Discussion Paper No. 598

THE SOURCES OF GROWTHAT DIFFERENT LEVELS OF DEVELOPMENT

Evan Osborne

October 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

The Sources of Growth at Different Levels of Development
Evan Osborne
Wright State U. Dept. of Economics and Osaka U. Institute of Social and Economic
Research
3640 Col. Glenn Hwy.
Dayton, OH 45435(937) 775 4599
(937) 775 2441 (Fax)evan.osborne@wright.edu

Abstract
Cross-country growth regressions have become an increasingly common tool in
empirical development research. But these regressions typically do not attempt todistinguish among countries in different stages of development. Two empirical methods
are used to test for such differences. Several of the factors known to affect economic
growth are shown to operate differently for countries in different portions of the global
income distribution. The results have implications for the role of financial markets,
openness and human capital in promoting growth.

What separates wealthy nations from poor ones is one of the cornerstones of
economic theory.1 But it has only been in recent years, with the development of powerfulcomputational techniques and data sets, that empirical analysis of growth and why it
happens (or, more revealingly, does not happen) has taken off. A common technique inthis analysis has been cross-country regressions in which national economic performance,
typically growth in per capita product, has been used as the dependent variable. Amongthe pioneering work in this regard is Barro (1991). The technique has also been applied
to investigate derivative issues such as foreign aid (Burnside and Dollar,2000) and theeffects of an open economy (Sachs and Warner, 1995; Rodriguez and Rodrik, 2001).
Yusuf and Stiglitz (2001) lay out what we know about achieving modernization inlight of this empirical revolution, which they refer to as the “settled issues” indevelopment economics. Among them are the importance of physical and human capital
accumulation (including not just education but knowledge available nonrivalrously to all
and produced by activities such as scientific research), low inflation, open trade, clean
governance, secure property rights, flexible labor markets and provision of social safety
nets. Other issues, e.g. the role of industrial policy, remain unsettled.
But a tacit assumption of much of this empirical growth literature, as well as theaforementioned theoretical consensus, is catholicity – the notion that whatever thesources of growth, in empirical testing the processes that create it ought to be assumed tooperate identically in all countries, be they highly advanced or pre-industrialized. The
1 . For example, in a letter to David Ricardo in 1817 Thomas Malthus is reported to havesaid that the question of development is “the grand object of all enquiries in Political
Economy” (Landes, 1999).

1

growth process is assumed to be the same in Sweden as it is in Bangladesh, with only
parameter values distinguishing the two. Typically this assumption plays out when datafrom all nations are thrown into a single regression, which is then subjected to a single
estimation technique at all levels of per capita income.
This paper investigates whether the findings of the growth literature are common
to countries at different levels of per capita income. The findings provide some insightwith respect to proper sequencing of reforms when political constraints exist, and explainsome of the stylized facts of development and the politics of international trade. Section I
describes the problem and some of the controversies in the literature its resolution might
address, Sections II and III contain the empirical results, and Section IV analyzes thefindings.

I. Growth nonlinearities
The approach bears some similarity to Barro (2000). There is a production
function that relates potential output to a vector of inputs. At the same time, societies
face constraints, imposed by government or beyond their control, that limit the efficiency
of input conversion, and hence cause output to fall short of the production frontier. To
the extent that these constraints are the results of policy choices, such choices thus havecosts that may or may not be worthwhile, but must be accounted for in assessing growth.
Actual output is then some transformation of available resources, e.g. schooling andphysical capital, and the choices that have been made or the constraints that are facedwith respect to macroeconomic management, political stability and so on.

2An issue of interest is whether the resources available and the choices made havedifferent effects at different levels of this function. Government spending, for example,
is sometimes both in theory (Krichel and Levine, 2001) and empirically (Barro, 2000;
Barro, 1991; Tavares and Wacziarg, 2001) shown to be negatively related to growth. Do
advanced societies more frequently fall into the trap of factional warfare over government
spoils to such an extent that they cripple the economy’s productive capacity (Olson,
1982)? Or is there a rent-seeking trap visible in government spending (or in economic
distortions) that tends to affect the poorest countries most (Krueger, 1974)? Is humancapital something that provides the most critical payoffs in the earliest stages of takeoff,
or is it a resource that causes the richest countries to further distance themselves frompoorer ones, owing to the increasing returns to scale and spinoffs it generates, in
combination with the tendency of richer countries to invest more in human capital thanpoorer ones (Bils and Klenow, 2000)? Adverse international developments might bethought to affect poorer countries most substantially because richer countries tend to havemore complex, diverse economies, so that it is easier to insure against suchdevelopments. Standard growth regressions, with their one-size-fits-all assumption about
the relation between growth and various independent variables, are incapable of resolving
such questions. In addition, the empirical approach in this paper can address the absenceof convergence by poorer countries to the income levels of the wealthiest ones. The
absence of such convergence has been a continuing mystery because it contradicts a
central prediction of the standard Solow (1956) growth model.

