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Abstract

This paper is an empirical test of what is called a unified theory of inequality and growth (Galor and Zeira, 1988, 1993; Galor and Moav, 2004; Galor, 2012) – in early stages of industrialization inequality enhanced the process of development by channeling resources towards individuals whose marginal propensity to save is higher, thus enhancing physical and human capital accumulation. In later stages of development, however, equality has stimulated human capital formation and growth and unequal distribution of income became a hurdle for economic development. A number of studies have found that human capital is higher and more evenly distributed in countries with lower income and wealth inequalities. In particular, Baten and Hippe (2018) argued that inequality in the distribution of land ownership in Europe (including Russia) in the 19th century had a negative impact on human capital formation (as measured by numeracy rate). In contrast, we find that in the regions of Russian Empire in 1897 uneven distribution of land was associated with higher levels of human capital, whereas the distribution of the human capital across the regional population was more even. The difference in the results is caused by the different measurements of land inequality.
MPRA
Munich Personal RePEc Archive
Human capital in the regions of the
Russian Empire and inequality in land
distribution at the turn of the 20th
century
Popov, Vladimir and Konchakov, Roman and Didenko,
Dmitry
RANEPA
14 January 2024
Online at https://mpra.ub.uni-muenchen.de/119796/
MPRA Paper No. 119796, posted 26 Jan 2024 07:26 UTC
1
Human capital in the regions of the Russian Empire and inequality in land distribution at the
turn of the 20th century
Vladimir Popov, Roman Konchakov, Dmitry Didenko
ABSTRACT
This paper is an empirical test of what is called a unified theory of inequality and growth (Galor and
Zeira, 1988, 1993; Galor and Moav, 2004; Galor, 2012) in early stages of industrialization
inequality enhanced the process of development by channeling resources towards individuals whose
marginal propensity to save is higher, thus enhancing physical and human capital accumulation. In
later stages of development, however, equality has stimulated human capital formation and growth
and unequal distribution of income became a hurdle for economic development.
A number of studies have found that human capital is higher and more evenly distributed in countries
with lower income and wealth inequalities. In particular, Baten and Hippe (2018) argued that
inequality in the distribution of land ownership in Europe (including Russia) in the 19th century had
a negative impact on human capital formation (as measured by numeracy rate) as landowners did not
have incentives to promote educational institutions or were not willing to pay the necessary taxes.
In contrast, we find that in the regions of Russian Empire in 1897 uneven distribution of land was
associated with higher levels of human capital (as measured by the average years of schooling and
literacy rate), whereas the distribution of the human capital across the regional population (as
measured by literacy and the proportions of inhabitants with higher, secondary and primary
education) was more even. The difference in the results is caused by the different measurements of
land inequality; our result is totally consistent with the unified theory of the inequality and growth.
Keywords: educational attainment, school enrollment, inequality, land distribution, growth.
JEL: D63, I24, J24, N93, R11.
2
Human capital in the regions of the Russian Empire and inequality in land distribution at the
turn of the XX century
Vladimir Popov, Roman Konchakov, Dmitry Didenko
Introduction
The most conventional link between human capital and inequality that is well studied in the literature
is about how the inequality in education (human capital) results eventually in the inequality in income
and wealth. If and when the returns to human capital are high, there are greater inequalities in income
and wealth distribution.
There is also the inverse link – the influence of wealth and income inequalities on the level and the
evenness of distribution of human capital. There are obvious externalities from education and
knowledge (social returns from are higher than private returns), so the theory predicts that, if human
capital is not treated as a public good and its formation is left to the market, there will be
underinvestment into education and the rich would be better educated than the poor. Hence in unequal
societies without free education, human capital would be lower, and the inequalities in the distribution
of human capital across the population would be larger.
Given these direct and backward links, there is a possibility of self-propelled process with multiple
(good and bad) equilibria. There may be a vicious circle: income and wealth inequalities => lower
and more unevenly distributed human capital => more income and wealth inequalities. But there is
also a chance for the virtuous circle, if the human capital formation is supported by the government
(free education): higher and more evenly distributed human capital => lower inequalities in income
and wealth => even less inequalities in human capital distribution.
The reasons for the market failure to ensure the optimal level of human capital may be different
credit constraints that do not allow poor individuals to borrow to make optimal investment into their
own education; political economy reasons, for instance, state capture, prohibiting the government to
support free education so as to compensate for the market failure – underinvestment into goods with
3
positive externalities (with social returns greater than private returns). And in the absence of
government investment into education, there may be another link between inequality in wealth and
incomes and human capital formation: rich individuals have higher savings rate than poor, so
investment into physical and human capital are higher than in more egalitarian societies. The natural
question is, of course, what are the actual links between human capital and income and wealth
inequality.
This paper investigates the relationship between the inequality in land distribution and the level of
human capital and its distribution across population using the dataset on the regions of the Russian
Empire in the end of the 19th century. We find that inequalities in the distribution of land (the ratio of
large to small land holdings) led to higher levels of human capital (as measured by the average years
of schooling and literacy rate), whereas the distribution of the human capital across the regional
population (as measured by literacy and the proportions of inhabitants with higher, secondary and
primary education) was more even. We compare our results with the other studies that produced
alternative estimates and analyze the reasons for the difference.
Literature review
The large body of evidence exists to prove that uneven distribution of human capital across population
is the source of inequalities in income and wealth (Becker and Chiswick 1966; Mincer 1974; Gregorio
and Lee, 1999; Lee, J.-W. and H. Lee. 2018 – just to cite some studies). Cross-country comparisons
found a basically positive relationship between educational attainment and income inequality
(Castelló and Doménech, 2002; De Gregorio and Lee, 2002). Some studies (Földvári and Van
Leeuwen, 2011) found a low impact of educational inequality on income inequality, especially in
developing countries, but a stronger positive relationship in economically advanced nations. This
may be due to the fact that efficiency of human capital utilization largely depends on its qualitative
characteristics (which are difficult to measure) and on the institutional environment.
There is much less agreement about the impact of inequality in income and asset distribution on
process of the formation of human capital. The dominant view today is that inequality harms growth
4
via different channels, in particular via the obstacles that it creates for the accumulation of human
capital.
Some studies actually use the data on the distribution of assets instead of distribution of incomes.
Easterly (2007) uses agricultural endowments as an instrument for inequality in cross-country
regressions to deal with endogeneity and measurement issues, and finds that inequality has been a
barrier to schooling and economic prosperity. Andersen (2015) argued that deficiencies arising from
both capital market imperfections and social rigidities imply that inequality may be a barrier to
education, which in turn makes inequality persistent and reduces growth. Deininger and Squire (1998)
ran cross-country regressions to show that Gini coefficients of land distribution have significant
adverse effect on education and economic growth.
Baten and Hippe (2018) used the indicator of numeracy measured via the age heaping method in
Europe (including Russia) in the 19th century and compared it with the inequality in the distribution
of land ownership (share of land plots of over 50 hectares in total land). They found a negative impact
on human capital formation, arguing that the effect is due to the lack of incentives for the landowners
to promote educational institutions or to pay the necessary taxes.
However, there is also a classical tradition to view inequality as conducive to the accumulation of
physical and human capital at a low level of development because inequality raises saving rate at a
low level of development. This is known as the Kaldor effect (Kaldor, 1955) if marginal
propensities to save increase with the growth of income, the increase in inequality will drive up
savings and investment ratios, accumulation of physical and human assets and economic growth.
Popov (2014) argues that this was the major reason of the acceleration of growth in Western countries
since the 16th century. Investment rate increased in Britain from 6% in 1760 to 12% in 1831, and
productivity growth rates increased to 1% annually after being virtually zero for millenniums. Today
the same effect is observed in less developed countries – Cook (1995) shows that regardless of the
inequality measure employed, some sort of Kaldor effect may be at work in developing countries.
