Chapter 13
The Determinants of
Economic Growth:
An Empirical Overview
© Pierre-Richard Agénor
The World Bank
1







Growth Accounting
The East Asian “Miracle”
Growth Regressions and convergence
Testing the Mankiw-Romer-Weil Model
The Empirics of Growth: Econometric Issues
The Econometric Evidence: An Overview
Catching Up or Falling Behind?
2
Growth Accounting
3


Two main types of empirical studies on the
determinants of economic growth: growth
accounting and cross-country regressions.
Growth accounting: consists of adding contributions
of growth of basic factor inputs (labor and capital) to
an unexplained (exogenous) residual that captures
improvements in technology.
4
Basic Growth Accounting Equation
 Suppose output is described by,
Y = AF(K, L). (1)


A is Hicks neutral: An increase in technological
progress raises the level of output without effecting
marginal product of inputs.
Differentiating with respect to time yields,
gY = gA + KgK + Lg
K = FKK/F; L = FLL/F.
(2)
5


L and K are equal, respectively, to the share of labor
and capital income in total output, and, by Euler’s
Theorem add up to 1.
Using above information, (2) can be written as,
gY/L = gA + KgK/L,
gY/L : growth of output per worker;
gA : Solow residual or rate of growth of total factor
productivity, TFP;
KgK/L: rate of growth of capital per worker.
6

With Cobb-Douglas specification (Y = AK L(1-)),
and K =  , L = 1 - , TFP growth,
gA = KgK/Y + (1 - K)gY/L .

In per capita (rather than per worker) terms, TFP,
gA = gY/N - KgK/N + (1 - K)gL/N .
7


Framework has been extended to include not only
investment in physical and human capital, but also
variables such as changes in the composition and
quality of factor inputs, economies of scale in
domestic markets, government regulations, labor
hoarding, and changes in rates of capacity use.
Nevertheless, land is still ignored, with the potential
of thus overstating capital’s share in the production
function and understating the rate of TFP growth.
8
Basic Growth Accounting Limitations
 No standard measurement method for estimating
growth rates of capital and labor.
Capital:
 Typically constructed using perpetual inventory
method: cumulating data on investment flows at
constant prices from sources such as the Penn
World Tables and assuming a constant depreciation
rate (4-6%).
 Quality-adjusted measures of capital: Roldos
(1997), for example, looked at relative rental rates in
Chile as proxy of capital quality.
 Most adjustments are ad hoc.
9
Labor:
 Measured using participation rates and work hours.
 Differences among types of workers and data
reliability across countries create problems for
comparative analysis.
 Quality adjusted labor: adjusting for different levels
of education with weights given to relative wages.
 Such adjustments may underestimate true TFP by
attributing a larger part of output increase to more
educated labor force (Sarel, 1997).
10
Estimation of Factor Shares
 Factor shares, e.g. K , , estimated from national
accounts data.
 Assumes perfect competition: each factor’s income
contribution will equal its marginal product.
 Monopoly profits tend to overstate elasticity of
output with respect to capital.
 Subsidies to capital-intensive industries may lead to
an overestimation of the share of capital in the
production process.
 Positive externalities, particularly increasing
returns to scale may underestimate the
contribution of capital and overestimate the true
11
degree of total productivity growth (see Barro,1998).
The East Asian “Miracle”
12
Some Facts:
 Growth in some East Asian Countries averaged 6%
between 1950-1992.
 Output per worker increased by more than 5% per
annum from 1960-94 in Korea, Singapore, Thailand,
and Taiwan.
 From 1980-95, Indonesia, Malaysia, Singapore, and
Thailand more than doubled their real income per
capita, compared with an increase of only 20% in
the United States (Sarel 1997, p. 369).
 Rapid pace of physical investment resulted in a
tenfold increase in the capital-labor ratio in
Singapore between 1960-92.
13
Explanations:
 Young (1995): nothing miraculous, rising
investment, increasing labor-force participation and
quality--not TFP, nothing exogenous.
 Collins and Bosworth (1996): From 1960-94,
average growth of output per worker equaled 4.2%
for East Asia
 Only 1.1% due to TFP growth.
 2.5% from high rate of physical capital
accumulation per worker
14
Something miraculous
 Sarel (1997): Found that TFP growth in East Asia
was much higher than Young’s results would have
suggested.
 Sarel attempted to capture the structure of
production across countries in estimating input
factor shares.
 Obtained capital shares, , ranging from .28 for
Thailand to .34 for Singapore. (by contrast, Young
had a capital share of .5 for Singapore)
 1978-96: TFP estimates very strong for Singapore
(2.2%), Malaysia and Thailand (2%), and much
less so in Indonesia (1.2%). By contrast, the U.S.
had a TFP growth of .3% during this period. 15
Growth Regressions
and Convergence
16
Diminishing Returns and Convergence

