metadata: authors: 0: author: Levine, Ross
1: author: Zervos, Sara
docna: 0: docna: Looking at the facts : what we know about
            policy and growth from cross-country analysis
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,Inflation,Public Finance Decentralization and
            Poverty Reduction,Crime and Society
teratopic: Poverty Reduction
abstracts: cdata!: What has the profession learned from
            cross-country regressions about the links between long-run
            growth and indicators of fiscal, monetary, trade, financial,
            and exchange-rate policies?  The authors find that:
            indicators of financial development are strongly associated
            with long-run growth; other individual policy indicators are
            only weakly linked to growth; and it is particularly
            difficult to find a consistent relationship between
            inflation and long-run growth.  For example, the inclusion
            or exclusion of one or two countries (Nicaragua and Uganda)
            out of more than 100 countries in the sample can lead to
            reaching three quite different conclusions:  (1) that only
            very high inflation is bad for growth; (2) that very high
            inflation in itself is not bad for growth, but small
            increases in inflation in moderate-inflation countries slow
            growth; or (3) that inflation is unrelated to growth.  The
            connections between policy indicators and growth are quite
            sensitive to slight alterations in the right-hand-side
            variables and to small changes in the sample of countries.
            And the daunting array of methodological problems limiting
            the ability to interpret cross-country regressions implies
            that, at best, they suggest interesting empirical
            regularities.  Cross-country regressions should not be used
            to predict by how much long-run growth will change when
            policies change. But beliefs about policy and growth that
            are not supported by cross-country evidence will tend to be
            viewed skeptically.  So, future work on the policy-growth
            nexus should integrate broad cross-country analyses with
            country case studies and investigations of specific firms.
repnme: repnme: Looking at the facts : what we know about
            policy and growth from cross-country analysis
display_title: Looking at the facts : what we know
            about policy and growth from cross-country analysis
listing_relative_url: /research/1993/03/699100/looking-facts-know-policy-growth-cross-country-analysis
topicv3: Economic Growth m1326241 471,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Fiscal Policy m1327144 445,Black Market m1439229 BLA-3388
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Policy Research Department
no_of_pages: 51
model_extractions: <!-- Start of picture text --> Policy Research|<br>Fnanco& andPvoScr<br>Development<br>WPS 1115<br><!-- End of picture text -->

- This paper a product of the Finance and Private Sector Development Division, Policy Research Departnent-ispartofalargereffortinthedeparunentto understandthe determinantsoflong-rungrowth. The study was funded by the Bank's Research Support Budget under research project "How Do National Policies Affect Long-Run Growth?" (RPO 676-66). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Daniele Evans, room N9-089, extension 38526 (March 1993,45 pages).

What has the profession leamred from crosscountry regressions about the links between long-run growth and indicators of fiscal, monetary, trade, financial, and exchange-rate policies?

Levine and Zervos find that: . Indicators of financial development are strongly associated with long-run growth.

* Other individual policy indicators are only weakly linkled to growth.

= It is particularly difficult to find a consistent relawionship between inflation and long-run grr **Avth.** For example, the inclusion or exclusion of one or two countries (Nicaragua and Uganda) out of more than 100 countries in the sample can lead to reach three quite different conclusions: (1) that only very high inflation is bad for growth, (2) **that** very high inflation in itself is not bad for growth, but small increases in inflation in moderate-inflation countries slow growth, or (3) that inflation is unrelated to growth The connections between policy indicators and growth are quite sensitive to slight alterations in the right-hand-side variables and to Ismall changes in the sample of countries.

And the daunting array of methodological problems limiting our ability to Interpret crosscountry regressions implies that, _at best,_ they suggest interesting empirical regularities. Crosscountry regressions should not be used to predict by how much long-run growth will change when policies change. But beliefs about policy and growth that are not supported by cross-country evidence will tend to be viewed skeptically. So, future worik on the policy-growth nexus siiould integrate broad cross-country analyses with country case studies and investigations of specific finns.

The Policy ResearchWorklng Paper Series disseminates the fidings of work under way in theBanlc An objective of the series is to get these findings out qciickly. even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissenination Center
# Looking at the Facts:

What We Know about Policy and Growth from Cross-Couutry Analysis Ross Levine Policy and Research Department The World Basnk Sara Zervos Department of Economics University of Rochester
# Table of Conteuts

I. Introduction **.....**

|1.Why Use Croes Country Regresl onso....o...o.oo.oo.ooo..ooI<br>2. Back to the "factsn**. ...**<br>**.. . ........**<br>**............**<br>**...**<br>**.......**<br>**3**|
|---|
|II.Extreme Bounds Analysis**..................... o............o.......**<br>5|
|loMotivationoooo..o<br>oo*<br>*o<br>oo<br>oo.oo.........eeo....o.......o......<br>2. Techniqueoo*oo<br>ooooo.o............oe....oo....oo..o..o..o..<br>6<br>3.Fiscal<br>policyindicatorso........o.<br>**.........................**<br>9<br>4.Monetary policy indicatorso....<br>**...**<br>**o.....**<br>**...........**<br>**o o **<br>o<br>**ool.....**11<br>5.International distortion indexes........................12<br>6. Financial policy indicators .............................<br>13|
|III. Trying To Find Facts: An Example: Inflation.................14|
|1. Setup ...............<br>o...ooooo ......<br>oooo.......o..o.o.o....14<br>2. Initial findings<br>_........................................_<br>_15_<br>3. Outliers: Inflation greater than 80 percent.............16<br>4.<br>Outliers: Inflation greater than 40 percentooo.***o..*.19<br>S.<br>Resolution<br>oo.o.oooooooooe.o..o.o.ooo....o.*.**..oo.*..<br>***..l9<br>6.<br>_<br>eteris paribus...............**.**...<br>o.o...o.......o.20|
|IV.Broad Policy Indexes<br>and Regim<br>es...........................21|
|1. Conglomerate indexeso.....................o.ooo.....o..o22<br>2. Results.........o....o<br>..<br>o.o.o..... oo......o.o...<br>oo..<br>23|
|V.<br>Conclusionso..ootoe**ooosooosooo9o24|
|Endnotes<br>...<br>oo.o.oooooooo*oooeooooo26<br>R-aferences...,,*oo*.ooooooo*ooo..*29<br>Tables.<br>o.ooo...ooooo*o*oo*eooooo33<br>Chartso-.s--<br>... o...<br>o.oo.*.**oooooo*o*<br>o<br>... ----<br>o41|

