As table 4 shows, historical serfdom was strongly associated with lower rates of urbanization (as opposed to city size) before the Revolution (columns 1 and 2). The reduction in the 1913 urbanization rate of about 3.8 percentage points implied by a standard deviation increase in serfdom is a large effect, given a mean of 10.1 and a standard deviation of 12.2 for the former. Columns 3 and 4 investigate industrial production using newly digitized district-level data from just after emancipation. We find a negative, albeit not statistically significant, association between serfdom and the number of firms per capita, but when we divide the ruble value of factory turnover in a district by the number of firms or factory workers, we find that worker productivity was significantly lower in areas with higher levels of serfdom. A 1 standard deviation increase in serfdom corresponded to about 16% lower industrial productivity.47

Table 5.
Structural Change, 1897 Employment, and the Heterogeneous Effects of Serfdom
Primary Employment 1897Secondary Employment 1897Industry Employment 1897Log Light Density, 2008
(1)(2)(3)(4)(5)(6)(7)(8)
Serfs % (1858) 0.077*  −0.008  −0.024  −0.820***
(0.043)  (0.023)  (0.022)  (0.295)
Corvée % (1858)  0.035**  −0.016*  −0.016*  −0.240***
(0.014)  (0.009)  (0.008)  (0.081)
Quitrent % (1858)  −0.015  0.026***  0.018**  −0.020
(0.010)  (0.008)  (0.008)  (0.080)
Household Serfs % (1858)  0.006  −0.008  −0.009*  −0.102
(0.012)  (0.006)  (0.005)  (0.091)
H0: Corvée $=$ Quitrent ($p$-value)  0.00  0.00  0.01  0.02
Flexible controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Distances: City and provincial capital ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Fixed effects Province Province Province Province Province Province Province Province
Observations 490 468 490 468 490 468 490 468
$R2$ 0.51 0.54 0.60 0.64 0.57 0.60 0.57 0.59
Number of clusters 50 49 50 49 50 49 50 49
Primary Employment 1897Secondary Employment 1897Industry Employment 1897Log Light Density, 2008
(1)(2)(3)(4)(5)(6)(7)(8)
Serfs % (1858) 0.077*  −0.008  −0.024  −0.820***
(0.043)  (0.023)  (0.022)  (0.295)
Corvée % (1858)  0.035**  −0.016*  −0.016*  −0.240***
(0.014)  (0.009)  (0.008)  (0.081)
Quitrent % (1858)  −0.015  0.026***  0.018**  −0.020
(0.010)  (0.008)  (0.008)  (0.080)
Household Serfs % (1858)  0.006  −0.008  −0.009*  −0.102
(0.012)  (0.006)  (0.005)  (0.091)
H0: Corvée $=$ Quitrent ($p$-value)  0.00  0.00  0.01  0.02
Flexible controls ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Distances: City and provincial capital ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Fixed effects Province Province Province Province Province Province Province Province
Observations 490 468 490 468 490 468 490 468
$R2$ 0.51 0.54 0.60 0.64 0.57 0.60 0.57 0.59
Number of clusters 50 49 50 49 50 49 50 49

The unit of observation is a district. Corvée, Quitrent, and Household Serfs are standardized variables (mean $=$ 0, SD $=$ 1). Flexible controls include eight dummies for cereal suitability and four dummies for quartiles of growing-season temperature, growing-season precipitation, the share of podzol soil, and river density, as well as linear controls of latitude and longitude of the district, the area covered by forest, ruggedness, the distance to the coast, and the distance to Moscow. Distances are the distance to the nearest city in 1600 and the distance to the provincial capital. Standard errors clustered at the province in parentheses. $*$$p<0.10$, **$p<0.05$, and ***$p<0.01$.

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