Search

Quality-adjusted population density (QAPD) is population divided by land area that has been adjusted for geographic characteristics. We derive weights on these geographic characteristics from a global regression of population density at the quarter-degree level with country fixed effects. We show, first, that while income per capita is uncorrelated with conventionally measured population density across countries, there is a strong negative correlation between income per capita and QAPD; second, that the magnitude of this relationship exceeds the plausible structural effect of density on income, suggesting a negative correlation between QAPD and productivity or factor accumulation; and third, that higher QAPD in poor countries is primarily due to population growth since 1820. We argue that these facts are best understood as results of the differential timings of economic takeoff and demographic transition across countries, and particularly the rapid transfer of health technologies from early to late developers.

Iodine deficiency is the leading cause of preventable mental retardation in the world today. The condition, which was common in the developed world until the introduction of iodized salt in the 1920s, is connected to low iodine levels in the soil and water. We examine the impact of salt iodization on cognitive outcomes in the United States by taking advantage of this natural geographic variation. Salt was iodized over a short period of time beginning in 1924. We use military data collected during World War I and World War II to compare outcomes of cohorts born before and after iodization in localities that were naturally poor and rich in iodine. We find that for the one-quarter of the population most deficient in iodine this intervention raised intelligence quotient (IQ) by approximately one standard deviation. Our results can explain roughly one decade’s worth of the upward trend in IQ in the United States (the Flynn effect). We also document a large increase in thyroid-related deaths following the countrywide adoption of iodized salt, which affected mostly older individuals in localities with high prevalence of iodine deficiency.

Abstra c t As populations age, the degree to which workers׳ human capital reflects the cutting edge of technology falls because education took place further in the past. This “pure vintage” effect of aging is well known. In this paper, we explore a second effect of aging: in an older population, older teachers pass on knowledge that was current further in the past. We show that this “teacher multiplier” can significantly increase the technological backwardness of the labor force. We present both an analytic model that can be solved for steady states and a numerical model that can describe transitions in the average vintage of human capital as population age structure changes over time. We also discuss evidence on the effect of age on the technological up-to-dateness of workers in general and teachers in particular.

I examine recent changes in African mortality and discuss their potential economic and demographic effects. Growth in life expectancy sharply departed from its trend after 1990, and then experienced a sharp acceleration after 2005. This latter acceleration was due overwhelmingly to improvements in HIV and malaria. Economists differ in their estimates of how large the structural effect of health on income is, with many estimates being relatively small. Taking seriously the delays built into many plausible causal channels would lead one to expect that any economic effects of these mortality changes, if they are detectable at all, will not appear for several decades. By contrast, the effect of declining mortality, especially from malaria, should soon be visible in data on population age structure in some countries.

We examine the effects of population aging due to declining fertility and rising elderly life expectancy on consumption possibilities in the presence of intergenerational transfers. Our analysis is based on a highly tractable continuous-time overlapping generations model in which the population is divided into three groups (youth dependents, workers, and elderly dependents) and lifecourse transitions take place in a probabilistic fashion. We show that the consumption-maximizing response to greater longevity in highly developed countries is an increase in fertility. However, with larger transfer payments, the actual fertility response will likely be the opposite, leading to further population aging.

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.

We construct a matrix showing the share of the year 2000 population in every country that is descended from people in different source countries in the year 1500. Using this matrix, we analyze how post-1500 migration has influenced the level of GDP per capita and within-country income inequality in the world today. Indicators of early development such as early state history and the timing of transition to agriculture have much better predictive power for current GDP when one looks at the ancestors of the people who currently live in a country than when one considers the history on that country’s territory, without adjusting for migration. Measures of the ethnic or linguistic heterogeneity of a country’s current population do not predict income inequality as well as measures of the ethnic or linguistic heterogeneity of the current population’s ancestors. An even better predictor of current inequality in a country is the variance of early development history of the country’s inhabitants, with ethnic groups originating in regions having longer histories of agriculture and organized states tending to be at the upper end of a country’s income distribution. However, high within-country variance of early development also predicts higher income per capita, holding constant the average level of early development.

