Määttä, I., C. Lessmann (2019): Human Lights, Remote Sensing 11(19):2194, 2019.
Abstract: Satellite nighttime light data open new opportunities for economic research. The data are objective and suitable for the study of regions at various territorial levels. Given the lack of reliable official data, nightlights are often a proxy for economic activity, particularly in developing countries. However, the commonly used product, Stable Lights, has difficulty separating background noise from economic activity at lower levels of light intensity. The problem is rooted in the aim of separating transient light from stable lights, even though light from economic activity can also be transient. We propose an alternative filtering process that aims to identify lights emitted by human beings. We train a machine learning algorithm to learn light patterns in and outside built-up areas using Global Human Settlements Layer (GHSL) data. Based on predicted probabilities, we include lights in those places with a high likelihood of being man-made. We show that using regional light characteristics in the process increases the accuracy of predictions at the cost of introducing a mechanical spatial correlation. We create two alternative products as proxies of economic activity. Global Human Lights minimizes the bias from using regional information, while Local Human Lights maximizes accuracy. The latter shows that we can improve the detection of human-generated light, especially in Africa.
Fabian, M., C. Lessmann and T. Sofke (2019): Natural Disasters and Regional Development - The Case of Earthquakes, Environment and Development Economics 24(5), 479-505, 2019.
Abstract: We analyze the impact of earthquakes on nighttime lights at a sub-national level, i.e. on grids of different size. We argue that existing studies on the impact of natural disasters on economic development have several important limitations, both at the level of the outcome variable – usually national income or growth – as well as on the level of the independent variable, e.g. the timing of an event and the measuring of its intensity. We aim to overcome these limitations by using geophysical event data on earthquakes together with satellite nighttime lights. Using panel fixed effects regressions covering the entire world for the period 1992-2013 we find that earthquakes reduce both light growth rates and light levels significantly. The effects are persistent for approximately 5 years, but we find no long run effects. The effects are strong and robust in a small grid and gets weaker the larger the unit of observation. National institutions and economic conditions are relevant mediating factors.
Lessmann, C. and A. Steinkraus (2019): The geography of natural resources, ethnic inequality and civil conflicts, European Journal of Political Economy 59, 33-51, 2019.
Abstract: We study whether the spatial distribution of natural resources across different ethnic groups within countries causes spatial inequality and the incidence of armed conflict. By providing a theoretical rent-seeking model and analysing a set of geo-coded data for mines, night-time light emissions, local populations and ethnic homelands, we show that the spatial distribution of resources is a major driving factor of ethnic income inequality. Moreover, a spatially unequal distribution of natural resources induces rent-seeking behaviour and thus increases the risk of civil conflicts. Consequently, we extend the perspective of the resource curse to explain cross-country differences in income inequality and the onset of civil conflicts.
Lessmann, C. and A. Seidel (2017): Regional inequality, convergence, and its determinants – A view from outer space, European Economic Review 92, 110-132.
Abstract: This paper provides a new dataset of regional income inequalities within countries based on satellite nighttime light data. First, we empirically study the relationship between luminosity data and regional incomes for those countries for which regional income data are available. Second, we use our estimation results for an out-of-sample prediction of regional incomes based on the luminosity data. These results enable us to investigate regional income differentials in developing countries that lack official income data. Third, we calculate commonly used measures of regional inequality within countries based on predicted incomes. An investigation of changes in the dispersion of regional incomes over time reveals that approximately 67-70% of all countries experience sigma-convergence. Forth, we study different major determinants of within-country changes in inequality, i.e., the determinants of the convergence process. We find evidence for an N-shaped relationship between development and regional inequality. Resources, mobility, trade openness, aid, federalism and human capital are also very important.
Achten, S. and C. Lessmann (2019): Spatial Inequality, Geography and Economic Activity, CESifo Working Paper No. 7547.