3II. Income as the dependent variable
To get a better handle on such questions, a first empirical model is developed thatis somewhat different from those in much of the growth literature. That work typicallyhas growth over some interval as the dependent variable, and either the beginning or the
average values over the interval of the exogenous variables. But while that approach isadequate to the task of investigating whether high-growing countries behave differently
from slowly growing ones, the question here is whether rich countries respond differently
to the exogenous variables from poor ones. Hence, the first tactic is to use the absolutelevel of per capita GDP rather than growth as the dependent variable. It, like all variables
unless otherwise noted, comes from the updated Barro-Lee data set. The productive
factors other than labor are assumed to be the country’s capital stock and its humancapital, as outlined below. Indeed, often the growth literature uses a flow (per capita
GDP growth) both as the dependent variable and for physical capital (the investment-toGDP ratio) but uses a stock for human capital. Here, both factor variables are expressed
as stocks, with per capita income taken as the flow output of these factor inputs plus theinstitutional, political and other factors that limit the productive power of those factors.
The base regression for this alternative specification, takes the following form:

PCGDP = a0 + a1 KAPW + a2 HUMCAP + a3 INFLATION + a4 DEMOCRACY+ a5 GOVT + a6 TERMS + a7 LIQUID + a8 INSTABILITY+ a9 OPEN + a10 PREMIUM(1)

4

PCGDP and KAPW are per capita GDP and non-residential capital per worker.
KAPW comes from the Penn World Tables v. 5.6. HUMCAP is the country’s
population’s contemporaneous average schooling. HUMCAP and KAPW are thus the
productive factors available. The other right-hand variables are familiar from the existing
empirical growth literature. INFLATION, GOVT, and INSTABILITY and are,respectively, the average annual rate of inflation, of government consumption lesseducation and defense spending as a percentage of GDP, and of the Barro/Lee measure ofpolitical instability during the previous five-year period. DEMOCRACY is the lagged
Barro (2000) measure of democracy. PREMIUM is the lagged logarithm of (1 + BMP),
where BMP is the black-market premium on the country’s exchange rate, which is often
used as a proxy for the amount of government distortion in the economy. TERMS is thechange during the previous five-year period in the country’s export to import relative
prices. OPEN is the average openness of the economy during the previous five years as
defined by Sachs and Warner (1995). LIQUID is the average during the previous fiveyear period of liquid liabilities to GDP. This variable might proxy either for thesophistication of a country’s financial markets, which has drawn a great deal of attention
after the villainous role in which they were cast in some accounts of the 1997 East Asiancrash, or the extent of public and private indebtedness.
But a simple OLS or panel regression would not enable an answer to the questions
outlined above. An alternate approach is to employ the quantile-regression technique of
Armstrong et al. (1979). Quantile-regression coefficients are estimated to minimize thesum of absolute values of errors, rather than the sum of squared errors. More importantly
for the problem here, coefficients can be estimated to minimize the sum of absolute error

5values relative to a particular point in the sample distribution of the left-hand variable. In
particular, for a quantile q of the dependent variable the coefficients minimizen∑ ( y − βx )a ,
i =1

i

i

i

where ai = 2q if the residual is positive and 2(1 – q) otherwise. The resulting coefficientestimates thus can be interpreted as the marginal impact of a change in one of the righthand variables for an observation at the quantile q in the distribution of the left-handvariable. Here, the regression line will be fitted through various quantiles of the sample
distribution of per capita GDP. The regressions are calculated using Stata/SE 7.0, whichcalculates coefficient standard errors via bootstrapping methods.
Table 1 presents the results of a standard median regression of (1), i.e. a
regression line through q = 0.5. In contrast to regressions that take growth as thedependent variable, measuring the extent to which constraints detract from the ability of agiven stock of factors to produce income yields somewhat different results. The two
productive factors are strongly significant, as would be required in almost any modern
theory of growth, but the only other variables that are significant are DEMOCRACY and
OPEN. Table 1 also presents, for comparison, OLS results. In addition to the previously
significant variables, TERMS is significant in the expected positive direction, and GOV isalso significant with a negative sign.
But employing the quantile-regression technique reveals subtleties in the results.
Table 2 presents the results of the estimations of (1) for the 0.2, 0.4, 0.6 and 0.8 quantiles.
The two factor coefficients are statistically significant at at least the ten-percent level6

