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

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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 rich countries of the world. Their turnover is shown to be highly counter-cyclical. Most studies of the internet's macroeconomic impact have focused on price and inventory flexibility. There has been little study, however, of the macroeconomic impact of reciprocal exchange networks like the Swiss Wirtschaftsring (“Economic Circle”), founded in the early 20th century. The experience of this network suggests that the credit it provides during recessions is highly stabilizing. This has important implications 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 prices more widely, e-commerce has forced better price flexibility and greater resistance to inflation (Greenspan, 1999). Better supply 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 macrostabilizing. As any loan-officer or central banker can attest, the prudent allocation of credit is both knowledge-intensive and highly 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 clearing house? 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 relevant information is centralized.

1

The ancient storehouse economies collapsed, and monetary1 systems evolved because the

The word “monetary” stems from the Latin Moneta, a surname of the mother goddess Juno, in whose temple Roman coins were 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 of money.


2 information required to coordinate a complex economy was far too great to be centralized (Stodder 1995). The internet is once again making large-scale information-centralization efficient, however and centralized barter is a new form of ecommerce. 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 prominent economists have speculated that computer-networked barter might eventually replace our decentralized money -- as well as its 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). Friedman's view that central banking may be seriously challenged was a lead topic at a World Bank conference on the "Future of Monetary Policy and Banking" (World Bank 2000). His warnings sparked a pair of skeptical reviews in the Economist 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 other's bids and offers - it will tend to be counter-cyclical. The central record of the value of such barter will track the bids (unmet demands) 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, B holds 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: A gives 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 to each firm, then there are no bilaterally improving trades. Without a sufficient value of decentralized money-stuff, some form of centralized credit accounting is necessary. In the simplest economies – a few households linked by long-term kinship relations – such centralized credit accounting can be each household’s reputational ‘capital’. But in larger and more complex societies this is unfeasible. In traditional and primitive economies, centralized ‘big-man’ or ‘storehouse’ households have been 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, with centralized credit accounts for each household and firm, and a record of all unmet bids and asks. This is far more knowledge than is available to any "central" bank -- the knowledge it has to set the money-supply basis of exchange. Its broad monetary


3 aggregates sit atop the decentralized "real" data in which investors and central bankers are interested. To get at this information, the bank can only scan indirect monetary indicators -- ratings of credit-worthiness, and statistical leading indicators. 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 world's largest barter exchange, the "Economic Ring" (Wirtschaftsring, or WIR) of Switzerland (Defila 1994, Stutz 1994). Such a centralized barter exchange, however, will have a 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 devotes a chapter of his General Theory (1936; Book VI, Chapter 23) to Gesell’s ideas. Despite criticisms, Keynes acknowledges that this “unduly neglected prophet” anticipated some of his own ideas. This link with Keynesian monetary theory should have 2

made Gesellian banking of some interest to macroeconomists. Only one contemporary economist, however, seems to have studied the macroeconomic record of WIR, the largest and most long-lived bank of this sort. Studer (1998) finds positive correlation between WIR credits advanced and the Swiss money supply, M1. This suggests that WIR follows a countercyclical credit "policy," one parallel to the monetary policy of the Swiss central bank itself. The data used in Studer's study, however, go back only as late as 1994. The present study has access to 9 more years of data, and makes use of cointegrationbased 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 than with, 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 banking tradition 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’s reciprocal 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 closely indeed, consistently maintaining a rate of about one-tenth the increase in the number of unemployed. Indeed, in the following regressions, the Unemployment term is the only one with strongly significant coefficients. The importance of Unemployment to WIR's Participant trend probably reflects its exclusion of "large" businesses, as established in the bank's rules since 1973 (Defila 1994). Employees in smaller, less diversified firms are probably more subject to unemployment risks.


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 the Money Supply is positive, from the cointegrating equation, in column (a): LnACCTS = 4.026392 + 0. 0998● LnUE + 1.132●LnMON, [5.158]*** [7.193]*** where both coefficients are significant at the 0.1 percent level. 3

(3)

LnACCTS, LnUE, LnMon, and all the other Swiss time

series to be considered here can be shown by Augmented Dickey-Fuller (A.D.F.) tests to be I(1) at the 5 percent level of significance, so cointegration is possible.4 The Johansen test results in column (a) of Table 2 show that cointegration is more likely 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 their coefficient 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 the present context, however, it seems reasonable to assume that the most important arrows of causality run from the Swiss macroeconomic variables to the still small Swiss WIR-Bank, and not in the opposite direction. The obvious test here is for Granger 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, there is 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 the number of Participants, as given both by the coefficients on the first lagged terms of each, and the summation of their lagged

2

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 on Money 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 1st differences in the test equation.


