During the period of reforms that began in the early 1990s, economic growth in Central America recovered from the poor performance of the 1980s.1 Yet, in spite of the region achieving a welcome degree of macroeconomic stability, growth in the 1990s still fell short of the record achieved in the 1960s and 1970s. It also fell short of more dynamic emerging market countries, notably in Asia. Despite the more recent stronger performance, there continue to be concerns about the region’s ability to grow at a pace that will significantly raise living standards and reduce poverty.2 Underscoring this challenge, even while the incidence of poverty edged down in the region during the 1990s, it remained well above that of Latin America as a whole at the end of the decade (Figure 2.1).
Poverty Rates, Nationally Defined Poverty Line
(Percent of population)
Source: Economic Commission for Latin America and the Caribbean, Social Panorama of Latin America, 2006.Poverty Rates, Nationally Defined Poverty Line
(Percent of population)
Source: Economic Commission for Latin America and the Caribbean, Social Panorama of Latin America, 2006.Poverty Rates, Nationally Defined Poverty Line
(Percent of population)
Source: Economic Commission for Latin America and the Caribbean, Social Panorama of Latin America, 2006.This chapter first reviews the stylized facts regarding the growth performance in Central America since the 1960s. It then analyzes the proximate sources of growth over this period using a standard growth-accounting framework, both for the region as a whole and for individual countries. The chapter then reviews some of the explanations for the region’s relatively unsatisfactory growth performance in the context of the broader literature on growth across countries, focusing on shortfalls in institutions and, in particular, on business regulations. Empirical support for the role of weak institutions is then presented in a panel regression framework, illustrating the possible importance of such effects for Central America.
Stylized Facts
Central America, and Latin America as a whole, experienced relatively rapid growth during the 1960s and 1970s. Real GDP growth in Central America averaged about 5½ percent during the two decades as a whole (Table 2.1 and Figure 2.2). Growth was similar across countries during the 1960s, ranging from Nicaragua at the high end with 7½ percent to the Dominican Republic at the low end with 4¾ percent. The positions of the best and worst performers reversed in the 1970s, however, and the dispersion increased sharply, as the Dominican Republic grew by 8¼ percent on average, while Nicaragua’s growth plunged to zero. Developments were more stable in other countries in the region during the 1960s and 1970s, with most witnessing relatively robust growth.
Central America: Long-Term Real GDP Growth by Country
(In percent a year)
Central America: Long-Term Real GDP Growth by Country
(In percent a year)
| 1960–69 | 1970–79 | 1980–89 | 1990–99 | 2000–05 | 1960–2005 | |
|---|---|---|---|---|---|---|
| Costa Rica | 5.9 | 6.3 | 2.1 | 5.4 | 3.7 | 4.7 |
| Dominican Republic | 4.8 | 8.2 | 3.8 | 4.6 | 4.3 | 5.1 |
| El Salvador | 6.0 | 3.9 | –2.1 | 4.9 | 2.2 | 3.0 |
| Guatemala | 5.5 | 5.9 | 0.9 | 4.1 | 2.7 | 3.8 |
| Honduras | 4.9 | 5.8 | 2.5 | 2.8 | 3.9 | 4.0 |
| Nicaragua | 7.4 | 0.0 | –0.9 | 3.0 | 3.3 | 2.6 |
| Central America | 5.7 | 5.3 | 1.2 | 4.4 | 3.4 | 3.9 |
Central America: Long-Term Real GDP Growth by Country
(In percent a year)
| 1960–69 | 1970–79 | 1980–89 | 1990–99 | 2000–05 | 1960–2005 | |
|---|---|---|---|---|---|---|
| Costa Rica | 5.9 | 6.3 | 2.1 | 5.4 | 3.7 | 4.7 |
| Dominican Republic | 4.8 | 8.2 | 3.8 | 4.6 | 4.3 | 5.1 |
| El Salvador | 6.0 | 3.9 | –2.1 | 4.9 | 2.2 | 3.0 |
| Guatemala | 5.5 | 5.9 | 0.9 | 4.1 | 2.7 | 3.8 |
| Honduras | 4.9 | 5.8 | 2.5 | 2.8 | 3.9 | 4.0 |
| Nicaragua | 7.4 | 0.0 | –0.9 | 3.0 | 3.3 | 2.6 |
| Central America | 5.7 | 5.3 | 1.2 | 4.4 | 3.4 | 3.9 |
Central America: Long-Term Real GDP Growth
(In percent)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Central America: Long-Term Real GDP Growth
(In percent)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Central America: Long-Term Real GDP Growth
(In percent)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).The picture changed dramatically in the 1980s, as the region was adversely affected by armed conflicts and external shocks, combined with poor policy responses, as in much of Latin America. The negative external shocks included the oil and debt crises, soaring inflation, and steep declines in the prices of coffee and fruits, which are among the region’s leading exports to the developed world. Consequently, Central America experienced a widespread economic downturn, with growth averaging only 1¼ percent in the 1980s. Nicaragua witnessed a particularly spectacular collapse, as political turmoil led to a sharp decline in real per capita GDP in 1979, followed by stagnation through the 1980s (Figure 2.3).3 El Salvador was also hard hit, experiencing a cumulative decline in output of almost 30 percent during 1979–83. Costa Rica, Guatemala, and Honduras had contractions in output in the early 1980s, albeit less severe than in Nicaragua and El Salvador. The Dominican Republic presents an interesting exception to the weak performance during this period, as growth continued at a relatively robust pace during the early 1980s before slowing later in the decade.
Real Per Capita GDP by Country
(In constant 2000 U.S. dollars)
Sources: Penn World Table Version 6.1; and IMF, World Economic Outlook database.Real Per Capita GDP by Country
(In constant 2000 U.S. dollars)
Sources: Penn World Table Version 6.1; and IMF, World Economic Outlook database.Real Per Capita GDP by Country
(In constant 2000 U.S. dollars)
Sources: Penn World Table Version 6.1; and IMF, World Economic Outlook database.After the “lost decade” of the 1980s, the 1990s ushered in signs of recovery in most Central American countries. Democratic changes, structural reforms, and global economic stability enabled the region to achieve modest but sustained increases in output, with real GDP growth averaging about 4½ percent during the decade. Even with this recovery, however, growth was a full percentage point below the average of the 1960s and 1970s, with only El Salvador approaching the rate achieved during those decades. During the first five years of the current decade, there were some renewed declines in growth in the region, with the pace on average falling to 3 percent. El Salvador experienced a particularly sharp reduction in growth to slightly less than 2 percent during this period, while Honduras was the only country in the group to witness some improvement in growth relative to the 1990s. By 2005, the level of real per capita GDP was higher than that in the late 1970s in only two countries—Costa Rica and the Dominican Republic (Figure 2.3).
