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


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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  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.


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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


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).

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

JEL classification

  • D12
  • H31
  • Q41
  • Q48