throughout. DEMOCRACY is significant in three out of the four quantiles (0.2, 0.6, 0.8),
with a positive sign. LIQUID is significant with a positive sign in the 0.2 and 0.4
quantiles, OPEN at 0.2, 0.6 and 0.8 and PREMIUM at 0.4.
To test whether growth effects differ at different levels of development is to ask
whether the differences in these coefficients are statistically significant. The test for thenull that the difference between any two coefficients for the right-hand variable j in thequantiles q and r, xjq –xjr, is zero is distributed F(1, n – k – 1), where k is the number ofright-hand variables. Table 3 lists the associated p-values for the right-hand variables
used in (2) for the 0.2 and 0.8 quantiles. The differences in four of the coefficients –HUMCAP, TERMS, LIQUID and OPEN – are significant at at least the ten-percent level.
A finding of immediate interest is that HUMCAP has a significantly higher coefficient at
the 0.8 than at the 0.2 quantile.
In addition, TERMS has a higher effect on gross domestic product for higher
levels of GDP, as does OPEN. These effects are not surprising in and of themselves, inthat the wealthier economies generally have more factor endowments per capita, and thus
the cost of a given amount of economic mismanagement or disturbance will be absolutely
higher. However, LIQUID has a coefficient that declines continuously as one moves up
from the 0.2 to the 0.8 quantiles. This is a result that is difficult to interpret cleanly,
depending as it does on what LIQUID is measuring, but it does suggest that welldeveloped financial markets are perhaps most critical for the poorest nations, even thoughsuch countries are of course the places where financial markets are typically the leastdeveloped.

7

The coefficients in Table 2 with statistically significant differences at the 0.2 and
0.8 quantiles are in bold. In each case the coefficient pattern is monotonic as the quantile
index increases. This consistency is on its own evidence of some nonlinearities in thedevelopment process. HUMCAP and OPEN have coefficients that increase continuously
from the 0.2 to 0.8 quantiles. This indicates that human capital is more productive forricher than for poorer countries, and that openness is also more beneficial as countries get
wealthier, at least in absolute terms. Note, however, that in percentage terms the effects
may be significantly greater for the poorest countries. Table 4 lists the values of the 0.2,
0.4, 0.6 and 0.8 quantiles for the left-hand variable PCGDP, and the proportion of thequantile coefficients from Table 2 as a percentage of these quantile values. These
represent a crude estimate of the proportionate effect on income (i.e., the growth boost ordrag) of a marginal increase in the variables in question for each stage of the global
income distribution. In the case of each of these two variables the percentages decline,
but the decline is significantly steeper for HUMCAP. Thus, an argument can be madethat human-capital is in absolute terms more important for the wealthiest nations, but inpercentage terms more important for the poorest nations. This in turn allows theinference that schooling improvements are a key part of whatever convergence exists.
Similarly, a failure to invest in human capital may be disastrous for the ability to catch
up. As for openness, somewhat surprisingly at first blush the effects are quite large in a
relative sense for wealthier countries. However, if one believes in endogenousgovernance theory (Becker, 1983), then this finding would explain the stylized fact,
visible both in cross-sectional contemporaneous government attitudes toward free-tradenegotiations and in the behavior of countries like Germany, Britain and the U.S. over the8

course of the industrialization process, that richer countries tend to be more enthusiastic
advocates of free trade. Richer countries at a moment in time tend to be moreenthusiastic promoters of openness, and countries that successfully industrialize tend tomove toward more openness unilaterally and to advocate it more strongly for others thanthey did in earlier stages of their own development.
TERMS has a consistently increasing coefficient. This is somewhat surprisingwhen compared to much of the conventional wisdom, at least in popular commentary,
about the relation between poverty and international economic developments. It is often
assumed that the poorest nations, often heavily dependent on commodities, are mostvulnerable to adverse price movements. Supposedly, they are almost totally dependent
on a small number of highly volatile commodity exports, as well as being heavily reliant
on importation to acquire many complex manufactured goods. One would expect thatterms-of-trade shocks would have the greatest effect on such countries.
In fact, consistent with the previous two variables there is a significant decline in
the percentage effect of TERMS (along with a relatively flat absolute effect) at the 0.2,
0.4 and 0.6 quantiles. But there is a substantial increase in the effect for the wealthiest
countries. Evidently, in the later stages of industrialization nations tend to become
significantly more dependent on trade. Whether that is because of greater export
dependence on frontier industries that are not produced in poorer countries, from productlifecycle effects or for some other reason is unclear, but the urgency of openness for themost advanced economies is a striking finding, and has interesting implications for thepolitics of international-trade negotiations.

