Abstract
During the last two decades there has been an intense debate over the efficacy and efficiency of international aid. The debate has focused on the performance of beneficiary countries in terms of economic growth. Yet little attention has been paid to the role of aid in income distribution within receiving countries, despite the fact that reducing inequality is an explicit aim of international aid. In this article, we analyse the role of aid in the evolution of income distribution over the last two decades for 18 Latin American countries. While Latin America is the most unequal region of the world, it includes some of the countries currently leading the reduction of inequality at the global level. The region is also currently losing a significant amount of aid disbursement. The main finding of our work, after controlling for several relevant variables, is that there has been a significant effect of international aid on reducing income inequality in Latin America.
Abstract
Au cours de ces deux décennies, un débat intense sur l’efficacité de l’aide internationale au développement a eu lieu. Pourtant, peu d’attention a été portée sur le rôle de l’aide au développement dans la distribution des revenus, en dépit du fait que la réduction des inégalités est l’un des buts explicites de l’aide internationale au développement. Dans cet article, nous analysons le rôle de l’aide au développement dans l’évolution de la distribution des revenus dans 18 pays d’Amérique Latine au cours de ces 20 dernières années. Bien que l’Amérique Latine soit la région du monde où il y a le plus d’inégalités sociales, elle comprend certains des pays qui sont actuellement les leaders dans la réduction des inégalités au niveau mondial. Actuellement, la région perd également une partie importante de l’aide au développement. Après avoir pris en compte plusieurs variables pertinentes, la conclusion principale de notre travail est que l’aide internationale au développement a joué un rôle significatif dans la réduction des inégalités de revenus en Amérique Latine.
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Notes
ODA reached USD 128.7 billion in 2010, representing a historical maximum and an increase of 6.5 per cent over 2009 and 0.32 per cent of DAC members’ GNI. In 2011, ODA was constant USD 2010 125.5 billion and 0.31 per cent of GNI.
In this article we use ‘aid’ and ‘ODA’ interchangeably.
Literature on aid and growth is still controversial (see McGillivray et al, 2006) even when meta-analysis techniques are used. Whereas the meta-analysis of Doucouliagos and Paldam (2011) does not find any significant effect of aid on growth, Mekasha and Tarp (2011) show positive results using meta-analysis as well. Some recent studies show a positive link using a variety of robust econometric techniques (Dovern and Nunnenkamp, 2007; Nowak-Lehmann et al, 2009; Arndt et al, 2010, 2011; Minoui and Reddy, 2010; Juselius et al, 2011; and Tezanos et al, 2013 for the Latin American case). Other authors even suggest that aid might actually do more harm than good (that is Easterly, 2006 and Moyo, 2009), usually looking at the stagnation of several African countries despite large aid inflows, together with evident corruption and mismanagement of resources.
Gini indexes among Latin American countries show wide dispersion, but even the most equal Latin American country (Uruguay with a Gini of 0.42 measured under disposable income) is still more unequal than the most unequal European country (Portugal with a Gini of 0.38) according to Goñi et al (2011). Since 2000 Latin America has averaged 0.50 in terms of Gini coefficient, while European countries have averaged 0.31.
Among others, the United Nations Millennium Declaration in 2000 states, ‘#2. We recognize that, in addition to our separate responsibilities to our individual societies, we have a collective responsibility to uphold the principles of human dignity, equality and equity at the global level’; and ‘#6. We consider certain fundamental values to be essential to international relations in the twenty-first century. These include: Equality. No individual and no nation must be denied the opportunity to benefit from development. The equal rights and opportunities of women and men must be assured’. The Paris Declaration (OECD, 2005) states, ‘#2. At this High-level Forum on Aid Effectiveness, we followed up on the Declaration adopted at the High-level Forum on Harmonization in Rome (February 2003) and the core principles put forward at the Marrakech Roundtable on Managing for Development Results (February 2004) because we believe they will increase the impact aid has in reducing poverty and inequality, increasing growth, building capacity and accelerating achievement of the MDGs’. Finally, the Accra Agenda for Action (OECD, 2008) states, ‘#3. We need to achieve much more if all countries are to meet the Millennium Development Goals (MDGs). Aid is only one part of the development picture. Democracy, economic growth, social progress, and care for the environment are the prime engines of development in all countries. Addressing inequalities of income and opportunity within countries and between states is essential to global progress’.
Both studies identified a stronger regressive effect in democratic countries but not in autocratic countries. The result is partially explained by rent-seeking activities and by the fact that aid is captured by local elites. Angeles and Neanidis (2009) and Hodler and Raschky (2010) find similar results.
Dolan and Tomlin (1980) run cross-section regressions with data for 66 developing countries and for 1970–1973. They find positive effects of aid on the Gini index and negative on the 20 per cent richest/40 per cent poorest income ratio, both statistically insignificant. Chong et al (2009) use panel data with 112 countries for 1972–2001. They use cross-section and GMM panel data techniques to study the impact of aid (measured as a percentage of GDP) on the Gini index, considering aid, its quadratic value, and an interactive term between aid and corruption. Most of their specifications yield non-significant results for aid.
