Download Stock Market Development and Cross

Stock Market Development and Cross-Country
Differences in Relative Prices∗
Borja Larrain†
Abstract
We observe a positive correlation between stock market capitalization and price
levels (or wages) within the group of countries with poorly developed stock markets.
Instead, we observe a negative correlation between these two variables within the group
of countries with more developed stock markets. In both groups there is a positive correlation between stock market capitalization and employment in services. This paper
argues that there is a causal relationship behind these correlations. Stock markets initially stimulate growth, pushing the demand for nontradables and increasing prices and
wages. Stock markets also promote a shift towards more capital-intensive technologies
in the tradable sector, increasing the migration of workers to services and eventually
putting downward pressure on wages and prices.
Keywords: Price Levels, Real Exchange Rate, Sectoral Employment, Stock Market Development, Wages.
JEL: F3, G0
∗
For comments and discussions I thank Mark Aguiar, Alan Ahearne, Miklós Koren, Felipe Varas, and
seminar participants at the Boston Fed and the Federal Reserve System Meeting on International Economic
Analysis in Washington. Maria Giduskova provided outstanding research assistance. Part of this paper was
written while I was an economist at the Federal Reserve Bank of Boston. The views expressed in this paper
are solely those of the author and do not reflect official positions of the Federal Reserve Bank of Boston or
the Federal Reserve System.
†
Pontificia Universidad Católica de Chile, Escuela de Administración, Avenida Vicuña Mackenna 4860,
Macul, Santiago, Chile. Tel: (56 2) 354-4025. E-mail: [email protected].
1
Introduction
The main stylized fact documented in this paper is that countries with highly developed
stock markets (mostly Anglo-Saxon countries such as Australia, U.K., U.S., etc.) have lower
price levels and lower wages than other countries with reasonably developed, although not
as highly developed, stock markets. These countries also have a very high share of total
employment in the services sector. These patterns are hard to explain completely with
standard theories (e.g. Balassa-Samuelson). For example, it is not clear why the U.S.
presents these patterns in comparison to, say, Germany which has a similar level of per
capita income.1 This paper argues that there is a causal relationship going from the stock
market to prices, wages and specialization.2 The analysis is not limited to well developed
stock markets; in fact, interesting differences are observed among less developed markets.
Among these, stock market development is positively correlated with prices and wages, and
also with the share of employment in services.
The Balassa-Samuelson hypothesis (1964), concerning the differential productivity of the
tradable sector, is still the most widely cited theory for long-run cross-country differences
in price levels (the real exchange rate). The positive correlation between income and price
levels is often taken as evidence in support of this hypothesis since higher income presumably
comes from higher productivity in the tradable sector. However, productivity and income
leave many differences unexplained, suggesting that something else is needed to complete the
picture. For example, when commenting on the behavior of the U.S. price level against other
industrial economies, Obstfeld and Rogoff (1996, p. 213) conclude that productivity differentials are only part of the story: "(...)International differences in government regulations,
1
According to the Penn World Table, the average per capita income in the 1990s was $29,152 in the U.S.
and $21,250 in Germany (PPP adjusted). Price levels, on the other hand, were 20% lower in the U.S. The
average monthly wage in manufacturing was $2,269 in the U.S. and $2,862 in Germany (PPP adjusted).
Employment in services was 73% of total employment in the U.S., while it was 60% in Germany.
2
Feldman and Xu (1999) also examine the role of financial development on price levels, but the analysis
is in terms of M2/GDP instead of stock market development like in this paper. Moreover, the focus of their
paper is on the time-series properties (cointegration) of financial development and price levels in a limited
number of countries.
2
trade policies, and market structure also seem relevant." This paper digs deeper into that
insight finding that the development of the stock market, a key feature of market structure,
is a powerful predictor of price levels even after controlling for income levels, government
size, trade openness and other country characteristics.
We know from the previous literature that the development of the stock market affects
quantities, but not that it affects prices. Stock markets foster the accumulation of capital
and growth (Levine (2004)), together with the allocation of resources to particular sectors
(Rajan and Zingales (1998)). We claim that the same mechanisms are behind our findings
on prices; in fact, we think that prices are a natural reflection of the effects of financial
development on growth and specialization in production.
Our explanation is the following. On one hand, stock market development stimulates
capital accumulation and growth. The increase in income drives up price levels in a way
similar to Balassa-Samuelson. On the other hand, stock market development promotes a
shift towards sectors that are more capital-intensive, particularly within tradables. More
workers migrate to services (nontradables) as labor demand falls in tradables, which puts
downward pressure on wages and prices. The domestic price level falls if specialization is
stronger than growth. The results in this paper suggest that specialization dominates among
highly developed stock markets, while growth dominates among the less developed. In other
words, factor accumulation is strong in the initial stages of development, while reallocation
dominates in later stages.
As the main results in this paper are empirical, it is worth discussing the identification
strategies that are used. The key obstacle is that the development of the stock market is an
endogenous variable so standard OLS assumptions are violated. We approach this problem
in two complementary ways. First, in cross-country regressions, the development of the
stock market is instrumented using the legal origin of the country (La Porta et al. 1997,
1998), which is a common strategy in the finance and growth literature (Levine (2004)).
The second identification strategy uses the time-series variation in prices and stock market
3
development together with the cross-country variation. The basic identifying assumption
is that unexpected changes in price levels are not correlated with previous levels of stock
market development and other controls. This allows suitable lags of the endogenous variables
as instruments. The GMM estimation in this case is reviewed by Arellano and Honoré
(2001), and it has been typically applied to micro-level panel data sets (see Beck, Levine,
and Loayza (2000) for a macro-finance application). This second strategy also illustrates
the dynamic impact of stock markets on price levels, which cannot be studied with the cross
section. It complements the cross-sectional approach because it does not rely on legal origins
as instruments and therefore it alleviates concerns about potential pitfalls with that set of
instruments.
Throughout the paper we focus on the effect of stock market development on price levels.
However, the same analysis can be applied to other forms of financial development such
as the banking system. In fact, the empirical results that we present still apply if we use
measures of banking development instead of stock market development. We decide to focus
on stock market development mostly because of our empirical strategy. Previous papers show
that the connection between legal origins and financial development is particularly strong
in the development of stock markets. Other financial structures are not so distinctively and
uniquely influenced by the legal origin of the country (Acemoglu and Johnson (2005)). By
focusing on stock markets our results can be considered to be conservative in terms of the
effect of financial development on relative prices.
The paper is organized as follows. Section 2 presents the evidence on the impact of
stock market development on price levels using a cross-section of countries. Section 3 uses
panel data techniques. Section 4 studies the allocation of employment across sectors and
the impact of stock market development on wages. Section 5 provides a stylized model for
interpreting the empirical results. Section 6 concludes.
4
2
Cross-Sectional Evidence on Stock Market Development and Price Levels
2.1
Data on Price Levels, Income and Stock Market Development
Price levels and per capita income are taken from the Penn World Table (PWT) version
6.1, which updates the work of Summers and Heston (1991). Price levels for each country
are reported relative to the corresponding price level in the United States. For this purpose,
the PWT researchers keep track of the cost of similar goods across countries, adjusting for
quality considerations.3 Within goods categories, the biggest variation in relative prices is
in nontradables (Summers and Heston (1991), Table I). Per capita income in each country
is also reported relative to the U.S. income.
The intersection of the PWT and the database of Beck, Demirgüç-Kunt, and Levine
(2001), which contains data on stock market capitalization over GDP, gives a sample of
90 countries in the period 1976 to 2000.4 Not all countries have data for the entire sample
period, and the coverage is particularly sparse in the early years. A detailed report of country
and period coverage of the different data sets employed in this paper is included in the web
appendix.