The slowing of GDP growth during 2000–05 raises the question of how much of the improved performance in the 1990s was transitory. In particular, the performance in the 1990s may have partly reflected temporary rebounds from the recessions of the 1980s. In addition, the durability of the region’s expansion during the past decade is in question given that it was primarily fueled by rising maquila exports to the United States that have been subject to increasing competition from other countries, particularly in Asia. To better understand the factors that have underpinned the changing growth fortunes of the region since the 1960s, the next section looks at the historical experience from the point of view of a growth-accounting framework, before turning to structural explanations for why growth in the region has not been stronger.
Growth Accounting
To assess the proximate determinants of the shifts in growth in the region since the 1960s, a growth-accounting exercise was performed that decomposed changes in output into the contributions of the factor inputs, leaving the unexplained component of growth as a residual (Box 2.1). In line with the terminology originally adopted by Solow (1957), we refer to this residual as total factor productivity (TFP). Over the longer term, TFP captures intangible aspects of human and technological progress that allow capital and labor to increase their productivity. Longer-term productivity is also influenced by the efficiency of the allocation of resources, as an inefficient allocation can take a country inside its production frontier even if labor and capital are fully utilized.
Over the period from 1960–2005, the growth-accounting framework suggests that TFP made virtually no contribution to output growth in Central America. Real output per worker in the region rose by an average of about 1¾ percent annually, but all of the increase reflected capital deepening as opposed to productivity growth (Table 2.2). This lack of productivity growth was characteristic of Latin America as a whole, although real output per worker grew even less quickly in Latin America due to a lower contribution of capital accumulation. In East Asia, by way of comparison, growth in real output per worker averaged 3¾ percent over this period, with both capital and TFP making significant contributions. The example of China is particularly notable, with a contribution of TFP to growth of close to 3 percentage points per year.
Growth Accounting Across Regions, 1960–2003
Growth Accounting Across Regions, 1960–2003
| Contributions of: | |||
|---|---|---|---|
| Growth in Output per Worker | Capital | TFP | |
| Central America (1960–2005) | 1.8 | 1.8 | 0.0 |
| Latin America | 1.0 | 0.9 | 0.1 |
| East Asia (excluding China) | 3.8 | 2.8 | 1.0 |
| China | 5.0 | 2.2 | 2.7 |
Growth Accounting Across Regions, 1960–2003
| Contributions of: | |||
|---|---|---|---|
| Growth in Output per Worker | Capital | TFP | |
| Central America (1960–2005) | 1.8 | 1.8 | 0.0 |
| Latin America | 1.0 | 0.9 | 0.1 |
| East Asia (excluding China) | 3.8 | 2.8 | 1.0 |
| China | 5.0 | 2.2 | 2.7 |
Growth Accounting Framework
We decompose output growth using the neoclassical Cobb-Douglas production function:
where A corresponds to total factor productivity, K to the physical capital stock, L to labor, and α to the share of capital. Taking logs and time derivatives gives the traditional equation for the estimated growth rate of total factor productivity (TFP):
TFP growth = Output growth - (Capital share)*Capital growth - (Labor share)*Labor growth.
Labor input is measured using the size of the labor force. The capital stock series were built using the perpetual-inventory method:
with an annual depreciation rate δ of 4 percent, while α was set to 33 percent, in line with typical estimates (see Loayza, Fajnzylber, and Calderon, 2005). The initial capital stock was calculated as
where Y0 is output in 1960, I is gross fixed investment in 1960, δ is the assumed depreciation rate, and g is average output growth during the 1960s.
Looking at the experience in Central America by subperiods, changes in the contribution of TFP have been the main factor explaining shifts in output growth over time (Figures 2.4 and 2.5).4 During the 1960s, the favorable growth performance reflected a contribution of TFP of slightly over 1 percentage point per year. While output continued to grow at a relatively rapid pace in the 1970s, its underpinnings changed, as TFP growth came to a halt while the contribution of capital accumulation increased. This unbalanced combination of capital accumulation without productivity growth proved unsustainable, however, and the shocks in the late 1970s and early 1980s led to both a collapse in TFP and weak investment. The recovery in the region during the 1990s was primarily associated with a return to positive productivity growth, albeit at a somewhat slower pace than in the 1960s, combined with some increase in the capital contribution. In terms of the experience over 2001–05, the slowing in growth was associated with a renewed decline in TFP, albeit at a more moderate pace than during the 1980s.
Central America: Growth Accounting
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and World Economic Outlook (2005).Central America: Growth Accounting
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and World Economic Outlook (2005).Central America: Growth Accounting
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and World Economic Outlook (2005).
Central America: Per Capita GDP and Total Factor Productivity
(Logarithms)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and IMF, World Economic Outlook (2005).Central America: Per Capita GDP and Total Factor Productivity
(Logarithms)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and IMF, World Economic Outlook (2005).Central America: Per Capita GDP and Total Factor Productivity
(Logarithms)
Source: IMF staff estimates using World Bank, World Development Indicators (2005) and IMF, World Economic Outlook (2005).There are interesting similarities and differences in the experiences of countries within the region using the growth-accounting framework. As is the case for the region as a whole, it is evident that variations in real per capita GDP, both across countries and over time, are closely associated with movements in TFP (Figure 2.6).5 From the early 1960s through the late 1970s, TFP rose in almost all of the countries in this group—the exception being Nicaragua, where productivity stagnated from the mid-1960s on. There was also a notable flattening of productivity growth in El Salvador in the 1970s. It is interesting that these two countries experienced the sharpest drops in output when conditions deteriorated at the end of the 1970s, suggesting that weak “fundamentals,” as reflected in the absence of sustained productivity growth, left them particularly exposed to unfavorable shocks.
