Elsevier

World Development

Volume 39, Issue 11, November 2011, Pages 2044-2053
World Development

Dodging Adverse Selection: How Donor Type and Governance Condition Aid’s Effects on School Enrollment

https://doi.org/10.1016/j.worlddev.2011.07.018Get rights and content

Summary

We employ AidData to test the effects of primary-education aid on school enrollment. We argue that the problem of adverse selection complicates both the allocation and the effectiveness of aid. We hypothesize that bilateral donors ought to have greater freedom to condition aid on recipient governance quality than multilateral donors, which are often bound by institutional rules to provide aid more impartially. Compared to their multilateral counterparts, bilateral donors may have advantages in overcoming adverse selection, resulting in bilateral aid’s boosting enrollments to a greater degree. AidData’s extensive coverage of multilateral aid enables this analysis for up to 100 low- and low-middle-income countries from 1995 to 2008. Latent growth regression analysis suggests that, compared to multilateral donors, bilateral donors indeed condition their primary education aid on recipient control of corruption and that bilateral aid is significantly related to improved enrollments.

Introduction

Grinding poverty plagues Eritreans, who eke out less than one dollar per day in per capita income and thus rank seventh on the list of poorest peoples in the world (World Bank, 2010). Apparently noticing Eritrea’s plight and seeking to boost its scarce human capital, donors sent roughly $220 million in foreign aid for improving Eritrean education during 1998–2008 (Findley et al., 2009). By the budget standards of advanced industrial economies the amount appears trivial, but at $20 million per year the aid represented more than 75% of total Eritrean government expenditures on education (World Bank, 2010). Effectively, foreign aid is the Eritrean education budget.

But an odd pattern has emerged in the beleaguered country: each year from 2005 to 2009 the net primary school enrollment rate – the proportion of school-aged children actually signed up for school – has dropped. It plummeted from 50% in 2005 to 39% in 2009 (World Bank, 2010). Many factors – including border disputes, regional war, political repression, flagging labor opportunities, volatile commodities markets, and climate strain, among others – likely contributed to this trend. But whatever donors intended with their education aid to Eritrea, it does not appear to have enabled more children to attend school.

Cases like Eritrea raise the possibility that education aid may prove ineffective more generally. And they provoke a more nuanced research question targeting the specific conditions under which aid may make a meaningful difference. Indeed, in this article we argue that the problem of adverse selection – the recipients most likely to seek and receive aid may be the least likely to use it effectively – complicates both aid allocation and aid effectiveness. Specifically, we contend that, in order to overcome adverse selection, donors must allocate aid strategically. And these selection effects in allocation will later condition aid effectiveness. We argue that, compared to bilateral donors, institutional rules and practices more tightly constrain multilateral donors, where broad coalitions of developing countries have seats and voting shares on development banks’ executive boards and can collude to demand financing with few strings attached. Thus, we expect bilateral donors to be more discriminating about the quality of governance among recipients and thus to act more strategically when allocating aid for primary education. These allocation strategies, then, should then influence the effectiveness of the aid in boosting primary-school enrollment rates.

We address this research question and test the argument empirically by employing AidData (Tierney et al., 2011), which, as other articles in this special issue have noted, significantly expands the coverage of aid information. Largely through adding money from multilateral banks and other donors that do not report to the OECD’s Creditor Reporting System (CRS), AidData nearly doubles the amount of development finance tracked by a single source, from $2.9 to $4.9 trillion. Given the amount of activity by multilaterals in the social sectors, particularly since the mid-1990s (see Lyne, Nielson, & Tierney, 2009), these new data should prove especially useful for understanding the relationship between education aid and education outcomes. Indeed, omission of these data – covering more than 40% of all development finance – from prior work may have biased previous results. For our empirical tests we apply latent growth (hierarchical linear modeling) estimation, which, by employing both random intercepts and random coefficients, is a more conservative technique for analyzing pooled time series data.

To date related studies have largely confined themselves to assessing education aid’s overall impact on economic growth, saying little about whether or not education aid actually leads directly to its more proximate anticipated outcomes. More recently, two studies have suggested that specifically-targeted education aid may have a positive influence on the key outcome of enrollment rates (Dreher et al., 2008, Michaelowa and Weber, 2007). We build on these studies by using AidData to nearly double the amount of development finance considered, isolating the effects of aid to primary schools, assessing bilateral and multilateral aid independently, and, critically, modeling the effects of adverse selection – measured as recipient corruption and autocracy – on the allocation and effectiveness of aid from bilateral versus multilateral donors. In what follows we introduce the substantive issue area of education aid, develop expectations for how education aid ought to affect enrollment rates conditional on donor type and governance, describe the data and estimation methods, present results, and draw conclusions.

Section snippets

Education aid and school enrollment

The bulk of the aid effectiveness literature has focused on broad questions of aggregate aid and its relationship to economic growth (Burnside and Dollar, 2000, Easterly, 2006, Easterly et al., 2004, Rajan and Subramanian, 2008). Results have been mixed, but the center of gravity has gathered around findings that suggest no relationship between aid and growth (Easterly et al., 2004, Rajan and Subramanian, 2008).

But economic growth is notoriously hard to predict, so a closer look at the effect

Data

While the ability of foreign aid to improve education outcomes is important in all recipient countries, we limit our analysis roughly 100 low- and lower-middle-income countries as defined by the World Bank. This is chiefly because primary enrollment rates frequently approach 100% among high- and upper-middle-income countries. The fact that the upper bound constrains the variance – added to the reality that the wealthier countries receive less aid per GDP (or none at all) – suggests that the

Methods

For our initial estimation, we employed a latent-growth, structural equation, mixed effects, or hierarchical linear model (HLM). This type of multilevel model is appropriately used to analyze time-series cross-sectional data like those found here. One drawback of the pooled time-series OLS method often used in aid-growth research is the significant difference in both intercepts and slopes across the countries being analyzed. With the type of data used for aid research, individual observations

Conclusions and implications

The results included here suggest some reinforcing evidence for previous findings that primary-education aid may cause limited increases to school enrollment. However, the results also suggest that those effects may not be universal. Indeed, separating bilateral from multilateral aid produces significantly different results for the two types. Consistent with our hypothesis, the results suggest that bilateral aid may overcome the problem of adverse selection somewhat better than multilateral

Acknowledgments

We are grateful to Axel Dreher, Michael Findley, Darren Hawkins, Christopher Kilby, Stephen Knack, Katja Michaelowa, Rich Nielson, Timmons Roberts, and Sven Wilson for helpful comments and guidance.

AidData (formerly known as Project-Level Aid or PLAID) was supported by the Bill & Melinda Gates Foundation; the William and Flora Hewlett Foundation; National Science Foundation grant SES-0454384; the College of William and Mary; and the College of Family, Home and Social Sciences, the Department of

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