Kelly Alverson Professor Farhi Economics 980l October 10, 2007

1. A Presentation of Rodrik, Subramanian, and Trebbi’s “Institutions Rule: The Primacy of Institutions Over Geography and Integration in Economic Development” (2004) Kelly Alverson Professor Farhi Economics 980l October 10, 2007

2. Contents • Introducing the Topic • Methodology • Results • Robustness of Results • Comparison to Related Studies • Implications for Future Research

3. Introduction

4. World Income Distribution • As of 2000, using “international” dollars adjusted for differences in purchasing power, average income levels differed by a factor of more than 100 among the world’s wealthiest and poorest countries • Sierra Leone had a per capita GDP of $490 in 2000, whereas Luxembourg had a per capita GDP of $50,061

6. Motivation • Rodrik et al. begin the article with the following quote from Adam Smith’s Wealth of Nations: “Commerce and manufactures can seldom flourish long in any state which does not enjoy a regular administration of justice, in which the people do not feel themselves secure in the possession of their property, in which the faith of contracts is not supported by law, and in which the authority of the state is not supposed to be regularly employed in enforcing the payment of debts from all those who are able to pay. Commerce and manufactures, in short, can seldom flourish in any state in which there is not a certain degree of confidence in the justice of government” • Basically, “commerce and manufactures” rely on solid institutions to prosper

7. Motivation • Key Question: What causes differences in national incomes, and what can be done to close the gaps • The answer to this question has great intellectual and more importantly, practical, significance

8. Three Theories • Three theories of what determines national income: • Geography: As a key determinant of climate, natural resources, disease, ease of transport, and diffusion of technology, affects the productivity of agriculture and human resources • “Integration” or International Trade • Moderate View: Once certain institutions are in place, trade can foster convergence • Maximal View: Major factor in the growth of poor countries • Institutions: The quality of a society’s institutions, in particular what kind of economic behavior they promote, matter most in determining growth. Protecting the rule of law and property rights are crucial to fostering economic development

9. Relation to the Solow Model • Assert physical and human capital “accumulation and technological change” are at best proximate causes of growth • Ask the next question, “why did some societies manage to accumulate and innovate more rapidly than others?” and explore “deeper” determinants of growth – i.e. institutions, integration, and geography

10. Methodology

11. OLS Regression • All three factors could potentially explain variation in income levels • The estimated coefficients are all positive, and in most cases, statistically significant • Does not account for reverse causality, omitted variable bias, measurement error

14. From a Broad Outlook… • Use Instrumental Variables (IV) Regression, by instrumenting for integration and institutions, to: • Determine how much variation in national incomes can be independently attributed to institutions, integration, and geography • Determine the nature of causal relationships among these three factors • For example, the authors need to show that “the rule of law and other aspects of the institutional environment are an independent determinant of incomes, and are not simply the consequence of higher incomes or of greater integration”

15. Regression Equation • Seeks to estimate the following equation: where y is income per capita, INS, INT, and GEO are standardized measures (subtract the mean, divide by standard deviation in order to directly compare coefficients) of institutions, integration, and geography, and e is random error • Utilizes the two-stage least squares method. The first stage regressions are: where INS and INT are regressed on the instruments, SM and CONST. SM refers to the settler mortality rate and CONST refers to the constructed trade share measure determined by Frankel and Romer • In the second stage, regress log income per capita on GEO and on the predicted values of INS, INT

17. A Note On IV Regression • Isolates the part of the regressor (institutions or integration) not correlated with the error term (which accounts for other, excluded determinants of income) in order to establish the causal effect of the regressor on the dependent variable (income) • For an instrument to be valid, it must be relevant (correlated with the regressor of interest) and exogenous (uncorrelated with the error term) • Done according to TSLS (Two Stage Least Squares Regression)

18. Choice of Instruments • Found instruments for institutions and integration • The problem of reverse causality did not apply for geography • For institutions, used “mortality rates of colonial settlers.” Acemoglu et al. (2001) argued this affected the type of institutions built in colonies of major European powers: in areas with serious health hazards, “their interests were limited to extracting as much resources as quickly as possible, and they showed little interest in building high-quality institutions” such as those to protect property rights and the rule of law • For trade, used “constructed trade share” based on a measure developed by Frankel and Romer (1999). They instrumented for actual trade/GDP by creating a predicted aggregate trade share for each country, based on the estimated coefficients from regressing bilateral trade flows (as a share of that country’s GDP) on country mass, distance between trade partners, and other geographical variables