2.2
Legal Origins as Instruments for Stock Market Development
The development of the stock market is treated as an endogenous variable, and it is instrumented by the origin of the country’s legal system. The five legal families considered are:
English, French, German, Scandinavian, and Socialist.5 More precisely, the set of instru3
Although surveyed goods are similar, there is variation in the expenditure weights used for the price level
in each country. I thank the referee for pointing this out. See also Bergstrand (1991) on non-homothetic
preferences and their impact on cross-country differences in prices.
4
We drop 6 countries with only one year of data after matching the PWT and the stock market database.
These countries are: Honduras, Kuwait, Kyrgyzstan, Mongolia, Oman, and Saudi Arabia. The results are
very similar if we include them. The rest of the countries have at least 3 years of data.
5
The most comprehensive list of countries with their legal origin can be found in Djankov, McLiesh, and
Shleifer (2006). The list of countries for this paper can be found in the web appendix.
5
ments is formed by four dummy variables, each dummy taking a value of one for countries
in a given family and zero for countries outside that family. The fifth family is absorbed by
the constant in the regressions.
Previous research shows that countries of the English legal origin have particularly welldeveloped stock markets, at least in the sample period that we cover (La Porta et al. (1997,
1998)).6 In econometric terms, we can be confident that legal origins are not weak instruments in this application, which is corroborated by the first-stage F -statistics reported
throughout the paper. Legal families are largely determined by colonial history, so reverse
causality is not likely to be a problem either (i.e., countries with certain price levels choosing
one set of legal rules). There can be a primitive driving force that determines prices and
legal origins simultaneously (e.g., cultural background). The existence of such a variable
would probably indicate that legal origins are endogenous, or correlated with the error term
in the price level regression. However, it is hard to pin down such primitive influence, and
probably even harder to spell out a mechanism through which it affects prices, wages and
employment in the ways described in this paper.
Perhaps a more serious threat, because it can never be completely ruled out, is the
exclusion restriction. The exclusion restriction requires that the effects of legal origins on
prices come only through the stock market, and not directly or through other variables.
One prominent possibility is that legal origins affect prices by influencing product market
regulation and labor regulation (Botero et al. (2004), Djankov et al. (2002), Pagano and
Volpin (2005)). We explore this issue later on and we conclude, at least preliminarily, that
the effect of legal origins seen through the stock market is separate from that of regulation.
Legal origins also affect other forms of financial development such as banking. However,
Acemoglu and Johnson (2005) argue that the clearest and strongest influence of legal origins
is seen on stock markets, and not in other financial institutions.
The legal origin is meant to capture the exogenous component of stock market develop6
Rajan and Zingales (2003) show that legal origins are not such good predictors of stock market development in the early 20th century.
6
ment, but by no means it is assumed to be its only determinant. For instance, income is
positively correlated with the development of stock markets. As Rajan and Zingales (2003)
point out, political economy considerations can also be important in explaining financial development (see also Pagano and Volpin (2005), Perotti and von Thadden (2006)). However,
the purpose of this paper is not to explain stock market development itself, but to study the
effect of its exogenous component on relative prices. For example, it is perfectly possible that
politicians influence the development of the stock market and through it price levels, but it
is hard to obtain a consistent measure of pro-market political forces for a wide cross-section
of countries that is, at the same time, clearly exogenous (i.e., a measure that can be used as
instrument). The real danger for our identification strategy is not that political forces affect
prices through the stock market, but rather through other channels, such as regulation, as
already mentioned.
2.3
Evidence within Groups of Stock Market Development
The basic regression for price levels is
log
µ
pj
pu.s.
¶
= α0 + α1
µ
Stock Marketj
GDPj
¶
+ α2 log
µ
yj
yu.s.
¶
+ j.
(1)
The dependent variable is the price level in country j relative to the price level in the U.
S. The stock market capitalization is measured as fraction of GDP. Per capita income (y)
in country j is also measured relative to the U.S. and is treated as an exogenous variable in
this section. The log specification for prices and income insures comparability with previous
literature, although the results are also strong with a linear specification (results not shown).
We run the regression with country-level averages for three time periods: 1976-1980, 19811990, and 1991-2000.7
Income serves as a catch-all proxy for several effects discussed in the previous literature,
such as differences in productivity (Balassa (1964) and Samuelson (1964)), differences in
7
We take averages first and then logs.
7
factor endowments (Bhagwati (1984)), and non-homothetic preferences (Bergstrand (1991)).
Froot and Rogoff (1995) and Rogoff (1996) provide surveys of these theories. Income can
also capture some of the growth effect of stock market development, so it is a strong control
variable, perhaps too strong. As long as income is not a perfect proxy for that effect, both
income and the stock market can enter regression (1) with a positive sign.
In this section we split the sample in two groups according to stock market development
in the 1990s, and we run regression (1) in each group separately. The purpose of this exercise
is to show the different impact of stock markets according to their stage of development, and
indirectly to showcase the differences in sample composition across time.
The top panel of Table 1 shows results for the more developed stock markets. In the three
time periods the coefficients on the stock market and income are statistically significant
at least at the 10% level (IV regressions). More interestingly, they have opposite signs.
While income has a positive sign as predicted by Balassa-Samuelson, the stock market has a
negative sign. In the last column we show the OLS regression which indicates that coefficients
become larger when applying instrumental variables. Using the IV coefficients in the 1990s
as a reference, an increase of 1 percentage point in stock market capitalization over GDP
lowers prices by about 0.4 percent (equivalently, a long-run depreciation of the real exchange
rate of 0.4 percent). An increase of 1 percent in income increases prices by 0.75 percent.
The sign of the coefficients is stable across time periods, which implies that the relationship
uncovered is not just a passing phenomenon.
Table 2 presents the stylized fact behind this regression. It shows data on prices, income
and stock market capitalization for high-income countries in 1991-2000. (Data for other
decades are very similar.) Countries are split in 4 groups according to their legal origin.
Focusing on group averages, we see that price levels increase as we move from top to bottom
while there is no clear pattern in income levels. At the same time, stock market capitalization
decreases as we move from top to bottom. Countries of the English legal origin have on
average the highest capitalization and the lowest price levels. The French, Scandinavian and
8
German countries have lower capitalization and higher prices than the English countries.
At a minimum, this table shows that there is variation in price levels that income fails to
capture, and that it can be potentially explained by stock market development.
The effect of stock market development is not the same across the two groups in Table
1. Among the less developed markets, a relative increase in stock market capitalization
increases prices. The effect is significant at the 1% level in 1991-2000. The coefficient in
the last decade implies that an increase of 1 percentage point in stock market capitalization
over GDP increases price levels by 14%. Since these are underdeveloped stock markets, a
1 percentage point increase in capitalization is a major change and therefore it has a big
impact on price levels. The results in the earlier sub-samples are not robust in part because
of sample attrition. We observe 15 less-developed stock markets in the 1980s, and only 6
in 1976-1980. The coefficient on income is never significant among these markets in the IV
regressions.
2.4
Non-monotonic Effect of Stock Market Development on Price
Levels
The evidence from different groups suggests that the stock market has a non-monotonic
effect on price levels. In this section we capture the non-monotonicity by adding the square
of stock market development to the basic regression in (1). We now use all countries in
the regression. We report results only with the 1991-2000 sample because it is the period
with more countries and with a balanced representation of different groups of stock market
development. This heterogeneity is key for identifying the non-monotonic effect of stock
markets as shown by Table 1. We come back to the time series of price levels and stock
market development in Section 3.
