Stockholm School of Economics

About: Stockholm School of Economics is a education organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Population & Entrepreneurship. The organization has 1186 authors who have published 4891 publications receiving 285543 citations. The organization is also known as: Stockholm Business School & Handelshögskolan i Stockholm.

Long run trends in the distribution of income and wealth

Abstract: This paper reviews the long run developments in the distribution of personal income and wealth. It also discusses suggested explanations for the observed patterns. We try to answer questions such as: What do we know, and how do we know, about the distribution of income and wealth over time? Are there common trends across countries or over the path of development? How do the facts relate to proposed theories about changes in inequality? We present the main inequality trends, in some cases starting as early as in the late eighteenth century, combining previous research with recent findings in the so-called top income literature and new evidence on wealth concentration. The picture that emerges shows that inequality was historically high almost everywhere at the beginning of the twentieth century. In some countries this situation was preceded by increasing concentration, but in most cases inequality seems to have been relatively constant at a high level in the nineteenth century. Over the twentieth century inequality decreased almost everywhere for the first 80 years, largely due to decreasing wealth concentration and decreasing capital incomes in the top of the distribution. Thereafter trends are more divergent across countries and also different across income and wealth distributions. Econometric evidence over the long run suggests that top shares increase in periods of above average growth while democracy and high marginal tax rates are associated with lower top shares.

Mapping poverty using mobile phone and satellite data

Abstract: Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.