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.

We assess quantitatively the effect of exogenous reductions in fertility on output per capita. Our simulation model allows for effects that run through schooling, the size and age structure of the population, capital accumulation, parental time input into child-rearing, and crowding of fixed natural resources. The model is parameterized using a combination of microeconomic estimates, data on demographics and natural resource income in developing countries, and standard components of quantitative macroeconomic theory. We apply the model to examine the effect of a change in fertility from the UN medium-variant to the UN low-variant projection, using Nigerian vital rates as a baseline. For a base case set of parameters, we find that such a change would raise output per capita by 5.6 percent at a horizon of 20 years, and by 11.9 percent at a horizon of 50 years.

We assess quantitatively the effect of exogenous reductions in fertility on output per capita. Our simulation model allows for effects that run through schooling, the size and age structure of the population, capital accumulation, parental time input into child-rearing, and crowding of fixed natural resources. The model is parameterized using a combination of microeconomic estimates, data on demographics and natural resource income in developing countries, and standard components of quantitative macroeconomic theory. We apply the model to examine the effect of a change in fertility from the UN medium-variant to the UN low-variant projection, using Nigerian vital rates as a baseline. For a base case set of parameters, we find that such a change would raise output per capita by 5.6 percent at a horizon of 20 years, and by 11.9 percent at a horizon of 50 years.

The Malthusian model of population and economic growth has two key components. First, there is a positive effect of the standard of living on the growth rate of population, resulting either from a purely biological effect of consumption on birth and death rates, or a behavioral response on the part of potential parents to their economic circumstances. Second, because of the existence of some fixed resource such as land, there is a negative feedback from the size of population to the standard of living. These two components generate a number of predictions. Specifically, in the absence of technological change or expansion in the stock of the fixed resource, population will be stable around a constant level. Second, without changes in the function generating population growth, technological improvements or increases in the stock of resources will eventually result in more people but not a higher standard of living. As a description of population-income interactions, the Malthusian model had a long period of success, covering most of human history in most of the world until the beginning of the industrial revolution. In this paper we ask whether the model has any relevance to the world today. For the first part of the model—the positive causality running from income to population growth—the answer is clearly no. For reasons that have not fully been determined, countries that get richer now see falling rather than rising rates of population growth. Regarding the second part of the model—whether higher population lowers the standard of living—some further clarification is required before we can even pursue this issue. First, it important to differentiate among the different channels through which population affects economic outcomes. We will characterize as non-Malthusian those channels that work through the growth rate or demographic structure of the population. These include the effect of population growth in diluting capital per worker; the effect of the population age structure (itself a function of fertility) on the ratio of working age adults to dependents; the association of lower fertility with higher human capital investment via a quality-quantity mechanism; and the effect of lower fertility in freeing up female labor for output production. We reserve the term Malthusian for channels having to do with the size of the population, such as the congestion of fixed resources. This channel was the one Malthus thought about, and it is also the only one that pins down the level of population in steady state, which matches historical experience. Thus, in our typology, it is perfectly possible for reductions in population growth to raise income per capita even though the Malthusian channel is irrelevant. A second issue to be clarified is at what geographic scale we are looking. It is possible that in a world with trade, a high level of population in a single country will not lower that country’s income relative to others, but that a world with more people will be worse off because of congestion of productive resources or the environment. We do not pursue that possibility here. Instead, we ask whether there are countries or subnational regions in the world where the local version of Malthusianism hold true. The likeliest place to look for Malthusian effects is among poor countries, for several reasons. First, poor countries have had (and are continuing to have) the largest increases in population. The population of Africa is expected to multiply by a factor of 9.8 between 1950 and 2050. In India, during the century of most rapid population growth (1920–2020) population is expected to multiply by a factor of 5.5. By contrast, in Europe over the period 1800–1900 (roughly the century of fastest population growth), population increased by a factor of 2.2. If the initial population in these regions represented some equilibrium in the relation between population and resources (given available technology), the more rapid population growth is more likely to result in a disequilibrium in this relationship. Second, poor countries are least able to use trade as a means of avoiding resource constraints. Finally, as discussed further below, poor countries empirically have much higher shares of natural resource rents in national income than do rich countries. The idea that poor countries might suffer negative economic effects from overpopulation has a long pedigree. However, in recent decades, the Malthusian perspective has fallen out of favor among development economists, who have stressed the substitutability of technology, capital, and labor for fixed factors, as well as the productive benefits of density per se or of the technological and institutional changes induced by population pressure (see Allen C. Kelley 2001). We take as an operative test of the Malthusian channel the answer to the question: if a country had fewer people but was otherwise unchanged in terms institutions, human and physical capital per capita, productivity, terms of trade, etc., would it be significantly better off in per capita terms?

