This paper overviews the role of slums in urban Africa, focusing on Nairobi. It reveals the characteristics of slums and how these have changed over time. Spatially disaggregated data show that slum areas are very dense with poor-quality buildings, lacking access to key services such as sewage disposal and electricity. However, improvements to building quality, public-service provision, and socioeconomic characteristics are mostly outpacing those seen in the formal sector. Measures such as child health and school attendance have caught up or are on pace to catch up in the near future with the formal sector, while improvements in building quality and service provision are advancing more slowly. We find significant heterogeneity across the city, and in particular that central slums look to be 'stuck' with low-quality buildings and poor service provision, though not with low socioeconomic indicators. We explore potential explanations for why slums located on highly prized land near the centre may be stuck with poor infrastructure.
Thanks to recent data availability, digitized transcriptions of Victorian censuses provide unprecedented historical big data on individuals in the past, but also with new methodological challenges like the classification of otherwise underreported entrepreneurs among a population sample of millions of individuals. This paper presents a methodological solution to accomplish the task of classifying entrepreneurs. We apply machine learning, including deep learning, to outperform a standard logistic regression algorithm. Our methodological developments traverse traditional disciplinary lines using state-of-the-art artificial intelligence methods. The main conclusion of the paper is that significant gains in performance can be achieved with historical archive data through machine learning to test economic theories on historical entrepreneurship. This suggests applicability to other disciplines in information sciences.
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