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We are grateful to Simon Johnson for many useful conversations, and for helpful comments from Chris Adam, Andy Berg, Abdoulaye Bio-Tchane, Aleš Bulíř, Nancy Birdsall, Francois Bourguignon, Ajay Chhibber, Michael Clemens, Tito Cordella, Shanta Devarajan, Alan Gelb, Ed Glaeser, Tim Harford, John Hicklin, Nurul Islam, Aart Kraay, Pravin Krishna, Anne Krueger, Michael Nowak, Jonathan Ostry, Alessandro Prati, Lant Pritchett, Rodney Ramcharan, Dani Rodrik, David Roodman, Shankar Satyanath, T.N. Srinivasan, Quy Toan-Do, Thierry Tressel, Alan Winters, and participants at the Center for Global Development Seminar in Washington D.C., and at seminars at Harvard and George Mason Universities. Manzoor Gill, Naomi Griffin, and Ioannis Tokatlidis provided superb research assistance.
There is a voluminous literature on aid effectiveness. Some key papers, in addition to those cited below, include, Alesina and Weder (2000), Bauer (1971), Collier and Dollar (2002), Dalgaard, Hansen, and Tarp (2004), Friedman (1958), Hansen and Tarp (2000), Roodman (2004), Svensson (2003), and World Bank (1998).
We also find that aid causes its adverse effects, at least in part, through an increase in the wages of skilled workers, which is one source of overvaluation.
Indirect evidence for the impact of the traded sector on long-run growth comes from results that show that exchange rate overvaluation have a negative impact on long-run growth (Easterly and Levine, 2003; and Acemoglu et. al. 2003). In a similar vein, Hausman et. al. (2004) show that a real depreciation can ignite growth spurts that could last up to 10 years.
Prati et. al. (2003) find an adverse competitiveness impact of aid based on cross-country regression analysis.
Given the rationale for instrumenting, all we need are predetermined instruments that correlate with aid but not with a country’s policies. We do not need to ensure our instruments are uncorrelated with growth.
Appendix 1 lists all countries for which data were available as well as those countries that were included in the econometric analysis.
Charts 1A and 1B depict the core result for the two decades. Also, the significance of the coefficient in the reduced form regression (columns 2 and 5 of Tables 2A and 2B) is reassuring (see Angrist and Krueger, 2001).
All standard errors in the second-stage regressions reported in this section are corrected to take account of the fact that the instrument used in the first-stage is estimated. The procedure used to make this correction is the same as that in Frankel and Romer (1999).
This procedure may select out the countries in which labor is most distorted, either because of aid flows themselves pushing up wages and moving labor intensity away from the average or because of other distortions. This is why one cannot use such correlations to examine whether the maintained assumption of a technological propensity to use labor is valid across countries. However, it is a useful robustness check.
The simplest example of aid providing more resources to the private sector would be one where the government reduces its borrowing from the banking system in response to the aid, and hence makes more credit available to the private sector.
One possible worry is that the financial dependence measure may not be appropriate for developing countries, but Rajan and Zingales (1998) show that their results hold also in a sample of low income countries. Another proxy for reliance on external finance may be the average size of establishments, with small (and thus young) establishments requiring more external finance than large establishments. When we include the average size of establishments in an industry in a country interacted with aid inflows, the coefficient for the labor intensity aid interaction still remains unchanged (estimates available from the authors).
The correlation between the labor-intensity and exportability measure is 0.34, suggesting that they are capturing similar things.
In our analysis, we measure the average overvaluation for each country for the period 1980-90 as the average of the values for 1980 and 1990. In other words, we estimate the Balassa-Samuelson relationship separately for 1980 and 1990, and average the overvaluation estimates over these two periods. We repeat this procedure for 1990 and 2000 to obtain the average overvaluation for 1990-00.
This procedure is similar to that used by Dollar (1992) to calculate the misaligment of the real exchange rate. It yields plausible estimates for the degree of over/under valuation. For example, most CFA countries seem to be over-valued in 2000 but also seem to be more undervalued in 1990 before the devaluation of the CFA franc. The estimates for many east Asian countries as well as China also seem plausible.
In the first-stage regressions, the coefficient on the instruments is .283 and .369 for the 1980s and 1990s, respectively, with corresponding t-values of 4.31 and 6.69.
We obtained very similar results when we used the Easterly and Levine (2003) measure of overvaluation. We also obtained similar results when we interacted the aid measure with the exportability indices (available from the authors). The coefficients in Tables 2 and 9 suggest that a 1 percentage point increase in the ratio of aid to GDP has the the same impact on labor-intensive sectors as a 3.8 percent overvaluation.
The coefficient of the aid-skill intensity of industry interaction term is 0.10 (standard error of 0.004) and significant at the 1 percent level.
Although in this paper, we consider the impact of remittances, it is possible to extend our methodology to other flows such as natural resources, foreign direct investment, and other capital flows.
Unlike the case of aid, where instrumentation is important to disentangle the impact of aid from that of policy, for remittances, which are private flows, instrumentation is less imperative.
Reliable data for remittances are available for a reasonable sample of countries only for the 1990s. Hence we restrict our analysis to this period.
Put another way, some of the ways to mitigate the adverse effects of aid inflows on the real exchange rate could also reduce potential benefits. For example, aid inflows could simply be stored as reserves and not spent, but this would not be particularly helpful for a resource-starved country.
The Trade and Production Database provides the WITS trade data at the 3-digit ISIC code. This database is available at: www.worldbank.org/research/trade.
Since local PPI was not available for all developing countries in IFS, alternative deflators needed to be used to construct the measure of real value added in local currency. Accordingly, whenever PPI was not available, we used the effective deflator constructed with the index of industrial production as in Rajan and Zingales (1998). This deflator is the ratio of the growth rate of nominal value added in the entire manufacturing sector (from the UNIDO database) to the growth rate of the index of industrial production (from IFS). Alternatively, a GDP deflator was used whenever these two series were not available.
954 observations for forty countries are used for the 1980s, and 642 observations for twenty eight countries are used for the 1990s.
In order to estimate equation 1, we needed to compute the share of a country’s total (i.e. bilateral and multilateral) aid that went to any particular recipient. To do this, we obtained a decomposition of multilateral aid into its underlying bilateral constituents. The OECD DAC database contains a series called “imputed” bilateral aid, which does precisely this.
In the Correlates of War database from which these data are obtained, there are 4 types of alliances: a common alliance; a defense alliance; a neutrality or non-aggression alliance; and an entente alliance. We use the last as it seems the most consistent with the economic relationships we are interested in.