Globalization and Income Convergence

Globalization and Income Convergence Kaitlyn R. Wolf College of Business and Economics West Virginia University Morgantown, WV 26506-6025 em : Kaitlyn.Wolf@mail.wvu.edu Andrew T. Young College of Business and Economics West Virginia University Morgantown, WV 26506-6025 ph: 304 293 4526 em : Andrew.Young@mail.wvu.edu JEL Codes: E02, O11, O43, O47 Keywords: globalization, institutions, income convergence This version: January 2014 1

Globalization and Income Convergence Abstract: The income convergence literature suggests that poor countries can catch-up to rich ones conditional on sharing certain characteristics with rich countries. Good institutions such as strong property rights and rule-of-law are key amongst those characteristics. From a policy perspective this is disheartening because economists have little understanding of how to transplant those institutions to developing countries. Worse, good informal institutions seem to be a necessary condition for formal institutions to stick. However, to the extent that good institutions can arise as a spontaneous order from individuals interacting with one another, allowing for an open society may be an effective development policy. To evaluate this proposition we explore whether or not increased globalization fosters income convergence. Based on a panel of up to 184 countries covering the years 1970 to 2009 we conclude that it does. In particular, the social dimension (as opposed to the economic or political dimensions) of globalization is robustly related to income convergence. JEL Codes: E02, O11, O43, O47 Keywords: globalization, institutions, income convergence 2

1. Introduction According to the World Bank, an estimated 1.29 billion people lived on less than $1.25 a day in 2008. That is a marked improvement over the 1.94 billion who lived in extreme poverty in 1981. Given population growth, this represents a difference between 22 percent of the developing world s people in 2008 versus a staggering 52 percent in 1981. 1 While the extent of poverty alleviation is impressive, the absolute number of people who are extremely poor remains very large. The stark contrast between per capita incomes in the developed versus the developing worlds has spawned a large empirical literature on convergence starting with Barro & Sala-i- Martin (1992) and Mankiw, Romer, & Weil (1992). 2 This literature asks whether poor economies tend to catch-up to richer ones. The consensus answer: yes, conditional on having characteristics similar to those of rich economies. The findings of early cross-country studies are consistent with what Barro (2012) calls the iron law of convergence : the gap between current and long-run income levels narrows at a rate of 2 percent annually. Given a 2 percent rate of convergence a country s income gap will be halved in about 35 years. Later studies using panel data and including country fixed effects in their estimations report considerably higher convergence rates between 4 and 10 percent per year (e.g., Islam (1995) and Caselli, Esquivel, & Lefort (1996)). 3 But even based on the pessimistic iron law one may find it encouraging that substantial improvements in well-being can occur within a generation or less. 1 http://siteresources.worldbank.org/intpovcalnet/resources/global_poverty_update_2012_02-29-12.pdf; last accessed September 11, 2012. 2 Sala-i-Martin (1996) provides a review of the early literature. 3 However, Barro (2012) argues that the inclusion of country fixed effects biases convergence rate estimates upwards and that this bias is likely as large as the downward omitted variable bias that fixed effects estimation attempts to alleviate. 3

However, one must keep in mind that cross-country convergence rates are conditional on poor countries being able to emulate the characteristics of rich countries. This reality can be disheartening, particularly in light of studies reporting that, among those characteristics, institutions rule (Rodrick, Subramanian, & Trebbi, 2004). Evidence suggests that institutions such as well-defined and enforced property rights (Knack & Keefer (1995), Hall & Jones (1999) and Acemoglu, Johnson, & Robinson (2001, 2002) and Acemoglu & Johnson (2005)) and the rule-of-law (Barro (1996) and Rodrick et al. (2004)) are critical. Indeed, Knack (1997) argues that where income convergence is absent, a lack of good institutions is to blame. Economists and policymakers have little understanding of how to transplant such institutions to developing economies and it is unlikely that any given institution political or economic can be reformed independent of complementary institutions (Sobel & Coyne, 2011). In particular, informal institutions (such as trust and a belief in self-determination) may be necessary for formal institutional reforms to stick (Williamson, 2009). 4 If economists and policymakers have little understanding of how to transplant property rights and the rule-of-law, then they have next to no understanding of how to transplant norms such as trust. Hayek (1960, p. 27) observes that informal institutions consist in large measure of forms of conduct which [an individual] habitually follows without knowing why [and] uses because they are available to him as a product of cumulative growth without ever having been designed by one mind. Hayek s (1945) arguments regarding the price system and the use of knowledge in society are well-known; his later arguments concerning the growth of knowledge in the evolution 4 Based on a cross-country sample and data from the World Values Surveys and European Values Surveys, Williamson (2009) finds that formal institutions are only positively related to growth if they are grounded in strong informal institutions. Her results are consistent with the regression theorem argument of Boettke, Coyne, and Leeson (2008) that the likelihood of an institutional change succeeding is a function of that institution s relationship to individuals in the previous time period. In their terminology, informal institutions provide the mētis that (formal) institutional changes may or may not stick to. 4

