Preface

Higher education plays an important role in promoting economic development and generating personal incomes. While these links have been established in theoretical models (e.g., Glomm and Ravikumar, 1992; Barro, 1998; Restuccia and Urrutia, 2004; Blankenau, 2005; De La Croix and Michel, 2007) they also have solid empirical support (Bassanini and Scarpetta, 2002; Checchi, 2006).

Consistent with these scientific results, past decades have witnessed an enormous expansion of the higher education systems in all developed countries and in most developing countries (Barr and Crawford, 1998; Checchi, 2006). At the same time, advances in information technologies and in statistical and social sciences have significantly improved the reliability of techniques that can be used for screening large populations. These advances are important for higher education worldwide, because they affect many of the mechanisms that are commonly used for rationing the available supply of higher educational services. In most countries, institutions of higher education are overcrowded, admission is therefore restricted and normally based on some mechanism through which students are screened (or tested) for their abilities. In Germany, for instance, new laws recently allowed publicly funded universities to select students on the basis of their scores in specifically designed admission tests. Presumably, this reform will greatly improve the availability and reliability of screening information about students’ abilities.

These observations beg important questions. What are the likely economic consequences, if selection procedures in higher education depend to an increasing degree on better screening techniques? Will such development improve the allocation of scarce resources in the higher education sector, thus leading to higher growth and economic welfare? If so, will the positive growth effects necessarily come at the cost of higher income inequality? And which government policies are suited for controlling or counteracting possible detrimental side effects of these developments? Focusing on different scenarios, we illustrate in this book how the answers to these questions vary with the funding structures for investments in higher education as well as with the students’ attitudes toward risk and with the availability of arrangements for sharing individual talent risks.

For the most part of our study we think of investments in higher education as being private investments, so that the returns to improved skills accrue to the students themselves. Thus we view higher education mainly as an investment opportunity for the individuals. Typically, the returns on such investments vary with the unknown abilities of the individuals. For some agents, investing in higher education may not be a profitable strategy. In modern societies, therefore, students are screened (or tested) for their abilities. The screening process generates noisy information about the students’ abilities that can be used in the decision-making process. Rational individuals will base their investment decisions on estimates about their abilities. The precision of these estimates, which depends on the reliability of the screening mechanism, thus affects the allocation of educational investments and, consequently, the growth path of the economy as well.

Insofar as economic growth is driven by aggregate human capital accumulation, better screening may have ambiguous implications for the efficiency of the growth process. One channel through which screening affects economic growth originates from the individuals’ decisions whether or not to invest in higher education. For agents with low abilities, the net returns on educational investments are negative. Yet, despite their low abilities, some of these agents will receive favorable signals (test results) that induce them to invest. Better screening reduces these misdirected investments as signals become less noisy, thereby reflecting the individual abilities more accurately.

Another channel originates from the individuals’ decisions how to relate investment volumes to the favorability of signals. Normally, the marginal return on educational investment is higher for individuals with higher abilities or talents. Thus, a given stock of aggregate human capital can be generated with less aggregate investment, if agents with more favorable signals invest more than agents with less favorable signals. Yet, as it turns out, in a rational agent’s decision problem investment in education is not always positively related to the favorability of a received signal. Depending on individual attitudes toward risk and on the availability of risk-sharing arrangements, agents with more favorable signals may choose to invest less in higher education. In that case, the equilibrium alignment of individual investment volumes and signals is detrimental to economic growth. More reliable screening further worsens the alignment of signals and investments, thus reducing economic growth through less efficient aggregate human capital formation.

Similar ambiguities arise when one tries to link the reliability of screening in higher education to the inequality of the income distribution. Individuals with more favorable signals have higher income prospects. Therefore, if more favorable signals induce higher investments in education, the distribution of incomes will become more unequal under more reliable screening. As argued above, however, there also exist plausible constellations where higher individual investments correspond to less favorable signals which imply lower income prospects. These are constellations in which more reliable screening in higher education may, in fact, reduce the inequality of the equilibrium income distribution.

The varied and diverse economic implications of more reliable screening in higher education raise a question about the role of government policy in this process. The government may influence the incentives for educational investments through tax-financed subsidies, through loan guarantees, or through a system of publicly provided student loans. While such policies may mitigate some negative side effects resulting from more reliable screening techniques, they also have the potential of generating new distortions in the higher education sector. For instance, tax-financed subsidies and loan guarantees may trigger investments in education that have negative net returns. And a system of publicly provided student loans may generate negative externalities if it coexists with competitive credit markets.

While subsequent chapters look at these questions from different perspectives, our analysis essentially uses a common theoretical framework throughout the book. This framework is laid out in detail in Chapter 4 and will be referred to in later chapters.

Chapter 1 presents basic concepts related to uncertainty and screening information. We define some notions of “informativeness” and discuss general conditions under which more reliable screening information is desirable. Chapter 2 studies the value of information in general equilibrium models. We show that more reliable information may be harmful, and we illustrate why the role of information tends to be less favorable in exchange economies than in production economies. Chapter 3 lays out some stylized facts regarding higher education and economic performance, and it relates the empirical evidence to the main research questions of the book.

In Chapter 4, we construct a dynamic model of production, screening, educational investment, and human capital accumulation. Physical capital is internationally mobile while human capital (labor) is immobile. All young individuals are tested (screened) for their unknown abilities. Educational investments are chosen after the individuals have learned their test results and have updated their beliefs accordingly. This economic set up serves as a benchmark model for the remainder of the book. As it turns out, more reliable screening does not necessarily lead to higher growth or higher welfare. Instead, the growth and welfare effects are determined by the interplay between the agents’ risk aversion and the available risk-sharing tools.

Markets for higher education financing tend to be imperfect, mainly because young individuals cannot provide sufficient collateral that would allow them to borrow against their future incomes. In addition, and further complicating loan arrangements, the beginning of repayment often lags behind the origination of a student loan by a long period of time. Chapter 5 explores the consequences of alternative forms of education financing that remove financial barriers for individuals, thereby allowing them to participate in the higher education system. Of special interest are loan programs with income contingent repayments that may accomplish some diversification of individual income risks. Friedman (1955, 1962) argues that such diversification is of prime importance, because it ensures that individuals can finance their educational investments on favorable terms and prevents an economy-wide underinvestment in education.

In the first part of Chapter 5, we compare three alternative funding schemes for higher education. These schemes differ with regard to the extent to which individual income risks are pooled and diversified. We find that the intermediate funding scheme with some, but restricted, risk sharing is first choice for financing higher education. In particular, this scheme is most efficient in terms of transforming educational investment into...