**Table 7:** Panel Data Analysis

|**(ln) Productivity**||**Nigeria**|||**Uganda**||
|---|---|---|---|---|---|---|
||OLS|FE|RE|OLS|FE|RE|
|Rural|-0.436<sup>_∗∗∗_</sup><br>(0.05)|-0.936<sup>_∗∗∗_</sup><br>(0.26)|-0.427<sup>_∗∗∗_</sup><br>(0.04)|-0.454<sup>_∗∗∗_</sup><br>(0.15)|-0.137<br>(0.48)|-0.661<sup>_∗∗∗_</sup><br>(0.08)|
|Female|-0.583<sup>_∗∗∗_</sup><br>(0.04)|-0.748<sup>_∗∗∗_</sup><br>(0.04)|-0.669<sup>_∗∗∗_</sup><br>(0.03)|-0.427<sup>_∗∗∗_</sup><br>(0.11)|-0.608<sup>_∗∗∗_</sup><br>(0.11)|-0.653<sup>_∗∗∗_</sup><br>(0.07)|
|Age: 25 to 34|0.220<sup>_∗∗∗_</sup><br>(0.07)|0.106<sup>_∗_</sup><br>(0.06)|0.197<sup>_∗∗∗_</sup><br>(0.05)|0.484<sup>_∗∗_</sup><br>(0.22)|0.620<sup>_∗∗∗_</sup><br>(0.19)|0.644<sup>_∗∗∗_</sup><br>(0.14)|
|Age: 35 to 44|0.373<sup>_∗∗∗_</sup>|0.262<sup>_∗∗∗_</sup>|0.328<sup>_∗∗∗_</sup>|0.357|0.694<sup>_∗∗∗_</sup>|0.636<sup>_∗∗∗_</sup>|
||(0.07)|(0.07)|(0.05)|(0.23)|(0.22)|(0.14)|
|Age: 45 and older|0.404<sup>_∗∗∗_</sup>|0.300<sup>_∗∗∗_</sup>|0.350<sup>_∗∗∗_</sup>|-0.002|0.519<sup>_∗∗∗_</sup>|0.278<sup>_∗∗_</sup>|
||(0.07)|(0.07)|(0.05)|(0.23)|(0.17)|(0.14)|
|Read & Write|0.425<sup>_∗∗∗_</sup>|0.115<sup>_∗∗_</sup>|0.306<sup>_∗∗∗_</sup>|0.775<sup>_∗∗∗_</sup>|-0.140|0.409<sup>_∗∗∗_</sup>|
||(0.04)|(0.06)|(0.04)|(0.14)|(0.14)|(0.08)|
|Migration||||0.233<sup>_∗_</sup><br>(0.12)|0.149<br>(0.11)|0.234<sup>_∗∗∗_</sup><br>(0.07)|
|Number of Adults|0.027<sup>_∗∗_</sup>|-0.037|0.010|0.112<sup>_∗∗∗_</sup>|0.088<sup>_∗∗∗_</sup>|0.115<sup>_∗∗∗_</sup>|
||(0.01)|(0.04)|(0.01)|(0.02)|(0.03)|(0.02)|
|Shock|-0.141<sup>_∗∗∗_</sup>|0.122<sup>_∗_</sup>|-0.027|-0.192<sup>_∗_</sup>|-0.074|-0.228<sup>_∗∗∗_</sup>|
||(0.05)|(0.07)|(0.04)|(0.11)|(0.08)|(0.06)|
|Credit|0.445<sup>_∗∗∗_</sup>|0.243<sup>_∗∗∗_</sup>|0.381<sup>_∗∗∗_</sup>|0.814<sup>_∗∗∗_</sup>|0.457<sup>_∗∗∗_</sup>|0.676<sup>_∗∗∗_</sup>|
||(0.06)|(0.07)|(0.05)|(0.10)|(0.11)|(0.07)|
|Firm Size|-0.110<sup>_∗∗∗_</sup><br>(0.03)|-0.124<sup>_∗∗∗_</sup><br>(0.04)|-0.094<sup>_∗∗∗_</sup><br>(0.03)|-0.016<br>(0.03)|-0.114<sup>_∗∗∗_</sup><br>(0.02)|-0.058<sup>_∗∗∗_</sup><br>(0.02)|
|Months in Operation||||0.117<sup>_∗∗∗_</sup><br>(0.02)|0.059<sup>_∗∗∗_</sup><br>(0.02)|0.078<sup>_∗∗∗_</sup><br>(0.01)|
|Constant|8.417<sup>_∗∗∗_</sup>|9.299<sup>_∗∗∗_</sup>|8.586<sup>_∗∗∗_</sup>|8.996<sup>_∗∗∗_</sup>|10.028<sup>_∗∗∗_</sup>|9.657<sup>_∗∗∗_</sup>|
||(0.12)|(0.21)|(0.11)|(0.33)|(0.45)|(0.24)|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|FE|No|Yes|No|No|Yes|No|
|RE|No|No|Yes|No|No|Yes|
|R<sup>2</sup>|0.163|0.115|0.147|0.165|0.108|0.173|
|N|7,755|7,755|7,786|4,501|4,501|4,506|

_Notes_ : Standard errors in parentheses; Household weights included. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 The significance of the effect of household characteristics varies. The number of adults in a household significantly increases labor productivity in Uganda per additional household member. In Nigeria, we only find a significant effect in the OLS regression. The effect of shocks is significant in some of the regressions, and the coefficients report in their majority a negative effect, where significant. Finally, we report the effects of enterprise characteristics on productivity. The effect of credit to expand the enterprise is significant and positive on labor productivity in all cases, and suggests that credit helps to improve the business performance. The variable months in operation, indicating how many months an enterprise was in operation during the past 12 months, reports a positive and significant association, and increases labor productivity per additional month in operation, possibly indicating improved capacity utilization.

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