The effect was very sensitive to sample countries and regions (in Latin America the effect found was the lowest, and the lower the initial inequality the lower the effect identified). The authors consider probit models owing to the double censored characteristic of the Gini variable [0–100] and use contemporaneous, one lag and two lags for the aid’s effect. Only 12 Latin American countries are considered and most of them received very low aid/GDP amounts in the years considered. In addition, the ordered probit technique implies fixing ratings that are to some extent arbitrary.
In the OECD-DAC taxonomy, the ‘good governance and civil society’ sector includes programmes such as strengthening public expenditure management, fiscal reforms and tax-assessment procedures, among many others.
OECD-DAC defines ‘Technical cooperation’ as ‘activities whose primary purpose is to augment the level of knowledge, skills, technical know-how or productive aptitudes of the population of developing countries’. A remarkable example of this kind is the EUROsociAL. EUROsociAL is the European Union’s regional technical cooperation programme to promote social cohesion in Latin America, to support national public policies aimed at improving levels of social cohesion, and to strengthen the institutions that implement such policies, including fiscal and employment. The first phase of the Programme (2005–2010) was co-financed by the European Commission, Spain and France. EUROsociAL-II, totally financed by the European Commission, was awarded to a consortium led by a Spanish institution: the International and Ibero-American Foundation for Administration and Public Policies (FIIAPP). Moreover, the Spanish Development Agency (AECID) finances technical assistances such as the Ibero-American Programme for Specialized Technical Training (PIFTE). Since 1987, hundreds of Latin American civil servants have attended courses on fiscal reform taught by the Spanish Institute for Fiscal Studies.
Countries with uncompleted data are (missing years in parentheses): Bolivia (2008); Ecuador (1990–1994); Guatemala (2007–2008); Nicaragua (1990–1991 and 2006–2008); Paraguay (1990–1994); and Peru (1990).
Although we rely on Gini coefficients from Martorano and Cornia to have the maximum number of observations possible, the correlation with Gini coefficients reported by the World Development Indicators dataset is higher than 0.9. Furthermore, although we present all our results with these Gini coefficients, main results hold when we experiment with other indices such as as the Theil index and Atkinson indices, which also have a correlation higher than 0.9 with the Gini coefficients used.
The amount of the reduction varies from −8.6 Gini points (Ecuador) to −1.2 (Costa Rica). Brazil in particular has experienced a steady reduction in income inequality, although its Gini values are still above the median. By contrast, Honduras, Peru and the Dominican Republic have experienced very volatile movements in their Gini indexes. As regards the year of the onset of the reduction in inequality, there is some concentration around 2003 (Paraguay, the Dominican Republic, Peru), but also in 2002 (Argentina), in 2001 (Panama and Costa Rica) and even earlier (1998 in Ecuador and El Salvador and 1995 in Mexico). Guatemala and Honduras experienced some reduction in 2002 and 2003, respectively, but inequality rose again afterwards. Finally, Mexico and Peru had their Gini over the median for some years (1995–2000 and, in the Peruvian case for 1998–1999 and 2003), but they managed to reduce it below the median later.
We use gross ODA to illustrate how many resources (loans and grants) arrive in each country for egalitarian purposes. Net ODA, in contrast, takes into account the returns of ODA loans to the donor. Loans represented more than 20 per cent of the net ODA in Argentina, the Dominican Republic, Panama, Brazil and Costa Rica.
Measured by the coefficient of variation (or volatility around the mean), the value for Latin America between 1990 and 2010 was 0.12, whereas it was 0.31 for Europe, 0.28 for Asia, 0.26 for Oceania, 0.24 for Africa and 0.21 among all recipient countries. The coefficient of variation fitted 0.10 for South America and 0.19 for North and Central America.
Most of the aid flows to Latin American countries came from bilateral donors (from a minimum of 48 per cent in Honduras or 55 per cent in Dominican Republic, to 90–93 per cent in Colombia, Panama, Brazil and Mexico in 2010). Spain, the United States, Japan and Germany have played a major role as bilateral donors. Multilateral institutions also play an important role in financing macro stability programmes. The European Union institutions are the biggest multilateral donors (in fact these institutions were the first donor in the Dominican Republic in 2009–10). EU ranks in the top 10 donors in the 18 countries. Other important multilateral donors are the IDB Spanish Fund and the Global Education Fund – except in Andean and Central American countries. IDA has been another important donor in Bolivia, Honduras and Nicaragua.
The remaining percentage mainly belongs to multi-sector activities, humanitarian aid and debt relief.
The regression of aid for education on social expenditure for education was statistically significant at 99 per cent of confidence. As suggested, aid may enhance domestic public investment in education and this might in turn be a channel for lower inequality.
