Evaluating the employment effects of environmental regulation without abatement cost data: A nonparametric cost function approach.

Environmental policymakers are concerned that environmental regulations reduce employment. A microeconomic analysis illustrates that environmental regulations have an uncertain effect on employment, making this an empirical question. A standard cost function model, used in the literature, requires a lot of data such as pollution abatement cost data to examine effects of environmental regulations on employment, but such survey data is not always available. In this paper, we develop a nonparametric cost function which alleviates the need for pollution abatement cost data. Our cost function, therefore, allows researchers and policy analysts to estimate employment changes associated with pollution abatement as well as measure the relative importance of other factors related to changes in employment with no pollution abatement cost data. Moreover, this is the first model using a cost function that incorporates the effect of structural change among industries within the economy on employment, which allows researchers to the examine how environmental regulations change the structure of the economy via a structural decomposition component. We illustrate how to operationalize our model using a balanced panel of industry-level data for 26 industries from 17 OECD countries (1995-2006). Our findings suggest that the change in employment due to regulatory induced increases in input costs exhibits both substantial variation among countries and substantial intra-country heterogeneity among industries.

Reconstructing Nonparametric Productivity Networks.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

Pollution Abatement and Employment.

Morgenstern et al. (2002) is well-known for its investigation of the employment effects of environmental regulations. However, the cost function specified in that paper is handicapped by its reliance on survey data of the costs of inputs assigned to pollution abatement. In this paper, we specify an input distance function that models the joint production of good and bad outputs. This allows us to measure the relative importance of factors associated with changes in employment without pollution abatement cost data. We operationalize our model using a sample of 80 coal-fired electric power plants from 1995-2005.

Brainstorm: occupational choice, bipolar illness and creativity.

Although economists have analyzed earnings, unemployment, and labor force participation for those with bipolar illness, occupational choice has yet to be explored. Psychological and medical studies often suggest an association between bipolar illness and creative achievement, but they tend to focus on eminent figures, case studies, or small samples. We seek to examine occupational creativity of non-eminent individuals with bipolar disorder. We use Epidemiologic Catchment Area data to estimate a multinomial logit model matched to an index of occupational creativity. Those with bipolar illness appear to be disproportionately concentrated in the most creative occupational category. Nonparametric kernel density estimates reveal that the densities of the occupational creativity variable for the bipolar and non-bipolar individuals significantly differ in the ECA data, and suggest that the probability of engaging in creative activities on the job is higher for bipolar than non-bipolar workers.

Evaluating health care efficiency.

In this chapter we propose a three-pronged approach to assessing efficiency of health care, including financial performance, performance in the production of (intermediate) medical outcomes and performance relating medical outcomes to patient health outcomes. Throughout we use frontier models which can be estimated in a number of ways, including DEA, stochastic frontiers and index numbers. We illustrate the health outcomes model with an application to cataract surgery patients in Sweden and use DEA as our estimator. Again, other frontier estimation methods as well as index numbers could be employed to explore other procedures' effectiveness and overall performance of services and/or hospitals and clinics.