1

U.S. Experience with Futures Transactions Taxes: 

Effects in a Highly Intermediated Market

Scott Mixon*

Abstract: 

The transactions tax on futures sharply reduced trading volume on wheat and corn contracts 

during the 1920s and 1930s but had no apparent effect on volatility or market quality. I find no 

evidence of a tax effect on open interest: I hypothesize this is because the relative magnitude of 

the tax was significantly higher for intermediaries than for other participants. Instead, the tax

appears to have substantially reduced intra-day trading but not longer-term positioning. Volumerelated proxies of liquidity therefore exhibit a strong relation with tax rates, but other measures 

of market quality show no relation to tax rates. In the long-run, however, exchange members 

doubled the minimum tick size in order to retain a large number of market makers and offset the 

impact of the tax. 

January 2016 

 

*

Office of the Chief Economist, Commodity Futures Trading Commission, 1155 21st Street, N.W., Washington, D.C. 

20581. smixon@cftc.gov, (202) 418-5771. I thank David Reiffen and seminar participants at the CFTC for helpful 

comments. 

The research presented in this paper was authored by Scott Mixon, a CFTC employee who wrote this paper in his 

official capacity with the CFTC. The Office of the Chief Economist and CFTC economists produce original research on 

a broad range of topics relevant to the CFTC’s mandate to regulate commodity future markets, commodity options 

markets, and the expanded mandate to regulate the swaps markets pursuant to the Dodd-Frank Wall Street Reform 

and Consumer Protection Act. These papers are often presented at conferences and many of these papers are later 

published by peer-review and other scholarly outlets. The analyses and conclusions expressed in this paper are 

those of the author and do not reflect the views of other members of the Office of Chief Economist, other 

Commission staff, or the Commission itself.
2

U.S. Experience with Futures Transactions Taxes: 

Effects in a Highly Intermediated Market 

Abstract: 

The transactions tax on futures sharply reduced trading volume on wheat and corn contracts 

during the 1920s and 1930s but had no apparent effect on volatility or market quality. I find no 

evidence of a tax effect on open interest: I hypothesize this is because the relative magnitude of 

the tax was significantly higher for intermediaries than for other participants. Instead, the tax

appears to have substantially reduced intra-day trading but not longer-term positioning. Volumerelated proxies of liquidity therefore exhibit a strong relation with tax rates, but other measures 

of market quality show no relation to tax rates. In the long-run, however, exchange members 

doubled the minimum tick size in order to retain a large number of market makers and offset the 

impact of the tax. 
3

The effects of a tax on financial transactions depend on the underlying market structure. Most 

studies find a sharp decline in transaction volume after a tax is imposed, and market quality 

measures often decline. The puzzle investigated in this paper is that, when taxes were levied on 

futures transactions in the U.S., trading volume fell but market quality measures did not 

otherwise deteriorate. There is no evidence futures end-users changed their usage of the 

contracts due to the tax. In this highly intermediated market, however, the middlemen felt 

disproportionate effects. In the short run (measured in years), intermediaries stayed in the 

market even as their revenues per client transaction were slashed by, in some cases, more than 

half. In the long run, however, the intermediaries effectively doubled the minimum tick size in 

order to preserve their numbers and effectively pass through the tax to customers. The increase 

in the tick size remained long after the tax was abolished.

The U.S. federal government levied a tax on futures transactions for more than twenty years. The 

tax, which applied to sales for future delivery but not to transactions in the cash markets, varied 

from one basis point of the notional value of the transaction to five basis points. The tax applied 

to all commodities at the same rate, and there were no exemptions for market making. A futures 

transaction tax was originally put in place to raise revenue for the Spanish American War (1898-

1901), revived to raise revenue for the First World War (1916-1918), and then put in place for 

general revenue purposes from 1919 to 1938. This paper provides the first empirical analysis of 

the tax and its effects over the 1921-1941 period.

The US experience is still relevant, because of multiple reasons, for 21st century policymakers 

considering the implementation of a transactions tax. First, the tax was imposed over decades of 

activity in the primary global derivatives market. Second, the tax varied exogenously because it 

was not tied explicitly to other market variables (such as expected volume) but was instead an 

outcome of the political process. Third, the analysis benefits from the substantial historical 

record, including extensive quantification and documentation of the market structure in 

existence at the time. Importantly, the length and variation of the tax rate allows analysis that is 

not feasible with recently imposed taxes. Long-run effects of the tax might only be visible after 

long periods of adjustment, especially if market participants hope to alter implementation of the 

tax. Given the prominence of the current debate regarding the introduction of financial 

transaction taxes in Europe and the US, it is important to extract all relevant lessons from past 

experience.

I find that the tax substantially reduced trading volume in wheat and corn futures. Regression 

estimates suggest that trading volume declined nearly 15% due to the tax when it was set at one 

basis point and declined more at higher levels of the tax. However, the tax did not have a direct, 

obvious effect on volatility or liquidity of futures markets. Unlike securities, futures contracts are 

in zero net supply; hence, I can measure the impact of the tax on end-users of the contracts by 

examining how open interest is affected by the tax. I find no significant change in open interest 
4

and conclude that the use of futures by end-users was not materially influenced by the tax. I 

conclude that the decline in transaction activity reflects less intermediation activity, rather than 

less activity by end-users of futures. This result is novel and is made possible by the use of 

futures market data (as opposed to securities market data) and the historical analysis of the 

market experience with the tax.

The conclusion is supported by direct examination of empirical proxies for liquidity and 

transaction costs. The Amihud (2002) measure, constructed as the ratio of absolute price 

changes to trading volume, is significantly lower at higher levels of the tax. If the analogous 

measure is constructed using the ratio of absolute price changes to changes in open interest, 

there is no evidence of a link to tax rates. Similarly, the closing range of daily prices shows no link 

to tax rates, nor does the Corwin-Schultz (2012) transaction cost estimator. These proxies are 

well explained by other effects such as market volatility, but they are not explained by the tax 

rate. There is little obvious evidence that market quality was diminished, although trading 

volume sharply declined. I conclude that there was a structural change in intermediation activity 

rather than an obvious relation to liquidity.

The modest direct impact on end-users of futures markets is underscored by the results of 

examining pricing behavior using synchronized data for cash and futures markets. The differential 

between cash and futures prices for wheat and corn is well explained during this period by the 

key, observable fundamental: visible supply (a proxy for convenience yield). In theory, the tax 

might have made futures less attractive to end-users, whether through a direct effect on returns 

or an indirect effect through diminished liquidity. The tax rate, however, appears to have had no 

material role in determining the relative prices of cash and futures. 

End-users of futures faced little direct cost due to the transactions tax. A hedger or speculator 

concerned about prices moving hundreds of basis points was not overly concerned with a tax of 

one basis point. Intermediaries bore the brunt of the tax and faced a marginal tax rate that was 

orders of magnitude larger. If an intermediary made $6.25 from capturing the typical 1/8 of a 

cent spread between bid and asked prices, a tax of one basis point of notional value represented 

roughly an 8% marginal tax rate when the market maker captured the spread on wheat priced at 

a dollar per bushel. At five basis points, the marginal rate was 40%. If market makers “split” a 

transaction to trade at 1/16 of a cent, the marginal tax rate reached 80% on a five basis point tax. 

Further, the tax was paid whether the market maker made or lost money.

1

 

However, end-users of futures ultimately faced a significant negative impact due to the disparity 

in the cross-sectional impact of the tax. In the short run, many market makers remained in the pit 

and continued to provide substantial liquidity, but they faced substantially diminished income 

 

1

The 80% marginal rate, for example, is computed as a tax of $2.50 per contract (0.05% tax rate x 5,000 bushels x 

$1.00 per bushel) on a tick value of $3.125 per contract (5,000 bushels per contract x 1/16 cent per bushel tick size). 
5

due to the tax. In 1933, exchange members effectively voted to increase the minimum tick size by 

eliminating “split” transactions that traded at 1/16 of cent increments. All end users now faced 

this increased tick size that effectively raised the price of a contract by $3.125 in order to offset a 

$2.50 tax.

The tax was abolished in 1938. In spite of the tick size increase, market makers were still 

pressured by a marginal tax rate of approximately 20% at this time (a 3 basis point tax worth over 

$1.00 versus a tick value of $6.25). In the long run, the competitive pressures would have 

inevitably led to a smaller number of market makers with less aggregate intermediation capacity. 

Less intermediation capacity would have meant that significant quantities traded would have 

resulted in worse execution prices (i.e., trades would have had larger price impact).2The 

commodity futures regulator weighed in on the tax, telling Congress that the tax placed a 

particularly heavy burden on market makers. The Chief of the regulator testified that elimination 

of the tax would increase the “stability and flexibility” of the market. Congress eliminated the tax 

in 1938, although the tick size increase from six years earlier remained in place. No other federal 

taxes have since been levied on futures transactions.

The structure of the paper is as follows. The first section discusses related literature. The second 

section provides historical background of the markets as context, with both a historical narrative 

of the US experience with futures transactions taxes and a quantitative description of the 

interwar commodity futures market. Section three uses the Grossman and Miller (1988) model to 

generate predictions for the equilibrium effect of a tax on a highly intermediated market. The 

fourth section covers the empirical analysis that is central to the paper. The fifth section discusses

the empirical results in their historical and institutional context. A final section concludes.

1. Related Literature. 

The empirical literature specifically relating transactions taxes to activity in derivatives markets is 

small, but there is a complementary, larger literature concerning transactions taxes applied to 

securities markets. The strongest finding from either literature: higher transactions taxes have a 

strong, negative impact on trading activity. Trading volume is often mobile across borders, so to 

the extent that equivalent trades are available offshore, volume often migrates after taxes are 

imposed in a given jurisdiction. The other prominent conclusion generally emerging from the 

studies is that market quality measures, such as bid-ask spreads, often deteriorate once taxes are 

imposed. However, there is no unanimous finding relating transactions taxes to volatility. 

 

2

Grossman and Miller (1988), in discussing the viability of competitive equilibria in markets with costly participation, 

describe exchange-driven restrictions on the number of market makers and minimum tick sizes as “Part of the art of 

managing a futures exchange...” (p. 630).
6

The most recent evidence is from the European experience. Following the financial crisis of 2008, 

the European Union debated transactions taxes on securities and derivatives. Advocates of a EUwide tax have had difficulty in achieving their goal, but transactions taxes were imposed in France 

and Italy. Colliard and Hoffmann (2015), Becchetti et al. (2014), and Meyer et al. (2014) examine 

the effects due to the French securities tax implemented in 2012. The tax programs reduced 

market quality: trading volume declined 20% even though market maker trades were exempt 

from the tax, but liquidity suppliers reduced market depth even as they kept spreads constant.

