Elsevier

Economics & Human Biology

Volume 31, September 2018, Pages 238-248
Economics & Human Biology

Night light intensity and women’s body weight: Evidence from Nigeria

https://doi.org/10.1016/j.ehb.2018.09.001Get rights and content

Highlights

  • We employ night light data as proxy for urbanization and related outcomes.

  • We investigate the relationship between night light intensity and women’s body weight.

  • We show nonlinear relationships between night light intensity and body weight outcomes.

  • The strength of the relationship differs across various stages of night light intensity.

  • Higher stages of nightlight intensities are associated with higher rates of overweight and obesity.

Abstract

The prevalence of overweight and obesity are increasing in many African countries and hence becoming regional public health challenges. We employ satellite-based night light intensity data as a proxy for urbanization to investigate the relationship between urbanization and women’s body weight. We use two rounds of the Demographic and Health Survey data from Nigeria. We employ both nonparametric and parametric estimation approaches that exploit both the cross-sectional and longitudinal variations in night light intensities. Our empirical analysis reveals nonlinear relationships between night light intensity and women’s body weight measures. Doubling the sample's average level of night light intensity is associated with up to a ten percentage point increase in the probability of overweight. However, despite the generally positive relationship between night light intensity and women’s body weight, the strength of the relationship varies across the assorted stages of night light intensity. Early stages of night light intensity are not significantly associated with women’s body weight, while higher stages of nightlight intensities are associated with higher rates of overweight and obesity. Given that night lights are strong predictors of urbanization and related economic activities, our results hint at nonlinear relationships between various stages of urbanization and women’s body weight.

Introduction

In the last few decades, many developing countries have been experiencing unprecedented levels of urbanization and economic growth. African nations, in particular, over the last two decades have experienced their highest-ever rate of urban growth, and their economies have sustained unprecedented rates of overall growth. The share of urban population has quadrupled since 1950, rising from about 10 percent to about 40 percent in 2014. This rapid urbanization is changing the continent’s demographic and nutritional landscapes, presenting both new opportunities and challenges for urban dwellers. Although urbanization improves access to health facilities and improved nutrition, it is also associated with major risk factors that may promote unhealthy lifestyles. It is commonly associated with a sedentary lifestyle, whose limited physical activities may lead to unhealthy weight gain and related cardiovascular diseases (Popkin, 1999; Harpham et al., 2004; Monda et al., 2007). This transition in lifestyle is commonly coupled with a nutritional transition, which involves a shift to more processed and fatty foods (Popkin, 2003; Popkin and Du, 2003; Popkin et al., 2012). Because of these transitions in lifestyle and nutrition, urbanization is commonly associated with a rise in noncommunicable diseases and associated mortality, mainly stemming from rises in the risk factors of overweight and obesity.

Due to these multifaceted attributes, contemporary urban expansion, on the one hand, has received a somewhat positive response from many economists and social scientists and, on the other hand, has been regarded more negatively in the public health and epidemiology literature (Leon, 2008). Indeed, the World Health Organization (WHO) classifies urbanization as a key threat to public health (WHO (World Health Organization), 2007).2 Given this mix of positive and negative externalities, it is important to identify the overall public health implication of urbanization to regulate and monitor contemporary urban expansions in developing countries.

Previous attempts to investigate the implications of urbanization have commonly employed dichotomous indicators of urbanization.3 These aggregate indicators of urbanization mask enormous heterogeneities across communities and villages.4 For instance, the indicators are commonly census- or survey-based, and as such cannot capture the enormous heterogeneities among urban areas and the rapid dynamics of urbanization (McDade and Adair, 2001; Vlahov and Galea, 2002; Dahly and Adair, 2007; Van de Poel et al., 2012). Rather than being a binary phenomenon, urbanization involves a continuum of rural-to-urban transformation at various stages and paces. Aggregate and binary indicators cannot uncover nonlinear relationships between urbanization and public health outcomes, and hence are not well suited for microlevel and dynamic analyses.5 Thus, researchers and urban planners are exploring alternative metrics that can sufficiently inform the dynamics and levels of urbanization.

The advent of satellite-based night light data offer a unique opportunity and potential to capture urban expansion. Based on the notion that light intensity per unit area corresponds to a reasonable measure of the degree of urbanization, night light intensity is proved to be a valid marker of urban expansion (Elvidge et al., 1997; Imhoff et al., 1997; Sutton, 1997; Henderson et al., 2003; Sutton et al., 2010; Storeygard, 2016). Night light data are measured with consistent quality across countries, regardless of different institutional capacities, allowing consistent measurement of urban growth across various communities and regions. For these reasons, night light data are increasingly being used as a proxy for urban expansion.