Do poor countries tend to grow faster than rich
countries?
Solow-Swan: Countries with same rate of
technological progress, , will all converge to a
balanced growth path in which the rate of growth of
income per capita is equal to . If production
technologies, saving rates, and population growth
rates are the same across countries, they will all
converge to the same level of income per capita as
well.
17

Diminishing returns to factor inputs imply that poor
countries (with a smaller base of capital and labor)
will grow at a faster pace than richer countries
faced with identical technology, saving rates, and
population growth rates.
18
Convergence and Cross-Section
Regressions
 and  convergence:
 Absolute -convergence or mean reversion: occurs
if poor economies tend to grow faster than richer ones.
  convergence: requires that the distribution of world
income become more equitable over time.
19
-convergence:
 Consider the following regression model,
lnyht = a0 + (1 - )lnyht-1 + uht, h = 1,…,N.
For h = N different countries.
a0: constant term.
20

Equation can also be rewritten as,
ln(yht/yht-1) = a0 - lnyt-1 + uht, h= 1,…N. (6)


In (6),  > 0, implies mean reversion.
In the long run, with yht - 1 = yht, expected real
income per capita is,
Elnyh = a0 /  .
21
-convergence:
 Requires a measure of the dispersion of income.
 Consider the sample variance of lnyht ,
t 2
N

1
2
(lny

)
=
ht
t
N - 1h = 1
t : sample mean of lnyht.

With N sufficiently large, sample variance can be
viewed as an accurate measure of the population
variance.
22

Equation (5) can be used to calculate the process
driving t2 over time,
t2  (1 - )2 t-12 + 2
solution of which is given by,
2 =
~


2
(9)
1 - (1 - )2
If  < 0 in (9), process driving t2 will be unstable.
Thus, absolute  convergence is necessary for 
convergence to take place.
23

Solution to (8) can also be written as,
t2 - ~ 2  (1 - )2(t-12 - ~ 2) (10)


(10) shows that if absolute  convergence holds in the
sample t2 will approach its long run value
monotonically.
-convergence is a necessary but not sufficient
condition for -convergence.

Figure 13.1.
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26
Testing the Mankiw-Romer-Weil Model

Core of model: differences in population growth and
capital accumulation account for variations in incomes
across countries.

Income per capita equation, as before is,

+



ln y = ln sK + ln sH ln (n + + )



~
with
 = 1/(1 -  - )
27

Mankiw-Romer-Weil use following measures to test
model:
y: logarithm of output per person in the population of
working age;
n: average population growth rate;
sK: ratio of gross savings to GDP;
sH: average fraction of the population of working age
that is enrolled in secondary school over the period
1960-85;
 + : set to 0.05 for all countries.
28
The Empirics of Growth:
Econometric Issues
29
Much of the empirical literature has focused on
cross-country regressions plagued by
methodological problems: measurement,
specification errors, simultaneity bias.
Other Difficulties:
 Heterogeneity between developing countries.
 Variations in data definitions across countries.
 Difficulty in separating trend component of output
growth from cyclical component.
 Mixing stationary and nonstationary variables can
lead to spurious results.
 Averaging implies that cross-country regressions do
not represent typical behavioral equations.