of institutions receive somewhat less attention. Above all, claims Hayek (1960, p. 30), we should provide the maximum of opportunity for unknown individuals to learn of facts that we ourselves are yet unaware of and to make use of this knowledge in their actions. Given our ignorance of how to transplant institutions, an alternative is to increase the opportunities for individuals in poor economies to observe, experience, and experiment with the institutions of rich economies. This can foster a process of institutional change where endogenous institutions emerge spontaneously as the result of individuals actions, but are not formally designed (Boettke, Coyne, & Leeson, 2008, p. 335). An open society may allow for piecemeal institutional change where individuals in developing countries adopt only the changes that are, at that time, compatible with their current institutional framework; but in doing so they modify that framework, setting the stage for future incremental adoptions. With this in mind, we employ a panel of data from up to 184 countries covering the 1970 to 2009 period to ask whether increased globalization is associated with a higher rate of income convergence. We estimate the horizontal convergence rate (i.e., the rate at which a country closes the gap between its per capita income level and that of the richest nation in the sample) as a function of the KOF globalization indices described in Dreher (2006). The definition of globalization in this context is the process of creating networks of connections among actors at multi-continental distances, mediated through a variety of flows including people, information and ideas (Dreher, 2006, p. 1092). Separate indices are reported for the economic, political, and social dimensions of globalization. We find that increased globalization has a statistically significant and large effect on convergence. In particular, the results suggest that social globalization is associated with faster horizontal convergence. 5

Our study is related to the growing empirical literature on cross-country institutional spillovers and their effects. For example, Seldadyo et al. (2010) and de Groot (2011) find that countries tend to cluster according to their institutional quality. Also, de Groot (2011) empirically identifies cross-country spillovers in political freedoms. Bosker and Garretsen (2009) report that economic growth in a country is positively linked to the institutional quality of its neighbors. Furthermore, Simmons and Elkins (2004) find that countries do not copy the policies of their neighbors indiscriminately; rather, they tend to copy only those associated with good economic outcomes. Given that institutional spillovers are important for economic outcomes, globalization ostensibly opens up the channels for those spillovers. 5 We have organized this paper as follows. Section 2 outlines our approach to estimating the horizontal convergence rate as a function of, among other things, the extent of globalization. The data that we employ is described in section 3. Results of our analysis reported and discussed in section 4. We then provide a concluding discussion in the 5 th and final section. 2. Empirical Model Our analysis follows Xu and Li (2008) in modeling changes in the relative incomes of countries across time. Specifically, we model what Xu and Li (2008) refer to as horizontal convergence the rate at which the gap narrows between any given country s income per capita and that of the richest country. 5 Sheehan and Young (2013) report that increased Internet access is generally associated with increases in economic freedom. Internet access is one sub-component of the KOF social globalization index. The exception that they document is countries that start from very high initial levels of economic freedom. In those cases, they argue, it is likely that the Internet fosters emotional contagion and an increased demand for collective action through political processes. 6

