001), using GIS mapping, suggests that
production technology in the tropics has lagged behind that in temperate zones in the two critical
areas of agriculture and health, and this has caused the former to lag behind in development.
Agricultural technology developed in temperate zones is unsuitable for the South, and poor public
health has slowed down the demographic transition from high fertility and mortality rates to low
21
fertility and mortality rates. This view is supported by Diamond (1997). However, as evidenced
by the later works cited earlier, most economists agree that geography only has an indirect impact
on the growth process, primarily through its impact on institutional quality.
Mocan (2008) looks at the causes and consequences of the prevalence of corruption in
different countries and finds that institutional quality affects the extent of corruption (along with
other variables like gender, education, the size of the city and incidents of war) as well as the
growth rate, but corruption has no direct effect on growth once the quality of institutions are
controlled for. The results indicate that a one-half standard deviation increase in institutional
quality (for instance from the level of Indonesia to the level of India) leads to a 0.7% increase in
annual per capita GDP growth. In their paper, Chinn and Ito (2006) focus on the links between
financial development, institutional development and capital account liberalization for a sample
of 108 countries. They find that better institutional quality enhances the effect of capital account
liberalization on the development of equity markets. This is achieved by mitigating financial
repression, allowing investors to engage in greater portfolio diversification and increasing the
efficiency of the financial system by weeding out the weaker institutions.
An interesting caveat is provided by Glaeser, La Porta, Lopez-de-Silanes and Shleifer
(2004). Their opinion is influenced to some extent by the divergent experiences of North and
South Korea. Every country faces a set of institutional opportunities, and this set is determined
largely by the human and social capital of its population. The greater the latter, the more
attractive are the institutional opportunities for the country.
As improving the quality of institutions is difficult for most countries, given the political
clout of the existing elite, and because policies do not have a direct impact on output and
productivity, no concrete policy prescription emerges from this literature.
For example,
Acemoglu, Johnson and Robinson (2001a) look at the success story of Botswana, which is
especially remarkable in the context of the economic woes of the other sub-Saharan African
nations.
They conclude that the primary reason is conducive pre-colonial institutions that
encouraged respect for private property and placed constraints on the political elite, coupled with
the fact that these were not altered by the British to any significant extent. In other words,
Botswana was lucky and no other country can follow its example. This is obviously not what
policymakers would want to hear.
22
For comparison purposes, this paper broadly follows the framework of Levine and
Zervos (1998), but also introduces institutional quality measures to determine whether like so
many other variables, financial variables too have no direct impact on growth, and it is
institutions that jointly determine both the extent of financial development and the rate of
economic growth. The remainder of the chapter is organized as follows. Section 2.2 describes
the data and the variables used. Section 2.3 presents the descriptive statistics. Section 2.4
describes the methodology and results and Section 2.5 concludes.
2.2. Data and Variables Used
2.2.1 Data Sources
The data on the banking sector and stock market variables are taken from the website of
Thorsten Beck (The World Bank). The data on institutional quality are from Kaufman, Kraay
and Zoido-Lobaton (1999, 2002), La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998, 1999),
World Health Organization (2005) and Acemoglu, Johnson and Robinson (2001). The data on
the remaining economic variables are from the IMF’s International Financial Statistics, the World
Bank’s World Development Indicators and UNESCO’s Statistical Yearbook.
The sample period is 1976 – 1993.
2.2.2 Variables
Output is produced using the neoclassical production function Y = AK L1- , where Y is
output, K is physical capital, L is labor input and A is productivity. Dividing both sides of the
production function by L, I measure output growth and capital stock growth in per worker terms.
Taking logs, differentiating with respect to time and assuming a capital share of
= 0.3,
productivity growth can be calculated as the residual (output growth – 0.3*capital stock growth).
I consider four dependent variables: output growth, capital stock growth, growth in
productivity and the gross private savings rate. Both output growth and capital stock growth are
measured in per worker terms.
23
The financial variables include banking sector as well as stock market variables. The
banking sector development indicator is bank credit, which is the amount of loans made by
deposit-taking banks to the private sector divided by the GDP. I look at four indicators of stock
market development. Capitalization equals the ratio of the value of listed domestic shares on
domestic exchanges to GDP. Turnover equals the ratio of the value of the trades of domestic
shares on domestic exchanges to the value of listed domestic shares. Value traded is the ratio of
the value of the trades of domestic shares on domestic exchanges to the GDP. Volatility is a 12month rolling standard deviation of stock returns.
The control variables I use are the logarithm of initial real per capita GDP, the logarithm
of the initial secondary school enrollment rate, the number of revolutions and coups, the initial
ratio of government consumption to GDP, the initial rate of inflation and the initial value of the
black market exchange rate premium.
The Kaufman, Kraay and Zoido-Lobaton measure of institutional quality is comprised of
over 300 governance indicators compiled from sources like Business Environment Risk
Intelligence, Freedom House, Gallup International, Political Risk Services and World Economic
Institute that are aggregated into six indices: voice and accountability, political stability,
government effectiveness, regulatory quality, rule of law and control of corruption. I take the
simple average of the six indices. Country scores range from -2.5 to 2.5, with higher scores
signifying better governance. Governance is broadly defined as the traditions and institutions by
which authority in a country is exercised, but there is no single acceptable definition of
governance. These indicators are based on guidelines established by the Institute for Governance,
IDEA and IMF.
Since institutional quality is likely to be correlated with one or more of the other
variables, I use five sets of instrument for institutional quality which have been previously used in
the literature. Settler mortality estimates for former colonies from Acemoglu, Johnson and
Robinson (2001) is a better instrument but greatly reduces the sample size. They argue that
European colonizers settled in significant numbers in colonies with low settler mortality rates
(like the U.S., Canada, Australia and New Zealand), and hence established the rule of law and
instituted measures for the protection of private property rights in these countries. In colonies
with high incidences of disease and mortality, the colonizers were only interested in extracting as
24
much wealth and resources as possible. Since institutional quality changes only gradually with
time, the poor institutions in these countries have persisted even after their independence. Hence
settler mortality rates are a good instrument for institutional quality. Settler mortality is in
logarithms, and is expressed as annualized deaths per thousand. In levels, it may exceed a
thousand as new ones replaced dead settlers. The drawback is that this data was maintained by
the army, navy and the church, and is not available for a lot of countries.
An alternative to using settler mortality rates as instrument is using an index of malaria
infection. The data is from the World Malaria Report (2005). Malaria has been either eradicated
or brought under control in several countries since the time of early European settlements, but
most of these successes are confined to temperate countries that had much lower rates to begin
with, or island nations like Taiwan and Mauritius. Malaria remains endemic throughout most of
Africa (411.1 per 1,000 in Tanzania and 409.3 per 1,000 in Malawi in 1990) and the highest rates
in the Americas continue to be in Guyana and French Guiana (31 and 50.8 per 1,000
respectively).
Data on latitude are from La Porta, Lopez-de-Silanes, Shleifer and Vishny (1999).
Latitude happens to be highly correlated with institutional quality, and there is no theoretical
basis for it to be correlated with any of our other causal variables. Latitude has been standardized
to vary between 0 and 1.
Higher latitudes imply more temperate climates that are more
conducive to European settlement and hence more likely regions to have good institutions.
The index of ethnolinguistic fragmentation gives the probability that two randomly
selected people from the country will not belong to the same ethnic and linguistic group. The
greater is the heterogeneity in the population, the more likely are the minority groups not
represented in the government to be persecuted. The government becomes more interventionist
and inefficient. The lack of agreement over the nature of public goods demanded by the various
groups leads to less of these goods being provided by the government, which adversely impacts
development. This data is also from La Porta, Lopez-de-Silanes, Shleifer and Vishny (1999).
As pointed out by La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998), legal origin
has an impact on economic outcomes because the English common law system, by design, limits
the powers of the sovereign, has a more independent judiciary and provides the greatest
25
protection to private property. Since the seventeenth century, it has been the English Parliament
and the judges who have influenced the laws, rather than the British Crown. The French, German
and Scandinavian civil codes, on the other hand, are merely designed to find a solution to a
dispute while at the same time maintaining or increasing the powers of the state. The civil law
system is based on a set of codified rules (following Codification in the nineteenth century),
rather than gradually evolving from case to case like common law. Countries following French
civil law also happen to provide the weakest protection to shareholders and creditors, while
German and Scandinavian civil law countries fall in the middle. Ethnolinguistic fragmentation,
latitude and legal origin all retain the original sample, whereas the index of malaria infection
slightly reduces the sample size.
2.3. Descriptive Statistics
Tables 2.1 and 2.2 present the summary statistics for the four dependent variables, one
banking sector variable, four stock market variables and two institutional quality variables.
Variable
Mean
Median
Maximum
Minimum
S.D.
Observations
Output Growth
0.021
0.019
0.097
-0.025
0.022
47
Capital Growth
0.028
0.024
0.095
-0.023
0.026
46
Productivity Growth
0.016
0.014
0.079
-0.019
0.017
46
Savings Rate
20.00
20.80
29.70
9.10
5.10
32
Capitalization
0.32
0.17
2.45
0.01
0.43
46
Value Traded
0.11
0.04
1.16
0.00
0.19
47
Turnover
0.30
0.23
2.05
0.01
0.33
46
Volatility
0.07
0.05
0.31
0.03
0.06
36
Bank Credit
0.80
0.75
2.27
0.12
0.50
47
Institutions
0.60
0.68
1.65
-1.00
0.77
47
Table 2.1: Summary Statistics1
1
Output, capital stock and productivity growth rates are in percentages per year. The financial variables
are expressed as ratios, with the exception of volatility, which is expressed as the standard deviation.
26
It is evident from Table 2.1 that the countries in the sample vary widely in their overall
economic as well as financial characteristics. Output growth rates are negative for countries like
Nigeria and Peru, and as high as 9.67% for Korea and 6.20% for Hong Kong. Capital stock
growth ranges from being negative for Bangladesh and Zimbabwe to 9.47% for Indonesia and
8.10% for Korea. Productivity declined for some Latin American and African countries, the
maximum decrease occurring in Venezuela, whereas it went up by 7.89% in Korea and 4.78% in
Hong Kong. Gross private savings rates varied from 9.06% in Bangladesh to 29.73% in Korea.
Market capitalization is the highest in Luxembourg (2.45), Singapore (1.29) and Hong Kong
(1.24) and the lowest in Bangladesh (0.0098); total value traded is the greatest in Taiwan (1.16)
and the least in Nigeria (0.00019); turnover is the most in Taiwan (2.05) and the least in Nigeria
(0.006); volatility is the largest in Argentina (0.31) and the smallest in Pakistan and U.S.A. (0.03
each). Bank credit is the highest in Luxembourg (2.27) and Japan (1.96) and the lowest in Peru
(0.12) and Zimbabwe (0.14). For institutional quality, the highest values are for Netherlands
(1.65) and Finland (1.62) and the lowest values are for Nigeria (-1) and Indonesia (-0.76).
Table 2.2 presents simple correlations among the variables with the data averaged over
the sample period. Hence each country has one observation for each variable. The main points
that emerge from Table 2 are that all the financial measures are positively and significantly
correlated with all the growth indicators except the gross savings rate, and they are significantly
positively correlated with institutional quality. Bank credit has especially high correlations with
the growth indicators (except the savings rate) and all of the stock market indicators. I take
volatility to be an inverse financial measure 2; hence the correlations for volatility are all negative.
Also, bank credit has a very high correlation with capitalization, which implies that the distinction
between equity and bank credit given to the private sector might be tenuous. Institutional quality
is also correlated positively with all the financial variables except volatility.
2
While limited stock price volatility is desirable as it reflects adjustments in response to new information in
efficient markets, excessive volatility implies that asset values are departing from their underlying
fundamentals. This might result in inefficient resource allocation and an upward pressure on the interest
rate as a result of increased uncertainty. The latter will have an adverse impact on investment and growth.
(see Arestis, Demetriades & Luintel, 2001)
27
Variable
Cap
Prod Savings Capita- Value Turn- VolaGrowth Growth Rate lization Traded over tility
Bank
Credit
Institutions
Output
Growth
0.773
0.957
0.447
0.037
0.522 0.487 -0.080
0.347
0.212
Capital
Growth
…
0.557
0.530
0.203
0.425 0.356 -0.104
0.324
0.167
Prod Growth
…
…
0.419
0.222
0.417 0.444 -0.169
0.372
0.233
Savings Rate
…
…
…
-0.079
0.160 0.447
0.119
0.119
0.240
Capitalization
…
…
…
…
0.331 0.050 -0.261
0.647
0.394
Value Traded
…
…
…
…
…
0.831
0.085
0.449
0.207
Turnover
…
…
…
…
…
…
0.186
0.328
0.069
Volatility
…
…
…
…
…
…
…
-0.404
-0.315
Bank Credit
…
…
…
…
…
…
…
…
0.662
Table 2.2: Correlations
2.4. Methodology and Results
2.4.1 Results from the Benchmark Model
This section reports the OLS results based on the maintained assumption that institutions
are exogenous to the growth process. I run both initial value regressions, where the dependent
variables averaged over the sample period are regressed on the initial values of the independent
variables, as well as contemporaneous regressions, where both sets of variables are averaged over
the sample period. Since the results are broadly similar for the two specifications, I only report
the initial value regressions in the paper, which is also the more revealing choice.
28
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
Bank Credit
0.016 (0.009)†
0.015(0.011)
0.013 (0.007)†
5.867 (2.9561*
Institutions
0.017 (0.008)*
0.009 (0.010)
0.018 (0.006)**
-1.707 (2.872)
R-square
0.43
0.36
0.42
0.33
Adjusted R-square
0.32
0.22
0.29
0.10
47
46
46
32
Observations
Table 2.3: Regressions on Bank Credit & Institutional Quality 3
I start with a regression of output growth on bank credit and institutional quality, along
with other explanatory variables4. The results are given in Column 2 of Table 2.3. Institutional
quality is significant at the 5% level, whereas bank credit is significant at the 10% level. Among
the control variables, initial real per capita GDP and revolutions and coups are significant at the
5% level and initial secondary school enrollment at the 10% level. Contemporaneous regressions
(not reported) give similar results. Replacing output growth by productivity growth as the
dependent variable yields very similar results. Institutional quality is now significant at the 1%
level and bank credit is significant at the 10% level; among the controls initial real per capita
GDP is significant at the 5% level and the initial black market premium at the 10% level, and
revolutions and coups come close to being significant. Neither independent variable is significant
for the capital stock growth regression, while bank credit is significant at the 5% level for the
savings rate regression.
3
** denotes significance at the 1% level.
* denotes significance at the 5% level.
† denotes significance at the 10% level.
4
These are the logarithm of initial real per capita GDP, the logarithm of the initial secondary school
enrollment rate, the number of revolutions and coups, the initial ratio of government consumption to GDP,
the initial rate of inflation and the initial value of the black market exchange rate premium.
29
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
Bank Credit
0.008 (0.009)
0.012 (0.011)
0.006 (0.007)
3.163 (3.115)
Turnover
0.025 (0.010)*
0.021 (0.013)†
0.018 (0.008)*
6.163 (6.534)
Institutions
0.017 (0.008)*
0.010 (0.010)
0.018 (0.006)**
2.169 (3.501)
R-square
0.56
0.52
0.53
0.45
Adjusted R-square
0.44
0.38
0.40
0.20
42
41
41
29
Observations
Table 2.4: Regressions on Bank Credit, Turnover & Institutional Quality
Regressing the four dependent variables on bank credit, turnover and institutions, along
with the other explanatory variables (Table 2.4) gives significantly different results. Bank credit
is insignificant in all specifications. Turnover is significant at the 5% level for output and
productivity growth and at the 10% level for capital stock growth, while institutional quality is
significant at the 5% level for output and at the 1% level for productivity growth. Neither
financial nor institutional quality variables help explain differences in private savings rates across
countries.
Among the controls, initial real per capita GDP and revolutions and coups are
significant at the 5% level for the output and productivity regressions, the black market premium
at the 5% level for the capital stock regression, the secondary school enrollment at the 10% level
for the output regression and the initial inflation rate at the 10% level for the capital stock
regression. All variables enter the regression with the expected sign. The R-square is around 0.5,
which is the standard value for most growth regressions. Contemporaneous regressions (not
reported) give similar results.
30
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
Bank Credit
0.009 (0.009)
0.012 (0.010)
0.006 (0.007)
2.538 (3.056)
Value Traded
0.082 (0.033)*
0.086 (0.044)†
0.060 (0.030)*
14.930 (14.753)
Institutions
0.019 (0.008)*
0.010 (0.0010
0.020 (0.007)**
3.278 (3.211)
R-square
0.54
0.54
0.51
0.46
Adjusted R-square
0.41
0.41
0.38
0.20
43
42
42
29
Observations
Table 2.5: Regressions on Bank Credit, Value Traded & Institutional Quality
Table 2.5 replaces turnover by value traded. As in the previous table, bank credit is
insignificant in all specifications. Value traded is significant at the 5% level for output and
productivity growth and at the 10% level for capital stock growth and insignificant for savings
growth. Hence stock market variables always seem to knock out banking development variables.
The competition generated as a result of trading in stock markets therefore has a large effect on
output, innovation and productivity, whereas bank lending might have a non-linear effect on
growth (which is why they show up as insignificant in the regressions). Institutional quality is
significant at the 5% level for output and at the 1% level for productivity growth and is
insignificant for the other two specifications. Among the control variables, initial real per capita
GDP and revolutions and coups are significant at the 5% level for the first three specifications,
along with the black market premium for capital stock growth. Initial government consumption
to GDP ratio and the initial inflation rate are significant at the 10% level for output and capital
stock growth respectively. Initial government consumption to GDP ratio and the initial inflation
rate are both significant at the 5% level for the savings rate. Replacing value traded by market
capitalization interestingly results in both market capitalization and bank credit being
insignificant in all four specifications, though the former comes close to being significant for
output growth. Institutional quality is significant at the 5% level for productivity growth and
insignificant in the other specifications.
31
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
Bank Credit
0.005 (0.009)
0.010 (0.011)
0.005 (0.007)
1.795 (3.138)
Capitalization
0.010 (0.012)
0.006 (0.015)
0.001 (0.009)
-8.831 (8.665)
Value Traded
0.067 (0.040)†
0.076 (0.048)
0.055 (0.031)†
23.861 (17.146)
Institutions
0.015 (0.009)†
0.008 (0.010)
0.017 (0.007)**
3.700 (3.234)
R-square
0.56
0.54
0.52
0.49
Adjusted R-square
0.42
0.38
0.36
0.20
42
41
41
29
Observations
Table 2.6: Regressions on Bank Credit, Market Capitalization, Value Traded &
Institutional Quality
As evidenced by Table 2.6, value traded remains significant even after controlling for
market capitalization for both output and productivity growth, and comes close to being
significant for capital stock growth; market capitalization, on the other hand, is insignificant in all
four specifications. Hence market capitalization seems to provide little information beyond value
traded. Bank credit is once again insignificant. Institutional quality is significant at the 1% level
for productivity and at the 10% level for output growth. Among the controls, initial real per
capita GDP, secondary school enrollment and revolutions and coups are significant at the 5%
level for output growth, initial real per capita GDP, revolutions and coups and black market
premium at the 5% level and the initial inflation rate at the 10% level for capital stock growth and
initial real per capita GDP and revolutions and coups at the 5% level for productivity growth.
None of our explanatory variables explain differences in savings rates. A partial explanation
might be the fewer observations for private savings rates.
32
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
Value Traded
0.058 (0.054)
0.055 (0.046)
0.038 (0.047)
8.111 (11.906)
Volatility
0.250 (0.194)
0.481 (0.164)**
0.125 (0.168)
110.246 (67.849)
Institutions
0.013 (0.010)
0.004 (0.009)
0.017 (0.009)†
0.417 (3.178)
R-square
0.46
0.66
0.39
0.76
Adjusted R-square
0.21
0.51
0.12
0.59
30
30
30
22
Observations
Table 2.7: Regressions on Value Traded, Volatility & Institutional Quality
Table 2.7 introduces volatility along with value traded as the financial variables. Value
traded is no longer significant. Neither is volatility for output and productivity growths, as we
might expect, but it is significant at the 1% level for capital stock growth and comes close to
being significant at the 10% level for the savings rate. This surprising result is similar to Levine
and Zervos (1998) and needs to be analyzed further. Volatility in stock prices adds uncertainty to
the value of household savings, which could encourage higher savings. Institutional quality is
only significant for productivity growth (at the 10% level). Hence overall this set of regressions
yields mixed results. As for the controls, initial real per capita GDP and revolutions and coups
are significant at the 5% level for the first three specifications, with secondary school enrollment
rate and the initial inflation rate being additionally significant for capital stock growth. The initial
inflation rate is significant at the 5% level and revolutions and coups at the 10% level for the
private savings rate.
I also undertook F-tests and log-likelihood ratio tests for the joint significance of our
explanatory financial variables.
Table 2.8 presents the F-statistic (with the probability of
rejecting the null in brackets) for the F-tests.
