WIR - James Stodder - WIR - Reciprocal Exchange and Macro-Economic Stability

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WIR - James Stodder - WIR - Reciprocal Exchange and Macro-Economic Stability

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WIR - James Stodder - WIR - Reciprocal Exchange and Macro-Economic Stability

  1. 1. RECIPROCAL EXCHANGE and MACRO-ECONOMIC STABILITY: Switzerland’s Wirtschaftsring http://www.rh.edu/~stodder/Stodder_WIR2.htm James Stodder (stodder@rh.edu), Rensselaer Polytechnic Institute at Hartford Hartford CT, 06120, USA (July 2005)An earlier version of this paper was published in the Proceedings of the International Electronic and Electrical Engineering (IEEE) Engineering Management Society Conference, in Albuquerque, New Mexico, August 2000.Abstract: Reciprocal credit and exchange networks or "barter rings" do billions of dollars of trade each year within the richcountries of the world. Their turnover is shown to be highly counter-cyclical. Most studies of the internets macroeconomicimpact have focused on price and inventory flexibility. There has been little study, however, of the macroeconomic impactof reciprocal exchange networks like the Swiss Wirtschaftsring (“Economic Circle”), founded in the early 20th century. Theexperience of this network suggests that the credit it provides during recessions is highly stabilizing. This has importantimplications for monetary theory and policy. I. Introduction Faster and cheaper information on the internet means greater macroeconomic stability. That, at least, is a well-publicized view of internet-based commerce. By making it possible for purchasing firms and households to compare pricesmore widely, e-commerce has forced better price flexibility and greater resistance to inflation (Greenspan, 1999). Bettersupply tracking and demand estimation also helps keeps inventories lean, thus tamping down unplanned inventories(Wenninger 1999), an important precursor of recession. But this literature on price and inventory flexibility has ignored another way that better information can be macro-stabilizing. As any loan-officer or central banker can attest, the prudent allocation of credit is both knowledge-intensive andhighly uncertain. What if, instead of trying to estimate the proper amount of money and credit to complete all transactions,current values bid by each potential purchaser, and asked by each potential seller, were precisely known by a central clearinghouse? The problem of how much money-stuff to create to balance aggregate supply and demand would largely disappear;money in the conventional sense would no longer exist. Such moneyless exchange took place in the ancient storehouse economies of the Middle East and the Americas(Polanyi 1947), and in the simplified models of microeconomic exchange -- both under conditions where the relevantinformation is centralized. The ancient storehouse economies collapsed, and monetary1 systems evolved because the1 The word “monetary” stems from the Latin Moneta, a surname of the mother goddess Juno, in whose temple Roman coinswere cast (Onions, 1966). The epithet Moneta is usually derived from monere, “to remind, admonish, warn, advise, instruct.”It seems fitting that in addition to being traditional maternal functions, these are among the chief information services ofmoney.
  2. 2. 2information required to coordinate a complex economy was far too great to be centralized (Stodder 1995). The internet isonce again making large-scale information-centralization efficient, however and centralized barter is a new form of e-commerce. Barter clearing-houses are growing with internet companies like swap.com, BarterTrust.com, and uBarter.com(Anders 2000). The implications of moneyless business are neither straightforward, nor without controversy. A few prominenteconomists have speculated that computer-networked barter might eventually replace our decentralized money -- as well asits centralized protector, central banking. Such questions have been asked by leading macroeconomists like Mervyn King,presently the Governor of the Bank of England (King 1999, Beattie 1999), and Benjamin Friedman of Harvard (1999). Friedmans view that central banking may be seriously challenged was a lead topic at a World Bank conference onthe "Future of Monetary Policy and Banking" (World Bank 2000). His warnings sparked a pair of skeptical reviews in theEconomist Magazine of London (2000a, 2000b). But no one, as far as I know, has looked at the direct evidence on this issue,the large-scale barter networks in existence for decades. II. Statement of the Argument If barter is informationally-centralized - on a network where, via a central resource, all parties can scan each othersbids and offers - it will tend to be counter-cyclical. The central record of the value of such barter will track the bids (unmetdemands) and asks (excess supplies) of all agents on the network. For a simple model of informationally centralized barter,consider firms, A, B, and C, each of which lacks one input -- a, b, and c, respectively. Let us say that A currently holds c, Bholds a and C holds b. This is shown in Figure 1 below. [ Please place Figure 1 about here.] If prices are set at unity, Pa = Pb = Pc = 1, the direction of mutually improving trade is obvious from the picture: Agives a unit of c to C, C gives a unit of b to B and, and B gives a unit of a to A. But if these are the only inputs of interest toeach firm, then there are no bilaterally improving trades. Without a sufficient value of decentralized money-stuff, some formof centralized credit accounting is necessary. In the simplest economies – a few households linked by long-term kinshiprelations – such centralized credit accounting can be each household’s reputational ‘capital’. But in larger and more complexsocieties this is unfeasible. In traditional and primitive economies, centralized ‘big-man’ or ‘storehouse’ households havebeen designated to keep these credit accounts (Stodder, 1995). The WIR bank in Switzerland can be seen as a more sophisticated answer to the same information problem, withcentralized credit accounts for each household and firm, and a record of all unmet bids and asks. This is far more knowledgethan is available to any "central" bank -- the knowledge it has to set the money-supply basis of exchange. Its broad monetary