Horizontal convergence is different than, though related to, the conditional convergence analyzed by Barro and Sala-i-Martin (1992) and Mankiw, Romer, & Weil (1992). In a conditional convergence regression of income growth, the estimated coefficient on the initial income level is used to infer the rate at which an economy approaches its balanced growth path. The balanced growth path itself is estimated via the inclusion of additional controls (e.g., the investment ratio; the population growth rate). Our approach, alternatively, takes the richest country s income level as the point of comparison and estimates the rate of horizontal convergence as a function of various controls, in particular the globalization indices. Horizontal convergence has at least a couple of advantages over the conditional convergence concept. First, we can make statements about the rate at which a country s standard of living is improving relative to the highest standard that is actually observed (as opposed to the unobserved and potentially quite low balanced growth path of that country). Second, we can estimate which factors are important determinants of how quickly that actual gap narrows. The horizontal convergence ratio is measured by the ratio of a country s (i s) income per capita to that of the richest in the same time period. Define this ratio as Si and then assume that, (2.1) S it 1 1 exp f Z it, where f(βzit) is the horizontal rate of income convergence, Zit is a vector of that rate s determinants, and β is a vector of parameters representing the relationships between the elements of Zit and the convergence rate. Equation (2.1) can be transformed into the form, it (2.2) L exp f Z it S it. 1 S it Our empirical model is derived by taking the natural log of (2), assuming a linear form for f, and appending an error term: 7

(2.3) it 0 j it, j it N ln L Z. j 1 Equation (2.3) specifies the horizontal convergence rate as a function of Zit. We estimate (2.3) by OLS. In some specifications we also include period fixed effects. In all cases we report heteroscedasticity-autocorrelation-consistent (HAC) standard errors. 3. Data For the construction of Sit (a country s relative real per capita GDP level in period t) we draw annual data from the World Bank s World Development Indicators (WDIs) from 1970 through 2009. We define 1 as the upper bound of. GDP per capita (GDP_PC) is reported in constant 2000 US$. We then take five year averages (1970-1974, 1975-1979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009). The panel is unbalanced with up to 184 countries. In principle, the highest average income country for each period constitutes the denominator of Sit. However, the highest average income country in our sample is typically, depending on the time period, Monaco or Liechtenstein. We consider these to be extreme outliers. (For 2005-2009 the former has a per capita GDP of $95, 885 while the latter has $80,388; by way of comparison the average US GDP per capita during that period is $37,905.) On a-priori grounds we are hesitant to use these countries as benchmarks. Therefore we instead take the highest average income OECD country as the benchmark for each period. The US provides this benchmark for 1970-1975 ($19,358) and 1975-1979 ($21,496); Switzerland for 1980-1984 ($29,271) and 1985-1989 ($31,761); and Japan for 1990-1994 ($35,206). For the remaining four periods Luxembourg is the benchmark ($39,264-$52,076). We also check the robustness of our results to using the US as the benchmark in every period. 8

We utilize the KOF indices as measures of different dimensions of globalization (Dreher (2006); Dreher et al. (2008b)). Broadly defined, globalization is conceived of as a process that erodes national boundaries, integrates national economies, cultures, technologies, and governance and produces complex relations of mutual interdependence (Dreher 2006, p.3). More specifically, the KOF indices attempt to gauge the networks and flows of ideas, people, capital, information, and goods across country borders. This index of globalization has been used widely in empirical studies and has been positively linked to several types of good economic outcomes. These outcomes include economic growth (Dreher, 2006), life expectancies ((Bergh and Nilsson, 2010a), and people s subjective evaluations of their own well-being (Hessami, 2011). 6 KOF rates 207 countries on the economic, social, and political dimensions of globalization. The overall globalization index (GLOB) is a weighted-average of the three dimension sub-indices. All of the KOF data is annual and the overall globalization index (as well as each of the economic, social, and political subindices) is on a scale of 0 to 100. We take 5-year averages corresponding to the same periods as our GDP data (1970-1974,..., 2000-2004, and 2005-2009). The economic globalization index (E_GLOB) is designed to incorporate two components: economic flows and restrictions to trade and capital. Data on trade, foreign direct investment, and portfolio investment are used to score economic flows; the index is increasing in these flows. Measures of hidden import barriers, mean tariff rates, and taxes on international trade are used 6 While the KOF social globalization index, specifically, has been positively linked to income inequality, particularly in developing economies (Bergh and Nilsson, 2010b), it has been negatively linked to gender inequality (Potrafke and Ursprung, 2012).Increased globalization has also been hypothesized to fiscally constraint governments by subjecting them to increased budgetary pressures from without. Dreher et al. (2008A) report evidence based on the KOF index that fails to confirm this so-called disciplining hypothesis. Samimi et al. (2012) report a negative relationship between the KOF index and inflation; alternatively, there authors fail to find an independent link between a more conventional measure of trade openness and inflation. 9

