Marital disruption and economic well-being: a comparative analysis

Summary.  Though there is a considerable literature concerned with the economic consequences of marital breakdown, there is still substantial disagreement in terms of its magnitude. One of the major problems underlying this debate is how economic well-being is defined. We implement several measures of well-being of monetary and multidimensional nature by using data from the European Community Household Panel. Another issue in this literature concerns selection bias of divorcing couples. We tackle this issue by using a propensity score matching technique combined with a difference-in-differences estimator. Results confirm the importance of the definition of well-being. We find a strong gender bias when using monetary measures but a considerably lower bias, and for some countries non-existent, when using non-monetary indices.     

Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing

Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, we must model the distribution of the treatment indicator given a vector of covariates. Much work has been done in the case where the covariates are fully observed. Unfortunately, many large scale and complex surveys, such as longitudinal surveys, suffer from missing covariate values. In this paper, we compare three different approaches and their underlying assumptions of handling missing background data in the estimation and use of propensity scores: a complete-case analysis, a pattern-mixture model based approach developed by Rosenbaum and Rubin (J Am Stat Assoc79:516–524, 1984), and a multiple imputation approach. We apply these methods to assess the impact of childbearing events on individuals’ wellbeing in Indonesia, using a sample of women from the Indonesia Family Life Survey. I am grateful to all the participants at the project “Poverty Dynamics and Fertility in Developing Countries” for their support and encouragement. Special thanks are due to Fabrizia Mealli for her insightful suggestions and discussions. I also thank Jungho Kim, who is the main author of the STATA code to produce Indonesia consumption expenditure. Finally, I thank Arnstein Aassve, and Letizia Mencarini for help working with the data and their very useful discussions, and Alexia Fuernkranz-Prskawetz, and Henriette Engelhardt for detailed comments and suggestions which have improved the paper. Financial support from CNR-EFS and COFIN 2005 is gratefully acknowledged.     

Another look into the effect of premarital cohabitation on duration of marriage: an approach based on matching

Summary.  The paper proposes an alternative approach to studying the effect of premarital cohabitation on subsequent duration of marriage on the basis of a strong ignorability assumption . The approach is called propensity score matching and consists of computing survival functions conditional on a function of observed variables (the propensity score), thus eliminating any selection that is derived from these variables. In this way, it is possible to identify a time varying effect of cohabitation without making any assumption either regarding its shape or the functional form of covariate effects. The output of the matching method is the difference between the survival functions of treated and untreated individuals at each time point. Results show that the cohabitation effect on duration of marriage is indeed time varying, being close to zero for the first 2–3 years and rising considerably in the following years.     

On the inefficiency of propensity score matching

Propensity score matching is now widely used in empirical applications for estimating treatment effects. Propensity score matching (PSM) is preferred to matching on X because of the lower dimension of the estimation problem. In this note, however, it is shown that PSM is inefficient compared to matching on X. Hence, matching on X should be considered as a serious alternative.     

Estimation of the average causal effect via multiple propensity score stratification

Suppose we are interested in estimating the average causal effect (ACE) for the population mean from observational study. Because of simplicity and ease of interpretation, stratification by a propensity score (PS) is widely used to adjust for influence of confounding factors in estimation of the ACE. Appropriateness of the estimation by the PS stratification relies on correct specification of the PS. We propose an estimator based on stratification with multiple PS models by clustering techniques instead of model selection. If one of them correctly specifies, the proposed estimator removes bias and thus is more robust than the standard PS stratification.     

Maximum entropy in the mean methods in propensity score matching for interval and noisy data

Abstract

In this paper, we propose maximum entropy in the mean methods for propensity score matching classification problems. We provide a new methodological approach and estimation algorithms to handle explicitly cases when data is available: (i) in interval form; (ii) with bounded measurement or observational errors; or (iii) both as intervals and with bounded errors. We show that entropy in the mean methods for these three cases generally outperform benchmark error-free approaches.     

An empirical study on the threshold cointegration of Chinese A and H cross-listed shares

We investigate the dynamic relationship between the prices of Chinese A and H market cross-listed shares using the Enders–Siklos threshold cointegration approach. Our data are the daily closing prices of the Hang Seng China AH (A) index and the Hang Seng China AH (H) index from 4 January 2006 to 1 November 2013. We find a threshold cointegration between these two indices, instead of the linear cointegration well established in the literature. The short-term adjustment to the equilibrium shows an asymmetric effect according to the price deviation from the equilibrium. Moreover, using a Granger causality test, we find a bi-directional causality between these two markets, indicating a close relationship between them. A pairs trading rule, based on the estimated threshold cointegration model, demonstrates the usefulness of our results as it generates a significantly higher return than a naive buy-and-hold trading rule.     

