Nonprofit colleges and universities have merged across the United States citing economies of scale and scope. Yet whether these mergers raise prices has not been empirically assessed. Using a retrospective merger evaluation approach, I estimate that the average merger between 2000 and 2015 increased tuition and fees by 5% to 7% relative to nonmerging institutions in the same state and sector (public or nonprofit). Effects on net prices are estimated imprecisely, but the results are suggestive that nonprofit colleges use mergers to increase price discrimination.

N his well-known review of the human capital approach to the study of income distribution Jacob Mincer makes the point that . . a better understanding of the relation between and earnings requires an understanding of the factors determining investment (1970, p. 18). The importance of this point has been lost somewhat in the past few years with the plethora of empirical examinations of the relationship between years of schooling (as the measure of human capital investment) and earnings. Nevertheless, it is clear that if schooling is itself the result of optimizing rather than random behavior, the typical earnings regression will overstate the contribution of schooling to earnings. A simple model that explains interpersonal differences in investments in formal schooling has been developed by Becker (1967, 1975) with more formal extensions of this model having been presented by Ben-Porath (1967) and Wallace and Ihnen (1975). Basically, Becker's model views the individual as maximizing the present value of his net earnings over the life cycle by investing in formal schooling up to the point at which the marginal rate of return from the equals the marginal financing costs. This may, in turn, be expressed in a conventional demand-supply framework. The demand for schooling is the product of two factors: the expectation of returns from a particular. level of schooling achievement and the probability that the particular individual will in fact succeed in attaining this level.' The first factor is largely determined by the exogenous forces of the labor market where individual differences arise because of imperfect knowledge. The second is largely a function of individual'capacities (ability), the schooling environment, and the extent to which the individual believes the schooling environment and curriculum will actually lead to an increase in his stock of human capital. The supply side of the model reflects the opportunity for which is determined, in large part, by the availability of financing funds. For youths enrolled in high school the major economic decision they face is whether to continue with the formal educational process. The primary alternatives to schooling are full-time participation in the labor force, service in the Armed Forces, marriage and work within the household. The data indicate that these alternatives are chosen, in varying degrees to the extent that only 75% of a schooling cohort that entered the fifth grade in 1964 graduated from high school in 1972 (U.S. Department of HEW, 1974, p. 14). A sizable proportion of young adults, therefore, terminate their educational before the completion of high school. This decision to drop out of high school has seemed to arouse considerable public and private concern. While there is now some evidence and concern regarding an overinvestment in college training (Freeman, 1975), the fact that young people drop out of high school usually raises the spectre of increased crime, drug usage, unemployment and a general alienation of youth from the adult community. The public result of this. concern has been a variety of dropout prevention programs which have been supported under Title VIII of the Elementary and Secondary Education Act together with a provision' of the Vocational Education Act. which directs Federal monies to areas of high need, including local areas with a high concentration of school dropouts. A variety of instructional methods have been supported in the dropReceived for publication May 9, 1977. Revision accepted for publication February 15, 1978. * University of South Carolina. The research reported herewas supported by the Office of the Assistant Secretary for Planning and Evaluation, Department of Health, Education and Welfare. That support, together with the comments of Caroline Clotfelter, Susan Cochrane, Elchanan Cohn, Linda Edwards, Robert Hauser and two anonymous referees, is gratefully acknowledged. The computational assistance of Jane Lee was also of great help. The conclusions expressed here do not necessarily reflect the position of the sponsoring agency. I This useful distinction is taken from Griliches (1973).

can take all the Sj and try to minimize the variance of vj via factor analysis. We could then find S and also see which Sj was most closely correlated with S. Unfortunately this technique requires that the Sj does not have common measurement error. Since none of the Sj are completely independent of all others (some common source of data is used), common error can creep in. In principle, if one data sourcebut not another -gave answers unacceptable in terms of the a priori considerations dictated by economics, we could eliminate the series. In the present instance, the only possibility would be the significance of NTW1 in the Sggc equations but not in the SOBi}B forms. The differences in cyclical behavior between series are disturbing. But none of the responses violate all saving theories especially since the more recent theoretical innovations, such as permanent income and the ratchet effect define saving to include purchases net of depreciation. Finally the relative quality of the data could be judged by a detailed examination of the primary data sources and subsequent manipulations. This cannot be done now since the last time the SEC and the OBE published detailed descriptions of their sources and manipulations was more than a decade ago and those descriptions in [3, 5] are out of date. Besides, the number of primary data sources used is quite large and diverse. Only a group of individuals familiar with the separate parts could hope to do a competent study. The conclusion, thus, is quite pessimistic. For the saving function, one of the most basic elements of macro-economics, the dynamic and cyclical characterization depends upon our choice of measurement of a given concept and we do not know which measurement is correct.

T HE standard neoclassical investment analysis postulates a fixed timing relationship between changes in the determinants of the desired stock of capacity and actual investment responses these changes elicit.' There are, however, good reasons to suppose that no such fixed timing relationship exists. This paper seeks to explore one class of variables which could influence firms' decisions concerning the time shape of the investment response to a given change in the desired stock of capacity. The influence of financial conditions on firms' investment decisions has long been recognized.2 What we hope to show is that firms will react differently to a given change in the desired stock of capacity if they are faced with differing financial constraints. Financial variables will play a dual role in the analysis. First, as will be evident below, standard neoclassical investment analysis requires a discount rate to determine the optimal equipment-output ratio. We believe that this discount rate is only mildly responsive to changes in financial market conditions. The relevant discount rate, which includes a substantial risk premium, should exhibit less variance over time than the short term cost of funds.3 Reasons for this lie in the ex post fixity of factor proportions and long term nature of investment in equipment. Financial variables should, however, exert an important impact on the time path chosen for the investment response to a given change in desired capacity. We assume that the cost of funds to the firm depends on the firm's ability to utilize retained earnings (cash flow) as well as the coniditions prevailing in the bond and equity markets. Cost is interpreted in a broad sense and definitely includes the risks which management may believe non-internal financing entails.4 Given this view of firm behavior, we expect that the firm will invest more slowly or postpone investment if it expects that either (a) the costs of not having needed capacity in place are outweighed by the gains to be had from making more extensive use of internal sources of finance during a slow expansion, or (b) the costs of outside sources of finance (or the opportunity cost connected with the investment use of retained earnings) will be lower during subsequent periods to such an extent that it is worth the delay. Firms may hasten their investment response even if this involves additional costs in terms of internal disruption, etc., if these costs are overcome by the present abundance of internal funds or low current finance costs. Section I integrates the effect of financial conditions into the standard analysis to produce an estimable investment relationship. Section II describes the method of estimation. Section III presents results and conclusions.

In 1991, the U.S. Council of Economic Advisers undertook an initiative to increase the quality of economic statistics. One specific objective was to reduce the size of revisions in GNP estimates. The authors present evidence that one straightforward and inexpensive way of forwarding this objective is for the Department of Commerce to utilize better publicly available information released by other governmental agencies. An important caveat, however, applies: the relationship appears to be nonlinear. Specifically, the inefficient use of information is concentrated in those quarters where the change in the preliminary GNP estimate is large in absolute value.

In order to measure the effect of tax credits on private R&D investment, researchers confront the difficult problem of finding an exogenous measure of tax policy that exhibits sufficient variation to support robust identification. This paper takes a new approach based on exploiting differences in the average capital-labor ratio of R&D investment across industries and variation in the tax treatment of different expenditure types across countries and over time. The estimated short-run elasticity is 0.50 which is somewhat more than double previous estimates derived from cross-country analysis.