“Barter rings improved” in the US and Switzerland do billions of dollars of trade


RECIPROCAL EXCHANGE NETWORKS:
IMPLICATIONS FOR MACROECONOMIC STABILITY
James Stodder (stodder@rh.edu),
Rensselaer at Hartford, Hartford CT, 06120

An earlier version of this paper was presented at the International Electronic and
Electrical Engineering (IEEE) Engineering Management Society (EMS) Conference, in
Albuquerque, New Mexico, August 2000.

Abstract:

"Barter rings" in the US and Switzerland do billions of dollars of trade each
year. Their turnover is seen to be counter-cyclical. Most studies of the internet's
macroeconomic impact focus on the stabilizing effect of greater price and inventory
flexibility. The pre-internet experience of these systems, however, suggests that, for
networks independent of direct monetary exchange, expanded credit availability may be
even more stabilizing.
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, ecommerce
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 macro-stabilizing. 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, the balancing of all supplies and demands were known and calculated,
through a central clearing house? The problem of how much money-stuff to create would
disappear; money, in the conventional sense, would no longer exist.
Such moneyless exchange took place in the ancient storehouse economies (Polanyi
1947), and in the simplified models of microeconomic exchange — both under conditions
where the relevant information is centralized. The ancient storehouses economies
collapsed, and monetary systems (from a root meaning 'to monitor') evolved because
the 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, and
centralized barter is an emerging form of e-commerce. Barter clearing-houses are
growing with internet startups like swap.com, BarterTrust.com, and uBarter.com (Anders
2000).
The possible implications of moneyless business are not 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 recently been asked by leading
macroeconomists like Mervyn King, Deputy 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
recent World Bank conference on the "Future of Monetary Policy and Banking" (World
Bank 2000). His warnings have even 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 records of such barter, possibly on computers, will track the bids (unmet
demands) and offers (excess supplies) of all agents on the network. 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 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 creditworthiness,
and statistical leading indicators.
This is not to imply that a centralized barter administration cannot make mistakes,
cannot extend too much or too little credit. 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). Yet only one contemporary economist, to my knowledge, has examined
the macroeconomic record of the WIR. Studer (1998) finds positive correlation 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. The data used in Studer's study, however, go back only to 1994.
This paper examines the historic data on two large barter exchanges — the WIR,
founded in 1930s Switzerland, and the International Reciprocal Trade Association
(IRTA), founded in the US in the early 1970s. The data will show that the economic
activity of both exchanges is counter-cyclical, rising and falling against, rather than with,
the business cycle.
III. The Data
Because the financial record of these exchanges is not widely known, I provide the basic
data. The North American data are available online (IRTA 1999). In the regressions to
follow, I have only used the series up to 1995, as the website states that the more recent
years are extrapolations.
Table 1: Volume of Corporate Barter,
North American Companies, 1974-1995
(in Millions of Current US Dollars)
Year Volume Year Volume
1974 $850 1986 $3200
1976 980 1987 3470
1977 1130 1988 3750
1978 1300 1989 4050
1979 1500 1990 4550
1980 1720 1991 5100
1981 1980 1992 5570
1982 2200 1993 6050
1983 2440 1994 6560
1984 2680 1995 7216
1985 2900
Source: Barter by North American Companies,
(http://ww2.dgsys.com/~irta/statisti.html).
Note that data for 1975 are missing, and in the present study, are
interpolated. For the regressions, these nominal figures were adjusted by
a 1992-based deflator for services, as explained in the text.
These IRTA data are evidently not of the highest quality. Table 1 shows clear roundingoff,
and should therefore be considered only a first-order approximation. Whatever
biases may have colored the compilation of this data, however, the desire to show a
counter-cyclical tendency was apparently not one of them. I know of no empirical studies
of the IRTA, apart from my own (Stodder 1998), that claim to find such macroeconomic
stabilization. Paradoxically, this is a source of some confidence.
Note that high-quality data on total barter transactions carried out though the IRTA do
exist, but are not in the public domain. All commercial barter credits count as regular
income and must be filed on Form 1099-B of the US Internal Revenue Service (IRTA,
1995). Since the IRTA Corporate Trade Council (CTC) for these years showed no
Canadian or Mexican companies, it is reasonable to conclude that most of the "North
American" barter is US.