9The final variable to have a statistically significant coefficient difference is
LIQUID. The finding of a declining coefficient throughout can be interpreted in two
ways. If LIQUID, as it is often assumed to, measures the maturity of a country’s
financial markets, then the results suggest that the greatest impact is felt in the poorest
countries. This is a result of substantial importance with respect to the question offinancing industrialization via banks versus capital markets, as well as whether foreign
portfolio capital should be allowed into the poorest countries. Evans, et al. (2002), among
others, support the importance of financial development in promoting growth even after
taking account of human capital. The results here add to that by indicating that this effectis greatest even in absolute (let alone proportionate) terms for the poorest countries. If
LIQUID is interpreted as indebtedness, then a high value in poor countries could imply
significant borrowing to fund high-return modernization investment opportunities nolonger available in mature economies. Similar high debt in the latter might indicate
profligacy rather than the aggressive accumulation of capital. It is of course possible that
LIQUID proxies for both phenomena at the same time, with the latter effect coming todominate in mature economies, where financial markets are already fully developed,
requiring a greater value of LIQUID to indicate some other phenomenon.

III. Growth as the dependent variable
It is also possible to test for different effects at different points of the sample
distribution of per capita income by employing interaction terms in a more conventional
growth regression. In that case the left-hand variable is the per capita growth rate rather

10than the level of per capita GDP. The following equation, standard in growth
regressions, is estimated as a panel model:

GROWTH = b0 + b1 INVGDP + b2 HUMCAP + b3 INFLATION + b4
DEMOCRACY + b5 GOVT + b6 TERMS + b7 LIQUID + b8 INSTABILITY+ b9 OPEN + b10 PREMIUM + b11 PCGDP + b11INTER(2)

GROWTH is average growth in per capita gross domestic product at 1985
international prices over a five-year period. INFLATION, DEMOCRACY, GOVT,TERMS, LIQUID, PREMIUM, OPEN, and INSTABILITY are similar to their definition in(1) except that they are the averages for the contemporaneous period rather than theprevious one. HUMCAP is the level of average schooling at the beginning of theinterval. Rather than using the stock of capital as a determinant of growth, capital
accumulation is measured by inclusion of INVGDP, the five-year average of investment
as a percentage of GDP. PCGDP is per capita gross domestic product, whose presence isdesigned to test for neoclassical convergence. In an ordinary regression, PCGDP should
for a given level of constraints have a negative sign, as the higher is per capita income thecloser a nation is to the technological frontier and the more slowly it should grow.
INTER is a general prefix for variables in which PCGDP is interacted with the otherright-hand variables.
To test the performance of random-effects and fixed-effects models, Table 5
reports the estimations of (2) without interaction terms by both techniques. The
performance of the random-effects model is clearly superior, and so for the interaction

11

models these are the only results that are reported. The results for this basic regression
are in agreement with many cross-country regression empirical models. Government
consumption, the black-market premium, and instability have a negative effect, as does
inflation, which is not typically included in such work. Investment, favorable
developments in the terms of trade, and openness have a positive effect. Schooling has apositive effect, which just fails to pass muster at the ten-percent level of significance (p <0.106). Per capita gross domestic product is negatively signed, as the neoclassical growthmodel predicts.
As for interaction terms, in the results listed in Table 6 two of them arestatistically significant, with signs the same as in the previous model. INTERPREMIUM
is significant with a negative sign, suggesting that government distortions do moredamage to the growth rate in poorer than in richer countries. And INTERHUMCAP has apositive sign, so that richer countries get more economic growth in percentage terms outof a given amount of population education than poorer ones. While INTEROPEN and
INTERDEM are not significant, they do have the predicted signs and do approachstatistical significance (p < 0.165 and p < 0.168 respectively). Of particular interest is theabsence of significance for INTERINV. Diminishing returns in a static Solow production
function would suggest that this coefficient would have a negative sign, as the growthachieved by adding capital is exhausted. The results suggest instead that innovation,
spillover from education or some other factor means that capital stock, at least as it ismeasured in the Penn World Tables, does not meaningfully suffer from a diminishingreturns problem. Such knowledge accumulation would counteract the natural12

diminishing-returns properties of physical capital by raising its marginal productivity,
and would satisfy the Solow notion of technological progress..

IV. Other Implications
Table 7 summarizes the findings with respect to statistical significance for both
regressions, for the variables that are common to both models. The criteria for inclusion
are statistical significance in at least two of the quantiles or a statistically significant
difference between the coefficients for the 0.2 and 0.8 quantiles.

