Analysis of Residential Lighting Fuel Choice in Kenya: Application of Multinomial Probability Models

Yong Jun Baek, Tae Yong Jung, Sung Jin Kang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Safe, clean, and affordable modern lighting services are crucial for improving the socio-economic welfare of the underprivileged people in developing countries. However, many of the Kenyan households are deprived of this service, and they continue to use traditional lighting devices to meet their lighting demand. It is essential to understand the determinants which influence the household energy choice to promote the household energy transition from traditional to modern lighting fuels. Therefore, this study examines the determinants of household lighting fuel choice with multinomial probability models using the survey data collected by the Kenya National Bureau of Statistics (KNBS) in 2015/16. The key findings of this study are as follows. First, the results of this study have empirically proven the energy ladder hypothesis as the probability of choosing modern lighting fuel increases with a female household head, and with improvements in income, wealth and education. The energy ladder hypothesis has been confirmed in both cases of the household with and without the choice of grid electricity. Second, different socio-economic determinants for on- and off-grid household fuel choice are identified, which are the location of household, marital status, and household size. This is an important finding which shows that different policy designs are required to promote energy transition in on- and off-grid households.

Original languageEnglish
Article number70
JournalFrontiers in Energy Research
Publication statusPublished - 2020 May 5

Bibliographical note

Publisher Copyright:
© Copyright © 2020 Baek, Jung and Kang.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Economics and Econometrics


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