33
Independent
Variables
Output Growth
Capital Stock
Growth
Productivity
Growth
Savings Rate
3.527 (0.026)*
1.962 (0.141)
2.431 (0.085)†
0.827 (0.496)
5.380 (0.010)**
2.753 (0.079)†
Turnover &
Value Traded
3.800 (0.033)*
2.408 (0.107)
3.341 (0.049)*
0.532 (0.596)
Capitalization &
Value Traded
3.131 (0.057)†
2.333 (0.114)
2.286 (0.119)
1.288 (0.299)
Bank Credit &
Turnover
3.475 (0.043)*
2.161 (0.132)
3.034 (0.063)†
0.815 (0.457)
1.765 (0.187)
1.363 (0.271)
0.867 (0.430)
0.417 (0.665)
2.912 (0.069)†
2.650 (0.087)†
2.581 (0.092)†
0.885 (0.429)
Turnover,
Capitalization &
Value Traded
Turnover &
Capitalization
Bank Credit &
Capitalization
Bank Credit &
Value Traded
3.524 (0.042)*
0.625 (0.546)
Table 2.8: Joint Significance of Financial Variables
The results confirm the earlier ones. Financial variables explain output growth and
productivity growth very well and capital stock growth to some extent; they are unable to explain
differences in savings rates across countries. The latter might also be explained by international
capital flows and not by internal saving. Stock market variables do a better job than banking
sector development indicators, even when being tested jointly. Log-likelihood ratio tests (not
reported) yield similar results.
Two main results emerge from the above exercise. First, stock market variables are more
important than banking sector variables in terms of explaining differences in growth experiences
across countries. Difference in institutional quality across countries is another important causal
variable. Among the controls, initial real per capita GDP and revolutions and coups are the most
important, and are significant at the 5% level in almost all the specifications. The first point
deserves attention, especially in light of the fact that firms depend a lot more on banks than the
equity market as a source of external finance even in countries with well-developed financial
34
systems like the U.S. The discipline shareholders impose on the management of the firms
mitigates the principal-agent problem by aligning their interests. This makes maximizing the firm
value the goal of the management, increasing efficiency, and in turn, productivity. There are
exceptions like early Indonesia, but this is by and large true. Stock markets also make financial
instruments less risky as investors can cash out relatively easily if they wish to alter their
portfolios.
Stock markets are easier to create in common law areas, which offer greater
opportunities for investment and growth by limiting expropriatory powers and protecting private
property.
Second, output and productivity growth are driven by the same factors, and the causal
variables do a much better job explaining them as compared to capital stock growth and
differences in private savings rates. The latter can’t be explained with these variables and are
presumably explained by other factors. Private capital formation depends on output growth and
FDI, and can be crowded out by public investment; public investment, on the other hand, is
influenced by the degree of external indebtedness and the ideology of the political party in power.
Private savings rates can be partly explained by differences in tax structure, output growth,
macroeconomic and political volatility and the old age dependency ratio.
2.4.2 2SLS Results
I also use a two stage least squares approach to control for potential endogeneity. In the
first stage, the measure of institutional quality is regressed on the distance from the equator,
settler mortality, index of malaria infestation and index of ethnolinguistic fragmentation rate in
four separate specifications. A legal origin dummy is used in the fifth specification. It takes the
value 1 for countries with English common law systems and 0 for countries with French, German
or Scandinavian civil law systems.
In the second stage, I regress our four dependent variables on the financial variables, the
institutional quality variables and the additional control variables. The regression I run is of the
form:
Growth indicator = β1 * vector of financial variables + β2 * vector of control variables
+ β3 * institutional quality + error term (1)
35
To further explore the impact of institutional quality vis-à-vis financial development on growth,
the coefficients of the financial variables are compared to those from equations of the form:
Growth indicator = β1 * vector of financial variables + β2 * vector of control variables
+ error term (2)
The results are similar across specifications, so only a couple of them are discussed. The second
stage results for the regression of output growth on bank credit and turnover, with latitude as the
instrument for institutional quality, are summarized in Table 2.9.
Independent Variable
Specification (1)
Specification (2)
Institutions (IV)
0.000 (0.007)
…
Bank Credit
0.013 (0.010)
0.013 (0.009)
Turnover
0.027 (0.011)*
0.027 (0.011)*
-0.014 (0.005)**
-0.014 (0.004)**
0.023 (0.010)*
0.023 (0.009)**
Revolutions & Coups
-0.035 (0.012)**
-0.035 (0.012)**
Initial Inflation Rate
-0.007 (0.014)
-0.007 (0.014)
Initial Gov Cons to GDP Ratio
-0.062 (0.048)
-0.062 (0.047)
Initial Black Market Premium
-0.000 (0.000)
-0.000 (0.000)
R-square
0.50
0.50
Adjusted R-square
0.36
0.38
42
42
Initial Real Per Capita GDP
Initial Sec School Enrollment
Observations
Table 2.9: Comparisons between Specifications (1) and (2) for Regression of Output
Growth on Bank Credit & Turnover
36
Institutional quality and bank credit are both insignificant, while turnover is significant at
the 5% level in both specifications. Among the control variables, initial real per capita GDP,
initial secondary school enrollment and revolutions and coups are all significant at the 1% level,
except for initial secondary school enrollment for specification (1), which is significant at the 5%
level. Replacing turnover with value traded (not reported) gives similar results, with value traded
being significant at the 5% level in both specifications; among the controls initial government
consumption to GDP ratio is now significant in one specification and close to being significant in
the other.
The results for the second stage regression of output growth on bank credit and market
capitalization, with latitude as the instrument for institutional quality, are summarized in Table
2.10.
Independent Variable
Specification (1)
Specification (2)
Institutions (IV)
0.009 (0.008)
…
Bank Credit
0.010 (0.010)
0.008 (0.010)
Capitalization
0.032 (0.014)*
0.023 (0.011)*
-0.019 (0.005)**
-0.016 (0.004)**
Initial Sec School Enrollment
0.023 (0.010)*
0.027 (0.009)**
Revolutions & Coups
-0.028 (0.012)*
-0.027 (0.012)*
Initial Inflation Rate
-0.009 (0.014)
-0.009 (0.014)
Initial Gov Cons to GDP Ratio
-0.045 (0.049)
-0.055 (0.048)
Initial Black Market Premium
-0.000 (0.000)
-0.000 (0.000)
R-square
0.49
0.48
Adjusted R-square
0.35
0.35
42
42
Initial Real Per Capita GDP
Observations
Table 2.10: Comparisons between Specifications (1) and (2) for Regression of Output
Growth on Bank Credit & Market Capitalization
37
Market capitalization is now significant at the 5% level in both specifications, so is
revolutions and coups.
Initial real per capita GDP is significant at the 1% level in both
specifications, and initial secondary school enrollment is significant at the 5% level for (1) and at
the 1% level for (2). Institutional quality and bank credit are both insignificant. However, when I
consider market capitalization and value traded together, as in the reduced form regressions, we
find that market capitalization is significant at the 10% level and that too only for specification
(1), whereas value traded is significant at the 10% level for both. Hence market capitalization
doesn’t give us much information beyond what is given by value traded.
The results hitherto found in the literature showing that institutional quality of countries
is the primary explanatory variable for growth is hence not applicable to the stock market
variables, even though banking development variables give us the traditional result. Institutional
quality is insignificant in our two-stage regressions, and so is bank lending; stock market
measures, on the other hand, are strongly significant.
Furthermore, the coefficients of the
financial variables change little while switching from specification (1) to (2) in case of both
turnover and value traded. This suggests that financial variables have an impact on growth that is
quite distinct from that of institutional quality. The results are broadly similar if latitude is
replaced with the index of ethnolinguistic fragmentation, index of malaria infection or the settler
mortality rate as the instrumental variable.
2.5. Conclusion
As Douglas North has pointed out, Western institutions are the by-product of 400 years
of economic development, and are not easily manipulable. A lot of the recent literature on
growth has focused on variations in the institutional quality of different countries as explanations
of cross-country growth differences. When standard growth regression variables are introduced
into this specification, only institutional quality remains significant, while the other variables
become insignificant. Hence the results emerging from this literature suggest that the only effect
of policy variables is their indirect effect on institutional quality; apart from that they have no
direct impact. This paper suggests that this is not true for stock market variables, implying that
countries stand to benefit from higher future growth rates by developing their financial sector.
While the IMF and the World Bank rightly recommend that countries bring their inflation rates,
fiscal deficits, government expenditures and exchange rate overvaluations under control, this
38
paper suggests that developing countries stand to gain from devoting some of their resources to
improving the efficiency of their financial markets.
Furthermore, given the extent of financial development, this paper suggests the opposite
conclusion, that institutions do not matter or matter only in their manifestation of financial
development. This dovetails well with Glaeser, La Porta, Lopez-de-Silanes and Shleifer (2004),
who find that most indicators of institutional quality as well as some of the instrumental variable
techniques that are used in this literature are flawed, and human capital is a more basic source of
growth than institutions. The European settlers in the largely temperate zone colonies of U.S.,
Canada, Australia and New Zealand brought with them their human capital rather than better
institutions, and the institutional quality instruments are even more highly correlated with human
capital both in the year 1900 and today. Moreover, their results suggest that poor countries get
out of poverty through good policies, which are often pursued by dictators. Improvements in
political institutions follow. South Korea, Taiwan and Singapore are excellent examples.
39
CHAPTER 3
Does Economic Freedom Mean Freedom to Grow?
3.1. Introduction
3.1.1 Literature on Economic Growth of the U.S. States
There is a large body of literature on the economic growth of the U.S. states. The
neoclassical growth models of Ramsey (1928), Solow (1956), Cass (1965) and Koopmans (1965)
all provide a theoretical framework suggesting that economies with low initial levels of per capita
output or income grow faster than those with high initial levels of per capita output or income.
Barro and Sala-i-Martin (1992) consider the neoclassical growth model to determine convergence
of U.S. states in terms of per capita state personal income since 1840 and per capita gross state
product since 1963. They find strong evidence of convergence, though the results fit into the
neoclassical model only with the assumption that diminishing returns to capital set in very slowly.
The rates of convergence are similar for gross state product and state personal income. The
authors explain this by assuming that the existing capital stock acts as the collateral for external
debt and consequently imposing a ceiling on the ratio of a state’s external debt to its capital stock.
Hence the residents of the state or the state government cannot borrow unlimited amounts to
finance investments in the state, leading to the convergence of gross state product and state
personal income. In a related paper, Barro and Sala-i-Martin (1991) also look at patterns of
convergence in 73 regions of Western Europe (from Belgium, Denmark, France, Germany, Italy,
the Netherlands and the United Kingdom) since 1950. The findings are similar to those of the
U.S. states, with a convergence rate of about 2% a year. Sala-i-Martin (1996) examines σ–
convergence (dispersion of real per capita GDP levels across countries decreases over time),
absolute β–convergence (poor countries grow faster than rich ones) and conditional β–
convergence (growth rate of an economy is positively related to the distance that separates it from
40
its own steady state) and applies them to a sample of 110 countries, a sub-sample of OECD
economies, the U.S. states, the prefectures of Japan and regions within several European nations.
He finds that except for the sample of 110 countries, all other samples strongly exhibit σ–
convergence and absolute β–convergence, whereas the former exhibits σ–convergence and
conditional β–convergence. The existence of β–convergence is a necessary condition for the
existence of σ–convergence. The speed of conditional convergence is around 2% a year.
Johnson and Takeyama (2001) use regression trees to ascertain the importance of initial
conditions in the growth experience of the 48 contiguous U.S. states. They assume that all states
have the same underlying fundamentals, so without differences in initial conditions they should
share the same steady state. Their results indicate that initial conditions matter because initial
capital stocks determine which states belong to which convergence club.
Quah (1996) shows that the uniform 2% rate of convergence that was the consensus at
the time can arise from factors unrelated to growth dynamics. In cross-country samples, the
author finds some evidence for persistence and convergence clubs, whereas the U.S. states clearly
exhibit convergence that is unambiguous up to sampling error. Evans and Karras (1996) focus on
the relationship between the growth rate of per capita output and the initial level of per capita
output and show that this approach is only accurate when regions have identical first-order
autoregressive dynamic structures and all permanent cross-economy differences are completely
controlled for. These conditions do not hold for our data sets. Using a different approach, they
find strong evidence for conditional convergence of the lower 48 U.S. states (1929-1991) and a
sample of 54 countries (1950-1990). Drennan and Lobo (1999) look at income convergence for
all the metropolitan areas of the U.S. for the period 1969-1995. Their results for β–convergence
indicate strong support for absolute convergence of per capita personal income and average wage
for the metropolitan areas. They do not test for conditional convergence as situations where
absolute convergence would occur, but conditional convergence would not, are extremely
unlikely. They also test for σ–convergence and find no evidence to support the hypothesis.
Some U.S. states have consistently grown more rapidly than the national average since
the Second World War, while others have consistently grown more slowly. Blanchard and Katz
(1992) look at regional booms and slumps in the U.S. in general and Massachusetts in particular
and find that growth rates return to normal after temporary ups and downs, but the trajectory of
41
employment is permanently altered. The main adjustment mechanism is the mobility of labor and
not job creation or job migration. Their findings also indicate that the mobility of labor in the
U.S. is mostly in response to changes in unemployment and not changes in consumption wages.
Glaeser, Kallal, Scheinkman and Shleifer (1992) test a data set on the growth of large
industries in 170 U.S. cities between 1956 and 1987 and find that cross-industry intellectual
externalities (rather than within-industry ones) are especially important for urban growth. Using
employment as the indicator, they find that industries grow slower in cities where they are heavily
represented; the primary metals industry, for instance, grew at a fast pace in Savannah, Georgia,
where it was relatively small in 1956, and contracted in Fresno, California, where it had a big
presence. Industries also grow faster in cities where the firm size for that industry is smaller than
the national average.
Lastly, city industries grow faster when the rest of the city is less
specialized. Glaeser, Scheinkman and Shleifer (1995) look at the relationship between urban
characteristics of 203 large U.S. cities in 1960 and urban growth rates between 1960 and 1990.
Urban growth is measured by population growth. Unlike countries, whose populations are
relatively immobile and population growth mostly differs due to differences in fertility,
population growth differences across cities reflect varying economic opportunities in the cities.
The authors find a positive relationship between income and population growth, both of which
are positively related to initial schooling and negatively related to initial unemployment and the
initial share of employment in manufacturing. They do not find any correlation between racial
composition, racial segregation or government expenditure (except spending on sanitation) and
urban growth. However, government debt is positively correlated with growth in later years.
They also do not find any evidence of convergence; bigger cities do not have lower population
growth rates and richer cities do not have lower income growth rates.
Turning the focus to the relationship between financial development and growth, one of
the most cited cross-country studies on the empirical front is King and Levine (1993). For a
sample of 77 countries, indicators of financial development like the size of the formal financial
sector relative to GDP, the size of commercial banks relative to the central bank, the fraction of
credit allocated to the private sector and the size of the credit issued to private firms relative to
GDP are significantly correlated with growth and each of these can significantly predict
subsequent values of the growth indicators. Levine and Zervos (1998) consider four different
dependent variables: economic growth, capital stock growth, growth in productivity and the
42
private savings rate and find that even after controlling for the standard variables, the extent of
development of the banking sector and the stock market both have significant positive impacts on
the first three dependent variables. Rajan and Zingales (1998) focus on a specific factor that
might cause increased financial intermediation to raise the rate of growth. They argue that
financial development makes external finance less costly and different industries have different
degrees of dependence on external finance. Hence industries like drugs & pharmaceuticals and
plastic products that use a lot of external capital should grow faster than industries like tobacco
and pottery that use the least. Their cross-country data indeed supports this view.
Among the early studies focusing on the finance-growth relationship in the U.S., Amel
and Liang (1992) find that deregulation in the U.S. banking industry has led to an increase in the
number of new branches but not in the number of de novo banks. This indicates a substitution in
favor of branch entry. They also conclude that fewer new entrants result from a change to interstate banking than from a change to intra-state banking. Calem (1994) finds that the small
banking sector has contracted in states that have relaxed intra-state branching restrictions, while
the relaxation of inter-state branching restrictions has not had an appreciable effect. Intra-state
branching restrictions prevented most banks from reaching their efficient size. Once the removal
of these restrictions enabled them to achieve their optimal scale, further deregulation was unlikely
to lead to additional contraction. The Riegle-Neal Act of 1994 came into effect on June 1, 1997,
and invalidated the laws in thirty six states that only allowed inter-state banking on a reciprocal or
regional basis. McLaughlin (1995) suggests that banking reforms will lead to faster consolidation
of the banking industry in the U.S., but although there has been rapid consolidation within state
borders, the changes across state borders will be incremental and localized and will not lead to
nationwide banking very soon.
This consolidation takes place through the bank holding
companies’ conversion of existing and acquired bank subsidiaries into branches.
A number of studies document the positive impact of banking deregulation in the U.S.
Schranz (1993) examines the impact of takeovers on firm value in the context of the banking
sector and finds that publicly traded banks in states that make takeovers easier are more
profitable.
Alternative mechanisms to maximize firm value like concentration of equity
ownership and management ownership of stock are observed in states that have restrictions on
takeovers. Berger, Kashyap and Scalise (1995) find that the post-deregulation banking sector in
the U.S. is characterized by a sharp fall in the number of banks and a sharp rise in the number of
43
bank failures, off-balance sheet activities, equity capital ratios, foreign bank lending to U.S.
corporations and the adoption of ATMs. Additionally, lending to both small and large businesses
fell in the first half of the nineties, whereas lending to medium-sized borrowers roughly stayed
constant. Improved technology and financial innovations may have led to big borrowers utilizing
alternatives sources of credit, whereas organizational diseconomies have made it harder for the
now larger banks to extend as much credit to small borrowers as before. Jayaratne and Strahan
(1996) study the impact of bank branching deregulations during the Reagan administration on
economic growth in the U. S. states and find that growth rates of real per capita output and
income increased substantially following the reforms. The cause of this increase was not an
increase in the quantity of bank lending but rather the quality of bank lending, as measured by
non-performing loans, the fraction of loans written off and the fraction of loans classified as
“insider loans”. Kroszner and Strahan (1999) use a hazard model to explain the timing of intrastate branching reforms. Much of the deregulatory patterns can be explained by the privateinterest theory of regulation, where better organized groups (large banks and bank-dependent
firms) use the coercive power of the state to capture rents at the expense of the less organized
ones (small banks and rival insurance firms). Hence reform occurs later in states where small
banks and stronger relative to big banks. The authors also argue that branching deregulation
began in the seventies due to the advent of ATMs, banking via mail and telephone (for example
checkable money market mutual funds and the Merrill Lynch Cash Management Account) and
falling transport and communication costs, all of which raised the elasticity of deposit supply.
This led banks to relax their guards in their fight to preserve their geographical monopolies.
Freeman (2002) arrives at a different conclusion by running robustness checks that
indicate that the growth effects of branching deregulation are significantly smaller than estimated.
Deregulation is endogenous to the economic conditions of the state, which results in an upward
bias for the Jayaratne and Strahan (1996) estimates. Banking deregulation in most states occurred
during downturns, when the banking sector was facing significant losses, often due to falling
commodity or housing prices. Wall (2004) finds that instead of a uniformly positive relationship,
banking deregulation led to decreases in entrepreneurship in some areas of the U.S. and increases
in others. This ranged from an 11.5% drop in the Mideast to a 15.1% increase in the Great Lakes.
Huang (2008) looks at 285 pairs of contiguous counties across U.S. state borders where intra-state
branching restrictions were removed earlier in one state than in the other. These counties should
44
be similar in both observable and unobservable conditions and grow at similar rates unless subject
to different regulations. Of the twenty three deregulation events during the period 1975-1990
considered by the author, growth significantly accelerates in only five cases, all of which
happened during 1985-1990.
Some of the explanations are that the U.S. economy is less
dependent on commercial banks than Europe and that the economic impact of banking
regulations in the U.S. has been overstated.
Beck, Levine and Levkov (2010) find that while U.S. income inequality widened during
the period, branching deregulation has lowered income inequality by reducing the income gap
between men and women and between skilled and unskilled workers.
Capital market
imperfections can hinder the poor from borrowing to finance their education (Galor and Zeira,
1993) and also prevent them from becoming entrepreneurs as they often lack collateral for bank
loans (Banerjee and Newman, 1993). However, the authors find no impact of deregulation on the
business income of the poor or on educational attainment.
Studies have addressed the effects of many other variables on state-level economic
growth. Of particular relevance to this paper are those related to public expenditure and taxes.
Ratner (1983) and Aschauer (1989) estimate production functions for private output where
government capital is one of the inputs. Ratner (1983) finds government capital to contribute to
private output, with an output elasticity of 0.06. However, private output is more elastic with
respect to private capital (around 0.22). Aschauer (1989) further disaggregates government
capital into military and non-military capital, and finds non-military capital to significantly
contribute to private output; the output elasticity is 0.39. A core infrastructure set of highways,
airports, mass transit, sewers, water systems, etc has the greatest impact on private productivity.
Lynde and Richmond (1993) estimate a translog profit function and find public capital to be a
significant part of the production process. About 40% of the 1% decrease in labor productivity
from the sub-period 1959-1973 to the sub-period 1975-1989 is a result of the fall in the public
capital to labor ratio, while the remainder is due to the effects of returns to scale, intermediate
goods prices and technology. The 5% positive contribution from the increase in the private
capital to labor ratio had a minor off-setting effect. Using panel data for the lower 48 states,
Evans and Karras (1994) consider the effects of different categories of government capital on the
private sector.
While current government education services are productive, other current
government services like health and hospital services, police and fire services and sewer and
45
sanitation services are not. Hence the lack of government investment in the U.S. is not as acute
as some believe. Holtz-Eakin (1994) also finds no impact of public sector capital on private
sector productivity after controlling for state-specific unobservables; an important reason is
crowding out. Turning to taxes, Jorgenson and Yun (1986) analyze the importance of tax policy
on capital allocation in an intertemporal general equilibrium model of the U.S. economy using
annual data from 1955 to 1980. Evaluating alternative tax policies, they conclude that a shift
from direct taxation of income to indirect taxation of consumption would result in significant
welfare gains (26-27% of the 1980 private national wealth). Jorgenson and Yun (1990) look at
the impact of the 1986 Tax Reform Act on U.S. economic growth. Forecasting the future growth
rate of the U.S. economy with and without the Tax Reform Act, the authors find that most of the
potential welfare gains from reform are lost even at moderate rates of inflation due to the lack of
inflation indexing of the tax base. In addition to indexing, future tax reforms should also include
income from household assets in the tax base and reducing the tax on business income.
