Losing Ground: Measuring the Welfare Effects of Retail Food Price Inflation during the COVID-19 Pandemic on Mexican Household
-
Noé J. Nava
,
Benjamin D. K. Wood
and
Rafael Garduño-Rivera
Abstract
As worldwide food prices rise, there is a growing interest in understanding the effect of these increases on households. Building on previous work, while applying recent methodological advances, we focus our attention on México during the COVID-19 pandemic. We document these price escalations for a basket of foods representative of Mexican households’ diets. The price increases are substantial across the basket, ranging from 20 percent in meat to 40 percent in tortilla. Using these estimates, we calculate the welfare effect from the recent food price escalation to cost households $17.07 billion annually, close to 1.5 percent of Mexico’s Gross Domestic Product in 2020. We estimate households would need to increase their food expenditure budgets by 28.66 percent, the compensating variation, to achieve pre-price increase utility levels.
Acknowledgments
The authors thank Metin Çakır for his tireless review of this manuscript. We also thank the co-editors of this special issue, Shawn Arita and Rebecca Nemec, along the two anonymous referees. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. This research was supported by the U.S. Department of Agriculture, Economic Research Service.
1A: Robustness Checks
Estimated parameters from estimation with ENIGH 2022.
| Parameter | Estimate | Parameter | Estimate | Parameter | Estimate |
|---|---|---|---|---|---|
| α 1 | 0.2695 | γ 33 | 0.0245 | θ 21 | −0.0367 |
| (0.0045) | (0.0046) | (0.0007) | |||
| α 2 | 0.2623 | γ 41 | −0.0226 | θ 22 | −0.0228 |
| (0.0038) | (0.0015) | (0.0006) | |||
| α 3 | 0.2640 | γ 42 | −0.0009 | θ 23 | −0.0510 |
| (0.0074) | (0.0014) | (0.0013) | |||
| α 4 | 0.1521 | γ 43 | 0.0052 | θ 24 | −0.0192 |
| (0.0039) | (0.0022) | (0.0007) | |||
| α 5 | 0.1175 | γ 44 | −0.0052 | θ 25 | −0.0174 |
| (0.0040) | (0.0018) | (0.0007) | |||
| β 1 | −0.0489 | γ 51 | −0.0581 | θ 31 | −0.0017 |
| (0.0022) | (0.0021) | (0.0001) | |||
| β 2 | −0.0125 | γ 52 | 0.0466 | θ 32 | −0.0008 |
| (0.0021) | (0.0019) | (0.0001) | |||
| β 3 | −0.0660 | γ 53 | 0.0617 | θ 33 | 0.0000 |
| (0.0043) | (0.0026) | (0.0001) | |||
| β 4 | −0.0325 | γ 54 | 0.0070 | θ 34 | 0.0008 |
| (0.0022) | (0.0016) | (0.0001) | |||
| β 5 | −0.0944 | γ 55 | 0.0146 | θ 35 | 0.0003 |
| (0.0025) | (0.0030) | (0.0001) | |||
| γ 11 | 0.0476 | θ 11 | 0.0040 | θ 41 | −0.0010 |
| (0.0027) | (0.0004) | (0.0003) | |||
| γ 21 | 0.0176 | θ 12 | −0.0038 | θ 42 | −0.0008 |
| (0.0018) | (0.0004) | (0.0003) | |||
| γ 22 | 0.0305 | θ 13 | 0.0189 | θ 43 | 0.0009 |
| (0.0023) | (0.0008) | (0.0005) | |||
| γ 31 | −0.0072 | θ 14 | 0.0002 | θ 44 | 0.0040 |
| (0.0025) | (0.0004) | (0.0003) | |||
| γ 32 | −0.0465 | θ 15 | 0.0174 | θ 45 | 0.0018 |
| (0.0023) | (0.0005) | (0.0003) |
-
Note: Standard errors are in parentheses. Source: Parameter estimates are our own using ENIGH 2022 data.
