Sources of income and changes in inequality in Early
Modern Spain: what can we learn from microsimulations? *
Esteban A. Nicolini
Universidad del Norte Santo Tomás de Aquino
Universidad Carlos III de Madrid
Universidad Nacional de Tucumán
Fernando Ramos Palencia
Universidad Pablo de Olavide de Sevilla
Ana Suárez Álvarez
Universidad Carlos III de Madrid
VERY PRELIMINARY VERSION. PLEASE DO NOT CITE WHITOUT
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Abstract
Among the most important hypotheses about the determinant of the changes of economic
inequality in early Modern Europe there are (1) structural change and expansion of modern
sectors in manufacturing and trade, (2) changes in functional distribution of incomes and in the
property of the means of production and (3) changes in labour retribution and skill premium.
Testing these hypotheses has proved to be very difficult because most of the relevant variables
are endogenous and there is scarce basic information to apply the required econometric tests.
In this paper we use a data set with more than four thousand household incomes in the
north of Spain circa 1750 to improve our understanding of the causes of changes in inequality.
Our data set provides information about different income sources (income from land, livestock,
labour, entrepreneurial activities, etc.), place of residence, occupation of the head of the
household, age and demographic structure of the household. With this unique data set we test
the quantitative relevance of the hypotheses mentioned above through very simple
microsimulations. In particular, we compare the actual levels of inequality observed in our
sample with the levels of inequality observed in a counterfactual distribution constructed to
simulate the income of each household after a given change in their different sources of income.
These counterfactuals are constructed under alternative scenarios: (1) an increase in the skill
premium, defined as the expansion in the labour earnings of those with high qualification
relative to those with low qualification; (2) a process of proletarianization understood as a
reduction of the share of artisans and self-employed and an increase in the share of unskilled
wage-earners; (3) an increase in the wage of unskilled workers keeping constant the incomes
from land (or an increase in land rents keeping constant the labour earnings); (4) a redistribution
of earnings coming from land. These exercises opens the possibility to test the accuracy of the
ratio between land rents and unskilled wages as a proxy measure of overall income inequality.
JEL Classification Numbers: D31, N33, O15
Keywords: inequality, Spain, income sources, real wage, skill-premium.
* The authors thank comments and previous discussions on the topic with Guido Alfani and Jaime Reis.
Esteban Nicolini gratefully acknowledges continuous financial support by the Universidad del Norte Santo
Tomás de Aquino and financial support by Ministerio de Ciencia y Tecnología –Argentina through PICT 24292013. Fernando Ramos acknowledges financial support by Spanish Ministry of Education and Sciences through
Project ECO2012-38028.
1
1. Introduction
Recent research on the evolution of economic inequality in the long run has
documented varied patterns in different countries in Early Modern Europe: increasing
inequality in a growing economy like Holland (Van Zanden 1995), increasing inequality in a
stagnant Italy (Alfani 2014) and decreasing or stable inequality in stagnant Spain (ÁlvarezNogal and Prados de la Escosura 2007) and Portugal (Pereira et al. 2011).
The ultimate determinants of those observed changes in inequality are not clearly
identified. Kuznets (1955) proposed the idea that the upsurge of inequality in several
countries during the process of industrialization was connected to the first stages of modern
economic growth and the shift of labourers from agricultural and rural activities to
occupations in services and industrial sectors in mostly urban agglomerations; in later stages
of industrialization, when most of the workers were already in the modern urban sector,
inequality tended to diminish. This dynamic generate the so-called inverted-U Kuznets’
curve. Van Zanden (1995) suggested that this link between inequality and structural change
was also present in pre-industrial Europa at least from the 16th century and proposed the
super-Kuznets’ curve whose left upward-sloped tail was expanded to cover the period before
the industrial revolution.1
There are other proposed mechanisms to account for changes in inequality in
Modern Europe. Van Zanden (1995) suggested that, in addition to the Kuznets approach,
there was a “classical” explanation related with changes in the functional distribution of
income. Within this general explanation, there were the process of proletarianization of
labour and the decline in real wages.2 More recently Alfani (2013) suggested that
demographic factors (like population pressure and rural-urban migration) should be
incorporated to the picture.
Testing the validity of these hypothesis is particularly difficult because the more
standard empirical approaches would require a kind of data usually unavailable for Modern
Europe. In fact, some of the estimations of economic inequality for this period are based on
approximations that do not use individual information but aggregated proxies like the
average income per social group (in a framework of social tables)3 or ratios between wages
and land rents or average income.4 When the underlying information is at the household
1
The fact that in Italy the observed growth in inequality is not associated to a process of economic growth
(Alfani 2014) suggests that changes in inequality do probably respond to more complex causes.
