Preliminary Programme

Recent models of Industrial Revolution argued that the relatively modest initial inequality in England might have been an influential factor in creating a modern market for consumer goods (and, perhaps, the motivation for an industrious revolution). It is, however, very difficult to measure inequalities before and during the Industrial Revolution period. This study employs the set of methods that developed around the phenomenon of “age heaping”, i.e. the tendency of poorly educated people to round their age erroneously – they answer more often “40”, if they are in fact 39 or 41, compared with better educated people.
In a related study, Crayen (2005) found that the relationship between illiteracy and age heaping for LDCs after 1950 is extremely close. She calculated age heaping and illiteracy for not less than 270,000 individuals that were organized by 416 regions, ranging from Latin America to Oceania. The correlation coefficient was as high as 0.7, even if only the age bracket of 23-42 year-olds was taken into account. Compared to the PISA results for numerical skills, the correlation was as high as 0.85.
The crucial advantage of those age heaping methods is that they are widely available for the early modern period, because many people were asked for their age in a more or less standardized way, when entering the military voluntarily, when they married etc.. In addition, they reflect numerical skills even more than literacy skills, which could be important for technical, commercial and craftsmen activities in the production process. We apply those methods to a wide sample of countries for which data are available. We assess the quality of the data carefully, by scrutinizing the institutional framework, selection processes, and the type of age questions asked in different situations, as far as this can be reconstructed. Moreover we use different units to assess inequality: We measure inequalities between regions, inequality between taller and shorter individuals (reflecting their nutritional status and perhaps social stratification), differences between occupations of middle/upper versus lower social status.
The results are facetted, as the history of inequality has always been. One particularly interesting result is that height was a good predictor of numeracy in all four countries: The taller half of the height distribution displayed much lower age heaping tendency, and hence higher numeracy. Moreover, the size of the difference varied by country and region. The largest inequality of numerical human capital was visible in the exceptional case of Paris, followed by Ireland. The more protein-rich Northeastern France resulted in only modest inequality. In England and the Northern United States, age heaping and inequality of numeracy was much lower than in France and Ireland. If the low inequality – growth relationship postulated by recent theoretical models would hold, we would expect the earliest industrial development in England, the Northern U.S. and the Northeast of France – and that is were it took place. (Show less)

In this paper we compare past and present globalisation with an aim to highlighting the different factors which caused wage inequality then and which are doing so now. Already we have constructed a ratio of wage inequality for 15 countries with different characteristics in the first period of globalisation (1870-1913) and the subsequent period of deglobalisation (1914-1930) and we have compared this pattern with wage inequality in the 1980s and 1990s. We have found that in the New World developed countries wage inequality increased in the globalisation period and decreased in the deglobalisation period. In most of the Old World countries wage inequality decreased in the globalisation period and this trend continued in most of these countries in the deglobalisation period. In the 1980s and 1990s most of the countries experienced an increase in wage inequality.
As a first step in disentangling the main factors contributing to wage inequality change, we compared past wage inequality with the present in order to find out whether there are general valid relationships for both periods. We analysed the impact of globalisation (trade and immigration), technological change and ‘institutional’ factors (education and the institutional structures of the labour market). We found that in the New World countries and the Old World countries, education attainment and labour institutions compensated for wage inequality change. The exceptions to the reduction in wage inequality were the countries that were less educated and or that had dictatorships that suppressed labour disputes. At present, in most of the developed and developing countries ‘institutional’ factors do not seem to be compensating for wage inequality change, thus the factors that favour rising wage inequality are dominating. We observe that countries which have not reduced the minimum wage or union density have not experienced wage inequality change. Perhaps these countries also have more egalitarian education systems or that these education systems have adapted to new technologies.
Now, we are interested in calculating the effect of globalisation, especially immigration, and technological and structural change on wage inequality change and also the extent to which the “institutional” factors compensated for this change. To do so, we intend using a decomposing analysis of wage inequality change using a calibrated general equilibrium model. A general equilibrium model allows us to specify the relationships between variables when there are multiple factors acting in different ways. The calibration of a general equilibrium model allows us to determine parameter values consistent with both the equilibria and the changes in exogenous variables contributing to the wage inequality change being decomposed. As in our case where there is very little data for most countries and periods, the calibration techniques have the advantage of choosing values for model parameters such that the model gives equilibrium solutions consistent with the data in both periods of globalisation and deglobalisation. The use of these techniques is relatively recent in the study of wage inequality changes (Abrego and Whalley, 2002) and here we will adapt this seminal paper, which considers only trade and technology factors, adding the effect of immigration and the above mentioned “institutional” factors. (Show less)

In this paper we mainly intend to re-assess the existing knwoledge on social inequality in the early modern cities of the Southern Low Countries. Hitherto especially aggregate inequality measurement methods (such as the Gini coefficient) were used. These, however, almost by definition yield an immobile and strongly polarised view upon preindustrial urban societies. Our intention is to refine the existing measurement methods in order to achieve a more balanced view in which the relative position of urban middling layers can -more accurately- be described and analysed. Fiscal data will be the backbone of this methodological as well as interpretative exploration. (Show less)