Studies of other tax rate changes found similar results. Pomeranets and Weaver (2011) conclude 

that increases in the New York State Securities Transaction Tax led to declines in market quality. 

Trading volume on the Taiwan Futures Exchange increased, and bid-ask spreads declined, but 

volatility was not particularly affected, following a May 2000 tax rate decrease for equity index 

futures (Chou and Lee (2002), Chou and Wang (2006)). Hu (1998) concluded that neither 

volatility nor turnover was generally affected when transaction taxes were changed in Asian 

markets over the 1975-1994 period. Umlauf (1993) documents the evisceration of Swedish 

equity market trading after a transactions tax was imposed in the 1980s.

A related literature connecting trading costs to volatility and trading volume in financial markets 

is relatively broad. Jones and Seguin (1997) found that the reduction in equity trading 

commission costs in May 1975 led to a decrease in volatility and increase in equity trading 

volume. In contrast, Liu and Zhu (2009) found that the 1999 deregulation of commissions in 

Japan led to increased price volatility. Surveying the literature, Wang and Yau (2012) conclude 

that there is no definitive link between transactions taxes and volatility, but that higher 

transactions taxes can dramatically reduce trading volume.

The general finding of Bjursell, Wang, and Yau (2012), Wang, Yau, and Baptiste (1998), and Wang 

and Yau (2000) is that higher transactions costs generate significantly lower trading volume and 

increase transactions costs and volatility for U.S. futures markets. They conclude that the 

elasticity of trading volume with respect to transactions costs is large enough that a tax of 2 basis 

points would completely eliminate futures trading in the major U.S. listed contracts. The key to 

this result is the magnitude of the assumed tax increase. For example, the authors find that such 

a tax would have added nearly $57 to the fixed transaction cost of $14.80 for S&P 500 futures 

contracts, leading to a 383% increase in costs due to the tax.

Rigorous theoretical analyses have shown that the welfare effects of transactions taxes in 

financial markets are ambiguous (Dow and Rahi (2000)). Recent work has highlighted that the 

impact of a particular tax depends on the cross-section of market participants, or the particular 

microstructure environment. Song and Zhang (2005) show that a tax may discourage noise 

traders who presumably increase volatility, but it will also discourage informed traders who aid 

price discovery. They also point out that a post-tax market that is thinner and less active might 

exhibit higher volatility to shocks. Pellizari and Westerhoff (2009) suggest that a transaction tax 
7

harms market quality in continuous double auction markets but would reduce market volatility in 

a specialist market.

In empirical work, Deng, Liu, and Wei (2014) examine cross-listed stocks in segmented markets 

and suggest that a tax might dissuade noise trading in markets dominated by uninformed traders 

but might dissuade informed trading in more mature markets. Colliard and Hoffmann (2015), 

studying effects of the 2012 transaction tax in France, highlight the liquidity provision of 

institutional investors and find significant erosion in market quality in stocks that do not rely on 

liquidity provision by tax-exempt market makers.

2. Historical Context 

a. Narrative 

The empirical focus of the paper is on the 1921-1941 period, but this represents only a portion of 

the transaction tax story for the US. Given the recent prominence of financial transaction taxes in 

the public sphere, including the post-2008 crisis introduction of taxes in France and Italy and 

protracted debates over a tax across the entire European Union, I augment the empirical analysis 

by providing broad historical context in this section.

Congress has enacted various taxes on derivatives, with the intention of either generating 

revenue or shaping/abolishing portions of the industry. The first U.S. federal derivatives tax 

appears to have been enacted in March 1863 on transactions in gold contracts. At the time, the 

greenback floated freely versus gold and a tax of 0.50% of the notional value plus 6% per annum 

(based on the tenor of the contract) was imposed in order to deter speculators from trading in 

gold. The tax was lowered in 1864 to 5 basis points, raised in 1865 to 10 basis points, and 

lowered yet again in 1866 to 1 basis point. It was finally abolished, along with stamp taxes on 

stocks, in 1872.

Agrarian sentiment against derivatives markets reached a peak in the early 1890s, and some of 

the proposed legislation in this vein included taxation measures designed to abolish derivatives 

trading. For example, the Butterworth Bill of 1890, never passed into law, would have imposed a 

licensing fee of $1,000 per year on dealers in futures and options, plus a tax of 20 cents per 

bushel of grain contracted (more than a 20% tax on the notional value for wheat) and 5 cents per 

pound on commodities such as cotton. Senator Ingalls of Kansas copied the language on taxation 

in a proposed amendment to the Sherman Anti-Trust Bill; Ingalls even moved that the title of the 

bill be changed to "A bill to suppress and punish unlawful trusts and combinations, to prevent 

dealing in options and futures, and for other purposes." The amendment did not make it into the 

final bill (McFarland, 1942). The Hatch Bill of 1892 required dealers to pay $2,000 per year and 20 
8

cents for each bushel of grain dealt in. The Hatch Bill passed both the Senate and House but was 

not enacted into law.3

A few years later, Congress imposed a variety of taxes as wartime measures to fund the Spanish 

American War. Taxes were levied on a wide range of occupations and goods. For example, banks, 

brokers, pawnbrokers, owners of theaters, circuses, and bowling alleys were taxed. Stamp taxes 

were imposed on telegraph messages, medicines, perfumes, and chewing gum. Among the 

documentary taxes was a tax of 1 bp on commodity transactions on a board of trade. The taxes 

were enacted at the end of June 1898 and were repealed in 1902.

Legislation designed to impede futures transactions was proposed at times during the early part 

of the 20th century, but the most far-reaching legislation was not enacted. For example, Senator 

James Clarke of Arkansas proposed a rider on a tariff bill in 1913 that would have imposed a 50 

cent per bale tax on cotton futures if the seller did not own physical cotton. Senator Cummins 

from Iowa later proposed an amendment for a 10 per cent tax on short sales of stock and on 

commodity futures.

Senator Thaddeus Caraway from Arkansas, in 1928, sponsored a bill to impose a 50 basis point 

tax on cotton and grain futures transactions. The bill was opposed by the Secretary of Agriculture 

and the head of the Grain Futures Administration and was defeated. Caraway introduced another 

bill the following year; that bill would have prevented the sale of cotton and grain futures unless 

the seller had, or could have, the commodity in hand. 

Congress also relied for a time on prohibitive taxes to shape the market structure for futures. The 

Cotton Futures Act of 1914 imposed a 2 cent per pound levy on all cotton traded for future 

delivery, unless the contract met various guidelines regarding cotton grading and delivery. The 

Future Trading Act of 1921 levied a 20 cent per bushel tax on grain option contracts (i.e., a 

$1,000 tax on an option valued at $5) and levied a 20 cent per bushel tax on grain futures 

contracts unless a) the seller had grain to deliver or b) the contract was traded on a board of 

trade that was designated as a “contract market” by the Secretary of Agriculture. Part of the Act 

was declared unconstitutional (based on its use of the power to tax) and was speedily replaced 

by the Grain Futures Act of 1922, which relied on Congress’s power to regulate interstate 

commerce instead. The Grain Futures Act did not regulate grain option contracts; the section of 

the Future Trading Act levying the 20 cent per bushel tax was not declared unconstitutional until 

January 1926, at which time futures option trading resumed on designated contract markets.

Legislation passed by Congress in the 20th century to impose taxes on futures trading was 

motivated more by a desire to generate revenue than by a desire to impede trading. Congress 

imposed a tax of one basis point on the notional value of futures trading in the Revenue Act of 

 

3

Cowing (1965) and Lurie (1979) provide useful discussions of “anti-option” sentiment during the late 19th century 

and early 20th century.
9

1914. The tax was abolished at the same time that many other war-time revenue measures 

ended, in the fall of 1916. A federal tax on futures trading was resurrected in late 1917 and was 

in continuous existence until 1938. The tax rate ranged over time from 1 to 5 basis points and 

applied to sales of all traded contracts at the same rate for a given period. The tax was paid by 

the seller of the futures contract, and it was evidenced by stamps applied to the trade memo.

The United States was not alone in levying taxes on grain futures transactions during this period. 

In 1923, the province of Manitoba levied a futures tax of 10 cents per 1,000 bushels of wheat

and lower amounts for other grain futures, but the act was soon declared invalid.

4

Russell (1937) 

provided details on commodity contracts across the globe, and he noted that wheat futures 

transactions on the Liverpool grain market had a tax at 6s per contract (on a commission of £10 

per contract), but there was no tax collected on London grain market transactions. Each trade on 

the Buenos Aires grain futures market was charged a registration fee of 16 pesos and a 

government tax of about 18 pesos, totaling about three quarters of a percent of the value.

Table 1 displays basic information on historical federal stamp taxes on futures contracts and 

summarizes some key facts of the discussion. A few key features of the taxes are worth noting. 

First, the taxes were initially imposed as emergency revenue measures during wartime. Taxes 

were imposed from 1898-1902 at a rate of 1 bp and from late 1914 to 1916 at a rate of 1 bp. The 

taxes were general revenue measures from late 1917 to 1938 and were not primarily associated 

with wars or with other objectives (e.g., to reduce speculation). Second, the Spanish-American 

war taxes applied equally to commodity transactions for future delivery or transactions for 

immediate delivery, but it was the exception. Taxes imposed in later years explicitly applied only 

to transactions for future delivery. Third, the tax rate fluctuated sharply, without an obvious time 

trend. The 1917 tax was imposed at 2 basis points (twice the wartime level), lowered to 1 bp in 

1924, raised by 400% to 5 bp in 1932, lowered to 3 bp in 1934, and then eliminated in 1938.

Fees have also been levied on futures trading at the state level in the U.S. Missouri imposed a 25 

cent per contract stamp tax on futures trades from 1907 to 1927, and Louisiana imposed a 2 

basis point tax on futures contracts from 1934 to 1940. Occupational taxes have also been 

imposed on brokers over the years, especially in the South. For example, depending on the size 

of the town, North Carolina charged from $50 to $300, Alabama charged $250 to $500, Georgia 

charged $1,000, and Tennessee charged from $200 to $400 in 1907 (Department of Commerce 

and Labor, 1907). These taxes were often applicable to bucket shops. 

