“Free market” is a summary term for an array of exchanges that take place in society. Each exchange is undertaken as a voluntary agreement between two people or between groups of people represented by agents. These two individuals (or agents) exchange two economic goods, either tangible commodities or nontangible services. Thus, when I buy a newspaper from a newsdealer for fifty cents, the newsdealer and I exchange two commodities: I give up fifty cents, and the newsdealer gives up the newspaper. Or if I work for a corporation, I exchange my labor services, in a mutually agreed way, for a monetary salary; here the corporation is represented by a manager (an agent) with the authority to hire. Both parties undertake the exchange because each expects to gain from it. Also, each will repeat the exchange next time (or refuse to) because his expectation has proved correct (or incorrect) in the recent past. Trade, or exchange, is engaged in precisely because both parties benefit; if they did not expect to gain, they would not agree to the exchange. This simple reasoning refutes the argument against free trade typical of the “mercantilist” period of sixteenth- to eighteenth-century Europe and classically expounded by the famed sixteenth-century French essayist Montaigne. The mercantilists argued that in any trade, one party can benefit only at the expense of the other—that in every transaction there is a winner and a loser, an “exploiter” and an “exploited.” We can immediately see the fallacy in this still-popular viewpoint: the willingness and even eagerness to trade means that both parties benefit. In modern game-theory jargon, trade is a win-win situation, a “positive-sum” rather than a “zero-sum” or “negative-sum” game. How can both parties benefit from an exchange? Each one values the two goods or services differently, and these differences set the scene for an exchange. I, for example, am walking along with money in my pocket but no newspaper; the newsdealer, on the other hand, has plenty of newspapers but is anxious to acquire money. And so, finding each other, we strike a deal. Two factors determine the terms of any agreement: how much each participant values each good in question, and each participant’s bargaining skills. How many cents will exchange for one newspaper, or how many Mickey Mantle baseball cards will swap for a Babe Ruth, depends on all the participants in the newspaper market or the baseball card market—on how much each one values the cards as compared with the other goods he could buy. These terms of exchange, called “prices” (of newspapers in terms of money, or of Babe Ruth cards in terms of Mickey Mantles), are ultimately determined by how many newspapers, or baseball cards, are available on the market in relation to how favorably buyers evaluate these goods—in shorthand, by the interaction of their supply with the demand for them. Given the supply of a good, an increase in its value in the minds of the buyers will raise the demand for the good, more money will be bid for it, and its price will rise. The reverse occurs if the value, and therefore the demand, for the good falls. On the other hand, given the buyers’ evaluation, or demand, for a good, if the supply increases, each unit of supply—each baseball card or loaf of bread—will fall in value, and therefore the price of the good will fall. The reverse occurs if the supply of the good decreases. The market, then, is not simply an array; it is a highly complex, interacting latticework of exchanges. In primitive societies, exchanges are all barter or direct exchange. Two people trade two directly useful goods, such as horses for cows or Mickey Mantles for Babe Ruths. But as a society develops, a step-by-step process of mutual benefit creates a situation in which one or two broadly useful and valuable commodities are chosen on the market as a medium of indirect exchange. This money-commodity, generally but not always gold or silver, is then demanded not only for its own sake, but even more to facilitate a reexchange for another desired commodity. It is much easier to pay steelworkers not in steel bars but in money, with which the workers can then buy whatever they desire. They are willing to accept money because they know from experience and insight that everyone else in the society will also accept that money in payment. The modern, almost infinite latticework of exchanges, the market, is made possible by the use of money. Each person engages in specialization, or a division of labor, producing what he or she is best at. Production begins

The foreign exchange market is the market in which foreign currency—such as the yen or euro or pound—is traded for domestic currency—for example, the U.S. dollar. This “market” is not in a centralized location; instead, it is a decentralized network that is nevertheless highly integrated via modern information and telecommunications technology. According to a triennial survey, the average daily global turnover (i.e., amount exchanged) in traditional foreign exchange markets reached $1.9 trillion in April 2004.1 In addition, there was $1.2 trillion of trading in derivatives such as forwards and options (see futures and options markets). In the spot market, parties contract for delivery of the foreign exchange immediately. In the forward market, they contract for delivery at some point, such as three months, in the future. In the option market, they enter a contract that allows one party to buy or sell foreign exchange in the future, but does not require it (thus the word “option”). Most of the trading is among banks, either on behalf of customers or on their own account. The counterparty to the transaction could be another dealer, another financial institution, or a nonfinancial customer. The survey reported that 89 percent of the trading involved the dollar on one side of the transaction or the other. (That the dollar is used as a “vehicle currency” explains why its trading volume is so high: someone wanting to go from the Malaysian ringgit to the South African rand passes through the dollar on the way.) Next, 37 percent of foreign exchange transactions involved the euro, 20 percent the yen, 17 percent the British pound, 