* We received helpful comments from Maria Carkovic, Rudiger Dornbusch, Stanley Fischer, Andrew Sheng, Mary Shirley, and David Zervoe. Support vas received from the World Bank research project "How Do National Policies Affect Long-RunGrowth? "
# I. INTRODUCTION

Economists have been seeking to comprehend why some countries are rich and others poor for well over 200 years. A better understanding of the national policies associated with long-run growth would both contribute to our ability to explain cross country differences in per capita incomes and provide a basis for making policy recommendations that could lead to improvements in human welfare. Recently, economists have used cross country regressions to search for empirical linkages between long-run growth and indicators of national policies (e.g., Roger Kormendi and Philip Meguire, 1985; Robert J. Barro, 1991). The large cross country growth literature has identified various fiscal, monetary, trade, exchange rate, and financial policy indicators that are significantly correlated with long-run growth. Yet, Ross Levine and David Renelt (1992) show that many of these findings are fragile to small alterations in the conditioning information set. That is, small changes in the right-hand-side variables produce different conclusions regarding the relationship between individual policies and growth. This paper's purpose is to take stock of what the profession has learned from cross country regression studies of the linkages between long-run growth and indicators of monetary, fiscal, exchange rate, trade, and financial policies.

# 1. Why use cross-countrv regressions?

To gauge what we have learned - and may potentially learn - from cross country regressions, we should humbly face the daunting array of methodological, conceptual, and measurement problems that plague our ability to interpret cross-country growth regressions confidently (see Levine and Renelt (1991)). While the title of this paper is "Looking at the Facts ... ," the inherent problems associated with cross-country
2 studies imply that, at best, we can only expect to unearth suggestive empirical regularities. Even settin8 aside measurement difficulties and issues of data consistency across countries and time, numerous interpretational problems plague cross-country investigations. It is not clear that we should include vastly different countries in the same regression. Regression analysis presupposes that observations are drawn from a distinct population, but as argued by Arnold Harberger (1987), Thailand, the Dominican Republic, Zimbabwe, Greece, and Bolivia may have little in common that merits their being put in the same regression. Thus, the statistical basis upon which we draw inferences from cross country analyses may be in doubt.

Furthermore, it ic conceptually difficult to interpret the coefficients on regressions that involve data for over 100 countries averaged over thirty years during which time business cycles, policy changes, and political disturbances have influenced economic activity. Many papers interpret the coefficients as elasticities, suggesting that if a policy indicator changes by one percent, growth will change by a percent corresponding to the coefficient on the policy indicator. These types of conceptual experiments should be treated skeptically as crose country regressions do not resolve causal issues, nor do the regressions ".. describe a single piece of machinery through time." (Harberger, 1987, p. 256) Cross country regression **s** hould be viewed as evaluating the strength of partial correlations and not as behavioral relationships tvi-t suggest how much growth will change when policies change.

"Looking at the facts" becomes even more opaque when the objects of analysis are national policies. In theoretical models of policy and growth, economists typically represent policy distortions with the greek letter tau. Not only do international data sets such as the International Financial Statistics and the Sumuers and Heston (1988) data not contain data series called tau, but it is very difficult to construct
3 proxies that measure policy actions. Instead of measuring executable policies, cross country regressions use policy indicators, such as the average ratio of exports to GDP or the average ratio of broad money to GDP over the past 30 years. Cross country regressions, therefore, do not typically link executable policies with growth.' The inherent statistical end conceptual obstacles to interpreting cross country studies limit what economists can learn about policy and growth from cross country regressions. Even if cross country regressions yield very "strong" results, these results should be viewed as suggestive empirical regularities, not as stylized facts nor as behavioral relationships on which to measure responses to policy changes. Cross country regressions, however, can be very useful. Along with other analytical methods, demonstrating that certain policy-growth relationships hold well across countries will influence beliefs about policy and economic performance. Similarly, beliefs about policy and growth that are not supported by cross country evidence will tend to be viewed skeptically.

# 2. Back to the "facts"

To examine the strength of the empirical relationship between longrun growth end various policy indicators, we slightly modify the approach taken in Levine and Renelt (1992), henceforth LR. LR examine whether the conclusions from existing cross-country studies of growth and policy are robust or fragile to small alterations in the conditioning set of information, i.e., do slight alterations in the right-hand-side iariables change the results for the variables of primary interest? LR examine the relationship between economic growth and a wide assortment of fiscal expenditure, fiscal revenue, monetary, trade, and exchange rate policy indicators as well as political and economic stability indexes for a broad cross-section of countries over the 1960-89 and 1974-89 periods.
4 They find that almost all cross-country regression results are sensitive to minor alterations in the conditioning set of variables.

