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Does aid improve democracy and governance? A meta-regression analysis

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Abstract

The paper provides a systematic and quantitative review of the empirical evidence on the effects of development aid on democracy and governance. We find that aid has had, on average, a zero or negative effect on democracy, except that it has had a positive effect on democratization in European transitional economies. Aid had a positive effect on governance during the Cold War period but has had no effect on governance in the post-Cold War period.

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Notes

  1. These papers are summarized in Doucouliagos and Paldam (2009).

  2. Easterly and Williamson (2011) argue that aid suffers from several problems, such as fragmentation and lack of specialization among donors. The Accra Agenda for Action also notes that aid effectiveness is reduced when there are too many duplicate initiatives (OECD 2008).

  3. Institutional quality can benefit citizens independently of its effect on growth. For example, Kurrild-Klitgaard et al. (2006: 307) find “that democracy leads to less terror”. Basuchoudhary and Shughart (2010) find that economic rights are more important than political rights in reducing the probability of terrorist attacks. Citizens might value the rule of law, civil liberties and political rights. Thus, even if aid has no effect on economic performance, it can increase welfare if aid results in more democracy and if democracy is a good. Aid could, however, reduce welfare. For example, Bjørnskov (2010) finds that aid increases the income share of the elites in democratic countries. Hence, if income equality is a desirable good, then aid could have an adverse effect on welfare. On the other hand, if aid does contribute to the recipient country’s economic development, then it might also affect the path of income inequality, e.g., in a Kuznets (1955) type manner. The net effect of aid on welfare remains an unresolved issue.

  4. For example, some studies of institutional development may use aid as a control variable. These studies might not be detected in searches of titles and abstracts that include the word “aid”.

  5. We excluded studies of the effect of aid on economic freedom, as there were relatively few such studies. An appendix with the full reference list of included studies can be found at http://www.deakin.edu.au/meta-analysis.

  6. Finkel et al. (2007) argue that there might not be any systematic bias in aid allocations. For example, some donors might give aid to non-democratic regimes while others give aid only to democratic regimes. Moreover, aid allocation motives can change over time. The consequence of this is that simultaneity might not be a severe problem. Hence, it is prudent to include all estimates and then conduct tests for differences between estimators.

  7. Vanhanen (2000) constructs an index of democracy by combining the degree of electoral competition with the percentage of the population who actually voted.

  8. See Stanley and Doucouliagos (2012) on the formula for converting regression outputs into partial correlations. Unfortunately, most studies do not provide sufficient information from which to calculate elasticities (the percentage change in institutional quality arising from a percentage change in aid).

  9. Studies differ in the way they scale the measures of democracy and governance. In some cases higher values mean more democracy (or better governance) whilst in others they mean less. Hence, in calculating partial correlations, we ensured consistency in the direction of the association by altering the sign where necessary so that a positive correlation means that aid improves democracy (or governance).

  10. The tetrachoric correlation measures the strength of association (correlation) between artificially dichotomized variables.

  11. For our dataset, both procedures yield essentially the same results.

  12. Usually there is a distribution of effect sizes. Hence, it is important to estimate MRA models rather than rely on a visual inspection of a graph.

  13. Correlations are truncated to range between −1 and +1. This truncation can, in some cases, result in downwardly biased estimates in MRA. Hence, we have also transformed the correlations into Fisher-z measures. This makes virtually no difference to the shape of the funnel plot nor to any subsequent MRAs. Hunter and Schmidt (2004) caution against using z-transformations on the grounds that it can result in upwardly biased estimates.

  14. Like all graphs, funnel plots are illustrative but they are no substitute for formal statistical testing.

  15. Precision squared is the inverse variance, which has been shown to produce ‘optimal’ weights in meta-analysis (Hedges and Olkin 1985).

  16. The fixed effects are jointly statistically significant (p-value = 0.00). Stanley and Doucouliagos (2012) argue that random effects can be quite problematic in MRA, especially if there is publication bias. The Hausman test rejects the null hypothesis that the preferred model is random effects MRA; χ 2 is 38.48 with a p-value of 0.005.

  17. The country classifications are adopted from the World Bank. Hence, Europe includes Cyprus and Turkey as well as European transitional economies. We also tried to separate this variable into European transitional countries and as well as into EU and non-EU members. Unfortunately, lack of data on the country composition of some samples means that adding the EU dummy reduces the sample size from 538 to 480. In these regressions the EU dummy has a positive coefficient (0.051) but is not statistically significant (t-statistic = 1.68). Consequently, the text reports our preferred results using the larger sample size and without the EU dummy.

  18. This result is surprising given the high correlation between measures of democracy. We re-ran the MRA using individual dummies for each of the democracy measures. The associated coefficients and t-statistics are: Polity =−0.05, t-statistic −4.68; Przeworski =−0.03, t-statistic −2.51; Vanhanen =0.06, t-statistic 4.57; Geddes =−0.03, −1.16; CGV =−0.04, −1.91. With the exception of Vanhanen, which is used in less than 1 % of the estimates, all the other measures produce larger adverse aid-on-democracy effects than the Freedom House measure.

  19. As noted earlier, the use of clustered standard errors is problematic in this literature given the relatively small number of clusters. As an alternative, we also estimated a linear hierarchical model with random effects, using Restricted Maximum Likelihood and also using Maximum Likelihood. Those results suggest that publication bias indeed exists in the literature and also that aid has no effect on democracy. Unfortunately, random effects models are highly problematic if there is indeed publication bias (see Stanley and Doucouliagos 2012).

  20. In the MRA of the aid-on-democracy data, the 2SLS/IV variable has a coefficient of −0.03, and the GMM variable has a coefficient of −0.05 and both are statistically significant. The null hypothesis that these coefficients are similar cannot be rejected (p-value =0.20).

  21. We also considered differences between the Country Policy and Institutional Assessments (CPIA) and Worldwide Governance Indicators (WGI) data, both of which are World Bank projects (Guillaumont 2009; Diarra and Plane 2011).

  22. In unreported regressions we also treated these variables separately. The 2SLS/IV dummy variable has a coefficient of 0.02 and the GMM dummy variable has a coefficient of 0.02. We accept the null hypothesis that these coefficients are similar (p-value =0.82).

  23. During the pre-1991 period there actually was little aid flowing to transitional countries. However, during this period aid was received by Cyprus, Turkey and other such nations.

  24. Australia has in recent years targeted some of its aid for the purpose of buying a seat on the UN’s Security Council.

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Acknowledgements

We thank Martin Paldam, Christian Bjørnskov, Andreas Freytag, Jakob de Haan, Erich Gundlach, Friedrich Schneider and Eric Uslaner, for useful comments and T.D. Stanley for technical advice on modeling. We take full responsibility for all errors.

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  1. School of Accounting, Economics and Finance, Deakin University, 221 Burwood Highway, Burwood, Victoria, 3125, Australia

    Zohid Askarov

  2. School of Accounting, Economics and Finance, and Alfred Deakin Research Institute, Deakin University, 221 Burwood Highway, Burwood, Victoria, 3125, Australia

    Hristos Doucouliagos

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Askarov, Z., Doucouliagos, H. Does aid improve democracy and governance? A meta-regression analysis. Public Choice 157, 601–628 (2013). https://doi.org/10.1007/s11127-013-0081-y

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