6 percent the Swiss franc, 5 percent the Australian dollar, and 4 percent the Canadian dollar. London is the world’s largest center for trading foreign exchange, with 31 percent of the global total turnover. Next comes New York at 19 percent, Tokyo at 8 percent, and Singapore and Frankfurt at 5 percent each. The exchange rate is the price of foreign currency. For example, the exchange rate between the British pound and the U.S. dollar is usually stated in dollars per pound sterling ($/£); an increase in this exchange rate from, say, $1.80 to say, $1.83, is a depreciation of the dollar. The exchange rate between the Japanese yen and the U.S. dollar is usually stated in yen per dollar (¥/$); an increase in this exchange rate from, say, ¥108 to ¥110 is an appreciation of the dollar. Some countries “float” their exchange rate, which means that the central bank (the country’s monetary authority) does not buy or sell foreign exchange, and the price is instead determined in the private marketplace. Like other market prices, the exchange rate is determined by supply and demand—in this case, supply of and demand for foreign exchange. Some countries’ governments, instead of floating, “fix” their exchange rate, at least for periods of time, which means that the government’s central bank is an active trader in the foreign exchange market. To do so, the central bank buys or sells foreign currency, depending on which is necessary to peg the currency at a fixed exchange rate with the chosen foreign currency. An increase in foreign exchange reserves will add to the money supply, which could lead to inflation if it is not offset by the monetary authorities via what are called “sterilization” operations. Sterilization by the central bank means responding to increases in reserves so as to leave the total money supply unchanged. A common way to accomplish it is by selling bonds on the open market; a less common way is to increase the reserve requirements placed on commercial banks. Still other countries follow some regime intermediate between pure fixing and pure floating (examples include bands or target zones, basket pegs, crawling pegs, and adjustable pegs). Many central banks practice “managed floating,” whereby they intervene in the foreign exchange market by “leaning against the wind.” To do so, a central bank sells foreign exchange when the exchange rate is going up, thereby dampening its rise, and buys when it is going down. The motive is to reduce the variability in the exchange rate. Private speculators may do the same thing:

Foreign aid as a form of capital flow is novel in both its magnitude and its global coverage. Though historical examples of countries paying “bribes” (see below) or “reparations” to others are numerous, the continuing large-scale transfer of capital from rich-country governments to those of poor countries is a post–World War II phenomenon. The origins of these transfers lie in the breakdown of the international capital market in the period between the two world wars and in the rivalry for political allies during the cold war. The breakdown of the international capital market provided the impetus for the creation of the World Bank at Bretton Woods. Its purpose was to provide loans at market interest rates to poor countries that were shut out of Western capital markets—especially the largest, the United States—because of their widespread defaults in the 1930s and the imposition of the U.S. government’s “blue sky” laws, which forbade U.S. financial intermediaries to hold foreign government bonds. Meanwhile, European markets were closed through exchange controls; the United Kingdom, for example, had its exchange controls until 1979. Official loans to poor countries at commercial interest rates, as laid down in the charter of the World Bank’s parent, the International Bank for Reconstruction and Development, would have been justified purely on efficiency grounds to intermediate the transfer of capital from where it was less scarce to where it was scarcer. This purely economic case was buttressed by political and, later, humanitarian justifications for concessional official flows, that is, loans with softer—that is, concessional—terms on interest and repayment. As to the political reasons for giving aid, little can be added to Lord Bauer’s devastating critique (Bauer 1976) that, instead of fostering Western political interests, foreign aid abetted the formation of anti-Western coalitions of Third World states seeking “bribes” not to go communist. A statistical study concluded that “as an instrument of political leverage, economic aid has been unsuccessful” (Mosley 1987, p. 232). The end of the cold war has removed this political motive. Currently, advocates of foreign aid emphasize the humanitarian and economic cases, though each rationale has seen many metamorphoses. The humanitarian case for concessional flows was based on an analogy with the Western welfare state. The idea was that just as many people favor welfare to transfer wealth from the relatively rich to the relatively poor within a country, so they favor welfare to transfer wealth from relatively rich countries to relatively poor ones. But many commentators not necessarily hostile to foreign aid—I. M. D. Little and J. Clifford, for example (Little and Clifford 1965)—emphasized that the humanitarian motives for giving aid may have justified transferring Western taxpayers’ money to poor people, but not to poor governments: the latter may have no effect on the former. With the likes of Marcos of the Philippines, Bokassa of the Central African Republic, Abacha of Nigeria, and a host of other kleptocratic “tropical gangsters” in power (Klitgard 1990), the money may simply be stolen. According to William Easterly, despite over $2 billion in foreign aid given to Tanzania’s government for roads, the roads did not improve. What increased was the bureaucracy, with the Tanzanian government producing twenty-four hundred reports a year for the one thousand donor missions that visited each year.1 Nor can the poor of the world claim a moral right to welfare transfers from the rich. While recipients of domestic welfare payments depend on the existence of a national society