3.1.2 Literature on Institutional Quality of the U.S. States
There is a significant body of literature that explores the relationship between
institutional quality and economic growth. Proponents of the institutions hypothesis are of the
view that institutional quality jointly determines both policy choices and the growth rate, but the
policies and the growth rate bear no causal connection. This makes it impossible to suggest
policy prescriptions for countries as institutions tend to be sticky, and apart from a few historic
exceptions, only evolve very slowly.
In one of the most cited papers on the subject, Easterly and Levine (1997) focus on subSaharan Africa to understand why different countries choose different public policies. Many
countries in the region have experienced negative per capita growth rates since the 1960s. They
find that much of the inadequate education system, political instability, large black market
exchange rate premiums, lack of financial development and physical infrastructure and
worrisome public health indicators like high infant mortality rates and inadequate calorie intake
can be explained by the very high levels of ethnic fragmentation in these countries. The borders
of most African countries were drawn up in a series of negotiations among European colonizers
solely with economic considerations in mind. Little or no heed was paid to keeping ethnic groups
46
together during this process. Some examples are the Congo Pedicle5 and the Caprivi Strip6.
Ethnic diversity translates into political polarization and enables rent-creation for the groups in
power at the expense of the other groups.
This prevents agreements about public goods
provisions and reduces the growth rate.
Alesina and Rodrik (1994) consider an endogenous growth model where there is
distributive conflict among economic agents who are endowed with different shares of labor,
which is non-accumulated, and capital, which is accumulated. Government services are financed
by a linear tax on capital. The authors theoretically show that an increase in inequality leads to
increased tax rates and reduced growth rates, and present empirical evidence that increases in
land ownership and income inequalities decrease subsequent economic growth. Persson and
Tabellini (1994) construct an overlapping generations model where agents live for two periods,
there is an income tax that is solely for redistributive purposes and this tax influences investment
in human capital. The model implies that investment and growth-promoting activities get taxed
in order to redistribute income in heterogeneous economies. They also present historical and
post-war empirical evidence that rising inequality reduces investment and hence depresses growth
rates. However, this relationship holds only in democracies. Alesina and Spolaore (1997)
analyze the equilibrium number of countries under different political regimes and economic
environments. This involves a trade-off between the benefits of large size (such as the decreasing
per capita cost of non-rival public goods with an increase in the number of people financing them
and increasing returns in the size of countries in models with increasing returns in a world with
partially free trade) and heterogeneous population. Their results suggest that democratization
leads to secessions, the equilibrium number of countries is inefficiently large, and this number
increases with the extent of economic integration. Bolton and Roland (1997) develop a model for
the breakup and unification of nations, where they too focus on the gains from efficiency vis-à-vis
the loss of control over political decisions. If income distribution varies widely across regions
5
The Congo Pedicle is a Congolese salient rich in copper deposits that juts into Zambia, almost cutting the
country in half. The pedicle was added to Congo by the political machinations of King Leopold II of
Belgium. The residents of the pedicle are largely overlooked by the government of Congo, while Zambian
residents who live near the pedicle have to travel more than 500 additional miles to avoid passing through
Congolese territory.
6
The Caprivi Strip is a narrow salient extending eastward from the northeastern corner of Namibia, that
was carved out in negotiations among Germany and Britain to connect the then German South-West Africa
to the Zambezi River and hence to German East Africa. It turned out that Victoria Falls lay 40 miles to the
east of Caprivi Strip’s easternmost point, which rendered the Zambezi non-navigable.
47
and the efficiency gains from staying together are small, then the equilibrium is separation. This
tendency to separate decreases with increase in factor mobility. They point to the breakup of
erstwhile Yugoslavia and Soviet Union as examples; the prospect of becoming a part of the
European Union someday represents potential economic gains, whereas the collapse of central
planning in the 1990s in these countries eliminated most economic benefits of staying together.
Alesina and La Ferrara (2000) argue that people prefer to self-segregate in a heterogeneous
society, as the physical interactions that are a pre-requisite for building up social capital are only
possible if everyone lives in the same community or travels to meet. Travel is costly, implying
that minority groups can either join a large heterogeneous group in which they remain the
minority, or not participate in the community at all. An increase in the area’s heterogeneity will
lead to an increased participation only if the minority is large enough to form its own group.
Alesina, Baqir and Easterly (1999) provide a theoretical model tying the ethnic
heterogeneity of a city to the type and quantity of public goods the city supplies. Testing their
model with data for U.S. cities, U.S. metropolitan areas and U.S. urban counties, the authors find
that after controlling for other factors, the shares of spending on education, roads, sewers and
trash pickup are inversely related to the census unit’s population heterogeneity. As an example,
they point to the adjacent counties of Prince George’s and Montgomery in Maryland; the former
is much more diverse than the latter, and while Prince George’s county schools are struggling,
Montgomery county public schools are nationally renowned.
Cutler and Glaeser (1997)
document that higher segregation in urban areas adversely affects education, income and social
indicators like single parenthood of the African American community. A one standard deviation
decrease in segregation would decrease the white-black difference in their model by about a third.
Poterba (1996) finds that an increase in the percentage of seniors in an area leads to a decrease in
public spending on education, and this effect is much stronger in areas with high levels of
heterogeneity among seniors and school-goers. Moreover, differences in the size of the schoolgoing population do not result in proportionate changes in education spending, leading to lower
levels of per capita spending on students in states with larger school-age populations.
Several studies have compared the tax and redistributive policies in Europe to that of the
United States. Tanzi and Schuknecht (2000) find that the average levels of health and education
indicators do not significantly differ among countries with large and small governments.
Atkinson (1995), however, finds that countries with larger governments and higher levels of
48
social spending have lower post tax income inequality. Post-tax levels of income inequality are
the highest in the U.S., followed by the U.K. and central and southern Europe, and the lowest in
the Nordic countries. Alesina, Glaeser and Sacerdote (2001) compare the redistributive policies
in European countries to that of U.S. states and find that factors like the variance and skewness of
the pre-tax income distribution, the volatility of income, the social costs of taxation and the
expected income mobility of the median voter do not explain the difference. The European
population is much more homogeneous than the American population. One of the effects of
racial heterogeneity in the U.S. is reluctance towards redistribution to the poor on the part of the
voters because of the recipients being mostly African American. The poor are widely perceived
as lazy in the U.S., whereas they are viewed as unfortunate in Europe. Other consequences
include the lack of a viable socialist party (which is also due to the lack of proportional
representation and the low population density) and limited political power of the poor. The
origins of the U.S. as the union of independent territories also created a federal structure that
wasn’t conducive to centralized redistributive policies.
An interesting caveat is provided by Alesina, Di Tella and MacCulloch (2004) in their
study of how inequality affects the response to survey questions on happiness. Controlling for
other variables, they find that individuals are less likely to report themselves as happy when
inequality is higher. This effect is stronger in Europe than in the United States. Additionally, it is
the poor and those to the left of the political spectrum who are bothered by inequality in Europe;
the rich are bothered by inequality in the U.S. The poor and those politically to the left in Europe
are more bothered by inequality than their counterparts in the U.S. These point to the belief in the
American Dream that it is easy to move up the income ladder by virtue of individual effort and
the corresponding belief in Europe that economic mobility is lower than in the U.S.
Tiebout (1956) addresses the problems with public goods provision that are created by a
heterogeneous electorate, and his solution to the problem is for individuals to sort them into
different communities depending on the type of public goods they desire. However, this sorting
mechanism might not work for several factors, including a limited number of such communities,
barriers to labor mobility and the economies of scale in providing public goods. Looking at a
sample of U.S. municipalities, all Boston-area municipalities and all U.S. counties (except
Alaska) from the mid-1800s to 1990, Rhode and Strumpf (2003) find evidence that long-run
trends in geographic segregation are not consistent with the Tiebout hypothesis that the choice of
49
residence depends only on the provision of public goods. With falling mobility costs, the Tiebout
model would indicate increasing heterogeneity in local public goods provisions (in terms of local
taxes per capita and school taxes per capita). Their results indicate the opposite. This partly
reflects the fact that most contemporary segregation occurs between neighborhoods (measured by
census tracts) within the same municipality, which consequently receive similar local public
services. Growing local government competition to attract individuals who are more desirable
than others (for instance the wealthy) would also act as an incentive for these local governments
to adopt similar policies (like reducing or eliminating local income taxes). Bayer (2000), in his
model, allows residential choice to be a function of employment location and community racial
composition.
Several papers have looked into the effect of population segregation on the public
provision of education. Durlauf (1996) and Benabou (1996) both show that racial and ethnic
difference between the suburbs and the inner cities have an adverse impact on the funding of
public schools, though efforts to equalize spending are counter-productive. Parents affect the
conditional probability distribution of the income of the next generation by their neighborhood
choice, which works through educational as well as sociological channels. More affluent families
self-segregate into more homogeneous neighborhoods. This makes income inequality more
persistent across generations, though that might not be the case with wealth. Goldin and Katz
(1999) look at high school graduation rates in the early twentieth century using data from the
1915 Iowa State Census and find that greater homogeneity in income, wealth, ethnicity and
religious beliefs all aided in the expansion of the high school system. Many states that led the
high school movement still score highly on various measures of social capital, and the social
capital assembled locally almost a hundred years ago still contributes to human capital formation
today. Hendricks (2004) looks at the disparate rates of educational attainment across the U.S.,
which differs nearly two-fold across the U.S. states. Massachusetts and Connecticut, at 35.5%
and 35.1% respectively, top the list of the percentage of people holding a college degree;
Arkansas and West Virginia, at 17.7% and 16.4% respectively, are at the bottom. These rates
also do not converge appreciably over time. As European institutions are largely the product of
western education, the varying percentages of the workforce with a college degree should
translate into qualitatively varying state-level institutions. States with highly educated workforce
employ skill-biased technologies within each industry and specialize in high-skill industries, but
50
they do not pay lower skill premiums than other states. Moreover, increasing urbanization and
population density lead to a more educated workforce. The explanation lies in theories based on
agglomeration economies.
Using 1980 census data, Braun (1988) looks at predictors of income inequality in U.S.
states. He considers the Gini coefficient, Theil index, coefficient of variation, four values of the
Atkinson measure and Nelson index as different measures of income inequality. They vary
widely across states; the Gini, for instance, ranges from 0.3163 in Wyoming and 0.3218 in New
Hampshire to 0.3997 in Louisiana and 0.4450 in the District of Columbia. The strongest
predictor for this variability turns out to be the standard deviation of number of years of
educational attainment. Mean family income is in second place, with much less predictive power
than education, followed by the percentage of females in the labor force and the percentage of
nonwhites.
The relationship between institutional factors and U.S. state-level economic growth has
also been addressed in political science literature.
Olson (1965, 1982) analyzed how
distributional coalitions like farmers, industrialists and labor unions often start out small, but
grow in influence over time and successfully lobby to steer policy in the direction of
protectionism and resistance to new technology.
The benefits will accrue to the coalition
members, who are relatively few, whereas the costs will be spread out among all consumers, who
are much more numerous. Hence there is little public resistance, but in most cases these policies
depress the growth rate. Gray and Lowery (1988) examine how Olson’s model holds up in
interest group politics and economic growth in U.S. states. They consider the exponential rate of
growth of income from labor and proprietors, exponential rate of growth of private nonfarm
income and exponential rate of growth of manufactures income as dependent variables and time
(measured by the number of years since statehood for non-southern states and the number of
years since 1865 for states in the Confederacy) and the number, size and nonencompassingness of
union and business groups and an indicator of interest group power relative to that of the
government as independent variables. They find that the number of years since statehood or the
Civil War and union power relative to the government are both negatively related to growth,
while business interest group power is positively related to growth. There is little evidence that
interest group influence increased with time since statehood or the Civil War.
51
Hero and Tolbert (1996) look at racial and ethnic diversity as theoretical and empirical
explanations for politics and policy in U.S. states. They consider an index of minority diversity
(blacks, Latinos and Asians as a ratio of the white population) as well as white ethnic diversity
(created from the percentage of white population who self-reported as Greek, Hungarian, Italian,
Polish, Portuguese, Russian or Irish in the census). Minority diversity is the least in Maine,
Vermont and South Dakota and the greatest in Texas, California and New Mexico; white ethnic
diversity is the least in Utah, North Dakota and South Dakota and the greatest in Rhode Island,
Massachusetts and Connecticut. The authors find that more minority diversity leads to inferior
educational and other social policy choices. This effect is magnified in more homogeneous
states.
Blacks have substantially lower graduation rates and higher suspension rates in
homogeneous states like Wisconsin, Minnesota and Washington than in ex-Confederate states
like South Carolina, Alabama and Texas. States with higher minority diversity spend less on
Medicaid and have higher infant mortality rates. Of the several states legislatures that passed
laws making English the official language, almost all had higher than average minority
population, indicating an anti-minority outlook.
The remainder of the chapter is organized as follows. Section 3.2 describes the data and
the variables used. Section 3.3 describes the methodology and results and Section 3.4 concludes.
3.2. Data and Variables Used
3.2.1 Data Sources
The data on gross domestic product by state, public expenditure and state and local
income taxes are from the Bureau of Economic Analysis. Price level data is from the Bureau of
Labor Statistics. The financial variables are from the Federal Deposit Insurance Corporation.
The data on economic freedom of the states is from The Fraser Institute. Ethnic fragmentation
index for the U.S. states are calculated from the U.S. Census Bureau data; the percentage of the
population who are foreign born are also from the U.S. Census Bureau. The percentage area and
population of a state that fall under the jurisdiction of the Voting Rights Act of 1965 are
calculated from the Department of Justice website and the U.S. Census Bureau. Sullivan’s
diversity index numbers are from Sullivan (1973).
52
The sample period is 1963 – 2010.
3.2.2 Variables Used
This paper focuses on gross domestic product by state rather than state personal income
to make the results comparable to cross-country studies. The principal difference between the
two is that gross domestic product by state counts capital income in the state where the
production process occurred, whereas state personal income counts it in the state the owner of the
asset resides. The neoclassical assumption of closed economies is not applicable to the U.S.
states, with free flow of labor, capital, goods and services and technology across state lines.
Hence there is a significant difference between gross domestic product by state and state personal
income. Also, in the event of technological parity, more globalized capital markets will speed up
the convergence for gross domestic product by state but slow down the convergence for state
personal income.
I run contemporaneous regressions with the independent variables averaged over the
sample period as well as regressions using the initial value for all independent variables. There is
one observation per variable per state. The dependent variable is the annual growth rate of gross
state product per capita. The independent variables include financial variables, an institutional
quality variable and control variables.
Gross domestic product (GDP) by state7 is the state counterpart of the national gross
domestic product. GDP by state is derived as the sum of the GDP originating in all the industries
in a state. An industry's GDP by state, or its value added, in practice, is calculated as the sum of
incomes earned by labor and capital and the costs incurred in the production of goods and
services. Hence it includes the wages and salaries earned by workers, the income earned by
individual or joint entrepreneurs as well as corporations and business taxes such as sales, property
and federal excise taxes that count as a business expense. Data on real GDP by state is in chained
(2005) dollars, and is hence an inflation-adjusted measure of each state’s gross product based on
7
GDP is calculated as the sum of what consumers, producers and the government spend on final goods and
services, plus investment and net foreign trade. In theory, incomes earned should equal what is spent, but
due to different data sources, income earned, usually referred to as gross domestic income (GDI), does not
always equal what is spent (GDP). The difference is referred to as the "statistical discrepancy."
53
national prices for that SIC category. Hence real GDP by state does not capture geographic
differences in the prices of goods and services that are produced and sold locally.
The financial variables include the assets of commercial banks as a percentage of the
gross state product, the total equity capital of commercial banks as a percentage of the gross state
product, loans made by commercial banks as a percentage of the gross state product, the loan to
equity ratio, total deposits of commercial banks as a percentage of the gross state product,
domestic deposits of commercial banks as a percentage of the gross state product, the number of
commercial bank branches per 1,000 residents in the state and the ratio of deposits of commercial
banks that failed or received assistance from the FDIC to the total deposits of FDIC-insured
commercial banks in the state.
The definitions and measurement methods of the banking variables I look at have been
adjusted multiple times over the sample period, hence I only discuss the current ones 8. The
majority of the data compiled by the Federal Deposit Insurance Corporation (FDIC) is from the
Federal Financial Institution Examination Council (FFIEC) Call Reports and the Office of Thrift
Supervision (OTS) Thrift Financial Reports submitted by all FDIC-insured depository
institutions.
FDIC-insured commercial banks include all commercial banks insured through the Bank
Insurance Fund (BIF) and all commercial banks insured through the Savings Association
Insurance Fund (SAIF) that are regulated by and submit financial data to one of the three Federal
commercial bank regulators (Board of Governors of the Federal Reserve System, Federal Deposit
Insurance Corporation and Office of the Comptroller of the Currency). Data on commercial
banks that are not insured by the FDIC (though they might be insured by a state insurance fund or
a private insurance company) are excluded from the analysis.
Assets refer to total assets owned by the institution including cash, loans, securities, bank
premises and other assets as of the last Call Report or Thrift Financial Report filed by the
institution. This total does not include off-balance-sheet accounts. Net loans and leases refer to
total loans and lease financing receivables minus unearned income and loan loss allowances.
Unearned income is the loan revenue that has been received in advance of its being earned. Loan
loss allowance is the allowance (reserve) that each bank must maintain for loan and lease losses
8
Definitions of financial variables are from FDIC’s “Statistics on Banking” (1997).
54
that is adequate to absorb estimated credit losses associated with its loan and lease portfolio
(which also includes off-balance-sheet credit instruments).
Total deposits refer to total deposits including demand deposits, money market deposits,
other savings deposits, time deposits and deposits in foreign offices as of the last Call Report or
Thrift Financial Report filed by the institution. Domestic deposits include all of these except the
deposits in foreign offices.
Total equity capital refers to total equity capital on a consolidated basis. It includes bank
equity capital (includes preferred and common stock, surplus and undivided profits) and the
equity capital component of non-controlling (minority) interests in consolidated subsidiaries.
Surplus refers to the portion of an institution's capital received for shares of stock sold in excess
of par value (excludes all surplus related to preferred stock). Undivided profits refer to undivided
profits, capital reserves, net unrealized holdings gains (losses) on available-for-sale securities,
other equity capital components, and accumulated gains (losses) on cash flow hedges.
The control variables I consider are total public expenditure and state and local income
tax receipts as a percentage of the gross state product and state personal income and initial gross
state product per capita.
The economic freedom index as calculated by The Fraser Institute measures institutional
quality in the state. The Fraser Institute publishes an overall index of economic freedom, as well
as three sub-indices on government size, takings and discriminatory taxation and labor market
freedom. The size of government index looks at government consumption as a percentage of the
GDP and transfers and subsidies as a percentage of the GDP. The takings and discriminatory
taxation index looks at total government revenue from own source as a percentage of the GDP,
the top marginal income tax bracket and its threshold, indirect tax revenue as a percentage of
GDP and sales tax as a percentage of GDP. The labor market freedom index considers minimum
wage legislation, government employment as a percentage of total employment, occupational
licensing and union density in the state. The indices range in value from 0 to 10, 0 indicating the
minimum economic freedom and 10 indicating the maximum economic freedom9.
9
Refer to Appendix B.2 for the economic freedom indices for the U.S. states.
55
The index of economic freedom is reflective of the underlying institutions in the state.
Since institutional quality is likely to be correlated with one or more of the other variables, I use
six sets of instruments for economic freedom. The percentage of foreign population is the ratio of
the population who was born outside the U.S. or Puerto Rico to the total population of the state.
The ethnic fragmentation index of the state is calculated according to the formula FRAC j
= 1 – Σ s2 ij, where sij is the share of ethnic group i in the total population of state j. The
population groups considered are Whites, Blacks or African-Americans, Asians, Native
Americans or Alaskans, Native Hawaiians or Other Pacific Islanders, Some Other Race and Two
or More Races. Persons of Hispanic or Latino origin may be any race. Sullivan’s diversity index
measures the social, economic and religious diversity of the U.S. states10. Since his data is from
1960-1963 (which is immediately prior to the start of the sample period in this paper) and because
of the popularity of the index in the political science literature, it is considered as an instrument of
choice. The percentage area and population of states that fall under the jurisdiction of Section 5
are calculated as the ratio of the total area and population of the counties and
cities/towns/plantations that were subject to Section 5 of the Voting Rights Act when it came into
effect in 1965 to the total area and population of the states respectively11.
3.3. Methodology and Results
3.3.1 Results from the Benchmark Model
This section presents the results for OLS regressions run on the assumption that
economic freedom (or institutional quality) is exogenous to the growth process. There is one
observation per variable per state. The earliest value for the economic freedom index that is
available is for 1981; data on the remaining variables are available from 1963. The initial value
regressions yield stronger results, and they are also the more appropriate specification of the two
since institutions evolve fairly slowly even at the state level.
10
Refer to Appendix B.4.
11
Refer to Appendix B.5.