Real Per Capita GDP and Total Factor Productivity by Country
(In logarithms)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Real Per Capita GDP and Total Factor Productivity by Country
(In logarithms)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Real Per Capita GDP and Total Factor Productivity by Country
(In logarithms)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).The early 1980s witnessed sharp declines in TFP and output in most of the other countries in the region, but they were generally less severe than those in Nicaragua and El Salvador. In Honduras, Guatemala, and Costa Rica, the recessions of the early 1980s were fairly short-lived, and renewed growth in productivity and output was evident by the middle of the decade (albeit at a subdued pace in Honduras). The only country to avoid a significant shock to output in the early 1980s was the Dominican Republic, although even its performance was relatively lackluster, as the downward trend in TFP that began in the mid-1970s continued through the 1980s.
The 1990s were characterized by a return to rising productivity levels in most of the region. Together with the Dominican Republic, the countries that recovered most quickly from the crises of the early 1980s—Costa Rica and Guatemala—also experienced the steadiest productivity growth during the 1990s. El Salvador and Nicaragua also witnessed some recovery in productivity in the 1990s, but the growth phases were shorter lived. Honduras was a notable exception to the picture of improvement in the 1990s, as productivity fell steadily throughout the decade.6
The more recent experience during 2000–05 indicates that the slowing in output growth in the region has been associated with renewed declines in productivity in several of the countries that experienced recoveries in the 1990s. This group includes Guatemala, El Salvador, the Dominican Republic, and Nicaragua. Productivity in Honduras appeared to have bottomed out in this period after the declines of the 1990s, but without signs of a meaningful recovery. This leaves Costa Rica as the only country in the group that has experienced steadily rising productivity levels since the shocks of the early 1980s. The weak productivity performance in most of these countries over the past five years underscores concerns about medium-term prospects in the absence of productivity-enhancing reforms. To better understand the underlying factors that may be holding back growth in the region, the next section reviews the evidence on structural impediments to growth, followed by an empirical analysis of the role of institutions in the context of Central America.
Bottlenecks to Productivity Growth
There is, of course, an extensive literature on the sources of growth in developing countries, and the factors that hold back progress in some countries, while encouraging it in others. Zettelmeyer (2006) surveys the evidence for Latin America, which is particularly relevant given that the experience in Central America has been similar to that in the rest of the region, at least in terms of GDP and productivity growth. He observes that the weak performance in Latin America has been associated with macroeconomic volatility, relatively high income inequality, and a low degree of external openness compared with other regions.7
In terms of macroeconomic volatility, the historical record for Central America appears to be typical of the rest of Latin America. The evidence presented in Sahay and Goyal (2006) indicates that the role of factors such as external growth, terms-of-trade shocks, and policy reversals has been similar in Central American countries to elsewhere, and that volatility in these factors has been associated with low growth throughout Latin America. The most dramatic example, of course, is the debt crisis of the early 1980s. As shown in Figure 2.7, real GDP per capita for the region as a whole declined by close to 20 percent from the late 1970s to the mid-1980s, and the growth path appears to have subsequently shifted down permanently. This shift is perhaps not surprising in light of the historical evidence on the effects of major shocks on long-term growth, indicating that some of the output loss tends to be permanent.8 But it is more notable that, after a period of stagnation, the subsequent slope of the growth path was flatter than prior to the “lost decade,” even as one might have expected partial recovery from the output losses of the 1980s, and also as the region regained macroeconomic stability (Sahay and Goyal, 2006).
Central America: Real Per Capita GDP
(Logarithm)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Central America: Real Per Capita GDP
(Logarithm)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).Central America: Real Per Capita GDP
(Logarithm)
Sources: World Bank, World Development Indicators (2005); and IMF, World Economic Outlook (2005).The evidence, then, suggests that factors beyond the historical record of macroeconomic volatility have been holding back growth in the region, at least since the beginning of the 1990s. Of the other two characteristics noted by Zettelmeyer (2006), income inequality in Central America is typical of that in Latin America as a whole (Sahay and Goyal, 2006), and thus would be expected to play a similar role. In contrast, regarding the third factor—trade openness—Central America has typically had higher trade shares than the rest of Latin America, and has also had more trade with the United States—although both gaps have closed somewhat in recent years (Figures 2.8 and 2.9).9 On this basis, one would expect trade to have positively affected growth in Central America in light of the extensive cross-country evidence linking trade and growth, and also the finding of Arora and Vamvakidis (2005) that income levels benefit positively from trading with faster-growing, more developed countries.
Trade Openness: Total Exports Plus Imports
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).Trade Openness: Total Exports Plus Imports
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).Trade Openness: Total Exports Plus Imports
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).
Exports to the United States
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).Exports to the United States
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).Exports to the United States
(In percent of GDP)
Sources: IMF, Direction of Trade database; and World Economic Outlook (2005).Part of the reason why trade has not provided a greater boost to growth in Central America may relate to the structure of trade in the region. Lederman and Maloney (2003) focus on factors specific to the composition of trade in Central America, such as resource abundance, export concentration, and the structure of intra-industry trade. They find that export concentration has been a significant factor hampering growth in the region—a critical conclusion, since more than 50 percent of the region’s exports are comprised of maquilas, which are exposed to direct competition from China and Mexico. Yet, studies of this nature do not explain why the structure of trade has evolved in a particular direction, and in particular why the region has not proven to be more dynamic in adjusting to changing trade opportunities, and in diversifying its export industries toward higher value-added activities.
The lack of dynamic adaptation in Central America’s trade raises the issue of the role that market flexibility and entrepreneurship play in translating the advantages of trade opportunities into economic growth. In this regard, Dollar and Kraay (2002) analyze the effects of both institutions and trade on growth, finding a joint role for trade and institutions in the long run. A possible interpretation is that sound institutions create the appropriate incentives for reallocating resources productively in the face of new trading opportunities. In the same vein, Bolaky and Freund (2004) examine the effects of trade openness on growth, arguing that trade does not stimulate growth in economies with excessive regulation because bureaucratic red tape prevents resources from moving into more productive sectors.10 Consequently, increases in trade are more likely to occur in the wrong sectors, amplifying existing distortions. Similarly, Chang, Kaltani, and Loayza (2005) find strong complementarities between institutional reforms and trade liberalization in enhancing growth.
