ECONOMIC-DEMOGRAPHIC MODELS

Economic-demographic models are designed to describe in formal terms the main effects of demographic change on economic activity and those of economic activity on demographic change. The goal of these models is to forecast how the linked population-economy system will evolve over time, provide insights into the effects of policy change, or both.

The attempt to understand how population processes interact with the economy was at the center of interest in the work of the classical economists, most notably T. R. Malthus (1766–1834), as well as Adam Smith (1723–1790), David Ricardo (1772–1823), and John Stuart Mill (1806–1873), although their theories seldom were expressed in terms of mathematical formulas. In the development of neoclassical economics through the first half of the twentieth century, consideration of such interactions was largely neglected. Subsequently, formal modeling of the mutual impact of demographic and economic variables received a strong impetus. This was in part the logical outcome of the novel ambition to construct comprehensive multivariate models of the workings of the economy and partly a result of the renewed post–World War II interest in the role of rapid population growth in the economic development of low-income countries. This entry discusses salient aspects of economic-demographic modeling efforts during the last few decades of the twentieth century.

Models of Developed Economies

Until the mid-1960s economic models were constructed principally to yield short-run forecasts. Because it was commonly believed that demographic effects were relatively unimportant in explaining variations in short-run economic activity, the models ignored demographic change, treating population as an exogenous variable. Similarly, economic effects were absent from demographic models because those models were concerned with long-run forecasts, time periods over which economic effects were considered unpredictable.

The first economic model to incorporate demography other than as an exogenous variable was the Brookings model of the U.S. economy, which was built in the 1960s. In this model population affected the labor market, which affected marriage and household formation, which in turn affected the economy through the nonbusiness construction sector. Population also affected government revenues and expenditures.

As described by Dennis Ahlburg, the next generation of models–mostly known by their abbreviated titles or institutional provenance, such as the Wharton, DRI, Chase, and Hickman-Coen models in the United States; the RDX and CANDIDE models in Canada; and the BACHUROO model in Australia–introduced fine-gauge age disaggregation in the production and consumption sectors. The DRI model introduced a highly elaborate demographic-economic system that forecast the size of consumer populations and the income available to those groups. The models also linked changes in age structure to changes in labor supply, unemployment, and wages.

In the late 1970s the Wharton model added an endogenous demographic sector that included births, marriage, divorce, work, and education as variables that affected and were affected by economic variables. The main linkages between the demographic sector and the economic model were income and labor force participation and household formation. Subsequently, the Wharton model introduced an endogenous demographic projection methodology so that it expressed the total fertility rate, migration rate, and life expectancy as functions of economic and demographic variables. These demographic variables in turn had effects throughout the economic model.

The main purpose of these models was shortrun forecasting, but they also could give policy-makers insights into the effects of policy changes without the need to carry out those changes. The usefulness of those simulations depended on the accuracy with which a model captured the economic and demographic structure of a country. Generally, demographic change in these models had only a relatively modest effect on the aggregate rate of economic growth.

Economic-demographic models of this type continue to be refined and applied widely. For example, the San Diego Association of Governments employs a simultaneous nonlinear model to produce medium-to long-term (20-year) forecasts of 700 economic and demographic variables. The Netherlands Interdisciplinary Demographic Institute is modeling economic-demographic scenarios for Europe at the national and regional levels. The International Institute for Applied Systems Analysis has developed a multiregional economic-demographic model to simulate various long-run economic growth scenarios for Europe and assess the economic impacts of population aging.

Models of Developing Countries

Formal economic-demographic models for developing countries were designed to illuminate the interaction between population and other variables in the development process and evaluate the consequences of various policies on economic and demographic variables. The best known of these models is the pioneering model by Ansley Coale and Edgar Hoover of the Indian economy, whose findings were cited widely to justify government interventions to limit fertility. In this model economic growth essentially depends on the resources devoted to productive investments. Population growth has a negative impact on economic growth because it increases current consumption and welfare-type outlays at the expense of savings and productive investments. Two shortcomings of the model are that it assumes costless fertility and mortality reduction and omits labor from the production function. This means that population growth adds consumers but does not add producers. Omitting labor is defensible only for medium-term calculations, the period of 15 to 20 years during which a decline in the birthrate leaves the size of the population of labor force age unaffected.