along with an index of capital controls to score trade and capital restrictions. The index is, of course, decreasing in these restrictions. Flows of information, ideas, images and people across international borders are the basis for the social globalization index (S_GLOB). This index involves three components. The first is personal contacts: the direct interaction among people across international borders using measures such as telecom traffic, tourism, and international letter volume. Information flows is the second component and is based on measures of Internet and television usage, as well as subscriptions to international newspapers. The third component is cultural proximity: the extent to which cultural beliefs move across borders. This component is based on measures such as the number of books imported and exported and the number of McDonald s restaurants and IKEA stores in a country. The social globalization index is increasing in each of these three components. Political globalization (P_GLOB) is the most straightforward of the globalization indices. It is based on the number of embassies and high commissions within a country; the number of international organizations of which the country is a member; as well as the number of UN peace missions the country has participated in. Political globalization is increasing in all of these measures. We include additional control variables from the WDIs. These control variables include gross capital formation (K); gross rates of primary school, secondary school, and tertiary school enrollment (PRIM_EDU, SEC_EDU, and TER_EDU, respectively); life expectancy (LIFE_EXP); and the rate of population growth (POP). Gross capital formation (the primary driver of convergence in neoclassical growth theory) is measured by investment as a percent of GDP. School enrollments are percentages of countries populations enrolled at each level and 10

control for investments in human capital. As a measure of the health of the labor force, life expectancy is included. The population growth rate is another standard control variable from neoclassical growth theory. Our dependent variable is also constructed from WDI data. GDP per capita is stated in constant 2000 US dollars. To control for political and economic institutions, three measures are used. Economic institutions are measured using the Economic Freedom of the World (EFW) Index from the Frasier Institute (Gwartney, Lawson, and Hall, 2011). The EFW ranking uses larger numbers to indicate larger levels of economic freedom within a country, with a maximum score of 10. Political institutions are controlled for using two measures, a measure of political freedom from the Freedom House and a measure of democracy from the Polity IV index. Freedom House creates a political freedom score based on an individual s ability to participate in the political process within their country, with lower scores indicating more freedom (Freedom House, 2012). Democracy is measured using the Polity IV Project s democracy variable, with accounts for three elements of democracy; the presence of institutions that allow citizens to express preferences about policies and leaders, whether or not formal constraints on the executive, and the availability of civil liberties to all citizens (Polity IV, 2013). Larger values for democracy indicate more democracy within the country. Combining these data we arrive at an unbalanced panel that includes up to 184 countries and covers 5-year periods from 1970 to 2009. Table 1 reports descriptions, sources, and summary statistics for all of the variables included in our analysis. 4. Results 11

Results from regressions of the specification shown in equation (2.3) are presented in Table 2. Columns 1 to 3, and column 6 provide pooled OLS results while columns 4 to 5 and 7 to 8 provide results that include period fixed effects to control for unobservable differences across time. Column (1) reports results from our baseline specification, including a positive and significant (1% level) estimated relationship between GLOB and the rate of horizontal convergence. Column (2) reports on an expansion of the baseline specification that includes the standard controls associated with neoclassical growth theory, and the column (3) specification includes both the neoclassical controls and measures of institutional quality. The estimated relationship between globalization and the horizontal convergence rate is positive and significant in all cases, However, the coefficient point estimate does decrease moving from column (1) to column (2), and then from column (2) to column (3). Including both neoclassical and institutional controls results in a point estimate of 0.020, implying that an increase in GLOB of 19 points (about 1 standard deviation) is all else equal associated with an increase in the horizontal convergence rate of about 38 percentage points. This is a large effect. A difference of 19 points is comparable to the 2005 globalization index differential between the US (about 76) and either Trinidad and Tobago or the Philippines (each about 54). The estimate, then, implies that if those latter countries were to become as globalized as the US then, all else equal, they would close the gap between their income and the richest country by an additional 38% over a 5 year period (or about 6.6% annually). Column (4) reports the results of an estimation analogous to the one in column (3) except that now period fixed effects are included. The coefficient estimate on GLOB increases to about 0.030, implying that a standard deviation increase in the globalization index is associated with, all else equal, an increase in the horizontal convergence rate of about 57 percentage points over 5 years (or about 9.5 percentage points annually). For this estimation (and all subsequent estimations 12

