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Each year, about 200,000 U.S. service members leave active duty and transition to civilian employment. Many of these service members find this transition difficult because some military occupations have no direct parallel in the civilian economy. In a previous study, researchers at the RAND Corporation developed a method of matching occupational characteristics from the civilian economy to occupations in the U.S. Army. In this report, the authors extend that method to the U.S. Navy, Marine Corps, and Air Force. The authors collected data from more than 5,100 active component enlisted personnel across the three service branches using the U.S. Department of Labor's (DoL's) Occupational Information Network (O*NET) survey. For each military occupation surveyed, the authors identified the most-similar civilian occupations by comparing service members' responses to the O*NET survey items with the responses that DoL obtained on those same survey items for almost 1,000 civilian occupations. This approach contrasts with existing methods for generating military-to-civilian occupation crosswalks, which rely on analyses of high-level job descriptions by occupational analysts. The authors were able to algorithmically match a military occupation to every civilian occupation and determine the best fit. The job-matching algorithm provides both high-quality occupational recommendations for each military occupation and the reasons that those matches are high quality. These results will be useful for service members who are leaving the military in search of civilian employment, job counselors, and employers in search of workers with specific skill sets.

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The Federal Emergency Management Agency (FEMA) increasingly seeks fixed-price contracts for Public Assistance expenditures to help communities rebuild after disaster. FEMA and those communities have large incentives to estimate costs correctly before contracts are signed. One challenge to providing an accurate estimate of construction costs is that the cost of rebuilding can be affected by the reconstruction effort itself. One way to account for such changes is to use a future price forecast (FPF) factor, which adjusts a project's cost estimate to account for overall price-level increases caused by a disaster. The purpose of this study was to help FEMA identify when to use an FPF. To carry out the study, the authors convened panels of experts to discuss scenarios of hypothetical disasters-in particular, how these disasters could affect local economies. They also tested a variety of community and disaster-related measures that might be predictive of large cost increases and explored the relationship between different types of skills and labor-mobility variables and disaster effects on construction costs. They then estimated how often disasters cause large increases in construction costs and identified criteria to use in determining when an FPF might be warranted. They also explored the implications of these criterion thresholds by estimating FPFs in some what-if scenarios.

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The U.S. Army has long recognized that the recruiting environment has a significant impact on its ability to recruit. Successfully achieving a mission goal is tremendously more difficult when the national unemployment rate is lower rather than higher. Additionally, when casualty rates increase or operational difficulties mount, recruiting difficulty worsens. The RAND Arroyo Center has built a forecasting model that provides a measure of the recruiting difficulty with up to a 24-month horizon. The recruiting difficulty index model consists of seven equations. Three of the equations are for outcomes reflecting recruiting difficulty, and four equations are related to the recruiting process and reflect decisions made by the Army in an ongoing effort to meet recruiting targets. The model's structure is as follows. First, the exogenous variables can affect all seven outcome variables. Second, the policy response variables-quick-ship bonuses, Military Occupational Specialty bonuses, duty recruiters, and conduct waivers-can be entered as explanatory variables in the equations indicating recruiting difficulty (in terms of the percentage difference between graduate-alpha contracts and mission, average months in the Delayed Entry Program [DEP], and training seat fill rate). Third, the criterion of mean-squared prediction error is used when estimating the model in deciding which variables to include as explanatory variables in each equation and whether lagged values of the dependent variables should be included in the explanatory variables (and, if so, how many lags). The resulting seven-equation model forecasts whether the Army is likely to face a difficult or easy recruiting environment.

Recruiting and enlistment --- Mathematical models. --- United States. --- Recruiting, enlistment, etc. --- Forecasting. --- Costs.

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Alternative procedures for obtaining Public Assistance from the Federal Emergency Management Agency allow an applicant to bundle projects together and to not build back to the same state as predisaster. Cost overruns are the applicant's responsibility, and cost savings can be invested in other mitigation and risk reduction activities. In most cases, current construction costs are a good proxy for future costs, accounting for inflation. In the case of Puerto Rico's recovery from Hurricane María, the scale of the recovery efforts relative to the size of the economy means that these efforts are likely to fundamentally change the economy in terms of labor, materials, and equipment. As a result, in this project, the authors aimed to develop estimates of future construction costs and build multiplicative factors that cost estimators can apply to current costs to reflect the future cost of construction. To do this, they developed a disaster recovery expenditure simulator based on historical obligations; created a model to estimate expenditure scenarios' effect on prices of labor, materials, and equipment; devised an econometric approach to estimate substitutability of labor; and developed a labor demand estimator. This report documents their approach, data, findings, and recommendations.