4. Urbanization and intergenerational mobility in Ethiopia
4.3. Results and discussion
4.3.2. Intergenerational mobility and urbanization
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In column 5 of Table A4.4, when children’s own education levels are controlled for, the parameter estimates related to parental occupation decline even further and the coefficient of skilled wage becomes statistically insignificant. This suggests that parents employed in better-paying occupations enhance employment opportunities for their children via investment in their education. Indeed, Table A4.9 in the Appendix shows that there is a strong positive correlation between the quality of parental employment and investment into children’s education – both in absolute terms and relative to total household expenditure. Lastly, the survey round dummy for 2014 is statistically insignificant. Together with the positive result in Table A4.3 for education, this indicates that while there was an improvement in educational status between 2014 and 2016, this did not translate into better occupational status.
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Table 4.4 Mobility in educational and occupational status by urbanization status
Urbanization status a) Total Rural Small towns Large towns
Panel A: Mobility in educational status b)
% in lower level than parents 14.2 12.9 12.6 16.8
% in the same level as parents 57.4 56.9 59.3 57.3
% in higher level than parents 28.4 30.2 28.1 25.9
Panel B: Mobility in occupational status c)
% in lower level than parents 8.4 4.3 6.2 14.5
% in the same level as parents 71.7 80.1 78.0 58.7
% in higher level than parents 19.9 15.7 15.9 26.8
Source: Author’s calculation based on LSMS-ISA (2014 & 2016)
Notes: a) Sum of the NTL at EA level is used to classify the households from rural (tercile with the smallest light intensity) to large towns (tercile with the highest light intensity). b) The first row is the percentage of children in a lower educational attainment group than their parents, and similarly for the second and third rows. C) The first row is the percentage of children in a lower occupational group than their parents, and similarly for the second and third rows.
Next, we re-estimate the basic multivariate model in equation 4.1 by disaggregating the sample households into rural, small towns, and large towns. Table 4.5 and Table 4.6 report extracted marginal effects from the ordered logit model for educational and occupational status, respectively. The results presented in Table 4.5 suggest that regardless of the location of residence, there is considerable persistence in educational status across generations. Compared to parents with no formal education, children from parents with some formal education are significantly more likely to attain higher educational status. However, inequality is much more pervasive in urban areas, particularly in large urban areas, than in rural areas. For instance, in rural areas, a child of parents with tertiary education is 25.4 percent more likely to attain tertiary education than a child of parents with no formal education. The corresponding figure in small and large urban areas is 36.8 percent and 41.2 percent, respectively. Similarly, a child of parents with secondary education is more than twice more likely to attain tertiary education in large urban areas than in rural or small towns. This suggests that urban areas tend to exacerbate rather than abate inequality in access to education. Since wellbeing and educational status are highly correlated, this leads to the further burgeoning of rural-urban as well as intra-urban economic inequality.
Unequal schooling attainment and increasing return to the level of education and skills in large urban areas are two key components of income inequality in developing countries (Autor 2020;
Glaeser 2020; Binder and Woodruff 2002).
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Table 4.5. Mobility in educational status, marginal effects, by urbanization status
(1) (2) (3) (4)
Child\Parent Education
No education
Elementary education
Secondary education
Tertiary education Panel A: Rural Areas a)
No education [Reference] -0.240*** -0.444*** -0.559***
(0.012) (0.019) (0.010)
Elementary education [Reference] 0.155*** 0.175*** -0.020
(0.007) (0.010) (0.034)
Secondary education [Reference] 0.067*** 0.194*** 0.325***
(0.005) (0.017) (0.016)
Tertiary education [Reference] 0.018*** 0.075*** 0.254***
(0.002) (0.010) (0.033)
Observations 17,351 17,351 17,351
Panel B: Small Towns
No education [Reference] -0.252*** -0.496*** -0.596***
(0.018) (0.027) (0.013)
Elementary education [Reference] 0.162*** 0.151*** -0.111**
(0.011) (0.024) (0.049)
Secondary education [Reference] 0.073*** 0.245*** 0.339***
(0.007) (0.030) (0.021)
Tertiary education [Reference] 0.018*** 0.099*** 0.368***
(0.003) (0.020) (0.064)
Observations 5,770 5,770 5,770
Panel B: Large Towns
No education [Reference] -0.246*** -0.411*** -0.490***
(0.014) (0.014) (0.011)
Elementary education [Reference] 0.096*** 0.022** -0.175***
(0.008) (0.009) (0.016)
Secondary education [Reference] 0.104*** 0.228*** 0.254***
(0.006) (0.011) (0.009)
Tertiary education [Reference] 0.045*** 0.161*** 0.412***
(0.003) (0.009) (0.021)
Observations 12,764 12,764 12,764
Source: Author’s calculation based on LSMS-ISA (2014 & 2016)
Note: Standard errors clustered at the household level in parentheses. Statistical significance indicated by: *** p<0.01,
** p<0.05, * p<0.1; a ) Thesum of the NTL at the EA level is used to classify the households from rural (tercile with the smallest light intensity) to large towns (tercile represents the highest light intensity).
