stock market transaction costs and economic growth …
TRANSCRIPT
STOCK MARKET TRANSACTION COSTS AND ECONOMIC GROWTH
by
GREGORY RICHARD EVANS
(Under the Direction of George Selgin)
ABSTRACT
Previous attempts to determine stock markets' contribution to economic growth have
measured stock market development by stock market size and activity. But stock market size
and activity are poor proxies of stock market development that lead to simultaneity and omitted
variable problems and unclear policy prescriptions. By looking at the efficiency of stock
markets, specifically how inexpensive it is to transact on, obtain information from, and enforce
contracts through them, the simultaneity and omitted variable problems can be overcome and
better policy prescriptions can be determined. To look at these costs, a measure of transaction
costs across different countries is needed. I estimate the cost of transacting on 95 exchanges in
74 countries using an approach suggested by Lesmond, Ogden, and Trzcinka (1999).
I use these estimates, and determinants of information and contract enforcement costs, to
supplant the proxy measures of stock market development used in previous models of stock
market development and economic growth. Though tepid, the results provide some support for
previous findings of a positive link between stock markets and economic growth and suggest that
reducing transaction costs is the most important cost reduced by stock markets.
Including the efficiency of stock markets in our understanding of stock market
development sheds more light on stock market policies and paves the way for additional
research. The results suggest that additional taxation of stock markets, through a securities
transaction tax and possibly capital gains taxes, would decrease economic growth. Based on the
estimated effects produced here, a 0.5% roundtrip tax on stock trades would be expected to
decrease current economic growth by $5 to $17 billion a year.
INDEX WORDS: economic growth; financial development; stock market; tobin tax
STOCK MARKET TRANSACTION COSTS AND ECONOMIC GROWTH
by
GREGORY RICHARD EVANS
B.S., Business Economics, Marquette University, 1998
M.S., Applied Economics, Marquette University, 1999
A Dissertation Submitted to the Graduate Faculty
Of The University of Georgia in Partial Fulfillment
of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
ATHENS, GEORGIA
2012
© 2012
Gregory Richard Evans
All Rights Reserved
STOCK MARKET TRANSACTION COSTS AND ECONOMIC GROWTH
by
GREGORY RICHARD EVANS
Approved:
Major Professor: George Selgin
Committee: Santanu Chatterjee
William Lastrapes
Scott Atkinson
Electronic Version Approved:
Maureen Grasso
Dean of the Graduate School
The University of Georgia
August 2012
iv
DEDICATION
To my wife, Crystal, I will always be in your pocket.
To my children, Savannah and Austin, never put your success in another person’s hands. Be
your own persons and control your own destinies.
To my parents, Cathy and Moe, you have always been there to support and help me in any way
that you could.
v
ACKNOWLEDGEMENTS
I would like thank Dr. Selgin for seeing me through on this dissertation. I am sure it was a much
longer journey than he thought it would be when he agreed to be my advisor.
I would like to thank Dr. Lastrapes, Dr. Chatterjee, and Dr. Atkinson for their input, particularly
at the Defense. They made this dissertation considerably better.
I would like to thank my wife, Crystal, for her help running the numerous models that had to be
run for the transaction cost estimates and for helpful comments reading my many drafts. I have
tried to limit the analogies to running water as best I could.
I would like to thank my mother, Cathy, for always offering ways to help me complete my
dissertation.
I would like to thank my father, Moe, for his help running the numerous models that had to be
run for the transaction cost estimates.
I would like to thank my brother, Bill, and Megan Friesen for their proofreading comments.
I would like to thank Sixto Alvarez and Thurman “Tap” Pearson for their help running the
numerous models that had to be run for the transaction cost estimates.
vi
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ............................................................................................................ v
LIST OF TABLES ....................................................................................................................... viii
LIST OF FIGURES ........................................................................................................................ x
CHAPTER 1 – STOCK MARKET DEVELOPMENT THEORY AND MEASUREMENT ....... 1
1.1 STOCK MARKET DEVELOPMENT IN THE LITERATURE ......................................... 3
1.2 BENEFITS OF MEASURING STOCK MARKET DEVELOPMENT BY COSTS .......... 6
1.3 DEFINING STOCK MARKET TRANSACTION COSTS ................................................. 8
1.4 THE TRANSACTION COST MODEL ............................................................................. 10
1.5 MARKET MICROSTRUCTURE INFLUENCES ............................................................. 13
CHAPTER 2 – ESTIMATING TRANSACTION COSTS .......................................................... 16
2.1 DATA CONSIDERATIONS .............................................................................................. 16
2.2 TRANSACTION COST ESTIMATES .............................................................................. 18
2.3 STOCK MARKET DEVELOPMENT AND TRANSACTION COSTS ........................... 23
CHAPTER 3 – ECONOMIC GROWTH REGRESSIONS ......................................................... 27
3.1 GENERAL SPECIFICATION ........................................................................................... 27
3.2 INSTRUMENTAL VARIABLES ...................................................................................... 28
3.3 REDUCED FORM CROSS-COUNTRY ESTIMATES .................................................... 30
3.4 REDUCED FORM FIXED-EFFECTS ESTIMATES........................................................ 33
3.5 COMPARISON TO REGRESSIONS WITH TURNOVER RATIO ................................. 34
3.6 REMAINING ISSUES ....................................................................................................... 35
CHAPTER 4 – POLICY IMPLICATIONS .................................................................................. 38
4.1 SECURITIES TRANSACTION TAXES ........................................................................... 38
4.2 CAPITAL GAINS TAXES................................................................................................. 44
4.3 SOURCES OF GROWTH .................................................................................................. 45
4.4 GROWTH VOLATILITY .................................................................................................. 47
4.5 DETERMINANTS OF TRANSACTION COSTS ............................................................. 49
vii
4.6 CONCLUSION ................................................................................................................... 50
BIBLIOGRAPHY ......................................................................................................................... 52
APPENDIX A - TABLES ............................................................................................................ 57
APPENDIX B - FIGURES ........................................................................................................... 75
viii
LIST OF TABLES
Page
TABLE 1 – COUNTRY AND EXCHANGE TRANSACTION COST ESTIMATES ................57
TABLE 2 – CORRELATIONS WITH TRANSACTION COST ESTIMATES ..........................60
TABLE 3 – CROSS COUNTRY REGRESSIONS OF STOCK MARKET DEVELOPMENT ..62
TABLE 4 – FIXED EFFECTS REGRESSIONS OF STOCK MARKET DEVELOPMENT ......63
TABLE 5 – COUNTRIES IN THE CROSS-SECTION REGRESSIONS ...................................64
TABLE 6 – SUMMARY STATISTICS FOR CROSS-SECTION COUNTRIES ........................64
TABLE 7 – INSTRUMENTAL VARIABLES CROSS-SECTION REGRESSIONS .................65
TABLE 8 – COUNTRIES IN 4 PERIODS, 5-YEAR AVERAGES PANEL DATA ...................66
TABLE 9 – SUMMARY STATISTICS FOR 4 PERIODS, 5-YEAR AVERAGES PANEL
DATA ............................................................................................................................................66
TABLE 10 – INSTRUMENTAL VARIABLES FIXED-EFFECTS REGRESSIONS ................67
TABLE 11 – CROSS-COUNTRY REGRESSIONS ....................................................................68
TABLE 12 – FIXED EFFECTS REGRESSIONS, 4 5-YEAR PERIODS, 1990-2009 ................68
TABLE 13 – COUNTRIES IN 3 PERIODS, 5-YEAR AVERAGES PANEL DATA .................69
TABLE 14 – SUMMARY STATISTICS FOR 3 PERIODS, 5-YEAR AVERAGES PANEL
DATA ............................................................................................................................................70
TABLE 15 – FIXED-EFFECTS REGRESSIONS, 3 5-YEAR PERIODS, 1993-2007 ................71
TABLE 16 – CROSS-COUNTRY REGRESSIONS WITH TURNOVER RATIO .....................72
TABLE 17 – FIXED-EFFECTS REGRESSIONS WITH TURNOVER RATIO ........................73
ix
TABLE 18 – SYSTEM DIFFERENCE ESTIMATOR REGRESSIONS .....................................74
x
LIST OF FIGURES
PAGE
FIGURE 1 ......................................................................................................................................75
FIGURE 2 ......................................................................................................................................75
FIGURE 3 ......................................................................................................................................75
FIGURE 4 ......................................................................................................................................75
FIGURE 5 ......................................................................................................................................76
FIGURE 6 ......................................................................................................................................76
FIGURE 7 ......................................................................................................................................76
FIGURE 8 ......................................................................................................................................76
FIGURE 9 ......................................................................................................................................77
FIGURE 10 ....................................................................................................................................77
FIGURE 11 ....................................................................................................................................77
FIGURE 12 ....................................................................................................................................77
FIGURE 13 ....................................................................................................................................78
FIGURE 14 ....................................................................................................................................78
FIGURE 15 ....................................................................................................................................78
FIGURE 16 ....................................................................................................................................78
FIGURE 17 ....................................................................................................................................79
FIGURE 18 ....................................................................................................................................79
FIGURE 19 ....................................................................................................................................79
xi
FIGURE 20 ....................................................................................................................................79
FIGURE 21 ....................................................................................................................................80
FIGURE 22 ....................................................................................................................................80
FIGURE 23 ....................................................................................................................................80
FIGURE 24 ....................................................................................................................................80
FIGURE 25 ....................................................................................................................................81
FIGURE 26 ....................................................................................................................................81
FIGURE 27 ....................................................................................................................................81
FIGURE 28 ....................................................................................................................................81
FIGURE 29 ....................................................................................................................................82
FIGURE 30 ....................................................................................................................................82
FIGURE 31 ....................................................................................................................................82
FIGURE 32 ....................................................................................................................................82
FIGURE 33 ....................................................................................................................................83
1
CHAPTER 1 – STOCK MARKET DEVELOPMENT THEORY AND MEASUREMENT
Researchers have attempted to measure stock markets’ contribution to economic growth
by including proxy measures of stock market development, such as stock market capitalization,
value traded, and turnover ratio, in economic growth regressions. Stock market development
“occurs when financial instruments, markets, and intermediaries ameliorate – though do not
necessarily eliminate – the effects of information, (contract) enforcement, and transaction costs”
(Levine 2005, 869). The traditional proxy measures of stock market development sometimes
give indications of how well stock markets ameliorate the effects of these costs but “too
frequently empirical measures of financial development do not directly measure” these costs
(Levine 2005, 922). These traditional proxy measures also suffer simultaneity and omitted
variable biases. Simultaneity bias can occur when economic growth leads to more investment
on, or in the operation of, a stock market, making the stock market larger, more active, or more
efficient. Omitted variable bias can occur when an unobserved variable, such as technological
advancement increases economic growth and improves stock market development. I develop
more exogenous and direct estimates of transaction costs to use, along with exogenous measures
of information and contract enforcement costs, in economic growth regressions. Replacing the
traditional proxy measures of stock market development with measures of the efficiency of stock
markets brings the research in line with the theorized benefits of stock markets and reduces the
biases inherent in using measures of stock market size and activity.