with some commonly accepted moral standard, there is no similar international society within which a right to aid can be established (Lal 1978, 1983). The vast majority of foreign aid has failed to alleviate poverty. It has improved the lot of poor people in a few cases. The people of Martinique, for example, are probably better off because the French government provides a very high percentage of their gross domestic product. Also, foreign aid helped wipe out river blindness in West Africa, keeping eighteen million children safe from infection.2 But a statistical study found that foreign aid “appears to redistribute from the reasonably well-off in the West to most income groups in the Third World except the very poorest” (Mosley 1987, p. 23). This is consistent with the evidence from both poor and rich countries that the middle classes tend to capture government transfers (see redistribution). By contrast, private transfers through either traditional interfamily channels or private charities (nongovernmental organizations, or NGOs) are more efficient in targeting these transfers to the poor, as well as in delivering health care and education (Lal and Myint 1996). The centralized bureaucracies of the Western aid agencies are particularly inept in targeting these transfers to the truly needy because they lack local knowledge. Moreover, there is evidence that these inefficient public transfers tend to crowd out more efficient private transfers (Lal and Myint 1996). Not surprisingly, therefore, despite their claim that their mission is to alleviate Third World poverty, official aid agencies are increasingly subcontracting this role to the NGOs. Whether this official embrace of the NGOs is in the NGOs’ long-term interest is arguable (Lal 1996). The political and humanitarian justifications for foreign aid are in tatters. What of the purely economic case? One such case was the “two-gap theory,” the idea that foreign aid was required to fill one of two shortfalls—in foreign exchange or savings—that depressed the growth rates of developing countries below some acceptable limit (Lal 1972). The alleged “foreign exchange” gap was based on dubious assumptions. One such assumption was “export pessimism,” the idea that poor countries would not generate many exports. Many development economists held this view despite a paucity of evidence for it (Lal 2002). Because both experience and theory have shown the irrelevance of this assumption, the “foreign exchange gap” justification for foreign aid has lost all force. Nor has the “savings gap” justification proved to be any more cogent. Contrary to the theory that foreign capital is necessary to supplement fixed and inadequate domestic savings, the savings performance of developing countries in the post–World War II period shows that nearly all of them (including those in Africa until the early 1970s) have steadily raised domestic savings rates since the 1950s (Lluch 1986). Moreover, a study of twenty-one developing countries between 1950 and 1985 confirms the commonsense expectation that differences in economic growth rates are related more to differences in the productivity of investment than to differences in investment levels (Lal and Myint 1996). Finally, statistical studies of the effects of foreign aid on growth and poverty alleviation have not been favorable (Easterly 2001). One found that, after correcting for the link between aid and income levels and growth, the effect of aid on growth is often negative (Boone 1994) (see Figure 1). A survey of other such studies concludes that “there is now widespread skepticism that concessional assistance does have positive effects on growth, poverty reduction or environmental quality” (Gilbert et al. 1999, p. F607). Figure 1 Foreign Aid and Growth Across Countries, 1960-2002 ZOOM  

For more than two centuries economists have steadfastly promoted free trade among nations as the best trade policy. Despite this intellectual barrage, many “practical” men and women continue to view the case for free trade skeptically, as an abstract argument made by ivory tower economists with, at most, one foot on terra firma. These practical people “know” that our vital industries must be protected from foreign competition. The divergence between economists’ beliefs and those of (even well-educated) men and women on the street seems to arise in making the leap from individuals to nations. In running our personal affairs, virtually all of us exploit the advantages of free trade and comparative advantage without thinking twice. For example, many of us have our shirts laundered at professional cleaners rather than wash and iron them ourselves. Anyone who advised us to “protect” ourselves from the “unfair competition” of low-paid laundry workers by doing our own wash would be thought looney. Common sense tells us to make use of companies that specialize in such work, paying them with money we earn doing something we do better. We understand intuitively that cutting ourselves off from specialists can only lower our standard of living. Adam Smith’s insight was that precisely the same logic applies to nations. Here is how he put it in 1776: It is the maxim of every prudent master of a family, never to attempt to make at home what it will cost him more to make than to buy.. . . If a foreign country can supply us with a commodity cheaper than we ourselves can make it, better buy it of them with some part of the produce of our own industry, employed in a way in which we have some advantage. Spain, South Korea, and a variety of other countries manufacture shoes more cheaply than America can. They offer them for sale to us. Shall we buy them, as we buy the services of laundry workers, with money we earn doing things we do well—like writing computer software and growing wheat? Or shall we keep “cheap foreign shoes” out and purchase more expensive American shoes instead? It is pretty clear that the nation as a whole must be worse off if foreign shoes are kept out—even though the American shoe industry will be better off. Most people accept this argument. But they worry about what happens if another country—say, China—can make everything, or almost everything, cheaper than we can.

