Measuring the Impact of Meat Packing and Processing Facilities in Nonmetropolitan Counties: A Difference‐in‐Differences Approach

Abstract

Considerable controversy exists regarding the costs and benefits of growth in the meat packing and processing industry for rural counties. This study investigates the effects of this industry on social and economic outcomes in nonmetropolitan counties of 23 Midwestern and Southern states from 1990 to 2000. Results suggest that as the meat packing industry's share of a county's total employment and wage bill rises, total employment growth increases. However, employment growth in other sectors slows, as does local wage growth. Industry growth has little impact on local crime rates or on growth of government spending on education, health, or police protection.

Inference Based on Alternative Bootstrapping Methods in Spatial Models With an Application to County Income Growth in the United States*

“…Instead, achieving sufficient scale to allow for the provision of public goods and services might take precedence. Although a number of studies have considered aggregate income measures such as growth in total county earnings (Kusmin, Redman, and Sears 1996; Aldrich and Kusmin 1997) and total county income growth (Artz, Orazem, and Otto 2007; Monchuk et al 2007), none of these explicitly controls for endogenous regressors in a spatial framework.…”

Impacts of national forests in the West on county population and employment

“…Our data only examines employment. One reason for the lack of a significant effect may be the small percentage of employment that forestry represents (see Artz et al (2007) for an examination of the meatpacking industry). Using 2013 Bureau of Labor Statistics data for two industry categories, Forestry and Logging (113) and Sawmills (321113) we find that the percentage of the labor market represented is less than 1% for all states.…”

A Century of Research on Rural Development and Regional Issues

“…We need new theoretically sound hypotheses to better identify the effects of new rural opportunities or policies on rural market signals and to identify the effects of changes in signals on behavior. We should use the most scientifically sound methods to estimate structural parameters and apply them to simulate rural economies, and continue to develop innovative econometric techniques, such as experimentalist approaches, to avoid selection biases and to identify "treatment effects" (e.g., Isserman and Merrifield 1982;Artz et al 2007). As Holmes (2010) argues, all three methods-structural, reduced form, and experimentalist-are needed to further our understanding of rural economic processes, policy effects, and outcomes.…”