Journal Article Choice of Technology in Low-Wage Countries: A Nonneoclassical Approach Get access Donald J. Lecraw Donald J. Lecraw University of Western Ontario Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 93, Issue 4, November 1979, Pages 631–654, https://doi.org/10.2307/1884473 Published: 01 November 1979

Previous estimates of the magnitude of highway congestion costs have employed equations relating the external cost imposed on motorists by an additional vehicle to the speed of traffic flow or the ratio of traffic flow to the maximum capacity of the road. While those equations may be accurate for rural roads and expressways, they may be less accurate on city streets where delays at intersections are a dominant factor in congestion costs. This study replaces single speed-volume equations with a traffic simulation model that replicates the queuing of vehicles at traffic lights, the dispersion of platoons of vehicles as they move from one intersection to another, and the interaction of intersecting traffic flows on an urban street network. The model is used with actual Toronto road and traffic data to produce new estimates of congestion costs on specific streets during the morning rush hour. The model produces a surprisingly high average congestion cost during the morning rush hour and a poor correlation of the results with those that would be estimated for the same traffic flows by the single equation models. The simulation technique allows the calculation of congestion costs on a street-by-street basis, generating the detailed information that would be necessary for a complex congestion pricing scheme. TRAFFIC CONGESTION IN URBAN AREAS has long been recognized as a technological external diseconomy causing serious urban problems. Highway engineering studies show that over a range of traffic flow levels observed on city streets an increase in the volume of traffic flow will reduce the speed of that flow for all motorists. While the additional driver perceives the impact of this lower speed upon himself, he is not faced with the social costs of the time lost to all other motorists as a result of his entering the road. The private cost of using the road, as perceived by the added motorist is thus below the social cost, often by a large amount. It has been shown that if the marginal external social cost of an additional vehicle-mile can be calculated and a toll is charged to all motorists equal to this marginal external cost, a Pareto improvement in the efficiency of using the road can be achieved.2 Mohring [10] has estimated the magnitude of such tolls as a function of the volume/capacity ratio of the highway, a measure of capacity utilization. Johnson [6] calculated a similar set of tolls as a function of the speed of movement of traffic on the road. Walters [15] estimated the elasticity of costs with respect to speed or time of travel. Smeed [13] calculated congestion costs as a function of speed and of the volume/capacity ratio.

In addition to the recent interest of the Securities and Exchange Commission, executive forecasts of earnings have received a considerable amount of attention in the academic literature (Basi, Carey, and Twark [1976], Lorek, McDonald, and Patz [1976], McDonald [1973], Copeland and Marioni [1972], Kapnick [1972], Daily [1971]). Much of this attention has focused either on the absolute or relative accuracy of such forecasts or on the ethical, legal, and practical problems of publishing and reviewing executive forecasts of earnings in external accounting reports. One aspect that has not been adequately considered is investor reaction to executive long-range forecasts of earnings. The purpose of this study is to investigate the information content of voluntarily disclosed long-range earnings forecasts by executives by determining security return reactions to a sample of such forecasts that were reported in the Wall Street Journal. The inclusion of management estimates of future earnings in annual reports is advocated on the assumption that such forecasts contain information, of interest to investors or other persons outside the firm, not otherwise publicly available. Not only do executives have information about internal and external factors expected to affect future operations and earnings, but they also exert considerable effort evaluating these factors and their impact on prospective operations in the normal planning function. Consequently, executive forecasts of earnings might be of inter-

The phenomenal growth in corporate merger activity of the 1960s revived interest in the motives and effects relating to corporate mergers. In recent years, many theories for explaining mergers have been discussed and tested in the literature of finance, law, and economics. Various authors have argued that motives for merger include increased market power [15, 21, 23], achievement of operating or managerial scale economies [2, 8], diversification [6], tax reduction [19], growth maximization [14, 16], and bankruptcy avoidance [7, 10, 12, 13]. The bankruptcy avoidance motive is perhaps the most recently articulated of all merger motives, and perhaps the only one for which no systematic attempts at empirical validation have been forthcoming.

The purpose of this paper is to provide evidence on the following question: Are there more banking offices available per person to furnish consumer and business services in branch banking states than in unit banking states? This question is a central part of a broader issue of what limitations should be placed on the ability of individual banks to branch. Indeed, in a recent review of the literature dealing with the branching question, and prepared for the Senate Banking Committee (McIntyre Committee), Guttentag [8] stated: “One of the most pervasive arguments for branch banking is that branch banks provide more office facilities than unit banking.” Yet the available evidence on the question is sparse and existing research contains methodological difficulties which make the findings of questionable value.

A TTEMPTS to ascertain the productivity of university resources have typically relied on simple regression techniques; some measure of output is regressed on variables reflecting university services with controls for student characteristics. The results of these studies can generally be described as inconclusive with respect to the questions of whether or not university inputs matter. For example, Astin (1968) failed to find any strong relationship between university resources and educational value-added, as measured by student performance on the Graduate Record Examination field test, controlling for scores on the National Merit Scholarship Qualifying Test upon entering college. However, Perl (1971) found a strong, statistically significant relationship between university expenditures per pupil and the proportion of graduates attending graduate and professional schools. Such differences in conclusions can be attributed to many factors. Various authors employ different measures for attainment, university services and the control variables. Also, differences in the levels of aggregation (e.g., university versus departmental observations) are important in rationalizing varying results. Beyond these specific differences, a deficiency common to each study is that they all treat university service flows as exogenous. Failure to account for at least student choices as to courses, major fields of study, and college or university of attendance implies that existing models are misspecified.1 Prior research has generally assumed that university resources (R) and student characteristics (X) combine to produce learning (Q):