Efficiency Effects on the U.S. Economy from Wireless Taxation

This paper measures for the first time the economic efficiency effects of the taxation of wireless services, which are taxed by federal, state, and local governments at relatively high rates in the range of 14%-25%. The paper concludes such taxes are a much greater drain on the economy than their direct costs. The taxes identified in this paper cost the economy $2.56 billion more than the$4.79 billion they raise in tax revenues. These taxes are raised from wireless consumers and thereby suppress demand for service, imposing an efficiency loss on the economy of $0.53 for every$1 currently raised in taxes. Prospective taxes will impose an efficiency loss of $0.72-$1.14 per additional dollar of tax revenue raised.

Sources of Bias and Solutions to Bias in the CPI

Four sources of bias in the Consumer Prices Index (CPI) have been identified. The most discussed is substitution bias, which creates a second order bias in the CPI. Three other changes besides prices changes create first order effects on a correctly measured cost of living index (COLI). (1) Introduction of new goods creates a first order effect of new good bias' (2) Quality changes in existing goods will lead to quality' bias, which has first order effects (3) Shifts in shopping patterns to lower priced stores can create first order outlet bias'. I explain in this paper that a pure price' based approach of surveying prices to estimate a COLI cannot succeed in solving the 3 problems of first order bias. Neither the BLS nor the recent report C. Schultze and C. Mackie, eds., At What Price (AWP, 2002), recognizes that to solve these problems, which have been long known, both quantity and price data are necessary. I discuss economic and econometric approaches to measuring the first order bias effects as well as the availability of scanner data that would permit implementation of the techniques. Lastly, I review recent research that demonstrates that these sources of bias are large in relation to measured inflation in the CPI.

Stochastic Problems in the Simulation of Labor Supply

Modern work in labor supply attempts to account for nonlinear budget sets created by government tax and transfer programs. Progressive taxation leads to nonlinear convex budget sets while the earned income credit, social security contributions, AFDC, and the proposed NIT plans all lead to nonlinear, nonconvex budget sets. Where nonlinear budget sets occur, the expected value of the random variable, labor supply, can no longer be calculated by simply 'plugging in' the estimated coefficients. Properties of the stochastic terms which arise from the residual or from a stochastic preference structure need to be accounted for. This paper considers both analytical approaches and Monte Carlo approaches to the problem. We attempt to find accurate and low cost computational techniques which would permit extensive use of simulation methodology. Large samples are typically included in such simulations which makes computational techniques an important consideration. But these large samples may also lead to simplifications in computational techniques because of the averaging process used in calculation of simulation results. This paper investigates the tradeoffs available between computational accuracy and cost in simulation exercises over large samples.

CPI Bias from Supercenters: Does the BLS Know that Wal-Mart Exists?

Hausman (2003) discusses four sources of bias in the present calculation of the CPI. A pure price' index based approach of surveying prices as done by the BLS cannot succeed in solving the problems of bias. We discuss economic and econometric approaches to measuring the first order bias effects from outlet substitution bias. We demonstrate the use of scanner data that permits implementation of techniques that allow the problem to be solved. In contrast, the current BLS procedure does not treat correctly outlet substitution bias and acts as if Wal-Mart does not exist. Yet, Wal-Mart offers identical food items at an average price about 15%-25% lower than traditional supermarkets. The BLS links out' Wal-Mart's lower prices. We find that a more appropriate approach to the analysis is to let the choice to shop at Wal-Mart be considered as a new good' to consumers when Wal-Mart enters a geographic market. This approach leads to a continuously updated expenditure weighted average price calculation. We find a significant difference between our approach and the BLS approach. Our estimates are that the BLS CPI-U food at home inflation is too high by about 0.32 to 0.42 percentage points, which leads to an upward bias in the estimated inflation rate of about 15% per year.

Consumer Benefits from Increased Competition in Shopping Outlets: Measuring the Effect of Wal-Mart

Consumers often benefit from increased competition in differentiated product settings. In previous research Hausman (1997a, 1997b, 1999, 2002) has estimated the increased consumer welfare from the introduction of new brand, e.g. Apple Cinnamon Cheerios, and new products, e.g. mobile telephones. In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of nontraditional retail outlets. Non-traditional outlets, including supercenters, warehouse club stores, and mass merchandisers have grown in popularity and nearly doubled their share of consumer food-at-home expenditures from 1998 to 20033. Within this non-traditional retail group, supercenters have experienced the largest increase over this time period, but warehouse club stores and dollar stores have also experienced significant increases in their share of the consumer food dollar as U.S. consumers attempt to find the best combination of prices and services at their retailer of choice.

Using a Laplace approximation to estimate the random coefficients logit model by non-linear least squares

Current methods of estimating the random coefficients logit model employ simulations of the distribution of the taste parameters through pseudo-random sequences. These methods suffer from difficulties in estimating correlations between parameters and computational limitations such as the curse of dimensionality. This paper provides a solution to these problems by approximating the integral expression of the expected choice probability using a multivariate extension of the Laplace approximation. Simulation results reveal that our method performs very well, both in terms of accuracy and computational time. This paper is a revised version of CWP01/06.