The literature, then, suggests an important link between the quality of the institutional framework and the benefits of trade openness. This is a key point for Central America, where business regulation is notably burdensome (Table 2.3), and institutional quality tends to be weak. The implications of this finding are particularly important as the region enters a new period of trade liberalization with the entry into effect of the CAFTA-DR with the United States and prospective trade negotiations with the European Union. Benefiting from the opportunities associated with this agreement can provide a fundamental stimulus to a more satisfactory growth performance in the region in the appropriate institutional environment.11
Ranking of Ease of Doing Business
Ranking of Ease of Doing Business
| Country | Rank |
|---|---|
| Costa Rica | 105 |
| Dominican Republic | 117 |
| El Salvador | 71 |
| Guatemala | 118 |
| Honduras | 111 |
| Nicaragua | 67 |
| Panama | 81 |
| United States | 3 |
| Canada | 4 |
| Chile | 28 |
| Mexico | 43 |
Ranking of Ease of Doing Business
| Country | Rank |
|---|---|
| Costa Rica | 105 |
| Dominican Republic | 117 |
| El Salvador | 71 |
| Guatemala | 118 |
| Honduras | 111 |
| Nicaragua | 67 |
| Panama | 81 |
| United States | 3 |
| Canada | 4 |
| Chile | 28 |
| Mexico | 43 |
Empirical Analysis
This section analyzes the empirical evidence on the effects of institutions, as well as other factors, on growth in Central American countries. The above discussion points to the lack of improvements in productivity as the proximate factor restraining growth in the region, notwithstanding its relative openness to trade. The absence of comparable international data on factors that might directly affect productivity, such as research and development and patent activity, precludes an analysis at the microeconomic level. In any case, these activities are likely to be endogenous to the institutional environment in a given country, defined to include the legal system, business regulations, etc. It seems more useful, then, both in terms of understanding deeper determinants of the factors driving productivity and the policy actions that could affect these factors, to focus on the role of such institutions in promoting growth. The empirical analysis examines the role of institutions as proxied by two widely used measures, and takes both a more recent cross-country perspective, as well as a longer-term dynamic perspective. Using a broad cross-country dataset12 and controlling for the key determinants identified in the extensive growth literature, we estimate a growth model whose coefficients allow us to verify that institutions matter for growth and to quantify the effect of improving institutions in Central America to levels attained by strong performers in Latin America, such as Chile.
The analysis begins with an examination of the effect of institutions on growth during the period beginning in the mid-1990s. Focusing on this more recent period has the advantage of abstracting from the effects on Central America the events of the 1980s and their immediate aftermath. Developments since the mid-1990s may be more reflective of the underlying productivity performance of these countries and its implications for future growth. The dependent variable is defined as average growth in real per capita GDP during 1995–2003. As a measure of the quality of institutions, we use the governance indicators of Kaufmann, Kraay, and Mastruzzi (2003). Two indexes are built with these indicators: Index 1 is the average of all the dimensions13 of their indicators, while Index 2 is the average of the two components most directly associated with business activity—i.e., regulatory burden and rule of law. We compare the explanatory power of these two indexes to determine how broadly the institutional environment should be defined for the purpose of assessing its impact on growth. We also introduce the political risk rating of the widely used International Country Risk Guide (ICRG) rating system as an additional robustness check.14 A potential criticism of the use of institutional indicators is that they are endogenous to other factors that may affect growth, biasing their estimated impact using ordinary least squares. To control for potential endogeneity, instrumental variables regressions were also performed, using legal origin and the English-speaking percentage of the population from the Dollar and Kraay (2002) dataset as instruments.15
Table 2.4 shows the results for the cross-section regressions using alternative definitions of the institutions index as the explanatory variable. The results are shown using ordinary least squares (OLS), as well as two-stage least squares (TSLS) using the instruments described above for the institutions variable. The signs of the parameter estimates are as expected in all of the regressions, although those on trade (trading partner growth and openness) and inflation are statistically insignificant. The coefficients on all of the institutions’ variables are significant, and of a similar magnitude in the regressions using OLS (Models 1 and 2) and TSLS (Models 3 and 4), suggesting that endogeneity is not a major problem. There is no clear “winner” between the broad and narrow definition of institutions—the coefficient on the latter is somewhat larger and more significant in the OLS regressions, while the reverse is true in the TSLS regressions.16 We interpret this as showing that both aspects of institutions—i.e., those directly affecting the business environment and those dealing with broader governance considerations—can affect the environment for growth, although the business environment institutions could be somewhat endogenous to other factors affecting growth. Furthermore, separating the “pure” effect of institutions from the effect that they may have on growth through their effect on investment (Models 5, 6, and 8 in Table 2.4) shows that the coefficient on institutions remains highly significant, and its magnitude does not decline substantively.
