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The use of an alternative statistic from the Entropy family of inequality, such as the Theil coefficient, does not alter the main findings, but the results are included in the Supplementary material, Appendix. As a first step toward the analysis of the results, figures 1 and 2 present the levels of estimated income inequality in the cities of the Southern Low Countries included in this study. Whether one looks at the Gini coefficient or at the share in income flowing to the top 5 percent of urban society, the pattern is roughly the same.
This rise spanned periods of urban commercial growth c. This suggests not only that inequality was not straightforwardly connected to the industrialization process, but also that its association with economic growth appears to be rather ambiguous a similar conclusion was reached in Alfani ; Alfani and Ammannati The general pattern is the same whether one considers the Gini coefficient or the income share flowing to the top 5 percent of the distribution: both measures suggest that the gaps between rich and poor grew wider during the early modern period, regardless of the specific method of summarizing the distribution.
In order to further explore these results, their interpretation has been divided into three parts. In the first segment, a simple Ordinary Least Squares OLS regression is introduced in order to study some of the main determinants affecting income inequality in this dataset. In the second section, I compare the experience of the Northern and Southern Low Countries during the early modern period in order to explore the relationship between growth and inequality. In the final part, I examine alternative factors that could explain changes in inequality in the Low Countries over time.
Nevertheless, for the sixteenth until the eighteenth centuries, a period for which we have overlapping data from both regions, the trend of rising inequality appears to be similar. Not only the trend, but also the absolute level of inequality in the Northern Low Countries remains broadly comparable to that in the Southern Low Countries figure 3.
Even in the seventeenth century, during the heyday of Holland's economic miracle, the level of inequality in towns such as Leiden and Alkmaar did not greatly surpass that in Aalst a Flemish town of comparable size. Only the high degree of inequality in Amsterdam appears as an outlier, which is easily attributable to the much greater size of its urban population. Housing inequality in the Northern and Southern Low Countries compared fourteenth to nineteenth centuries. A more elaborate analysis of the determinants of inequality in the early modern Low Countries can be undertaken by means of an OLS regression on the Gini coefficient table 1.
The Gini coefficients from towns in both the Northern and Southern Low Countries are taken as the dependent variable in the model. Since most variables commonly invoked in modern econometric studies of inequality are not readily available for the pre-industrial era, some perhaps rather rough proxies have been included here as independent variables instead.
Population size and relative population change could be derived thanks to the data collected by Jan De Vries and Paul Bairoch De Vries ; Bairoch et al. Population change, in this case, refers to the relative growth of the city's population size during the previous fifty years. Results from OLS regressions on the Gini coefficient in urban case studies of the Low Countries fifteenth to nineteenth centuries a. Sources : see Supplementary material, Appendices A and C. Testing for the influence of economic growth and the development of living standards is considerably more difficult since GDP per capita and real wage estimations are not generally available at the local level for all the towns under scrutiny.
Instead of using Angus Maddison's regional GDP per capita estimates Bolt and Van Zanden , I have opted for an alternative proxy for economic development: the deflated average rental value of houses. Since several studies have demonstrated how the development of housing rents in the pre-industrial Low Countries accurately reflects economic performance, this can be considered a relatively reliable proxy for per capita economic performance Van Ryssel ; Soly Real wages are more difficult to obtain for each of the case studies, since that would require price and wage series for all of the towns.
Rethinking Separate Spheres
However, a comparison of the available wage and grain price series for most of the towns studied here indicates that prices and wages tended to co-vary from place to place price and wage data in Verlinden and Scholliers That is to say: where wages were lower for instance in Aalst and Kortrijk, when compared with Ghent or Antwerp , grain prices were usually lower to a similar degree a similar remark in Van Zanden Therefore, the real wage indices for the Northern and Southern Low Countries constructed by Robert Allen, and based on price and wage data from Amsterdam, and Bruges, Ghent, and Antwerp respectively can serve as an acceptable proxy.
Dummy variables have been added to look at the effect of the local characteristics of a case study: whether the town was located in the Northern Low Countries, and whether it was a maritime port city, or a capital city. A more detailed description of all the variables used can be found in the Supplementary material, Appendix. What are the main interpretations to be derived from this analysis? Although population size appears to be a strong predictor of urban inequality levels model A , the effect can at least partially be explained by a range of related variables model B.
Nevertheless, larger cities tend to have higher levels of inequality. A more diverse economic structure, but also higher levels of immigration needed to sustain larger urban population sizes in the context of low or negative urban natural demographic growth seem likely candidates to explain this effect. Since the towns under scrutiny generally tended to grow through time—and especially during the eighteenth and nineteenth centuries—this in itself partially helps to explain the growing inequality pattern established earlier.