No other federal tax has been levied on futures trading since 1938, although discussion of 

imposing a tax has been prominent at various times. Edwards (1992) and Schwert and Seguin 

(1993) discussed various federal proposals that had been made. Proposals have sometimes 

 

4

“Manitoba loses long battle to tax grain trade,” Winnipeg Tribune, page 1, 24 March 1925.
10

surfaced outside the federal level, e.g., Chicago Mayor Richard Daley proposed, and quickly 

removed, a tax in his budget for 1974.

5

Separate from a tax designed to reduce financial market activity or to raise general revenue, 

there have been discussions regarding a user fee to fund regulatory work. For example, the 

Futures Trading Act of 1992 mandated a study of the feasibility of a user fee for futures trading to 

offset funding for the Commodity Futures Trading Commission (CFTC), leading to a Government 

Accounting Office Study a year later (United States General Accounting Office, 1993). Such a user 

fee was contemplated in the Futures Trading Act of 1978 that first reauthorized the CFTC, was 

proposed by the Reagan administration for the 1983 fiscal year, and was subsequently proposed 

in federal budgets from 1991-1994, 1996, 2003, 2007-2010, and 2012-2015.6The Securities and 

Exchange Commission collects transaction fees on securities and security futures (fees on the 

latter are technically termed “assessments”). The National Futures Association collects 

transactions fees on transactions on futures and options on futures.

b. Quantitative description of interwar market structure 

Given the political importance of agriculture in financial market discussions of the early 20th

century, regulators collected copious data on trading activity during the interwar period. I use the 

data to quantify basic time series and cross-sectional features of the market in this subsection. 

Table 2 displays summary statistics for futures trading activity and volatility in wheat, corn, and 

oats; the statistics cover tax regimes over the period 1921-1941. Trading volume is measured as 

the average daily volume of all contracts at the Chicago Board of Trade, and the volatility is the 

annualized Parkinson (1980) estimate based on the high and low prices of the nearby contract 

during the month. The striking observation is that the trading activity was generally trending 

downward over the period, with trading in the late 1930s and early 1940s less than half of the 

trading volume from the early 1920s. This occurred despite the removal of transactions taxes in 

1938. The decline has been attributed to the substantial intervention of the federal government 

in farm programs to reduce volatility in agricultural prices (e.g., Working, 1954). The average 

volatility over the 1938-1941 sample is, in fact, the lowest value in the table across all three 

grains. Volatility does not appear to exhibit a particular trend, although the 1932-1934 period 

exhibited exceptionally high volatility. This period coincides with the highest tax rate in the 

sample (5 basis points), but it also coincides with the depths of the Great Depression. Similarly, 

open interest exhibits no obvious trend, although the highest levels of open interest occur during 

the middle part of the sample.

 

5

“Mayor’s plan to tax stock, commodities transfers assailed,” Chicago Tribune, 19 November 1973. 

6

“Recovery of the cost of CFTC regulation ... through transaction fees and license transfers the regulatory cost from 

the general taxpayer to the identifiable beneficiaries.” Executive Office of the President, Office of Management and 

Budget, Major Themes and Additional Budget Details, Fiscal Year 1983, p. 218. 
11

Quantification of the cross-section of trading activity is consistent with the observation that 

these futures contract markets were well-intermediated by scalpers, or pit locals. These traders 

typically held very small or no positions overnight, but they transacted large amounts of volume 

during the day. These intermediaries provided immediacy to longer-term buyers or sellers in 

exchange for the bid-ask spread. Scalpers were involved in a substantial amount of the trading 

volume. The available data suggests that from one-third to one-half of trade sides were 

associated with scalpers; the figure tended to be highest for wheat, lower for corn, and lowest 

for oats.7 Other participants who traded with a generally longer holding period, often on behalf 

of other participants, can be classified into exchange members and exchange non-members. 

Table 3 quantifies the cross-section of market activity in the wheat futures market. The Grain 

Futures Administration reported trading volume broken out by various participant types for a 

special study conducted on trading from January to October 1927 (United States Department of 

Agriculture (1930)). The raw data provides daily buy volume and sell volume by participant type

for over half of the observed trading volume, where the original classification was performed by 

the Grain Futures Administration. I define the volume traded by trader type i, Vit, as the sum of 

buys and sells for day t, and the daily change in long and short open interest for trader type i as 

∆OIit

LONG and ∆OIitSHORT, respectively. The table reports averages of the following ratio and its 

components:

Ratioit =

|∆OIit

LONG| + |∆OIitSHORT|

Vit

This ratio compares the gross position changes to the total trading volume for a given day; I 

average it across all days in the sample. A value near zero indicates that intraday trading rarely 

resulted in a gross position change, while a value closer to unity indicates that nearly all trading 

was associated with a change in position that was not offset by another trade that day. The table 

displays averages for the ratio’s numerator, the denominator, the ratio, and the associated 

fraction of volume for each participant type.

Based on this metric, scalpers clearly exhibit trading behavior that differs from other traders and 

is consistent with the description of them as intermediaries. The ratio for scalpers is quite close 

to zero at 0.013 because they traded huge quantities (approximately 45% of the classified buys 

plus sells) and generally ended the day flat. Speculators traded 38% of the classified volume but, 

in aggregate, maintained large overnight positions; the ratio is therefore higher at 0.140. 

Spreaders, who transacted related contracts across exchanges or across expiries (e.g., Chicago 

wheat versus Kansas City wheat), accounted for just 6.3% of volume. Finally, accounts classified 

as predominately hedging accounted for 10.5% of the volume. Not surprisingly, these accounts 

 

7

These characteristics are quantified in various reports of the Grain Futures Administration; see the Appendix for 

more details.
12

maintained overnight positions to an even larger extent than predominately speculative accounts 

did; the ratio is correspondingly higher at 0.224.

Table 4 displays information on the transactions costs associated with wheat futures activity. One 

part of the cost is the commission charged for executing the trade (or the clearing fee charged to 

locals). Another part is the bid-ask spread implicitly paid each time a round-turn transaction was 

undertaken. The table displays, for each of the three commodities examined, the median clearing 

fee / commission charge for the three participant types, and the minimum bid-ask spread for the 

1921-1941 sample period. Values are shown as percentage of the notional value of a 5,000 

bushel contract. The table also shows, for convenience, a weighted average fee, where the 

weights reflect the typical participant breakdown for each commodity.

The table makes clear that scalpers paid extremely low clearing fees to the exchange. Exchange 

members paid five times as much as scalpers, and exchange non-members paid ten times as 

much. The exchanges were well aware that scalpers facilitated liquidity for longer-term traders

(both members and non-members), and scalpers thrived in a low marginal profit, high volume 

business. Longer-term traders would not be as sensitive to transactions costs as scalpers, and 

they also had exogenous motivations to enter the futures market other than flipping a contract in 

order to profit by an eighth of a cent per bushel. The table also makes clear that blended 

transactions costs were relatively low for wheat but were three times as much for oats. This 

pattern partially reflects the heavier weighting of non-members, who paid the highest rates, 

relative to scalpers, in the less active markets. 

The final two columns display the bid-ask spread, as a percentage of notional value of the 

contract, for two separate periods. Prior to August 1933, traders could split transactions by 

offering half the quantity of a trade at a specified price and offering half at one tick above that 

price. The effect was to average the total quantity over the two price points and therefore allow 

trades at a sixteenth of a cent per bushel increment rather than an eight of a cent per bushel 

increment. Obviously, constructing this measure as a percentage commingles the average price 

per bushel across time and across products with the discrete tick values. Wheat, with an average 

price per bushel just above $1.00, has the lowest bid-ask spread of 0.05% in the early part of the 

sample and 0.14% in the latter part. Oats has an average price of $0.40 per bushel and has the 

highest percentage spreads of 0.15% in the early part of the sample and 0.36% in the latter part. 

Corn’s average price and spreads are about halfway between those for wheat and oats. The 

normalization is useful to gauge the magnitude of the tax to existing costs. For scalpers, the other 

real cost is the clearing fees; the bid-ask spread is more analogous to potential revenue. For nonscalpers, the costs include commissions as well as the bid-ask spread. The table makes clear that 

the magnitude of the tax was relatively small for some participants (e.g., non-exchange member 

participants in corn and oats) but huge for scalpers.
13

Traders were well aware of the costs of trading and tried to optimize their costs with respect to 

their trading style. Irwin (1933) examined the transaction level data of the traders who had been 

classified as “speculators” for the January – October 1927 wheat data and concluded that traders 

within this categorization were heterogeneous with respect to their trading frequency. In 

particular, he found that some of these traders were very active and maintained low end of day 

net exposures, whereas others were more directional and traded less frequently. He divided the 

traders into two groups. Of the 198 traders examined, the 85 in the “most active” grouping were 

predominately exchange members (72%) and the 113 in the “less active” grouping were 

predominately non-members (75%). 

3. Theory. 

This section explores the likely impact of a transactions tax in the futures market using the model 

of Grossman and Miller (1988). A key takeaway from the literature is that a “one-size fits all” 

conclusion regarding transactions taxes is elusive, and that the facts and circumstances for a 

given financial market should be taken seriously. Grossman and Miller’s model focuses on the 

equilibrium provision of liquidity by intermediaries and is well-suited for examining futures 

markets. I consider the model’s predictions for a fixed transaction cost increase for 

intermediaries (i.e., a tax) and show comparative static results for open interest, total trading 

volume, and the amount of trading volume that is intermediated by scalpers as the transaction 

tax changes. The model predicts that open interest and customer volume remains unchanged but 

that total trading volume declines due to a decline in scalper volume. Scalper volume declines 

because there are fewer market makers in equilibrium after the cost increase.8

The trade in the Grossman and Miller model is described as follows. At date one, a customer 

comes to the futures market to hedge a cash position with which he has been endowed (e.g., a 

hedger with a long physical position wishes to go short futures). Not finding another customer 

with an offsetting demand, the customer trades with market makers (scalpers) who facilitate part 

of the customer’s desired trade (the customer wants to trade a total quantity denoted i). At date 

two, a customer with precisely the offsetting physical position of the original customer (e.g., a 

hedger with a short cash position wishes to go long futures) comes to the market. The late

customer trades with the market maker and the early customer; both customers are now 

completely hedged and the market makers have no position. 