Previous researchers on financial development have identified factors related to
information and contract enforcement costs that should be exogenous to current economic
2
growth. An index measuring the quality of company annual reports in 1990 indicates
information costs. The quality of information companies provided in annual reports in 1990
likely was not influenced by economic growth from 1990 to 2009, unless companies were
anticipating economic growth and constructing their reports accordingly. The origin of the
commercial law system indicates contract enforcement costs. The people establishing the legal
system in different countries were likely not influenced by current economic growth. There has
not, however, been a satisfactory development of a broad set of transaction cost estimates. This
research will change that. Transaction costs are determined by the structure of the exchange,
taxes, fees, and commissions and should be shielded from current economic growth and
unobserved effects like technology.1
This paper will seek to advance our understanding of stock markets’ contribution to
economic growth by developing a set of transaction cost estimates that span 74 countries for up
to 20 years to use in cross-section and fixed-effects economic growth regressions to replace the
previously used proxy measures for stock market development. This approach will help to
alleviate the simultaneity and omitted variable problems inherent in the stock market
development proxies and produce more detailed information on the importance of stock market
efficiency. The results suggest a modest contribution from stock markets to economic growth.
Transaction costs appear to be the more important cost that stock markets reduce. Having more
specific detail on the effects of these costs on economic growth allows for examination specific
policy proposals, such a securities transaction tax.
1 The assumption that economic growth and omitted variable bias do not affect transaction costs is probably the
most questionable assumption made here. This effect, however, is likely small. Economic growth does not
influence taxes, fees, and trading methods (quote driven or order driven market). Technological advancements are
also unlikely to influence taxes, fees or trading methods. They may, however, influence commissions, opportunity
costs, and the trading software exchanges use. Still, the feedback from economic growth to transaction costs is
likely to be considerably smaller than the feedback to size and activity since economic growth attracts additional
investment. I will also make use of econometric techniques to attempt to further control for the potential
simultaneity and omitted variable problems.
3
1.1 STOCK MARKET DEVELOPMENT IN THE LITERATURE
Stock markets arise to improve liquidity and facilitate investment in multiple risky
projects, thereby spreading out the risk from any one project (Levine 1991, 1446). Stock
markets improve liquidity by reducing the cost of transacting on equity shares, reducing the cost
of acquiring financial information and enforcing financial contracts. With lower transaction
costs, investors can trade in and out of stocks without having their savings consumed by fees.
Low transaction costs also help investors reduce risk by enabling them to afford to invest in
multiple projects. Lower information costs help investors to know more about more projects and
to know which investments to trade in and out of and when.
These private benefits give rise to a stock market but the existence of a stock market also
brings public benefits. Stock markets enhance economic activity by producing information,
monitoring investments, facilitating risk management, mobilizing savings, and easing exchange
of goods and services (Levine 2006, 869). The lower the cost of transacting on a stock market
and obtaining information, the more people will trade on the stock market and the better the
stock market should perform these economic activity enhancing functions. Measuring stock
market development by size and activity hints at these costs but does not measure them. Further,
a growing economy is likely to result in a larger and more active stock market leading to
simultaneity and omitted variable problems when used in economic growth regressions.
Researchers have focused on correcting the simultaneity and omitted variable problems
but have done so in unsatisfactory ways. Raymond Atje and Boyan Jovanovic (1993, 632) make
one of the first attempts to empirically measure the effect of stock market development on
economic growth. They use cross-section data for 39 countries from 1980 to 1988. While the
4
authors find support that the value traded on the stock market positively affects economic
growth, Richard Harris (1997, 140) identifies several problems with the study. Atje and
Jovanovic primarily attempt to control for the simultaneity and omitted variable problems with
the stock market development proxy measures by using lagged and initial values of investment
and value traded. Using initial values implies an information loss since theory suggests a
relationship between economic growth and contemporaneous stock market development. The
estimated effects are potentially inconsistent since the initial values are imperfect proxies for
contemporaneous values. Harris uses these lagged and initial values as instruments in a two-
stage least squares regression and finds it reduces the significance of value traded’s effect on
economic growth. Harris’s findings suggest that if value traded is an accurate measure of stock
market development, then stock market development does not affect economic growth.
Ross Levine and Sara Zervos (1998, 538) find evidence to support the theory that stock
market development increases economic growth, though they also use initial levels of the stock
market development proxies to control for the simultaneity and omitted variable problems like
Atje and Jovanovich did. Specifically, more value traded and turnover are associated with higher
rates of economic growth. They expand the number of countries from Atje and Jovanovic’s
study to 47 and the time period to 17 years, with data from 1976 to 1993.
Ross Levine (1998, 597), Ross Levine (1999, 30) and Ross Levine, Norman Loayza and
Thorsten Beck (2000, 34) use Rafael LaPorta, Florencio Lopez-De-Silanes, Andrei Shleifer, and
Robert Vishny’s (1997, 1138) origin of the commercial law system in a country as an
instrumental variable to correct for the simultaneity and omitted variable problems with
economic growth and bank development. The origin of the legal system helps determine how
creditors are treated and property rights are enforced. Thorten Beck, Asli Demirguc-Kunt and
5
Ross Levine (2001, 497) demonstrate that market capitalization differs significantly across
countries with different legal origins, making it a candidate to be an exogenous determinant of
contract enforcement costs.
Philip Arestis, Panicos Demetriades and Kul Luintel (2001, 17) use quarterly time series
data for 5 developed economies to investigate the effect of stock markets on economic growth.
The authors find stock markets contribute only modestly to economic growth, though this finding
may largely be a result of the two stock market variables they use: capitalization and volatility.
Levine and Zervos find both of these variables to be unreliable determinants of economic
growth. Specifically, the effect of stock market capitalization and volatility on economic growth
is influenced greatly by which countries are included in the regression. The use of quarterly data
is also questionable when looking at economic growth because economic growth is a long-run
concept that looks beyond a given business cycle. A recession is, at a minimum, two quarters
long, so one time period would only be half a recession at best.
Peter Rousseau and Paul Wachtel (2000, 1934) use the difference estimator, an
estimation technique that uses lagged values of the variables as instruments for the first-
differenced variables, to look at the effect of stock market development on economic growth.
The authors use annual data, which is an improvement upon the quarterly data Arestis et al.
(2001) use. Rousseau and Wachtel (1935) find evidence supporting a positive link between
stock market development, as measured by value traded, and economic growth.
Building on the use of the difference estimator, Thorsten Beck and Ross Levine (2004,
436) use the system difference estimator to control for the feedback between turnover ratio and
economic growth. The system difference estimator estimates a system of equations, the first
equation is the difference estimator with the lagged levels of the variables used as instruments
6
for the first-difference of the variables, and the second equation uses first differences as
instruments for the levels. Beck and Levine use five-year averages and expand the time period
to 1998, though there are only 40 countries in their study. Beck and Levine (428) narrow the
focus of stock market development to turnover ratio, deeming market capitalization to lack
theoretical and empirical significance and value traded to be an inadequate and biased measure
of liquidity. Five-year increments, while better than quarterly or annual data, are still not
necessarily long enough to accurately measure long-run growth.
1.2 BENEFITS OF MEASURING STOCK MARKET DEVELOPMENT BY COSTS
The simultaneity and omitted variable problems can be addressed further by better
identifying the benefits of stock market development, specifically with exogenous measures of
information, transaction, and contract enforcement costs. LaPorta et al. (1997) and Beck et al.