5
The unified theory of inequality and growth (Galor and Moav, 2004; Galor and Zeira, 1988, 1993;
Galor, 2012) predicted exactly such kind of relationships. Inequality in the distribution of income
may have an adverse effect on the growth process in a non-poor economy, whereas …. inequality in
poor economies may induce investment in human capital and may thus increase the long-run level of
income per capita” (Galor, 2012, p.20). The model was based on a natural assumption of credit
constraints (prohibiting poor individuals to borrow money for education) and the growing role of
human capital (as opposed to physical capital) in the economic development process1. But it is quite
obvious that these assumptions are not crucial, if there are externalities from education (social returns
are greater that private returns) and the state is not compensating the private underinvestment into
human capital.
However, Galor explicitly argued that “unequal distribution of land has been a hurdle for economic
development. While industrialists have had an incentive to support education policies that foster
human capital formation, landowners, whose interests lay in the reduction of the mobility of their
labor force, have favored policies that deprived the masses of education” (Galor, 2012)2.
Our results, reported in the next sections, suggest that inequality in the distribution of land in the
regions of the Russian Empire at the turn of the 20th century (the period of industrialization) had a
positive effect on both the level and the evenness of the distribution of the human capital. We
discuss the reasons for the divergence with Galor (2012) and Baten and Hippe (2018) studies in the
last section after presenting the results.
1 “In early stages of industrialization, as physical capital accumulation was a prime engine of growth, inequality enhanced
the process of development by channeling resources towards individuals whose marginal propensity to save is higher. In
later stages of development, however, as human capital has become a main engine of growth, equality, in the presence of
credit constraints, has stimulated human capital formation and growth” (Galor, 2012).
2 “Economies in which land and other natural resources have been more equally distributed have implemented earlier
public education campaigns and have benefited from the emergence of a skill-intensive industrial sector and a rapid
process of development. In contrast, among economies marked by a more unequal distribution of ownership over land
and other natural resources, resource abundance that was a source of richness in the early stages of development has led
in later stages to under-investment in human capital, an unskilled labor-intensive industrial sector, and a slower growth
process. Thus, variation in the distribution of ownership over land and other natural resources across countries has
contributed to disparity in human capital formation and the industrial composition of the economy, and thus to divergent
development patterns across the globe” (Galor, 2012, p. 29).
6
Data
Our dependent variables are the level of human capital and inequalities in the distribution of human
capital. The level of human capital is measured by the share of those with literacy skills3 and average
years of schooling. For measuring of the inequality in the distribution of human capital we constructed
the index of inequality as the ratio of the number of residents with secondary and higher education
degrees to the number of residents with primary degree (or to the average years of schooling).
There is a weak negative relationship between these indicators provinces with higher level of
literacy and years of schooling have lower inequalities in the distribution of human capital (fig. 1)4.
As explanatory variables, we used all available data on the distribution and concentration of the land
(share of small and large land plots in total and private land, concentration indices, share of allotted
plots in total land)), on economic and demographic conditions (density of the population, share of
rural population, share of industry in employment and value added, agricultural yields, gross regional
product (GRP) per capita, institutional arrangements (share of serfs before 1861, community
redistribution of land practices, local governments expenditure, size of education facilities).
Below is the list of variables that turned out to be significant in our regressions with explanations.
Distribution of land property:
Distribution of land possessions (surveys conducted in 1877 and 1905 by the Central Statistical
Committee of the Ministry of Internal Affairs published in TsSK MVD, 1880-1885, 1907) provide
the data on the distribution of private land (excluding peasants’ allotments owned by their agricultural
communities) and all land (including these peasants’ allotments).
3 Only reading skills in the native language were considered.
4 In another paper (Popov, Konchakov, Didenko, 2024, forthcoming) we consider changes in the flows of human capital,
namely Gross Enrollment Ratios for different levels of education from 1897 to 1914 based on (DNP, 1998) for primary
education (Kessler, Markevich, 2014) for secondary and higher levels in 1897 and (Kessler, Markevich, 2020, for
population in 1897; TsSK MVD, 1915, 1916, for population and education facilities in 1914).
7
Fig. 1. Literacy rate and inequality in the distribution in human capital (share of inhabitants
with secondary and higher degrees divided by the average number of the years of schooling) in
the regions of the Russian Empire in 1897
Source: See data section.
We used different indicators in the regressions (not all are reported, only the ones with the best
results), including:
- Share of the landlords’ estates of over 500 dessiatines (1 dessiatine = 1. 09 hectare) in total land
area in 1877 and 1905,
- Share of peasants’ land holding of over 10 dessiatines in total peasants’ land holdings in 1877,
- Share of private land holdings of less than 10 dessiatines in total private land in 1877,
- Share of allotted land in total land.
The Emancipation Act of 1861 gave personal freedom to serfs and land allotments were allocated to
former serfs’ and to the peasants that were working on the land that belonged to the state, Tsar’s
family and monasteries. When peasants were freed in 1861, they were given a choice of buying out
land allotments (with redemption payments that were abolished only in 1907) or continuing with rent
or corvée contract (abolished in 1881).
Akmola region
Amur region
Arkhangelsk province
Astrakhan province
Baku province
Bessarabian Governorate
Warsaw Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Dagestan region
Yekaterinoslav Governorate
Elisavetpol Governorate
Yenisei province
Transbaikal region
Transcaspian region
Irkutsk province
Kazan province
Kalisz Governorate
Kaluga province
Kara Governorate
Kielce Governorate
Kyiv province
Kovno province
Kostroma provinceKuban region
Courland Governorate
Kursk province
Kutaisi province
Livland Governorate
Lomzhinsky province
Lublin Governorate
Minsk province
Mogilev province
Moscow province
Nizhny Novgorod province
Novgorod province
Region of the Don Army
Olonets province
Orenburg province
Oryol province
Sakhalin island
Penza province
Perm province
Petrokovskaya province
Plock Governorate
Podolsk province
Poltava province
Primorsky region
Pskov province
Radom Governorate
Ryazan province
Samarkand region
Samara province
St. Petersburg Governorate
Saratov province
Sedlec Governorate
Semipalatinsk region
Semirechensk region
Simbirsk province
Smolensk province
Stavropol province
Suwalki Governorate
Syrdarya region Tauride province
Tambov province
Tver province
Terek region
Tiflis Governorate
Tobolsk province
Tomsk province
Tula province
Turgai region
Ural region
Ufa province
Fergana region
Kharkov province
Kherson province
Chernigov province
Black Sea Governorate
Erivan Governorate
Estonian province
Yakutsk region
Yaroslavl province
12345
Inequality in the distribution of human capital
0 20 40 60 80
The proportion of literates in 1887,%
8
The data on railroad length and railway stations per 1 km in 1910 were extracted from the official
statistics reported in TsSK MVD (1915).
Other things being equal, inequality of land distribution was observed mostly in remote regions (low
density of population and railway network and relatively high GRP per capita), where the share of
the peasants’ allotments in total land was low (large peasants’ allotments contributed to the more
even land distribution) and the agricultural community did not carry out land redistribution.5
We also computed the land distribution inequality index (similar to the decile or Palma ratio) as the
ratio of the area of all land holdings over 500 dessiatines divided by the area of landholding of less
than 10 dessiatines for private land and for all land (table 1)6.
The highest private land distribution inequality coefficient (over 500) in 1877 was in ethnic provinces
of the Empire (Baltics Courland, Lifland, Estland and Kovno governorates, Bessarabia, Minsk,
Vitebsk, Kiev governorates; no data on Caucuses and Central Asia), and in the non-ethnic, mostly
Russian newly colonized regions in the outskirts of the Empire in the North, Volga, Urals,
Novorossiya regions (Olonets, Astrakhan, Samara, Saratov Ufa, Perm, Orenburg, Ekaterinoslav,
Kherson; no data on Siberia and Far East) see table 1 (highlighted in yellow). And the lowest
(below 100) index of the inequality of private land distribution was observed mostly in the Central
5 LnINEQindex1877 = 4.0*** – 0.02**POPDENS +.002**GRPcap – 3.9** RAILeng1910 – 0.04**ALLOTsh1877
0.02** SERFshare1858 – 0.7** COMMdist1900,
Robust standard errors, N=47, R2 = 0.54. Here and later – standard notations: *** - significant at 1%, **- 5%, *- 10%.