Resource efficiency versus resource accumulation
Initially the differences between the results for the level and growth rate of percapita income are worth noting. The essence of a neoclassical production function
augmented by human capital is confirmed by the significance and sign of KAPW and
INVGDP in, respectively, the income and growth equations, along with AVGSCHOOL.
The variables that are and are not significant after accounting for the level of factors
provide an interesting interpretation relative to other literature in the growth literature. It
is possible to distinguish between two sources of growth-destroying effects. One effectdiscourages the accumulation of productive factors, which has for decades been
considered a critical part of the industrialization process. This would amount to lesser
amounts of KAPW and AVGSCHOOL. The other sort of inefficiency causes the existing
factor stocks to be used more or less inefficiently, so that a given amount of factor stocks
yields less output. This will show up as variables taking statistical significance in theincome regressions after factor amounts are accounted for.

13

Thus, the significant and substantially positive effect of openness and democracy
on income after taking account of factor amounts suggests that these two variables allow
resources to be used more efficiently. Elementary comparative-advantage theory
provides a straightforward explanation for the first variable. Countries exposed tointernational commerce will use resources to greater effect than countries that protect. As
for democracy, the interpretation is somewhat more ambiguous. One possibility is thecontroversial and oft-studied hypothesis of Wittman (1989), which argues that democracyis the most efficient form of governance because it amounts to more competitive politicalmarkets. The efficiency is defined relative to general voter preferences over all objects ofchoice rather than income per se, but it is surely conceivable that fewer restraints on
overall economic performance would be one, and perhaps a substantial, object of suchpreferences.

The importance of human capital
The most robust result is the increasing effect of human capital as one moves up
the standard-of-living distribution. There is an increasing absolute though not relative
effect in the income regression, and a positive interaction effect on the growth rate in
Section III. This is an important result in that it supports the notion that human capital, atleast to the extent that schooling proxies for it, is an important factor in continuously
raising an economy’s potential. Despite the rediscovery of the importance of humancapital as a source for growth in Romer (1986), Lucas (1988) and the rest of the newgrowth literature, some work (Hendricks, 2002, Bils and Klenow, 2000) has downplayed
its importance. But the results here provide some nuance to the existing theories of14human capital and development. Given the consistency across both estimation
techniques, schooling in particular if not human capital in general has greater effects onricher than on poorer countries.
This is a result that holds equal parts promise and peril. Peril, because it suggeststhat the absence of convergence may be a recurring problem absent massive injections ofhuman capital into poorer countries. If richer countries continually increase theiradvantage in human capital over poorer ones by greater investments in it, then it may beeven more difficult for poor countries to close the gap. Promise, because even if it isdifficult a remedy – substantially higher investments in human capital in poorer countries– suggests itself immediately. While the optimism of those – e.g., Page (1994) – whoargue that human capital improvements will help bring about substantial convergence
may be misplaced with respect to the difficulty of the task, optimism with respect to its
efficacy if accomplished is supported by the results here.
In addition, the evidence is relatively good that openness has positive effects
throughout the distribution. In absolute terms the effects of openness seem to increase
for richer nations. In percentage terms the effect is somewhat greater for the poorest
nations. Unlike human capital, the remedy for openness is administratively very simple,
if not necessarily politically so. If one is giving advice to political authorities with atleast some freedom of movement, then openness should loom large in therecommendations made. Recall that the analysis of the first estimation combined withendogenous-governance theory suggests that richer countries will often find openness intheir own interest.

15

The agreement of the two models with respect to TERMS is noteworthy. There isa widespread belief that poorer nations are more vulnerable to adverse developments ininternational commerce. Indeed, the "small open economy” is a pillar of development
modeling. The basic stereotype has it that these countries tend to depend heavily oncommodity or, sometimes, manufactured exports to the industrial world, and thus whencommodity prices decline or industrial-country growth slows, the effect on these nations
is magnified significantly. In fact, the results suggest that wealthier countries are moresensitive, at least in absolute-dollar terms, to terms-of-trade developments.
With a moment’s reflection, the finding should perhaps not be surprising.
Contrary to the image of trade-dependent poor countries, Table 8 suggests that in fact
wealthier nations depend more on foreign trade than poorer ones. The top portion depicts
data from the Penn World Tables v. 6.1 for exports plus imports as a percentage of GDP
in 2001 for the 0.2, 0.4, 0.6 and 0.8 percentiles of the distribution of real per capita GDP
in 2000. The poorest countries in 2001, those with per capita GDP of less than $1589.20,
had a ratio of 0.6282, while those with a per capita income higher than $14,589.78 had aratio of 0.9880. Richer countries trade relatively more, and thus ought to be more
vulnerable to changes in their terms of trade, other things equal.
This notion of relatively high poor-country vulnerability to international
developments is further undermined by examining changes in TERMS as a function of
PCGDP. The bottom portion of Table 8 shows the average value of TERMS for 19901995, with the sample again stratified at the same percentile boundaries. While the very
poorest countries had the worst average shocks during this period, the 0.2-0.4 group had
the best, with a positive average change in terms of trade. Over the entire Barro-Lee