Almost all the other early economic-demographic models of low-income countries agreed with the Coale-Hoover conclusion that rapid population growth slowed the pace of economic growth, although the mechanisms yielding that out-come varied. An exception was the model proposed by Julian Simon. Simon's model assumed that relatively rapid population growth produces strong economic growth, at least in the long run. Output in the model is a positive function of "social overhead capital" (better roads and communication, economies of scale in production, improved government and organization, and better health services). Social overhead capital in turn is a costless function of population growth; this is an important and questionable assumption. The significance of Simon's model is that it suggests that although the short-term impact of population growth may be negative, there may be more than compensating positive effects in the future. Thus, the net impact of population growth for various specified time horizons is an open empirical question.

Later models became larger and more complex. As discussed by Dennis Ahlburg, the Bachue series of models developed by the International Labour Organization contained multisectoral input-output submodels and treated population in a highly disaggregated way (by age, sex, location, and education). Those models endogenized the components of population change and determined both the level of employment and the size distribution of incomes across households. The Bachue models are intended to be long-term policy-oriented simulation models rather than short-term forecasting models.

Models of this type have had only a limited impact on policy analysis and planning because of their complexity, the often conflicting specifications of key economic-demographic relationships, very different empirical estimates of those relationships, and the fact that a single equation (the production function) often has a dominant impact on the properties of the model regardless of the specification of the rest of the model. (The strengths and weaknesses of these models are discussed in Brian Arthur and Geoffrey McNicoll's, Warren Sanderson's, and Ahlburg's works.)

In the 1990s a series of country-specific models was constructed that added environmental interactions to the economic-demographic system to produce population-development-environment (PDE) dynamic simulations. Only sectors considered important to a particular country are modeled in detail, and simplicity of specification is emphasized to aid comprehension of the user. Wolfgang Lutz, Alexia Prskawetz, and Sanderson discuss examples of these models.

Economic Growth Models

Interest in the determinants of economic growth, which was the motivation for some of the earliest neoclassical growth theories, resurfaced in the late 1980s with simple single-equation models that tried to explain relative rates of economic growth across countries. Economic growth was expressed as a function of economic, demographic, institutional, and other variables.

The dominant model in this so-called new growth theory comes from the work of Robert Barro and is derived from an extended version of the neoclassical growth model. It embodies the idea of conditional convergence: The lower the starting level of real per capita gross domestic product in relation to its long-term or steady-state level, the higher the predicted growth rate. The significance for demography is that the long-term growth rate can be affected by the growth rate of population and by other factors, such as the savings rate, that may be affected by demographic change.

Capital had always played a critical role in models of economic growth, and the new growth models broadened the concept of capital to include education and health. Economic growth depended on the relationship between the initial and target levels of output. The target level of output depended on government policies (including not only spending and tax rates but also the rule of law, the protection of property rights, and political freedom) and household behavior (savings, labor supply effort, fertility, and health). Geographic endowments such as a temperate climate and ecological conditions that impede the spread of diseases or favor cash crops also can affect economic growth directly or through their impact on institutions.

Barro estimated the basic model with data on a panel of about 100 countries from 1960 to 1990. He found that economic growth was higher the lower the fertility rate, the longer the life expectancy, and the higher the level of education. These arguments are similar to those of Coale and Hoover. Other determinants of growth were the maintenance of the rule of law, lower levels of government consumption, lower inflation, and improved terms of trade.

Allen Kelley and Robert Schmidt and others have extended the demographic specification of the basic growth model and explored the adjustment or transition to the long-run equilibrium. Those researchers argued that the impact of demography on economic growth was a function of the levels of fertility and mortality rates, the timing of changes in fertility and mortality, and the sensitivity of the economy to those changes. Differences in levels and timing can create significant shifts in the age structure of the population that can affect economic growth in addition to the direct effect of births and deaths. Disaggregating population change into its components and more fully specifying the dynamics allow the effect on economic growth to be positive, negative, or zero. Many earlier models did not allow such flexibility. Population size and density also appear in some of those models. A larger population can lead to economies of scale in the provision of roads, communication systems, research and development, and markets and institutions. Higher population densities can lead to lower per-unit costs and increased efficiency of investments, particularly in agriculture. The effects of population size and density have been found to vary considerably across countries.