including period fixed effects) the F-statistic associated with a test of joint insignificance of the period effects rejects the null at the 1% significance level. These tests results suggest that there is relevant unobserved time variation in horizontal convergence rates that is not captured by our explicit control variables. However, we also note that the R 2 reported in column (3) is 0.679 and while the inclusion of period fixed effects results in an increase (to 0.844) is not particularly large. Therefore while the inclusion of period effects appears to be appropriate, our explicit control variables are accounting for about 68% of the variation in horizontal convergence rates. Focusing on column (4) (which we consider to be our preferred specification) most of the other control variables enter with the expected signs and are statistically significant (5% level or better). In particular, life expectancy and institutional quality are positively associated with horizontal convergence rates. (Recall that the POL_FREE measure is one where lower values correspond to more political freedoms.) Of the educational measures, only secondary enrollment enters significantly but the estimated effect is positive. The only result that is perhaps surprising is that population growth enters positively and significantly as a determinant of convergence rates. Standard neoclassical growth theory suggests a negative sign on this variable (e.g., Barro and Salai-Martin, 1992, p. 225). The estimated effect is fairly small: a standard deviation increase in a country s population growth rate (about 1.6%) is associated with an increase in the horizontal convergence rate of about 11 percentage points over five years (or more than 2 percentage points annually). Since this is only a control variable in our analysis we do not dwell on it any further. Columns (5) through (8) report various robustness checks. First, column (5) reports results that use the US as the benchmark per capita income level in each period (in place of the richest OECD country). The positive and significant relationship between globalization and the horizontal convergence rate is robust to this change in the benchmark. Quantitatively the coefficient estimate 13

on GLOB increases from 0.030 (column 3) to 0.044 (column 4). (Since this is a robustness check and the replacement of the US as the benchmark is somewhat arbitrary we do not put too much stock into this increase.) Then columns (6), (7), and (8) report results that are analogous to those reported in columns (3), (4) and (5) save for the fact that the Polity IV measure of democracy (DEMOC) replaces Freedom House s political freedom score (POL_FREE). This substitution of measures of the quality of political institutions does not change the results meaningfully. Table 3 presents the results of estimations that consider countries economic, social, and political globalization scores separately (E_GLOB, S_GLOB, and P_GLOB, respectively). All specifications in table 3 include period fixed effects. The estimations reported in columns (1) through (4) include the Freedom House measure of political institutions as a control; the estimations reported in columns (5) through (6) include the Polity democracy index. All estimations include investment, education, and population growth rate controls. When only economic globalization is included (columns (1) and (5)) it enters positively and significantly (5% level). The coefficient point estimates are both 0.011; considerably smaller than that associated with the overall globalization index in column (4) of table 2. The sample standard deviations on GLOB and E_GLOB are similar (17.525 versus 19.111) so the difference in coefficient estimates is meaningful. The coefficient estimates on the political globalization sub-index (columns (3) and (7)) are also small. The sample standard deviation of P_GLOB is larger than that of the other two sub-indices (26.458) but its effect is still roughly similar to that of economic globalization. Alternatively the estimated effects of social globalization (columns (2) and (6)) are comparable to that associated with the overall globalization index. These estimated effects are both statistically significant at the 1% level. Globalization, as measured by the KOF index, contributes to income convergence; in particular, the social dimension the flow 14

