Illuminating economic growth

Hu, Yingyao; Yao, Jiaxiong

doi:10.1016/j.jeconom.2021.05.007

Cited by 60 publications

(23 citation statements)

References 18 publications

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“…GDP data in developing countries are particularly error-prone and could be subject to outright manipulation [25]. The presence of measurement error on both sides of the equation has long been recognized in the economics literature focused on estimating the relationship between nighttime lights and GDP, or optimal combinations of both [2,3,17,26].…”

Section: Methodsmentioning

confidence: 99%

“…We study what the literature calls the structural relationship (how lights react to GDP), as opposed the predictive relationship (how lights predict GDP), but make no attempt to estimate the structural relationship net of measurement error. Other work focuses on the country level and proposes a method of identifying the elasticity under the presence of very general forms of measurement error using proxies for the statistical capacity of each country [17]. We use subnational data where variation in statistical capacity is not helpful for identification.…”

Section: Methodsmentioning

confidence: 99%

“…Interpreting how changes in nightlights reflect unmeasured changes in economic activity requires having a reliable estimate from settings where both nightlights and economic activity are measured. Recent work has explored how the relationship between growth in nightlights and economic activity varies across contexts, both at national scale [17] and at smaller geographies [7]. Some studies conclude that the subnational nightlights-GDP relationship is not stable in emerging and advanced economies [18].…”

Section: Introductionmentioning

confidence: 99%

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What Can We Learn from Nighttime Lights for Small Geographies? Measurement Errors and Heterogeneous Elasticities

Bluhm

McCord

2022

Remote Sensing

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Nighttime lights are routinely used as a proxy for economic activity when official statistics are unavailable and are increasingly applied to study the effects of shocks or policy interventions at small geographic scales. The implicit assumption is that the ability of nighttime lights to pick up changes in GDP does not depend on local characteristics of the region under investigation or the scale of aggregation. This study uses panel data on regional GDP growth from six countries, and nighttime lights from the Defense Meteorological Satellite Program (DMSP) to investigate potential nonlinearities and measurement errors in the light production function. Our results for high statistical capacity countries (the United States and Germany) show that nightlights are significantly less responsive to changes in GDP at higher baseline level of GDP, higher population densities, and for agricultural GDP. We provide evidence that these nonlinearities are too large to be caused by differences in measurement errors across regions. We find similar but noisier relationships in other high-income countries (Italy and Spain) and emerging economies (Brazil and China). We also present results for different aggregation schemes and find that the overall relationship, including the nonlinearity, is stable across regions of different shapes and sizes but becomes noisier when regions become few and large. These findings have important implications for studies using nighttime lights to evaluate the economic effects of shocks or policy interventions. On average, nighttime lights pick up changes in GDP across many different levels of aggregation, down to relatively small geographies. However, the nonlinearity we document in this paper implies that some studies may fail to detect policy-relevant effects in places where lights react little to changes in economic activity or they may mistakenly attribute this heterogeneity to the treatment effect of their independent variable of interest.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What Can We Learn from Nighttime Lights for Small Geographies? Measurement Errors and Heterogeneous Elasticities

Bluhm

McCord

2022

Remote Sensing

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“…Night light data has many advantages like it is readily available, publicly, and unbiased data sets. Thus, the research on NTL images data sets is reproducible and verifiable [6]. There are presently two programs for utilizing NTL images: Defence Meteorological Satellite Program Operation Line Scan system (DMSP-OLS) (10) and Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) (11) .…”

Section: Data Setsmentioning

confidence: 99%

“…It is also difficult to establish consistent and credible economic inequality estimates at the disaggregated level in developing nations due to the lack of consistent income data at the sub-state or district level. It is well-established in research published by international and national organizations namely the World Bank (6) and Reserve Bank of India RBI [3] that Night time light and GDP are highly correlated with each other. Moreover, various usages of NTL have been studied like spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters.…”

Section: Introductionmentioning

confidence: 99%

Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India

Verma¹,

Mehta²,

Bhandari³

2022

IJST

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Objectives: Application of trend detection and fuzzy time series models to predict the growth of Uttarakhand state using night-Time Lights (NTL) dataset (Visible Infrared Imaging Radiometer Suite (VIIRS) night-time lights). Methods: In this work, we propose a methodology to analyze trends and estimate different fuzzy time series models for prediction in the context of forecasting the economic growth of a low-earning income Uttarakhand state. Mann-Kendall tests are used for performing trend analysis of Uttarakhand (India) using night-time luminosity data sets. Forecasting methods based on fuzzy time series models have been presented to deal with uncertainties induced by ambiguities, vagueness, and some other non-probabilistic factors, and have widely been employed in many problems domains. The simulation is done on the Jupyter Notebook platform using night-time luminosity datasets. Findings: We conclude that Uttarakhand and Himachal Pradesh show positive growth in trends for their night-time lights monthly average intensity with magnitude of 0.002182 and 0.002558 respectively. Night time luminosity database can be used as a proxy indicator for the economic growth of the Uttarakhand state as the correlation between NTL time series data of Uttarakhand and its Gross Development Product (GDP) is 0.795641021. Fuzzy time series models can also be used to predict the growth of the Uttarakhand state and the High Order Fuzzy Time Series model gives the accuracy metrics of root mean square (RMSE) as 0.11. Novelty: No previous research work is performed on trend analysis and prediction of growth of a Uttarakhand state using night-time luminosity data sets.

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Big Data and Computational Social Science for Economic Analysis and Policy

Manzan

2022

Handbook of Computational Social Science for Policy

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The goal of this chapter is to survey the recent applications of big data in economics and finance. An important advantage of these large alternative datasets is that they provide very detailed information about economic behaviour and decisions which has spurred research aiming at answering long-standing economic questions. Another relevant characteristic of these datasets is that they might be available in real time, a property that can be used to construct economic indicators at high frequencies. Overall, big alternative datasets have the potential to make an impact on economic research and policy and to complement the information used by governmental agencies to produce the official statistics.

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Illuminating economic growth

Cited by 60 publications

References 18 publications

What Can We Learn from Nighttime Lights for Small Geographies? Measurement Errors and Heterogeneous Elasticities

What Can We Learn from Nighttime Lights for Small Geographies? Measurement Errors and Heterogeneous Elasticities

Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India

Big Data and Computational Social Science for Economic Analysis and Policy

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