LnINEQindex1877 – natural logarithm of the index of inequality of distribution of all land in 1877;
POPDENS – density of the population in 1904, number of people per 1 sq. km;
GRPcap – GRP per capita in 1897, rubles;
RAILeng1910 – Engel coefficient in 1910 (density of the railway network). Engel coefficient, E, is equal to the length
of railways in the region divided by the square root of a multiple of area and population of the region: E
l/√ S*N,
where l is the length of the transport network, km; S is the area of region, thousand km2; N is the total population,
thousands of people;
ALLOTsh1877 – share of allotment land in total land in 1877, %;
SERFshare1858 – share of serfs in total population in 1858;
COMMdist1900 – The existence of a community with redistribution of allotted land in 1900. The community was an
equalization institution, hindering the polarization of peasants.
6 For private land the numerator includes private lands of the nobility, whereas the denominator does not include
allotments held by peasants, normally in communal ownership. For all land the index includes all land holdings.
9
and close to Central regions of the Empire (highlighted in red in the table 1) – Archangelsk, Vladimir,
Vologda, Vyatka, Grodno, Kaluga, Kostroma, Kursk, Mogilev, Moscow, Nizhny Novgorod,
Novgorod, Oryol, Poltava, Pskov, Ryazan, Smolensk, Tver, Tula, Kharkov, Yaroslavl.
Table 1. Inequalities in the distribution of private land and all land in the regions of the
Russian Empire in 1877 (highlighted in yellow provinces with highest (above 200)
private land inequality index, highlighted in red provinces with lowest (below 100)
private land inequality index)
Region
Allotment size in
dessiatines per capita
of the male
population, average
for the province,
1880
Inequality
index for
all land
Inequality
index for
private
land
Share of
allotments
land in total
land, %
Akmola region
Amur region
Arkhangelsk province 2,8 0,0 0,0 96,6
Astrakhan province 11 0,3 3545,7 79,1
Baku province
Batumi district
Bessarabian Governorate 4,1 1,1 221,4 53,0
Warsaw Governorate
Vilna province 2,7 0,8 101,8 46,9
Vitebsk province 3,2 1,2 220,1 41,2
Vladimir province 3,3 0,5 30,1 58,9
Vologda province 6,2 0,5 20,9 70,0
Volyn province 2,6 1,1 100,7 45,5
Voronezh province 3,3 0,4 156,4 69,6
Vyborg Governorate
Vyatka province 6,1 0,1 71,8 89,5
Grodno province 3,1 0,7 23,0 52,0
Dagestan region
Ekaterinoslav Governorate 3,6 1,2 1668,5
Elisavetpol Governorate
Yenisei province
Transbaikal region
Transcaspian region
Irkutsk province
Kazan province 3,6 0,2 146,3 81,6
10
Kalisz Governorate
Kaluga province 2,7 0,5 28,6 57,0
Kara Governorate
Kielce Governorate
Kyiv province 1,9 0,9 434,3 50,1
Kovno province 3,7 0,7 210,1 48,2
Kostroma province 4 1,0 42,7 42,8
Kuban region
Courland Governorate 3,1 2,6 2567,4 0,0
Kursk province 2,2 0,3 15,8 63,7
Kutaisi province
Livland Governorate 3 5.8 61628,0 0,0
Lomzhinsky province
Lublin Governorate
Minsk province 3,8 2,6 242,1 30,0
Mogilev province 3,1 1,2 76,0 40,7
Moscow province 2,9 0,4 28,8 59,7
Nizhny Novgorod province 2,9 0,6 41,1 58,3
Novgorod province 5,6 1,4 63,5 39,4
Region of the Don Army 2
Olonets province 18,7 0,4 285,0 70,6
Orenburg province 16,2 0,2 15570,8 81,9
Oryol province 2,4 0,9 39,6 41,3
Sakhalin island
Penza province 2,7 0,6 136,1 58,4
Perm province 6,4 2,2 20769,9 41,0
Petrokovskaya province
Plock Governorate
Podolsk province 1,8 0,9 176,8 50,5
Poltava province 2,2 0,6 23,9 49,4
Primorsky region
Pskov province 3,5 0,8 36,5 43,9
Radom Governorate
Ryazan province 2,2 0,4 23,9
Samarkand region
Samara province 6,2 0,9 1109,0 67,3
St. Petersburg Governorate 5,1 1,7 168,6 36,5
Saratov province 3,5 0,8 288,1 54,5
Sedlec Governorate
Semipalatinsk region
Semirechensk region
11
Simbirsk province 2,6 0,6 139,6 56,6
Smolensk province 3,5 0,9 70,0 44,4
Stavropol province
Suwalki Governorate
Syrdarya region
Tauride province 6,5 2,1 104,2 43,2
Tambov province 2,7 0,5 116,6 59,5
Tver province 3,4 0,4 12,4 58,3
Terek region
Tiflis Governorate
Tobolsk province
Tomsk province
Tula province 2 0,5 37,8 50,8
Turgai region
Ural region
Ufa province 8,4 1,1 820,2 73,1
Fergana region
Kharkov province 2,6 0,4 50,6 61,3
Kherson province 3,4 1,5 2429,1 39,9
Chernigov province 2,9 0,5 33,1 54,2
Black Sea Governorate
Erivan Governorate
Estland province 2,1 205,7 950,2
Yakutsk region
Yaroslavl province 3,2 0,4 8,6 54,5
Source: Surveys conducted in 1877 and 1905 by the Central Statistical Committee of the Ministry of
Internal Affairs published in TsSK MVD, 1880-1885, 1907.
Index of inequality of distribution for all land is way lower than the same index for private land, but
the natural logs of two indicators are very much correlated (fig. 2) and both work in regression
reported in the next section.
The share of large estates (over 500 dessiatines) fell in all but 3 provinces and distribution of land in
1905 became slightly more even as compared to 1877, but huge inequalities persisted. The land
distribution inequality index (ratio of the area of holdings over 500 dessiatines to the area of holdings
12
below 10 dessiatines) increased in 1877-1905 in 9 provinces (Archangelsk, Vladimir, Vyatka,
Kostroma, Moscow, Olonets, Saratov, Tauride, Estland) out of 48 (fig. 3).
Fig. 2. Index of inequality in the distribution of all land and private land in 1877 (natural
logarithms)
Source: Computed from Table 2.
Inequality in the distribution of land (concentration indices) were higher in the non-central regions of
the Empire they had mostly low density of the population and railway network, were relatively
richer than regions in the Central Russia and had low share of serfs in 1858 and little distribution of
land by the community in 1900.
Demographics:
Total number of people in the region,
Population density,
Share of urban population.
Astrakhan province
Bessarabian Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Yekaterinoslav Governorate
Kazan province
Kaluga province
Kyiv province
Kovno province
Kostroma province
Courland Governorate
Kursk province
Livland Governorate
Minsk province
Mogilev province
Moscow province
Nizhny Novgorod province
Novgorod province
Olonets province
Orenburg province
Oryol province
Penza province
Perm province
Podolsk province
Poltava province
Pskov province
Ryazan province
Samara province
St. Petersburg Governorate
Saratov province
Simbirsk province
Smolensk province
Tauride province
Tambov province
Tver province
Tula province
Ufa province
Kharkov province
Kherson province
Chernigov province
Estonian province
Yaroslavl province
2 4 6 8 10 12
Inequality of distribution index for private land, ln
-2 0 2 4 6
Inequality of distributions index for all land, ln
13
Fig. 3. The inequality index of private land distribution (ratio of the area of holdings over 500
dessiatines to the area of holdings below 10 dessiatines) in 1877and 1905
Source: Computed from Surveys conducted in 1877 and 1905 by the Central Statistical Committee
of the Ministry of Internal Affairs published in TsSK MVD, 1880-1885, 1907.