16

sample, there is a weakly positive correlation between PCGDP and TERMS, with ρ =0.0785 (p < 0.05). But in general, from the perspective of the effect of shocks tointernational relative prices, there does not appear to be any clear connection between
place in the global income distribution and damage from global trade developments, andsome reason for concluding the opposite.
Finally, the significance of DEMOCRACY in the lowest quantile of the income
regressions is perhaps modest evidence against the dismal theory of democracy that has
gained some currency in recent years (Zakaria, 2003; Kaplan, 2000; Cheung, 1998).
Unalloyed democracy in the absence of the cultural infrastructure of liberal societies, aswell as few limits on what democratically elected legislative majorities are allowed to do,
is said to enable the worst, most predatory human instincts to take flight and thus destroy
not just growth but social stability itself. But DEMOCRACY is statistically significant
and positively signed at the lowest levels of the distribution as well as higher ones in thequantile regressions, although there is no evidence that the effect is more powerful inabsolute terms for poorer nations. However, even assuming equal effects in absoluteterms the proportionate effects in poor countries may well be much greater. While theabsence of significance in the interaction regression means that the positive effects ofdemocracy in poor countries, famously recounted by Sen (1981), are not overwhelmingly
supported, at a minimum the predatory, pessimistic theories of democracy are rejected.

V. Conclusion
There do appear to be some differences over the income distribution with respect
to the determinants of growth. These differences are most compelling with respect to17

education and openness. The failure to converge can be attributed to failures to invest in
human capital (or at least schooling), and wealthy countries appear to benefit most fromopenness. In addition, there is some evidence for varying effects with respect to theterms of trade and governmental distortions. And one of the two estimation techniques is
consistent with a hypothesis that well-developed financial markets are important to help apoor country take off.
It would be surprising if it were otherwise. The division of labor, the extent ofmarkets and the microeconomic information they reveal, legal structure, engagement withthe world and a myriad of other details relevant to growth and prosperity are substantiallydifferent in countries in Europe, North America and East Asia than in Africa, Latin
America and South Asia. Abstraction is an indispensable tool in advancing the frontiersof knowledge, but like everything it is subject to diminishing returns. It was probably too
much to hope for that a single, simple production function would be sufficient to describe
the growth process in societies as different as those we see. At the same time, further
understanding of those differences may allow the design of better development policy,
avoiding some of the mistakes of the past that assumed a uniform set of problems
requiring a single solution.
In addition, the results may point the way toward further research on proper
sequencing of reforms in economies where many problems must be solved. Openness,
evidently, is a reform that carries great benefits for the very poorest countries even as it isadministratively straightforward. Similarly, human capital seems to be of singularimportance, at least in proportional terms. In decades past physical capital accumulation
was seen as the key to prosperity, but the findings here provide more support for an18

approach that emphasizes the need to accumulate human capital.

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Solow, Robert M. “A Contribution to the Theory of economic Growth.” The
Quarterly Journal of Economics 70 (1), February 1956, 65-94.
Tavares, Jose and Wacziarg, Romain. “How Democracy Affects Growth.”
European Economic Review 45 (8), August 2001, 1341-1378.
Wittman, Donald. “Why Democracies Produce Efficient Results.” Journal of
Political Economy 97 (6), December 1989, 1395-424.
Yusuf, Shahid and Stiglitz, Joseph E. “Development Issues: Settled and Open.”
In Gerald M. Meier and Joseph E. Stiglitz (eds.), Frontiers of Development Economics:
The Future in Perspective. Washington, The World Bank, 2000, 227-268.
Zakaria, Fareed. The Future of Freedom: Illiberal Democracy at Home andAbroad. New York: W.W. Norton, 2003.

21

Table 1
Median regression, per capita income as dependent variable
Variable
CONSTANTKAPW
HUMCAP
INFLATION
DEMOCRACY
GOVT
TERMS
LIQUIDOPENNESSPREMIUM

Coefficient-561.8331(-1.05)0.2064341***(11.92)420.4742***(78.45306)-707.0079(-1.31)
958.4864**(2.02)-3110.622(-1.40)2102.984(0.94)
961.1991(1.58)
843.7941**(2.51)-344.6247(-0.91)

R2:
0.7321
N:
214
Note: * denotes significance at the ten-percent level.
** denotes significance at the five-percent level.
*** denotes significance at the one-percent level.
**** denotes significance at the 0.1-percent level.

