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.

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.

Time, Due Process, and Representation: An Empirical and Legal Analysis of Continuances in Immigration Court

Since 2014, U.S. immigration courts have expedited the cases of many children and families fleeing persecution in Mexico and Central America. This Article conducts an empirical and legal analysis of this policy, revealing that reasonable time between immigration court hearings is necessary to protect the statutory and constitutional rights to legal representation. A large majority of immigrants facing deportation- including those part of the recent surge of children and families from Central America and Mexico-appear at their first deportation hearing without a lawyer, often because they cannot afford one. When an immigrant appears without a lawyer and does not expressly waive his or her right to counsel, the immigration judge (IJ) must grant a continuance that allows a reasonable period of time for an immigrant to search for and retain counsel. Yet existing law does not specify what period of time is reasonable, and the courts of appeals disagree over how closely to scrutinize an IJ\u27s decision to deny a continuance. In this Article, we use schedule data from the Executive Office for Immigration Review to show that the length of a continuance has a large effect on immigrants\u27 likelihood of finding counsel, of appearing at subsequent hearings, and of eventually avoiding removal. Our analysis demonstrates that shorter continuances for unrepresented children and families prevented many from finding counsel and avoiding deportation. In light of these findings, we examine the due process and statutory consequences of an IJ\u27s decision to deny a continuance or to grant an overly short continuance. We conclude by recommending that initial continuances of fewer than ninety days should be presumptively invalid

Manipulation and the causal Markov condition

This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We argue that violations of CM also violate widely shared expectations—implicit in the manipulability conception—having to do with the absence of spontaneous correlations. They also violate expectations concerning the connection between independence or dependence relationships in the presence and absence of interventions

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.