Constituents’ economic interests and senator support for spending limitations

Peltzman [Peltzman, S., 1984. Constituent interest and congressional voting. Journal of Law and Economics 27, 181–210] argues that if constituents’ economic interests have well-defined “winners and losers” and are appropriately measured, then constituents’ economic interests, and not legislator ideology, are the most important determinates of legislator voting. We test Peltzman's theory by examining senatorial voting on three mandated spending limitation bills. We find, consistent with Peltzman's theory, that the ratio of federal spending in a senator's state to federal taxes paid by that state, and not a senator's personal ideology, matters on legislation where there are well-defined economic “winners and losers.” This is particularly important because unlike other constituents’ economic interest measures that only impact a fraction of the constituency, the ratio of federal spending to federal taxes in a state represents the economic interests of all the constituents in a state.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Scaling up CBOs for second‐order devolution in welfare reform

The Personal Responsibility and Work Opportunity Act of 1996 created a paradigm shift in the financing, organization, and delivery of welfare programs in the United States. The act shifted revenue and authority to states, giving them great discretion to determine the specifics of their programs. First‐order devolution, combined with time limits and work requirements, set in motion a chain of events that moved Temporary Assistance to Needy Families (TANF) recipients into the labor force and off state welfare rolls. Second‐order devolution shifted revenue and authority to community‐based organizations (CBOs) to help former recipients remain employed, advance to higher paying jobs, and move their families toward economic self‐sufficiency. Early findings from project innovations in Texas and other states raise doubts about the capacity of these organizations to achieve these goals. State funders will need to provide ongoing technical assistance and support to “scale up” the capacity of CBOs to plan, implement, and manage local innovations in welfare reform.     

Teaching Teachers to Teach Corporate Governance Differently

Journal of Management and Governance -     

THE DEVELOPMENT OF AN INTEGRATIVE CURRICULUM IN AN UNDERGRADUATE BUSINESS PROGRAM*

This paper describes how the core portion of the undergraduate business program at Indiana University was revised to facilitate integration of basic functionally oriented decision making skills and thus help students gain a deeper understanding of the interdependence of business decisions. It relates, in essence, why and how a single computer-oriented case study which demanded a solution to a large-scale, unified finance-marketing-production problem was used at Indiana University, mentions how we plan to accomplish further integration of functional material, and provides some insight into student reaction to what was, in fact, a massive project from both student and administrative points of view.     

A METHOD FOR SOLVING LINEAR PROGRAMS WITH VARIABLE RESOURCES

This paper is concerned with the solution of linear and linear goal programming problems in which the values of the right-hand side parameters are not fixed constants. Specifically, we are concerned with linear optimization problems in which the right-hand sides of the constraining equations are free to vary subject to a set of linear constraining equations. By formulating a relaxed linear program wherein the right-hand sides are treated as variables, we show how it is possible to solve one larger linear program that yields as a solution not only the optimal values for the decision variables, but also the optimal values for the right-hand sides.     

MULTIPLE REGRESSION ANALYSIS APPLIED TO RESIDENTIAL PROPERTIES

An interesting and promising innovation in the assessment of property for tax purposes is the application of multiple regression analysis. Sales prices are regressed on various housing characteristics, and models developed from sold properties are used to generate value estimates for unsold properties. Two important issues relating to the development of such models are (1) the extent to which real estate markets are stable in terms of structural relationships over time, and (2) the extent to which sales prices reflect actual market values. The first issue is important because it affects how current sales must be before they can be used in model development, as well as the amount of information the assessor must collect and maintain on properties. The second issue is important because it concerns the amount of measurement error in the models and the extent to which the assessor/analyst can unambiguously interpret residual variance. These issues are investigated by comparing regression models developed from a sample of properties in Eugene, Oregon, which sold in each of two well separated time periods.