Mark Ups and Pass-Through in Small and Medium Retailers for Rice, Tomato Sauce and Oil

Pablo Blanchard

doi:10.1515/jafio-2023-0059

Article

Mark Ups and Pass-Through in Small and Medium Retailers for Rice, Tomato Sauce and Oil

Pablo Blanchard

Published/Copyright: May 21, 2024

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From the journal Journal of Agricultural & Food Industrial Organization Volume 22 Issue 1

Abstract

In this paper, we recover and decompose markups, and estimate the pass-through rates from cost to prices in small and medium retail stores for oil, tomato sauce, and rice in Uruguay using a structural model of demand and assumptions about the competitive behavior of producers. The market power for these products has been under study by the Commission of Promotion and Defense of Competition since 2016, and the proposed methodology allows for a deeper exploration of the measurement and understanding of the origin of that market power. In addition to providing a fundamental input for competition defense policies in Uruguay, this study enhances the international academic literature by contributing evidence on cost-to-price pass-through in a developing economy with potentially greater market power than that found in developed countries. About 65 % of the market power under the Nash Bertrand assumption is explained by the ability of firms to differentiate products and 35 % by the ownership structure in the case of oil and sauce. In the case of rice, 49 % is explained by differentiation and 51 % for ownership structure. Finally, the pass-through rates are low for the three products, being under both behavioral assumptions lower than 55 % for the three products.

Keywords: market power; cost pass-through; discrete choice models; product differentiation

JEL Classification: D43; L11; L81

Corresponding author: Pablo Blanchard, Universidad de la República Uruguay, Montevideo, 11200, Uruguay, E-mail: pabloblanchard88@gmail.com

Appendix A

See Table A.2.

Table A.1:

Descriptive statistics of demographics.

Departamento	Localidad	Municipio	Age	Education years	Sex
Artigas	Artigas		45.91	8.684	0.455
Artigas	Bella Union		44.76	8.305	0.507
Canelones	Barros Blancos		43.29	7.678	0.478
Canelones	Canelones		46.06	8.961	0.464
Canelones	La Paz		44.92	8.688	0.454
Canelones	Las Piedras		43.92	8.214	0.443
Canelones	Pando		43.90	8.575	0.480
Canelones	Parque Del Plata		47.59	9.508	0.447
Canelones	Paso Carrasco		43.04	8.821	0.487
Canelones	Pinar		43.67	10.46	0.491
Canelones	Progreso		43.56	7.864	0.480
Canelones	Salinas		46.75	9.780	0.468
Canelones	San Ramon		46.75	7.972	0.499
Canelones	Santa Lucia		46.43	8.699	0.474
Canelones	Sauce		45.54	8.363	0.469
Canelones	Solymar		46.04	10.93	0.466
Canelones	Toledo		42.67	8.001	0.471
Cerro Largo	Melo		46.87	8.392	0.442
Colonia	Carmelo		47.23	8.436	0.466
Colonia	Colonia		46.64	9.127	0.454
Durazno	Durazno		46.22	8.534	0.465
Flores	Trinidad		48.69	8.035	0.500
Florida	Florida		47.19	8.720	0.453
Lavalleja	Minas		48.57	8.523	0.450
Maldonado	Maldonado		44.67	8.553	0.468
Maldonado	Piriapolis		48.53	9.397	0.470
Maldonado	Punta Del Este		47.46	11.98	0.465
Maldonado	San Carlos		44.03	8.524	0.457
Montevideo	Montevideo	A	45.83	8.123	0.454
Montevideo	Montevideo	B	43.90	12.21	0.449
Montevideo	Montevideo	C	47.37	11.01	0.451
Montevideo	Montevideo	CH	48.02	13.07	0.437
Montevideo	Montevideo	D	45.32	8.490	0.444
Montevideo	Montevideo	E	47.31	11.62	0.486
Montevideo	Montevideo	F	44.06	8.211	0.471
Montevideo	Montevideo	G	46.38	8.897	0.424
Paysandu	Paysandu		46.62	9.071	0.472
Rio Negro	Fray Bentos		45.36	8.844	0.481
Rio Negro	Young		44.51	7.978	0.529
Rivera	Rivera		45.52	8.432	0.433
Rocha	Rocha		46.88	8.710	0.456
Salto	Salto		44.77	8.739	0.467
San Jose	Ciudad Del Plata		41.76	7.740	0.468
San Jose	Libertad		45.09	7.983	0.470
San Jose	San Jose De Mayo		46.42	8.555	0.484
Soriano	Dolores		45.12	8.309	0.476
Soriano	Mercedes		46.46	8.997	0.457
Tacuarembo	Paso De Los Toros		46.97	7.785	0.468
Tacuarembo	Tacuarembo		45.53	8.298	0.436
Treinta Y Tres	Treinta Y Tres		47.46	8.862	0.474

Notes. Average age, education years and sex by region, obtained from Census 2011.

Table A.2:

First stage logit model.

