Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images

Otchia, Christian S.; Asongu, Simplice A.

doi:10.1108/jes-05-2020-0201

Cited by 13 publications

(11 citation statements)

References 65 publications

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“…Similar to the previous paper, this paper is also based on satellite "image" data, whereas our work here is based on processed (numeric) nighlight data, in order to avoid the computational complexity associated with processing satellite images. Otchia, et al [30] use Machine Learning methodologies on nighttime data to study the industrial progress in Africa. This research further confirms the validity of using nighttime data for gauging the economic progress for regions where there is a gap in the data or the quality of data is poor.…”

Section: Related Workmentioning

confidence: 99%

ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Musthyala¹,

Rudrajit²,

Hritish³

et al. 2022

Preprint

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Policy makers often make decisions based on parameters such as GDP, unemployment rate, industrial output, etc. The primary methods to obtain or even estimate such information are resource intensive and time consuming. In order to make timely and well-informed decisions, it is imperative to be able to come up with proxies for these parameters which can be sampled quickly and efficiently, especially during disruptive events, like the COVID-19 pandemic. Recently, there has been a lot of focus on using remote sensing data for this purpose. The data has become cheaper to collect compared to surveys, and can be available in real time. In this work, we present Regional GDP NightLight (ReGNL), a neural network based model which is trained on a custom dataset of historical nightlights and GDP data along with the geographical coordinates of a place, and estimates the GDP of the place, given the other parameters. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and is able to predict the GDP for both normal years (2019) and for years with a disruptive event (2020). ReGNL outperforms timeseries ARIMA methods for prediction, even during the pandemic. Following from our findings, we make a case for building infrastructures to collect and make available granular data, especially in resource-poor geographies, so that these can be leveraged for policy making during disruptive events. CCS Concepts: • Computing methodologies → Neural networks; Supervised learning by regression; • Applied computing → Economics.

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Section: Related Workmentioning

confidence: 99%

ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Musthyala¹,

Rudrajit²,

Hritish³

et al. 2022

Preprint

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“…Notably, there are a few empirical studies which have tested for a possible non- Our study draws and improves on three aspects of the current literature. Firstly, we expand on the literature for African economies of which the works of Nonso (2015) and Otchia and Asongu (2020) are currently the only studies available in the literature which provide regression estimates between gdp and night light intensity. Secondly, we differ from Nonso (2015) and Otchia and Asongu (2020) by taking a country-specific, time series approach to estimating the night light gdp relationship as in the studies of Guerrero and Mendoza (2019) for Mexico, China and Chile.…”

Section: Continued On the Next Pagementioning

confidence: 99%

“…The main contribution of our study is that it is the first to examine the relationship between night light and gdp for individual African countries, whereas previous African-related studies have relied on panel-based estimations (Nonso 2015;Otchia and Asongu 2020). Consequently, by taking a country-specific approach using more advanced empirical techniques, our study distinguishes between those African countries which display significant correlations between night light intensity and gdp and those which do not.…”

Section: Introductionmentioning

confidence: 97%

“…Thirdly, previous studies do not adequately account for possible time-varying and asymmetric effects of night light activity on economic growth. Whilst some studies attempt to test for nonlinear effects by including a 'squared interactive term' in the estimated regression, they all fail to observe a significant coefficient on the interactive term (Zhao, Currit, and Samson 2011;Henderson, Storeygard, and Weil 2012;Hu and Yao 2019;Chanda and Kabiraj 2020;Otchia and Asongu 2020).…”

Section: Introductionmentioning

confidence: 99%

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Using Nighttime Luminosity as a Proxy for Economic Growth in Africa: Is It a Bright Idea?

Hamman

Phiri

2022

MGT

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In this paper, we question whether night light luminosity data can beused as an alternative measure of GDP in 49 African countries. For thisto be proven true, evidence of significant relationships between night lightdata and GDP time series variables needs to be confirmed through empiricalanalysis. In differing from previous studies, we employ pooled meangroup (PMG) cross sectional cointegration estimators and wavelet coherencetools to examine the cointegration relationships and time-frequencysynchronizations between GDP and DMSP-OLS night light intensity forannual data collected between 1992 and 2012. All in all, we find little evidenceof significant relationships between nighttime data and GDP for individualAfrican countries and therefore caution policymakers in strictlyusing DMSP-OLS data to create synthetic measures of economic growth.Possible avenues for future research are further recommended at the endof the study.

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“…Random forest (RF) is a classification and regression approach that is composed of a succession of prediction trees, each of which is generated using a randomly chosen random vector that is unrelated to the vector input [8]. A numerical value is given to the prediction tree instead of a class label, as is done with the random forest classifier [9].…”

Section: Regression On a Random Forestmentioning

confidence: 99%

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

Sopandi

2021

J. Appl. Data Sci.

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In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. We utilized 132 field measurements comprising this dataset. 88 models were trained using observations, while the remaining 44 were used to validate it. The cumulative time (Tf), the impurity type (It), the impurity concentration (Ci), and the moisture content (Wc) were utilized as input variables, and the rate of infiltration was employed as the output. To evaluate the efficiency of the two modeling methodologies, correlation coefficients we estimated root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative square error are all terms that may be used to describe errors (RRSE). The random forest regression approach outperforms the other two models when compared to evolutionary data (ANN and M5P model tree). Using a random forest as a model, regression can properly estimate the infiltration rate within a 25% error range. According to the results of the sensitivity research, cumulative time plays an important influence in determining the soil's penetration rate.

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Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images

Cited by 13 publications

References 65 publications

ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Using Nighttime Luminosity as a Proxy for Economic Growth in Africa: Is It a Bright Idea?

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

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