(2001) make considerable progress on identifying determinants of financial information and
contract enforcement costs. These studies do not address transaction costs, however. Adding
stock market transaction costs will provide a fuller explanation of stock market development and
allow for better control of the simultaneity and omitted variable problems with economic growth.
Even if the simultaneity and omitted variable problems are corrected, there are other
benefits to measuring stock market development by information, transaction, and contract
enforcement costs instead of by the traditional proxy measures. Size and activity of a stock
market do not measure how well the stock market functions (Levine 2006, 922). Looking at the
inner workings of the stock market will help us understand what particular aspects and features
of a market promote good, or bad, outcomes. While research suggests a positive effect of stock
markets on economic growth in general, some research indicates that stock markets hurt resource
7
allocation. Stock markets may have poor incentives to gather information, lead to weak
corporate governance, and have incentives to engage in short-sighted trading (Levine 2006, 882).
The thought that markets are weak at corporate governance stems from the thought that
corporate takeovers are the only tool of corporate governance. Corporate takeovers may be
unlikely to occur in a market system because insiders have better information than outsiders
(Stiglitz 1985, 137), other investors can free-ride simply by watching the market activities of
bigger investors (Grossman and Hart 1980, 43), and poison pills put in place by existing
managers reduce incentives to take over a firm (DeAngelo and Rice 1983, 332). These
weaknesses all rely on takeovers being unlikely and being the sole means of exerting corporate
governance.
Takeovers are not, however, the sole means of exerting corporate governance. While
takeovers certainly lead to corporate control, corporate governance can also come from corporate
influence. Influence can come, for example, from research departments at large investment
houses issuing buy, hold, or sell ratings. Determining and issuing these ratings is an information
cost and the ability to act on the information is determined by transaction costs. Similarly, the
lower the cost to trade a company’s stock, the easier it is to sell (buy) the stock to punish
(reward) bad (good) behavior. As long as the managers of the company care about the stock
price, such trading will influence their behavior. Measuring transaction costs does not directly
address corporate governance but it does measure an important tool for exerting corporate
governance. Looking at size and activity of stock markets does not necessarily look at the tools
of corporate governance.
The other main critique of market systems is that stock markets encourage short-sighted
trading. The easier and cheaper it is for investors to seek immediate gains, the less likely
8
investors will be to stick with an investment through the ups and downs of a long project. Short-
sighted investors will lead to short-sighted managers. Robert Pollin, Dean Baker, and Marc
Schaberg (2003, 530) argue that encouraging short-sighted behavior leads to an inefficient
allocation of resources. If this critique is valid, then one of the primary motivations for stock
markets, to facilitate investment in long term projects, would be in doubt. Incorporating a
measure of transaction costs into economic growth research will allow for a test of these
competing theories.
Information, transaction, and contract enforcement costs also give a better indication of
proverbial rocks or dams that might prevent capital from flowing to its most productive uses.
Stock market size and activity do not give an indication of whether the capital is moving to
where it is most productive, simply that it exists and is moving. Therefore, it is important to
measure the effect of information, transaction, and contract enforcement costs on economic
growth and not just the effect of stock market size and activity on economic growth. Looking
specifically at the effects of these proverbial rocks and dams we will also be able to make more
informed policy decisions.
1.3 DEFINING STOCK MARKET TRANSACTION COSTS
Larry Harris (2003, 421) identifies three types of transaction costs: explicit costs, implicit
costs, and missed trade opportunity costs. Explicit costs are those visible to an accountant such
as commissions, exchange fees, and taxes. Implicit costs do not involve a direct expenditure of
funds but involve some type of opportunity cost. The missed trade opportunity costs are implicit
costs that arise specifically from missing out on a trade. Adding up the various explicit costs is
9
straightforward as long as all of the commissions, fees, and taxes associated with a trade can be
defined and identified.
The explicit costs are not always straightforward however. Commissions, for example,
vary by quality of service and by quantity of trades. The commissions for full-service brokerage
firms are higher than for discount and online brokerages. In Korea, for example, commissions
through a traditional broker are up to four times more than commissions from online trading
(Batista & Palmiero 2002, 253).
While exchange fees, stamp duties and transfer fees are relatively straightforward,
government tax laws can make them more complicated. Morocco subjects commissions and fees
to the value-added tax, while Switzerland exempts banking and securities transactions from the
tax. Even within countries, the tax treatment is not always consistent. In Switzerland, asset
management services are not exempt from the value-added tax, so the cost to transact would be
different based on how a trader interacts with the market.
Harris’s implicit costs arise from price effects of a trade, as indicated by bid-ask spreads.
The bid-ask spread represents the difference between the price buyers pay for a stock and the
price sellers receive for it. If a trader were to buy a stock and sell it immediately, the trader
would lose the difference between the bid and the ask.
Missed trade opportunity costs come from investors offering a bid that is just below the
ask or asking a price that is just above the bid, which results in missing out on the trade. Had the
investor offered a slightly better price, she would have consummated the trade and earned the
return. Harris's definition of implicit costs needs to be expanded to include opportunity costs to
the investor for having to spend time deciding on a trade and contacting a broker. The time it
takes to contact a broker can also result in missed trades. If an investor can enter an order online,
10
as opposed to trying to get in touch by phone with a broker, the investor is less likely to miss the
trade.
The most common empirical measure of transaction costs has been the bid-ask spread
plus a representative commission. This approach suffers from the fact it overlooks explicit and
opportunity costs, and also from the fact commission data are not always available for a cross-
section of countries for long periods of time. An available and comparable measure of
transaction costs across countries is needed for better research on the relationship between stock
market development and economic growth.
David Lesmond, Joseph Ogden, and Charles Trzcinka (1999) develop a model to estimate
transaction costs that accounts for these problems. They use a Tobit model with frictions based
on expected and actual stock returns. Since the estimates are based on returns, the transaction
costs are derived from investors’ decisions regarding their own costs rather than a researcher
estimating the specific costs the investors would consider. Also, since return data are more
readily available than spread and commission data, the model can be used to estimate transaction
costs for numerous stock exchanges. I use this model to estimate transaction costs for 95
exchanges in 74 countries.
1.4 THE TRANSACTION COST MODEL
The Tobit model by Lesmond et al. (1999) accounts for the explicit and implicit costs
considered by traders by using data on individual stock and index returns to measure transaction
costs. An investor will trade on new information if the expected gain from that information
exceeds the cost of making the trade. When transaction costs are higher than the expected return,
trades will not occur. The threshold return for trades to take place can thus be regarded as a
11
measure of the overall cost of transacting in the market in question. Hard to measure costs, such
as the opportunity cost to contact a broker, are included implicitly in the estimates from Lesmond
et al.'s model, making the estimates a more complete measure of transaction costs than the
common bid-ask spread plus commission.
In applying the Lesmond et al. approach, the expected returns are represented by a local
market index. While individual stocks do not necessarily move with an index, variations are
assumed to be independently and identically distributed.
The relationship between a stock's actual return, Rjt, and its gross return, , is thus
represented as
, (1)
where
if
, (2)
if , (3)
if
. (4)
Rmt, α1j and α2j are the market return, the selling threshold and the buying threshold, respectively.
If the gross return is between these thresholds, then the stock is expected to have an actual return
of zero. If the gross return is less than α1j, then the actual return is expected to equal the gross
return less this threshold. On the other end, if the gross return exceeds α2j, then the actual return
is expected to equal the gross return less this threshold.
The total cost to buy and sell the stock is the difference between α1j and α2j. Since α1j is a
negative number, this difference becomes the sum of the two costs. The transaction costs are
thus estimated as percentages of price, or return. Since the transaction costs are estimated as
returns, they are estimated as completely variable costs without an explicit fixed cost component.
12
Lesmond et al. develop this model on two quote-driven markets (the New York Stock
Exchange, NYSE and the American Stock Exchange, AMEX), and it has been used to estimate
transaction costs on two order-driven markets (the Tunisia Stock Exchange (Bellalah et al. 2006)
and the Oslo Stock Exchange (Odegaard 2008)). Lesmond et al. compare their estimates with
spread plus commission data and found both measures of transaction costs follow similar time
patterns, with peaks and valleys occurring at similar intervals. Mondher Bellalah, Adel
Boubaker, and Saber Sebai (2006) and Bernt Odegaard (2008) compare estimates using this
model with the spread estimates using the approaches by Richard Roll (1984) and Thomas
George, Gautam Kaul, and Michigan Nimalendran (1991). Again, the alternative estimates
follow similar time patterns, though the estimates using Lesmond et al.'s model are greater than
both spread estimates, as expected.
Randi Naes, Johannes Skjeltorp, and Bernt Odegaard (2008, 22) also estimate transaction
costs with the model by Lesmond et al. and report correlations between transaction cost
estimates and various other measures of transaction costs and liquidity for the Oslo Stock
Exchange. They find that Lesmond et al.'s Tobit estimates are least correlated with quoted bid-
ask spreads and turnover. This result also indicates that the Lesmond et al. approach provides
other information than the popular measure of transaction costs and one of the popular measures
of stock market development.