The equilibrium volume in the model is described as follows. Given m market makers, the 

aggregate volume traded by these scalpers in equilibrium is V

S

(m) = i (

m

1+m

), and the total 

 

8

Throughout the paper, I interchangeably use the terms scalper, local, market maker, and intermediary, depending 

on the context.
14

volume traded in the model is V(m) = i (1 +

m

1+m

). Scalper volume ranges from zero to i and 

the total volume traded ranges from i to 2i as the number of market makers ranges from zero to 

infinity; scalper volume ranges from zero to one-half as a fraction of total volume. Open interest, 

which we identify as the open positions after trading occurs in period 2 (i.e., the end of trading), 

is i, irrespective of the number of market makers.

The number of market makers, m, is made endogenous by assuming that potential market 

makers have a utility function over wealth parameterized as U(w) = −e

−awand are subject to 

the equilibrium condition that the market maker’s utility is the same as his initial utility; 

EU(w0 − c + (P̃2 − P1

̃ )x1

m = EU(w0). In this equation, w0 is initial wealth, c is a fixed, 

exogenous cost paid to become a market maker, x1

m is the market maker demand function for 

period 1, and P̃

t

is the price for the underlying at time t. For a risk aversion parameter a and initial 

cost c, Grossman and Miller effectively show that the number of market makers is given by the 

equation m(ac) =

a(var(P2

)var(i))

1

2

(e

2ac−1)

1

2

− 1.

If the potential scalper’s cost of participating moves from c to c*, then the total volume when the 

cost is c* (as a fraction of the volume under the original price c) is given by

V(m(ac∗

))⁄V(m(ac)) =

i(1+

m(ac∗)

1+m(ac∗)

)

i(1+

m(ac)

1+m(ac)

)

= (

2a(var(P2

)var(i))

1

2−(e

2ac∗

−1)

1

2

2a(var(P2

)var(i))

1

2−(e

2ac−1)

1

2

). Hence, if c* > c, clearly 

V(m(ac∗

)) < V(m(ac)). Equivalently, the higher participation cost reduces the number of 

market makers m, and scalper volume declines although customer volume is unchanged. 

Obviously, aggregate scalper volume as a fraction of total volume also declines as the 

participation cost rises. 

The model formalizes the idea that market makers provide immediacy to traders who have longer 

trading horizons. Absent market makers, these longer-term trader might ultimately obtain the 

same positions, but they would do so with some delay. It follows that a decrease in the number 

of market makers would reduce the volume of trade without materially affecting the open 

interest. However, a higher participation cost for market makers reduces the amount of 

immediacy provided and reduces trading volume. The cross-sectional prediction from the model 

is that the volume decline would be larger for more intermediated markets; i.e., that wheat and 

corn would show a large effect and that oats would show the most modest effect.

4. Empirical Analysis. 

This goal of this section is to use regression analysis to isolate the channels by which the 

transaction tax impacted the futures markets. The first phase of the analysis relates trading 
15

activity and volatility to the tax rate; it provides strong evidence that transaction volume declined 

due to the tax, but no evidence that open interest (i.e., longer-term positioning) was affected. 

The second phase is to search for evidence that the tax was important enough to drive a wedge 

between futures and physical market prices; I find no evidence that it does. The final phase of the 

empirical analysis is to relate empirical proxies for liquidity to the tax rate. Standard market 

variables such as volatility explain much of the variation in these measures of market quality. 

Additionally, the tax rate is a significant explanatory variable for volume-related liquidity 

measures, but the tax rate is not significantly related to other measures of liquidity. Taken 

together, these results appear consistent with the tax having a strong contemporaneous effect on 

trading volume but otherwise did not have a strong influence on the market. 

a. Direct impact on market activity 

 

i. Effect on quantities and volatility around event dates 

This subsection presents the results from regressions designed to test for a shift in futures 

trading volume, or futures price volatility immediately after a change in the tax rate. The tables

provide coefficients and associated t-statistics for regressions of the form:

yt = α0 + α1 Dt + errort., (1)

where Dtis a dummy variable taking the value of unity following a change in the tax rate and zero 

before the change. The regressions are estimated using 30 trading days of data prior to, and 30 

days of trading data immediately following, a change in the rate. The availability of data limits the 

analysis to wheat and corn contracts. Three versions of the regression in equation (1) are 

estimated for each event; the events are the effective dates of the changes in tax rates in 1924 

(down), 1932 (up), 1934 (down), and 1938 (up). One regression has the log trading volume for 

the active futures contract on the left hand side, and two regressions have a log volatility 

measure on the left hand side. The first volatility regression utilizes the log of the day-to-day 

volatility of the futures price, measured as Volatility1 = (π/2)

1/2

|∆ln(pt)|, while the second 

volatility regression utilizes the log of the intraday volatility of the futures price (i.e., the 

Parkinson (1980) estimator), measured as 1/(4ln(2)) x |Hight − Lowt

|.

Tables 5 and 6 display the daily regression results for wheat futures and corn futures, 

respectively. For convenience, the tables display both the pre-event and post-event predicted 

values of the left hand side variable. Both values are exponentiated for ease of interpretation; 

i.e., the table shows exp(α0) and exp(α0+α1). The general patterns from the regressions indicate 

that volume and intraday volatility moved inversely with tax rates, but the day-to-day volatility 

and the spread measure did not. For example, the regressions for 1934 suggest a 74% rise in 

wheat futures volume (from 21,000 contracts traded per day to 37,000 traded per day) and a 

33% rise in corn futures volume (from approximately 8,000 to 10,500 contracts) when the tax 
16

rate moved from 5 bp to 3 bp. The regressions simultaneously predict a dramatic increase in 

intraday volatility by over 50 bp per day (from 1.2% to 1.8% in wheat and from 1.4% to 2.1% in 

corn). 

The regressions are unambiguous with respect to volume: seven out of eight regressions show an 

inverse relation with taxes, with five of these significant at conventional levels. The regressions 

also show a discernable pattern for intraday volatility: six of eight regressions display an inverse 

relation with tax rates (four of them significantly so). Five of the eight day-to-day volatility 

regressions show an inverse relation, but only one of those is significant at conventional levels, 

suggesting much weaker evidence than for the intraday volatility regression.

While the regressions on daily data are suggestive regarding the impact of transactions taxes, 

they leave much to be desired. They do not take into account the differing magnitudes of the tax 

rate changes, nor do they take advantage of the long time series of data across the differing 

regimes. Next, I estimate regressions using the entire 1921-1941 sample to determine more 

systematic impacts of the transactions tax.

ii. Contemporaneous effects on quantities and volatility 

I estimate the elasticities of trading volume, price change volatility, and open interest with 

respect to transaction taxes in this subsection. I find strong evidence that trading volume was 

negatively related to the tax rate, based on the monthly regressions utilizing the entire sample 

period from 1921 to 1941. There is no evidence that open interest was related to the tax rate, 

and there is a small amount of evidence that volatility was related to the tax rate.

The elasticity values are first estimated on the 21 years of monthly data using the specification

yt = ∑ αi

21

i=1 + ∑ αj

12

j=1 + βln(1 + τt

) + errort

, (2)

where ytis the dependent variable of interest, the αi’s are annual marketing year dummy 

variables, the αj’s are monthly seasonal dummy variables, and τtis the federal stamp tax rate 

applicable during the majority of month t. The marketing year for wheat is July through June of 

the following calendar year during this era, it is September through August of the following 

calendar year for corn, and it is June through May of the following calendar year for oats. 

Estimation is over the period January 1921 through December 1941. The omitted groups are the 

1941-1942 marketing years (e.g., July to December 1941 for wheat). The regressions are 

estimated separately for each of the three commodity futures using data from the Chicago Board 

of Trade. Various studies of income tax effects have used similar methods to estimate elasticities 

by using time dummies or trend functions to isolate the relevant effect, e.g., Romer and Romer 

(2014), Saez (2004).
17

A value of β less than zero suggests that a higher tax rate decreases the dependent variable, 

which in the first exercise will be trading volume. In terms of magnitude, it is useful to note that 

an estimate of β equal to 1,000 implies a volume decline of slightly less than 10% when the tax 

rate changes from zero to 0.01%. Such a linear rule of thumb decreases in accuracy for larger 

values of β with, for example, β = -2,000 implying a volume decline of 18%.

It is worth elaborating that the regressions in this section assume that the tax rate is exogenous 

to the left hand side variables such as trading volume. In defense of this assumption, note that 

the purpose of the tax was to raise general revenue and was not targeted to raise revenue for a 

specific purpose. In the modern securities markets, this assumption would be less defensible: the 

Securities and Exchange Commission budget, for example, is offset by transactions fees. These 

fees, in turn, are mechanically set such that the predicted level of trading volume multiplied by 

the fee equals the target revenue. The generally positive correlation between trading activity and 

volatility further complicates analysis. Hence, the exogeneity assumption would require more 

careful examination if I were looking at the current environment. The historical narrative, 

however, suggests no obvious link between the dependent variables and the tax rates for the 

analysis presented here.

The dependent variable for the volume regressions is the log of average daily trading volume (all 

contracts combined) during month t. These elasticity estimates are in the first column of Table 7. 

In this and in all other cases, standard errors use the method of Newey-West (1987) with one lag.

The elasticity estimates for trading volume have the expected sign and are statistically significant 

at conventional levels for wheat and for corn. The elasticity for wheat has a t-statistic of -1.98; 

the elasticity for corn has a t-statistic of -3.63. 

The economic magnitude of the tax is given by the estimated percentage impact (1 + τ)

β − 1. 

This function suggests that, compared to a zero tax, a one basis point tax reduced average daily 

volume by 13% for wheat, 14% for corn, and 5% for oats, although the estimate for oats is not 

significant. Two standard error confidence intervals constructed by the delta method suggest that 

the impact of a one basis point tax was in the range (-25.8%, -0.8%) for wheat, in the range (-

21.6%, -7.0%) for corn, and (-22.8%, 12.6%) for oats. A five basis point tax had predicted effect of 

-51% and -54% for wheat and corn, respectively. The two standard error confidence interval is (-

86.3%, -15.7%) for wheat and (-73.5%, -34.1%) for corn. The predicted impacts are quite large, 

although the confidence intervals are also quite large.

There are estimates of modern elasticities of grain futures trading volume with respect to 

transactions costs. Wang, Yau, and Baptiste (1998) estimate the elasticity for the CBOT Wheat 

contract at -0.116 (with a standard error of 0.20) for the period January 1990 – April 1994. 