2 In addition, Van Zanden suggests the possible importance of the accumulation of capital in the hand of urban
citizenry in the junctions of international trade (Van Zanden 1995, p. 656).
3 For instance Lindert and Williamson (1982) for England and Milanovic, Lindert and Williamson (2010) for
twenty eight different pre-industrial societies.
4 For instance Alvarez Nogal and Prados de la Escosura (2007) for Spain.
2
level, it is quite common that the variable used to calculate inequality is neither income nor
wealth but a proxy whose relation with those variables is not established beyond dispute. For
instance, Soltow and Van Zanden (1998) use the rental value of dwelling to approximate
income and Alfani (2013) uses real estate (lands and buildings) to approximate wealth.5 As
long as these variables are linear transformations of the variable of interest they can be safely
used to measure inequality. However, given that they do not provide information on the
different sources of income, it is quite difficult to assess how certain economic changes
would affect incomes in the different parts of the distribution. In order to be able to explore
the quantitative validity of the available hypothesis, it is necessary to have information about
the different sources of income (mainly labour and land) for each household, on the
occupation of the head of the household and on the skill associated to each occupation.
In the middle of the 18th century, the Spanish monarchy decided to change the tax
system prevailing in Old Castile and undertook a massive recollection of information about
the economic situation of each locality in that region. Given that the idea was to assess the
tax capacity of households (and institutions like the church), detailed information about some
assets, incomes and demographic characteristics of the population was registered. The
surviving records of the EC provide a unique source to analyse incomes of individual
households in many regions of Spain with the particular advantage that not only a detailed
description of many characteristics of each household is provided but also that the income
of each household is registered as the sum of the different sources of income (labour, land,
and livestock among others). This characteristic of the data opens the possibility of check
how inequality is affected by the impact of some specific shocks in certain income sources
of particular subsets of the distribution. For this research, we use a data set with more than
four thousands observations from the province of Palencia that can be considered
representative of the approximately twenty five thousand households in that province.
The main contribution of this paper is to reformulate some of the main available
hypothesis for changes in inequality in Modern Europe in terms of specific economic
changes in some incomes in certain sub-sets of the households and then provide an
assessment of the plausible quantitative impact of each hypothesis over income inequality.
Our results suggest that only an important redistribution of land income and a considerable
5
A quite common idea is that the different components of wealth and income are highly correlated. For
instance Alfany (2013, p. 8) says that “For preindustrial societies, in which most of the product was agrarian,
wealth inequality is a good proxy of income inequality, not only because the property of land (or more precisely,
the right to the use of land) was of great importance in defining how the total product was distributed, but also
because it is very unlikely that, in such a society, income and wealth inequality could move in different
directions”.
3
reduction in wages of unskilled workers produce a significant change in income inequality;
in general, the impacts of the other hypotheses on the observed inequality in our data set are
rather small.
The rest of the paper is structured as follows: in the second section the data set is
presented and in the third one we characterize the household incomes of relevant subsets of
the population (discriminated mainly according to occupation and skill level). In the fourth
section we describe the way in which the counterfactuals linked to each hypothesis are
generated and present the results of each of them. In the Section 5 the conclusion is
presented.
2. The data
The Ensenada Cadastre (EC hereafter), a census carried out by the Marquis of
Ensenada during the reign of Ferdinand VI (1746-1759), was aimed at the simplification and
reform of the crown of Castile’s complex system of taxation. The main target was the
introduction of a ‘single tax’, payable by all persons and calculated proportionally according
to their level of wealth. The determination of the amount payable by each individual involved
an exhaustive effort which has not been equalled in Spain to date and which had no parallels
in Modern Europe. The peculiarity of the census stems from the fact that it not only included
peasants and the middle classes but also the higher classes (aristocracy and clergy), often
exempt from certain taxes. The census included individual and institutional wealth
statements supervised by teams of experts who appraised the value of the real estate
registered. Individual declarations were afterwards grouped at the local and provincial level
for the calculation of the ‘single tax’.
The historical record of the census is essentially divided into ‘Respuestas Generales’, so
defined by the Junta Real, and the ‘Respuestas particulares’, defined by Antonio Matilla (1947).
The Respuestas Generales were the answers given by each town to a set of forty questions on
such issues as name and location, the number of citizens and the citizens’ sources of wealth.