Lesmond et al.'s model has attracted some attention from financial development and
macroeconomics economists. Ross Levine and Sergio Schmukler (2006) use a simplified
version of the model to estimate stock market liquidity in developing countries. Rather than
using it as a measure of transaction costs, however, Levine and Schmukler take the frequency of
zero return trading days as an indicator of market liquidity. Randi Naes, Johannes Skjeltorp, and
13
Bernt Odegaard (2011) use estimates from the model to examine the link between liquidity and
the business cycle in the United States and Norway. Despite these researchers using transaction
costs as measures of stock market development, there is still a need for a broad cross-country set
of transaction cost estimates.
1.5 MARKET MICROSTRUCTURE INFLUENCES
Before estimating the transaction costs for a broad cross-section of countries, I examine
how market microstructures influence transaction costs. Understanding stock market
microstructure, or the process and outcomes of trading stocks under explicit trading rules
(O’Hara 1995, 1), serves as a way to see if the estimates line up with expectations. The explicit
costs and implicit costs can be influenced by the microstructure of the stock market. Market
microstructure can be broken down into three categories: the degree of continuity, the extent of
reliance on dealers, and the degree of market transparency (Naes 2008, 4).
Continuity is how frequently trading occurs. Electronic trading generally allows for
exchanges to have continuous trading. In contrast, periodic trading systems hold call auctions at
certain points in time. The primary benefit of a continuous trading system is liquidity, though a
periodic call auction may be more efficient as it is easier to match up buyers and sellers at once
than to match them as the orders are placed (Naes 2008, 6). A preference for continuous trading
versus periodic call auctions could be an example of traders valuing liquidity over efficiency. In
practice, many exchanges combine the two trading schedules. Poland, for example, has
continuous trading from 10:00-4:00, but also holds two single-price auctions from 11:15-11:45
and 3:00-3:30.
14
The second consideration is whether orders are matched in an order-driven or quote-
driven system. Order-driven markets match orders based on predetermined criteria, usually price
and time. With the advent of electronic market systems, the matching of orders based on
decision criteria is fast and efficient. In quote-driven markets, market makers quote bid and ask
prices for a stock and are responsible for matching orders. Market makers earn their revenue
from the spread between the bid and ask prices. Quote-driven markets are more liquid because
the market maker will often fulfill one end of a transaction if an order cannot be matched. In
order to do ensure orders can be matched, market makers have inventories of their assigned
stocks. Mark Garman (1976) and Yakov Amihud and Haim Mendelson (1980), find inventory
costs are an important cost for a market maker to consider when setting the spread. Market
makers also have pricing power over the spread. Amber Anand, Carsten Tanggaard, and Daniel
Weaver (2009) find that spreads drop when the Stockholm Stock Exchange allowed companies
to negotiate with market makers.
As with the decision on trading frequency, several markets have become hybrids between
quote-driven and order-driven market systems. In a hybrid system, the primary matching system
is order-driven, but market makers are available to insure liquidity. The NYSE is an example of
a hybrid market. The NYSE began as a quote-driven market but now runs most trades through a
limit order book with market makers still available for liquidity.
The last consideration is transparency. Order-driven markets often have the best
transparency since the orders go into a public order book. Quote-driven markets are less
transparent since the availability of information depends on the market maker. Several studies
(Admati & Pfleiderer 1991, Forster & George 1992, Benveniste et al. 1992), though not all (e.g.
Madhavan 1995), find that transparency is good for both liquidity and transaction costs. Based
15
on the research behind these three considerations, we should expect order-driven markets with
public limit order books and at least some periodic trading to have the lowest transaction costs.
16
CHAPTER 2 – ESTIMATING TRANSACTION COSTS
Developed stock markets are supposed to reduce financial transaction, information, and
contract enforcement costs. Researchers studying stock market development have used
imperfect measures of development that do not necessarily reflect these costs. At best,
researchers have focused on only two of these three costs, though often times on none of them.
Previous researchers have either estimated transaction costs using the Lesmond et al.
model for only a few countries or have used a simplified version of the model for several
countries. In this chapter, I use the approach developed by Lesmond et al. to estimate and
evaluate the transaction costs for 95 exchanges in 74 countries. I then test these estimates as an
explanatory variable in models of stock market development.
2.1 DATA CONSIDERATIONS
I construct annual estimates of transaction costs using daily data on individual stock
returns for 95 exchanges in 74 countries from 1990 to 2009. Transaction costs are estimated by
firm and averaged over the exchange. I assign a firm to the exchange and country where its
primary quote appears. The initial data included 97,128 firms with 248,908,929 observations,
though for reasons given below, the final data set was smaller. The expected market return is
represented by a local index given by DataStream.
Data are not available on all exchanges for the entire time period, and the data for some
years are sparse. An exchange is deemed to be active when the local index has a non-zero return
and the number of stocks on the exchange in a given year is not too different from most other
17
years. For example, for the exchange in Sao Paulo, Brazil there are 1, 3, 4, and 7 stocks with
useable data from 1990 to 1993, and then in 1994 there are 163. I, therefore, exclude the data
from 1990 to 1993.
I also only keep stocks that had evidence of trading activity on at least 20% of the trading
days. Since the model relies on the occurrence of zero-return days, there is a tradeoff between
trying to use only good data and keeping with the rationale underlying Lesmond et al.'s model.
In a paper on international stock market efficiency, John Griffin, Patrick Kelly, and Frederico
Nardari (2006) use a 30% cutoff. I chose a 20% cutoff because, based on the data, it is less
restrictive of stocks with minimal trading activity. Future researchers looking to make use of this
data would be able to set their own cutoff.
Another consideration with the data is that it is organized at the exchange level and not
simply by country. Most countries only have one exchange, so this problem is only a concern
for the 12 countries with multiple exchanges. Countries with multiple exchanges segment firms
of different sizes and types among their exchanges. Countries with only one exchange serve all
of their firms on that exchange. The cost of transacting on the stocks of smaller firms tends to be
greater than the costs of transacting on the stocks of larger firms. Lesmond et al. build the model
on return data from the NYSE, the main market in the United States, and the AMEX, a smaller
market in the United States. The estimated transaction costs differed by as much as 36%.
Furthermore, the NYSE had lower transaction costs for larger sized firms than the AMEX, and
the AMEX had lower transaction costs for smaller firms than the NYSE. Countries whose stock
markets serve a broader segment of stocks should be considered to have more developed stock
markets than countries whose stock markets serve only a narrow group of stocks. The
transaction costs, however, may indicate the opposite.
18
2.2 TRANSACTION COST ESTIMATES
The average estimated transaction costs, along with minimum, maximum and trend, for
each exchange are given in Table 1. For brevity, only the estimates of the total cost to trade are
given, though separate costs to buy and sell are available. I have confidence in the results for
four reasons. First, the results are similar in level and variation to those produced by other
researchers using the same model. Lesmond et al. (1999) and Naes et al. (2011, 146) produce
transaction cost estimates for the NYSE from 1963 to 1990 and from 1947 to 2008, respectively.
The Lesmond et al. estimates average 3.3% and the Naes et al. estimates average 3.5% overall,
4.0% from 1990 to 1999, and 2.7% from 2000 to 2008. My estimates average 2.3%. My
estimates are also lower in the 2000 to 2008 time period than in the 1990 to 1999 time period,
similar to the estimates from Naes et al.
Odegaard (2008, 4) and Naes et al. (2011, 146) use the same approach to estimate
transaction costs for Norway from 1980 to 2007. Both studies find costs ranging from a little
more than 3% to almost 10% with the costs in most years between 4% and 7%. My estimates
range from 2.4% to 11.7% with most years also between 4% and 7%.
Second, exchanges within country follow an expected ordering, with primary exchanges
having lower costs than secondary exchanges. The exchanges within country also have similar
trends. The estimated transaction costs on the NYSE are lower than the estimated costs for the
AMEX. This ordering holds in my estimates for the Toronto and TSX exchanges in Canada, the
Tokyo and JASDAQ exchanges in Japan, the Korean and KOSDAQ exchanges in Korea, the
Main Board, the 2nd
Board, and the MESDAQ in Malaysia, the Singapore and Catalist exchanges
in Singapore, and the Taiwan and Gre Tai exchanges in Taiwan.
19
Similarity in trends within a country can be seen in the Canadian (Figures 2 and 3),
Chinese (Figures 4 and 5), and Malaysian (Figures 15, 16, and 17) exchanges. The estimated
transaction costs for the Chinese exchanges are stable except for a spike in both exchanges in
2006. The Canadian exchanges are more volatile with high costs in the early 1990s, followed by
a drastic decrease in costs, only to have another increase in costs in 2001. Transaction costs on
the Canadian exchanges then decrease substantially, only begin increasing again in 2008. The
costs on each of the Malaysian exchanges are generally in the low to mid-single digits until a
sudden increase in 2007. While the size of the increase is different on the different exchanges,
each exchange experienced a spike beginning in 2007.
Third, there is considerable time series variation in the estimates. It is common for the
estimated transaction costs on an exchange to triple, if not more, throughout the time period. For
example, in Venezuela, estimated costs range from less than 5% to more than 25%. For the
NYSE, the estimates range from less than 1% to greater than 6%. These estimates might seem at
first brush like a weakness of the estimates. Other researchers, however, have found similar
results using the same and different methods.