This paper has three parts. The first part modifies the LR analysis in four ways. First, based on work by King and Levine (1992a,b,c,d), we include indicators of the level of domestic financial sector development that were not included in LR. Second, based on work by Jong-wha Lee (International Monetary Fund), we use an improved measure of the black market premium. Third, based on Easterly and Rebelo (1993), we use a measure of the total public sector surplus. Fourth, we U63 a reduced form specification based on Barro (1991) since these "Barrostyle" regressions are frequently used.' We find a few robust regularities. FLrst, various indicators of financial sector development are robustly associated with long-run growth (as first noted by King and Levine (1992c)). Second, unlike LR, the black market exchange rate premium is negatively related to long-run growth in the "Barro-style" regression framework used in tili paper. Third, as in LR, a host of monetary, fiscal, and trade indicators are not robustly related to growth even in the Barro-framework.

The second part of this paper studies the relationship between inflation and growth. Here we move beyond simply altering the conditioning information set. After demonstrating that inflation and growth are not strongly correlated in simple regressions, we attempt to discover whether the relationship between growth and inflation is different in "very" high inflation countries as opposed to countries with more "moderate" inflation rates. This allows us to illustrate c number of additional difficulties - such as defining "outliers" and altering the sample of countries - associated with attempting to draw interpretable results from cross-country regressions. We find that although economists would almost unanimously argue that high inflation is bad for growth, this result is difficult to find in a broad cross-section of countries.
5 Finally, instead of focusing on the partial correlation between individual policy indicators and growth, we construct three indexes designed to characterize **the** macroeconomic, international, and dometic financial sector onvironments. These three indexes are constructed by combining individual policy indicators. In this way, we attempt to broaden the scope of our analysis by moving away from the narrow focus on indiviual Indicators and moving toward, for example, an overall index of macroeconomic stability. Furthermore, using these overall indexes, we study policy regimes. By classifying countries as having "good" or "bad" financial, macroeconomic, and international policies, we can categorize countries into eight different "regimes." The investigation of policy regimes suggests that the state of the financial sector is importantly associated with long-run growth and the overall index of macroeconomic stability is more strongly linked vith growth than any individual fiscal or monetary indicator.

# II. EXTREME BOUNDS ANALYSIS

# 1. Motivation

Previous cross-country growth analyses identify over fifty different policy and political indicators as significantly correlated with long-run per capita growth rates.' Are these results believable? Should they change our views and policies? To answer these questions, we need an empirical definition of "believable." Levine and Renelt (1992) (LR) use a narrow definition to show that most existing "empirit al facts" are not believable. Their definition of believable is derived from Edward Leamer's work on extreme bounds analysis (EBA).' Basically, LR show that small alterations in the conditioning information set, i *e, small alterations in the right-hand-side variables, change the statistical significance of most existing results. LR term results that
6 do not withstand small alterations in the conditioning information set "fragile" and those results that do withstand there alterations "robust." Furthermore, LR show that past results typically rely on searching beyond standard regression specifications to find the "right" set of right-handside variables that produce "good" results. We use the LR approach to further investigate the robustness of partial correlations between growth rates and "policy" variables over the 1960-1989 period for broad crosesection of about 100 coun'ries.

# 2. Techniue

The EBA employs the linear, ordinary least squares regression framework:

> **_GPW aPI_** + **_PON+,Z U_**

(1) where GYP is the growth rate in GDP per capita averaged over the 19601989 period for a cross-section of up to 100 countries, I is a set of base variables always included in the regression, M is the variable of particular analytical interest, and Z is a set of variables chosen from a pool of variables that we believe represent appropriate conditioning information. The EBA involves varying the Z variables to determine whether the coefficient on the N variable is consistently signif_cant and of the same sign when the conditioning information set varies. If the coefficient on the N variable is consistently significant and of the same sign we call this result "robust." If the coefficient on the N variable changes sign or becomes insignificant, we call this result "fragile."
**7**

Our I variables - the base set of variables that we always include - in the regressions are LSEC, the log of **the** initial (1960) secondary school enrollment rate, LYO, the log of initial real GDP per capita, and REVC, the number of revolutions and coups.5 **This** is a different set of I variables from the set used in LR. We choose this new set of I variables %ecause they correspond to the "Barro-style" regressions that have become the standard cross-country growth regression. **Thus,** choosing these I variables facilitates comparisons with other ***tudies.** We began by using the complete set of Barro (1991) control variables but dropped the log of the initial p%rimary schnol enrollment rate, the numbot of assassinations, and the 1960 average deviation from unity of **the** purchasing power parity index for investment goods since the inclusion of these variables did not importantly alter our findings.' Thus, equation (1) becomes

> _GYP - C + PLYO 2_<sup>_LSEC+_</sup> _3_<sup>_RvC_+</sup><sup>_Pr_</sup> PIZ + _u._ (2)

First, for each variable of interest, X, we run a base regression with only the basic set of variables included, i.e., we do not include any Z variables. This base regression determines whether the variable of interest is significantly correlated with long-run growth after controlling for a base set of variables designed to control for initial conditions and the degree of political stability.

Second, we run separate regressions including each variable - one at a time - from the pool of potential Z variables in regression equation f'). Then we run separate regressions including every combination of two variables from the pool of potential Z variables; finally, we run separate regressions including every combination of three variables from the pool of potential Z variables. Out of all of these regressions, we
8 compute the extreme upper and lower bound on the coefficient Sm. The extreme upper bound is equal to the Lighest calculated **value** of A6 + 2 * the standard error of 0A; the extreme lower bound is **the** lowest calculated value of fi3 - 2 * the standard error of 4*. For example, In some cases the addition of only one Z **variable** may produce the extreme upper bound for A,, while the addition of thr-e Z variables produces the extreme lower bound.