These are reported in the publications of the First General Census of the Russian Empire in 1897
(Troinitskii, ed., 1898-1905), and structured into the data set in Kessler, Markevich, 2020). The data
on population in 1914 were directly extracted from the official statistics reported in TsSK MVD
(1915). The data on provinces area were borrowed from the official data of the time and on the basis
of processing of the original maps in Strel’bitskii, 1915; GSh, 1884, 1921, into digital GIS systems).
Level of development, structure of the economy, incomes and well-being:
Gross regional product per capita in 1897 (Markevich, 2019, 2022).
Land productivity (grain yields), reported by the Central Statistical Committee of the Ministry
of Internal Affairs (processed in Obukhov, 1927)7.
The share of the labor force in industry (reported in the publications of the First General Census
of the Russian Empire in 1897, and processed into the data set in Kessler, Markevich, 2020).
7 Reliability of this kind of data is discussed in Kuznetsov (2012).
1
10
100
1000
10000
100000
1 10 100 1000 10000 100000
Land inequality index-1905
Land inequality index-1877
14
The share of large industry in value added – assuming that gross output in industry was 2 time
larger than value added (Markevich, 2019, 2022).
The latter two indicators – the share of industry in employment and in total value added (GRP) are
correlated (fig. 4), which is how it should be, of course – it could be regarded as one more test of the
quality of the data.
Fig. 4. The share of large industry in value added and the share of employment in industry in
1897, %
Source: See text.
Education facilities (reported in TsSK MVD, 1916):
Number of primary and secondary schools per 100 000 inhabitants
Number of students in education facilities per 100 inhabitants (Gross Enrollment Ratio)
Institutional environment:
0 10 20 30
Share of labor force in industry in 1897, %
0 20 40 60
Share of large-scale industry in GDP
15
The share of serfs in the population in 1858. This is viewed as an obstacle to the accumulation
of human capital and industrial development (Markevich, Zhuravskaya, 2018). It was the highest in
the regions of Central Russia and in Lithuania, Ukraine and Belarus.
The existence of a commune with redistribution of allotted land in 1900. The community was
an equalization institution, hindering the migration of labor from the agricultural sector to the
industrial sector (Markevich, Zhuravskaya, 2018). The redistribution of land by the commune
discouraged social polarization of peasants and prevented the growth of inequalities.
The average annual expenditures of local self-government bodies per capita in 1868-1903, in
rubles. The measure captures the level of development of local self-government institutions that
moderate social tensions and promote economic development (urban upravy: Konchakov and
Didenko, 2022; rural zemstva: Markevich, Zhuravskaya, 2018). These expenditures are for all
purposes (not only for education) and are in current rubles (without deflation), so should be
interpreted with care.
Results
Literacy rates (as well as very close indicators of the share of inhabitants with primary education and
the average number of the years of schooling) are positively correlated with the indices of inequality
of distribution of all land and private land (fig. 5), whereas inequality in the distribution of human
capital (percent of residents with secondary and higher levels of education / average number of years
of schooling) is negatively linked with inequality in land distribution (fig. 6). Although the correlation
coefficients are not high (0.3-0.6 in the first case and 0.2-0.3 in the second case), the inclusion of the
control variables into the right hand side produces very robust results with high R2.
Among the control variables are population density, level of urbanization, share of industry in
employment and value added, GRP per capita, harvest yields, share of serfs in rural population before
1861, dummy for the redistribution of land in the agricultural community.
The link with the inequality in the distribution of land is positive – as regressions reported in table 2
suggest, the higher the index of inequality and the share of small (below 10 dessyatines) land plots,
16
and the lower the share of large land plots (over 10 dessiatines)8, the higher is the level of human
capital (literacy and the average number of years of schooling). Naturally, the number of students
attending primary schools per 100 inhabitants has a positive impact on the level of literacy (whereas
the number of students in secondary and higher levels educational facilities is insignificant). In the
next section we explain that the financing of these educational facilities was carried out mostly by
the urban (not rural) local authorities and by central government and happened mostly in relatively
rich periphery provinces with higher revenues of the local governments. That is why the inclusion of
the zemstvo (local rural governments) expenditures variable turns out to be insignificant (in table 2 –
not shown) or the variable even acquires the negative sign (table 3).
Fig. 5. The literacy rate in 1897 (%) and the indices of inequality of distribution of all land
and private land in 1877 in Russia’s regions
Source: see data section.
8 The share of small private land holdings (less than 10 dessiatines) in the total private land and of large peasants’ land
plots (over 10 dessiatines) in total land characterizes polarization of the distribution of land – the higher the former and
the lower the latter, the higher is inequality in land distribution.
Astrakhan province
Bessarabian Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Yekaterinoslav Governorate
Kazan province
Kaluga province
Kyiv province Kovno province
Kostroma province Courland Governorate
Kursk province
Livland Governorate
Minsk province
Mogilev province Moscow province
Nizhny Novgorod province
Novgorod province
Olonets province
Orenburg province
Oryol province
Penza province
Perm province
Podolsk province
Poltava province
Pskov province
Ryazan province
Samara province St. Petersburg Governorate
Saratov province
Simbirsk province
Smolensk province
Tauride province
Tambov province
Tver province
Tula province
Ufa province
Kharkov province
Kherson province
Chernigov province
Estonian province
Yaroslavl province
Astrakhan province
Bessarabian Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Yekaterinoslav Governorate
Kazan province
Kaluga province
Kyiv province Kovno province
Kostroma province
Courland Governorate
Kursk province
Livland Governorate
Minsk province
Mogilev province Moscow province
Nizhny Novgorod province
Novgorod province
Olonets province
Orenburg province
Oryol province
Penza province
Perm province
Podolsk province
Poltava province
Pskov province
Ryazan province
Samara province
St. Petersburg Governorate
Saratov province
Simbirsk province
Smolensk province
Tauride province
Tambov province
Tver province
Tula province
Ufa province
Kharkov province
Kherson province
Chernigov province
Estonian province
Yaroslavl province
-5 0 5 10
0 20 40 60 80
The proportion of literates in 1887,%
LN of Inequality of land distributions index for all agricultural land
LN of Inequality index for distribution of private land
Fitted values
Fitted values
17
Fig. 6. Inequality in human capital (percent of residents with secondary and higher levels
of education / average number of years of schooling) in 1897 and inequality in land
distribution in Russia’s regions in 1877
Source: See data section.
The number of the average years of schooling is very much correlated with the literacy rate and with
the share of inhabitants with primary education (fig. 7), but we ran the regression with these
dependent variables anyway to check the robustness of the conclusions. The results are in table 3 and
they strengthen the previous statements human capital is higher in regions with most unequal
distribution of land, relatively rich regions with high urbanization, low population density, low share
of serfs before 1861 and no redistribution of land by the community.
The share of land plots of over 10 dessiatines in total land has a negative impact on impact on
educational level, whereas the share of land plots of less than 10 dessiatines in private land has a
positive impact (polarization promotes inequality and human capital accumulation).