Budget and Marshallian price elasticities from estimation using ENIGH 2022.
| With respect to | Price | ||||||
|---|---|---|---|---|---|---|---|
| Tortilla | Cereal | Meat | Dairy | Fruits and vegetables | Other | Budget | |
| Quantity | |||||||
| Tortilla | −0.5067 | 0.2430 | 0.0587 | −0.1036 | −0.3632 | 0.1322 | 0.5395 |
| (0.0109) | (0.0155) | (0.0056) | (0.0066) | (0.0220) | (0.0080) | (0.0303) | |
| Cereal | 0.2251 | −0.6849 | −0.2846 | 0.0466 | 0.4406 | −0.4118 | 0.6690 |
| (0.0161) | (0.0137) | (0.0201) | (0.0043) | (0.0313) | (0.0291) | (0.0255) | |
| Meat | −0.0137 | −0.1598 | −0.8986 | 0.0258 | 0.2460 | −0.1500 | 0.9503 |
| (0.0109) | (0.0120) | (0.0121) | (0.0027) | (0.0124) | (0.0075) | (0.0084) | |
| Dairy | −0.1238 | 0.0546 | 0.1229 | −0.9967 | 0.1222 | 0.1196 | 0.7012 |
| (0.0152) | (0.0118) | (0.0182) | (0.0153) | (0.0050) | (0.0047) | (0.0151) | |
| Fruits and vegetables | −0.3393 | 0.3665 | 0.4722 | 0.0824 | −0.8581 | −0.5080 | 0.7843 |
| (0.0217) | (0.0211) | (0.0280) | (0.0114) | (0.0171) | (0.0263) | (0.0158) | |
| Other | −0.0489 | −0.1859 | −0.1798 | −0.0271 | −0.2406 | −0.6989 | 1.3813 |
| (0.0054) | (0.0064) | (0.0104) | (0.0035) | (0.0082) | (0.0051) | (0.0072) | |
-
Note: Standard errors are in parentheses. Source: Elasticity estimates are our own using ENIGH 2022 data.
2A: Regional Aggregation of Mexican States
Regional aggregation of Mexican states.
| Region | States |
|---|---|
| Border | Chihuahua |
| Coahuila | |
| Nuevo Leon | |
| Tamaulipas | |
| Center | Ciudad de Mexico |
| Estado de Mexico | |
| Hidalgo | |
| Tlaxcala | |
| Veracruz | |
| Central Pacific | Colima |
| Jalisco | |
| Michoacan | |
| North Center | Aguascalientes |
| Durango | |
| Guanajuato | |
| Queretaro | |
| San Luis Potosi | |
| Zacatecas | |
| North Pacific | Baja California |
| Baja California Sur | |
| Nayarit | |
| Sinaloa | |
| Sonora | |
| Peninsula | Campeche |
| Chiapas | |
| Quintana Roo | |
| Tabasco | |
| Yucatan | |
| South Pacific | Guerrero |
| Morelos | |
| Oaxaca | |
| Puebla |
References
Agyei, S. K., Z. Isshaq, S. Frimpong, A. M. Adam, A. Bossman, and O. Asiamah. 2021. “COVID-19 and Food Prices in Sub-Saharan Africa.” African Development Review 33 (S1): S102–13. https://doi.org/10.1111/1467-8268.12525.Search in Google Scholar
Arita, S., J. Grant, S. Sydow, and J. Beckman. 2022. “Has Global Agricultural Trade been Resilient under Coronavirus (COVID-19)? Findings from an Econometric Assessment.” Food Policy 107: 102204. https://doi.org/10.1016/j.foodpol.2021.102204.Search in Google Scholar
Attanasio, O., V. Di Maro, V. Lechene, and D. Phillips. 2013. “Welfare Consequences of Food Price Increases: Evidence from Rural Mexico.” Journal of Development Economics 104: 136–51. https://doi.org/10.1016/j.jdeveco.2013.03.009.Search in Google Scholar
Banco de México. 2023. “Monetary Policy Statement.” Banco de México: Press Release June 22, 2023. https://www.banxico.org.mx/publications-and-press/announcements-of-monetary-policy-decisions/%7BBE9A474F-C31A-105C-704C-F0D83F946952%7D.pdf (accessed November, 2023).Search in Google Scholar
Banks, J., R. Blundell, and A. Lewbell. 1997. “Quadratic Engel Curves and Consumer Demand.” The Review of Economics and Statistics 79 (4): 527–39. https://doi.org/10.1162/003465397557015.Search in Google Scholar