Kelley and Schmidt developed several extended growth models with data from a panel of 86 countries for the period 1960–1995. (A single model could not suffice because of disagreement among economists on structural details.) They reached the qualified conclusion that declining fertility and mortality reinforce each other in encouraging economic growth. Because fertility and mortality declines necessarily offset each other in their effect on population growth, this finding underscores the importance of distinguishing the components of population change rather than using population growth as a single variable. It also illustrates the importance of specifying the dynamics of the effect of demography on economic growth (current high fertility decreases growth, but fertility lagged by a generation increases growth). Kelley and Schmidt also found small positive effects of population size and density.

The "Demographic Gift"

The effect of changes in age structure on economic growth, a relationship treated in the Coale-Hoover model, was investigated further by researchers in the 1990s. Coale and Hoover had argued that high fertility resulted in a high youth dependency rate that depressed aggregate savings rates and thus economic growth. However, this is just the initial phase of the changes in age structure that accompany the demographic transition. The ratio of the population of labor force age to total population is low when fertility is high, rises as declining fertility lowers child dependency, and eventually falls as population aging sets in. The second phase of this shift is a period in which the demands the population makes on resources are relatively low but the economic contributions (through work, savings, and investments) are potentially great. The resulting (potential) impetus on economic growth has been referred to as the "demographic gift" or "demographic bonus."

David Bloom and Jeffrey Williamson found that the rate of economic growth rose faster as the ratio of the working-age population to the total population rose. Although the exact mechanisms by which this effect occurs are unclear, it seems that changes in the age structure are associated with shifts from unpaid work to paid work, increased health and education of workers, and increases in savings and capital accumulation.

Ronald Lee, Andrew Mason, and Tim Miller showed that during the demographic gift period savings and wealth can increase faster, spurring economic growth because of favorable shifts in the age structure and changes in life expectancy and total fertility. In a case study of Taiwan they found that as much as one-half of the increase in savings rates was due to demographic factors. Other studies investigated the influence of demographic change on the "economic miracle" of East Asia in the 1980s and early 1990s and concluded that the demographic gift caused one-quarter to one-third of the rapid economic growth that took place.

The conclusion that can be drawn from this event is that under the right conditions it is possible for a rapid demographic transition to generate large increases in savings and wealth that can stimulate economic growth. It appears that the "demographic gift" of rapidly falling fertility and mortality can translate into higher economic growth if there are supportive policies, markets, and institutions. The practical challenge indicated by the modeling is to bring about conditions that will convert the "gift" into the reality of economic growth.

bibliography

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Ahlburg, Dennis A. 1987a. "The Impact of Population Growth on Economic Growth in Developing Nations: The Evidence from Macroeconomic-Demographic Models." In Population Growth and Economic Development: Issues and Evidence, ed. D. Gale Johnson and Ronald D. Lee. Madison: University of Wisconsin Press.

——. 1987b. "Modeling Economic-Demographic Linkages: A Study of National and Regional Models." In Forecasting in the Social and Natural Sciences, ed. Kenneth C. Land and Stephen H. Schneider. Dortrecht, the Netherlands: D. Reidel.

Arthur, W. Brian, and Geoffrey McNicoll. 1975. "Large-Scale Simulation Models in Population and Development: What Use to Planners?" Population and Development Review 1: 251–265.

Barro, Robert J. 1997. Determinants of Economic Growth. Cambridge, MA: MIT Press.

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Coale, Ansley J., and Edgar M. Hoover. 1958. Population Growth and Economic Development in Low-Income Countries. Princeton, NJ: Princeton University Press.

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Lee, Ronald D., Andrew Mason, and Tim Miller. 2001. "Saving, Wealth, and Population." In Population Matters: Demographic Change, Economic Growth, and Poverty in the Developing World, ed. Nancy Birdsall, Allen C. Kelley, and Steven W. Sinding. Oxford: Oxford University Press.

Lutz, Wolfgang, ed. 1994. Population-Development-Environment: Understanding Their Interactions in Mauritius. Berlin: Springer-Verlag.

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MacKellar, Landis, and Tatiana Ermolieva. 1999. "The IIASA Social Security Reform Project Multiregional Economic-Demographic Growth Model: Policy Background and Algebraic Structure." Interim Report IR-99-007. Laxenburg, Austria: International Institute for Applied Systems Analysis.

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San Diego Association of Governments. 1993. DEFM Forecast 1993 to 2015. Volume 1: Model Overview. San Diego, CA: San Diego Association of Governments.

Simon, Julian L. 1977. The Economics of Population Growth. Princeton, NJ: Princeton University Press.

Dennis Ahlburg

Economic-Demographic Models

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