These extreme bounds can help clarify the degree of confidence that one can place in the partial correlation **between** growth (GYP) and the H variable. If a policy indlcator is robustly correlated with long-run growth, then one should feel more confident about its association with growth than an indicator which has a fragile link. If a result is fragile, the following cables will also Indicate how many and which Z variables are causing the "weakness." For instance, If a result is classified as fragile: 0, the zero indicates that the M variable is insignificant without adding any additional Z variables; _I._ **e.,** the M variable enters insignificantly in the base rgression. If a result is classified as fragilet 1, the "one" indicates that the H variable is significantly correlated with growth in the base regression but the inclusion of only one additional right-hand-side variable causes the partial correlation between growth and the H variable to turn insignificant.

The pool of variables from which ve allow the BA to choose Z- variables includes the average inflation rate (PI), the standard deviation of Inflation (STPI), the government tiscal surplus ratio to GDP (T-RY), imports plus exports as a share of GDP (TRD), the black market premium (BHP), and liquid liabilities as a ratio to GDP (LLY), for a total of seven possible Z variables. 7 We believe this broad set of policy indicators represents a reasonable set of information upon which to condition our beliefs regarding the association between individual
9 policy indicators and growth. For each **K variable, this** pool is restricted by excluding any variable which, a priori, we think may measure the same phenomenon. For example, when TRD **ts** the K variable, we exclude the black market premium from **the** Z pool **as** both variables may reflect aspects of international policy. By eliminating such duplication, we give each M variable **a** better chance **at** achieving the "robust" status.

# 3. Fiscal golic? indicators

Table 1 presents the sensitivity results for four fiscal policy indicators. Many empirical livestigations Into the relationship between average per capita growth and fiscal policy use measures of the size of government in the economy and measures of goverment deficits. Consequently, we examine two of each of these types of fiscal policy indicators.

The first variable GOV, the ratio of government consumption to GDP, attempts to measure the role of the government In economic activity. Barro (1990) shows that if countries are choosing the optimal amount of fiscal expenditures and taxes, then the ratio of government expenditures or revenues to GDP should be unrelated to long-run growth. On the other hand, many policy arguments are based on the asumptions that the size of government expenditures is typically larger than optimal and that government expenditures are spent on the wrong things. However, measuring whether government spending is 'too" large or whether expenditures are "mis-spent" is difficult. An important problem with GOV is that it is an aggregate measure of government size and, therefore, does not capture the distribution of expenditures, the efficiency with which the government uses any given level of expenditures, or whether the government size is suboptimal.
10 The EBA results for GOV show that regardles of the conditioning set of information, the partial correlation between GOV and growth is always negative but never significant at the 0.05 level. This may reflect optimal fiscal policy or that COV is poorly measured.

Further examining the link between fiscal policy and growth, Barro (1991, p. 430) argues that "expenditures on *ducatiou and defense are more like public investmen **t** hen public consumption," and therefore he constructs the variable government consumption expenditures minus education and defense expenditures divided by GDP, over the 1970-85 period, and calls this variable HSGVXDXE. This variable is also fragile. When LLY, TRD, and STPI are included the coefficient on HSGVXDXE is insignificant.8 In another attempt to link government actions and growth, many studies examine the role of government fiscal surpluses and deficits. Government deficits are frequently considered bad for growth, or, sometimes, deficits are viewed as indicative of structural problems associated with poor growth. We use two measures of fiscal financing to investigate these claims. Firet, we study the ratio of the central government surplus to GDP (SURY). The EBA shows that only with particular combinations of explanatory variables does this variable have a significant partial correlation with growth. SURY does not enter significantly in the base regression, but the LBA finds that when PI, STPI, LLY are included the coefficient on SURY becomes significant. In our sample, we found two outliers for the variable SURY as shown in Graph I (Bolivia and Israel). Removal of these countries did not change the EBA results. Running the entire EBA after first omitting outliers on all variables does not change this paper's conclusions. SURY, however, does not accurately measure the size of the entire public sector deficit and may, therefore, be both incomplete and inconsistent across countries. For example, in many countries, the
11 government owns public enterprises and local governments and municipalities play important fiscal roles. Therefore, Easterly and Rebelo (1993) calculate a measure of the total public sector surplus, PSSUR, in an attempt to rectify these measurement problems. As can be seen at the end of Table l, however, PSSUR is also fragile. Thus, even when extensive efforts are made to measure the total public sector surplus in a consistent fashion across countries, it is difficult to find an insensitive relationship between fiscal financing and long-run growth. Though all four of these fiscal variables enter with the predicted sign, none is robust to slight alterations in the conditioning set of information. These results make us uncomfortable with using these (frequently cited) fiscal indicators in making policy recommendations. The fragile relationship between aggregate fiscal policy indicators and growth, however, does not rule out fiscal policy's importance in affecting long-run growth. Ae mentioned above, these indicators are not equal to policieo; they suffer from measurement problems, and they may be too aggregate to be informative. We conclude that there may be no substitute for a detailed examination of the types of fiscal expenditures and taxes, and the efficiency with which government provides services and collects taxes, within the context of individual country circumstances.