Odegaard (2008, 7-8) calculates the bid-ask spread, the relative spread and the Roll
(1984) measure of trading costs for the Oslo exchange. The high for the bid-ask spread is 7
times the low, and the high for the relative spread is almost 4 times the low. The Roll measure
gave a tighter range of trading costs, ranging from under 2% to almost 4%. Lesmond et al.
calculate the spread plus commission for the NYSE and find the highest estimates to be up to 5
times the lowest estimates.
The transaction costs estimates have such a large time series variation owing largely to
liquidity concerns. Some of the costs associated with buying stock, like taxes, are pre-
20
determined, or at least stable, such that they would not fluctuate wildly from year to year. The
spread however, is set by the market maker and subject to more frequent changes. With the
spread, the market maker covers: order processing costs such as costs incurred in setting up,
fixed exchange fees, etc.; risk of holding inventory, which is related to the volatility; and
adverse information costs such as the risk of trading with a counterparty with superior
information (Plerou, et al. 2005, p. 046131-1). The first source of costs is predetermined and
should not change wildly from year to year. The other two sources of cost arise from the risks
the market-maker faces and are much more variable. The more risk the market-maker perceives,
the greater the spread between the bid and the ask.
A market maker is likely to perceive more risk of holding inventory in a down market.
That is, if prices are falling, then more people are selling than buying so the market-maker will
likely accumulate inventory and will want to set the spread higher to account the increased
inventory costs. Inversely, if prices are rising, then more people are buying than selling so the
market maker will likely reduce inventory and will have lower inventory costs. As mentioned in
Section 1.4, research suggests market makers consider inventory costs when setting the spread.
The time series variation leads to the last reason to have confidence in the results.
Historical events are visible in the time series variation of the estimates. Specifically, transaction
costs are high during periods when market-makers would likely perceive more inventory risk
because of down markets and transaction costs lower with expectations of up markets. For
example, the Asian Financial Crisis can be seen by spikes in the estimated costs for the
exchanges in Hong Kong (Figure 10), Indonesia (Figure 13), Japan (Figures 14 and 15),
Malaysia (Figures 15, 16, and 17), the Philippines (Figure 20), South Korea (Figures 27 and 28),
and Thailand (Figure 31).
21
The estimated costs for the Russian exchanges (Figures 23, 24, and 25) are similarly
volatile during the Russian debt crisis. Former Soviet Bloc countries, such as Croatia (Figure 6),
the Czech Republic (Figure 7), Estonia (Figure 9), Hungary (Figure 11), Poland (Figures 21 and
22), and Slovenia (Figure 26), had similar spikes between 1997 and 2000. While Mexico (Figure
18) actually experienced decreasing costs during the Mexican Peso Crisis, Argentina (Figure 1)
experienced increased costs during an economic crisis in 2001 and 2002. Costs in Sri Lanka
(Figure 29) spiked during a recession in 2001. Venezuela (Figure 33) also had a considerable
increase in costs beginning in 1999, which is when Hugo Chavez was elected. Though there
have been some fluctuations, costs are about double the pre-election period.
In 1998, the NOREX trading system was formed with the exchanges in Denmark (Figure
8), Iceland (Figure 12), Norway (Figure 19), and Sweden (Figure 30). Since the new system was
formed, the transaction costs in those countries have followed similar trends. Since 2002, the
transaction costs in all four countries have decreased. Transaction costs have increased in all of
these exchanges in costs in the last two years of data.
There are some questionable results that deserve attention. The estimates for the Chinese
exchanges (Figures 4 and 5) were remarkably low and stable, except for the spike in 2006. The
low results seem incorrect based on a summation of known explicit costs. Commissions on the
Shanghai exchange are 0.7%, and trades are subject to a 0.1% transfer fee and a stamp duty of
0.3%. At a minimum, the estimated round-trip transaction costs for Shanghai should be at least
1.8%. Similarly, the Shenzhen exchange has brokerage commissions of 0.43%, a stamp duty of
0.3%, a stock exchange charge of 0.03%, a clearing charge of 0.05%, and a China Securities
Regulatory Commission fee of 0.0046%. Transaction costs on the Shenzhen exchange should
not be less than 1.6%. The estimated costs average only 0.5% and 0.6%. One possible reason
22
for the low estimated costs is investors being more concerned with missed trade opportunities
than explicit trading costs.
There is a spike in the estimated transaction costs for the Chinese exchanges in 2006.
This spike may be attributed to efforts by the Chinese government to slow investment growth by
raising reserve requirements 3 times in the second half of the year. The government continued
raising reserve requirements after 2006, however, the estimated transaction costs returned to the
pre-spike range.
The cost estimates for the NYSE (Figure 33) reached a low of 0.3% in 2005, 2006, and
2007. The low transaction costs can be expected in part because of the move to decimal pricing
(Batista & Palmiero 2002, 480) and advances in technology. These years preceded the Financial
Crisis of 2008 but even after the liquidity crunch, the estimated stock market transaction cost
only increased slightly. The lack of a spike is contrary to the experience in most other countries.
The reason may be the response to the crisis. The Federal Reserve pursued a loose monetary
policy when the crisis set in. Also, the other financial crises were regional, which meant
investors seeking quality could substitute away from the affected region. The Financial Crisis of
2008 was more global than the others, so a flight to quality would still send investors to the
United States.
There is also evidence that this approach does not work for all exchanges. The cost
estimates for the over-the-counter (OTC) markets in the United States are particularly high. The
estimates can be partially explained by the nature of the OTC markets in the United States. The
OTC markets typically serve penny stocks, so higher transaction costs are expected. There is
another, more problematic, cause of the high estimates. The estimates are based on comparing a
stock’s actual return to its expected return. A local index is used as a proxy for the expected
23
return. If that index does not accurately represent the expected return for the stock, then the
model will produce inaccurate estimates. For all of the U.S. markets, the S&P 500 Composite is
used as the expected market return. The expected return for penny stocks is probably not well
represented by the return for the S&P 500 Composite.
In general, the estimated transaction costs hold up to inspection of individual exchanges.
The results for the exchanges in China and for some of the secondary exchanges and OTC
markets are suspect. Most of the estimates, however, appear to be reasonable estimates of the
cost of transacting on various stock exchanges.
2.3 STOCK MARKET DEVELOPMENT AND TRANSACTION COSTS
The purpose of estimating transaction costs is to gain a better understanding of stock
market development and improve our understanding of its economic effects. Theory suggests
transaction costs would be negatively correlated with the measures of stock market development.
That is, lower transaction costs should mean greater stock market development. There are 67
exchanges with both transaction cost estimates and data for market capitalization, value traded,
and turnover. Correlations are given in Table 2. Of those cases, theory holds 50, 48, and 42
times for market capitalization, value traded, and turnover. Data on these measures come from
Thorsten Beck, Asli Demirguc-Kunt, and Ross Levine (2000). In countries with multiple
exchanges, the data on capitalization, value traded and turnover are aggregated by combining the
primary and secondary stock exchanges in those countries. The transaction cost estimates are
separated by exchange. For Canada, for example, this separation means there are separate
correlation results between capitalization and transaction costs for the Toronto exchange and for
the TSX, though the capitalization numbers are the same in both cases.
24
These results are simple correlations without any controls. To gain a better
understanding of the relationship between transaction costs and stock market development, I
estimate cross-section and fixed-effects regressions. Previous researchers have identified several
other explanatory variables for stock market development. These control variables include legal
tradition, shareholder rights, property rights, accounting standards, political openness, trade, and
geography (e.g. Beck et al. 2001, Chinn & Ito 2006, Law & Habibullah 2009). The first three
variables indicate how costly it is to enforce contracts in a country. Legal tradition indicates how
well a country protects shareholders and creditors and comes from LaPorta et al. (1997).
Shareholder rights indicate how well minority shareholders are protected from exploitation by
majority shareholders and come from Beck et al. (2001). Property rights indicate how well
property is protected in a country and come from the Index of Economic Freedom.
Information costs are indicated by accounting standards in each country. The accounting
standards measure is based on the quality of reporting in annual reports and comes from the
Center for International Financial Analysis through Beck et al. (2001).
There are 3 additional control variables included that are thought to be determinants of
financial development. The first two pertain to how likely a country is to foster competition.
Political openness comes from Beck et al. (2001) and is a composite index variable measuring
how closed a political system is, access to institutional structures for political expression, access
of non-elites to executive offices, and how competitive the selection process for executives is.
Openness to trade comes from the World Bank Development Indicators and is measured by the
sum of imports and exports divided by GDP. Both variables indicate how open the entrenched
interests in a country are to competition. Since a stock market broadens access to financial
25
capital, a stock market should foster a more competitive environment and be more accepted in a
country where the entrenched interests are more open to competition.
The final variable controls for the incentive a country may have to invest in long term
projects and the country's need for a stock market. Geography is linked with health and disease.
In countries with lower life expectancies, investors may not be interested in investing in long
term projects. Geography is measured by World Bank classification of a country as having a
tropical climate and comes from Beck et al. (2001).