19. …Continued • Frankel and Romer assert that trade has a causal impact on income, and Acemoglu et al. assert that institutions causally impact income. However, Frankel and Romer do not control for the effect of institutions and Acemoglu et. al do not control for the effect of integration on income (and on the quality of institutions) • Rodrik et al. use the two instruments simultaneously in a series of regressions to isolate the individual effects of institutions, integration, and geography on national income • Additionally, look at the first stage regressions to figure out causal links among these three factors

20. Key Variables • LCGDP95: Natural log of GDP per capita (in PPP-adjusted US dollars) for 1995 was used as the measure of national income or economic performance (dependent variable). Source: Penn World Tables • RULE: A standardized index used to indicate institutional quality, is an aggregate measure of elements that protect property rights and the strength of the rule of law. Varies between -2.5 (weakest) and 2.5 (strongest). From Kaufmann et al. (2002), refers to 2001 and approximates for 1990’s institutions • LCOPEN: Natural log of nominal openness, given by the ratio of nominal imports plus exports divided by GDP. From Penn World Tables, Mark 6, average overall 1950-98 available data • DISTEQ: Distance of the capital city from the equator in degrees (measured as abs(Latitude)/90) • LOGEM4: Natural log of European settler mortality (expressed as deaths per annum per 1,000 population) • LOGFRANKROM: Natural log of constructed openness (i.e. predicted trade share as computed by Frankel and Romer)

22. Country Samples • Three samples: • The 64-country sample used by Acemoglu et al. • A 79-country sample, the largest sample that can be used along with the Acemoglu instrument • A 137-country sample that requires two alternative instruments for institutional quality to be used: EURFRAC (fraction of population speaking either English, French, German, Portuguese or Spanish as first language) and ENGFRAC (fraction of population speaking English), developed by Hall and Jones (1999) – presumably a measure of the colonial European power’s commitment to building institutions in a particular country, as opposed to trying to quickly extract resources • Not used because the instruments fail to pass the over-identification tests, hence at least one of the instruments is endogenous

23. Results

24. Summary of Results • The quality of institutions has the greatest direct effect on income (always enters with the correct sign and is statistically significant) • Controlling for institutions, integration has no direct effect on income, at times entering the regression with a negative sign, and geography has a weak direct effect on income (shown in tests not reported in the paper that involve interactions among some of the geography variables and using different functional forms for the geography variable) • The quality of institutions positively and significantly effects integration (note: an increase in the settler mortality rate negatively effects integration). Geography also significantly effects the quality of institutions, and thus, indirectly income

27. IV Regression Results: Table 3 • Column 6 is the preferred specification. The quality of institutions, significant at the 1% level, is the only statistically significant coefficient. A one standard deviation increase in the rule of law index corresponds to an increase in log incomes of 1.98. The estimated coefficients of integration and geography are both statistically insignificant and negative, when controlling for institutions • Although the magnitude of the coefficient slightly differs, the same results hold for the large sample (column 9)

28. IV Regression Results: Table 3 • Panel B (First stage regression results for the endogenous variables institution and integration) reveals that settler mortality has a negative, statistically significant (at the 1% level) effect on integration, integration has a positive but statistically insignificant effect on institutions, and that geography has a statistically significant impact (at the 1% level) on the quality of institutions • Sokoloff and Engerman discuss the latter, asserting that the differing factor endowments of the New World colonies affected the degree of socio-economic inequality in those places, and thus, the development of institutions • The F-statistics from the first stage regressions are also above 10 (in all cases), indicating that the instruments are not weak

30. IV Regression Results: Table 4 • In Table 4, the quality of institutions and integration were separately regressed on geography and on each other, using IV regression (Panel B) • The quality of institutions has a positive, statistically significant (at the 5% level) effect on integration: a unit increase in the quality of institutions increases the trade share by .45 units • The estimated effect of integration on the quality of institutions was positive but statistically insignificant

