A mixture likelihood approach for generalized linear models

A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this approach handles many of the existing latent class regression procedures as special cases, as well as a host of other parametric specifications in the exponential family heretofore not mentioned in the latent class literature. As such we generalize the McCullagh and Nelder approach to a latent class framework. The parameters are estimated using maximum likelihood, and an EM algorithm for estimation is provided. A Monte Carlo study of the performance of the algorithm for several distributions is provided, and the model is illustrated in two empirical applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45934/1/357_2005_Article_BF01202266.pd

Breaking the glass ceiling: empowering female entrepreneurs through female mentors

Among the millions of entrepreneurs in developing economies, few are able to earn a decent livelihood. To help these entrepreneurs succeed, governmental and nongovernmental organizations invest billions of dollars every year providing training programs. Many of these programs involve providing entrepreneurs with mentors. Unfortunately, the effects of these programs are often muted, or even null, for women-owned firms. Against this backdrop, we tested whether gender-matching, where female entrepreneurs are randomly paired with a female mentor, could help address the gender gap. Findings from a randomized controlled field experiment with 930 Ugandan entrepreneurs show that although mentor gender has little impact on male entrepreneurs, it has a powerful impact on female entrepreneurs. Firm sales and profits of female entrepreneurs guided by a female mentor increased by, on average, 34% and 29% compared to the control group. And these estimates are even larger for female entrepreneurs with high aspirations. In contrast, female entrepreneurs guided by a male mentor did not significantly improve performance compared to the control group. We provide suggestive mechanism evidence that female mentor-mentee arrangements were characterized by more positive engagements

Note—Extending the Rotterdam Model to Test Hierarchical Market Structures

Two issues that have implications across a firm's marketing activities are the estimation of the market response functions, and the determination of the structure of the market in which its products compete. In a recent paper, Clements and Selvanathan (Clements, K. W., E. A. Selvanathan. 1988. The Rotterdam demand model and its application to marketing. (Winter) 60–75.) describe how the Rotterdam Demand System can be used to study these two issues in a joint framework. In this note we extend their model to test hierarchical market structures using data at the brand/form level. We also incorporate idiosyncratic response coefficients that capture the effects of advertising and/or other nonprice marketing mix variables separate from the effects of a price change. These extensions provide additional insights into the nature and extent of competition among brands within a product class.market structures, demand estimation, separability of preferences

Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach

The purchase timing decision is an important component of the dynamics of a household's purchase behavior. This decision is influenced by marketing and other variables, and the modeling of this dependence has recently received attention in the literature. In this paper, we build on previous studies and develop a comprehensive stochastic model that incorporates the major factors influencing interpurchase times. Specifically, we use a generalized version of Cox's proportional hazard model to test among competing probability distributions for the interpurchase times while incorporating effects due to marketing variables, observed household characteristics, and unobserved heterogeneity across households. The empirical finding from analyzing the IRI coffee data, suggests that the interpurchase times cannot be adequately described by probability distributions such as exponential, Erlang-2 or Weibull. The effects of unobserved heterogeneity are significant, and they impact the estimates of the effects of the covariates. We also find that a nonparametric procedure for estimating the effects of unobserved heterogeneity provides the best overall fit to the data and yields covariate estimates that are more consistent with prior expectations. Our model is validated by replicating the substantive empirical findings on an additional product category.hazard function, purchase timing, unobserved heterogeneity