metadata: authors: 0: author: Levine, Ross
1: author: Renelt, David
docna: 0: docna: A sensitivity analysis of cross-country
            growth regressions
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Economic Theory & Research,Economic Adjustment and Lending,Industrial Economics,Public Sector Economics,International Trade and Trade Rules,Economic Growth,Public Finance Decentralization and
            Poverty Reduction,Crime and Society
teratopic: Poverty Reduction
abstracts: cdata!: A vast amount of literature uses
            cross-country regressions to find empirical links between
            policy indicators and long-run average growth rates. The
            authors study whether the conclusions from existing studies
            are robust or fragile when small changes in the list of
            independent variables occur. They find that although
            "policy" appears to be importantly related to
            growth, there is no strong independent relationship between
            growth and almost every existing policy indicator. They also
            find that very few macroeconomic variables are robustly
            correlated with cross-country growth rates. They clarify the
            conditions under which one finds convergence of per capita
            output levels and confirm the positive correlation between
            the share of investment in GDP and long-run growth. They
            conclude that all findings using the share of exports in GDP
            could be obtained almost identically using the total trade
            or import share and also that few commonly used fiscal
            indicators are robustly correlated with growth. Finally, the
            authors highlight the importance of considering alternative
            specifications in cross-country growth regressions.
repnme: repnme: A sensitivity analysis of cross-country
            growth regressions
display_title: A sensitivity analysis of
            cross-country growth regressions
listing_relative_url: /research/1991/03/700021/sensitivity-analysis-cross-country-growth-regressions
topicv3: Economic Growth m1326241 471,Trade m1326924 698,Fiscal Policy m1327144 445,Population, Aging and Health m1327736 640,Black Market m1439229 BLA-3388
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Policy, Research & Ect Affairs
no_of_pages: 48
model_extractions: <!-- Start of picture text --> oo| Polley. Research, and External Affairs |<br>!<br>a<br>WORKING PAPERS<br>Macroeconomic Adjustment |<br>and Growth<br>Country Economics Department<br>The Worid Bank<br>March 1991<br>WPS 609<br><!-- End of picture text -->

# A Sensitivity Analysis of Cross-Cou ntry Growth Regressions

## Ross Levine and David Renelt

A vast literature uses cross-country regressions to find empirical links between policy indicators and long-run average growth rates. But conclusions from those studies are fragile if there are small changes in the independent variables.

The Policy, Research, and Extemal Affairs Complex distnbutes PRE Working Papers to disseminate the findings of work in progress and to encourage the exchange of ideas among Bank staff and all others interested in devclopment issues. These papers carry the names of the authors, reflect only their views, and should be used and ciled accordingly The findings, interpretations, and canclusions are the authors’ own, They should not he attnbuted to the World Bank, ts Board of Directors, its management, or any of its member countnes.
<!-- Start of picture text --> Policy, Research, and External Affairs<br><!-- End of picture text --> <!-- Start of picture text --> = WORKINGPAPERS<br>| Macroeconomic Adjustment |<br>and Growth :<br>WPS 609<br><!-- End of picture text -->

This paper — a product of the Macroeconomic Adjustment and Growth Division, Country Economics Department is part of a larger effort in PRE to understand empirically the links between policy and longrun growth. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact CECMG staff, room N11-024, extension 39175 (43 pages, with tables).

A vast literature uses cross-country regressions to find empirical links between policy indicators and long-run average growth rates.

* Confirm the positive correlation between the share of investment in GDP and long-run growth.

Levine and Renelt study whether the conclu¢ Conclude that all findings using the share sions from existing studies are robust or fragile if | exports in GDP could be GDP could be could be be obtained almost identithere are small changes in the list of independent cally using the total trade or import import share. variables. (Cross-country growth studies that use use export

¢ Conclude that all findings using the share of exports in GDP could be GDP could be could be be obtained almost identically using the total trade or import import share. (Cross-country growth studies that use use export indicators should not be interpreted as studying the relationship between exports and growth per se but rather as between growth and trade defined more broadly.) Their conclusion: they are fragile.

They find that although “policy” — broadly defined — appears to be importantly related to growth, there is no strong independent relationship between growth and almost every existing policy indicator. Levine and Renelt: « Find that many commonly used fiscal indicators are not robustly correlated with growth.

¢ Find that very few macroeconomic variables are robustly correlated with cross-country growth rates.

* Highlight the importance cf considering altemative specifications in cross-country growth regressions.

* Clarify the conditions under which onc finds convergence of per capita output levels.

| The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal | | TheAffairsfindings,Complex.interpretations,An objective andof theconclusionsseries is to getin thesethese findingspapers dooutnotquickly,necessarilyeven representif presentationsofficial areBankJes; thanpolicy.fully polished. Produced by the PRE Dissemination Center
# TABLE OF CONTENTS

|||Page|
|---|---|---|
|I.|Introduction|1|
|Il.|Methodology<br>and Data|3|
|III.|Basic Variables<br>and<br>Some<br>First Results|7|
|IV.|Macroeconomic<br>Variables<br>and Growth|ll|
||A.<br>Illustrative Overview|il|
||B.<br>Fiscal Policy Indicators|14|
||Cc.<br>TradePolicyIndicators|19|
||<br>D.<br>Monetary and Political Indicators|23|
|Vv.|Sensitivity of the Sensitivity Analysis and Variable Groupings|24|
|VI.|Conclusion|26|
||Bibliography|29|
||Data<br>Set Variables<br>and Sources<br>Tables|32<br>36|

We have received helpful comments from Robert Barro, Surjit Bhalla, Maria Carkovic, David Dollar, Bill Easterly, Dale Jorgenson, Lant Pritchett, Cani Rodrik, Andy Rose, Larry Summers, and seminar participants at Harvard University and the World Bank.
l re Introduction A large literature uses cross-country regressions to search ror emoirical Linkages between long-run average growth rates anu a variety of economic, political, and institutional factors suggested by theory. Most investigators consider only a small number of explanatory variables in attempting to establish a statisticaliy significant relationship between growth and a particular variable of interest. For example, many authors who examine the relationship between measures of fiscal policy and growth ignore the potential importance of trade policy, while those authors who study the empirical ties between trade and growth commonly ignore the role of fiscal policy.+ Given that over 50 variables have been found to be significantly correlated with growth in at least one regression, readers may be uncertain as to the confidence they should place in the findings of any one study. This paper addresses the question: How much confidence should we have in the conclusions of cross-country growth regressions? We find that few findings can withstand slight alterations in the list of explanatory variables.

We use a variant of Leamer’s (1983) extreme bounds analysis to test the robustness of coefficient estimates to the inclusion of other relevant variables. Wwe study a large number of variables that have be2n used as explanatory variables in a broad collection of growth studies. In examining the strength of the statistical relationship between each variable and growth, we add explanatory variables that have been identified as important by the 1 Studies of fiscal policy that exclude trade inaicators include Landau (1983), Ram (1986), Grier and Tullock (1989), and Barro (1990, 1991). Feder (1983) and Edwards (1989) study trade policy but ignore fiscal indicators. Kormendi and Meguire (1985) and Romer (1990a) include variables for both.

2 See Levine and Renelt’s (1990) review of the empirical growth literature.

1
empirical growth literaturs. The relationship between growth and a particular variable of inter.st is considered robust if it remains statuisticaliy significant when other variables are included. Even thcugh we try not to include variables that on a priori grounds measure the same phenomenon as the variable of interes:, almost all identified relationships become insignificant with the inclusion of only one additional variable, and many publicized coefficients actually change sign when other explanatory variables are added. Two themes emerge from our investigation. First, measures of economic policy are related to long-run growth. Taken individ: ally or in groups, there are many cross-country econometric specifications in which macroeconomic policy indicators are significantly correlated with long-run average growth rates. The second theme is that the cross-country statistical relationships between long-run average growth rates and almost every particular macroeconomic indicator considered by the profession are fragile: small alterations in the "other" explanatory variables overturn past results. In particular, the broad array of fiscal expenditure variables, monetary policy indicators, political stability indexes, human capital and fertility measures considered by the profession are not robustly correlated with growth; and newer indicators that we have assembled to capture exchange rate, tax, and fiscal expenditure policies are also not robustly correlated with growth. This implies that there is sot a strong independent statistical relationship between most popular macroeconemic indicators and growth even though “policy” - defined broadly - appears to be importantly related to growth. Besides demonstrating the fragile relationship between many policy indicators and growth, this paper clarifies the conditions under which one finds evidence of convergence of per capita output levels, confirms the positive 2
correlation between growth and the share of investment in GDP, and takes some additional steps toward improving our understanding of the complex relationship between trade and growth. We find that the ratio of trade to output is robustly, positively correlated with the investment share which is in turn robustly correlated with cross-country growth rates.

Before detailing the methodology and the results, it is important to emphasize this paper's bouncaries. We do not estimate a structural model, establish causal links, identify growth determinants, make policy recommendations, improve the measurement of policy indicators, or run the full gamut of sensitivity analyses discussed by Leamer (1985). We simply examine whether partial correlations that have drawn the attention of a large empirical literature are robust or fragile to small changes in the list of right-hand-side variables. We find that they are generally fragile.