Astrakhan province
Bessarabian Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Yekaterinoslav Governorate
Kazan province
Kaluga province
Kyiv province
Kovno provinceKostroma province
Courland Governorate
Kursk province
Livland Governorate
Minsk province
Mogilev province Moscow province
Nizhny Novgorod province
Novgorod province
Olonets province
Orenburg province
Oryol province
Penza province
Perm province
Podolsk province
Poltava province
Pskov province
Ryazan province
Samara province St. Petersburg Governorate
Saratov province
Simbirsk province
Smolensk province
Tauride province
Tambov province
Tver province
Tula province
Ufa province Kharkov province
Kherson province
Chernigov province
Estonian province
Yaroslavl province
Astrakhan province
Bessarabian Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Yekaterinoslav Governorate
Kazan province
Kaluga province
Kyiv province
Kovno province
Kostroma province
Courland Governorate
Kursk province
Livland Governorate
Minsk province
Mogilev province Moscow province
Nizhny Novgorod province
Novgorod province
Olonets province
Orenburg province
Oryol province
Penza province
Perm province
Podolsk province
Poltava province
Pskov province
Ryazan province
Samara province
St. Petersburg Governorate
Saratov province
Simbirsk province
Smolensk province
Tauride province
Tambov province
Tver province
Tula province
Ufa province
Kharkov province
Kherson province
Chernigov province
Estonian province
Yaroslavl province
-5 0 5 10
12345
Inequality in HC (>secondary education/ average schooling years)
LN of Inequality of land distributions index for all agricultural land
Fitted values
LN of Inequality index for distribution of private land
Fitted values
18
Table 2. Regression of literacy rate and share of inhabitants with primary education on land
inequality indices and control variables
Dependent variable Share of inhabitants with
primary education in
1897, %
Literacy rate in 1897, %
Equation, N //
Indicator 1,
N= 48 2,
N=48 3,
N = 47 3,
N=48 4,
N =48
5,
N=48 6,
N=48 8,
N = 47
Index of inequality of private
land distribution in 1877,
times
.0003
*** .0002
* .0004
*** .0001
**
Ln of the index of inequality
of all land distribution in
1877, times
3.3*** 3.3
***
Ln of the index of inequality
of private land distribution in
1877, times
2.2
** 3.11
** 7.11
*** 2.1
*
Share of large peasant land
holdings (more than 10
dessiatines) in total land
-17.2
*** -20.6
*** -21.7
*** -36.5
** -31.4
*** -17.6
*** -22.0
*** -22.1
***
Share of small land holdings
(less than 10 dessiatines) in
total land, %
2.3
** 2.2
**
GRP per capita in 1897, rubles
.18
*** .13
*** .20
*** .19
***
Share of urban population,
1877, % .6
*** .87
*** .76
*** .7
***
Share of serfs in rural
population in 1858, % -.15
** -.13*
Population density in 1904,
inhabitants per 1 sq. km -.22
** -.17
** -.2
*** -.37
*** -.29
*** -.21
** -.15
** -.2
***
Dummy variable for the
community redistribution of
land in 1900
-17.0
*** -17.4
*** -13.0
*** -17.7
*** -16.6
*** -12.7
***
Share of employment in
industry in 1897, % .57*
Number of students in primary
education facilities per 100
inhabitants in 1897
3.3** 3.3**
Constant 33.6
*** 39.3
*** 13.6 17.0
*** 31.4
*** 32.9
*** 37.4
*** 14.1
R
2
, % 74 79 85 62 73 76 79 86
19
Fig. 7. Average years of schooling in 1897 and literacy rate (%) in 1897
Source: See data section.
Akmola region
Amur region
Arkhangelsk province
Astrakhan province
Baku province
Bessarabian Governorate
Warsaw Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Dagestan region
Yekaterinoslav Governorate
Elisavetpol Governorate
Yenisei province
Transbaikal region
Transcaspian region
Irkutsk province
Kazan province
Kalisz Governorate
Kaluga province
Kara Governorate
Kielce Governorate
Kyiv province
Kovno province
Kostroma province
Kuban region
Courland Governorate
Kursk province
Kutaisi province
Livland Governorate
Lomzhinsky province
Lublin Governorate
Minsk province
Mogilev province
Moscow province
Nizhny Novgorod province
Novgorod province
Region of the Don ArmyOlonets province
Orenburg province
Oryol province
Sakhalin island
Penza province
Perm province
Petrokovskaya province
Plock Governorate
Podolsk province
Poltava province
Primorsky region
Pskov province
Radom Governorate
Ryazan province
Samarkand region
Samara province
St. Petersburg Governorate
Saratov province
Sedlec Governorate
Semipalatinsk region
Semirechensk region
Simbirsk province
Smolensk province
Stavropol province
Suwalki Governorate
Syrdarya region
Tauride province
Tambov province
Tver province
Terek region
Tiflis Governorate
Tobolsk province
Tomsk province
Tula province
Turgai region
Ural region
Ufa province
Fergana region
Kharkov province
Kherson province
Chernigov province
Black Sea Governorate
Erivan Governorate
Estonian province
Yakutsk region
Yaroslavl province
0 .5 1 1.5 2
Average years of schooling in 1897, years
0 20 40 60 80
The proportion of literates in 1887,%
Akmola region
Amur region
Arkhangelsk province
Astrakhan province
Baku province
Bessarabian Governorate
Warsaw Governorate
Vilna province
Vitebsk province
Vladimir province
Vologda province
Volyn province
Voronezh province
Vyatka province
Grodno province
Dagestan region
Yekaterinoslav Governorate
Elisavetpol Governorate
Yenisei province
Transbaikal region
Transcaspian region
Irkutsk province
Kazan province
Kalisz Governorate
Kaluga province
Kara Governorate
Kielce Governorate
Kyiv province
Kovno province
Kostroma province
Kuban region
Courland Governorate
Kursk province
Kutaisi province
Livland Governorate
Lomzhinsky province
Lublin Governorate
Minsk province
Mogilev province
Moscow province
Nizhny Novgorod province
Novgorod province
Region of the Don ArmyOlonets province
Orenburg province
Oryol province
Sakhalin island
Penza province
Perm province
Petrokovskaya province
Plock Governorate
Podolsk province
Poltava province
Primorsky region
Pskov province
Radom Governorate
Ryazan province
Samarkand region
Samara province
St. Petersburg Governorate
Saratov province
Sedlec Governorate
Semipalatinsk region
Semirechensk region
Simbirsk province
Smolensk province
Stavropol province
Suwalki Governorate
Syrdarya region
Tauride province
Tambov province
Tver province
Terek region
Tiflis Governorate
Tobolsk province
Tomsk province
Tula province
Turgai region
Ural region
Ufa province
Fergana region
Kharkov province
Kherson province
Chernigov province
Black Sea Governorate
Erivan Governorate
Estonian province
Yakutsk region
Yaroslavl province
0 .5 1 1.5 2
Average years of schooling in 1897, years
0 20 40 60 80
Share of residents with primary education in 1897, %
20
Table 3. Regressions of the average number of the years of schooling on land inequality indices
and control variables
Dependent variable Average number of the years of schooling
Equation, N //
Indicator 1,
N= 48 2,
N = 48 3,
N=47 4,
N = 47 5,
N=48 6,
N =34
Index of inequality of private
land distribution in 1877,
times
Index of inequality of all land
distribution in 1877, times .003***
Ln index of inequality of
private land distribution in
1877, times
.16*** .10*** .07*** .07**
Share of large peasants’ land
holdings (more than 10
dessiatines) in the total land
-.8*** -.06*** -.7*** -.7***
Share of small private land
holdings (less than 10
dessiatines) in the total private
land, %
.11** .07** .07*** .07*** .02***
GRP per capita in 1897, rubles
.006*** .003** .002***
Share of urban population,
1877, % .03*** .03*** .01*** .02***
Share of serfs in rural
population in 1858, % -.003* -.003**
Population density in 1904,
inhabitants per 1 sq. km -.008** -
.007**
*
-.004** -.004***
Dummy variable for the
community redistribution of
land in 1900
-.3*** -.3*** -.34***
Average annual expenditures
of local self-government
bodies (zemstva) per capita in
1868-1903, rubles
-.06***
Number of students in primary
education facilities per 100
inhabitants in 1897
.09*** .05***
Constant -.21 -.31 .46*** .66** .33** .23***
R
2
, % 29 71 84 87 89 98
The inclusion of the zemstvo expenditure per person variable into the right hand side yields an
unexpected result the variable acquires the negative sign, i.e. the higher were the zemstva total
21
expenditure per capita, the lower was human capital (equation 6 in table 3). This result points out to the
existence of alternative mechanism of the financing of human capital (not via zemstva expenditure).
The number of primary school students per 100 inhabitants (flow) predictably has a positive impact on
the level of human capital measured by the number of the years of schooling (stock) equation 4,5,
which means that there was another way of financing of schools except for zemstva financing.
To put it differently, zemstva most likely were doing the right thing spending more in regions with
lower educational levels, but these efforts did not succeed in increasing noticeably the level of human
capital in the region. We deal with the issue in the next section (“Interpretation”), showing that human
capital in relatively rich periphery regions with high inequality in land distribution was higher because
of the educational expenditure of the central government and local city authorities, and despite the
lower expenditure of the zemstva.