Table 3 shows the basic result for the regression with the quadratic term. We find a
positive coefficient on the level of stock market capitalization and a negative coefficient
on the quadratic term. Both coefficients are significant at the 1% level and are larger in
9
the IV regression. The relative magnitudes of these coefficients imply that stock market
development reduces prices only after it surpasses high levels of capitalization (1.8 times
GDP following the IV regression).
The IV regression in the first column of Table 3 implies that an increase of 1 percentage point in stock market capitalization over GDP from the sample median–20 percent of
GDP–, increases prices by 3.9 percent (an appreciation of the real exchange rate of 3.9
percent). An increase of 1 percent in income increases prices by 0.18 percent. The effect of
income in the IV regression is smaller than the standard in the literature, although the OLS
coefficient is in line with previous papers.
The first-stage F -statistic shows that legal origins are important explanatory variables
for the development of the stock market on top of per capita income. The test of joint
insignificance of the instruments is rejected at the 5% level (Table 3, first column). The
J-test with a p-value of 20% implies that we cannot reject the validity of legal origins as
instruments at conventional levels of significance.
Table 3 also reports regressions where we add trade openness (from the PWT) and the
interaction of openness and income.8 These terms capture an alternative non-monotonic
channel related to trade. We expect a positive coefficient on openness since trade promotes
growth (Frankel and Romer (1999)), and a negative coefficient on the interaction term since
the trade specialization of rich countries in more capital-intensive sectors can put downward
pressure on prices. Table 3 shows that we obtain the opposite signs as predicted by this
hypothesis (Broda (2006) reports the same signs that we find). Signs change depending
on whether the interaction is included or not, and also in the IV regressions where these
coefficients are not significant. The stock market coefficients are still significant. Since we
find little support for the openness channel, and since it does not affect the stock market
variables, we exclude openness in much of the analysis that follows. This does not affect the
results that are reported.
8
Regressions with openness squared instead of the interaction term with income give very similar results
and do not affect the stock market variables.
10
Figure 1 illustrates the non-linear relationship between prices and the stock market in
the raw data (not controlling for income). The quadratic pattern can be seen in the three
time periods. In the 1990s, only 8% of the sample is past the inflection point in the figure
(1.2 times GDP). This shows that only countries with very deep stock markets, mostly rich
Anglo-Saxon countries, experience a drop in price levels after a sustained increase throughout
most of the development path. This is not at odds with Table 1, which does not necessarily
imply that all developed stock markets have price levels below those of the less developed
markets.
2.5
Additional Determinants of Price Levels
Table 4 adds to the basic specification some variables that are frequently considered to be
determinants of price levels. For instance, higher government consumption as a fraction of
GDP can increase prices if it is tilted towards nontradable goods (De Gregorio, Giovannini, and Wolf (1994)). We get instead a negative coefficient on government consumption,
although not significant.9
A similar argument applies to the net foreign asset position of the country if domestic and
foreign preferences are different. This is related to what Keynes called the transfer problem
(Lane and Milesi-Ferretti (2004)). We find a positive sign on this variable as most previous
studies, but not enough statistical significance.10
De Gregorio and Wolf (1994) argue that terms of trade shocks can have an effect similar
to that of income, increasing the prices of nontradables and price levels. We find instead a
negative coefficient on the terms of trade growth, but only significant at the 10% level. Lane
and Milesi-Ferretti (2004) report positive and negative coefficients on terms of trade shocks
depending on the sample.11
9
General government final consumption expenditure as percentage of GDP is obtained from the World
Development Indicators.
10
The net foreign asset position as a fraction of GDP is taken from Lane and Milesi-Ferretti (2001, 2007).
The measure in Kraay et al. (2000) gives similar results (not reported).
11
Terms of trade come from the Global Development Network Growth Database available at the World
11
Finally, Broda (2006) shows that countries with flexible exchange rates tend to have lower
price levels, particularly among developing economies. We find the same dampening effect
on prices in the whole sample when using Reinhart and Rogoff (2004)’s index of exchange
rate flexibility, although the effect is not statistically significant.
Overall, adding these controls does not reduce the significance of the stock market variables. The magnitude of the coefficients is somewhat affected only when all controls are
included. It is important to keep in mind that these controls are potentially endogenous and
can introduce several biases to the regression. In order to preserve the maximum number of
countries in the sample we keep only income and the stock market variables in the regressions
that follow.
2.6
Are Legal Origins Working Through the Stock Market?
The IV regression assumes that legal origins have no effect on price levels except through
the stock market. However, recent studies show that legal origins are correlated with laws
beyond those affecting the development of the stock market–in particular, the laws regulating product market competition (Djankov et al. (2002)) and the labor market (Botero
et al. (2004), Pagano and Volpin (2005)). Countries with an English legal origin have less
restrictive labor regulation, and it is possible that this affects prices, although the direction
of the effect is unclear. For instance, tighter labor regulation can increase wages and prices
in the short run, but reduce them in the long run (Blanchard and Giavazzi (2003)).
The relevance of regulation is tested using the number of procedures required to start
a business (a measure of the regulation of entry) and an index of labor law.12 We do not
have a time-series for these measures of regulation, but only one observation per country.
Regulation is also treated as an endogenous variable and it is instrumented using the set
Bank’s website and originally compiled by William Easterly. It is necessary to use growth rates because the
terms of trade are presented in the form of country indexes, which cannot be compared across countries.
12
The number of procedures is taken from Djankov et al. (2002). The labor law index is the country
average of the employment laws index, the collective relations laws index, and the social security laws index
from Botero et al. (2004).
12
of dummies that represent legal origins. It is important to emphasize that we have four
dummies in the set of instruments and not just one categorical variable (e.g., a variable
that takes a value of 1 if the country is of the English legal origin, 2 if the country is of
the French origin, 3 if German and so on). This is relevant because the legal families do
not have the same ranking across all dimensions. For example, while the English countries
have the most developed stock markets, they have a tougher regulation of entry than the
Scandinavian countries. At the same time, the Scandinavian countries have highly regulated
labor markets, almost as regulated as Socialist countries, which have the less developed stock
markets. French countries have less regulated labor markets than Scandinavian countries,
but more regulated entry, and so on.
Only the labor law index comes in significantly and with a negative sign in Table 5. The
stock market variables are still significant and of comparable magnitude to previous tables.
This evidence supports the idea that the effect of legal origins that comes through the stock
market is an independent channel and not simply a proxy for regulation. Admittedly, these
regressions do not exhaust all possibilities. Regulation, for example, can act in more subtle
ways that the proxies we use here. However, the regressions in Table 5 show some encouraging
results in the sense that our identifying assumption is not patently violated.
3
Panel Data Evidence on Stock Market Development
and Price Levels
3.1
Identifying Assumptions
In this section, we present a time-series analysis which strengthens our results in several
respects. First, with the time-series we can assess the dynamic effect of stock market development on price levels, unlike the case of the pure cross-sectional analysis. Second, using
panel data helps us to control for country unobservables, in particular for those unobserv-
13
ables that potentially invalidate legal origins as instruments. A third advantage is that we
can control for the endogeneity of income and not only for the endogeneity of stock market
development.
Consider the following version of regression (1) that accounts for the time dimension and
unobserved country-specific effects (μj ):
log
µ
pj,t
pu.s.,t
¶
= α1
µ
Stock Marketj,t
GDPj,t
¶
+ α2 log
µ
yj,t
yu.s.,t
¶
+ μj +
j,t .
(2)
First differencing equation (2) eliminates the country-specific effect,
∆ log
µ
pj,t
pu.s.,t
¶
µ
Stock Marketj,t
= α1 ∆
GDPj,t
¶
+ α2 ∆ log
µ
yj,t
yu.s.,t
¶
+∆
j,t .
