10 research outputs found
Measuring segregation on small units : a partial identification analysis
We consider the issue of measuring segregation in a population of small units, considering establishments in our application. Each establishment may have a different probability to hire an individual from the minority group. We define segregation indices as inequality indices on these unobserved, random probabilities. Because these probabilities are measured with error by proportions, standard estimators are inconsistent. We model this problem as a nonparametric binomial mixture. Under this testable assumption and conditions satisfied by standard segregation indices, such indices are partially identified and sharp bounds can be easily obtained by an optimization over a low dimensional space. We also develop bootstrap confidence intervals and a test of the binomial mixture model. Finally, we apply our method to measure the segregation of foreigners in small French firms
Estimating selection models without instrument with Stata
Ministry of Education, Singapore under its Academic Research Funding Tier
A convenient method for the estimation of the multinomial logit model with fixed effects
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey
International audienceSummary Linear regressions with period and group fixed effects are widely used to estimate policieâs effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates if the policyâs effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue and that proposes alternative estimators robust to heterogeneous effects. We use those alternative estimators to revisit Wolfers (2006a)
Fuzzy differences-in-differences with Stata
International audienceDifferences-in-differences evaluates the effect of a treatment. In its basic version, a âcontrol groupâ is untreated at two dates, whereas a âtreatment groupâ becomes fully treated at the second date. However, in many applications of this method, the treatment rate increases more only in the treatment group. In such fuzzy designs, de Chaisemartin and DâHaultfĆuille (2018b, Review of Economic Studies 85: 999â1028) propose various estimands that identify local average and quantile treatment effects under different assumptions. They also propose estimands that can be used in applications with a nonbinary treatment, multiple periods, and groups and covariates. In this article, we present the command fuzzydid, which computes the various corresponding estimators. We illustrate the use of the command by revisiting Gentzkow, Shapiro, and Sinkinson (2011, American Economic Review 101: 2980â3018)
The MarcinkiewiczâZygmund law of large numbers for exchangeable arrays
International audienceWe show a Marcinkiewicz-Zygmund law of large numbers for jointly, dissociated exchangeable arrays, in L r (r â (0, 2)) and almost surely. Then, we obtain a law of iterated logarithm for such arrays under a weaker moment condition than the existing one
A Cautionary Tale on Instrumental Calibration for the Treatment of Nonignorable Unit Nonresponse in Surveys
<p>Response rates have been steadily declining over the last decades, making survey estimates vulnerable to nonresponse bias. To reduce the potential bias, two weighting approaches are commonly used in National Statistical Offices: the one-step and the two-step approaches. In this article, we focus on the one-step approach, whereby the design weights are modified in a single step with two simultaneous goals in mind: reduce the nonresponse bias and ensure the consistency between survey estimates and known population totals. In particular, we examine the properties of instrumental calibration, a special case of the one-step approach that has received a lot of attention in the literature in recent years. Despite the rich literature on the topic, there remain some important gaps that this article aims to fill. First, we give a set of sufficient conditions required for establishing the consistency of instrumental calibration estimators. Also, we show that the latter may suffer from a large bias when some of these conditions are violated. Results from a simulation study support our findings. Supplementary materials for this article are available online.</p