We assess quantitatively the effect of exogenous health improvements on output per capita. Our simulation model allows for a direct effect of health on worker productivity, as well as indirect effects that run through schooling, the size and age-structure of the population, capital accumulation, and crowding of fixed natural resources. The model is parameterized using a combination of microeconomic estimates, data on demographics, disease burdens, and natural resource income in developing countries, and standard components of quantitative macroeconomic theory. We consider both changes in general health, proxied by improvements in life expectancy, and changes in the prevalence of two particular diseases: malaria and tuberculosis. We find that the effects of health improvements on income per capita are substantially lower than those that are often quoted by policy-makers, and may not emerge at all for three decades or more after the initial improvement in health. The results suggest that proponents of efforts to improve health in developing countries should rely on humanitarian rather than economic arguments.

We construct a matrix showing the share of the year 2000 population in every country that is descended from people in different source countries in the year 1500. Using this matrix, we analyze how post-1500 migration has influenced the level of GDP per capita and within-country income inequality in the world today. Indicators of early development such as early state history and the timing of transition to agriculture have much better predictive power for current GDP when one looks at the ancestors of the people who currently live in a country than when one considers the history on that country's territory, without adjusting for migration. Measures of the ethnic or linguistic heterogeneity of a country's current population do not predict income inequality as well as measures of the ethnic or linguistic heterogeneity of the current population's ancestors. An even better predictor of current inequality in a country is the variance of early development history of the country's inhabitants, with ethnic groups originating in regions having longer histories of agriculture and organized states tending to be at the upper end of a country's income distribution. However, high within-country variance of early development also predicts higher income per capita, holding constant the average level of early development.

We examine the dynamic interaction of the population age structure, economic dependency, and fertility, paying particular attention to the role of intergenerational transfers. In the short run, a reduction in fertility produces a %u201Cdemographic dividend%u201D that allows for higher consumption. In the long run, however, higher old-age dependency can more than offset this effect. To analyze these dynamics we develop a highly tractable continuous-time overlapping generations model in which population is divided into three groups (young, working age, and old) and transitions between groups take place in a probabilistic fashion. We show that most highly developed countries have fertility below the rate that maximizes steady state consumption. Further, the dependency-minimizing response to increased longevity is to raise fertility. In the face of the high taxes required to support transfers to a growing aged population, we demonstrate that the actual response of fertility will likely be exactly the opposite, leading to increased population aging.

This paper examines the evolution of the relationship between population growth technological change and the standard of living. It develops a unified model of growth population and technological progress that encompasses three distinct regimes: Malthusian Post-Malthusian and Modern Growth. The analysis focuses on two differences between these regimes--in the behavior of income per capita and in the relationship between the level of income per capita and the growth rate of population. In the Malthusian regime population growth is positively related to the level of income per capita. Technological progress is slow and is matched by proportional increases in population so that output per capita is stable around a constant level. In the post-Malthusian regime the growth rates of technology and total output increase. Population growth absorbs much of the growth of output but income per capita does rise slowly. The economy endogenously undergoes a demographic transition in which the traditionally positive relationship between income per capita and population growth is reversed. In the Modern Growth regime population growth is moderate or even negative and income per capita increases rapidly. Moreover there are two forces that drive the transitions between these regimes: 1) technological progress is driven both by increases in the size of the population and by increases in the average level of education; 2) technological progress creates a state of disequilibrium which raises the return of human capital and induces parents to substitute child quality for quantity.