In this paper, we employ satellite-based night light intensity data as a proxy for urbanization and related economic activities. We thus investigate the relationship between night light intensity and women’s body weight outcomes. Despite the increasing use of night light for measuring urbanization, we are not aware of any study using night light data to study individuals’ body weight outcomes.6 Thus, unlike previous studies that employed binary rural-urban indicators, we employ night lights as a continuous, disaggregated, and objective proxy for urbanization. This allows the detection of relatively small variations and uncovers potentially nonlinear relationships between night light and body weight.

We focus the study on Nigeria, a country that provides an interesting context to address our research question for several important reasons. Nigeria is going through rapid urban expansion, concurrent with reasonably increasing trends of overweight and obesity. In 2000 about 44 percent of the Nigerian population used to live in urban areas, while recent predictions show that the urban population will hit 65 percent by 2020. The prevalence as well as trends of adult overweight and obesity are also increasing overtime (Chukwuonye et al., 2013).7 The rates of overweight and obesity in our sample period (2008–2013) increased by about 24 and 40 percent, respectively. These trends reinforce the need for careful analysis of the distribution and determinants of body weight outcomes. We focus on women’s overweight and obesity trends, since these two indicators are commonly associated with a higher risk of noncommunicable diseases and associated mortality.

We employ two rounds (2008 and 2013) of georeferenced and nationally representative Demographic and Health Survey (DHS) data from Nigeria. The DHS data provide detailed public health indicators and body weight outcomes for both urban and rural dwellers. We merge these georeferenced DHS data with night light intensity data for the survey clusters in which the DHS sample households reside. We employ both nonparametric and parametric estimation approaches that exploit the cross-sectional and longitudinal variations in night light intensity. Thus, the longitudinal nature of the night light data allows us to examine potential dynamic relationships between night light intensity and women’s body weight.

The remainder of the paper is organized as follows: In Section 2 we discuss alternative measures of urbanization and potential advantages of using night light data. In Section 3 we present the key variables used in the empirical analysis. Section 4 presents the empirical model and estimation strategy. We present and discuss the empirical results in Section 5. Section 6 provides concluding remarks and policy implications.

Section snippets

Measuring urbanization

While the globe continues to register unprecedented levels of rural-to-urban transformations, we still lack an accurate measure of the level and dynamics of urbanization. Most urban expansions in developing countries are accompanied by local economic, infrastructural, and technological developments. While we have standard measures of this economic progress in the aggregate, especially at national and regional levels, it is difficult to measure at the disaggregated level, such as the community

Data sources

The main data source for this study is the Nigerian Demographic and Health Survey (NDHS). We employ two rounds of these surveys (2008 and 2013), which are nationally representative surveys covering both urban and rural households. The sampling design involves a three-stage stratified sampling strategy. In the first stage, localities were selected with probability proportional to population size and independent of selection in each sampling stratum. In the second stage, enumeration areas were

Parametric and conditional regressions

Estimating the relationship between night light intensity and body weight requires recalling the potential determinants of body weight we discussed in Section 2.2. Furthermore, quantifying the overall effects of urbanization on body weight may suffer from endogeneity problems arising from omitted attributes. This is plausible given that urbanization programs and trends are accompanied by rapid economic and infrastructural growth, which can affect livelihoods and body weight. Thus, the

Results and discussion

Before discussing our results, it is worth clarifying how to interpret our estimates. Although we are exploiting longitudinal variations in night light intensity, so that time-invariant unobserved heterogeneities are innocuous, our estimates might not sufficiently inform causal inference due to the endogeneity problems discussed in Section 4. Furthermore, night light might capture not only rural-urban transformations but also accompanying local economic developments and spatial variations in

Concluding remarks

Overweight and obesity are increasingly becoming major public health problems in African urban centers. These trends have been particularly rapid in the last two decades, just as many African countries have been experiencing their highest-ever urban growth. Thus, many attribute these trends in body weight to contemporary urban expansion and associated nutritional transitions and changes in lifestyle. Indeed, there exists an evolving interest in unpacking the role of urbanization in driving

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    1

    Amare is affiliated with the Nigeria Strategy Support Program (NSSP), managed by the International Food Policy Research Institute (IFPRI) and financially supported by the United States Agency for International Development (USAID) in connection with the Feed the Future Nigeria Agricultural Policy Project.

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