# II. Methodology and Data

There does not exist a consensus theoretical framework to guide empirical work on growth. Many researchers begin with a neoclassical production function and add variables thet correspond wi.h their views of growth [Feder (1983) and Ram (1986)]. Others use endogenous growth models that highlight a few aspects of growth [Romer (1989) and Barro (1990, 19$1)]. Still others use a variety of theoretical models to motivate an assortment of variables that they use in exploratory empirical studies [Kormendi and Meguire (1985) and Grier and Tullock (1989)}. This has produced a diverse and sometimes unwieldy literature, where few studies control for the variables analyzed by other researchers. One common feature of most cross-country growth regressions is that they are linear (see: Barro (1990, 1991), Kormendi and McGuire (1985), Grier and Tullock (1989), but 3
also see Romer (1989)): researchers regress the average growth rate for a samle of count tes on a g oup of explanatory variables and focus on the coefficients of one or two variables of interest. This paper studies whether we should have much confidence in the conclusions that have been drawn from these regressions.

Since past cross-country studies do not provide much evidence regarding the sensitivity of their findings to small alterations in the list of explanatory variables, this paper uses a variant of Leamer's extreme bounds analysis to examine the robustness of a variety of "fiudings* described in the empirical growth literature (See Leamer (1983, 1985) and Leamer and Leonard (1983)}). We first describe the extreme bounds procedure and then return to discuss and study the empirical growth literature.

As in the studies we are considering, our extreme bounds test is based on linear equations explaining output growth and investment: where Y is either per capita GDP growth or the share of investment in GDP, I is a set of variables thas are always included in the regression, M is the variable of interest, and 2 is a subset of variables chosen from a large pool of variables identified by past studies as important explanatory variables of growth. Our extreme bounds procedure involves varying the subset of Z-variables included in the regression to find the widest range of coefficient estimates on the variable of interest, M, that is accepted by standard hypothesis tests. In particular, we first choose a variable that has been the focus of past empirical studies, M, and vericy the bi-variate sign and significance of these past 4
findings with our data set. Tnen we compute the regression results for all possible linear combinations of up to three 2-variables and identify the highest and lowest values for the coefficient on the variable of interest, 8,, that can not be rejected at the 0.05 significance level.? The de ree of confidence that one can have in the partial correlation between the Y and M variables can be inferred from the extreme bounds on the coefficient 6). If the coefficient remains significant and of the same sign at the extreme bounds, then one can maintain a fair amount of confidence in that correlation. In such a case, we refer to the result as "robust." If the coefficient does not remain significant or the coefficient changes sign, then one might feel less confident in the relationship between the M and Y variables. In this case, we refer to the result as “*fragile.* One possible objection with this extreme bounds procedure is that it introduces multicollinearity, inflates the coefficient standard errors, and exaggerates the range on the coefficient of interest. Leamer (1978, p.170-181), however, points out that the multicollinearity problem is really a weak-data problem. If one is unable to find robust partial correiations in a crosssection regression it simply means that there is not encugh independent variation in that variable to explain cross-country differences in growth. It is only when one identifies a significant correlation while controlling for other relevant variables that one can have much confidence in the correlation. Nonetheless, we restrict the extreme bounds procedure in two ways to avoid some potential difficulties. First, to the list of variables always included in the extreme bounds regressions, the I-variables, we only allow the procedure to 3 We add and subtract two standard errors to each coefficient. Only the actual B's are reported in the tables with the standard errors.

5
choose up to three 2-variables from the large pool of variables identified as potentially important for explaining cross-country growth differentials. Consequently, we restrice the total number cof explanatory variables included in any one regression to be eight or less.* The set of variables from which we draw combinations of Z-variables, however, is larger than that considered by any other investigation. The second way we Limit the extreme bounds procedure is that for every variable of interest, M, we restrict the pool of variables from which we choose 2-variables. We exclude variables that, a priori, measure the same phenomenon. For example, when we examine the relationship between growth and the rate of domestic credit creation over the 1960-89 period, we do not allow the inflation rate to be a 2-variable. These restrictions make it more difficult to implicate past findings as fragile.° The data are primarily from the World Bank National Accounts Data Base and, when available, cover the period 1960 to 1989. The data set includes 119 countries but the major oil exporters are excluded from the analysis. Detailed government expenditure and tax information are from the International Monetary Fund’s Government Financial Statistics and begin in 1974 for most countries. Data on -lack market exchange rates, measures of educational attainment, civil liberties, etc. are obtained from a variety of sources listed in the Data Appendix. We also use Barro’s (1990, 1991) data set, which is composed primarily of the Summers and Heston (1988) data set, to compare our results with 4 This total is similar to that used by Kormendi and Meguire (1985) and less than Barro (1991) who uses between eight and fourteen explanatory variables. 5 Also, before labelling a result as ‘“robust,” we conduct additional sensitivity analyses by examining different groups of countries, time periods, and expanding the number of variables that can be included as Z-variables.

6
other papers. We find similar results with the two data sets, but see Kravis and Lipsey (1990) for a comparison of the two data sets.

III. aA_Basic Set of Variables for Growth Regressions and Some Firs. Results The choice of the included variables, ° was based on past empirical studies and economic theory. When the dependent variable is the average annual growth rate in GDP per capita (GYP), the I-variables are the investment share of GDP (INV), the initial level of real GDP per capita in 1960 from Summers-Heston (RGDP60), the initial secondary school enrollment rate as a proxy for human capital (SEC), and the average annual rate of population growth (GPO). Although few empirical studies include all of these variables, most studies control for some subset. Of 41 growth studies surveyed in Levine and Renelt (1990), 33 included the investment share, 29 included population growth, 13 included a human capital measure, and 18 included a measure of initial income. In addition, Mankiw, Romer, and Weil (1990) empirically show that the variables we term *included-variables" enter with the signs predicted by their human-capitalaugmented neoclassical growth uodel, and the included variables are consistent with a variety of "new" growth models that rely on constant returns to reproducible inputs or endogenous technological change [e.g., Barro (1990, 1991) and Romer (1989, 1990a)]. Furthermore, with these I-variables, we can confirm the findings of a large assortment of empirical studies; and, in recognition of the issues raised by McAleer, Pagan, ard Volker (1985), we show that small changes in the included variables do not alter this paper’s conclusions. There are statistical and conceptual problems with using these I- variables. In keeping with this paper's focus on assessing the statistical fragility of past findings, we discuss these problems only triefly. Measurement 7
oroblems with initial income and secondary school enrollment rates may induce biased results.® In the case of populetion growth, census data may be very poor, and the causal links with per capita output growth are ambiguous [Becker, Murphy, and Tamura (1990)]. Furthermore, in the case of secondary school enrollment rates, human capital represents more than formal schooling, and enrollment rates do not control for educational quali.y. Nonetheless, other measures of education (primary enrollment, literacy) yield similar rezults.’ There are also problems with including the ratio of physical capital investment to GDP as an I-variable. The causal relationship between growth and investment is ambiguous, and the theoretical justification for iucluding many variables in growth regressions is that they may explain investment Tf investment is already included, the only channel through which other explanatory variables can explain cross-country per capita growth differentials is the efficiency of resource allocation. To partially clarify this ambiguity, we investigate the ability and robustness of macroeconomic "variables of interest" to explain the ratio of physical investment to GDP.

6 For example, if initial income is mis-measured, the estimated coefficient on initial income will be biased towards being negative. Romer (1989) uses instrumental variables for initial income and the literacy rate and finds that they become insignificant. However, it is unclear that the instruments used (consumption of newsprint and number of radios per capita) are adequate and he uses literacy rates which we find are not as strongly correlated with per capita growth as primary and secondary school enrollr -. rates. , Secondary enrollment may be preferable to primary and literacy rates because many countries have reached the upper bound for these other measures. A measure of average years of schooling of the labor force around 1380 constructed by Psacharopoulos and Arriagada (1986) was found to be highly correlated with the secondary enrollment ratio.

8
The regression results with the I-variables over the 1960-89 period are GYP = -0.83 - 0,35*RGDP60 - 0.38*GPO + 3.17*SEC + 17.5*INV (2) (0.85) (0.14) (0.22) (1.29) (2.68) R* = 0.46 n= 10).

The coefficient standard errors are in parenthesis. The variables have the signs predicted by a wide class of models, and all but population growth are significant at the 0.05 significance level. The I-variables explain about half of the cross-section variance in growth rates.

Table 1 presents extreme bounds tests for each of the I-variables choosing only from the pool of doubtful variables available for the 1960-89 period. The investment coefficient is positive and robust. At the lower bound, tie coefficient on the investment share is 12.8 with a t-statistic of 4.2. The investment coefficient remains significantly positive even when we allow the extreme bounds procedure to choose five Z-variables. This robust positive re'ationship between per capita GDP growth and the investment share is in accord with a wide assortment of growth studies.

A second important finding presented in Table 1 is the robust negative partial correlation between tne average per capita growth rate over the 19601989 period and initial income in 1960. The coefficient on initial income is often used to test the convergence hypothesis: a poor country - other things equal - tends to grow faster than a rich country .® DeLong (1988) and Romer 8 anrther definition of convergence is when the cross-country dispersion of per capita income levels declines over time. Poorer countries growing faster than richer countries will lead toward reductions in dispersion, but new shocks may work in the other direction. See Quah (1990) and Barro and Sala-i-Martin (1990).

9
(1987), for example, argue that there is little empirical support for convergence. In accord with Barro (1991) and Mankiw, Romer, and Weii (1990), however, we find that there is a robust negative correlation between growth and initial income if one includes population growth, the investment share and either the initial level of secondary school enrollment or the init’al level of primary school enrollment as proxies for the initial level of human capital.?9 But, if one excludes initial human capital measures or the investment share, the correlation between growth and initial income is fragile. Thus, although there are specifications that yield evidence of both convergence and non-convergence, we could not overturn the finding of convergence when we included population growth, the investment share and the initial secondary (or primary) school enrollment rates. When we excluded the four fastest and four slowest growers over the 1960-1989 period, the relationship between growth and initial income remained robustly negative. 1° Convergence, however, did not hold very strongly over the 1974-1989 period.