政府支出对经济增长贡献率的测度研究

由于受国际金融危机的影响,各国政府都在不同程度上扩大了其支出水平,推动经济的发展与复苏。文章首先建立政府支出对经济增长贡献率关系的理论模型,然后基于我国的相关数据,运用联立方程组模型,进行实证分析。实证结果表明,政府支出对经济增长不仅有直接的拉动作用,而且还会通过消费、投资的促进作用间接地拉动经济增长,同时还对政府支出对经济增长的贡献率进行了估计。     

A Class of Permutation Tests for the Equality of Two Marginal Survival Functions Using Paired Censored Data

We studied several test statistics for testing the equality of marginal survival functions of paired censored data. The null distribution of the test statistics was approximated by permutation. These tests do not require explicit modeling or estimation of the within-pair correlation, accommodate both paired data and singletons, and the computation is straightforward with most statistical software. Numerical studies showed that these tests have competitive size and power performance. One test statistic has higher power than previously published test statistics when the two survival functions under comparison cross. We illustrate use of these tests in a propensity score matched dataset.     

An empirical investigation of the box-cox model and a nonlinear least squares alternative

In this paper we present a generalized functional form estimator, recently developed by jeffrey Wooldridge; and then we compare it empirically to the popular Box-Cox (BC) estimator using three data sets. We begin by briefly reviewing the drawbacks of the BC estimator. We Then introduce the nonlinear lest squares (NLS) alternative of Wooldridge which retains the desirable qualities of the BC estimator without the associated theoretical problems. We continue by applying both the BC and the NLS models to data from three classic hedonic regression studies and then compare the estimation resuts-point estimates, inferences and fitted values. The estimations include a wage rate equation, and two computer hedonic regression equations, one using data from a classic study by Gregory Chow and the other using an IBM data set that formed the basis of the new official BLS computer price index.     

An empirical investigation of the box-cox model and a nonlinear least squares alternative

In this paper we present a generalized functional form estimator, recently developed by jeffrey Wooldridge; and then we compare it empirically to the popular Box-Cox (BC) estimator using three data sets. We begin by briefly reviewing the drawbacks of the BC estimator. We Then introduce the nonlinear lest squares (NLS) alternative of Wooldridge which retains the desirable qualities of the BC estimator without the associated theoretical problems. We continue by applying both the BC and the NLS models to data from three classic hedonic regression studies and then compare the estimation resuts-point estimates, inferences and fitted values. The estimations include a wage rate equation, and two computer hedonic regression equations, one using data from a classic study by Gregory Chow and the other using an IBM data set that formed the basis of the new official BLS computer price index.     

Evaluating the effect of education on earnings: models, methods and results from the National Child Development Survey

Summary.  Regression, matching, control function and instrumental variables methods for recovering the effect of education on individual earnings are reviewed for single treatments and sequential multiple treatments with and without heterogeneous returns. The sensitivity of the estimates once applied to a common data set is then explored. We show the importance of correcting for detailed test score and family background differences and of allowing for (observable) heterogeneity in returns. We find an average return of 27% for those completing higher education versus anything less. Compared with stopping at 16 years of age without qualifications, we find an average return to O-levels of 18%, to A-levels of 24% and to higher education of 48%.     

高端业务对人力资源服务企业效益影响研究

人力资源服务业是服务业中的"朝阳产业",属于生产性服务业,它的高速发展促进了各行各业人力资源的优化配置,产生显著的溢出效应已成为现代服务业新的增长点,同时人力资源服务企业的发展状况及其统计信息也已成为社会各界广泛关注的问题和需求。通过问卷调查方式收集的人力资源服务企业数据,可分析样本企业的基本现状,基于此数据可将该领域的研究从行业层面推进到业务类型层面,并采用倾向得分匹配方法研究人力资源服务高端业务对企业效益的影响,研究结果表明开展高端业务的企业比未开展高端业务企业的销售利润率高1.97个百分点。     

An empirical investigation of different operating characteristics of several estimators of the intraclass correlation in the analysis of binary data