Free-market environmentalism emphasizes markets as a solution to environmental problems. Proponents argue that free markets can be more successful than government—and have been more successful historically—in solving many environmental problems. This interest in free-market environmentalism is somewhat ironic because environmental problems have often been seen as a form of market failure (see public goods and externalities). In the traditional view, many environmental problems are caused by decision makers who reduce their costs by polluting those who are downwind or downstream; other environmental problems are caused by private decision makers’ inability to produce “public goods” (such as preservation of wild species) because no one has to pay to get the benefits of this preservation. While these problems can be quite real, growing evidence indicates that governments often fail to control pollution or to provide public goods at reasonable cost. Furthermore, the private sector is often more responsive than government to environmental demands. This evidence, which is supported by much economic theory, has led to a reconsideration of the traditional view. The failures of centralized government control in Eastern Europe and the Soviet Union awakened further interest in free-market environmentalism in the early 1990s. As glasnost lifted the veil of secrecy, press reports identified large areas where brown haze hung in the air, people’s eyes routinely burned from chemical fumes, and drivers had to use headlights in the middle of the day. In 1990 the Wall Street Journal quoted a claim by Hungarian doctors that 10 percent of the deaths in Hungary might be directly related to pollution. The New York Times reported that parts of the town of Merseburg, East Germany, were “permanently covered by a white chemical dust, and a sour smell fills people’s nostrils.” For markets to work in the environmental field, as in any other, rights to each important resource must be clearly defined, easily defended against invasion, and divestible (transferable) by owners on terms agreeable to buyer and seller. Well-functioning markets, in short, require “3-D” property rights. When the first two are present—clear definition and easy defense of one’s rights—no one is forced to accept pollution beyond the standard acceptable to the community. Local standards differ because people with similar preferences and those seeking similar opportunities often cluster together. Parts of Montana, for example, where the key economic activity is ranching, are “range country.” In those areas, anyone who does not want the neighbors’ cattle disturbing his or her garden has the duty to fence the garden to keep the cattle out. On the really large ranches of range country, that solution is far cheaper than fencing all the range on the ranch. But much of the state is not range country. There, the property right standards are different: It is the duty of the cattle owner to keep livestock fenced in. People in the two areas have different priorities based on goals that differ between the

An econometric model is one of the tools economists use to forecast future developments in the economy. In the simplest terms, econometricians measure past relationships among such variables as consumer spending, household income, tax rates, interest rates, employment, and the like, and then try to forecast how changes in some variables will affect the future course of others. Before econometricians can make such calculations, they generally begin with an economic model, a theory of how different factors in the economy interact with one another. For instance, think of the economy as comprising households and business firms, as depicted in Figure 1. Households supply business firms with labor services (as tailors, accountants, engineers, etc.) and receive wages and salaries from the business firms in exchange for their labor. Using the labor services, businesses produce various outputs (clothing, cars, etc.) that are available for purchase. Households, using the earnings derived from their labor services, become the customers who purchase the output. The products the businesses produce wind up in the households, and the wage and salary payments return to the businesses in exchange for the products the households purchase. This chain of events, as shown by the activities numbered 1–5 in Figure 1, is a description—or diagrammatic model—of the operation of a private-enterprise economy. It is obviously incomplete. There is no central bank supplying money, no banking system, and no government levying taxes, building roads, or providing education or national defense. But the essentials of the economy’s private sector—working, producing, and buying products and services—are represented in a useful way in Figure 1. Figure 1. ZOOM   The diagrammatic model of Figure 1 has certain disadvantages when it comes to representing quantities such as the value of the wage and salary payments or the number of cars produced. To represent magnitudes more conveniently, economists employ a mathematical model, a set of equations that describe various relationships between variables. Consider household purchases of output, shown as activity 4 in Figure 1. If W is the value of the wages and salaries households earn, and C is household expenditures on clothing, then the equation C = .12W states that households spend 12 percent of their wages and salaries on clothing. An equation could also be constructed to represent household purchases of cars or any other goods and services. Indeed, each of the activities pictured in Figure 1 can be represented in the form of an equation. Doing so may take a blend of economic theory, basic economic facts about the particular economy, and mathematical sophistication; but once done, the result would be a mathematical or quantitative economic model, which is but one important step away from an econometric model. In the equation for clothing purchases, C = .12W, “12 percent” was selected purely for illustrative purposes. But if the model is to say anything useful about today’s American economy, it must contain numbers (econometricians and others applying similar statistical methods refer to such numbers as “parameters”) that describe what actually goes on in the real world. For this purpose, we must turn to the