Henderson paper

• • Night lights data can improve economic growth estimates for countries with unreliable national income accounts.
  • A methodology combining lights data with income measures enhances growth analysis.
  • Elasticity of measured GDP growth to lights growth is estimated at around 0.3.
  • Differentiating between good and bad data countries in analysis reveals small differences.
  • Night lights data can aid in sub-national growth analysis where detailed income data is lacking.
• The elasticity of measured GDP growth to night light growth is estimated at around 0.3, indicating that the two variables are closely related.
• Income per capita has increased over time due to technological progress, but this trend may not continue as technology becomes saturated.
• Combining lights data with income measures improves growth analysis by providing more reliable estimates for countries with unreliable national income accounts.
• What are the key findings regarding the use of night lights in analyzing economic growth?

  • The estimated elasticity of measured GDP growth with respect to lights growth is approximately 0.3.
  • Revised growth estimates for countries with low statistical capacity are based on a combination of national income accounts and predicted income growth from lights data.
  • An estimate of the structural elasticity of growth in night lights with respect to true GDP growth is slightly above one.
• Are there any limitations or drawbacks associated with using night lights as an indicator of economic activity?
• Yes, some limitations include the fact that night lights do not capture all forms of economic activity (e.g., agriculture) and may be influenced by factors other than economic output (e.g., weather conditions). Additionally, the quality of satellite imagery used to measure night lights varies widely, which could affect the accuracy of the results.
• How does the relationship between night lights and economic activity vary across different regions of the world?
• Is it possible to distinguish between "good" and "bad" data countries when using night lights as an indicator of economic activity?
• Can night lights be used as a proxy for economic development in developing countries without access to reliable income statistics?
• The author suggests using a simple model where lights growth depends linearly on true GDP growth, with a coefficient close to unity. This allows for the estimation of true GDP growth when only lights data are available.
• How can night lights data be used to improve estimates of true economic growth?

  Night lights data can be used to predict income growth by estimating the elasticity of measured GDP growth with respect to lights growth. By combining lights data with income measures, revised growth estimates can be generated for countries with limited statistical capacity, resulting in more accurate assessments of true economic growth.
• Section I Summary:

  Section I introduces the concept of using night lights data to analyze economic growth in regions where traditional national income data is lacking.

  Night lights data collected by satellites provide a new way to study economic activity and growth patterns at subnational levels.

  • The availability of night lights data allows for broader research questions and analysis beyond traditional national income data, opening up new avenues for empirical growth analysis.
• SECTION 1 SUMMARY

  • Night lights data collected by satellites from the United States Air Force Defense Meteorological Satellite Program offer a unique opportunity to study economic growth patterns.
  • These data provide insights into regions with poor data quality, allowing for improved estimates of true income growth.
  • The use of night lights data expands the scope of research questions in growth analysis beyond traditional national income data, enabling a more comprehensive understanding of economic development.
• Night lights data


  Satellite data from the United States Air Force Defense Meteorological Satellite Program to measure the intensity of Earth-based lights
• Night lights data


  • Can be utilized to assess economic growth, particularly in regions defined by geographic, economic, or health metrics
  • Available since the 1970s
  • Provides a consistent and comprehensive measure of economic activity that is available over time and covers almost all inhabited areas globally
  • Suitable for predicting economic growth
• Using night lights data to predict GDP growth


  Implications for improving estimates of true income growth, especially in regions with limited or unreliable national income accounts
• Methodology for combining changes in night lights with measured income growth

  1. Estimate the elasticity of measured GDP growth with respect to lights growth

  2. Produce revised growth estimates for countries with limited statistical capacity

  3. Obtain an estimate of the structural elasticity of growth in night lights concerning true GDP growth
• Lights in an area

  Reflect total intensity of income, which is increasing in both income per person and number of people
• Night lights are suitable for predicting economic growth because they provide a consistent and comprehensive measure of economic activity that is available over time and covers almost all inhabited areas globally
• Unlike other proxies like electricity consumption, night lights data can be used to estimate income growth accurately, especially in regions with unreliable national income accounts
• Combining changes in night lights with measured income growth can enhance the accuracy of economic growth estimates, making night lights a valuable tool for analyzing economic trends across countries and regions
• Luminance


  Measurements captured by night lights data that can vary across countries due to factors like climate, auroral activity, and economic activities
• Estimating equation to relate lights to GDP growth

  1. Outline assumptions about error structures

  2. Develop a statistical framework to combine measures of lights growth with measures of GDP growth to improve estimates of true income growth
• The study explores the predictive power of electricity consumption and lights on GDP growth, estimating elasticities and structural relationships between luminance and GDP
• Data quality rankings


  A process to measure the quality of data or classify countries into different data quality groups, important for improving the accuracy of income growth estimates
• The study discusses the application of night lights data in assessing growth in regions defined by geographic, economic, or health metrics rather than political borders
• Coastal areas versus inland regions in sub-Saharan Africa

  Despite the perceived advantages of coastal locations for trade and economic development, inland areas experienced higher growth in terms of lights data over a 15-year period
• Primate cities versus hinterland areas

  Despite the historical focus on primate cities, hinterland areas have shown comparable or even faster growth rates, raising questions about the effectiveness of favoring primate cities in policy-making and resource allocation
• Malaria prevalence and economic growth in sub-Saharan Africa

  Regions with lower malaria prevalence experienced faster economic growth rates compared to areas with higher malaria prevalence, indicating a potential negative impact of malaria on economic development
• The reduction in malaria transmission post-initiatives did not consistently lead to higher GDP growth, suggesting other factors may influence economic growth outcomes