Abstract: We study the effect of spatial inequality on economic activity. Given that the relationship is highly simultaneous in nature, we use exogenous variation in geographic features to construct an instrument for spatial inequality, which is independent from any man-made factors. Inequality measures and instruments are calculated based on grid-level data for existing countries as well as for artificial countries. In the construction of the instrumental variable, we use both a parametric regression analysis as well as a random forest classification algorithm. Our IV regressions show a significant negative relationship between spatial inequality and economicactivity. This result holds if we control for country-level averages of different geographic variables. Therefore, we conclude that geographic heterogeneity is an important determinant of economic activity.
Düben, C. and M. Krause (2019): Population, Light, and the Size Distribution of Cities, ECINEQ Working Paper 2019 - 488.
Abstract: We provide new insights on the city size distribution of countries around the world. Using geo-spatial data and a globally consistent city identication scheme, our data set contains 13,844 cities in 194 countries. City size is measured both in terms of population and night time lights proxying for local economic activity. We find that Zipf's law holds for many, but not all, countries in terms of population, while city size in terms of light is distributed more unequally. These deviations from Zipf's law are to a large extent driven by an undue concentration in the largest cities. They benefit from agglomeration effects which seem to work through scale rather than through density. Examining the cross-country heterogeneity in the city size distribution, our model selection approach suggests that historical factors play an important role, in line with the time of development hypothesis.
Bluhm, R., A. Dreher, A. Fuchs, B. Parks, A. Strange, and M. Tierney (2018): Connective Financing: Chinese Infrastructure Projects and the Diffusion of Economic Activity in Developing Countries. AidData Working Paper #64.
Abstract: How do development projects influence the geographic distribution of economic activity within low-income and middle-income countries? Existing research focuses on the effects of Western development projects on inter-personal inequality and inequality across different subnational regions. However, China has recently become a major financier of economic infrastructure in Africa, Asia, Latin America, the Middle East, and Central and Eastern Europe, and it is unclear if these investments diffuse or concentrate economic activity. We introduce an original dataset of geo-located Chinese Government-financed projects in 138 countries between 2000 and 2014, and analyze the effects of these projects on the spatial distribution of economic activity within host countries. We find that Chinese development projects in general, and Chinese transportation projects in particular, reduce economic inequality within and between subnational localities. Our results suggest that Chinese investments in “connective infrastructure” produce positive economic spillovers that lead to a more equal distribution of economic activity in the localities where they are implemented.
Bluhm, R. and M. Krause (2018): Top lights – Bright cities and their contribution to economic development,
Abstract: The commonly-used satellite images of nighttime lights fail to capture the true brightness of most cities. We show that night lights are a reliable proxy for economic activity at the city level, provided they are first corrected for top-coding. We present a stylized model of urban luminosity and empirical evidence which both suggest that these ‘top lights’ follow a Pareto distribution. We then propose a simple correction procedure which recovers the full distribution of city lights. Applying this approach to cities in Sub-Saharan Africa, we find that primate cities are outgrowing secondary cities but are changing from within.
Bluhm, R. and M. H. L. Wong (2017): Neighborhood disputes? Spatial inequalities and civil conflict in Africa, mimeo, University of Hanover.
Abstract: High levels of spatial inequality are associated with slow growth and civil conflict in developing countries, yet we know very little about whether these findings are driven by localized tensions or nation-wide grievances. In this paper we develop novel measures of local spatial inequalities which can be applied to neighborhoods of varying sizes. Using geographic information systems, we use these measures to capture economic differences between neighboring ethnic groups and administrative regions in Africa. We show that greater ethnic inequalities are robustly associated with a higher propensity of experiencing civil conflict within a particular region, while spatial inequalities among first-level administrative regions are not. Interestingly, once comparisons include groups living further away this association strengthens, while reducing the size of the neighborhood often weakens the estimated effect. We also show that the effect of local between-group inequality on conflict varies with the historical formation of language groups.