The fiscal cost of the provision of basic public services, subsidies for expenditure on food and basic citizen income per household in Costa Rica, Guatemala and El Salvador during the COVID-19 pandemic: An expenditure analysis


  • Luis Miguel Galindo UNAM and CIDE
  • Fernando Filgueira UDELAR
  • Marike Blofield GIGA Institute
  • Carlos Alberto Francisco Cruz UNAM


COVID-19, consumption, basic public services, food, basic income, fiscal costs


The objective of this article is to estimate the fiscal costs, using income and expenditure surveys, of the provision of basic public services (electricity, water, telephone and internet) for the 40% of the population with the lowest incomes, the provision of a subsidy of 50% of actual food expenditure for the 40% of the population with the lowest incomes and the provision of a basic income per household equivalent to the value of the poverty line for households under the poverty line in Costa Rica, Guatemala and El Salvador during the COVID-19 pandemic. These fiscal options are a fundamental component of any public health strategy against the COVID-19 considering they give economic viability to the population during the isolation and mobility restrictions period and financial support during the economic and social emergency. The results show that the fiscal costs of the provision of basic public services to 40% of the population with the lowest incomes or other fiscal measures considering less ambitious targets are heterogeneous between these Central American countries because of previous conditions and public policies but are reasonable and possible to cover under the actual circumstances.


Al-Habashene, F. & Al-Majali, (2014). Estimating the Engel curves for Household expenditure in Jordan from 2010 to 2011, European Scientific Journal, 10(2), pp. 267-282.

Alvarez, F. E., D. Argente, & F. Lippi (2020). A simple planning problem for COVID-19 lockdown (No. w26981). National Bureau of Economic Research.

Attanasion, O, A. Meshard & E. Battistin (2012). Food and cash transfers: evidence from Colombia, Economic Journal, 122, 559, pp. 92-124.

Baker, S.R., R. Farrokhnia, S. Meyer, M. Pagel & C. Yanellis (2020). How does household spending respond to an epidemic? Consumption during the 2020 COVID-19 pandemic, technical report, NBER, working paper, No. 26949.

Banks, J., R. Blundell & A. Lewbel (1997). Quadratic Engel curves and consumer demand, Review of Economics and Statistics, 79(4), pp. 527-539.

Battistin, E. & M. De Nadai (2015). Identification and estimation of Engel curves with endogenous and unobserved expenditures, Journal of Applied Econometrics, vol. 30, pp. 487-508.

Blofield, M. & F. Filgueira (2020). COVID-19 and Latin America: social impacts, policies and a fiscal case for an emergency social protection floor, Social protection program, CIPPEC.

Blundell, R.W., X. Chen & D. Kristensen (2007). Semi-nonparametric IV estimation of shape-invariant Engel curves, Econometrica, 75(6), Noviembre, pp. 1613–1669.

Bodenstein, M.; G. Corsetti & L. Guerrieri (2020). Social distancing and supply disruptions in a pandemic, Finance and Economic Series, Division of Research, Statistics and Monetary Affairs, federal Reserve Board, Washington, DC.

Bottan, Nicolás, Bridget Hoffmann & Diego Vera-Cossio (2020). Coronavirus Survey Results Show Big Impacts, Linkages between Labor Markets and Inequality. Ideas Matter Blog. Inter-American Development Bank. May 8.

Chai, A. & A. Moneta (2010). Retrospectives: Engel curves, Journal of Economic Perspectives, American Economic Association, vol. 24(1), pp. 225-240. DOI: 10.1257/jep.24.1.225

Deaton, A. & J. Muellbauer (1980). Economics and Consumer Behavior, Cambridge University Press.

Deb, P., D. Furceri, J.D. Ostroy & N. Tawk (2020). The Economic Effects of COVID-19 Containment Measures, working paper, FMI 20/158, august, pp 32-75.

Dingel, J. & B. Neiman (2020). How Many Jobs Can be Done at Home? Journal of Public Economics, 189, DOI: 10.1016/j. jpubeco.2020.104235.

ECLAC (2020). Childhood and urban housing inequality in Latin America and the Caribbean. Informative note (published online).

Eichenbaum, M. S., S. Rebelo &M. Trabandt (2020). The macroeconomics of epidemics (No. w26882). National Bureau of Economic Research.

Headey, D.D. & H.H. Alderman (2019). The Relative Caloric Prices of Healthy and Unhealthy Foods Differ Systematically across Income Levels and Continents. The Journal of Nutrition, 149(11): 2020–2033.

Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the econometric society, 153-161.

Hoffmann, R., & A. L. Kassouf (2005). Deriving conditional and unconditional marginal effects in log earnings equations estimated by Heckman's procedure. Applied Economics, 37(11), 1303-1311.

IMF (2020) World Economic Outlook, June 2020: A Crisis Like No Other, An Uncertain Recovery.

Jordá, O., S.R.R Singh & A.M. Taylor (2020). Longer-run economic consequences of pandemics, Technical Report, NBER, Working Paper No. 26934.

Levinson, A., & J. O’Brien (2019). Environmental Engel curves: Indirect emissions of common air pollutants. Review of Economics and Statistics, 101(1), 121-133.

Najera, H. & C. Huffman (2020). Estimación del costo de eliminar la pobreza extrema por ingreso en México en tiempos de COVID, Documento de trabajo, Programa de Estudios para el desarrollo, UNAM.

Nakamura, E., J. Steinsson & M. Liu (2016). Are Chinese growth and inflation too smooth? Evidence from Engel curves. American Economic Journal: Macroeconomics, 8(3), 113-44. DOI: 10.1257/mac.20150074

Pesaran, M.H. (2015). Time series and panel data econometrics, Oxford University Press.

Raddatz, C. (2009). The wrap of god: macroeconomic costs of natural disasters, Policy Research working paper, no. WPS 5039, Washington, D.C. Banco Mundial.

Sager, L. (2019). Income inequality and carbon consumption: Evidence from Environmental Engel curves. Energy Economics, 84, 104507.

World Bank (2020) La economía en los tiempo del COVID-19.