5 coefficients, as shown in Table 4. Even though the long-term cointegrating relationship seen in equation (3) has been seen to be 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 the error-correction term is always negative in all regressions. In Table 2, column (a), for example, a negative coefficient on the error 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 of Participants is also positive, we must have some negative feedback from Unemployment or Money supply if we are not to have an explosive system. Note in Table 2 (and all other regression Tables) that such negative feedback is provided by a negative 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 the number of unemployed workers in Switzerland.6 While WIR account holders and the unemployed are not likely to often be the same people, this close fit between the two series is clearly an important counter-cyclical trend. WIR accounts are also sufficiently 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 a whole, 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 long term, 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) [6.091]

(4)

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 along with 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) [5.116] [-4.390]

5

(5)

In the estimates that follow, it will be noted that the summed coefficients on the lags of the dependent variable (in this example, D(LnAccts)) is always close to unity after a sufficient number of lags. This is necessary for a VAR process that is neither 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 for that exogenous factor. 7 We did attempt to use the quadratic form for Money, similar to equation (2) above for the North American Data, but found the matrix became nearly singular, and therefore could not be estimated.


6 with both coefficients significant at 0.1 percent. Note in column (e), however, that when Money Supply is left out of the cointegrating equation (and thus allowing us to use a longer time series), the sign on GDP would becomes positive, and thus apparently pro-cyclical. This makes sense as a secular rather than cyclical effect – simply the long term trend for WIR Turnover to expand along with the Swiss economy as a whole. (Note that this positive secular association is very close indeed, as reflected by both the significance of the cointegrating relationship in the Johansen test, and by the goodness of fit statistics.) Turning to the effects of Money Supply and GDP on Turnover, columns (a) and (b) of Table 4 show the coefficient on the first lag of each significant at the 10 and 5 percent levels, respectively. The medium-term effects of Money Supply and GDP are shown by their summation terms, which are significant at the 10 or 5 percent levels, depending on how many lags 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 well Granger-cause each other, with P-values slightly greater than 5 percent; (b) LnGDP Granger-causes LnTURN, but not the reverse, and (c) LnMON almost certainly Granger Causes LnGDP, but not the reverse. We need not accept at face vale the seemingly incredible claim of (a), that the movement of a few billion Swiss Francs in WIR accounts could determine the monetary policy of the Swiss central bank. We should recall the interpretation of Robert Shiller and Campbell (1998): that Granger causality may mean only that one time series accurately anticipates or predicts variation in a second series, without causing 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’s operations; i.e., part of the foregoing statistic of annual Turnover. It will be seen that Credits are even more counter-cyclical than the total volume of Turnover itself. With LnCRED as the logged value of Credit, the form of the cointegrated equation in column (a) is: LnCRED(-1) = 15.410 + 5.380●LnMON(-1) – 6.221●LnGDP(-1) [4.649] [2.923]

(6)

[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 Again, 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 defined

as currency in circulation and most forms of ordinary bank money (checking deposits and savings accounts), these estimates imply 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, M3 over M2, in the Swiss monetary system. Over the past 20 years, the Swiss National Bank (the central bank) ratio M3/M2 has never 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. Preliminary estimates on this same Swiss National Bank data indicate that M2 itself has a positive income elasticity of about 2, a figure in line with a recent survey of the literature (Gerlach-Kristen, 2001). This must be compared with a long-term income elasticity of WIR Turnover and Credits that is not only negative (and thus counter-cyclical) when used as an independent variable alongside 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 highly counter-cyclical. There remains the vital question, however, as to why this counter-cyclicity occurs. A basic difference of opinion exists within macroeconomic theory as to whether instability is more due to price rigidity, or to inappropriate levels of 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 of

macroeconomics, most commentary on the impact of e-commerce has concentrated on prices, as we have seen. But if a barter