relevant historical data to find out what percentage of household income Americans do, in fact, typically spend on clothing. The column headed “Total” in Table 1 shows the percentage of (after-tax) income Americans spent on clothing (including shoes) for each of the years 1995–2002. One fact is immediately obvious: 12 percent was way off. If it had been left in the model, it would have led to a substantial overestimate of clothing purchases and would have been useless to understand or predict behavior in the American economy. Something closer to 4.21 percent, the average of the annual values in the “Total” column, would more accurately reflect total annual spending on clothing and shoes as a percentage of household income in the United States. Table 1 Spending on Clothing and Shoes, 1995-2002 % of Household Income Year Total Total – 100 1995 4.5 2.6 1996 4.4 2.6 1997 4.3 2.6 1998 4.2 2.7 1999 4.3 2.8 2000 4.1 2.7 2001 4.0 2.6 2002 3.9 2.6 Average 4.21 2.65 A more careful look at the facts, however, reveals that 4.21 percent may not adequately represent the actual behavior. There has been substantial annual variation—from as much 4.5 percent to as little as 3.9 percent—in household income spent on clothing and shoes. What is more, there appears to be a downward trend, with the larger percentages coming in the mid-1990s and the smaller percentages coming more recently. The following simple statistical procedure takes care of these objections. Start with total annual spending on clothing and shoes, subtract $100 billion, and then calculate the balance—annual spending on clothing and shoes beyond the first $100 billion—as a percentage of household income. The column headed “Total − 100” in Table 1 shows the result—a very satisfactory result, with little annual variation around the average of 2.65 percent and no apparent trend over time. You might well wonder what this subtraction of $100 billion represents. Here is a useful way to think about it. The U.S. population averaged 277.6 million persons during 1995–2002. Therefore, the value $100 billion represents, in round numbers, $360 per person ($100 billion divided by 277.6 million persons). The facts in Table 1 suggest that an expenditure on clothing and shoes averaging about $360 per person per year is a base, or minimally acceptable, amount in the United States these days. Once that minimum is accounted for, additional purchases of clothing and shoes will amount to 2.65 percent of household income. In other words, Americans spend more on clothing and shoes the higher their household income, but they spend at least $100 billion per year. And the best forecast of the total that will be spent is: $100 billion plus an additional 2.65 percent of household income. In equation form, this is represented by C = 100 + .0265W, a far cry from the C = .12W we began with. The fact that the parameter values 100 and .0265 in the clothing equation were determined by using the relevant data is what gives us reason to believe that the equation says something meaningful about the economy. Using the data to determine or estimate all the parameter values in the model is the critical step that turns the mathematical economic model into an econometric model. An econometric model is said to be complete if it contains just enough equations to predict values for all of the variables in the model. The equation C = 100 + .0265W, for example, predicts C if the value of W is known. Thus, there must be an equation somewhere in the model that determines W. If all such logical connections have been made, the model is complete and can, in principle, be used to forecast the economy or to test theories about its behavior. Actually, no econometric model is ever truly complete. All models contain variables the model cannot predict because they are determined by forces “outside” the model. For example, a realistic model must include personal income taxes collected by the government because taxes are the wedge between the gross income earned by households and the net income (what economists call disposable income) available for households to spend. The taxes collected depend on the tax rates in the income tax laws. But the tax rates are determined by the government as a part of its fiscal policy and are not explained by the model. If the model is to be used to forecast economic activity several years into the future, the econometrician must include anticipated future tax rates in the model’s information base. That requires an assumption about whether the government will change future income tax rates and, if so, when and by how much. Similarly, the model requires an assumption about the monetary policy that the central bank (the Federal Reserve System in the United States) will pursue, as well as assumptions about a host of other such “outside of the model” (or exogenous) variables in order to forecast all the “inside of the model” (or endogenous) variables. The need for the econometrician to use the best available economic judgment about “outside” factors is inherent in economic forecasting. An econometrically based economic forecast can thus be wrong for two reasons: (1) incorrect assumptions about the “outside” or exogenous variables, which are called input errors; or (2) econometric equations that are only approximations to the truth (note that clothing purchases beyond the minimum do not amount to exactly 2.65 percent of household income every year). Deviations from the predictions of these equations are called model errors. Most econometric forecasters believe that economic judgment can and should be used not only to determine values for exogenous variables (an obvious requirement), but also to reduce the likely size of model error. Taken literally, the equation C = 100 + .0265W means that “any deviation of clothing purchases from 100 plus 2.65 percent of household income must be considered a random aberration from normal or expected behavior”—one of those inherently unpredictable vagaries of human behavior that continually trip up pollsters, economists, and others who attempt to forecast socioeconomic events. The economic forecaster must be prepared to be wrong because of unpredictable model error. But is all model error really unpredictable? Suppose the forecaster reads reports that indicate unusually favorable consumer reaction