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Institution

Center for Global Development

NonprofitWashington D.C., District of Columbia, United States
About: Center for Global Development is a nonprofit organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Poverty. The organization has 1472 authors who have published 3891 publications receiving 162325 citations.


Papers
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Journal ArticleDOI
TL;DR: This pedagogic paper first introduces linear GMM, and shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way.
Abstract: This working paper by CGD research fellow David Roodman provides an introduction to a particular class of econometric techniques, dynamic panel estimators. The techniques and their implementation in Stata, a statistical software package widely used in the research community, are an important input to the careful applied research CGD advocates. The techniques discussed are specifically designed to extract causal lessons from data on a large number of individuals (whether countries, firms or people) each of which is observed only a few times, such as annually over five or ten years. These techniques were developed in the 1990s by authors such as Manuel Arellano, Richard Blundell and Olympia Bover, and have been widely applied to estimate everything from the impact of foreign aid to the importance of financial sector development to the effects of AIDS deaths on households. The present paper contributes to this literature pedagogically, by providing an original synthesis and exposition of the literature on these dynamic panel estimators, and practically, by presenting the first implementation of some of these techniques in Stata. Stata is designed to encourage users to develop new commands for it, which other users can then use or even modify. In this paper Roodman introduces abar and xtabond2, which is now one of the most frequently downloaded user-written Stata commands in the world. Stata's partially open-source architecture has encouraged the growth of a vibrant world-wide community of researchers, which benefits not only from improvements made to Stata by the parent corporation, but also from the voluntary contributions of other users. Stata is arguably one of the best examples of a combination of private for-profit incentives and voluntary open-source incentives in the joint creation of a global public good.

5,458 citations

Journal ArticleDOI
TL;DR: This paper introduced linear generalized method of moments (GMM) estimators for situations with small T, large N panels, with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals.
Abstract: The Arellano-Bond (1991) and Arellano-Bover (1995)/Blundell-Bond (1998) linear generalized method of moments (GMM) estimators are increasingly popular. Both are general estimators designed for situations with “small T, large N” panels, meaning few time periods and many individuals; with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals. This pedagogic paper first introduces linear GMM. Then it shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it shows how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The Center for Global Development is an independent think tank that works to reduce global poverty and inequality through rigorous research and active engagement with the policy community. Use and dissemination of this Working Paper is encouraged, however reproduced copies may not be used for commercial purposes. Further usage is permitted under the terms of the Creative Commons License. The views expressed in this paper are those of the author and should not be attributed to the directors or funders of the Center for Global Development.

5,416 citations

Posted Content
TL;DR: This work estimates the relationship between household wealth and children’s school enrollment in India by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights, and shows that this index is robust to the assets included, and produces internally coherent results.
Abstract: The relationship between household wealth and educational enrollment of children can be estimated without expenditure data. A method for doing so - which uses an index based on household asset ownership indicators - is proposed and defended in this paper. In India, children from the wealthiest households are over 30 percentage points more likely to be in school than those from the poorest households, although this gap varies considerably across states. To estimate the relationship between household wealth and the probability that a child (aged 6 to 14) is enrolled in school, Filmer and Pritchett use National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993. In developing their estimate Filmer and Pritchett had to overcome a methodological difficulty: The NFHS, modeled closely on the Demographic and Health Surveys, measures neither household income nor consumption expenditures. As a proxy for long-run household wealth, they constructed a linear asset index from a set of asset indicators, using principal components analysis to derive the weights. This asset index is robust, produces internally coherent results, and provides a close correspondence with data on state domestic product and on state level poverty rates. They validate the asset index using data on consumption spending and asset ownership from Indonesia, Nepal, and Pakistan. The asset index has reasonable coherence with current consumption expenditures and, more importantly, works as well as - or better than - traditional expenditure-based measures in predicting enrollment status. The authors find that on average a child from a wealthy household (in the top 20 percent on the asset index developed for this analysis) is 31 percent more likely to be enrolled in school than a child from a poor household (in the bottom 40 percent). This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to inform educational policy. The study was funded by the Bank`s Research Support Budget under the research project Educational Enrollment and Dropout (RPO 682-11).

4,966 citations

Journal ArticleDOI
TL;DR: This article reviewed the evidence on the effects of instrument proliferation, and described and simulated simple ways to control it, and illustrated the dangers by replicating Forbes [American Economic Review (2000) Vol. 90, pp. 869-887] on income inequality and Levine et al. [Journal of Monetary Economics] (2000] Vol. 46, pp 31-77] on financial sector development.
Abstract: The difference and system generalized method of moments (GMM) estimators are growing in popularity. As implemented in popular software, the estimators easily generate instruments that are numerous and, in system GMM, potentially suspect. A large instrument collection overfits endogenous variables even as it weakens the Hansen test of the instruments’ joint validity. This paper reviews the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it. It illustrates the dangers by replicating Forbes [American Economic Review (2000) Vol. 90, pp. 869–887] on income inequality and Levine et al. [Journal of Monetary Economics] (2000) Vol. 46, pp. 31–77] on financial sector development. Results in both papers appear driven by previously undetected endogeneity.

3,429 citations

Journal ArticleDOI
TL;DR: In this paper, the authors review the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it, and illustrate the dangers by replicating two early applications to economic growth: Forbes (2000) on income inequality and Levine, Loayza, and Beck (2000).
Abstract: The Difference and System generalized method of moments (GMM) estimators are growing in popularity, thanks in part to specialized software. But as implemented in these packages, the estimators easily generate results by default that are at once invalid yet appear valid in specification tests. The culprit is their tendency to generate instruments that are a) numerous and, in System GMM, b) suspect. A large collection of instruments, even if individually valid, can be collectively invalid in finite samples because they overfit endogenous variables. They also weaken the Hansen test of overidentifying restrictions, which is commonly relied upon to check instrument validity. This paper reviews the evidence on the effects of instrument proliferation, and describes and simulates simple ways to control it. It illustrates the dangers by replicating two early applications to economic growth: Forbes (2000) on income inequality and Levine, Loayza, and Beck (2000) on financial sector development. Results in both papers appear driven by previously undetected endogeneity.

3,350 citations


Authors

Showing all 1486 results

NameH-indexPapersCitations
William Easterly9325349657
Michael Kremer7829429375
George G. Nomikos7020213581
Tommy B. Andersson7021615167
Mark Rounsevell6925320296
David Hulme6932418616
Lant Pritchett6826035341
Jane E. Freedman6534813704
Arvind Subramanian6422020452
Dale Whittington6326510949
Michael Walker6131914864
Sanjeev Gupta5957514306
Joseph C. Cappelleri5948420193
Nathaniel P. Katz5821118483
Anthony Bebbington5724713362
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20236
202221
2021225
2020202
2019229
2018240