8

The Swiss National Bank (http://www.snb.ch/e/daten/daten_u_sta.html) shows that the ratio M2/M1 remained close to 2 from 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 exchange's members charge prices that do not diverge significantly from its cash prices -- those charged to their nonmembers -- then counter-cyclicity may derive from barter's ability to create credit. WIR activities are highly public and centralized, subject to the scrutiny of other customers, and so unlikely to allow confidential discounts. Also, prices for goods and services advertised in the WIRPlus magazine (2000-2005) are regularly quoted in WIR-credit prices that are higher than their price in Swiss Francs, so this does not seem to be downward price flexibility. Lower prices on barter than cash would tend to divert trade to the former. This would be undesirable for most businesses, 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 since WIR's bylaws restrict membership to small and medium businesses (Defila 1994), members will usually have comparatively little price-setting power. Thus, the counter-cyclical history of WIR is likely due more to its credit creation than its added price flexibility. Inventory flexibility, however, could also be a factor, even before wide-scale use of computers. If such network 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 unlike neoclassical theory (Romer 1997, Howitt and Phillipe 1998, Arthur 1996). Such industries are therefore likely, especially as their 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," yet they appear strongly counter-cyclical. It may be important to understand why. To quote Mervyn King (1999), now Governor of the Bank of England, electronic exchange may build a world in which "central banks in their present form would no longer exist; 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: JulyAugust. 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 Wirtschaftsring," 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 for the 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 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.htm

FIG. 1.


10 A c

C

B

b

a

Figure 3: Unemployed Swiss Workforce and WIR Accounts, 1948-2003 90

200 Accounts

UE

180

80

WIR Accounts (thousands)

140 60 120 50 100 40 80 30 60 20

40

10

20

0

0

Unemployment (thousands)

160

70

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947


11 Figure 4: Swiss Money Supply (M2) and WIR Turnover, in 1990 Swiss Franks, 1960-2003

M oney Supply (M illions of S.F.)

500,000

Money Supply

2,000

Turnover

400,000 1,500 300,000 1,000 200,000

500

100,000

-

0

WIR Turnover (M illions of S.F.)

2,500

600,000

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

1960


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/

Turnover

1948

814

1.1

0.3

Year Participants Turnover Credit Credit/

Turnover

0.2727

1976

23,172

223.0

82.2

0.3686

23,929

233.2

84.5

0.3623

1949

1,070

2.0

0.5

0.2500

1977

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

0.2460

1980

24,227

255.3

94.1

0.3686

1981

24,501

275.2

103.3

0.3754

1952

2,941

12.6

3.1

1953

4,540

20.2

4.6

0.2277

1954

5,957

30.0

7.2

0.2400

1982

26,040

330.0

127.7

0.3870

1983

28,418

432.3

159.6

0.3692

1955

7,231

39.1

10.5

0.2685

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

0.2285

1988

46,895

1,329

437.3

0.3290

1989

51,349

1,553

525.7

0.3385

1960

12,567

67.4

15.4

1961

12,445

69.3

16.7

0.2410

1962

12,720

76.7

19.3

0.2516

1990

56,309

1,788

612.5

0.3426

62,958

2,047

731.7

0.3574

1963

12,670

83.6

21.6

0.2584

1991

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

0.2222

1994

79,766

2,509

904.1

0.3603 0.3782

1966

15,076

121.5

27.0

1967

15,964

135.2

37.3

0.2759

1995

81,516

2,355

890.6

1968

17,069

152.2

44.9

0.2950

1996

82,558

2,262

869.8

0.3845

1997

82,793

2,085

843.6

0.4046

1969

17,906

170.1

50.3

0.2957

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

78,505

1,691

791.5

0.4681

77,668

1,650

784.4

0.4754

1974

20,902

200.0

73.0

0.3650

2002

1975

21,869

204.7

78.9

0.3854

2003

Sources: Data to 1983 are from Meierhofer (1984). Subsequent years are from the annual Rapport de Gestion and communications 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) d'Affaires, 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 Table 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)

Johansen Cointegration Test, P-Value: LnACCTS(-1)

.01

.10

.10

.20

1.000000

1.000000

1.000000

1.000000

-0.229313 [-14.210]***

-0.279225 [-5.067]***

-9.958488

-9.633628

LnUE(-1)

-0.099794 [-5.158]***

LnMON(-1)

-1.131793 [-7.193]***

-1.852541 [-14.646]***

4.026392

12.90664

C Error Correction:

(c)

(d)

