Reviews the book "Readings in Auditing," 3rd ed., by J. Herman Brasseaux and John D. Edwards.

POLICIES to promote price and income stability in agriculture have often been justified by the belief that stability would help farmers make better consumption and investment decisions. However, review of literature makes it abundantly clear that the consequences of instability are matters of debate among economists. For instance, Caine (1966, p. 16) believes that a main evil resulting from fluctuations in income is lowering of the level of capital expenditure. Others argue that farmers adapt to the exigencies of fluctuating income and that instability, per se, has little influence on consumption and investment (e.g., Campbell, 1964, p. 59).1 In this paper, consumption and investment functions are estimated for two groups of southern Minnesota farmers with contrasting degrees of income stability. Since various hypotheses exist about investment and consumption behavior, alternative models are outlined in the first section. Consequently, this paper provides empirical evidence for evaluating alternative models as well as assessing the effects of instability. The data and the estimation procedures are briefly described in the second section, and the empirical results are presented in the third.

In a brilliant and pioneering paper, John Harris and Michael Todaro introduced a model with two sectors, manufacturing (urban) and agriculture (rural), a (sticky) minimum wage in manufacturing and consequent unemployment. They also introduced a labor allocation mechanism under which, instead of the usual equalization of actual wages, the actual rural wage was equated with the expected urban wage; the latter was defined as the (sticky) minimum wage weighted by the rate of employment, so that, unlike in the standard rigid-wage models of trade theory, the unemployment resulting from the minimum wage is to be construed as specific to the urban sector. In the context of this model, Harris and Todaro analyze two policies: a wage subsidy policy in the manufacturing sector and a labor-mobility restriction policy. They argue that the former, as well as the latter, can be used to improve welfare, defined as a function of available goods in the usual way; but that, to attain the optimal first best solution, both policies are necessary.

In applications of linear regression analysis, the unknown error covariance matrix has to be somehow estimated. This can lead to biased estimates of the covariance matrix of the regression coefficients. Since such bias is difficult to eliminate completely, its sensitivity to alternative estimates of error covariances is studied by Watson, Theil, Malinvaud, and others with the help of bounds on the bias derived under certain assumptions. This paper gives similar bounds under less restrictive assumptions, and illustrates them in the context of heteroscedasticity and autocorrelation problems. In particular, for the first order error autocorrelation coefficient of p the upper bound on proportionate bias is shown to be reasonably approximated by (1 + p)/(l - p) - 1.

The distribution of personal income is approximated by a two-parameter gamma density function (Pearson Type III). The two parameters may be considered as indicators of scale and of inequality, respectively. Maximum likelihood estimates of the parameters are derived from a random sample using graphical techniques, and a likelihood ratio test for the hypothesis that the inequality parameter is the same for different distributions is presented. The derivation of both the estimates and the test statistic requires computing the arithmetic and geometric means from the sample. An empirical application, including a comparison of the gamma and lognormal distributions to demonstrate the better fit of the gamma, is made to personal income data in the United States for the years 1960 to 1969. Using the gamma density, inequality is shown to decrease when unemployment or inflation decreases, or when the real national product increases.