When economists look to the sky
Economists are increasingly turning to data from satellite images to estimate the level of economic activity in regions where economic data is erratic or unavailable
One key innovation by the finance ministry in its annual Economic Survey has caught everyone’s attention this year: the use of satellite data. The first volume of the Survey released in February had used data from satellite images to calculate built-up area and estimate potential property tax collections to show how Indian cities are failing to t ap a lucrative source of revenue (bit.ly/2x1o7cf). The second volume released in August uses satellite data to show that India may be more urbanized than thought previously ( bit.ly/2x39q9k).
The use of satellite data is new in Indian policymaking but a growing body of economic research has relied on mining such data over the past few years to answer questions relating to growth and poverty in regions where official data is either unavailable or unreliable. What started as a satellite programme run by the US department of defence to gauge cloud cover in the 1960s has increasingly become an important resource for economists.
Night-time lights or night-lights data contain the data on energy emitted or reflected back from the surface of the earth to the sky. Economists figured that this data tends to correlate with economic activity. Thus, for countries with poor data, night-lights data has been particularly useful in estimating the level of economic activity and gross domestic product (GDP). A classic example is Myanmar, which stopped publishing its national accounts statistics in 1989. Using night-lights data, economists have shown that the country’s economy is growing at a very slow pace. A 2012 research paper (bit.ly/2gb9qe4) by the Japan-based think-tank IDE-JETRO used night-lights data to show that most economic activity is concentrated in regions surrounding the capital Yangon. Also, the regions bordering China and Thailand grew at much faster pace than those bordering Bangladesh and India, the study suggests.
There has been similar uncertainty about growth in North Korea after sanctions were imposed against it. Night-lights data have once again come to the rescue. As a celebrated image (go.nasa.gov/ 1c545wo) confirms, there is a stark difference between South and North Korea; the North is almost completely dark while the South seems to be well-lit. A recent study (stanford.io/2vcksuv) by Yong Suk Lee of Stanford University using night-lights data shows that economic activity appears to be concentrated in urban areas, and particularly so in the capital Pyongyang. Lee found that economic sanctions decreased luminosity in the hinterlands but increased luminosity in urban areas, especially the urban core, suggesting that the dictatorship may have “countered the effects of sanctions by reallocating resources to the urban areas”. Also, economic activity as measured by luminosity or night-lights seemed to have increased in regions bordering China, Lee’s study showed.
In India, night-lights data has been used to understand the effects of the reorganization of states. In 2000, three new states were created—uttarakhand, Jharkhand and Chhattisgarh. A 2015 study (bit.ly/2wv97as) by Sam Asher of the World Bank and Paul Novosad of Dartmouth College, using census and nightlights data, suggested that there has been marked improvement in economic activity in the newly created states.
A recent research paper by (bit.ly/2vcol7l) analysts Praveen Chakravarty and Vivek Dehejia of the IDFC Institute in Mumbai used night-lights data to show that both inter-state and intra-state inequality in India has been growing.
The Economic Survey this year also documented the widening regional inequality in India, and attributed it to the differing quality of governance. But Chakravarty and Dehejia argue that much of the differences in economic activity may be driven by network effects.
“The conventional channel of economic convergence is diminishing returns to capital, but we would argue that this is offset by the opposite phenomenon, of agglomeration economies in capital accumulation and network externalities which increase, rather than decrease, the marginal productivity of capital as its stock increases,” the duo write. “…That is why Apple has chosen to locate its new manufacturing unit in India in wealthy and expensive Karnataka rather than (say) poor and cheaper Bihar, and why, by extension, it has chosen to locate in wealthy and expensive Bengaluru rather than (say) poor and cheaper Shimoga. If low labour costs and a putatively higher return to capital were drivers, as conventional theory suggests, we ought to have seen Apple locate in Bihar rather than Karnataka or at least within Shimoga rather than Bengaluru.”
There are two serious limitations of nightlights data that researchers have been grappling with for some time. One, satellites that record this data do not have the capacity to detect artificial lights with precision and only capture the lights emitted from vehicular traffic, rooftops and streets. The second is more serious: night-lights data do not distinguish between the poorest and the poor region. Beyond a threshold, all is dark in the satellite images.
Research by Charlotta Mellander of the Jönköping International Business School (bit.ly/2gbguf3) and co-authors, based on a study in Sweden, suggests that while the correlation between night-time lights and economic activity is strong enough to make it a relatively good proxy for population and establishment density, the correlation is weaker in relation to wages. The researchers found the link between light and economic activity, especially estimated by wages, to be “slightly overestimated in large urban areas, and underestimated in rural areas”.
To get around some of these issues, researchers have begun to use data from daytime satellite images, which are fed into a machine learning algorithm to estimate levels of poverty, capital stock, and economic activity (bit.ly/2gu8qhw). Multiple images, captured on several different days, are often com-
Regional inequality: Night-time views of India and surrounding areas in 2012 (top) and 2016. Economists believe that night-time lights or night-lights data tends to correlate with economic activity.