Table 4 reports the results of the regressions, explaining the indicators of inequality in the distribution
of human capital – after the inclusion of all possible controls (urbanization and the share of industry
in employment and value added, population density, GRP per capita and harvest yields, share of serfs
before 1861 and community redistribution of land dummy) it turns out that more even distribution of
human capital across population was associated with higher, not lower inequality in land distribution.
This is true for both indicators of the evenness of the distribution of human capital that we use – share
of the inhabitants with secondary and higher educational degree divided by the average number of years
of schooling and the ratio of the number of inhabitants with secondary and higher degrees to those with
primary degree only.
Such a relationship is not surprising the bulk of the educational activities were taking place at the
primary level (the number of inhabitants with secondary and higher educational degrees was in most
regions less than 1% of the population), so the regions with higher share of inhabitants with primary
education were also the regions with the low inequality in the distribution of human capital.
22
Table 4. Regressions of indices of inequality in the distribution of human capital on land
distribution inequality and control variables
Dependent variable Share of the inhabitants with secondary
and higher educational degree divided by
the average number of years of schooling
Number of inhabitants
with secondary and
higher degree to those
with primary degree only
Equation, N //
Indicator 1,
N= 49 2,
N=48 3,
N=47 4,
N =47 5,
N=48 6,
N=47
Index of inequality of private
land distribution in 1877, times
-.00001
*** -4.6e-07
***
Ln of the index of inequality of
private land distribution in
1877, times
-.07*** -.05* -.005***
Share of small private land
holdings (less than 10
dessiatines) in the total private
land
-.005***
GRP per capita in 1897, rubles
Share of urban population,
1877, % .03*** .04*** .04*** .04*** .002*** .002***
Share of serfs in rural
population in 1858, %
Population density in 1904,
inhabitants per 1 sq. km .009**
* .008***
Dummy variable for the
community redistribution of
land in 1900
.9*** .9*** .9*** .8*** .03*** .03***
Average harvest yield for 10
years, c/ha (1907 year - the
middle of the period)
.09** .08** .003** .003**
Share of industry in GRP in
1897, % -
.008**
Share of employment in
industry in 1897, % -.03** -.04*** -.03*** -.001** -.001***
Constant .5*** .2 .6* .8*** -.01 .02
R
2
, % 66 67 70 71 74 79
Discussion and interpretation
The negative relationship between the share of land holdings larger than 50 hectares (ha) in the total
land area and the level of numeracy that was found by Baten and Hippe (2018) does not mean that
inequality is negatively related to numeracy. The land plots of over 50 ha, according to Baten and
23
Hippe, accounted on average for 37 to 65% of total land in 6 European countries (Spain, Italy,
Hungary, Russia, Poland, UK) at a time. The threshold of 50 hectares is too low to characterize
inequality because it includes the estates with a land area of a good half of the total. For the sake of
the argument, imagine a country with very even distribution of land holdings (one half of all plots
with the size of 51 hectares, the other half – 49 hectares); the share of land plots of over 50 hectares
would be 51% in this case. And if the distribution would be totally even (all land plots have equal
size of 51 hectares), the share of land holdings of over 50 hectares would be 100%. It is not a 100%
inequality, but a 100% equality.
In 2000 the average size of the farm was 40 ha in Germany, 45 ha in France, 178 ha in the USA, 273
ha in Canada (Lowder, Skoet, Raney, 2016). The average size of collective and state farms in the
USSR was several hundred hectares. A 50 hectares’ land plot is just a square with a side of a 700
meters only.
To put it differently, the share of large land holdings in total land could be an indicator of high
inequality only if the threshold is high enough. The 50-hectare threshold is extremely low for Europe
and especially for Russia in 1877 the estates with over 500 dessiatines (1 dessiatine = 1.09 ha)
accounted for over 50% of agricultural land in all provinces except for 2 (Yaroslavl’ and
Archangelsk); in some provinces the share of these large estates was as high as 95% (Popov,
Konchakov, Didenko, 2023).
In 1905 only in 9 provinces of European part of Russia out of 50 the share of land holdings of under
100 dessyatines in total private land exceeded 20%, in most provinces it was less than 10%, i.e. about
90% of total land was occupied by land estates of over 100 dessyatines (TsSK MVD (1907).
So, if the indicator of the share of land holdings over 50 hectares characterizes inequality, it is only
in a way opposite to the Baten-Hippe interpretation. The higher the indicator, the lower is the share
of land plots of less than 50 hectares (small land plots that accounted probably for only 5% or so of
total private land), and the lower, not higher, the inequality in land distribution.
24
These are exactly our results – the share of land plots of over 10 dessyatines in total land area has a
negative impact on human capital. These results support the Baten and Hippe findings, but they
should be interpreted differently. Strictly speaking, it is possible to have a very even and a very
uneven land distribution patterns with the same share of land plots of over 10 dessiatines in total land:
but if the average size of the plot is well above 10 dessiatines, the high share of these relatively small
plots is likely to point out to a greater equality in land distribution (more land in the hands of small
owners – something that usually happens after the egalitarian land reform dividing large estates into
small farms).
Or, to be more precise, this indicator characterizes not the inequality, but the scale of relative poverty
– if all peasants have plots of over 10 dessiatines, there are no relatively poor peasants (with plots of
less than 10 dessiatines). No wonder, in our regressions the stock of the human capital is negatively
linked to the share of land plots of over 10 dessyatines (at a lower stage of development the
accumulation of human capital is faster in unequal and deeply polarized societies with poverty on the
one end and wealth on the other).
The anecdotal evidence of the distribution of land in Russia may be even more telling. The Russian
noblemen, the Orlov brothers, for instance, after helping Catherine the Great to take the throne in
1762, were given in 1768 the huge estate on the Volga river (instead of several smaller estates in
Central region) with an area of over 100,000 dessiatines and nearly 10,000 serfs (the area of the
country of Luxemburg is only 2.5 times larger). In the Chekov’s “Cherry Orchard” play the medium
size estate that was sold by the landlady Ms. Ranevskaya to the industrialist Mr. Lopakhin had an
area of 1100 hectares.
The regions of the Russian Empire that had high inequality in land distribution were relatively more
prosperous and located not in the center, but in the periphery of the Empire (Popov, Konchakov,
Didenko, 2023). Land distribution statistics is available only for the European part of the country, but
for this limited sample one can observe the positive link between the level of development (GRP per
capita) and the inequality in land distribution (fig. 8): in more affluent and less populous regions
(periphery) inequality in land distribution was higher (see footnote 4).
25
Fig. 8. Indices of inequality in land distribution (ln) in 1877 and GRP per capita in 1897,
rubles
Source: See data section.
So, the human capital was higher in the relatively prosperous regions with high inequality in the
distribution of land – exactly as regressions in the previous section suggest, showing positive impact
of GRP per capita, harvest yields, and inequality in land distribution on the literacy rates, years of
schooling and evenness in the distribution of educational attainments among population.
The natural question, of course, is about the mechanism at work ensuring that provinces with higher
inequality in land distribution had higher and more evenly distributed human capital. It does not seem
to be caused by the rural local government zemstva educational activity9. If the indicator of
zemstva expenditure per capita in 1868-1903 is added into the right hand side of the equation
9 Educational activities of the zemstva were divided between rural (most) and (somewhat) urban areas. The former were
district administrations (uezdnye zemstva), which dealt with rural primary schools, while provincial ones (gubernskie
zemstva) did so with secondary, vocational and higher schools which were mostly urban (Abramov, 1996, p. 110-126).
Various zemstva actors were vocal supporters of proliferation of education for the masses. Similar views came from the
government officials, for instance Nikolai Bogolepov, rector of Moscow University in 1893, subsequently curator of
Moscow educational district and Minister of Education in 1898-1901 (Alston, 1969, p. 141).
0
2
4
6
8
10
12
-2
-1
0
1
2
3
4
5
6
0 50 100 150 200 250 300 350
GRP per capita, rubles
Inequality index for all land, ln
Inequality index for private land, ln
26
explaining the level and evenness of distribution of human capital, it acquires the negative sign or is
insignificant (table 4)10.