  3. 3. 3aggregates sit atop the decentralized "real" data in which investors and central bankers are interested. To get at thisinformation, the bank can only scan indirect monetary indicators -- ratings of credit-worthiness, and statistical leadingindicators. Of course a centralized barter administration can still make mistakes, extending credit too much or too little.Credit "inflation" was indeed evident in the early history of the worlds largest barter exchange, the "Economic Ring"(Wirtschaftsring, or WIR) of Switzerland (Defila 1994, Stutz 1994). Such a centralized barter exchange, however, will havea better knowledge base on which to extend credit than any central bank. The WIR was inspired by the ideas of an early 20th-century economist, Silvio Gesell (Defila 1994). Keynes devotesa chapter of his General Theory (1936; Book VI, Chapter 23) to Gesell’s ideas. Despite criticisms, Keynes acknowledges thatthis “unduly neglected prophet” anticipated some of his own ideas. This link with Keynesian monetary theory should have 2made Gesellian banking of some interest to macroeconomists. Only one contemporary economist, however, seems to havestudied the macroeconomic record of WIR, the largest and most long-lived bank of this sort. Studer (1998) finds positivecorrelation between WIR credits advanced and the Swiss money supply, M1. This suggests that WIR follows a counter-cyclical credit "policy," one parallel to the monetary policy of the Swiss central bank itself. The data used in Studers study,however, go back only as late as 1994. The present study has access to 9 more years of data, and makes use of cointegration-based methods of time series analysis. The present paper examines the historic data on a large barter exchanges -- the WIR, founded in 1930s Switzerland.These data will show that the economic activity of this exchange is counter-cyclical, rising and falling against, rather thanwith, the business cycle. III. Data and Regression Results Because the financial record of these exchanges is not widely known, I provide the basic data. The Swiss bankingtradition is well-known for the quality of its private records. The WIR bank gives us 56 years of data on Participants(numbers of household or firm members), Turnover (account activity), and Credit advanced (in the form of credit to one’sreciprocal exchange account, not in terms of Swiss currency): [Place Table 1 about here.] As Figure 2 below shows, growth in the number of WIR Participants has tracked Swiss Unemployment very closelyindeed, consistently maintaining a rate of about one-tenth the increase in the number of unemployed. Indeed, in the followingregressions, the Unemployment term is the only one with strongly significant coefficients. The importance of Unemploymentto WIRs Participant trend probably reflects its exclusion of "large" businesses, as established in the banks rules since 1973(Defila 1994). Employees in smaller, less diversified firms are probably more subject to unemployment risks.
  4. 4. 4 [Place Figure 2. about here.]In Table 2 below, it is clear that the long-term relationship between the number of WIR Accounts, Unemployment, and theMoney Supply is positive, from the cointegrating equation, in column (a): LnACCTS = 4.026392 + 0. 0998● LnUE + 1.132●LnMON, (3) [5.158]*** [7.193]***where both coefficients are significant at the 0.1 percent level. 3 LnACCTS, LnUE, LnMon, and all the other Swiss timeseries to be considered here can be shown by Augmented Dickey-Fuller (A.D.F.) tests to be I(1) at the 5 percent level ofsignificance, so cointegration is possible.4 The Johansen test results in column (a) of Table 2 show that cointegration is morelikely when both LnUE and LnMON are used. [Place Table 2. about here.]Now Unemployment and Money Supply are almost certainly highly collinear. Note that by isolating them, as in Table 2,columns (b) and (c), the Johansen cointegration test is only significant at the 10 percent level. It is seen, however, that theircoefficient signs do not change in the cointegrating equation.Whenever we add a variable to an Error Correction Model, there is a potential for one more cointegrating equation. In thepresent context, however, it seems reasonable to assume that the most important arrows of causality run from the Swissmacroeconomic variables to the still small Swiss WIR-Bank, and not in the opposite direction. The obvious test here is forGranger causality. In Table 3, we can reject the null hypotheses that a) the Money Supply and Unemployment variables, LnMon and LnUE, do not reciprocally Granger-cause each other; and that b) LnUE does not Granger-cause Turnover, LnACCTS.We cannot, however, reject the hypothesis that LnMON and LnACCTS do not Granger-cause each other. In summary, thereis little doubt that Turnover is more affected by, rather than affecting, the other economy-wide macro-economic variables –as is only reasonable. Consequently, the cointegrating equation can reasonably be written in the form of (3) above. [Place Table 3. about here.]It is interesting here to look at the short-term and medium-term effects of Money Supply and Unemployment upon thenumber of Participants, as given both by the coefficients on the first lagged terms of each, and the summation of their lagged2 Keynes notes that “Professor Irving Fisher, alone amongst academic economists, has recognised [this movement’s]significance,” and gives his own prediction that “the future will learn more from the spirit of Gesell than from that of Marx.”3 Note that there are two decades of observations missing in most of the regressions in Table 2, since our OECD data onMoney Supply and Inventories only go back to 1960.4 Unless otherwise specified, all unit-root tests in this paper are of the Augmented Dickey-Fuller form, with lagged 1stdifferences in the test equation.
