Identifying the most deprived in rural Ethiopia and Uganda: a simple measure of socio-economic deprivation

ABSTRACT

The Extreme Deprivation Index uses easily verifiable answers to ten questions about the ownership of the most basic non-food wage goods – things that poor people in a variety of rural contexts want to have because they make a real difference to the quality of their lives. Using this Index, we define rural Ethiopians and Ugandans who lack access to a few basic consumer goods as ‘most deprived’: they are at risk of failing to achieve adequate education and nutrition; becoming pregnant as a teenager; remaining dependent on manual agricultural wage labour and failing to find to a decent job. As in other African countries, they have derived relatively little benefit from donor and government policies claiming to reduce poverty. They may continue to be ignored if the impact of policy on the bottom 10% can be obscured by fashionably complex indices of poverty. We emphasise the practical and political relevance of the simple un-weighted Deprivation Index: if interventions currently promoted by political leaders and aid officials can easily be shown to offer few or no benefits to the poorest rural people, then pressures to introduce new policies may intensify, or at least become less easy to ignore.

KEYWORDS:

• Ethiopia
• Uganda
• poverty
• index
• assets

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Christopher Cramer http://orcid.org/0000-0003-1792-7374

Notes

1. Wittenberg and Leibbrandt, “Measuring Inequality”.

2. Vaz and Alkire, “Measuring Autonomy”; O’Hara and Clement, “Power as Agency”; Shobhit, “The Gross Toilet Index”.

3. Ravallion, “Are the World's Poorest”.

4. Beegle et al, “Poverty in a Rising Africa,” 108

5. Kaiser et al, “Measuring Material Wealth,” 2.

6. Lesnoff, “Uncertainty Analysis”; Himelein et al., “Sampling Nomads”.

7. Filmer and Scott, “Assessing Asset Indices”.

8. Ngo and Christiaensen, “The Performance,” 2. The initial information required for Scorecards and other types of survey instrument, including the World Bank instrument known as the Survey of Well-Being via Instant and Frequent Tracking (SWIFT), is a very recent (and high cost) Household Expenditure Survey (Dang et al., “Data Gaps,” 24).

9. Ngo and Christiaensen, “The Performance”; McBride and Nichols, “Retooling Poverty Targeting”; Dang et al., “Data Gaps”. Some good reasons for mistrusting measures of poverty in Uganda using levels of expenditure per capita and derived from the Household Budget Surveys are discussed in Daniels and Minot, “Is Poverty Reduction Overstated”.

10. The full range of consumer goods examined, as well as details concerning the project’s methods, can be examined at: http://ftepr.org/

11. Pritchett and Spivack, “Estimating Income/Expenditure Differences”.

12. Tusting et al., “Measuring Socioeconomic Inequalities,” 651.

13. Ngo and Christiaensen, “The Performance,” 4.

14. Chai and Moneta, “Retrospectives: Engel Curves”; Anker, “Engel's Law”.

15. Black et al., “Maternal and Child Undernutrition”.

16. Haushofer and Shapiro, “Household Response,” 30.

17. Browne et al., “Expenditure Elasticities,” 571.

18. Pouw and Elbers, “Modelling Priority Patterns,” 1367.

19. The average data on the ownership of consumer goods suggests lower levels of deprivation in the Uganda sample, but some of the research sites in Uganda – the Kaweri and Ngomba coffee sites, for example – showed very low levels of ownership of consumer goods.

20. Kaiser et al., “Measuring Material Wealth,” 3.

21. The Bank grudgingly admits that in the Ethiopian context ‘the MPI does not fully reflect living standards’ (World Bank, “Ethiopia Poverty Assessment,” 34).

22. Deaton, “Measuring Poverty”.

23. Cramer et al., “How to Do,” 173–4 and 181. Recent LSMS-type surveys in Uganda and Ethiopia required an expenditure of about US $400 per surveyed household (Kilic et al., “Costing Household Surveys,” 21).