The programme has allowed the rehabilitation and construction of libraries in 770 schools, better infrastructure in 31 educational centres, better equipment in 90 schools and better capabilities for 1497 teachers. See Cassimon et al (2009) for an independent evaluation and UNESCO (2006) or Salles Almeida (n.d.) for a review. The total amount of debt swaps reached USD 773.99 million for 1992–2007. Italy, Germany, Spain, France, the United States, Finland, Canada and Switzerland were the donors involved. We only use the inputs as a potential impact on inequality. We have not found an evaluation that shows a causal and direct impact of debt swaps on inequality in Latin America.
We based our work on the Chong et al’s (2009) analysis of the impact of aid on inequality, as it is the closest paper we could find in terms of our empirical objective, and so as to have a benchmark for our results. However, while Chong et al perform an analysis for a world sample, we focus specifically on Latin America. Our focus allows us to derive context-specific policy implications of major relevance for the region today. Additionally, our focus extends Chong et al by using a longer time span, as well as considering other variables relevant for the analysis in Latin America.
However, minimum wages in the formal sector may increase inequality if higher minimum wages create a greater divide between formal and informal sectors, especially relevant in Latin American countries, where large informal sectors are prevalent. Additionally, higher minimum wages might favour top percentiles of the wage distribution when wages are indexed based on minimum wages. (Arango and Pachon (2004) in fact find regressive effects of minimum wages in Latin American countries like Colombia.)
The polity score ranges from +10 (strongly democratic) to −10 (strongly autocratic). Democracy is conceived as three essential, interdependent elements: (i) the presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders; (ii) the existence of institutionalized constraints on the exercise of power by the executive; and (iii) the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. Autocracy is defined as a distinctive set of political characteristics: restriction or suppression of competitive political participation; chief executives chosen through a regularized process of selection within the political elite, and once in office they exercise power with few institutional constraints and so on.
We report different combinations of up to seven control variables. Introducing more explanatory variables would dramatically reduce our degrees of freedom as we only have 18 observations for our cross-section (this does not affect our key result for aid, however).
Given that we have significantly more observations than in the cross-section, in our pooled estimations we can introduce more control variables simultaneously. Main results for aid hold if we lag one period right-hand side variables, and use annual data or a 3-year moving average. As in the cross-section results, using aid per capita instead of aid as a percentage of GDP does not change the main results.
If this were the case, our coefficient for aid would be biased towards a positive sign. Hence, a negative coefficient in our OLS estimations would represent the upper bounds of an unbiased result, which actually supports our hypothesis of a negative effect of aid on inequality.
Random Effects (RE) estimations allow us to control for unobserved country-specific characteristics retaining cross-sectional differences. However, if the country-specific characteristics are correlated with the regressors – which is highly likely – RE is inconsistent and Fixed Effects (FE) estimations should be preferred. FE also controls for time-invariant country-specific effects but does not solve reverse causality. Furthermore, FE only considers variations within countries. As our variables are highly persistent over time, FE is expected to worsen dynamic bias concerns (see, for instance, Ostry et al 2014).
Under these specific conditions, System-GMM estimates are expected to be more efficient than any other dynamic GMM estimators. Previous estimations have been carried out under a similar theoretical framework with static, autoregressive dynamic and simulated maximum likelihood techniques, finding an egalitarian effect of aid in lower-middle income Latin American countries (González and Larrú, 2012). In this article we apply the System-GMM approach to deal with endogeneity, using internal instruments, in order to improve the causal effects of the independent variables, and considering a wider set of ODA measures.
Serial correlation tests, along with tests for overidentifying restrictions, are standard to check the validity of instruments. For instruments to be valid, first-order serial correlation, but not second-order, is expected. We report ar1, ar2 and Hansen tests in the results tables.
The coefficients for aid are slightly larger than in our pooled estimations, confirming our intuition about the bias towards a positive sign (see note 24). Aid remains negative and significant if we control for minimum wages, unemployment rates, terms of trade, or political context (polity2). We cannot control for all of them simultaneously in our system-GMM estimations if we want to keep a reasonable fit between the number of instruments and the number of observations. Results are available upon request.
For annual data we replace the literacy rate with the educational Gini variable due to data availability.
We find similar results when we measure aid as ODA in per capita terms, rather than as a percentage of GDP. Aid loses significance, however, if we exclude debt cancellation, suggesting a relevant role of this mechanism (as discussed in the third section). Results are available upon request.
The coefficient for lagged Gini in our benchmark estimation (column 1) is 0.66, between Cornia (2012), 0.63, and Chong et al (2009), 0.78.
Our coefficient for fdi in column 5 of Table 6 (0.034) is in fact very close to the one obtained by Cornia (2012), 0.035.
In the unique statistically significant specification for aid in Chong et al (2009), the coefficient for aid/GDP was 0.634 (SE=0.336) and for square aid was 0.022 (SE=0.011). However, this result is not strictly comparable to ours because of differences in sample, aid and liquid liabilities indicators.
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Castells-Quintana, D., Larrú, J. Does Aid Reduce Inequality? Evidence for Latin America. Eur J Dev Res 27, 826–849 (2015). https://doi.org/10.1057/ejdr.2014.67
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DOI: https://doi.org/10.1057/ejdr.2014.67