But zemstva accounted for only part of the expenditure for education and this part formally was only
auxiliary (Abramov, 1996, p. 26). Even though zemstva share was the largest part of the expenditure
in the 1870s-80s, in the 1890s the share of the central government (including the Holy Synod) was
increasing and exceeded that of the zemstva by the 1900s (Didenko, 2021, p. 137-138).
The financing of education at the turn of the century came from several sources – Ministry of
Peoples’ Enlightenment, local rural authorities (zemstva), local urban authorities (upravy), fees for
educational services, church authorities, charitable donation. In the regions of European part of the
country the share of zemstva was about 1/3 of total financing with similar amounts coming from the
Ministry, whereas in the Eastern regions the share of the financing from the Ministry was usually
over 50%, and zemstva did not exist at all, even though there were zemstva taxes collected and
managed by the central government (Didenko, 2021).
Sample data on financing of education (only for 14 regions – 8 provinces in the European part of
Russia and 6 provinces in Siberia and Far East) are presented in table 5. The data suggest that total
expenditures for education per capita as a rule were several times higher in Siberia and Far East
than in the European regions of the country (fig. 9), and this was true for two major components of
these expenditures – central government financing via the Ministry of Peoples’ Enlightenment and
local city governments financing, but not the zemstva.
In the European provinces city authorities’ (upravy) share in total education expenditures did not
change much over time, it was about 10% in 1870-1914 (Didenko 2021, table 8 and Table 5).
10 Zemstva expenditure are not linked to literacy levels, but depend positively on GRP per capita and negatively – on the
share of serfs in rural population in 1858.
ZEMSTVOexp35 = 1.3*** -.004* GRPcap – .009*SERFshare1858, robust standard errors,
N=34, R2 = 0.25. Here and later – standard notations: *** - significant at 1%, **- 5%, *- 10%.
ZEMSTVOexp35 – average annual expenditures of local self-government bodies (zemstva) per capita in 1868-1903,
rubles,
GRPcap – GRP per capita in 1897, rubles,
SERFshare1858 – share of serfs in rural population in 1858, %.
27
Table 5. Expenditure on education per capita by major sources in 14 regions of the
Russian Empire in 1897, rubles
Region
Education
expenditure
per capita,
total
Education
expenditure
per capita,
zemstva
Education
expenditure per
capita, central
government
Education
expenditure
per capita,
cities
GRP
per
capita
Inequality
index for
all land
European part of Russia
Voronezh
governorate 0.29 0.08 0.09 0.04 42 0.36
Vologda
governorate 0.48 0.18 0.15 0.02 49 0.49
Kaluga
governorate 0.68 0.19 0.15 0.05 55 0.47
Kursk
governorate 0.32 0.11 0.09 0.02 47 0.30
Perm’
governorate 0.43 0.20 0.18 0.03 69 2.15
Ryazan
governorate 0.38 0.13 0.03 0.03 49 0.44
Saratov
governorate 0.50 0.11 0.17 0.15 70 0.85
Yaroslavl’
governorate 0.48 0.16 0.20 0.07 119 0.39
Siberia and Far East
Primorskiy
region 1.57 0.01 1.27 0.17 294
Amur
region 1.36 0.05 0.69 0.36 148
Yenisey
governorate 0.82 0.06 0.26 0.06 86
Tomsk
governorate 0.57 0.04 0.42 0.03 66
Irkutsk
governorate 0.99 0.04 0.60 0.11 102
Tobol’sk
governorate 0.39 0.07 0.11 0.03 49
Source: Governors’ annual reports; Kessler, Markevich, 2014; numbers highlighted in red are
calculated by Didenko based on model assumptions (Didenko, 2021).
In the Far East and Siberia, the central government played a greater role in financing education than
in the European provinces. Also, in most of the Far Eastern and some of the Siberian provinces
28
institutional structure of financing education was shifted to city administrations. They were more
active in proliferation and financing of schooling (especially primary) than their counterparts in
European Russia11.
Fig. 9. Educational expenditure per capita in selected regions and GRP per capita in 1897
Source: Table 5.
Unlike zemstva, central government and city administrations (upravy) were spending money in
relatively well-off regions with high inequality in land distribution, and their spending resulted in
relatively higher levels of human capital in these regions.
Why the central government and the city authorities were spending more money per capita on
education in relatively prosperous regions with already high levels of educational attainments? Galor
(2012, p. 44) is citing Johnson (1969), claiming that large land owners were not interested in the
education of the peasants (trying to keep them in the villages) unlike industrialists that were interested
11 See e.g. Shilov (2008, p. 20, 418-420).
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 50 100 150 200 250 300
EDUCATION EXPENDITURE PER CAPITA, RUBLES
GRP PER CAPITA, RUBLES
Education
expenditure per
capita, total
Education
expenditure per
capita, central
government
Education
expenditure per
capita, cities
Education
expenditure per
capita, zemstva
29
in educated labor force in the cities12. It could be true, but their interests did not determine the
financing flows in any case. Neither the central government budget, nor city finances were dependent
on land taxes. In 1897 only 3% of total tax revenues of the government budget came from land taxes,
whereas over 1/3 of all tax revenues were collected from the excise tax on alcoholic beverages
(Shatsillo, 2003, table 2).
In contrast, zemstva revenues came from taxation of real estate property (most) and entrepreneurial
activities (a lot) with certain ceiling limits established by the central government (Abramov, 1996, p.
14-15, 20-21; Naftziger, 2011, p. 400).
To put it differently, there was a lack of pro-active education policy before the first Russian
revolution, the government mostly relied on the market in the formation of human capital. Various
levels of governments (except for zemstvo that were created in 1864 and by 1914 existed in rural
areas of 43 regions of European Russia) were going with the grain, spending money on education in
relatively wealthy provinces, where the revenues of the budgets were higher. And, as was argued
previously, these were exactly the periphery provinces with the high inequality in land distribution.
Zemstvas were created to bridge the gap that emerged between the government and the rural areas
after the abolition of serfdom and the loss of the gentry’s control over the village. The rural elections
were designed to ensure the pre-dominance of large rural landowners at the expense of the peasantry
and towns. However, the zemstva staff was hired from gymnasium and university graduates, i.e. not
from the gentry, but from the “third estate” (Alstom, 1969, p. 59; Eklof, 1986, p. 55-56, 61-62).
In the 20th century (and perhaps even since the 1890s13), especially after the first Russian revolution,
the government stepped up its efforts in the formation of human capital – in 1908-12 the discussions
in the State Duma (created during the first Revolution) resulted in the decision to introduce obligatory
12 “Provincial councils dominated by wealthier landowners were responsible for their local school systems and were
reluctant to favor the education of the peasants (Johnson, 1969)” (Galor, 2012, p. 44). Similar pattern for the period of
the 1860s-80s is thoroughly documented in (Eklof, 1986, p. 72-83).
13 “A revolution in school finances occurred in the 1890s, the result of a joint government-zemstvo endeavor— both had
given low priority to popular education until 1890, but both moved rapidly after that date to bring about universal
education.” (Eklof, 1986, p. 88).
30
primary education in the European part of Russia – by 1918, and in the whole Empire – by the end
of the 1920s. The bill was finally voted down by the State Council14, but the number of primary
schools and gymnasiums in 1897-1914 increased 1.6 times, the number of schoolchildren 2.1
times15, the number of secondary schools and gymnasiums – 2.0 times, the number of schoolchildren
in them – 2.5 times16. The share of the entire population that was actively attending schools increased
threefold from 1.7% in 1897 to 5.7% in 1915 (Dennis, 1961).
Also, the education expenditures of zemstva has grown significantly: their share in the total
expenditures increased from 7.7% in 1871 to 28.1% in 1913 (Naftziger, 2011, p. 400). The level of
representation of the peasant curia had a positive effect on the level of zemstvo expenditures on
education (Naftziger 2011, p. 415-431).