Beckman, Jayson, Noé J. Nava, Angelica S. Williams, and Steven Zahniser. 2024. “Land Competition and Welfare Effects from Mexico’s Proposal to Ban Genetically Engineered Corn.” American Journal of Agricultural Economics 106 (3), https://doi.org/10.1111/ajae.12463.Search in Google Scholar
Capacci, S., and M. Mazzocchi. 2011. “Five-A-Day, a Price to Pay: An Evaluation of the UK Program Impact Accounting for Market Forces.” Journal of Health Economics 30 (1): 87–98. https://doi.org/10.1016/j.jhealeco.2010.10.006.Search in Google Scholar
Deaton, A., and J. Muellbauer. 1980. “An Almost Ideal Demand System.” The American Economic Review 70 (3): 312–26.Search in Google Scholar
De Hoyos, R., and D. Medvedev. 2009. “Poverty Effects of Higher Food Prices: A Global Perspective.” Review of Development Economics 15 (3): 387–402. https://doi.org/10.1596/1813-9450-4887.Search in Google Scholar
Deslandes, A. 2021. “Soaring Tortilla Prices Hit Struggling Mexican Households Hard.” Al Jazeera. https://www.aljazeera.com/economy/2021/7/28/soaring-tortilla-prices-hit-struggling-mexican-households-hard (accessed November 27th, 2023)Search in Google Scholar
Dong, D., B. W. Gould, and H. M. Kaiser. 2004. “Food Demand in Mexico: An Application of the Amemiya-Tobin Approach to the Estimation of a Censored Food System.” American Journal of Agricultural Economics 86 (4): 1094–107. https://doi.org/10.1111/j.0002-9092.2004.00655.x.Search in Google Scholar
Dyer, G. A., and J. E. Taylor. 2011. “The Corn Price Surge: Impacts on Rural Mexico.” World Development 39: 1878–87. https://doi.org/10.1016/j.worlddev.2011.04.032.Search in Google Scholar
Eftimov, T., G. Popovski, M. Petkovic, B. K. Seljak, and D. Kocev. 2020. “COVID-19 Pandemic Changes the Food Consumption Patterns.” Trends in Food Science and Technology 104: 268–72. https://doi.org/10.1016/j.tifs.2020.08.017.Search in Google Scholar
Food and Agriculture Organization (FAO). 2023. “World Food Situation.” https://www.fao.org/worldfoodsituation/foodpricesindex/en/ (accessed November 27th, 2023)Search in Google Scholar
García-Enríquez, J., and C. A. Echevarría. 2016. “Consistent Estimation of a Censored Demand System and Welfare Analysis: The 2012 VAT in Spain.” Journal of Agricultural Economics 67 (2): 324–47. https://doi.org/10.1111/1477-9552.12144.Search in Google Scholar
Haile, M. G., M. Kalkuhl, and J. von Braun. 2016. “Worldwide Acreage and Yield Response to International Price Change and Volatility: A Dynamic Panel Data Analysis for Wheat, Rice, Corn, and Soybeans.” American Journal of Agricultural Economics 98 (1): 172–90. https://doi.org/10.1093/ajae/aav013.Search in Google Scholar
Han, J., B. D. Meyer, and J. X. Sullivan. 2020. “Income and Poverty in the COVID-19 Pandemic.” National Bureau of Economic Research No. w27729.10.3386/w27729Search in Google Scholar
Hanke, S. H. 1996. “Currency Board for Mexico.” Central Banking 6 (4).Search in Google Scholar
Heien, D., and C. R. Wesseils. 1990. “Demand System Estimation with Microdata: A Censored Regression Approach.” Journal of Business and Economic Statistics 8 (3): 365–71. https://doi.org/10.1080/07350015.1990.10509807.Search in Google Scholar
Hevia, F. J., and S. Vergara-Lopez. 2022. “Rezago de Aprendizajes Básicos y Brecha Digital en el Contexto de COVID-19 en México: El Caso de Xalapa, Veracruz.” Perfiles Educativos 44 (176): 8–21. https://doi.org/10.22201/iisue.24486167e.2022.176.60478.Search in Google Scholar