31. Explanation for the Greater Impact of Institutions • The authors conclude that the greater impact of institutions results from: the estimated direct effect on income being positive and large, the estimated direct effect of trade being negative (albeit statistically insignificant), and the indirect effect of trade on institutions is positive, although small and statistically insignificant • Combining the statistically significant results from Tables 3 and 4 for geography, Rodrik et al. find that the overall effect of a unit change in geography corresponds with an increase in log incomes of 1.49 units (.75*1.98). The authors assert that this larger impact results from the indirect effect on income that geography has through determining institutional quality (i.e. according to column 1 of Table 4, a unit change in geography corresponds with a .75 increase in the quality of institutions)

32. Application to the Solow Model • To address how institutions, integration, and geography influence growth, the authors regressed income per worker and the three proximate determinants, physical capital per worker, human capital per worker, and total factor productivity (a labor-augmenting parameter) on the “deeper” determinants

34. Application to the Solow Model • In all cases, the quality of institutions has a positive, statistically significant impact on income, capital accumulation, and productivity • The quality of institutions has the quantitatively greatest impact on physical capital accumulation. The authors suggest that this result emphasizes the important role that preventing property expropriation plays in enhancing the incentive to invest in physical capital • In every regression, the estimated coefficient on geography is negative, but statistically insignificant. The estimated coefficient on integration is similarly negative in every case

35. Robustness of Results

37. Table 6 • Checks the robustness of the results to “influential” observations (Ethiopia and Singapore are omitted in columns with one asterisk, Ethiopia is omitted in columns with two asterisks), neo-European countries (which consist of Australia, Canada and New Zealand, all of which are omitted in columns with three or four asterisks), and the inclusion of regional dummies • The effect of institutions and the conclusions about the (lack of) impact of geography and integration remains the same • The magnitude of the coefficient on institutions increases when neo-European countries are excluded, implying that the quality of institutions matters more in countries aside from those • The authors also control for legal origin, origin of colonizer, and religion (columns 3, 4, 5). Although these additional explanatory variables are at times independently or jointly significant, they do not affect the impact of institutional quality on income, nor the (lack of direct) impact of integration and geography. In fact, when controlling for these variables, the magnitude of the coefficient on institutional quality increases

39. Table 7 • Columns 1- 7 tests whether the choice of measure for the geography variable affects the results by substituting in a number of alternative measures • Some include: “percent of a country’s land in the tropics, access to the sea, number of frost days per month in winter, area covered by frost, whether a country exports oil, and mean temperature” • None of the substitutes alter the effect of the institution variable, nor the impact of the integration variable, the estimated coefficient of which remains negative and statistically insignificant • The oil dummy was the only substitute variable statistically significant (at the 5% level) in determining income

40. Table 7 • Also tested two geography variables related to malaria incidence used by Sachs (2003) in response to an earlier version of the paper • MAL94P: An estimate of the fraction of a country’s population that lives with the risk of malaria transmission • MALFAL: Multiplies MAL94P by an estimate of the fraction of malaria cases that involve the fatal species • However, malaria incidence is endogenous (i.e. perhaps greater income enables a country to obtain more vaccines, reducing incidence) and so, Sachs instruments for both measures using an index of “malaria ecology” (ME) • Malaria incidence is shown (in columns 8 and 9) to have a very negative and statistically significant impact on income. Although the estimated coefficient on the quality of institutions decreases, it is still statistically significant at the 1% level • Concerns over the endogeneity of the instrument • Malaria incidence is also prevalent in sub-Saharan Africa, and so the variable is correlated with regional dummies. Upon the inclusion of regional dummies (columns 10 and 11), neither malaria variable is statistically significant. In column 10, the quality of institutions only remains statistically significant at the 90% level

