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Relative Effects of Income and Consumption Poverty on Time Poverty in Ghana

Abstract

One strand of the literature suggests that higher income individuals are less susceptible to time poverty because they can afford to hire others to assist them with household duties, while another strand postulates a potential trade-off between income poverty and time poverty. This study examined the relationship between time poverty and income/consumption poverty among households in Ghana, using data from three Ghana Living Standard Surveys. The descriptive analysis showed that while time poverty has declined since the 1998/1999 survey period, it is still more prevalent among women, urban residents, those with low levels of education and the non-poor. The regression estimates confirm the trade-off hypothesis, but the subsample analysis across gender and periods of the survey reveals some element of the counter-argument to the trade-off hypothesis among females. The Ministry of Employment and Labour Relations should collaborate with other allied bodies such as the Ghana Employers Association and trade unions to design labour market policies that will create flexible work conditions for especially time-poor women. Such policies should prioritize early childhood education in public schools, promote the provision of onsite day care services by employers, and the incentive to use public transport to ease time-consuming car travel in congested cities.

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Fig. 1

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 2

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 3

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 4

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 5

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 6

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Fig. 7

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Notes

  1. 1.

    The concept of feminisation of poverty means one or a combination of the following three hypotheses: (1) there is a higher incidence of poverty among women than among men; (2) women’s poverty is more severe than men’s; and (3) over time, the incidence of poverty among women is increasing compared to men (Cagatay 1998).

  2. 2.

    See the works of Mothersbaugh et al. (1993) and Hamermesh and Lee (2007) for discussions on how the subjective approach has been applied to assess the effect of perceived time pressure on adherence to recommended dietary practices (RDPs), and how time stress varies by gender.

  3. 3.

    Note that in the regression analysis, education was categorised into none, basic education and secondary plus. This was because the test of the statistical difference in the extent of the effect of secondary and other, higher levels of education was not significant. It was therefore statistically intuitive to combine the two categories into a single category.

  4. 4.

    A detailed explanation of and distinction between organic solidarity and mechanical solidarity can be found in the study by Beck (2015).

  5. 5.

    The three dummy variables, which took on the value of 1 if a survey was conducted in a particular year and 0 if otherwise, were included in the analysis. The survey years were 1998/1999 (GLSS4), 2005/2006 (GLSS5) and 2012/2013 (GLSS6). To avoid a potential dummy variable trap, the year 1998/1999 was kept as a reference category.

References

  1. Angrist, J., & Hahn, J. (2004). When to control for covariates? Panel asymptotics for estimates of treatment effects. Review of Economics and Statistics,86(1), 58–72.

  2. Arora, D. (2015). Gender differences in time-poverty in rural Mozambique. Review of Social Economy,73(2), 196–221.

  3. Asmah, E. E., & Orkoh, E. (2017). Self-care knowledge of hypertension prevention and control among women in Contemporary Ghana. American Journal of Health Education,48(6), 374–381.

  4. Bardasi, E., & Wodon, Q. (2010). Working long hours and having no choice: Time poverty in Guinea. Feminist Economics,16(3), 45–78.

  5. Beck, U. (2015). The reinvention of politics: Rethinking modernity in the global social order. New York: Wiley.

  6. Bhattacharya, J., Goldman, D., & McCaffrey, D. (2006). Estimating probit models with self-selected treatments. Statistics in Medicine,25(3), 389–413.

  7. Blackden, M., & Wodon, Q. (2006). Gender, time use, and poverty in sub-Saharan Africa (Vol. 73). Washington, DC: World Bank Publications.

  8. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association,90(430), 443–450.

  9. Burchardt, T. (2008). Time and income poverty. Centre for Analysis of Social Exclusion: London School of Economics.

  10. Cagatay, N. (1998). Gender and poverty. Social Development and Poverty Elimination Division. Working Paper Series 6. New York: UNDP.

  11. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge: Cambridge University Press.

  12. Chatzitheochari, S., & Arber, S. (2012). Class, gender and time poverty: A time-use analysis of British workers’ free time resources. The British Journal of Sociology,63(3), 451–471.

  13. Chen, Y., Jaupart, P., Moreno-Monroy, A., & Picarelli, N. (2017). Unequal commutes. Job accessibility & employment in Accra. International Growth Centre (IGC). Retrieved February 2, 2019 from https://www.theigc.org.