# 4. Monetarv nolicv indicators

Inflation may be related to growth through many channels. Although we discuss these channels and examine the relationship between inflation and growth in greater detail in the next section, Table 2 shows che results for two monetary policy indicators, the average rate (PI) and the standard deviation of the inflation rate (STPI). Though they reflect not only monetary policy but also shocks and other policies, these have been widely used in empirical investigations of the link between monetary policy and growth (see, for example, Kormendi and MegLrire (1985)). The
12 results of the sensitivity **lnalysi** show that both variables are fragile; no specification yields a significant partial correlation. Sven after removing BMP from the Z variable pool, since BMP may reflect an inconsistent combination of exchange rate and monetary policies, PI and STPI remain fragile.

# 5. International distortion indexes

Much theory suggests that openness to international trade will spur economic growth.' Unfortunately, there does not exist a good indicator of international trade policy. In an impressive empirical paper, Pritchett (1991) shows that most cross-country indicators of trade policy are not highly correlated with themselves! Thus, different trade policy indicators tend to produce different rankings of countries in terms of openness. Following tradition, we use the share of total trade in GDP **(TRDO)1** TRD is fragile. The addition of LLY, the ratio of liquid liabilities to GDP, produces an insignificant coefficient on TRD. As depicted in Graph 2, we found three TRD "outliers" (Hong Kong, Luxembourg, and Malta). When these three countries are:removed from the sample, TRD is insignificantly correlated with growth even in the base regression.

The black market premium, BMP, is often used as a general index of international distortions. Intuition suggests that larger black market premia will be associated with slower growth. However, the black market premium suffers several drawbacks as an indicator of policy. One problem with the BMP is that it is a general index of distortion... A country could have a freely floating currency and zero black market premium but still impose severe trade restrictions. Similarly, the combination of a fixed exchange rate and inflationary monetary policy could produce a large black market exchange rate premium even with a
13 relatively open trade **regime.** Thuas it **is** difficult to link BMP with any single policy.

We use a measure of the black market premium constructed by Jongwha Lee that improves upon the measure used in the LR. Lee precisely matches the date at which the official end black market exchange rates are compared to better calculate the black market premium for each country. As shown in Table 2, BMP has a robust negative correlation with long-run growth. Though difficult to interpret precisely, this result suggests a negative association between international distortions end growth. As shown in Graph 3, there are three countries with BIP values that appear to constitute outliers (Ghana, Burma, and Uganda). Excluding these countries alone does not change the robust finding."

# 5. Financial Policv indicators

The traditional view of financial intermediaries depicts these institutions as passive coordinators between households who save and businesses which invest. In contraot, new research suggests an intrinsic link between financial Intermediaries and economic growth. '2 This new view posits that economies with more developed and more efficient financial systems will be able to more effectively allocate savings to the best investments. This in turn leads to increased productivity, potentially higher savings rates, and faster growth.

To examine the relationship between financial policy and growth, we use three variables constructed by King and Levine (1992a) to reflect the level of financial sector development. To represent the size of the financial system, we uso LLY, the ratio of liquid liabilities to GDP.'" As shown in Table 3, LLY earns the robust classification. The significant, positive partial correlation between growth and LLY shows that countries with larger per capita growth rates tend to have larger
14 financial systems. Omitting the outliers depicted in Graph 4 does not alter this result.

To examine the relationship between growth and the types of financial intermediaries that are conducting financial intermediation, we examine the variable BANK, which equals deposit money bank domestic credit divided by deposit money banks domestic credit plus central bank domestic credit. Again confirming the findings in King and Levine (1992c), BANK is positively and robustly correlated with long-run growth. Finally, to examine the importance of where the financial system allocates credit, we perform the EBA on the variable PRIVATE, which equals the credit to the private sector divided by total domestic credit. Once again, PRIVATE enters with a positive and robust coefficient. These findings help support the new view of the role of financial intermediary services in long-run growth.

## _11. TRYING TO FIND FACTS: AN EXAMPLE

# 1. Setup

Arguably, the single most studied issue in economics is the relationship between money and economic activity."' rheory suggests that inflation may affect growth by influencing capital accumulation, inducing agents to shift out of socially productiv **e** ndeavors into rentseeking activities, or causing people to substitute out of money exchange into transactions technologies that require more time and effort. Similarly, inflation may influence investment decisions by increasing uncertainty. In addition to the many existing models, many more could (and certainly will be) created that exemplify the linkages between inflation and long-run growth.

Perhaps just as important as the debates surrounding theoretical models of inflation and growth is the generally accepted policy
15 conclusion that inflation is, in most cases, bad for long-run growth. A poll of economists would probably find us recommending that coterie paribus lower inflation is better than higher inflation. If we went to a country with a 100 percent inflation, we would tend to recommend that the country pursue policies designed to reduce the inflation rate. Moreover, interrnational organizations in the business of giving economic policy advice would, almost unanimously, argue that a policy of lower inflation is better than a policy of higher inflation; one rarely sees the International Monetary Fund or the World Bank recommanding that countries increase their inflation rates. Given this uncharacteristically unified view among economists and policy analysts, we should expect a negative relationship between growth and inflation to "jump-out" at us from the data. Yet, no empirical evidence strongly supports the contention that countries with higher inflation rates tend to have slower long-run growth rates ceteris paribus. A cross-country analysis of the relationship between growth and inflation, therefore, offers a particularly appealing opportunity to illustrate a few of the difficulties inherent in trying to identify the "facts" concerning policy and growth.

# 2. Initial findings

In a cross-section of f02 countries, the correlation between the average annual real per capita growth rate (GYP) and the average annual inflation rate (PI) over the 1960-1989 period is -0.17 with a P-value of 0.10. Though weak, any model would suggest controlling for other factors when examining the relationship between inflation and growth.

