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Loyalty programs (LP) and rewards are ubiquitous in retailing, designed to increase customer expenditures and retention, gather abundant customer-level data, and design individually targeted coupons. Although studies have analyzed the individual impacts of LP rewards and targeted coupons on shopping trip incidence and expenditures, they have not compared the two instruments. By investigating the LP of a leading German grocery retailer that uses both LP rewards (points to redeem for free products) and individually targeted coupons, the current study establishes a comparison of their profitability and relative contributions to increasing shopping trips and expenditures. The investigated LP also relies on in-store kiosks to interact with customers, so they can decide when to redeem LP points and request coupons. By jointly modeling the effects of LP point redemptions and coupons on shopping trip incidence and expenditures, using their decision to access the kiosk as a dependent variable and rich data that span thousands of customers over 60 weeks, this study reveals that both instruments increase shopping trip incidence, kiosk access, and expenditures. The LP point redemptions have a stronger effect on shopping trip incidence; coupons have a stronger effect on kiosk access. Although a single redemption of LP points leads to a greater increase in expenditures than a single coupon redemption (€1.46 vs. €0.48), coupons yield three times greater expenditures after controlling for usage frequency; lower discounts also contribute to coupons’ higher profitability. Therefore, LP point redemptions and individually targeted coupons are complementary tools for retailers. This empirical study provides practical insights and pertinent managerial implications regarding the management and design of modern LPs.

This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on quantiles. The quantile treatment effects (QTE) estimator reduces to quantile regression when selection for treatment is exogenously determined. QTE can be computed as the solution to a convex linear programming problem, although this requires first-step estimation of a nuisance function. We develop distribution theory for the case where the first step is estimated nonparametrically. For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles. Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution.

In this paper, we consider the semiparametric identification and estimation of a heteroscedastic binary choice model with endogenous dummy regressors and no parametric restriction on the distribution of the error term. Our approach addresses various drawbacks associated with previous estimators proposed for this model. It allows for: general multiplicative heteroscedasticity in both selection and outcome equations; a nonparametric selection mechanism; and multiple discrete endogenous regressors. The resulting three-stage estimator is shown to be asymptotically normal, with a convergence rate that can be arbitrarily close to n-½ if certain smoothness assumptions are satisfied. Simulation results show that our estimator performs reasonably well in finite samples. Our approach is then used to study the intergenerational transmission of smoking habits in British households.

In this paper, we investigate the drivers and obstacles to switch the electricity supplier for Austrian households. Since the liberalization of the market in 2001, the annual switching rates have remained at surprisingly low levels, leading to a loss of significant savings potential for Austrian households. The results of our exploratory study suggest that the knowledge about tariffs and electricity suppliers is the driving force for the decision to switch. The lack of information might also explain the low incentive effect of savings potentials. Thus, we suggest that for price competitive electricity suppliers, it might be more effective to familiarize customers with tariff structures and savings potentials than to advertise with “green electricity” as it is the common strategy at the moment.

This article presents a new Qual VAR model for incorporating information from qualitative and/or discrete variables in vector autoregressions. With a Qual VAR, it is possible to create dynamic forecasts of the qualitative variable using standard VAR projections. Previous forecasting methods for qualitative variables, in contrast, produce only static forecasts. I apply the Qual VAR to forecasting the 2001 business recession out of sample and to analyzing the Romer and Romer narrative measure of monetary policy contractions as an endogenous variable in a VAR. Out of sample, the model predicts the timing of the 2001 recession quite well relative to the recession probabilities put forth at the time by professional forecasters. Qual VARs-which include information about the qualitative variable-can also enhance the quality of density forecasts of the other variables in the system.

The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy-to-implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root-n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data-generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.

This paper examines the effect of the visits of U.S. Presidents and Secretaries of State to a country on bilateral trade flows between that country and the United States. The official visits dummy variables are derived from the historical archives of the U.S. Department of State, while bilateral trade with the United States as a percentage of total trade with the world is compiled from UNCOMTRADE. The fixed effects estimation shows that the visits of U.S. officials to the country do not have a statistically significant effect on the bilateral trade variable. However, the issue of endogeneity cannot be ignored. As much as trade flows may increase after the visits of American officials to the country, these officials may be tempted to visit their major trading partners as well. This is either to ensure the smooth flow of trade, to increase the volume and value of trade, or to contain any trade disputes. This highlights an issue of reverse causality. To address the issue of potential endogeneity, we use the Endogenous Treatment model. The estimation results provide evidence that the official visits of U.S. Presidents and U.S. Secretaries of State have a statistically significant positive effect on bilateral trade with the United States. This is robust even after the inclusion of other control variables identified by the literature as confounding factors for bilateral trade flows. When we examine the effect of different types of visits, the results show that bilateral meetings during the official visits of U.S. Presidents and U.S. Secretaries of State have a statistically significant positive effect. These results imply that the visits of U.S. officials are taken as an opportunity to conclude trade agreements, to contain trade disputes, and to discuss the elimination of trade barriers between the two countries. This enhances the level of bilateral trade between the country and the United States. The paper has multiple policy implications tat are discussed in detail.

In this paper, we consider the nonparametric identification and estimation of the average effect of a dummy endogenous regressor in models where the regressors are weakly but not additively separable from the error term. The model is not required to be strictly increasing in the error term, and the class of models considered includes limited dependent variable models such as discrete choice models. Conditions are established conditions under which it is possible to identify the average effect of the dummy endogenous regressor in a weakly separable model without relying on parametric functional form or distributional assumptions and without the use of large support conditions.

This article examines the relationship between economic openness and internal conflict. The article first discusses different theoretical perspectives on how openness affects a country's internal stability and how internal conflict affects openness. Next, empirical estimates of the relationship between conflict and openness are presented, where conflict is measured with both a civil war dummy variable and an events dataset. The correlation between openness and conflict in the data is negative: more open countries have less internal conflict by some measures. However, internal conflict affects the level of openness, which suggests that openness should be treated as an endogenous variable. When the effect of openness on conflict is estimated using instrumental variables or full information maximum likelihood to control for endogeneity, openness does not significantly reduce internal conflict. There is robust evidence, on the other hand, that conflict within a country reduces its international trade. Together, these results suggest that the negative correlation between openness and conflict emerges because stability facilitates international trade rather than because trade flows reduce internal conflict.

In this article we estimate a state-contingent production frontier for a group of farms while endogenously estimating the number of states of nature induced by unobserved environmental variables. This estimation is conducted by using a birth-death Markov chain Monte Carlo method. State-contingent output is estimated conditioned on an observed input vector and an a priori unknown number of unobserved states, each of which is modeled as a component of a mixture of Gaussian distributions. In a panel data application, state-independent dummy variables are used to control for time effects. The model is applied to 44 rice farms in the Philippines operating between 1990 and 1997. The endogenous estimation procedure indicates a unimodal posterior probability distribution on the number of states, with a median of three states. The estimated posterior coefficient values and their economic implications are compared to those of previous research that had assumed a fixed number of states determined exogenously. Goodness-of-fit testing is performed for the first time for a state-contingent production model. The results indicate satisfactory fit and also provide insights regarding the degree of estimation error reduction achieved by utilizing a distribution for the number of states instead of a point estimate. All of our models show significant improvement in terms of mean squared error of in-sample predictions against previous work. This application also demonstrates that using a state-independent dummy time trend instead of a state-contingent linear time trend leads to slightly smaller differences in state mean output levels, although input elasticities remain state-contingent.