of information and the ideas and images of individuals from other countries appears to have a particularly large effect. Finally, columns (4) and (8) present results with the inclusion of all three subcomponents together. Column (4) reports that, upon inclusion of E_GLOB, S_GLOB, and P_GLOB, only the social measure of globalization remains significant. In column (6), when the Polity IV index replaces the Freedom House measure of political freedoms, both social and political globalization enter significantly (1% level). However, again the estimated effect of S_GLOB is considerably larger than that associated with political globalization. Indeed, the point estimates on both variables (0.033 and 0.006) are virtually the same as when each variable in included separately; without the other. Column (6) provides some evidence that the political dimension of globalization is relevant to income convergence, but columns (4) and (6) both provide evidence that the significant and large estimated effect of social globalization is robust. Table 4 presents the results of estimations of the horizontal convergence rate when globalization is not controlled for but, as in Xu and Li (2008), political and economic institutions are. Column (1) presents results of estimates based on controlling for political institutions using POL_FREE whereas column (2) presents results of estimations using the DEMOC measure of political institutions. The results suggest that previous estimates of the relationship between economic and political institutions may be overstated. Column (1) in table 4 is analogous to column (4) in table 2 and column (4) in table 3, except that globalization controls are not included in table 4. The point estimates for ECON_FREE and POL_FREE are meaningfully larger in table 4 than in both specifications of interest in tables 2 and 3. In particular, the coefficient estimate on ECON_FREE increases from between 0.215 and 0.219 to 0.332 (an increase of more than 51%). These results suggest that the importance of these institutional 15

variables as determinants of horizontal convergence rates is likely overestimated when globalization measures are omitted. Column (2) in table 4 is most similar to column (7) in table 2 and column (8) in table 3. Again, the point estimates for the institutional controls (ECON_FREE and DEMOC) are smaller in absolute value in tables 2 and 3 than in table 4. These results, again, suggest that estimates of the relationship between horizontal convergence and institutions may be overestimated when globalization measures are not controlled for. Economic and political freedoms are, of course, highly correlated with globalization. Indeed, these freedoms are what allow for the integration that constitutes globalization. Therefore, we do not interpret our results as discounting the importance of such institutions. Rather, globalization and, in particular, the social integration of countries with one another is likely an important channel through which economic and political freedoms lead to income convergence. Again, these freedoms provide the maximum of opportunity for unknown individuals to learn of facts that we ourselves are yet unaware of and to make use of this knowledge in their actions (Hayek, 1960, p. 30). Whereas economic and political freedoms allow for individuals to better use the knowledge available to their society, the globalization that those freedoms promote leads to the growth of knowledge that is there to be used. 5. Conclusions We explore whether increased globalization promotes income convergence. Based on a panel of up to 184 countries covering the years 1970 to 2009 we conclude that it does. In particular, the social dimension (as opposed to the economic or political dimensions) of globalization is robustly related to income convergence. 16

The implications of our results are twofold. First, the estimated impact of economic and political freedoms on income convergence from previous research (e.g., Xu and Li (2008)) is overestimated given that the effects on convergence from globalization have yet to be accounted for. In particular, the promotion of globalization is likely a channel through which the positive correlation between such freedoms and income convergence (in part) manifests itself. Our research adds to the literature by presenting estimates of the association between globalization and income convergence, while controlling for institutional factors. Specifically, our results suggest that social globalization may be of particular importance for increasing the rate of horizontal convergence. Additionally, our results present evidence that increases in globalization may allow for the spread of beneficial institutions and policies across country borders, and in turn may positively impact living standards in globalized countries. Future research in this area may look at the channels through with globalization promotes convergence, and how globalization spreads institutions. 17

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Table 1. Descriptions, sources, and summary statistics for variables. Variable Description Source Min. Max. Mean St. Dev. GDP_PC GDP per capita (constant 2000 US$) World Bank WDIs 85.21 95,885.27 7,049.92 11,172.99 S Ratio of GDP to Highest Income Country GDP Xu and Li (2008),WDIs 0.002 1.000 0.190 0.262 S US Ratio of GDP to US GDP Xu and Li (2008),WDIs 19358.434 37905.256 28098.800 6285.936 L Logistic Transformation of S equal to S/(1-S) 0.002 179.135 0.705 5.505 Logistic Transformation of S L US equal to S US /(1- US S US ) 0.002 4987.859 7.184 157.355 LN(L) Natural Log of Logistic Transformation of S -6.335 5.188-2.574 1.922 LN(L US ) Natural Log of Logistic Transformation of S US -6.036 8.515-2.378 1.969 LN(S US ) Natural Log of Ratio of GDP to US GDP GLOB Overall Globalization Index KOF 13.809 92.375 44.948 17.525 E_GLOB Economic Globalization Index KOF 9.575 97.921 49.469 19.111 S_GLOB Social Globalization Index KOF 6.130 92.456 40.452 20.827 P_GLOB Political Globalization Index KOF 1.000 97.698 46.070 26.458 DEMOC Polity IV Democracy Score Polity IV Project 0.000 10.000 4.159 4.086 ECON_FREE Economic Freedom of the World Score Fraser Institute 1.782 9.141 5.934 1.347 POL_FREE Freedom House Political Freedom Score Freedom House 1.000 7.000 3.845 2.020 K Gross Capital Formation (% of GDP) World Bank WDIs 3.575 86.793 23.121 8.194 PRIM_EDU Primary School Enrollment Rate (Gross) World Bank WDIs 11.518 216.724 96.080 24.572 SEC_EDU Secondary School Enrollment Rate (Gross) World Bank WDIs 0.140 164.595 59.092 34.012 TER_EDU Tertiary School Enrollment Rate (Gross) World Bank WDIs 0.000 100.100 17.997 19.034 LIFE_EXP Life Expectancy at Birth World Bank WDIs 28.871 82.557 64.296 10.889 POP Population Growth Rate World Bank WDIs -4.645 16.245 1.799 1.643 21

