THE ROLE OF LABOR AND MARRIAGE MARKETS,PREFERENCE HETEROGENEITY,AND THE WELFARE SYSTEM IN THE LIFE CYCLE DECISIONS OF BLACK,HISPANIC, AND WHITE WOMEN*

Using data from the NLSY79, we structurally estimate a dynamic model of the life cycle decisions of young women. The women make sequential joint decisions about school attendance, work, marriage, fertility, and welfare participation. We use the model to perform counterfactual simulations designed to shed light on three questions: (1) How much of observed minority–majority differences in behavior can be attributed to differences in labor market opportunities, marriage market opportunities, and preference heterogeneity? (2) How does the welfare system interact with these factors to augment those differences? (3) How can new cohorts that grow up under the new welfare system (Temporary Aid for Needy Families) be expected to behave compared to older cohorts?     

Quasi-structural estimation of a model of childcare choices and child cognitive ability production

This article evaluates the effects of maternal vs. alternative care providers’ time inputs on children’s cognitive development using the sample of single mothers in the National Longitudinal Survey of Youth. To deal with the selection problem created by unobserved heterogeneity of mothers and children, we develop a model of mother’s employment and childcare decisions. We then obtain approximate decision rules for employment and childcare use, and estimate these jointly with the child’s cognitive ability production function. To help identify our selection model, we take advantage of the plausibly exogenous variation in employment and childcare choices of single mothers generated by the variation in welfare rules across states and over time created by the 1996 welfare reform legislation and earlier State waivers.     

New business with the new military

A $200 billion market has appeared on your business horizon, but you may not have noticed it. It's the U.S. military--the new U.S. military. Virtually all aspects of the military are changing to ensure it can fight unpredictable threats while sustaining the infrastructure needed to support and train forces. The military is turning to non-traditional business partners to meet a wide range of needs, from health care to housing to information technology. The Defense Department is yielding its monopoly on every aspect of national security and adopting a more businesslike model in which the military's warfighting capabilities are supported through outsourcing and business alliances. Civilians are replacing military personnel in many noncombat roles. Military functions with corporate equivalents are candidates for outsourcing and privatization. Market standards are replacing the heavy customization that has locked many companies out of this marketplace. The authors have participated in the transformation process from different perspectives--one civilian, the other military. Together, they highlight the prospects that transformation is creating for companies outside the traditional defense industry and reveal paths to success in this complex market. They also present six principles for doing business with the military that require persistence, integrity, and a willingness to master the intricacies of a distinctive culture. By understanding the logic of military transformation, executives can identify and create vast new business opportunities. And by mastering the six principles, they can build profitable long-term relationships.     

SIMULATED MAXIMUM LIKELIHOOD ESTIMATION BASED ON FIRST‐ORDER CONDITIONS*

I describe a strategy for structural estimation that uses simulated maximum likelihood (SML) to estimate the structural parameters appearing in a model's first‐order conditions (FOCs). Generalized method of moments (GMM) is often the preferred method for estimation of FOCs, as it avoids distributional assumptions on stochastic terms, provided all structural errors enter the FOCs additively, giving a single composite additive error. But SML has advantages over GMM in models where multiple structural errors enter the FOCs nonadditively. I develop new simulation algorithms required to implement SML based on FOCs, and I illustrate the method using a model of U.S. multinational corporations.     

Theory-Driven Choice Models

We explore issues in theory-driven choice modeling by focusing on partial-equilibrium models of dynamic structural demand with forward-looking decision-makers, full equilibrium models that integrate the supply side, integration of bounded rationality in dynamic structural models of choice and public policy implications of these models.     

Smoothly mixing regressions

This paper extends the conventional Bayesian mixture of normals model by permitting state probabilities to depend on observed covariates. The dependence is captured by a simple multinomial probit model. A conventional and rapidly mixing MCMC algorithm provides access to the posterior distribution at modest computational cost. This model is competitive with existing econometric models, as documented in the paper's illustrations. The first illustration studies quantiles of the distribution of earnings of men conditional on age and education, and shows that smoothly mixing regressions are an attractive alternative to nonBayesian quantile regression. The second illustration models serial dependence in the S&P 500 return, and shows that the model compares favorably with ARCH models using out of sample likelihood criteria.     

Identifying individual differences: An algorithm with application to Phineas Gage

In many research contexts it is useful to group experimental subjects into behavioral “types.” Usually, this is done by pre-specifying a set of candidate decision-making heuristics and assigning each subject to a heuristic in that set. Such approaches might perform poorly when applied to subjects with prefrontal cortex damage, because it can be hard to know what cognitive heuristics such subjects might use. We suggest that the Houser, Keane and McCabe (HKM) robust classification algorithm can be a useful tool in these cases. An important advantage of this classification approach is that it does not require one to specify either the nature or number of subjects' heuristics in advance. Rather, both the number and nature of the heuristics are discerned directly from the data. To illustrate the HKM approach, we draw inferences about heuristics used by subjects in the well-known gambling task [Bechara, A., Damasio, A.R., Damasio, H., Anderson, S., 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7–12].     

EXPLORING THE USEFULNESS OF A NONRANDOM HOLDOUT SAMPLE FOR MODEL VALIDATION: WELFARE EFFECTS ON FEMALE BEHAVIOR*

A particularly challenging use of decision‐theoretic models in economics is to forecast the impact of large changes in the environment. The problem we explore in this article is how to gain confidence in a model's ability to predict the impact of such large changes. We show that an approach to validation and model selection that includes the choice of a “nonrandom holdout sample,” a sample that differs significantly from the estimation sample along the policy dimension that the model is meant to forecast, can be fruitful.