Knowledge that Transforms

IN this paper is examined some of the determinants of earnings of males in a high powered occupation, chemical engineering. Models such as those developed here can be useful to researchers interested in earnings functions, the theory of occupational choice, economic growth and returns to investments in human capital. In addition, earnings models can be help'ful to individuals who must make promotion and salary decisions. The impact of education on earnings has received a great deal of attention in social, political and economic circles. Previous studies show that persons who obtain higher levels of education earn higher incomes. However, comparing average incomes of individuals who differ only in levels of educational attainment may overstate the influence of education since schooling and non-schooling factors other than the amount of formal schooling cause differences in individual incomes. Such factors include socio-economic background, demographic characteristics, innate ability and the quality of formal education. Emphasis in this study is on the quality of education and student ability. The author was able to find only two attempts at simultaneously estimating the impact of student ability and school quality on the earnings of persons in professional occupations.' This paper is viewed as an exploratory effort in this direction. The basic earnings functions to be estimated are described in section I. The data sources, key proxy variables in the regression analysis and the regression findings are discussed in section II. Section III is a summary of the main findings.

N. J. Ireland, G. Briscoe, D. J. Smyth, Specification Bias and Short-Run Returns to Labour: Some Evidence for the United Kingdom, The Review of Economics and Statistics, Vol. 55, No. 1 (Feb., 1973), pp. 23-27

They indicate that purchases of durable goods (DUR) are the third most volatile component behind inventory investment (INV I) and federal government expenditures (F GOV). The correlation coefficients between GNP and its components listed in the second row of table 1 show that durable goods purchases have the third hiighest covariance with GNP after inventory investment and nonresidential investment (NRI). Theoretically, these purchases represent either changes in the size of the household portfolio through changes in the flow of savings, or a reallocation of accumulated wealth among assets in response to changes in rates of return. Empirically, some effort has been given to examining the separate influences of rates of return and income. Hamburger (1967) found that interest rates, the price of durable goods relative to other prices faced by the consumer, and disposable personal income all have a significant impact on purchases of durable goods. portance of these variables as sources of fluctuation in purchases. Motley (1970) included a user cost of real assets variable in addition to the rate on savings deposits and expected income, and found in sharp contrast to Hamburger that none of them had a significant influence on the demand for the sum of durables and housing. The analysis of fluctuations in durable good purchases presented here differs from its prede-

OINCE 1970, Chinese sources have ended a decade of silence on quantitative developments in the economy by releasing statistical information which illuminates economic trends during the 1960's as well as after the Cultural Revolution (1966-1968). The purpose of this article is to combine this new material with previously available data to construct a series of industrial gross ou;tput value for most years of the period 1952-1971.' The years for which data gaps prevent us from offering output estimates include two periods of cyclical fluctuation: the Great Leap Forward and its aftermath (1958-1962), during which output spurted and then declined; and the Cultural Revolution, which saw a pause or temporary drop in industrial output. The principal result of this study is the annual output series for 1952-1971 shown in table 1. Our findings indicate that China has experienced substantial industrial growth during the past two decades, -and suggest an upward revision Of previous Western es!timates of her industrial progress, especially for the period since 1957.

Hiroki Tsurumi, A Comparison of Alternative Optimal Models of Advertising Expenditures: Stock Adjustment Vs. Control Theoretic Approaches, The Review of Economics and Statistics, Vol. 55, No. 2 (May, 1973), pp. 156-168

R ECENT attempts to measure changes in the quality of labor inputs in production have been based either on changes in years of schooling and other quality determining factors (Denison (1967) and Jorgenson and Griliches (1967)) or on changes in occupational mix (Griliches (1967) and Raimon and Stoikov (1967)).' Both measures raise serious problems. The first, because of lack of data, has not been able to adjust for differences in ability, on-the-job training or a number of other quality determining factors so that they do not even purport to have exhausted all significant quality differences.2 On the other hand, studies using occupational data have been based essentially on Bureau of Census major occupational groups i.e., eleven groups -and as the authors themselves were aware, these are uncomfortably broad. The purpose of this paper is to discuss still another (albeit related) approach to measuring changes in the quality of labor in production and to present the results for manufacturing industries. The approach is to first develop a wage rate index which measures changes in prices of a homogeneous bundle of labor services over time. The wage rate index is then used to deflate a time series of average hourly earnings (i.e., the cost of a changing bundle of labor services per man-hour) in order to obtain the desired index of quality per manhour. This index, in effect, measures changes in labor quality by changes in occupational mix -i.e., increases in quality per man-hour are measured by shifts from lower to higher paid occupations. The major difference between the approach used here and that in the previously cited studies is that my results are based on very detailed occupational data by sex and industry. Because of limited coverage, the indexes of quality per man-hour are for production (blue collar) workers in 25 selected manufacturing industries. On the basis of these series and some additional assumptions, I also attempt to expand the results to all employees in manufacturing in order to estimate the contribution of changes in labor quality to the growth in output and output per man-hour in this sector. In particular, changes in quality per manhour are divided between those resulting from shifts among production and nonproduction (white collar) workers and those resulting from changes in quality per man-hour within these two groups.