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290
Nonparametric estimation of average treatment effects under exogeneity: a review
- REVIEW OF ECONOMICS AND STATISTICS
, 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
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Cited by 630 (25 self)
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Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional-form assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this
Some practical guidance for the implementation of propensity score matching
- IZA DISCUSSION PAPER
, 2005
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Matching as Nonparametric Preprocessing for Reducing Model Dependence
- in Parametric Causal Inference,” Political Analysis
, 2007
"... Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other ..."
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Cited by 334 (46 self)
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Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological
Large Sample Properties of Matching Estimators for Average Treatment Effects
- ECONOMETRICA 74,235-267
, 2006
"... Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not ap ..."
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Cited by 318 (18 self)
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Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N1/2-consistent in general and describe conditions under which matching estimators do attain N1/2-consistency. Second, we show that even in settings where matching estimators are N1/2-consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R.
Alternative Approaches to Evaluation in Empirical Microeconomics
, 2002
"... Four alternative but related approaches to empirical evaluation of policy interventions are studied: social experiments, natural experiments, matching methods, and instrumental variables. In each case the necessary assumptions and the data requirements are considered for estimation of a number of ke ..."
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Cited by 158 (3 self)
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Four alternative but related approaches to empirical evaluation of policy interventions are studied: social experiments, natural experiments, matching methods, and instrumental variables. In each case the necessary assumptions and the data requirements are considered for estimation of a number of key parameters of interest. These key parameters include the average treatment effect, the treatment of the treated and the local average treatment effect. Some issues of implementation and interpretation are discussed drawing on the labour market programme evaluation literature.
On the failure of the bootstrap for matching estimators.”
, 2006
"... Abstract Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping methods for inference. No formal justification for the use of the bootstrap has been provided. Here we show that the bootstrap is in general not valid, even in the simple c ..."
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Cited by 87 (4 self)
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Abstract Matching estimators are widely used for the evaluation of programs or treatments. Often researchers use bootstrapping methods for inference. No formal justification for the use of the bootstrap has been provided. Here we show that the bootstrap is in general not valid, even in the simple case with a single continuous covariate when the estimator is root-N consistent and asymptotically normally distributed with zero asymptotic bias. Due to the extreme non-smoothness of nearest neighbor matching, the standard conditions for the bootstrap are not satisfied, leading the bootstrap variance to diverge from the actual variance. Simulations confirm the difference between actual and nominal coverage rates for bootstrap confidence intervals predicted by the theoretical calculations. To our knowledge, this is the first example of a root-N consistent and asymptotically normal estimator for which the bootstrap fails to work. JEL Classification: C14, C21, C52 Keywords: Average Treatment Effects, Bootstrap, Matching, Confidence Intervals * We are grateful for comments by Peter Bickel. Financial support for this research was generously provided through NSF grants SES-0350645 (Abadie) and SES 0136789 (Imbens). †
Trade, standards and poverty: Evidence from Senegal, World Development
, 2009
"... A major concern about trade is that, while it may accelerate growth, the poor many not benefit (proportionately). An emerging literature on global supply chains adds additional critiques, arguing that increasing standards are trade barriers for developing countries and cause the marginalization of p ..."
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Cited by 48 (14 self)
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A major concern about trade is that, while it may accelerate growth, the poor many not benefit (proportionately). An emerging literature on global supply chains adds additional critiques, arguing that increasing standards are trade barriers for developing countries and cause the marginalization of poor households. This paper is the first to quantify income and poverty effects of such high-standards trade and to integrate labor market effects, by using company and household survey data from the vegetable export chain in Senegal. We find that exports have grown sharply despite increasing standards, with important income and poverty effects. Regional poverty is 14 % points lower due to vegetable exports. Tightening food standards induced a shift from smallholder contract-based farming to large-scale integrated estate production, altering the mechanism through which poor households benefit: through labor markets instead of product markets. The impact on poverty reduction is stronger as the poorest benefit relatively more from working on large-scale farms than from contract farming.
Do property titles increase credit access among the urban poor? Evidence from a nationwide titling program
- Processes of Large-Scale Land Acquisition by Investors Case Studies from Sub-Saharan Africa Global Land Grabbing. In: International Conference on Global Land Grabbing Global Land Tool Network (GLTN) 2012. Available at: http://www.gltn.net/index.php/land-t
, 2006
"... The collateral value of landholdings is generally assumed to increase with ownership rights, thereby improving credit access among landholders. In situations of poverty, however, this outcome is uncertain because of other significant barriers to lending. To test whether proof of property ownership ..."
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Cited by 46 (1 self)
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The collateral value of landholdings is generally assumed to increase with ownership rights, thereby improving credit access among landholders. In situations of poverty, however, this outcome is uncertain because of other significant barriers to lending. To test whether proof of property ownership promotes the use of low-income housing as collateral, we evaluate the impact on credit supply of obtaining a property title through a landtitling program in Peru. By directly observing whether loan applicants are requested to provide collateral, we can isolate the effect of property titles on credit supply from their effect on demand by comparing loan approval rates when titles are requested to rates when they are not. Our results indicate that property titles are associated with approval rates on public sector loans as much as 12% higher when titles are requested by lenders and no relationship between titles and approval decisions otherwise. In contrast, there is no evidence that titles increase the likelihood of receiving credit from private sector banks, although interest rates are significantly lower for titled applicants regardless of whether collateral was requested. The failure of commercial banks to increase their rate of lending to households that obtain property titles through government programs has important implications for the potential effects of property reform on economic growth and poverty reduction. One explanation for this failure is that titling programs reduce banks' perceptions of their ability to foreclose. This is supported by data from Peru indicating that individuals with title have less fear of losing property in cases of default.
