Inequality in the very long run: inferring inequality from data on social groups

Modalsli, Jørgen

doi:10.1007/s10888-014-9279-6

Cited by 23 publications

(27 citation statements)

References 15 publications

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“…Properly collected and intelligently processed big data are able to provide significant insights in various application fields. For example, statistic inferences based on big data analytics have shown their powers in biology [1–3], medical science [4, 5], sociology [6–8], geography [9], zoology [10], intelligent transportation [11, 12], to name a few. Among all these, traffic behavior belongs to the scope of Cyber-Physical Systems(CPS), which aims to answer the questions from deeply intertwined physical and cyber components [13], and two examples are unsignalized intersections with heterogeneous urban traffic [14] and empirical investigation on driver behavior [15].…”

Section: Introductionmentioning

confidence: 99%

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

Zhang

Wang

2016

PLoS ONE

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How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers’ destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in revealing some dark side investigation.

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Section: Introductionmentioning

confidence: 99%

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

Zhang

Wang

2016

PLoS ONE

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“…In our preferred estimates, all of the households in a cell are assigned its mean income, and, by default, we do not assume that incomes overlap across groups. Recent work by J⊘rgen Modalsli (2011) suggests that this simplifying assumption may not lead to the underestimation of inequality when within-group dispersion takes particular forms, even in studies with only a few dozen cells. Our use of 85 cells for each of fifty provinces suggests that there is little possible bias from using cell means 34…”

Section: Russian Income Inequality C 1904: Resultsmentioning

confidence: 99%

Russian Inequality on the Eve of Revolution

Nafziger

Lindert²

2014

J. Econ. Hist.

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Careful handling of an eclectic data set reveals how unequal were the incomes of different classes of Russians on the eve of Revolution. We estimate incomes by economic and social class in each of the fifty provinces of European Russia. On the eve of military defeat and the 1905 Revolution, Russian income inequality was middling by the standards of that era, and less severe than is inequality today in China, the United States, and Russia itself. We note how the interplay of some distinctive fiscal and relative-price features of Imperial Russia might have shaped the now-revealed level of inequality.

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“…In our preferred estimates, all of the households in a cell are assigned a mean income, and, by default, we do not assume that incomes overlap across groups. Recent work by Modalsli (2011) suggests that this simplifying assumption may lead to limited underestimates of inequality when within-group dispersion takes particular forms. Our alternative estimates (available at http://gpih.ucdavis.edu) do relax this assumption in several ways; also see the discussion of similar issues in Milanovic et al (2011).…”

Section: Russian Income Inequality C 1904: Resultsmentioning

confidence: 99%

Russian Inequality on the Eve of Revolution

Nafziger¹,

Lindert²

2012

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Just how unequal were the incomes of different classes of Russians on the eve of Revolution, relative to other countries, to Russia's earlier history, and to Russia's income distribution today? Careful weighing of an eclectic data set provides provisional answers. We provide detailed income estimates for economic and social classes in each of the 50 provinces of European Russia. In 1904, on the eve of military defeat and the 1905 Revolution, Russian income inequality was middling by the standards of that era, and less severe than inequality has become today in such countries as China, the United States, and Russia itself. We also note how the interplay of some distinctive fiscal and relative-price features of Imperial Russia might have shaped the now-revealed level of inequality.

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Inequality in the very long run: inferring inequality from data on social groups

Cited by 23 publications

References 15 publications

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

Russian Inequality on the Eve of Revolution

Russian Inequality on the Eve of Revolution

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