Although the US has more complete public economic statistics than almost any other
country, the Swiss banking tradition is famous for the quality of its private records. The
WIR, organized as a bank, is no exception, giving three time-series to the US one.
Table 2: Barter Turnover, Number of Firms, and Credit-on-Turnover, WIR-Bank,
1948-99 (in Millions of Current Swiss Franks)
Year Turnover Participants Credit Year Turnover Participants Credit
1948 1.1 814 0.3 1974 200.0 20902 73.0
1949 2.0 1070 0.5 1975 204.7 21869 78.9
1950 3.8 1574 1.0 1976 223.0 23172 82.2
1951 6.8 2089 1.3 1977 233.2 23929 84.5
1952 12.6 2941 3.1 1978 240.4 24479 86.5
1953 20.2 4540 4.6 1979 247.5 24191 89.0
1954 30.0 5957 7.2 1980 255.3 24227 94.1
1955 39.1 7231 10.5 1981 275.2 24501 103.3
1956 47.2 9060 11.8 1982 330.0 26040 127.7
1957 48.4 10286 12.1 1983 432.3 28418 159.6
1958 53.0 11606 13.1 1984 523.0 31330 200.9
1959 60.0 12192 14.0 1985 673.0 34353 242.7
1960 67.4 12567 15.4 1986 826.0 38012 292.5
1961 69.3 12445 16.7 1987 1065.0 42227 359.3
1962 76.7 12720 19.3 1988 1329.0 46895 437.3
1963 83.6 12670 21.6 1989 1553.0 51349 525.7
1964 101.6 13680 24.3 1990 1788.0 56309 612.5
1965 111.9 14367 25.5 1991 2047.0 62958 731.7
1966 121.5 15076 27.0 1992 2404.0 70465 829.8
1967 135.2 15964 37.3 1993 2521.0 76618 892.3
1968 152.2 17069 44.9 1994 2509.0 79766 904.1
1969 170.1 17906 50.3 1995 2355.0 81516 890.6
1970 183.3 18239 57.2 1996 2262.0 82558 869.8
1971 195.1 19038 66.2 1997 2085.0 82793 843.6
1972 209.3 19523 69.3 1998 1976.0 82751 807.7
1973 196.7 20402 69.9 1999 1833.0 82487 788.7
Sources: Data to 1983 are from Meierhofer (1984). Subsequent years are from
Annual Reports and WIR, public relations department (2000).
IV. The Regression Results
United States
Figures 1 and 2 below give visual evidence of Corporate Barter's "mirror image" or
negative correlation with US GDP, and its more positive correlation with Wholesale
Inventories.
To deflate the nominal IRTA data of Table 1, the 1992 chained price index for Services
was used. By most accounts US corporate barter is heavily weighted toward services
(Healey 1996), especially in media and advertising. Gross Domestic Product is in real
terms, using a 1992 chained deflator, from the Economic Report of the President (1996).
Figure 1: Annual Change in US GDP and Corporate Barter (1992 Prices), 1974-95.
Figure 2: Annual Change in US Wholesale Inventories (left axis) and Corporate
Barter (right axis) 1992 Prices, 1974-95.
Right-hand-side variables (in Table 3) are a Time trend, Wholesale Inventories, the
percentage of Unemployment, and the Gross Domestic Product of the US economy.
There is clear multicollinearity between these last two, as demonstrated by the Rsquared
term being virtually unchanged when either one of them is dropped, in the last
three estimates. Inventories show less multicollinearity, going "both ways" in the
business cycle — rising with expected upturns, but also with unexpected downturns. As a
result of this independence, the coefficient on Inventories is significant throughout.
Estimates in Table 3 are first-order auto-regressive (AR1). Durbin Watson statistics fall
mostly into the indeterminate area, so the null hypothesis of no auto-correlation can be
rejected at level 5 percent. Regression [4] shows positive auto-correlation.
The coefficient on each variable is significant in at least one equation. All coefficients
have signs consistent with the hypothesis of barter being counter-cyclical.