D(LNACCTS) D(LNACCTS) D(LNACCTS) D(LNACCTS)

CointEq1

-0.099030 [-4.530]***

-0.061111 [-2.509]*

-0.046168 [-4.189]***

-0.021387 oo [-1.680]

D(LnACCTS(-1))

0.786376 [6.128]***

0.912327 [6.419]***

0.709588 [ 5.099]*

0.728941 [ 5.476]***

D(LnACCTS(-2))

0.011106 [0.073]

0.071534 [0.389]

0.186215 [ 1.131]

-0.029469 [-0.184]

D(LnACCTS(-3))

0.016312 [0.108]

-0.164988 [-0.968]

-0.082101 [-0.503]

0.326010 [ 2.268]*

D(LnACCTS(-4))

0.201309 oo [1.783]

0.151225 [1.040]

0.137935 [ 1.104]

-0.346573 [-3.106]**

∑t D(LnACCTS(-t))

1.01510 [13.392]***

0.970097 {10.395]***

0.951636 [11.925]***

[7.930]***

D(LnUE(-1))

0.003822 [0.801]

4.393E-03 [ 1.156]

7.167E-03 [ 0.967]

D(LnUE(-2))

-0.018154 [-3.219]**

-0.016161 [-3.839]***

-0.015226 oo [-1.971]

D(LnUE(-3))

-0.000628 [-0.115]

-4.846E-03 [-1.139]

-2.387E-03 [-0.302]*

D(LnUE(-4))

-0.013051 [-3.444]**

-0.015700 [-3.699]***

-7.389E-03 [-1.016]

∑t D(LnUE(-t))

-0.02801 [-2.455]*

-0.032315 [-3.930]***

-0.017835 [-1.405]

D(LnMON(-1))

-0.122727 [-2.852]**

-0.103439 oo [-2.043]

D(LnMON(-2))

-0.051672 [-0.948]

-0.085094 o [-1.642]

D(LnMON(-3))

-0.118640 [-1.398]

-8.616E-03 [-0.168]

D(LnMON(-4))

-0.039625 [-0.490]

-3.743E-03 [-0.054]

∑t D(LnMON(-t))

-0.332664 oo [-1.870]

-0.200892 [-1.384]

Constant

0.010038 [1.254]

4.566E-03 [0.730]

5.096E-03 [ 1.078]

0.019485 [ 2.213]*

39 0.926 0.886 117.969 -5.472 -4.869 0.986 0.613 0.148 0.243 0.820

39 0.859 0.815 107.929 -5.022 0.905 0.951 0.159 0.073 0.476 0.905

39 0.879 0.842 111.685 -5.215 -4.788 0.397 0.131 0.457 0.083 0.624

50 0.859 0.827 104.540 -3.782 -3.399 0.000 0.133 0.065 0.008 0.973

Observations R-squared Adj. R-squared Log likelihood Akaike AIC Schwarz SC P-val. LM test (1) P-val. LM test (2) P-val. LM test (3) P-val. LM test (4) P-val. LM test (5)

0.678909

* 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 Table 3: Pairwise Granger Causality tests: WIR Accounts, Unemployment, and Money Supply (M2), 1960-2002

Lags: 4 Null Hypothesis: No Granger Causality of LnUE upon LnACCTS LnACCTS upon LnUE

Obs. F-Statistic

P-value

50

1.16002 0.93729

0.34209 0.45176

43

2.33012 1.94024

0.07577 0.12620

LnMON upon LnACCTS 40 LnACCTS upon LnMON

0.93776 1.23926

0.45511 0.31474

LnMON upon LnUE 39 LnUE upon LnMON

6.32342 2.74761

0.00082 0.04650

43

2.44883 2.24093

0.07940 0.10019

41

2.23398 1.41519

0.10207 0.25524

LnMON upon LnUE 40 LnUE upon LnMON

7.07425 3.32948

0.00084 0.03130

LnUE upon LnACCTS LnACCTS upon LnUE

Lags: 3 LnUE upon LnACCTS LnACC upon LnUE LnMON upon LnACCTS LnACCTS upon LnMON


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 Test, P-Value:

0.10

0.05

0.10

0.01

0.05

1.000000

1.000000

1.000000

1.000000

1.000000

LnTURN(-1)

LnMON(-1) -4.874644 -8.244366 -1.830236 [-3.586]*** [-5.116]*** [-6.091]*** LnGDP(-1)

