3. Heterogeneous effect of urban proximity on nutritional outcomes
3.6. Mechanisms
In the previous sections, we showed that an increase in the proximity to urban areas (as measured by lower transportation cost) and the size of the nearest urban areas lead to an improvement in nutritional status. In this section, we highlight the major mechanisms that underpin this basic finding. First, the descriptive result in Table 3.1 shows that, on average, large-town households are wealthier and more educated than small-town households. Furthermore, Figure 3.3 shows that both wealth and level of education decline along the transportation cost gradient. While we control for the direct effect of both of these factors in all the regressions, the indirect effects might still explain the basic result. Empirical studies conducted in low-income countries indicate that differences in household wealth are the single most important explanatory factor of differences in health and nutrition outcomes (Headey et al. 2015; Headey, Hoddinott, et al. 2017).
Panel A: wealth index by transportation cost Panel B: education level by transportation cost
Figure 3.3. Wealth index and education level by transportation cost
Source: Authors’ computation based on LSMS Survey (2012, 2014, and 2016)
Other complementary channels that might underlie the spatial pattern in health and nutrition outcomes include differences in access to water and sanitation, public services, employment opportunity, and maternal education and time use. These channels are discussed in more detail below.
3.6.1. Water, Sanitation, and Hygiene (WASH)
Access to clean water, sanitation, and hygiene (WASH) is a fundamental factor to improve health and nutritional status (Humphrey 2009; Spears 2013). Evidence shows that access to WASH is vital, inter alia, to improve child and maternal health, reduce water-borne diseases, promote the quality of food hygiene, and reduce inequality based on gender and disability (see Joanna &
Oliver, 2016 for review). However, poor access to WASH is widespread across SSA countries. As of the year 2017, less than 30 percent of the region’s population had access to basic sanitation (e.g. a clean and safe toilet), and basic handwashing facilities with soap and water. Moreover, 39 percent of the population in the region do not have access to safe drinking water (WHO and UNICEF 2017). This has severe social and economic implications. Estimates show that the lack
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of access to improved WASH is the second biggest cause of child mortality in Africa. On average, close to 4,000 children under the age of five die every day from WASH-related diseases in the region (UNDP 2006).
While WASH coverage is generally low in the region, there is a significant spatial disparity between and within countries. For instance, Table A3.4 presents the distribution of households’ access to water and sanitation facilities by place of residence in Ethiopia49. It shows that smaller towns tend to have a larger share of households with substandard housing; i.e., fewer rooms, and inferior quality housing (roof, floor, and wall). Less than 1 percent of small-town households use improved cooking fuel, more than a third resort to open defecation, fetch drinking water from unprotected spring/hole, and use potentially harmful sources of lighting. Half of the households travel more than 15 minutes to a source of drinking water. Table 3.6 shows that even after accounting for household and location characteristics, the quality of housing and WASH systematically vary based on both the degree of urban proximity as well as the size of the proximate urban areas.
Given that WASH is a key element of health and nutrition, these observed differences across rural-urban areas may partly explain the spatial difference in health and nutrition outcomes in the country. Therefore, policy interventions that target improvement in WASH are likely to be effective to enhance the overall health and nutrition status as well as reduce the disparity across regions50. Table 3. 6: Urbanization and access to clean Water, Sanitation, and Hygiene (WASH)
Roof Floor Toilet Drinking water
ln(Transportation cost) -0.067*** -0.025*** -0.034*** -0.078***
(0.010) (0.006) (0.008) (0.010)
Large town, yes=1 0.052 0.049*** 0.085*** 0.119***
(0.033) (0.019) (0.027) (0.036)
Household & location characteristics Yes Yes Yes Yes
Zonal fixed effects Yes Yes Yes Yes
Number of observations 14,048 14,048 14,048 14,040
R2 0.383 0.480 0.344 0.422
Adjusted R2 0.378 0.476 0.340 0.418
Source: Author’s calculation based on LSMS-ISA (2012, 2014 & 2016)
Note: Clustered standard errors in parentheses. Statistical significance indicated by: *** p<0.01, ** p<0.05, * p<0.1;
Coefficients, household, and location characteristics are omitted to preserve space.
3.6.2. Access to public services
Access to public services such as roads, schools, health posts, and communication infrastructure has been shown to be an important determinant of health and nutrition outcomes (Hirvonen et al.
2017; Hoddinott et al. 2015; Stifel and Minten 2017; World Bank 2020). Therefore, the difference in nutritional status between small- and large-town households might be associated with differences in access to these services. Table A3 in the appendix shows that the distance to public services is significantly shorter for large-town households than for small-town households.
