Empirical Evidence on Growth: A Closer Look on Cross-Country Regressions

1. Empirical Evidence on Growth:A Closer Look on Cross-Country Regressions Presentation by Dejan Jasnić and Philipp Wahlen 24. November 2008

2. Regression Analysis Characteristics of relationship between dependent variable and independent variables y = f(x)  strength, direction, type • Simple linear regressions: y = a + bx • Multivariate regressions: y =  + 1x1 + 2x2 +… + nxn Cross-Country Regressions

3. Regression Analysis – scatter plot What does it tell us about strength, direction and type of the relationship? Cross-Country Regressions

4. Regression Analysis – growth regressions Multivariate regressions: y =  + 1x1 + 2x2 +… + nxn Complexity What is the right model? Right variables? More major shortcomings: • parameter heterogeneity, outliers, measurement error, endogenity • assumption that economic growth operates according to universal laws across countries through the time Cross-Country Regressions

5. Cross-Country Regressions • Recall:By regressingannual growth onpolitical and economic indicatorsacross countries researchers attempt to find generally valid driving forces of economic growth. • What might be problematic with this approach? Cross-Country Regressions

6. Basic problems Levine and Zervos (2001) identify the following problems: • Statistical entries are sometimes inconsistently or inaccurately measured • Construction of proxies that measure policy actions is difficult • Sala-i-Martin (1997) gives example of human capital – how to measure it? • Regression analysis presupposes that observations are drawn from a distinct population • Growth is often averaged over 30 years – including business cycles, policy changes and political disturbances – which makes interpretation of coefficients conceptually difficult • Cross-country regressions do not address causal issues per se. • Relationships might be discontinuous or non-linear Cross-Country Regressions

7. Further problems Also McCartney (2006) brings forward fundamental critique: • Cross-country regressions (averaging growth rates) typically do not consider structural breaks • Growth patterns in development countries are discontinuous, periods of fast growth are succeeded by those of slow growth. Growth accelerations are common (see also Hausmann et al. 2005). • Interactions between policy measures cannot be feasibly incorporated into econometric cross-country models • Simple cross country regressions do not account for so-called hysteresis effects • Temporary economic shocks that have a permanent impact on economic growth; threshold effects; virtuous and vicious circles • Static regressions do not consider dynamic effects • Does a policy have impact during one or a few business cycles only or on long term growth? Cross-Country Regressions

8. Two problems to focus on All considered papers name at least one of these two difficulties and make suggestions how to deal with them: • Growth theories are not explicit about what variables should be in a growth regression • Take “A”, the “level of technology” in many growth models) • Many empirical economists are tempted to try around with indicators without a theoretical backup • Most variables are significant in some combinations and insignificant in others • The problem is mentioned by Sala-i-Martin (1997), McCartney (2006), Brunetti (1997), and Levine and Zervos (2001) • Government policy is probably endogenous and not random • Governments adopt certain policies, because they are optimizing over policies in some objective function. Then the estimated coefficients will be biased. • The problem is mentioned by Rodrik (2005) and Brunetti (1997) Cross-Country Regressions

9. log of secondary school enrolment rate in 1960 • log of initial real GDP per capita • population growth • ratio of investment to GDP Levine and Renelt (1992) Regressors and robustness of results (1) Two papers, Levine and Zervos (2001) and Sala-i-Martin (1997) are specifically concerned with the issue: • Levine and Zervos cite papers by Levine and Renelt (1992) and Levine and Zervos (1993) that have used Extreme Bounds Analysis (EBA) to identify reliable determinants of growth regressor of interest growth rate = β1I + β2M + β3Z set of fixed regressors 3 out of 7 possible regressors Cross-Country Regressions

10. log of secondary school enrolment rate in 1960 • log of initial real GDP per capita • population growth • ratio of investment to GDP Levine and Renelt (1992) Regressors and robustness of results (2) • Both cited papers take 1960-1989 average growth in a cross-section of over 100 countries as regressand • EBA checks, whether a variable is positive and significant in all regression variants; then it is called robust • Hardly any relationship is found to be robust in this approach regressor of interest growth rate = β1I + β2M + β3Z set of fixed regressors 3 out of 7 possible regressors Cross-Country Regressions