  5. 5. 5coefficients, as shown in Table 4. Even though the long-term cointegrating relationship seen in equation (3) has been seen tobe a positive association between all three terms, the short-term and medium-term effects are seen to be largely negative.5 This is a necessary condition for stability in an error correction model of this type. Note that the coefficient on theerror-correction term is always negative in all regressions. In Table 2, column (a), for example, a negative coefficient on theerror term means that the number of Participants in WIR will grow when Unemployment and Money Supply are ‘too large’;i.e., when the error-correction term is itself negative. Since the medium-term effect of lagged Participants upon growth ofParticipants is also positive, we must have some negative feedback from Unemployment or Money supply if we are not tohave an explosive system. Note in Table 2 (and all other regression Tables) that such negative feedback is provided by anegative sign not only on the coefficients for the short-term lagged variables, but on those coefficients’ summation as well. From Figure 2 one can see that the number of WIR accounts has been, for over 50 years, roughly half as large as thenumber of unemployed workers in Switzerland.6 While WIR account holders and the unemployed are not likely to often bethe same people, this close fit between the two series is clearly an important counter-cyclical trend. WIR accounts are alsosufficiently numerous, relative to the Swiss labor force, for this trend to have substantial counter-cyclical impact. We now consider the positive association between WIR Turnover on Accounts, and the Swiss Money Supply as awhole, as measured by M2. Their long term common trend is seen in Figure 3: [Place Figure 3. about here.]The log of Money Supply (LnMON) and Total Turnover (LnTURN) in the WIR bank are positively associated in the longterm, from the first two cointegrating equations estimated in Table 4. 7 In column (c), this is of the form: LnTURN(-1) = -16.479 + 1.830●LnMON(-1) (4) [6.091]where the coefficient on LnMON is significant at the 0.1 percent level. [Place Table 4. about here.]Overall goodness of fit is comparable, however, by both R-squared and Aikake or Schwartz criteria, if we include GDP alongwith Money Supply as an independent variable determining Turnover. In column (b) this gives the cointegrating equation: LnTURN(-1) = 59.278 + 8.244●LnMON(-1) - 12.546●LnGDP(-1) (5) [5.116] [-4.390]5 In the estimates that follow, it will be noted that the summed coefficients on the lags of the dependent variable (in thisexample, D(LnAccts)) is always close to unity after a sufficient number of lags. This is necessary for a VAR process that isneither explosive (summed coefficients greater than 1) nor vanishing (summed coefficients less than 1).6 Changes in Swiss guest worker policies have probably affected the number of unemployed, but we do not account here forthat exogenous factor.7 We did attempt to use the quadratic form for Money, similar to equation (2) above for the North American Data, but foundthe matrix became nearly singular, and therefore could not be estimated.
  6. 6. 6with both coefficients significant at 0.1 percent. Note in column (e), however, that when Money Supply is left out of thecointegrating equation (and thus allowing us to use a longer time series), the sign on GDP would becomes positive, and thusapparently pro-cyclical. This makes sense as a secular rather than cyclical effect – simply the long term trend for WIRTurnover to expand along with the Swiss economy as a whole. (Note that this positive secular association is very closeindeed, as reflected by both the significance of the cointegrating relationship in the Johansen test, and by the goodness of fitstatistics.) Turning to the effects of Money Supply and GDP on Turnover, columns (a) and (b) of Table 4 show the coefficienton the first lag of each significant at the 10 and 5 percent levels, respectively. The medium-term effects of Money Supplyand GDP are shown by their summation terms, which are significant at the 10 or 5 percent levels, depending on how manylags we use. As was argued with equation (3), there is little question about the direction of causality. Granger causality results,shown in Table , are less ambiguous for the 3 lag specification. These results imply that (a) LnMON and LnTURN may wellGranger-cause each other, with P-values slightly greater than 5 percent; (b) LnGDP Granger-causes LnTURN, but not thereverse, and (c) LnMON almost certainly Granger Causes LnGDP, but not the reverse. We need not accept at face vale theseemingly incredible claim of (a), that the movement of a few billion Swiss Francs in WIR accounts could determine themonetary policy of the Swiss central bank. We should recall the interpretation of Robert Shiller and Campbell (1998): thatGranger causality may mean only that one time series accurately anticipates or predicts variation in a second series, withoutcausing that variation, even a stochastically deterministic sense. [Place Table 5. about here.]In Table 6 we turn to the third of the Swiss WIR-Bank time series: the total value of Credit extended as part of the Bank’soperations; i.e., part of the foregoing statistic of annual Turnover. It will be seen that Credits are even more counter-cyclicalthan the total volume of Turnover itself. With LnCRED as the logged value of Credit, the form of the cointegrated equationin column (a) is: LnCRED(-1) = 15.410 + 5.380●LnMON(-1) – 6.221●LnGDP(-1) (6) [4.649] [2.923] [Place Table 6. about here.]with the coefficients on LnMON and LnGDP significant at the 0.1 and 0.5 significance level, respectively. From Table 7,we see that (a) LnMon almost certainly Granger-causes LnGDP, but not the reverse; (b) lnMON and lnCRED do not appear to Granger-cause each other; while (c) LnCRED appears to Granger-cause LnGDP.
