Unionizing the Jungles: Labor and Community in the Twentieth-Century Meat-packing Industry

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Dangers, tensions lurk in meatpacking industry

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The development screen the examples use of maintained with Persia in the western Century typically a tremendous review at the discovery of Noah, Abraham, omics or intransigence widely. The parameter attached to a factor will be positive if the factor raises utility. If individuals are allowed to migrate freely, expected utility must be equal across counties in equilibrium. Empirical Strategy Equation 4 provides the basis for the empirical specification.

The vector of dependent variables is composed of K elements,. The vector Xi,t includes the K base period values of the endogenous elements,. Because K is very large, we use up many degrees of freedom if all elements enter with a separate coefficient. To shrink the dimensionality, we performed a principle component analysis over all values of the endogenous variables.

Results were not sensitive to the inclusion or exclusion of various subsets of these variables or to the use of the actual variables rather than the principle components.

We expect that the impact of MPP may vary according to its size relative to the size of the host community. To account for this, we measure by a series of dummy variable representing various MPP shares of total county employment. The data are not publicly available, but they allowed us to generate aggregated results using these broader measures of industry size.

These dummy variable measures which span the period to 5 satisfied their confidentiality restrictions, whereas actual measures of firm size did not. These dummy variables are summarized in Table 1. The base case is no industry employment in The key parameters are the. Negative values suggest that meatpacking lowers the growth of the dependent variable. Failure to reject joint significance tests of these four parameters suggests that meatpacking presence does not affect the dependent variable.

The parameter on meatpacking concentration is complex and must be evaluated carefully. It includes three terms, the impact of meatpacking on utility, ; the impact of the dependent variable on utility, ; and the impact of wages on utility 0. We illustrate these points with two examples that we actually find in our empirical work below. First, take a case where we are very certain that the dependent variable lowers utility: the local poverty rate.

The result implies that counties that benefit from the presence of a local meatpacking plant accept some associated negative consequences such as a higher incidence of poverty, leaving their overall utility unchanged. For a second example, suppose that rural Midwest counties attach positive utility weights to increasing the population of white school children, implying that 0. In this case, the local community accepts a lower fraction of white school children in exchange for the benefits of a local meatpacking plant. By now, some readers may be confused because it seems so foreign to view meatpacking presence as a positive element in utility.

That is precisely why this paper is necessary— preconceptions regarding the value of meatpacking have not been subjected to rigorous tests.

The Jungle and the Meatpacking Industry

In fact, most of our results are consistent with meatpacking being a positive influence on rural communities, as will be shown below. The other critical parameters are and. In this expanded specification, the effect of gaining the industry when a county had no industry jobs in is captured by 2.

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If the sum of the parameters is positive and significant, then a rising MPP employment share over the decade will be associated with growth in the dependent variable. Initially, we assumed that the error terms in 5 were purely random. We allow for cross- equation correlation in the disturbances and estimate equation 5 using seemingly unrelated regression SUR estimation for each hypothesis 6. For each set of equations a Breusch-Pagen test of independence was easily rejected, validating the use of SUR over equation by equation ordinary least squares.

Equation 5 is applied to data from non-metropolitan counties in twenty-three Midwestern and Southern states to test four hypotheses regarding the impacts of local meatpacking and processing firms on local immigration and other social outcomes. Decennial Census, the U. Census of Governments, or the National Center for Educational Statistics and are available at the county level 8. The specific starting and ending dates for the relative change in outcomes depends on data availability. For measures based on the U.

Decennial Census, the start and end dates are and , respectively. For the education measures, data on the number and demographics of students are available for and , but measures on students receiving free and reduced lunches and English language learners are only available between and The choice of the time period was dictated by data availability; is the earliest year available in the LDB. While the shift of meat packing and processing into rural areas began earlier, a considerable amount of change occurred in the s.

Forty-three percent of the sample counties counties had MPP employment in By , the industry had newly entered 91 counties while exiting counties in the sample. In addition, average county- industry employment rose nearly 50 percent over the decade.

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Furthermore, Kandel and Parrado find that effect of meat processing employment on Hispanic population growth was roughly five times larger in the s than in the s. Our estimates are presented in table 2. The constant term represents the average change in the dependent variable for counties without MPP employment, conditional on the starting value and the additional control variables.

These measures control for plausible sources of cross-county variation in the dependent variables other than MPP to minimize the chance that our estimated effects of meatpacking are due to missing variables bias rather than a true MPP effect. The first is a test of the joint significance of the. Failure to reject this test implies that industry presence has no impact on growth in the dependent variable. Given the number of estimates presented in table 2, we are not able to discuss all findings in detail.

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Instead we will focus our discussion on the key findings for each our four hypotheses. However, to insure that readers understand how we derive our interpretation of the results, we present a detailed discussion of all the estimates for one dependent variable: total population growth. Those results are reported in the first column of table 2: Hypothesis A.

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Since the dependent variable is measured as a log change over the decade, the estimates of can be interpreted as the added effect of industry presence on growth in the outcome relative to not having the industry. Counties with MMP employment share between 5 and 10 percent grew an added 3 percentage points relative to non-host counties. Counties with MPP employment share exceeding 20 percent grew an additional 9 percentage points over the decade.

The joint test of significance for the is rejected, confirming the effect of MPP employment share on total population growth. However the coefficient is so small less than 0. Meatpacking plants change the population demographics The first common belief is that the shift of meatpacking plants from urban to rural areas has changed the demographics of host communities.