Bjursell, Wang and Yau (2012) estimate the elasticity at -0.98 (with a standard error of 0.09) for 

the period January 2007 – December 2010. Wang and Yau (2011) use a value of $16.01 for 

observed transactions costs in their analysis. For wheat prices of roughly $6 per bushel during 
18

the second, this translates into transactions costs of roughly 5 bps. Therefore, Wang and Yau’s 

results suggest that a one basis point increase in transactions costs (due to a tax) would lead to a 

20% decline in trading volume (20% increase in costs times an elasticity of minus one). This is 

comparable to the 13% predicted decline in the model under consideration here. 

Table 7 also provides the estimates for the volatility regressions in the second data column; the 

dependent variable is the log of the Parkinson (1980) High-Low range volatility estimate during 

month t. The results provide some evidence that the tax rate is correlated with the level of the 

tax rate, but it is not statistically significant for wheat or oats. There is statistical significance at 

conventional levels for corn, however. For a one basis point tax, the predicted impact on volatility 

is -9% for corn, with a two standard deviation confidence interval of (-14.7%, -3.4%). A five basis 

point tax has a predicted impact of -38% with a confidence interval of (-57.1%, -18.4%). 

It is more intuitive to examine the counterfactual implied by the model, by assuming that the tax 

rates had been zero for each month in the sample. The observed median monthly volatility was 

22.4% for corn, and the median counterfactual with zero taxes was 27.4% (the median monthly 

difference is four volatility points). Hence, the model predicts that volatility would have been four 

to five volatility points higher in absence of the tax. Divide these figures by the square root of 300 

to convert them into implied typical price movements per trading day, and the observed volatility 

was 1.3% per day with a counterfactual of 1.6% if the tax rate were zero. The economic 

magnitude of the effect is not large and is less than a reduction in variation of 2 price ticks per 

day (0.35% = 2 x 0.125 cents / 72 cents per bushel). Despite the statistical significance of the 

volatility elasticity, I conclude that the tax might have slightly dampened market moves but that 

volatility would have remained in the same general range (i.e., in the 20 – 30% range).

The third data column of Table 7 displays the results of the regressions when month-end open 

interest is the dependent variable. There is no evidence that the tax rate exerted a direct effect 

on the overall open interest in the contracts. In fact, while all of the estimated coefficients for 

these regressions are positive (which would correspond to an increase in open interest as the tax 

rate increased) the low t-statistics suggest a lack of a systematic relationship between taxes and 

open interest.

Finally, the rightmost column of Table 7 displays the results of regressing the ratio of volume to 

open interest on the dummies and the tax rate. As expected given the results of the volume and 

open interest regressions, there is substantial evidence that this ratio is inversely related to the 

tax rate for wheat and corn. The t-statistics are -2.24 and -2.16, respectively.

iii. Contemporaneous effects on pricing 

The empirical evidence thus far suggests that end-users of futures did not modify their day-today activity in futures trading due to the transactions tax. One other source of impact on end-
19

users is through the pricing of futures relative to the cash market. The tax during the interwar 

period was levied on the sale of futures contracts but was not levied on cash market sales. If the 

existence of the tax drove a wedge between the two prices, then end-users would have borne 

that burden. Did the tax make futures prices noticeably cheaper or more expensive relative to 

cash prices as it fell or rose? The following regression results show that the convenience yield is 

readily explained by the visible supply variable. However, there is no statistically significant 

evidence that the tax rate impacted futures pricing relative to the cash market.

The exchanges were vocal that the tax, especially when levied at five basis points, put the smooth 

functioning of agricultural futures markets at risk of collapse. When the tax was raised to this 

level, journalists wrote “President Hoover and leaders in Congress were warned today by a 

committee representing nine principal American commodity exchanges that grain farmers of the 

nation faced the possibility of heavy penalties and a breakdown of marketing machinery during 

the heavy movement of the crops this Summer unless the tax on futures trading was reduced.”

The exchanges declared that “A grave situation has developed as a result of the utterly 

prohibitive tax. Genuine alarm prevails through agriculture and the agricultural trades that the 

startling restriction of markets may make it impossible to absorb the new incoming crop. ... there 

is real danger that during the heavy crop movement period the weight of hedges may prove too 

great for the markets. ... Congress, without such action [to reduce the tax] can only be construed 

as utterly disregarding the welfare of agriculture in this pressing emergency.”9

Consider the archetypal hedger/speculator model for agricultural commodities. If hedgers 

shorted futures in order to hedge the physical commodity and were insensitive to price (i.e., had 

a perfectly inelastic demand for short futures), then the futures price would adjust based solely 

on the willingness of speculators to go long the futures (i.e., the speculative demand curve for 

long futures exposure). If the demand curve for speculative futures exposure were to shift 

materially downward due to the transactions tax, a sharp drop in futures prices might result. This 

effect could be a direct result of lower expected returns or, more interestingly, an indirect result 

due to lower liquidity in the market. Higher taxes would mean lower futures prices, after 

controlling for the cash market conditions.

The empirical work shown below attempts to operationalize the tax’s impact in a way that is 

consistent with the ideas expressed by the exchanges. The analysis relies on the traditional 

Theory of Storage for its structure. The basic theory is that the futures price at a given point in 

time reflects the cash market price (St), the financing cost (measured at the annual rate r), and 

the convenience yield associated with storing the physical commodity (measured at the annual 

rate c) (Brennan, 1958). For a futures contract evaluated at date t and expiring at date T, the 

 

9

“Reduction in Futures Tax Urged Upon Hoover; High Levy Called Threat to Crop Marketing,” New York Times, July 

14, 1932.
20

equilibrium relation among these variables is Ft,T = Ste

(r−c)(T−t)

. In the regressions, I examine 

whether the tax rate is useful in explaining the observed relation between futures and cash 

market prices after controlling for this theoretical relation. In order to operationalize the 

regression, I first solve for the convenience yield (which measures the relative value of holding 

futures market exposure versus cash market exposure) and test if its values are well-described as 

a function of the visible supply and the tax rate.

The regression associated with this hypothesis has the following form:

ln(Ft,T) − ln(St)

(T − t)

− r = α + β ln(Visible Supplyt

) + γ(tax ratet) + errort

.

The dependent variable in the regression equals minus one times the convenience yield c as a 

function of futures and cash prices and the time to expiry (T-t). The right hand side of the 

regression contains the log of the visible supply, which is an observable economic variable that is 

likely to affect the convenience yield, c; marginal convenience yield is high when inventories are 

low and declines as inventories rise. Hence, theory suggests that the visible supply coefficient 

would be positive and statistically significant. The right hand side of the regression also includes 

the tax rate, allowing the rate to drive a wedge between futures and cash market prices.

The regressions use end-of-month or mid-month cash market prices, as available, and 

contemporaneous futures prices from January 1921 until December 1938 for wheat, corn, and 

oats. Data sources include Hoffman (1932), Howell (1948), and Working (1934). The relation 

between open interest, hedging activity, and visible supply was quite strong during the interwar 

period, justifying the use of visible supply as a proxy variable for convenience yield in these 

tests.10 Towards the end of the sample, this relation begins to break down, as government 

supports of grain prices intensified. Hence, I end these regressions at the end of 1938 (see the 

unpublished appendix for details on data construction). Table 8 displays the results of the 

regressions.

The regressions show a strong contemporaneous relation between the pricing of futures 

contracts relative to cash market prices and the visible supply of the underlying commodity. The 

coefficient on the visible supply variable is statistically significantly negative, with t-statistics in 

the -3 to -6 range. The fit of the equations is quite good, with R2values in the 30-65% range. This 

is clear evidence that, during the interwar period, the Theory of Storage explains the relative 

pricing of futures and cash quite well. The data are strong enough to reject the idea that the 

pricing was random; observable variables explain a significant portion of the variability. With 

 

10 Visible supply data for wheat are adjusted to remove cash holdings of the Federal Farm Board’s Grain Stabilization 

Corporation, which held virtually the entire visible supply in summer 1931 (unintentionally cornering the market).

The regression results are not qualitatively different when dummy variables are included for May and June 1931.
21

respect to the tax rate, however, there is no strong evidence that the futures prices or the 

convenience yield were related to the tax rate. The regressions do not support this interpretation 

of the exchanges’ warnings of a “grave situation”. 

Alternatively, hedger demand for futures might not be perfectly inelastic and might depend on 

the tax rate. High taxes on futures transactions might lead hedgers to increase the convenience 

yield ascribed to holding the physical commodity. This could be due to a direct effect or a 

liquidity-induced, indirect effect. In this case, a simple regression allowing for this effect includes 

the log of visible supply on the right hand side and an interaction term between the visible 

supply and the tax rate. In empirical tests (reported in the unpublished appendix), the results of 

this test are almost identical to the ones described above. There is little evidence to support the 

idea that the tax rate had a material impact on futures pricing relative to the cash market prices.

b. Direct impact on Liquidity 

It is perhaps not surprising that the transactions tax impacted futures trading volume, but the 

open question is whether liquidity was impacted. The next set of regressions is designed to 

address the question as directly as possible. The regressions use standard proxies for liquidity as 

dependent variables and allow the tax rate to impact the proxy as an independent variable. The 

regressions also control for standard explanatory variables for market liquidity: volatility of the 

underlying asset and open interest of the futures contract. 

The regression specification has the form

ln(Illiquidityt

) = a + b0 ln(Volatilityt) + b1 ln(OIt) + b2 ln(1 + τt) + errort

.

To account for endogeneity between contemporaneous values of dependent and independent 

variables, the regressions are estimated using instrumental variables, with one lag each of the 

open interest and volatility as instruments.

I use four illiquidity proxies in the regression analysis; two are pure price-based measures and 

two are variants of the Amihud (2002) measure. I construct the Corwin and Schultz (2012) 

illiquidity measure using the daily high, low, and closing price (mid-point of closing range) of a 

nearby futures contract. The “Closing Range” variable represents daily prices in various parts of 

the futures trading pit at the time of market closure each day. I also construct an Amihud 

measure using the absolute value of the log price change divided by the absolute change in open 

interest for the nearby futures contract. Finally, I construct a standard Amihud measure that is 

the ratio of the absolute value of the log price change divided by the daily contract volume for 

the nearby futures contract.

These illiquidity statistics are computed using daily data hand collected from various statistical 

bulletins produced by the USDA. The daily data are aggregated into monthly statistics and used in 
22

regressions spanning the period August 1923 (after open interest data become available) to 

December 1938. The data cover wheat and corn futures contracts.

