Discussion Paper No. 316
January 29, 2022
The Fiscal State in Africa: Evidence from a Century of Growth
Authors:Albers, Thilo N. H. (HU Berlin and Lund University)
Jerven, Morten (Norwegian University of Life Sciences)
Suesse, Marvin (Trinity College Dublin)
What is the level of state capacity in developing countries today, and what have been its drivers over the past century? We construct a comprehensive new dataset of tax and revenue collection for 46 African polities from 1900 to 2015. Descriptive analysis shows that many polities in Africa have been characterized by strong growth in fiscal capacity. As a next step, we explain this growth using a fixed-effects long-run panel setting. The results show that canonical state-building factors such as democratic institutions and interstate warfare can increase revenue collection, while government turnover reduces it. Access to external credit and foreign aid are even more important, and both negatively affect fiscal capacity. In addition, access to external revenues, especially from commodity exports and debt, moderates the operation of canonical state-building factors such as democracy and conflict. These insights add important nuances to established theories of state building. Not only are states in Africa more capable than hitherto thought, but the international environment shapes their capacity, both directly and indirectly.
Keywords:fiscal capacity; taxes; Africa; statehood; resources; external finance
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Discussion Paper No. 315
January 29, 2022
Perks and Pitfalls of City Directories as a Micro-Geographic Data Source
Authors:Albers, Thilo N. H. (HU Berlin)
Kappner, Kalle (HU Berlin)
Historical city directories are rich sources of micro-geographic data. They provide information on the location of households and firms and their occupations and industries, respectively. We develop a generic algorithmic work flow that converts scans of them into geo- and status-referenced household-level data sets. Applying the work flow to our case study, the Berlin 1880 directory, adds idiosyncratic challenges that should make automation less attractive. Yet, employing an administrative benchmark data set on household counts, incomes, and income distributions across more than 200 census tracts, we show that semi-automatic referencing yields results very similar to those from labour-intensive manual referencing. Finally, we discuss potential applications in economic history and beyond.