These records therefore offer a general perspective on each town; although the information
contained in them is less exhaustive than that reported in the Respuestas particulares, the
amount of information on vital economic variables – for example production processes or
demography – is nevertheless still huge. Specifically, the Mapas o Estados Generales
compiles very valuable information about the net income of domestic economies at the
provincial level.
The Respuestas Particulares, on the other hand, recorded the individual wealth of each
person, commons and ecclesiastical properties. In this regard the census was extremely
4
detailed. In the first place, the so called ‘Memoriales’ recorded the name of the head of the
family and of the rest of family members (spouse, children, other family members and
domestics), along with their respective ages and occupations. Equally, the head declared
urban and rural properties owned, mortgages (owed and owned), and livestock. Finally, the
head of the family stated the remuneration received in the exercise of his job (recorded as
‘personal’) and of other activities (land leases, exchange, etc.); the wages earned by other male
members of the family and the domestic servants were similarly recorded. It must be
highlighted that the head of the family rarely included the wages earned by his wife and
daughters in his statement. These statements were signed by the head of the family or by a
third person if they were illiterate. In general, the information contained in these Memoriales
was the most extensive, because afterwards officials only used it partially in the elaboration
of the Libros de Hacienda (Camarero Bullón 1989, 1991).
After this, and once the supervisors had been consulted, the Oficiales de la Única
Contribución compiled this information into two kinds of book: (i) Libros de Cabeza de Familia
with personal information (name of the head of the family, age and number of people
contributing to the domestic economy, including children, siblings, domestic servants,
employees, apprentices, etc.); and (ii) Libros de Hacienda o de lo Personal, the annual income
derived from urban and rural leases, mortgages and other duties, livestock and personnel
(including daily wages and the utilities connected with the specific trade). The analysis of the
supervisors, who were generally well acquainted with the locality that they were inspecting,
was exhaustive, including the physical measurement of plots of land and houses and a very
close supervision of wages.
A. Database
From this census we have collected 4131 records in the province of Palencia, in the
North of Spain. Each record has information on household’s income, occupation of the head
of the household, place of residence, and demographic characteristics of the household.
Following Nicolini and Ramos (2015), we have distinguished the following sources of gross
income (all measured in reales) in the Cadastre: (i) income derived from land; (i) income
derived from non-land properties: urban and rural buildings (for instance, houses in the cities
or mills in the countryside); (iii) income derived from livestock; (iv) ‘Censos’ (taxes, fees or
credits and/or taxes, fees or debts from land or properties or, exceptionally, from financial
assets or liabilities); and (v), personal or labor earnings. The labor earnings are the sum of
other five components: (a) labor income of the main activity of the head of the household ;
(b) income from trade associated to the main activity; (c) labor income derived from a second
5
occupation; (d) income from trade associated to other activities different from the main
activity; (e) finally, income derived from agro-pastoral activities in land rented from others.
This is net income after paying the land rents. We have compiled these data for ten localities:
Bustillo de la Vega, Cevico Navero, Hontoria de Cerrato, Palencia city, Paredes de Campo,
Resoba, Valberzoso, Villabellaco, Villabermudo and Villarramiel. In order to make our data
set representative of the whole set of households in Palencia, we have calculated weights that
correct the fact that not all the households had the same probability of being included in the
data set.6
We have crossed the list of occupations resulting from the EC with the professions
listed in History of Work Information System (HISCO). From the 1950s onwards, the
International Labour Organization has developed an International Standard Classification of
Occupations (ISCO) that allows us to classify professional activities across the world. This
is an international and historical occupational information system that simultaneously
connects the classifications with those that are currently used. The information system uses
the Historical International Classification of Occupations (HISCO) to combine various types
of information on activities and functions in historical scenarios. Additionally we have used
the web associated to HISCO: http://historyofwork.iisg.nl/index.php; the primary,
secondary, tertiary system of occupational coding developed by Wrigley and Davies; and, the
information from van Leeuwen and Maas (2011) who develop a new class scheme HISCLASS- created for the purpose of making comparisons across different periods,
countries and languages. Thus, we have got the occupational skills (high, medium, low and
unskilled) and the type of profession (manual versus non-manual). Finally we have
distinguished between self-employed or wage-earning worker considering the criteria used in
Ensenada Cadastre.
3. Characteristics of the households
We have used three criteria to characterize the different groups of individuals (i) level
of labour skills, (ii) distinction between self-employed and the wage-earning workers and (iii)
manual or non-manual professions.