(3)
We assume that the variables are weakly exogenous, that is, they may be correlated with
past and current shocks, but not with future shocks. In particular, the levels of income
and stock market development are assumed to be uncorrelated with future unanticipated
shocks to price levels (future
j,
i.e., unanticipated shocks from the point of view of the
econometrician). With forward looking agents, it is reasonable to assume that stock markets
can respond to anticipated shocks to prices, but not to unanticipated shocks. Under the
assumption that the errors
j,t
are serially uncorrelated, we can use the following moment
conditions to identify the coefficients in the last regression,
0
∆
E[Xj,t−s
j,t ]
= 0 for s > 2; t = 3, .., T
(4)
The expectation is taken across countries. The matrix Xj,t−s contains lagged levels of
stock market capitalization and income as columns. This matrix can also contain current
levels of strictly exogenous variables included in the regression, which serve as instruments for
themselves (e.g., time dummies). Arellano and Bond (1991) develop a GMM estimator based
on these moment conditions. In subsequent work, Arellano and Bover (1995) and Blundell
and Bond (1998), show that we can improve the estimation by considering additional moment
14
conditions that deal with errors in levels and not only in differences. The main reason
for the poor performance of the estimation based solely on (4) is that past levels can be
weak instruments for current differences if the time series are close to unit roots. Intuitively
speaking, first differences are pure innovations if a time series is a random walk, and therefore
past levels contain no valuable information about current changes.
Under the further assumption that the first differences ∆Xj,t−s are uncorrelated with the
country-specific effects μj , we can add the following moment conditions,13
0
E[∆Xj,t−s
j,t ]
= 0 for s > 1; t = 3, .., T
(5)
The usefulness of moment conditions in (5) can be understood intuitively as the reverse
of the problem of past levels as weak instruments. First differences are permanent shocks if
a time series is a random walk, and therefore, past changes contain important information
for future levels (i.e., they are strong instruments.) At least in our application, stock market
development is growing steadily over time so we suspect that adding this second set of moments conditions can improve the identification substantially.14 It is important to note that
some of the moment conditions in equation (5) can be redundant depending on the number
of lags considered in equation (4) (Arellano and Bover (1995)). Following the literature we
refer to the estimator derived from moments conditions (4) and (5) as system GMM.15
The econometric techniques described here are appropriate for panels with a large number
of individuals and a short time dimension. Therefore, for each country we average the data
over non-overlapping 5-year periods starting with 1976-1980, which follows the strategy of
Beck, Levine, and Loayza (2000).16 This also serves to smooth business-cycle fluctuations
since our focus is the long-run behavior of price levels.
13
This assumption does not imply that the levels of stock market development are uncorrelated with the
country-specific effects, but that this correlation does not change over time.
14
In contrast, price levels and income look stationary. The reason for this is that they are measured relative
to a reference country (the U.S.).
15
System GMM is estimated using the Stata command xtabond2 developed by Roodman (2005).
16
This implies that T = 5 in this application.
15
We present two identification tests. The first test about the validity of the instruments
is the standard test of overidentifying restrictions or J -test. A second test proposed by
Arellano and Bond (1991) checks for second-order autocorrelation in ∆
j,t
which deals with
the assumption of no serial correlation in the error term. Failure to reject the null hypothesis
in both cases provides support for the empirical model.
3.2
Results with Panel Data
Table 6 presents the results for the panel regression in (2) including the quadratic term
of stock market capitalization. All regressions include time dummies (not reported in the
tables). We start by considering income as weakly exogenous as the other variables.
The first column in Table 6 shows the results when all available lags are used in moment
condition (4). In this case only the first lag is relevant in equation (5) since the other moment
conditions are implied by (4). Coefficients on the stock market terms are smaller than the
previous cross-sectional estimates, but they are still sizeable. From the sample median, an
increase of 1 percentage point in stock market capitalization over GDP increases prices by
0.8 percent. The relative magnitude of the linear and quadratic terms still implies that the
inflection point in the relationship between prices and the stock market is observed at high
levels of capitalization. The J -test provides support for the model, but the AR(2) test rejects
the null at the 4% level, which is an indication of poor instruments.
Bond (2002) notes that estimates based on a smaller set of instruments can solve biases
that arise from having too many moment conditions (an overfitting problem; see also Roodman (2006)).17 The second column in Table 6 reduces the number of instruments by ignoring
very long lags. The results become stronger in terms of magnitude and statistical significance. The identification tests lend support for this smaller set of instruments since now both
tests are easily passed.18 One justification for the improvement in this second model is that
17
This is also the reason for using the collapse option in xtabond2 as we do for the regressions in Table
18
Other variations in the instrument set give similar results. The key difference with respect to the first
6.
16
long lags of levels are weak instruments for current differences. Excluding longer lags from
the moment conditions in (4) implies that some moment conditions in (5) are not redundant
now, and adding these moment conditions in levels improves the estimation substantially.
A perhaps more intuitive reason for why this second model works better has to do with the
changing characteristics of the sample at different horizons. Moment conditions that use
long lags are tilted towards well developed markets because they have longer time series.
As we saw in the cross-section, the identification of the stock market coefficients depends
upon observing countries within a wide range of stock market development. Therefore, the
moment conditions with long lags contain potentially less useful information because they
are characterized by less heterogeneity.
The third column in Table 6 shows the results with two-step GMM, including the finite
sample correction suggested by Windmeijer (2005). Results are very similar to the case of
one-step GMM.
The coefficient on income is not statistically significant in the first three columns of
Table 6 when income is assumed to be weakly exogenous. The fourth column treats income
as strictly exogenous as in the cross-sectional regressions. Now income has a positive and
significant coefficient as before. The stock market terms are still significant and within one
standard deviation of the baseline GMM estimates.
The last two columns in Table 6 show results of the OLS and the country-fixed effect
regressions for comparison with the GMM estimation. The coefficients in the OLS regression
have the same signs as before, and they are statistically significant, but magnitudes are
smaller. The OLS and fixed effect estimations have several biases in comparison with system
GMM (Arellano and Honoré (2001)).19
model is to include more moment conditions with errors in levels.
19
The web appendix shows results for the GMM estimation when the other determinants of price levels
presented in table 4 are included.
17
4
Evidence on Employment Shares and Wages
4.1
Employment Shares and Stock Market Development
The mechanism behind the change in relative prices entails a reallocation of labor across
sectors and changes in wages. In this section we explore these implications directly. We
first focus on the relation between stock market development and employment in different
sectors. We follow the literature by considering services as mostly nontradable, and industry
(manufacturing and others) as relatively more tradable.
Table 7 presents regressions where the dependent variable is the average share of total
labor in a country that is employed in services and industry in the 1990s (The omitted sector
is agriculture.)20 Regressions with data from previous decades cover approximately half the
countries of the 1990s, and we do not present them. The explanatory variables are the stock
market capitalization and its quadratic term, plus per capita income and its quadratic term.
These last two terms are motivated by the non-linear effect of income on specialization as
documented by Imbs and Wacziarg (2003).21
The regressions with industry employment show a clear negative effect of stock market
development. The quadratic term is positive and significant, but very small. For instance,
only one country in our sample is past the inflection point in the quadratic relationship
implied by these coefficients, so we can conclude that the effect is mostly negative. The
marginal effect of income is positive, contrary to the effect of the stock market, meaning
that an increase in per capita income increases employment in industry. The quadratic term
of income is economically insignificant (i.e., no country in the sample is past the inflection
point in this case.)