to the latest styles in clothing. Suppose, on this basis, the forecaster believes that next year’s clothing purchases are likely to exceed the usual minimum by something closer to 3 percent than to the usual 2.65 percent of household income. Should the forecaster ignore this well-founded belief that clothing sales are about to “take off,” and thereby produce a forecast that is actually expected to be wrong? The answer depends on the purpose of the forecast. If the purpose is the purely scientific one of determining how accurately a well-constructed model can forecast, the answer must be: Ignore the outside information and leave the model alone. If the purpose is the more pragmatic one of using the best available information to produce the most informative forecast, the answer must be: Incorporate the outside information into the model, even if that means effectively “erasing” the parameter value .0265 and replacing it with .0300 while generating next year’s forecast. Imposing such “constant adjustments” on forecasts was at one time disparaged as entirely unscientific. These days, many researchers regard such behavior as inevitable in the social science of economic forecasting and have begun to study how best—from a scientific perspective—to incorporate such outside information. Much of the motivation behind trying to specify the most accurately descriptive economic model, trying to determine parameter values that most closely represent economic behavior, and combining these with the best available outside information arises from the desire to produce accurate forecasts. Unfortunately, an economic forecast’s accuracy is not easy to judge; there are simply too many dimensions of detail and interest. One user of the forecast may care mostly about the gross domestic product (GDP), another mostly about exports and imports, and another mostly about inflation and interest rates. Thus, the same forecast may provide very useful information to some users while being misleading to others. For want of anything obviously superior, the most common gauge of the quality of a macroeconomic forecast is how accurately it predicts real GDP growth. Real GDP is the most inclusive summary measure of all the finished goods and services being produced within the geographic boundaries of the nation. For many purposes, there is much value in knowing with some lead time whether to expect real GDP to be increasing at a rapid rate (a booming economy with a growth rate above 4 percent), to be slowing down or speeding up relative to recent behavior, or to be slumping (a weak economy with a growth rate below 1 percent or even a recessionary economy with a negative growth rate). The information contained in Figure 2 can be used to judge, in the summary fashion just indicated, the econometric forecasting accuracy achieved by the Research Seminar in Quantitative Economics (RSQE) of the University of Michigan over the past three-plus decades. The RSQE forecasting project, dating back to the 1950s, is one of the oldest in the United States. Figure 2 compares, for each of the years 1971–2003, the actual percentage change in real GDP (the economy’s growth rate) with the RSQE forecast published in November of the preceding year. There are several ways to characterize the quality of the RSQE forecasting record. Although the forecasts missed the actual percentage change by an average of only 1.1 percentage points (measured by the average forecast error without regard to sign), the forecast error was as small as 0.5 percentage point or less in thirteen of the thirty-three years shown. On the other hand, six years had forecast errors of 2 percentage points or more, and for 1982 and 1999, the forecast errors were 3.1 and 3.0 percentage points, respectively. But, despite some relatively large errors, there was never a boom year that RSQE forecast to be a weak year; never a weak year that RSQE forecast to be a boom year; and just a few instances—most recently, 1999 and 2001—in which the forecast really went “the wrong way” in the sense of missing badly on whether the economy’s growth rate was about to increase or decrease relative to the preceding year’s growth rate. The discussion, so far, has focused on what is referred to as a structural econometric model. That is, the econometrician uses a blend of economic theory, mathematics, and information about the structure of the economy to construct a quantitative economic model. The econometrician then turns to the observed data—the facts—to estimate the unknown parameter values and turn the economic model into a structural econometric model. The term “structural” refers to the fact that the model gets its structure, or specification, from the economic theory that the econometrician starts with. The idea, for example, that spending on clothing and shoes is determined by household income comes from the core of economic theory. Economic theories are both complex and incomplete. To illustrate: • Does this year’s spending on clothing depend only on this year’s income or also on the pattern of income in recent years? • How many years is “recent”? • Don’t other variables, such as the price of clothing relative to other consumer goods, matter as well? This situation makes it far more difficult than implied to this point to specify the economic model one must begin with to wind up with a structural econometric model for use in forecasting. In recent years, econometricians have found that it is possible to do economic forecasting using a simpler, nonstructural, procedure without losing much forecast accuracy. Although the simpler procedure has significant costs, these costs do not show up in the normal course of forecasting. This will be explained after a quick introduction to the alternative procedure known as “time-series forecasting.” The idea of time-series forecasting is easily explained with the aid of Figure 3, which shows year-by-year changes in spending on clothing and shoes starting in 1981 and Figure 2 RSQE Forecast Accuracy: Real GDP Growth, 1971-2003 (Actual vs. RSQE Forecast from the Preceding November) ZOOM   Figure 3 Spending on Clothing and Shoes, Year-to-Year Changes, 1981-2002 ZOOM   going through 2002. The horizontal line marks the average annual change of $8.8 billion. Most of the year-to-year changes are in the range of $4.4–$13.2 