22

Table 1 (continued)
OLS regression, per capita income as dependent variable
Variable
CONSTANTKAPW
HUMCAP
INFLATION
DEMOCRACY
GOVT
TERMS
LIQUIDOPENNESSPREMIUM
R2:
N:

Coefficient-796.4741*(-1.67)0.1832118****(12.32)492.1253****(68.82155)-626.496(-1.23)1459.35****(3.56)-3992.995**(-1.98)3757.837*(1.91)
524.2403(0.99)
940.583****(3.21)-410.8241(-1.11)0.9036214

23

Table 2
Quantile regression, per capita income
Quantile
Variable
CONSTANTKAPW
HUMCAP
INFLATION
DEMOCRACYGOVTERMS
LIQUIDINSTABILITYOPENNESSPREMIUM
R2
N

20

40

60

80

-1226.731*.2182572****217.7103*
60.3768
631.3259*
243.5417
1715.722559.503**-812.8924
498.277*-547.6386
0.6284
214-909.4372*.22582****351.7136****-269.5202
654.5446-1034.914
1257.807
1515.678*-915.4862
516.4058-529.0416**
0.7090-121.6807.2271579****366.2904****-841.2787
878.7279*-3225.528
1591.667
392.0297-729.2297
1170.474**-400.5238
0.741363.90375.2109841****479.1935****-492.9678
1048.76*-3103.941
7709.693**-336.2946
523.5452
1742.675****-247.4111
0.741324

Table 3p values, .20 and .80 quantiles
VariableХ2

p-value

KAPW
AVGSCHOOL
INFLATION
DEMOCRACYGOVTERMS
LIQUIDINSTABILITY
OPEN
PREMIUM

0.04
3.43
0.27
0.50
1.05
4.51
9.05
1.37
8.40
0.33

0.85
0.06540.60610.47900.30680.03490.00300.24320.0042
0.568225

Table 4
Percentage effects of quantile coefficients
Quantile values (per capita GDP)0.2
816.600.4
1570.600.6
2916.400.8
6807.20
Coefficients/quantile values
HUMCAP
0.2
0.267
0.4
0.223
0.6
0.126
0.8
0.070
OPENNESS
0.2
0.610
0.4
0.329
0.6
0.401
0.8
0.256
TERMS
0.2
2.101
0.4
0.801
0.6
0.546
0.8
1.133
LIQUID
0.2
3.134
0.4
0.965
0.6
0.134
0.8
-0.04926

Table 5
Basic growth regression, fixed and random effects
Variable
CONSTANTINVGDPHUMCAP
INFLATION
DEMOCRACYGOVTERMS
LIQUIDINSTABILITYOPENNESSPREMIUM
PCGDP
R2
F
Х2
N

Fixed effects
0.0530635****(4.00)
0.0567861(1.44)-0.0023464(-1.01)-0.0282204***(-2.66)-0.0076354(-1.01)
0.0416894(0.78)
0.0625473***(2.64)-0.0191694(-1.27)-0.0195017*(-1.92)0.011293(1.45)-0.011382**(-1.98)-0.00000499****0.0311
10.56****
N/A

Random Effects
0.0293929****(5.16)
0.0859588****(3.86)
0.0015898(1.62)-0.0256605****(-3.50)0.0000331(0.01)-0.1055809****(-4.53)0.0807883****(3.57)-0.0068543(-1.01)-0.018558**(-2.32)0.0179489***(4.68)-0.0110383***(-2.65)-0.00000443****0.3719N/A
231.49****

403

27

Table 6
Growth equation, interaction effects
Variable
CONSTANTINVHUMCAP
INFLATION
DEMOCRACY
GOVT
TERMS
LIQUIDINSTABILITYOPENNESSPREMIUM
PCGDP
INTERINV
INTERHUMCAP
INTERINF
INTERDEM
R2
N

Coefficients
0.0294214**** 0.0340182**** 0.0291521**** 0.0340783****(04.36)(5.39)(5.07)(5.11)
0.0859038*** 0.0999748**** 0.0865625**** 0.0948079****(2.95)(4.20)(3.87)(4.06)
0.0015866
0.0001417
0.0016042
0.001453(1.60)(0.11)(1.63)(1.45)-0.0256627**** -0.0228742*** -0.02237*-0.0237326***(-3.49)(-3.02)(-1.79)(3.16)
0.0000412
0.0024887
0.0000867-0.0055311(0.01)(0.47)(0.02)(-0.85)-0.1055545**** -0.1154642**** -0.1062903**** -0.115508****(-4.44)(-4.69)(-4.54)(-4.66)0.0807503**** 0.0832152**** 0.0804417**** 0.081449****(3.56)(3.69)(3.54)(3.60)-0.0069007-0.0062657-0.0068587-0.006609(-1.01)(-0.89)(-1.01)(-0.96)-0.0184441**-0.0165828**-0.0185551**-0.0171911**(-2.31)(-2.04)(-2.32)(-2.12)0.0179564**** 0.01837****0.0177223**** 0.0174987****(4.67)(4.64)(4.54)(4.47)-0.0110377*** -0.0108922*** -0.0113439*** -0.0109476***(-2.64)(-2.58)(-2.65)(-2.61)-0.00000444*** -6.96E-06**** -4.37E-06**** -6.89E-06****(-2.92)(-4.57)(-6.15)(-3.59)2.38E-08(0.01)
1.65E-07*(1.89)-8.84E-07(-0.32)2.60E-06(1.38)
0.37190.37710.37210.3722403