Cross-Country Growth Regressions
(t-statistics in parentheses)
Cross-Country Growth Regressions
(t-statistics in parentheses)
| (1) OLS | (2) OLS | (3) 2SLS | (4) 2SLS | (5) OLS | (6) OLS | (7) OLS | (8) OLS | |
|---|---|---|---|---|---|---|---|---|
| Log of initial GDP per capita (1995) | –1.26 | –1.35 | –1.55 | –1.38 | –1.07 | –1.42 | –1.43 | –1.12 |
| (–5.58) | (–6.14) | (–3.09) | (–2.69) | (–4.59) | (–6.31) | (–5.30) | (–4.28) | |
| Institutions KKM Index 1 | 1.91 | … | 2.46 | … | 1.79 | 1.49 | ||
| –5.68 | … | –2.53 | … | –5.45 | –4.05 | |||
| Institutions KKM Index 2 | … | 2.09 | … | 2.07 | ||||
| … | –6.61 | … | –1.83 | |||||
| Institutions ICRG Index | 0.19 | 0.17 | ||||||
| –5.21 | –4.74 | |||||||
| Inflation | –0.02 | –0.03 | –0.02 | –0.03 | –0.02 | –0.01 | 0.00 | 0.01 |
| (–1.10) | (–1.53) | (–0.96) | (–1.65) | (–1.29) | (–1.31) | –0.23 | –0.36 | |
| Education | 0.04 | 0.04 | 0.04 | 0.04 | 0.02 | 0.05 | 0.04 | 0.02 |
| –3.60 | –3.49 | –3.28 | –3.05 | –1.80 | –5.83 | –2.90 | –1.31 | |
| Government consumption | –0.10 | –0.08 | –0.10 | –0.09 | –0.06 | –0.06 | –0.05 | –0.01 |
| (–3.08) | (–2.88) | (–2.67) | (–2.76) | –1.67 | (–1.94) | (–1.37) | (–0.20) | |
| Growth in trading partners | 0.22 | 0.04 | 0.34 | 0.05 | –0.08 | 0.37 | 0.05 | |
| –0.44 | –0.07 | –0.72 | –0.09 | (–0.17) | –0.73 | –0.10 | ||
| Openness | 0.00 | |||||||
| (–0.30) | ||||||||
| Investment | 0.13 | 0.12 | 0.13 | |||||
| –4.27 | –3.94 | –3.56 | ||||||
| Constant | 9.78 | 10.52 | 11.7 | 10.83 | 6.61 | 8.03 | –2.85 | –4.87 |
| –5.42 | –6.04 | –3.03 | –2.63 | –3.82 | –4.81 | (–1.46) | (–2.58) | |
| Number of observations | 91 | 91 | 84 | 84 | 84 | 119 | 84 | 80 |
| R-squared | 0.40 | 0.41 | 0.40 | 0.42 | 0.51 | 0.43 | 0.41 | 0.50 |
Cross-Country Growth Regressions
(t-statistics in parentheses)
| (1) OLS | (2) OLS | (3) 2SLS | (4) 2SLS | (5) OLS | (6) OLS | (7) OLS | (8) OLS | |
|---|---|---|---|---|---|---|---|---|
| Log of initial GDP per capita (1995) | –1.26 | –1.35 | –1.55 | –1.38 | –1.07 | –1.42 | –1.43 | –1.12 |
| (–5.58) | (–6.14) | (–3.09) | (–2.69) | (–4.59) | (–6.31) | (–5.30) | (–4.28) | |
| Institutions KKM Index 1 | 1.91 | … | 2.46 | … | 1.79 | 1.49 | ||
| –5.68 | … | –2.53 | … | –5.45 | –4.05 | |||
| Institutions KKM Index 2 | … | 2.09 | … | 2.07 | ||||
| … | –6.61 | … | –1.83 | |||||
| Institutions ICRG Index | 0.19 | 0.17 | ||||||
| –5.21 | –4.74 | |||||||
| Inflation | –0.02 | –0.03 | –0.02 | –0.03 | –0.02 | –0.01 | 0.00 | 0.01 |
| (–1.10) | (–1.53) | (–0.96) | (–1.65) | (–1.29) | (–1.31) | –0.23 | –0.36 | |
| Education | 0.04 | 0.04 | 0.04 | 0.04 | 0.02 | 0.05 | 0.04 | 0.02 |
| –3.60 | –3.49 | –3.28 | –3.05 | –1.80 | –5.83 | –2.90 | –1.31 | |
| Government consumption | –0.10 | –0.08 | –0.10 | –0.09 | –0.06 | –0.06 | –0.05 | –0.01 |
| (–3.08) | (–2.88) | (–2.67) | (–2.76) | –1.67 | (–1.94) | (–1.37) | (–0.20) | |
| Growth in trading partners | 0.22 | 0.04 | 0.34 | 0.05 | –0.08 | 0.37 | 0.05 | |
| –0.44 | –0.07 | –0.72 | –0.09 | (–0.17) | –0.73 | –0.10 | ||
| Openness | 0.00 | |||||||
| (–0.30) | ||||||||
| Investment | 0.13 | 0.12 | 0.13 | |||||
| –4.27 | –3.94 | –3.56 | ||||||
| Constant | 9.78 | 10.52 | 11.7 | 10.83 | 6.61 | 8.03 | –2.85 | –4.87 |
| –5.42 | –6.04 | –3.03 | –2.63 | –3.82 | –4.81 | (–1.46) | (–2.58) | |
| Number of observations | 91 | 91 | 84 | 84 | 84 | 119 | 84 | 80 |
| R-squared | 0.40 | 0.41 | 0.40 | 0.42 | 0.51 | 0.43 | 0.41 | 0.50 |
The next set of robustness checks allow for exploring the time variation that is left unexplored through averaging the data. A panel of three five-year nonoverlapping averages of the dependent and explanatory variables was constructed.17 The estimation of a fixed-effects panel regression18 shows that the coefficient on the quality of institutions as measured by the ICRG political index is robust in magnitude, and continues to be significant (Models 9 and 10 in Table 2.5).19 Similarly unchanged is the coefficient on investment. The coefficient on the openness variable is significant, and its effect is in line with that estimated in other papers such as Arora and Vamvakidis (2005). The coefficient on inflation20 is also significant and negative in this specification.
Fixed-Effects (FE) Panel Regressions
(t-statistics in parentheses)
Fixed-Effects (FE) Panel Regressions
(t-statistics in parentheses)
| (9) FE | (10) FE | ||
|---|---|---|---|
| Log of initial GDP per capita (1995) | –5.92 | –7.40 | |
| (–4.91) | (–6.02) | ||
| Institutions ICRG Index | 0.18 | 0.13 | |
| –5.41 | –3.46 | ||
| Inflation | –1.34 | –1.09 | |
| (–2.97) | (–2.47) | ||
| Education | 0.00 | –0.16 | |
| –0.11 | –0.33 | ||
| Government consumption | –0.10 | –0.16 | |
| (–3.08) | (–2.28) | ||
| Openness | 0.03 | 0.02 | |
| –3.07 | –2.42 | ||
| Investment | 0.11 | 0.15 | |
| –1.90 | –2.64 | ||
| Time dummies | |||
| 1990–94 | –1.53 | ||
| (–3.12) | |||
| 1995–99 | –1.11 | ||
| (–3.63) | |||
Fixed-Effects (FE) Panel Regressions
(t-statistics in parentheses)
| (9) FE | (10) FE | ||
|---|---|---|---|
| Log of initial GDP per capita (1995) | –5.92 | –7.40 | |
| (–4.91) | (–6.02) | ||
| Institutions ICRG Index | 0.18 | 0.13 | |
| –5.41 | –3.46 | ||
| Inflation | –1.34 | –1.09 | |
| (–2.97) | (–2.47) | ||
| Education | 0.00 | –0.16 | |
| –0.11 | –0.33 | ||
| Government consumption | –0.10 | –0.16 | |
| (–3.08) | (–2.28) | ||
| Openness | 0.03 | 0.02 | |
| –3.07 | –2.42 | ||
| Investment | 0.11 | 0.15 | |
| –1.90 | –2.64 | ||
| Time dummies | |||
| 1990–94 | –1.53 | ||
| (–3.12) | |||
| 1995–99 | –1.11 | ||
| (–3.63) | |||
Overall, the results from the empirical analysis suggest an important link between the quality of the institutional framework and growth. Institutional quality, both broadly and more narrowly defined, is found to have a significant and robust impact on growth, above and beyond its effect through investment. Some direct tests of the hypothesis that benefits of trade openness to growth are linked to the quality of the institutional framework provide tentative support—the coefficient on an interaction term of institutional quality and openness is positive and significant, suggesting that greater benefits to trade accrue to countries with better institutions, but the empirical evidence is not robust.21
How do institutions in Central America fare? Figure 2.10 and Table 2.6 show the scores for the Central American countries on the indices of institutional quality, along with those for Chile, which has the highest scores in the Latin American region, and the average score for the overall sample of countries. Only Costa Rica has institutional scores above the sample mean, but they still fall notably short of Chile’s scores. El Salvador and the Dominican Republic are in the middle of the rankings for Central America, with Guatemala, Honduras, and Nicaragua lagging.