  7. 7. 7Again, the Shiller-Campbell (1998) interpretation of (c) is suggested: prediction, rather than deterministic causality. The log [Place Table 7. about here.]form allows us to interpret coefficients in elasticity terms. Our tentative empirical conclusions from Tables 5 and 7 are that: • A 1 per cent increase in the long-term money supply (M2) is reflected in a long-term increase of between 4.9 to 8.2 per cent in the annual Turnover of WIR accounts (Table 7: a) and b)), and between 5.4 to 9.4 percent in the Credits advanced on that Turnover (Table 9: a) and b)). • A 1 percent decrease in long-term GDP is reflected in a long-term counter-cyclical increase of between 5.8 to 12.5 percent in WIR Turnover (Table 7: a) and b)), and between 6.2 to 14.2 percent in the Credits advanced on that Turnover (Table 9: a) and b)). These estimates show that WIR-Bank pursues a counter-cyclical policy that is very aggressive. Since M2 is definedas currency in circulation and most forms of ordinary bank money (checking deposits and savings accounts), these estimatesimply that WIR-Bank’s creation of money and credit is many times more sensitive to economic conditions than is M2 itself.WIR increases its turnover by a money-multiplier several times higher than the ratio of the broadest measure of money, M3over M2, in the Swiss monetary system. Over the past 20 years, the Swiss National Bank (the central bank) ratio M3/M2 hasnever been greater than 3.2.8 WIR-Bank Turnover shows M2 income elasticity that is perhaps twice this ratio. Furthermore,WIR extends its credit even more aggressively than its Turnover, as has been seen. The counter-cyclical trend of Turnover and Credit is far more pronounced than that of M2 itself. Preliminaryestimates on this same Swiss National Bank data indicate that M2 itself has a positive income elasticity of about 2, a figure inline with a recent survey of the literature (Gerlach-Kristen, 2001). This must be compared with a long-term income elasticityof WIR Turnover and Credits that is not only negative (and thus counter-cyclical) when used as an independent variablealongside M2, but is also about twice as great in absolute value as the consensus income elasticity on M2. V. Conclusions and Implications There is substantial evidence for the general form of our hypothesis, that centralized barter exchange is highlycounter-cyclical. There remains the vital question, however, as to why this counter-cyclicity occurs. A basic difference ofopinion exists within macroeconomic theory as to whether instability is more due to price rigidity, or to inappropriate levelsof money and credit. Keynes (1936) recognized that both conditions can and do apply, and that either can lead to instability. The reigning macroeconomic consensus, as represented by Mankiw (1993), puts the blame more on rigid prices;economists like Colander (1996) stress monetary and credit conditions. Reflecting the "sticky price" consensus ofmacroeconomics, most commentary on the impact of e-commerce has concentrated on prices, as we have seen. But if a barter8 The Swiss National Bank (http://www.snb.ch/e/daten/daten_u_sta.html) shows that the ratio M2/M1 remained close to 2from 1984 to 2004, taking values between 2.2 and 1.7. Over this same period, the ratio M3/M1 has varied from 3.2 to 1.9.
  8. 8. 8exchanges members charge prices that do not diverge significantly from its cash prices -- those charged to their non-members -- then counter-cyclicity may derive from barters ability to create credit. WIR activities are highly public and centralized, subject to the scrutiny of other customers, and so unlikely to allowconfidential discounts. Also, prices for goods and services advertised in the WIRPlus magazine (2000-2005) are regularlyquoted in WIR-credit prices that are higher than their price in Swiss Francs, so this does not seem to be downward priceflexibility. Lower prices on barter than cash would tend to divert trade to the former. This would be undesirable for mostbusinesses, since cash is almost always more fungible than exchange credits (Healey 1996).9 The possibility remains that barter may have forced greater flexibility in network members cash prices. But sinceWIRs bylaws restrict membership to small and medium businesses (Defila 1994), members will usually have comparativelylittle price-setting power. Thus, the counter-cyclical history of WIR is likely due more to its credit creation than its addedprice flexibility. Inventory flexibility, however, could also be a factor, even before wide-scale use of computers. If suchnetwork exchanges are indeed counter-cyclical, this is emphatically not the case for all "network economies".Telecommunications networks are highly subject to increasing returns to scale, unlike older industries – and unlikeneoclassical theory (Romer 1997, Howitt and Phillipe 1998, Arthur 1996). Such industries are therefore likely, especially astheir importance to the economy increases, to fuel greater pro-cyclical instability. Reciprocal exchange networks like those studied here also have increasing returns and "network externalities," yetthey appear strongly counter-cyclical. It may be important to understand why. To quote Mervyn King (1999), now Governorof the Bank of England, electronic exchange may build a world in which "central banks in their present form would no longerexist; nor would money….The successors to Bill Gates could put the successors to Alan Greenspan out of business."References AGHION, Philippe, and Peter Howitt (1998) Endogenous Growth Theory, Cambridge: MIT. ANDERS, George (2000) "First E-Shopping, Now E-Swapping" Wall St. Journal, New York; Jan.17. ARTHR, W. Brian (1996) “Increasing Returns and the New World of Business”, Harvard Business Review, Boston: July- August. BEATTIE, Alan (1999) "Internet Heralds Coincidence of wants," Financial Times, Dec. 6. COLANDER, David (1996), ed., Beyond Micro-foundations: Post Walrasian Macroeconomics. Cambridge, UK: Cambridge University Press. DEFILA, Heidi (1994) "Sixty Years of the WIR Economic Circle Cooperative: Origins and Ideology of the Wirtschafts- ring," WIR Magazin, September. (Translation by Thomas Geco.) http://www.ex.ac.uk/~RDavies/arian/wir.html. Economic Report of the President (1996), Washington, DC: US Government Printing Office. Economist Magazine (2000a), "Economics Focus: Who Needs Money?" January 22. _________________ (2000b), "Economics Focus: E-Money Revisited," July 22. FRIEDMAN, Benjamin (1999) "The Future of Monetary Policy", International Finance, December. GERLACH-KRISTEN, Petra (2001) “The Demand for Money in Switzerland 1936-1995”, Zeitschrift für Volkswirtshaft und Statistik, Vol. 137, No. 4, pp. 535-554. GREENSPAN, Alan (1999) "Testimony before the Joint Economic Committee," US Congress, June 14. HEALEY, Nigel (1996) ΑWhy is Corporate Barter?≅ Business Economics, April, Vol. 31, no. 2.9 WIR credits themselves cannot be exchanged for cash at a discount, a decision historian Defila (1994) sees as crucial forthe organization. (Note however, that the ability to charge lower prices in cash than in WIR-credits is nearly equivalent,since one might buy a good for WIR-credits and then sell it at the going rate -- for a smaller amount of Swiss Franks.)