Table 2. Regressions of the horizontal convergence rate on globalization indices and other controls, 1970-2009. (1) (2) (3) (4) (5) (6) (7) (8) (Pooled OLS) (Pooled OLS) (Pooled OLS) (Period Effects) (Pooled OLS) (Period Effects) (Period Effects) (L US ) (Period Effects) (L US ) GLOB 0.076*** 0.046*** 0.020** 0.030*** 0.044*** 0.026*** 0.034*** 0.053*** (0.002) (0.004) (0.008) (0.007) (0.007) (0.009) (0.007) (0.007) K 0.002 0.009-0.009-0.011* 0.008-0.008-0.010* (0.006) (0.008) (0.006) (0.006) (0.008) (0.006) (0.006) PRIM_EDU -0.006** -0.012*** -0.003-0.004* -0.011*** -0.001-0.001 (0.002) (0.003) (0.002) (0.002) (0.003) (0.002) (0.002) SEC_EDU 0.008*** 0.021*** 0.018*** 0.020*** 0.018*** 0.015*** 0.015*** (0.003) (0.004) (0.003) (0.003) (0.004) (0.003) (0.003) TER_EDU -0.016*** -0.015*** 0.002-0.004-0.012*** 0.006** 0.001 (0.003) (0.004) (0.003) (0.004) (0.004) (0.003) (0.003) LIFE_EXP 0.085*** 0.082*** 0.060*** 0.059*** 0.084*** 0.053*** 0.047*** (0.008) (0.009) (0.007) (0.008) (0.009) (0.008) (0.008) POP 0.084** 0.117** 0.068** 0.108*** 0.083 0.056 0.079* (0.036) (0.052) (0.034) (0.037) (0.056) (0.040) (0.042) ECON_FREE -0.100* 0.219*** 0.123** -0.080 0.224*** 0.138*** (0.060) (0.048) (0.049) (0.060) (0.047) (0.046) POL_FREE -0.247*** -0.114*** -0.123*** (0.037) (0.025) (0.030) DEMOC 0.057*** 0.028** 0.021* (0.017) (0.011) (0.011) Countries 184 167 105 105 104 108 108 107 Observations 1,205 913 587 587 567 608 608 589 F-stat. (redundant effects) 220.68*** (0.000) 169.77*** (0.000) 262.24*** (0.000) 220.73*** (0.000) Adj. R 2 0.476 0.622 0.679 0.844 0.794 0.671 0.853 0.833 Notes: *, **, and *** denote, respectively, significance at the 10, 5, and 1 percent levels. HAC standard errors are in parentheses. Dependent variable is ln(l it ), where. Constants are included in regressions though not reported above. Period Effects are fixed period effects. The null hypothesis for the redundant fixed effects test is that the fixed effects are jointly insignificant. L US indicates that dependent variable relative per capita GDP was calculated using US GDP at the benchmark for all periods. Panel is unbalanced and based on 5-year periods (1970-1974, 1975-1979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009). 22