Comparison of Institutional Ratings
Note: ICRG = International Country Risk Guide; KKM = Kaufman, Kraay, and Mastruzzi (2003).Comparison of Institutional Ratings
Note: ICRG = International Country Risk Guide; KKM = Kaufman, Kraay, and Mastruzzi (2003).Comparison of Institutional Ratings
Note: ICRG = International Country Risk Guide; KKM = Kaufman, Kraay, and Mastruzzi (2003).Scores of Institutional Quality
(Average of dimensions)
Scores of Institutional Quality
(Average of dimensions)
| Country | Index 1—“Broad” | Index 2—“Narrow” |
|---|---|---|
| Average of 91 countries | 0.28 | 0.34 |
| Chile | 1.16 | 1.33 |
| Costa Rica | 0.88 | 0.80 |
| Dominican Republic | –0.12 | –0.05 |
| El Salvador | 0.01 | 0.23 |
| Guatemala | –0.48 | –0.21 |
| Honduras | –0.38 | –0.35 |
| Nicaragua | –0.35 | –0.37 |
Scores of Institutional Quality
(Average of dimensions)
| Country | Index 1—“Broad” | Index 2—“Narrow” |
|---|---|---|
| Average of 91 countries | 0.28 | 0.34 |
| Chile | 1.16 | 1.33 |
| Costa Rica | 0.88 | 0.80 |
| Dominican Republic | –0.12 | –0.05 |
| El Salvador | 0.01 | 0.23 |
| Guatemala | –0.48 | –0.21 |
| Honduras | –0.38 | –0.35 |
| Nicaragua | –0.35 | –0.37 |
The question, then, is how much improvements in institutional quality could contribute to higher growth rates in the region? Table 2.7 addresses this issue by hypothetically assuming that the institutional scores of the countries in Central America were raised to Chile’s level, and then calculating the resulting change in the fitted value for growth using the parameter estimates from the regression framework.22 The effects would be significant, pointing to improvements in growth ranging from around 0.5 percentage points per year in Costa Rica to 3 percentage points or more in the countries with the weakest institutional scores.23 Of course, transformations in institutions of this magnitude would take a considerable amount of time to realize. But the results indicate that institutional reforms could play a major role in putting Central American countries on more satisfactory growth paths.24
Estimated Impact on Growth of Improving Institutions
(Percentage points, change in predicted value)
Estimated Impact on Growth of Improving Institutions
(Percentage points, change in predicted value)
| Country | Model 1 | Model 2 | Model 5 | Model 7 | Model 8 |
|---|---|---|---|---|---|
| Costa Rica | 0.6 | 1.1 | 0.5 | 0.5 | 0.4 |
| Dominican Republic | 2.5 | 2.9 | 2.3 | 1.4 | 1.2 |
| El Salvador | 2.3 | 2.3 | 2.1 | 0.9 | 0.8 |
| Guatemala | 3.1 | 3.2 | 3.0 | 1.8 | 1.6 |
| Honduras | 3.0 | 3.5 | 2.8 | 3.0 | 2.6 |
| Nicaragua | 2.9 | 3.5 | 2.7 | 4.2 | 3.7 |
Estimated Impact on Growth of Improving Institutions
(Percentage points, change in predicted value)
| Country | Model 1 | Model 2 | Model 5 | Model 7 | Model 8 |
|---|---|---|---|---|---|
| Costa Rica | 0.6 | 1.1 | 0.5 | 0.5 | 0.4 |
| Dominican Republic | 2.5 | 2.9 | 2.3 | 1.4 | 1.2 |
| El Salvador | 2.3 | 2.3 | 2.1 | 0.9 | 0.8 |
| Guatemala | 3.1 | 3.2 | 3.0 | 1.8 | 1.6 |
| Honduras | 3.0 | 3.5 | 2.8 | 3.0 | 2.6 |
| Nicaragua | 2.9 | 3.5 | 2.7 | 4.2 | 3.7 |
Conclusions
While the recovery in growth in Central America in the 1990s provided a welcome contrast to the “lost decade” of the 1980s, the record was still not fully satisfactory, and the more recent performance raises concerns about long-run prospects. The region has failed to regain the high productivity and growth rates observed in some previous periods, such as the 1960s. Growth has also been insufficient to decisively reduce poverty and narrow the income gap with more developed countries. In most countries in Central America, the level of productivity is below that observed in the late 1970s. In addition, the slowing of output growth and weak productivity during the 2000–05 period raises questions about the prospects for the region to enter a period of sustained rapid growth, even with the opportunities that arise given the implementation of CAFTA-DR.