Table 1 also includes extreme bounds tests for the secondary enrollment rate in 1960 and population growth. The secondary school enrollment rate enters with a significantly positive coefficient if the appropriate Z2-variables are chosen. But, this proxy for initial human capital enters insignificantly with other plausible specifications.?+ Thus, one should not feel very comfortable 9 the coefficient on initial income is insignificant for some specifications if the literacy rate is used to proxy for initial human capital.

10 However, if one excludes all OECD countries the results are not robust: the regression with the I-variables and the average inflation rate yields an insignificant negative coefficient on initial income.

11 These results are unchanged with primary school enrollment rates, literacy rates, or the educational attainment levels computed by Psacharopoulos and Arriagada (1986). Also, if one excludes the continent dummies, the extreme bounds procedure still finds that education variables are not robustly correlated 10
concluding that “the growth rate is substantially positively related to the starting amount of human capital.* (Barro, 1991, p.22}] Similarly, one should cautiously interpret Barro’s (1991) conclusions that lower fertility rates are correlated with faster growth. For some specifications, population growth enters with a significantly negative coefficient, but it enters with a positive but insignificant coefficient with other plausible 2-variables.

The extreme bounds of the I-variables are also computed using the investment share as the dependent variable. As Table 1 illustrates, none of the coefficients are robust. In fact, the coefficient on initial income takes-on both significant positive and negative values. Interestingly, the finding of a non-robust relationship between initial income and the investment share combined with the finding of a robust negative partial correlation between initial income and growth (when controlling for population growth, the investment share, and initial human capital) suggest that per capita income convergence does not operate primerily through increases in domestic savings or international capital flows.

# IV. Macroeconomic Variables and Growth

# A. Illustrative Overview

This paper's primary aim is to evaluate the degree of confidence one should have in the partial correlations between growth and popular macroeconomic indicators. This introductory subsection uses two relatively comprehensive studies of growth and some simple correlations to illustrate this paper's two major themes: many indicators of policy - taken individually or in groups - are with average growth rates.

ll
correlated with growth, but the relationship between any particular indicator or group of indicators and cross-country growth rates is fragile. After this subsection illustrates these findings, the following subsections conduct a systematic series of extreme bounds tests to determine the robustness of past findings.

Tables 2 and 3 anticipate this paper's findings. Country's that grew faster than average over the 1960-89 period tended to have a higher share of exports in GDP, a higher share of investment in GDP, larger primary and secondary school enrollment rates, a lower black market exchange rate premium, and lower inflation rates than slower growing countries. Similarly Table 3 shows that the investment share, the export share, the black market premium, and the indexes for revolutions/coups and civil liberties are significantly correlated with the average real per capital growth rate. Importantly, however, none of these variables is significantly correlated with the residuals from the regression of growth on the I-variables (the investment share, initial income, initial secondary school enrollment rate, and population growth). Thus, while many policy indicators are significantly related to growth, this relationship depends importantly on what factors are being held constant.

Kormendi and Meguire (1985) and Barro (1991) use a variety of macroeconomic variables to explain growth, and they present intuitively appealing results. Table 4 presents equations based on these studies. Equaticn 2 is nearly a replication of Barro’s (1991) work and includes the share of investment in GDP, a measure of initial human capital, population growth, and initial income along with the ratio of government consumption expenditures to GDP, a dummy variable for socialist economic systems, indicators for revolutions and coups, and dummy variables for Latin America and Africa. Barro's data is 12
based on Summers and Heston (1988). All variables are of the anticipated sign and initial income, the investment share, primary school enrollment rate, government consumption share, revolution and coups and the continent dummies are significant. Equation 3 is based on Kormendi and Meguire (1985) and includes initial income, the investment to GDP ratio, population growth, the average annual growth rate in the share of government consumption to GDP over the 196089 period, the average annual growth rate of domestic credit, the standard deviation of domestic credit growth, the average growth rate in the share of exports to GDP, and a measure of civil liberties. As in Kormendi and Meguire, this equation uses World Bank and IMF data. The coefficients have the anticipated signs and initial income, the investment share, population growth, average growth rate of domestic credit and standard deviation of the growth rate of domestic credit are significant at the 0.05 level. The equations explain 68 and 61 percent respectively of the cross-country variation in growth rates.

Since both equations appear reasonable but include different independent variables, readers may be wary of the findings of each study. To highlight this quandary, we combine the two equations using the union of the two sets of explanatory variables used by Kormendi and Meguire (1985) and Barro (1991). These results are shown in equations 4 and 5, based on World Bank and Summers and Heston data respectively. Only the investment share, initial income, and continent dummies remain significant with both data sets. The standard deviation of domestic credit growth remains significant with World Bank data while the government consumption share and the revolution and coup indicator is significant with the Summers-Heston data. Since the continent dummies simply suggest the importance of omitted variables, the results imply that only the share of investment in GDP and the initial income level - out of the long list 13
of explanatory variables given in Table 4 - have an independent, statistically significant correlation with cross-country growth differentials computed both from World Bank and Summers and Heston data.

These results suggest that many popular cross-country growth findings are sensitive to the inclusion of explanatory variables. More fundamentally, they illustrate that it is very difficult to isolate a strong empirical relationship between any particular macroeconomic policy indicator and long-run growth. The subsections that follow systematically examine the robustness of fiscal policy, trade policy, monetary policy, and political stability indicators that have been the focus of past studies. In addition, we study international distortion and tax indicators that have not received much attention.

# B. Fiscal Policy Indicators

The first variables that we analyze using the extreme bounds procedure are fiscal policy indicators. One of the most important and frequently studied issues in economics is the role of fiscal policy in economic development. Empirical attempts to link aggregate measures of fiscal policy with average per capita growth rates in cross-country studies have tended to use (1) measures of the overall size of the government in the economy; (2) disaggregated measures of government expenditures; or (3) measures of the growth rate of government expenditures. In addition to examining these fiscal indicators, we examine the role of government deficits and disaggregated measures of government taxes.

Before presenting our results, it is worth mentioning some problems with these fiscal policy measures. Governments may provide growth-promoting public goods and design taxes to close the gap between private and social costs. On the other hand, governments may waste funds, funnel resources to endeavors that 14
do not encourage growth, and impose taxes and regulations that distort private decisions. Aggregate measures of government size will not capture the potentially important implications of how total government expenditures are allocated. Furthermore, even if government funds are always spent on growthpromoting goods, there may be complex, non-linear tradeoffs between the beneficial effects of government services and the deleterious implications of distortionary taxes (see: Barro (1990) and Easterly (1990)}.+2 Linear, crosscountry regressions will not appropriately capture these relationships. In addition, disaggregated measures of government expenditures and tax sources are only available for a limited number of countries since the 1970s and are particularly prone to measurement problems. Moreover, since government resources may be spent effectively or ineffectively, using simple expenditure data without accounting for government efficiency may yield inaccurate measures of the actual delivery of public services. While recognizing these problems, we focus on examining the robustness of past findings.

A common measure of the role of government in economic activity is the ratio of government consumption expenditures to GDP [e.g., Easterly (1990), Romer (1989), and Landau (1983)]. Table 5-A reports extreme bounds tests of this variable for the period 1960-1989. The coefficient is not robust although the estimated coefficient is always negative.?> 12 see Levine (1991) for an analysis of the effects of different types of taxes on long-run growth.

13 similar results were obtained with the Summer-Heston data set, but if the extreme bounds procedure is prohibited from choosing the ratio of government investment expenditures to GDP as a Z~-variable, the relationship is robustly negative (with the Summers-Heston data set).

15
Although subject to data limitations, the ratio of total governrent expenditures to GDP is perhaps a mcre complete proxy for the relative size of the government in economic activity than the ratio of government consumption expendicure to GDP. We compute the extreme bounds on the ratio of total government expenditures to GDP for 1974-1989. The partial correlation with the average annual growth rate of per capita GDP is not robust. The sign of the coefficient remains negative but becomes insignificant when other variables are added.

The effect of government expenditures on economic growth, however, may depend on the way in which funds are allocated, not merely total expenditures by the government. Barro (1991) attempts to capture this difference empirically by removing education and defense expenditures from government consumption. In Table S-A, we provide extreme bounds results using the ratio of government consumption less defense and education expenditures to GDP over the 1974-1989 period during which IMF data is available for a broad range of countries. Barro (1991) finds this measure using Summers and Heston data to be negatively correlated with growth measured over the period 1960-1985. We find that the coefficient on government consumption expenditures less defense and education payments is always negative but becomes insignificant when the standard deviation of domestic credit growth and the Africa dummy are added.14 Continuing to examine the effects of the disaggregated components of total government expenditures, we tested in turn the ratios o1 government capital formation, government education expenditures, and government defense expenditures to GDP. None of these variables were robustly correlated with 14 We find that relationship between growth measured over the 1960-1989 period and the government consumption less defense and education indicator measured over the 1974-1989 period is robustly negative as in Barro (1991).

16
average growth rates in a cross-section of countries. Interestingly, given the strong positive relationship between the share of total investment in GDP and growth, the lack of a robust positive correlation between government investment and growth suggests that the returns are lower to public than private investment.

The central government surplus was used to explore the potential negative effects of government deficits. This variable enters with a positive and robust coefficient when we exclude measures of taxes. When we allowed the extreme bounds procedure to control for the mode of government financing, however, the coefficient on the surplus variable becomes insignificant when the ratio of export tax revenue to exports and the black market exchange rate premium are included as Z-variables.