5.760765 [ 2.300]*

12.54567 [ 4.390]***

Constant -17.08268

-59.27782

-5.997376 -3.430694 [-5.758]*** [-8.093]*** 16.47885

68.250310

36.04694

Error Correction: D(LnTURN) D(LnTURN) D(LnTURN) D(LnTURN) D(LnTURN) Coint. Equation -0.079224 [-2.644]*

-0.040163 [-2.771]**

-0.076565 [-2.906]**

-4.023E-03 [-0.27497]

-0.044820 [-2.388]*

0.686543 [4.693]***

0.766490 [ 4.87723]

0.585649 [ 4.372]***

0.128213 [ 0.82271]

0.302734 [ 2.281]*

D(LnTURN(-1))

0.573234 [3.247]**

0.589409 [3.737]***

D(LnTURN(-2))

0.323044

0.324488 [1.94044]

0.248226

0.96926 [8.694]***

0.91390 [9.741]***

0.93477 [9.561]***

D(LnMON(-1)) -0.360272 oo [-1.802]

-0.363954 oo [-1.835]

-0.066805 [-0.482]

D(LnMON(-2)) -0.193820 [-0.938]

-0.186259 [-0.997]

-0.313684 [-2.361]*

-0.38049 oo [-1.948]

o

[1.595] D(LnTURN(-3)) ∑t D(LnTURN(-t))

oo

o

[1.602]

0.072986 [0.372] 0.894704 0.888383 [8.767]*** [14.965]***

D(LnMON(-3))

0.087171 [0.462]

∑t D(LnMON(-t))

-0.46692 [-1.105]

-0.55021 oo [-1.825]

D(LnGDP(-1))

-1.41559 [-2.521]*

-1.345752 [-2.459]*

-1.071322 [-2.503]*

-0.761633 oo [-1.924]

D(LnGDP(-2))

0.095148 [0.153]

0.233857 [0.515]

0.841125 [ 2.065]*

0.686411 oo [ 1.957]

D(LnGDP(-3))

0.089058 [0.200]

∑t D(LnGDP(-t))

-1.23139 oo [-1.866]

-1.11189 [-2.094]*

-0.230197 [-0.516]

-0.075222 [-0.177]

0.034573 oo [1.90229]

0.038895 [2.294]*

0.011874 [1.194]

5.738E-03 [ 0.433]

-3.128E-03 [-0.213]

40 0.796 0.726 69.274 -2.914 -2.449 0.970 0.753 0.367 0.806

41 0.769 0.720 68.968 -2.974 -2.640 0.557 0.530 0.711 0.147

41 0.743 0.706 66.750 -2.963 -2.713 0.707 0.843 0.905 0.234

41 0.715 0.675 64.661 -2.862 -2.611 0.179 0.012 0.936 0.937

53 0.837 0.820 73.698 -2.555 -2.332 0.517 0.067 0.518 0.818

Constant Observations R-squared Adj. R-squared Log likelihood Akaike AIC Schwarz SC P-val. LM test (1) P-val. LM test (2) P-val. LM test (3) P-val. LM test (4)

Sources: WIR Annual Reports (Rapport de Gestion), World Bank Development Indicators, 2004


16 Table 5: Pairwise Granger Causality tests: WIR Turnover, GDP, and Money Supply (M2), 1960-2003

Lags: 3 Null Hypothesis: No Granger Causality of LnMON upon LnTURN LnTURN upon LnMON

Obs. F-Statistic

P-value

41

2.79406 2.61596

0.05506 0.06691

LnGDP upon LnTURN LnTURN upon LnGDP

44

3.71742 2.10155

0.01966 0.11666

LnGDP upon LnMON LnMON upon LnGDP

41

1.07191 14.3525

0.37395 3.3E-06

42

0.77078 1.26698

0.46994 0.29362

LnGDP upon LnTURN LnTURN upon LnGDP

44

2.78191 2.87996

0.07423 0.06814

LnGDP upon LnMON LnMON upon LnGDP

42

0.86046 20.5973

0.43126 9.7E-07

Lags: 2 LnMON upon LnTURN LnTURN upon LnMON


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 Test, P-Value:

0.07

0.05

0.15

0.05

0.01

LnCRED(-1)

1.000000

1.000000

1.000000

1.000000

1.000000

LnMON(-1) LnGDP(-1) Constant Error Correction:

