Satellite Night-Light GDP Analysis
Combined VIIRS night-light and World Bank indicators to examine whether satellite-observed brightness can complement conventional measures of national economic activity.
Quick read
The through-line of the work.
Open
The starting question
Can night-light intensity help explain GDP where official statistics are limited, and do urban population and electricity access change that relationship?
Build
What I checked
I started with VIIRS night-light data and World Bank GDP, population, urban-population, and electricity-access indicators and moved through Integrate country-year data -> Prepare the variables -> Test the relationship -> Define responsible use.
Turn
What changed my view
Night-light intensity explains 81.9% of GDP variation in the simple model (R-squared = 0.819, N = 791), reproducible via reproduce.py in the repository.
Close
Where I draw the line
R-squared = 0.819 describes explanatory fit in the simple model; it is not 81.9% prediction accuracy.
01 · Start
The question I began with
Can night-light intensity help explain GDP where official statistics are limited, and do urban population and electricity access change that relationship?
02 · Material
The data and evidence
03 · Work
How I worked through it
- 01
Integrate country-year data
Merged satellite brightness and economic indicators at the country-year level.
- 02
Prepare the variables
Applied log transformations to skewed variables and centered moderator variables.
- 03
Test the relationship
Estimated the baseline regression and added urban-population and electricity-access interaction terms.
- 04
Define responsible use
Interpreted model fit alongside infrastructure effects, missing data, and non-economic sources of light.
04 · Takeaway
What the numbers suggested
Night-light intensity explains 81.9% of GDP variation in the simple model (R-squared = 0.819, N = 791), reproducible via reproduce.py in the repository.
Urban population and electricity access produced statistically significant interaction effects in the report.
The result is explanatory and should not be described as prediction accuracy or causal proof.
Not claimed
- Does not claim predictive accuracy. R-squared is in-sample explanatory power.
- Does not claim causation. Brightness does not create GDP.
- Does not propose a new method. It checks how far a known relationship holds.
05 · Use
Where this helps
Night-light data can provide a complementary signal for early market screening, country-risk research, and economic monitoring where conventional reporting is limited.
06 · Boundary
What still needs checking
- •The final country count and the 973/820/791 dataset stages require a reproducible data dictionary.
- •An earlier-year Albania record and the final brightness definition require reconciliation.
- •Fixed-effects and out-of-sample analysis are needed before making predictive claims.
07 · Figures
Tables and figures
Alternative-data pipeline
Verified sourceThe diagram shows how satellite and economic indicators enter a country-year analysis. It explains provenance and process; it does not establish that night lights cause GDP.
Source · GDP project report, PPTX, XLSX, and SPSS output
Simple-model fit
Verified sourceReproduced R-squared
0.819
N = 791 · slope 0.834 · p < 0.001
Recomputed by the repository's reproduce.py from the SPSS analysis file. It is explanatory fit within the sample, not 81.9% prediction accuracy and not causal evidence.
Source · reproduce.py · SPSS analysis file (.sav) · ln(GDP) ~ ln(brightness)
Distinct dataset stages
Verified source973
Raw rows
820
Merged observations
791
GDP observations
973, 820, and 791 refer to raw rows, merged observations, and GDP-usable observations. They are different processing stages and must not be treated as interchangeable sample sizes.
Source · reproduce.py row counts · project spreadsheet and SPSS file