Instituto Nacional de Estadísticas y Geografía (INEGI). 2020. “Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH). Subsystem of Demographic and Social Information – 2020.” Database.Search in Google Scholar
Instituto Nacional de Estadísticas y Geografía (INEGI). 2023a. “Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH). Subsystem of Demographic and Social Information – 2022.” Database.Search in Google Scholar
Instituto Nacional de Estadísticas y Geografía (INEGI). 2023b. “Indice Nacional de Precios al Consumidor (INPC).” Database.Search in Google Scholar
Ivanic, M., and W. Martin. 2008. “Implications of Higher Global Food Prices for Poverty in Low-Income Countries.” Agricultural Economics 36 (1): 405–16. https://doi.org/10.1596/1813-9450-4594.Search in Google Scholar
Jordá, Ó., S. R. Singh, and A. M. Taylor. 2022. “Longer-run Economic Consequences of Pandemics.” The Review of Economics and Statistics 104 (1): 166–75. https://doi.org/10.1162/rest_a_01042.Search in Google Scholar
LaFrance, J. T. 1993. “Weak Separability in Applied Welfare Analysis.” American Journal of Agricultural Economics 75 (3): 770–5. https://doi.org/10.2307/1243589.Search in Google Scholar
Millán-Guerrero, R. O., R. Caballero-Hoyos, and J. Monárrez-Espino. 2021. “Poverty and Survival from COVID-19 in México.” Journal of Public Health 43 (3): 437–44. https://doi.org/10.1093/pubmed/fdaa228.Search in Google Scholar
Nava, N. J., and D. Dong. 2022. “The Impact of Taxing Sugar-sweetened Beverages in México: A Censored QUAI Demand System Approach.” Journal of the Agricultural and Applied Economics Association 1 (1): 18–32. https://doi.org/10.1002/jaa2.6.Search in Google Scholar
Nava, N. J., W. Ridley, and S. Dall’Erba. 2023. “A Model of the U.S. Food System: What are the Determinants of the State Vulnerabilities to Production Shocks and Supply Chain Disruptions?” Agricultural Economics 54 (1): 95–109. https://doi.org/10.1111/agec.12750.Search in Google Scholar
Organization for Economic Cooperation and Development (OECD). 2023. “Inflation (CPI) Indicator.” OECD Data. https://data.oecd.org/price/inflation-cpi.htm#indicator-chart (accessed November, 2023).Search in Google Scholar
Phaneuf, D. J., C. L. Kling, and J. A. Herriges. 2000. “Estimation and Welfare Calculations in a Generalized Corner Solution Model with an Application to Recreational Demand.” The Review of Economics and Statistics 82 (1): 83–92. https://doi.org/10.1162/003465300558650.Search in Google Scholar
Pollak, R., and T. J. Wales. 1981. “Demographic Variables in Demand Analysis.” Econometrica: Journal of the Econometric Society 49: 1533–51. https://doi.org/10.2307/1911416.Search in Google Scholar
Poi, Brian P. 2012. “Easy Demand-system Estimation with Quaids.” STATA Journal 12 (3): 433–46. https://doi.org/10.1177/1536867x1201200306.Search in Google Scholar
Ray, Ranjan. 1983. “Measuring the Costs of Children: An Alternative Approach.” Journal of Public Economics 22 (1): 89–102. https://doi.org/10.1016/0047-2727(83)90058-0.Search in Google Scholar
Ridley, W., S. B. Akhundjanov, and S. Devadoss. 2023. “The COVID-19 Pandemic and Trade in Agricultural Products.” The World Economy 46 (8): 2432–55. https://doi.org/10.1111/twec.13376.Search in Google Scholar
Shonkwiler, J. S., and S. T. Yen. 1999. “Two-step Estimation of a Censored System of Equations.” American Journal of Agricultural Economics 81 (4): 972–82. https://doi.org/10.2307/1244339.Search in Google Scholar
