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In Capital in the Twenty-First Century, Thomas Piketty uses the market value of tradable assets to measure both productive capital and wealth. As a measure of wealth this is problematic because it ignores the value of human capital and transfer wealth, which have grown enormously over the last 300 years. Thus the constancy of the wealth/income ratio as portrayed in his data is an illusion. Further, the types of wealth that he does not measure are more equally distributed than tradable assets. The approach also incorrectly identifies capital gains due to reduced discount rates as increases in the capital stock.

M-Pesa, a service operated by the mobile phone network Safaricom in Kenya, allows users to deposit money onto their telephone handsets, transfer e-money to another user with a simple text message, and withdraw cash at one of thousands of outlets throughout the country. The system is safer, cheaper, and far faster than the money transfer systems that it replaced. Although only five years old, M-Pesa has achieved remarkable penetration into the Kenyan economy. As of September, 2011, there were 32,000 M-Pesa outlets at which individuals could exchange cash for e-float or vice-versa, in a country that as of 2009 had 491 bank branches, 500 postbank branches, and 352 ATMs (Mas and Ng’weno, 2009). In the six month period April-September, 2011, the volume of transfers was Ksh. 314 billion, compared to nominal GDP of Ksh. 2.99 trillion that year. (For the period from which our data are drawn, the market exchange rate was approximately 75 Ksh/dollar and the PPP exchange rate was approximately 35 Ksh./dollar). At present, M-Pesa is primarily a money transfer system. However, it has the potential to evolve in two exciting directions: first as a gateway via which unbanked households can access financial services, and second, as a transaction medium, with e-money partially replacing cash.1 Even in its current form, however, M-Pesa represents a dramatic change in the economic environment of Kenyan households. Further, data on how households use M-Pesa allows for insight into the objectives of and constraints on their money management choices, and consumption more generally.

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.

Saving and growth are strongly positively correlated across countries. Recent empirical evidence suggests that this correlation holds largely because high growth leads to high saving, not the other way around. This evidence is difficult to reconcile with standard growth models, since forward-looking consumers with standard utility should save less in a fast-growing economy because they know they will be richer in the future than they are today. We show that if utility depends partly on how consumption compares to a “habit stock” determined by past consumption, an otherwise-standard growth model can imply that increases in growth can cause increased saving. (JEL D91, E21, O40)

This paper develops a unified growth model that captures the historical evolution of population, technology, and output. It encompasses the endogenous transition between three regimes that have characterized economic development. The economy evolves from a Malthusian regime, where technological progress is slow and population growth prevents any sustained rise in income per capita, into a Post-Malthusian regime, where technological progress rises and population growth absorbs only part of output growth. Ultimately, a demographic transition reverses the positive relationship between income and population growth, and the economy enters a Modern Growth regime with reduced population growth and sustained income growth. (JEL J13, O11, O33, O40)

This paper examines how population growth affects the average level of utility particularly the consumption per capita. It also focuses on the effects of population growth on the ratio of dependent consumers to working-age adults. The model employed in this paper has three demographic groups: working-age adults who produce and consume and the young and elderly who only consume. This study concluded that the transition to lower population growth requires a long period of reduced dependency in which society benefits from lower spending on children while it has yet to pay for higher old-age dependency. The dependency level after 30 years is not significantly different from that which would exist in an optimal stable population. Any rise in fertility that would decrease old-age dependency in the long run would require a lengthy period of higher-than-steady-state dependency.

This paper examines a novel mechanism linking fertility and growth. Household fertility is determined by relative wages of women and men. Increasing women's wages reduces fertility by raising the cost of children relatively more than household income. Lower fertility raises the level of capital per worker which in turn, since capital is more complementary to women's labor input than men's, raises women's relative wages. This positive feedback leads to the possibility of multiple steady-state equilibria. Countries with low initial capital may converge to a development trap with high fertility, low capital, and low relative wages for women.