Stock market development induces a higher share of employment in services. Since income
20
The employment shares are taken from the World Development Indicators. Employment is reported for 3
sectors: agriculture, industry, and services. Industry corresponds to sectors 2-5 (construction, manufacturing,
mining, and utilities) and services correspond to sectors 6-9 of the ISIC-Rev.2 codes used by the International
Labor Organization.
21
The regressions using log-employment shares and log-income, together with the stock market variables,
give very similar results.
18
and the stock market have effects in the same direction it is harder to identify the coefficients
precisely. Nevertheless, the last IV regression indicates a strong effect of the stock market
and not of income. In terms of magnitude, an increase of 1 percentage point in stock market
capitalization from the sample median increases the share of employment in services by 0.8
percentage points (the average share of employment in services is 55 percent.)
Overall, this evidence suggests that higher income shifts employment away from agriculture and towards industry and services, while stock market development induces a further
migration of labor into services.
4.2
Wages and Stock Market Development
We put together a cross-section of wages by countries and sectors using data from the ILO
in the 1990s (because data in previous decades are very limited.)22 The advantage of these
data is the wide sectoral coverage. Disadvantages are the differences in the methodology for
data collection, which potentially reduce the comparability of the series across countries.23
In our analysis we compare wages across countries, but within sectors. For example, we
compute wage differentials between the U.S. and Germany only for workers in construction.
Even though frictionless models speak of one national wage, we do not attempt to aggregate
wages across sectors for a particular country. Our results confirm that even if perfect wage
equalization within a country fails, we can still observe cross-country wage differentials that
mimic the price differentials already documented.
We adjust wages to take into account purchasing power differences across countries. The
idea is to obtain the relative cost of labor expressed in terms of a comparable bundle of goods.
The cross-country dispersion in wages is even greater if we do not adjust for purchasing
power. First we express wages in U.S. dollars of the year 2000 using the nominal exchange
rate and the U.S. CPI as deflator. Then we multiply wages by a purchasing power parity
22
We restrict the sample to sectors with at least 3 years of data. Results do not hinge on this restriction.
See Freeman and Oostendrop (2000) for related criticism, although with respect to the ILO October
Inquiry data that intends a much more detailed coverage by occupations.
23
19
factor.24 This factor is the ratio of GDP in 2000 "international dollars" to GDP in 2000
U.S. dollars for a given country in a year as reported in the World Development Indicators.
An international dollar has the same purchasing power over GDP as one U.S. dollar in the
United States.25
Similar patterns as with price levels can be seen in wages. Within the group of welldeveloped stock markets, those countries with higher stock market capitalization tend to have
lower wages. For instance, wages in manufacturing are lower in Anglo-Saxon countries such
as the U.S. ($2,269) or Ireland ($2,081) than in countries such as France ($2,310), Germany
($2,862), or Sweden ($2,499). In Table 8 we show the results of our basic regression using
ILO wages as the dependent variable. First we run regressions by sector so each data point
represents a country, similarly to our regressions for price levels. The coefficient on income is
positive and significant in all regressions. The linear term of stock market development has
a positive sign and the quadratic term has a negative sign in all regressions. The linear term
is significant in 8 of the 9 sectors, and the quadratic term is significant in 4 of the 9 sectors
(manufacturing, construction, retail, and transport). Manufacturing is the sector with the
widest coverage across countries so it is reassuring that all coefficients are significant and
with the expected sign in this regression. The magnitudes are also comparable to those in
the price level regressions. Taking the regression in manufacturing as reference, an increase
of 1 percentage point in stock market development increases wages by 2.7 percent. This
represents an increase of $38 from an average monthly wage in manufacturing of $1,389.
Labor law is an important determinant of wages across countries (Freeman and Oostendrop (2000)). Table 5 already showed that tighter labor regulation has a downward effect on
price levels. Consistent with that effect, labor regulation has a negative impact on wages as
24
The nominal exchange rate is taken from the IFS, except for Romania and Turkey for which we use
the ratio of GDP in local currency units to GDP in U.S. dollars as reported in the World Development
Indicators.
25
This conversion factor is conceptually equivalent to deflating wages by the price levels reported in the
PWT. The correlation between the two deflators in our sample is 0.82. Using the PPP factor from the WDI
allows us to present new data and examine the robustness of the results shown with PWT price levels in
tables 1-7.
20
seen on the right-hand-side panel of Table 8. Countries with heavily regulated labor markets
include rich countries such as Scandinavian countries, but also former communist countries
and other less developed countries such as Panama or Venezuela. Once the effect of income
is taken out, tighter labor regulation clearly decreases wages in IV and OLS regressions,
including or not the development of the stock market.
Including the labor law index reduces the number of observations by approximately 15%
to 20%. Standard errors are consequently larger and we lose more than half of the significant
coefficients on the linear term of stock market development. The quadratic term is robust
to the inclusion of labor law, and its magnitude tends to increase. This is probably due to
an increasing importance of more developed markets in the sample. In any case, the basic
message is still present. Labor law seems to be an important channel that affects wages, but
by no means it replaces the effect of stock markets.
The last row in Table 8 shows regressions that pool all the sectors together. These regressions include sector fixed effects. The standard errors are clustered by country to account
for within-country correlation across sectors. Clustering is essential since the explanatory
variables are country-level measures. The coefficients on the linear and quadratic terms of
stock market development are statistically significant at the 5% level. Significance is reduced
when the labor law index is included, but coefficients are still of similar magnitude.
5
Growth, Specialization, and Relative Prices
The theory we present here serves as possible justification for the empirical results just
documented. First, it motivates the main channels of influence of stock market development
with results from previous literature. Then it provides a simple model to illustrate the
connection between these channels and price levels.
21
5.1
Motivation
There is a large literature that studies the impact of stock market development on growth and
specialization. In a recent survey, Levine (2004) concludes that the evidence strongly suggests
a positive influence of stock markets on growth. Importantly, reverse causality does not seem
to drive the relationship. The literature also shows that the development of the financial
system is a source of comparative advantage and therefore that it leads to specialization.
Sectors that are heavy users of financial markets benefit from financial development in an
analogous way as sectors that are heavy users of a factor benefit from the abundance of that
factor. Rajan and Zingales (1998) develop a measure of the financial dependence of different
industries and they show that financial development disproportionately affects the growth
rates of those industries with a higher financial intensity. Beck (2003) and Svaleryd and
Vlachos (2005) confirm the idea of the comparative advantage by showing that countries with
better financial systems specialize their exports and production in the financially dependent
sectors identified by Rajan and Zingales.26
The evidence indicates that industries with high financial intensity have higher capital
intensity. For example, Rajan and Zingales report a correlation of 0.81 between financial
dependence and investment intensity, which they define as the ratio of capital expenditures
over property, plant and equipment (see also Carlin and Mayer (2003) and Svaleryd and
Vlachos (2005)). Therefore, the specialization induced by financial development is tilted
towards capital-intensive technologies.
Motivated by these previous findings, we view stock market development as a twodimensional process. On one hand, stock markets lead to higher capital accumulation and
higher growth. This growth channel can be understood as the result of relaxing financing
constraints and undertaking more projects. Simultaneously, stock markets promote a shift
towards technologies that are more capital-intensive. This specialization channel implies a
26
Beck (2002) shows that financial development constitutes a comparative advantage for manufacturing
as a whole when compared to other sectors.
22
change in the composition of the projects that are financed. In the model, we view this channel as a shift in the underlying production function towards higher capital intensity. Our
stylized model illustrates how these two channels can affect price levels and employment
shares.
