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CESIFO ECONOMIC STUDIES
CONFERENCE ON UNDERSTANDING
THE CHINESE ECONOMY
10 – 11 June 2005
CESifo Conference Centre, Munich
India China: Diverging to Converge
Surjit S. Bhalla
CESifo
Poschingerstr. 5, 81679 Munich, Germany
Phone: +49 (89) 9224-1410 - Fax: +49 (89) 9224-1409
E-mail: [email protected]
Internet: http://www.cesifo.de
Preliminary Draft
Comments Welcome
India China: Diverging to Converge
By
Surjit S. Bhalla *
June 1, 2005
* Managing Director, Oxus Research & Investments.
Oxus Research & Investments
S-160 Panchshila Park
New Delhi, 110017
Phones: (91) (11) 51751020-22
e-mail: [email protected]
Paper prepared for the CESifo Economic Studies Conference on Understanding the
Chinese Economy, ifo Institute, Munich, Germany, June 10-11, 2005. I would like to
thank Tirthatanmoy Das for excellent research assistance.
Introduction
Section 2: Under-valuation of exchange rate and its effect on growth1
The relationship between trade and growth is a controversial, and much studied, issue.
While theory is unambiguous (trade reform can only increase growth), empirical validity
has been harder to achieve. The simplest measure of the importance of trade is the
share of traded goods as a percent of GDP. As is well known, individual country factors
(size, geography etc.) can make the trade share significantly different for otherwise
identical economies. However, change in trade shares can be a useful proxy for the
change in trade policy, and indeed has been used as such by Dollar-Kraay(2001).
Even this simple proxy for change in trade policy runs into empirical problems. As
pointed out by Rodrik, a change in trade share can easily be a consequence of higher
growth, as its cause. There is a simple trade liberalization variable – the magnitude of
trade tariffs. This simple variable, however, has several drawbacks; first, what is needed
is a weighted tariff rate, and this is rarely available; second, this variable does not
provide any information on non-tariff barriers.
Sachs-Warner(1995) develop a trade liberalization or openness variable based on five
different criteria: coverage of non-tariff barriers, average tariff rates, black market
premium on currency, a socialist economic system and a state monopoly on major
exports. Theirs is a zero-one binary variable signifying whether an economy was closed
or open, and this index exists for all years till 1994.
In addition to the above trade liberalization variables, one additional variable is
constructed and used to estimate the impact of trade policy on growth. It is a variable
often speculated upon i.e. the exchange rate under-valuation in an economy.
Under-valuation of the exchange rate: There are several methods which can be used to
measure the under-valuation of a currency. (See IMF documents for measurement of the
real effective exchange rate, see John Williamson (??) for measurement of the FEER,
and see Bhalla(1999) for the PPP+ method of estimating overvaluation). This papers
offers yet another definition. It is that undervaluation (UV) can be measured simply as
the ratio of per capita income measured in current US dollars (a proxy for labor costs) to
1
This section draws from Bhalla(2002), “Trade Growth and Poverty: Re-examining the Linkages”,
paper presented at the World Bank – Asian Development Bank conference in Seoul, Korea, Oct.
2
per capita income measured in current PPP dollars (a proxy for the productivity of labor
in international prices). An increase in this ratio means a relative increase in costs and
therefore lower investments and growth; a decrease represents an increase in
competitiveness, profitability, investments and growth.
(1)
UV = per capita income in US $ / per capita income in PPP $
= US $ exchange rate / PPP$ exchange rate
Some properties of the value of the US dollar/PPP dollar exchange rate (UV)
The purchasing power of developing economies in terms of PPP prices is greater than in
terms of US dollars i.e. a PPP dollar goes further, often several times more, to purchase
an equivalent amount of goods. For developed economies, the two exchange rates are
approximately equal; for developing economies, the US dollar exchange rate is
considerably below the PPP exchange rate, often by an order of magnitude (three to five
times).
At any point in time, the level of under-valuation in an economy is a function of several
factors, most importantly the share of non-tradeables in the economy, the openness of
the economy to world trade, the level of development etc. With globalization, the
allocation of capital and manufacturing to different economies is increasingly being
determined by the competitiveness of the domestic labor force. Differences in capital
costs, and transportation costs, have become negligible. With floating exchange rates,
the market will, and does, arbitrate present competitiveness with future growth
possibilities e.g. due to convergence, the developing economy US dollar exchange rate
will have a tendency to appreciate, ceteris paribus.
