**Table 5. Regional summary statistics**

||**AFR**||**EAP**||**ECA**||**LAC**||**MNA**||**SAR**||**High In**|**come**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|**Mean**|**Sd**|
|L-grow|0.041|0.172|0.054|0.093|0.022|0.124|0.027|0.140|0.016|0.148|0.029|0.083|0.027|0.140|
|LP-grow|-0.059|0.596|-0.042|0.148|-0.014|0.269|-<br>0.024|0.234|-0.126|0.248|-0.026|0.149|-0.032|0.249|
|Foreign Share|0.086|0.253|0.036|0.169|0.052|0.197|0.100|0.283|0.054|0.207|0.008|0.077|0.066|0.236|
|Own Largest|0.873|0.226|0.849|0.223|0.812|0.252|0.718|0.274|0.752|0.285|0.781|0.266|0.794|0.265|
|Exporter|0.190|0.392|0.197|0.398|0.235|0.424|0.297|0.457|0.234|0.424|0.164|0.370|0.246|0.431|
|Once-lagged GDP (log)|6.501|0.710|7.975|0.217|8.050|0.900|8.251|0.593|7.550|0.585|6.921|0.326|9.170|0.567|
|In Big City|0.584|0.493|0.856|0.334|0.189|0.303|0.705|0.456|0.454|0.498|0.436|0.496|0.297|0.444|
|Corruption Obstacle|0.446|0.285|0.140|0.199|0.441|0.247|0.644|0.190|0.800|0.236|0.444|0.310|0.395|0.174|
|Overdraft Facility|0.271|0.212|0.282|0.286|0.369|0.266|0.579|0.233|0.323|0.256|0.522|0.262|0.416|0.312|
|Trade Credit|0.244|0.149|0.521|0.244|0.364|0.189|0.562|0.186|0.384|0.191|0.501|0.214|0.538|0.174|
|Inf. Competition|0.653|0.212|0.499|0.269|0.416|0.198|0.690|0.156|0.440|0.195|0.408|0.223|0.367|0.216|
|Skilled Labor Obstacle|0.311|0.193|0.233|0.218|0.322|0.193|0.644|0.144|0.320|0.203|0.316|0.223|0.457|0.209|
|Labor Regulation Obstacle|0.259|0.179|0.112|0.161|0.161|0.134|0.532|0.186|0.287|0.161|0.293|0.202|0.299|0.215|
|Tax Rate Obstacle|0.599|0.230|0.295|0.280|0.525|0.211|0.641|0.199|0.548|0.178|0.540|0.240|0.682|0.217|
|Land Access Obstacle|0.460|0.209|0.188|0.204|0.209|0.158|0.297|0.119|0.323|0.217|0.340|0.255|0.224|0.157|
|Outage|0.824|0.183|0.506|0.315|0.442|0.214|0.543|0.196|0.669|0.320|0.674|0.282|0.317|0.177|
|Web Intensity|0.314|0.213|0.589|0.320|0.553|0.227|0.607|0.186|0.486|0.239|0.475|0.255|0.679|0.197|
|Security Cost|0.032|0.027|0.009|0.011|0.018|0.019|0.018|0.014|0.012|0.015|0.012|0.010|0.012|0.012|
|Capacity Agglomeration|0.183|0.109|0.489|0.203|0.263|0.099|0.369|0.104|0.261|0.132|0.367|0.128|0.266|0.091|
|PM 2.5|13.682|8.064|45.697|22.510|14.636|4.974|6.128|3.249|10.085|2.986|45.894|18.379|12.528|4.675|

As an additional exercise, we look at how BE effects differ by city size of a firm’s location and explore whether there are sorting effects of firms across cities. Sorting effects can be present for different reasons as specific characteristics of cities may attract certain types of firms. For example, big cities could provide better infrastructure and access to finance. They also usually provide better amenities that can attract more technologically-advantaged firms and access to better education that could impact workers’ qualifications.

To investigate how BE effects differ by city size of a firm’s location, we interact the dummy variable of whether the firm is located in a city of more than a million residents with the BE variables.<sup>11</sup> To reduce multicollinearity, we drop those interaction terms that are statistically

> 11 The data allow us to potentially delve into richer ways of how city size affects firm growth. In particular, we know whether the firm is located in a small city (i.e., less than 50,000, between 50,000-250,000, 250,000 to one million, capital city). In empirical explorations, we found that being affiliated with medium cities (i.e., 250,000 to a million) or small cities (i.e., 50,000 to 250,000) did not have statistically significantly different effects from being affiliated with little cities (i.e., fewer than 50,000 people). We thus only keep the dummy of being affiliated with big cities.

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