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Relative Economic Position and Subjective Well-Being in a Poor Society: Does Relative Position Indicator Matter?

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Abstract

Concern for relative income (or status in general) may have important implications for poverty and individual well-being. This paper examines the impact of relative economic position on individual’s level of well-being among poor communities in rural Ethiopia. The analysis uses a self-reported measure of overall life-evaluation as a measure of individual well-being. Despite the fact that well-being is multidimensional, the impact of non-money metric measures of relative economic position on individual well-being has not been given a lot of attention in the literature. In this study, relative economic position is measured using consumption data, asset index, and respondent’s own perception of relative wealth. The asset index captures the non-monetary dimensions of economic welfare, including education, physical assets, and social capital. We use data from the 2004 and 2009 waves of the Ethiopia Rural Household Survey and employ a multilevel modelling technique to account for individual and group level heterogeneity in our empirical analysis. We find no significant relationship between individual well-being and relative economic position measured with in consumption terms. In contrast, we do find a significant negative impact of relative position on individual well-being when we use asset indices and respondent’s own perception of relative wealth to measure relative economic position. Our findings suggest that when individuals compare themselves with others, they evaluate various aspects of their life, including their financial conditions, asset holdings, and social relations, which are hardly captured by consumption or income data in many poor countries.

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Notes

  1. The classic early references are Smith (1776), Marx (1849), and Veblen (1899) (as cited in Heffetz and Frank 2008). Other studies also modelled the interdependency of individual preference functions (see eg. Scitovsky 1976; Frank 1985; Leibenstein 1950; Pollak 1976; Neumark and Postlewaite 1998; Clark and Oswald 1998).

  2. Using panel data from the UK, Brown et al. (2015) study also show that positive effect of relative position is observed when spatial reference groups are used in place of individual characteristics such as age groups. Brown et al. (2015), however, argue that the positive effect of relative income may well have been area wealth effects.

  3. The survey was conducted by the Economics Department of Addis Ababa University and the Centre for the Study of African Economies at Oxford University. We thank the Centre for African Studies for providing us with the data and related documents. The data set is available at: http://www.ifpri.org/dataset/ethiopian-rural-household-surveys-erhs.

  4. For each household, different livestock types transformed into a standard livestock unit known as a Tropical Livestock Unit (TLU).

  5. Birr is Ethiopian currency. 1 USD = 8.31 ETB in 2004, while 1 ETB = 8.96 USD in 2009.

  6. Results from the MCA show that the first dimension explains about 83% of the observed inertia in 2004 and 89.9% in 2009 (results not presented here). Thus, the first dimension explains a large proportion of the variance in the set of variables indicating the non-monetary welfare. Based on the first dimension poor households can be identified as those households with no educated household member, have small land and livestock holdings. They do not have assets such as radio and farming equipment (hoe and plough). They were not able to store crops from the previous harvest, have experienced food shortages, and they do not have access to cash if they need it. In contrast, rich households have family members with higher education, have more land and livestock, have assets (radio and plough), able to store crops from the previous harvest, can get access to cash if they need it, and they do participate in social networks (equb, edir, and labour sharing arrangements).

  7. We combine code 7 with code 6, and combine code 1 and code 2 due to the very low number of observations for codes 7 and 1. For ease of interpretation, the scaling here is reversed from 1 (poor) to 5(rich).

  8. The magnitude of the ICC at the village level in our study is similar to those observed by other related studies. However, comparing ICC estimates across different studies might be a bit tricky as different studies use different levels of analyses (most use two-level modelling). In addition, estimates of ICC could vary across different studies due to variations in the extent of sample homogeneity. For instance, using a three-level multilevel modelling (i.e. individuals nested in households and households nested in districts) a study by Ballas and Tranmer (2012) find that the proportion of the district level variance from the total variance for individuals SWB measure is 9.4%, while the figure is 14.63 and 84.44% for household and individual level variances. The corresponding figures for a happiness measure are 0, 8.2, and 91.8% respectively. Using a two-level multilevel modelling (individuals nested in countries), Novak and Pahor (2017) find that the proportion of the variance due to the level 2 variable (country) is 7.6%. Likewise, using a two-level multilevel modelling (individuals nested within country-period combinations) Rözer and Kraaykamp (2013) find that the ICC at the country-year level is 18.1%.

  9. Since both the direction of the effect and significance of the variables are similar in the multilevel linear mixed-effects model and the multilevel mixed-effects ordered probit model estimates we report estimation results from the multilevel mixed-effects ordered probit regression only based on Model 5 specification (Table 5 in the “Appendix”). Table 5 in the “Appendix” also presents estimation results using a pooled OLS and pooled ordered probit approaches with cluster-standard errors (CRSEs) that are adjusted at village levels.

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Acknowledgements

Muna Shifa would like to thank the National Research Foundation (NRF) for funding her post-doctoral research. Murray Leibbrandt acknowledges the Research Chairs Initiative of the South African National Research Foundation and the South African Department of Science and Technology for funding his work as the Research Chair in Poverty and Inequality.

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  1. Southern Africa Labour and Development Research Unit, School of Economics, University of Cape Town, Rondebosch, Cape Town, 7700, South Africa

    Muna Shifa & Murray Leibbrandt

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  1. Muna Shifa
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Correspondence to Muna Shifa.

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Appendix

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Table 5 Determinants of subjective well-being
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Shifa, M., Leibbrandt, M. Relative Economic Position and Subjective Well-Being in a Poor Society: Does Relative Position Indicator Matter?. Soc Indic Res 139, 611–630 (2018). https://doi.org/10.1007/s11205-017-1739-5

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