Thus, we run a regression of GYP on PI including the logarithm of real per capita GDP in 1960 (LYO) to control for initial income, the logarithm of the secondary school enrollment rate in 1960 (SEC) to control for initial investment in human capital, and the number of revolutions and coups over the 1960-89 period (REVC) to control for
16 political instability. 1 The results presented in Table 1 suggest that Initially rich countries grow more slowly than initially poorer countries, that those countries that began the thirty year period with more students enrolled in secondary schools grow faster than countries with lower secondary school enrollment rates, and that countries that experienced more revolutione and coups grew more slowly than more stable societies. But, the regression results presented in Table 4 indicate that inflation is not significantly related to long-run growth at standard significance levels as the t-statistic for the coefficient on inflation is only 0.58. Thus, a simple negative association between inflation and growth still does not "jump-out" at us." 3. Outliers: l,fUtion areater than 80 Percent per annum The relationship between inflation and growth may, however, be discontinuous or non-linear. Consider, for example, two alternative hypotheses about the growth-inflation relationship. First, inflation rates may have to reach extremely high levels before people significantly alter how they allocate their time and resources. Thus, marginal changes in moderate inflation rates - say from one to two percent - may not be negatively associated with growth, but very high inflation rates - say over 80 percent - may be associated with a break-down in normal economic relationships and slower long-run growth rates. We call this the "highpi" hypothesis since the greek letter pi often represents inflation. Alternatively, people in countries with very high inflation for very long periods may become inured to inflation and develop a host of mechanisms for coping with inflation, so that growth is unrelated to very high inflation. Changes in inflation in moderate inflation countries may, however, be negatively associated with growth since moderate inflation countries have not become "desensitized." We call this the desensitize hypothesis.
17 These two hypothesee obviously do not cover the full range of potential explanations relationship between inflation and growth. Furthermore, perspectives on the inflation-growtht nexus should be exemplified in models that clarify hypotheses and suggest appropriate econometric specifications.17 Here, we use simple dummy variable procedures to identify and control for countries with very high inflation rates. We allow countries with very high inflation rates to have different slope and intercept coefficients from moderate inflation countries. Then, we test versions of the high-pi and desensitize hypotheses.

Consider the simple scatter plot of the average annual Inflation rates for 102 non-oil producing countries in Graph 5. There are clearly outliers in the sample, but where should one draw the line between high and moderate? As can be seen in the scatter plot, the inflation rates of both 80 and 40 percent suggest relatively clear demarcations. We examine both. First define high inflation as those countries with inflation rates over 80 percent. Define the dummy variable HIPI80 as having a value of 1 for those countries with average inflation rates greater than 80 percent over the 1960-1989 period and a value of 0 otherwise. We run the regression: _GrP - PC + 0LRGDP + 0 3LSEC + p4"RC + IPuzZ80_ + _P6PI*HZPI8O + ,PI._ (3) The coefficient on HIPI80, 8,, indicates whether countries with inflation rates over 80 percent per annum have a different intercept than countries with inflation rates of less than 80 percent. The regression defined in equation (3) also permits the slope coefficient on inflation to differ
18 between high and non-high inflation countries. The regression implies that a marginal increase in inflation is associated with a change in per capita growth defined by = _P 6 HXP180 + P7 aGY,P l 0.pxO_1_ = _P6 + P7 (4) aG IH:IPIGoO_ = 7Thus, if _P6_ is significantly different from zero, high inflation countries respond differently from non-high inflation countries to changes in the inflation rate. If & plus p, is not significantly different from zero, then a marginal increase in inflation in a high inflation country is not associated with a change in real per capita GDP. Regression 2 of Table 4 incorporates these new variables and demonstrates a potentially eppealing "finding." When we define high inflation as countries with average annual inflation rates over the 19601989 period of greater than 80 percent, we find support for the desensitize hypothesis; very high inflation is not negatively associated with growth, but increases in inflation in moderate inflation countries are negatively linked with growth. If a country such as Italy, with a thirty year average annual inflation rate of 10 percent instead had an average inflation rate of 5 percent, this regression (taken literally) implies that Italy would have grown an extra 0.2 percent per year in per capita terms. Cumulating over thirty years, this means that Italy's per capita income in 1990 would be about 1.3 million Lira higher.' These results are consistent with the story that countries with high inflation rates over 30 years become "desensitized" to inflation, but countries
19

# with moderate inflation rates exhibit a significant negative relationship between growth and inflation.

# 4. Outliers: Inflation greater than 40 percent ner annum

In coutrast, when we define high inflation as countries with average inflation greater than 40 percent, we find evidence for the highpi hypothesis; countries with very high inflation rates have slower growth, but inflation increases in moderate inflation countries are not negatively linked to growth (regression 3 in Table<sup>**4).19**Thus, using 40</sup> percent inflation as the definition of "high" leads to a different conclusion from when we defined "high" as inflation greater than 80 percent per annum.

# S. Resolution: The "over-im2ortance" of a counle of countries?

The dichotomous interpretations evoked by the two outlier choices warrant some additional digging. One or some of the five countries with inflation between 401 and 802 are causing the coefficients in the regressions to jump around. These countries are Israel, Nicaragua, Uganda, Uruguay and Zaire. When these countries are included as high inflation countries, we conclude that (1) high inflation is negatively associated with growth, but (2) increases in inflation in moderateinflation-countries are not linked to growth. On the other hand, if these five countries are not counted as high inflation countries, we conclude that (1) high inflation is not negatively associated with growth, but (2) increases in inflation in moderate-iuflation-cot.atries are negatively linked to growth.