The data range from 1995 to 2005 with 31 countries in the cross-section regressions. I
include countries with at least 10 years of data during this time. There are separate regressions
for each measure of stock market development. Transaction costs and the measures of stock
market development all enter the regression in log form. The cross-section specification is
, (5)
where is a stock market development measure that is capitalization, turnover ratio, or value
traded and is a series of control variables described above. Results are given in Table 3.
The results support the theory that transaction costs are an important determinant of stock market
development. In all of the measures of stock market development, the direction of effect
between transaction costs and stock market development is negative as expected, with almost all
results being statistically significant. Reducing the cost of transacting on the stock market in a
country by one percent would suggest an increase in stock market development of about a half a
percent.
There are 38 countries in the fixed effects regressions. The fixed effects specification is
, (6)
26
where the subscript t is the time period and is the country-specific effects. The time variation
in the fixed effects regression also reduces the available control variables. Only property rights
and international trade are included in . Results are given in Table 4. The fixed-effects
regression results are all statistically significant and indicate an economically significant, though
smaller, relationship between transaction costs and stock market development. From the fixed-
effects results, reducing transaction costs in a country by one percent would suggest an increase
in stock market development of about a quarter of a percent.
One final interesting note on the results is that the other consistently significant variable
is accounting standards. Recall that accounting standards are a measure of information cost.
The significance of stock market transaction cost and accounting standards indicate transaction
costs and information costs are very important for the development of stock markets. Based on
these findings, future research to measure information costs across stock markets may be
beneficial to help better understand the contribution of stock markets to economic growth.
27
CHAPTER 3 – ECONOMIC GROWTH REGRESSIONS
In this chapter, I improve our understanding of stock markets’ role in economic growth
by using exogenous measures of transaction, information, and contract enforcement costs in
economic growth regressions. As Beck and Levine. (2004, 427-428) note when using the
traditional proxy measures of stock market development,
“there are important weaknesses with the measures. Theory focuses on the role that stock
markets and banks may play in reducing informational asymmetries and lowering
transaction costs. We do not, however, have direct measures of the degree to which
markets and banks in a broad cross-section of countries ameliorate information and
transaction costs.”
With exogenous measures of transaction, information, and contract enforcement costs I can
avoid using proxy measures for stock market development and the burden of simultaneity and
omitted variables that plague them. The measures also bring the empirical work in line with the
theory. The findings are far from definitive, but they do add to the growing body of evidence
that suggests a link between stock markets and economic growth in a much cleaner method than
previously used. Also, the estimated effects for transaction costs can be used to evaluate policy
proposals such as a securities transaction tax that I discuss in Chapter 4.
3.1 GENERAL SPECIFICATION
Previous researchers have used a general form to represent stock markets’ relationship
with economic growth of
28
, (7)
where GROWTH is the annual growth rate of real GDP per capita, SD is stock market
development, and X is a set of exogenous control variables. Unfortunately, stock market
development, particularly the proxy variables traditionally used to measure it, is aided by
economic growth,
, (8)
where Z is a set of exogenous determinants of stock market development. Since economic
growth influences stock market development, using the traditional proxy measures in an
economic growth regression causes a simultaneity problem that leads to inconsistent estimates.
Also, any unobserved components of Z, such as advances in technology, that are not actually
exogenous can lead to omitted variable bias.
3.2 INSTRUMENTAL VARIABLES
Rather than using the proxy measures of stock market development, I use transaction
costs, an exogenous component of Z, to serve as an instrument for turnover ratio in economic
growth regressions. Beck and Levine (2004) suggest turnover ratio is the preferred proxy
measures of stock market development. Capitalization is not a good variable because theory
does not suggest that simply listing shares on the stock market will influence resource allocation
and growth (Beck and Levine 2004, 428). Value traded is not a good proxy because it measures
the size of the stock market relative to the size of the economy and not stock market liquidity.
Value traded is also potentially more biased than turnover ratio because value traded includes
share prices that would likely be bid up with expectations of higher economic growth (Beck, et al
2004, 428).
29
The system of equations I estimate is
, (9)
, (10)
where TR is turnover ratio, and the hat over TR indicates it is the predicted values of TR from
the first equation. Some of the variables in X appear in all of the regressions. The constant
controls are: the log of the initial level of GDP per capita, the log of one plus the average years
of schooling, and the log of credit issued by the banking sector as a percentage of GDP. The
remaining controls in X are interchanged in separate regressions. The remaining controls are the
log of government consumption as a percentage of GDP, the log of imports and exports divided
by GDP, and the log of one plus CPI inflation.
I estimate both cross-section and fixed-effects models. Countries in the cross-section
regressions are given in Table 5 and summary statistics are given in Table 6. To compare with
previous results, the transaction cost estimates are matched up to the data from Beck and Levine
(2004). Although the transaction costs are estimated for 74 countries, Beck and Levine only
have 40 countries and of those, only 29 of those countries have both transaction cost and
turnover ratio data. Results are given in Table 7. The estimated effects are at best only a tenth
the size of the estimated effects Beck and Levine find. I bootstrap with a thousand repetitions to
account for the small sample size and none of the results are statistically significant.
Countries in the fixed-effects regressions are given in Table 8 and summary statistics are
given in Table 9. There are more countries in the fixed-effects regression as I am not matching
up to the regressions by Beck and Levine. There are almost 50 countries in the instrumental
variables fixed effects regressions. Some of the 74 countries I estimate transaction costs for only
have a few years of data and some do not have data for turnover ratio. The instrumental
30
variables fixed-effects results are given in Table 10. Unlike in the cross-section regressions, the
estimated effects from the fixed-effects regressions are considerably larger than the results Beck
and Levine find. The estimated effects are more than 8 times greater than Beck and Levine find.
Despite the larger estimated effects, none of the estimated effects are statistically significant.
The strange results likely result from transaction costs being a weak instrument for
turnover ratio. To be a valid instrument for turnover ratio in an economic growth regression,
transaction costs must be uncorrelated with the error term in Equation 10 such that
. (11)
This condition is assumed to hold, although in the introductory section I gave some reasons why
it might not hold that I will revisit in Section 3.6. The second criterion for transaction cost to be
a valid instrument for turnover ratio is for transaction costs to be partially correlated with
turnover ratio. Despite the findings in Chapter 2, this assumption does not appear to hold. The
F-statistic from the first equation is never greater than 2, suggesting the transaction cost
estimates do not serve as a good instrument turnover ratio. The weakness of transaction costs
may at least partially explain the statistically and economically insignificant results found in the
instrumental variables regressions.
3.3 REDUCED FORM CROSS-COUNTRY ESTIMATES
For a different approach, I replace the traditional proxy measures of stock market
development with measures of stock market efficiency from Z and estimate a reduced form
equation. Specifically, I use exogenous measures of transaction, information, and contract
enforcement costs so the economic growth equation becomes
, (12)
31
where TC is transaction costs, IC is information costs, and CE is contract enforcement costs.
Including measures of these costs also allows for better policy prescriptions than when including
the proxy measures of stock market development.
In Chapter 2, I develop estimates of transaction costs. I also discuss variables that
represent information costs and contract enforcement costs. A measure of accounting standards
from the Center for International Financial Analysis represents information costs. A set of
dummy variables identifying the origin of the commercial law system represents contract
enforcement costs. The regression specification I estimate is
, (13)
where AC is accounting standards, LOB is British legal origin, LOF is French legal origin, and
LOS is Scandinavian legal origin. Socialist legal origin is the excluded dummy variable.
The results are given in Table 11 and standard errors are derived by bootstrapping with
1,000 repetitions to account for the small sample size. The variables related to stock market
costs all enter in level form, as does the growth rate of real GDP per capita, so the coefficient
interpretation is straightforward. The coefficients no longer represent stock market development
generally, rather they are estimated effects of the individual costs stock markets theoretically
reduce. The estimated effect of transaction cost is consistently negative. Lower transaction costs
indicate a more developed stock market, which theory suggests leads to more economic growth.
The estimated effect size ranges from -0.07 and -0.16 so a one-percentage point decrease in
transaction costs would suggest an increase in economic growth between 0.07 and 0.16
percentage points. A one standard deviation decrease in a country’s transaction costs would
suggest an increase in economic growth between 0.33 percentage points and 0.77 percentage
points. These results, however, are not statistically significant.
32
The estimated effects for information costs are unexpected. Accounting standards has a
negative estimated effect, which is counterintuitive since a higher score on accounting standards
indicates better information is available in those countries. Better information availability should
be good for economic growth. The results are statistically significant but economically small.
The estimated effect size is only -0.002. A one standard deviation improvement in accounting
standards would suggest a decrease in economic growth of only 0.026 percentage points. In fact,
a country moving from the minimum accounting standard to the highest account standard would
expect to see a change in economic growth of only 0.11 percentage points. It may be that the
effort expended on keeping high accounting standards exceeds the benefit of low information
costs.
The estimated effects for contract enforcement costs are all small and statistically
insignificant. The greatest magnitude of effect is 0.031 and the smallest is -0.001. Countries
with British and Scandinavian origins for their commercial law systems do slightly better than
countries with socialist origins. Countries with French origins for their commercial law systems
perform slightly worse than countries with socialist origins.