Growth Accounting, by Country
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators, and IMF, World Economic Outlook. Note: TFP = total factor productivity.Growth Accounting, by Country
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators, and IMF, World Economic Outlook. Note: TFP = total factor productivity.Growth Accounting, by Country
(Contribution in percentage points)
Source: IMF staff estimates using World Bank, World Development Indicators, and IMF, World Economic Outlook. Note: TFP = total factor productivity.Actual and Predicted Growth, Cross-Sectional Regressions, 1995–2003
Actual and Predicted Growth, Cross-Sectional Regressions, 1995–2003
| Country | Actual Growth | Model 1 | Model 2 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|
| Costa Rica | 2.3 | 1.8 | 1.5 | 1.7 | 1.4 | 1.6 | 1.4 |
| Dominican Republic | 3.8 | 1.3 | 1.2 | 0.9 | 1.0 | 1.9 | 1.4 |
| El Salvador | 1.2 | 1.3 | 1.7 | 0.9 | 1.0 | 2.0 | 1.6 |
| Guatemala | 0.8 | 0.3 | 0.6 | 0.2 | 0.0 | 1.0 | 0.9 |
| Honduras | 0.3 | 0.7 | 0.6 | 1.6 | 1.7 | 0.6 | 1.5 |
| Nicaragua | 1.4 | 1.1 | 1.0 | 2.2 | 2.4 | -0.3 | 1.0 |
| Average | 1.6 | 1.1 | 1.1 | 1.2 | 1.3 | 1.1 | 1.3 |
Actual and Predicted Growth, Cross-Sectional Regressions, 1995–2003
| Country | Actual Growth | Model 1 | Model 2 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|
| Costa Rica | 2.3 | 1.8 | 1.5 | 1.7 | 1.4 | 1.6 | 1.4 |
| Dominican Republic | 3.8 | 1.3 | 1.2 | 0.9 | 1.0 | 1.9 | 1.4 |
| El Salvador | 1.2 | 1.3 | 1.7 | 0.9 | 1.0 | 2.0 | 1.6 |
| Guatemala | 0.8 | 0.3 | 0.6 | 0.2 | 0.0 | 1.0 | 0.9 |
| Honduras | 0.3 | 0.7 | 0.6 | 1.6 | 1.7 | 0.6 | 1.5 |
| Nicaragua | 1.4 | 1.1 | 1.0 | 2.2 | 2.4 | -0.3 | 1.0 |
| Average | 1.6 | 1.1 | 1.1 | 1.2 | 1.3 | 1.1 | 1.3 |
Both the growth literature and the empirical evidence for Central America suggest that the quality of institutions, including those related to business activity and governance more generally, significantly affect growth performance. This is an area in which there is scope for considerable improvement in most Central American countries—indeed, weaknesses in institutions may explain why these countries have failed to fully capitalize on the trading opportunities offered by proximity to developed markets and a relatively high degree of trade openness. The evidence suggests that the beneficial effects of trade are only likely to be realized in an environment where sound institutions promote an efficient allocation of resources and a dynamic business environment. The estimated impact of improving institutions on growth is large, and suggests that changes in this area could play an important role in putting Central America on a higher sustained growth path.
Appendix 2.1. Countries Included in the Sample
Specifications with 91 countries: Argentina, Australia, Austria, Bangladesh, Barbados, Belgium, Benin, Bolivia, Brazil, Burkina Faso, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Republic of Congo, Costa Rica, Cyprus, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador, Fiji, Finland, France, Gambia, Germany, Ghana, Greece, Guatemala, Guyana, Honduras, Hong Kong SAR, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kenya, Korea, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mexico, Morocco, Mozambique, Nepal, the Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, the Philippines, Poland, Portugal, Saudi Arabia, Senegal, Sierra Leone, Singapore, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Syria, Togo, Tunisia, Turkey, Uganda, United Kingdom, United States, Uruguay, Venezuela, Zambia, Zimbabwe.
Appendix 2.2. Fit of the Regression Model for the Central American Countries
Table 2.A.1 compares the actual growth rates of the Central American countries with the predicted values obtained from the regression models. In general, the models tend to underpredict growth in the Central American countries, with the exception of Honduras, for which the models consistently overpredict growth, and El Salvador, for which all specifications predict growth relatively well. In addition, the predicted values from the specifications that include investment ratios (Models 5, 6, and 8) substantially overestimate the growth rates in Honduras and Nicaragua, where the predicted growth rates are about 1 percentage point higher than the average actual growth rate. Honduras and Nicaragua both have average investment levels above the sample median of 21 percent of GDP (at 25 and 27 percent, respectively).
References
Acemoglu, Daron, Simon Johnson, and James Robinson, 2001, “The Colonial Origins of Comparative Development: An Empirical Investigation,” American Economic Review, Vol. 91, No. 5 (December), pp. 1369–401.
Arora, Vivek, and Athanasios Vamvakidis, 2005, “How Much Do Trading Partners Matter for Economic Growth?” Staff Papers, International Monetary Fund, Vol. 52, No. 1.
Berg, Andy, Carlos Leite, Jonathan Ostry, and Zettlemeyer, Jeromin 2006, “What Makes Growth Sustained?” (unpublished; Washington: International Monetary Fund).
Bolaky, Bineswaree, and Caroline Freund, 2004, “Trade, Regulations and Growth,” World Bank Policy Research Working Paper No 3255 (Washington: World Bank).
Cabrera, Melgar, Oscar, Ovidio, Julieta Fuentes, Hada Desiree de Morales, 2005, “Hechos estilizados en el crecimiento económico de El Salvador 1978–2004: Una propuesta de acciones de política económica en el corto y mediano plazo,” Departamento de Investigacion Economica y Financiera, Banco Central de Reserva de El Salvador.
Cerra, Valerie, and Chaman Saxena, 2005, “Growth Dynamics: The Myth of Economic Recovery,” IMF Working Paper 05/147 (Washington: International Monetary Fund).
Chang, Roberto, Linda Kaltani, and Norman Loayza, 2005, “Openness Can Be Good for Growth: The Role of Policy Complementarities,” World Bank Policy Research Working Paper No. 3763 (Washington: World Bank).
Djankov, Simeon, Caralee McLiesh, and Rita Ramalho, 2005, “Regulation and Growth,” Social Science Research Network Electronic Papers. Available via the Internet: http://ssrn.com/abstract=893321.
Dollar, David, and Aart Kraay, 2002, Institutions, Trade and Growth (Washington: World Bank).
Easterly, William, and Ross Levine, 2001, “It’s Not Factor Accumulation: Stylized Facts and Growth Models,” Social Science Research Network Electronic Papers. Available via the Internet: http://ssrn.com/abstract=269108.
