Statistical Matching Methods Research Articles

Unemployed and Their Caseworkers: Should They be Friends or Foes?

Summary In many countries, caseworkers in public employment offices have dual roles of counselling and monitoring unemployed people. These roles often conflict, which results in important caseworker heterogeneity: some consider providing services to their clients and satisfying their demands as their primary task. However, others may pursue their own strategies, even against the will of the unemployed person. They may assign jobs and labour market programmes without the consent of the unemployed person. On the basis of a very detailed linked jobseeker–caseworker data set for Switzerland, we investigate the effects of caseworkers’ co-operativeness on the probabilities of employment of their clients. Modified statistical matching methods reveal that caseworkers who place less emphasis on a co-operative and harmonic relationship with their clients increase their chances of employment in the short and medium term.

로버스트 회귀모형을 이용한 자료결합방법

서로 다른 출처로부터 얻어진 데이터 파일들을 하나의 데이터 파일로 만드는 통계적 자료결합방법은 공통변수와 서로 다른 고유변수를 포함하여 변수들 간에 존재하는 관련성에 대해 살펴볼 수 있다. Robin (1986)이 제안한 일반회귀모형의 예측값을 이용한 통계적 결합방법은 자료에 대한 다변량 정규성을 가정하기 때문에 이 가정을 위반하는 자료를 이용하는 것은 많은 문제를 수반한다. 본 연구는 제공파일의 고유변수에 모분포를 반영하지 못하는 특이점이 존재하는 경우, 일반회귀모형을 이용한 통계적 결합방법의 대안으로 로러스트 회귀추정방법을 이용한 자료결합방법을 제안하였다. 나아가 로버스트 회귀모형을 이용한 결합방법과 일반회귀모형을 이용한 결합방법에서의 상관관계 및 결정계수 보존에 관한 성능을 비교하기 위하여 모의실험을 수행하였다. Statistical matching techniques whose aim is to achieve a complete data file from different sources. Since the statistical matching method proposed by Rubin (1986) assumes the multivariate normality for data, using this method to data which violates the assumption would involve some problems. This research proposed the statistical matching method using robust regression as an alternative to the linear regression. Furthermore, we carried out a simulation study to compare the performance of the robust regression model and the linear regression model for the statistical matching.

A Caseworker Like Me - Does the Similarity between Unemployed and Caseworker Increase Job Placements?

This paper examines whether the chances of job placements improve if unemployed persons are counselled by caseworkers who belong to the same social group, defined by gender, age, education, and nationality. Based on an unusually informative dataset, which links Swiss unemployed to their caseworkers, we find positive employment effects of about 4 percentage points if caseworker and unemployed belong to the same social group. Coincidence in a single characteristic, e.g. same gender of caseworker and unemployed, does not lead to detectable effects on employment. These results, obtained by statistical matching methods, are confirmed by several robustness checks.

Unemployed and Their Caseworkers: Should They Be Friends or Foes?

In many countries, caseworkers in a public employment office have the dual roles of counselling and monitoring unemployed persons. These roles often conflict with each other leading to important caseworker heterogeneity: Some consider providing services to their clients and satisfying their demands as their primary task. Others may however pursue their strategies even against the will of the unemployed person. They may assign job assignments and labour market programmes without consent of the unemployed person. Based on a very detailed linked jobseeker-caseworker dataset, we investigate the effects of caseworkers' cooperativeness on the employment probabilities of their clients. Modified statistical matching methods reveal that caseworkers who place less emphasis on a cooperative and harmonic relationship with their clients increase their employment chances in the short and medium term.

Unemployed and Their Caseworkers: Should They Be Friends or Foes

In many countries, caseworkers in a public employment office have the dual roles of counselling and monitoring unemployed persons. These roles often conflict with each other leading to important caseworker heterogeneity: Some consider providing services to their clients and satisfying their demands as their primary task. Others may however pursue their strategies even against the will of the unemployed person. They may assign job assignments and labour market programmes without consent of the unemployed person. Based on a very detailed linked jobseeker-caseworker dataset, we investigate the effects of caseworkers' cooperativeness on the employment probabilities of their clients. Modified statistical matching methods reveal that caseworkers who place less emphasis on a cooperative and harmonic relationship with their clients increase their employment chances in the short and medium term.

Using an alternative evaluation measure for assessing juvenile diversion programs

This paper describes the effectiveness of six juvenile diversion programs in the metropolitan area of Denver, Colorado. Program success was measured by recidivism reduction. However, by using a one-for-one statistical client-baseline matching method and a variable definition of recidivism, we managed to overcome drawbacks that often occur when using recidivism as an outcome indicator. Together, program participants showed a 26% reduction in recidivism rate over that of a matched baseline sample. Although four of the six programs studied accounted for the overall success rate, we explain why two of the programs showed no significant reductions in recidivism rates.

AN EXAMPLE OF THE USE OF STATISTICAL MATCHING IN THE ESTIMATION AND ANALYSIS OF THE SIZE DISTRIBUTION OF INCOME*

This paper focuses on the use of statistical matching in the estimation and analysis of the size distribution of family unit personal income. The paper begins with a brief discussion of data on the size distribution of income in the U.S. and their limitations. Several methods of improving or augmenting those data are described, and earlier examples of statistical matching for that purpose are mentioned. A brief summary of the types of statistical matching methods which have been used is also presented. Then a recent example of statistical matching carried out at the Office of Research and Statistics, Social Security Administration, is described, and the effects on the size distribution of adjusting and augmenting the initial data using the statistically matched data from that example are shown. Material relating to the accuracy of that statistical match is presented in the appendix.