Descriptive statistics
| Variable | Mean | SD | Minimum | Maximum |
|---|---|---|---|---|
| Local share of informal firms | 0.42 | 0.31 | 0 | 1 |
| ln (Agglomeration, δ = 2) | 7.81 | 0.87 | 4.35 | 10.30 |
| Agglomeration, δ = 2 | 3,648 | 4,020 | 77 | 29,804 |
| ln (Formal agglomeration, δ = 2) | 7.40 | 1.03 | 3.35 | 10.07 |
| Formal agglomeration, δ = 2 | 2,749 | 3,343 | 29 | 23,709 |
| Firm’s size | 5 | 35 | 1 | 2,415 |
| Jobs | 727 | 1,417 | 0 | 16,087 |
| Formal jobs | 551 | 1,191 | 0 | 13,172 |
| Informal jobs | 176 | 324 | 0 | 3,319 |
| Job density (per km2) | 3,109 | 5,794 | 0 | 43,435 |
| Population | 5,972 | 5,763 | 0 | 45,702 |
| Population density (per km2) | 21,296 | 13,022 | 0 | 56,716 |
| Neighbourhood Area (km2) | 0.36 | 0.50 | 0.02 | 7.84 |
| Distance from CBD (km) | 3.79 | 2.47 | 0 | 16.43 |
| Distance from main corridors (km) | 0.56 | 0.62 | 0 | 6.55 |
| Variable | Mean | SD | Minimum | Maximum |
|---|---|---|---|---|
| Local share of informal firms | 0.42 | 0.31 | 0 | 1 |
| ln (Agglomeration, | 7.81 | 0.87 | 4.35 | 10.30 |
| Agglomeration, | 3,648 | 4,020 | 77 | 29,804 |
| ln (Formal agglomeration, | 7.40 | 1.03 | 3.35 | 10.07 |
| Formal agglomeration, | 2,749 | 3,343 | 29 | 23,709 |
| Firm’s size | 5 | 35 | 1 | 2,415 |
| Jobs | 727 | 1,417 | 0 | 16,087 |
| Formal jobs | 551 | 1,191 | 0 | 13,172 |
| Informal jobs | 176 | 324 | 0 | 3,319 |
| Job density (per km2) | 3,109 | 5,794 | 0 | 43,435 |
| Population | 5,972 | 5,763 | 0 | 45,702 |
| Population density (per km2) | 21,296 | 13,022 | 0 | 56,716 |
| Neighbourhood Area (km2) | 0.36 | 0.50 | 0.02 | 7.84 |
| Distance from CBD (km) | 3.79 | 2.47 | 0 | 16.43 |
| Distance from main corridors (km) | 0.56 | 0.62 | 0 | 6.55 |
Notes:
338 neighbourhoods. In levels, the agglomeration variable has a mean equal to 3,648, and the standard deviation is 4,020. We use these data to calculate the effect of a one standard deviation change
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