Abstract: Stata 14 provides several commands for fitting item response theory (IRT) models. IRT has a long history in test development and psychometrics and is now being adopted more broadly in fields such as health services research. In this presentation, I will provide an overview of IRT, demonstrate how to fit models with binary items, and discuss postestimation tools such as plotting characteristic curves and information functions. I will also briefly demonstrate how to fit Bayesian IRT models using Stata. This is a nontechnical talk with an emphasis on concepts and no prior knowledge of IRT or Bayesian statistics is assumed.

Abstract: The literature on statistical methods of program evaluation is mainly focused on estimating effects of binary treatments. Moreover, even papers focused on multivalued treatment effects implicitly assume that there is a certainty about the emergence of an alternative treatment. Thus the uncertainty concerning the choice of an appropriate hypothetical counterfactual outcome has not been modeled.

A new, generalized concept of counterfactual causality has been proposed—average treatment effect on the treated is defined as a difference between observed income and a convex linear combination of all possible counterfactuals weighted using estimated propensity score values. Under this framework, not only counterfactual incomes can be estimated but also the hypothetical emergence of a counterfactual treatment can be modeled, because it depends on similar characteristics as the potential outcomes.

The mmatch Stata program implements the proposed framework. The proposed concept of causality has been illustrated using the data on unemployment rates and level of formal education using EUSILC data for Poland.

Abstract: Since the seminal paper of Rosenbaum and Rubin, propensity score (PS) has played a significant role in the causal inference framework. It aims to indicate similar units that will be matched or to provide appropriate weights. PS has gained its great popularity by dramatically reducing dimensionality in estimation. Further development of related methods has turned the attention of researchers to the dual nature of PS as a covariate balancing score and conditional probability of treatment assignment. Imai and Ratkovic (2014) exploit the aforementioned duality by deriving a set of appropriate moment conditions and thereby introduce a PS estimator that optimizes the covariate balance—covariate balancing propensity score (CBPS). The paper introduces a new Stata user-written function CBPS that implements the CBPS method within a generalized method of moments framework. The short description of the estimator and the function is presented. Additionally, an empirical exercise that concerns a relationship between a woman's fertility and her labor supply using the exogenous variation due to twin births (Rosenzweig and Wolpin 1980; Angrist and Evans 1998) is provided. The paper also compares the CBPS method with classical PS estimators in unfavorable data environment of a high degree of heterogeneity among women, low fraction of twin births, and exogeneity of the treatment variable with respect to covariates. Moreover, to my knowledge, this is the first paper that concerns the labor supply of Polish women using the first-birth twins identification strategy.

Abstract: Propensity score matching (PSM) has become by far the most commonly used matching method to estimate causal treatment effects. The goal of the matching is twofold. On the one hand, we use PSM to overcome that counterfactual situation when we want to compare the outcome of the treated observations with the results of the treated observations if they were not treated. PSM helps us to find close matches to compare by using observations' corresponding features. On the other hand, PSM can be used to reduce the imbalance within the dataset, which is an obvious source of model dependence. However, after researchers have taken a position to apply PSM, they are faced with many questions related to its implementation—which alternative matching algorithm to choose or trimming to determine the common support—for the actual dataset. This presentation will provide a brief summary about the implementation of PSM and show some tradeoffs regarding bias and efficiency on a real-life dataset.

Abstract: Travel-mode choices made by citizens of the city are an important factor determining congestion, level of pollution, and noise, especially in rapidly developing agglomerations. Analysis of these decisions and factors by which they are determined should be considered as meaningful step in the projecting city’s urban and infrastructural policy.

Nowadays, Łódź experiences deep infrastructural changes, the progressive aging of society, and a shift in demographic structure. Therefore, Łódź is interesting with regard to the travel behavior of its inhabitants.

The purpose of this study is to identify what determines the decisions of citizens of Łódź in their daily travel activity. The database used in empirical part of this paper was established in the quality of life study of the citizens of Łódź and its spatial diversification. This dataset allows us to include more explanatory variables than in standard travel-mode choice studies. To find the determinants of travel behavior we estimate ordered logistic regression models, and, where needed, their generalized versions. Presenting our results, we compare the outcomes for different districts of Łódź in order to investigate spatial differences.

The results show that there are significant differences between the determinants of different modes of transport in a spatial dimension. As expected, we observe a high impact of socio-demographic variables on mode choice. Also, the attitudes and opinions concerning the state of the city's infrastructure and effectiveness of the functioning of public transportation system have the effect on the frequency of the usage of particular travel modes.

Abstract: This research attempts to evaluate some implications of the earnings risk of investments in human capital on workers mobility in the United Kingdom. Previous studies show that risk affects individual educational and occupational choices. Given the fact that training outcomes and their usefulness for the current and future employer can hardly be predicted; earnings risk associated with investments in training can significantly affect employees' future mobility. The focus of the research is on the following main objectives:

1. Estimating the level of earnings risk associated with investments in training within different occupation groups in the United Kingdom.
2. Analyzing the extent of job mobility in the United Kingdom.
3. Investigating the impact of the risk of investments in training on employees’ job mobility.

The empirical part of the research aims to verify the research hypothesis that the risk of investments in training has a significant impact on employees' voluntary mobility. Using the rich panel data from the first eighteenth waves of the British Household Panel Survey, we intend to examine the effect of the risk on employees' mobility in the labor market. In the study, the risk associated with investments in training is analyzed from the worker's perspective. In addition, two types of mobility in the labor market are considered: internal mobility, which refers to the job positions change without leaving the current employer, and external mobility, which is connected with the employer change.

Abstract: The aim was to check if a public institution's performance is evaluated the same way by two different societies and how it is related with their satisfaction and trust toward the institutions based on data from the European Social Survey (ESS) in Poland (PL) and Germany (GE). The hypothesis of equal coefficients and means within and between countries for adequate variables representing the constructs mentioned above were checked via testing configural, metric, and scalar invariance. Our analysis was based on three rounds of ESS (2010, 2012, 2014) separately as well as three rounds jointly for a single country and between countries (MGCFA with the ADF method of estimation) performed in Stata. The analysis evaluated the stability of results obtained in all rounds, and so far, the various models without and with restrictions were evaluated at least to obtain the partial invariance and to control the requested quality of models (that is, RMSEA for PL and GE for 2012 were equal to 0.043). Additionally, not only comparability with the latest round will be presented but also how and in what way using various weights available for correct analysis in ESS will change final SEM results. At the end, we will provide conclusions on how it is possible based on ESS to make cross-country comparisons with SEM analysis in the analyzed topics in Stata and what we can learn from this analysis.

Abstract: The Porter hypothesis claims that there is a positive relationship between environmental regulation, environmental innovation, and business competitiveness. However, the empirical results in the literature remain inconclusive. In this paper, we limit the investigation to the relationship between environmental innovation and business competitiveness. This relationship is tested using a firm-based German panel-data and a dynamic limited dependent variable model. We estimate the impact of a combination of time-varying and time-invariant regressor on return on sales. Namely, R&D intensity, the size and the market share on the one hand, and the sector and the region of the business on the other hand. The results show that there is indeed an overall positive relationship between environmental innovation and business competitiveness. However, when one controls for marketing intensity or limiting the data to specific sectors, the relationship becomes insignificant because of omitted variable bias. These results help explain why some researchers have come to find a positive effect of eco-innovation on business competitiveness while others have not.