These statistics are expected to provide reasonable measures of various aspects of illiquidity in 

commodity markets. Marshall et al. (2012) conclude that the standard Amihud measure is highly 

correlated with intraday measures of transactions costs for modern commodity futures data, and 

they also find evidence that the Corwin-Schultz estimator is related to intraday transactions 

costs.

Table 9 displays the regression results. The first data column, labeled “Volatility”, illustrates that 

the illiquidity measures are strongly positively related to the contemporaneous volatility of the 

underlying futures contract. All of the coefficients are positive and all but one have t-statistics 

greater than two. The second data column, labeled “Open Interest”, shows some explanatory 

power. The coefficient is negative in all but one case, but there is only modest evidence for 

statistical significance. Two of the eight associated t-statistics are highly significant, both for corn 

regressions. 

The third data column, labeled “Tax Rate”, is the one of major interest. The results indicate that 

the coefficient on the log of one plus the tax rate is highly significant in two of the eight 

regressions. The significance shows up in both the wheat regression (t-statistic of 2.53) and the 

corn regression (t-statistic of 5.31), but only for the Amihud statistic. The coefficient does not 

appear statistically important in any of the other displayed regressions. 

The regression results clearly show that the illiquidity statistics are not random; they 

systematically vary with important market variables. However, these measures do not covary 

predictably with the transaction tax rate, except for the single statistic that is volume-based. 

Higher tax rates are associated with less volume, as seen in the regressions displayed earlier, but 

those same sets of regressions provided no evidence of volatility covarying with tax rates. This 

result carries through to the Amihud statistic, which shows less volume per unit of volatility at 

higher tax rates. 

Similarly, the previous regression results show no evidence of a link between tax rates and open 

interest, and this message is the same one conveyed in the Amihud illiquidity statistic regression

using changes in open interest for the denominator. There is no evidence that the typical change 

in open interest per unit of volatility varies predictably as tax rates change. 

I conclude from these regressions that market liquidity was not tightly linked to the tax rate. For 

both wheat and corn, and for three of the four measures of illiquidity, the regressions provide no 

evidence that the tax rate was a driver of trading liquidity. However, the strong link between the 

Amihud measure and tax rates is suggestive of a structural shift in intermediary behavior rather 

than a broad-based change in trading activity across all types of market participants. 
23

5. Discussion. 

The other traders in the wheat futures pit booed him. That was the response to the first pit 

trader who attempted to make a trade at a sixteenth of a cent after the five basis point tax (on 

notional value) was implemented. Although the minimum tick size was nominally one-eighth of a 

cent per bushel, traders could readily “split” one transaction: half of the contracts at one tick and 

half at the next higher tick. But traders who attempted to split a trade just after the tax was 

implemented were greeted with such a hostile, loud response by their peers that the 

transactions were rarely completed.11 The tax mattered greatly to market makers.

The scalpers had been paying $0.50 in taxes per contract, and the eighths they were attempting 

to capture were worth $6.25 per contract. But the new tax rate implemented in 1932 was 

approximately $2.50 per contract, paid whether the scalper made a profit or loss on a trade. If 

one thinks of market makers as earning the bid-ask spread on a “round-trip” transaction, then 

the tax of $5.00 per round-trip represented an 80% reduction in their proceeds from such a 

trade. If trades were executed at sixteenths instead of eighths, the same $2.50 tax meant their 

potential profit was now less than a quarter of what it would have been the day before. Not 

surprisingly, exchange members eliminated split transactions a year later. Figure 1 displays the 

closing range of prices (a proxy for the bid-ask spread, or market liquidity) for wheat and corn 

futures; the rule change in July 1933 is clearly evident.

Exchange members had long been vocal in opposing the tax. In 1924 testimony against the 2 

basis point tax in effect at the time, the Chicago Board of Trade’s representative claimed 

“Everybody who deals in grain, either as a buyer or a seller, knows that the Chicago wheat market 

and grain market are depressed. Why? We think very largely because they being taxed practically 

out of existence. ... This has so depressed the Chicago wheat market that something like 40 per 

cent of the traders have quit and a very large amount of the trading has gone to other 

markets.”12

Nonetheless, exchange rules regarding the minimum tick size remained constant until July 1933, 

after the tax rate had been raised to 5 basis points. Government intervention during the late 

1930s to stabilize commodity prices acted to reduce trading volume. Exchange members actively 

kept this constraint in place: an amendment to re-authorize split quotations was rejected 433 to 

97 in 1939. Interestingly, at the same meeting, the members voted down an amendment to raise 

minimum commission charge on job lots originating outside Chicago.13 Hence, it appears that 

 

11 “New U.S. Taxes Arouse “Boos” in Wheat Pit,” by O.A. Mather, Chicago Daily Tribune, 22 June 1932, p.1.

12 Philip Campbell, representing Chicago Board of Trade, Revenue Revision, 1924, Hearings before the Committee on 

Ways and Means House of Representatives (p. 231). The Grain Futures Administration noted that the alleged 

movement of U.S.-based speculators to other markets was a “myth”: Report of the Chief of the Grain Futures 

Administration, 1931, p.10.

13 “Chicago Pit’s Members Balk Rule Changes,” Chicago Daily Tribune, 30 March 1939, p.1.
24

members opted to keep in place the tick size constraint (which was invisible to outside 

customers) while rejecting a visible price increase to customers. The empirical results presented 

in this paper suggest that the brunt of the tax was borne by the market makers in the trading pit, 

and customer volume was not materially impacted by the tax. Even so, the pit remained highly 

competitive. Volatility did not appear to increase due to a thin market, and measures of market 

depth and liquidity based on prices and open interest were not directly affected by the tax. A 

simplistic reading of the results would suggest that the tax did not materially affect the 

functioning of the market, except for reducing the profits of some middlemen. 

The futures market regulator did not agree with this simplistic conclusion that the tax “did not 

matter”. In particular, the Chief of the Commodity Exchange Authority (CEA), in testimony 

entered in the Congressional record, argued that the tax should be abolished. A second-best 

option was to reduce it from 3 basis points to 1 basis point, albeit with a removal of the tax 

exemption for “scratch” trades (i.e., a buy and a sell transaction at the same price). The Chief 

explicitly recognized that the highly competitive market maker activity was central to the proper 

functioning of the futures market. He stated that reducing the tax would lead to “...a larger 

volume of trading and a corresponding increase in the stability and flexibility of the market which 

will be of value both to producers and consumers.”

He stated that “It would be of value to the commodity markets if this tax could be eliminated 

entirely. It places a particularly heavy burden on the scalpers who give flexibility to the market. In 

fact, without the presence of scalpers a futures market cannot function efficiently in that hedgers 

desiring to sell a future as protection against loss would be compelled to sell at a lower price and 

hedgers desiring to buy a future as protection against the sale of flour would be compelled to pay 

a higher price than justified.”

14

The CEA Chief’s statement that reducing the significant tax burden on scalpers would add to the 

“stability and flexibility of the market” can be interpreted in more modern terms. Recent 

research highlights the importance of intermediary inventory capacity. Kirilenko, et al. (2014) 

investigated the “Flash Crash” of May 2010 and concluded that the sharp intraday movement in 

S&P 500 e-mini contract prices was due to a large amount of customer demand that temporarily 

overwhelmed intermediary capacity. Intermediaries were unable to absorb all of the customer 

demand near the current price and were unable to find enough immediately available customers 

to take the other side of the desired trades. The price temporarily dropped sharply as the market 

absorbed this imbalance. Hendershott and Menkveld (2014) study New York Stock Exchange 

 

14 J.W.T. Duvel, Chief, Commodity Exchange Administration, Revenue Act of 1938, Hearings Before the Committee 

on Finance United States Senate, Seventy-Fifth Congress Third session on H.R. 9682 An Act to Provide Revenue, 

Equalize Taxation and for Other Purposes, Part 4, March 30, 1938. (p. 115-6)
25

specialists and find economically large price pressure effects due to risk-averse intermediaries 

supplying liquidity to investors with asynchronous arrival times. 

In light of studies such as these, one argument against the tax is that the long-run effect was to 

reduce the risk-bearing capacity of market makers. The earlier discussion of Grossman and 

Miller’s (1988) model suggests that trading volume due to intermediaries declines in a post-tax 

equilibrium because the number of market makers declines. In practice, the typical liquidity 

measures of the futures markets show no obvious relation to the tax rate, but the aggregate riskbearing capacity of the traders in the pit surely declined. This effect might have been gradual, as 

scalpers recognized that the tax was subject to change or removal. Scalpers also faced frictions in 

transferring to other lines of work and may have opted to bear the reduced revenues and profit 

in the short run.

6. Conclusion. 

Federal stamp taxes on futures transactions during the 1920s and 1930s led to fairly immediate, 

contemporaneous changes in trading volume. The analysis in this paper suggests that a one basis 

point tax on the sale of wheat futures contracts reduced average daily trading volume by 13%. 

However, end-users of futures – the traditional hedgers and speculators – did not materially 

adjust trading activity because of the tax. The reduction in volume reflected a decline in 

intermediation activity. 

Despite the measurable change in trading volume, several measures of market illiquidity show no 

obvious correlation with the tax rates. The Corwin-Schultz (2012) transaction cost statistic, the 

closing range of futures prices, and a version of the Amihud (2002) statistic using open interest 

(absolute price change divided by change in open interest) each show no contemporaneous 

changes attributable to tax rates. These statistics do covary, however, with standard explanatory 

variables such as the volatility of the futures price. 

The tax rates do show a strong link to the traditional Amihud (2002) statistic (absolute price 

change divided by trading volume): higher tax rates suggest a higher measure of the statistic. 

Given the structure of the market and the body of results, I interpret this finding to mean less 

intermediation volume rather than less liquidity.

The intermediaries (i.e., locals or pit scalpers) were the ones who bore the most significant tax 

burden in the short run. The tax rate was a tiny fraction of the notional value of a futures 

contract, but it was the same order of magnitude as the expected return for intermediaries. A 

speculator or a hedger who had a horizon of weeks or months and concerned himself with price 

changes of, say, 5 to 10% per month might have viewed a 5 basis point tax as virtually irrelevant: 
26

the equivalent of coming into the market a few seconds earlier or later. The market maker who 

had a holding period of, say, 5 minutes and hoped to make $6.25 as an intermediary might view 

that 5 basis point marginal tax (worth $2.50) quite differently.

