Globalization, Democracy, and Social Spending in Latin America, 1980-1997

 

 

 

 

 

George Avelino

Fundação Getulio Vargas

Av 9 de Julho2029

01313-902 São Paulo, SP, Brazil

avelino@fgvsp.br

 

 

David S. Brown

Department of Political Science – MS24

Rice University

P.O. Box 1892

Houston, TX 77005-1892

 

 

 

 

Wendy Hunter

Department of Government

Burdine Hall 536

Austin, Texas 78712-1087

Phone: (512) 471-5121

Fax: (512) 471-1061

 

 


INTRODUCTION

The globalization of markets for capital, goods, services, and information that has taken place in the past fifteen or so years is without historical parallel.  Against a wider context of international integration, Latin America has experienced the most dramatic change in its economic policy orientation since World War II.  Latin American governments have instituted a broad array of reforms aimed at integrating their economies into global markets.  While many other regions have made changes in a similar direction, few have undergone as rapid and thorough a transformation as Latin America.

            While globalization has provided some groups in society with new opportunities for social mobility, it has created new sources of inequality and insecurity for others.  Most would agree that economic openness puts employers under greater pressure to reduce labor costs, and that individuals who possess the skills, knowledge, and resources associated with internationally competitive sectors benefit more from the new market-oriented system than those who do not. 

            In this light, questions about how states provide for the welfare of citizens in the contemporary international economic system gain new relevance.  How has the international integration of markets for goods, services, and capital affected the social policy decisions of Latin American governments?  More specifically, have governments become less generous toward citizens in response to the pressures generated by greater economic openness?  Or, have they created stronger safety nets and new forms of social assistance in order to meet the new social challenges of globalization?   

            This paper examines the impact globalization and democracy has on social spending in Latin America.  We use time-series cross-sectional (TSCS) analysis to test whether the empirical patterns observed in the OECD nations are observed in a developing region that has undergone dramatic economic and political change over the last two decades.  We build on previous work in several ways.  First, we use data on social spending that maximizes cross-country comparability.  Second, we test whether our results are sensitive to different measures of globalization and democracy.  Third, since different social programs reach different constituencies, we disaggregate social spending to determine whether the effects of globalization and democracy vary by program.  Finally, we provide the first compositional analysis of social spending using a comprehensive framework developed by Gary King and Jonathan Katz (2000).  By examining the distribution of resources to health, education, and social security, we gain a more refined understanding of the interaction between globalization, democracy, and social spending.

            Several empirical patterns emerge from our analysis.  First, confirming previous results from Kaufman and Segura (forthcoming), we find trade openness has a strong negative impact on the amount of resources devoted to social spending.  Second, we find democracy has a strong positive correlation with social spending both as a percentage of GDP and in per capita terms.  To understand the causal links that connect globalization, democracy, and social spending, we break social spending down into several categories.  We find that democracy’s biggest impact on social spending is through education while trade openness has its largest impact on health spending. We also find that increased trade openness influences the distribution of resources to social security, education, health, and a residual category of programs that includes housing, sanitation, and other poverty alleviation programs.  Countries that trade a relatively high percentage of their GDP protect education and health while allocating greater resources to the residual category.  The increasingly large allocation of resources to the residual category, however, comes at the direct cost of social security. Finally, we find that democracies allocate relatively more resources to health, education, and the residual category at the direct cost of social security. 

            The paper proceeds as follows.  Section one introduces the theory and previous empirical work that relates globalization to government spending.  Section two describes our data and the model we use to test the hypotheses derived from the previous section.  Section three presents the results.  Section four extends the analysis by conducting a compositional analysis of the social welfare budget.  Section five concludes the paper by discussing the implications of our findings as well as identifying some questions that remain unanswered.

 

THEORY

A growing literature addresses the interaction between globalization, domestic politics, and government spending (Cameron 1978; Katzenstein 1985; Hicks and Swank 1992; Pierson 1996; Powers and Kostadinova n.d.; Rodrik 1998, 1999, 2001; Garrett 1998 and 2001; Garrett and Mitchell 1999; Huber 1999; Hurrell and Woods 1999; Garrett and Nickerson 2001; Adsera and Boix forthcoming;, Kaufman and Segura forthcoming; Huber and Stephens forthcoming).  The question at the core of this literature involves whether governments respond to the challenges of globalization with social policy choices that are oriented more toward cutting costs ("efficiency") or protecting people's welfare ("compensation"). 

            The central notion of the efficiency approach is that governments will reduce taxes and social welfare expenditures that diminish profits, discourage investment, and therefore threaten economic growth and international competitiveness.  Social services burden business through the distortion of labor markets and higher taxes.  If governments borrow to pay for these services, the higher real interest rates that result further depress investment.  In short, the efficiency approach posits economic openness places important constraints on welfare spending, leaving governments little choice but to restrict their social outlays. 

            The compensation perspective recognizes the constraints imposed by economic integration on the social policy options of governments, yet accords greater weight to the countervailing demands imposed by citizens seeking protection from the state.  It stresses the perception among top elected officials and bureaucrats that the social instability and political discontent engendered by internationalization could ultimately endanger the model of economic openness as well as their careers.  The core contention of the compensation thesis is that government officials use the latitude they have to strengthen social insurance mechanisms and cushion citizens from the vagaries of the international economy. 

            Social expenditures are a clear general measure of the extent to which governments contract or expand their commitments to citizens.[1]  Fluctuations in spending can provide clues about the constraints facing public officials, their latitude for responding to those constraints, and the relative weight they place on competing priorities.   The quantitative dimension inherent in studies of expenditures is conducive to clarity and comparability across countries.   Yet because welfare states may change in kind as well as in quantity  (similar amounts of money may fund very different types of programs and constituencies), case studies and small-N comparisons that examine the transformation of social programs in detail are hence necessary complements to large N-analysis.