56
Independent Variables
GSP Per Capita Growth
(Initial Value)
0.121 (0.065)*
GSP Per Capita Growth
(Contemporaneous)
0.084 (0.070)
Economic Freedom
0.300 (0.086)**
0.291 (0.106)**
Public Expenditure
1.575 (1.267)
2.590 (1.389)*
State Income Tax Receipts
0.520 (4.460)
-1.150 (4.578)
-0.008 (0.002)**
-0.008 (0.002)**
R-Square
0.38
0.33
Adjusted R-Square
0.31
0.25
50
50
Bank Assets
Initial GSP Per Capita
Observations
Table 3.1: Regressions on Bank Assets & Economic Freedom 12
Table 3.1 reports the results for regression of the growth rate of gross state product per
capita on bank assets, economic freedom, public expenditure as a percentage of the gross state
product, state income tax receipts as a percentage of the state personal income and the initial level
of gross state product per capita. Bank assets are significant at the 5% level and economic
freedom and initial gross state product per capita are significant at the 1% level for the initial
value regression. Bank asset is insignificant (though barely out of the 10% range) and public
expenditure is significant at the 5% level for the average value regression. Table 3.2 presents the
results for the regression on bank loans, economic freedom and the control variables.
12
The coefficients and standard errors are multiplied by 100.
57
Independent Variables
GSP Per Capita Growth
(Initial Value)
0.199 (0.096)*
GSP Per Capita Growth
(Contemporaneous)
0.142 (0.103)†
Economic Freedom
0.300 (0.085)**
0.286 (0.105)**
Public Expenditure
1.559 (1.253)
2.563 (1.381)*
State Income Tax Receipts
0.586 (0.044)
-1.144 (4.556)
-0.008 (0.002)**
-0.008 (0.002)**
R-Square
0.39
0.33
Adjusted R-Square
0.32
0.26
50
50
Bank Loans
Initial GSP Per Capita
Observations
Table 3.2: Regressions on Bank Loans & Economic Freedom
Independent Variables
GSP Per Capita Growth
(Initial Value)
1.389 (0.587)*
GSP Per Capita Growth
(Contemporaneous)
1.133 (0.568)*
Bank Loans over Equity Capital
0.060 (0.046)†
0.008 (0.047)
Economic Freedom
0.289 (0.085)**
0.294 (0.087)**
Public Expenditure
1.202 (1.271)
1.534 (1.269)
State Income Tax Receipts
1.041 (4.429)
0.450 (4.502)
-0.009 (0.002)**
-0.008 (0.002)**
R-Square
0.41
0.39
Adjusted R-Square
0.33
0.30
50
50
Equity Capital
Initial GSP Per Capita
Observations
Table 3.3: Regressions on Equity Capital, Loan-to-Equity Ratio & Economic Freedom
The results are broadly similar when equity capital is considered as the financial variable.
A more revealing specification is one that regresses state growth rates on equity capital, the ratio
of bank assets or bank loans to equity capital and economic freedom. The assets to equity ratio or
the loans to equity ratio are akin to the return on equity (or the ROE) and are indicative of bank
58
performance. The results are presented in Table 3.3; equity capital is significant at the 5% level
in both specifications and the loan to capital ratio is significant at the 10% level in the initial
value specification, indicating that capital deepening matters more for growth than the
performance of the banks themselves.
Table 3.4 presents the results for the growth rate of gross state product per capita
regressed on total deposits in addition to the control variables. The financial variables are once
again significant at the 5% level in case of both initial value regressions, whereas bank loans are
significant at the 10% level and public expenditure is significant at the 5% level for the initial
value regression and the average value regression respectively. Once again, total deposits are just
outside the 10% range in the average value regression. Economic freedom and the initial gross
state product per capita are significant at the 1% level in both specifications. Replacing total
deposits by domestic deposits (not reported) yields very similar results.
Independent Variables
GSP Per Capita Growth
(Initial Value)
0.192 (0.111)*
GSP Per Capita Growth
(Contemporaneous)
0.136 (0.117)
Economic Freedom
0.303 (0.086)**
0.298 (0.105)**
Public Expenditure
1.555 (1.273)
2.609 (1.392)*
State Income Tax Receipts
0.840 (4.485)
-0.901 (4.582)
-0.008 (0.002)**
-0.008 (0.002)**
R-Square
0.37
0.32
Adjusted R-Square
0.30
0.25
50
50
Total Deposits
Initial GSP Per Capita
Observations
Table 3.4: Regressions on Total Deposits & Economic Freedom
Branch density is an important determinant of access to finance, though its importance
has been declining due to innovations like ATMs and online and phone banking. Table 3.5
presents the results for the growth rate of gross state product per capita regressed on the number
of branches per 1,000 people in the state and the control variables. The number of branches is
59
significant at the 5% level for the initial value regression and somewhat outside the 10% range for
the average value regression.
Independent Variables
Number of Branches
GSP Per Capita Growth
(Initial Value)
1.560 (0.906)*
GSP Per Capita Growth
(Contemporaneous)
0.969 (0.926)
Economic Freedom
0.332 (0.088)**
0.320 (0.104)**
Public Expenditure
1.141 (1.264)
2.416 (1.393)*
State Income Tax Receipts
0.282 (4.486)
-1.330 (4.608)
-0.007 (0.002)**
-0.007 (0.002)**
R-Square
0.37
0.32
Adjusted R-Square
0.30
0.24
50
50
Initial GSP Per Capita
Observations
Table 3.5: Regressions on Number of Branches & Economic Freedom
The final financial variable considered, the ratio of deposits of commercial banks that
failed or received assistance from the FDIC to the total deposits of FDIC-insured commercial
banks, can be interpreted as an inverse financial measure. Table 3.6 considers the ratio of
deposits of commercial banks that failed or received assistance from the FDIC to the total
deposits of FDIC-insured commercial banks in the state as the independent financial variable.
Higher values of this ratio indicate a greater vulnerability of the commercial banking system in
the state. As can be expected, failures and assistance transactions do not contribute to growth; the
co-efficient is negative (though insignificant) in the average value specification, while it is
positive but very small in the initial value specification.
60
Independent Variables
GSP Per Capita Growth
(Initial Value)
1.023 (1.368)
GSP Per Capita Growth
(Contemporaneous)
-1.322 (1.423)
Economic Freedom
0.300 (0.091)**
0.319 (0.109)**
Public Expenditure
1.244 (1.330)
2.523 (1.462)*
State Income Tax Receipts
0.664 (4.946)
-1.12563 (4.995)
-0.008 (0.003)**
-0.008 (0.003)**
R-Square
0.33
0.30
Adjusted R-Square
0.20
0.22
50
50
Failures/Assistance Transactions
Initial GSP Per Capita
Observations
Table 3.6: Regressions on Failures and Assistance Transactions & Economic Freedom
Independent Variables
Bank Assets & Equity Capital
F-statistic (Probability > F)
2.19 (0.12)
Bank Assets & Bank Loans
3.95 (0.03)*
Bank Assets & Number of Branches
2.48 (0.10)†
Bank Assets, Equity Capital & Bank Loans
2.60 (0.07)†
Bank Assets, Equity Capital & Number of Branches
1.89 (0.15)
Equity Capital, Bank Loans & Total Deposits
2.63 (0.06)†
Equity Capital, Total Deposits & Number of Branches
1.85 (0.15)
Bank Loans, Total Deposits & Number of Branches
2.45 (0.08)†
Table 3.7: Joint Significance of Financial Variables
Table 3.7 reports the results for the F-tests for the joint significance of the financial
variables. The results reported are for the initial value regressions. The results once again show
that the strength of the commercial banking sector in the state is an important predictor of growth
61
in gross state product. The results suggest that of all the variables considered, bank assets have
the strongest explanatory power.
3.3.2 Principal Component Analysis
It is worth noting that in the results presented, the effect on the growth rate of gross state
product is remarkably constant regardless of the financial variable considered. So are the strong
effects of economic freedom (or institutional quality) and the initial level of gross state product
per capita. The former is to some extent a reflection of the high correlation between the various
commercial banking indicators. To further explore this issue, I undertake a principal component
analysis of all the financial variables. The eigenvalues and the proportion of variability explained
by the components are presented in Table 3.8.
Component
Component 1
Eigenvalue
4.091
Proportion
0.682
Component 2
1.082
0.180
Component 3
0.765
0.128
Component 4
0.055
0.009
Component 5
0.005
0.001
Component 6
0.002
0.000
Table 3.8: Principal Components Analysis
The regression results with the first principal component as the explanatory financial
variable are presented in Table 3.9. The results show that the financial variable is significant at
the 1% level in the initial value specification and at the 5% level in the average value
specification. Economic freedom and initial per capita gross domestic product by state are both
significant at the 1% level.
62
Independent Variables
GSP Per Capita Growth
(Initial Value)
0.049 (0.020)**
GSP Per Capita Growth
(Contemporaneous)
0.038 (0.022)*
Economic Freedom
0.296 (0.084)**
0.276 (0.104)**
Public Expenditure
1.574 (1.231)
2.553 (1.363)*
State Income Tax Receipts
0.560 (4.347)
-1.192 (4.499)
-0.008 (0.002)**
-0.008 (0.002)**
R-Square
0.41
0.35
Adjusted R-Square
0.34
0.27
50
50
First Principal Component
Initial GSP Per Capita
Observations
Table 3.9: Regressions on the First Principal Component
This reinforces the importance of financial development as a pre-requisite to economic
growth. These results are not sensitive to the inclusion of Delaware, North Carolina and South
Dakota, all of which witnessed a surge in banking activity after changing their corporate tax
codes, or by the inclusion of the geographically discontiguous states of Alaska and Hawaii.
3.3.3 2SLS Results
Economics and political science literature suggests that a heterogeneous population leads
to different interest groups jockeying for political influence, and this reduces the likelihood of
consensus on growth-promoting public expenditure. Hence ethnically diverse states should have
worse institutions. I consider five instrumental variables individually and jointly to control for
this potential endogeneity. The ethnic fragmentation index for the state, the Sullivan diversity
index and the percentage of the population who are foreign-born have been used in the literature
in the past, whereas the percentage of the geographical area and population of the state that are
subject to Section 5 of the Voting Rights Act of 1965 are new instruments.
The ethnic
fragmentation index is calculated from population estimates for Whites, Blacks or AfricanAmericans, Asians, Native Americans or Alaskans, Native Hawaiians or Other Pacific Islanders,
Some Other Race and Two or More Races.
race.
Persons of Hispanic or Latino origin may be any
The Sullivan’s diversity index is based on education, income, occupation, housing,
63
ethnicity and religion. The higher is the percentage of population in a state that is foreign born,
the greater is the demographic heterogeneity in the state. Section 5 of the Voting Rights Act
governs changes to voting laws and regulations that could disproportionately affect minority
voters in states or areas that have a history of suppression of minority voting or intimidation of
minority voters. This includes African Americans in most southern states, Latinos in Texas and
Native Americans in South Dakota. This is reflective of the underlying tension between the
different population groups, which would again be a roadblock to the development of strong
socio-political institutions. I use a two stage least squares approach to control for potential
endogeneity, where economic freedom is instrumented by an ethnic fragmentation index for the
state, the Sullivan diversity index, the percentage of the population who are foreign-born and the
percentage of the geographical area and population of the state that are subject to Section 5 of the
Voting Rights Act13. Each of these variables are exogenous to the growth process, but are
correlated with the extent of economic freedom.
Independent
Variables
Bank Assets
Area under
Section 5 of VRA
0.121 (0.059)*
Population under
Section 5 of VRA
0.121 (0.059)*
All IVs
0.122 (0.056)**
Bank Equity
1.093 (0.461)*
1.089 (0.466)*
1.142 (0.404)**
Bank Loans
0.199 (0.071)**
0.199 (0.072)**
0.200 (0.066)**
Total Deposits
0.193 (0.107)*
0.194 (0.107)*
0.191 (0.103)*
Domestic Deposits
0.243 (0.104)*
0.243 (0.105)*
0.245 (0.094)**
Table 3.10: Instrumental Variable Results – Financial Variables14 15
13
The Voting Rights Act of 1965 was enacted to prevent the disenfranchisement of minority voters.
Section 5 of the act states that any change to voting regulations in “covered jurisdictions” (those having a
history of obstructing minority voting) requires a preclearance by the Department of Justice. The
percentage area and population that are regulated by Section 5 are considered separately due to the large
quantitative impact of several jurisdictions that have small areas but significant populations. Examples are
Manhattan, Brooklyn and the Bronx in New York and Honolulu County in Hawaii.
14
All standard errors reported are robust standard errors.
15
Coefficients are multiplied by 100.
64
The results for the initial value regressions are presented in Table 3.10. The first stage
regressions indicate poor fits for the instruments used in the existing literature, namely
ethnolinguistic fragmentation, the Sullivan’s diversity index and the percentage of the population
that are foreign born. The area and population of the state that are under the jurisdiction of
Section 5 of the Voting Rights Act are good fits, hence these results are discussed. All the
financial variables for both these specifications are significant at the 5% level, with bank loans
being significant at the 1% level.
I also consider all five instruments at once. The results again
strongly uphold the importance of financial deepening for state-level growth.
3.4. Conclusion
This paper shows that financial variables had a significant impact on the economic
growth rates of the U.S. states over a sample period that is much longer than the ones considered
in the current literature. This effect is distinct from that of institutional quality that is captured by
The Fraser Institute’s economic freedom index, which is a function of the size of the state
government, state tax policies and labor laws in the state. The economic freedom index is highly
correlated with economic growth. There are states that have made significant changes to the
extent of their economic freedom; economic freedom index scores have risen substantially in
Connecticut, Delaware and Nevada over the years. Such changes, however, tend to be politically
divisive and are hard to achieve. Blue states generally have higher state and local income taxes
than red states, and government size and policy regarding labor laws vary widely across the
nation. In swing states, they can change with each or every other election cycle. Recently passed
(and in one case, subsequently overturned) laws regarding collective bargaining rights in Ohio
and Wisconsin and the lowering of state income tax rates in Maine in 2011 are excellent
examples. Given these political realities, financial deepening offers a clear way for states to
attain higher growth. Additionally, as the bargaining power of financial intermediaries increase,
they can steer the states towards greater economic freedom.
Economics and political science literature suggests that a heterogeneous population leads
to different interest groups jockeying for political influence, and this reduces the likelihood of
consensus on growth-promoting public expenditure. Hence ethnically diverse states should have
worse institutions. At the same time, regulations are reflective of underlying institutions, and
financial intermediaries relocate to states with fewer regulations.
65
I jointly consider five
instrumental variables to control for this potential endogeneity. The ethnic fragmentation index
for the state, the Sullivan diversity index and the percentage of the population who are foreignborn have been used in the literature in the past, whereas the percentage of the geographical area
and population of the state that are subject to Section 5 of the Voting Rights Act of 1965 are new
instruments. The instrumental variables results once again uphold the importance of financial
development for economic growth.
66
CHAPTER 4
How Banking Deregulation Affects Growth:
Evidence from a Panel of U.S. States
4.1. Introduction
4.1.1 Chronology of U.S. Banking Regulations
The history of American banking started with the founding of the Bank of Pennsylvania
in Philadelphia on July 17, 1780. The goal was to provide funding for the Continental Army
fighting in the Revolutionary War, the Commonwealth of Pennsylvania and the merchants in
Philadelphia.
The Bank of North America superseded it in 1781 and operated until
Pennsylvania’s legislature repealed its charter in 1785, during which it was the first de facto
central bank in the country. It was re-chartered in 1787 with restrictions placed on its new
activities. Other financial institutions were founded after the end of the war. The Bank of New
York (founded in 1784) became the first bank to lend money to the federal government when
treasury secretary Alexander Hamilton negotiated a $200,000 loan in 1789. This was followed by
other banks in Boston in 1784, Providence in 1791 and Baltimore in 1795.
The Bank of the United States, established in 1791, was the first real attempt at
establishing a central bank that would take care of the financial needs of the federal government.
This was partly based on the success of the Bank of England, which was established in 1694
when King William III found himself in need of funds to fight a war against France. Hamilton
struck a deal with Southern senators to get them to support the bank in return for relocating the
capital to the bank of the Potomac. It was different from today’s central banks in many ways:
foreigners (primarily the Bank of England) owned part of it and it was only one of several banks
67
that issued currency. However, supporters of states rights (mostly from the South) clashed with
the federalists (who were mostly Northerners) and the bank’s charter lapsed in 1811 under
President James Madison. Along with The Bank of North America and the Bank of New York,
First Bank of the United States stocks were the first to be traded in the New York Stock
Exchange. The War of 1812 was the principal factor leading to the chartering of the Second
Bank of the United States in 1816. Its legality was upheld by the Supreme Court case McCulloch
v. Maryland, but its charter lapsed in 1836 due to pressure from President Andrew Jackson, which
was both political and a response to widespread corruption. It continued briefly as an ordinary
bank until declaring bankruptcy in 1841. Both the Bank of the United States and the Second
Bank of the United States were government sponsored enterprises that had monopoly in interstate banking, implying that the U.S. did have inter-state banking in its early years.
The period from 1837 to 1863 is called the free banking period.
Until 1863, all
commercial banks were chartered by states. This posed several problems. Each bank would
issue its own banknotes; since these did not always trade one-for-one (the size, financial strength
and proximity of the issuing bank determined the size of the discount), this effectively lead to
hundreds of different currencies in circulation. Governments of some states abused their charter
by forcing banks within their territory to accept and hold risky bonds that the states themselves
issued. There was considerable counterfeiting of currency, and reserve ratios and bank capital
were often insufficient. About half the banks failed, and their average lifespan was short. Some
local financial institutions overtook central bank functions, like the New York Safety Fund
insuring the deposits of member banks. To rectify this situation with the state banks, the National
Bank Acts were passed in 1863 and 1864, creating a new system of federally chartered banks
called national banks, supervised by the Office of the Comptroller of the Currency, an
independent bureau of the U. S. Treasury. The act also introduced a 10% tax on notes issued by
state banks, leaving notes issued by national banks untaxed. The aims were to create a national
currency, damage the interests of the banking sector in the Confederate South (where a majority
of the state banks were located) and, along with income taxes and excise duties, help finance the
civil war for the Union.
In the dual banking system that arose as a result, the number of state banks began to go
down and the number of national banks began to go up. The west coast saw its first national bank
in 1865 with the establishment of the First National Bank of Portland. Another response was the
68
adoption of checking accounts. Some of these new banks were urban, while others were rural.
Rural banks lent money to farmers in spring (prior to the planting season) and the farmers repaid
them in fall (after the harvest). The rural banks kept these deposits with the larger urban banks
and again withdrew the funds in spring (when they would be loaned out again). Urban banks
would generally anticipate these flows, but sometimes they would be short on cash and the result
would be bank runs. Adding to the problem, currency had to be backed by treasury securities and
when security values fluctuated, banks had to either call in loans or borrow. The worst liquidity
crises occurred in 1873, 1884 and 1893, and these would cause recessions in the U. S. economy.
Excessive corporate speculation had caused banks to overlend in the beginning of the
1900s. When the large Heinze Trust Company failed in 1907, people panicked and the resulting
financial crisis was so bad that the U.S. government turned to J. P. Morgan to borrow funds for
the ailing banking system. This underscored the need for having a central bank for the country.
The Aldrich-Vreeland Act was passed in 1908, establishing the National Monetary Commission,
which recommended the creation of the Fed. A few years later, the Federal Reserve Act or GlassOwen Act was passed in 1913 under President Woodrow Wilson, creating the Federal Reserve
System. All national banks were required to become members of the system and became subject
to its regulations. State banks could opt to become a member (though this was not required), but
most did not because of the costs of membership stemming from the Fed’s regulations. The Fed
had considerable independence in the 1920s, but lost most of it (the Secretary of Treasury was a
member of its Board of Governors) until it signed The Accord with the Treasury in 1951; it could
now independently conduct monetary policy, while the Treasury oversaw fiscal policy.
According to Mishkin (2009), almost 9,000 banks failed during the Great Depression;
some deposits were lost and a significant portion was tied up during the bankruptcies. To prevent
future depositor losses from such failures, the Federal Deposit Insurance Corporation (FDIC) was
established by the Banking Act of 1933, commonly known as the Glass-Steagall Act. This
provided federal insurance on bank deposits. Members of the Federal Reserve System were
required to purchase FDIC insurance. Non-member banks could also choose to do so and almost
all of them did. It also became easier for the Fed to offer rediscounts.
Conflicts of interest arising from speculative activities of commercial banks were blamed
for many of the bank failures. Hence the Glass-Steagall Act also introduced prohibitions on
commercial banks underwriting or dealing in corporate securities; they could, however, sell new
69
issues of government securities. Similarly, it prohibited investment banks from engaging in
commercial banking activities. Commercial or investment banks could also not act as insurance
companies. Commercial banks had to sell off their investment banking operations. First National
Bank of Boston spun off its investment banking operations into the First Boston Corporation,
which is now part of Credit Suisse First Boston. Most predominantly investment banks sold off
their commercial banking operations and restructured solely as investment banks. An exception
was J. P. Morgan, which discontinued its investment banking business and reorganized as a
commercial bank, but some of its senior officers went on to form Morgan Stanley, another very
important investment banking firm.
Deregulation in the banking sector started in the eighties. The Depository Institutions
Deregulation and Monetary Control Act of 1980 repealed Regulation Q, the provision of GlassSteagall Act that enabled the Fed to regulate interest rates paid on savings deposits. It also
allowed banks to merge and credit unions and savings and loan associations to offer checking
accounts. The arguments in favor of preserving the rest of the act were to prevent conflict of
interest between lending and investing by the same institution, limiting the market power of
financial intermediaries, limiting the size of government bailouts if insured deposits were lost due
to speculation and limiting risk exposure for depository institutions. The arguments in favor of
repeal were preventing the hitherto regulated banks from losing market share to less regulated
institutions like private equity firms and foreign banks, creating separate subsidiaries for lending
and investing activities, diversification reducing overall risk and the fact that there were no such
restrictions in most other countries. Moreover, consumers invested more during booms and saved
more during recessions, hence they would be able to both at the same institution, whose finances
would also be more stable.