Even so, by 1914 Russia was very much behind European countries in this respect – the number of
school attendees was only 59 per 1000 inhabitants as compared to 143 in Austria, 152 in Great Britain,
175 in Germany, 213 in the US, 148 in France, 146 in Japan (Mironov, 2018, p. 759).
But in 1897, when the government was going largely with the flow, and its educational expenditure
were determined by the relative incomes of the regions in question, human capital formation was
proceeding slowly and mostly in rich regions with high inequality in land distribution.
This picture is consistent with other research on human capital in the Russian regions at the turn of
the 20th century. The paper by Popov, Konchakov and Didenko (2023) finds a positive correlation
between the growth of social protest before the first Russian revolution and the inequality in land
distribution. It turns out that all three indicators of the unrest – increase in peasants’ uprising, strikes
at industrial enterprises and crimes against persons – were higher in the regions with high inequality
in the land distribution. These were mostly the provinces of the periphery of the Empire, with
14 About the discussion of the bill and its legislative track see Santa Maria (1990, p, 56-57).
15 As it follows from the data in DNP (1898) and TsSK MVD (1916).
16 As it follows from the data in Kessler and Markevich (2014) and TsSK MVD (1916).
31
generally higher GRP per capita and incomes than in the center and with a higher level of human
capital (literacy and average years of schooling).
Whereas literacy had a negative effect on the increase in domestic violence, it had either a significant
positive effect on social unrest (increase in strikes) or was insignificant (increase in peasants’ unrest).
Success rate of strikes, though, was linked positively with education (literacy rate and the average
number of years of schooling) in 1895-99, but in 1900-04 the relationship was negative: in the late
19th century strikes were successful mostly in educated regions, whereas in 1900-04 less educated
regions became successful in their strikes’ activity as well.
Conclusions
The major result of this paper is different from many studies of inequality and human capital. We
find that in the regions of Russian Empire in 1897 the uneven distribution of land was associated with
higher levels of human capital (as measured by the average years of schooling and literacy rate),
whereas the distribution of the human capital across the regional population (as measured by the
proportions of inhabitants with higher, secondary and primary education) was more even.
Regions with higher human capital were located mostly in the periphery of the Russian Empire, were
relatively well-off (higher agricultural yields and higher per capita incomes) and had higher per capita
educational expenditure financed by the central government and local city authorities.
We interpret the results in the framework of the unified theory of the inequality and growth: at low
levels of development (industrialization stage) greater inequality in asset distribution and related
inequality in incomes lead to the increase in the savings rate and encouraged investment into physical
and human capital, thus stimulating incomes and growth. Higher incomes in turn yielded higher
revenues for the central and local governments, so expenditure for education also rose.
It was not local rural authorities zemstva that managed to increase the level of human capital:
even though they were spending more per capita in regions with low level of education, these
spending did not make much of a difference – human capital in these regions remained low.
32
This insight can add an important argument to the debates of the time whether zemstva could
transform Russian communal pre-capitalist village into the capitalist “American type” farming or
whether the zemstva activities were just a palliative care that did not affect the root causes of
inequality. Narodniks (one group of socialist reformers) believed that transition to socialism was
possible through the agricultural community and zemstva activities, whereas Marxists (social
democrats at the time) considered a revolution a sine qua non for social progress17.
The revealed link between the inequality and human capital is in line with previous research showing
that higher land inequality was associated with more advanced capitalist transformation of the rural
social structures (buyouts of community land by the peasants and purchases of the land of the nobility
by merchants and industrialists), higher social protest and higher human capital (Popov, Konchakov,
Didenko, 2023).
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Acknowledgements
This study was supported by the Ministry of Science and Higher Education of the Russian Federation
(grant ID: 075-15-2022-325). The authors thank our colleagues Evgenii Grishin, Maria Karpenko,
Igor Kuznetsov, Anna Nifontova, and Ruben Vartanian for their assistance in data processing. Special
gratitude is to Maria Karpenko for her assistance in organizing the research.
... The previous research with incomplete data revealed that zemstva expenditure on education per capita were higher in regions with low level of education, but these spending did not make much of a differencehuman capital in these regions remained relatively low (Popov, Konchakov, Didenko, 2024). The results reported in this paper provide additional and more rigorous proof that zemstva activities and the increase in their spending for education in 1897-1913 contributed to the spread of primary education and to the decline in the inequality of the distribution of human capital not only between the regions< but also within the regions (ratio of secondary to primary education enrollment). ...
... One of the results of the previous research is that human capital (as measured by literacy rates and years of schooling), as well as evenness in the distribution of educational attainments among population, was higher in the relatively prosperous regions of the Russian Empire in 1897 with higher GRP per capita, harvest yields, and inequality in land distribution (Popov, Konchakov, Didenko, 2024). ...
... Data on 14 regions seemed to suggest that it was not caused by the rural local governmentzemstvaeducational activity 2 . If the indicator of zemstva expenditure per capita in 1868-1903 is added into the right hand side of the equation explaining the level and evenness of distribution of human capital, it acquires the negative sign or is insignificant (Popov, Konchakov, Didenko, 2024, table 4) 3 . ...
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The previous research with incomplete data revealed that zemstva were spending more per capita in regions with low level of education, but these spending did not make much of a difference – human capital in these regions remained relatively low (Popov, Konchakov, Didenko, 2024). The results reported in this paper provide additional and more rigorous proof that zemstva activities and the increase in their spending for education in 1897-1913 contributed to the spread of primary education and to the decline in the inequality of the distribution of human capital within the regions (ratio of secondary to primary education enrollment). But we also show that there were more powerful forces at play – education for tuition fees, central government and city/town administration financing, that were pushing the development in an opposite direction, increasing the secondary education enrollment in most regions faster than the primary education enrollment. The result was the widening gap between low and high educated individuals that could have contributed to the formation of the intelligentsia phenomenon – educated intellectuals that were not able to find the proper place in the national economy to apply their knowledge. Intelligentsia opposition to the tsarist regime, however, did not take violent forms –regions with fast growing educational disparities registered lower, not higher increases in peasants’ unrest, industrial strikes and crimes against persons.
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Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million farms worldwide, most of which are small and family-operated. It shows that small farms (less than 2 ha) operate about 12% and family farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information.
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The rise of the West is often attributed the presence of certain features in Western countries from the 16th century that were absent in more traditional societies: the abolition of serfdom and Protestant ethics, the protection of property rights, and free universities. The problem with this reasoning is that, before the 16th century, there were many countries with social structures that possessed these same features that didn't experience rapid productivity growth. This book offers a new interpretation of the 'Great Divergence' and 'Great Convergence' stories. It explores how Western countries grew rich and why parts of the developing world (South and East Asia and the Middle East) did not catch up with the West from 1500 to 1950 but began to narrow the gap after 1950. It also examines why others (Latin America, South Africa, and Russia) were more successful at catching up from 1500 to 1950, but then experienced a slowdown in economic growth compared to other developing countries. Mixed Fortunes offers a novel interpretation of the rise of the West and of the subsequent development of 'the rest' and China and Russia, important examples of two groups of developing countries, are examined in greater detail.
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We document a very large increase in agricultural productivity, peasants’ living standards, and industrial development in the 19th century Imperial Russia as a result of the abolition of serfdom. We construct a novel province-level panel dataset of development outcomes and conduct a difference-in-differences analysis relying on cross-sectional variation in the shares of serfs and over-time variation in emancipation controlling for region-specific trends. We disentangle the effects of the emancipation and the subsequent land reform and show that land reform contributed negatively to agricultural productivity in contrast to a large positive effect of the emancipation. The evidence is consistent with the increase in the power of the peasant commune as the channel of the negative effect of the land reform. The different organizational forms of serfdom were associated with different levels of nutrition of serfs and productivity. The emancipation of serfs from estates where serfs were obliged to work on the landlord’s farm (corvee, barshchina) caused an increase in height of their children by 1.6 centimeters. Estates where serfs were required to make in kind payment to the landlord (quitrent, obrok) were equally productive, but, in contrast, their emancipation did not lead to rise in their height. Commitment to an implicit longer-term contract on the amount of serf obligations to landlords, practiced in some estates, made serfdom more productive.