             Latin America constitutes an interesting and relatively understudied region for the analytical questions at hand.  The majority of studies aimed at understanding globalization's effects on social protection focus on OECD countries (e.g. Cameron 1989, Garrett 1998; Garrett and Mitchell 1999; Katzenstein 1985; Pierson 1996; Rodrik 1998; Hicks and Swank 1992).  This literature grew in part out of a concern that social welfare states in industrialized democracies would undergo severe erosion as increased trade with low wage economies placed downward pressure on wages and benefits.  At the same time, capital liberalization allowed investors to look abroad for higher returns on their investments, causing some concerns over capital flight.  Some authors pay special attention to the developing world (e.g. Garrett and Nickerson 2001; Rodrik 1999), but only one major quantitative study (Kaufman and Segura forthcoming) focuses specifically on Latin America. 

            A number of factors that set Latin America apart from other regions—especially Western Europeimpinge upon the ability and/or inclination of Latin American governments to respond to globalization with robust welfare programs.  On the one hand, some of these factors encourage actions in accordance with the efficiency thesis.  Others make Latin America more likely to adopt compensatory schemes.

            The relative weakness of unions and paucity of Social Democratic parties, a historical support base for universal social protection policies in Western Europe, deprives Latin American citizens of two key organizational means to defend social services against budgetary cuts.  Thus, while Cameron (1978) finds that trade openness in Western Europe resulted in the provision of greater public resources for social protection, such an outcome may not hold in Latin America. 

            The rapid and dramatic process of stabilization and structural adjustment in the wake of the Latin American debt crisis—and the active accompanying role played by the International Monetary Fund—is without parallel in the developed world.  IMF prescriptions for attaining fiscal solvency depend on reducing government expenditure on social programs by introducing user fees in health and education, and by affecting a more efficient distribution of goods and services to the poor.[2]  Governments reluctant to initiate such actions still acknowledge the importance of expressing to the IMF their intent to adopt these economic reforms.  

            Finally, the comparative weakness of Latin American states exposes welfare programs to the risk of retrenchment.  The state in most Latin American countries, while never as strong as in most Western European countries, was weakened further by the economic crisis of the 1980s and 1990s.   Governments in the region are notorious for their inability to carry out some of the most essential tasks—tax collection—necessary to maintain generous welfare support (Huber 1999).  

            Other factors relevant to Latin America provide some reason to expect globalization might promote compensation.  Greater trade volatility heightens the insecurity of citizens unless governments take active measures to provide for social protection.  Most Latin American countries tend to have relatively specialized patterns of trade compared to their counterparts in the developed world.  With higher levels of volatility, the effects of trade exposure may be more severe, inducing a higher level of government spending in relation to total trade.  This hypothesized relationship leads us to examine the role played by new democratic regimes.

Noted earlier, most of the previous empirical work focuses on the OECD nations for which comparable and extensive data are available. Understandably, these studies take stable democratic institutions as given. In these models, democracy has only an indirect effect: it works as a channel through which the effects of other relevant variables (such as political parties and union strength) can be analyzed.  Although it is important to consider political factors other than regime type, issues relating to regime type and regime transition still deserve attention. At least two reasons justify this claim.

First, we need to know more about the conditions that make democracies work: the conditions that enable them to achieve economic growth, material security, freedom of arbitrary violence, and other widely desirable objectives (Przeworski et all, 1995).  Due to the endemic political instability that has characterized developing countries, much of the comparative work on democratization assumes these new regimes were inherently fragile. These works were mostly concerned in constructing etiologies of types of regime change or of emerging democratic regimes.  Virtually ignored in this literature is the impact of democracy over public policies.[3] 

The recent political and economic transformations experienced by many developing countries offer a unique opportunity to explore questions about how different political regimes react to external economic shocks (Rodrik, 1999). According to Adserá and Boix (forthcoming), the interaction between democracy and globalization has a strong positive effect on government expenditures.  As Latin American countries comprise a significant part of the recent wave of democratization, they provide a great opportunity to analyze the effects of different political regimes over these policy options.

Second, despite the euphoria sparked by the widespread democratization among developing countries, current attitudes toward new democracies are mixed. Although they represent an important change in comparison to previous authoritarian institutions, Latin American democracies have been criticized for not having fulfilled many of the expectations they generated. This growing disenchantment with democracy is particularly acute in the provision of public services, an area in which democratization was expected to have a tangible impact on the plight of the poor.[4] 

As the empirical literature makes clear, democracies alone are unlikely to reverse deeply entrenched patterns of poverty and social inequality. Nevertheless, the prevalence of new democracies headed by governments with a presumed interest in maintaining social stability and winning re-election would seem to auger well for social welfare programs.  The social dislocations produced by restructuring an economy toward competition in the international marketplace affect middle class as well as poorer segments of the population.  The Middle Class is not only well represented at the ballot box, it is also crucial to public opinion formation.  Rebellion among indigenous peasant producers in southern Mexico, food riots in Argentina, and strikes by public sector workers in a number of countries are among the expressions of protest that have emerged in the last decade.  The widespread institution of social emergency programs, such as PRONASOL in Mexico and FONCODES in Peru, suggests that governments in the region are not unaware of the need to secure support for themselves and for their economic reforms. 