  9. 9. 9 KING, Mervyn (1999) "Challenges for Monetary Policy: New and Old." Paper prepared for the Symposium on “New Challenges for Monetary Policy” sponsored by the Federal Reserve Bank of Kansas City at Jackson Hole, Wyoming, 27 August 1999. http://www.bankofengland.co.uk/speeches/speech51.pdf . IRTA (1995), “Fact Sheet: Why Business People Barter.” http://ww2.dgsys.com/~irta/fswhybpp.html, Falls Church, Virginia: IRTA. KENNEDY, Peter (1998) A Guide to Econometrics, Fourth Edition, Cambridge, Mass.: MIT Press. KEYNES, John Maynard (1936) The General Theory of Employment, Interest, and Money. New York: Harcourt Brace Jovanovich, 1964. MADISON, Angus (1995) Monitoring the World Economy, 1820-1992, Paris: OECD. MAGENHEIM, E. and P. Murrell (1988) "How to Haggle and Stay Firm: Barter as Hidden Price Discrimination" Economic Inquiry, July, Vol. 26, no.3. MANKIW, N. (1993) editor of “Symposium on Keynesian Economic Theory Today,” Journal of Economic Perspectives, Vol. 7, no. 1. MIEIERHOFER, L. (1984) Volkswirtsaftliche Analyse des WIR-Wirtschaftsrings, WIR: Basel, Switzerland. MITCHELL, B.R. (1998) International Historical Statistics, Europe, 1750-1993, UK: MacMillan. OECD (2000) Economic Outlook, Jan., Paris: OECD. _____ (1999) Economic Surveys: Switzerland, Paris: OECD. _____ (1998) Main Economic Indicators, Historical Statistics, 1960-97 [computer file], Paris: OECD. ONIONS, C.T., (1966) Editor, The Oxford Dictionary of English Etymology, Oxford: Clarendon Press, 1995. PLATNER, SAMUEL B. (1929), A Topographical Dictionary of Ancient Rome, London: Oxford University Press. POLANYI, Karl (1947) The Great Transformation: the political and economic origins of our time, Boston: Beacon Press, 1957. ROMER, Paul (1986) "Increasing Returns and Long-Run Growth," Journal of Political Economy Vol. 94, no. 5 (October): pp. 1002-37. SHILLER, Robert, and John Campbell (1998) "Interpreting Cointegrated Models,” Journal of Economic Dynamics and Control, Special Issue, Masanao Aoki (ed.), "Economic Time Series Models with Random Walk and Other Nonstationary Components," Vol. 12. pp. 505–522 SKIDELSKY, Robert (1992) John Maynard Keynes: The Economist as Savior, 1920-37, Vol. 2 of the author’s 3 part biography, New York: Penguin. STODDER, James (1998) "Corporate Barter and Macroeconomic Stabilization," International Journal of Community Currency Research, Vol.2, no.2, http://www.geog.le.ac.uk/ijccr/volume2/2js.htm. _____________ (1995) “The Evolution of Complexity in Primitive Economies: Theory,” and "…Empirical Evidence," Journal of Comparative Economics, Vol. 20: no 1., February and no. 2, May. STUDER, Tobias (1998) WIR in Unserer Volkwirtschaft, Basel: WIR. STUTZ, Emil (1984) "Le Cercle Économique-Societé Coopéreative WIR - Une Retrospective Historique," Basle: WIR. WIR (2004) E-mail statistics from Public Relations, December. WIR (2003) Rapport de Gestion, Various Years, Basle: WIR WENNINGER, John (1999) "Business-to-Business Electronic Commerce," Current Issues in Economics and Finance, June, Volume 5, no. 10. WORLD BANK (2004) World Development Indicators, http://www.worldbank.org/data/wdi2004/index.htm. ____________(2000) "Future of Monetary Policy and Banking Conference: Looking Ahead to the Next 25 Years," July 11, 2000, World Bank,Washington, D.C., www.worldbank.org/research/interest/confs/upcoming/papersjuly11/papjuly11.htmFIG. 1.
  10. 10. 10 A c C B b a Figure 3: Unemployed Swiss Workforce and WIR Accounts, 1948-2003 90 200 Accounts UE 80 180 160 70 140 Unemployment (thousands)WIR Accounts (thousands) 60 120 50 100 40 80 30 60 20 40 10 20 0 0 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
  11. 11. 11 Figure 4: Swiss Money Supply (M2) and WIR Turnover, in 1990 Swiss Franks, 1960-2003 600,000 2,500 500,000M oney Supply (M illions of S.F.) Money Supply Turnover 2,000 WIR Turnover (M illions of S.F.) 400,000 1,500 300,000 1,000 200,000 500 100,000 - 0 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
