Knowledge that Transforms

Women, youth, and minorities are frequently cited as an almost one-word explanation for declining productivity growth in the U.S. labor force as measured at the macro level. While some economists have proposed theories to account for productivity slowdown which emphasize such things as declining capital-labor ratio (J. R. Norsworthy, Michael Harper, and Kent Kunze, 1979); energy constraint (Edward Hudson and Dale Jorgenson, 1978); and macro instability (Richard Nelson, 1980; Michael Mohr, 1980), many analysts have cited the changing composition of the labor force as a significant contributing factor. (See, for example, Edward Denison, 1979; Norsworthy et al.; Frank Gollop and Jorgenson, 1980.) These assertions are tantamount to suggesting that admitting certain groups into the labor force necessarily lowers productivity and should not go unchallenged for several reasons. Inferring negative productivity coefficients to particular groups is likely to reinforce statistical discrimination whereby employers use perceived characteristics of groups as information shortcuts for making decisions about individuals (Edmund Phelps, 1972). It also has considerable implication for future U.S. productivity trends as the demographic composition of the labor force continues to change, and for formulating appropriate labor policies. This paper examines labor market trends and productivity measurement methodology as it pertains to labor force composition, and concludes that whether women, youth, and minorities have contributed significantly to the decline in productivity growth, and are likely to do so in the future, depends on how productivity is defined and measured. Current definitions of productivity are essentially based on micro concepts. I argue that they may be misleading indicators of trends in economic efficiency at the macro level, especially under the conditions of social change and labor-supply growth which characterized the 1970's.

The scholarly debate over the nature and cause of the significant racial disparities in prison incarceration rates in the United States has taken on renewed intensity in recent years. Two sorts of activities have spurred the debate. On one hand, researchers such as Alfred Blumstein (1982), Jan Chaiken and Marcia Chaiken (1982), and Joan Petersilia (1983) have begun to use powerful analytic and conceptual tools to scrutinize the hypothesis that racism or racial discrimination exists in the criminal justice system, or that it is the cause of the racial disproportionality of our prisons. On the other hand, minority scholars and public opinion leaders have begun a very visible and vocal attack on the results of the conventional social science community. (See, for example, National Minority Advisory Council on Criminal Justice, 1980.) These activities have stimulated much discussion among public policymakers and legislators. Ranking black members of the U.S. Congress, for example, have gone on record by questioning social science research findings that purport to show that racial discrimination in certain aspects of the criminal justice system does not exist - or, at least, that its alleged existence is not a cause of the greater representation of blacks in the prisons or the criminal population. Economists have not been leaders or even active participants in this debate. This is surprising for several reasons. Many of the conventional tools of econometrics can be called upon to resolve some of the statistical issues in dispute; post-Beckerian models are likely to yield more than negligible benefits in sorting out the theoretical effects of punishment on criminal activities; and radical labor market paradigms may prove useful in examining the historical evolution of prisons and punishment in America. A brief overview of a number of different areas of research on race and punishment will illustrate the inherent potential as well as the unrealized promise of economic approaches.

(3) WA = R1Pne + 1 _ R A n R2U, where pe is the forward-looking expectation and f31 is the credibility coefficient (or degree of belief) attached to the model and the announced policy rule. Total expectation is the weighted sum of the forwardand backward-looking beliefs. In the case of the simplest monetaristrational expectations viewpoint, P, =1, and pe = m*, where m* is the announced monetary target (with the trend in velocity offset by the economy's natural rate of growth). Aggregate supply price will be

