Multinomial Logit Models with Implicit Variable Selection

Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this paper we are developing a boosting technique called multinomBoost that performs variable selection and fits the multinomial logit model also when predictors are high-dimensional. Since in multicategory models the effect of one predictor variable is represented by several parameters one has to distinguish between variable selection and parameter selection. A special feature of the approach is that, in contrast to existing approaches, it selects variables not parameters. The method can distinguish between mandatory predictors and optional predictors. Moreover, it adapts to metric, binary, nominal and ordinal predictors. Regularization within the algorithm allows to include nominal and ordinal variables which have many categories. In the case of ordinal predictors the order information is used. The performance of the boosting technique with respect to mean squared error, prediction error and the identification of relevant variables is investigated in a simulation study. For two real life data sets the results are also compared with the Lasso approach which selects parameters

Variable Selection in General Multinomial Logit Models

The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate. In this paper we are proposing a sparsity-inducing penalty that accounts for the special structure of multinomial models. In contrast to existing methods, it penalizes the parameters that are linked to one variable in a grouped way and thus yields variable selection instead of parameter selection. We develop a proximal gradient method that is able to efficiently compute stable estimates. In addition, the penalization is extended to the important case of predictors that vary across response categories. We apply our estimator to the modeling of party choice of voters in Germany including voter-specific variables like age and gender but also party-specific features like stance on nuclear energy and immigration

Investigating distance effects on environmental values: a choice modelling approach

Analysis of the relationship between distance and willingness to pay (WTP) is important for estimation and transfer of environmental benefits. Several contingent valuation (CV) studies have investigated this topic, but results are mixed. This paper describes a choice modelling (CM) application that estimates distance effects on parameters of three environmental attributes. Combinations of these attributes create different management policies for native vegetation. The CM study is based on a sampling procedure that provides a geographically balanced sample and statistical tests to choose the best specification of the distance–WTP relationship. Welfare analysis shows that disregarding distance causes under-estimation of individual and aggregated benefits and losses, seriously misdirecting resource allocation.choice modelling, geographical distance, spatial heterogeneity, stratified sample, Environmental Economics and Policy, Resource /Energy Economics and Policy,

Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints

In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category. When parameters are penalized, shrinkage of estimates should not depend on the reference category. In this paper we investigate ridge regression for the multinomial logit model with symmetric side constraints, which yields parameter estimates that are independent of the reference category. In simulation studies the results are compared with the usual maximum likelihood estimates and an application to real data is given

The Value of Habitat and Agriculture

The report summarises the results of a non-market valuation study of Habitat and Agriculture in the Upper South East (Upper SE) of South Australia. A non-market valuation technique known as choice modelling was used to elicit values in the form of willingness to pay from people in the Upper SE, Adelaide and the rest of the State. The regional and state-wide preferences for the habitat improvement provide one more piece of information relating to the difficult issues around the configuration of the cleared and uncleared landscape in the Upper South East of South Australia.Australia;habitat;agriculture;land