The provision banning bank holding companies from holding
companies other than commercial banks was repealed by the Gramm-Leach-Bliley Act (also
known as the Financial Services Modernization Act) of 1999. So the commercial bank-holding
company Citicorp and insurance company Travelers Group combined in 1998 to form Citigroup
(in anticipation of a change in the law in the near future).
The other significant banking reform during the Reagan presidency was the Garn-St.
Germain Depository Institutions Act of 1982, which deregulated savings and loan associations
and permitted adjustable rate mortgages by banks to ensure a steady margin for lenders.
70
Another prominent feature of the U.S. commercial banking industry is the very high
number of banks, a result of past regulations that restricted banks from opening more branches.
The McFadden Act of 1927 prohibited banks from branching in other states. On the coasts, banks
could open branches throughout a state, whereas in the interior states it was even more restrictive.
Arkansas, Colorado, Florida, Illinois, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska,
North Dakota, Oklahoma, Texas, West Virginia, Wisconsin and Wyoming had unit banking,
which limited banks to a single office. Historically Americans have been hostile towards large
banks, especially in states with large farming populations. Furthermore, the Douglas Amendment
to the Bank Holding Company Act, passed in 1956, established that the Board of Governors of
the Fed had to approve the establishment of bank holding companies, which are corporations that
own several banks. Bank holding companies headquartered in a particular state were also
prevented from buying banks in other states.
There were two responses to the branching restrictions. One was the emergence of bank
holding companies. The holding company can own a controlling interest in several banks and
thus overcome branching restrictions on individual banks.
Initially they were restricted to
holding banks only in the state where they were headquartered. Maine in 1978 became the first
state that allowed out-of-state bank holding companies to purchase banks within the state,
followed by Alaska and New York in 1982. Bank holding companies own almost all of the larger
banks today. The other response was the proliferation of automated teller machines (ATM).
Banks argued that if they didn’t own or rent the ATM but allowed it to be owned by someone else
and paid for each transaction with a fee, then the ATM should not be subject to branching
regulations. Regulatory agencies and courts generally concurred with this; Cirrus, NYCE and
other companies operate such ATMs where customers of almost all banks can withdraw money.
The number of banks in the U. S. held steady at around 14,000 till the mid –1980s. After
that, it started to decline. The reason was consolidation (mergers and acquisitions) spurred by
deregulation.
The Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994
overturned the McFadden Act, so we now have a truly nationwide banking system. All states
except Hawaii had already relaxed their branching restrictions by varying degrees by the time of
the act. States had the option of opting out of interstate branching before the Riegle-Neal Act
came into effect in mid-1997, but only Texas and Montana did so.
Advocates of bank
consolidation say consolidation induces economies of scale (increasing the bank size helps spread
71
out the fixed costs) and economies of scope (one institution can provide many different services).
These lead to more efficient banks that can choose the services that they want to provide without
any interference. Critics say that the elimination of small banks will lead to less lending to small
businesses and a few banks dominating the industry, making it less competitive. However, the
entry of out-of-state banks will presumably make up for the loss of some of the local banks.
100000
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
Banks
Branches
Offices
1934 1944 1954 1964 1974 1984 1994 2004
Year
Figure 4.3: Number of U.S. Commercial Banks, Branches & Offices
The Federal Deposit Insurance Corporation Improvement Act of 1989, on the other hand,
increased regulation. Until then, state banks in some states had to purchase FDIC insurance as a
requirement under state law, whereas those in other states could choose whether to do so. The act
made it mandatory for all deposit-taking banks to have FDIC insurance, in addition to having a
primary federal regulator.
4.1.2 Literature on Banking Deregulation and Growth
There is an abundance of literature on the impact of financial development on the
economic growth of a country. Not all economists agree on this issue. The debate originated
from the differing opinions of Robinson (1952), who believed that the causality ran from
72
economic growth to financial development as countries with good growth prospects were the
ones that were more likely to develop their financial sector, and Schumpeter (1912), who said that
the causality was from financial development to growth, as a country stymied by inadequate
financial capital could not grow.
A huge body of work has been done on money and banking since, and on the theory,
empirics and country studies of banking deregulation in particular. An early theoretical work by
Holmstrom and Tirole (1997) yields variable results of banking integration on state level output
volatility. Bankers can reduce volatility by monitoring firms better, but they can also increase
volatility by shirking their monitoring responsibilities.
Spieker (2004) looks at the 15% increase in the number of branches between 1994 and
2003 that has accompanied the 29% decline in the number of institutions, even as technological
innovations such as automated teller machines and increased broadband capacity reduce the need
to have branches. He concludes that the passage of the Riegle-Neal Act along with changes to
state laws lead to consolidation by converting multiple banks held by holding companies into
branches, leading to staff and overhead reductions and enabling them to undertake higher risks
for higher returns from diversification. States with unit banking laws and those that were late in
relaxing branching laws saw greater increases in branch numbers, whereas important banking
markets like California, New York and North Carolina actually saw decreases. Demographic
trends (population and employment growth) and traffic patterns affecting access to the location in
localized markets also helped to explain branch growth.
Beck, Demirguc-Kunt and Levine (2004) use data from 69 countries for 1980-1997 and
find that after controlling for a variety of factors like regulatory policies, institutional quality,
macroeconomic conditions and economic shocks, systemic banking crises are less likely in
countries with more concentrated banking systems that have fewer banks. Concentration is taken
to be the ratio of the assets of the three largest banks to total bank assets; a systemic crisis is
defined as a period where the banking system is unable to perform any intermediary role for the
economy and emergency assistance is given to the banking system, or if non-performing assets
reach 10% or more of total assets, or rescue costs reach 2% of more of the GDP. Fewer banks
imply more market power and higher profits that help these banks better weather adverse shocks.
High profits increase the charter value of the banks and thereby reduce the risk-taking incentive
73
for managers. It is also easier to regulate fewer banks. Allen and Gale (2000) reinforce this with
their result that the U.S., with its large number of banks, has had a more unstable financial history
than the U.K. or Canada, with much fewer banks. This is in contrast to Boyd and De Nicolo
(2005), who argue that more market power in concentrated banking systems enable banks to
charge higher interest rates and thereby encourage risky behavior on the part of the borrowing
firms. Beck, Demirguc-Kunt and Levine also find that regulations and federal institutions that
limit competition are also associated with more unstable banking systems. Collectively, their
results suggest that concentration is an incomplete measure of bank competitiveness.
Looking into possible mechanisms linking banking integration and economic volatility,
Morgan, Rime and Strahan (2003) find that the correlation between collateral values (based on
housing prices) and growth increases and correlation between bank capital (based on book
values) and growth decreases with banking integration.
Hence integration stabilizes state
business cycles by decreasing the exposure of the state economy to downturns in the state
banking system. State sensitivity to changes in the value of the collateral increases, but the
former effect dominates.
Morgan, Rime and Strahan (2004) also investigate how bank
integration following deregulation has impacted state business cycles. Theoretically, cross-state
asset holdings can reduce risk and smooth consumption and hence reduce output volatility.
Specifically for banks, it can reduce bank capital and loan supply shocks.
Capital market
integration could also increase output volatility as capital moved from stagnant economies to
vibrant ones. For banks, it can amplify firm collateral or loan demand shocks. The relative
magnitude of the effects determines the net effect on volatility. The authors find that the
amplitude of state business cycles over 1976 to 1994 diminish as state banking systems become
more integrated with those of other states. The decline in volatility is especially pronounced for
the oil-producing states of Wyoming, Montana, Oklahoma, Texas, North Dakota and Louisiana.
Hence multi-state banking has made state business cycles smaller and more similar to one
another.
These results are very different from the ones with country panel data. There is less
evidence that the entry of foreign banks into hitherto protected banking markets of small
developing countries has decreased economic volatility. Morgan and Strahan (2004) find a
positive relationship between business cycle volatility and the market share of foreign banks,
though the relationship is not significant.
74
Williamson (1989) compared the experiences of U.S. and Canadian banks during the
Great Depression. The Canadian banks were already highly integrated across the provinces and
territories in the 1930s, whereas the U.S. essentially had fifty separate banking systems, one per
each state. Almost all the Canadian banks survived the depression, whereas almost a third of the
American banks failed. However, the downturn in Canada was almost as severe as the downturn
in the U.S. Hence bank integration stabilized the banking system but not output.
Correa (2008) finds that bank integration following deregulation in the U.S. reduced the
financing constraints of publicly-traded firms that are more heavily dependent on external
financing through an increase in the share of locally headquartered geographically diversified
banks. More technologically advanced banks acquiring local banks might have better screening
and monitoring tools that enable more firms to obtain credit.
Multi-state banks can also
geographically diversify their assets, thus reducing their exposure to idiosyncratic state-level
risks. Deregulation also allows banks to have more business loans in their portfolio without
increasing their overall risk, thus passing on the economies to external finance-dependent firms.
The sensitivity of investment to internal funds goes down as firms are able to obtain external
funds at a cheaper rate. The interest rate gap for short term loans between large and small or
medium sized firms also decreases significantly following bank integration. Correa also finds
that smaller firms indeed have access to more credit following deregulation, especially if a multistate bank headquartered in that state increases its market share. Hence local changes are more
important for improving access to credit than the new lending practices of out-of-state banks.
Deregulation increases competition in the retail loan market. Hubbard and Palia (1995)
find that management discipline increases due to an increase in the risk of takeovers following
deregulation, as evidenced by the turnover and sensitivity of pay to performance of senior bank
executives. Jayaratne and Strahan (1998) find decreases in loan losses and operating costs after
branching reforms. Stiroh and Strahan (2003) find an increased correlation between performance
and market share following deregulation, implying that competition is reallocating more assets to
the more efficient banks.
Demsetz and Strahan (1997) observe that larger bank holding
companies are able to offer more business loans while operating at higher leverage. Houston,
James and Marcus (1997) and Houston and James (1998) find that bank holding companies
reallocate funds among their banks by using their internal capital markets; local economic effects,
75
as a result, do not have a significant impact on individual banks. The result is an increase in
supply of loanable funds.
Several studies have also documented the negative effects of banking sector deregulation.
Larger banks are often more reluctant to lend to smaller firms with information asymmetry. A
personal relationship between a small bank and a small firm often ends with the small bank being
acquired by another bank. Moreover, large firms can raise funds from public debt or equity
markets without going to banks, whereas smaller firms are more dependent on banks. Berger and
Udell (1996) find a negative relationship between the average bank size and lending to small
businesses for a specific market. Peek and Rosengren (1998) find that acquired banks often have
their management replaced and start following the lending habits of the acquiring bank after the
acquisition. In a study of Norwegian firms, Karceski, Ongena and Smith (2005) find that small
firms are adversely affected by bank mergers and acquisitions and are also the least likely to
switch to a different bank. In a study of Italian firms, Bonaccorsi Di Patti and Gobbi (2007) find
that firms borrow less from banks involved in mergers, either as the acquirer or as the acquired,
though this effect is no longer discernible after three years.
Among the early studies focusing on the finance-growth relationship in the U.S., Amel
and Liang (1992) find that deregulation in the U.S. banking industry has led to an increase in the
number of new branches but not in the number of de novo banks. This indicates a substitution in
favor of branch entry. They also conclude that fewer new entrants result from a change to interstate banking than from a change to inter-state banking. Calem (1994) finds that the small
banking sector has contracted in states that have relaxed intra-state branching restrictions, while
the relaxation of inter-state branching restrictions has not had an appreciable effect. Intra-state
branching restrictions prevented most banks from reaching their efficient size. Once the removal
of these restrictions enabled them to achieve their optimal scale, further deregulation was unlikely
to lead to additional contraction. The Riegle-Neal Act of 1994 came into effect on September 29,
1995, and invalidated the laws in thirty six states that only allowed inter-state banking on a
reciprocal or regional basis. McLaughlin (1995) suggests that banking reforms will lead to faster
consolidation of the banking industry in the U.S., but although there has been rapid consolidation
within state borders, the changes across state borders will be incremental and localized and will
not lead to nationwide banking very soon. This consolidation takes place through the bank
holding companies’ conversion of existing and acquired bank subsidiaries into branches.
76
A number of studies document the positive impact of banking deregulation in the U.S.
Schranz (1993) examines the impact of takeovers on firm value in the context of the banking
sector and finds that publicly traded banks in states that make takeovers easier are more
profitable.
Alternative mechanisms to maximize firm value like concentration of equity
ownership and management ownership of stock are observed in states that have restrictions on
takeovers. Berger, Kashyap and Scalise (1995) find that the post-deregulation banking sector in
the U.S. is characterized by a sharp fall in the number of banks and a sharp rise in the number of
bank failures, off-balance sheet activities, equity capital ratios, foreign bank lending to U.S.
corporations and the adoption of ATMs. Additionally, lending to both small and large businesses
fell in the first half of the nineties, whereas lending to medium-sized borrowers roughly stayed
constant. Improved technology and financial innovations may have led to big borrowers utilizing
alternatives sources of credit, whereas organizational diseconomies have made it harder for the
now larger banks to extend as much credit to small borrowers as before. Jayaratne and Strahan
(1996) study the impact of bank branching deregulations during the Reagan administration on
economic growth in the U. S. states and find that growth rates of real per capita output and
income increased substantially following the reforms. The cause of this increase was not an
increase in the quantity of bank lending but rather the quality of bank lending, as measured by
non-performing loans, the fraction of loans written off and the fraction of loans classified as
“insider loans”. Kroszner and Strahan (1999) use a hazard model to explain the timing of intrastate branching reforms. Much of the deregulatory patterns can be explained by the privateinterest theory of regulation, where better organized groups (large banks and bank-dependent
firms) use the coercive power of the state to capture rents at the expense of the less organized
ones (small banks and rival insurance firms). Hence reform occurs later in states where small
banks and stronger relative to big banks. The authors also argue that branching deregulation
began in the seventies due to the advent of ATMs, banking via mail and telephone (for example
checkable money market mutual funds and the Merrill Lynch Cash Management Account) and
falling transport and communication costs, all of which raised the elasticity of deposit supply.
This led banks to relax their guards in their fight to preserve their geographical monopolies.
Freeman (2002) arrives at a different conclusion by running robustness checks that
indicate that the growth effects of branching deregulation are significantly smaller than estimated.
Deregulation is endogenous to the economic conditions of the state, which results in an upward
77
bias for the Jayaratne and Strahan (1996) estimates. Banking deregulation in most states occurred
during downturns, when the banking sector was facing significant losses, often due to falling
commodity or housing prices. Wall (2004) finds that instead of a uniformly positive relationship,
banking deregulation led to decreases in entrepreneurship in some areas of the U.S. and increases
in others. This ranged from an 11.5% drop in the Mideast to a 15.1% increase in the Great Lakes.
Huang (2008) looks at 285 pairs of contiguous counties across U.S. state borders where intra-state
branching restrictions were removed earlier in one state than in the other. These counties should
be similar in both observable and unobservable conditions and grow at similar rates unless subject
to different regulations. Of the twenty three deregulation events during the period 1975-1990
considered by the author, growth significantly accelerates in only five cases, all of which
happened during 1985-1990.
Some of the explanations are that the U.S. economy is less
dependent on commercial banks than Europe and that the economic impact of banking
regulations in the U.S. has been overstated.
Beck, Levine and Levkov (2010) find that while U.S. income inequality widened during
the period, branching deregulation has lowered income inequality by reducing the income gap
between men and women and between skilled and unskilled workers.
Capital market
imperfections can hinder the poor from borrowing to finance their education (Galor and Zeira,
1993) and also prevent them from becoming entrepreneurs as they often lack collateral for bank
loans (Banerjee and Newman, 1993). However, the authors find no impact of deregulation on the
business income of the poor or on educational attainment.
This paper explores whether the findings of Jayaratne and Strahan (1996) and Rajan and
Zingales (1998) hold for the U.S. states and industries. Jayaratne and Strahan (1996) look at the
impact of bank branching deregulations during the Reagan administration on economic growth in
the U. S. states and find that growth rates increased substantially following the reforms.
Furthermore, the cause of this increase was not an increase in the quantity of bank lending but
rather the quality of bank lending, as measured by non-performing loans, the fraction of loans
written off and the fraction of loans classified as “insider loans”. Rajan and Zingales (1998)
focus on a specific factor that might cause increased financial intermediation to raise the rate of
growth. They argue that financial development makes external finance less costly and different
industries have different degrees of dependence on external finance. Hence industries like drugs
& pharmaceuticals and plastic products that use a lot of external capital should grow faster than
78
industries like tobacco and pottery that use the least. Their cross-country data indeed supports
this view.
The central question addressed in this paper is whether industries that are more dependent
on external financing grew at a faster rate in the fifty U.S. states and the District of Columbia.
Additionally, the paper also looks at the effect of deregulation on the commercial banks
themselves. The remainder of the chapter is organized as follows. Section 4.2 describes the data
and the variables used. Section 4.3 describes the methodology and results and Section 4.4
concludes.
4.2. Data and Variables Used
4.2.1 Data Sources
The data on state personal income and gross domestic product by state are from the
Bureau of Economic Analysis. Price level data is from the Bureau of Labor Statistics. The data
on commercial banking is compiled by the Federal Deposit Insurance Corporation (FDIC).
External finance dependence data is from Rajan and Zingales (1998).
The sample period is 1963 – 1997 for state personal income and gross domestic product
by state, and 1963 – 2010 for the commercial banking indicators. For state personal income, data
from 1963 to 1974 are based on the 1967 SIC, those from 1975 to 1987 are based on the 1972
SIC 16 and those from 1988 to 1997 are based on the 1987 SIC. Populations are mid-year
estimates by the Census Bureau. For gross state product, the data from 1963 to 1986 are based on
the 1972 SIC and the data from 1987 to 1997 are based on the 1987 SIC. While data is available
beyond 1997, the transition from the Standard Industrial Classification (SIC) definitions to the
North American Industry Classification System (NAICS) definitions results in a discontinuity
that affects both the levels and the growth rates of state-level estimates. Certain data are not
released by the government to avoid disclosure of confidential information and certain other data
are not available for some years, limiting the sample size in some cases.
16
SIC stands for Standard Industrial Classification, which is a U.S. government system for categorizing
industries. The North American Industry Classification System (NAICS), a collaborative effort by the
U.S., Canada and Mexico, replaced it in 1997.
79
4.2.2 Variables Used
I consider three sets of dependent variables: growth rate of state personal income by
industry, growth rate of gross domestic product by state at the industry level and growth rate of
commercial banking indicators (number of commercial banks, number of branches, number of
branches per bank, number of offices, assets, bank equity, total deposits and loans). These are
regressed on a deregulation dummy and additional control variables. A few states deregulated
prior to 1963 and one had not deregulated by 1997, and these are dropped from the sample for the
OLS state personal income and gross domestic product by state regressions 17. The states that
deregulated prior to 1963 are also dropped from the OLS commercial banking indicator
regressions.
The BEA defines state personal income as the income received from all sources by all
persons residing in a particular state. Hence it is the sum of net earnings by place of residence,
property income and personal current transfer receipts. Net earnings by place of residence is
earnings by place of work (the sum of wage and salary disbursements, supplements to wages and
salaries, and proprietors' income) less contributions for government social insurance, plus an
adjustment to convert earnings by place of work to a place-of-residence basis. Property income
is sum of rental income, dividend income and interest income. Personal income is measured
before the deduction of personal income taxes and other personal taxes and is reported in current
U.S. dollars.
Gross domestic product (GDP) by state18 is the state counterpart of the national gross
domestic product. GDP by state is derived as the sum of the GDP originating in all the industries
in a state. An industry's GDP by state, or its value added, in practice, is calculated as the sum of
incomes earned by labor and capital and the costs incurred in the production of goods and
services. Hence it includes the wages and salaries earned by workers, the income earned by
17
Refer to Appendix C.1 for a chronology of deregulation by different states.
18
GDP is calculated as the sum of what consumers, producers and the government spend on final goods
and services, plus investment and net foreign trade. In theory, incomes earned should equal what is spent,
but due to different data sources, income earned, usually referred to as gross domestic income (GDI), does
not always equal what is spent (GDP). The difference is referred to as the "statistical discrepancy."
80
individual or joint entrepreneurs as well as corporations and business taxes such as sales, property
and federal excise taxes that count as a business expense. Data on real GDP by state is in chained
(2005) dollars, and is hence an inflation-adjusted measure of each state’s gross product based on
national prices for that SIC category. Hence real GDP by state does not capture geographic
differences in the prices of goods and services that are produced and sold locally.
The definitions and measurement methods of the banking variables19 I look at have been
adjusted multiple times over the sample period, hence I only discuss the current ones. The
majority of the data compiled by the Federal Deposit Insurance Corporation (FDIC) is from the
Federal Financial Institution Examination Council (FFIEC) Call Reports and the Office of Thrift
Supervision (OTS) Thrift Financial Reports submitted by all FDIC-insured depository
institutions.
FDIC-insured commercial banks include all commercial banks insured through the Bank
Insurance Fund (BIF) and all commercial banks insured through the Savings Association
Insurance Fund (SAIF) that are regulated by and submit financial data to one of the three Federal
commercial bank regulators (Board of Governors of the Federal Reserve System, Federal Deposit
Insurance Corporation and Office of the Comptroller of the Currency). Data on commercial
banks that are not insured by the FDIC (though they might be insured by a state insurance fund or
a private insurance company) are excluded from the analysis. Unit banks have only one office, at
which deposits are received or other banking business is conducted; banks with branches have
one or more offices in addition to the head office. Branches include all offices of a bank other
than its head office, which receive deposits, pay checks or lend money. Banking facilities
separate from a banking house, those at government installations, offices, agencies, paying or
receiving stations, drive-in facilities and other limited-purpose facilities are defined as branches
under the FDI Act. This is despite the fact that such facilities are not considered to be branches
under the state law in some states. Contractual branches are not included.
