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on Economic Growth |
| By: | Nick Obradovich; Ömer Özak; Ignacio Martín; Ignacio Ortuño-Ortín; Edmond Awad; Manuel Cebrián; Rubén Cuevas; Klaus Desmet; Iyad Rahwan; Ángel Cuevas |
| Abstract: | Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by the currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable, and high-resolution measurement of behaviorally revealed cultural values and preferences. We employ publicly available data across nearly 60,000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. We first validate that cultural distances calculated from this measurement instrument correspond to traditional survey-based and objective measures of cross-national cultural differences. We then demonstrate that this expanded measure enables rich insight into the cultural landscape globally at previously impossible resolution. We analyze the importance of national borders in shaping culture, explore unique cultural markers that identify subnational population groups, and compare subnational divisiveness to gender divisiveness across countries. The global collection of massive data on human behavior provides a high-dimensional complement to traditional cultural metrics. Further, the granularity of the measure presents enormous promise to advance scholars' understanding of additional fundamental questions in the social sciences. The measure enables detailed investigation into the geopolitical stability of countries, social cleavages within both small and large-scale human groups, the integration of migrant populations, and the disaffection of certain population groups from the political process, among myriad other potential future applications. |
| JEL: | C80 J10 J16 O10 R10 Z10 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27827&r=all |
| By: | Hodler, Roland; Lechner, Michael; Raschky, Paul A. |
| Abstract: | We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict. Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices. |
| Keywords: | Resource curse, mining, economic development, conflict, causal machine learning, Africa |
| JEL: | C21 O13 O55 Q34 R12 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2020:16&r=all |
| By: | Klaus Ackermann; Alexey Chernikov; Nandini Anantharama; Miethy Zaman; Paul A Raschky |
| Abstract: | Reliable data about the stock of physical capital and infrastructure in developing countries is typically very scarce. This is particular a problem for data at the subnational level where existing data is often outdated, not consistently measured or coverage is incomplete. Traditional data collection methods are time and labor-intensive costly, which often prohibits developing countries from collecting this type of data. This paper proposes a novel method to extract infrastructure features from high-resolution satellite images. We collected high-resolution satellite images for 5 million 1km $\times$ 1km grid cells covering 21 African countries. We contribute to the growing body of literature in this area by training our machine learning algorithm on ground-truth data. We show that our approach strongly improves the predictive accuracy. Our methodology can build the foundation to then predict subnational indicators of economic development for areas where this data is either missing or unreliable. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.05455&r=all |
| By: | Bartels, Charlotte (DIW Berlin); Jäger, Simon (Massachusetts Institute of Technology); Obergruber, Natalie |
| Abstract: | What are the long-term economic effects of a more equal distribution of wealth? We exploit variation in historical inheritance rules for land in Germany. In some German areas, inherited land was to be shared or divided equally among children, while in others land was ruled to be indivisible. Using a geographic regression discontinuity design, we show that equal division of land led to a more equal distribution of land; other potential drivers of growth are smooth at the boundary and equal division areas were not historically more developed. Today, equal division areas feature higher average incomes and a right-shifted skill, income, and wealth distribution. Higher top incomes and top wealth in equal division areas coincide with higher education, and higher labor productivity. We show evidence consistent with the more even distribution of land leading to more innovative industrial by-employment during Germany's transition from an agrarian to an industrial economy and, in the long-run, more entrepreneurship. |
| Keywords: | distribution, economic growth, economic development |
| JEL: | D3 O1 O4 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13665&r=all |