Relative to large-town households, small-town households live further away from roads, markets,
49 This pattern is similar across the rest of Sub-Saharan Africa (See WHO and UNICEF, 2017).
50 A study by UNDP (2006) indicates that Sub-Saharan Africa might save a total $23.5 billion - 5% of GDP- if the entire population had access to basic, low-cost water and sanitation technology.
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schools, health posts, and financial services.51 Furthermore, Table 3.7 shows that large-town households outperform small-town households in terms of access to radio, television, electricity, and mobile phones. These disparities are directly related to differences in wealth. Indirectly, and perhaps more importantly, they represent households' varying access to information, which has been shown to be a key predictor of nutritional outcomes (Hirvonen et al. 2017).
Table 3.7. Urbanization and access to public services and local institutions
Hospital Electricity Mobile phone Radio TV
ln(Transportation cost) -0.077*** -0.097*** -0.041*** -0.027*** -0.031***
(0.014) (0.010) (0.006) (0.005) (0.006)
Large town, yes=1 0.087* 0.130*** 0.056** 0.015 0.040***
(0.052) (0.032) (0.022) (0.018) (0.013) Household & location characteristics Yes Yes Yes Yes Yes
Zonal fixed effects Yes Yes Yes Yes Yes
Number of observations 14,048 14,048 14,048 14,048 14,048
R2 0.30 0.58 0.39 0.22 0.67
Adjusted R2 0.30 0.57 0.38 0.22 0.67
Source: Author’s calculation based on LSMS-ISA (2012, 2014 & 2016)
Note: Clustered standard errors in parentheses. Statistical significance indicated by: *** p<0.01, ** p<0.05, * p<0.1;
Coefficients, household, and location characteristics are omitted to preserve space
3.6.3. Employment opportunity
Another factor to consider is whether opportunities for nonfarm employment differ across urban areas, which could explain the variation in nutritional outcomes. We examine the likelihood of participation and intensity of household employment in off-farm wage employment and non-farm self-employment. Table 3.8 shows that large-town households are more likely to work, and for more hours per week, in wage employment compared to small-town households; but the differences are not significant in the case of non-farm self-employment. Kamei & Nakamura (2020) reported similar results based on spatial analysis of the Ethiopian urban labor market. The results also indicate that households that are better connected to urban areas have better labor-market opportunities related to non-farm activities. This suggests that policy interventions aimed at improving rural infrastructure are likely to improve nutritional outcomes through the labor market.
Table 3.8. Urbanization and patterns in employment status
Wage employment Non-farm self-employment
Participation # hours Participation # hours
ln(Transportation cost) -0.014*** -0.056*** -0.023*** -0.094***
(0.004) (0.016) (0.007) (0.025)
Large town, yes=1 0.050*** 0.215*** -0.001 0.005
(0.013) (0.049) (0.021) (0.086)
Household & location characteristics Yes Yes Yes Yes
Zonal fixed effects Yes Yes Yes Yes
Number of observations 14,039 14,039 14,039 14,039
R2 0.229 0.245 0.154 0.168
Adjusted R2 0.224 0.240 0.148 0.162
Source: Author’s calculation based on LSMS-ISA (2012, 2014 & 2016)
Note: Clustered standard errors in parentheses. Statistical significance indicated by: *** p<0.01, ** p<0.05, * p<0.1;
Coefficients, household, and location characteristics are omitted to preserve space.
51 This pattern is consistent across most of the selected services except for primary schools and health posts. These two public services are available in every village by government policy.
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Spatial differences in maternal education, employment, and time use might also explain the heterogeneous impact of urban size on nutritional outcomes. Empirical evidence suggests that maternal education (Alderman and Headey 2017; Emily, Juan, and David 2012; Headey et al.
2015) and maternal productive employment (Alderman et al. 2001) are important determinants of dietary diversity and child health. To test this, we examine the association between maternal education and time use with transportation cost and size of the proximate urban areas in a multivariate regression framework.
Table 3.9 shows that, when compared to mothers in small towns, mothers in large towns are more likely to be educated and more likely to be wage employed. Furthermore, Kamei & Nakamura (2020) reported that, in comparison to small towns, women in large towns are less likely to be unemployed or out of the labor force.
Table 3.9. Urbanization and mothers’ education & time use
Maternal
Education
Maternal Employment
ln(transportation cost) -0.008** -0.050***
(0.003) (0.006)
Large town, yes=1 0.032** 0.064***
(0.013) (0.017)
Household & location characteristics Yes Yes
Zonal Fixed Effects Yes Yes
Number of observations 11,961 11,961
R2 0.440 0.359
Adjusted R2 0.435 0.354
Source: Author’s calculation based on LSMS-ISA (2012, 2014 & 2016)
Note: Clustered standard errors in parentheses. Statistical significance indicated by: *** p<0.01, ** p<0.05, * p<0.1;
Coefficients, household, and location characteristics are omitted to preserve space.
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