A Bright Idea for Measuring Economic Growth.

Most readers of this article are already familiar with images of the “Earth at Night,” which are really montages showing satellite views of visible lights.1 Looking at such images, it is easy to see that the brightness of visible lights is strongly related to both population density and income per capita. The effect of population density shows up in the visibility of urban agglomerations, coastlines, river valleys, and even the higher density of population along the paths of highways and railroads. Similarly, the Northeastern United States is far brighter than the mountain West, even though they have similarly levels of income. The effect of income is similarly easy to see. Sharp economic boundaries, such as the inter-Korean border show up plainly. India, with a population density slightly higher than Japan, is significantly dimmer, The observation that income per capita is one of the determinants of visible light suggests that one might potentially use visible light to help measure income. In this paper, we explore the usefulness of these night lights data to economists studying issues related to economic growth and the national or local level. In particular, we ask the extent to which lights data can augment or entirely replace data on Gross Domestic Product.2 There are a number of obstacles to using lights visible from space to measure income, broadly falling into two categories. First, the relationship between economic activity and the true amount of light emanating from the Earth’s surface is not constant across time and space. Light visible from space is a byproduct of consumption and production activities, but light is hardly produced in a fixed ratio to output. Some productive activities such as steelmaking produce a lot of light; others such as software design produce very little. Countries or regions may differ in the fraction of their economic activity that takes place after dark (for example, Las Vegas vs. Salt Lake City). Patterns of settlement (for example, whether people live in multi-storey buildings), the availability of hydropower, etc. may all affect how much light is visible from space for a given level of income and population. Second, true light is imperfectly measured by the satellites. Humidity, reflectivity, and time periods excluded from coverage because of sunlight, moonlight, and cloud cover contamination all differ across the globe. Sensor scale for recording lights in practice varies across satellites and as satellites age over time. If one is measuring economic growth, some of the location-specific factors are differenced out. The rest we treat as measurement error in the relationship between GDP growth and light growth. We note that while we focus on measuring total income growth of regions, one might be also interested in growth in income per capita. We don’t pursue that here although we have explored the issue by using population growth numbers to supplement lights growth. Population is both conceptually and practically easier to measure than is income, so it is also feasible to alternatively focus on income growth per capita. Despite problems, the night lights data have several advantages. The first has to do with the nature of measurement error in lights data. A number of studies have recently reminded economists that much of the data on GDP growth in developing countries is plagued by serious measurement error. For example, Johnson et al. (2009) study revisions in the Penn World Tables, a standard measure of GDP. They find that comparing version 6.1 of that data, released in 2002, with version 6.2 released in 2006, the standard deviation of the change in countries’ average growth over the period 1970–1999 was 1.1% per year (which is very large relative to the average growth rate of 1.56% per year). This indicates that at least one of the measures, and presumably both, contained a great deal of error. We do not claim that the measurement error in the night lights data is smaller than that in conventional data, but we do think that the two forms of measurement error are poorly or not correlated. It is well know that combing two problematic measures can produce a composite with smaller measurement error than either of them. A second advantage of the night lights data is that they are available for regions for which standard GDP measures are not. These include sub-national units such as cities and regions, as well as entities that cross national borders, like biomes. Economic analysis of growth and of the impacts of policies and events on cities and regions of many countries is hindered by a complete absence of any regular measure of local economic activity. Much of the interesting variation in economic growth takes place within rather than between countries. However, for the vast majority of economics research, “empirical analysis of growth” has become synonymous with use of national accounts data. The night lights data offer a tool that allows the researcher to set aside this limitation. The rest of this paper is organized as follows. In Section I, we briefly discuss the night lights data. Section II outlines our statistical approach to combining data from lights with conventional measures of GDP, and presents results of an exercise applying our technique to a set of low and middle income countries. Section III discusses an application beyond the measurement of national income. We refer the reader to our companion paper, Henderson, Storeygard, and Weil (2009), for more detailed discussion of many of the technical and statistical issues raised here.