5.2
Model
The model follows the analysis of long-run price levels developed in Obstfeld and Rogoff
(1996, ch. 4). Imagine a small open economy in a world with two goods, one tradable
and one nontradable. The law of one price holds for the tradable good and therefore any
difference in national price levels is explained by the price of the nontradable good, p. The
tradable good has a fixed price of one and is the numeraire. Consumers have homothetic
preferences over the consumption of tradables and nontradables, U(CN , CT ) = CNθ CT1−θ . The
parameter θ is the fraction of income that consumers spend in nontradables (0 < θ < 1).
Consumers are endowed with a fixed amount of labor, L, and capital, K. Tradables and
nontradables are produced domestically. Nontradables are produced using only labor, YN =
LN . This production function implies that the price of nontradables is equal to the domestic
wage. The production function for the tradable good is YT = AKTα L1−α
T , where A is total
factor productivity and α is the capital intensity of tradables (0 < α < 1). Alternatively,
capital can be converted into tradables at no cost, which implies that the world interest rate
r has to be positive (a no-arbitrage condition). We also assume that the world interest rate
is not "too low" in the sense that r > αA.27
Labor is mobile across sectors in the economy, but not internationally, which implies that
LN + LT = L. Capital is mobile across and within countries, except for one restriction. The
tradable sector faces a capital constraint of KT < K̄. This constraint can be the result of
an (unmodeled) agency problem or asymmetric information. We assume initially that this
27
Intuitively, this condition says that the world interest rate is higher than the share of total factor
productivity that can be attributed to capital. This condition is necessary for some the comparative statics
that follow in propositions 1 and 2.
23
constraint is binding (KT = K̄) in the sense that the marginal product of capital evaluated
at K̄ is higher than the world interest rate. Consumers can lend or borrow the difference
K − K̄ at the world interest rate, therefore K − K̄ is the long-run foreign asset position of
the country. Total income in this economy is YT + pYN + r(K − K̄), with a fraction θ spent
in nontradables and 1 − θ spent in tradables. The market clearing conditions are CN = YN
for nontradables and CT = YT + r(K − K̄) for tradables.
In terms of this model we view stock market development as affecting two key parameters.
First, stock market development implies a relaxation of the capital constraint, i.e., an increase
in K̄. We view this constraint as representing domestic financial development and not only
as a restriction on foreign borrowing. For example, in this model it is possible to see a
country with a productive sector that is capital constrained, and at the same time to see
a positive net foreign asset position. This happens when K > K̄. In this case, an increase
in K̄ allows more domestic capital to flow to the tradables sector instead of going overseas.
Only when K < K̄ the capital constraint affects foreign borrowing.
Second, the specialization induced by stock markets is captured by an increase in the
capital intensity of the tradable sector, i.e., an increase in α. The following propositions
summarize the comparative statics concerning these two key parameters. A detailed analysis
of these propositions and the rest of the model is provided in the web appendix.
Proposition 1: When the capital constraint on the tradable sector is binding and the
foreign asset position of the country is not extreme:
1.1 (Growth) The price level increases as the constraint is relaxed,
in employment in the tradable sector,
∂ log LT
∂ log K̄
∂ log p
∂ log K̄
> 0. The change
, inherits the sign of the foreign asset position.
1.2 (Specialization) Increasing capital intensity leads to lower price levels,
lower employment in the tradable sector,
∂ log LT
∂α
∂ log p
∂α
< 0, and
< 0.
The intuition for the growth channel is simple. The higher availability of capital increases
the marginal product of labor in the tradable sector, which leads to higher wages, and
24
consequently higher price levels. This effect induces labor to move to the tradable sector. At
the same time, higher income increases the demand for nontradables, which induces labor to
move to the nontradable sector. In the case of K = K̄, i.e., when the foreign asset position
is zero, these two effects cancel out so that no labor needs to move across sectors to reach a
new equilibrium price. More generally, labor moves across sectors as the capital constraint
is relaxed.
Specialization reduces the amount of labor needed in the tradable sector and pushes
labor towards the nontradable sector. The resulting increase in the supply of nontradables
puts downward pressure on prices and wages. The sign of the comparative statics in the
case of specialization can be determined analytically when the foreign asset position is zero.
However, they apply more generally within reasonable bounds of the foreign asset position
as we show with numerical examples in the web appendix.
In the model we artificially separate growth and specialization in order to stress their
different implications for price levels. Most probably, both channels are at work simultaneously in the data. However, we can suspect that the growth channel stops being relevant as
stock markets reach a certain level of development. Simply put, at some point the capital
constraint has to stop being binding. If we think in terms of world equilibrium, it seems
implausible that the world interest rate is below the marginal product of capital everywhere.
When the capital constraint is not binding, stock markets can continue to affect price levels
and employment through specialization. This is summarized in our second proposition.
Proposition 2 (Specialization without a binding capital constraint): When the capital
constraint on the tradable sector is not binding, specialization leads to lower price levels,
∂ log p
∂α
< 0, and lower employment in the tradable sector,
∂ log LT
∂α
< 0. These effects do not
depend on the foreign asset position of the country, which is defined as K − KT in this case.
This last proposition can help us to interpret the observed negative relationship between
stock markets and price levels among countries that have already achieved a certain level
25
of market development. The model suggests that in those countries capital constraints
are not binding anymore. Among countries with poorly developed stock markets, where
the capital constraint is still binding, we can expect a positive relationship if the growth
channel is strong. In other words, our interpretation of the empirical findings among less
developed markets is that initially stock markets relax capital constraints more than induce
specialization.
6
Conclusions
This paper documents a new fact: stock market development has a non-monotonic effect
on price levels and wages. The causality runs, as far as we can tell, from the stock market
to price levels as shown by two different identification strategies. The first strategy relies
on legal origins as instruments and the second strategy uses panel data techniques. The
explanation for this relationship is based on the growth and specialization patterns induced
by the development of stock markets. There is ample evidence in the literature for these
two channels, although the implications for price levels have not been studied before. We
also document a negative influence of stock market development on the employment share
in industry (and positive in services).
From the standpoint of political economy, it is important that stock market development
does not have a monotonic effect on wages. Our results show that the development of
the stock market increases wages during early stages. This can be viewed as a "win-win
situation," where capitalists and workers benefit. However, the fall in wages that occurs in
later stages, with the consequent opposition of workers, may explain why some countries
reverse the development of their stock markets (Rajan and Zingales (2003)). How these
political considerations determine the level and structure of financial development is an
interesting area of present and future research (see also Pagano and Volpin (2005), and
Perotti and von Thadden (2006)).
26
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Table 1
The Effect of Stock Market Development on Price Levels by Groups
The dependent variable is the log price level relative to the U.S. taken from the Penn World Table 6.1. The
independent variables are: (1) Stock market capitalization over GDP from Beck et al. (2001), and (2) Log
of per capita income relative to the U.S. from the Penn World Table 6.1. All variables are averages for each
country in the sample period indicated in the heading of the column. In the IV regressions the instruments
for the stock market capitalization are dummies reflecting the legal origin of the country (English, French,
German, Scandinavian or Socialist). Countries are divided into highly and poorly developed according to
their average stock market development in 1991-2000. The constant in the regression is not reported.
Robust standard errors are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
1976-1980
IV
Highly Developed Stock Markets
Stock Market Cap.
-0.63
1981-1990
IV
**
(0.32)
Log Income
No. Countries
Poorly Developed Stock Markets
Stock Market Cap.
