Table 7: 2SLS for Working Hours and Underemployment

|Sample|Private|+Self-emplo|yment||Private||Sel|f-employme|nt|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||OLS|2SLS|2SLS|OLS|2SLS|2SLS|OLS|2SLS|2SLS|
|**Panel A: Lo**|**g of workin**|**g hours**||||||||
|Ln Pop|0.121<sup>_⇤⇤⇤_</sup>|0.063<sup>_⇤⇤⇤_</sup>|0.070<sup>_⇤⇤⇤_</sup>|0.031<sup>_⇤⇤⇤_</sup>|0.014<sup>_⇤⇤⇤_</sup>|0.044<sup>_⇤⇤⇤_</sup>|0.110<sup>_⇤⇤⇤_</sup>|0.061<sup>_⇤⇤⇤_</sup>|0.042|
||(0.013)|(0.006)|(0.026)|(0.007)|(0.003)|(0.011)|(0.014)|(0.011)|(0.031)|
|Addis Ababa|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Underidentifc|ation|2.839|17.97||3.706|16.45||2.743|18.06|
|Weakidentifca|tion|58.54|72.39||188.0|96.59||32.18|62.50|
|N|47,153|47,153|47,153|16,199|16,199|16,199|30,954|30,954|30,954|
|**Panel B: Un**|**deremploy**<br>|**ment**<br>||||||||
|Ln Pop|-0.038<sup>_⇤⇤⇤_</sup>|-0.023<sup>_⇤⇤⇤_</sup>|-0.022<sup>_⇤⇤_</sup>|-0.018<sup>_⇤⇤⇤_</sup>|-0.012<sup>_⇤⇤⇤_</sup>|-0.018<sup>_⇤⇤⇤_</sup>|-0.034<sup>_⇤⇤⇤_</sup>|-0.021<sup>_⇤⇤⇤_</sup>|-0.015|
||(0.005)|(0.003)|(0.010)|(0.005)|(0.002)|(0.007)|(0.006)|(0.004)|(0.012)|
|Addis Ababa|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Underidentifc|ation|2.832|17.74||3.694|16.61||2.736|17.72|
|Weakidentifca|tion|57.67|71.83||185.5|97.90||31.91|61.77|
|N|49,884|49,884|49,884|16,979|16,979|16,979|32,905|32,905|32,905|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Dependent variables are (a) the log of hours worked during the last week and (b) a binary indicator about underemployment (one = underemployment; zero = not underemployment). The estimation includes control variables for Demographic: Gender, Age (10 years), Marital Status, and Household Size (and square term) and Education: No education, Incomplete primary, Complete primary, Incomplete secondary, Complete secondary, Post-secondary, and Adult education, as well as year fixed e↵ects.

elasticity estimate in the 2SLS without Addis Ababa fixed e↵ects is 5.7 percent (column 3). Adding Addis Ababa fixed e↵ect reduces the elasticity to 3.2 percent, which is not statistically significant. These results imply that the wage premium of large towns is driven by Addis Ababa.

# **5.5 Robustness Check**

## **Excluding Recent Migrants**

Although our analysis has instrumented the town population size, an endogeneity bias due to the sorting of workers is a lingering concern. To assess the bias, we tap into the migration information available in our data.<sup>11</sup> We reestimate our OLS and 2SLS models by excluding recent migrants to towns to highlight agglomeration e↵ects on the stayers. We expect this to reduce the size of the estimated agglomeration e↵ects if sorting has contributed to the e↵ects. Table 9 summarizes the results. As expected, excluding rural-to-urban migrants (columns 2 and 5) and both rural-tourban and urban-to-urban migrants (columns 3 and 6) lower the absolute values of the coefficient estimates for the log of town population. Nevertheless, we still observe significant associations between town population size and the probability of engaging in self-employment jobs.

> 11Unfortunately, only the LFS has such migration information, reducing the sample size for this analysis.

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