Aid and growth at the regional level

Abstract

This paper brings the aid effectiveness debate to the sub-national level. We hypothesize the nonrobust results regarding the effects of aid on development in the previous literature to arise due to the effects of aid being insufficiently large to measurably affect aggregate outcomes. Using geocoded data for World Bank aid to a maximum of 2,221 first-level administrative regions (ADM1) and 54,167 second-level administrative regions (ADM2) in 130 countries over the 2000-2011 period, we test whether aid affects development, measured as nighttime light growth. Our preferred identification strategy exploits variation arising from interacting a variable that indicates whether or not a country has passed the threshold for receiving IDA's concessional aid with a recipient region's probability to receive aid, in a sample of 478 ADM1 regions and almost 8,400 ADM2 regions from 21 countries. Controlling for the levels of the interacted variables, the interaction provides a powerful and excludable instrument. Overall, we find significant correlations between aid and growth in ADM2 regions, but no causal effects.

Aid on Demand: African Leaders and the Geography of China's Foreign Assistance

“…We rely on a georeferenced dataset provided by AidData (2015) in collaboration with the World Bank that consists of all World Bank projects approved between 2000 and 2011. The dataset includes 3,534 projects and 41,307 project locations, comprising total commitments of almost USD 370 bn (Dreher and Lohmann 2015).…”

“…We exclude those projects that are nation-wide in scope, for which no or unclear information on their location is provided, and projects that are allocated to the central government and therefore cannot be attributed to a specific region. In total, about 40 percent of all projects are assigned to a distinguishable location (Dreher and Lohmann 2015). Our main variable of interest is a binary indicator variable Birthregion ict , which is equal to 1 if the political leader of country c in year t was born in administrative region i, and 0 otherwise.…”

International organizations and development finance: Introduction to the special issue

“…For example, the Brazys et al (2017) contribution to this special issue is representative of a broader Bgeospatial turn^in aid research that leverages subnational sources of variation to better understand the motivations for and impacts of international development finance (Winters 2014;Dreher and Lohmann 2015;Nunnenkamp et al 2016a, b;Briggs 2017). With data on the precise locations and timing of specific interventions funded by bilateral and multilateral development finance institutions and subnationally geocoded outcome data, the literature is now uncovering new knowledge about the features of multilateral development finance that are truly distinctive.…”

“…This geo-spatial turn has also led to a new set of aid effectiveness studies that seek to achieve causal identification with finer-grained data on the geographical scope and timing of specific interventions and (both intended and unintended) outcomes that are measured on similar spatial and temporal scales (Dreher and Lohmann 2015;Campbell et al 2016;Marty et al 2017). Several of these studies have presented evidence that bilateral and multilateral sources of development finance can have substantially different impacts on economic, environmental, and governance outcomes (Dreher et al 2016;Buchanan et al 2016;BenYishay et al 2016;Isaksson and Kotsadam 2016).…”

Do We Know What We Think We Know? Aid Fragmentation and Effectiveness Revisited

“…8 See Dreher and Langlotz (2015) and Dreher and Lohmann (2015) for recent attempts to identify causal effects of aid on growth. 9 The regression we build upon is presented by Clemens et al (2012) in Table 7, column 7.…”