Total equity capital refers to total equity capital on a consolidated basis. It includes bank
equity capital (includes preferred and common stock, surplus and undivided profits) and the
equity capital component of non-controlling (minority) interests in consolidated subsidiaries.
Surplus refers to the portion of an institution's capital received for shares of stock sold in excess
19
Definitions are from FDIC (1997).
81
of par value (excludes all surplus related to preferred stock). Undivided profits refer to undivided
profits, capital reserves, net unrealized holdings gains (losses) on available-for-sale securities,
other equity capital components, and accumulated gains (losses) on cash flow hedges.
Net loans and leases refer to total loans and lease financing receivables minus unearned
income and loan loss allowances. Unearned income is the loan revenue that has been received in
advance of its being earned. Loan loss allowance is the allowance (reserve) that each bank must
maintain for loan and lease losses that is adequate to absorb estimated credit losses associated
with its loan and lease portfolio (which also includes off-balance-sheet credit instruments).
Total deposits refer to total deposits including demand deposits, money market deposits,
other savings deposits, time deposits and deposits in foreign offices as of the last Call Report or
Thrift Financial Report filed by the institution.
Assets refer to total assets owned by the
institution including cash, loans, securities, bank premises and other assets as of the last Call
Report or Thrift Financial Report filed by the institution. This total does not include off-balancesheet accounts.
The control variables I use are public expenditure as a percentage of the gross state
product and state personal income and state and local income tax receipts as a percentage of gross
state product and state personal income.
4.3. Methodology and Results
4.3.1 OLS Regressions: State Personal Income
This section reports the results of the growth rate of state personal income by industry
regressed on a dummy that assumes the value 0 for years prior to deregulation in that state and 1
for the deregulating and subsequent years. Hence the baseline regressions I run for each industry
in each state are of the form:
Yit/Yit-1 = α + βDt + εt
82
where Yit/Yit-1 is the growth in industry j in state i between periods (t – 1) and t and Dt is
deregulation dummy which equals 0 before the state allowed branching via mergers &
acquisitions (henceforth M&A) and equals 1 afterwards. I run two sets of regressions, one with a
current dummy and the other with the dummy lagged by a year. The results are broadly similar
for the two. In a third specification, public expenditure and state and local income tax receipts as
a percentage of state personal income are added as control variables. Only two results each from
the least and most external finance-dependent industries are discussed as the results from other
industries are broadly similar.
An issue that arises here is the positive correlation of the error terms over time. The long
run variance will be greater than the short run variance. The issue is addressed by using
heteroskedasticity and autocorrelation (HAC) consistent estimates to correct the standard error.
Using the selection criterion k = 4(T/100)1/4, where k is the lag length and T is the number of
observations, results in a lag length of 3.
I start with a regression of the growth rate of tobacco products (SIC 456) on the
deregulation dummy. The sample is limited by the relatively smaller number of states that
produce more than $10 million worth of tobacco products annually.
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
GA
TN
KY
VA
PA
CT
IN
NY
FL
MA
Coeff - S.E.
Coeff + S.E.
Coefficient
Figure 4.2: Tobacco Products (SPI)
83
This is the sector considered by Rajan and Zingales that is expected to exhibit the least
increase in growth rates following deregulation.
As seen in Figure 4.2, deregulation had a
significantly positive impact on growth in three states and a significantly negative impact on just
one. The deregulatory effect is negative in only four out of ten states.
The caveat needs to be added that the tobacco industry is unlike most of the other
industries I consider in some ways. Tobacco consumption has been steadily falling across the
U.S. following successful anti-smoking campaigns both by the government and by private
agencies. The industry has also been hit hard by lawsuits that have resulted in the courts
awarding significant sums of money to the plaintiffs. It is hence surprising that the output of
tobacco products has grown substantially in so many states following deregulation. This could
reflect the American tobacco giants’ strategy of successful expansion into markets abroad.
0.08
0.06
0.04
Coeff - S.E.
0.02
Coeff + S.E.
0
-0.02
AR FL TX VA MS GA KS ND ME AL LA PA NJ IL OK WV NY VT UT
Coefficient
-0.04
-0.06
Figure 4.3: Food & Kindred Products (SPI)
Figure 4.3 presents the results for food and kindred products (SIC 453), which are
broadly similar.
This is another sector that relies very little on external financing, but
deregulation has a counter-intuitive significantly positive effect on growth in six states and a
significantly negative effect in just two.
84
0.4
0.2
0
-0.2 WY VT UT FL AL VA KS MA KY NH GA NE OH MI NJ CT IL NM
-0.4
Coeff - S.E.
-0.6
Coeff + S.E.
-0.8
Coefficient
-1
-1.2
-1.4
-1.6
Figure 4.4: Electronic & Other Equipment (SPI)
Figures 4.4 and 4.5 discuss the results for two industries that are among the most
dependent on external financing. Figure 4.4 presents the results for electronic and other electric
equipment (SIC 432). The results are striking because the sectoral growth rate significantly
slowed down in many states after deregulation. Growth rates slowed significantly in fourteen
states and rose significantly in just one. In the full sample, growth rates decreased for thirty out
of the thirty-seven states in the sample.
0.2
0.15
0.1
0.05
Coeff - S.E.
0
Coeff + S.E.
-0.05
FL WY NH NE TN MA VT NJ WV KS PA OR ME WA MI ND GA IL MT
-0.1
-0.15
-0.2
Figure 4.5: Industrial Machinery & Equipment (SPI)
85
Coefficient
Figure 4.5 presents the results for industrial machinery and equipment (SIC 429), which
is the most external finance-dependent sector in Rajan and Zingales that corresponds to one of the
3-digit SIC categories.
Growth in this sector exhibits a similar slowdown; a dozen states
witnessed a significant decrease in growth rates following deregulation, whereas only one state
saw a significant increase. Overall, growth rates for the sector slowed down in twenty-seven
states and increased in only eleven in the sample of thirty-eight states.
The results are nearly identical if the deregulation dummy is lagged by a year. Adding
public expenditure and state and local income tax receipts as a percentage of state personal
income as control variables does not alter the results.
Two main results emerge from the above exercise. First, industries that aren’t too
dependent on external financing were not adversely affected by the deregulation of the
commercial banking sector. In many cases, they exhibited strong growth in the post-deregulation
period. Second, and much more interesting, are the results for the industries that are heavily
dependent on bank lending. Instead of witnessing accelerating growth rates as a result of
increased supervision by lending institutions, the average growth rate of these industries have
been markedly slower in the post-deregulation period.
4.3.2 OLS Regressions: Gross Domestic Product by State
I also run the industry-level regressions using gross domestic product by state instead of
state personal income.
While there are some differences with the state personal income
specification, the two sets of results are broadly similar.
86
0.3
0.2
0.1
Coeff - S.E.
0
Coeff + S.E.
GA
TN
VA
AL
CT
PA
IL
KY
NJ
WV NY
IN
FL
-0.1
Coefficient
-0.2
-0.3
Figure 4.6: Tobacco Products (GSP)
The results for tobacco products are displayed in Figure 4.6.
Deregulation had a
significantly positive effect on growth in six states; nowhere did it have a significantly negative
effect on growth. Overall, deregulation boosted growth in ten out of the thirteen states in the
sample.
0.25
0.2
0.15
Coeff - S.E.
0.1
Coeff + S.E.
0.05
Coefficient
0
-0.05
FL VA HI NH WY WV NJ MT WI IL OH VT OR NY NE WA LA MO UT
-0.1
Figure 4.7: Food & Kindred Products (GSP)
87
Figure 4.7 presents the results for food products, which is another sector that depends
very little on external capital. The results are broadly similar to that of tobacco products, with
deregulation significantly increasing growth in thirteen states and significantly decreasing growth
in none. Overall, the growth rate increased in thirty-three states and decreased in five.
Figures 4.8 and 4.9 present the results for the two industries that are among the most
heavily dependent on external financing. These results are more mixed. As evidenced by Figure
4.8, deregulation significantly positively affected the growth rates of electronic and other
equipment in four states and significantly negatively affected the growth rates in two. Overall,
the sectoral growth effect of deregulation was positive in twenty-four states and negative in
fourteen.
0.6
0.4
0.2
Coeff - S.E.
0
-0.2
LA ME UT ND MS KS OH NJ NY CT MA IN NE WV GA NH WI NM
Coeff + S.E.
Coefficient
-0.4
-0.6
-0.8
Figure 4.8: Electronic & Other Equipment (GSP)
Figure 4.9 presents the results for industrial machinery. Deregulation had a significant
positive impact on sectoral growth in five states and a significant negative impact in three.
Overall, the deregulatory effect is positive in twenty-three states and negative in fifteen.
88
0.2
0.15
0.1
0.05
Coeff - S.E.
0
Coeff + S.E.
-0.05
FL AR WY NE VA NJ NH MA WI WV IN OR MO KS NM MN GA ND LA
Coefficient
-0.1
-0.15
-0.2
Figure 4.9: Industrial Machinery & Equipment (GSP)
Lagging the deregulation dummy by a year and adding public expenditure and state and
local income tax receipts as a percentage of gross domestic product by state for the particular year
and state as control variables once again yields similar results. This implies that unlike in a crosscountry setting, banking deregulation has not been an important driver of sectoral economic
growth in the U.S.
4.3.3 OLS Regressions: Commercial Banking Indicators
I also regress various commercial banking indicators like the number of banks, branches
and offices, loans, total deposits, domestic deposits, bank assets and bank equity to examine the
impact of deregulation on the performance of the banks themselves.
The strongest impact of the lifting of restrictions on intra-state branching was on the
number of commercial banks. Small local banks found themselves competing with other banks
for customers for the first time and many were unable to compete with their more efficient
counterparts. As evidenced by Figure 4.10, the effect of deregulation was negative in every state
in the sample and the number of banks dropped significantly in thirty-four out of the thirty-nine
states.
89
0.02
PA
VT
MS
WA
MN
KY
GA
KS
WI
UT
MA
CT
IN
AL
AR
ME
OR
NH
-0.02
HI
TX
0
-0.04
Coeff + S.E.
-0.06
Coeff - S.E.
-0.08
Coefficient
-0.1
-0.12
-0.14
-0.16
Figure 4.10: Number of Commercial Banks
Deregulation is expected to have a very different effect on the numbers of bank branches
and bank offices. Branches include all offices of a bank other than its head office where
customers make deposits, take out loans and cash checks. Offices include all locations of the
bank, including its head office and offices that do not perform depository activities.
0.4
0.3
0.2
-0.2
-0.3
-0.4
Figure 4.11: Number of Branches per Bank
90
WY
HI
TX
OR
KS
MI
WA
WI
LA
NM
OH
OK
NH
AL
NY
NE
-0.1
NJ
Coeff - S.E.
VT
0
FL
Coeff + S.E.
WV
0.1
Coefficient
While the number of commercial banks was decreasing as a result of post-deregulation
consolidation in the banking industry, the number of branches was rising. Figure 4.11 presents
the results for growth in branches per bank. Deregulation had a positive effect on branch growth
in nineteen states and a negative effect in twenty. Branch growth increased significantly in seven
states and decreased significantly in eleven. This effect might appear weak given that in the postderegulatory era we have banks that have branches on both coasts, but technological innovations
such as automated teller machines and increased phone and internet banking have simultaneously
reduced the need to have branches. Additionally, the number of branches of a few big banks
might have grown significantly (for instance Chase’s current push into California) but the number
of branches of smaller and medium sized banks might not have grown so much.
0.2
0.15
0.1
OH
OR
CT
VA
MT
AR
ND
NE
NY
CO
MS
IA
IL
NM
NJ
-0.05
LA
Coeff - S.E.
TX
0
VT
Coeff + S.E.
NH
MI
0.05
Coefficient
-0.1
-0.15
-0.2
Figure 4.12: Bank Assets
Figure 4.12 depicts the impact of branching deregulation on bank assets. Assets include
cash, loans, securities and physical assets like buildings and equipment. Deregulation increased
the growth rate of bank assets in nine states and decreased it in thirty states. Bank asset growth
decreased significantly in twelve states; no state saw a significant increase in the growth rate of
bank assets.
91
The impact of deregulation on bank equity, which is the sum of bank equity capital and
the equity capital component of minority stakes in consolidated subsidiaries, is depicted in Figure
4.13. Deregulation increased the growth rate of bank equity in twenty states and decreased it in
nineteen. The increase was significant in four states and the decrease was significant in three
states in the sample. Hence apart from the number of banks and the number of branches, the
strongest impact of deregulation was on bank equity, which would reflect the weeding out of the
more inefficient banks in a more competitive environment.
0.3
0.25
0.2
0.15
Coeff + S.E.
0.1
Coeff - S.E.
0.05
Coefficient
UT
MA
ME
AL
GA
WA
MT
PA
HI
MS
MO
LA
NE
CO
IA
NM
NJ
MI
-0.05
FL
IN
0
-0.1
-0.15
Figure 4.13: Bank Equity
Other banking indicators considered (total deposits and loans) yield similar but weaker
results, indicating that bank branching deregulation did not have a significant impact on these
aspects of bank performance.
4.3.4 Fixed Effects Regressions: External Financing
I estimate the impact of the lifting of branching restrictions on various industry categories
using a fixed effects model of the form:
92
Yit/Yit-1 = αi + βt + γDit + εit,
where Yit/Yit-1 is the growth of industry j in state i between periods (t – 1) and t, αi is the statespecific component of long run economic growth, β t is the common economy-wide shock to
growth at time t and Dit is the deregulation dummy for state i in year t.
The results for select industries for the full sample of fifty states and the District of
Columbia are presented in Table 4.1. Deregulation appears to have a significant impact on the
growth rate of industry sectors in the gross state product specification, with the less external
finance-dependent industries of tobacco products, food and kindred products and paper and paper
products exhibiting the highest increase. Using the sub-set of the sixteen former unit banking
states does not significantly alter these results20. Neither does the inclusion of Delaware, South
Dakota and North Carolina, all of which reformed their tax structures to become more bankfriendly.
The coefficients are smaller and the results are more mixed in the state annual personal
income specification, with deregulation having a negative effect on the growth rates of the
apparel, printing and publishing and electronics sectors. The difference in estimates in the two
specifications can be the result of the industrial classifications being similar but not identical,
different dates for switching over from the 1972 to the 1987 SIC, as well as statistical
discrepancy. Additionally, gross domestic product by state counts capital income in the state
where the production process occurred, whereas state personal income counts it in the state the
owner of the asset resides. The neoclassical assumption of closed economies is not applicable to
the U.S. states, with free flow of labor, capital, goods and services and technology across state
lines. Hence there is a significant difference between gross domestic product by state and state
personal income. Also, in the event of technological parity, more globalized capital markets will
speed up the convergence for gross domestic product by state but slow down the convergence for
state personal income.
20
These states are Arkansas, Colorado, Florida, Illinois, Iowa, Kansas, Minnesota, Missouri, Montana,
Nebraska, North Dakota, Oklahoma, Texas, West Virginia, Wisconsin and Wyoming.
93
Industrial Sector
Tobacco Products
Apparel
Gross State Product
**
0.061 (0.025)
State Personal Income
0.033 (0.028)†
0.002 (0.013)
-0.032 (0.028)
Food & Kindred Products
0.046 (0.009)**
0.007 (0.003)*
Paper & Products
0.026 (0.010)**
0.007 (0.018)
Printing & Publishing
0.007 (0.008)
-0.007 (0.008)
Furniture
0.025 (0.012)*
0.034 (0.026)†
Fabricated Metal Products
0.026 (0.012)*
0.013 (0.007)*
Motor Vehicles
0.098 (0.038)**
0.015 (0.023)
Industrial Machinery
0.029 (0.017)†
0.002 (0.009)
Electronic & Other Equipment
0.015 (0.021)
-0.006 (0.036)
Table 4.1: Industry Fixed Effects
While Table 4.1 corrects for state and time fixed effects, Tables 4.2 and 4.3 more
formally address the issue of industry business cycles for the gross domestic product by state and
state personal income specifications respectively. The differences in coefficient values in the two
specifications reflect noisy data as well as variances in accounting for factor income. Lagged
values of the dependent variable are significant in many of the specifications.
Though the
coefficients of the deregulation dummies exhibit no significant change, they increase slightly in
many cases. They are also significant in more specifications, especially in case of gross domestic
product by state.
94
Industrial Sector
Lagged Growth
Deregulation Dummy
Tobacco Products
0.031 (0.043)
0.064 (0.024)**
Apparel
-0.007 (0.025)
0.005 (0.013)
Food & Kindred Products
0.035 (0.025)†
0.049 (0.00)**
Paper & Products
0.094 (0.025)**
0.028 (0.010)**
Printing & Publishing
0.043 (0.024)*
0.014 (0.008)*
Furniture
-0.005 (0.025)
0.034 (0.012)**
-0.095 (0.024)**
0.034 (0.012)**
Motor Vehicles
-0.015 (0.026)
0.101 (0.038)**
Industrial Machinery
-0.024 (0.025)
0.036 (0.017)*
Electronic & Other Equipment
0.026 (0.025)
0.048 (0.021)*
Fabricated Metal Products
Table 4.2: Industry Fixed Effects with Lagged Dependent Variables (GSP)
Industrial Sector
Lagged Growth
Deregulation Dummy
Tobacco Products
0.021 (0.037)
0.043 (0.030)†
Apparel
-0.029 (0.025)
-0.033 (0.029)
Food & Kindred Products
0.189 (0.024)**
0.009 (0.003)**
Paper & Products
-0.008 (0.026)
-0.006 (0.019)
Printing & Publishing
0.244 (0.024)**
-0.004 (0.004)
Furniture
0.061 (0.034)**
0.039 (0.027)†
Fabricated Metal Products
0.139 (0.024)**
-0.008 (0.007)
0.023 (0.026)
-0.009 (0.023)
Industrial Machinery
0.112 (0.025)**
-0.014 (0.009)†
Electronic & Other Equipment
-0.075 (0.025)**
-0.066 (0.037)*
Motor Vehicles
Table 4.3: Industry Fixed Effects with Lagged Dependent Variables (SPI)
95
To explore whether the results of Rajan and Zingales (1998) hold up for the U.S., I also
estimate a fixed effects model of the form:
Yijt/Yijt-1 = αij + βt + γDit + δDitFj + ε ijt
where Yijt/Yijt-1 is the growth of industry j in state i between periods (t – 1) and t, αij is the statespecific component of long run economic growth of industry j, β t is the common economy-wide
shock to growth at time t, Dit is the deregulation dummy for state i in year t and F j is the
numerical measure of the extent that industry j is dependent on external finance. If Rajan and
Zingales (1998) are correct in their assumption that industries that are more dependent on external
finance should grow faster than other industries following deregulation, then we should obtain
δ > 0.
Variable
Full Sample
Unit Banking
γ (GSP)
0.003 (0.006)
0.003 (0.009)
δ (GSP)
-0.000 (0.017)
-0.004 (0.025)
γ (SPI)
-0.004 (0.011)
-0.002 (0.013)
δ (SPI)
-0.043 (0.029)†
-0.059 (0.036)†
Table 4.4: External Financing & Growth Effects
The results are presented in Table 4.4. The deregulation dummy has a weak effect on
growth, entering the regression with a small positive coefficient in the gross state product
specification and with a small negative coefficient in the state annual personal income
specification. The more revealing result is that the coefficient of the deregulation dummy
interacted with the external dependence term is negative in both specifications, and significantly
negative when we consider state annual personal income. These results are broadly similar for
the full sample and the former unit banking states. The growth rate of industries more dependent
on external financing in the post-deregulation period was slower than the growth rate of the less
96
dependent ones. Possible explanations are the increasing market share of non-bank financial
intermediaries, the general decline of manufacturing industries in the U.S. and the terms of trade
moving in favor of agriculture.
The weak deregulation coefficients, on the other hand, could be indicative of the impact
of deregulation being a temporary level effect and not a longer-term growth effect. To explore
this further, I also estimate a fixed effects model of the form:
log Yijt = αij + βt + γDit + δDitFj + εijt
where Yij is the output of industry j in state i at time t, αij is the state-specific component of long
run economic growth of industry j, β t is the common economy-wide shock to growth at time t, Dit
is the deregulation dummy for state i in year t and F j is the numerical measure of the extent that
industry j is dependent on external finance.
Variable
Full Sample
Unit Banking
γ (GSP)
-0.006 (0.010)
0.031 (0.015)*
γ (SPI)
-0.014 (0.011)
-0.012 (0.016)
Table 4.5: Level Effects
The results from Table 4.5 do not indicate any appreciable level effects. The only
specification where the deregulation dummy has a positive effect is in the case of the unit banking
states using gross domestic product by state. The effect of the deregulation dummy is negative,
though small, in both the full sample specifications. These results reinforce the earlier finding of
the absence of growth effects. This could indicate commercial banks losing ground to other
forms of financial intermediaries. The Federal Reserve Bank did not pay interest on the required
reserves of commercial banks until 2008. Consequently, these alternative financial institutions
also had a competitive edge vis-à-vis banks for a long time as they are usually not subject to
reserve requirements.
97
4.3.5 Fixed Effects Regressions: Commercial Banking Indicators
Aside from sectoral growth effects, branching reforms should have a direct impact on the
commercial banks themselves. As local banking markets open up, banks witness increased
competition from other in-state banks establishing new branches in areas hitherto only served by
them. We would expect fewer surviving banks, which in turn are larger in size, have more
branches, are better managed and have stronger balance sheets than before.