our earlier article, we developed model which supported the hypothesis that the proportion of the staff budget which each senator from 48 states returned unspent 1978 was significantly influenced by their political philosophy: conservatives, on average, returned higher proportion than liberals. their comment, Steven Cobb and Robert Hagemann emphasize that some state may have been omitted and, as result, the coefficients estimated from our model may be biased. They develop an approach intended to correct this, that is, a regression estimated on differences between state senators will automatically purge the regression of all state effects (p. 524), and find that the relationship between the proportion returned and political philosophy is statistically significant. Although concern about omitted is justified, there are two shortcomings with the approach that Cobb and Hagemann employ that deserve mention. First, differencing is an appropriate technique to use with cross-section data, as Carl Christ observed, because the cross-section has no analogy to the value the time series case (1966, p. 210).1 time-series, the data are uniquely ordered, but this is true cross section. The data which Cobb and Hagemann difference are cross section of two on senators within each of 48 states. The crux of the issue is which senator is subtracted from which within each state, because in economics there is usually no meaningful way to order ... cross-section observations (p. 209).2 As an illustration, consider three states (A, B, C), each with two senators (denoted by subscripts). Several differencing schemes can be proposed which generate for each state: (A,-A2),(Bj-B2), (Cl-C2); (A1-A2), (B1-B2), (C2-C1); and, (A1-A2), (B2-BB), (C2-C1), among others. Each scheme generates different set of values for the dependent variable and for the independent variable measuring political philosophy. The different data sets will produce confficting estimates of the coefficients their model and choosing among them is necessarily arbitrary. When all permutations and combinations among 48 states are considered, Cobb and Hagemann's approach produces hundreds of different data sets that yield hundreds of conflicting estimates for the same parameters. Of course, the within states can be ordered by some rule (political philosophy, proportion returned unspent, tenure office, age, etc.),3 but the choice of rule is itself arbitrary. Second, for purposes of exposition, consider the where senators are either extreme liberals or conservatives; liberals spend all of their staff budgets, whereas conservatives spend none of their funds; also, every state has two senators with the same political philosophy. this ideal case, our model will verify that political philosophy determines spending behavior, but the model presented by Cobb and Hagemann will not, for when differences are taken, the value of the dependent variable (and the independent variable for political philosophy) for every observation is identically zero and no empirical estimates can be obtained whatsoever. One *George Mason University, Fairfax, VA 22030. Research support provided by the Sarah Scaife Foundation and the Earhart Foundation is gratefully acknowledged. 'In strict sense, Cobb and Hagemann are differencing, employing lagged values. However, Christ explicitly points out that ...using first differences as is equivalent to using variables (p. 177). 2Christ adds that In pure cross-section modelthe typically have no natural order, though certain cases we can imagine putting them the order of size, social status, or distance from some focal point, or what not (p. 209, emphasis added). 3If political philosophy is used, the question then becomes which measure of political philosophy. We used three: the American Conservative Union, the AFLCIO, and the Americans for Democratic Action rankings of senators.

To understand the significance of the and wealth elasticities estimated in my 1975 article and reestimated for a variety of samples by George Perkins, it is useful to recall the legal controversy that surrounded education finance in the early 1970's. In the historic and much publicized case of Serrano vs. Priest (1971), the California Supreme Court held that California's system of educational finance violated California's state constitution because local educational outlays were related to local property values. Similar cases were being introduced in the courts of other states and in the U.S. Supreme Court. Perhaps the most commonly discussed legal remedy in cases like Serrano was a plan referred to as power (DPE) by its original advocates, J. E. Coons, W. H. Clune, and S. D. Sugarman in their very influential book (1970). District power equalization is a matching grant formula that makes each percentage point of tax rate levied on the market value of local property produce the same revenue, independent of the actual local tax base. To be more precise, let W, be the tax base (wealth) per pupil in school district i and 6i be the tax rate chosen by school district i. The per pupil tax raised locally would therefore be Ti =61iWi. District power equalization would make the total per pupil revenue of district i proportional to 6i but independent of W, or Ri = 6iW*, where W* is the equivalent tax base implicitly assigned to all school districts by the DPE matching rate formula. The matching rate for district i (min) is the number of state level dollars given to district i for every dollar of revenue that they raise locally; therefore 1 + mi = R/Ti = W*/Wi. The DPE formula is important because it implies that the local price of educational outlays is proportional to wealth. If district i's of buying educational outlays is defined as the amount that the district must produce in local tax revenue per dollar of total spending, the DPE formula implies pi = TIRi = WJ/W*. In terms of the pricewealth elasticity of my earlier paper and Perkins' comment, district power equalization implies v =-1. My reason for estimating and wealth elasticities of demand for local school districts was to answer the following two questions. First, if the courts required the state governments to finance education in a way that eliminated the currently observed association between local wealth and educational outlays, what would be the appropriate matching formula? Second, if the courts mandated the district power equalizing rule, what would be the resulting association between wealth and educational outlays? To make these ideas more precise, I decided to measure the association by the elasticity of per pupil education outlays with respect to per pupil wealth and called this elasticity the degree of wealth neutrality. By applying Theil's famous formula for specification bias, I showed that the wealth neutrality (a) can be written