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We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

Educational achievement as measured by students’ performance in international tests is receiving scant attention in the educational research. The launch of these tests helped the educational community to better understand the factors underlying students’ achievement and guided the policy makers in their policies. However, Middle East and North African (MENA) countries were underrepresented in these discussions mainly because of data scarcity. Fortunately, Trends in International Mathematics and Science Study (TIMSS) 2007 data provides data for 15 MENA countries for students at the eighth grade and for many MENA countries; this is the first time that such data is available. This chapter aims to present estimates of students’ family background over mathematics and science scores. The results show that students with abundant home resources perform better than those with less home resources. Moreover, expatriate students in Gulf countries perform better than natives.

The purpose of this paper is to identify the factors that determine demand for crop insurance in Poland.

To examine the determinants of decisions regarding crop insurance, the authors used logistic regression. The base source of data for the analysis was the 2013 FADN sample. The scale of yield losses, the indemnities received and the Arrow-Pratt risk aversion coefficient were examined in a representative sample of farms in consecutive years in the period 2004-2013.

Losses are the major determinants of crop insurance uptake. Additionally, it was observed that the economic determinants are in line with the expected utility theory, while contrary to expectations, farmer’s characteristics such as education level, age or even risk aversion did not prove to have any influence on crop insurance uptake.

The FADN sample is representative as regards the type of farming, economic size of farm and location of the farm. Every farm in the sample represents a specific number of similar farms in the population. However, it must be emphasised that the representativeness of the sample with respect to other determinants, e.g., yield losses in previous years, using crop insurance or the farmers’ age and education has not been verified due to lack of data characterizing the general population with regard to these factors.

It could be argued that the system of crop insurance subsidies should be targeted to encourage the farmers who previously had not used insurance to join the system.

The paper presents the analysis of crop insurance uptake in a country with a strongly polarised agriculture. The Polish farm sector consists of 1.4 million farms with sizes ranging from 1 ha to over a few thousands hectares. The research is based on a data set of 5,202 farms which contains data from ten years (2004-2013). The novelty of the methodological approach is that it includes information on the number of farms represented by every farm in the FADN sample in the Horvitz-Thompson estimator in order to achieve results which are valid for the general population of Polish farms.

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of conditional multinormality we show that our estimator is efficient and provide exact formulae for its asymptotic variance. Simulation results show that our estimator outperforms popular alternatives and is robust to departures from the parametric assumptions adopted in the benchmark case. We illustrate our estimator by examining the portfolio allocation decision of Italian households.

The research compared the predictors of work‐related stress for policemen and policewomen. Stressors included workplace problems, token status in the organization, low family and coworker support, and community and organizational conditions.

In 11 police departments, racial and ethnic minorities were oversampled. Of 2,051 officers sampled, 46.2 percent responded. Questions and scales were adapted from prior research on both males and females. Regression analysis revealed the strength of individual predictors of stress, the variance explained by workplace problems, and the additional variance explained by social support, token status, and community and organizational context.

Workplace problems explained more male's than female's stress. Regardless of gender, the strongest predictor of stress was bias of coworkers, and a weaker predictor was language harassment. Just for males, lack of influence over work and appearance‐related stigmatization were additional predictors. Workplace problems explained gender differences in stress that were related to token status as a female.

The sample was not representative of all police in the USA. Measures of community and organizational characteristics were highly intercorrelated, so they could not be examined separately. Especially, for women, there is a need to identify additional sorts of influence on stress.

Although individual interventions and coping strategies are important for reducing police officer stress, changes in the organizational context also deserve attention. There is a need to develop and test interventions to reduce bias among coworkers, to contain language harassment, and to provide police with an increased sense of control over their work.

The paper focuses on stress within the policing environment.

The literature on the union wage premium is among the most extensive in labour economics but unions’ effects on other aspects of the wage-effort bargain have received much less attention. The purpose of this paper is to contribute to the literature through a study of the union premium in paid holiday entitlements.

The authors examine the size of the union premium on paid holidays over time, with a particular focus on how the premium was affected by the introduction of a statutory right to paid holidays. The data come from nationally representative surveys of employees and workplaces.

The authors find that the union premium on paid holidays is substantially larger than the union premium on wages. However, the premium fell with the introduction of a statutory minimum entitlement to paid leave.

This is the first study to examine explicitly the interaction between union representation and the law in this setting. The findings indicate the difficulties that unions have faced in protecting the most vulnerable employees in the UK labour market. The authors argue that the supplanting of voluntary joint regulation with statutory regulation is symptomatic of a wider decrease in the regulatory role of unions in the UK.