On changes in under-valuation
The change in the numerator of the equation defining UV (costs or per capita income
measured in US dollars) reflects the changes in the cost of production. The change in
the denominator, (“revenues” or per capita income measured in PPP dollars) can be
considered as a measure of productivity growth in an economy. (Actually, this change is
an under-estimate of productivity growth in traded goods, and is larger for economies
2002.
3
whose manufacturing sector is growing faster). The (log) difference between the two,
therefore, reflects the change in costs relative to productivity, or change in
competitiveness. With development, the ratio UV should converge towards unity i.e. the
expectation is that with development, this ratio should converge towards unity. To the
extent it does not, one can infer that the US dollar exchange rate is not being allowed to
appreciate with the change in productivity.
We therefore have the following theoretical predictions. First, that at any point in time, a
developing economy will have a lower value of UV then a developed economy. Second,
that a developed economy will have a UV ratio close to unity. Third , that with
development, UV should converge towards unity. If this convergence is not allowed to
take place, then it follows that economies are intervening to keep their exchange rates
competitive, or undervalued. It is alleged that several East Asian economies have
successfully followed the path of currency undervaluation as a basis for achieving higher
growth.2 It is a moot question, however, whether such under-valuation is empirically
associated with higher growth?
The hypothesis to be tested is therefore whether a change in competitiveness i.e. a
move towards greater undervaluation, or a decline in the US dollar/PPP ratio, is
positively associated with higher growth. Because of the forces of convergence,
economies with greater distance between the value of UV and unity are expected to
grow at a faster rate i.e.
y = a + b*(uv)
where lower case represents log growth rate and b is expected to be negative. In other
words, as an economy becomes less undervalued, it grows at a faster rate, ceteris
paribus.
This undervaluation of currency variable, like all such proxies, has two problems. First,
there is the problem that PPP productivity growth includes productivity changes of nontradeables. But the bias of this phenomenon is in the right direction i.e. tradeable goods
4
productivity growth is likely to be even higher than measured PPP growth. Second, is the
phenomenon of convergence i.e. the natural process of development should lead to an
increase in the undervaluation ratio towards unity, and therefore a positive relationship
between the change in under-valuation and economic growth. Given that the null
hypothesis is that an increase in UV leads to a decline in economic growth, both the
biases are in the right direction i.e. if a negative relationship is observed between a
change in undervaluation and a decline in economic growth, then a fairly robust result is
obtained.
Determinants of growth
In its most general form, the model relating per capita income growth to its determinants
is as follows:
(1)
y = f(F, z )
where y is growth in per capita (log) income measured in constant PPP dollars, F is a
vector representing the influence of various fixed factors or initial conditions (e.g.
geography, initial level of income, education, etc.) , and z is a vector represents the
influence of various variables (e.g. share of investment in GDP, growth in the working
age population, policy induced variables like the US exchange rate relative to the PPP
rate, etc.)
It is important to control for some important growth determining variables before a
relationship between policy and output response can be estimated. Two of the most
important such “control” variables are the share of investment in GDP and initial income.
The first, not surprisingly, is one of the most important determinants of GDP. If a variable
is significant in the presence of an investment/GDP variable, then it can be considered to
be robust to alternative specifications. It is also common, and correct, to control for initial
levels of income in order to properly account for catch-up. In a capital and technology
mobile world, late-comers have an advantage in that they have low labor costs, and can
proceed rapidly to international technology levels. During the transition stage, economic
2
Bhalla(1999a,1999b) argues that the Chinese devaluation of 1990-1993 laid the foundations of
the East Asian currency crisis of 1997-1998. The PPP+ model developed in these papers
5
and productivity growth in these economies will be higher than that predicted by other
factors like investment. The initial level of income variable captures this catch-up.
Results:
Various models and variables were tried; the most robust variables were log of initial per
capita income; log of initial years of education; latitude in degrees; share of investments
in GDP, and the log change in undervaluation. The time-period tested was 1960 to 2003,
with data aggregated into non over-lapping periods (i.e. 1960 to 1964, 1965 to 1969,
1970 to 1974 etc.). Similar results are reported for whether the period of analysis is 1960
to 2003 or 1980 to 2003; similar results are obtained whether developed and Eastern
European economies are included or excluded. Given that Eastern European economies
went through a structural break in the 1990s, and that the developed economies are at
the frontier in terms of technology (little catch-up) and have a UV ratio already close to 1,
the sample chosen is all developing economies for the period 1980-2003.
