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Cash transfers and residential demand for electricity: insights from BISP, Pakistan

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Abstract

We examine the causal impacts of the cash transfer program, namely the Benazir Income Support Program (BISP), on residential demand for electricity among ultra-poor in Pakistan. We also analyze the effects of BISP cash transfers on a household’s decision to acquire electrical appliances. The empirical analysis is based on the fuzzy regression discontinuity design (RDD) using primary data collected from 1200 households. We find that BISP cash transfer has a significant positive impact on electricity demand among the target group. The cash transfer positively affects the use of few essential electric appliances, such as a washing machine and refrigerator, but not all electrical appliances. The electricity demand mainly stems from the additional use of existing electrical devices. Therefore, the extra income from BISP may not allow the recipients to move up the electric appliances ladder. The provincial analysis shows that the impact of BISP cash transfers on electricity demand varies across provinces and the development level, signifying the importance of regional heterogeneities, such as electricity supply. The findings suggest that cash transfers may facilitate the transition from traditional to modern energy to overcome the rising pollution problem and protect health. The expansion in the cash transfer program demands continuous investment in the power sector to fulfill the growing need for electricity.

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Data availability

The data and replication files are available on request from the corresponding author.

Notes

  1. A bulk of literature show that CTPs significantly reduce poverty and improve educational, health, and nutritional outcomes (Fiszbein and Schady 2009; Alatas et al. 2012; Banerjee et al. 2017; García and Saavedra 2017; Taaffe et al. 2017; WB 2017; GoP 2018; Alix-Garcia et al. 2019; Angeles et al. 2019; Cahyadi et al. 2019).

  2. The tariff differential subsidy is the difference between the electricity tariff including surcharges paid by the end consumers and the “allowable costs” of electricity utilities determined by the regulator, namely the National Electric Power Regulatory Authority (NEPRA). The tariff differential subsidy allows the government of Pakistan to implement an identical tariff structure across Pakistan and to allow subsidies to only deserving consumers (Walker et al. 2017).

  3. According to Household Integrated Economic Survey (HIES) 2018–2019, the average monthly income of bottom quintile (lowest 20%) is Rs. 23192 (in nominal terms) and per month stipend amount is Rs. 2000 (GoP 2020c). These figures reveal that BISP quarterly cash transfers, on average, constitutes around 8.5% of the total income of bottom quintile.

  4. Debnath et al. (2019) have also used stratified random sampling technique to collect household data to access the impact of rehabilitation program on appliances ownership and its implications on demand for residential electricity in India.

  5. Table 7 provides the list of districts along with population count and incidence of poverty.

  6. We over-sampled the households (1600 households) to achieve the desired sample size (1200 households), keeping in mind a 20% non-response rate. Each household was visited twice before declaring it as a non-response household. Enumerators were asked to record non-response households and those who refused to give the interview. Around 50 households were dropped from the main dataset at the cleaning stage due to non-response or refusal status. See Appendix Figure 10 for graphical representation of sampling framework.

  7. https://designer.mysurvey.solutions/Identity/Account/Login?ReturnUrl=%2F

  8. Various studies have used RDD to estimate the impact of BISP on key socio-economic indicators (Ambler and de Brauw 2017, 2019; GoP 2018; Mustafa et al. 2019; Nawaz and Iqbal 2020a)

  9. In RDD setting, the identification of control and treatment groups is based on cutoff point. The selection in the program can be probabilistic or deterministic (Hahn et al. 2001). The regression discontinuity follows a sharp design in case of deterministic selection while it follows a fuzzy design in case of probabilistic selection. The sharp RDD assumes perfect selection, i.e., eligible can only become beneficiary while ineligible could not. In case of fuzzy RDD, some eligible households might not get benefits while ineligible might get benefits. The fuzziness arises due to imperfect implementation, compliance, and procedural requirements. Earlier Ambler and de Brauw (2019) and Nawaz and Iqbal (2020a) use fuzzy RDD to assess the effect of BISP cash transfers on labor supply and fuel choices, respectively.

  10. The weak association between cash transfers and electricity demand with optimal bandwidth needs special consideration. One possible reason could be the small number of observations across the cutoff point while applying optimal bandwidth to estimate RDD ( shown in Table 5 and Appendix Table 12)

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Acknowledgments

The authors are thankful to anonymous referees for their valuable inputs to improve the quality of the paper. The view presented in the article is those of the authors. The authors are responsible for any errors. The authors are also thankful to the Benazir Income Support Program (BISP) for providing NSER data for sampling purposes.

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Authors and Affiliations

  1. Pakistan Institute of Development Economics (PIDE), Islamabad, Pakistan

    Nasir Iqbal

  2. COMSATS University Islamabad, Islamabad, Pakistan

    Saima Nawaz

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  1. Nasir Iqbal
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Contributions

Both authors contributed equally to this work.

Nasir Iqbal contributed by doing data analysis, conducting literature review, and drafting results and discussion sections along with the conclusion.

Saima Nawaz contributed by developing questionnaire, conceptualization study, and drafting methodology with sampling framework.

Corresponding author

Correspondence to Nasir Iqbal.

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The survey team took permission from respondents (sampled households) before starting an interview. Those households were interviewed, who gave consent to participate in the survey.

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The authors declare that they have no conflict of interest.

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Appendix

Appendix

Fig. 10
figure 10

Access to electricity connection across various income groups. Source: author’s calculation based on data given in GoP (2017a). Note: quintile analysis is based on household income given in the Household Integrated Economic Survey (HIES) 2015–2016 (GoP 2017a

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Fig. 11
figure 11

Access to electricity connection across regions. Author’s calculation based on data given in GoP (2017a). Note: see Appendix Fig. 10

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Fig. 12
figure 12

Access to electricity connection across provinces. Author’s calculation based on data given in GoP (2017a). Note: see Appendix Fig. 10

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Fig. 13
figure 13

Penetration of electric appliances across income groups and provinces. Source: author’s calculation based on data given in GoP (2017a). Note: see Appendix Fig. 10

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Fig. 14
figure 14

Penetration of electric appliances. Source: author’s formulation based on survey data

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Fig. 15
figure 15

Cattaneo RDD manipulation test (plot). Source: author’s formulation

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Fig. 16
figure 16

Impact of BISP on per capita electricity expenditure. Source: authors’ analysis of survey data

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Table 6 Access to electricity at the national and provincial level
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Table 7 List of districts along with population count and incidence of poverty
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Table 8 Household characteristics
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Table 9 Impact of BISP cash transfer on electric appliances: overall sample
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Table 10 Impact of BISP cash transfer on electric appliances: province wise analysis
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Table 11 Impact of BISP cash transfer on electric appliances: district wise analysis
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Table 12 Impact of BISP cash transfer on electricity expenditures
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Table 13 Sharpened q values
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Iqbal, N., Nawaz, S. Cash transfers and residential demand for electricity: insights from BISP, Pakistan. Environ Sci Pollut Res 28, 14401–14422 (2021). https://doi.org/10.1007/s11356-020-11384-w

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