The end-users bore the tax burden in the long run, however. Exchange members effectively voted 

to double the minimum tick size a year after the tax rate was raised to its highest level. This 

action was designed to put a floor on the minimum profit for a market maker’s transactions and 

effectively allow the tax burden to be passed on to the end-user. The doubling of the tick size 

raised the contract price by $3.125 in order to offset a tax of $2.50. The tick size increase 

remained in place long after the tax was abolished. 

The Chief of the Commodity Exchange Authority (CEA) recommended that Congress abolish the 

tax because of its disproportionate impact on intermediaries. Standard models suggest that 

higher transaction costs for intermediaries means fewer intermediaries and less aggregate 

intermediation capacity. Recent research, such as Hendershott and Menkveld (2014), focuses on 

the importance of intermediary capacity in price discovery. Kirilenko et al. (2014) explain the 

2010 “Flash Crash” as an intermediation capacity issue. While the regressions presented in this 

paper show no compelling evidence that liquidity was contemporaneously correlated with tax 

rate changes, they are silent about the long-run implications of the tax such as a reduction in 

intermediation capacity. 

Policymakers have recently imposed transactions taxes in parts of Europe and continue 

discussing implementation of transactions taxes in the U.S. and across Europe. The analysis in this 

paper highlights that the facts and circumstances of a particular market are important for 

understanding the impact of a given tax. The implications of a tax for a dealer market dominated 

by a few large dealers and end-users might be quite different than the implications for a highly 

intermediated futures market with many pit traders who engage in scalping activity. Similarly, a 

floor-based futures market with scalpers physically located in the pit might react quite differently 

than a virtual market with an electronic central limit order book. The composition of market 

participants, the institutional details of the market, and the precise implementation method all 

affect the impact of a tax.

 

 
27

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30

Tables and Figures
31

Table 1. 

Federal Stamp Taxes Imposed on Futures Transactions in the United States, 1898-1938 

Description Start Date End Date 

Taxed 

Transactions 

Rate as percent of 

notional value 

Spanish-American War 

Tax 

July 1, 1898 June 30, 1902 Futures and cash 1 basis point

Federal Tax Dec 1, 1914 Sep 8, 1916 Futures 1 basis point

Federal Tax 

Dec 1, 1917 July 2, 1924 Futures 2 basis points

July 3, 1924 June 20, 1932 Futures 1 basis point

June 21, 1932 May 9, 1934 Futures 5 basis points

May 10, 1934 June 30, 1938 Futures 3 basis points
32

Table 2. 

Average Trading Volume, Price Volatility, and Open Interest in Grain Futures, 1921 - 1941 

Period 

Tax 

Rate 

Average Daily Volume 

(millions of bushels) 

Average Monthly 

Volatility (Annualized) 

Average Open Interest 

(millions of bushels) 

 Wheat Corn Oats Wheat Corn Oats Wheat Corn Oats

Jan 1921 – 

June 1924 

2 bp 32.8 15.9 5.2 22.4% 22.5% 24.1% 89.5 56.9 21.9

July 1924 – 

June 1932 

1 bp 39.8 16.5 3.7 25.0% 26.3% 26.9% 116.4 58.1 37.3

July 1932 – 

Apr 1934 

5 bp 29.1 8.8 2.8 34.7% 39.4% 44.0% 140.2 71.4 37.8

May 1934 – 

June 1938 

3 bp 27.1 8.0 2.6 22.9% 24.3% 26.9% 101.6 44.9 32.6

July 1938 – 

Dec 1941 

0 bp 15.9 3.8 1.0 22.0% 20.3% 23.6% 74.0 37.6 12.5

 

 

 
33

Table 3. 

Wheat Futures: Average Daily Aggregate Position Change and Volume by Participant Type, Jan – 

October 1927 (1,000s of bushels) 

Participant Type 

Scalpers Speculators Spreaders Hedgers

Average Aggregate End-ofDay Position Change 175.9 1,690.0 429.0 734.7

Average Daily Volume 

(Buys + Sells) 

14,625.7 12,319.4 2,022.7 3,395.0

Ratio of Aggregate Position 

Change to Daily Volume 

(Average) 

0.013 0.140 0.239 0.224

Percent of Classified Daily 

Volume 

45.2 38.1 6.3 10.5

 
34

Table 4. 

Median Values of Commission and Minimum Spread, as Percentage of Notional Value, 1921 - 1941 

Price / bu 

($) 

Weighted 

Average 

Commission / 

Clearing Fees 

Clearing Fees: 

Scalpers* 

Commission: 

Members 

Commission: 

NonMembers 

Minimum 

Spread 

(1/1921 – 

7/1933) 

Minimum 

Spread 

(8/1933 - 

12/1941) 

Wheat 1.05 0.10% 0.02% 0.12% 0.24% 0.05% 0.14%

Corn 0.72 0.15% 0.03% 0.17% 0.35% 0.08% 0.21%

Oats 0.40 0.32% 0.06% 0.32% 0.63% 0.15% 0.36%

Computations performed on data covering the period January 1921 – December 1941.

Minimum bid-ask spread is computed using the minimum tick size (half a tick prior to August 

1933 and one tick afterwards). CBOT contracts were tradable in split quotations until July 29, 

1933. Contracts could explicitly be traded with one half of the total traded at a given tick and one 

half traded at the next tick, resulting in trades at sixteenths.

* Clearing Fees for trades other than scratch trades. Clearing fees for scratch trades were 1/25 

the clearing fees ($0.05 vs. $1.25 per contract) for non-scratch trades.

 

 

 

 
35

Table 5. Daily Regression Results: Wheat Futures 

The table displays results from univariate regressions on daily data for wheat futures on the 

Chicago Board of Trade. For each row, the estimated regression is of the form

yt = α0 + α1 Dt + errort,

where Dtis a dummy variable taking a value of zero for the 30 trading days prior to the tax rate 

change and unity for the 30 trading days after the tax rate change. Independent variables are 

defined as follows: rows labeled “Volume” utilize the log of trading volume for the active wheat 

contract, rows labeled “Volatility1” utilize the log of (π/2)

1/2

|∆ln(pt)|, rows labeled “Volatility2” 

utilize the log of 1/(4ln(2)) x (Hight − Lowt

), where Hight and Lowt are the daily high and low, 

respectively, of the log futures price. Values shown in the table are for exp(α0) (pre-event) and 

exp(α0 + α1) (post-event). Newey-West t-statistics with two lags are displayed; coefficients having 

t-statistics with absolute magnitude greater than two are shaded.

Year 

Change in Tax 

Rate 

Independent 

Variable 

Pre-event Post-event 

t-statistic for 

change 

1924 -1 bp

Volume 24,588 44,694 4.03

Volatility1 0.91% 1.62% 3.12

Volatility2 0.90% 1.35% 3.42

1932 +4 bp

Volume 28,926 17,805 -5.51

Volatility1 1.68% 1.60% -0.31

Volatility2 1.39% 1.31% -0.49

1934 -2 bp

Volume 21,139 36,821 3.12

Volatility1 1.46% 1.37% -0.25

Volatility2 1.23% 1.77% 2.00

1938 -3 bp

Volume 27,548 21,278 -3.13

Volatility1 1.32% 0.99% -1.16

Volatility2 1.53% 1.06% -3.48

 

 
36

Table 6. Daily Regression Results: Corn Futures 

The table displays results from univariate regressions on daily data for corn futures on the 

Chicago Board of Trade. For each row, the estimated regression is of the form

yt = α0 + α1 Dt + errort,

where Dtis a dummy variable taking a value of zero for the 30 trading days prior to the tax rate 

change and unity for the 30 trading days after the tax rate change. Independent variables are 

defined as follows: rows labeled “Volume” utilize the log of trading volume for the active wheat 

contract, rows labeled “Volatility1” utilize the log of (π/2)

1/2

|∆ln(pt)|, rows labeled “Volatility2” 

utilize the log of 1/(4ln(2)) x (Hight − Lowt

), where Hight and Lowt are the daily high and low, 

respectively, of the log futures price. Values shown in the table are for exp(α0) (pre-event) and 

exp(α0 + α1) (post-event). Newey-West t-statistics with two lags are displayed; coefficients having

t-statistics with absolute magnitude greater than two are shaded.

Year 

Change in Tax 

Rate 

Independent 

Variable 

Pre-event Post-event 

t-statistic for 

change 

1924 -1 bp

Volume 13,491 20,860 3.10

Volatility1 1.13% 1.53% 1.10

Volatility2 0.98% 1.48% 2.38

1932 +4 bp

Volume 3,395 3,362 -0.07

Volatility1 1.49% 1.76% 0.80

Volatility2 1.24% 1.31% 0.42

1934 -2 bp

Volume 7,946 10,538 1.39

Volatility1 1.37% 1.85% 1.32

Volatility2 1.36% 2.11% 2.47

1938 -3 bp

Volume 4,639 6,608 3.40

Volatility1 0.72% 0.88% 0.73

Volatility2 0.75% 0.89% 1.16

 
37

Table 7. Estimated elasticities 

The table displays results from univariate regressions of a) log of average daily trading volume

(“Volume”), b) log of monthly Parkinson volatility estimate (“Volatility”), c) log of month-end 

open interest (“Open Interest”), and d) average daily volume divided by month-end open interest

(“Volume/Open Interest”). Regressions use monthly data and maximally cover the period January 

1921-December 1941; regressions involving open interest begin the August 1923. Regressions 

are separately estimated for each commodity and each independent variable. Newey-West tstatistics with three lags are displayed; coefficients having t-statistics with absolute values greater 

than two are shaded. 

Commodity Volume Volatility Open Interest Volume/ Open 

Interest 

Wheat 

Elasticity -1428.8 -1363.0 93.2 -1572.1

(t-statistic) (-1.98) (-1.43) (0.25) (-2.24)

Adjusted R2(%) 65.3 42.8 78.6 67.8

Corn 

Elasticity -1544.4 -947.2 85.6 -993.8

(t-statistic) (-3.63) (-3.05) (0.64) (-2.16)

Adjusted R2(%) 75.6 31.1 75.8 68.6

Oats 

Elasticity -522.6 -639.0 187.8 -691.2

(t-statistic) (-0.56) (-0.89) (0.33) (-1.85)

Adjusted R2(%) 79.0 40.6 88.1 65.6

 

 

 

 

 

 

 
38

Table 8. 

Impact of the tax on futures pricing 

The table displays results from regressions 

ln(Ft,T)−ln(St

)

(T−t)

− r = α + βln(Visible Supplyt

) + γ(tax ratet) + errort

.

