Samuel Bazzi

Counting Chickens when they Hatch: Timing and the Effects of Aid on Growth†

Recent research yields widely divergent estimates of the cross‐country relationship between foreign aid receipts and economic growth. We re‐analyse data from the three most influential published aid–growth studies, strictly conserving their regression specifications, with sensible assumptions about the timing of aid effects and without questionable instruments. All three research designs show that increases in aid have been followed on average by increases in investment and growth. The most plausible explanation is that aid causes some degree of growth in recipient countries, although the magnitude of this relationship is modest, varies greatly across recipients and diminishes at high levels of aid.

Blunt Instruments: On Establishing the Causes of Economic Growth

Concern has intensified in recent years that many instrumental variables used in widely-cited growth regressions may be invalid, weak, or both. Attempts to remedy this general problem remain inadequate. We demonstrate that a range of published growth regressions may contain spurious results because of hidden problems with the instrumental variables they use. We urge several steps to surpass these difficulties: grounding of growth regressions in slightly more generalized theoretical models, deployment of the latest methods for estimating sensitivity to violations of exclusion restrictions, opening the black box of GMM with supportive evidence of instrument strength, and utilization of weak-instrument robust tests and estimators.

A Reply to “A Replication of “Counting Chickens When They Hatch” (Economic Journal 2012)”

The regressions in Clemens et al. (2012) are fully replicable with open-access data and code. Roodman (2015) alters the regression specifications in that paper by adding twice-lagged aid, after which he cannot reject the null hypothesis of a zero effect of aid on growth. We show, with Roodman’s data and code, that his altered specifications have very low power to reject the null–roughly 0.1 to 0.2. In other words, there is an 80-90% chance that Roodman’s altered regressions fail to reject the null by construction. This renders the exercise uninformative about the robustness of the findings in Clemens et al. (2012) or, more generally, about the effect of aid on growth.

Finding the Poor vs. Measuring Their Poverty: Exploring the Drivers of Targeting Effectiveness in Indonesia.

Centralized targeting registries are increasingly used to allocate social assistance benefits in developing countries. This paper provides the first attempt to identify the relative importance of two key design issues for targeting accuracy: (i) which households to survey for inclusion in the registry and (ii) how to rank surveyed households. We evaluate Indonesia’s Unified Database for Social Protection Programs (UDB), among the largest targeting registries in the world, used to provide social assistance to over 25 million households. Linking administrative data with an independent household survey, we find that the UDB system is more progressive than previous, program-specific targeting approaches. However, simulating an alternative targeting system based on enumerating all households, we find a one-third reduction in undercoverage of the poor compared to focusing on households registered in the UDB. Overall, we identify large gains in targeting performance from improving the initial registration stage relative to the ranking stage. JEL classification: D61, I32, I38

Assessing the Spatial Concentration of Indonesia's Manufacturing Sector: Evidence from Three Decades

Beyond the role of economic forces, many theories of economic geography emphasize the way politics can shape the spacial configuration of economic activity. We investigate the impact of changes in political regimes on industrial concentration using 30 years of data on Indonesian manufacturers. These data span both the reign of Suharto, one of the strongest central governments in Southeast Asia, and its collapse and the subsequent decentralization of power. Using the canonical measure of Ellison and Glaeser, we show that in the mid 1980s, Indonesias firms exhibited a similar degree of agglomeration as seen in the United States. Spatial concentration then declined until the 1998 Asian Financial Crisis, and has since begun to rise during the decentralization period. We also measure concentration using the continuous measure developed by Duranton and Overman (2005), and find that the agglomeration exhibited by Indonesian firms is also broadly similar to that documented by Duranton and Overman (2005 ) for the United Kingdom, although localization drops off more gradually in Indonesia than in the United Kingdom. Using this continuous measure of agglomeration, we identify 32 manufacturing clusters in Indonesia, and investigate the correlates of concentration. We find that the most robust drivers of agglomeration have been natural resources and supply chain linkages, especially with respect to explaining long-term changes in spatial concentration.