  14. Costa, J., Hailu, D., Silva, E., & Tsukada, R. (2009). The implications of water and electricity supply for the time allocation of women in rural Ghana. Working Paper Number 59, International Policy Centre for Inclusive Growth (IPC-IG).

  15. Coudouel, A., Hentschel, J. S., & Wodon, Q. T. (2002). Poverty measurement and analysis. In J. Klugman (Eds.), A sourcebook for poverty reduction strategies (Vol. 1, pp. 27–74). Washington, DC: World Bank Group.

  16. Ferrant, G. (2015). Time use as a transformative indicator for gender equality in the post-2015 agenda. Paris: OECD Development Centre. Retrieved August 20, 2018 from https://oecd.org/dev/poverty/Time%20use%20_final_2014.pdf.

  17. Filippini, M., Greene, W. H., Kumar, N., & Martinez-Cruz, A. L. (2017). A note on the different interpretation of the correlation parameters in the Bivariate Probit and the Recursive Bivariate Probit. Economics Letters,167, 104–107.

  18. Floro, M. S. (1995). Women’s well-being, poverty, and work intensity. Feminist Economics,1(3), 1–25.

  19. Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica: Journal of the Econometric Society, 52(3), 761–766.

  20. Gammage, S. (2006). A menu of options for intra-household poverty assessment. Washington, DC: United States Agency for International Development (USAID).

  21. Gammage, S. (2010). Time pressed and time poor: Unpaid household work in Guatemala. Feminist Economics,16(3), 79–112.

  22. Ghana Statistical Service. (2000). Ghana Living Standard Survey. Report of the Fourth Round (GLSS4). Ghana Statistical Service. http://www.statsghana.gov.gh/nada/index.php/catalog/14. Accessed 20 June 2017.

  23. Ghana Statistical Service. (2007). Pattern and trends of poverty in Ghana 1991‒2006. http://s3.amazonaws.com. Accessed 20 June 2017.

  24. Ghana Statistical Service. (2012a). 2010 Population & housing census: Summary report of final results. Ghana Statistical Service. http://www.statsghana.gov.gh/docfiles/2010phc/Census2010_Summary_report_of_final_results.pdf. Accessed 20 June 2017.

  25. Ghana Statistical Service. (2012b). How Ghanaian women and men spend their time. Ghana time-use survey 2009. Main report. http://www.statsghana.gov.gh/nada/index.php/catalog/53/related_materials. Accessed 20 June 2017.

  26. Ghana Statistical Service. (2014a). Ghana Living Standard Survey Round 6 (GLSS6). Poverty profile in Ghana (2005‒2013). Accra: Ghana Statistical Service. http://www.statsghana.gov.gh/nada/index.php/catalog/72. Accessed 20 June 2017.

  27. Ghana Statistical Service. (2014b). Ghana Living Standard Survey Round 6 (GLSS 6): Main report. http://www.statsghana.gov.gh/nada/index.php/catalog/72. Accessed 20 June 2017.

  28. Hamermesh, D. S., & Lee, J. (2007). Stressed out on four continents: Time crunch or yuppie kvetch? The Review of Economics and Statistics,89(2), 374–383.

  29. Harvey, A. S., & Mukhopadhyay, A. K. (2007). When twenty-four hours is not enough: Time poverty of working parents. Social Indicators Research,82(1), 57–77.

  30. Ilahi, N. (2000). The intra-household allocation of time and tasks: What have we learnt from the empirical literature? Policy Research Report on Gender and Development. Working Paper Series No 13. Washington, DC: World Bank.

  31. Kalenkoski, C. M., Hamrick, K. S., & Andrews, M. (2011). Time poverty thresholds and rates for the US population. Social Indicators Research,104(1), 129–155.

  32. Kizilirmak, B., & Memis, E. (2009). The unequal burden of poverty on time use. Working Paper 572. Annandale-on-Hudson, NY: The Levy Economics Institute of Bard College.

  33. Lawson, D. (2007). A gendered analysis of time poverty: The importance of infrastructure. UK, Economic and Social Research Council Global Poverty Research Group Working Paper GPRG-WPS-078.

  34. Levin, C. E., Ruel, M. T., Morris, S. S., Maxwell, D. G., Armar-Klemesu, M., & Ahiadeke, C. (1999). Working women in an urban setting: Traders, vendors and food security in Accra. World Development,27(11), 1977–1991.

