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The Czech Republic, like many countries in Central and Eastern Europe, has a tremendous task ahead in transforming its economy, and the individual enterprises that contribute to it, into a competitive, modern, economy. This requires a major attitude change for most organisations and requires skills and experience which are significantly under‐developed. Change is starting to happen and the techniques of industrial engineering are being harnessed to facilitate organisational change. Outlines the transformation required, the approach and the steps being taken, and the supporting and facilitating processes providing the necessary knowledge and expertise.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.

This study aims to examine whether announcements of mandatory sustainability disclosure affect corporate sustainability performance (CSP).

The authors use a quasi-experiment provided by mandatory sustainability disclosure announcements that occurred in 21 countries from 2006–2016. A difference-in-differences method is adopted. The authors restrict the drawing of all candidate treatment and control firms to a pool of firms that did not disclose sustainability information one year before the announcements.

The authors find that the announcements of mandatory sustainability disclosure are positively related to CSP. The positive effect is more pronounced for firms in countries with higher anticipation effects and lower awareness effects. Specifically, the authors find that the effect of the announcements is more pronounced in a country where the rule of law is higher and stakeholders are less likely to initiate communication about sustainability with firms, and with fewer active participants in and signatories to the United Nations Global Compact initiative. The findings hold under different robustness analyses.

The study enriches the knowledge about the effect of the announcements of comprehensive mandatory sustainability disclosure by analysing the consequences of these announcements. In the contribution to this growing stream of research, the authors provide evidence on the consequences of the announcements based on a cross-country sample and importantly, focusses on the non-economic consequences.

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.

A food security indicator for technology impact assessment is needed that can be constructed with available data, is comparable over time and space, and represents the multiple dimensions of food security.

In this chapter, we review some commonly used food security indicators, analyze the extent to which these indicators satisfy key criteria, and introduce a food security indicator constructed for use in an economic impact assessment and that exhibits a number of desirable properties.

This income-based indicator is similar to a consumption-based poverty indicator, utilizing an estimate of the income required to purchase a food “basket” that meets nutritional requirements and comparing the food security income requirement to a household’s per capita income.

The applicability of the indicator is illustrated with an analysis of the impacts of legume inoculation technology developed for smallholder farms in Tanzania and other parts of Africa. We conclude with a discussion of suggested improvements for food security indicators used for technology impact assessment.

This chapter estimates the negative effect of smoking on earnings in the context of a developing country. Using data from the 2005 Albania Living Standards Measurement Survey, models are estimated by parametric and semi-parametric methods to account for the effect of observable and unobservable characteristics that could affect individual smoking decisions and earnings. Information on the smoking behaviour of parents is used to address the endogeneity of the smoking decision. The results show that, after controlling for observed individual characteristics and parental education and taking into account unobserved heterogeneity in personal characteristics, smoking is found to have a substantial negative impact on earnings. The main results are robust to a range of alternative specifications. On average, smokers’ earnings are 19–23 percent lower than the earnings of similar non-smokers.

We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.

In an evaluation of a job training program, the causal effects of the program on wages are often of more interest to economists than the program's effects on employment or on income. The reason is that the effects on wages reflect the increase in human capital due to the training program, whereas the effects on total earnings or income may be simply reflecting the increased likelihood of employment without any effect on wage rates. Estimating the effects of training programs on wages is complicated by the fact that, even in a randomized experiment, wages are truncated by nonemployment, i.e., are only observed and well-defined for individuals who are employed. We present a principal stratification approach applied to a randomized social experiment that classifies participants into four latent groups according to whether they would be employed or not under treatment and control, and argue that the average treatment effect on wages is only clearly defined for those who would be employed whether they were trained or not. We summarize large sample bounds for this average treatment effect, and propose and derive a Bayesian analysis and the associated Bayesian Markov Chain Monte Carlo computational algorithm. Moreover, we illustrate the application of new code checking tools to our Bayesian analysis to detect possible coding errors. Finally, we demonstrate our Bayesian analysis using simulated data.

This chapter reviews the existing empirical evidence on how social insurance affects health. Social insurance encompasses programs primarily designed to insure against health risks, such as health insurance, sick leave insurance, accident insurance, long-term care insurance, and disability insurance as well as other programs, such as unemployment insurance, pension insurance, and country-specific social insurance programs. These insurance systems exist in almost all developed countries around the world. This chapter discusses the state-of-the art evidence on each of these social insurance systems, briefly reviews the empirical methods for identifying causal effects, and examines possible limitations to these methods. The findings reveal robust and rich evidence on first-stage behavioral responses (“moral hazard”) to changes in insurance coverage. Surprisingly, evidence on how changes in coverage impact beneficiaries’ health is scant and inconclusive. This lack of identified causal health effects is directly related to limitations on how human health is typically measured, limitations on the empirical approaches, and a paucity of administrative panel data spanning long-time horizons. Future research must be conducted to fill these gaps. Of particular importance is evidence on how these social insurance systems interact and affect human health over the life cycle.

Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on average, individuals benefited from the program in the form of higher weekly earnings and employment prospects. At the same time, “young adults” (ages 20–24) realized much higher impacts relative to “adolescents” (ages 16–19). Employing recent nonparametric bounds for causal mediation, we investigate whether these two groups’ disparate effects correspond to them benefiting differentially from distinct aspects of Job Corps, with a particular focus on the attainment of a degree (GED, high school, or vocational). We find that, for young adults, the part of the total effect of Job Corps on earnings (employment) that is due to attaining a degree within the program is at most 41% (32%) of the total effect, whereas for adolescents that part can account for up to 87% (100%) of the total effect. We also find evidence that the magnitude of the part of the effect of Job Corps on the outcomes that works through components of Job Corps other than degree attainment (e.g., social skills, job placement, residential services) is likely higher for young adults than for adolescents. That those other components likely play a more important role for young adults has policy implications for more effectively servicing participants. More generally, our results illustrate how researchers can learn about particular mechanisms of an intervention.