One of the issues that arise while discussing branching deregulation decisions by states is
spatial considerations and endogeneity in the timing of deregulation. Contrary to what the
competitive framework suggests, geographically adjacent states did not necessarily deregulate at
the same time; nor did states deregulate at the trough of the business cycle. For instance, Ohio
deregulated intra-state branching in 1979, whereas among the neighboring states Pennsylvania
deregulated in 1982, Michigan and West Virginia in 1987, Indiana in 1989 and Kentucky in 1990.
California deregulated long before 1960, whereas Oregon deregulated much later in 1985.
Maryland had deregulated by 1960, whereas Virginia only did so in 1978. The hold-out state that
was the last to deregulate was Iowa in 1999, whereas neighboring South Dakota had deregulated
by 1960.
Kroszner and Strahan (1999) explore this issue in depth and conclude that it is not
competition among states that lead to deregulation. Instead, much of the deregulatory patterns
can be explained by the private-interest theory of regulation, where better organized groups (large
banks and bank-dependent firms) use the coercive power of the state to capture rents at the
expense of the less organized ones (small banks and rival insurance firms). Hence reform occurs
later in states where small banks are stronger relative to big banks. The authors also argue that
branching deregulation began in the seventies due to the advent of ATMs, banking via mail and
telephone (for example checkable money market mutual funds and the Merrill Lynch Cash
Management Account) and falling transport and communication costs, all of which raised the
elasticity of deposit supply. This led banks to relax their guards in their fight to preserve their
geographical monopolies.
The impact of the lifting of branching restrictions on various commercial banking
indicators is estimated using a fixed effects model of the form:
98
Yit/Yit-1 = αi + βt + γDit + εit
where Yit/Yit-1 is the growth in the banking indicator j in state i between periods (t – 1) and t, αi is
the state-specific component of long run economic growth, β t is the common economy-wide
shock to growth at time t and Dit is the deregulation dummy for state i in year t. Once again, the
full sample of fifty states and the District of Columbia as well as the sub-set of former unit
banking states are considered in two separate specifications. The results are presented in Table
4.6.
Banking Indicator
Full Sample
Unit Banking
Number of Banks
-0.039 (0.003)**
-0.043 (0.002)**
Number of Bank Branches
-0.056 (0.006)**
-0.075 (0.016)**
Number of Branches per Bank
-0.013 (0.007)*
-0.025 (0.017)†
Number of Bank Offices
-0.019 (0.002)**
-0.003 (0.003)
Assets
-0.020 (0.023)
-0.026 (0.006)**
Bank Equity
-0.004 (0.020)
-0.005 (0.006)
Total Deposits
-0.021 (0.030)
-0.027 (0.006)**
Loans
-0.022 (0.025)
-0.027 (0.009)**
Table 4.6: Commercial Banking Indicator Fixed Effects
These results are significantly different from the findings of Jayaratne and Strahan
(1996), and using lagged values of the M&A dummy does not significantly alter them. As can be
expected, the strongest effect of the deregulation is on the number of commercial banks operating
in a state. This went down significantly following deregulation, with a higher rate of decrease in
the sixteen former unit banking states. While the absolute number of bank branches and bank
offices have continued to grow, the rate of growth of bank branches, bank offices and branches
99
per bank have slowed down following deregulation, though the slowdown has been smaller in the
former unit banking states.
The results indicate a smaller number of bigger organizations. The U.S. banking system
prior to deregulation resembled the banking system in present-day Western Europe, where most
banks are usually confined within national boundaries except a few service branches. Postderegulation, it has inched somewhat closer to Canada, which has a handful of big banks, all of
which have nationwide presence.
Small banks are vulnerable due to inventory issues and
overheads. A bigger bank obviously benefits from economies of scale due to the drop in
overhead costs. Hence we could argue that banking is a natural monopoly. However, the greater
the distance between managers and shareholders, the more severe is the principal-agent problem
and the consequent diseconomies of scale. Often it is one rogue trader or department that is
responsible for the troubles faced by a big bank; examples are Nick Leeson at Barings Bank,
Jerome Kerviel at Societe Generale and AIG’s credit-swap department. So medium-sized banks
are the best for the economy. However, bailouts support bigger banks as they get bailed out
while smaller banks are allowed to fail. This offsets the diseconomies of scale. As a result, the
concentration of the five biggest banks has grown post-2008.
Perhaps most interestingly, the annual growth rates of bank assets, bank equity, total
deposits and loans all decreased in the post-deregulation period. This slowdown is especially
marked in the former unit banking states.
The results are not sensitive to the inclusion of
Delaware, South Dakota and North Carolina, which have witnessed rapid growth in banking
industry as a result of tax incentives. This observation might reflect the acquiring and subsequent
conversion of former unit banks (or other small banks in the non-unit banking states) into
branches of the acquiring bank and their eventual closure if the branches turned out to be
unprofitable. This would leave some locations unserved or underserved by banks. It could also
be the result of an increase in the number of credit unions or other alternatives to commercial
banks in these states. It should be noted that political influence of the agricultural sector (farming
and ranching), which has traditionally supported smaller banks, is especially strong in the former
unit banking states.
100
4.4. Conclusion
The results of this paper indicate that contrary to Rajan and Zingales (1998), the relaxing
of branching restrictions did not lead to an increase in the growth rates of industries heavily
dependent on bank lending in the U.S. These results hold across industries and across states for
different specifications after adjusting for public spending and the amount of state and local
income tax receipts.
While seemingly intriguing, an explanation for the results could be that commercial
banks were facing increasing competition from other sources of external financing and therefore
losing market share to these new institutions. Furthermore, several of the industries that use little
external financing are in the primary sector, while most of the industries that use extensive
external financing are in manufacturing. The decline in growth rates of these industries in the
post-deregulation period might be due to the decline of U.S. manufacturing industries due to
outsourcing, and not a reflection of the supervisory performance of bank loan officials. It could
also reflect terms of trade moving in favor of agriculture.
The other point that emerges is that the strongest effect of branching deregulation was on
the banks themselves. Until deregulation, the number of banks steadily grew in most states to
cater to a growing population. This trend was even more pronounced in the unit banking states.
As the commercial banking industry was forced to consolidate from increasing competition, the
number of banks decreased significantly, while the number of branches and offices increased.
However, the annual growth rates of the number of branches, number of offices, assets, bank
equity, total deposits and loans all slowed down in the post-deregulation period. This slowdown
is especially marked in the former unit banking states. This could be because acquiring banks
converted former unit banks (or other small banks in the non-unit banking states) into branches
and eventually closed them if the branches turned out to be unprofitable. This would leave some
locations unserved or underserved by banks.
It could also reflect credit unions and other
alternatives to commercial banks increasing their market share in these states because of the
strong influence of the agricultural sector, which has traditionally been a vocal opponent of large
banks.
101
CHAPTER 5
Concluding Remarks
This dissertation adds to the body of literature that reinforces the importance of financial
development, institutions and regulations as necessary pre-conditions for sustained economic
growth. My first essay underscores the importance of this conclusion in a cross-country setting,
while the second essay chronicles its importance for the fifty U.S. states. Together, they suggest
that financial development as a policy pre-requisite is necessary not just at the initial stages of
development, but continues to stay relevant for mature economies like the U.S. that are relatively
free of capital constraints.
However, with this simple policy prescription come caveats that are often overlooked.
We must ensure that the country is on the right trajectory of financial development. Evidence
points to the development of stock markets having a stronger growth effect compared to banking
sector development. This point deserves attention as firms depend a lot more on banks than on
equity markets as a source of external finance. The discipline shareholders impose on the
management mitigates the principal-agent problem by aligning their interests. Stock markets also
make financial instruments less risky by making it easier for investors to cash out, and tend to
develop in common law areas that limit expropriatory powers and protect private property.
Additionally, regulatory changes in the financial sector might not have the consequences
regulators intended for a variety of reasons. The lack of appreciable growth effects of the
relaxation of branching restrictions in the U.S. states provides a prime example.
As the
commercial banking industry was forced to consolidate from increasing competition, the number
of banks decreased significantly, while the number of branches and offices increased. However,
the annual growth rates of the number of branches, number of offices, assets, bank equity, total
deposits and loans all slowed down in the post-deregulation period. This could be because
acquiring banks converted former unit banks (or other small banks in the non-unit banking states)
102
into branches and eventually closed them if the branches turned out to be unprofitable, which
would leave some locations unserved or underserved by banks.
emergence of credit unions and other alternatives to commercial banks.
103
It could also reflect the
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[146] Wall, Howard J. “Entrepreneurship and the Deregulation of Banking”, Economics Letters,
Vol. 82, No. 3, March, 2004, pp. 333-339
[147] World Health Organization “World Malaria Report”, 2005, pp. 275-279
[148] Young, Alwyn “The Tyranny of Numbers: Confronting the Statistical Realities of the East
Asian Growth Experience”, The Quarterly Journal of Economics, Vol. 110, No. 3, August, 1995,
pp. 641-680
117
A: Appendix for Chapter 2
Appendix A.1: Countries in the Sample
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Canada
Chile
Colombia
Cote d’Ivoire
Denmark
Egypt
Finland
France
Germany
Greece
Hong Kong
India
Indonesia
Israel
Italy
Jamaica
Japan
Jordan
Luxembourg
Malaysia
Mexico
Morocco
Netherlands
New Zealand
Nigeria
Norway
Pakistan
Peru
Philippines
Portugal
Singapore
South Korea
Spain
Sweden
Taiwan
Thailand
Turkey
U.K.
U.S.A.
Venezuela
Zimbabwe
118
Appendix A.2: Components of Kaufman, Kraay and Zoido-Lobaton’s Governance Indices
1. Voice and Accountability
Change in government, orderly transfer (EIU)
Legal system, transparency, fairness (EIU)
Civil liberties: Freedom of speech, of assembly and demonstration, of religion, equal opportunity, of
excessive governmental intervention (FH)
Political rights: Free and fair elections, representative legislature, free vote, political parties, no dominant
group, respect for minorities (FH)
Free press: Law and practice, independence and violations (FH)
Military in politics: Reduces accountability (PRS)
Democratic accountability: Responsiveness of the government to its people, free and fair elections (PRS)
Business is kept informed of important developments in rules and policies (WDR)
Business has a voice to express its concerns over changes in laws or policies (WDR)
Political process: Elections, party configuration, political competition and participation (FHNT)
Civil society: Volunteerism, trade unionism, professional associations (FHNT)
Independent media (FHNT)
Media: Independence and quality (PERC)
Transparency of the business environment (PERC)
Transparency: The government communicated its intentions successfully (WCY98)
2. Political Instability and Violence
Risk reduction of GDP due to major urban riot, major insurgency/rebellion, military coup, political
terrorism, political assassination and civil war (DRI)
Armed conflict, war (EIU)
Social unrest (EIU)
Terrorist threat, political violence (EIU)
Internal conflict: Political violence and governance, from no tolerance of arbitrary violence to civil war torn
countries (from best to worse score) (PRS)
Ethnic tensions: Based on intolerance and prone to conflict (PRS)
Terrorism as an obstacle to business development (WDR)
Likelihood of unconstitutional government changes (WDR)
Fractionalization of the political spectrum (BERI)
Fractionalization by ethnic, language and religious groups (BERI)
Restrictive (coercive) measures to retain power (BERI)
Organization/power of radical group (BERI)
Societal conflict: Strikes, violence and demonstrations (BERI)
Constitutional changes, assassinations and guerillas (BERI)
Political stability (CEER)
Likelihood of dramatic change in institutions (GCS97, GCS98)
The highest power is always peacefully transferred (GCS97, GCS98)
Likelihood of dramatic change in institutions (GCSA)
Government coups or political instability as an obstacle to development (GCSA)
Tribal conflict as an obstacle for business development (GCSA)
119
3. Government Effectiveness
Government policy (pro-business) (EIU)
Government/Institutional efficacy (EIU)
Red tape/Bureaucracy (EIU)
Institutional failure: Institutional rigidities that hinder bureaucratic efficiency (DRI)
Government ineffectiveness: Quality of the government’s personnel (DRI)
Government instability: High turnover that lowers the quality of the government’s personnel (DRI)
Government stability: Its ability to carry out programs (PRS)
Bureaucratic quality: Civil service’s institutional strength, free from political influences (PRS)
Likelihood that when a government official acts against the rules, one can go to another official or a
superior and get correct treatment (WDR)
Management time spent with bureaucrats (WDR)
The efficiency of customs (WDR)
The general condition of roads you use (WDR)
The efficiency of mail delivery (WDR)
The quality of public health care provision (WDR)
Government efficiency in delivering services (WDR)
Predictability of changes in rules and laws (WDR)
Credibility of government’s commitment to policies (WDR)
Bureaucratic delays (BERI)
Government and administration: Decentralization and transparency (FHNT)
Competence of public sector personnel relative to private sector (GCS98)
Wasteful government expenditure (GCS98)
Government commitments are honored by new governments (GCS98)
Strength and expertise of the civil service to avoid drastic interruptions in government services in times of
political instability (GCS98)
Management time spent with bureaucracy (GCS98)
Public service vulnerability to political pressure (GCS98)
Competence of public sector personnel relative to private sector (GCSA)
Wasteful government expenditure (GCSA)
Government commitments are honored by new governments (GCSA)
Strength and expertise of the civil service to avoid drastic interruptions in government services in times of
political instability (GCSA)
Effective implementation of government decisions (WCY98)
Bureaucracy as an obstacle to business development (WCY98)
Exposure of public service to political interference (WCY98)
4. Regulatory Burden
Regulations that impose a burden on business (HFWSJ)
Government intervention in economy (HFWSJ)
Wage/Price control (HFWSJ)
Trade policy (tariff and non-tariff barriers to trade) (HFWSJ)
Capital flows and foreign investment (financial regulations for foreigners) (HFWSJ)
Banking (free from government intervention, domestic financial regulations) (HFWSJ)
Export regulations (DRI)
Import regulations (DRI)
Other regulations (regulatory burden) (DRI)
Legal restrictions on ownership of business by non-residents (DRI)
Legal restrictions on ownership of equity by non-residents (DRI)
120
Regulations of starting new business as an obstacle to business development (WDR)
Price controls as an obstacle to business development (WDR)
Regulations on foreign trade as an obstacle for business development (WDR)
Foreign currency regulations as an obstacle for business development (WDR)
General uncertainty about the costs of regulations as an obstacle for business development (WDR)
Price liberalization (EBRD8)
Trade Regulations (EBRD8)
Competition policy (EBRD8)
Banking: Extensiveness of legal rules (EBRD8)
Banking: Effectiveness of legal regulations (EBRD8)
Securities: Extensiveness of legal rules (EBRD8)
Securities: Effectiveness of legal regulations (EBRD8)
Protection of domestic banks from foreign competition (GCS98)
Barriers to entry in banking sector (GCS98)
Interest rates are heavily regulated (GCS98)
Participation of private sector in infrastructure projects (GCS98)
Extent of market competition (GCS98)
Effectiveness of anti-trust policies (GCS98)
Negative impact of tariffs on costs and availability of equipment and materials (GCS98)
Negative impact of hidden barriers to trade (GCS98)
The tax system hinders business competitiveness (GCS98)
Extent of market competition (GCSA)
Effectiveness of anti-trust policies (GCSA)
Costs of uncertain rules, laws or government policies (GCSA)
Negative impact of tariffs on costs and availability of equipment and materials (GCSA)
Negative impact of hidden barriers to trade (GCSA)
Openness of public sector contracts to foreign investors (GCSA)
Policies for dividend remittances as obstacles to development (GCSA)
Dominance of state owned or state controlled enterprises (GCSA)
Regulatory burden (GCSA)
State interference in private business (GCSA)
Regulatory discretionality (vagueness of regulations) (GCSA)
The tax system hinders business competitiveness (GCSA)
Protection of domestic banks from foreign competition (GCSA)
Regulations for starting a business as an obstacle to business development (GCSA)
Price controls as an obstacle to business development (GCSA)
Regulations on foreign trade as an obstacle to business development (GCSA)
Foreign currency regulations as an obstacle to business development (GCSA)
Transfer costs associated with exporting capital as an obstacle to business development (GCSA)
General uncertainty on costs of regulations as an obstacle to business development (GCSA)
Legal regulation of financial institutions (WCY98)
Protectionism as an obstacle to imports from abroad (WCY98)
Controls on foreign investors’ ownership of companies (WCY98)
Obstacles to foreign bidders on public contracts (WCY98)
Political system as an obstacle to development (WCY98)
Real personal taxes as a burden to work initiative (WCY98)
Real corporate taxes as a burden to entrepreneurial activity (WCY98)
Legal framework as an obstacle to competitiveness (WCY98)
Custom’s administration as a burden to international trade (WCY98)
Price controls (WCY98)
Competition laws as an obstacle to fair competition (WCY98)
121
5. Rule of Law
Losses and costs of crime (DRI)
Kidnapping of foreigners (DRI)
Enforceability of private contracts (DRI)
Enforceability of government contracts (DRI)
Corruption in banking (EIU)
Crime (EIU)
Black market (HFWSJ)
Property rights (HFWSJ)
Law and order transition (PRS)
Theft and crime (now) (WDR)
Confidence in authority to secure property (now) (WDR)
Unpredictability of the judiciary (now) (WDR)
Crime and theft as obstacles to business (WDR)
Enforceability of contracts (BERI)
Rule of law (CEER)
Rule of law (FHNT)
Extent of tax evasion (GCS97, GCS98)
Costs of organized crime for business (GCS97, GCS98)
Police effectiveness in safeguarding personal security (GCS97, GCS98)
Intellectual property protection (GCS97, GCS98)
Compliance with court rulings and/or arbitration awards (GCS97, GCS98)
Legal system effectiveness at enforcing commercial contracts (GCS97, GCS98)
Independence of the judiciary from interference by the government and/or parties to the dispute (GCS97,
GCS98)
Private business has recourse to independent and impartial courts for challenging the legality of
government actions (GCS97, GCS98)
Likelihood of winning a court case filed against the government (GCS97, GCS98)
Legal system effectiveness at enforcing commercial contracts (GCS97, GCS98)
Private business capacity to file lawsuits at independent and impartial courts against government (GCS97,
GCS98)
Citizens’ willingness to accept legal means to adjudicate disputes rather than depending on physical force
or illegal means (GCS97, GCS98)
Legal system effectiveness at enforcing commercial contracts (GCSA)
Private business has recourse to independent and impartial courts for challenging the legality of
government actions (GCSA)
Private business can readily file and try lawsuits against other business, foreign or domestic, at independent
and impartial courts (GCSA)
Citizens’ willingness to accept legal means to adjudicate disputes rather than depending on physical force
or illegal means (GCSA)
Costs of organized crime for business (GCSA)
Costs of petty crime and theft for business (GCSA)
Police effectiveness in safeguarding personal security (GCSA)
Extent of tax evasion (GCSA)
Crime and theft as an obstacle to business development (GCSA)
The parallel economy as an obstacle to business development (WCY98)
Extent of tax evasion (WCY98)
Confidence in the fair administration of justice in the society (WCY98)
Confidence among people that their person and property is protected (WCY98)
Protection of intellectual property (WCY98)
122
6. Graft
Corruption among public officials, effectiveness of anticorruption initiatives (DRI)
Corruption among public officials (EIU)
Corruption in the political system as a “threat to foreign investment” (PRS)
Frequency of “additional payments” to “get things done” (WDR)
Corruption as “obstacle to business” (WDR)
Mentality regarding corruption (BERI)
Effect of corruption on “attractiveness of country as a place to do business” (CEER)
Perceptions of corruption in civil service, business interests of policymakers (FHNT)
Frequency of “cases of corruption” among public officials (GALLUP)
Irregular, additional payments connected with import and export permits, business licenses, exchange
controls, tax assessments, police protection or loan applications (GCS97, GCS98)
Frequency of “irregular payments” to officials and judiciary (GCS97, GCS98)
Irregular, additional payments connected with import and export permits, business licenses, exchange
controls, tax assessments, police protection or loan applications (GCSA)
Corruption as an obstacle to business development (GCSA)
Effect of corruption on business environment for foreign companies (PERC)
Improper practices in the public sphere (WCY98)
123
Appendix A.3: Sources of Kaufman, Kraay and Zoido-Lobaton’s Governance Indices
Business Environment Risk Intelligence (BERI)
The Wall Street Journal Central European Economic Review (CEER)
Standard and Poor’s DRI/McGraw-Hill (DRI)
European Bank for Reconstruction and Development (EBRD)
The Economist Intelligence Unit (EIU)
Freedom House (FHFW/FHNT)
Gallup International Association (GALLUP)
World Economic Forum (GCS/GCSA)
Heritage Foundation/Wall Street Journal (HFWSJ)
Political Risk Services, International Country Risk Guide (GRS/ICRG)
Political and Economic Risk Consultancy (PERC)
Institute for Management Development (WCY)
World Bank/University of Basel (WDR)
124
Appendix A.4: Institutional Quality
Country
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Canada
Chile
Colombia
Cote d'Ivoire
Denmark
Egypt
Finland
France
Germany
Greece
Hong Kong
India
Indonesia
Israel
Italy
Jamaica
Japan
Jordan
Luxembourg
Malaysia
Mexico
Morocco
Netherlands
New Zealand
Nigeria
Norway
Pakistan
Peru
Philippines
Portugal
Singapore
South Korea
Spain
Sweden
Taiwan
Thailand
Turkey
United Kingdom
United States
Venezuela
Zimbabwe
Voice &
Accountability
0.49
1.63
1.45
-0.01
1.41
0.58
1.39
0.62
-0.15
-0.57
1.63
-0.67
1.63
1.15
1.46
1.05
0.01
0.36
-1.13
1.06
1.28
0.75
1.14
0.15
1.49
-0.09
-0.11
-0.24
1.64
1.47
-1.23
1.67
-0.44
-0.69
0.63
1.48
0.13
0.91
1.36
1.60
0.71
0.22
-0.88
1.51
1.52
0.15
-0.67
Political
Stability
0.51
1.18
1.38
-0.40
0.82
-0.32
1.03
0.45
-1.29
-0.14
1.29
-0.07
1.51
0.65
1.32
0.21
0.92
-0.04
-1.29
-0.46
1.16
-0.34
1.15
-0.06
1.40
0.55
-0.35
0.09
1.48
1.42
-1.05
1.41
-0.65
-0.53
0.27
1.39
1.39
0.16
0.58
1.41
0.94
0.25
-0.94
0.92
1.10
-0.25
-0.54
125
Government
Effectiveness
0.26
1.46
1.22
-0.56
0.88
-0.22
1.72
1.17
-0.06
-0.18
1.72
-0.14
1.63
1.28
1.41
0.56
1.25
-0.26
-0.53
0.69
0.77
-0.48
0.84
0.63
1.67
0.71
0.18
0.27
2.03
1.57
-1.32
1.67
-0.74
0.17
0.13
1.15
2.08
0.41
1.60
1.57
1.29
0.01
-0.41
1.97
1.37
-0.85
-1.13
Country
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Canada
Chile
Colombia
Cote d'Ivoire
Denmark
Egypt
Finland
France
Germany
Greece
Hong Kong
India
Indonesia
Israel
Italy
Jamaica
Japan
Jordan
Luxembourg
Malaysia
Mexico
Morocco
Netherlands
New Zealand
Nigeria
Norway
Pakistan
Peru
Philippines
Portugal
Singapore
South Korea
Spain
Sweden
Taiwan
Thailand
Turkey
United Kingdom
United States
Venezuela
Zimbabwe
21
Regulatory
Quality
0.67
0.96
0.90
-0.16
0.79
0.13
0.87
0.90
0.29
0.15
1.05
0.12
1.14
0.71
0.89
0.60
1.21
-0.04
0.12
0.53
0.59
0.76
0.39
0.42
0.95
0.48
0.61
0.22
1.14
1.20
-0.35
0.93
-0.20
0.67
0.57
0.89
1.24
0.22
0.86
0.85
0.83
0.19
0.59
1.21
1.14
0.09
-0.34
Rule of
Law
0.32
1.60
1.81
-0.93
0.80
-0.22
1.55
1.09
-0.78
-0.33
1.69
0.13
1.74
1.08
1.48
0.50
1.33
0.16
-0.92
0.97
0.86
-0.73
1.42
0.71
1.62
0.83
-0.47
0.68
1.58
1.82
-1.10
1.83
-0.76
-0.52
-0.08
1.08
1.94
0.94
1.03
1.62
0.93
0.41
-0.01
1.69
1.25
-0.66
-0.15
Control of
Corruption
-0.27
1.60
1.46
-0.29
0.67
0.06
2.06
1.03
-0.49
-0.08
2.13
-0.27
2.08
1.28
1.62
0.82
1.31
-0.31
-0.80
1.28
0.80
-0.12
0.72
0.14
1.67
0.63
-0.28
0.13
2.03
2.07
-0.95
1.69
-0.77
-0.20
-0.23
1.22
1.95
0.16
1.21
2.09
0.63
-0.16
-0.35
1.71
1.41
-0.72
-0.32
Overall index is the simple average of the six indices.