A. Skill level
Following the premises specified by the History of Work Information System
(HISCO), the occupational skills of each head of the household can be found in the database.
They are divided into four different categories: high, medium, low and unskilled. In table 1,
6
For more details about the weights see Nicolini and Ramos (2015).
6
it is observed that most individuals are unskilled (43.9%), while they are also the group of
workers with the lowest average income. Regarding the inequality index for the incomes per
skill group, it can be seen that the unskilled workers belong to the group with the lowest level
of inequality, followed by the low-skilled workers. The fact that the income inequality in
these two groups is the lowest justifies the use of the land rent/wage ratio or total
income/wage ratio (Álvarez-Nogal and Prados de la Escosura, 2007). The reason is because
the ratio between land rents and wages measure the weight between the most unequally
distributed factor (land) and the most equal (unskilled workers’ wages).
Table 1
Income distribution according to skill level:
inequality measures
Skill level
Mean income
(in reales)
Individuals (%)
High
Relative Mean
Proportion of
income (%)
Gini
GE(2)
2.60%
2406.95
2.382
6.20%
0.529
0.583
Medium
12.37%
2090.06
2.068
25.57%
0.504
0.809
Low
27.03%
1179.47
1.167
31.55%
0.334
0.352
Unskilled
43.94%
702.14
0.695
30.53%
0.323
0.235
0.07%
136.04
0.135
0.09%
0.633
0.756
13.36%
457.89
0.453
6.05%
0.726
4.645
between-group inequality
0.821
within-group inequality
0.142
Disabled people
NA
B. Self-employed workers and the wage-earning workers.
As shown in the table, most of the workers in the sample belong to the wage-earning
workers (71%), although their average income is less than half of that earned by selfemployed workers. Regarding the inequality in each group, it is observed that inequality is
higher among self-employed workers than the wage-earning workers. This could be because
the wage-earning workers group is mostly made up of unskilled workers, among which there
less inequality is shown.
Table 2
Income distribution according to worker type (self-employed or wageearning worker): inequality measures
Self-employed
14.27%
Mean
income
(in reales)
2077.44
Wage-earning worker
70.83%
918.49
0.909
Not specified
14.91%
427.29
0.423
Worker type
Individuals
(%)
Proportion
of income
(%)
2.056
29.33%
Relative
Mean
Gini
GE(2)
0.512
0.830
64.37%
0.369
0.389
6.30%
0.728
4.813
between-group inequality
0.856
7
within-group inequality
0.107
C. Manual and non-manual professions.
The distinction between manual (categories 1 to 5) and non-manual occupations
(categories 6 to 12) is made by using the HISCLASS system, which is closely linked to the
HISCO classification. In this database, another category is added, numbered as 0 (see table),
which includes the individuals with high incomes who do not belong to either of the
mentioned categories, for example, nobles and aristocrats.
Table 3
Sample distribution according to the HISCLASS classification
Class
number
Class label
Manual /
non manual
Weighted
No.
No.
0*
High incomes (landowners, gentry, aristocracy, etc.)
non-manual
116
129
1
Higher managers
non-manual
30
54
2
Higher professionals
non-manual
128
488
3
Lower managers
non-manual
31
55
4
Lower professionals, and clerical and sales personnel
non-manual
75
298
5
Lower clerical and sales personnel
non-manual
36
210
6
Foremen
Manual
0
0
7
Medium skilled workers
Manual
324
1244
8
Farmers and fishermen
Manual
393
6532
9
Lower skilled workers
Manual
994
5502
10
Lower skilled farm workers
Manual
0
0
11
Unskilled workers
Manual
644
648
12
Unskilled farm workers
Manual
1036
6770
Source: van Leeuwen and Maas (2011),, p. 57
As shown, most workers have manual occupations (95%) and their average income
is practically half of that earned by workers with non-manual professions. Inequality is just
as low among the manual occupations.