Table 3. Regressions of the horizontal convergence rate on economic, social, and political globalization indices and other controls, 1970-2009, using period fixed effects. (1) (2) (3) (4) (5) (6) (7) (8) (Period Effects) (Period Effects) (Period Effects) (Period Effects) (Period Effects) (Period Effects) (Period Effects) (Period Effects) E_GLOB 0.011** -0.004 0.011** -0.005 (0.005) (0.005) (0.005) (0.005) S_GLOB 0.031*** 0.033*** 0.030*** 0.033*** (0.005) (0.004) (0.004) (0.004) P_GLOB 0.004* 0.003 0.008*** 0.006*** (0.002) (0.002) (0.002) (0.002) K -0.013** -0.005-0.013** -0.004-0.014** -0.006-0.012** -0.003 (0.006) (0.006) (0.006) (0.006) (0.006) (0.005) (0.006) (0.006) PRIM_EDU -0.004* -0.001-0.004* -0.001-0.002 0.000-0.002 0.000 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) SEC_EDU 0.021*** 0.016*** 0.024*** 0.016*** 0.019*** 0.015*** 0.022*** 0.016*** (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) (0.002) (0.003) TER_EDU 0.005* 0.002 0.003 0.001 0.010*** 0.005* 0.007*** 0.003 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) LIFE_EXP 0.068*** 0.051*** 0.068*** 0.050*** 0.064*** 0.049*** 0.062*** 0.046*** (0.007) (0.007) (0.008) (0.008) (0.007) (0.007) (0.008) (0.007) POP 0.042 0.047 0.054 0.057* 0.022 0.053 0.048 0.075** (0.034) (0.033) (0.037) (0.035) (0.039) (0.034) (0.042) (0.036) ECON_FREE 0.269*** 0.203*** 0.329*** 0.215*** 0.292*** 0.219*** 0.344*** 0.232*** (0.050) (0.047) (0.046) (0.051) (0.049) (0.042) (0.039) (0.049) POL_FREE -0.135*** -0.099*** -0.147*** -0.099*** (0.025) (0.023) (0.025) (0.024) DEMOC 0.037*** 0.031*** 0.037*** 0.029*** (0.011) (0.010) (0.011) (0.010) Countries 105 105 105 105 108 108 108 108 Observations 587 587 587 587 608 608 608 608 F-stat. (redundant effects) 209.94*** (0.000) 235.12*** (0.000) 197.52*** (0.000) 207.87*** (0.000) 231.37*** (0.000) 264.30*** (0.000) 210.63*** (0.000) 261.30*** (0.000) Adj. R 2 0.835 0.852 0.832 0.853 0.840 0.859 0.839 0.861 Notes: *, **, and *** denote, respectively, significance at the 10, 5, and 1 percent levels. HAC standard errors are in parentheses. Dependent variable is ln(l it ), where. Constants are included in regressions though not reported above. Period Effects are fixed period effects. The null hypothesis for the redundant fixed effects test is that the fixed effects are jointly insignificant. L US indicates that dependent variable, relative per capita GDP, was calculated using US GDP at the benchmark for all periods. Panel is unbalanced and based on 5-year periods (1970-1974, 1975-1979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009). 23

Table 4. Regressions of the horizontal convergence rate on economic and political institutional measures and other controls, 1970-2009, using period fixed effects. (1) (2) (Period Effects) (Period Effects) K -0.013** -0.015** (0.006) (0.006) PRIM_EDU -0.004* -0.002 (0.002) (0.002) SEC_EDU 0.024*** 0.022*** (0.002) (0.002) TER_EDU 0.005 0.010*** (0.003) (0.003) LIFE_EXP 0.069*** 0.065*** (0.007) (0.008) POP 0.043 0.029 (0.036) (0.039) ECON_FREE 0.332*** 0.356*** (0.046) (0.039) POL_FREE -0.148*** (0.024) DEMOC 0.040*** (0.011) Countries 105 109 Observations 588 612 F-stat. 209.85*** 214.79*** (redundant effects) (0.000) (0.000) Adj. R 2 0.831 0.833 Notes: *, **, and *** denote, respectively, significance at the 10, 5, and 1 percent levels. HAC standard errors are in parentheses. Dependent variable is ln(lit), where. Constants are included in regressions though not reported above. Period Effects are fixed period effects. The null hypothesis for the redundant fixed effects test is that the fixed effects are jointly insignificant. GDP at the benchmark for all periods. Panel is unbalanced and based on 5-year periods (1970-1974, 1975-1979, 1980-1984, 1985-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009). 24