24. Jayne, “Africa's Changing Farmland”; Deininger, “Smallholders’ Land Ownership,” 16.

25. Earlier research has shown the limited ability of logistic regression to explain female vulnerability in rural Mozambique (Oya and Sender, “Divorced, Separated and Widowed”).

26. Casteneda et al., “Who Are the Poor,” 21.

27. ICF International (http://www.statcompiler.com).

28. Alderman and Headey, “How Important Is Parental Education,” 456.

29. Bado and Susuman, “Women's Education”.

30. On the relationship between mothers’ secondary education and stunting in Ethiopia see Ambel and Huang, “Maternal Education,” 14.

31. Keats, “Women's Schooling,” 23.

32. Tusting et al., “Measuring Socio-Economic Inequalities,” 653.

33. http://databank.worldbank.org/data/reports.aspx?source=Education-Statistics:-Education-Attainment&preview=off

34. Both of these surveys indicate that the rural adult illiteracy rate is higher in Ethiopia (about 60%) than in Uganda (about 30%).

35. Basole and Basu, “Non-food Expenditures”.

36. See http://www.epdc.org/.

37. Hernández, “Are Wealth Indices Good Predictors”; Herrador et al., “Low Dietary Diversity”; Hirvonen et al., “Seasonality and Household Diets”; Hirvonen et al., “Children's Diets”; Muhoozi et al., “Nutritional and Developmental Status”.

38. Milazzo and van de Walle, “Women Left Behind?”; Liu et al., “Female-Headed Households”.

39. FTEPR methods are described in detail in Cramer et al., “How to Do”.

40. Marshall et al., “Child Marriage”; Semahegn and Mengistie, “Domestic Violence”; Bantebya et al., “Adolescent Girls”.

41. The relevant coded answers to the question about the frequency of support were: regularly/often; sometimes; and never.

42. Sharp et al., “Destitution,” 56.

43. Saloojee and Coovadia, “Maternal Age Matters,” e342.

44. Pradhan and Canning, “The Effect of Schooling,” 1.

45. Van Campenhout, “Poverty and Its Dynamics,” 150.

46. Abebaw and Admassie, “Correlates of Extreme Poverty,” 127.

47. In Vietnam, LSMS results linking poverty with large households have been criticised in detail by Ngan et al., “Migration, Employment and Child Welfare”. In Bangladesh, the use of per capita expenditure has been shown to obscure dramatic changes in welfare/poverty captured by other indices and research (Davis and Baulch, “Parallel Realities,” 133).

48. DHS 2011: authors’ calculations.

49. Siyoum, “Broken Promises,” 50 et seq.

50. Palacios-Lopez et al., “How Much of the Labor,” 7.

51. Lowder et al., “Transformation in the Size”.

52. Davis et al., “Are African Households,” 161 and Table A2.

53. Tamru and Minten, “Value Addition and Processing,”8.

54. Central Statistical Authority and World Bank, “Ethiopia Rural Socioeconomic Survey,” 31.

55. Chiputwa, Spielman and Qaim, “Food Standards, Certification and Poverty,” 404; Chiputwa, “Old Request”.

56. In the USA after 1945, a surprisingly similar story of cumulative disadvantage – rooted in a labour market that provided inadequate job opportunities for people with low education – is discussed in Case and Deaton, “Mortality and Morbidity,” 29 et seq.

57. Ravallion, “Are the World's Poorest,” 7.

58. Ibid, 23.

59. World Bank, “Ethiopia Poverty Assessment,” 12 and 49.

60. Gebrehiwot and Castilla, “Do Safety Net Transfers,” 24–8.

61. Sharp et al., “Destitution,” 21.

62. Cochrane and Tamiru, “Ethiopia's Productive Safety Net,” 657; Roelen et al., “Evaluation,” 22.

63. Lefort, “Free Market Economy”.

64. Cochrane and Tamiru, “Ethiopia's Productive Safety Net,” 655.

65. Hickey and Bukenya, “The Politics,” 18.

66. Cramer et al., “Fairtrade Cooperatives”.

Additional information

Funding

The award of a Leverhulme Emeritus Fellowship (EM-2015-009) helped John Sender to complete this paper.