The growth rate of government expenditures was also tested because Ram (1986) argues that this measure is positively related to growth. An obvious theoretical problem with this is that if government services are 4 normal good, one would expect growth in government services to parallel income growth. This measure enters with a positive but not a robust coefficient; when the average annual growth rate of exports [studied by Feder (1983)] and the change in exports as a share of GDP [studied by Romer (1989a)] are included, the coefficient on the growth rate of government consumption expenditures becomes insignificant. The high r-square of this equation (.98) suggests that one only need put in the growth rates of enough components of GDP to explain the crosssection variance in growth.

Extreme bounds tests of the ratio of export tax receipts to exports, the ratio of import tax receipts to imports, the ratio of corporate tax receipts to GDP, and the ratio of individual income tax receipts to GDP did not yield any 17
robust relationships. The coefficient on each of these variable changes sign when the Z-variables are changed.

The second part of Table 5-B presents extreme bounds t:'.. of fiscal indicators with the investment share as the dependent variable. Although many theoretical predictions of a negative relationship between the size of the government and growth are based on a negative impact of government activity on capital accumulation, none of the fiscal policy measures has a robust relationship with the investment share. Indeed, the total expenditure and government consumption measures were actually positively related to the investment share.

In this subsection, we could not find a robust cross-country relationship between a diverse collection of fiscal policy indicators and growth. Specifically, although there are econometric specifications that yield significant coefficient estimates between specific fiscal policy indicators and growth, the coefficients on these same variables become insignificant when the right-hand-side variables are slightly altered. Interestingly, standard fiscal indicators entered with the predicted sign for many econometric specifications when investment was included in the regression but these same indicators were insignificantly related to investment (or they entered with the "wrong" sign). Thus, fiscal policy - to the extent that it has an independent relationship with growth - is correlated with the "efficiency of resource allocation" as opposed to the accumulation of physical capital. These results suggest chat the interactions between fiscal policy, investment, and growth may be more complicated than can be captured in simple linear models using fairly aggregate measures of fiscal activity.

18
# c. International Trade and Price Distortions

Over two hundred years ago Adam Smith argued that openness to international markets could enhance productivity by encouraging specialization that would be unprofitable in smaller markets. Recently, this argument and other theoretical ties between trade and growth have been formalized by RiveraBatiz and Romer (1989), Grossman and Helpman (1989c), and Romer (1986, 1990b). Although theoretical discussions frequently focus on the relationship between international trade and growth, empirical examinations have typically examined the relationship between exports and growth. Consequently, we examine the robustness of export indicators used in past studies. In addition, we examine the relationship between growth and import indicators, total ‘rade indicators, and more direct estimates of trade policy and the distortion between domestic and international prices.

Three important results emerge from these extreme bound tests. First, if one substitutes imports or total trade for exports in cross-country growth or investment regressions one obtains essentially the same coefficient estimate and coefficient standard « ror.+> Thus, researchers who identify a significant correlation using an export performance measure should not associate this result with exports per se, because it could be obtained using a corresponding measure of imports or total trade. Second, the share of trade in GDP is robustly positively correlated with the share of investment in cpp. Finally, when controlling for the share of investment in GDP, we could not find a robust 15 Although this result may not be surprising, it seems to be frequently overlooked as many authors interpret their results as establishing an exclusive relationship between exports and growth.

16 The black market exchange rate premium is negatively - though not strictly robustly - correlated with the investment share.

19
independent relationship between any trade or internationa. price distortion indicator and growth. +? These three results indicate that an important part of the relationship between trade and growth is based on erhanced resource accumulation and not necessarily on the improved allocation of resources.

The major results are in Table 6. Two measures of trade performance that have received considerable attention are the export share of GDP and the growth rate of the export share. The results suggest that there is little link between che export (or import or trade) share and growth once investment is controlled for as none of the coefficients are significant. Measures of export share growth, import share growth, and total trade share growth were tried and also found to be insignificantly related to growth.

Arguing that exports have important externality effects, Feder (1983) uses export growth and export growth times the share of exports in GDP to explain Cross-country growth differentials. Table 6-A presents results showing that export growth is not robustly related to growth when government growth variables are included. Similar results hold for exports multiplied by export share or when we substitute imports or total trade for exports. Given the national accounts identity, even if we found a robust relationship, it is not clear what worthwhile inferences could be drawn.

Romer (1990a) finds a strong link between the export share and investment. We confirm this result. The extreme lower bound still indicates a significant, positive correlation between the investment and export shares. The SummersHeston data set produced similar results. Total trade was also tested and the trade-investment link was still robust. Since outliers may be especially 17 pollar’s (1990) measure of the distortion between domestic and foreign prices, however, is negatively - though not strictly robustly - correlated with long-run average growth rates.

20
important with this measure (i.e. Hong Kong and Singapore have export/GDP ratios greater than one), countries with export shares greater than .75 were dropped. The export share remained robust in explaining investment.*% The export share was also found to be robust in the growth equation when investment was dropped. These results suggest an important two-link chain between trade and growth through investment. Interestingly, however, the theoretical ties between growth and trade typically seem to run through improved resource allocation and not through a higher investment share.

We also examined more direct measures of trade policy. Leamer (1988) uses the Hecksher-Ohlin-Vanek trade model to construct measures of "openness" and "intervention." The intervention index represents the deviation between the actual and predicted pattern of trade. The openness index represents the difference between the actual and predicted level of trade (as opposed to the pattern of trade). The openness index is constructed so that a higher value represents more “openness.” As Table 6-A indicates, neither Leamer's (1988) intervention or openness indexes is robustly correlated with average long-run growth rates when the I- variables are included in the regression. Both of Leamer’s indexes, however, are robustly, positively correlated with the investment share as depicted in Table 6-B. On the one hand this is not surprising because both of Leamer's indexes are highly (e.g., 0.70) and significantly (at the 0.01 level) correlated with the share of trade in GDP, which we found to be significantly correlated with the investment share. On the other hand, these results are difficult to 18 when the extreme bounds test is done on the subset of countries for which disaggregated tax data exist (over the 1974-1989 period), the coefficient on the export share becomes insignificant when the ratio of corporate tax receipts to GDP is included.

21
interpret because the intervention and openness indexes are positively (e.g., 2.63) and significantly (at the 0.01 level) correlated with each other.9 We also examine measures of international price distortions. Dollar (1990) constructs an index of the distortion between domestic and international prices (1976-1985) from the Summers and Heston data set. This ‘real exchange rate distortion" index is significantly positively correlated with the black market exchange rate premium, but it is negatively correlated with the ratio of trade to GDP. These correlations plus the analysis by Pritchett (1990) suggest that one may want to interpret Dollar’s (1990) index as a general measure of international distortions and not a narrow measure of trade policy.

Table 6-A shows that Dollar’s index is negatively though not robustly correlated with growth when the extreme bounds procedure is allowed to choose the African and Latin American continent dummies and the average fiscal deficit to GDP ratio. If the continent dummies are excluded, however, the partial correlation between growth and the real exchange rate distortion index is negative and robust. But, if one restricts the analysis to countries for which Summers and Heston actually collect price data (the benchmark countries) and excludes those countries for which extrapolation techni:.es were employed to construct price data, the results are not robust. If one adds the government consumption less defense and education spending to GDP ratio to the I-variables, the coefficient on the distortion index becomes insignificant.

Finally, we examine the average black market exchange rate premium. Since this variable represents the interactions of many policies, it is very difficult 19 after carefully examining the relationship between different measures of trade policy, Pritchett (1990) concludes that the “alternative objective summary measures of policy outward orientation produce entirely different country rankings ...* (p. 29) This assessment has obviously dour implications for attempts to quantify the relationship between trade policy and long-run growth.

22
to interpret this variable as an indicator of any one policy. From Table 6, the black market exchange rate premium is not robustly currelated with growth or the investment share.*? [t does, however, take three additional variables to make the partial correlation of the investment share with the black market premium insignificant. Thus, this conglomerate index of policy distortions does have a generally negative relationship with the investment share.

# D. Monetary and Politica. Indicators

This section examines the empirical relationship between long-run average growth rates and measures of monetary policy and indicators of the political climate. Previous researchers have explored the relationship between measures of monetary policy and growth. Kormendi and Meguire (1985) find that the growth rate of inflation and the standard deviation of money supply shocks are negatively related to growth. Grier and Tullock (1989) find that the standard deviation of inflation is negatively related to growth using a pooled crosssection, time-series analysis. The regression results in Table 4 already suggest that these results are sensitive to the chosen econometric specification. Table 7 indicates that the average growth rate of domestic credit and the standard deviation of inflation are not robustly correlated with growth or the investment share. In fact, the standard deviation of the inflation rate is actually positively related to the investment share for a variety of econometric specification. We also tried inflation and measures of the growth rate of various monetary aggregates and found similarly fragile results. Of course, these measures of monetary policy may be simplistic. Thus, 20 similar results were found for just developing countries.

23
future theoretical and empirical work should focus on deriving empirically testable predictions regarding monetary policy and long-run growth.

The profession has also used a variety of political indicators in searching for explanations of long-run growth. Kormendi and Meguire (1985) find that greater civil liberties are positively related with growth, while Barro (1991) finds that war and revolutions decrease growth. We find (Table 7) that a revolutions and coups index (REVC) and the Gastil index of civil liberties (CIVL) are not robu-tly correlated with long-run economic growth rates, and the coefficients on these political indicators experienced sign changes when the Z- variables are changed. The revolution and coup indicators, however, is robustly, negatively correlated with the investment share if the extreme bounds procedure is not allowed to include the index of civil liberties (Table 7). Thus, not surprisingly, countries that experience a high number of revolutions and coups tend to be countries that invest less of their resources than countries with stable political environments.