Easterly, William, Norman Loayza, Peter Montiel, 1997, “Has Latin America’s Post-Reform Growth Been Disappointing?” Journal of International Economics, Vol. 43 (November): 287–311.
Faal, Ebrima, 2005, “GDP Growth, Potential Output, and Output Gaps in Mexico,” IMF Working Paper 05/93 (Washington: International Monetary Fund).
International Monetary Fund (IMF), 2003, World Economic Outlook, World Economic and Financial Surveys (Washington, April).
Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi, 2003, “Governance Matters III: Governance Indicators for 1996–2002” (Washington: World Bank).
Daniel, Lederman, and William Maloney, 2003, “Trade Structure and Growth,” World Bank Policy Research Working Paper No. 3025 (Washington: World Bank).
Loayza, Norman, Pablo Fajnzylber, Cesar Calderon, 2005, Economic Growth in Latin America and the Caribbean. Stylized Facts, Explanations, and Forecasts (Washington: World Bank).
Sahay, Ratna, and Rishi Goyal, 2006, “Volatility and Growth in Latin America: An Episodic Approach” (unpublished; Washington: International Monetary Fund).
Singh, Anoop, and Martin Cerisola, 2006, “Sustaining Latin America’s Resurgence: Some Perspectives from the Historical Experience,” paper prepared for the IMF-World Bank Annual Meetings Seminar (September).
Solimano, Andrés and Raimundo Soto, 2005, “Economic Growth in Latin America in the Late 20th Century: Evidence and Interpretation,” ECLAC Economic Development Division Macroeconomía del desarrollo series No. 33 (Santiago, Chile: Economic Commission on Latin America and the Caribbean).
Solow, Robert M., 1957, “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics, Vol. 39, pp. 312–20.
World Bank, 2005a, “DR-CAFTA: Challenges and Opportunities for Central America,” Central America Department and Office of the Chief Economist, Latin America and Caribbean Region. Available via the Internet: http://lnweb18.worldbank.org/LAC/LAC.nsf/ECADocByUnid/3D4B4F71AE8214FE8525702D00710E58?Opendocument.
World Bank, 2005b, Doing Business in 2005: Removing Obstacles to Growth (Washington: World Bank, International Finance Corporation, and Oxford University Press).
Zettelmeyer, Jeromin, 2006, “Growth and Reforms in Latin America: A Survey of Facts and Arguments,” IMF Working Paper 06/210 (Washington: International Monetary Fund).
Covers the countries that form part of the Central America-Dominican Republic Free Trade Agreement (CAFTA-DR) with the United States: Costa Rica, the Dominican Republic, Guatemala, Honduras, Nicaragua and El Salvador.
These concerns are underscored by the evidence in Berg and others (2006) that Latin American countries in general have experienced difficulties in sustaining growth over longer periods of time.
In 1979, the Somoza government was overthrown and a new governing coalition dominated by the Frente Sandinista de Liberacion Nacional (FSLN) assumed power.
This is characteristic of the findings of Easterly and Levine (2001) for a wider range of countries. See also Faal (2005) for evidence on Mexico, and Cabrera Melgar, Fuentes, and Morales (2005) for El Salvador.
The results of the growth accounting exercise for each individual country are shown in Appendix Figure 2A.1.
Recent revisions of the national accounts in Costa Rica and Nicaragua introduced breaks in 1991 and 1994, respectively, with improvement in the coverage focused principally on the services and informal sectors. Thus, the increases in the contribution of total factor productivity to growth for Costa Rica in 1992 and Nicaragua in 1995 at least partly reflect a break in the coverage of the GDP series.
See also Easterly, Loayza, and Montiel (1997) and Solimano and Soto (2005) for discussions of the growth performance of Latin America.
Cerra and Saxena (2005) use panel data to analyze how fast economies recover after a shock, finding that output losses during recessions are followed by partial but not full recovery phases, such that some of the output loss is permanent.
Since the Caribbean Basin Initiative in 1983, Central American countries have benefited from trade preferences in U.S. markets that have allowed duty-free access for a wide range of products.
Djankov, McLiesh, and Ramalho (2005) also analyze how regulations affect growth in a cross-country context, finding that improving its index of business regulations from the worst to the best quartile can result in a 2½ percentage point increase in a country’s average annual growth. Loayza, Fajnzylber, and Calderon (2005) also find a strong relationship between regulations and growth.
See World Bank (2005a) for a discussion of the opportunities presented by CAFTA-DR.
The country list is provided in Appendix 2.1.
The dimensions consist of government effectiveness, control of graft or corruption, political instability and violence, regulatory burden, voice and accountability, and the rule of law.
The overall political risk rating reflects risk ratings of the following components: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religion in politics, law and order, ethnic tensions, democratic accountability, and bureaucratic quality.
Acemoglu, Johnson, and Robinson (2001) explore the role of colonial history in determining institutions. They find that high mortality rates led European settlers to establish “extractive states.” In other regions, where mortality rates were lower, they were more likely to establish European-style institutions where the rule of law prevailed and investment was encouraged. Hence, early Latin American colonies were endowed with institutions and regulations that favored exploitation, empowering a narrow elite and restricting other economic activity.
Nesting the hypothesis of which is more important by including both definitions in the same regression yields a similar conclusion—neither is highly significant due to multi-collinearity, but the narrow definition is more significant using OLS and the broad definition using TSLS.
In order to construct the panel, the original estimation period of 1995–2003 was extended to 1990–2004.
The choice of a fixed-effects versus a random-effects specification is justified by a Hausman test.
The governance indicators employed in the cross-section regressions above have only been available since the mid-1990s and thus could not be used in this specification.
Measured as the log of (1 + inflation).
The interaction term is significant and positive in the cross-sectional regressions using both the ICRG and the Kaufmann, Kraay, and Mastruzzi indices, but is not significant in the fixed-effects panel regression.
The fit of the regression models for the Central American countries is shown in Table 2A.1.
Given the robustness of the coefficient on institutional quality, the estimated impact of raising the quality of institutions is shown in Table 2.7 only for a representative subset of all the estimated specifications. These cover the use of three different institutional measures (the narrow and broad definitions of the Kaufmann, Kraay, and Mastruzzi index and the International Country Risk Guide political risk index), and the inclusion of investment levels as a control variable.
