Using Night-Time Lights and Statistical Data to Measure Regional Inequality in Turkey

Using Night-Time Lights and Statistical Data to Measure Regional Inequality in Turkey

Copyright: © 2024 |Pages: 35
DOI: 10.4018/979-8-3693-2683-1.ch012
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Abstract

Poverty and inequality are the outstanding challenges in both developing and developed countries in the globe. Using Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) images and socio-economic data from administrative sources, this chapter focuses on the association between nighttime lights and economic activities with an aim of computing regional income inequality indices for the year 2015 in Turkey. Gini, the Atkinson and Theil statistics were used to establish regional inequality indices using both NTL and statistics data. The findings indicated that urban NTLs are strongly correlated with economic activity while the correlation is much weaker regarding rural nightlights and agricultural output. It can be noted that there was increasing regional inequality in north-west, south, and south-east regions whereas regional equality was more homogeneously distributed. The results indicated that NPP-VIIRS nightlight data can help to perform regional inequality assessments for the urban areas in Turkey.
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1. Introduction

Income inequality is a growing concern for both the advanced economies and developing countries. The risks of divisions are sound in the former case while the inequality trends are mixed in the latter with some countries associated with declining inequality whereas others are experiencing the growing trend. OECD reported that ‘the average income of the richest 10% population is about nine times that of the poorest 10% across the OECD countries…’ (Keeley, 2015) and that ‘…in Turkey, Chile and Mexico has inequality fallen but in the latter two countries the upper-group incomes are still more than 25 times those of the poorest’ (OECD, 2019). Widening inequality has significant implications on economic and social development. It can cause macroeconomic instability, lead to sub-optimal use of human resources, cause investment-reducing political and economic instability and raise crisis risk (Dabla-Norris et al., 2015). Therefore, it has received considerable attention from theoretical and empirical research and policies to address poverty and disparities.

In the literature there are various studies focusing on measuring inequality, its mechanism and consequences and its policy implications. The measures and methods that are commonly used in the analysis of inequality are explained in Jenkins (1991), De Maio (2007), and Cowell (2011). The regional inequality has been researched employing the traditional inequality measures based on geographical variations across countries and regions (Piketty and Saez, 2003; Anand and Segal, 2008; Reardon and Bischoff, 2011; Xie and Zhou, 2014; Han et al., 2016; Solt, 2020). The causes and consequences of inequality were covered internationally (Bergh and Nilsson, 2010; Agnello et al., 2012; Dabla-Norris et al., 2015; Roser and Cuaresma, 2016; Buttrick and Oishi, 2017; Furceri and Ostry, 2019) whereas policy implications were examined in a more local context (Fortin et al., 2012; Franzini and Raitano, 2015; Shahbaz et al., 2017).

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