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Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

Programs are typically evaluated through the average treatment effect and its standard error. In particular, is the treatment effect positive and is it statistically significant? In theory, programs should be evaluated in a decision framework, using social welfare functions and posterior predictive distributions for outcomes of interest. This chapter discusses the use of stochastic dominance of predictive distributions of outcomes to rank programs, and, under more restrictive parametric and functional form assumptions, the chapter develops intuitive mean-variance tests for program evaluation that are consistent with the underlying decision problem. These concepts are applied to the GAIN and JTPA datasets.

We apply a recently suggested econometric approach to measure the effects of active labor market programs on employment, unemployment, and wage histories among participants. We find that participation in most of these training programs produces an initial locking-in effect and for some even a lower transition rate from unemployment to employment upon completion. Most programs, therefore, increase the expected duration of unemployment spells. However, we find that the training undertaken while unemployed successfully increases the expected duration of subsequent spells of employment for many subpopulations. These longer spells of employment come at a cost of lower accepted hourly wage rates.

Although the theoretical trade-off between the quantity and quality of children is well established, empirical evidence supporting such a causal relationship is limited. This chapter applies a recently developed nonparametric estimator of the conditional local average treatment effect to assess the sensitivity of the quantity–quality trade-off to functional form and parametric assumptions. Using data from the Indonesia Family Life Survey and controlling for the potential endogeneity of fertility, we find mixed evidence supporting the trade-off.

Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.

Lechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection-on-observables-type assumptions (sequential conditional independence assumptions). Lechner (2004) proposed matching estimators for this framework. However, many practical issues that might have substantial consequences for the interpretation of the results have not been thoroughly investigated so far. This chapter discusses some of these practical issues. The discussion is related to estimates based on an artificial data set for which the true values of the parameters are known and that shares many features of data that could be used for an empirical dynamic matching analysis.

Climate change, deforestation and hydropower dams are contributing to environmental change in the Lower Mekong River region, the combined effects of which are felt by many rural Cambodians. How people perceive and manage the effects of environmental change will influence future adaptation strategies. The objective of this research was to investigate whether the use of a low-cost, explicitly spatial method (participatory mapping) can help identify locally relevant opportunities and challenges to climate change adaptation in small, flood-prone communities. Four villages along the banks of the Mekong River in Kratie Province, Cambodia, were the subject of this research. To identify perceived environmental hazards and adaptive responses, eight workshops were conducted using focus-group interviews and participatory mapping. The communities’ responses highlight the evolving nature of environmental hazards, as droughts increase in perceived importance while the patterns of wet season flooding were also perceived to be changing. The attribution of the drivers of these hazards was strongly skewed towards local factors such as deforestation and less towards regional or global drivers affecting the hydrology of the Mekong and climate patterns. Combining participatory mapping with focus-group interviews allowed a greater depth of understanding of the vulnerabilities and opportunities available to communities than reliance on a single qualitative method. The study highlights the potential for a bottom-up transfer of information to strengthen existing climate change policies and tailor adaptation plans to local conditions.

The purpose of this study is to understand how, when, and why emergency medicine residents learn while working in the chaotic environment of a hospital emergency room.

This research used a qualitative interview methodology with thematic data analysis that was verified with the entire population of learners.

Analysis of the data revealed four different types of learning episodes, each with facilitating factors. The episodes varied in intensity, duration, and the degree of motivation and self‐direction required of the learner. One episode could prompt another. Learning occurred both individually and in social interaction in the workplace during the episode, as well as outside of the workplace environment after the experience had occurred.

Recommendations for individuals to maximize their learning related to this chaotic work environment are identified, along with associated implications for their trainers. These suggestions advocate for current apprenticeship approaches to training to include a developmental perspective, providing effective feedback and supporting learner self‐assessment and reflection.

This paper makes an original contribution to the literature by describing the process of learning by emergency medicine residents in the chaotic work setting of an emergency department. The paper also expands understanding of the types of learning episodes and the factors that contribute to their occurrence. Finally, the research illustrates how the voice of the learners can be used to inform their training.

The purpose of this paper is to empirically examine the relationship between founder-chief executive officers (CEOs) and firm performance. Specifically, the paper explores two opposing arguments on the performance implications of founder-CEO leadership. The first theoretical perspective argues that founder-CEOs positively contribute to firm performance since they bring passion, vision, and external legitimacy to the organization. The contrary resource-based perspective, argues that while founder-CEOs help in the early years of the firm, they become less effective as the firm evolves into a complex bureaucracy since they lack the necessary managerial skills.

In order to test these perspectives, the paper develops a matched sample of 82 US manufacturing firms and compared their performance using both accounting and market-based measures. Independent sample t-tests and analysis of variance were used to empirically test the opposing predictions. Data were obtained from the Mergent Online database as well as official proxy filings of sample firms.

The results of the data analysis indicate that there is a statistically significant performance difference between founder-led and non-founder led firms. Such performance difference is especially evident when the paper focusses on accounting-based firm performance measures such as return on assets and return on investment. Surprisingly, founder-led firms performed worse than those led by non-founder CEOs. The follow-up analysis indicates a significant difference in age and size among sample firms led by founders and non-founders such that founder-led firms tend to be younger and smaller in size.

Unlike other studies in the literature that found a strong positive impact of founder-CEOs, the findings of the study provided empirical support for the resource-based explanation of founder-CEO impact on firm performance. Specifically, the findings reported here contribute to understanding the role of founder-CEOs in the context of executive succession, strategy selection as well as organizational evolution.

This study makes original contribution to the on-going research on strategic leadership by exploring the performance effect of founder-CEOs and the corresponding alternative theoretical explanations. In addition, the inclusion of both accounting and market-based (Tobin's Q) dependent variables provide a broader measure of firm financial performance.

This chapter demonstrates that fixed-effects and first-differences models often understate the effect of interest because of the variation used to identify the model. In particular, the within-unit time-series variation often reflects transitory fluctuations that have little effect on behavioral outcomes. The data in effect suffer from measurement error, as a portion of the variation in the independent variable has no effect on the dependent variable. Two empirical examples are presented: one on the relationship between AFDC and fertility and the other on the relationship between local economic conditions and AFDC expenditures.