Knowledge that Transforms

Randolph C. Martin, Robert E. Graham, Jr., The Impact of Economic Development Administration Programs: Some Empirical Evidence, The Review of Economics and Statistics, Vol. 62, No. 1 (Feb., 1980), pp. 52-62

T IME affects an individual's earnings in three ways. First, his earnings depend on his age and experience, as he acquires skills through experience or as his productivity changes during the aging process. Second, the rental rate on human capital varies over time with relative factor supplies, consumer demand, and technical change. Finally, workers in different cohorts may attain different earnings levels for a variety of reasons. Common experiences of certain cohorts, such as war or depression, may permanently affect their lifetime careers, as might the quality of schooling received by the cohort, or the tightness of the labor market as the cohort entered its first jobs. This paper attempts to estimate these on the earnings of white American men by using panel data on a cross-section of workers. The estimated vintage effects, beside being of interest for their own sake, allow at least tentative evidence on several recent hypotheses about the labor market. Vintage effects for this paper are defined as the differences in earnings between cohorts that cannot be explained by either secular growth or the normal age-experience profile of earnings. A cohort is defined as a set of all workers who entered the labor force at the same time. Since cohort effects, as defined, must have lasted into the observation period, 1968-76, to be observed, we cannot measure any temporary advantage or disadvantage a cohort may have held before the observation period. The estimates, therefore, attempt to measure long-run effects of vintage on earnings or the long-run difference between cohorts abstracting from age and calendar time. The major problem in the analysis is to disentangle empirically the effects of age, time, and vintage on earnings. At the very least, one must observe cross-sections of workers at different points in time. However, a further necessary condition is some restriction on the functional form which allows vintage effects to be identified. This problem is handled in two ways: 1. restrictions on the functional form of the earnings function that are robust to alternative functional forms; 2. direct tests of alternative hypotheses about the cause of vintage effects.

Box, George E. P., and David R. Cox, An Analysis of Transformations, Journal of the Royal Statistical Society, series B, 26 (Apr. 1964), 211-243. Boyes, W. J., An Application of Specification Error Tests-The Liquidity Trap, Manchester School of Economics and Social Studies (1978), 139-151. Chang, Hui-shyong, Forms and the Demand for Meat in the United States, this REVIEW 59 (Aug. 1977), 355-359. Greenfield, J. N., Effects of Price Changes on the Demand for Meat, Monthly Bulletin of Agricultural Economics and Statistics 23(12) (1974), 1-8. Ramsey, James B., Limiting Functional Forms for Demand Functions: Tests of Some Specific Hypotheses, this REVIEW 56 (1974), 468-477.

Louis N. Christofides, Robert Swidinsky, David A. Wilton, A Microeconometric Analysis of Spillovers within the Canadian Wage Determination Process, The Review of Economics and Statistics, Vol. 62, No. 2 (May, 1980), pp. 213-221

That it is easier to measure the costs of price regulation than the benefits is evident in the conflicting results of studies made to determine welfare gains. An econometric study based on a simple theory of demand by customer class concludes that: (1) the effectiveness of regulation across states is irregular, which suggests that state regulatory agencies confront firms with highly variable political climates and may serve customers with an uneven quality of benefits; (2) the pattern of cross-subsidization of prices that penalized residential customers in 1969 was gone or shifted to favor residential customers by 1974; (3) a more-detailed re-evaluation of the presumed benefits and costs of nonuniform pricing may be warranted; and (4) welfare improvements appear to be possible. The dominant question that emerges is how to explain what underlying forces contribute to the substantial state-to-state and between-customer variations in regulatory impact. 13 references, 6 tables.

T HE demand for private protection against crime1 and the interaction between private security and public safety have been analyzed in recent studies. While the deterrent effect of public law enforcement has been treated extensively in the literature, systematic empirical analyses of the effect of private enforcement on crime are few.2 Our emphasis is on a form of private behavior that may substantially affect the success of public law enforcement: victims' reporting of crimes to the police. Because apprehension of criminals crucially depends on the reporting of crimes, and because reporting increases the chances of apprehension and conviction, reporting should deter potential offenders. To study this deterrent effect, we chose to concentrate on residential burglary. Our data, collected in the National Crime Panel (NCP) victimization surveys, reveal that only about half of all burglaries were reported to the police. Our basic premise is that among potential victims who offer the same expected gross returns, those perceived by burglars as more likely to report are less attractive targets. This perceived reporting probability is then a victim-specific deterrent variable. The NCP victimization survey data provide opportunities to improve upon previous studies of deterrence that use only aggregate crime statistics. First, they allow us to construct a victim-specific deterrence variable, rather than to assume that all victims in a state or city impose on the offender the same amount of risk. Second, information on loot from burglary enables us to measure directly the illegal returns. Third, in contrast to police-recorded crime statistics, the NCP surveys include information on all crimes, reported or not. Finally, our data on individuals make it easier to avoid the simultaneity problem encountered in studies that employ data on aggregates.3 Consequently, this study tests the deterrence hypothesis more directly.

FOOD is macroeconomically important in poor countries, for obvious reasons. Agriculture (mainly staple food production, in most places) may make up half or more of total valueadded, and the average share of consumer expenditure going to food products is likely to be around 50% as well. Among the poor, the food budget share may be as high as three-quarters. Given all dimensions of the food sector (including production, processing, transportation and distribution activities, and consumption), the policy instruments that are supposed to influence its behavior also have noticeable macro repercussions. These have been little traced in the literature, though they can be important for all kinds of planning. In the medium to long run, for example, newly fashionable Basic Needs development strategies will require sustained growth in food intake on the part of the poorest half of the population in many countries. By Engel's Law, rapidly increasing staple food consumption imposes strong constraints on both growth and composition of aggregate demand. In the short run, food supply shortfalls and the threat of rising prices have forced more than one set of ministers to exit from the back door with rioters out front in the street. Corrective measures that they or their successors impose dramatically shift the balance of payments and government deficit positions. This paper is devoted to sketching how a simple macroeconomic model can provide guidance on how to calculate probable impacts of food policy changes in a poor country. We work specifically with Pakistan, and have tried to be as realistic as possible in designing an analytical scheme that can fit the available data, and give policy-relevant information. These requisites lead us to set up the data for accounting consistency in a Social Accounting Matrix (or SAM), discussed in the following section. Section III then outlines the structure of the analytical model built around the SAM, and section IV explains how it works. Several possible food policy interventions in Pakistan are discussed in section V, along with some implied policy conclusions. 1