billion, and only one change, that of 2001, is well outside that range. The year-to-year changes, in other words, appear to be stable. Some are above $8.8 billion and some are below; 1983–1988 exhibited a string of changes that were all close to $11 billion, but that was unusual. More often, one year’s change is little guide to the next year’s change, as the changes jump around too much. So, a forecasting rule that says next year’s spending on clothing and shoes will be $8.8 billion more than this year’s spending makes good sense. And that, for this simple case, is the essence of time-series forecasting. Look carefully at the historical behavior of the variable of interest, and if that behavior is characterized by some kind of stability, come up with a quantitative description of that stability and use it to construct the forecast. It is not always easy to “see” the stability that can be counted on to provide a reliable forecast, and econometricians have developed sophisticated procedures to tease out the stability and measure it. In general, the time-series procedure and the structural model procedure seem to produce comparably good, or bad, forecasts for a year or two into the future. But the time-series procedure has the distinct advantage of being far simpler. We can forecast spending on clothing and shoes without having to worry about the theoretical relationship between spending and household income. It need not be specified and its parameters need not be estimated; just focus on the clothing variable itself. So, where are the significant costs in using the time-series forecasting procedure? They come from the fact that the procedure gives a numerical answer and nothing else. If the user of the forecast—for example, a clothing manufacturer—asks why the forecast says what it does, the time-series econometrician can answer only, “Because that’s the way spending on clothing has behaved in the past,” not, “Because household income is going to rise sharply in response to an expansionary monetary policy which is being conducted in order to . . .” In short, there is no economics in the analysis in the first place. If there were, the user would be able to respond, “That makes sense; I’ll plan on the basis of the forecast”; or, alternatively, “I think that forecast is too good to be true because I’m convinced that expansionary monetary policy is about to be reversed, and so I’m shaving the forecast in my planning.” Time-series forecasting leaves the user “hanging”: just take it or leave it. Because many forecasters work with structural models, users can acquire not only the various numerical forecasts, but also the economic analysis that accompanies and justifies, or explains, each forecast. A user who has to act on the basis of a forecast and can choose among the alternative forecasts available is surely getting much more information when those forecasts have a structural economic basis. Finally, and related to the preceding discussion, structural models are the “only game in town” when it comes to the important area of econometric policy analysis or other “what if” calculations. Thus, a baseline forecast may be calculated using a structural econometric model and the best information available to the forecaster. And then someone asks, “What if Congress raises the income tax rate by five percentage points?” This single perturbation is then imposed on the original calculation, and the forecast is recalculated to show the model’s evaluation of the effect on the economy of the posited change in government fiscal policy. Economists commonly employ such calculations in the process of providing advice to businesses and units of government. The practical validity of such applications depends on how well the model’s structure represents the economic behavior that is central to the “what if” question being asked. All models are merely approximations to reality; the issue is whether a given model’s approximation is good enough for the question at hand. Thus, making structural models more accurate is a task of major importance. As long as model users ask “what if,” structural econometric models will continue to be used and useful. About the Author Saul H. Hymans is an emeritus professor of economics and statistics and director of the Research Seminar in Quantitative Economics at the University of Michigan. Further Reading   Howrey, E. Philip, Saul H. Hymans, and Michael R. Donihue. “Merging Monthly and Quarterly Forecasts: Experience with MQEM.” Journal of Forecasting 10 (May 1991): 255–268. Hymans, Saul H., Joan P. Crary, and Janet C. Wolfe. “The U.S. Economic Outlook for 2004–2005.” In The Economic Outlook for 2004, Proceedings of the Fifty-first Annual Conference on the Economic Outlook, Ann Arbor, Mich., 2004. Pp. 1–84. Kennedy, Peter. A Guide to Econometrics. 5th ed. Cambridge: MIT Press, 2003. Especially chaps. 18 and 19. Klein, Lawrence R., ed. Comparative Performance of U.S. Econometric Models. Oxford: Oxford University Press, 1991. Especially chaps. 1, 3, 10, 11, and 12. Klein, Lawrence R., and Richard M. Young. An Introduction to Econometric Forecasting and Forecasting Models. Lexington, Mass.: Lexington Books, 1980.   (0 COMMENTS)

Financial regulation in the United States, and elsewhere in the developed world, breaks down into two basic categories: safety-and-soundness regulation and compliance. While this entry focuses on U.S. financial services regulation, it broadly reflects what occurs elsewhere. Financial institutions serve various purposes. Depository institutions (banks, savings and loans [S&Ls], and credit unions) transform liquid liabilities (checking accounts, savings accounts, and certificates of deposit that can be cashed in prior to maturity) into relatively illiquid assets, such as home mortgages, car loans, loans to finance

