Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
Agricultural, Food, & Resource Economics
Non Technical Summary
This research will investigate the widespread effects of domestic and international regulation targeting the food system on economic returns to agriculture.Thebrunt of this research will focus on identifying the economic impacts of domestic programs and policies on agricultural production and pricing. Specific policies analyzed will likely include antitrust and market regulation, the economics of grades and standards, and commodity programs. The aim of the policy analysis will be to evaluate policy effectiveness, impacts and outcomes, paying close attention to the unintended and indirect consequences of such policies.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The overall objective of this project is to apply econometric and numerical analysis to understand how domestic and international regulation affects economic returns to agriculture. An important focus of the program will also be to communicate implications of the findings to stakeholders in Michigan. The program will also generate methodological contributions for policy analysis. The sub-objectives, and some of the specific application areas will be applied over the next five years, are reported below: Objective 1: Evaluate the direct and unintended consequences of policy and regulation, with particular emphasis on regulations affecting Michigan agribusiness.A precise understanding of the impacts of the effects of policy are necessary to evaluate the effectiveness/return on investment for policy. Additionally, as policies and regulations change incentives for affected individuals, they can generate unintended effects which undermine or reduce the intended benefits of the policy. One such application could involve the impacts of Farm Bill Commodity Program and Crop Insurance rules on the incidence of Prevent Plant for program crops. Objective 2: Analyze the political economy of policy administration, and determine the implications for Michigan stakeholdersThe political incentives created by policy administration can lead to socially sub-optimal regulatory design as politicians seek to benefit their own interests. One application of this affecting Michigan agriculture would be to analyze the effects of the Market Facilitation Payments - issued by executive order - rather than through the legislative process, on voting decisions in the 2020 Presidential election. Objective 3: Analyze the appropriate method by which to communicate policy-based extension to Michigan stakeholders.Over the last several years, university extension budgets have fallen into decline. This reality places substantial constraints on the ability to communicate policy extension via traditional in-person meetings throughout the state. At the same time, information technology allows a single person to communicate to a much larger audience, as long as the person can effectively engage the stakeholder audience. A potential research effort would be to study the effects of in-person meetings versus e-meetings by which to communicate 2020 Farm Bill Commodity Program choices on program sign-ups and election choices.
Project Methods
This research will use a combination of legal and economic analysis to understand the impacts of regulation on the returns to agriculture. Within the economic analysis, both conceptual and econometric modeling will be used.Legal/Institutional AnalysisThe first step in analyzing the impacts of regulation on agriculture will involve gaining a thorough understanding of the regulatory/legal details and institutions surrounding the affected industry(ies). To accomplish this, I will read regulatory text and surrounding legislative documents, including related case law. In this type of analysis, it is necessary to understand not only the effects of the law as written, but also the effects of administration. To gain an understanding of the institutional details for the affected industry(ies), I will speak to stakeholders. Conceptual Modeling Based on the results of the legal and institutional analyses, I will develop a conceptual framework (usually presented as a mathematical optimization model) based on economic theory to demonstrate how the regulation will affect the behavior of the regulated agents, as well as the behavior of upstream and downstream agents (Varian, 2014). I will use the conceptual framework to develop testable hypotheses for how the regulation will likely affect economic returns to agriculture. Econometric ModelingFollowing the development of the conceptual model and testable hypotheses on the effects of the regulation, I will collect data and develop econometric (i.e., statistical regression) models to determine whether the hypotheses hold and to empirically quantify the effects of the regulation. The most common econometric techniques I use for these purposes are (i) price analysis (time series econometrics), (ii) panel data methods, and (iii) gravity modeling. Below I briefly explain each of these analytical methods:Price Analysis (Time Series Econometrics)One common method for investigating the extent to which regulation has impacted economic returns to agriculture is to determine whether the implementation of the regulation aligns with change in the historical relationship between the price of the affected product(s) and a bundle of prices that were likely not affected by the regulation, but are otherwise correlated with the affected price(s). To determine whether the regulation aligns with a change in the historical relationship between prices, I commonly use structural break analysis (Quandt, 1960; Kim and Siegmund, 1989; Andrews, 1993). Consider the hypothetical scenario in which there exists one price affected by the regulation (Pa) and one price unaffected by the regulation (Pc), such that price series Pa and Pc are correlated in both a statistical and economic sense. One could assess the historical relationship between these series via regression analysis. One can implement the structural break test in one of two ways. First, one can treat the timing of the break as "known" and test whether the coefficients of the regression change in the period prior to versus after the known break date. Alternatively, one can treat the timing of the break as "unknown" and test every feasible whether coefficients change at every feasible break date. Using this procedure, one would identify the most likely candidate break date (via either theSupremum Wald or Supremum Likelihood Ratio statistic) and then assess whether the timing of the break aligns with the timing of regulatory change. After testing for a break, one could model the co-integrating relationship that existed prior to the break using a regression analysis and then develop a counterfactual price series for what prices would have been occurred had the regulation not been enacted. The impacts of the regulation are estimated as the difference between this counterfactual price series and the actual prices observed in light of regulatory adoption. Panel Data MethodsOne can often develop a richer understanding of the impacts of regulation on the economic behavior of affected agents with longitudinal data. Ideally, with this type of data, the regulation would affect different agents at different times and in differing intensities. If one has observations on the behavior of multiple agents over multiple periods of time, one exploit variation in the treatment variable to measure the impacts of the regulation on economic behavior using an econometric model (Cameron and Trivedi, 2009). Gravity Modeling The econometric gravity model is a special case of panel data analysis commonly employed to measure the impacts of policy on international trade flows. In the gravity model, one hypothesizes that the value of bilateral trade in a given product is a function of the economic mass of the importing and exporting countries (defined as GDP), time-invariant factors specific to the bilateral trade relationship, and a set of controls, commonly including things such as foreign exchange rates. One then assesses the impacts of regulation by comparing outcomes for trade flows and time periods "treated" with the regulation versus those that are not. Because a large percentage of trade flows are zero values, one commonly uses the Poisson pseudo-maximum likelihood (PPML) technique (Silva and Tenreyro, 2006) to estimate gravity relationships.Partial Budget Analysis Finally, based on the outcomes of the econometric model, one can employ partial budget analysis to examine the impacts of the regulatory change on producer revenues, margins, or profits (Kay et al. 1994).