30




Problems with linear models and ordinary least
squares (OLS):
Omitted variable (or specification) bias.
Endogeneity bias: failure to account for the
endogenous nature of some of the explanatory
variables.
Alternatives approaches to cross-country
regressions:
Quah, (1996a,1996b, 1997): jointly analyzed - and
-convergence using stochastic kernels.
Others suggested time series regressions for
individual countries.
31

Arestis and Demtriades (1997): used a cointegration
approach to analyze determinants of growth in an
individual context.
32
The Econometric Evidence:
An Overview
33



Selective overview of literature on cross-country
growth regressions.
Motivated by the results of Levine and Renelt
(1992), emphasizing the fragility of many of the
cross-country regression results they reviewed.
Many recent studies have used improved
econometric methods.
34
Saving and Physical and Human Capital
 Levine and Renelt (1992): both saving and physical
investment rates positively and significantly
correlated with average growth rates.
 Few have tried to account for the quality of
investment--Asia crisis demonstrated that high rates
of investment not a panacea for sustained growth.
 Barro (1991, 1997), Benhabib and Spiegel (1994):
the initial level of education was an important
determinant of subsequent growth.
 In contrast to Levine and Renelt (1992): suggested
that the significance of human capital indicators in
growth regressions was not always robust to the
inclusion of other variables.
35
Saving and Physical and Human Capital


Recent evidence less conclusive:
Pack and Page (1994): argued that part of the
growth effects attributed to investment and education
may reflect ensuing changes in the sectoral structure
of production.
Bils and Klenow (1998): expected growth causes an
increase in enrollment rates rather than causality
running from schooling to growth.
36
Fiscal Policy



Recent evidence on relationship between
government expenditures and growth mixed.
Devarajan, Swaroop, and Zou (1996): no
relationship between growth and the level of
expenditures.
Barro (1997): Government consumption
expenditures--calculated by deducting defense and
education expenditures from general consumption
spending--measured in proportion of GDP
negatively correlated with growth.
37



Devarajan, Swaroop, and Zou (1996), in contrast,
found a positive relationship between public
consumption expenditures (as measured by current
outlays as a share of total expenditures) and
economic growth.
Caselli, Esquivel, and Lefort (1996) also found a
positive effect on growth of government expenditure
(net of military and educational spending) as a share
of output.
Easterly, Loayza, and Montiel (1997) found no
significant effect of the share of government
consumption in GDP on growth in Latin America.
38
Role of public and private investment in growth:
 Public Investment as,
 Complementary to private investment:
infrastructure improvements can increase the
marginal product of private capital.
 Competitive with private initiative: substitution
causes a crowding out of private capital with
adverse effects on growth.
Evidence ambiguous.
 1970-90: Khan and Kumar (1997): private investment
consistently more productive than public investment.
 Knight, Loayza, and Villanueva (1993) and Nelson and
Singh (1994): Level of public investment in
infrastructure significant positive effect on growth.
39
Easterly and Rebelo (1993):
 Public investment,
 in transportation and communications positively
related to growth,
 in state-owned enterprises had no effect on
growth,
 in agriculture had a negative effect.
Devarajan, Swaroop and Zou (1996):
 Inverse relationship between public investment and
growth.
 This suggests that governments may have been
misallocating expenditures in favor of capital
expenditures (as opposed to outlays on, say,
40
infrastructure maintenance).
Inflation and Macroeconomic Stability



Fischer (1993) positive link between growth and
macroeconomic stability.
De Gregorio (1992, 1993) negative relationship
between level of inflation, variability of inflation, and
growth in Latin America.
Sarel (1996); relationship between growth and
inflation nonlinear: changes in inflation are significant
(insignificant) when it is high (low).
41
Sarel (1996):
 High inflation (above 8% per annum) has a negative
and statistically significant effect on growth.
 Doubling in the rate of inflation (e.g. 20 to 40%)
reduces economy's growth rate by 1.7%.
Barro (1997):
 Inflation has negative effect but smaller.
 Increase in average inflation by 10% per year
lowers the growth rate by .2 - .3%.
 Relationship only exists with annual inflation over
40%.
Bruno and Easterly (1998):
 No robust relationship with inflation less than 40%.
42
Other measures of macroeconomic policy uncertainty:
 Simple crude indicator of instability: unweighted
sum of the mean inflation rate, the standard
deviation of inflation, mean budget and external
current account deficits as a percentage of GDP,
and mean terms-of-trade changes.
 More precise measure of policy variability
(Aizenman and Marion, 1993): used standard
deviation of the real exchange rate, standard
deviation of inflation and domestic credit growth, and
composite indicators using foregoing variables.
43
More precise measure of policy variability
proposed by Aizenman and Marion (1993):
 standard deviation of the real exchange rate,
inflation, domestic credit growth, as well as
composite indicators including all of the foregoing
variables;
 negative relationship between policy variability and
growth, although not robust for some indicators.
Bleaney (1996):
 1980-90 cross-sectional study.
44

Macroeconomic instability (measured by the fiscal
balance and the degree of volatility of the real
exchange rate) had a significant negative effect on
the rate of economic growth and possibly also a
negative effect on investment.
45
Financial Factors


Causality difficult to establish.
Demetriades and Husein (1996) studied 16 countries:
 4 displayed causality from financial depth to
growth.
 7 countries displayed a feedback relationship
between finance and growth.
 4 displayed causality from growth to financial
depth.
46
Financial Repression and Growth
Roubini and Sala-i-Martin:
 Negative effect of the bank reserve ratio on growth.
 However, reserve ratios can be a poor proxy for
repression.
Arestis and Demetriades (1997):
 used a weighted index of five principal controls on
banking sector;
 actually found a mildly positive effect of financial
repression on economic growth in Korea.
47
Openness and External Trade
 Benefits of trade openess: technology spillovers,
access to specialized inputs etc.
Sachs and Warner (1997a):
 Classified 117 countries as either open or closed.
 Constructed index of openess, based on five
criteria: non-tariff barriers, average tariff levels, the
parallel market exchange rate, existence of
monopolies for major exports and political regime.
 More open countries grew by 2 to 2.5% more than
others.
48
Greenaway, Morgan, and Wright (1998):
 Cross-country regression covering 73 countries.
 Measured openness along with other control
variables---initial income per capita, the ratio of
domestic investment to output, and an index of the
terms of trade.
 Used a dynamic regression framework to
investigate potential lagged effects.
Results:
 Trade openness has an identifiable impact on
growth.
 However, the growth effect had a J-curve feature:
only over time did impact becomes positive.
49

Has implications for the sustainability of trade
reforms.
50
Political Variables and Income
Inequality



Political and civil liberties; not only desirable in
themselves but also, by providing a foundation for
imposing the rule of law, may play an important
economic role.
Dasgupta 1995: liberties are positively and
significantly correlated with improvements in income
per capita, life expectancy at birth, and the infant
survival rate.
Growth effects of political factors have been the
subject of a variety of cross-country empirical
studies.
51
Brunetti (1997):
 Five groups of political variables
 Measures of democracy, including the Gastil
index.
 Measures of government (or institutional)
instability.
 Indicators of political violence, including
measures of political strikes, protest
demonstrations, riots, armed attacks, political
assassinations, and executions.
 Subjective political indicators.
 Democracy indicators, measures of government
stability, and indicators of political violence not
52
robustly correlated with growth.



Survey-based measures of political risk appear more
closely correlated with growth (Mauro, 1997).
Evidence suggests no clear correlation between
income inequality and economic growth.
Studies have also found no systematic relationship
and no clear causal link between income inequality
and political instability.
53
Catching Up or Falling Behind?
54
Solow-Swan model:
 Implies convergence across countries in both growth
rates and income levels if production technology,
savings rates, and the rate of technological progress
are the same.
 Convergence can be inhibited through, for example,
trade barriers, that limit possible access to more
advanced technologies.
55
AK model:
 Along with other endogenous growth models, imply a
sustained difference in growth and income levels.
 Externalities to specialized inputs remove diminishing
returns characteristic of capital and thus remove the
primary driver of convergence in the Solow-Swan
model.
56