Fixed factors:
Catch-up or initial per capita income: The catch-up variable, log of per-capita income in
1980, has a coefficient value of –1.25 (tstat = 3.8). The China value for 1980, PPP$ 2.1
per capita per day or log(2.1) is .74. Corresponding values for India: 3.0 and 1.1. So
China is expected to have a higher growth rate of 0.9*1.25 or approximately 1.1 percent
per annum.
Initial mean years of education: Education is proxied by the mean log number of years of
education of the population aged 15 and over (variable obtained from Barro-Lee). It is
mostly significant and positive with a coefficient value around 1.74 e.g. China, which had
a mean value for years of education of 4.8 in 1980, or (log(4.76) is equal to 1.18). India
had a lower value of mean education in 1980, 3.3 years. So the extra growth rate for
China is 1.74*(1.56 -1.18) or approximately 0.7 percent per annum.
Geography, latitude: The coefficient of this variable is not as robust as the others, and in
our “final” regression, its coefficient had a value of 0.02(t-value of 1.8, or significance at
the 7 percent level of confidence). China is about 14 degrees north of India (latitude of
suggests that the Chinese yuan is today undervalued by as much as 40-50 percent.
6
37 versus 23 degrees for India) and is therefore ‘able” to grow at a 14*.02 or 0.3 percent
per annum faster than India.
The influence of all the fixed factors is about a 2.1 (=1.1 + 0.7 + 0.3) percent per annum
faster rate for China. Going forward, the catch-up influence has now moved against
China (relative to India) by about 0.9 percent per annum (China’s per capita income
today is about twice that of India). The education advantage of China is less today than
in 1980 – 6.4 versus 5.3 years for a growth advantage of about 0.3 percent per annum
(compared to 0.7 earlier). It is unlikely that the geography advantage is as high, or even
significant, today as it was 25 years ago. So, going forward, because of fixed factors
alone, India is expected to grow faster, ceteris paribus, by about 0.6 percent per annum.
Policy variables:
Change in undervaluation of currency: This is the most robust of all the policy variables
tried. It’s coefficient is always negative, its t-statistic comfortably above 2, and its
magnitude in a narrow range centered around -0.14. This means that for every (log) 10
percent change in undervaluation i.e. an appreciation of the local currency against the
US dollar by approximately 10 percent, the growth rate of the economy declines by 1.4
percent per annum. This is a rather large estimate.
Fiscal deficit: A perspective on the magnitude of the change in undervaluation coefficient
is revealed by the magnitude of the coefficient of a popular and much discussed policy
variable - the fiscal deficit (fiscal deficit as a percentage of GDP). Coincidentally, it’s
coefficient is approximately the same – about 0.12. A reduction of 5 percentage points in
the fiscal deficit (which will bring China to zero and India to about 4 percent) in either
China or India would be in the nature of a revolution; yet, the growth advantage from
following this long-run policy would be a mere 0.6 percent per annum.
It is likely that prevention of a mere 2 percent revaluation, by purchasing and
accumulating foreign reserves, is considerably less painful than a 2.5 percentage point
reduction in the fiscal deficit. Or that a 4 percent prevention of appreciation is equivalent
in impact on the growth rate for the reduction in fiscal deficit by more than half in India or
elimination of the fiscal deficit (or elimination of the non-performing loans) in China. This
large bang for the policy buck of undervaluation helps explain why countries like China
7
(and earlier Japan and Korea) have followed a mercantilist trade policy for decades; and
why countries like India have learnt this historic lesson and are now actively preventing
the exchange rate from appreciation today.
Share of Investment in GDP: Not surprisingly, the share of investment in GDP is a major
explanator of growth rates. The coefficient is stable at around 0.14 i.e. each 10
percentage point increase in the share of investment leads to a 1.4 percentage point
increase in the growth rate. China’s share of investment in GDP is around 45 percent
today, India’s around 28 percent. So the extra investment in China allows it to grow at a
(45-28)*0.14 or a 2.4 percentage point faster rate than India. It is unlikely that China will
be able to sustain this level of investments. The investment rate peaked in Korea at
around 37 percent in the mid-nineties; today it is back to a level in the high 20’s. India is
rapidly approaching the 30 percent level of investment, and over-time the two countries
should converge to the low 30s, and especially if China adopts a freer exchange rate
policy (see above).
