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Estimating The Impact Of Transitory Income Fluctuations On Home Maintenance Expenditures: A Heterogeneous Growth Model Approach

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    Estimating the Impact of Transitory Income Fluctuations on Home Maintenance Expenditures: A Heterogeneous Growth Model Approach There are two approaches to isolating transitory income fluctuations. The first approach is to find instruments for transitory income changes. Examples might include an indicator for temporary layoffs and changes in hours worked. This approach is difficult to implement with the post - 1985 AHS data since it does not include most of the candidate instruments that have been used in the literature. The second approach is to assume a specific model for the earnings process and then use the structure of that model to construct the transitory income component. We follow this strategy. Consider the following specification for household earnings, (1) ln( Y it ) = X it β + μ it + ε it , where Y it is the i t h household’s earnings in year t , X it is a set of demographic and human capital variables capturing life - cycle earnings profiles, μ it is the permanent component of residual earnings, and ε it is the transitory component of residual earnings. The permanent component is typically modeled either as a random walk or a heterogeneous growth process. 5 Baker (1997) tests the random walk specification against the heterogeneous growth specification as given in equation (2) using Panel Study of Income Dynamics data and a common set of controls for the life - cycle earnings profiles, the sample used in the estimation (by about 30%) and increases the average length of the panel for each household. Those details are available upon request. For example, using the Panel Study of Income Dynamics , they report that temporary layoffs are not a significant predictor for household income changes. Thus, at least some of the candidate instruments are not very powerful. On the random walk specification, see MaCurdy (1982), Abowd and Card (1989), Topel (1991), Topel and Ward (1992), and Moffitt and Gottschalk (1995). (2) μ it = γ i + λ i Exp it , where Exp it is the potential labor market experience for the i th household. He finds the data to be more supportive of the heterogeneous growth specification. We use the heterogeneous growth specification to estimate the transitory residual earnings component in two steps. In the first step, equation (1) is estimated by regressing log earnings on set of demographic and human capital variables. 6 In the second step, equation (2) is estimated for each household by regressing its earning residuals ( ) it it μ + ε on the household head’s potential labor market experience, which is measured as the age of the head minus imputed years of schooling minus six. The residuals from these second - stage regressions serve as our estimates of the transitory residual earnings component, ε it . 7 Using these estimates, we can decompose family log earnings into its permanent component ( ln P ˆ ˆ ˆ it it i i it Y ≡ X β + γ + λ Exp ) and its transitory component ( ln T ˆ it it it Y ≡ ε + m ), where ˆ it m represents the combined effects of measurement and estimation error. Following Dynarski and Gruber (1997), we restrict our sample to households with heads between the ages of twenty and fifty - nine. In contrast to those authors, we include female - headed households as well as male - headed households. We drop observations if any of the income variables are allocated, and further restrict the sample to houses located in 114 SMSAs for which we can merge in Freddie Mac repeat - sale house price data. Metro area house price data is employed to control for possible home “equity” effects on the household’s maintenance decision.

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