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

As an economic system, fascism is socialism with a capitalist veneer. The word derives from fasces, the Roman symbol of collectivism and power: a tied bundle of rods with a protruding ax. In its day (the 1920s and 1930s), fascism was seen as the happy medium between boom-and-bust-prone liberal capitalism, with its alleged class conflict, wasteful competition, and profit-oriented egoism, and revolutionary Marxism, with its violent and socially divisive persecution of the bourgeoisie. Fascism substituted the particularity of nationalism and racialism—“blood and soil”—for the internationalism of both classical liberalism and Marxism. Where socialism sought totalitarian control of a society’s economic processes through direct state operation of the means of production, fascism sought that control indirectly, through domination of nominally private owners. Where socialism nationalized property explicitly, fascism did so implicitly, by requiring owners to use their property in the “national interest”—that is, as the autocratic authority conceived it. (Nevertheless, a few industries were operated by the state.) Where socialism abolished all market relations outright, fascism left the appearance of market relations while planning all economic activities. Where socialism abolished money and prices, fascism controlled the monetary system and set all prices and wages politically. In doing all this, fascism denatured the marketplace. Entrepreneurship was abolished. State ministries, rather than consumers, determined what was produced and under what conditions. Fascism is to be distinguished from interventionism, or the mixed economy. Interventionism seeks to guide the market process, not eliminate it, as fascism did. Minimum-wage and antitrust laws, though they regulate the free market, are a far cry from multiyear plans from the Ministry of Economics. Under fascism, the state, through official cartels, controlled all aspects of manufacturing, commerce, finance, and agriculture. Planning boards set product lines, production levels, prices, wages, working conditions, and the size of firms. Licensing was ubiquitous; no economic activity could be undertaken without government permission. Levels of consumption were dictated by the state, and “excess” incomes had to be surrendered as taxes or “loans.” The consequent burdening of manufacturers gave advantages to foreign firms wishing to export. But since government policy aimed at autarky, or national self-sufficiency, protectionism was necessary: imports were barred or strictly controlled, leaving foreign conquest as the only avenue for access to resources unavailable domestically. Fascism was thus incompatible with peace and the international division of labor—hallmarks of liberalism. Fascism embodied corporatism, in which political representation was based on trade and industry rather than on geography. In this, fascism revealed its roots in syndicalism, a form of socialism originating on the left. The government cartelized firms of the same industry, with representatives of labor and management serving on myriad local, regional, and national boards—subject always to the final authority of the dictator’s economic plan. Corporatism was intended to avert unsettling divisions within the nation, such as lockouts and union strikes. The price of such forced “harmony” was the loss of the ability to bargain and move about freely. To maintain high employment and minimize popular discontent, fascist governments also undertook massive public-works projects financed by steep taxes, borrowing, and fiat money creation. While many of these projects were domestic—roads, buildings, stadiums—the largest project of all was militarism, with huge armies and arms production. The fascist leaders’ antagonism to communism has

The European Union (EU) includes twenty-seven countries and 490 million people. In 2005, the EU had a $13 trillion (€11 trillion) economy, a single market, and for some member countries, a single currency. A growing number of political and economic decisions are made on a pan-European level in Brussels. The origins of the EU are usually traced to the European Coal and Steel Community (1952). Heavily regulated coal and steel industries of Germany and France were to be administered by a supranational authority. Economic benefits of supranational control over one sector of the economy were expected to lead to demands for supranational management of other economic sectors. But supranationalism, characterized by bureaucratic planning and regulation, could not produce economic growth. Ludwig Erhard’s free-market reforms in West Germany in the late 1940s (see german economic miracle) provided an alternative model for development, and the resulting economic growth made a strong case for Europe-wide liberalization. The 1957 Treaty of Rome created the European Economic Community (EEC). The EEC abolished internal tariffs and quotas and established a customs union. The treaty made provisions for the eventual liberalization of movement of labor, services, and capital. Despite the Treaty of Rome’s many imperfections, the economic interdependence between nations that it produced was believed to make future armed conflict less likely. As nineteenth-century French economic journalist

Positive externalities are benefits that are infeasible to charge to provide; negative externalities are costs that are infeasible to charge to not provide. Ordinarily, as Adam Smith explained, selfishness leads markets to produce whatever people want; to get rich, you have to sell what the public is eager to buy. Externalities undermine the social benefits of individual selfishness. If selfish consumers do not have to pay producers for benefits, they will not pay; and if selfish producers are not paid, they will not produce. A valuable product fails to appear. The problem, as David Friedman aptly explains, “is not that one person pays for what someone else gets but that nobody pays and nobody gets, even though the good is worth more than it would cost to produce” (Friedman 1996, p. 278). Admittedly, the real world is rarely so stark. Most people are not perfectly selfish, and it is usually feasible to charge consumers for a fraction of the benefit they receive. Due to piracy, for example, many people who enjoy a CD fail to pay the artist, which reduces the incentive to record new