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Self-Disconnection Among Pre-Payment Customers - A Behavioural Analysis

  • Brutscher, P.
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    In this paper, we revisit the problem of self-disconnection among prepayment energy customers. Using metering data from 2.3 million electricity pre-payment customers, we study how often households with an electricity pre-payment meter tend to self-disconnect over the course of a year - and why they do so. What we find is that, in any given year, the majority of households (ca. 78%) do not self-disconnect; ca. 12% self-disconnect once; ca. 3% selfdisconnect more often than four times. We also find that most selfdisconnections (ca. 62%) last for less than one day; between 72% and 82% last for less than two days; 12%-18% last for more than 3 days. As for the main driver of self-disconnection, we identify financial constraints. This suggests that it is likely to be difficult/expensive to reduce the total number of self-disconnections. In the last part of the paper, we argue, however, that it may (still) be possible to reduce the negative impact of self-disconnection in a relatively inexpensive way - at least to some extent - by helping households to better smooth their self disconnections over the course of a year.

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    File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe1214.pdf
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    Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 1214.

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    Date of creation: 21 Mar 2012
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    Handle: RePEc:cam:camdae:1214
    Contact details of provider: Web page: http://www.econ.cam.ac.uk/index.htm

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    1. Laibson, David I., 1997. "Golden Eggs and Hyperbolic Discounting," Scholarly Articles 4481499, Harvard University Department of Economics.
    2. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
    3. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    4. Wedel, M, et al, 1993. "A Latent Class Poisson Regression Model for Heterogeneous Count Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 397-411, Oct.-Dec..
    5. Ted O'Donoghue & Matthew Rabin, 2001. "Choice And Procrastination," The Quarterly Journal of Economics, MIT Press, vol. 116(1), pages 121-160, February.
    6. Paxson, C.H., 1991. "Consumption And Income Seasonality In Thailand," Papers 150, Princeton, Woodrow Wilson School - Development Studies.
    7. Dean Karlan & Nava Ashaf & Wesley Yin, 2006. "Deposit collectors," Natural Field Experiments 00205, The Field Experiments Website.
    8. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    9. Tversky, Amos & Kahneman, Daniel, 1986. "Rational Choice and the Framing of Decisions," The Journal of Business, University of Chicago Press, vol. 59(4), pages S251-78, October.
    10. Sokbae 'Simon' Lee, 2004. "Endogeneity in quantile regression models: a control function approach," CeMMAP working papers CWP08/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    12. Mullahy, John, 1997. "Heterogeneity, Excess Zeros, and the Structure of Count Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 337-50, May-June.
    13. Jonathan J. Morduch & Hall S. Stern, 1995. "Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh," Harvard Institute of Economic Research Working Papers 1728, Harvard - Institute of Economic Research.
    14. Gritz, R. Mark, 1993. "The impact of training on the frequency and duration of employment," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 21-51.
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