Regressions use monthly data and cover the period January 1921-December 1938. Corn and oats 

data are unavailable for the period October 1930-June 1931. Regressions include an AR(1) 

correction. Coefficients having t-statistics with absolute values greater than two are shaded.

Commodity Intercept (tstatistic) Ln(Visible Supply) 

(t-statistic) 

Tax Rate (tstatistic) Adj. R2 

Wheat -1.06 0.24 -107.39 37.0%

(-8.01) (7.76) (-0.67)

Corn -0.65 0.22 -167.59 59.0%

(-6.40) (7.13) (-0.68)

Oats -0.56 0.14 37.92 55.2%

(-4.76) (4.27) (0.17)

 

 

 

 

 
39

Table 9. 

Relating futures market liquidity and tax rates 

The table displays results from regressions of various illiquidity proxies on three explanatory 

variables: the log of Parkinson volatility of the futures contract, the log of month-end open 

interest of the futures contract, and the log of one plus the transactions tax rate. The regressions 

are estimated separately for each illiquidity proxy and for each commodity (wheat and corn). 

Regressions utilize monthly observations spanning the period August 1923 to December 1938. 

Illiquidity measures are constructed using daily data within each month. T-statistics based on 

Newey West (1987) standard errors with 3 lags are shown in parentheses below coefficients; 

coefficients with t-statistics greater than two in absolute magnitude are shaded.. The column 

marked “GR2” displays the Pesaran and Smith (1994) Generalized R2.

Illiquidity 

Measure 

Commodity Volatility Open 

Interest 

Tax Rate GR2 

Corwin-Schultz 

Wheat 

0.95 -0.17 -27.46 40.0

(9.27) (-1.42) (-0.18)

Corn 

1.09 -0.05 -121.50 35.9

(9.17) (-0.62) (-0.55)

Closing Range 

Wheat 

0.33 -0.09 -106.54 17.3

(3.55) (-1.14) (-0.63)

Corn 

0.10 0.00 7.75 1.8

(1.73) (0.04) (0.05)

Amihud 

(Open Interest) 

Wheat 

0.60 -0.46 16.53 1.4

(2.24) (-1.33) (0.04)

Corn 

0.89 -1.36 360.76 27.3

(4.28) (-8.00) (0.80)

Amihud (Volume) 

Wheat 

0.68 -0.35 1025.91 22.2

(4.24) (-1.65) (2.53)

Corn 

0.47 -1.01 2471.09 50.6

(2.92) (-7.37) (5.31)

 
40

Figure 1. Futures Closing Range, 1921-1938 
41

APPENDIX TO 

“U.S. Experience with Futures Transaction Taxes” 

NOT FOR PUBLICATION 

Contents 

Detail on Trading Fees/Commissions...........................................................................................................42

Detail on Estimated Impact of Tax (Standard Errors computed via Delta Method) .....................................45

Detail on Hedging and the Visible Supply of Wheat ....................................................................................48

Appendix Supplement to Table 8 (Impact of the tax on futures pricing).....................................................50

 

 
42

Detail on Trading Fees/Commissions 

Table A1. Computation of Weighted Average Fees / Commissions per Contract 

 Fees / 

Commissions per 

contract 

Volume Weights 

Commission Type Wheat Corn Oats 

Scalper (Scratch Trades) $0.05 30.0% 30.0% 20.0%

Scalper (Non-Scratch Trades) $1.25 20.0% 20.0% 20.0%

Member $6.25 19.2% 17.8% 21.4%

Non-Member $12.50 30.8% 32.2% 38.6%

Weighted Average cost per 

contract 

 $5.32 $5.40 $6.42 

 

Total scalper volume estimates and scratch trade volume estimates 

The assumption in the computations above utilize the approximations of 50% scalping volume for 

wheat and corn and 40% scalping volume for oats. Scratch trades are assumed to be 30% of all 

volume for wheat and corn and 20% for oats. These values are assumed to hold on all dates. The 

Grain Futures Administration estimated that 50% of all wheat trades in 1923 involved a scalper, 

and that about 30% of all trades were scratch trades (Report on the Grain Trade, Vol. 7, p. 101). It 

further estimated that, for the period 1921-1923, that about 30% of all wheat trading was 

scratch trades, 28% in corn, and 20% in oats. “These figures are estimates based on and 

corresponding with the averages obtained from direct computations relating, for wheat, to the 

first 4 months of each of these 3 years, and for corn and oats to the last 3 months of 1922 and 

the first 4 months of 1923.” (United States Department of Agriculture Statistical Bulletin no. 6, 

Oct. 1924, Grain Futures: Daily Data, p.3.) Table A2 provides quantitative detail on the available 

estimates of scalping volume during this era.

 
43

Table A2. Fraction of Futures Volume with a Scalper Involved 

Period Wheat Corn Oats 

Jan 1916 – April 1917a33% 29% 11%

May 1917 – Dec 1917b N/A 41% 27%

Jan 1925 – April 1925c53% N/A N/A

Jan 1927 – Oct 1927d45% 29% N/A

Notes:

a. Report on the Grain Trade, Vol 7, p. 100. These values may underestimate the volume 

attributable to scalping activities, especially scratch trades (trades made on the same day 

at the same price), which may not have been reported to the extent that other trades 

were reported.

b. Report on the Grain Trade, Vol 7, p. 100. Trade in wheat futures was suspended during 

this period.

c. U.S Department of Agriculture (1926), Grain Futures Administration, Fluctuations in 

Wheat Futures, (US 69th Congress, 1st Session, Senate Document No. 135).

d. U.S Department of Agriculture (1930), Grain Futures Administration, Reports by Members 

of Grain Futures Exchanges, Part 2 (US 71st Congress, 2nd Session, Senate Document No. 

123).

 

 

 

 

 

 

 

 

 

 

 

 
44

Member vs. Non-Member volume 

The estimates of member and non-member volume in Table A1 are split according to the 

proportions implied by Table A3. The assumption is that 50% of volume is due to non-scalpers in 

wheat and corn and 60% of volume is due to non-scalpers in oats. 

Table A2 displays the Federal Trade Commission provides volume estimates for 1916-1917 by 

commission type, where “House Trades” reflect the minimum pit brokerage costs paid to a 

broker for a trade. 

Table A3. Weighted Average Costs of Trading, from Federal Trade Commission data 

 

 Cost / 

contract ($) 

Wheat 

(% of volume) 

Corn 

(% of volume) 

Oats 

(% of volume) Trade Type 

House Trades 0.75 13.38 16.07 19.54

Cleared for Others 1.25 33.39 34.42 20.24

Made for Members 6.25 20.41 17.62 21.45

Made for Non-Members 12.50 32.82 31.89 38.77

Source: Federal Trade Commission Report on the Grain Trade, vol. 7, pp. 111-112

 

Figure A.1 

Value of weighted average commission costs plus minimum spread as percentage of notional value, 

1921-1941. 

 
45

Detail on Estimated Impact of Tax (Standard Errors computed via Delta Method) 

Figure A.2: Estimated Impact of Tax on Futures Trading Volume. 

Panel A: Wheat 

 

Panel B: Corn 

 

Panel C: Oats 

 
46

Figure A.3: Estimated Impact of Tax on Futures Open Interest. 

Panel A: Wheat 

 

Panel B: Corn 

 

Panel C: Oats 

 

 

 
47

 

Figure A.4: Estimated Impact of Tax on Futures Volatility. 

Panel A: Wheat 

 

Panel B: Corn 

 

Panel C: Oats 

 

 
48

Detail on Hedging and the Visible Supply of Wheat 

Figure A.5: Wheat futures hedging and the visible supply of wheat 

The chart displays the visible supply of wheat (NBER series m05001d and m05001e) less the 

Grain Stabilization Corporation cash wheat holdings for 1930-1932 (from Anne E. Peck, “The 

Futures Trading Experience of the Federal Farm Board.” Futures Trading Seminar Proceedings, 

Vol. IV, Chicago Board of Trade, Chicago, IL, 1976, pp.23-56), the open interest of CBOT wheat 

futures, and the total (long plus short) hedging of wheat with CBOT futures (from the Commodity 

Exchange Authority). The chart validates the strong relation between visible supply and futures 

hedging during the 1920s and 1930s, with the relation weakening at the end of the period due to 

the influence of the Commodity Credit Corporation.

Working (“Whose Markets? Evidence on Some Aspects of Futures Trading,” Journal of Marketing, 

19(1): 1-11, 1954) describes the dynamics: 

“When surplus stocks of wheat accumulated in the late 1920's, leading to increased hedging, 

open contracts rose on all markets. When the Grain Stabilization Corporation took over much of 

this surplus in 1930-31, so that the stocks were no longer hedged, open contracts dropped. 

When a new surplus appeared at the end of the 1930's, federal loans and direct holding by the 

Commodity Credit Corporation created an actual shortage of wheat supplies in commercial 

hands, and hedging and open contracts in wheat futures dropped sharply.” 
49

Raw and Adjusted Visible Supply of Wheat Grain Stabilization Corp. Holdings as % of Visible 

Supply 

Note that the chart adjusts for the GSC holdings but not for the Commodity Credit Corporation 

(CCC) data. The total amount of wheat pledged as collateral or held by the CCC is available 

annually and is shown in the following table (from United States Department of Agriculture, 

Agricultural Statistics, 1942, p. 730).

1938 1939 1940 1941 

Quantity (millions of bushels) 85.7 167.7 278.3 347.4

 

 
50

Appendix Supplement to Table 8 (Impact of the tax on futures pricing)

The table displays results from regressions 

ln(Ft,T)−ln(St)

(T−t)

− r = α + βln(Visible Supplyt

) + γln(Visible Supplyt)x(tax ratet) + errort

.

Regressions use monthly data and cover the period January 1921-December 1938. Corn and oats 

data are unavailable for the period October 1930-June 1931. Regressions include an AR(1) 

correction. Coefficients having t-statistics with absolute values greater than 2 are shaded.

Commodity Intercept 

(t-statistic) 

Ln(Visible Supply) 

(t-statistic) 

Interaction 

(t-statistic) 

Adj. R2 

Wheat -3.53 0.68 -42.87 40.3%

(-8.60) (8.40) (-1.34)

Corn -0.68 0.23 -25.38 58.9%

(-7.17) (6.19) (-0.34)

Oats -0.56 0.14 28.07 55.2%

(-4.95) (3.73) (0.40)