Even though population size is a rather strong predictor of inequality in this model, the addition of a number of other independent variables nevertheless significantly improves the explanatory value of the model. Population change, which serves as a proxy for migration, does not turn out to be significant. In contrast, both the average value of houses in a town which serves as a proxy for income per capita and the real wage level are significant and produce relatively strong positive and negative effects, respectively.
At the same time, the fact that lower wages tend to contribute to higher inequality levels perhaps helps to explain why studies are increasingly finding pre-industrial rises in inequality throughout Europe—even in the absence of economic growth. Therefore, regression equation C presents an alternative model in which both variables have been left out.
This model exclusively considers the strength of the independent variables of a prior causative nature: location in the Southern or Northern Low Countries, maritime and capital economic functions, spells of pre-industrial economic efflorescence, and industrialization. Strikingly, the dummy variable for the Northern Low Countries does not turn up as significant in the regression in either model B or C.
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This indicates that there were no large differences in urban inequality levels between North and South, even after the Revolt, when controlling for economic growth and real wages. Taken together, these times and places did not experience significantly higher levels of inequality when controlling for the other independent variables. Interpreted in another way, this confirms what was also suggested by figure 3 : inequality was not significantly lower in the Southern Low Countries after the Dutch Revolt than it had been before, or than it became afterward in the North.
The presence of a maritime port function seems to have had a slightly depressing effect upon local inequality levels, although this effect is no longer significant in model C. At the very least this indicates that international trade did not exert a direct positive influence on inequality. In contrast, if a town carried the administrative or political functions of a capital city, that tended to increase the local level of inequality.
This suggests that political factors had an important role to play, in both direct and indirect ways.
Lastly, the dummy variable for industrialization is significant and points to a positive effect. Thus, over and above the influence of economic growth, the mechanization of industry during the nineteenth century went hand in hand with deepening inequality. The comparison between the Northern and Southern Netherlands and the results from the regression analysis suggest that largely similar dynamics characterized the relationship between economic development and inequality in the Low Countries before and after the Dutch Revolt. Therefore, the relative lack of economic growth in the South poses the question: what explanatory variables must be invoked to explain the similarity in the path of inequality to the Northern Low Countries?
Two main explanatory factors will be considered in more detail: 1 the respective roles of labor and capital in pre-industrial economic development, and 2 the role of institutions. This means that much attention is again paid to the functional distribution of income, which is the distribution of income shares flowing to each of the factors of production within an economy, i.
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Recently, Piketty has argued that the growth of inequality today, and the high levels of inequality before the twentieth century, can be explained by the fact that in the long term, the price of capital tends to be higher than the price of labor. The argument is not entirely dissimilar to the one put forward by Van Zanden for the pre-industrial period, since he argued that the primary cause for the growth of inequality in the early modern Northern Low Countries was the discrepancy between the falling factor price of labor i.
Yet not only the respective prices of capital and labor matter, so also do their distribution across the population. For the classical economists factor endowments presented themselves as external to the laws of economic growth, as determined by historical processes of political or otherwise extra-economic developments.
Unfortunately, by neglecting the question of how these changes in the functional distribution of income influenced the personal distribution of income, this historiography has largely failed to connect to the findings of economic historians of the nineteenth and twentieth centuries. Can the arguments about factor prices and factor endowments be invoked for the case of the Southern Low Countries—where, contrary to early modern England and the Northern Low Countries, economic growth was very slow, if not nonexistent before the end of the eighteenth century?
Figure 4 shows the ratio of the real wage to GDP per capita in the Low Countries from the sixteenth to the end of the nineteenth century.
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At the beginning of the period under scrutiny, roughly the period —, real wages were high relative to the GDP per capita. In fact, almost everywhere in Europe real wages were at a high point following the relative scarcity of labor resulting from the drop in population caused by the ecological and epidemiological crisis of the late Middle Ages Allen The demographic effect of the Black Death on the labor supply in the Southern Low Countries was reinforced by specific institutional arrangements.
After craft guilds had gained access to urban political power around the beginning of the fourteenth century, the export-oriented textile industries in the Flemish cities became increasingly subject to corporatist organization and regulation Lis and Soly The industrial structure of the region was transformed from a low-wage economy based on a high degree of labor division to a skill-intensive export industry in which the quality of labor formed the foundation of added value and economic gains Van Der Wee Thus, the scarcity of labor relative to the stock of capital, buttressed by the emergent institutional framework of corporatism, provided for a relatively high price of labor.
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Moreover, since small-scale production by independent master-artisans dominated production relations, factor endowments of capital were less unequal than they had been before. The result appears to have been a decline in inequality during the fifteenth century—confirmed at least by the case of Bruges, where a prolonged decline in inequality set in after the fourteenth century.
Although the institutional context remained the same, this situation of relative equality would come under growing pressure as the second half of the sixteenth century proceeded, especially when price inflation and wage rigidity slowly undermined the purchasing power of urban wage laborers. Unsurprisingly, this went hand in hand with an upswing in inequality in most of the towns surveyed here.