Log Income
No. Countries
0.47
-0.39
**
(0.16)
***
0.60
***
IV
1991-2000
OLS
-0.38 *
-0.05
(0.21)
(0.07)
0.75
***
0.68
(0.05)
(0.06)
(0.08)
(0.05)
30
40
45
45
-15.11
13.95
14.03 **
2.06
(17.93)
(22.36)
(7.13)
(0.92)
0.20
0.24
0.21
0.30
(0.26)
(0.16)
(0.16)
(0.10)
6
15
45
45
***
**
***
Table 2
Price Levels, Income, and Stock Market Development in High-Income Countries
All variables are country averages in the period 1991-2000. Prices and income are shown relative to U.S.
levels. Both are taken from the Penn World Table 6.1. Stock market capitalization over GDP is taken from
Beck et al. (2001). The table shows the 30 countries with highest income in the period 1991-2000
according to income levels in the Penn World Table.
Country
English Legal Origin
Australia
Barbados
Canada
Cyprus
Hong Kong
Ireland
Israel
New Zealand
United Kingdom
United States
Singapore
Average
French Legal Origin
Belgium
France
Italy
Luxembourg
Netherlands
Portugal
Spain
Average
German Legal Origin
Austria
Germany
Japan
Korea, Republic of
Slovenia
Switzerland
Taiwan
Average
Scandinavian Legal Origin
Denmark
Finland
Iceland
Sweden
Norway
Average
Pi/Pusa
1991-2000
Yi/Yusa Stk Mkt Cap
0.90
0.53
0.90
0.75
0.88
1.04
0.96
0.86
1.03
1.00
0.90
0.89
0.77
0.50
0.80
0.53
0.86
0.68
0.55
0.59
0.69
1.00
0.79
0.70
0.69
0.42
0.72
0.20
2.34
0.56
0.41
0.51
1.32
1.05
1.50
0.88
1.13
1.16
0.99
1.13
1.12
0.73
0.93
1.03
0.73
0.71
0.70
1.22
0.74
0.48
0.54
0.73
0.50
0.49
0.28
1.52
0.96
0.31
0.42
0.64
1.20
1.23
1.50
0.68
0.66
1.51
0.83
1.09
0.74
0.73
0.81
0.45
0.46
0.86
0.51
0.65
0.14
0.34
0.72
0.37
0.07
1.62
0.80
0.58
1.31
1.19
1.21
1.30
1.34
1.27
0.82
0.67
0.75
0.72
0.83
0.76
0.41
0.76
0.30
0.81
0.29
0.51
Table 3
The Effect of Stock Market Development on Price Levels: Whole Sample
The dependent variable is the log price level relative to the U.S. taken from the Penn World Table 6.1. The
independent variables are: (1) Stock market capitalization over GDP and its quadratic term from Beck et al.
(2001), (2) Log of per capita income relative to the U.S. from the Penn World Table 6.1, (3) Trade
openness defined as exports plus imports over GDP also from the PWT, and (4) Interaction term of trade
openness and per capita income. All variables are averages for each country in 1991-2000. In the IV
regression the instruments for the stock market capitalization are dummies reflecting the legal origin of the
country (English, French, German, Scandinavian or Socialist). The constant in the regression is not
reported. Robust standard errors are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
Stock Market Cap.
IV
5.00
***
(1.59)
Stock Market Cap. Squared
-2.77
(0.98)
Log Income
OLS
0.67
***
(0.23)
***
-0.30
***
(0.10)
0.18
0.44
(0.16)
(0.07)
Trade Openness
IV
6.41
1991-2000
OLS
** 0.56
(2.74)
(0.24)
-4.02
(1.80)
***
**
-0.20
0.10
0.46
(0.25)
(0.07)
1.03
-0.15
(0.75)
(0.07)
R2
First-Stage F-statistic
J-test
p-value J-test
90
90
90
0.66
3.06
3.26
0.20
90
IV
4.66
*
-2.95
(1.23)
***
**
OLS
0.50
**
(0.22)
**
-0.22
**
(0.11)
0.02
0.39
(0.21)
(0.08)
0.08
-0.37 ***
(0.00)
(0.13)
0.010
0.004
(0.000)
(0.002)
90
0.67
4.87
1.82
0.40
**
(1.94)
(0.11)
Trade Openness x Income
No. Countries
**
90
0.69
2.19
3.78
0.15
***
**
Table 4
The Effect of Stock Market Development on Price Levels: Additional Determinants of Prices
The dependent variable is the log price level relative to the U.S. taken from the Penn World Table 6.1. The independent variables are: (1) Stock market
capitalization over GDP and its quadratic term from Beck et al. (2001), (2) Log of per capita income relative to the U.S. from the Penn World Table 6.1, (3)
Government final consumption expenditure over GDP from the WDI, (4) Net external position as fraction of GDP from Lane and Milesi-Ferreti (2007), (5)
Terms of trade growth from the World Bank, and (6) Index of exchange rate flexibility from 1 (least flexible) to 6 (most flexible) taken from Reinhart and Rogoff
(2004). All variables (except for the net external position which is measured in 1995) are averages for each country in 1991-2000. In the IV regressions the
instruments for the stock market capitalization are dummies reflecting the legal origin of the country (English, French, German, Scandinavian or Socialist). The
constant in the regression is not reported. Robust standard errors are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
IV
IV
Stock Market Cap. 4.09 *** 7.19 **
(1.29)
(3.52)
Stock Market Cap. Squared -2.26 *** -3.93 **
(0.82)
Log Income 0.28 **
(0.14)
1991-2000
IV
IV
IV
IV
4.11 *** 4.70 *** 4.05 *** 3.41 **
(1.46)
(1.63)
(1.14)
(1.99)
(0.87)
(1.00)
(0.76)
-0.13
0.27 **
0.22
0.23 *
0.32 ***
(0.37)
(0.14)
(0.16)
(0.13)
(0.13)
Government Expenditure -1.61
(2.27)
0.43
0.13
(0.38)
(0.16)
Terms of Trade Growth
-2.73 *
-5.10 **
(1.39)
(2.38)
ER Flexibility
No. Countries 89
First-Stage F-statistic 3.75
J-test 5.68
p-value J-test 0.06
(1.00)
-2.30
(1.73)
Net External Position
(1.68)
-2.28 *** -2.59 *** -2.21 *** -1.83 *
86
3.06
1.71
0.43
83
3.39
2.34
0.31
90
3.12
4.12
0.13
-0.07
-0.06
(0.06)
(0.06)
84
3.84
4.54
0.10
76
5.99
3.60
0.17
Table 5
Are Legal Origins Working Through the Stock Market?
The dependent variable is the log price level relative to the U.S. taken from the Penn World Table 6.1. The independent variables are: (1) Stock market
capitalization over GDP and its quadratic term from Beck et al. (2001), (2) Log of per capita income relative to the U.S. from the Penn World Table 6.1, (3) Log
of the number of procedures from Djankov et al. (2002), and (4) Labor law index from Botero et al. (2004). All variables are averages for each country in 19912000. In the IV regressions the instruments for the stock market capitalization, the number of procedures and the labor law index are dummies reflecting the legal
origin of the country (English, French, German, Scandinavian or Socialist). The constant in the regression is not reported. Robust standard errors are reported
below the coefficients. Significance: * 10%, ** 5%, *** 1%.
Stock Market Cap.