# V. Sensitivity of the Sensitivity Analysis and Variable Groupings

The selection of the I-variables was based on theoretical grcounds, past empirical findings, and the ability to replicate past finding with this set of included variables. Nonetheless, we examined the robustness of our findings to alterations in the I-variables. We conducted the entire series of extreme Lounds procedures with two alternative sets of I-variables. The first set is the original I-variables plus the African and Latin American dummy variables. We add the continent dummies because a number of previous researchers have found significant effects for African and Latin American dummy variables (See Barro (1990, 1991), Romer (1989, 19908), and Grier and Tullock (1989)]. The second 24
alternative set of I-variables includes only the investment share because initial income, population growth, and education may be particularly poorly measured. The alternative choices of the I-variables did not alter the results.

In addition, we examined the importance of maximizing the difference in the G,’s rather than the difference in bounds (8,’s plus two coefficient standard errors) to isolate the source of our findings. We found that this alteration in the extreme bounds procedure did not alter the results. This suggests, as does the fact that coefficient standard errors are generally Similar between upper and lower bounds, that alterations in the 2-variables change the estimated betas more than the standard errors. Thus, when this procedure identifies a past result as fragile, this is because it finds a different coefficient estimate not a different coefficient standard error. Finally, we made some attempts to gauge the notion that policies should be interpreted more broadly than any particular measure of fiscal, trade, or monetary performance can capture. For example, the black market exchange rate premium is related to exchange rate policy, monetary policy, trade policy, and political uncertainty, thus it may be “*unfair" to include other policy indicators while examining the partial correlation between the black market premium and long-run growth rates. Of course, if a significant coefficient is then found when other policy indicators are excluded, the significance should not be interpreted as representing a correlation between growth and the black market premium per se, but between growth and a general indicator of "distortions." Consequently, we used factor analysis to construct "aggregate policy indicators* from groups of individual policy indicators. For example, we tested the robustness of various "international" distortion indexes, “domestic” 25
distortion indexes, and “uncertainty” indexes constructed from up to four individual indicators. None were robustly correlated with growth. This again indicates the difficulty of isolating the independent importance of any single policy. National policies appear to be a complex package, and future research may wish to focus on macroeconomic policy regimes as opposed to any particular policy.

# VI. Conclusion

Theoretical and empirical research on economic growth suggest a number of channels through which macroeconomic policies may affect long-run growth. We employ a version of Leamer’s extreme bounds analysis to test the robustness of a wide assortment of macroeconomic indicators in explaining cross-country growth differentials. We find that very few macroeconomic variables are robustly correlated with cross-country growth rates. We do, however, identify some correlations that are - with some qualifications - robust to slight alterations in the List of independent variables. This will hopefully provide useful information for future theoretical and empirical work.

We briefly summarize our findings:

- (1) We found a positive and robust correlation between average growth rates and the average share of investment in GDP.

- (2) We found a positive and robust correlation between the share of investment in GDP and the average share of trade in GDP over the 1960-1989 period.

- (3) In terms of the convergence hypothesis, we found a negative and robust correlation between the level of initial income and growth when investment and a measure of the initial level of human capital are included and the equation is estimated over the 1960-1989 period.

26
- (4) We found that all findings using the share of exports in GDP could be obtained almost identically using the total trade or import share. Thus, cross-country growth studies that use export indicators should not be interpreted as studying the relationship between growth and exports per se but rather as between growth and trade defined more broadly.

- (8) We found that many fiscal indicators are not robustly correlated with growth. Total government expenditures as a fraction of GDP, government consumption expenditures as a share of GDP, government consumption expenditures less defense and education spending as a fraction of GDP, the average growth rate of government expenditures, the growth rate of the share of government expenditures in GDP, and deficits were found not to be robust in explaining growth. Also, disaggregated measures of fiscal expenditures - public investment expenditures, education expenditures, and defense expenditures as a shares of GDP - were not robustly correlated with growth. Similarly, disaggregated tax revenue data - the ratio of export taxes to exports, the ratio of import taxes to imports, the ratios of corporate, individual, and social security taxes in GDP - were not robustly correlated with growth.

(6) A variety of measures of trade - average growth rates, shares of GDP, and average growth rates of shares - were found to not be robustly correlated with growth when the investment share is included. (7) Finally, a large assortment of other economic and political variables were found not to be robustly correlated with growth including measures of investment in human capital, the average black market exchange rate premium, the standard deviation of the black market premium, the average inflation rate, the standard deviation of the inflation rate, the average 27
growth rate in domestic credit, the standard deviaticn in the growth rate ef domestic credit, measures of civil liberties, coups, socialist pcomomies, mixed economies, etc. The indicator of revolutions and coups is negatively correlated with the investment share.

Using a linear framework, we have tried to distinguish partial growth correlations that seem robust from those that are fragile. We find that although there are many econometric specification in which macroeconomic policy indicators + taken individually or in groups - are significantly correlated with growth, the cross-country statistical relationship between long-run average growth rates and almost every particular macroeconomic policy indicator are fragile. These results suggest that there is not a strong independent statistical relationship between popular macroeconomic policy indicators and growth even though "policy" defined more broadly is importantly related to growth. Econometrically, this paper highlights the importance of considering alternative specifications in cross-country growth regressions. Finally, we may need to go beyond aggregate macroeconomic indicators in linear cross-country growth regressions to more fully understand economic growth.

28
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29
- -----. 1985. "Sensitivity Analyses Would Help." American Economic Review 75, (June) p.308-313.

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- Pritchett, Lant. 1990. "Measuring Outward-Orientation in LDCs: Can It Be Done?" The World Bank.

- Psacharopoulos, George and Ana-Maria Arriagada. 1986. "The Educational Attainment of the Labor Force: An International Comparison." World Bank ETD WP#38.

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-----. 1990b. "Endogenous Technological Change.* Journal of Political Economy 98 (October), $1:71-102.

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- Summers, Robert, and Alan Heston. 1988. “A New Set of International Comparisons of Real Product and Price Levels Estimates for 130 Countries, 1950-1985." Review of Income and Wealth 34 (March) p.1-25.

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31
||Cata<br>Set<br>Variables<br>and<br>Sources|
|---|---|
|AFRITA|Dummy<br>for<br>Subsaharan Africa|
|AREA|Land area<br>(200s<br>sq KM)<br>S:WBSI|
|BMP|Black Market<br>Exchange<br>Rate<br>Premium<br><br><br><br>|
||S:Picks Currency Yearbook,<br>Wood<br>(38),<br>WB updates|
|BMS|Standard<br>Dev.<br>BMP|
|ISD<br>(Lh)|Central Government Gross Capital Formation<br>S:IMFGFS|
|CIVL|Index<br>of Civil<br>Liberties<br>S:Gastil,<br>Barro|
|cTX<br>(Ll)|Ratio Central Govt Corporate<br>Income Tax Revenue<br>to GDP<br>S:IMFGFS|
|DEE<br>(L)|Ratio Central Govt Defence Expenditure to GDP<br>S: IMFGFS|
|DEF<br>(L)|Ratio Central Govt<br>Deficit<br>to GDP<br>S: IMFGFS|
|RERD|Real Exchange Rate Distortion<br>S:Dollar<br>(90),<br>Calculated with<br>SH|
|RERDB|RERD<br>for<br>SH benchmark countries|
|EDE<br>(L)|Ratio Government Educational Expenditures to GDP<br>S:IMFGFS|
|SBC|Growth<br>rate<br>of Domestic<br>Credit<br>S:IMFIFS|
|SGCFD|Government Capital formation deflated with SH prices<br>S:Barro<br>fromIMFGFS,<br>Summers-Heston<br>(88)<br>[SH]|
|GG|<br><br><br><br>Growth of government consumption expenditures<br>S:WBNA|
|GM|Growth of<br>imports<br>S:WBNA|
|SOV|Government Consumption<br>share<br>of GDP<br>S:WBNA|
|SOVYK<br>(L)|Government Consumption<br>less Defense<br>and Education<br>share<br>of<br>GDP|
||GOV<br>-<br>DIE<br>-<br>EDE|
|SPO|Growth of Population<br>S:WBSI|
|SR|Growth of<br>real per capita GDP<br>S:SH|
|SSG|Growth<br>of<br>the<br>Share<br>of Government<br>S:Growth of Share of Government Consumption|
|SX|Growth of Exports<br>S:WBNA|
|SYP|Growth of per capita GDP<br>S: WENA|
|HSGVX|Share real Government Consumption expenditures minus defense<br>education expenditures<br>S:Barro<br>fromSH,IMFGFS|
|INV|<br>Investment<br>share<br>of GDP<br>S:WBNA|
|ITX<br>(L)|Share of central govt individual income tax to GDP<br>|
||S: IMFGFS|
|LAAM|Dummyvariable<br>forLatinAmerica|