8
Table 4
Income distribution according to profession type (manual vs. non-manual):
inequality measures
Profession
type
Manual
Non-manual
Individuals
(%)
Mean
income
(in reales)
Relative
Mean
Proportion
of income
(%)
GINI
GE(2)
94.37%
1060.95
0.9534
89.98%
0.418
0.731
5.63%
1982.55
1.7816
10.03%
0.539
0.760
between-group inequality
0.018
within-group inequality
0.763
4. Microsimulations
There are various hypotheses that try to explain why inequality increased in the preindustrial economies of early modern Europe (Van Zanden 1995, pp. 655-661). For classic
economists (Smith, Ricardo and Marx), inequality is caused by changes in the functional
distribution of income, involving four processes: proletarianization of the labour factor,
reduction of real wages in the long run, capital accumulation in the main urban centres for
international trade and the concentration of capital in powerful urban groups. Traditionally,
Kuznets (1955) suggests that modern economic growth is related to the shifting of labour
from sectors with low wage and productivity levels (agriculture) to sectors with a high wage
and productivity level (industry and services). According to this author, in the first stages of
economic growth, there is a rise in inequality that will progressively decline. More recent
approaches to the study of inequality highlight changes related to productivity and variations
in income distributions due to the skill premium, that is, the skilled/unskilled wage ratio.
In this section, several of these hypotheses will be tested by creating counter-factual
income distributions (microsimulations). Specifically, given the characteristics of the data in
the Ensenada Cadastre, the three hypotheses of the effects on inequality are analyzed: (i) the
process of proletarianisation, (ii) a decline of real wages, and (iii) variations in the skill
premium. To study the effects of the hypotheses described, changes are made to the incomes
of different household subgroups in alternative scenarios. In parallel, the standard measures
of inequality are calculated – Gini index and Generalised Entropy Index (2) – to value how
these changes affected inequality. Finally, the results obtained are compared with the
methodology used by Álvarez-Nogal and Prados de la Escosura (2007). These authors use
the ratios between land rents and total incomes with unskilled workers’ wages as an
approximate measure of total inequality.
9
The simulations are based on changes in the database so that a counter-factual
income distribution is produced. This distribution is compared with the initial distribution
to see the effect of the changes on inequality. To do so, the inequality indices of both
distributions are compared.
A. Process of proletarianisation
The effect of a process of proletarianisation on inequality is studied using two
scenarios.
Scenario 1. Reduction of the proportion of artisans and self-employed workers, and an increase in
the proportion of unskilled wage earners.
The occupations are classified into two groups: self-employed workers and artisans,
and unskilled wage earners. The process of proletarianisation in this case causes an increase
in the proportion of unskilled wage earners and a reduction in artisans and self-employed
workers. The analysis adopts two different procedures: a) the unskilled wage earners’ average
wage is attributed to the group of artisans and self-employed workers; b) for each quintile of
workers, the self-employed and artisans are allocated the average wage of each quintile of
unskilled wage earners.
Scenario 2. Reduction of the proportion of workers with non-manual professions, and an increase of
workers with manual professions.
Manual and non-manual occupations are distinguished by the HISCLASS
classification described earlier. Given that the working class professions are usually manual,
a decrease in the proportion of workers with non-manual occupations in favour of an
increase in the proportion of workers with manual occupations would be construed as a
process of proletarianisation. The analysis is conducted with two different procedures.
Firstly, it is assumed that the workers who “proletarianise” (non-manual) earn a wage equal
to the average wage of manual workers before this process took place. Secondly, a
distribution of labour incomes for non-manual workers similar to that of manual workers is
imposed. In this particular case, each quintile of non-manual workers is assigned the average
wage of each quintile of manual workers.
Initially, this process of proletarianisation, understood as a process whereby nonmanual workers begin to work in manual professions, would give rise to a small reduction in
inequality. Although, if it is understood as a process whereby self-employed workers begin
to receive the wages of unskilled workers, or low skilled and medium skill workers, this would
not give a conclusive result about the changes in inequality. However, the division between
10
the working class and self-employed workers in a pre-industrial society is complicated to
make, and this could be the reason behind the difficulty of drawing a solid conclusion7.
Table 5
Inequality measures in the counterfactual distributions
Process of proletarianisation
Mean wage
Quintiles mean wage
Self-employed vs.
Unskilled workers
b) Unskilled &
a) Unskilled
Low skilled
GINI
GE (2)
GINI
GE (2)
0.4867
0.9921
0.4849
0.9854
0.4919
1.0058
0.4932
0.9967
Manual vs.
Non-manual
GINI
0.4752
0.4769
GE(2)
0.9386
0.9411
B. Reduction of real wages
The simulation to see the effect on inequality of a decrease in the real wages of all
the individuals will consist of applying percentage reductions to personal incomes.
Specifically, variations of 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 and 60 percentage points. As
well as the Gini index and the generalised entropy index, two ratios are calculated: the ratio
of land rents/unskilled wages, using the mean of both variables and the ratio between total
incomes and unskilled workers’ wages.