Table 3: US IRTA Corporate Barter, as Explained by Macroeconomic Variables
Dependent Variable: Corporate Barter, 1974-1995
(t-stats in italics, * : p-value < 0.05, o : p <0.10)
Equation [1] [2] [3] [4]
Variable
Constant 1407.73 -344.37 2174.86 1070.110
0.641 -0.446 3.496* 2.363
Time 132.118 71.835 159.491 131.782
1.659 1.977 o 6.120* 5.099*
Wholesale Inv. 15.635 17.656 14.135 8.869
2.801* 3.512* 3.825* 2.719*
Unemploy. -0.342 55.034
-0.851 2.172*
GDP 18.279 -0.468
0.365 -2.345*
Regress. Mthd AR1 AR1 AR1 AR1
R-squared 0.892 0.893 0.890 0.861
Adj. R-squared 0.867 0.875 0.871 0.846
Durbin-Watson 1.323 1.271 1.305 0.824
Rho 0.929 0.927 0.929 0.927
t-stat. of Rho 14.788* 14.706* 15.166* 15.014*
Log likelihood -124.14 -124.60 -124.22 -127.16
Observations 22 22 22 22
Sources: IRTA (1995a) and Economic Report of the President (1996). Also, see Stodder (1998).
Switzerland
As Figure 3 below shows, growth in the number of WIR Participants has tracked Swiss
Unemployment very closely, 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 firms are generally more subject to unemployment risks. Note that only 40
observations were available in these regressions, since the OECD data on Inventories
only go back to 1960.
Figure 3: Change in number of Swiss Unemployed (in 1000s, left axis) and in
number of WIR Participant-Accounts (in 1000s, right axis), 1948-99.
To deflate the WIR data, a chained price deflator on 1990 GDP is used. In Table 4 the
dependent variable is the change in number of Participants. Right-hand-side variables
are the Change in Unemployment, Change in Gross Domestic Product, and Change in
all Private Inventories — all in actual and not in percentage terms. The Durbin-Watson
statistics show the hypothesis of no positive correlation cannot be rejected at 5 percent.
Turnover is seen to be largely pro-cyclical, rising and falling in tandem with the change in
GDP and against changes in Inventories (See Figures 4 and 6). Credit advanced by the
WIR, on the other hand, is highly counter-cyclical, correlated against GDP and with
Inventories (See Figures 5 and 7).
Figure 4: Change in Swiss GDP (left axis), and Change in Total WIR Turnover (right axis), both in
1990 Swiss Franks, 1948-99.
Figure 5: Change in Swiss GDP (left axis), and Change in Credits Advanced in WIR (right axis), both
in 1990 Swiss Franks, 1948-99.
Figure 6: Change in Swiss Inventories, Millions of 1990 Swiss Franks (left axis), and Change in
Annual Turnover in WIR (right axis), 1960-99.
Figure 7: Change in Swiss Inventories, Millions of 1990 Swiss Franks (left axis),
and Total Credits advanced in WIR (right axis), 1960-99.
In Table 4 below, Change in the number or WIR Participants is regressed against
Change in Unemployment (in thousands, not as a percentage), Change in real GDP (in
1990 Swiss Franks), and Change in Real Inventories (also 1990 based.) The impression
of an overwhelming correlation between membership and unemployment, seen in Figure
4, is confirmed. R-squared terms are relatively low, however, and the Durbin-Watson
term is in the indeterminate region, so the null hypothesis of first-order autocorrelation
cannot be rejected at the 5% level.
Table 4: Participants in the WIR Barter Network, as Explained by Macroeconomic
Variables 1960-1999
Dependent Variable: Change in Number of WIR Participants**
(t-stats in italics, * : p-value < 0.05, o : p <0.10)
Equations [1] [2] [3]**
Variable
Constant 1381.46 1368.13 -9.011
1.299 1.291 -2.808*
Change Unemploy. 19.299 20.280 0.0242
3.130* 3.088* 1.485*
Change GDP 0.01251 1.892
0.470 5.767*
Change Inventories -0.0674 -0.0824 -0.363
-1.992* -1.761 o -2.272*
Regression Method AR1 AR1 AR1
R-squared 0.279 0.283 0.963
Adj. R-squared 0.240 0.223 0.960
Durbin-Watson Stat. 1.344 1.37051 0.627
Rho (autocorrelation) 0.9163 0.915 0.986
t-statistic of Rho 16.691* 16.251* 63.098*
Log likelihood -318.006 -317.884 58.191
Number 40 40 36
** natural log of original, not change term, in [3]
Sources: OECD: "Historical Statistics" (1998), "Economic Surveys: Switzerland" (1999);
IMF: "Economic Outlook" (2000); Madison (1995); and Mitchell (1998).
