Journal of Official Statistics, Vol.22, No.3, 2006. pp. 453485

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Forecasting Labor Force Participation Rates

Motivated by the desire to project the financial solvency of Social Security, the article discusses forecasts of labor force participation rates. The data are highly multivariate in the sense that, at each time point, rates are disaggregated by gender, age, marital status as well as the presence of young children in the household; taken together, there are 101 demographic cells at each point in time. Thirty-one years of data from the U.S. Bureau of Labor Statistics are available. As input to Social Security projections, it is desirable to produce forecasts over a 75-year time horizon.

The purposes of this article are to give a structure to the problem of forecasting labor force participation rates, to summarize the data and to show how to implement different types of basic models.

For the basic structure, the article explores the use of statistical techniques commonly employed in demography for forecasting vital rates such as mortality and fertility. Specifically, we examine principal components techniques popularized by Lee and Carter (1992), as well longitudinal data techniques, for forecasting.

In summarizing the data, we find that differencing rates captures much of the dynamic movement of rates, as measured by short-range out-of-sample validation. Moreover, a logistic transformation stabilizes long-range forecasts.

The long-range forecasts varied substantially over the type of model selected, ranging from an average of 88.9% for females (78.4% for males) to 65.1% (61.3%) over a 75-year horizon. This large difference reflects the well-known fact that sex-specific models can summarize small historical differences yet project them into large differences in the distant future. To remedy this, we propose a simple autoregressive model that captures gender differences with common parameters. This simple model generates a 71.4% average participation rate for females (77.5% for males), a modest increase (decrease) compared to current rates.

Longitudinal data, principal components, demographic forecasting, JEL classification, Primary J110, Secondary J210

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