Using machine learning to assess the livelihood impact of electricity access

  • Devarajan, S. Africa’s statistical tragedy. Rev. Income Wealth 59S9–S15 (2013).

    Article Google Scholar

  • Burke, M., Driscoll, A., Lobell, D. & Ermon, S. Using satellite imagery to understand and promote sustainable development. Science 371eabe8628 (2021).

    Article CAS PubMed Google Scholar

  • Yeh, C. et al. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nat. Commun. 112583 (2020).

    Article ADS CAS PubMed PubMed Central Google Scholar

  • Jean, N. et al. Combining satellite imagery and machine learning to predict poverty. Science 353790–794 (2016).

    Article ADS CAS PubMed Google Scholar

  • Chi, G., Fang, H., Chatterjee, S. & Blumenstock, JE Micro-estimates of wealth for all low- and middle-income countries. Proc. Natl Acad. Sci. USA 119e2113658119 (2022).

    Article PubMed PubMed Central Google Scholar

  • Steele, JE et al. Mapping poverty using mobile phone and satellite data. JR Soc. Interface 1420160690 (2017).

    Article PubMed PubMed Central Google Scholar

  • Pokhriyal, N. & Jacques, D. Combining disparate data sources for improved poverty prediction and mapping. Proc. Natl Acad. Sci. USA 114E9783–E9792 (2017).

    Article ADS CAS PubMed PubMed Central Google Scholar

  • Huang, L., Hsiang, S. & Gonzalez-Navarro, M. Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. Preprint at https://arxiv.org/abs/2104.11772 (2021).

  • The World Bank. Access to electricity (% of population)—Uganda. https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?locations=UG (2021).

  • International Energy Agency (IEA). World energy outlook 2019 (2019).

  • International Energy Agency (IEA). Africa energy outlook 2019 (2019).

  • Lenz, L., Munyehirw, A., Peters, J. & Seivert, M. Does large-scale infrastructure investment alleviate poverty? Impacts of Rwanda’s electricity access roll-out program. World Dev. 8988–110 (2017).

    Article Google Scholar

  • Chakravorty, U., Emerick, K. & Ravago, M.-L. Lighting up the last mile: the benefits and costs of extending electricity to the rural poor. Resources for the Future Discussion Paper 16–22 (2016).

  • Dinkelman, T. The effects of rural electrification on employment: new evidence from South Africa. Am. Econ. Rev. 1013078–3108 (2011).

    Article Google Scholar

  • Lee, K., Miguel, E. & Wolfram, C. Does household electrification supercharge economic development? J. Econ. Perspective. 34122–144 (2020).

    Article Google Scholar

  • Lee, K. et al. Electrification for “under grid” households in rural Kenya. Dev. Eng. 126–35 (2016).

    Article Google Scholar

  • Bayer, P., Kennedy, R., Yang, J. & Urpelainen, J. The need for impact evaluation in electricity access research. Energy Policy 137111099 (2020).

    Article Google Scholar

  • Bernard, T. Impact analysis of rural electrification projects in sub-Saharan Africa. World Bank Res. Obs. 2733–51 (2012).

    Article Google Scholar

  • Jaeger, DA, Joyce, TJ & Kaestne, R. A cautionary tale of evaluating identifying assumptions: did reality TV really cause a decline in teenage childbearing? J. Bus. Econ. Stat. 38317–326 (2020).

    Article MathSciNet Google Scholar

  • Kahn-Lang, A. & Lang, K. The promise and pitfalls of differences-in-differences: reflections on 16 and Pregnant and other applications. J. Bus. Econ. Stat. 38613–620 (2020).

    Article MathSciNet Google Scholar

  • Sahn, DE & Stifel, D. Exploring alternative measures of welfare in the absence of expenditure data. Rev. Income Wealth 49463–489 (2003).

    Article Google Scholar

  • Filmer, D. & Scott, K. Assessing asset indices. Demography 49359–392 (2012).

    Article PubMed Google Scholar

  • In He, K., Zhang, X., Ren, S. & Sun, J Proc. European Conference on Computer Vision – ECCV 2016 (eds Leibe, B., Matas, J., Sebe, N. & Welling, M.) 630–645 (2016).

  • Athey, S., Bayati, M., Doudchenko, N., Imbens, G. & Khosravi, K. Matrix completion methods for causal panel data models. J. Am. Stat. Assoc. 1161716–1730 (2021).

    Article MathSciNet CAS Google Scholar

  • Doudchenko, N. & Imbens, G. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. Preprint at https://arxiv.org/abs/1610.07748 (2016).

  • Jedwab, R. & Storeygard, A. The average and heterogeneous effects of transportation investments: evidence from Sub-Saharan Africa 1960–2010. J. Eur. Econ. Assoc. 201–38 (2022).

    Article Google Scholar

  • Uganda National Roads Authority. Connecting Uganda. https://www.unra.go.ug/home (2021).

  • Collins Bartholomew Ltd. Collins mobile coverage explorer (2014).

  • World Bank Group. Poverty maps of Uganda (2018).

  • World Bank Group. Uganda systematic country diagnosis: boosting inclusive growth and accelerating poverty reduction (2015).

  • Burlig, F. & Preonas, L. Out of the darkness and into the light? Development effects of rural electrification. Energy Inst. Haas WP 26826 (2016).

    Google Scholar

  • Lee, K., Miguel, E. & Wolfram, C. Experimental evidence on the economics of rural electrification. J. Polit. Econ. 1281523–1565 (2020).

    Article Google Scholar

  • Filmer, D. & Pritchett, LH Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography 38115–132 (2001).

    CAS PubMed Google Scholar

  • Omulo, G., Banadda, N. & Kiggundu, N. Harnessing banana ripening process for banana juice extraction in Uganda. Afr. J. Food Sci. 6108–117 (2015).

    Google Scholar

  • Ministry of Energy and Mineral Development. Uganda’s Sustainable Energy for All (SE4ALL) initiative action agenda (2015).

  • Ugandan Energy Sector GIS Working Group. Distribution lines operational (2016) (2017).

  • Kingma, DP & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2017).

  • OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org (2019).

  • Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econ. 225254–277 (2021).

    Article MathSciNet MATH Google Scholar

  • Callaway, B. & Sant’Anna, PHC Difference-in-differences with multiple time periods. J. Econ. 225200–230 (2021).

    Article MathSciNet MATH Google Scholar

  • Abadie, A., Diamond, A. & Hainmuelle, J. Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. J. Am. Stat. Assoc. 105493–505 (2010).

    Article MathSciNet CAS Google Scholar

  • Leave a Reply

    Your email address will not be published. Required fields are marked *