Of these five crucial countries, two experienced extreme political disruptions over the sample period: Uganda and Nicaragua. Do we want the experiences in Uganda and Nicaragua to determine our opinion of the relationship between growth and inflation? We believe that most
20 economists would feel uncomfortable with the complex events that occurred in Uganda and Nicaragua playing such a pivotal role.

To test whether these two countries are responsible for the variations in results, the same regressions are run excluding these countries. Support for both hypotheses breaks down at the 0.05 significance level. Removal of both countries causes a reversion co the original results that inflation is unrelated to growth (regressions 2A and 3A in Table 4). These results demonstrate that it is difficult to find "facts." Unless researcher **s** tudy the sensitivity of their results to small variations in the sample of countries and changes in the conditioning information set, the results should be regarded with skepticism.

# 6. Ceteris varibus

Finally, we think it is worth pointing out that the relationship between inflation and growth depends importantly on which explanatory variables are included in an attempt to hold other things equal. For example, in the last regression which defines high inflation as greater than 80 percent but excludes Nicaragua and Uganda (regression 2A), regression 5 indicates that when we add the ratio of government consumption expenditures to _GDP_ (GOV), we again find support for the desensitize hypothesis; inflation becomes significantly negatively correlated with growth in moderate inflation countries. But, regression 6 shows that when we include both GOV and TRD, inflation is not significantly related to growth "ceteris paribus." Thus, the cloice of the conditioning information set, i.e., the definition of ceteris paribus, importantly alters the conclusions one would draw on the relationship between growth and inflation.

Besides forming the conclusion that inflation is not robustly linked to growth, this evidence suggests that any link between policies
21 and long-run growth must be scrutinized carefully. The investigating economist can easily "find" several distinct but appealing "facts." Not only must the result be robust to variations in the explanatory variables, but it must also be checked for outliers, changes in the _deflnition_ of an outlier, and for small variations in the sample.20

# IV. BROAD POLICY INDXES AND REGIMES

The results thus far are disturbing. Except for the black market premium and the indicators of the development of the domestic financial system, there is not a strong statistical association between an assortment of economic indicators and long-run growth. The macroeconomic policy indicators are not strongly linked to growth, indicators of trade performance are also not closely tied to growth, and the strong negative association between growth and the black market premium is difficult to interpret since the black market premium reflects many policies. Yet, when giving policy recommendations, most economists would still argue that "macroeconomic stability" and "openness to the international market place" help countries grow faster. Where the term "macroeconomic stability" refers to a conglomerate evaluation of the macroeconomic environment and not any one indicator; and, "openness to the international market place" refers to a conglomeration of tariff, nontariff, and exchange rate policies meant to quantify the ease with which residents can interact with the rest of the world. Therefore, listead of examining the partial correlation between individual indicators and growth, it may be worthwhile studying the relationship between indexes designed to measure the overall macroeconomic environment and the overall openness to international interactions. Furthermore, these indexes allow us to study policy regimes.
22 Levine and Ronelt (1992) use factor analysis to construct international, macroeconomic, and uncertainty indexes by extracting the largest principle component from a group of individual indicators. They find that none of these indexes is robustly correlated with long-run growth. We try a more subjective approach.

# 1. Conalomerate indexes

We form a conglomerate index for international, financial and macroeconomic policy indexes. Each of these indexes is composed of transformed variables. After removing all outliers in the sample as defined in Graphs 1-5,21 each variable (V) which is to be incorporated into an index is transformed as follow.s TI(V) - (Vi ABS _(WAN_ (V)) ) / _AS_ (MEAN (V) ), where i indicates a country, ABS is the absolute value operator, and MEAN (V) represents the mean of the variable V across countries. Therefore, the transformation involves a standardization of each series around the mean of the series. If the value of V is exactly at the mean, the value of T(V) is zero. A value of V above the mean corresponds to a value of T(V) greater than zero.

We wanted growth to be positively associated - from an intuitive - perspective with higher values of the transformed variables. Thus, for variables such as inflation and the black market premium, which are thought to be negatively correlated with growth, the transformation is multiplied by negative one. Therefore, countries with a high fiscal surglus relative to the mean will have a positive value for the transformed variable while countries with high inflation relative to the mean will have a negative value for the transformed variable.
23 The separate international, finance, and macro indexes are formed by summing the pertinent transformed variables. The international index, INTL, is composed of the transiormed BMP and TRD, the finance index, FINANCE, is the sum of transformed LLY, BANK and PRIVATE, and the macro index, MACRO, is the sum of the transformed PI and SURPLUS. As mentioned before, these indexes _are_ formed so that a higher value of the index intuitively corresponds to higher growth.

# 2. Results

As an initial test of the partial correlation between these indexes and growth, these variables vere included in the basic regressions shown earlier. Table 5 shows that in this basic framework, each index is positively, sigaificantly correlated with growth. However, when all three indexes are included together, only FINANCE remains significant at the 0.05 level (MACRO is significant at the 0.08 level and INTL at the 0.12 level). Still, these results suggest that a "sound" economic environment, defined in terms of financial, international, and macroeconomic policies is positively linked with growth.' We also created and attempted to examine the importance of policy regimes. All three of the indexes were ranked in descending order and then the sample was divided in half. For each index, the top half of the countries (those with larger indexes and thus "better" policies) were given a value of 1 while the bottom half (those with "worse" policies) were given a value of 0. Consequently, this categorization defines eight different policy regimes. For example, countries can have a 1,C,0 which means good FINANCE but bad INTL and MACRO, while 1,0,1 indicates good FINANCE and MACRO but bad INTL, and so forth.

