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A stochastic frontier estimator of the aggregate degree of market power exerted by the US meat packing industry

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Abstract

The objective of this study is to measure the aggregate degree of market power exercised by the US meat packing industry with the employment of the recently developed stochastic frontier estimator (SFA) of market power. Furthermore, the present work shows that the SFA estimation technique can be used in order to measure the sum of oligopsonistic and oligopolistic power along a food supply chain. Annual time series data for the period 1970–2011 were employed. The empirical results reveal that, in the US meat packing industry, the farm-to-wholesale price spread is 3.74% above the marginal processing cost. These findings indicate that rather a small percentage of the farm-to-wholesale price spread can be attributed to market power in the US meat packing sector.

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Notes

  1. Red meat includes beef, pork, veal, lamb and mutton. Beef and pork account for more than 99% of the red meat production.

  2. Although concentration data are useful for describing an industry, high levels of concentration is not a sufficient condition to conclude that firms engage in non-competitive behavior (McCorriston 2002).

  3. Mergers and acquisitions in the US meatpacking industry have resulted in multi-output firms, i.e. firms slaughtering both beef and pork. Hence, one can assume the unit of analysis to be either the meatpacking plant or a single firm operating multiple plants.

  4. There are also high levels of concentration at the retail level of the meat industry. Accordingly, at the last stage of the meat marketing channel we find firms with potentially high degree of market power as well. This work focuses on the estimation of oligopolistic and oligopsonistic power exerted by meat processors and does not model for bilateral oligopoly power between packers and retailers.

  5. In some occasions a conversion parameter \(k < 1\) is used in order to capture the cattle/hog to beef/pork transformation. The majority of the empirical studies in the literature maintain the assumption that \(k=1\). The present works adopts the same assumption.

  6. In the present study, the dependent variable on the right hand side of Eq. (13) is the aggregate meat (beef and pork) revenue over the total costs, namely the sum of beef revenue and pork revenue over the total costs of meat processing (Table 1).

  7. Data at regional level could have been employed as well. The present work uses data at country level in order to obtain comparable results with the majority of the studies in the relevant literature. As Azzam and Pagoulatos (1990) point out, little can be known about how the presence or absence of market power is obscured by too much or too little aggregation.

  8. NBER-SIC2011 database reports deflators for the f.o.p. material and energy.

  9. Assuming a 20-year equipment working life in the food processing industry and a linear form, a value of 0.05 was applied to the depreciation rate (Lopez et al. 2017).

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Correspondence to Dimitrios Panagiotou.

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Appendix

Appendix

Description of the variables and their sources are as follows:

Source: NBER-CES Manufacturing Industry Database/SIC2011 (meatpacking)

$$\begin{aligned} L & = {\text{Production worker hours (million hours)}} \\ W_{L}&= \frac{{\text{Production worker wages (million}} \$)}{{\text {L}}} \\ W_{K}&= {\text{interest rate + depreciation rate}} \\ W_{M}& = {\text{Deflator for MATCOST (1987 = 1.00)}} \\ W_{E}&= {\text{Deflator for ENERGY (1987 = 1.00)}} \\ C& = {\text{LABOR COST + CAPITAL COST + MATERIAL COST + ENERGY COST}}. \end{aligned}$$

Source: United States Department of Agriculture-Economic Research Service

$$\begin{aligned} Q &= \text {Commercial meat (beef and pork) production (carcass weight, million lbs)}\\ (P-W) &= \text {Farm-wholesale price spread (cents per retail pound equivalent)}. \end{aligned}$$

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Panagiotou, D., Stavrakoudis, A. A stochastic frontier estimator of the aggregate degree of market power exerted by the US meat packing industry. Econ Polit Ind 45, 387–401 (2018). https://doi.org/10.1007/s40812-018-0093-1

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  • DOI: https://doi.org/10.1007/s40812-018-0093-1

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