In Table 5, annual Real Turnover in WIR, again in 1990 Swiss Franks, is regressed
against the same variables as in Table 4 above. Note that Turnover is correlated with
Unemployment, and thus counter-cyclical to this extent — just as Membership was in the
previous table. However, we now find a positive correlation with GDP, and a negative
correlation with Inventories — and thus a pro-cyclical relationship with these variables.
Most coefficients are significant, but the low R-squared and Durbin-Watson terms do not
inspire confidence.
In Table 6, I regress Credit against a slightly different set of variables, here using
Change in Gross Capital (which includes inventories) rather than the change in
inventories itself. With decreased value of existing capital stock in a recession, this
emphasizes the counter-cyclical aspect even more. The regressions with high Durbin-
Watson statistics [1] and [2], show low R-squares, and those with high R-squares, the
log forms [3] and [4], show low Durbin-Watsons.
Despite their plausible signs and significance of the coefficients, none of the regressions
in Tables 4-6 are convincing, because of possible auto-correlation and low R-squared
problems. In the final regressions, Table 7 below, these problems are partly resolved.
Table 5: Total Turnover in the WIR Barter Network, as Explained by
Macroeconomic Variables 1960-1999
Dependent Variable: Change in Annual Turnover of WIR-Bank
(t-stats in italics, * : p-value < 0.05, o : p <0.10)
Equations [1] [2] [3]** [4]**
Variable
Constant 1381.46 1368.13 -9.011 -38.792
1.299 1.291 -2.808* -5.077*
Change Unemploy. 19.299 20.280 0.0242 0.1068
3.130* 3.088* 1.485* 2.677*
Change GDP 0.01251 1.892 3.594
0.470 5.767* 5.752*
Change Inventories -0.0674 -0.0824 -0.363
-1.992* -1.761 o -2.272*
Regression Method AR1 AR1 AR1 AR1
R-squared 0.279 0.283 0.963 0.0417
Adj. R-squared 0.240 0.223 0.960 .261E-2
Durbin-Watson Stat. 1.344 1.37051 0.627 0.772
Rho (autocorrelation) 0.9163 0.915 0.986 0.971
t-statistic of Rho 16.691* 16.251* 63.098* 38.808*
Log likelihood -318.006 -317.884 58.191 13.034
Number 40 40 36 52
** natural log of original, not "change" term,
used
in [3], [4].
Sources: Same as Table 4.
Table 6:Credit Advanced in the WIR Barter Network, as Explained by
Macroeconomic Variables 1960-1999
Dependent Variable: Change in Annual Credit Advanced by WIR-Bank
(t-stats in italics, * : p-value < 0.05, o : p <0.10)
Equations [1] [2] [3]** [4]**
Variable
Constant 11.034 10.172 -1.112 –25.554
1.186 1.17157 -0.336 -4.578*
Change Unemploy. 0.433 0.455 0.0692 0.0538
2.154* 2.373* 1.864 o 1.697 o
Change GDP -0.207 2.6536
-0.370 4.488*
Change Gross Cap. 0.5734 -0.179
2.024* -0.566
Regression Method AR1 AR1 AR1 AR1
R-squared 0.103 0.101 0.281 0.690
Adj. R-squared 0.0662 0.0829 0.237 0.661
Durbin-Watson Stat. 2.490* 2.497* 0.592 0.818
Rho (autocorrelation) 0.6967 0.689 0.994 0.964
t-statistic of Rho 6.902* 6.82298 116.617 30.564*
Log likelihood -224.595 -224.665 27.130 34.412
Number 51 51 36 36
** natural log of original, not "change" term,
used
in [3], [4].
Sources: Same as Table 4.
In Table 7, I use the ratio of Credit over total barter Turnover. In contrast to the results of
Table 4, the regressions on number of WIR participants, I find that the Unemployment
term is now not significant. The "Change in Inventories and Change in GDP, are highly
significant in regression [2]. The Durbin-Watson statistic for this equation, however,
indicates that the null hypothesis of first-order autocorrelation cannot be rejected at five
percent.
