Unemployed couples: the labour market effects of making both partners search for work

Summary.  The paper evaluates the effect of a recent change to unemployment benefit in the UK which requires both partners in a couple (rather than just one) to search for work. The difference-in-differences estimator is extended in two ways. First, variations in when the change was implemented are exploited to test and adjust for bias resulting from differential trends among the control group. Second, the approach is combined with matching to relax functional form restrictions. After several months, positive effects on exiting from benefits were detected but effects on entry to jobs were less apparent.     

The effect of careers guidance for employed adults on continuing education: assessing the importance of attitudinal information

Summary. The validity of the matching estimator in programme evaluation depends on the completeness of the set of variables that are used for matching. When an attitudinal variable is relevant for the decision about participation, but is either unmeasured or measured only after entry to the programme, estimates of effects may be biased or difficult to interpret. This issue was investigated with data from an evaluation study of careers guidance for employed adults which utilized the method of propensity score matching. Job satisfaction, measured shortly after entry to the programme, was found to be strongly associated with participation but might itself have been influenced by the early experience of careers guidance. Estimates of the effects of guidance were considered both including and excluding the job satisfaction measure from the participation model. Data experiments with adjusted values of job satisfaction were also performed. The results illustrate that omitted attitudinal information poses a particular difficulty for the matching estimator.     

Does residential mobility improve educational outcomes? Evidence from the Netherlands

This paper explores the impact of residential mobility on educational outcomes. By considering a large Dutch city with substantial internal residential mobility, we examine how residential mobility influences the decision of students to drop out of school. The paper exploits a rich administrative dataset with extensive information on educational, individual, family, housing and moving characteristics of students. It combines a matching design with a multivariate regression analysis, such that the evaluation draws on a well-comparable control group for the treated students. Accounting for individual, family, educational, neighborhood and housing characteristics, as well as for school and year fixed effects, we observe that residential mobility increases the probability of school dropout in the first few years after moving. The estimated effect changes, however, to a lower risk of early school leaving after an initial period, and then changes again to a higher risk after 6 years. This effect remains, regardless the level of education the students attended, or whether the student moves to a better or a worse neighborhood.     

城市住房公积金运行中的社会排斥探析

运用社会排斥理论探讨了当前城市住房公积金制度在缴存政策、覆盖范围、存贷关系等方面存在的制度性排斥问题,在此基础上建议,今后完善城市住房公积金制度应以扩充大非公有制组织缴存面、降低中低收入家庭住房贷款门槛、拓展公积金使用范围、加大对弱势群体政策保障力度等作为重点与主要方向。     

都市青年择偶场域分析——基于南京大型相亲会的实地研究

当代都市青年的择偶方式与过程正在发生新变化。大型相亲会作为社会为解决择偶问题所做的一种努力,为都市青年提供了一个新型的择偶场域。实地研究发现,大型相亲会为有择偶需求的都市青年提供了交流平台与见面机会,但在实际解决择偶问题上却表现出极大无奈。信息不对称和舆论魔力导致供需双方发生利益冲突、策划精英与媒体共谋制约活动的公益性、市场化运作与相亲市场恐慌导致婚配逻辑畸形错位,以及婚配主体选择意愿的模糊性等极大地制约了相亲会功效的发挥。     

Correlation and efficiency of propensity score-based estimators for average causal effects

Propensity score-based estimators are commonly used to estimate causal effects in evaluation research. To reduce bias in observational studies, researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probability weighting, and doubly robust estimators change under the case of correlated covariates. Propositions regarding the large sample variances under certain assumptions on the data-generating process are given. The propositions are supplemented by several numerical large sample and finite sample results from a wide range of models. The results show that the covariate correlations may increase or decrease the variances of the estimators. There are several factors that influence how correlation affects the variance of the estimators, including the choice of estimator, the strength of the confounding toward outcome and treatment, and whether a constant or non-constant causal effect is present.     

The matching, birthday and the strong birthday problem: a contemporary review

This article provides a contemporary exposition at a moderately quantitative level of the distribution theory associated with the matching and the birthday problems. A large number of examples, many not well known, are provided to help a reader have a feeling for these questions at an intuitive level.     

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

ABSTRACT

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.     

Make assurance double sure: combination of two disclosure limitation methods and estimation of general regression models

In order to guarantee confidentiality and privacy of firm-level data, statistical offices apply various disclosure limitation techniques. However, each anonymization technique has its protection limits such that the probability of disclosing the individual information for some observations is not minimized. To overcome this problem, we propose combining two separate disclosure limitation techniques, blanking and multiplication of independent noise, in order to protect the original dataset. The proposed approach yields a decrease in the probability of reidentifying/disclosing individual information and can be applied to linear and nonlinear regression models. We show how to combine the blanking method with the multiplicative measurement error method and how to estimate the model by combining the multiplicative Simulation-Extrapolation (M-SIMEX) approach from Nolte (, 2007) on the one side with the Inverse Probability Weighting (IPW) approach going back to Horwitz and Thompson (J. Am. Stat. Assoc. 47:663–685, 1952) and on the other side with matching methods, as an alternative to IPW, like the semiparametric M-Estimator proposed by Flossmann (, 2007). Based on Monte Carlo simulations, we show that multiplicative measurement error combined with blanking as a masking procedure does not necessarily lead to a severe reduction in the estimation quality, provided that its effects on the data generating process are known.