In terms of growth rates, it is unambiguously the case that China’s growth rate has been
a few percentage points faster than India for the last two decades (about 4-5 percentage
points higher in per-capita terms, and 2.5-3.5 percentage points faster in aggregate
terms). This excess has been achieved by favorable initial conditions, a higher
investment rate, and a policy of exchange rate under-valuation. Each of these factors
has contributed about a third to overall “excess” growth for China. Going forward, an
expected appreciation of the Chinese exchange rate and less advantageous initial
conditions should mean a somewhat lower than India growth rate for China, ceteris
paribus. Any excess growth rate observed for China will have to be a function of how
easy it is for China to sustain the rather high investment levels of 40 percent and above.
Realistically, and on the basis of experience of other countries, the investment ratio for
China should fall back to the mid-30s level i.e. investment as a share of GDP of around
35 percent. When that happens, the Indian growth rate will be higher than China’s.
Combining all the three factors (initial conditions, exchange rate change and investment
levels) India’s growth rate should exceed China’s by as early as 2010. This is a radically
different forecast than that articulated by other experts e.g. Virmani(2005) (who has the
8
growth rate not converging till ??) and the market-oriented BRIC’s report by Goldman
Sachs (2003).
Section 3: Poverty decline
How China and India have similar poverty levels for the $ a day poverty line.
See power point presentation
Section 4: Improvement in non-monetary living standards
See power point presentation
Section 5: Conclusions
See power point presentation
9
Appendix – 1
Variable Definitions and Construction
GDP Per Capita: measured in nominal PPP$ and nominal US$ . Source: World Bank,
World Development Indicators, CD ROM, 2002 (hereafter referred to as WBCD)
Mean years of education, population above 15 years of age: Source: Barro–Lee
Schooling Dataset
Trade shares: Source: World Bank, World Development Indicators, CD ROM, 2002
Undervaluation of currency: Ratio of “dollar GDP per capita to PPP dollars per capita” in
nominal terms. Source: WDI 2003.
10
Appendix – 2
Dependent variable zY: average annual growth in per capita GDP, PPP$, five year nonoverlapping intervals.
Ledu80: log initial mean years of education, in 1980
LY80: log initial per capita income, in 1980
Fvcen_lat: fixed variable, latitude in degrees
Xinv: share of investment in GDP
Zuv: log growth in uv; uv = ratio of per capita GDP (US nominal $)/ per capita GDP
(nominal PPP$)
Xgdef: share of fiscal deficits in GDP
Sample from 1980 to 2003, developing economies only.
Regression with robust standard errors
Number of obs
F( 5,
285)
Prob > F
R-squared
Root MSE
=
=
=
=
=
291
13.27
0.0000
0.1853
3.8147
-----------------------------------------------------------------------------|
Robust
zY |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------ledu80 |
1.747099
.460566
3.79
0.000
.8405563
2.653641
lY80 | -1.252541
.3353922
-3.73
0.000
-1.912701
-.5923805
fvcen_lat |
.0218118
.0120427
1.81
0.071
-.0018921
.0455158
xinv |
.141996
.0315304
4.50
0.000
.079934
.204058
zuv | -.1389842
.0426752
-3.26
0.001
-.2229827
-.0549858
_cons | -2.426196
.9339141
-2.60
0.010
-4.26444
-.5879515
------------------------------------------------------------------------------
11
With fiscal deficit as an additional variable:
Regression with robust standard errors
Number of obs
F( 6,
213)
Prob > F
R-squared
Root MSE
=
=
=
=
=
220
10.80
0.0000
0.2210
3.774
-----------------------------------------------------------------------------|
Robust
zY |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------ledu80 |
2.245282
.6968745
3.22
0.001
.8716279
3.618935
lY80 | -1.500832
.4052932
-3.70
0.000
-2.299732
-.7019329
fvcen_lat |
.0236374
.0130831
1.81
0.072
-.0021516
.0494264
xinv |
.1279774
.0392932
3.26
0.001
.0505241
.2054307
zuv | -.1432515
.0502421
-2.85
0.005
-.242287
-.0442161
xgdef |
.1227456
.0549199
2.23
0.026
.0144895
.2310017
_cons | -1.846862
1.244362
-1.48
0.139
-4.299705
.60598
------------------------------------------------------------------------------
.
12
References (Incomplete)
Ahluwalia, Montek S. 1976. Inequality, Poverty, and Development. Journal of
Development Economics vol.3, Sept, p. 307-342
Ahluwalia, Montek S. 1977. Rural Poverty and Agricultural Performance in India. Journal
of Development Studies 22: 298-323.
Ahluwalia, Montek S., N. Carter, and H. Chenery. 1979. Growth and Poverty in
Kommentar: Page: 1
AU: Please add vol., no. & pp.
Kommentar: Page: 1
AU: Please add no. & pp.