The results of the inequality indicators when facing a decrease in real wages (Table
6) clearly show a considerable rise in inequality. The Gini index rises by almost 9 points in
relation to the initial distribution when the real wages of all the individuals are reduced by
60%; the increase in the entropy index is also significant. Regarding the ratios, it is observed
that their behaviour is more sensitive than that of the inequality indices. In both ratios, there
is a higher increase in inequality than in the standard measures of inequality, so it could be
confirmed that the changes to inequality are exaggerated. This exaggeration of the changes
to inequality is particularly noticeable in the case of the ratio of land rents/unskilled wages,
given that the numerator of this quotient remains constant when the wages are reduced.
Their reduction is larger than that of the ratio of total incomes, in which both terms of the
quotient (total incomes and unskilled wages) are reduced by the decline in wages.
7
In both scenarios, there could be two possible bias when it comes to estimating inequality. Firstly, the results
could be overestimating the reduction in inequality, as we are supposing that the process of proletarianisation
affects all the individuals in the sample who were previously self-employed (scenario i) or non-manual workers
(scenario ii). Secondly, it is to be expected that a process of proletarianisation causes a reduction in the wages
of all wage-earning workers as there is an increase in the labour on offer for this type of worker; this would
boost inequality even more.
11
Table 6
Microsimulation 2: Decrease of real wages
Land rent /
Total income/
unskilled wage unskilled wage
ratio
ratio
Wage decrease
Gini
GE (2)
5%
0.490
0.992
0.818
2.689
10%
0.494
1.022
0.863
2.776
15%
0.499
1.056
0.914
2.873
20%
0.505
1.092
0.971
2.982
25%
0.511
1.132
1.036
3.106
30%
0.517
1.175
1.110
3.248
35%
0.524
1.222
1.195
3.411
40%
0.532
1.274
1.295
3.601
45%
0.540
1.331
1.412
3.827
50%
0.549
1.393
1.553
4.097
55%
0.559
1.462
1.726
4.427
60%
0.570
1.537
1.942
4.840
Variation (%)
16%
55%
137%
80%
C. Changes in the remuneration of labour and skill premium.
In this hypothesis, the effects of increases in the skill premium are studied, the
definition of which is a rise in the labour incomes of individuals with higher skill levels in
relation to the incomes of individuals with less skills. Based on this premise, two types of
microsimulations will be done: (i) only increasing the personal incomes of high-skilled
workers; and (ii) increasing the wages received by the high-skilled and medium-skilled
workers. The wages in both scenarios are increased by 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55
and 60 percentage points. Finally, the total inequality is calculated once the changes and
assumptions are applied. The results obtained are shown in table 7.
12
Table 7
Microsimulation 3: Changes in the skill premium
Increases in
personal
income
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
High skills levels
High and medium skills levels
GINI
GE (2)
GINI
GE (2)
0.486
0.486
0.487
0.488
0.488
0.489
0.489
0.490
0.491
0.491
0.492
0.493
0.966
0.968
0.971
0.974
0.977
0.981
0.984
0.988
0.993
0.997
1.002
1.007
0.486
0.487
0.488
0.489
0.490
0.491
0.492
0.494
0.495
0.496
0.498
0.499
0.965
0.967
0.970
0.973
0.976
0.980
0.984
0.989
0.994
1.000
1.006
1.012
A rise in the skill premium increases the inequality in the two simulations performed,
given that there would be more difference between the labour incomes received by workers
with less skills and those who are high-skilled, or high-skilled and semi-skilled. It is useful to
highlight that there is high wage dispersion in the counter-factual distributions. Inequality
rises to a greater extent when wage increases for high-skilled and medium-skilled workers are
considered, than when only the high-skilled workers are considered. However, the increases
in inequality due to higher wages based on superior skills are very small. In the most extreme
case, the Gini index goes from 0.485 in the initial distribution to 0.499 in the counter-factual
distribution, in which the wages of high-skilled and medium-skilled workers have risen by
60%. In this scenario, the highest increase observed in the Gini index is slightly less than 3%
(for example, when the wages of the high-skilled and medium-skilled workers are changed
by 60%). This corroborates Van Zanden (1995, p. 661) who indicates that the rise in
inequality produced by the increase in the skill premium is quite small, given that the
percentage of high-skilled and medium-skilled professionals in a pre-industrial economy is
normally quite small. In the case of Palencia, only 2.6% of workers are high-skilled and 12.4%
are medium-skilled.