We report the average growth rate of the countries in each regime in Table 6. Those countries with the "best" economic regime (1,1,1) have an average growth rate three times that of the "worst" regime (0,0,0).
24 One of the most ptominent features of this analysis is the strength of the relationship between the measures of financial sector development and growth. For example, going from the "all bad" regime (0,0,0) to good FINANCS but bad INTL and MACRO doubles the average growth rate. In general, the effects of having a good versus bad INTL or MACRO rank are mixed.

# VI. CONCLUSION

We Identify two broad findings. First, cross country regressions show that indicators of financial development are strongly associated with long-run growth. Since changes in these financial development indicators are linked to changes in financial sector policies (see e.g., King and Levine, 1993b), the link between financial sector policies and long-run growth deserves more attention. Second, it is extremely difficult to identify believable links between a wide assortment of indicators of individual policies and long-run growth, although there is some evidence that general indicators of international distortions are negatively associated with growth. Most notably, we could not find robust ties between indicators of monetary or fiscal policy and long-run growth.

The empirical connection between policy indicators and growth seems to be quite sensitive to slight alterations in the right-hand-side variables and to small changes in the sample of countries. Future cross country work on the relationship between policy and long-run grcJth will need to develop innovative ways of improving available policy indicators, defining policy regimes, and examining interactions among policies and their effects on growth. Easterly and Rebelo (1993), for example, importantly improve existing measures of a range of fiscal revenue and expenditure indicators for many countries over the 1960-1990 period.
25 Finally, however, broad cross country regression analyses of policy and growth will need to be closely integrated with country case studies and firm level investiga ions.
26 NOTES

1. Moreover, when studies measure the average inflation rate or average tax rate over the last 30 years, they do not distinguish between, **say,** a hyper-inflationary episode lasting a few year. and sustained high inflation lasting 30 years.

2. Levine and Renelt (1992) use a different specification from Barro (1991). The differences are noted below.

3. See Levine and Renelt (1991).

4. See Leamer (1973, 1983, 1985) and Leamer and Leonard (1983).

5. Note that this basic set of always included variables is different from those used in LR. When GYP is the dependent variable, LR's I variables are initial income, initial secondary school enrollment, population growth, and the ratio of investment to GDP. LR also investigate the robustness of the partial correlation between the investment share and each H variable.

6. In addition, we do not include the ratio of real government consumption less defense and education expenditures to GDP, which is part of Barro's (1991) set of control variables, because (1) this fiscal variable is a contemporaneous economic policy indicator and not a variable to control for initial conditions or political stability and (2) it is averaged over the 1970-85 period rather than over the 1960-89 period that we examine. We do, however, examine this fiscal expenditure variable as an M variable.

7. For the pool of Z variables, LR use PI, STPI, GOV, **_TRD,_** REVC, the growth rate of domestic credit, and the standard deviation of the growth rate of credit.
27 8. When the average growth rate of domestic credit (GDC) is added to the pool of Z variables, HSGVXDXE changes to the classification fragile: 2. The addition of LLY and GDC to the base regression causes _1_ to become insignificant.

9. See Grossman and Helpman (1990, 1991) and Rivera-Batiz and Romer (1991).

10. As LR show, very similar results emerge with the export to GDP ratio or the import to GDP ratio.

11. If these outliers are excluded and the standard deviation of domeetic credit growth, STGDC, is added to the Z variable pool, BMP becomes "fragile" when PI and STGDC are both included.

When the EBA is done using the specification in Levine and Renelt (1992), the partial correlation between BMP and both growth and the investment share are fragile. Thus, the difference between the findings in this paper and LR is a product of using different I-variables, not from using a different measure of the black market exchange rate.

12. See Greenwood and Jovanovic (1990), Bencivenga and Smith (1991), Levine (1991), Roubini and Sala-i-Martin (1991, 1992), King and Levine (1992a,b,c,d), Saint-Paul (1992), and DeGregorio and Guidotti (1992). But, also see Dornbusch and Reynoso (1989) for a different perspective. 13. Liquid liabilities equals M1 plus interest bearing liabilities of the banking system, plus demand and interest bearing liabilities of non-bank financial intermediaries.

14. See the extensive review by Orphanides and Solow (1990) and the papers by Fischer (1979), Stockman (1981), and DeGregorio (1991, 1992). Also, see Fischer (1992) for a study of the ties between macroeconomic factors and growth.

15. Adding the initial literacy rate, initial primary school enrollment,
28 the number of assassinations, or the number of wars to this control set of variables does not change this paper's results.

16. We only look at the average annual inflation rate. This rate may be strongly influenced by a few observations and therefore not adequately represent the inflation rate in any time period. We get similarly inconclusive results when we use the standard deviation of inflation instead of the average inflation rate.

17. Similarly, as discussed in Section II, there are so many endemic problems with cross-country analy.es of growth that we should not push the econometrics beyond the low quality and limited interpretability of available data.

18. Italy's GDP per capita in 1990 was about 22.7 million Lira, and its average real per capita growth rate over the thirty years between 1960 and 1990 was about 3.5 percent per annum. 19. The coefficient indicates that countries in the HIPI40 group grow an average 0.02z slower per year. While statistically significant, this is economically minute.

20. Also see the paper by Easterly, Kremer, Summers, and Pritchett (1992) that examines other sensitivity analyses.

21. The results do not depend on removing the outliers.

22. Only FINANCE is robust when the extreme bounds analysis is performed.