Estimating Market Power in Agricultural Industries Using the Production Function Approach: Evidence from the Philippines
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Gabriel Angelo B. Domingo
Abstract
Market power represents distortions that hinder the efficient allocation of resources within an economy. The significance of measuring such distortions is particularly pronounced in Philippine agriculture which has been subject to periodic food affordability crises. This paper is the first to directly quantify markups and assess returns to scale for agricultural suppliers and manufacturers. Using establishment-level surveys for Philippine agricultural suppliers and manufacturers from 2012 to 2018, I estimate a national average markup parameter for suppliers and manufacturers through production function regressions. I use panel data models to consistently estimate output elasticities for materials’ expenditure with which I back out theory-consistent markups and scale economies separately for agricultural suppliers and manufacturers. For suppliers, I find average markups of 8 % and constant returns to scale. For agricultural manufacturers, I find markups above 50 % and modest but statistically significant increasing returns to scale of 11 %. These estimates are consistent with the theoretical relationship between markups, profits, and returns to scale. Estimates of fixed costs over time inform us that markups are a more important factor keeping profit rates elevated for manufacturing than for upstream suppliers.
Appendix
CPBI and ASPBI Sections for Module A and Module C Included in the Dataset
This is the PSIC listing for module A and C:
see Appendix Table 1.
PSIC Sections for Module A and Module C (Agriculture Manufacturers only).
| Module A | Module C |
|---|---|
| Crop And Animal Production, Hunting And Related Service Activities - 01 | Manufacture Of Food Products −10 |
| Forestry And Logging - 02 | Manufacture Of Beverages −11 |
| Fishing And Aquaculture - 03 |
Number of Firms in the Sample by Number of Years
see Appendix Figure 1.
Count of establishments by No. of Years.
Using COGS Based Material Expenditure
see Appendix Table 2.
Panel data models, aggregated PSIC-5 with COGS materials expenditure full Sample.
| First diff | FE | |
|---|---|---|
| L-K | 0.391 | 0.401 |
| (0.071) | (0.092) | |
| M-K | 0.358 | 0.299 |
| (0.057) | (0.139) | |
| Num.Obs. | 318 | 318 |
| R2 | 0.558 | 0.513 |
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