Identification strategy, results and discussion

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simple OLS estimates of the βs would be biased. To minimize this problem, the panel structure of the data is used to estimate the EA fixed effects model. The EA fixed effect can capture time-invariant differences in welfare across different villages, implying that the parameters associated with SOL estimate the effect of urbanization on household welfare. Moreover, several socio-demographic characteristics of the household that might be associated with urbanization such as education and wealth levels of the households, are controlled for, to address many sources of concern regarding omitted variables bias.

In all regressions, a year dummy is included to account for aggregate shifts in welfare or correlated shifts in the right-hand side variables. Since surveyed households are sampled from stratified village level samples and households from the same village might share common unobservable characteristics, standard errors in all regressions are clustered at the village level.

2.3.2. Results and discussion

Urbanization and welfare

Panel A in Table 2.2 presents the pooled OLS regression result of household welfare. Two regression models are estimated for each of the outcome variables. First, a simple unconditional regression of an outcome variable (e.g. per capita expenditure) is estimated on SOL and survey period dummy. In the subsequent regression, the model is extended by accounting for household and village characteristics as well as the zones of residence. The estimation result shows that urbanization is strongly and positively associated with household welfare, measured in terms of per capita expenditure and diet diversity score. It also shows that urbanization is associated negatively with the food security gap. Specifically, a doubling of the SOL is associated with a 5 percent increase in consumption per capita, a 1.6 percent increase in HDDI, and a 0.4 percent reduction in the food security gap.

Panel B of Table 2.2 presents the estimation result of urbanization on the same outcome variables based on the EA fixed effect. Since these estimators are immune to time-invariant village-level heterogeneities, they identify the causal welfare effect of urbanization (Wooldridge 2002).

Although the sizes of the magnitudes are notably larger than the coefficients from the pooled regression, the fixed effect estimators report a qualitatively similar result for consumption and diet diversity. The coefficient of food security score is, however, not statistically significant.

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Table 2.2. Association between urbanization and household welfare

ln(Expenditure) Diet Diversity score Food security Gap

Panel A: Pooled OLS

ln(Sum of Nighttime light) 0.096*** 0.047*** 0.028*** 0.016*** -0.007*** -0.004**

(0.007) (0.008) (0.002) (0.002) (0.001) (0.002)

HH & village characteristics ^a) No Yes No Yes No Yes

Zone Fixed Effect No Yes No Yes No Yes

Number of observations 9,215 9,210 9,606 9,600 9,606 9,600

R2 0.123 0.375 0.151 0.339 0.013 0.216

Adjusted R2 0.122 0.368 0.151 0.332 0.013 0.207

Panel B: EA fixed effect

ln(Sum of Nighttime light) 0.151*** 0.112*** 0.042*** 0.031*** 0.002 0.005 (0.031) (0.025) (0.007) (0.006) (0.016) (0.012)

HH & village characteristics ^a) No Yes No Yes No Yes

EA Fixed Effect Yes Yes Yes Yes Yes Yes

Number of observations 9,215 9,210 9,606 9,600 9,606 9,600

R2 0.004 0.156 0.007 0.103 0.000 0.103

Adjusted R2 0.004 0.154 0.007 0.102 -0.000 0.102

Source: Author’s calculation based on LSMS-ISA (2014 & 2016)

Notes: Village clustered standard error in parentheses: *** p<0.01, ** p<0.05, * p<0.1. The Sum of Nighttime light (SOL) represents the sum of NTL intensity around EAs. ^a) Coefficients omitted to preserve space. For estimation results of the full model, see Tables A2.3 and A2.4 in the appendix.

Heterogeneity in welfare across city hierarchy

This sub-section employs a parametric regression method to estimate the heterogeneity in the effect of urbanization on household welfare. As discussed before, this proceeds by splitting the sample households into clusters of urbanization based on Hansen's (2000) threshold method. This is tantamount to estimating equation (2.3) with dummy variables representing different levels of urbanization (and a rural household as a reference). The results presented in Figure 2.10 show the coefficient estimates from this model after accounting for household and location characteristics. It shows that all else the same, on average, households in intermediate and large towns consume more per capita than those in rural areas. They also fare better in terms of diet diversity score and food security. Specifically, compared to an average household in rural areas, the per capita consumption is 20 percent higher; diet diversity is higher by 10 percent (approximately by one food group); the food security gap is lower by two weeks for a household in an intermediate- or large- town. The welfare level of a household in a small town is largely comparable to a household in rural areas except for consumption expenditure which is about 10 percent higher in small towns than in rural areas.

In line with the s-shaped welfare pattern from the non-parametric regression, Figure 2.10 also shows that differences between small and intermediate towns are much larger than that between rural areas and small towns as well as between intermediate towns and large towns. That is, while small towns resemble rural areas, intermediate and large towns appear quite similar in terms of average welfare levels, and there is a huge difference between small and intermediate towns.

Furthermore, in almost all regressions, the magnitude of the effect is larger for the intermediate

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towns than for large towns. Moreover, the standard errors corresponding to parameter estimates of large urban areas are larger than those of the intermediate towns, suggesting that inequality in welfare is relatively more prevalent in large urban areas. Table A2.8 in the appendix corroborates this and shows that regardless of the type of measure adopted, welfare inequality is substantially higher in large urban areas. Therefore, while intermediate- and large- towns are comparable in terms of average welfare outcomes, intermediate towns appear to be more inclusive. This result aligns with recent and increasing evidence of intermediate towns having a greater impact on employment generation and overall poverty reduction in developing countries (Christiaensen, De Weerdt, and Todo 2013; Dorosh and Thurlow 2014; Kanbur et al. 2019).

From the comparison of the conditional and unconditional regression coefficients of the full results presented in Table A2.5 and Table A2.6 in the Appendix, it appears that wealth status and human capital endowment of the household head are important drivers of welfare differences. That is, the heterogeneity of the link between welfare and urbanization over different stages is mediated by the spatial distribution of human capital difference, in line with the human capital theory (HCT).

However, even after differences in wealth, human capital, and institutional differences across locations are factored in, the spatial disparities in household welfare are considerably minimized but not eliminated. The next section highlights the main underlying factors for this spatial pattern.

Figure 2.10: Association between stages of urbanization and household welfare

Source: Author’s calculation based on LSMS-ISA (2014 & 2016)

Notes: For all regression, standard errors are clustered at the village level. Rural areas, small towns, intermediate towns, and large towns in this figure were generated from the sum of NTL intensity around EA using the Hansen (2000) threshold method, respectively. Other control variables are omitted to preserve space. For estimation results of the full model, see Tables A2.5 and A2.6 in the appendix.

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