 

MODEL SPECIFICATION

To test hypotheses on the influence of globalization and democracy on social spending, we examined annual data for 19 Latin American countries between 1980 and 1997.[5] The data was compiled by a team of researchers assembled by the United Nations Commission for the Latin American and the Caribbean (ECLAC/CEPAL).[6] This data set provides a unique opportunity to study the relationship between democratization and social spending for two reasons. First, with the exception of Cuba and Haiti, the set includes all Latin American countries. Second, the persistent problem of data comparability is minimized by the ECLAC, which conducted studies of each country for the expressed purpose of producing comparable data on social spending.[7]

The data form a Times-Series Cross-Sectional (TSCS) data set in which each country-year represents a single observation. Although pooling the data has the obvious benefit of increasing the number of observations, it can violate at least two of the basic assumptions that underlie Ordinary Least Squares (OLS) estimation. First the temporal structure of the data increases the chance of autocorrelation, violating the OLS assumption that the errors are independent of each other. Second, the cross-sectional structure of the data increases the chance that the variance in the error terms may differ across countries and that there will be spatial processes that affect different panels simultaneously (i.e., a currency crisis in Argentina effects Brazil). The consequence of these violations is that OLS coefficient estimates are still unbiased but inefficient.

In order to deal with these problems we followed Beck and Katz (1995, 1996) and used panel corrected standard errors. To deal with the problem of auto-correlation we included a lagged dependent variable and a set of “n” country and “t” year dummies. The inclusion of a lagged dependent variable is based on two assumptions. First the autocorrelation problem is limited to the first-order correlation, a plausible assumption given the short period covered by the data. Second the autocorrelation is not unit specific; rather, it is assumed to be common across all pooled units.[8] Finally, but no less important, including a lagged dependent variable allows one to address autocorrelation without transforming the data, which complicates the interpretation of regression coefficients.

The inclusion of a set of “n” country dummies addresses the heteroscedasticity problem by controlling for country-specific effects. It assumes that these effects are fixed over time, allowing a different intercept for each country. This statistical technique has two other consequences that are worth mentioning.

First, the combination of these dummy variables may be highly correlated with other independent variables, producing multicollinearity problems within the model and reducing the efficiency of the estimates. Multicollinearity will be particularly acute in relation to variables that are relatively invariant, or fixed, within each country along the 18-year period. This prevents the inclusion of some variables traditionally used in cross-sectional models that explain welfare spending variation in OECD countries: the institutional characteristics of social programs.

Second, the exclusion of relevant variables from the model specification should lead to bias in estimates of the coefficients. From this perspective, the set of dummies summarizes the differences between countries caused by relevant variables that can be considered as fixed over time. The country dummies can also account for the differences caused by unmeasured relevant variables, a very common situation among developing countries for which it is hard to find complete and comparable data.  In sum, while the inclusion of country dummies has some disadvantages, it insures that no relevant, and relatively stable, cross-sectional variable is excluded from the model.[9] 

Finally, the inclusion of year dummies takes into account time specific effects. For instance, if all countries are subject to a common external shock, the effects of this shock over our dependent variables need to be controlled. This problem is particular important because of the debt crisis endured by Latin American countries during the end of the last century.  Therefore, we will employ the following baseline equation:

Social Spendingi,t =ai + dt + b1 Social Spendingi,t-1 + b2 Pop65 i,t

+ b3 Unemployment i,t + b4 Level of Development i,t + b5 Growth i,t 

+ b6 Democracy i,t + b7 Financial Liberalization i,t + b8 Trade Openness i,t

+ e i,t.

In this equation, the terms a and d represent country and year dummies, the b’s are the parameter estimates and e represents the error term.  Finally, the subscripts i and t represent the country and year of observations respectively.

Social Spending is the dependent variable, measured in two ways: as a percentage of GDP and in 1990 per capita dollars. At first, it will be measured as a percentage of GDP and in 1990’s per capita dollars. Results for more disaggregated levels of social spending and for changes in the composition of the social welfare budget will be shown later.

The measure for democracy conceives democratization as a distinct process and measures its effects by using a dummy variable for the political regime, coding one for democracies and zero for the residual category of authoritarian regimes. The measure is drawn from Alvarez et all (1996). Based on Dahl’s (1971) minimalist definition of a democratic regime, the authors focus on contestation as the essential institutional feature of democracies.[10] We followed Alvarez et. al in their classification.  To check the stability of our results with respect to the measure of democracy, we ran every regression using a continuous variable derived from Gurr’s POLITY IV data.  We subtracted Gurr’s AUTOC score from his DEMOC score, producing a more continuous measure that ranges from –10 (most authoritarian) to 10 (the most democratic). 

Gobalization is measured by two indicators. The first is trade openness: the sum of imports and exports as a percentage of the GDP. It is important to note that the inclusion of country dummies in all equations takes into account countries’ fixed characteristics (such as the size of the population and the distance from major trade partners) that may influence their exposure to international trade.[11] Therefore, we are confident that the coefficient on the trade openness term represents government policy choices.

The second indicator is the degree of international financial liberalization, drawn from Morley, Machado, and Pettinato (1999), and defined as “the average of four components which reflects the sectoral control of foreign investment, limits on profit and interest repatriation, controls on external credits by national borrowers and capital outflows.” The index is based on information from IMF”s Annual Report on Exchange Arrangements and Exchange Restriction, and is normalized between zero and one, with one being the country observation with the smallest legal restrictions on capital flows.[12] 

We also include four control variables traditionally used in the social spending empirical literature. The first is demographic: pop65 which is defined as the percentage of the population that is 65 years or older. Due to the impact of demographic characteristics over health care and social security, we expect a higher percentage of elderly people in the population to be positively related to social spending. The data for this variable came from the WDI 2000.

The second traditional control variable is the unemployment rate. Despite the existence of few public unemployment programs in Latin America, we expect this coefficient to be positive: high unemployment rates should be correlated with increased social spending. The data was drawn from various issues of ECLAC’s yearly report on the Economic Survey of Latin America.