Since the previous regressions show Credit as correlated with Inventories, while
Turnover volume is correlated with GDP, the ratio of Credit to Turnover in Table 7,
therefore, correlates with Inventories and against GDP. As in the IRTA regressions of
Table 3, however, collinearity is evident between the GDP and Inventory terms. In both
cases some functional relationship is likely, although it is not specified here.
Table 7: WIR Credit-Turnover Ratio, as Explained by Macroeconomic
Variables 1948-1999
Dependent Variable: Annual Ratio of Credit to Turnover,
(t-stats in italics, * : p-value < 0.05, o : p <0.10)
Equations [1] [2] [3] [4]
Variable
Constant 1.86E-01 1.89E-01 1.92E-01 2.45E-01
5.314* 5.448* 5.067* 11.192*
Time 4.66E-03 4.49E-03 4.34E-03 3.04E-03
4.402* 4.648* 4.123* 4.400*
Unemploy. -5.50E-05
-0.384
Change GDP -1.53E-06 -1.52E-06 -8.73E-07
-2.633* -2.659* -1.767 o
Chnge Invnt. 2.23E-06 2.23E-06 3.63E-07
2.089* 2.129* 0.434
Regress.Mthd AR1 AR1 AR1 AR1
R-squared 0.457 0.419 0.343 0.568
Adj.R-squrd 0.394 0.396 0.307 0.550
Durb.-Watson 2.069* 1.664 2.229* 2.413*
Rho 0.832 0.983 0.846 0.788
t-stat. of Rho 9.788* 65.533* 10.709* 9.057*
Log liklhood -111.74 -416.85 -108.08 -132.53
Number 40 40 40 52
Sources: Same as Table 4.
V. Conclusions and Implications
The Swiss results are less persuasive than the US, perhaps due to the poorer coverage
of its national data (Maddison 1995, p. 135) — as opposed to its barter exchange data.
Nevertheless, there is substantial evidence for the general form of our hypothesis, that
centralized barter exchange is 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 consensus around the "sticky price" school of
macroeconomics, most commentary on the impact of e-commerce has concentrated on
prices, as we have seen. But if a barter exchange's members charge prices that do not
diverge significantly from its cash prices — those charged to their non-members — then
counter-cyclicity may derive from barter's ability to create credit.
The two barter exchanges studied here have very different pricing practices. The North
American IRTA is likely to benefit its participants through greater price flexibility, and
even under-the-table "discounts" off the list price (Magenheim and Murrell 1988). The
Swiss WIR, by contrast, is unlikely to engage in pricing that differs substantially from
cash deals. WIR credits cannot be exchanged for cash at a discount, a decision historian
Defila (1994) sees as crucial for the organization.
The IRTA is a loose affiliation of "barter middle-men," not a nationally centralized
exchange like the WIR-bank. The totality of the US barter exchanges is far smaller than
WIR, both absolutely and relative to the national economy. IRTA activities are less public
and less centralized, and therefore, far less subject to the scrutiny of other customers.
Prices available to members of the WIR, by contrast, are usually matters of public
information. Lower prices on barter than cash would surely divert trade to the former.
This is undesirable for most businesses. Within a cash-wide economy, other things
being equal, cash is always preferred (Healey 1996).
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 had little price-setting power. Thus,
the counter-cyclical history of WIR is likely more due to its credit creation than to added
price flexibility. Inventory flexibility, however, could also be a factor, even before widescale
use of computers. The IRTA's counter-cyclical path probably derives from all three
causes, with effects more closely balanced.
Whatever the causes, if these network exchanges are indeed counter-cyclical, this is not
the case for all "network economies". Telecommunications networks are highly subject
to increasing returns to scale, unlike older industries — and standard neoclassical theory
(Romer 1997, Howitt and Phillipe 1998). Such industries are likely to fuel faster growth,
but also, as their importance to the economy increases, greater macroeconomic
instability.
Networks like those studied here also have increasing returns and "network
externalities," yet appear to be counter-cyclical. Although started long before the
internet, these networks offer important historical evidence on the macroeconomics of
barter.
It is not too soon to begin studying this evidence. To quote Mervyn King, Deputy
Governor of the Bank of England, the logic of electronic barter may imply that "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." (King 1999)
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