Smith, T. G. 2014. “Feeding Unrest: Disentangling the Causal Relationship between Food Price Shocks and Sociopolitical Conflict in Urban Africa.” Journal of Peace Research 51 (6): 679–95. https://doi.org/10.1177/0022343314543722.Search in Google Scholar
Tallis, G. M. 1965. “Plane Truncation in Normal Populations.” Journal of the Royal Statistical Society: Series B (Methodological) 27 (2): 301–7. https://doi.org/10.1111/j.2517-6161.1965.tb01497.x.Search in Google Scholar
Tauchmann, H. 2010. “Consistency of Heckman-type Two-step Estimators for the Multivariate Sample-Selection Model.” Applied Economics 42 (30): 3895–902. https://doi.org/10.1080/00036840802360179.Search in Google Scholar
U.S. Department of Agriculture, Foreign Agricultural Service (USDA, FAS). 2022a. “COVID-19 Working Paper: U.S.-Mexico Agricultural Trade in 2020.” https://www.ers.usda.gov/webdocs/publications/103088/ap-97.pdf?v=7168.5 (accessed November 27th, 2023).Search in Google Scholar
U.S. Department of Agriculture, Foreign Agricultural Service, (USDA, FAS). 2022b. “Government of Mexico Program to Combat Food Price Inflation and Scarcity.” https://fas.usda.gov/data/mexico-government-mexico-program-combat-food-price-inflation-and-scarcity (accessed November 27th, 2023).Search in Google Scholar
Valero-Gil, J. N., and M. Valero. 2008. “The Effects of Rising Food Prices on Poverty in México.” Agricultural Economics 39 (1): 485–96. https://doi.org/10.1111/j.1574-0862.2008.00354.x.Search in Google Scholar
Vargas-Lopez, Adrian, Clara Cicatiello, Ludovica Principato, and Luca Secondi. 2022. “Consumer Expenditure, Elasticity and Value of Food Waste: A Quadratic Almost Ideal Demand System for Evaluating Changes in Mexico during COVID-19.” Socio-Economic Planning Sciences 82: 101065. https://doi.org/10.1016/j.seps.2021.101065.Search in Google Scholar
Wales, T. J., and A. D. Woodland. 1983. “Estimation of Consumer Demand Systems with Binding Non-negativity Constraints.” Journal of Econometrics 21 (3): 263–85. https://doi.org/10.1016/0304-4076(83)90046-5.Search in Google Scholar
Wood, B. D. K., C. H. Nelson, and L. Nogueira. 2012. “Poverty Effects of Food Price Escalation: The Importance of Substitution Effects in Mexican Households.” Food Policy 37 (1): 77–85. https://doi.org/10.1016/j.foodpol.2011.11.005.Search in Google Scholar
World Bank. 2023. “GDP (Current US$) – Mexico Online.” World Bank Data Bank worldbank.org (accessed December 10, 2023).Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Causes and Consequences of Retail Food Price Inflation: Introduction to the Special Issue
- Research Articles
- Food Price Inflation in the United States as a Complex Dynamic Economic System
- Rural-Urban Food Price Inflation Disparities in the United States
- Losing Ground: Measuring the Welfare Effects of Retail Food Price Inflation during the COVID-19 Pandemic on Mexican Household
Articles in the same Issue
- Frontmatter
- Editorial
- Causes and Consequences of Retail Food Price Inflation: Introduction to the Special Issue
- Research Articles
- Food Price Inflation in the United States as a Complex Dynamic Economic System
- Rural-Urban Food Price Inflation Disparities in the United States
- Losing Ground: Measuring the Welfare Effects of Retail Food Price Inflation during the COVID-19 Pandemic on Mexican Household