The population of wealthy countries is getting much older. Between 2005 and 2035, the number of elderly in wealthy countries will more than double, but the number of workers will barely change. This historically unprecedented demographic change portends enormous fiscal stresses because of the high and growing cost of meeting government pension and health-care commitments to the elderly. Indeed, these projected payments are so high that collecting them may not be feasible, either economically or politically. The costs associated with the coming generational storm will bankrupt the governments of most wealthy countries unless major and painful adjustments are made now. “Bankrupt” is a strong word, but entirely appropriate in this context. When countries’ governments go bankrupt, they stop paying what they owe. They may default explicitly by reneging on principal and interest payments on their debt. Or they may fail to pay promised benefits and meet other spending commitments; some people regard this as default. A particularly popular way of implicitly defaulting on spending obligations and on debt is to use inflation to do the dirty work. The government simply prints the money it needs to “meet” its spending obligations. The increase in the money supply generates inflation, which waters down the real value of the government’s spending and reduces the real value of its debt. Although bankruptcies of national governments are not

Fiscal policy is the use of government spending and taxation to influence the economy. When the government decides on the goods and services it purchases, the transfer payments it distributes, or the taxes it collects, it is engaging in fiscal policy. The primary economic impact of any change in the government budget is felt by particular groups—a tax cut for families with children, for example, raises their disposable income. Discussions of fiscal policy, however, generally focus on the effect of changes in the government budget on the overall economy. Although changes in taxes or spending that are “revenue neutral” may be construed as fiscal policy—and may affect the aggregate level of output by changing the incentives that firms or individuals face—the term “fiscal policy” is usually used to describe the effect on the aggregate economy of the overall levels of spending and taxation, and more particularly, the gap between them. Fiscal policy is said to be tight or contractionary when revenue is higher than spending (i.e., the government budget is in surplus) and loose or expansionary when spending is higher than revenue (i.e., the budget is in deficit). Often, the focus is not on the level of the deficit, but on the change in the deficit. Thus, a reduction of the deficit

The Original Federal Reserve System Several monetary institutions appeared in the United States prior to the formation of the Federal Reserve System, or Fed. These were, in order: the constitutional gold (and bimetallic) standard, the First and Second Banks of the United States, the Independent Treasury, the National Banking System, clearinghouse associations, and the National Reserve Association. The Fed was the last such institution founded. Although it has endured, the present-day Fed would be unrecognizable to its founders. The original Federal Reserve Act became law in December 1913. The “Federal” in the title implied that the law applied to the whole country, and “Reserve” emphasized the new institution’s role as a reserve holder and reserve supplier for the commercial banking system. The twelve regional Federal Reserve Banks, according to the Federal Reserve Act, were “to furnish an elastic currency” by discount[ing] notes, drafts, and bills of exchange arising out of actual commercial transactions.. . . The time, character, and volume of sales of [this eligible] paper . . . shall be governed with a view to accommodating commerce and business and with regard to their bearing upon the general credit situation of the country. Conspicuously absent in the title or anywhere else in the act were the words “central bank.” The primary reason for this omission was the term’s unpopularity with the populist wing of the Democratic Party. Republicans had accepted the label, but, after 1912, no longer controlled either Congress or the White House. Therefore, the new institution could not be a “central bank.” That term, many congressmen objected, implied monopolistic control by Wall Street bankers, who would keep interest rates “high” and conspire with speculators to cause panics. However, under Democratic sponsorship, the new institution could be an autonomous group of regional reserve-holding supercommercial banks, and the act passed through Congress in this guise. The U.S. banking and financial system at this time had recognizable faults that some bankers, financial experts, politicians, and economists thought needed correcting. The major monetary institution of the era was the self-regulating gold standard, which functioned much as was expected. However, the commercial banking system included both state banks—chartered by state governments—and national banks—licensed as such by the comptroller of the currency. Because of the overlap in regulatory jurisdictions, a plethora of regulations made banking operations difficult. Legal reserve requirements, for example, varied significantly at the state and national levels, and almost always made banking less flexible and more precarious. The banking system’s fragility appeared whenever some random and unforeseen financial shock put undue pressure on the banking system to provide “emergency” liquidity. The Federal Reserve System was the institutional answer to this perceived problem. Just as the gold standard worked through market forces to provide a proper quantity of gold-based money, so the new Federal Reserve Banks would augment the gold standard to ensure that the commercial banking system could issue the proper quantity of bank-created money in a timely fashion. The twelve regional Federal Reserve Banks were to be located in major cities. Each bank was to operate autonomously in its region. A Fed Bank had a board of directors and an executive structure similar to that of any commercial bank or business firm. Commercial banks in a Federal Reserve district could become members of the Federal Reserve if they fulfilled certain requirements, including buying stock in their regional Fed Bank according to a formula based on their capital value. The Fed Bank then paid them a statutory annual return of 6 percent on the value of this stock. Member commercial banks therefore became the “stockholders” of the Fed banks. Also included was a Federal Reserve Board located in Washington, D.C., and housed in the Treasury Department. Members of the board, appointed by the president, served staggered ten-year (later fourteen-year) terms. The