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Permanent hearing loss is one of the most common disabilities among sailors. Although noise-induced injury is preventable by limiting exposure, it is generally irreversible once it occurs. In 2004 the Veterans Administration (VA) spent $108 Million dollars in disability payments to 15.8 thousand former Navy personnel for hearing loss. This represents an increase of $65 Million in spending by the VA on Navy hearing disability payments since 1999. The focus of this study is to find out how hearing loss relates to service time spent aboard ships, in order to reduce disabilities and costs. Over a career in the US Navy, Service Members characteristically are posted to a variety of stations, both afloat and ashore. Many of these posts have high noise levels, such as certain ships; and particular ratings have high exposure, such as jobs near machinery. If assignments of higher risk both in location and tasking can be identified, then focused prevention programs can be implemented, such as closer monitoring of all personnel, preventive measures in key high risk locations and ratings, focused noise control in ship design, and perhaps better rotational schedules. All these preventative methods may reduce damage to the hearing of Navy personnel. This will preserve quality of life for personnel, and save millions of dollars for government.

In a previous study, CNA analysts used data from past DoD A-76 competitions to construct a model of savings and projected the potential savings from additional DoD Commercial Activities (CA) competitions. In this paper, we use an alternative approach for estimating savings from future DoD CA competitions. We estimate two separate bidding equations - one for the in-house team bid and another for the minimum contractor bids, along with an equation for baseline cost. Based on these estimated equations, one could then indirectly project future savings in the A-76 inventory as the difference between predicted baseline cost and the predicted winning bid. Using the new approach, we project an annual savings of $6 billion if the entire 1995 DoD CA inventory were competed under A-76 rules.

This study estimates the earnings losses of workers who lose their jobs in a plant closing. A unique data set was used: Social Security earnings records of over 9,000 workers employed in plants that actually closed. Separate estimates are made for workers by age and sex and the effects on losses of economic and demographic variables are also estimated. Alternative methodologies are discussed and used to estimate losses of workers who never work after the plant closing. (Author)

This paper estimates the value employees place on stable employment. Here the term 'stable employment' means a relatively low probability of temporary and/or permanent layoffs. This value is estimated by regressing individual wage rates on exogenous variables and proxy variables for unstable employment. The sign and size of the coefficients on these proxy variables in the wage equation measures the value of stable employment in terms of the hourly wage rate. The wage equation is estimated using the Michigan and Parnes survey data. The results indicate that the wage elasticity with respect to instability is .3. This means that if one industry is 50 percent more stable than another, then other things equal, the more stable industry would have a 15 percent lower wage rate. (Author)

In recent years there have been a large number of studies that deal with the problem of selectivity bias in the data. Here the term 'selectivity bias' refers to non-randomly distributed observed data. This non-randomness can occur whenever the data we have are generated by the choices that individuals make. A review of selectivity problems in econometric models can be found in two papers by Maddala (1977). The purpose of the present paper is to review several models not discussed in Maddala's (1977) papers, and to give a further interpretation of the covariance terms that are particular to selectivity models. This should enhance the understanding of these models.

This paper estimates the responsiveness of receipts of 49 state governments to fluctuations in economic activity. The concluding sections uses these estimates to evaluate the allocation of counter-cyclical revenue sharing grants among states and makes some suggestions for improvements in the allocating formula.... Allocations, Cycles, Econometrics, Economics, Grants, Local government, State government, Revenue sharing, Unemployment.

When estimating models with pooled cross-section and time-series data (e.g. estimating demand equations for all 50 states) one has to decide whether or not to pool the data. The usual procedure is to first test for the overall homogeneity (equality) of the coefficients. If this hypothesis is not rejected, then a single equation is estimated with pooled data. If the hypothesis is rejected, further hypothesis testing may be necessary. For example, if the model contains more than one coefficient the equality constraint may be rejected for only a subset of the coefficients. In this case the data is pooled and dummy variables are used with the subset of coefficients for which the equality constraint does not hold. There are at least three problems with this procedure of pooling (or not pooling) after some preliminary tests of significance. First, as noted in Maddala, it raises problems about the inference from the pooled model. Second, there is the related question of what significance level to use when deciding whether or not to pool. Third, the choice of estimates to select from is quite limited. That is, one must pick either the pooled or the non-pooled estimate, even if these two estimates are very different. The problems suggest that an alternative (or hybrid) method of handling pooled cross-section and time-series data is needed. The purpose of this paper is to propose such a method.

A model which extends the switching regression models and combines several different limited dependent variable models into a general framework is introduced. Methods to get consistent estimates and asymptotic efficient estimates are derived. Our estimation procedures are then used to study a housing expenditure model which takes into account the simultaneous determination of whether or not to own, and how much to spend.