Taken together the results indicate stock markets have a modest effect on economic
growth at best. I test the joint significance of the measures for transaction, information, and
contract enforcement costs and find they are not jointly significant. Also, the reduction of
different costs has different direction of effects on economic growth. One possible cause of the
small estimated effects for information costs and contract enforcement costs is that the variables
used to represent those costs are not direct measurements of the costs like the transaction cost
measure is. More work to develop actual measures of those costs could help to improve the
estimated effects. More work to develop better measures of information and contract
33
enforcement costs might also focus on developing measures of those costs with a time series
component that would allow for a panel examination of how those costs affect economic growth.
3.4 REDUCED FORM FIXED-EFFECTS ESTIMATES
Since the transaction cost estimates are available over time as well as across countries,
they can be used in panel regressions. The information and contract enforcement costs do not
vary over time, however, so the new regression equation is simply
, (14)
where is the country-specific effects and t is the time period. The data are averaged into four
five-year periods from 1990 to 2009. 2 There are more than 60 countries available since turnover
ratio is no longer needed for these regressions.
Results for this data are given in Table 12. The estimated effects for transaction costs
have the expected negative sign, are statistically significant, and are larger than the cross-country
estimated effects. The estimated effects are between -0.172 and -0.225. A one standard
deviation decrease in transaction costs would suggest an increase in economic growth between
0.81 and 1.1 percentage points.
The panel results not only support the findings from the cross-section regression of a
negative relationship between transaction costs and economic growth, they also suggest the
effect might be larger. The ability to trade in and out of stock holdings does seem to matter for
economic growth. One note of caution on the panel results is warranted. Since information and
contract enforcement costs are not in the regression there is a potential for omitted variable bias
leading to inflated, or deflated, estimated effects of transaction costs. This would only be a
2 The data was also divided into three five-year periods from 1993 to 2007 to see if the cutoff years for the time
periods made a difference. Countries and summary statistics are given in Tables 13 and 14. The results are given in
Table 15. The cutoff points do not seem to make a difference.
34
problem, however, if information or contract enforcement costs mattered for economic growth
and mattered for transaction costs.
The cross-section results raise doubts about the importance of information and contract
enforcement costs for economic growth. It is also not likely that those costs are very important
for transaction costs since transaction costs are driven by fees, commissions, bid-ask spreads and
liquidity concerns. Information costs could play a role in the bid-ask spread as higher
information costs give more pricing power to the market maker. That pricing power relates to
only one component of transaction costs, however, and any bias it introduced would depend on
the effect size of information costs on economic growth.
3.5 COMPARISON TO REGRESSIONS WITH TURNOVER RATIO
To see if the smaller estimated effects I find are a function of the new approach I use or
something else, I estimate the same cross-section regression without controlling for simultaneity
or omitted variables that Beck and Levine (2004) estimate but on the smaller time period and
number of countries. I also estimate fixed-effects regressions without controlling for
simultaneity or omitted variables. The cross-section regression results are given in Table 16 and
the fixed-effects results are given in Table 17. Turnover ratio is not statistically significant in
any of the cross-section or fixed-effects regressions. The estimated effect sizes are a tenth to a
third of the estimated effects found by Beck and Levine. These results suggest that the time
period and countries in the sample are important factors in measuring the relationship between
stock markets and economic growth. The small and statistically insignificant estimated effects
for transaction, information, and contract enforcement costs may be the result of stock markets
not exerting as much influence on economic growth as they did in previous time periods.
35
3.6 REMAINING ISSUES
These results modestly support the previous research that stock market development
contributes positively to economic growth. The contribution does not appear to be large and
comes mainly from transaction costs. This small effect may result from poor variables used to
stand in for information and contract enforcement costs. Transaction costs were the only costs
that were directly estimated and their estimated effects were considerably larger than the
estimated effects for the other costs. It is possible that better measures of the other costs would
have a similar result. The smaller and insignificant findings using turnover ratio as a proxy
measure of stock market development than other studies find suggest the link between stock
markets and economic growth may not be as strong as it once was.
Another potential problem with these results is the assumption that the costs are
exogenous to economic growth. This assumption is likely valid for the variables representing
contract enforcement and information costs. The origin of the commercial law system was likely
not influenced by current economic growth. Accounting standards are also determined prior to
current economic growth. As mentioned in Chapter 2, however, the transaction cost estimates
are influenced by liquidity concerns. These concerns are particularly noticeable with business
cycle fluctuations. Economic growth, however, is a long-run concept that is not influenced by
short-run fluctuations. Looking at transaction costs over a similarly long period of time should
remove the influence of the short-run liquidity concerns.
The simultaneity and omitted variable problems may not be completely eliminated by
using the transaction cost estimates either. Economic growth may lead to additional resources
being devoted to developing the stock market, which could reduce transaction costs. Also,
36
unobserved effects, such as the internet, could improve both economic growth and transaction
costs. This effect, however, is likely small. Economic growth does not influence taxes, fees, and
trading methods (quote driven or order driven market). Technological advancements are also
unlikely to influence taxes, fees or trading methods. They may, however, influence
commissions, opportunity costs, and the trading software exchanges use. If transaction costs are
not sufficiently exogenous to economic growth, then the results from instrumental variables
regressions in Section 3.2 would be invalid. Still, the simultaneity and omitted variable
problems with economic growth and transaction costs are likely to be considerably smaller than
the simultaneity and omitted variable problems with size and activity since economic growth
attracts additional investment.
To allow for the possibility the exogeneity assumption does not hold for transaction costs,
I also use the system difference estimator by Arellano-Bover (1995). The system difference
estimator builds on the Arellano and Bond (1991) approach by combining the regression in
differences, the Arellano-Bond procedure, with a regression in levels. The regression in
differences handles endogeneity by assuming that lagged values of the explanatory variables in
levels are not correlated with the differenced error terms. The regression in levels, on the other
hand, assumes differenced explanatory variables are not correlated with the error term in levels
or the country specific effects. Using Equation 13, the following moment conditions are:
( ) for s ≥ 0; t = 3, . . ., T, (15)
for s ≥ 0; t = 3, . . ., T, (16)
for the regression in differences and
( ) for s = 1, (17)
for s = 1, (18)
37
for the regression in levels, where X includes all of the previously mentioned explanatory
variables except the level of real GDP per capita and Y is the level of real GDP per capita rather
than the growth rate.
This estimator will be consistent as long as the instruments are valid and the error terms
do not exhibit serial correlation. The instruments are considered valid if the moment conditions
just described hold in the sample. Serial correlation is considered not to exist if there is no
second-order serial correlation in the differenced error term. Both tests are constructed such that
not rejecting the null hypothesis is evidence in favor of the model. One other note on the test is
that the standard errors in the system difference estimator could be biased downwards,
particularly if the number of instruments exceeds the number of countries. In this model, the
number of countries, around 60, exceeds the number of instruments, around 20. I also use small
sample properties and the robust two-step estimator to correct for the downward bias.
Results of the system difference estimator are given in Table 18. The test results on the
suitability of the model are mixed. Serial correlation is not a problem as the null hypothesis of
no serial correlation is not rejected in any of the regressions. The quality of the instruments is
troublesome with the null hypothesis that the moment conditions holding being rejected in two of
the four regressions. The lack of quality results for the model overall may result from only
having only four time periods. While there are enough periods to estimate the model, the method
is based on using multiple lags as instruments. With only four time periods, there simply are not
many lags to be used. The results generally support the effect size found in the other regressions
as well as their statistical insignificance.
38
CHAPTER 4 – POLICY IMPLICATIONS
Using measures of transaction, information, and contract enforcement costs as measures
of stock market development opens up new opportunities for research in ways that give clearer
policy guidance than when stock market development is measured by the traditional proxy
measures. The link between stock market activity and economic growth has been one of indirect
association. Active stock markets probably have lower information, transaction, and contract
enforcement costs than less active stock markets so they probably do a better job of producing
information, monitoring investments, facilitating risk management, mobilizing savings, and
easing exchange of goods and services too (Levine 2006, 869).
These positive outcomes from stock markets are probably not the direct result of size or
activity, however. If they were, then governments could increase economic growth simply by
buying and selling shares of stock. It is more likely that greater stock market activity results
from reduced information, transaction, and contract enforcement costs and those reduced costs
also lead to greater economic growth. Gaining a better understanding of how important these
costs are for economic growth will lead to better policy prescriptions than simply increasing
trading activity. By looking specifically at transaction costs, for example, we can evaluate
policies that would affect the cost of trading.
4.1 SECURITIES TRANSACTION TAXES
One such policy is the proposal to tax securities transactions. The logic behind this
policy is that the tax would reduce the volatility of stock markets by driving out speculators.
39
Since stock market volatility can be destabilizing to an economy, decreasing it is good. The
estimates of transaction costs will help us evaluate whether such taxes would reduce stock
market volatility or not. Regardless of whether the taxes reduce stock market volatility or not,
the research in the previous chapter refutes the overall benefits of securities transaction taxes in a
way the other research on stock market development does not. By estimating the direction and
size of the effect transaction costs have on economic growth, I show by how much a securities
transaction tax would negatively affect economic growth.