IV
3.68
***
(1.38)
Stock Market Cap. Squared
-2.05
OLS
0.65
(0.25)
***
(0.74)
-0.30
0.27
(0.13)
(0.08)
Log Number of Procedures
-0.12
-0.13
(0.27)
(0.09)
**
0.42
Labor Law Index
***
-2.09
0.65
72
72
***
-0.25
0.56
**
***
(0.07)
**
-1.05
(1.15)
(0.44)
72
72
0.68
1.98
0.91
0.64
*
(0.11)
**
(0.27)
0.66
5.69
3.25
0.07
OLS
0.42
(0.24)
(0.69)
***
-2.36
2
R
First-Stage F-statistic
J-test
p-value J-test
(1.30)
(0.11)
Log Income
No. Countries
1991-2000
IV
*** 2.97 **
**
Table 6
The Effect of Stock Market Development on Price Levels: Basic Panel Data Evidence
The dependent variable is the log price level relative to the U.S. taken from the Penn World Table 6.1. The independent variables are: (1) Stock market
capitalization over GDP and its quadratic term from Beck et al. (2001), and (2) Log of per capita income relative to the U.S. from the Penn World Table 6.1 All
variables are non-overlapping five-year averages for each country in 1976-2000. System GMM is estimated using the xtabond2 routine for Stata. Columns (1),
(2) and (4) use one-step GMM, column (3) uses two-step GMM. All variables are considered to be weakly exogenous, except for income in column (4) that is
considered strictly exogenous. The last two columns present OLS and country-fixed effect regressions respectively. Time dummies in the regressions are not
reported. Robust standard errors are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
Stock Market Cap.
GMM
(1)
0.86
Stock Market Cap. Squared
-0.25
GMM
(2)
1.73
(0.54)
Log Income
**
(0.78)
*
-0.62
1976-2000
2-step GMM
GMM
(3)
(4)
1.34
**
0.95
(0.55)
**
-0.51
**
0.19
*
-0.06
(0.42)
***
-0.41
FE
**
(0.09)
(0.12)
*
-0.02
-0.01
0.13
0.39
(0.29)
(0.34)
(0.28)
(0.07)
(0.03)
(0.09)
90
314
0.32
0.17
14
90
314
0.32
0.05
14
90
314
0.32
0.08
12
90
314
90
314
same as (2)
same as (2),
income exog.
(0.03)
0.00
(0.30)
Xt-2
ΔXt-1, ΔXt-2
(0.21)
0.07
(0.15)
No. Countries
90
No. observations
314
Hansen J-test p-value
0.20
AR(2) Test p-value
0.04
Moment Conditions
17
Instruments:
Difference equation Xt-2, Xt-3, Xt-4
Levels equation
ΔXt-1
(0.19)
OLS
***
0.45
(0.04)
***
0.29
***
Table 7
The Effect of Stock Market Development on Labor Shares of Industry and Services
The dependent variable is the share of total labor that is employed in industry or services in a country, as reported in the WDI. The independent variables are: (1)
Stock market capitalization over GDP and its quadratic term from Beck et al. (2001), (2) Per capita income relative to the U.S. and its quadratic term from the
Penn World Table 6.1. All variables are averages for each country in 1991-2000. In the IV regressions the instruments for the stock market capitalization are
dummies reflecting the legal origin of the country (English, French, German, Scandinavian or Socialist). The constant in the regression is not reported. Robust
standard errors are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
1991-2000
Industry
Stock Market Cap.
IV
-0.16
**
(0.08)
Services
OLS
-0.03
IV
-0.71
(0.02)
(0.22)
Stock Market Cap. Squared
0.33
***
0.22
(0.09)
**
0.10
(0.04)
Income Squared
**
0.71
**
(0.17)
No. Countries
74
2
R
First-Stage F-statistic
J-test
p-value J-test
74
74
0.10
3.07
8.61
0.04
0.08
IV
0.07
OLS
-0.01
IV
1.07
(0.09)
(0.02)
(0.44)
**
-0.57
(0.03)
***
(0.17)
-0.41
***
(0.05)
(0.13)
Income
OLS
-0.17
0.47
(0.25)
***
(0.12)
**
-0.35
0.25
(0.11)
0.33
(0.06)
**
(0.13)
74
74
0.34
4.36
1.62
0.45
**
74
***
(0.09)
**
0.02
(0.04)
0.06
0.61
(0.31)
(0.19)
0.00
-0.29
(0.27)
(0.15)
74
74
0.37
3.07
9.62
0.02
**
OLS
-0.04
0.39
4.36
1.59
0.45
***
*
Table 8
The Effect of Stock Market Development and Labor Law on Wages
The dependent variable is the log monthly wage in international dollars for each of the 9 sectors of the ISIC-Rev.2 classification. The independent variables are:
(1) Stock market capitalization over GDP and its quadratic term from Beck et al. (2001), (2) Log of per capita income relative to the U.S. from the Penn World
Table 6.1, and (3) Labor law index from Botero et al. (2004). All variables are averages in 1991-2000. Regressions are estimated using instrumental variables.
The instruments for the stock market capitalization are dummies reflecting the legal origin of the country (English, French, German, Scandinavian or Socialist).
The constant in the regression is not reported. Robust standard errors are reported below the coefficients. Pooled regressions (last row) include sector dummies,
but they are not reported. In this case, robust standard errors clustered by country are reported below the coefficients. Significance: * 10%, ** 5%, *** 1%.
1991-2000
Stock Market
Cap. Squared
-8.27
Stock
Market Cap.
Agriculture, Hunting, Forestry and Fishing
8.96
(8.95)
Mining and Quarrying
3.39
**
(1.59)
Manufacturing
3.65
***
(1.41)
Electricity, Gas and Water
2.69
Construction
2.93
**
***
(1.10)
Wholesale and Retail Trade and Restaurants and Hotels
3.65
3.41
**
3.00
***
1.99
**
*
(1.11)
All Sectors (Pooled Regression)
3.07
(1.16)
(1.20)
(0.14)
-2.28
**
***
0.62
-1.26
0.43
(1.06)
(0.14)
-1.74
-2.45
-1.77
**
0.67
**
0.57
0.40
0.29
(0.16)
-0.81
0.53
(0.85)
(0.14)
**
0.53
(0.13)
3.25
**
(1.42)
***
68
2.85
*
(1.72)
***
***
58
58
***
50
***
57
(0.15)
(1.20)
(0.87)
59
(0.20)
*
1991-2000 with Labor Law Index
Stock Market Labor
Log
Cap. Squared
Law
Income
-2.93
-2.33 *
1.07 ***
Stock
Market Cap.
3.07
(3.84)
***
(0.14)
-1.65
-1.76
N
37
(0.17)
(1.01)
(1.51)
Community, Social and Personal Services
0.49
(1.08)
(1.24)
Financing, Insurance, Real Estate and Business Services
(0.27)
-1.84
(0.74)
(1.57)
Transport, Storage and Communication
(10.31)
(0.96)
(1.25)
Log
Income
0.78 ***
*
***
***
48
48
483
1.89
*
(4.02)
(1.39)
-1.37
1.22
0.31
(1.02)
(1.13)
(0.25)
-2.63
** -3.19 *
(1.05)
(1.67)
(0.33)
1.10
-1.11
-1.10
0.67
(0.72)
(0.95)
(0.21)
1.80
-1.61
(0.74)
** -2.29 *
(1.25)
*
1.04
3.02
-4.46
-7.18 **
1.82
(2.57)
(3.42)
(0.87)
2.49
-2.40
** -3.11 **
1.02
(1.57)
(0.98)
(1.40)
(0.38)
2.46
-2.99
(2.45)
(1.51)
(2.73)
1.38
-2.36
-5.43 *
1.55
(2.38)
(1.62)
(2.89)
(0.63)
-2.27
** -3.04 **
1.04
(0.91)
(1.42)
(0.35)
2.35
(1.37)
*
***
56
***
47
***
48
**
42
***
48
**
40
**
40
***
399
(0.26)
(3.86)
** -5.12 *
49
(0.41)
(0.97)
(1.15)
N
29
1.22
(0.62)