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Responsiveness of low-income households to hybrid price/non-price policies in the presence of energy shortages: evidence from Colombia

Abstract

At the beginning of 2016, Colombia was experiencing an energy shortage, and in order to avoid mandatory power cuts, the government launched an unexpected hybrid price/non-price energy-saving policy. In this paper, I evaluate how low-income households in a major Colombian city respond to this policy. Using hourly household electricity consumption data, I find that, on average, households reduce electricity consumption by 4.5% as a result of the policy. It is striking that even low-income households, who consume relatively small amounts of electricity, respond to energy-saving policies and engage in conservation behaviors in the short term. In my analysis, I also find that the effect is stronger the higher the household pre-treatment electricity consumption levels and smaller among poorer households. However, the heterogeneity in terms of income level vanishes once I control for household pre-program electricity consumption levels. Finally, my point estimates are comparable to the impact estimates of policies that are similar to the one I analyze in this paper.

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Notes

  1. Cali is the third largest city in Colombia, with a population of around 2,500,000 people.

  2. According to the World Bank, World Development Indicators (2017), Colombia has a population of 49,000,000 people and a GDP per capita (PPP) of 14,158 USD (2016), with 27.8% of the people living in poverty (2015) and high levels of inequality (a Gini coefficient of 53.5 (2014) ). The economic conditions in Cali, in terms of per capita income, poverty rates, and inequality levels, are similar to the country’s average.

  3. Technically, the benchmark corresponds to the amount of consumption reported in the last bill before March 7, 2016.

  4. Given that Colombia has an increasing two-part block price structure for low-income households, this incentive (disincentive) represents a different percentage of the actual price depending on the household’s socioeconomic group. The block pricing structure is explained in “Electricity supply in Cali.”

  5. Cali is a city as well as a municipality. In Colombia, cities or municipalities are divided into administrative areas or segments called comunas. This classification only applies to the urban areas of each municipality.

  6. Gran Encuesta Integrada de Hogares

  7. The average hourly consumption multiplied by 720 (24 × 30) in the case of monthly consumption, and by 8760 (24 × 365) in the case of annual consumption.

  8. EMCALI officials’ suggested explanation.

  9. The average hourly consumption multiplied by 24.

  10. Baraldi and Enders (2010)

  11. To calculate the total electricity consumption for two pre-treatment weeks, I take the difference between the “readings” from March 6 and February 21. In the same way, to calculate the consumption and for the two post-treatment weeks, I take the difference between the “readings” from March 20 and March 6. Therefore, I have two measures of total 2-week consumption: one measure for before and other measure for after the policy implementation day.

  12. The results are similar for all window sizes but I chose the 7-day window estimations because this is the smallest window size that has all the days of the week on each side of the policy implementation day.

  13. The hourly 2-week average consumption is the total consumption of the 2 weeks before (after) the policy implementation day, divided by 336 (14 × 24) days.

References

  • Agostini, C., et al. (2012). Residential Demand for Electric Energy in Chile, Journal Economía Chilena (The Chilean Economy), 15(3), 64-83

  • Alberini, A., & Miller, M. (2016). Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: evidence from U.S. data. Energy Policy, 97, 235–249.

    Article  Google Scholar 

  • Alberini, A., Gans, W., & Velez-Lopez, D. (2011). Residential consumption of gas and electricity in the U.S.: the role of prices and income. Energy Economics, 33(5), 870–881. https://doi.org/10.1016/j.eneco.2011.01.015.

    Article  Google Scholar 

  • Alcaldía de Cali, Cali en Cifras (2015). Retrieved from http://planeacion.cali.gov.co/caliencifras/Documentos%20pdf/Caliencifras2015.pdf. Accessed 2 Jan 2018.

  • Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95, 820–842.

    Article  Google Scholar 

  • Allcott, H., & Mullainathan, S. (2010). Behavior and energy policy. Science, 327(5970), 1204–1205. https://doi.org/10.1126/science.1180775.

    Article  Google Scholar 

  • Andor, M., et al. (2017). Social norms and energy conservation beyond the USA replication. Retrieved from https://www.econstor.eu/bitstream/10419/170701/1/1000718255.pdf. Accessed 2 Jan 2018.

  • Baraldi, A., & Enders, C. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(2010), 5–37. https://doi.org/10.1016/j.jsp.2009.10.001.

    Article  Google Scholar 

  • Delmelle, E., et al. (2016). A spatial model of socioeconomic and environmental determinants of dengue fever in Cali. Colombia: Acta Tropica.

  • Departamento Administrativo Nacional de Estadística, Gran Encuesta Integrada de Hogares - GEIH – (2016). Retrieved from https://formularios.dane.gov.co/Anda_4_1/index.php/catalog/427. Accessed 2 Jan 2018.

  • Filippini and Pachuari. (2004). Elasticities of electricity demand in urban Indian households. Energy Policy, 32(3), 429-436

  • Gillingham, et al. (2006). Energy efficiency policies: a retrospective examination. Annual Review of Environment and Resources, 31, 162–192.

  • IEA. (2005). Saving electricity in a hurry: strategies to deal with temporary shortfalls in electricity supplies. Paris: International Energy Agency.

    Google Scholar 

  • Ito, K., et al. (2017). The persistence of moral suasion and economic incentives: field experimental evidence from energy demand. American Economic Journal: Economic Policy. (forthcoming).