	(1)	(2)	(3)
Variables	Price	Price	Price
1 instrument	0.445	−1.358**	−0.925
	(1.659)	(0.617)	(0.757)
2 instrument	4.180**	−0.472	0.184
	(1.954)	(0.916)	(0.870)
3 instrument	−2.835	−2.300**	−0.924
	(2.281)	(1.119)	(0.854)
4 instrument	−0.242	−2.603**	−0.131
	(2.109)	(1.053)	(0.833)
5 instrument	5.547***	0.126	−0.982
	(1.748)	(0.988)	(0.799)
6 instrument	−0.337	−4.553***	−1.382*
	(1.753)	(1.020)	(0.740)
7 instrument	1.671	4.101***	−1.207
	(1.655)	(1.354)	(0.783)
8 instrument	−2.049	−2.596**	1.204
	(1.556)	(1.193)	(0.985)
9 instrument	−0.717	−4.024***	0.477
	(1.540)	(1.105)	(1.086)
10 instrument	0.024	−1.671	−1.187
	(1.494)	(1.240)	(1.006)
11 instrument	−4.362*	−0.551	−5.691***
	(2.639)	(1.334)	(1.012)
12 instrument	3.932	−2.726**	1.724*
	(2.394)	(1.285)	(0.984)
13 instrument	4.796*	0.689	−2.821***
	(2.723)	(1.047)	(0.819)
14 instrument	−6.215**	−9.480***	3.870***
	(2.700)	(1.214)	(0.762)
15 instrument	−9.771***	0.672	−1.565*
	(3.138)	(1.244)	(0.893)
16 instrument	6.969*	−3.335***	−1.694*
	(3.675)	(1.039)	(0.969)
17 instrument	−4.161	−0.796	−1.092
	(3.364)	(0.986)	(0.930)
18 instrument	8.269***	−5.024***	−1.944**
	(3.007)	(1.105)	(0.982)
19 instrument	−7.032**	−0.798	3.950***
	(2.856)	(1.169)	(0.977)
20 instrument	3.507	−1.116	−1.770*
	(2.396)	(1.235)	(1.050)
21 instrument	−3.059*	−1.131	−3.549***
	(1.613)	(1.374)	(0.822)
22 instrument	−4.876**	−1.477	−2.410***
	(2.288)	(1.218)	(0.665)
Observations	2751	3360	3525
R-squared	0.826	0.926	0.937
Product	Oil	Sauce	Rice

Notes. First stage of IV regressions reported in Table 3. For hypothesis testing we use p-values with significance levels: ***p < 0.01, **p < 0.05, *p < 0.1.

Table A.3:

Margins by product for oil.

	Single product	Current ownership	Collusion
Condesa soja	22.2	36.4	55.8
Diez soja	12.8	12.8	23.3
Demas soja	98.1	98.1	192.5
Optimo canola	8.4	32.3	49.7
Optimo girasol	10.6	24.3	36.0
Optimo girasol altoleico	−22.6	−8.8	1.2
Revelacion soja	12.1	12.1	57.2
Rio de la plata soja	11.3	11.3	51.2
Uruguay girasol	11.4	27.5	42.5

Notes. Presented are means of the brand-locality-quarter observations, weighted by the sales. Margins are defined as (p − mc)/p. Margins computed based on the full model reported on Table 4.

Table A.4:

Margins by product for sauce.

	Single product	Current ownership	Collusion
Cololo	6.0	6.0	37.5
Conaprole	9.2	9.2	31.3
De Ley	24.9	35.8	50.0
Don Perita	12.3	12.3	61.0
Gourmet	16.9	28.4	42.1
Gourmet napolitana	16.9	35.7	50.5
Qualitas	13.7	42.3	62.4
Rigby	16.4	19.8	70.5
Rigby italiana	15.1	19.8	60.0
Pure de tomate Big	13.9	13.9	54.7

Notes. Presented are means of the brand-locality-quarter observations, weighted by the sales. Margins are defined as (p − mc)/p. Margins computed based on the full model reported on Table 4.

Table A.5:

Margins by product for rice.

	Single product	Current ownership	Collusion
Aruba patna	16.2	35.3	72.8
Blue patna	12.7	30.4	44.5
Blue parna parboiled	11.0	29.6	41.1
Casarone	14.2	14.2	67.7
Green chef	21.2	31.0	45.6
Saman blanco	15.0	21.3	44.8
Saman Parboiled	12.2	21.6	42.5
Saman patna	12.2	24.4	49.4
San jose	15.0	15.0	68.8
Shiva patna	20.3	46.5	68.8

Notes. Presented are means of the brand-locality-quarter observations, weighted by the sales. Margins are defined as (p − mc)/p. Margins computed based on the full model reported on Table 4.

A.2 Appendix: Demand Estimation Details

The implementation of the estimation of the model proposed by Berry, Levinsohn, and Pakes (1995) requires determining a method to approximate the integral, an optimization algorithm, initial values, and convergence criteria. Brunner et al. (2017) discuss implementation alternatives so that the estimation results are adequate and the R package “BLPestimatematoR” is provided, which is used in the present work to carry out the estimation.

Regarding the simulation to approximate the integral of the market shares, 200 MLHS (Latin hypercube sampling draws) draws are used. The sensitivity of the results to increasing the number of extractions to 1000 is tested, corroborating that there are no relevant differences in the results. The number of extractions cannot be greater than the number of extractions of the observable characteristics of the individuals. The algorithm used for optimization is BFGS (Broyden–Fletcher–Goldfarb–Shanno).

Regarding the iterations of the contraction, a maximum of 5000 iterations is set or until it is less than 1e−06. Finally, following Nevo (2000) and Chidmi and Lopez (2007), as starting guesses for the average utility vector (γ) the results obtained in the logit model are used.

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Received: 2023-12-07

Accepted: 2024-01-30

Published Online: 2024-05-21

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Articles in the same Issue

https://doi.org/10.1515/jafio-2023-0059

Keywords for this article

market power; cost pass-through; discrete choice models; product differentiation