We also include the level of economic development, defined as the log of Gross Domestic Product per capita and measured in PPP dollars.  Including income in the equation takes into account Wagner’s Law which states the level of public spending is positively correlated with levels of economic development. Finally, the annual growth rate of GDP per capita is included to control for the effects of economic volatility on government spending. Data for both variables were drawn from the WDI 2000.

 

RESULTS

Aggregate Measures

Table 1 presents the results at the aggregate level.  Several patterns can be observed from the estimates.  First, the lagged dependent variable in every regression is significant which comes as no surprise.  Although there is a strong correlation between the dependent variable and its lag, the coefficient on the lagged dependent term is not close to one, ranging from .72 to .76.  Consequently, the unit root problem is not a concern.  Second, none of the economic controls are correlated with social spending.  Given the use of both country and year dummy variables in each regression, there is little cross-sectional variance left for the control variables to explain.  Third, the coefficient on the democratic dummy variable is statistically significant and positive in every regression.  To test the stability of the result with respect to the operationalization of democracy, we substituted in Gurr’s POLITY IV measure of democracy.  In every regression the Gurr measure score confirmed the results we obtained with Przeworski et. al’s dichotomous operationalization.  Finally, the coefficient for the trade openness variable is significant and negative: as trade openness increases, the resources governments devote to social programs decrease. 

<Table 1>

            The substantive impact of increased trade and democratization is significant.  The democracy dummy variable is relatively easy to interpret.  The coefficient on the democratic dummy variable indicates that the difference between democratic and authoritarian regimes is roughly .8 percentage points of GDP.  Of course, for the larger economies (Brazil, Mexico, and Argentina), a .8 percentage point difference is substantial.  In Brazil’s 1 trillion dollar economy, for example, .8 percentage points is equivalent to 800 million dollars.  The same pattern is observed when we use social spending in dollars per capita as the dependent variable.  The coefficient on the democratic dummy term ranges from $35 dollars per capita to $45 dollars per capita among the same six regressions.  Given that total social spending in some countries is less than $45 dollars per capita (Paraguay, Bolivia, Peru), the difference between democratic and authoritarian regimes is substantial.  Even among the bigger spenders (Chile, Costa Rica, Brazil spend between $400-$500 per capita), the $35 to $45 difference amounts to roughly 20 percent of all social spending.  Regressions for the per capita spending figures can be found in Appendix 1.

            The impact trade openness has on social spending is substantial as well.  To illustrate the constraints trade openness places on social spending among the Latin American countries, we plotted the predicted values and 95% confidence intervals for social spending as trade openness ranges from 0 to 120.  Bear in mind that the inter-quartile range of the data runs from 30% to 60%.  In other words, 50% of the cases have economies in which trade represents 30-60% of GDP.  Holding all other variables constant at their means, varying trade openness from 30% to 60% results in a -1.73 percentage point change (95% confidence interval from -2.6 to -.88).  In per capita terms, governments in more open economies (at the 75% percentile) will spend approximately  $71 less than their more closed counterparts (95% confidence interval is -115.03 to  -25.50).  Although the differences at first seem substantial, it is important to note that the ability for countries to move increase trade openness is somewhat circumscribed.  For example, the difference between Brazil’s highest and lowest trade openness figure is roughly 8 percentage points.  Argentina, another fairly large economy, is also somewhat limited (11 percentage points).

<Figure 1>

The estimates we report withstood a number of tests for stability.  First, we tested the stability of the results with respect to our measure of globalization. For instance, Rodrik (1998) argues the most important issue is the “exposure to external risk” brought about by economic internationalization rather than trade shares alone. However, the inclusion of variables that measure “terms of trade risk” or “export concentration” did not cause any noticeable change in our results.[13] We also substituted our variable for capital liberalization with another variable that measurers the net inflows of Foreign Direct Investment as a percentage of the GDP.  Using the alternative measure based on Foreign Direct Investment had no effect on our estimates.

Second, we subjected our models to bootstrapping: we took each country out, one a time, to see if its absence affected the estimates.  This is particularly important given the TSCS nature of the data since influential points are probably clumped together and will not, by themselves, appear to have an effect on the results.  We found that the coefficients for the democracy dummy and trade openness variables remained significant throughout the procedure. 

            Also presented in the regression tables is a model that includes an interactive term for democracy and trade openness and an interactive term for democracy and capital liberalization.  The estimates indicate a strong interactive relationship exists between democracy and trade openness.  However, in direct contrast to findings by others (Garrett and Nicerson 2001; Adserá and Boix, forthcoming), we find that the interaction between democracy and trade openness has a strong negative impact on social spending.  Rather than dampening the negative correlation between social spending and trade openness, democracy seems to amplify its negative impact.  This finding comes with two caveats.  First, democracies continue to outspend their authoritarian counterparts until trade represents roughly 57% of their GDP. Roughly 75% of all cases lie below that figure: in 75% of the cases, the predicted value for democratic regimes is higher.  Second, when examining spending as a percentage of GDP, the interactive term loses its magnitude and significance when Paraguay is removed from the sample.  It is also important to point out that the negative coefficient may be driven by the significant advantage democracies hold over authoritarian regimes when trade comprises a smaller percentage of GDP.  Because of the linearity imposed by our model, the large advantage at low levels of trade openness translates into an authoritarian advantage at the highest levels (see Figure X)..

<figure 2>

            Previous empirical work seeks to establish whether governments respond to globalization by either becoming more efficient (spending less) or by compensating the losers (spending more).  However, as Kaufman and Segura note, the number of constituencies that benefit from social spending varies dramatically.  Consequently, we might expect that if democratic institutions provide some form of compensation, they would allocate resources to those sectors most affected by increasing competition in the market.  Examining whether trade openness and democracy affect all components of social spending equally will help us determine whether changes in spending can be attributed to the compensation or efficiency hypotheses.