  12. 12. 12 Table 1: Participants, Total Turnover, Credit, and Credit/Turnover, WIR-Bank, 1948-2003 (Total Turnover and Credit Denominated in Millions of Current Swiss Franks) Year Participants Turnover Credit Credit/ Year Participants Turnover Credit Credit/ Turnover Turnover 1948 814 1.1 0.3 0.2727 1976 23,172 223.0 82.2 0.3686 1949 1,070 2.0 0.5 0.2500 1977 23,929 233.2 84.5 0.3623 1950 1,574 3.8 1.0 0.2632 1978 24,479 240.4 86.5 0.3598 1951 2,089 6.8 1.3 0.1912 1979 24,191 247.5 89.0 0.3596 1952 2,941 12.6 3.1 0.2460 1980 24,227 255.3 94.1 0.3686 1953 4,540 20.2 4.6 0.2277 1981 24,501 275.2 103.3 0.3754 1954 5,957 30.0 7.2 0.2400 1982 26,040 330.0 127.7 0.3870 1955 7,231 39.1 10.5 0.2685 1983 28,418 432.3 159.6 0.3692 1956 9,060 47.2 11.8 0.2500 1984 31,330 523.0 200.9 0.3841 1957 10,286 48.4 12.1 0.2500 1985 34,353 673.0 242.7 0.3606 1958 11,606 53.0 13.1 0.2472 1986 38,012 826.0 292.5 0.3541 1959 12,192 60.0 14.0 0.2333 1987 42,227 1,065 359.3 0.3374 1960 12,567 67.4 15.4 0.2285 1988 46,895 1,329 437.3 0.3290 1961 12,445 69.3 16.7 0.2410 1989 51,349 1,553 525.7 0.3385 1962 12,720 76.7 19.3 0.2516 1990 56,309 1,788 612.5 0.3426 1963 12,670 83.6 21.6 0.2584 1991 62,958 2,047 731.7 0.3574 1964 13,680 101.6 24.3 0.2392 1992 70,465 2,404 829.8 0.3452 1965 14,367 111.9 25.5 0.2279 1993 76,618 2,521 892.3 0.3539 1966 15,076 121.5 27.0 0.2222 1994 79,766 2,509 904.1 0.3603 1967 15,964 135.2 37.3 0.2759 1995 81,516 2,355 890.6 0.3782 1968 17,069 152.2 44.9 0.2950 1996 82,558 2,262 869.8 0.3845 1969 17,906 170.1 50.3 0.2957 1997 82,793 2,085 843.6 0.4046 1970 18,239 183.3 57.2 0.3121 1998 82,751 1,976 807.7 0.4088 1971 19,038 195.1 66.2 0.3393 1999 82,487 1,833 788.7 0.4303 1972 19,523 209.3 69.3 0.3311 2000 81,719 1,774 786.9 0.4437 1973 20,402 196.7 69.9 0.3554 2001 80,227 1,708 791.5 0.4634 1974 20,902 200.0 73.0 0.3650 2002 78,505 1,691 791.5 0.4681 1975 21,869 204.7 78.9 0.3854 2003 77,668 1,650 784.4 0.4754Sources: Data to 1983 are from Meierhofer (1984). Subsequent years are from the annual Rapport de Gestion and communi- cations with the WIR public relations department (2000, 2004). The first three series names (Participants, Turnover, and Credit) are given in the annual report in French as Nombre de Comptes-Participants, Chiffre (o Volume) dAffaires, and Autres Obligations Financières envers Clients en WIR, respectively. Both Turnover and Credit are denominated in Swiss Francs, but the obligations they represent are payable in WIR-accounts. In the regressions, all monetary series were deflated by the1990 GDP deflator.
  13. 13. 13Table 2: Account Activity in WIR Exchange Network, as Explained by Unemployment and Money Supply 1960-2002* [t-stats] in parentheses, ***: p-value < 0.001, **: p-value < 0.01, * : p-value < 0.05, oo: p <0.1; o: p <0.15 Cointegrating Eq: (a) (b) (c) (d) Johansen Cointegration .01 .10 .10 .20 Test, P-Value: LnACCTS(-1) 1.000000 1.000000 1.000000 1.000000 LnUE(-1) -0.099794 -0.229313 -0.279225 [-5.158]*** [-14.210]*** [-5.067]*** LnMON(-1) -1.131793 -1.852541 [-7.193]*** [-14.646]*** C 4.026392 12.90664 -9.958488 -9.633628 Error Correction: D(LNACCTS) D(LNACCTS) D(LNACCTS) D(LNACCTS) CointEq1 -0.099030 -0.061111 -0.046168 -0.021387 oo [-4.530]*** [-2.509]* [-4.189]*** [-1.680] D(LnACCTS(-1)) 0.786376 0.912327 0.709588 0.728941 [6.128]*** [6.419]*** [ 5.099]* [ 5.476]*** D(LnACCTS(-2)) 0.011106 0.071534 0.186215 -0.029469 [0.073] [0.389] [ 1.131] [-0.184] D(LnACCTS(-3)) 0.016312 -0.164988 -0.082101 0.326010 [0.108] [-0.968] [-0.503] [ 2.268]* D(LnACCTS(-4)) 0.201309 0.151225 0.137935 -0.346573 oo [1.783] [1.040] [ 1.104] [-3.106]** ∑t D(LnACCTS(-t)) 1.01510 0.970097 0.951636 0.678909 [13.392]*** {10.395]*** [11.925]*** [7.930]*** D(LnUE(-1)) 0.003822 4.393E-03 7.167E-03 [0.801] [ 1.156] [ 0.967] D(LnUE(-2)) -0.018154 -0.016161 -0.015226 oo [-3.219]** [-3.839]*** [-1.971] D(LnUE(-3)) -0.000628 -4.846E-03 -2.387E-03 [-0.115] [-1.139] [-0.302]* D(LnUE(-4)) -0.013051 -0.015700 -7.389E-03 [-3.444]** [-3.699]*** [-1.016] ∑t D(LnUE(-t)) -0.02801 -0.032315 -0.017835 [-2.455]* [-3.930]*** [-1.405] D(LnMON(-1)) -0.122727 -0.103439 oo [-2.852]** [-2.043] D(LnMON(-2)) -0.051672 -0.085094 o [-0.948] [-1.642] D(LnMON(-3)) -0.118640 -8.616E-03 [-1.398] [-0.168] D(LnMON(-4)) -0.039625 -3.743E-03 [-0.490] [-0.054] ∑t D(LnMON(-t)) -0.332664 -0.200892 oo [-1.870] [-1.384] Constant 0.010038 4.566E-03 5.096E-03 0.019485 [1.254] [0.730] [ 1.078] [ 2.213]* Observations 39 39 39 50 R-squared 0.926 0.859 0.879 0.859 Adj. R-squared 0.886 0.815 0.842 0.827 Log likelihood 117.969 107.929 111.685 104.540 Akaike AIC -5.472 -5.022 -5.215 -3.782 Schwarz SC -4.869 0.905 -4.788 -3.399 P-val. LM test (1) 0.986 0.951 0.397 0.000 P-val. LM test (2) 0.613 0.159 0.131 0.133 P-val. LM test (3) 0.148 0.073 0.457 0.065 P-val. LM test (4) 0.243 0.476 0.083 0.008 P-val. LM test (5) 0.820 0.905 0.624 0.973* Note: For Column (d), which only includes Accounts and Unemployment, sample is extended to its maximum, 1948-2002. Sources: WIR Annual Reports (Rapport de Gestion), World Bank Development Indicators, 2004.
