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Over the last few decades, Indian cities have increasingly been recognised as the engines of growth. Going by the contributions of metropolitan centres like Mumbai and Bangalore, it is clear that cities have played a major role in keeping the country's economy moving. Hence, it is vital that these engines keep on running smoothly.
Like engines, cities also require all the nuts and bolts to be in place and well-oiled to function smoothly. For instance, a city can achieve its complete potential only when all of its segments, ranging from public health to sanitation, are in place. A city can run at maximum capacity if the crucial economic activities such as trade and investment increase. Similarly, without proper sanitation facilities, cities will suffer the same fate as an engine would if it didn't have a clean exhaust. Keeping these considerations in mind, it is distressing that we do not know how well our growth engines are doing.
India suffers from a dearth of economic indicators at the city level. The current sources to study city data are limited to the collection of demographic data once a decade. Furthermore, the private sector, which has often played a critical role in aggregating data on national economies, has also done only limited research at the city level. All in all, there is a scarcity of robust data on basic economic parameters such as the Gross Domestic Product or net exports at the city level. This limitation has capped both our ability to capture the potential of these cities, or to appropriately plan for its development.
The most commonly used indicator to measure a city's economic growth is its GDP. However, it requires information such as investment or consumer spending at city level, which are not easily accessible. This shortcoming can be overcome by using alternative indicators or proxies. One such indicator that was promoted by various economists and sociologists in the late 1990s and early 21st century was night-time light data. Night-time light data is a measure of the artificial illuminations in the night.
These artificial illuminations can be used as a proxy for night-time activities and electricity consumption in the region. The high density of night-light can be due to street lights, vehicular lights or due to economic activities that take place in the night such as construction, road work, etc. All of these activities are clear indicators of the magnitude of economic activity in the region. Moreover, comparing luminosity with other indicators such as population density in the region also helps provide a fair estimate of growth in the cities.
The primary night-time light has been captured by the U.S. Air Force's Defence Meteorological Satellite Program since the mid 1960s. Although the programme was started with the objective of studying the worldwide cloud cover, a branch of it now specialises in night-time observations of lights and combustion sources worldwide. The National Centre for Environmental Information , created under the program, provides the geophysical data on an open source platform.
This dataset provided by the Satellite Program became a pool of information that economists and sociologists could study. Economist William Nordhaus and sociologist Xi Chen have done a lot of work explaining the applicability of night-time light data. In several of their papers, they have reinstated the relevance of night-time light data, specially for regions with low-quality statistical systems. As per the research, "the use of luminosity as a proxy for output [is] most beneficial for countries with the poorest statistical systems".
Night-time lights have also been used to measure the human development as it helps show the "spatial depiction of differences in development within countries". For such studies, the night-time light data is compared with the population density of the region. Sweden is a good example of a country with robust night-time light datasets that have been used to understand the relation between night-time light data and economic activities. Night-time light data has also been used to decipher the increasing urban-rural inequality in countries with very little access to data like North Korea .
That said, the night-time light data also suffers from high measurement error. These errors specifically tend to show up in areas that are prone to high background noise data, such as fires used for warmth, cooking, etc. Further, the presence of reflections from snow or water bodies tend to distort the data. However, efforts are being made to disentangle signal from noise.
It is clear that luminosity data is a good proxy for economic statistics, especially in Indian cities which lack reliable measurement parameters.