Kommentar: AU: pp.?
Developing Countries. Journal of Development Economics 6, 399-341.
Barro, Robert and Jong Wha Lee. 2001. International Data on Educational Attainment:
Updates and Implications. NBER Working Paper 7911. Cambridge, MA: National
Kommentar: AU: Cited in
table 12.2 and in the text of
chap. 12; please add.
Bureau of Economic Research.
Bhalla, Surjit S. 1997a. Freedom and Economic Growth: A Virtuous Cycle? In
Kommentar: AU: Cited in
chap. 11; please add.
Democracy’s Victory and Crisis, ed. Axel Hadenius. Cambridge, UK: Cambridge
University Press.
Bhalla, Surjit S (2002a), Imagine there’s no country: Poverty, Inequality and Growth in
the Era of Globalization, Institute of International Economics, Sept 2002; first
draft, 2001.
Bhalla, Surjit S (2002b), Poor Results: A Comparative analysis of Estimates of Global
Inequaliity and world Poverty’, paper prepared for the CESifo conference on
Globalization, Inequality and Well-Being, Munich, Germany, Nov. 8-9, 2002.
Chen, Shaohua, Gaurav Datt, and Martin Ravallion. 1994. Is Poverty Increasing in the
Developing World. Review of Income and Wealth 40, no. 4 (December): 359-376.
Chen, Shaohua, and Martin Ravallion. 2000. How Did the World’s Poorest Fare in the
1990s? Development Research Group, World Bank, Washington. Photocopy.
Kommentar: AU: Please give
page nos.
Kommentar: AU: Please
supply missing info.
Deaton, Angus. 2001a. Counting the World’s Poor: Problems and Possible Solutions.
Princeton University, Princeton, NJ. Photocopy (August).
Deininger, Klaus, and Lyn Squire. 1996. A New Data Set Measuring Income Inequality.
World Bank Economic Review vol.10 no. 3 (September): 565-592.
Deininger, Klaus, and Lyn Squire. 1998. New Ways of Looking at Old Issues: Inequality
and Growth. Journal of Development Economics 57, no. 2: 259-87.
13
Kommentar: AU: Please give
vol., no. & pp.
Kommentar: AU: Please
supply missing info.
Dollar, David, and Aart Kraay. 2000. Growth Is Good for the Poor. World Bank,
Washington. Photocopy (March).
Dollar, David, and Aart Kraay. 2001, Trade, Growth, and Poverty. World Bank,
Washington. Photocopy (March).
Frankel, Jeffrey A. and David Romer (1999). “Does Trade Cause Growth?” The American
Economic Review, (June) 379-399.
Levine, Ross, and David Renelt. 1992. “A Sensitivity Analysis of Cross-Country Growth
Regressions,” American Economic Review, September, 82(4): pp. 942-963.
Ravallion, Martin, and Chen, Shaohua. 1996. What Can New Survey Data Tell Us about
Recent Changes in Distribution and Poverty?, World Bank, Policy research
working paper 1694, December.
Reddy, G. Sanjay, and Thomas W. Pogge. 2002. How Not to Count The Poor.
www.socialanalysis.org.
Kommentar: AU: Please give
the full URL for this document,
not just the Web site.
Rodrik, Dani. 1997. Has Globalization Gone Too Far? Washington: Institute for
International Economics.
Rodrik, Dani. 1998. Where Did All the Growth Go? External Shocks, Social Conflict, and
Growth Collapses. Harvard University, Cambridge, MA. Photocopy (August).
Sachs, Jeffrey, and Andrew M. Warner. 1995. Economic Convergence and Economic
Policies. Harvard Institute for International Development, Cambridge, MA.
Photocopy (February).
Sachs, Jeffrey D. and Andrew Warner. 1995. “Economic Reform and the Process of
Global Integration.” Brookings Papers on Economic Activity, (1), 1-118.
Williamson, John. 1990. Latin American Adjustment: How Much Has Happened?
Washington: Institute for International Economics.
World Bank. 1991. Growth, Poverty Alleviation and Improved Income Distribution in
Malaysia: Changing Focus of Government Policy Intervention Report 8667-MA.
Washington: World Bank.
World Bank. 1991. World Development Report – The Challenge of Development
World Bank. 2001. World Development Report – Attacking Poverty
14
Kommentar: AU: Moved into
text, per Institute style.
World Bank. 2002a. Globalization, Growth, and Poverty. New York: Oxford University
Press.
15
Kommentar: AU: Moved into
text—this and one below.