Publisher Summary This chapter explores the ways in which changing the age structure of the population in the macroeconomic models affects variables, such as consumption, wages, government spending, and saving. The chapter also presents the basic data on the ways in which the age structure of the population is evolving over time, considers the effects of population aging on production and consumption in the economy as a whole, and discusses the effects of aging. One of the salient themes of this chapter is that the “costs” of population aging currently in prospect are to a large extent simply the passing of the transitory benefits of reduced fertility. A second theme of this chapter is that although aging will not appreciably change the overall burden of transfers that society makes to dependents, it will greatly change the channels through which these transfers flow. Population aging has a larger effect on individual transfer schemes considered in isolation than on the net flow of transfers taken together. In the case of government transfer schemes to dependents, which focus particularly on the elderly, the average age of providing is younger than the average age of receiving—and thus population aging enlarges the burden of such transfer schemes.

Changes in fertility have long been recognized as important correlates of economic growth with the relationship between the level of fertility and the level of income per capita being one of the strongest observable correlations in cross-country data. This paper examines the linkages between fertility and growth theory. The structure of the model is presented in sections on production couples decision making system dynamics and steady-state equilibria. The first component of the model is that increases in capital per worker raise womens relative wages since capital is more complementary to womens labor input than to mens. The second component is that increasing womens relative wages reduces fertility by raising the cost of children more than household income. Finally lower fertility raises the level of capital per worker. A positive feedback loop generates demographic transition a rapid decline in fertility accompanied by accelerated output growth.

Both theory and examination of data suggest a relation between people's age and the amount they save. It is thus natural to examine how changes in demographics will affect saving rates. I would like to take up the questions of whether the theories of saving that we have at our disposal are sufficient for such a task, and whether a more satisfactory answer could be provided by making better use of the available data. Auerbach and Kotlikoff (AK) begin their exercise by measuring agespecific saving coefficients in cross-section data. Given such estimates, one way to forecast future saving would be simply to multiply these coefficients by the number of people in each age group, and see how saving would be altered over time. Why might this exercise not work? Clearly, the answer is that in the future, the cross-section age/saving profile may be different from the one observed today. To see why this might be, consider the reasons for saving. Some determinants of the age/saving profile may not vary systematically with demographic factors such as an individual's stage of life cycle, receipt of bequests, tastes, health status, etc. But many other factors which affect saving will change as the age structure of the population changes. For example

This paper explores the impact of demographic changes on the housing market in the US, 1st by reviewing the facts about the Baby Boom, 2nd by linking age and housing demand using census data for 1970 and 1980, 3rd by computing the effect of demand on price of housing and on the quantity of residential capital, and last by constructing a theoretical model to plot the predictability of the jump in demand caused by the Baby Boom. The Baby Boom in the U.S. lasted from 1946-1964, with a peak in 1957 when 4.3 million babies were born. In 1980 19.7% of the population were aged 20-30, compared to 13.3% in 1960. Demand for housing was modeled for a given household from census data, resulting in the finding that demand rises sharply at age 20-30, then declines after age 40 by 1% per year. Thus between 1970 and 1980 the real value of housing for an adult at any given age jumped 50%, while the real disposable personal income per capita rose 22%. The structure of demand is such that the swelling in the rate of growth in housing demand peaked in 1980, with a rate of 1.66% per year. Housing demand and real price of housing were highly correlated and inelastic. If this relationship holds in the future, the real price of housing should fall about 3% per year, or 47% by 2007. The theoretical model, a variation of the Poterba model, ignoring inflation and taxation, suggests that fluctuations in prices caused by changes in demand are not foreseen by the market, even though they are predictable in principle 20 years in advance. As the effects of falling housing prices become apparent, there may be a potential for economic instability, but people may be induced to save more because their homes will no longer provide the funds for retirement.