2017-2018 Seminar Series
February 23, 3:00-4:30, Filley Hall 210 Lindon Robison, Michigan State University
The Role of Social Capital and Relational Goods in Health Care Decisions
Dr. Lindon J. Robison is Professor of Agricultural and Resource Economics (AFRE) in the tenure stream at Michigan State University. He has published numerous books and articles, including the text for the department’s capstone Agri-business management course 435 which he also teaches. He has consulted for governments, firms, and international organizations such as the World Bank, particularly in Latin America. He has worked for the US Government as an agricultural economist, has been a visiting faculty member at Brigham Young University, the University of Minnesota, and the Swedish University of Agricultural Sciences in Uppsala Sweden. He has won many academic awards including Best Ph.D. thesis for his work on risk and portfolio management of rural banks and in 2012 was made a fellow of the Institutional and Behavioral Economics section of the Agricultural and Applied Economics Association (AAEA). His most frequently cited works include The Competitive Firm’s Response to Risk which he authored with Peter J. Barry and “Is Social Capital Really Capital?” which he authored with Allan A. Schmid and Marcelo E. Siles. His pioneering research focuses on the role of social capital (relationships of caring, trust, and regard) on establishing the terms and level of trade—that has been applied to minimum sell land and car prices, the likelihood of loan approval, and medical screen decisions. His most recent publications describe social capital motives and distinguish between relational goods and commodities.
March 2, 3:00-5:00, Nebraska East UnionJohn Antle, Oregon State University
Data, Economics and Computational Agricultural Science
A 2017 special issue of Agricultural Systems on “Next Generation Data, Models and Knowledge Products” presented a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. The authors envisage computational agricultural research and development that could complement, and increasingly substitute for, conventional experimental methods. They argue that significantly improved data and models could contribute to development of advanced farm-management systems that could accelerate the adoption and efficient use of more productive and more sustainable technologies. Various ongoing efforts by public sector organizations such as USDA and private firms such as Microsoft are working to develop and implement new technologies consistent with this vision, including new sensors, “big data,” and artificial intelligence. In this lecture I propose that economics has an important role to play in these developments. I draw parallels between advances in artificial intelligence and machine learning and recent developments in microeconometrics, and implications for the potential and challenges of “smart farming” systems and their role in generating data for computational science.
March 9, 3:00-4:30, Filley Hall 210 Kostas Karantininis, Swedish University of Agricultural Sciences
April 6, 3:00-4:30, Filley Hall 210Lisa House, University of Florida
September 22, 3:00-4:30, Filley Hall 210 Murray Fulton, University of Saskatchewan
The Political Economy of Good Governance
Abstract: In recent years there has been a marked increase in interest in what constitutes good government, good governance and quality of government. In addition to a broad consensus that government is no longer the key player in governing the economy, a concern has emerged that pursuing economic growth alone will not generate the best outcomes for society. In this paper, we examine these questions through a political economy model of governance in which power, economic payoffs and governance arrangements co-develop. Using this model we explore how corruption and ignorance affect the two underlying political economy problems of wealth generation and wealth distribution. We show, as other authors have done, how corruption generates outcomes that fail to grow the pie, while at the same time generating distributional outcomes that are highly disadvantageous. We then show how ignorance can have one of two effects, depending on the context. In the one case, ignorance—through its impact on transaction costs—can result in a failure of the pie to grow, often with detrimental distributional impacts. In the other case, ignorance can lead to increases in the size of the pie, albeit at the cost of redistributing the benefits of this growth to a particular group to such an extent that political instability ensues.
October 13, 3:00-4:30, Filley Hall 210 Marco Costanigo, Colorado State University
A Belief-Preference Model of Choice for Experience and Credence Goods
Abstract: We develop a methodology addressing the issue of confounded beliefs and preferences in models of discrete choice. First, we formalize the theoretical framework and logical underpinnings of a belief-preference model of choice for experience and credence goods, where subjective beliefs relate to uncertain product quality. Then, we present the experimental procedure within the context of an online choice experiment studying consumer food preferences. The empirical strategy leverages information from a quality sorting task to identify and estimate beliefs, while choice data are used to recover preferences. By conditioning product choices on predicted quality perceptions, the issue of endogenous beliefs is resolved.
November 10, 3:00-4:30, Filley Hall 210 Sara Savastano, World Bank and University of Rome Tor Vergata
Farm Size and Productivity A “Direct-Inverse-Direct” Relationship
Abstract: This paper proposes a new interpretation of the farm size–productivity relationship. Using two rounds of the Ethiopian Rural Household Survey, and drawing on earlier work on five countries in Sub-Saharan Africa, the paper shows that the relationship between farm size and productivity is neither monotonic nor univocal. Most previous studies that tested the inverse farm size–productivity relationship used ordinary least squares estimation, therefore reporting parameter estimates at the conditional mean of productivity. By expanding these important findings to consider the entire distribution of agricultural productivity, the analysis finds sign switches across the distribution, pointing to a “direct-inverse-direct” relationship. Less productive farmers exhibit an inverted U-shape relationship between land productivity and farm size, while more productive farmers show a U-shape relationship that reverses the relationship. In both cases, the relationship points toward a threshold value of farm size; however, the threshold is a minimum for the less productive farmers and a maximum for the more productive ones. To the left of the threshold, for very small farmers, the relationship between productivity and farm size is positive; for the range of middle farm size, the relationship is negative; and to the right of the threshold, the relationship is direct (positive) again. From a policy perspective, these findings imply that efficiency enhancing and redistributive land reform should consider farm size in the proper context of the present and potential levels of agricultural productivity. The results and their policy implications underline the relevance of the most recent efforts of the international development community to collect more reliable georeferenced data on farm size and agricultural productivity.