 

Health, Education, and Social Security

Health, education, and social security are the main components of social spending yet serve very diverse segments of the population.  As Kaufman and Segura note, social security outlays may be most susceptible to globalization since they comprise an important part of the wage bill (Kaufman and Segura, forthcoming).  Health and education, however, are not as directly tied to the costs born by employers.  There may be important differences between health and education as well.  Although there are certainly important societal effects of an increasing supply of educational opportunity, the direct beneficiaries of increasing access to education are the young students themselves along with their parents.  Those benefiting from health expenditures represent a wider segment of the population.  Since the groups that benefit directly from spending on health, education, and social security vary, we estimated the previous models for each category of spending separately.

            Table 2 reports the results for three components of social spending for both the standard model and a model with interactions.  A clear pattern emerges from the estimates reported in Table 2.  Democracy’s positive impact on social spending is channeled through education.  The democratic dummy variable has a positive and statistically significant coefficient in both equations for education. 

<Table 2>

Although the democracy variable maintains its positive coefficient, in the health equation it cannot be distinguished from zero at any acceptable level of confidence.  The consistent pattern in the health regressions is the strong and negative association between the trade openness variable and spending.  With respect to social security, we find that only the interactive term between democracy and trade openness exhibits a strong negative correlation with spending. 

            Along with our findings in the aggregate analysis, the following pattern begins to emerge.  Although trade openness is negatively correlated with aggregate social spending, its main effects are found in health spending and education spending.  Democracy’s positive association with social spending is manifested in higher rates of spending on education.  The interaction between democracy and trade openness is always negative and significant.  Democracies, it seems, are much quicker to cut social spending as larger segments of the economy are exposed to trade.  But, as we saw in Figure 2, a large number of democracies (democracies with relatively low levels of trade to GDP) actually spend more than authoritarian regimes (roughly 60 percent of our cases).  The picture, consequently, is somewhat murky.  In terms of the compensation versus efficiency hypotheses, the dummy variable term for democracy indicates democratic regimes compensate exposure to trade by increasing social spending.  Trade openness, however, forces governments to cut back on spending particularly in health and education.  In countries with relatively small trade sectors (i.e. Brazil, Argentina, Mexico, Colombia, Peru, Venezuela), democratic regimes spend more than authoritarian regimes.  In countries with relatively high levels of trade (i.e. Costa Rica, Panama, Jamaica, Nicaragua, Paraguay), democratic regimes spend less than their authoritarian counterparts.  However, as noted earlier, there are reasons to suspect that the strong negative coefficient is being driven by the substantial advantage democratic regimes hold at low levels of trade openness.  In fact, when we eliminate the largest economies (those with trade representing 30 percent or less of GDP), the interactive term between democracy and trade openness becomes insignificant while the democracy dummy variable still maintains its strong positive coefficient. 

            Although disaggregating social spending into health, education, and social security reveals some interesting patterns, several important questions remain unanswered.  We don’t know, for example, whether democracies spend more on education by taking from health or social security.  We know that trade openness does not seem to affect the overall size of social security, but what we don’t know is whether maintaining social security comes at the cost of health and education.  According to the estimates from the regressions reported in Table 2, the answer would be yes.  However, the regression analysis used heretofore cannot answer these questions.

 

COMPOSITIONAL ANALYSIS

Compositional analysis allows us to examine the effect globalization and democracy have on a number of budget categories simultaneously.  Although the literature focuses on education, health, and social security, an average of 10 percent of all social spending in Latin America went to other programs: housing, sanitation, and other poverty alleviation programs.  Politically important programs such as PRONASOL in Mexico and FONCODES in Peru do not show up in budgetary outlays destined for education, health, or social security.  Given, their importance, it is important to employ an analysis that can estimate the impact of globalization and domestic political institutions on social spending programs simultaneously. 

            Previous work that tests the compensation or efficiency hypotheses focuses exclusively on the size of overall government spending.  Given the wide array of groups that benefit from different social programs, the distribution of resources within social spending can illuminate how domestic political institutions and economic phenomena affect the allocation of resources among different political constituencies.  Previous work relies on artificially dichotomizing the social welfare budget: separate regressions are run for each component or all remaining programs are lumped into a residual category.  Rather than dichotomizing or simplifying the budget to accord with readily available methodologies, we can use an approach developed by King and Katz that provides a systematic framework to examine the distribution of shares among more than one category (King and Katz 2000).  More importantly, their approach allows us to account for parts of the welfare budget that have been ignored.  Only through compositional analysis can we obtain the entire picture in order to understand the allocative strategies politicians use in response to changing global circumstances.

            Rather than provide an accounting of the methodological issues relevant to compositional analysis, we refer the reader to King and Katz (2000) as well as to Aitchison (1986).  To perform the compositional analysis, we began with the social spending data on health, education, social security, and a residual category “other” which is comprised of housing, sanitation, and other welfare programs.  Housing, sanitation, and other programs not budgeted through health, education, or social security represent 10 percent of social spending in Latin America.  To estimate the correlation between globalization, democracy, and the distribution of government resources on different social programs, we ran a series of regressions in which each component is divided by a standard category (we chose social security).  Once expressed as a ratio, the term is logged.  Predicted values are generated by the n-1 regressions (n being the number of categories).  We present the resulting regressions in Table 3.  Predicted values are generated for the three categories which can then be subtracted from one, producing the predicted percentage of the fourth category (in our case social security).

<Table 3>

Two distinct patterns are observed when we plot the predicted values for each category and vary trade openness and democracy.  Figure 3 below provides the predicted shares going to social security, health, education, and our residual (“other”) category as trade openness varies increases from 0 to 100, holding all other variables constant at their mean values.  As trade openness increases, health and education’s share of the budget remain fairly constant while the budget share allocated to the residual category increases dramatically.  Maintaining spending on education, health, and the residual category all come at the cost of social security.  Even though trade openness has a negative impact on the overall size of the budget, the biggest cuts come from social security, not health or education.  The shifting of resources from social security in order to maintain housing, sanitation, health, and education has very important political implications for the Latin American cases.  Given the relatively narrow segment of the population that benefits from social security spending, redistributing resources to housing, sanitation, and other welfare programs while maintaining health and education suggests that some form of compensation is occurring. 

Though somewhat limited, available evidence suggests that social security transfers are regressive compared to either education or health (Petrei, 1996). In addition, many Latin American governments have created new types of social programs (our residual category) with the declared objective to circumvent old and vitiated distributive schemes in order to target the poor more efficiently (Graham 1994). Therefore, redistributing funds from social security to health, education, and other welfare programs suggests that even though trade openness has a negative impact on the overall size of social spending, it compels governments to shift resources among programs to compensate those most adversely affected.  Finally, since education and health are generally regarded as representing more productive forms of social investment, we can say trade openness compels Latin American governments to invest more efficiently.

<Figure 3>

            The estimates also suggests that resources are allocated to programs that reach a larger segment of the population (health and education) as well as to the poor (narrowly focused welfare programs).  Not only is the pattern above more efficient in terms of investing in the economy, there is a political logic lurking underneath.  Democracy’s impact on the relative shares devoted to health, education, and welfare may be even more pronounced (Figure 4 below).  To examine democracy’s effect on the allocation of resources, we substituted Gurr’s democracy score for the dichotomous measure.  Figure 4 below shows what happens to the composition of the social budget as democracy increases from –10 (the most authoritarian score) to 10 (the most democratic).  If at the aggregate level the relationship between social spending and trade openness conforms to the efficiency hypothesis, the pattern described in Figure 4 portrays the political equivalent of the compensation hypothesis.  Holding all other factors constant at their mean, there is a slight increase in the shares allocated to health and education at the apparent cost of social security.  Given the narrow segment of the population that receives social security benefits, the pattern above illustrates a certain political logic.  Not only does democracy protect aggregate levels of social spending, it protects the shares allocated to programs that reach large segments of the electorate.

<Figure 4>

 

CONCLUSION

Using data collected on social spending for the Latin American countries between 1980 and 1997, we set out to test whether the compensation or efficiency hypotheses held for the Latin American continent.  As the previous pages revealed, the story is somewhat more complicated than a simple confirmation of one hypothesis over another.  Specifically, and in direct contrast to previous work by on the OECD countries, we find that trade openness has a negative impact on the allocation of resources to social programs.  The strong, negative correlation between trade openness and social spending confirms recent work by Kaufman and Segura (forthcoming).  We find, however, in contrast to Kaufman and Segura, that democracy has a consistent positive influence on social spending. 

Interacting democracy and trade openness produced a very contradictory result, implying that democracies do not compensate the losers as trade openness increases.  Instead, at first glance, it appears democracies accelerate the efficient allocation of resources away from social spending as trade openness increases.  Further investigation, however, showed that the negative coefficient on the interactive term was generated by the large democratic economies that outspent their authoritarian counterparts.  Finally, we analyzed the allocation of resources between social spending programs to better understand whether globalization and democracy perform compensating or efficiency functions both in terms of economic investment and politics.  Democracies protect spending on programs that reach large segments of the population while globalization leads to a more efficient allocation of resources among individual programs.

            All we have done here is to establish some interesting empirical patterns, more theoretical and empirical work is needed to actually explain this complex set of phenomena.  At the very least, we have established there are some heretofore unobserved patterns that deserve further scrutiny.  Left unanswered, for example, is why trade openness has a consistently negative correlation with social spending in Latin America when similar models for the OECD nations produce contradictory results.  More work is also needed to explain why our estimates of democracy’s impact on social spending differ from those produced by Kaufman and Segura.  Finally, more work is needed on correctly specifying a model that interacts globalization with domestic political institutions.  By pursuing these questions in greater depth we can better understand the constraints and opportunities that globalization and democratization afford.

 


Table 1

Social Spending as a Percentage of GDP Regressed on Control

Variables, Democracy, Capital Liberalization, and Trade Openness.

 

 

(1)

(2)

(3)

(4)

(5)

(6)

 

 

 

 

 

 

 

Social Spending/GDPt-1

0.759***

0.767***

0.760***

0.756***

0.759***

0.726***

 

(0.052)

 

(0.054)

(0.053)

(0.059)

(0.052)

(0.055)

GDP/capitat

-1.342

-1.333

-1.321

-0.384

-1.399

-0.850

 

(1.343)

 

(1.338)

(1.389)

(1.424)

(1.378)

(1.276)

Economic Growtht

-0.346

-0.207

-0.360

-1.228

-0.427

-0.739

 

(1.103)

 

(1.068)

(1.127)

(1.201)

(1.093)

(1.122)

Unemploymentt

0.066*

0.067*

0.066*

0.065

0.065*

0.057

 

(0.037)

 

(0.040)

(0.037)

(0.041)

(0.037)

(0.036)

% of Pop. over 65t

-1.160

-1.332

-1.158

-1.122

-1.161

-0.831

 

(0.797)

 

(1.002)

(0.797)

(0.794)

(0.800)

(0.780)

Democracy Dummyt

0.774***

0.827***

0.755***

0.586***

0.761***

2.955***

 

(0.235)

 

(0.255)

(0.264)

(0.189)

(0.240)

(0.440)

Trade Opennesst

-0.043***

-0.046***

-0.043***

-0.045***

-0.044***

-0.027***

 

(0.010)

 

(0.012)

(0.010)

(0.010)

(0.010)