The multivariate ordered logit regression result for occupation across rural-urban areas is presented in Table A4.5 (coefficients) and Table 4.6 (marginal effects). Table A4.5 shows that, for large urban areas, once educational attainment is accounted for, parental occupation ceases to be significantly associated with individual occupation levels. For rural and small-town sub-samples, only parental occupation in self-employment is associated significantly with individual occupations. This observation indicates that the strong child-parent correlation in occupation, shown in Table 4.4, is being mediated by children’s education69. That is, parents employed in better-paying occupations enhance employment opportunities for their children via investment in education of their children. Indeed, Table A4.6 in the appendix shows that there is a strong positive correlation between the quality of parental employment and investment into children’s education – both in absolute terms and relative to total household expenditure.
69 Note that individuals’ education levels, especially post-secondary level education, appear statistically significant across all levels of urbanization (Table A4.5).
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Table 4.6 representing the marginal effect extracted from the basic model, further corroborates the results reported above. Once individual education level is accounted for, large urban areas offer better mobility in occupational status, as compared to rural areas and small towns. This result has huge policy implications. It suggests that policy interventions that effectively address inequality in access to educational opportunities in urban areas might help to address both the inequality in welfare and inefficiency in the labor market. Since the level of education is an important determinant of occupational status (see Table A4.5) and productivity (Barro 2001;
Becker 1994), raising the average schooling of disadvantaged individuals and backward regions should indeed reduce inequalities in welfare and inefficiency in the labor market. For similar findings elsewhere, see (World Bank, 2009; World Bank, 2011).
Table 4.6. Mobility in occupational status, marginal effects, by urbanization status
(1) (2) (3) (4)
Child\Parent Occupation Elementary occupation
Unskilled wage
Self- Employment
Skilled Wage
Panel A: Rural Areas a )
Elementary occupation [Reference] -0.080** -0.101*** -0.027
(0.040) (0.023) (0.051)
Unskilled wage [Reference] 0.034** 0.042*** 0.012
(0.016) (0.009) (0.022)
Self-Employment [Reference] 0.035* 0.045*** 0.012
(0.018) (0.011) (0.022)
Skilled Wage [Reference] 0.010* 0.013*** 0.003
(0.006) (0.003) (0.006)
Observations 13,217 13,217 13,217
Panel B: Small Towns
Elementary occupation [Reference] 0.049 -0.175*** 0.035
(0.041) (0.032) (0.098)
Unskilled wage [Reference] -0.019 0.053*** -0.013
(0.016) (0.009) (0.039)
Self-Employment [Reference] -0.024 0.093*** -0.017
(0.020) (0.018) (0.048)
Skilled Wage [Reference] -0.006 0.029*** -0.004
(0.005) (0.007) (0.011)
Observations 4,585 4,585 4,585
Panel B: Large Towns
Elementary occupation [Reference] -0.085*** -0.071*** -0.152***
(0.020) (0.013) (0.047)
Unskilled wage [Reference] 0.023*** 0.020*** 0.036***
(0.005) (0.004) (0.008)
Self-Employment [Reference] 0.031*** 0.026*** 0.055***
(0.007) (0.005) (0.017)
Skilled Wage [Reference] 0.031*** 0.025*** 0.061***
(0.008) (0.005) (0.023)
Observations 10,689 10,689 10,689
Source: Author’s calculation based on LSMS-ISA (2014 & 2016)
Note: Standard errors clustered at the household level in parentheses. Statistical significance indicated by: *** p<0.01,
** p<0.05, * p<0.1; a ) The sum of the NTL at the EA level is used to classify the households from rural (tercile with the smallest light intensity) to large towns (tercile represents the highest light intensity).
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