Where Do the Sick Go? Health Insurance and Employment in Small and Large Firms

Small firms that offer health insurance to their employees may face variable premiums if the firm hires an employee with high-expected health costs. To avoid expensive premium variability, a small firm may attempt to maintain a workforce with low-expected health costs. In addition, workers with high-expected health costs may prefer employment in larger firms with health insurance rather than in smaller firms. This results in employment distortions. We examine the magnitude of these employment distortions in hiring, employment, and separations, using the Medical Expenditure Panel Survey from 1996 to 2001. Furthermore, we examine the effect of state small group health insurance reforms that restrict insurers’ ability to deny coverage and restrict premium variability on employment distortions in small firms relative to large firms. We find that workers with high-expected health cost are less likely to be new hires in small firms that offer health insurance, and are less likely to be employed in insured small firms. However, we find no evidence that state small group health insurance reforms have reduced the extent of these distortions. Estimating the magnitude of employment distortions in insured small firms is essential in refining reforms to the small group health insurance market. The difficulties that small firms face in obtaining and maintaining health insurance for their employees have been widely documented (Brown, Hamilton and Medoff, 1990; McLaughlin,1992; Fronstin and Helman, 2000). Only 45% of firms with fewer than 50 employees offer health insurance compared to 97% of firms with 50 or more employees (AHRQ, 2002). This low proportion has been attributed, in part, to the high administrative cost of health insurance for small firms, the low demand for insurance among workers in these firms, and the unwillingness of insurers to take on small firm risks (McLaughlin, 1992, Fronstin and Helman, 2000, Monheit and Vistnes, 1999). In recent decades, small firms that provide health insurance to their employees were in a precarious position. Their premiums were calculated yearly, based on the expected value of their health care utilization. Hence, a single high cost employee could lead to a substantial surcharge on the premiums for the firm (Zellers, McLaughlin, and Frick, 1992). In a survey of small employers that did not offer health insurance, 75 percent claimed that an important reason for not offering insurance was high premium variability (Morrisey, Jensen and Morlock, 1994). Concerns about these problems fueled the passage of numerous state small group health insurance reforms in the 1990s that implemented premium rating reforms and restrictions on pre-existing condition exclusions. While a few states have implemented premium rating reform that has severely restricted small group insurers’ ability to use health status to set premiums, in most states, these reforms have been moderate. Assuming that firms are unable to perfectly tailor individual wages to individual health insurance costs, unexpectedly high premiums may impose a large burden on small firms. Paying high premiums, possibly financed by borrowing at high interest rates, may increase the risk of bankruptcy. If small firms choose not to pay high premiums, and instead drop insurance coverage, they renege on the implicit compensation contract with workers. Employers may opt to raise employee contributions to cover higher costs but large increases may lead to healthier employees dropping coverage. Faced with this predicament, small firms may choose to prevent expensive premium variability by maintaining a work force that has a low-expected utilization of health care services. Thus, the link between employment and health insurance in small firms may result in a welfare loss if it prevents individuals with high-expected health costs from being employed in small firm jobs in which they may have high match specific productivity. Employers may obtain information about employees’ medical conditions in several ways. Before the passage of the 1990 Americans with Disabilities Act (ADA), half of all employers conducted pre-employment medical examinations (U.S. Congress, 1988). Most small group employers required the completion of a family health questionnaire for insurance coverage (Zellers et al., 1992, Cutler 1994). While ADA now restricts employer inquiries on employee health, it does not apply to firms with under 15 employees. In addition, employer compliance with the ADA may be hindered because its stipulations about pre-employment health inquiries are vague. Medical inquires are allowed if they pertain to the applicant’s ability to perform the job. In addition, medical information is explicitly allowed in the use of medical underwriting for insurance (Epstein, 1996). The media continues to report cases where employers easily obtain employee medical records (Rubin, 1998), or employees have been laid-off because of high health costs (O’Connor, 1996), or employee premiums have been adjusted to reflect the employee’s claims experience (Kolata, 1992). The Health Insurance Portability and Accountability Act of 1996 (HIPAA) includes a nondiscrimination provision that bars a group health plan or issuer from discriminating in eligibility or contributions on the basis of a health status-related factor. However, HIPAA allows medical underwriting and allows insurers to rate groups of employees based on health status as long as the premium rate for all employees is blended. This stipulation prevents employers from requiring higher cost employees to contribute a higher premium share, but does not shield employers from bearing the costs for a sick worker. Economists have typically believed that health insurance is an attribute of “good jobs” and workers do not choose jobs based on whether or not the job provides health insurance. In fact, this precept is behind the notion that employment is a mechanism for minimizing adverse selection in the market for health insurance (see, for example, Gruber and Levitt, 2000). However, a number of recent studies have suggested that worker demand for health insurance may play an important role in employment decisions. Workers with high-expected family costs may prefer jobs that offer health insurance, and conversely, workers with low preferences for health insurance may sort into jobs that lack health insurance. (Monheit and Vistnes, 1999, Monheit and Vistnes, 2006, Royalty and Abraham, 2005, Bundorf and Pauly, 2004). In this paper, we use the Medical Expenditure Panel Survey (MEPS) from 1996 to 2001 to examine the magnitude of employment distortions for workers with high-expected health costs. Since health insurance and employment are linked, health insurance may be an important determinant of employment outcomes. High-expected health costs may reduce the probability that workers are employed in firms where they have the highest match specific productivity. We estimate the magnitude of distortions in hiring, employment, and separations. Furthermore, we examine the effect of state small group health insurance reforms that restrict insurers’ ability to deny coverage and restrict premium variability on employment distortions in small firms relative to large firms. Estimating the magnitude of employment distortions in insured small firms and understanding the effect of small group regulation on these distortions is essential in deciding optimal public policy towards the small group health insurance market.

Consumers' Preferences for GM Food and Voluntary Information Acquisition: A Simultaneous Choice Analysis

Previous research studies directed at the influence of information on consumers' preferences and choices of food in the context of genetically modified (GM) food assume that information is exogenous, in that this is provided to consumers from external sources. Information made available to consumers is also typically treated as being received and processed. Other literature and observation suggests that these two features tend not to apply in practice. Using data from a choice experiment on consumers' choices for genetically modified food in which respondents were able to voluntarily access information, this study allows information to be endogenous; consumers' product choices and information access decisions are examined within a simultaneous choice framework. We find that these two types of decisions are related, but not entirely as might be expected from the existing agricultural economics literature since those with more negative attitudes toward GM food were most likely to access information made available. Our results are consistent with research findings in the social psychology literature. There is heterogeneity across consumers in the relationship between information access and consumer choices which may reflect differentiation in attitudes to GM food.Genetically modified food, information search, multinomial logit models, simultaneous modeling., Food Consumption/Nutrition/Food Safety, Research and Development/Tech Change/Emerging Technologies, Q13, Q18, C8,

HOW DO PUBLIC INSTITUTIONS SELECT COMPETITIVE AGRICULTURAL R&D PROJECTS? - THE CASE OF AN ITALIAN REGION

This paper analyses, through a Random Utility Model (RUM), how a public institution selects among competitive agricultural R&D projects on the basis of observable distinctive features. In particular, we aim at verifying if, which and how other criteria, beyond the pure scientific value, are decisive for selection. From such information, like cost, duration, etc., the institution must infer about the unobservable actual ability, effort and reliability of the scientists themselves. Such analytical framework is empirically applied to a real case, the agricultural R&D activity funded by the Emilia-Romagna Region (Italy) between 2001 and 2006.Public Agricultural R&D Funding, Random Utility Model, Logit Model, Agribusiness, Community/Rural/Urban Development, Public Economics,