Three Essays in Economics

My dissertation consist of three essays analyzing the results of decisions made by workers, both on the microeconomic as well as the macroeconomic level.My first essay, which is a coproduction with Wayne Grove and Andrew Hussey, investigates the determinants of the gender wage gap. Specifically, the paper points out that noncognitive skills, preferences for life and career, but also preferences for work ethics and work environment, are able to account for as much as one third of the explained portion of the gender wage gap.My second essay, which is co-authored with Dr. Pinaki Bose, provides a possible explanation why some tax amnesties are successful in terms of revenuecollection and participation rates (for example Ireland, Colombia, India twice, and France), whereas others are not. In particular, I am modeling the taxpayer\u27s decision whether to acceptan amnesty offer from the tax authority and derive conditions under which she will be inclined to do so. The resultsshow that if economic conditions change substantially, for example by a tradeliberalization of the domestic country, a perfectly rational agent will find itoptimal to accept a tax amnesty.In my third essay, I am developing a theoretical model identifying the relationship between the volatility of private sector wages and growth. The model suggests two distinct channels in which wage volatility affects growth: a positive direct way (working through precautionary savings) and a negative indirect way (working through the mediating role of government size). Applying a 3SLS approach to a panel of 19 countries, my empirical analysis provides strong evidence for the existence of both effects. Thus, this paper establishes wage volatility as a growth determinant and explains why previous growth analyses on other sorts of volatility could not reach a consensus, as the indirect effect was not recognized

Try, try again: Lessons learned from success and failure in participatory modeling

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors' cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems

Try, Try Again: Lessons Learned from Success and Failure in Participatory Modeling

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors’ cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems