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8. B Additional Information on Inequality Data and Sources B.1 Dataset The inequality data and the sources used to construct the panel dataset have been extensively discussed by Alfani et al. (2022) and Schaff (2022). This Appendix provides some essential background information. The community-level inequality panel employed here differs from Alfani et al.
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58. Second, it provides all local inequality estimates including the propertyless households — the only sub-category among the poor that was sometimes missing from the tax registers — where these were recorded in the tax registers. Of course, what constituted propertylessness was dependent on local understanding of the concept, and even if people without property were reported, we cannot exclude with absolute certainty that some poor people were not captured by the tax registers. Since Alfani et al. (2022) have been primarily interested in providing macro-level estimates of inequality for Germany comparable to estimates from other European areas, they have dropped the propertyless from their community-level distributions. However, for the purposes of this paper it seemed adequate to base the analysis on the complete local wealth distributions, including also the covered propertyless households.
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62. The propertyless were a small part of the total population in preindustrial Germany. Their share ranged between 0.81 percent in 1400 and a high of 8.4 percent in 1550. Reassuringly, Figure 10 shows that the hypothetical exclusion of all the propertyless from the wealth distributions reduces the wealth share of the poor, as measured by the wealth share of the bottom 50 percent, only marginally in preindustrial Germany (data from Alfani et al. 2022). This implies that inequality estimates where some of the poor are missing should be interpreted as lower bound estimates. Most importantly for my analysis, that is, regressions with unit- and time-fixed effects, including or dropping the propertyless does not change the trend of the wealth share of the poor. Even more reassuringly, the propertyless were missing from the tax registers of one town in my sample only, Heilbronn. As shown below, dropping Heilbronn from the sample does not change the results.
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63. This dataset also differs from Schaff (2023) in that the main outcome variable — the wealth share of the bottom 20 percent of the population — has been newly constructed from the household-level wealth information. Other outcomes, such as the Top 1 percent, the middling 40 percent and the Arguably another important provision was the legalisation of Calvinism.
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