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Protection and the Determinants of Household Income in Tanzania 1991 – 2007

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  • Vincent Leyaro
  • Oliver Morrissey
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    Abstract

    This paper analyses the association between household characteristics – in particular size and location, and for the household head age, sector of employment (and the tariff applicable to that sector) and education - and household income using data from the Tanzania Household Budget Survey for the years 1991/92, 2000/01 and 2007. The static analysis of the determinants of household income is based on the full sample and is complemented by a dynamic analysis using a pseudo-panel (representative households). Larger households have lower income; living in urban areas is associated with income around one quarter higher than rural households; and location in the Coastal zone, which includes Dar es Salaam, increases household income by about 15% compared to the poorest region (Central). Years of education of the household head is associated with higher income: each additional year of education adds about 4.5%. Average incomes of agriculture households are lower than for manufacturing households, but within each broad sector incomes appear to be higher in sub-sectors with higher tariffs. Household income tends to increase in both tariffs and education, but the effect of tariffs diminishes or becomes negative for household heads with secondary education and alters over time. Observing that tariffs offer less protection to the incomes of more educated workers compared to less educated (less skilled) workers is consistent with better educated workers being more productive and therefore in firms, or sectors, better able to compete with imports. Given data limitations it would be incorrect to infer a causal effect of tariffs on household incomes. Nevertheless, the analysis is informative about the effect of the cross-sector pattern of tariff protection on household incomes allowing for other determinants.

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    File URL: http://www.nottingham.ac.uk/credit/documents/papers/10-03.pdf
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    Bibliographic Info

    Paper provided by University of Nottingham, CREDIT in its series Discussion Papers with number 10/03.

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    Handle: RePEc:not:notcre:10/03

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    Postal: School of Economics University of Nottingham University Park Nottingham NG7 2RD
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    Web page: http://www.nottingham.ac.uk/economics/
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    Keywords: Household Income; Sector Tariffs; Education; Tanzania.;

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    1. L. Alan Winters & Neil McCulloch & Andrew McKay, 2004. "Trade Liberalization and Poverty: The Evidence So Far," Journal of Economic Literature, American Economic Association, vol. 42(1), pages 72-115, March.
    2. Nicita, Alessandro, 2004. "Who benefited from trade liberalization in Mexico? Measuring the effects on household welfare," Policy Research Working Paper Series 3265, The World Bank.
    3. McKenzie, D.J.David J., 2004. "Asymptotic theory for heterogeneous dynamic pseudo-panels," Journal of Econometrics, Elsevier, vol. 120(2), pages 235-262, June.
    4. Ariel Barraud & German Calfat, 2008. "Poverty Effects from Trade Liberalisation in Argentina," Journal of Development Studies, Taylor & Francis Journals, vol. 44(3), pages 365-383.
    5. Vincent Leyaro & Oliver Morrissey & Trudy Owens, . "Food Price Changes and Consumer Welfare in Tanzania 1991 – 2007," Discussion Papers 10/01, University of Nottingham, CREDIT.
    6. Van der Walle, Dominique & Cratty, Dorothyjean, 2003. "Is the emerging nonfarm market economy the route out of poverty in Vietnam?," Policy Research Working Paper Series 2950, The World Bank.
    7. Eric V. Edmonds, 2007. "Child Labor," NBER Working Papers 12926, National Bureau of Economic Research, Inc.
    8. Mª Dolores Collado, 1998. "Estimating binary choice models from cohort data," Investigaciones Economicas, Fundación SEPI, vol. 22(2), pages 259-276, May.
    9. Ann Harrison, 2007. "Globalization and Poverty," NBER Books, National Bureau of Economic Research, Inc, number harr06-1, May.
    10. Reimer, Jeffrey J., 2002. "Estimating the poverty impacts of trade liberalization," Policy Research Working Paper Series 2790, The World Bank.
    11. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    12. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    13. Girma, Sourafel, 2000. "A quasi-differencing approach to dynamic modelling from a time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 98(2), pages 365-383, October.
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