This paper is concerned with properties (bias, standard deviation, mean square error and efficiency) of twenty six estimators of the intraclass correlation in the analysis of binary data. Our main interest is to study these properties when data are generated from different distributions. For data generation we considered three over-dispersed binomial distributions, namely, the beta-binomial distribution, the probit normal binomial distribution and a mixture of two binomial distributions. The findings regarding bias, standard deviation and mean squared error of all these estimators, are that (a) in general, the distributions of biases of most of the estimators are negatively skewed. The biases are smallest when data are generated from the beta-binomial distribution and largest when data are generated from the mixture distribution; (b) the standard deviations are smallest when data are generated from the beta-binomial distribution; and (c) the mean squared errors are smallest when data are generated from the beta-binomial distribution and largest when data are generated from the mixture distribution. Of the 26, nine estimators including the maximum likelihood estimator, an estimator based on the optimal quadratic estimating equations of Crowder (1987), and an analysis of variance type estimator is found to have least amount of bias, standard deviation and mean squared error. Also, the distributions of the bias, standard deviation and mean squared error for each of these estimators are, in general, more symmetric than those of the other estimators. Our findings regarding efficiency are that the estimator based on the optimal quadratic estimating equations has consistently high efficiency and least variability in the efficiency results. In the important range in which the intraclass correlation is small (≤0 5), on the average, this estimator shows best efficiency performance. The analysis of variance type estimator seems to do well for larger values of the intraclass correlation. In general, the estimator based on the optimal quadratic estimating equations seems to show best efficiency performance for data from the beta-binomial distribution and the probit normal binomial distribution, and the analysis of variance type estimator seems to do well for data from the mixture distribution.     

Exploring the economic and social determinants of psychological well-being and perceived social support in England

Summary.  A fundamental focus of Government concern is to enhance well-being. Recently, policy makers in the UK and elsewhere have recognized the importance of the community and society to the well-being of the nation as a whole. We explore the extent to which economic and social factors influence the psychological well-being of individuals and their perceptions of the social support that they receive, using Health Survey for England data. We employ a random-effects ordered probit modelling approach and find that unobserved intrahousehold characteristics help to explain the variation in our dependent variables, particularly for co-resident females. Our results indicate that individuals with acute and chronic physical illness, who are female, unemployed or inactive in the labour market and who live in poor households or areas of multiple deprivation report lower levels of psychological well-being. Reduced perceptions of social support are associated with being male, single or post marriage, from an ethnic minority, having low educational attainment and living in a poor household, but are not statistically related to area deprivation measures. These findings may help to inform the contemporary policy debate surrounding the promotion of individual well-being and community, through the alleviation of social exclusion.     

An investigation of the effects of inspection errors on the run-length control charts

For continuous inspection schemes in an automated manufacturing environment, a useful alternative to the traditional p or np chart is the Run-Length control chart, which is based on plotting the run lengths (the number of conforming items) between successive nonconforming items. However, its establishment relies on the error-free inspection assumption, which can seldom be met in practice. In this paper, the effects of inspection errors on the Run-Length chart are investigated based on that these errors are assumed known. The actual false alarm probability and the average number inspected (ANI) in the presence of inspection errors are studied. This paper also presents the adjusted control limits for the Run-Length chart, which can provide much closer ANI curves to the ones obtained under error-free inspection.     

An investigation of duration dependence in the American stock market cycle

This paper investigates the duration dependence of the US stock market cycles. A new classification method for bull and bear market regimes based on the crossing of the market index and its moving average is proposed. We show evidence of duration dependence in whole cycles. The half cycles, however, are found to be duration independent. More importantly, we find that the degree of duration dependence of the US stock market cycles has dropped after the launch of the NASDAQ index.     

大学生校园创业行为的影响因素研究——基于四川省九所高校实证研究

大学校园不仅是获取知识的摇篮,也是大学生创业的起点。经历校园创业积累知识与经验为毕业后成功创业打下坚实基础。文献回顾发现,国内外学者对大学生校园创业研究较少,因此,本研究主要探索各种影响大学生校园创业行为因素,提炼出学校创业教育、创业动机、企业家精神、创业的成功概率、创业技能五个关键影响因子,同时也为解决大学生创业难问题提出了新的思路。     

An empirical comparison of Canonical Correspondence Analysis and STATICO in the identification of spatio-temporal ecological relationships

The wide-ranging and rapidly evolving nature of ecological studies mean that it is not possible to cover all existing and emerging techniques for analyzing multivariate data. However, two important methods enticed many followers: the Canonical Correspondence Analysis (CCA) and the STATICO analysis. Despite the particular characteristics of each, they have similarities and differences, which when analyzed properly, can, together, provide important complementary results to those that are usually exploited by researchers. If on one hand, the use of CCA is completely generalized and implemented, solving many problems formulated by ecologists, on the other hand, this method has some weaknesses mainly caused by the imposition of the number of variables that is required to be applied (much higher in comparison with samples). Also, the STATICO method has no such restrictions, but requires that the number of variables (species or environment) is the same in each time or space. Yet, the STATICO method presents information that can be more detailed since it allows visualizing the variability within groups (either in time or space). In this study, the data needed for implementing these methods are sketched, as well as the comparison is made showing the advantages and disadvantages of each method. The treated ecological data are a sequence of pairs of ecological tables, where species abundances and environmental variables are measured at different, specified locations, over the course of time.