D. Land redistribution
One of the possible reasons for changes in inequality is the modification of the
distribution of land. The accumulation of larger proportions of total available land in hands
of the elites in the top of the distribution has been traditionally regarded a powerful way in
which economic disparities expand (Banerjee 1999). The debates about the possible impacts
13
of this process in pre-industrial Europe have been linked to seminal transformation of social
and economic relationships in 16th century (Brenner 1976) or the consequences of enclosures
and their acceleration in the 18th century (Allen 1992, Overton 1996).
In this paper we analyse what would be the impact on income inequality of a
significant redistribution of the property of land from the small-holders in the bottom of the
distribution to the owners of large land-holdings in the top. For doing so we will focus on
those household that receive some income from land and we will assume the all the income
of the 80 % of the households with the lowest income from this source (i.e. the 80 % of
landowners with the smallest holdings) is set to zero and that income is redistributed among
the 20 % of the households with the highest income coming from this source. Two choices
are made that produce four alternative scenarios: regarding the redistribution of income
among the richest 20 % we can assume that each household in that group receive the same
amount of income or that, for making the redistribution less egalitarian, the first decile
receives 2/3 of the income and the second decile receives 1/3. Regarding the bottom of the
distribution, there are some households whose total income, after the redistribution, drops
below the income of a standard unskilled worker in agriculture. It is possible to leave these
incomes in that way or to assume, that there is a reservation wage (that of mancebos del
campo) and therefore to set in that level the income of all the households that fall below it.
Table 8
Inequality measures in the counterfactual distributions
Land redistribution.
a) Discriminating between
the two richest deciles
b) Uniform rents to the
richest 20 %
Simple land redistribution
GINI
0.5640
GE(2)
1.2410
GINI
0.5728
GE(2)
1.1048
With a mínimum reservation wage
0.5542
1.2087
0.5581
1.0653
The results are that this massive redistribution of land produces a significant change
in income inequality. From the original Gini index of 48.54, the counterfactual distributions
generate Gini indices between 55.43 and 57.28.
E. Changes in the functional distribution compared with the evolution of land rents
(or total incomes)/unskilled wages ratios.
Finally, the effect of changes in the functional distribution of income on inequality is
studied. To see this effect, two different scenarios are studied. Firstly (scenario 1), an increase
in unskilled workers' wages is considered, while land rents remain constant. In this case, the
analysis is conducted with two different procedures: a) only unskilled workers’ wages are
14
increased; and b) unskilled and low-skilled workers’ wages are increased. Secondly (scenario
2), an increase in land rents is supposed, while labour incomes remain constant. In this
second scenario, increases in land rents for all the households in the sample are assumed; the
increases are on the same scale as those in the first assumption. This means that wages and
land rents are increased by 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 and 60 percentage points.
Subsequently, the inequality indices are calculated for each of the changes in the different
scenarios described and the ratios between land rents or total incomes and unskilled workers’
wages.
In the table, the results for inequality are shown, considering different wage increases
and the two variants mentioned in the previous paragraph (a and b). In both alternatives, an
identical evolution in inequality is observed. Inequality drops, given the rise in the wages of
both groups of individuals with lower wages and wage dispersion is reduced. In addition, it
can be observed that the ratios – if either the unskilled workers' wages are used or both the
unskilled and low-skilled wages – reflect the same trend in inequality, but with much more
sensitivity. In other words, the changes to income inequality are exaggerated. As seen in table
8, the variation of the ratios, both considering the smallest wage increase (5%) and the largest
(60%), is very different from the change shown in the Gini index. The generalised entropy
index shows an even more pronounced variation.
Table 8
Scenario 1: Wage increases in unskilled and low-skilled workers
Wage
increase
Gini
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
Variation (%)
0.481
0.477
0.474
0.470
0.467
0.463
0.460
0.457
0.454
0.452
0.449
0.447
-7.1%
Unskilled wage
increase
GE (2)
Land
rent /
wage
0.946
0.740
0.928
0.706
0.912
0.675
0.896
0.647
0.880
0.621
0.865
0.597
0.851
0.575
0.837
0.555
0.823
0.536
0.810
0.518
0.798
0.501
0.785
0.485
-16.9% -34.4%
Total
income
/ wage
2.508
2.414
2.328
2.249
2.177
2.110
2.048
1.990
1.937
1.887
1.840
1.797
-28.4%
Unskilled & low skilled wage
Increase
Gini
GE (2)
Land
Total
rent /
income/
wage
wage
0.481
0.936
0.642
2.192
0.476
0.910
0.613
2.125
0.472
0.885
0.586
2.063
0.468
0.861
0.562
2.007
0.465
0.839
0.539
1.955
0.461
0.818
0.519
1.907
0.458
0.798
0.499
1.863
0.455
0.780
0.482
1.822
0.452
0.762
0.465
1.783
0.450
0.745
0.449
1.747
0.447
0.729
0.435
1.714
0.445
0.713
0.421
1.683
-7.4%
-23.8%
-34.4%
-23.2%
15
In table 9, the results for scenario 2 are shown. It is observed that when land rents
increase, inequality rises. However, this result is to be expected as land is the most unequally
distributed productive factor. The ratios proposed by Álvarez-Nogal and Prados de la
Escosura (2007) also reflect this growing trend of inequality. As in scenario 1, there is more
sensitivity than in the standard measures of inequality. Similarly, the variation in the GE
index (2) is notably larger than that in the Gini index. The reason is that the GE (2) index is
much more sensitive to changes in the upper end of the distribution.