  14. 14. 14Table 3: Pairwise Granger Causality tests: WIR Accounts, Unemployment, and Money Supply (M2), 1960-2002 Lags: 4 Null Hypothesis: No Granger Obs. F-Statistic P-value Causality of LnUE upon LnACCTS 50 1.16002 0.34209 LnACCTS upon LnUE 0.93729 0.45176 LnUE upon LnACCTS 43 2.33012 0.07577 LnACCTS upon LnUE 1.94024 0.12620 LnMON upon LnACCTS 40 0.93776 0.45511 LnACCTS upon LnMON 1.23926 0.31474 LnMON upon LnUE 39 6.32342 0.00082 LnUE upon LnMON 2.74761 0.04650 Lags: 3 LnUE upon LnACCTS 43 2.44883 0.07940 LnACC upon LnUE 2.24093 0.10019 LnMON upon LnACCTS 41 2.23398 0.10207 LnACCTS upon LnMON 1.41519 0.25524 LnMON upon LnUE 40 7.07425 0.00084 LnUE upon LnMON 3.32948 0.03130
  15. 15. 15 Table 4: Turnover in the WIR Exchange Network, as Explained by Money Supply (M2) and GDP, 1960-2003 [t-stats] in parentheses; ***: p-value < 0.001, **: p-value < 0.01, * : p-value < 0.05, oo: p <0.1; o: p <0.15 Cointegrating Equation: (a) (b) (c) (d) (e) Johansen Cointegration 0.10 0.05 0.10 0.01 0.05 Test, P-Value: LnTURN(-1) 1.000000 1.000000 1.000000 1.000000 1.000000 LnMON(-1) -4.874644 -8.244366 -1.830236 [-3.586]*** [-5.116]*** [-6.091]*** LnGDP(-1) 5.760765 12.54567 -5.997376 -3.430694 [ 2.300]* [ 4.390]*** [-5.758]*** [-8.093]*** Constant -17.08268 -59.27782 16.47885 68.250310 36.04694 Error Correction: D(LnTURN) D(LnTURN) D(LnTURN) D(LnTURN) D(LnTURN) Coint. Equation -0.079224 -0.040163 -0.076565 -4.023E-03 -0.044820 [-2.644]* [-2.771]** [-2.906]** [-0.27497] [-2.388]* D(LnTURN(-1)) 0.573234 0.589409 0.686543 0.766490 0.585649 [3.247]** [3.737]*** [4.693]*** [ 4.87723] [ 4.372]*** D(LnTURN(-2)) 0.323044 oo 0.248226 0.302734 0.324488 0.128213 o o [1.595] [1.94044] [1.602] [ 0.82271] [ 2.281]* D(LnTURN(-3)) 0.072986 [0.372] ∑t D(LnTURN(-t)) 0.96926 0.91390 0.93477 0.894704 0.888383 [8.694]*** [9.741]*** [9.561]*** [8.767]*** [14.965]*** D(LnMON(-1)) -0.360272 -0.363954 -0.066805 oo oo [-0.482] [-1.802] [-1.835] D(LnMON(-2)) -0.193820 -0.186259 -0.313684 [-0.938] [-0.997] [-2.361]* D(LnMON(-3)) 0.087171 [0.462] ∑t D(LnMON(-t)) -0.46692 -0.55021 -0.38049 [-1.105] oo oo [-1.825] [-1.948] D(LnGDP(-1)) -1.41559 -1.345752 -1.071322 -0.761633 [-2.521]* [-2.459]* oo [-2.503]* [-1.924] D(LnGDP(-2)) 0.095148 0.233857 0.841125 0.686411 [0.153] [0.515] oo [ 2.065]* [ 1.957] D(LnGDP(-3)) 0.089058 [0.200] ∑t D(LnGDP(-t)) -1.23139 -1.11189 -0.230197 -0.075222 oo [-2.094]* [-0.516] [-0.177] [-1.866] Constant 0.034573 0.038895 0.011874 5.738E-03 -3.128E-03 oo [2.294]* [1.194] [-0.213] [1.90229] [ 0.433] Observations 40 41 41 41 53 R-squared 0.796 0.769 0.743 0.715 0.837 Adj. R-squared 0.726 0.720 0.706 0.675 0.820 Log likelihood 69.274 68.968 66.750 64.661 73.698 Akaike AIC -2.914 -2.974 -2.963 -2.862 -2.555 Schwarz SC -2.449 -2.640 -2.713 -2.611 -2.332 P-val. LM test (1) 0.970 0.557 0.707 0.179 0.517 P-val. LM test (2) 0.753 0.530 0.843 0.012 0.067 P-val. LM test (3) 0.367 0.711 0.905 0.936 0.518 P-val. LM test (4) 0.806 0.147 0.234 0.937 0.818Sources: WIR Annual Reports (Rapport de Gestion), World Bank Development Indicators, 2004
  16. 16. 16Table 5: Pairwise Granger Causality tests: WIR Turnover, GDP, and Money Supply (M2), 1960-2003 Lags: 3 Null Hypothesis: No Granger Obs. F-Statistic P-value Causality of LnMON upon LnTURN 41 2.79406 0.05506 LnTURN upon LnMON 2.61596 0.06691 LnGDP upon LnTURN 44 3.71742 0.01966 LnTURN upon LnGDP 2.10155 0.11666 LnGDP upon LnMON 41 1.07191 0.37395 LnMON upon LnGDP 14.3525 3.3E-06 Lags: 2 LnMON upon LnTURN 42 0.77078 0.46994 LnTURN upon LnMON 1.26698 0.29362 LnGDP upon LnTURN 44 2.78191 0.07423 LnTURN upon LnGDP 2.87996 0.06814 LnGDP upon LnMON 42 0.86046 0.43126 LnMON upon LnGDP 20.5973 9.7E-07