11. Regressors and robustness of results (3) • The Levine and Renelt (1992) paper is criticized by Sala-i-Martin (1997) for employing a robustness test that coefficients cannot survive • It is in the nature of statistical tests that they yield insignificant results every once in a while, even if the assessed coefficient is significant; thus, when taking into account many regressions, one should use softer measures of robustness • Sala-i-Martin • Uses 63 variables collected from different papers • Runs 30,856 regressions for each of 58 “variables of concern” of the type on the previous two slides • Weights estimated coefficients using integrated likelihoods • Constructs a distribution of the weighted coefficients and then ranks coefficients by the percentage of their cumulative density functions that concentrates in either the positive or the negative quadrant • Furthermore, for every variable he records the percentage of regressions in which significance is recorded Cross-Country Regressions

12. Regressors and robustness of results (4) Cross-Country Regressions

13. Endogeneity of government behavior (1) • Recall: Government policies are probably endogenous and not random • Governments adopt certain policies, because they are optimizing some objective function. Then the estimated coefficients will be biased. Rodrik (2005) shows how cross-country growth regressions might yield negative coefficients for policy measures, even if the country is better off with the policy: • Rodrik introduces the following simple model: • Growth is determined by: g(s, θ, φ) = (1 – θ(1-s))A – φα(s) –ρα´(s)>0, α´´(s)>0 • Government behavior is given by maximization over s: max u = λg(s, θ, φ) + π(s) π´(s)>0, π´´(s)<0 government ability market imperfection policy tool weight placed on growth Cross-Country Regressions

14. Endogeneity of government behavior (2) • Then, under the assumption- that governments differ in their weights for growth, or- or that the severity of market failures differs between the countries,the estimated coefficient should be negative • Mathematical derivations can be studied in the paper • Intuition: • Assuming that governments can create rents for themselves from a certain policy, they will adopt the policy to a farther extent than optimal for growth; dg/ds < 0 evaluated at this point • Growth is decreasing in the market failure parameter; optimal extent of policy implementation is growing in market failure parameter; this leads to a seemingly negative effect of policy on growth in the model, dg/ds < 0 • Example: Ownership of banks around the world (as in La Porta, Lopez-de-Silanes, and Shleifer, 2002) Cross-Country Regressions

15. Endogeneity of government behavior (3) How to deal with the problem: • Rodrik sees no opportunities for field experiments, in which policies would be randomized over situations; governments will not readily allow for such experiments • Instrumental variables are hard to find in the context of cross-country regressions (Rodrik, Brunetti 1997) • Furthermore, they are not able to identify the success of purposeful policy action, which we are most interested in (Rodrik) • Finally, the common use of past values of variables as instruments bears the danger of autocorrelation (Brunetti) • Rodrik demands that before regressing and testing • a full theoretical model must be specified; • this should incorporate a likely channel through which policies might operate Cross-Country Regressions

16. General outlooks • Rodrik (2005): See previous slide; in general very pessimistic about the sense of general cross-country regressions • Just as well Levine and Zervos (2001); they call for better measures of policies and for closer investigation of interaction between policies and the interactions‘ effect on growth; thus they do not seem to completely discard the approach • Sala-i-Martin (1997) acknowledges problems but recommends not to be too pessimistic; quite a few indicators can be found as very robust under the right robustness test • Brunetti (1997) claims that with the development of more advanced policy indicators, the predictions seem to become more robust; furthermore, he calls for the identification of IV to deal with the endogeneity issue • McCartney (2006), however, suggests to discard the cross-country regression approach as a whole, as it rests on the neo-classical assumption of mechanically equal growth processes over all countries Cross-Country Regressions

17. Used Papers • Ross Levine and Sara Zervos (2001): What We Have Learned About Policy and Growth from Cross-Country Regressions? • Xavier Sala-i-Martin (1997): I Just Ran Four Million Regressions • Dani Rodrik (2005): Why We Learn Nothing from Regressing Economic Growth on Policies • Aymo Brunetti (1997): Political Variables in Cross-Country Growth Analysis • Matthew Mc Cartney (2006): Can a Heterodox Economist Use Cross-country Growth Regressions? Cross-Country Regressions