28

Table 6 (continued)
CONSTANTINVHUMCAP
INFLATION
DEMOCRACY
GOVT
TERMS
LIQUIDINSTABILITYOPENNESSPREMIUM
PCGDP
INTERGOVT
INTERTERMS
INTERLIQ
INTERINST
INTEROPEN
R2
N

0.029835**** 0.0297138**** 0.0310608**** 0.029476**** 0.0319996****(4.96)(5.20)(4.99)(5.17)(5.25)
0.0856521**** 0.0864426**** 0.0887415**** 0.0857621**** 0.0916444****(3.82)(3.88)(3.90)(3.84)(3.99)
0.0015703
0.0014731
0.001629
0.0016325*
0.0017827*(1.59)(1.48)(1.63)(1.65)(1.74)-0.0257431**** -0.0260241**** -0.0256058**** -0.0259708**** -0.0242784****(-3.50)(-3.54)(-3.47)(-3.52)(-3.25)-0.0001233
0.0001666
0.0000767-0.0000499
0.0006344(-0.02)(0.03)(0.02)(-0.01)(0.12)
0.1095543**** -0.105583**** -0.106595**** -0.1051353**** -0.1149391****(-3.78)(-4.53)(-4.51)(-4.50)(-4.61)0.0807778**** 0.1035216*** 0.0805468**** 0.0809099**** 0.0836443****(3.56)(2.93)(3.56)(3.57)(3.69)-0.0065453-0.0066815-0.0129592-0.0068369-0.0074391(-0.94)(-0.98)(-1.18)(-1.01)(-1.07)-0.0186995*** -0.018435**-0.0185461**-0.0229689*-0.0176572**(-2.33)(-2.30)(-2.31)(1.65E-06)(-2.18)0.0178216**** 0.0175439**** 0.0182996**** 0.0180408**** 0.0121056*(4.58)(4.53)(4.68)(4.69)(2.08)-0.0110032*** -0.0112022*** -0.0108172*** -0.0108523*** -0.0112932***(-2.63)(-2.68)(-2.57)(-2.59)(-2.68)-4.54E-06**** -4.37E-06**** -4.91E-06**** -4.49E-06**** -5.74E-06****(-5.40)(-6.33)(-4.93)(-6.41)(-4.84)
1.96E-06(0.24)-8.21E-06(-0.84)
8.92E-07(0.69)
1.65E-06(0.43)
1.45E-06(1.39)
0.37200.37300.37210.37220.3730403

29

Table 6 (continued)
CONSTANTINVHUMCAP
INFLATION
DEMOCRACY
GOVT
TERMS
LIQUIDINSTABILITYOPENNESSPREMIUM
PCGDP
INTERPREM
R2
N

0.0298322****(5.23)
0.0901599****(4.02)
0.0015529(1.58)-0.0249731***(-3.41)0.0001089(0.02)-0.1165666****(-4.82)0.0763674****(3.36)-0.0063684(-0.93)-0.0186363**(-2.33)0.0165205****(4.20)-0.003628(-0.06)-4.33E-06****(-6.30)-3.62E-06*(-1.70)0.3765403

30

Table 7
Summary results

IncomeGrowthPositive Sign

Negative Sign

KAPW, HUMCAP, OPEN,
DEMOCRACY, LIQUIDINVGDP, PREMIUM,TERMS, OPEN,
INSTABILITY,
DEMOCRACY,PREMIUM, LIQUID

31

PREMIUM, GOVT,
INSTABILITY, INFLATION

Table 8
Per capita income, trade and trade shocks
Percentile (200)0-0.2
0.2-0.4
0.4-0.6
0.6-0.8
0.8-1Upper Income level
1589.203847.1326732.241
14,589.78
39,656.49(X + M)/GDP
0.62820.83940.837897.59
98.80

Percentile (1990)
0-0.2
0.2-0.4
0.4-0.6
0.6-0.8
0.8-1Upper Income level
914.6
2092.63994.2
10,416.6
32,014

Mean, TERMS, 1990-1995-0.01296
0.0035436-0.003544-0.003636-0.005315

Sources: PWT v6.1 (top), Barro-Lee (bottom)

32