  17. 17. 17 Table 6: Credit in the WIR Exchange Network, as Explained by Money Supply (M2) and GDP, 1960-2003 (standard errors) and [t-stats] in parentheses, ***: p-value < 0.001, **: p-value < 0.01, * : p-value < 0.05, oo: p <0.1; o: p <0.15 Cointegrating Equation: (a) (b) (c) (d) (e) Johansen Cointegration 0.07 0.05 0.15 0.05 0.01 Test, P-Value: LnCRED(-1) 1.000000 1.000000 1.000000 1.000000 1.000000 LnMON(-1) -5.379977 -9.364830 -2.393765 -3.307454 [-4.649]*** [-4.930]*** [-10.275]*** [-9.373]*** LnGDP(-1) 6.221436 14.21366 -5.372218 [2.923]** [ 4.257]*** [-6.514]*** Constant -15.41026 -64.91655 24.63596 61.54493 35.66864 Error Correction: D(LnCRED) D(LnCRED) D(LNCRED) CointEq1 -0.086261 -0.025332 -0.095920 -0.025688 -0.124612 [-2.705]* oo [-2.361]* [-1.840] [-0.891] [-3.686]*** D(LnCRED(-1)) 0.530399 0.626873 0.692746 0.736722 0.263086 [3.029]** [3.682]*** [4.204]*** oo [ 4.265] [ 2.012] D(LnCRED(-2)) 0.080187 0.177155 0.053711 0.047170 0.484968 [0.401] [0.991] [0.263] [ 0.220] [ 4.379]*** D(LnCRED(-3)) 0.350244 0.148277 0.046772 0.150581 oo [0.850] oo [1.743] [ 0.260] [ 1.189] ∑t D(LnCRED(-t)) 0.96083 0.80403 0.894735 0.830665 0.898635 [6.721]*** [6.640]*** [6.521]*** [5.700]*** [9.616]*** D(LnMON(-1)) -0.556649 -0.339104 -0.204543 [-2.276]* o [-1.262] [-1.600] D(LnMON(-2)) -0.006542 0.099423 -0.081741 [-0.030] [0.468] [-0.512] D(LnMON(-3)) 0.038870 -0.121267 [0.187] [-0.789] ∑t D(LnMON(-t)) -0.52432 -0.23968 -0.407551 [-1.108] [-0.718] [-1.293] D(LnGDP(-1)) -1.439979 -1.000384 -0.128998 0.025940 [-2.156]* o [-1.601] [-0.261] [ 0.046] D(LnGDP(-2)) -0.362967 -0.019670 -0.156727 -0.386 [-0.552] [-0.039] [-0.294] [-0.631] D(LnGDP(-3)) -0.075068 0.563805 -0.208374 [-0.148] [ 1.247] [-0.425] ∑t D(LnGDP(-t)) -1.87801 -1.02005 0.278079 -0.568319 oo [-2.188]* [-1.729] [0.083] -0.872 Constant 0.049263 0.035872 0.014946 1.137-E3 0.010071 [2.212]* oo [1.156] [1.861] [ 0.070] [ 0.491] Observations 40 41 40 40 52 R-squared 0.689 0.629 0.636 0.598 0.713 Adj. R-squared 0.581 0.550 0.557 0.510 0.668 Log likelihood 65.478 63.999 62.375 60.374 57.314 Akaike AIC -2.724 -2.732 -2.719 -2.619 -1.897 Schwarz SC -2.259 -2.397 -2.381 -2.281 -1.596 P-val. LM test (1) 0.950 0.360 0.598 0.120 0.000 P-val. LM test (2) 0.946 0.532 0.912 0.130 0.118 P-val. LM test (3) 0.939 0.940 0.404 0.123 0.726 P-val. LM test (4) 0.458 0.263 0.252 0.812 0.750Sources: WIR Annual Reports (Rapport de Gestion), World Bank Development Indicators, 2004
  18. 18. 18Table 7: Pairwise Granger Causality tests: WIR Credits, GDP, and Money Supply (M2), 1960-2003 Lags: 3 Null Hypothesis: No Granger Obs. F-Statistic P-value Causality of LnMON upon LnCRED 41 1.16580 0.33707 LnCRED upon LnMON 0.83257 0.48531 LnGDP upon LnCRED 44 0.57871 0.63267 LnCRED upon LnGDP 2.61165 0.06579 LnGDP upon LnMON 41 1.07191 0.37395 LnMON upon 14.3525 3.3E-06 LnGDP Lags: 2 LnMON upon LnCRED 42 1.32894 0.27710 LnCRED upon LnMON 1.36067 0.26902 LnGDP upon LnCRED 44 0.41267 0.66474 LnCRED upon LnGDP 3.81860 0.03058 LnGDP upon LnMON 41 0.86046 0.43126 LnMON upon 20.5973 9.7E-07 LnGDP

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