  35. Marra, G., Papageorgiou, G., & Radice, R. (2013). Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing. Australian & New Zealand Journal of Statistics,55(3), 321–342.

  36. Moe, K. (2008). Women, family, and work: Writings on the economics of gender. New York: Wiley.

  37. Morris, S. (2007). The impact of obesity on employment. Labour Economics,14(3), 413–433.

  38. Mothersbaugh, D. L., Herrmann, R. O., & Warland, R. H. (1993). Perceived time pressure and recommended dietary practices: The moderating effect of knowledge of nutrition. The Journal of Consumer Affairs,27(1), 106–126.

  39. Mullahy, J., & Sindelar, J. L. (1994). Alcoholism and income: The role of indirect effects. The Milbank Quarterly, 72(2), 359–375.

  40. National Development Planning Commission. (2015). Ghana Millennium Development Goals 2015 report. Retrieved February 11, 2018 from http://www.gh.undp.org/content/ghana/en/home/library/poverty/2015-ghana-millennium-development-goals-report.html.

  41. Noh, H., & Kim, K. (2015). Revisiting the ‘feminisation of poverty’ in Korea: Focused on time use and time poverty. Asia Pacific Journal of Social Work and Development,25(2), 96–110.

  42. Orkoh, E. (2018). Gender welfare effects of regional trade integration on households in Ghana. In World Bank Group & World Trade Organization (Eds.), Trade and poverty reduction: New evidence of impacts in developing countries (pp. 36–57). Geneva: World Trade Organization.

  43. Parra, J. C., & Wodon, Q. (2010). Gender, time use, and labor income in Guinea: Micro and macro analyses. Munich: University Library of Munich.

  44. Ribeiro, L. L., & Marinho, E. (2012). Time poverty in Brazil: Measurement and analysis of its determinants. Estudos Econômicos (São Paulo),42(2), 285–306.

  45. Rogers, B. L., & Schlossman, N. P. (1990). Intra-household resource allocation: issues and methods for development policy and planningPapers prepared for the workshop on methods of measuring intra-household resource allocation, Gloucester, Massachusetts, USA, October 1983 (Vol. 15). United Nations University Press.

  46. Spinney, J., & Millward, H. (2010). Time and money: A new look at poverty and the barriers to physical activity in Canada. Social Indicators Research,99(2), 341–356.

  47. Theodoropoulos, H. (1999). Review of the rural/urban problem from an interdisciplinary perspective. Prospettive e proposte mediterranee-Rivista di Economia, Agricoltura e Ambiente, (3), 24–31.

  48. Udry, C., & Woo, H. (2007). Households and the social organization of consumption in southern Ghana. African Studies Review,50(2), 139–153.

  49. Vickery, C. (1977). The time-poor: A new look at poverty. Journal of Human Resources, 12(1), 27–48.

  50. Wodon, Q., & Bardasi, E. (2006). Measuring time poverty and analyzing its determinants: Concepts and application to Guinea. Economics Bulletin, AccessEcon,10(12), 1–7.

  51. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge, MA: MIT Press.

  52. World Bank. (2012). World Development Report 2012: Gender equality and development. Washington, DC: World Bank.

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Correspondence to Emmanuel Orkoh.

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Appendix: Descriptive Statistics of Inequality in Time Poverty

Appendix: Descriptive Statistics of Inequality in Time Poverty

See Figs. 8, 9, 10, 11 and Tables 6, 7.

Fig. 8
figure8

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Poverty gap and severity of time poverty across years of survey.

Fig. 9
figure9

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Gap and severity of time poverty by gender and geographical location.

Fig. 10
figure10

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Gap and severity of time poverty across level of education.

Fig. 11
figure11

Source: Prepared by the authors using Ghana Living Standard Survey (GLSS) datasets

Gap and severity of time poverty by ethnic affiliation of respondent.

Table 6 Pooled sample estimates of time poverty by geographical location.
Table 7 Estimates of time poverty by survey year, poverty indicator and geographical location.

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Orkoh, E., Blaauw, P.F. & Claassen, C. Relative Effects of Income and Consumption Poverty on Time Poverty in Ghana. Soc Indic Res 147, 465–499 (2020) doi:10.1007/s11205-019-02158-0

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Keywords

  • Income poverty
  • Consumption poverty
  • Time poverty
  • Recursive bivariate regression