Table 9
Scenario 2: Increases in land rents
Land rent /
Total income /
Total income /
unskilled & low
unskilled & low
unskilled wage
wage
wage
0.708
2.650
2.300
Land rent
increase
Gini
GE (2)
Land rent /
unskilled wage
5%
0.488
0.985
0.816
10%
0.490
1.007
0.854
0.742
2.689
2.334
15%
0.492
1.029
0.893
0.775
2.727
2.367
20%
0.494
1.051
0.932
0.809
2.766
2.401
25%
0.496
1.074
0.971
0.843
2.805
2.435
30%
0.498
1.096
1.010
0.876
2.844
2.468
35%
0.501
1.119
1.049
0.910
2.883
2.502
40%
0.503
1.141
1.087
0.944
2.922
2.536
45%
0.505
1.163
1.126
0.978
2.960
2.570
50%
0.507
1.186
1.165
1.011
2.999
2.603
55%
0.509
1.208
1.204
1.045
3.038
2.637
60%
0.512
1.230
1.243
1.079
3.077
2.671
Variation (%)
4.9%
24.9%
52.4%
52.4%
16.1%
16.1%
5. Conclusions
From all the scenarios considered, everything indicates that the decrease in real wages
and land redistribution are the hypothesis that had the largest impact on income disparities.
This supports the results obtained by Van Zanden (1995, p. 661), who argues that the
hypotheses suggested by classic economists provide the best explanation for the evolution
of inequality. In line with Van Zanden’s results, it can also be seen that the wage increases
for individuals with higher skill levels do not have a significant impact on inequality. It is
probable that the relative lack of significance of the variations in the skill premium has to do
with the reduced importance of skilled workers in a pre-industrial society. Regarding the
process of proletarianisation, no conclusive results are drawn. The results of the second
scenario (increase of manual workers and decrease of non-manual workers) show a slight
reduction in inequality. In addition, it could be the case that the results of this scenario are
16
overestimated, as they do not include the increase in the incomes of the small group of
owners of the means of production. Finally, although there are many arguments regarding
the evolution of inequality, several of them would only produce small quantitative impacts.
This would put into question Van Zanden’s (1995, p. 661) assertion that the growth in
inequality in the Netherlands is “over-explained”.
On the other hand, the ratios suggested by Williamson (2002) to measure the
evolution of inequality and calculated by Álvarez-Nogal and Prados de la Escosura for
Modern Spain (2007) move in the same direction than the Gini index but they are much
more sensitive than the traditional indicators to the changes implied by the hypotheses
presented in the paper. Why? The ratio between land rents and unskilled workers’ wages
measures the difference between the two tails of the income distribution. A ratio that uses
the average total incomes as a numerator compares the lower end of the distribution with its
upper half. In comparison, the Gini index is more sensitive to what happens to the mean of
income distribution. Consequently, the changes implemented in the last section have more
effect on the ratios, given that they measure changes in land rents (upper tail) and changes
in unskilled workers’ wages (lower tail).
Nevertheless, when a decrease of real wages is suggested, it can be observed that
these ratios are also more volatile than in the Gini index. In this case, the changes to obtain
the counter-factual distributions are made to all the individuals in the sample, not only to
those who are at the ends of the distribution. This suggests that the greater volatility of the
ratios occurs in various circumstances, not only when the components that clearly affect
them to a greater extent are changed. Initially, these ratios present a result that is closer –
albeit always higher – to the generalised entropy index 2. This index is more sensitive to
changes in the upper end of the distribution, which can be seen more clearly in the ratio total
incomes/unskilled workers’ wages.
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