32
|LEAM1|Measure<br>of overall<br>trade openness<br>S:Leamer<br>(88)|
|---|---|
|LEAM2|Measure of overall<br>trade<br>intervention<br>S:Leamer<br>(88)|
|LIT|Literacy<br>rate<br>in<br>1960<br>S:WBSI|
|MIX|Dummy variable<br>for mixed government<br>S:Gastil,<br>Barro|
|MSG|Growth of<br>import<br>share<br>S:WBNA|
|MTX<br>(L)|Ratio of<br>import taxes<br>to imports<br>S:IMFGFS, IMFIFS|
|MP|Measure of openness based on import penetration<br>S:Residuals of regres.<br>of M on RGDP60(&sqr),AREA,<br>POP|
|M|Import<br>share of GDP<br>S:WBNA|
|OECD|Dummy for OECD|
|OIL|Dummy for OPEC|
|PI|Average inflation of GDP deflator<br>S:WBNA|
|POP70|Population in 1970<br>$:SH|
|PRI|Primary enrollment<br>rate<br>1960|
||$:Barro<br>from UNESCO,ILO|
|PRJ|Primary enrollment<br>rate 1970<br>$:Barro<br>from UNESCO,ILO|
|REVC|Number of revolution and coups per year<br>S:Barro<br>from Banks|
|RGDPxx|Real GDP per capita<br>in 19xx<br>$:SH|
|SCOUT|Dummy for outward orientation based<br>S:Syrquin and Chenery<br>(88)|
|SEC|Secondary enrollment<br>rate<br>1960<br>S:Barro<br>from UNESCO, ILO|
|SED|Secondary enrollment<br>rate<br>1970<br>$:Barro<br>from UNESCO,ILO|
|SGOV|Real Government Consumption Share<br>$:SH|
|SINV|Real<br>Investment<br>Share<br>S:SH|
|soc|Dummy<br>for socialist economy<br>S:Barro<br>from Gastil|
|SST<br>(L)|Ratio Social Security taxes<br>to GDP<br>S: IMFGFS|
|STDD|Standard Deviation of GDC<br>(growth domestic<br>credit)<br>S:IMFIFS|
|STDI|Standard Deviation of<br>PI<br>(inflation)<br>S:WBNA|
|TAX<br>(L)|Ratio central government tax revenue to GDP<br>S$:IMFGFS|
|TEX<br>(L)|<br>Ratio total government expenditure to GDP<br>S: IMFGFS|
|TRD|Ratio total<br>trade<br>(X+M)<br>to GDP<br>|
||S:WBNA|
|XSG|GrowthofexportshareofGDP|

33
S:WBNA XTX (L) Ratio central govt export tax revenue to exports S: IMFGFS x Export share of GDP S:WBNA YRSCH Average years of schooling of labor force #1980 S:Psacharopoulos and Arriagada (86) WBNA World Bank National Accounts Database WBSI World Bank Social Indicators IMF IFS International Monetary Fund--International Finance Statistics IMF GFS International Monetary Fund--Government Finance Statistics (L) indicates only available for 1974-1989 period 34
# Country List 119 Country Sample

|:|AFG|Afghanistan|40|HTI|Haiti|80|PRY|Paraguay|
|---|---|---|---|---|---|---|---|---|
|2|D2A|Algeria|41|HND|Honduras|81|PER|Peru|
|3|AGO|Angola|42|HKG|Hong Kong|82|PHL|Philippine|
|4|ARG|Argentina|43|ISL|Iceland|83|PRT|Portugal|
|5|<br>AUS|<br> Australia|44|IND|India|84|RWA|Rwanda|
|6|AUT|Austria|45|IDN|Indonesia|§5|SAU|Saudi Arab|
|?|BGD|Bangladesh|46|IRN|Tran|86|SEN|Senegal|
|8|BRB|Barbados|4?|TRQ|Iraq|87|SLE|SierraLeo|
|9|<br>BEL|<br> Belgium|48|IRL|Ireland|88|SGP|<br>Singapore|
|10|BOL|Bolivia|49|ISR|Israel|89|SOM|Somalia|
|ll|BWA|Botswana|30|ITA|Italy|90|ZAF|SouthAfri|
|12.|<br>~=BRA|<br> Brazil|5.|JAM|Jamaica|91|ESP|<br>Spain|
|13.|BDI|Burundi|52|JAP|Japan|92|LKA|SriLanka|
|14|<br>CMR|<br> Cameroon|53|JOR|Jordan|93|SDN|<br>Sudan|
|13|CAN|Canada|54|KEN|Kenya|94|Sw2|Swaziland|
|16|CAF|Cent. Afr.<br>Rep|55|KOR|Korea|95|SWE|Sweden|
|17|TCD|Chad|56|KWT|Kuwait|96|CHE|Switzerlan|
|18|CHL|Chile|57|LSsO|Lesotho|9?|SYR|Syria|
|19|COL|Colombia|58|LBR|Liberia|98|OAN|Taiwan|
|20|COG|Congo|59|LUX|Luxembourg|99|TZA|Tanzania|
|21|CRI|CostaRica|60|MDG|Madagascar|100|=THA|Thailand|
|22.|<br>«CIV|<br> Cote D'Ivoire|61|MwI|Malawi|101|TGO|Togo|
|23|CYP|Cyprus|62|MYS|Malaysia|102|TTO|Trin.<br>and|
|24|DEN|Denmark|63|MLI|Mali|103|TUN|Tunisia|
|25|DOM|DominicanRep.|64|MLT|Malta|104|TUR|Turkey|
|26|<br>ECU|<br> Ecuador|6S|MRT|Mauritania|105|UGA|Uganda|
|27|EGY|Egypt|66|MUS|Mauritius|106|GBR|GreatBrit|
|28|<br>SLV|<br> El<br>Salvador|€7|MEX|Mexico|107|USA|<br>United<br>Sta|
|29|ETH|Ethiopia|68|MAR|Morocco|108|URY|Uruguay|
|30|<br>FJI|<br> Fiji|69|MOZ|Mozambique|109|VEN|Venezuela|
|31|FIN|Finland|70|NLD|Netherland|110|YEM|Yemen|
|32|FRA|France|71|=N2L|NewZealan|112|ZAR|Zaire|
|33.|<br>GAB|<br>Gabon|<br>72,|<br>=0NIC|<br>Nicaragua|112|ZMB|zambia|
|34|<br>GMB|<br>Gambia|73,|«~NER|Niger|113|2WE|Zimbabwe|
|35.|<br>DEU|<br>Germany|74|NGA|Nigeria|114|~=BUR|Burma|
|36|<br>GHA|<br>Ghana|75|NOR|Norway|115|<br>GUY|Guyana|
|37|<br>GRC|<br> Greece|76|OMN|Oman|116|BEN|Benin|
|38|GTM|Guatemala|77|PAK|Pakistan|117|HVO|Burkina<br>Fa|
|39|GNB|Guinea-Bissau|78|PAN|Panama|118|NPL|Nepal|
||||79|~=PNG|Pap.NewG|119|SUR|Suriname|

35
TABLE 1

# Sensitivity Results for Basic Variables

Non-Oil Countries Regressions on Growth of Per Capita Income 1960-1989

||Beta|Stan.Err.<br>T-Stat|N|Re|Cther Incl. Vars.*|
|---|---|---|---|---|---|
|Investment<br>|Share<br>of <br>|GDP<br>(INV)<br><br>||||
|High??|19.555|2.845<br>6.87|89|56|STDI, BMP, GOV|
|Low|12.762|3.070<br>4.16|97|46|XSG,LIT,REVC|
|RealGDPp|ercapita|in1960<br>(RGDP60)||||
|<br>High|<br>-.299|<br><br>.138<br>2.16|97|-50|LAAM,XSG,GOV|
|Low|-.541|142<br>3.82|78|- 60|<br>GDC, BMP,LIT|
|Growth<br>in<br>|Population<br>|(GPO)<br><br>||||
|<br>High|<br>131|221<br>59|100|.84|x22,LAAM,PRI|
|Low|-.546|234<br>2.33|90|-56|<br>X,BMP,MIX|
|Secondary|School Enr|ollment Ratio<br>in 1960<br>(SEC)||||
|High|3.708|1.222<br>3.04|84|-55|X,GOV,GDC|
|Low|.572|1.208<br>47<br>Regression on Investment<br>S|91<br>hare|64<br> 1960-19|BMP,LAAM,AFRICA<br>89|
||Beta|Stan.Err.<br>T-Stat|N|rR?|Other Incl. Vars.+*|
|Real GDP p|er capita|in 1960<br>(RGDP60)<br>1960-1989||||
|High|.007|003<br>2.23|85|.07|GbdC,SOC,STDI|
|Low|-.010|.005<br>2.15|89|625|CIVL, BMP, GOV|
|Growth<br>in|Population|(GPO)<br>1960-1989||||
|<br>High|<br>006|007<br>.82|93|25|CIVL,LAAM,<br>BMP|
|Low|-.016|006<br>2.65|86|09|SOC ,STDI,GDCc|
|Secondary|School Enr|ollment Ratio<br>in 1960<br>(SEC)|1960|-1989||
|High|059|.042<br>1.39|84|-15|CIVL,GDC,GOV|
|Low|-.003|039<br>+07|90|21|BMP,GOV,CIVL|

* INV, RGDP60. GPO, and SEC included in all Growth regressions ** no additional variables included in the Investment regressions

2lthe high beta is the coefficient from regression which obtains the highest beta bound in the extreme bounds analysis. The low beta is from the regression which obtains the lowest beta bound. 22the underlined variables show additional variables which make the coefricient of interest insignificant or change sign. In this case the regression of GYP on INV,RGDP,SEC,GPO, and X yields an insignificant coefficient on GP).