  • Jessoe, K., et al. (2014a). Knowledge is (less) power: experimental evidence from residential energy use. American Economic Review, 104(4), 1417–1438. https://doi.org/10.1257/aer.104.4.1417.

  • Jessoe, K., et al. (2014b). Towards understanding the role of price in residential electricity choices: evidence from a natural experiment. Journal of Economic Behavior and Organization, 107(A), 191–208. https://doi.org/10.1016/j.jebo.2014.03.009.

  • Little, R., & Rubin, D. (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: Wiley. https://doi.org/10.1002/9781119013563.

    Book  MATH  Google Scholar 

  • Maddock et al. (1992). “Estimating Electricity Demand: The Cost of Linearising the Budget Constraint,” The Review of Economics and Statistics, 74(2), 350–354.

  • McRae, S. (2015a). Efficiency and equity effects of electricity metering: evidence from Colombia, Working Paper, University of Michigan. Retrieved from http://www.sdmcrae.com/wp-content/uploads/unmetered.pdf. Accessed 2 Jan 2018.

  • McRae, S. (2015b). Infrastructure quality and the subsidy trap. American Economic Review, 105(1), 35–66. https://doi.org/10.1257/aer.20110572.

    MathSciNet  Article  Google Scholar 

  • Medina, C., & Morales, L. F. (2008). Demand for public utility services and deadweight loss of subsidies: the case of Colombia. Desarrollo y Sociedad, 1, 1–42.

  • Pasquier, S. B. (2011). Saving electricity in a hurry updates 2011, Information Paper. Paris, France: International Energy Agency.

  • Reiss, P., & White, M. (2008). What changes energy consumption? Prices and public pressure. RAND Journal of Economics, 39(3), 636–663. https://doi.org/10.1111/j.1756-2171.2008.00032.x.

    Article  Google Scholar 

  • Shi, G., et al. (2012). Estimating elasticity for residential electricity demand in China. Scientific World Journal.

  • The World Bank, World Development Indicators (2017). Retrieved from https://data.worldbank.org/data-catalog/world-development-indicators. Accessed 2 Jan 2018.

  • Wolfram, et al. (2012). How will energy demand develop in the developing world? Journal of Economic Perspectives, 26(1), 119–138. https://doi.org/10.1257/jep.26.1.119.

    Article  Google Scholar 

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Acknowledgements

I wish to thank “Empresas Municipales de Cali” (EMCALI) for granting me access to the data. I am grateful to Héctor Peña, Iván Sanclemente, Juan Guillermo Blanco, Leticia González, and Santiago Londoño for their invaluable help, as well as Adriana Fagua and Cristina Arango for the initial contact. Diego Bohórquez provided valuable information on Cali’s socioeconomic groups. I gratefully acknowledge the financial support from the Department of Agricultural and Resource Economics at the University of Maryland. Finally, I am especially grateful to Anna Alberini for her help, motivation, and guidance throughout this process.

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Correspondence to José M. Eguiguren-Cosmelli.

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This article belongs to the Topical Collection: Energy and Climate Economic Modelling

Guest Editors: Milan Ščasný and Anna Alberini

Appendices

Appendix 1. Example of electricity bill

Fig. 6
figure 6

Photo of a bill from EMCALI

Appendix 2. Alterative empirical approach

Another way to check my findings is to estimate the following regression for “T = 0” and “T = 1” separately,

$$ {e}_{iwdh}={\alpha}_{idh}+{X}_{wdh}\beta +{\varepsilon}_{iwdh} $$

Then, using the estimates from each period separately, I use data from “T = 1” to calculate the average of the predicted consumption of electricity. That is, I use data from “T = 1” and the estimates of the model at “T = 0” to predict \( {\widehat{e}}_{iwdh} \) and then calculate \( \overline{{\widehat{e}}_{wd}} \) for each “w” and “d” of “T = 1.” Then, I do the same but using the estimates of the model at “T = 1”. Finally, I plot them in Fig. 7, which shows that the predictions for “T = 1” using the model from “T = 0” are, for all the days after the policy implementation day, higher than the predictions for “T = 1” using the model from “T = 1.” Actually, the mean difference in the predictions is 0.0063 kWh while the median is 0.007 kWh, which are equivalent to a reduction in electricity consumption of 3.6 and 4%, respectively. These results are a little bit lower than the estimates from both of my empirical approaches, but completely in line with them.

Fig. 7
figure 7

Predicted consumption for each day after the treatment

Appendix 3. Additional figures and tables

Fig. 8
figure 8

Percentage changes by pre-consumption decile (I do not include the estimated percentage change for the first decile because, given the pre-treatment consumption was close to 0, it was so large)

Fig. 9
figure 9

Percentage changes by % of missing values

Fig. 10
figure 10

Number of light-days-hours

Fig. 11
figure 11

Temperature in Cali

Table 16 Estimated effect of the policy by income level

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Eguiguren-Cosmelli, J. Responsiveness of low-income households to hybrid price/non-price policies in the presence of energy shortages: evidence from Colombia. Energy Efficiency 11, 641–661 (2018). https://doi.org/10.1007/s12053-017-9595-3

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Keywords

  • Energy policy
  • Household behavior
  • Social norms
  • Colombia
  • Smart meters

JEL classification

  • D12
  • H31
  • Q41
  • Q48