-5.379977 -9.364830 -2.393765 [-4.649]*** [-4.930]*** [-10.275]*** 6.221436 [2.923]**

14.21366 [ 4.257]***

-15.41026

-64.91655

-3.307454 [-9.373]*** -5.372218 [-6.514]***

24.63596

61.54493

35.66864

D(LnCRED) D(LnCRED) D(LNCRED)

CointEq1

-0.086261 [-2.705]*

-0.025332 oo [-1.840]

-0.095920 [-2.361]*

-0.025688 -0.124612 [-0.891] [-3.686]***

D(LnCRED(-1))

0.530399 [3.029]**

0.626873 [3.682]***

0.692746 [4.204]***

0.736722 [ 4.265]

D(LnCRED(-2))

0.080187 [0.401]

0.177155 [0.991]

0.053711 [0.263]

0.047170 0.484968 [ 0.220] [ 4.379]***

D(LnCRED(-3))

0.350244 oo [1.743]

0.148277 [0.850]

0.046772 [ 0.260]

∑t D(LnCRED(-t))

0.96083 [6.721]***

0.80403 [6.640]***

0.894735 [6.521]***

D(LnMON(-1))

-0.556649 [-2.276]*

-0.339104 o [-1.600]

-0.204543 [-1.262]

D(LnMON(-2))

-0.006542 [-0.030]

0.099423 [0.468]

-0.081741 [-0.512]

D(LnMON(-3))

0.038870 [0.187]

∑t D(LnMON(-t))

-0.52432 [-1.108]

-0.23968 [-0.718]

D(LnGDP(-1))

-1.439979 [-2.156]*

-1.000384 o [-1.601]

-0.128998 [-0.261]

0.025940 [ 0.046]

D(LnGDP(-2))

-0.362967 [-0.552]

-0.019670 [-0.039]

-0.156727 [-0.294]

-0.386 [-0.631]

D(LnGDP(-3))

-0.075068 [-0.148]

0.563805 [ 1.247]

-0.208374 [-0.425]

∑t D(LnGDP(-t))

-1.87801 [-2.188]*

-1.02005 oo [-1.729]

0.278079 [0.083]

-0.568319 -0.872

Constant

0.049263 [2.212]*

0.035872 oo [1.861]

0.014946 [1.156]

1.137-E3 [ 0.070]

0.010071 [ 0.491]

40 0.689 0.581 65.478 -2.724 -2.259 0.950 0.946 0.939 0.458

41 0.629 0.550 63.999 -2.732 -2.397 0.360 0.532 0.940 0.263

40 0.636 0.557 62.375 -2.719 -2.381 0.598 0.912 0.404 0.252

40 0.598 0.510 60.374 -2.619 -2.281 0.120 0.130 0.123 0.812

52 0.713 0.668 57.314 -1.897 -1.596 0.000 0.118 0.726 0.750

Observations R-squared Adj. R-squared Log likelihood Akaike AIC Schwarz SC P-val. LM test (1) P-val. LM test (2) P-val. LM test (3) P-val. LM test (4)

0.263086 oo [ 2.012]

0.150581 oo [ 1.189]

0.830665 0.898635 [5.700]*** [9.616]***

-0.121267 [-0.789] -0.407551 [-1.293]

Sources: WIR Annual Reports (Rapport de Gestion), World Bank Development Indicators, 2004


18 Table 7: Pairwise Granger Causality tests: WIR Credits, GDP, and Money Supply (M2), 1960-2003 Lags: 3

Null Hypothesis: No Granger Causality of LnMON upon LnCRED LnCRED upon LnMON

Obs. F-Statistic

P-value

41

1.16580 0.83257

0.33707 0.48531

LnGDP upon LnCRED 44 LnCRED upon LnGDP

0.57871 2.61165

0.63267 0.06579

LnGDP upon LnMON 41 LnMON upon LnGDP

1.07191 14.3525

0.37395 3.3E-06

42

1.32894 1.36067

0.27710 0.26902

LnGDP upon LnCRED 44 LnCRED upon LnGDP

0.41267 3.81860

0.66474 0.03058

LnGDP upon LnMON 41 LnMON upon LnGDP

0.86046 20.5973

0.43126 9.7E-07

Lags: 2 LnMON upon LnCRED LnCRED upon LnMON


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