126
Overall
Index21
0.33
1.41
1.37
-0.40
0.90
0.00
1.44
0.88
-0.41
-0.19
1.59
-0.15
1.62
1.03
1.36
0.62
1.01
-0.02
-0.76
0.68
0.91
-0.03
0.94
0.33
1.47
0.52
-0.07
0.19
1.65
1.59
-1.00
1.53
-0.59
-0.18
0.22
1.20
1.46
0.47
1.11
1.52
0.89
0.15
-0.33
1.50
1.30
-0.37
-0.53
Appendix A.5: Instrumental Variables
Country
Argentina
Australia
Austria
Bangladesh
Belgium
Brazil
Canada
Chile
Colombia
Cote d’Ivoire
Denmark
Egypt
Finland
France
Germany
Greece
Hong Kong
India
Indonesia
Israel
Italy
Jamaica
Japan
Jordan
Luxembourg
Malaysia
Mexico
Morocco
Netherlands
New Zealand
Nigeria
Norway
Pakistan
Peru
Philippines
Portugal
Singapore
South Korea
Spain
Sweden
22
Latitude
0.3778
0.3000
0.5244
0.2667
0.5611
0.1111
0.6667
0.3333
0.0444
0.0889
0.6222
0.3000
0.7111
0.5111
0.5667
0.4333
0.2461
0.2222
0.0556
0.3478
0.4722
0.2017
0.4000
0.3444
0.5494
0.0256
0.2556
0.3556
0.5811
0.4556
0.1111
0.6889
0.3333
0.1111
0.1444
0.4367
0.0136
0.4111
0.4444
0.6889
Ethnolinguistic
Fragmentation
0.1769
0.1128
0.0332
0.0000
0.3638
0.0558
0.3762
0.0506
0.0558
0.8565
0.0275
0.0231
0.1050
0.1455
0.0438
0.0778
0.2368
0.7422
0.6906
0.3271
0.0389
0.0125
0.0099
0.0297
0.2167
0.6104
0.1741
0.3480
0.0634
0.1476
0.8567
0.0699
0.6216
0.4316
0.7238
0.0025
0.3215
0.0000
0.2745
0.0650
Settler
Mortality
68.9
8.55
NA
71.41
NA
71
16.1
68.9
71
668
NA
67.8
NA
NA
NA
NA
14.9
48.63
170
NA
NA
130
NA
NA
NA
17.7
71
78.2
NA
8.55
200424
NA
36.99
71
NA
NA
17.7
NA
NA
NA
Risk of
Malaria22
0.1
0
0
0.5
0
3.8
0
0
2.8
40.9
0
0
0
0
0
0
0
2.4
0.9
0
0
0
0
0
0
2.8
0.5
0
0
0
13
0
0.7
1.3
1.4
0
0
0
0
0
Legal
Origin23
F
E
G
E
F
F
E
F
F
F
S
F
S
F
G
F
E
E
F
E
F
E
G
F
F
E
F
F
F
E
E
S
E
F
F
F
E
G
F
S
Standardized reported malaria rates per 1,000
23
E stands for English common law; F, G & S stand for French, German & Scandinavian civil laws. There
are no countries with Socialist legal systems in the sample.
24
Settler mortality is expressed as annualized deaths per 1,000 settlers. This number can exceed 1,000 as
settlers who passed away were replaced by new arrivals from the colonizing country.
127
Taiwan
Thailand
Turkey
U.K.
U.S.
Venezuela
Zimbabwe
0.2589
0.1667
0.4333
0.6000
0.4222
0.0889
0.2222
0.2551
0.3569
0.1636
0.1063
0.2090
0.0525
0.5986
NA
NA
NA
NA
15
78.1
NA
128
0
5
0.2
0
0
2.4
63.3
G
E
F
E
E
F
E
B: Appendix for Chapter 3
Appendix B.1: Components of Fraser Institute’s Economic Freedom Index
Area 1: Size of Government
1A: General Consumption Expenditures by Government as a Percentage of GDP
1B: Transfers and Subsidies as a Percentage of GDP
1C: Social Security Payments as a Percentage of GDP
Area 2: Takings and Discriminatory Taxation
2A: Total Tax Revenue as a Percentage of GDP
2B: Top Marginal Income Tax Rate and the Income Threshold at Which It Applies
2C: Indirect Tax Revenue as a Percentage of GDP
2D: Sales Taxes Collected as a Percentage of GDP
Area 3: Labor Market Freedom
3A: Minimum Wage Legislation
3B: Government Employment as a Percentage of Total State Employment
3C: Union Density
129
Appendix B.2: Economic Freedom Index
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
25
Economic Freedom Index25
6.1
5.7
6.6
6.2
6.6
7.4
7.1
7.9
6.6
7.3
5.7
6.5
7.0
6.9
6.9
6.7
6.0
7.0
5.8
6.5
6.8
6.1
6.9
5.6
6.6
5.7
7.1
7.5
7.1
6.5
5.4
6.5
7.2
6.4
6.3
6.8
6.7
6.5
6.0
6.2
7.2
6.8
7.5
7.2
5.8
6.8
6.5
Values are for 2009.
130
West Virginia
Wisconsin
Wyoming
5.4
6.3
7.2
131
Appendix B.3: Instrumental Variables
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Ethnic
Fragmentation26
0.461
0.524
0.447
0.382
0.616
0.330
0.383
0.477
0.591
0.409
0.547
0.724
0.203
0.460
0.279
0.164
0.291
0.223
0.505
0.093
0.570
0.343
0.355
0.267
0.513
0.300
0.195
0.254
0.534
0.117
0.499
0.499
0.531
0.482
0.186
0.301
0.461
0.294
0.315
0.328
0.483
0.254
Foreign-Born
Population
0.031
0.070
0.140
0.042
0.269
0.097
0.131
0.084
0.120
0.188
0.094
0.173
0.063
0.135
0.044
0.039
0.061
0.030
0.034
0.033
0.128
0.143
0.062
0.068
0.020
0.036
0.020
0.059
0.192
0.052
0.202
0.098
0.214
0.071
0.024
0.038
0.051
0.096
0.055
0.127
0.045
0.027
Sullivan’s
Diversity Index27
0.352
0.456
0.471
0.333
0.499
0.455
0.543
0.460
NA
0.421
0.364
0.528
0.386
0.507
0.409
0.419
0.407
0.366
0.440
0.456
0.454
0.541
0.476
0.468
0.330
0.425
0.467
0.439
0.469
0.494
0.538
0.465
0.556
0.342
0.459
0.458
0.370
0.425
0.489
0.522
0.345
0.425
26
Constructed from population estimates for Whites, Blacks or African-Americans, Asians, Native
Americans or Alaskans, Native Hawaiians or Other Pacific Islanders, Some Other Race and Two or More
Races. Persons of Hispanic or Latino origin may be any race. Data is from April 2010.
27
Based on education, income, occupation, housing, ethnicity and religion. Values are from 1960-1963.
132
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
0.370
0.477
0.254
0.091
0.487
0.391
0.117
0.251
0.175
0.042
0.161
0.078
0.033
0.102
0.122
0.013
0.045
0.031
133
0.346
0.443
0.396
0.467
0.392
0.450
0.371
0.484
0.441
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
% Area covered by
Section 5 of Voting Rights Act
100
100
100
0
4.752
2.046
2.358
0
0
13.895
100
19.457
3.710
0
0
0
0
0
100
2.135
0
2.564
0.075
0
100
0
0
0
0
4.039
0
11.095
0.344
39.952
0
0
3.866
0
0
0
100
4.523
0
100
0
0
100
0
0
0
4.909
134
% Population covered by
Section 5 of Voting Rights Act
100
100
100
0
2.376
12.161
2.329
0
0
8.872
100
69.334
1.706
0
0
0
0
0
100
1.424
0
1.939
0.109
0
100
0
0
0
0
1.304
0
8.661
28.211
43.144
0
0
1.275
0
0
0
100
2.815
0
100
0
0
100
0
0
0
8.120
Appendix B.4: Components of Sullivan’s Diversity Index
1. Educational Variable:
a) Less than 5 years of education
b) More than 5 years of education but did not finish high school
c) High school education
d) College education
2. Income Variable:
a) Families that earn less than $6,000 a year
b) Families that earn between $6,000 and $10,000 a year
c) Families that earn over $10,000 a year
3. Occupational Variable:
a) White collar occupations
b) Other occupations
4. Housing Variable:
a) Home ownership
b) Renter occupied
5. Ethnic Variable:
a) Foreign stock
b) Native stock
6. Religious Variable:
a) Jewish
b) Catholic
c) Protestant and Other
135
Appendix B.5: Jurisdictions currently covered by Section 5 of the Voting Rights Act (1965)
States covered as a whole:
Alabama
Alaska
Arizona
Georgia (except City of Sandy Springs)
Louisiana
Mississippi
South Carolina
Texas (except Northwest Austin Municipal Utility District Number One and Jefferson County Drainage
District Number Seven)
Virginia (except Amherst, Augusta, Bedford, Botetourt, Culpeper, Essex, Frederick, Greene, James City,
Middlesex, Page, Pulaski, Rappahannock, Roanoke, Rockingham, Shenandoah, Warren and Washington
Counties and Cities of Bedford, Fairfax, Harrisonburg, Manassas Park, Salem, Williamsburg and
Winchester)
Covered counties in states not covered as a whole:
California:
Kings County (except portion covered by Alta Irrigation District)
Merced County
Monterey County
Yuba County
Florida:
Collier County
Hardee County
Hendry County
Hillsborough County
Monroe County
New York:
Bronx County
Kings County
New York County
North Carolina:
Anson County
Beaufort County
Bertie County
Bladen County
Camden County
Caswell County
Chowan County
136
Cleveland County (except City of Kings Mountain)
Craven County
Cumberland County
Edgecombe County
Franklin County
Gaston County (except City of Kings Mountain)
Gates County
Granville County
Greene County
Guilford County
Halifax County
Harnett County
Hertford County
Hoke County
Jackson County
Lee County
Lenoir County
Martin County
Nash County
Northampton County
Onslow County
Pasquotank County
Perquimans County
Person County
Pitt County
Robeson County
Rockingham County
Scotland County
Union County
Vance County
Washington County
Wayne County
Wilson County
South Dakota:
Shannon County
Todd County
Covered townships in states not covered as a whole:
Michigan:
Allegan County: Clyde Township
Saginaw County: Buena Vista Township
New Hampshire:
Cheshire County: Rindge Town
Coos County: Millsfield Township, Pinkhams Grant, Stewartstown Town and Stratford Town
Grafton County: Benton Town
Hillsborough County: Antrim Town
Merrimack County: Boscawen Town
Rockingham County: Newington Town
137
Sullivan County: Unity Town
Bailed-out jurisdictions formerly covered by Section 5 of the Voting Rights Act (1965)28
Wake County, North Carolina - January 23, 1967
Curry, McKinley and Otero Counties, New Mexico - July 30, 1976
Towns of Caswell, Limestone, Ludlow, Woodland, New Gloucester, Sullivan, Winter Harbor, Chelsea,
Sommerville, Charleston, Waldo, Beddington, and Cutler, Plantations29 of Carroll, Nashville, Reed and
Webster, Unorganized Territory of Connor, Maine - September 17, 1976
Choctaw and McCurtain Counties, Oklahoma - May 12, 1978
Campbell County, Wyoming - December 17, 1982
Towns of Amherst, Ayer, Belchertown, Bourne, Harvard, Sandwich, Shirley, Sunderland, and Wrentham,
Massachusetts - September 29, 1983
Towns of Groton, Mansfield, and Southbury, Connecticut - June 21, 1984
El Paso County, Colorado - June 30, 1984
Honolulu County, Hawaii - July 31, 1984
Elmore County, Idaho - September 22, 1966; July 31, 1984
City of Fairfax, Virginia, including the City of Fairfax School Board - October 21, 1997
Frederick County, Virginia, including the Frederick County School Board the Towns of Middletown and
Stephens City; and the Frederick County Shawneeland Sanitary District - September 10, 1999
Shenandoah County, Virginia including the Shenandoah County School Board, the Towns of Edinburg,
Mount Jackson, New Market, Strasburg, Toms Brook, and Woodstock, the Stoney Creek Sanitary District,
and the Toms Brook-Maurertown Sanitary District - October 15, 1999
Roanoke County, Virginia, including the Roanoke County School Board and the Town of Vinton - January
24, 2001
City of Winchester, Virginia - June 1, 2001
City of Harrisonburg, Virginia, including the Harrisonburg City School Board - April 17, 2002
Rockingham County, Virginia, including the Rockingham County School Board and the Towns of
Bridgewater, Broadway, Dayton, Elkton, Grottoes, Mt. Crawford, and Timberville - May 24, 2002
Warren County, Virginia, including the Warren County School Board and the Town of Front Royal November 26, 2002
28
Jurisdictions can seek exemption from Section 5 coverage through the process of “bail out” by obtaining
a declaratory judgment from the District Court for the District of Columbia.
29
In Maine, a plantation is a civil division that is in between a township (or unorganized territory) and a
town. They lack the power to make local ordinances.
138
Greene County, Virginia, including the Greene County School Board and the Town of Standardsville January 19, 2004
Pulaski County, Virginia, including the Pulaski County School Board and the Towns of Pulaski and Dublin
- September 27, 2005
Augusta County, Virginia, including the Augusta County School Board and the Town of Craigsville November 30, 2005
City of Salem, Virginia - July 27, 2006
Botetourt County, Virginia, including the Botetourt County School Board and the Towns of Buchanan,
Fincastle, and Troutville - August 28, 2006
Essex County, Virginia including the Essex County School Board and the Town of Tappahannock January 31, 2007
Middlesex County, Virginia, including the Middlesex County School Board and the Town of Urbanna January 7, 2008
Amherst County, Virginia, including the Town of Amherst - August 13, 2008
Page County, Virginia, including the Page County School Board and the Towns of Luray, Stanley, and
Shenandoah - September 15, 2008
Washington County, Virginia, including the Washington County School Board and the Towns of Abington,
Damascus, and Glade Spring - September 23, 2008
Northwest Austin Municipal Utility District Number One, Texas - November 3, 2009
City of Kings Mountain, North Carolina - October 22, 2010
City of Sandy Springs, Georgia - October 26, 2010
Jefferson County Drainage District Number Seven, TX - June 6, 2011
Alta Irrigation District, CA - July 15, 2011
City of Manassas Park, VA - August 3, 2011
Rappahannock County, VA, including the Rappahannock County School Board and the Town of
Washington - August 9, 2011
Bedford County, VA, including the Bedford County School Board - August 30, 2011
City of Bedford, VA - August 31, 2011
Culpeper County, VA, including the Culpeper County School Board and the Town of Culpeper - October 3,
2011
James City County, VA - November 9, 2011
City of Williamsburg, VA, including the Williamsburg-James City County School Board - November 28,
2011
139
C: Appendix for Chapter 4
Appendix C.1:
Years of Relaxing Out-of-State Bank Entry and Intrastate Branching
30
Restrictions
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
Interstate Banking
1987
1982
1986
1989
1987
1988
1983
1988
1985
1985
1985
1994
1985
1986
1986
1991
1992
1984
1987
1978
1985
1983
1986
1986
1988
1986
1993
1990
1985
1987
1986
30
Intrastate Branching
1981
1960
1960
1994
1960
1991
1980
1960
1960
1988
1983
1986
1960
1988
1989
1999
1987
1990
1988
1975
1960
1984
1987
1993
1986
1990
1990
1985
1960
1987
1977
Branching dates reflect years when states started permitting branching via mergers and acquisitions. This
usually predates the year when de novo branching was permitted by several years. Dates are from Amel
(1993), Kroszner and Strahan (1999) and Goetz (2009).
140
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
1989
1982
1985
1991
1985
1987
1986
1986
1984
1986
1988
1985
1987
1984
1988
1985
1987
1988
1987
1987
141
1991
1976
1960
1987
1979
1988
1985
1982
1960
1960
1960
1985
1988
1981
1970
1978
1985
1987
1990
1988
Appendix C.2: Degree of Dependence on External Financing by Sector31
ISIC Code
314
361
323
3211
324
372
322
353
369
313
371
311
3411
3513
341
342
352
355
332
381
3511
331
384
354
3843
321
382
3841
390
362
383
385
3832
3825
356
3522
Industrial Sector
Tobacco
Pottery
Leather
Spinning
Footwear
Nonferrous Metal
Apparel
Petroleum Refineries
Nonmetal Products
Beverages
Iron & Steel
Food Products
Pulp, Paper
Synthetic Resins
Paper & Products
Printing & Publishing
Other Chemicals
Rubber Products
Furniture
Metal Products
Basic excluding Fertilizers
Wood Products
Transportation Equipment
Petroleum & Coal Products
Motor Vehicles
Textile
Machinery
Ship
Other Industries
Glass
Electric Machinery
Professional Goods
Radio
Office & Computing
Plastic Products
Drugs
External Dependence
-0.45
-0.15
-0.14
-0.09
-0.08
0.01
0.03
0.04
0.06
0.08
0.09
0.14
0.15
0.16
0.18
0.20
0.22
0.23
0.24
0.24
0.25
0.28
0.31
0.33
0.39
0.40
0.45
0.46
0.47
0.53
0.77
0.96
1.04
1.06
1.14
1.49
Capital Expenditures
0.23
0.20
0.21
0.16
0.25
0.22
0.31
0.22
0.21
0.26
0.18
0.26
0.20
0.30
0.24
0.39
0.31
0.28
0.25
0.29
0.30
0.26
0.31
0.23
0.32
0.25
0.29
0.43
0.37
0.28
0.38
0.45
0.42
0.60
0.44
0.44
Notes: This table reports the median level of external financing and capital expenditure for ISIC industries
for the U.S. during the 1980s. The data is from Standard & Poor’s Compustat (1994). External
dependence is the fraction of capital expenditures not financed with cash flows from operations. Cash flow
from operations is defined as the sum of funds from operations, decreases in inventories, decreases in
receivables and increases in payables. Capital expenditures are the ratio of capital expenditures to net
property plan and equipment.
31
Rajan & Zingales (1998)
142