36
TABLE 2

# Cross-Country Averages: 1960-89 (excluding oil countries)

||Fastgrowers|Slowgrowers|t-~-stat|
|---|---|---|---|
|Share<br>of<br>investment<br>in GDP|0.23|0.17|5.18|
|Secondary school enrollment<br>rate<br>(1960)|0.30|0.10|5.46|
|Primary<br>school enrollment<br>rate<br>(1960)|0.90|0.54|6.10|
|Government consumption/<br>GDP|0.16|0.12|3.26|
|Inflation<br>rate|12.34|31.13|-1.74|
|Black market exchange<br>rate premium|13.57|57.15|-3.79|
|Shareofexports<br>toGDP|0.32|0.23|2.31|

Note: Mean growth rate = 1.92 Fastgrowers: Greater than the mean growth rate. (n = 56) Slowgrowers: Less than the mean growth rate. (n = §3)

## TABLE 3

## CROSS-COUNTRY CORRELATIONS

||GYP|INV|RES**|x|GOV|PI|BMP|REVC|CIVL|
|---|---|---|---|---|---|---|---|---|---|
|GYP|1.00|0.59*|0.73*|0.32*|0.09|-0.16|-0.38*|-0.36*|-O0.41*|
|INV||1.00|0.00|0.50*|0.28*|-0.04|-0.43*|-0.40*|-2,.33|
|RES**|||1.00|0.09|-9.13|-0.17|-0.13|-0.16|-D.ls|
|X||||1.00|0.15|~0.15|-0.22*|~0.34*|-l 238|
|GOV|||||1.00|-0.16|-0.19|-0.29*|-3.19|
|PI||||||1.00|0.18|0.46*|2.92|
|BMP|||||||1.00|0.47*|1.38*|
|REVC||||||||1.00|9.508|
|CIVL|||||||||1.009|

**RES The residual of the OLS regression of average per capita growth (GYP) on the I-Variables: initial income (RGDP60), population growth (GPO), secondard school enrollment rate (SEC), and the investment share (INV).

* significantly different from zero at the 0.05 significance level.

37
TABLE 4

# Cross-Country Growth Regressions Dependent Variable is Average Annual Growth Rate of Per Capita Income

|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|
|Period<br>1960-89|1960-85|1960-89|1960-85|1960-85|
|Data Set<br>WB|SH|WB|WB|SH|
|Independent Variables<br><br>|||||
|Constant<br>-.83|2.01|. 86|47|2.95|
|(.85)|(.83)|(.89)|(1.18)|(1.16)|
|Initial GDP/Capita<br>-.35¢#<br><br>|-.69#<br>|-.304<br>|-. 408<br>|-.564<br>|
|RGDP60<br>(.14)|(.12)|(.11)|(.13)|(.13)|
|Investment Sh.<br>17.498|9.314|16.778|13.4468|10.32¢|
|INV<br>(2.68)|(2.08)|(2.62)|(3.13)|(2.50)|
|Popul.Growth<br>-.38|.08|-.53#|-.15|-.02|
|<br><br>GPO<br>(.22)|(.18)|(.18)|(.19)|(.19)|
|Secondary Enroll.<br>3.174|1.21||-63|.95|
|SEC<br>(1.29)|(1.17)||(1.26)|(1.25)|
|Primary Enroll.|1.794||91|1.92|
|PRI|(.58)||(.73)|(.79)|
|Government Share|-6.374||-.59|-6.756|
|GOV|(2.03)||(3.73)|(2.39)|
|Growth Gov Share||-.08|||
|GSG||(.06)|||
|Socialist Econ|-.25||-.21|-.15|
|$0c|(.38)||(.45)|(.44)|
|Revolution&Coup|-1.76#||-.86|-1.788|
|<br>REVC|(.52)||(.62)|(.60)|
|AfricaDummy|1.2464||-1.36#|-1.82¢|
|<br>AFRICA<br>|(.37)<br>||(.48)<br>|(.46)<br>|
|Latin America Dummy|-1.18#||-1.348|-1.824|
|LAAM<br><br>|(.33)||(.38)<br>|(.37)<br>|
|Growth of Dom.<br>Credit||0194|.013|208|
|GDC||(.009)|(.008)|(.007}3|
|Stan.<br>Dev.<br>Dom.<br>Cred.||-.0094|-.0064|-.003|
|STDD||(.003)|(.003)|(.0C3)|
|ExportShareGrowth||090|.023|-.027|
|<br>XSG||(.052)|(.047)|(.046)|
|CivilLiberties||-.22|.O1|.15|
|<br>CIVL||(.11)|(.13)|(.13)|
|Number of Obs.<br>101<br><br>|103<br>|83<br>|84<br>|84<br>|
|Re<br>46|68|61|.67|73|

# f=significant at 52, Standard errors in parentheses

Regressions 1,3,& 4 use World Bank data for growth and national accounts while numbers 2 & 5 use Summers-Heston (1988) data 38
TABLE S-A Sensitivity Results for Fiscal Variables Regressions on Growth of Per Capita Income

|Beta|Stan.Err.|T-Stat<br>N|Re|Other Incl. Vars.*|
|---|---|---|---|---|
|Government Consumption|Share<br>(GOV)|1960-1989|||
|High<br>-1.621|3.355|48<br>78|-61|BMP,MIX,AFRICA|
|Low<br>~7.189|3.611|1.99<br>78|-59|LAAM, BMP, GDC|
|Government Consumption <br><br>|Share minus <br>|Def.<br>and Educ. Ex<br><br>|p<br>(GOVX<br>|)<br>1974-'989<br>|
|High<br>- 8.648|6.741|1.28<br>63|.62|STDD,AFRICA,LAAM?>|
|Low<br>21.024|7.482|2.81<br>59|-58|BMP,GDC,PI|
|Total Government Expen|diture Share|(TEX)<br>1974-1989|||
|High<br>- 2.750|2.055|1.34<br>67|.54|X, BMP, GDC|
|Low<br>-<br>7.784|2.135|3.65<br>82|-51|X,MIX, LAAM|
|CentralGovernmentSur|plus/Deficit|asShare<br>(DEF)|1974-19|89|
|<br>High<br>22.495|<br>4.863|<br><br>4.62<br>78|52|AFRICA,X,MIX|
|Low<br>13.044<br><br>|5.584<br>|2.34<br>65<br><br>|37<br>|STDD, BMP, REVC<br>|
|Low<br>5.297|6.181|86<br>53|.58|XTX,BMP,MTX|
|(with tax receipt v|ariables)||||
|Growth of Government C|onsumption Sp|ending<br>(GG)<br>1960-|1989||
|High<br>2271|.054|5.01<br>89|.66|REVC, BMP, GOV|
|Low(exXG)<br>106|052|2.03<br>85|64|GDC,STDD,X|
|Low(inXG)<br>.017|.012|1.41<br>95|.98|XG,XSG,GOV|

* INV, RGDP, GPO, and SEC included in all Growth regressions. Underlined variables show additions which make coefficient insig. or change signs

23 I€ one excludes the continent dummies, the extreme bound procedure chooses X, BMS, STDD as 2-variables. This yields a coefficient on GOVX of -13.5 and a t-statistics of 1.81, which is not significant at the 0.05 level.

39
TABLE 5-8 Sensitivity Results for Fiscal Variables Regressions on Investment Share

||Beta|Stan.Err.|T-Stat|N|Re|Other Incl. Vars.**|
|---|---|---|---|---|---|---|
|Sovernment|Consumption|Share<br>(GOV)|1960-1989||||
|High|.309|-114|2.71|101|.09|LAAM,MIX,STDI|
|Low|.083|.110|.76|78|.36|X, BMP ,GDC|
|Government<br>|Consumption<br>|Share<br>(GOVX<br>|)<br>1974-198<br>|9<br>|||
|<br>High|<br>-.004|<br><br>.175|.03|75|.09|AFRICA, LAAM, CIVL24|
|Low|~.616|.203|3.03|59|18|BMP,GDC,CIVL|
|Total Gover|nment Expend|iture Share|(TEX)<br>1974|-1989|||
|High|-119|-056|2.14|86|.08|CIVL,SOC,MIX|
|Low|039|-052|.74|85|-20|X, AFRICA, LAAM|
|CentralGov|ernmentSurp|lus/Deficit|asShare|(DEF)|1974-19|89|
|<br>High|<br>.050|<br>-165|<br>31|74|.08|AFRICA,MIX,<br>BMP|
|Low|-.067|-191|.35|65|.04|STDI,BMP,GDC|

** no additional variables included in the Investment regressions

Underlined variables show additions which make coefficient insignificant or change signs 24 T£ one does not allow the extreme bounds procedure to choose the continent dummies, the coefficient on GOVX becomes insignificant with the inclusion of STDI.

