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Social Mobility in India
Rapid rise in income inequality in India is a serious concern. While the...
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Hidden Undernutrition: How universal cutoffs can fail to capture stunting in low and middle income countries
Stunting, or impaired child growth due to undernutrition, has multiple n...
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A Mathematical Model of Population Growth as a Queuing System
In this article, a new mathematical model of human population growth as ...
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Understanding Innovation to Drive Sustainable Development
Innovation is among the key factors driving a country's economic and soc...
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Combinatorial Models of Cross-Country Dual Meets: What is a Big Victory?
Combinatorial/probabilistic models for cross-country dual-meets are prop...
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On the limits of algorithmic prediction across the globe
The impact of predictive algorithms on people's lives and livelihoods ha...
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What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC
We analyze which forces explain inflation and how in a large panel of 12...
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Modeling PKT at a global level: A machine learning approach
It is well-accepted that the ability to go from one place to another, or mobility, contributes significantly to one's wellbeing. The need for mobility is universal, but the demand for mobility shows a great variation on a country basis. This particular study looks at what are some of the most important factors on a global level that can help in predicting the passengerkilometers-travelled or passenger-miles-travelled (PKT/PMT) on a country by country basis. This particular work tries to quantify the impact of some of the key variables like Gross Domestic Product (GDP), population growth, employment rate, number of households, age demographics within the population and macroeconomic variables on the total vehicle-based travel within each country. A panel-based regression model is developed to identify the effect of some of the key macroeconomic variables on the countries' PKT growth.
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