A Bivariate Probit Model for Testing Joint Effect of Affordability and Desirability to Connect Electricity in Mtwara and Pwani, Tanzania
Corresponding Author(s) : Amina Suleiman Msengwa
Journal of Humanities & Social Science (JHSS),
Vol. 9 No. 2 (2020): SPECIAL ISSUE OF SCIENCE, 2020
Abstract
The objective of this study is to determine perceptions of joint effect of
affordability and desirability to connect electricity in a household to propose an
appropriate advocacy strategy for the regions of Mtwara and Pwani in Tanzania.
Bivariate probit model is used to analyse data involving 162 households.
Constructed Likert Scales of delightful and wary perceptions on oil- and gas-based
on three questions with Likert response formats are used as part of the
explanatory set of variables in the model. Marginal mean effects of the bivariate
probit model indicate that wary perceptions on the use of gas are statistically
significant at the 5% level for joint affordability to connect and the desirability to
connect electricity in own household. Since perceptions are usually rooted in the
culture of a relevant community and can be nurtured through education and
general information gathering, it is recommended that appropriate advocacy
programmes be mounted in Pwani and Mtwara regions.
Keywords
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References
African Economic Outlook (AEO). 2016. Sustainable Cities and Structural Transformation, AfDB
/OECD /UNDP. OECD Publishing, Paris.
Ajzen, I. 2012. Martin Fishbein's Legacy: The Reasoned Action Approach. The Annals of the American
Academy of Political and Social Science, 640, 11-27. Retrieved April 21, 2020, from
www.jstor.org/stable/23218420
Campbell, B. S. Vermeulen, J. Mangono & R. Mabugu, 2003. The Energy Transition in Action: Urban
Domestic Fuel Choices in Urban Zimbabwe. Energy Policy, 553–562.
CDC Group Plc. 2016. What Are the Links Between Power, Economic Growth and Job Creation?.
Development Impact Evaluation: Evidence Review.
Cheng, Chao-Yo & J. Urpelainen, 2014. Fuel Stacking in India: Changes in the Cooking and Lighting
Mix, 1987–2010. Energy, Elsevier, 76(C): 306-317.
Chiappori, P. A., C. Meghir, 2014. Intra-Household Welfare. NBER Working Paper 20189.
Cronbach, L. 1951. Coefficient Alpha and the Internal Structure of Test. Psychometrika, 16: 297–334.
Dong, X., G. Pi, Z. Ma & C. Dong, 2017. The Reform of the Natural Gas Industry in the PR of China.
Renewable and Sustainable Energy Reviews, 73(February): 582–593.
Dyer, P., D. Gursoy, B. Sharma & J. Carter, 2007. Structural Modeling of Resident Perceptions of Tourism
and Associated Development on the Sunshine Coast, Australia. Tourism Management 28: 409 – 422.
Faes, C., H. Geys, M. Aerts, G. Molenberghs & P.J. Catalano, 2004. Modeling Combined Continuous
and Ordinal Outcomes in a Clustered Setting. JABES 9(4): 515-530.
Fitzmaurice, G. M. & N. M. Laird, 1995. Regression Models for a Bivariate Discrete and Continuous
Outcome With Clustering. Journal of the American Statistical Association, 90: 845–852.
Gajewski, B. J., D. K. Boyle & S. Thompson, 2010. How a Bayesian Might Estimate the Distribution
of Cronbach’s Alpha From Ordinal-Dynamic Scaled Data: A Case Study Measuring Nursing Home
Resident Quality of Life. Methodology: European Journal of Research Methods for the Behavioral and
Social Sciences, 6(2): 71–82.
Graham, Jm M. 2006), Congeneric and (Essentially) Tau-Equivalent Estimates of Score Reliability:
What They Are and How to Use Them. Educational and Psychological Measurement, Volume 66
Number 6, December 2006, pp. 930 -944.
Gueorguieva, R. V. & A. Agresti, 2001. A Correlated Probit Model for Joint Modeling of Clustered
Binary and Continuous Responses. Journal of the American Statistical Association, 96: 1102–1112.
Lipton, M. 1977. Why Poor People Stay Poor: Urban Bias in World Development. Cambridge, M A: Harvard
University Press.
Mac Kinnon, M. A., J. Brouwer & S. Samuelsen, 2018. The Role of Natural Gas and Its Infrastructure
in Mitigating Greenhouse Gas Emissions, Improving Regional Air Quality, and Renewable
Resource Integration. Progress in Energy and Combustion Science, 64: 62–92.
Marra, G. & R. Radice. 2011. Estimation of a Semiparametric Recursive Bivariate Probit Model in the
Presence of Endogeneity. Canadian Journal of Statistics, 39(2): 259–279.
Masera, O. R., B. D. Saatkamp & D. M. Kammen, 2000. From Linear Fuel Switching to Multiple
Cooking Strategies: A Critique and Alternative to the Energy Ladder Model. Journal of World
Development, 2083–2103.
Mullahy, J. 2011. Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome
Models, With Applications in Health Economics. National Bureau of Economics Research.
Working Paper 17588, Cambridge, MA 02138.
Nansaior, A., A. Patanothai, T. A. Rambo & S. Simaraks, 2011. Climbing the Energy Ladder Or
Diversifying Energy Sources? the Continuing Importance of Household Use of Biomass Energy
in Urbanizing Communities in Northeast Thailand. Biomass and Bioenergy: 4180–4188.
United Republic of Tanzania (URT). 2017. Energy Access Situation Report, 2016. Tanzania Mainland.
February 2017.
Yijun, L. & M. Li. 2016. Impacts of Low Oil Price on China and the World Natural Gas Industry Chain.
Natural Gas Industry, B3: 493–503.
Zhang, D. & Q. Ji, 2018. Further Evidence on the Debate of Oil-Gas Price Decoupling: A Long Memory
Approach. Energy Policy 113: 58–75.
Zheng, F. M. Leornard & S. Westra, 2015. Efficient Joint Probability Analysis of Flood Risk. Journal of
Hydroinformatics 17.4.