40
TABLE 6-A

# Sensitivity Results for Trade Variables

Regressions on Growth of Per Capita Income

|Beta|Stan.Err.|T-Stat|N|R?|Other Incl. Vars.*|
|---|---|---|---|---|---|
|Exports as percentage o|f CDP<br>(X)|1960-1989||||
|High<br>1.3549|.7782|1.74|97|.55|GOv,AFRICA|
|Low<br>-<br>.2641|9659|227|79|.59|PI,GDC, BMP|
|Imports as percentage o|f GDP<br>(M)|1960-1989|.|||
|High<br>1.6395|8982|1.83|c§|.53|GOV,AFRICA|
|Low<br>-1.8000|1.0722|1.68|78|.58|PI,GDC, BMP|
|Growth of Exports<br>(avg.|annual)|(GX)<br>1960-19|89|||
|High<br>-2517|-0349|7.21|96|71|AREA, REVC, GOV<br>|
|Low<br>(exGG)<br>.2141|-0381|§.62|78|71|GOV,GDC,<br>BMP|
|Low (inGG)<br>.0085|-9103|-83|86|-98|GSG,GG,STDD|
|Growth of Imports<br>(avg.<br><br>|annual)<br>|(GM)<br>1960-19<br>|89<br>|||
|High<br>2845|0424|6.71|95|69|GOV, LAAM,GSG|
|Low<br>(exGG)<br>.1940|.0454|4.27|86|71|STDD,LAAM,AFRICA|
|Low (inGG)<br>.0137|0184|.74|87|97|GSG,GG, BMP|
|Openness measure based|on factor|adjusted tra|de-Leam|er<br>(LEA|M1)<br>1974-1989|
|High<br>- 8478|1.5798|54|49|. 58|AFRICA, LAAM, PT|
|Low<br>-3.3110|1.4062|2.35|38|74|AFRICA, LAAM, SOVX|
|Trade<br>Distortion based|on H-O dev|iations-Leame|r<br>(LEA|M2)<br>197|4-1989|
|High<br>~5659|1.8665|.30|49|58|AFRICA,LAAM,<br>PI|
|Low<br>~5.8597|2.1404|2.74|38|.63|AFRICA,BMS,CEE|
|Black Market Exchange R<br><br>|ate Premiu<br>|m<br>(BMP)<br>1960<br>|-1989<br>|||
|High<br>-.0014|0026|.53|31|.58|PI,SOC,MIX|
|Low<br>-.0053|0030|1.78|79|.39|LAAM, REVC,S2C|
|Real Exchange Rate Dist|ortion-Dol|lar<br>(RERD)<br>1|974-198|9||
|High(wdum)<br>-.0059|.0057|1.04|81|- 66|AFRICA, LAAM, CEF|
|High<br>-.0167|.0069|2.42|64|.55|GOVX,PI,STOD|
|Low<br>-.0227|0071|3.18|74|44|PI,STDI,DEE|

*INV, RGDP, GPO, and SEC included in all Growth regressions Underlined variables show additions which make coefficient insig. or change signs

41
TABLE 6-B

# Sensitivity Resules for Trade Variables

Regressions on Investment Share

|Beta|Stan.Err.<br>T-Stat<br>N|R?|Other Incl. Vars.**|
|---|---|---|---|
|Exportsaspercentage|ofGDP<br>(X)|||
|<br>High<br>+1600|<br><br>.0298<br>§.31<br>86|+26|GOC,STOI|
|Low<br>-0952|02446<br>3.90<br>100|-35|GOV, REVC, STDI|
|Opennessmeasurebased|onfactoradjustedtrade-Leam|er(LEA|M1)1974-1989|
|<br>High<br>-152|<br>.953<br>2.89<br>47|<br>.18|<br>LAAM,STDI,GDC|
|Low<br>-104|-049<br>2.11<br>4?|.30|<br>AFRICA,<br>REVC , GDC|
|TradeDistortionbased|onH-Odeviations-Leamer<br>(LEA|M2)<br>19|74-1989|
|<br>High<br>-240|<br><br>044<br>5.44<br>47|-42|LAAM,GDC,CIVL|
|Low<br>1177|.038<br>4.60<br>49|<br>-55|AFRICA, CIVL, REVC|
|BlackMarketExchange|RatePremium(BMP)<br>1960-1989|||
|<br>High<br>-~.00020|<br><br>0001<br>1.58<br>78|-19|GDC,GOV,REVC|
|<br>Low<br>- .00042|-0001<br>3.98<br>81|-21|<br>LAAM,MIX, STDD|
|RealExchangeRateDis|tortion-Dollar<br>(RERD)<br>1974-198|9||
|<br>High<br>-.0000|0002<br>O01<br>64|25|AFRICA,GDC<br>,GOVX|
|<br>Low<br>-.0005|0002<br>2.85<br>91|-i1|<br>LAAM,MIX,GDC|

** no additional variables included in the Investment regressions

Underlined variables show additions which make coefficient insig. or change signs 42
TABLE 7 Sensitivity Results for Monetary, Exchange Rate, and Political Variables Regressions on Growth of Per Capita Income 1960-1989

|Beta|Stan.Err.|T-Stat|N|Re|Other Incl. Vars.*|
|---|---|---|---|---|---|
|Srowth<br>of Domestic Cre|dit<br>(GDC)|||||
|High<br>0011|-0061|.18|85|.66|LAAM,MIX,AFRICA|
|Low<br>-.0054|-0065|.83|85|<br>.5?|X,50C|
|Standard Deviation of|the Rate<br>of|Inflation|(STDI)|||
|High<br>-.00027|-0007|-37|101|-49|LAAM,REVCMIX|
|Low<br>-~.00124|0007|1.84|85|59|<br>GDC,CIVL,REVC|
|RevolutionandCoups|(REVC)|||||
|<br>High<br>-6391|-7699|-83|79|-60|GDC,BMP,PI|
|Low<br>-1.4701|<br>-6371|2.31|101|-52|<br>AFRICA|
|CivilLiberties<br>(high|er=lessCIVL)|||||
|<br><br>High<br>1043|<br>1458|.72|79|-63|GDC,BMP,AFRICA|
|Low<br>~.3491|-1268|2.75|101|52|MIX,LAAM|

*INV, RGDP, GPO, and SEC included in all Growth regressions

Sensitivity Results for Monetary, Exchange Rate, and Political Variables Regression on Investment Share 1960-1989

|Beta|Stan.Err.|T-Stat|N|rR?<br>=|Other Incl. Vars.|
|---|---|---|---|---|---|
|Growth of Domestic Cr|edit<br>(GDC)|1960-1989||||
|High<br>-00041|0003|1.54|85|18|CIVL,REVC,SOC|
|Low<br>-00003|0003|-10|86|.03|SOC,MIX|
|Standard Deviation of|the Rate of|Inflation|(STDI)|1960-198|9|
|High<br>.00006|00002|2.357|92|-32|REVC<br>,LAAM,BMP|
|Low<br>-.00001|-00002|355|105|-08|<br>AFRICA,<br>SOC,MIX|
|RevolutionandCoups<br><br>|(REVC)<br>1960-<br>|1989<br>||||
|<br>High<br>~.0347|0228|1.52|88|234|X,CIVL,$TDD*>|
|Low<br>~ .0880|-0241|3.65|85|-17|<br>SOC,MIX,GDC|
|CivilLiberties<br>(high|erzlessCIVL)|1960-198|9|||
|<br><br>High<br>0029|<br>-0061|70|85|27|REVC,AFRICA,<br>GDC|
|Low<br>-.0108|0032|3.38|88|-15|<br><br>SOC,MIX,STDD|

25 Tf the extreme bounds procedure is not allowed to choose CIVL, the coefficient on REVC is robustly negative even when the procedure is allowed to choose continent dummies.

43
# PREWorkingPaper Series

||Iie|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS596_|The Mexican Sugar Industry:<br>Problems and Prospects|Brent Borrell|February 1991|P. Kokila<br>33716|
|WPS597_|Rent Sharing in ‘he Multi-Fibre<br>Arrangement: Theory and Evidence<br>from U.S. Apparel Importsfrom<br>Hong Kong|Refik Erzan<br>Kala Krishna<br>LingHuiTan|February 1991|G. llogon<br>33732|
|WPS598|Africa Region Population Projections:<br>1990-91 Edition|Patience W. Stephens<br>Eduard Bos<br>MyT. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS599_|Asia Region Population Projections:<br>1990-91 Edition|EduardBos<br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS600|Latin America and the Caribbean<br>Region Population Projections:<br>1990-91 Edition|My T. Vu<br>Eduard bos<br>Patience W. Stephens<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS601_|Europe, Middle East, and North<br>Africa Region Population Projections:<br>1990-91 Edition|Eduard Bos<br>Patience W. Stephens<br>My T. Vu<br>Rodolfo A. Bulatao|February 1991|O. Nadora<br>31091|
|WPS602_|Firm Output Adjustment to Trade<br>Liberalization: Theory with<br>Application to the Moroccan<br>Experience|Mark A. Dutz|February 1991|S. Fallon<br>38009|
|WPS603_|The Role of Officially Supported<br>Export Credits in Sub-Saharan<br>Africa's External Financing|Asli Demirgiig-Kunt<br>Refik Erzan|February 1991|G. llogon<br>33732|
|WPS604|Foreign Trade and Its Relation to<br>Competition and Productivity in<br>Turkish Industry|Faezeh Foroutan|February 1991|S. Faiion<br>37942|
|WPS605|Overview of Contractual Savings<br>Savings Institutions|Dimitri Vittas<br>Michael Skully|March 1991|W. Pitayato-<br>nakarn<br>37668|
|WPS606|Adjustment Policies and Investment<br>Performance in Developing<br>Countries: Thoory, Country Experienc<br>andPolicyImplications|Luis Serven<br>Andrés Solimano<br>es,|March 1991|E. Khine<br>39361|
# PRE Working P Seri

||Iie|Author|Date|Contact<br>foLpaper|
|---|---|---|---|---|
|WPS607|Abolishing Green Rates: The Effects<br>on Cereals, Sugar, and Oilseeds<br>in WestGermany|Donald F. Larson<br>Simon Glance<br>Brent Borrell<br>Merlinda Ingco<br>Jonathan Coleman|March 1931|D. Gustafson<br>33714|
|WPS608|Cross-Country StudiesofGrowth<br>and Policy: Methodological,<br>Conceptual, and Statistical Problems|Ross Levine<br>David Reneit|March 1991|CECMG<br>39175|
|WPS609_|A Sensitivity Analysis of Cross-|Ross Levine|March 1991|CECMG|
||CountryGrowthRegressions|DavidRenelt||39175|