Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space

Intergenerational Mobility of Education in Europe: Geographical Patterns, Cohort-Linked Measures, and the Innovation Nexus

“…4 Using machine-learning techniques, the authors classify annual composites of Landsat satellite pixels from 1984 through 2020 into six different categories that describe terrestrial features of the earth with similar surface characteristics, the surface groups (built-up land, grassland, cropland, forest, land without buildings or vegetation, and water). These surface groups can be aggregated at any regional level and explain a large part of the variation in regional economic activity even at low levels of aggregation (Lehnert et al, 2023). We aggregate the surface groups to modified nested European NUTS boundary shapefiles.…”

“…𝑀𝑀 is the degree of intergenerational mobility, as a weighted average of the mobility of the three cohorts (as previously described). 𝜃𝜃 is a vector of contemporary controls for region-specific characteristics in 𝑡𝑡 − 1, namely proxy measures for local economic activity extracted from daytime satellite imagery collected via Landsat satellites (see Lehnert et al, 2023). 𝐼𝐼 is a vector of controls for cohort-specific characteristics: average years of education, coefficient of variation of years of education, and cohort-specific initial conditions (see Section 2); again as a weighted average across the three cohorts.…”

“…Cohort-specific initial conditions include a summary indicator for historical precipitation, temperature, sea level pressure, relative humidity, wind speed, and global radiation associated with the respective cohorts, also weighted by cohort-participation weights. Contemporary controls include variables indicative of structural transformation and local development retrieved fromLehnert et al (2023). Last row shows the elasticity computed based on the regression coefficient applying the procedure explained inBellemare and Wichman (2019).…”