(0.010)

Capital Liberalizationt

-0.893

-0.899

-0.920

-0.616

-0.891

0.339

 

(0.753)

 

(0.731)

(0.725)

(0.820)

(0.753)

(0.919)

Fiscal Decifict

 

0.013

 

 

 

 

 

 

(0.022)

 

 

 

 

 

Civil Wart

 

 

-0.061

 

 

 

 

 

 

(0.181)

 

 

 

 

Debt Service Ratiot

 

 

 

0.027***

 

 

 

 

 

 

(0.006)

 

 

 

Inflationt

 

 

 

 

0.000

 

 

 

 

 

 

(0.000)

 

 

Trade Openness X

 

 

 

 

 

-0.044***

Democracy Dummy

 

 

 

 

 

(0.008)

 

Capital Liberalization X

 

 

 

 

 

-0.882

Democracy Dummy

 

 

 

 

 

(0.910)

 

Observations

214

208

214

214

214

214

 

 

 

 

 

 

 

Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%                      

 

 

 

Figure 1

Predicted Values and 95% Confidence Intervals For Social Spending

As a Percentage of GDP at Various Levels of Trade Openness


Figure 2

Interaction Between Democracy and Trade Openness When Holding

All Variables at the Means and Varying Trade Openness

 

 

 

 

 


Table 2

Regression for Education, Health, and Social Security

 

 

(1)

(2)

(3)

(4)

(5)

(6)

 

Education

 

Education

Health

Health

Social

Security

 

Social Security

Lagged Dependent

0.751

0.695

0.549

0.563

0.826

0.812

Variable

(29.40)**

 

(16.98)**

(16.37)**

(17.00)**

(8.52)**

(8.93)**

Unemployment

0.461

0.330

0.803

0.657

1.205

0.611

 

(2.21)*

 

(1.66)

(4.86)**

(3.53)**

(1.20)

(0.66)

GDP/capita (log)

18.118

33.638

39.705

45.237

11.533

17.655

 

(1.50)

 

(2.89)**

(4.04)**

(4.26)**

(0.72)

(1.05)

Economic Growth

-1.111

-15.287

1.404

-3.056

-17.260

-37.484

 

(0.10)

 

(1.35)

(0.09)

(0.19)

(0.72)

(1.52)

Population 65 and

-11.316

-6.979

0.051

0.245

25.743

22.550

Above

(1.29)

 

(0.79)

(0.02)

(0.07)

(2.28)*

(1.88)

Democracy Dummy

11.730

58.212

0.805

10.250

14.632

23.987

1=Democracy

(5.45)**

 

(5.25)**

(0.39)

(1.23)

(1.72)

(0.78)

Trade Openness

-0.300

-0.028

-0.257

-0.166

-0.011

0.260

 

(2.51)*

 

(0.27)

(3.80)**

(2.43)*

(0.06)

(1.22)

Capital

-1.788

26.317

16.748

16.746

-5.986

-34.987

Liberalization

(0.32)

 

(1.77)

(3.04)**

(1.48)

(0.22)

(0.82)

Democracy X

 

-21.766

 

3.777

 

48.739

Capital Lib.

 

(0.91)

 

 

(0.32)

 

(0.91)

Democracy X

 

-0.853

 

-0.311

 

-1.022

Trade Openness

 

(7.30)**

 

(3.98)**

 

(2.76)**

 

 

 

 

 

 

 

Constant

-60.109

-212.090

-269.675

-315.371

-201.810

-203.189

 

(0.70)

 

(2.93)**

(3.55)**

(3.92)**

(2.06)*

(1.93)

Observations

200

200

183

183

177

177

 

 

 

 

 

 

 


Panel-corrected z-statistics in parentheses: * significant at 5% level; ** significant at 1% level

 

 


 

Table 3

Regressions Used to Calculate the Predicted Values for

Each Category of Social Spending

 

(1)

(2)

(3)

 

Health

 

Education

Other

Log(Health/Social Sec.)t-1

0.545

-0.016

-0.771

 

(5.17)**

 

(0.22)

(2.61)**

Log(Educ./Social Sec.)t-1

0.009

0.573

0.782

 

(0.09)

 

(5.29)**

(2.50)*

Log(Housing/Social Sec.)t-1

0.007

0.005

0.494

 

(0.30)

 

(0.26)

(4.93)**

Unemploymentt

-0.006

-0.002

-0.010

 

(0.95)

 

(0.29)

(0.78)

GDP/capita (logged)t

-0.196

-0.053

0.106

 

(1.47)

 

(0.45)

(0.25)

Economic Growtht

0.776

0.174

0.543

 

(4.04)**

 

(0.93)

(1.16)

% of Pop. over 65t

-0.162

-0.283

-0.314

 

(1.92)

 

(3.44)**

(1.09)

Democracy Dummyt

0.259

0.268

0.485

 

(4.27)**

 

(3.85)**

(2.47)*

Exports + Imports/GDPt

0.004

0.003

0.010

 

(2.00)*

 

(1.82)

(1.23)

Capital Liberalizationt

0.482

0.371

0.278

 

(3.39)**

 

(2.84)**

(0.78)

Constant

1.595

0.973

-0.765

 

(1.98)*

 

(1.28)

(0.33)

Observations

180

180

180

 

 

 

 

Panel-corrected z-statistics in parentheses * significant at 5% level; ** significant at 1% level                                          

 

 


 

Figure 3

Predicted Values of Budget Shares as Trade Openness

Varies from 0 to 100

 


 

Figure 4

Predicted Values of Budget Shares as Democracy - Autocracy

Varies from –10 to 10

 

 


 

Appendix A

Regression of Social Spending Per Capita on Control Variables,

Democracy, Trade Openness, and Capital Liberalization

 

