Left in the Dark? Oil and Rural Poverty

Smith, Brooke M.; Wills, Samuel

doi:10.1086/698512

Cited by 36 publications

(25 citation statements)

References 101 publications

(60 reference statements)

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“…In this study, a special attention was paid to rural residents. The context of the study is associated with the problems of poverty and social exclusion in rural areas (Barbier and Hochard, 2018;Aggarwal, 2018;Smith and Wills, 2018;Thalassinos et al, 2019). The aim of the study is to reveal and determine the current state of poverty in rural areas in the countries and regions of the Visegrád Group.…”

Section: Purpose Methodology and Sources Of Informationmentioning

confidence: 99%

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Piwowar¹,

Dzikuć²

2020

ERSJ

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Purpose: The aim is to reveal and determine the current state of poverty in rural areas in the regions of the Visegrád Group. By comparing the basic features used for measuring poverty, it has been pointed out that there are spatial differences between the regions analyzed. Design/Methodology/Approach: The basic indicators used in this area include the income measure, the relative poverty indicator (at-risk poverty rate-ARPR) and the aggregate indicator (at-risk-of-poverty and social exclusion-AROPE). Findings: In many households in rural areas, as in Poland and Hungary, there is a problem consisting in the fact that financial resources are insufficient to cover the basic needs. As it results from the analyses (especially from at-risk-of-poverty indicator after deducting housing costs), the level of satisfying the remaining needs in rural areas is very low. Practical Implications: This issue is important not only in theoretical term, but also for application purposes. Many households in the Visegrád Group area experience poverty. According to the analyses, the problem of the risk of poverty concerns especially rural residents in Poland and Hungary. It is necessary to take measures in the sphere of social policy that would limit the threat of social exclusion. Originality/Value: Social exclusion and poverty are alarming problems for modern societies they are obstacles in achieving sustainable social development. The paper discusses important and current issues related to poverty and social exclusion in rural areas in the Visegrád Group countries.

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Section: Purpose Methodology and Sources Of Informationmentioning

confidence: 99%

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Piwowar¹,

Dzikuć²

2020

ERSJ

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“…The Wealth Index (WI) (Figure 2a) drawn from the Demographic and Health Surveys (DHS) program [43][44][45] was used as the dependent variable of the poverty estimation model. WI is computed as the first principal component of household's ownership of selected assets (such as televisions and bicycles, materials used for housing construction, types of water access and sanitation facilities) and has been used as a reflection of household poverty level in previous studies [28,30,46]. The WI value is an integer ranging from 1 to 5, indicating the lowest, second, middle, fourth, and the highest asset levels.…”

Section: Datamentioning

confidence: 99%

Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

et al. 2019

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Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km × 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy. The results show that the R2 between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R2 between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty.

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“…Lowe (2014) suggests averaging across satellites in the years with two satellites; this reduces measurement error variance if errors are random, but if one satellite has a fixed tendency to detect differently than another, it may be better to include satellite fixed effects that place more weight on the within-satellite correlation across years, than on the between-satellite and within-year correlation. Henderson et al (2012) discuss the intercalibration approach in the remote-sensing literature but reject it in favor of using year dummies in their regressions, an approach followed in several other studies (e.g., Smith and Wills, 2018). Yet, these year dummies will not help with any fixed tendency for one satellite to detect light at brighter levels than what is detected by another.…”

Section: Signal Error and Temporal Comparabilitymentioning

confidence: 99%

“…The discussion in Section 2.3 suggests that detecting night lights by satellites is more appropriate for cities than for rural areas, due to the type of lights that are needed (such as high pressure sodium lamps) in order to be detected from space and due to the underlying differences in population density that produce more concentrated sources of lights. A few of the studies in Table 3 focus only on cities, but many also cover rural areas, and some rely on rates of detecting lights for villages or rural areas as outcomes of interest (e.g., Baskaran et al, 2015;Smith and Wills, 2018). The national-level findings from Keola et al (2015) and the regional-level findings from Gibson et al (2019), that DMSP night lights are negatively correlated with GDP in times or places where agriculture is more important, raise some doubts about what effects are being identified when DMSP lights data are a proxy for village-level activity or rural poverty.…”

Section: Some Uses Of Night Lights Data In Economicsmentioning

confidence: 99%

NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES¹

Gibson

Olivia

Boe‐Gibson

2020

Journal of Economic Surveys

112

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Night lights, as detected by satellites, are increasingly used by economists, typically as a proxy for economic activity. The growing popularity of these data reflects either the absence, or the presumed inaccuracy, of more conventional economic statistics, like national or regional GDP. Further growth in use of night lights is likely, as they have been included in the AidData geoquery tool for providing subnational data, and in geographic data that the Demographic and Health Survey links to anonymized survey enumeration areas. Yet, this ease of obtaining night lights data may lead to inappropriate use, if users fail to recognize that most of the satellites providing these data were not designed to assist economists, and have features that may threaten validity of analyses based on these data, especially for temporal comparisons, and for small and rural areas. In this paper, we review sources of satellite data on night lights, discuss issues with these data, and survey some of their uses in economics.

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Left in the Dark? Oil and Rural Poverty

Cited by 36 publications

References 101 publications

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES¹

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Left in the Dark? Oil and Rural Poverty

Cited by 36 publications

References 101 publications

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Poverty and Social Exclusion: Is this a Problem in Rural Areas in the Visegrad Group Countries?

Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES1

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NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES¹