42. Table 8 • Robustness checks regarding the integration variable • Omission of market size regressors: Frankel and Romer (1999) argued that small countries participate in more trade, studies examining the effect of trade on income should thus control for country size. When area and population were included (column 1), the size of the coefficient on quality of institutions is unaffected, although statistical significance decreases to 5%. (Note: the sign on integration changes to positive, although remains statistically insignificant) • “Real Openness”: Calculated as the ratio of trade to PPP GDP, rather than nominal GDP which Alcala and Ciccone (2004) argued was a better measure of integration. The coefficient on institutional quality increased in magnitude, although statistical significance dropped to the 5% level (see column 5). Integration and geography enter the regression negatively, although the coefficients are statistically insignificant • In Appendix A, argue that there are problems with using the Alcala and Ciccone measure of real openness. In their regressions, real openness and nominal openness are defined by log Ropen = log Open + log P, where P is a country’s price level. However, Rodrik et. al assert that a country’s price level is highly correlated with income/productivity, guaranteeing that there will be a strong positive correlation between real openness and income • Moreover, for Alcala and Ciccone’s assertion to be valid, they need to show that instrumentation should fail when nominal openness is used, and work when real openness is used which they don’t

43. Table 8 • In columns 2, 4, and 6 the authors used a different instrument for integration than Frankel and Romer, who defined the dependent variable in their gravity equation as trade to PPP-adjusted GPD (which would be more consistent with Alcala and Ciccone’s measure of real openness). The authors instead defined the dependent variable as trade to nominal GDP. The results, however, are extremely similar; hence, the choice of instruments does not affect the authors’ main results • Substituted a measure of policy openness in column 7 for integration, promoted by Sachs and Warner (1995) and Krueger and Berg (2002). The coefficient on the quality of institutions remains positive and statistically significant, although at the 5% level and the “policy” variable enters the regression negatively (albeit, it is not statistically significant)

44. Comparison to Related Studies

45. Causal vs. Instrumental • Rodrik et al. assert that Acemoglu et al. and Easterly and Levine (2003) assign “a causal role to the settler mortality instrument” with the latter authors using “it as a geographical determinant of institutions such as ‘crops and germs,’ rather than viewing it as a device to capture the exogenous source of variation in institutions. Our view is that we should not elevate settler mortality beyond its status as an instrument, and avoid favoring either colonial view of development…or a geography-based theory of development”

46. The Effectiveness of Policy • Easterly and Levine (2003) “assert that (macroeconomic) policies do not have an effect on incomes, once institutions are controlled for” • Define policy as a flow variable and institutions as a stock variable, or “the cumulative outcome of past policy actions” where d is the rate at which institutional quality (I) decays and where a represents the impact of policy (p) on institutional quality • Should not regress income levels on institutional quality and policies: Incomes move slower whereas policy can suddenly change, and measures of institutional quality already account for the effect of policies • If income can be specified as ln y = BI + u, then the effect of policies can be determined by: i.e. by regressing the growth of income (found by taking its derivative) on policies

47. Related Theories About Institutions • Hall and Jones (1999) assert that “differences in capital accumulation, productivity, and therefore output per worker are driven by differences in institutions and government policies, which we call social infrastructure. We treat social infrastructure as endogenous, determined historically by location and other factors captured in part by language” • Use Sachs and Warner’s measure of a country’s openness to trade as a measure of social infrastructure • Use geographic distance from the equator and whether a Western European language, and in particular English, is spoken as the mother language as instrumental variables. Assert that countries influenced by Western Europe were more likely to develop social infrastructure more conducive to economic growth (by enhancing productivity) • As aforementioned, Sokoloff and Engerman (2000) assert “ascribing difference in development to differences in institutions raises the challenge of explaining where the differences in institutions come from” which they attribute to initial conditions, or “factor endowments”

48. Implications for Future Research

49. The Road Ahead… • While Rodrik et al. showed the importance of institutions in determining national income, the findings provide little guidance as to how, in practice, to improve the quality of institutions • “Our indicators of institutional quality are investors’ and other observers’ ratings of the institutional environment. They quantify these observers’ views as to the likelihood that investors will retain the fruits of their investments, the chances that the state will expropriate them, or that the legal system will protect their property rights…it remains unclear how the underlying evaluations and perceptions can be altered” • What form should property rights take? Would a system of private property rights be more effective compared to other schemes of property rights? • “There is growing evidence that desirable institutional arrangements have a large element of context specificity, arising from differences in historical trajectories, geography, political economy, or other initial conditions” • One size does not fit all