 

(1)

(2)

(3)

(4)

(5)

(6)

 

 

 

 

 

 

 

Social Spending/capitat-1

0.738***

0.708***

0.745***

0.743***

0.741***

0.704***

 

(0.055)

 

(0.062)

(0.056)

(0.058)

(0.055)

(0.054)

GDP/capitat

22.980

15.123

27.501

32.062

27.599

96.199*

 

(53.274)

 

(49.949)

(54.690)

(57.143)

(52.146)

(54.192)

Economic Growtht

48.507

39.884

45.564

40.112

56.119

-1.148

 

(45.718)

 

(47.450)

(45.622)

(50.500)

(48.916)

(50.863)

Unemploymentt

2.839

2.264

2.725

2.740

2.872

2.136

 

(1.961)

 

(1.963)

(1.974)

(2.044)

(1.951)

(1.802)

% of pop. over 65t

2.043

21.050

2.326

1.586

1.824

5.215

 

(30.612)

 

(33.071)

(31.641)

(30.947)

(30.477)

(32.965)

Democracy Dummyt

43.295***

35.409***

37.712***

41.709***

44.591***

104.437***

 

(12.042)

 

(11.037)

(12.441)

(11.248)

(12.896)

(34.474)

Trade Opennesst

-1.773***

-1.629***

-1.774***

-1.792***

-1.728***

-0.771***

 

(0.320)

 

(0.311)

(0.327)

(0.338)

(0.325)

(0.291)

Capital Liberalizationt

-56.259

-62.480*

-64.408*

-53.249

-56.238

-86.418*

 

(34.918)

 

(35.917)

(38.894)

(35.272)

(35.210)

(52.324)

Fiscal Deficitt

 

-4.069***

 

 

 

 

 

 

(0.927)

 

 

 

 

Civil Wart

 

 

-18.881*

 

 

 

 

 

 

(9.895)

 

 

 

Debt Service Ratiot

 

 

 

0.265

 

 

 

 

 

 

(0.296)

 

 

Inflationt

 

 

 

 

0.003

 

 

 

 

 

 

(0.004)

 

Trade Openness X

 

 

 

 

 

-3.121***

Democracy Dummy

 

 

 

 

 

 

(0.431)

Capital Liberalization X

 

 

 

 

 

80.000

Democracy Dummy

 

 

 

 

 

 

(61.559)

Observations

214

208

214

214

214

214

 

 

 

 

 

 

 

Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%  

 

 


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[1]While summary figures do not address the distributional impact of social spending, there is some evidence that they exert a positive impact on the poorer sectors of the population in Latin America (Petrei 1996; Mostajo 2000). 

[2]See Deacon (1999: 222) for a list of the IMF's prescriptions vis-`a-vis the social policies of borrowing member countries.  See also Grosh (1996) for the theory behind targeting and its practice. 

[3] For a survey of different “types of democracy”, mostly based on institutional characteristics, see Collier and Levistky (1997). As the ability of the poor to make effective demands depends on the institutional design of democratic regimes, a natural extension of the work done here is to test the impact of different types of democracy over public policies.

[4] A recent poll by “Latinobarometro”, published in “The Economist” (2001), attested the decline of the democracy support in Latin America. This disenchantment, however, does not imply in disregarding that changes in democracies are usually moderate and incremental as claimed by many authors (Huntington, 1989; Schmitter and Karl, 1991). In most cases, the disenchantment stems from the perception that new democracies have not represented a shift in government priorities, even an incremental one, toward the interests of the poor.

[5] The countries are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic,  Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, and Venezuela. The full data matrix, therefore, comprises a maximum of 342 observations (19 countries x 18 years). Missing data, however, implied that we analyzed smaller data sets, depending on the country and year coverage of variables.

[6] ECLAC/CEPAL  "Base de Datos de Gasto Social - División de Desarrollo Social de la Cepal, actualizada hasta fines de 1998."

[7] This project has yielded two publications: Cominetti and Di Gropello (1994) and Cominetti and Ruiz (1997).

[8] As argued by Beck and Katz, (1995: 638), “The assumption of unit-specific serial correlations also seems odd at a theoretical level. Time-series cross-section analysis assume that the ‘interesting’ parameters of the model, b, do not vary across units; this assumption of pooling is at the heart of TSCS analysis. Why not should we expect the ‘nuisance’ r to not show similar pooling? r can be interpreted as how long it takes for prior shocks to be removed from the system. Why should this ‘memory’ be the only model parameter that varies from unit to unit?” See also, Beck and Katz (1996).

[9] As stressed by Stimson (1985), the estimated dummy coefficients are not explanation, but rather summary measures of our ignorance about the causes of between-units differences. Following Przeworski and Teune (1970), one would say that the dummies represent our inability to “substitute the name of variables for the names of social systems.” (p.8)

[10] “Our purpose is to distinguish regimes that allow some, even if limited, regularized competition among conflicting visions and interests from those in which some values or interests enjoy a monopoly buttressed by a threat or the actual use of force.”  (Alvarez et all, 1996: 4). See Huntington (1991: 266-67) for a similar theoretical point.

[11] See Rodrik (1998: 1026), who calls this exogenous component of trade shares as the “natural openness for each country.”

[12] The difference between each country absolute index and the least liberalized country year observation is expressed as a percentage of the difference between the maximum and minimum absolute indexes for all countries over the entire period.

[13] The first variable is the standard deviation of the first logarithm differences of the terms of trade for each country. Data on terms of trade were drawn from ESDB/IADB, available at the IADB Internet site (www.iadb.org). Export concentration is measured as the summation of the percentage share of the ten most important export products on the total exports for each country. Data from this last variable was collected from ECLAC/CEPAL, Statistical Yearbook of Latin America, various issues.