Proposals to tax securities have popped up every decade for the last 30 years. Although
the proposals can be traced back to Keynes' General Theory of Employment, Interest and Money
where he likened Wall Street to a casino (159), the modern push for the tax originated with
James Tobin's (1978, 6) advocacy for a tax on foreign exchange transactions. Lawrence
Summers (1989) calls for a 0.5% tax on debt and equity securities trades. Pollin et al. (2003)
continue the push for a 0.5% tax on securities transactions. In 2009, House Resolution 1068,
called the “Let Wall Street Pay for Wall Street's Bailout Act,” was introduced calling for
unspecified securities transaction taxes.
Proponents of a securities transaction tax cite two primary reasons to support it: it would
discourage short-term speculation, and it would raise tax revenue. Short-term speculation, the
thinking goes, leads to excessive volatility, a diversion of human and physical capital into the
financial sector away from more socially profitable activities, and short-sighted corporate
investment decisions (Summers 1989, 165). Pollin et al. (2003, 532) propose two other negative
effects of speculation in financial markets: herd behavior that leads to deflationary spirals and
weakened macroeconomic stabilization tools. The suggested cure, however, has little to do with
the disease.
40
Levine and Zervos (1998, 550) find a negative relationship between volatility and
economic growth. To the extent that speculators are the cause of excessive volatility, reducing
speculation may be beneficial to economic growth. But not all speculators are destabilizing.
Speculation can be loosely broken out into two groups: trading against price movements and
trading with price movements. Traders who trade going against price movements would buy a
stock that seems undervalued and sell a stock that seems overvalued. These traders would push a
stock's price back towards its fundamentals determined value and reduce volatility. Traders who
trade going with price movements would sell when prices are falling and buy when prices are
rising. These traders would be expecting the trend to continue and would reinforce it by their
actions. They would potentially drive the price further away from its fundamentals determined
value. These “noise traders” would lead to the herd mentality that leads to deflationary spirals
and increases volatility.
The advocates of a securities transaction tax seek to discourage noise traders. A
securities transaction tax, however, does not discriminate between noise traders and investors
trading on fundamentals. The hope is that noise traders would be more affected by the tax
because they trade more than investors who trade on fundamentals and who employ a buy and
hold strategy. But since investors who buy and hold do not, by definition, trade frequently, they
do not help a stock move back toward a fundamentals determined price. It takes traders who are
willing to make trades whenever a stock price has deviated from its fundamentals determined
value to keep the price near that value. To reduce the influence of noise investors, a market
needs to increase short-term trading based on fundamentals relative to short-term trading based
on trends. A securities transaction tax would not do so.
41
Evidence indicates that a securities transaction tax would actually increase volatility.
Charles Jones and Paul Seguin (1997, 729) look at the deregulation of commissions in the United
States. Steven Umlauf (1993, 233) looks at changes in securities taxes in Sweden. Harald Hau
(2006, 864) looks at changes in the tick size on the Paris Bourse. All of these researchers find a
positive relationship between transaction costs and volatility, indicating a securities transaction
tax would increase volatility. This finding is the opposite effect proponents of such a tax intend.
There is some research indicating securities transaction taxes do work to dampen
volatility, but this evidence is weak at best. Wade Brorsen (1991, 161) and Shing-Yang Hu
(1998) find some support for taxes reducing volatility, but the effects were either short-run or not
robust. Richard Roll (1989, 140) looks at the performance of financial markets in 23 countries
around the 1987 crash and found weak support for transaction taxes reducing volatility. Roll
relies on a dummy variable for whether a country had a securities transaction tax or not, but only
four countries in his sample did not have the tax. Also, while the results consistently show the
expected negative result between the presence of a tax and volatility, these results are seldom
significant and are sensitive to the inclusion of Mexico. When Mexico is excluded, the results
are no longer even weakly significant nor do they have the expected negative relationship. Roll
is also looking specifically around the 1987 crash. Tax proponents are concerned generally
about market volatility, but it is particularly destabilizing market crashes that concern them the
most. In one of the most extreme crashes, the ability to dampen volatility appears to be lacking.
The transaction cost estimates produced here can provide more direct evidence on whether
taxing stock market transactions would reduce stock market volatility.
Even in advocating for a securities transaction tax, Pollin et al. (538) admit that the
evidence is mixed on whether or not increased transaction costs reduce stock market volatility.
42
There is another avenue, according to Pollin et al., for securities transaction taxes to reduce the
negative effects of volatility. By reducing the size of the market, a lender of last resort, such as
the government, will be able to have greater influence during a crisis. Since securities
transaction taxes may well increase volatility, it seems counterproductive to want to strengthen
the ability of the government to fix a problem that is exacerbated by the very attempt to
strengthen the government. Also, strengthening the government's ability to manage a crisis in
the stock market would come at the expense of economic growth.
Atje and Jovanovic (1993, 632), Levine and Zervos (1998), Arestis et al. (2001, 17),
Rousseau and Wachtel (2000, 1934), and Beck and Levine (2004, 436) find a positive
relationship between stock market size and/or activity and economic growth. Furthermore, the
research in the previous chapter refutes the benefits of a securities transaction tax even further.
The cost of transacting on stock markets is negatively related with economic growth. That is, it
is not just stock market activity that matters for growth, the cost of trading matters too. A tax
that increases the cost of trading securities would thus decrease economic growth.
The other concerns raised regarding excessive volatility also seem to have little to do
with the cost of transacting on stock markets and would not necessarily be addressed by a
securities transaction tax. The finance industry is not a socially undesirable industry, so capital
invested in finance does not necessarily have a more socially profitable pursuit. Like all
industries, the socially desirable allocation of capital depends on the cost of capital and the return
generated by that capital. A decrease in transaction costs would change this allocation in favor
of more capital in the financial sector, but if well-functioning stock markets increase economic
growth, then more capital would be a good thing. If there is reason to believe there is excess
43
investment in the financial sector, it is better to look at non-market reasons for either the excess
return in finance or depressed returns in other sectors.
Another concern is that stock markets encourage managers to focus on short-run gains at
the expense of long-run planning. This focus, however, happens because of investors, not low
transaction costs. To the extent that low transaction costs increase the number of investors in a
stock market, they will increase the number of investors interested in long-term planning. Paul
Davidson (1998) discusses how a securities transaction tax will reduce trading volume and
increase volatility. A thicker market will be more likely to have traders with more diverse views.
The final point in favor of a securities transaction tax is that it generates tax revenue.
While the tax may not discourage negative activity, if it provides tax revenue that could offset
other more distortionary taxes or fund more productive government activities, then the tax could
still be a good idea. Given that the tax is likely to reduce economic growth, the usefulness of the
tax would depend on the gains from government using the tax revenue exceeding the lost
economic growth.
Pollin et al. (2003) estimate tax revenue for a 0.5% roundtrip securities transaction tax on
equity securities to range from $27.5 billion to $54.9 billion based on 1997 trading data. Based
on the results from the previous chapter, a 0.5% tax per transaction would have been associated
with 0.035 to 0.11 percentage points slower economic growth in the United States. GDP in 1997
was $8.3 trillion, so the tax would have been associated with GDP growth between $2.9 billion
and $9.3 billion less that year than without the tax.3 These numbers do not include the loss in tax
revenue from other taxes. Ulauf (1993) finds that Swedish transaction taxes reduced capital
gains tax collections by about the same amount as the taxes collected by the transaction tax. If
economic growth decreases, then income taxes, payroll, and corporate taxes would decrease too.
3 Based on 2011 GDP, the lost growth would range between $5 billion and $17 billion a year.
44
A securities transaction tax not only reduces economic growth but it may decrease overall tax
revenue.
Whatever appeal a securities transaction tax may have, the empirical evidence does not
support the theoretical predictions of its advocates. Even if the large and active markets were
susceptible to destabilizing volatility, the evidence presented in this dissertation indicates
increasing transaction costs on those markets is not a wise cure. The cost of transacting on a
stock market is shown to be important for economic growth. The efficiency of stock markets
matters. Getting capital flowing on a market is important, but it is also important not to impede
that capital. That is exactly what a securities transaction tax would do.
4.2 CAPITAL GAINS TAXES
Like transaction costs, capital gains taxes affect investor decisions to trade. The tax
treatment of short-term and long-term gains can reduce market volatility by punishing different
types of trading behavior more than others. As with securities transaction taxes, capital gains
taxes alter trading behavior and can be expected to affect economic growth. Evidence of
behavior resulting from tax considerations can be found in increased trading around the dates of
capital gains tax changes and December trading to take the tax deduction from losses. Since
capital gains taxes affect trading decisions, the transaction costs estimated here may fully capture
the economic effect of these taxes. However, they may not, so the effect of capital gains taxes is
worth further examination.
If short-term capital gains are taxed at a higher rate than long-term gains, investors are
encouraged to hold stocks for longer periods of time and discouraged from short-term
speculation. As stated previously, discouraging short-term trading does not mean discouraging
45
only destabilizing trading. Also, by giving a tax advantage to short-term losses, policy makers
are encouraging investors to take losses on short-term holdings over long-term holdings, which
can reinforce a deflationary spiral.
The effect on trading behavior of capital gains taxes is well documented. George
Constantinides (1984) uses simulations to demonstrate how traders can increase profits by using
timely trading strategies based on when gains and losses are realized. Edward Dyl (1977) and
Joel Slemrod (1982) find empirical support for the hypothesis that capital gains taxes lead to
abnormally high selling of down stocks and abnormally low selling of up stocks in December.
Ross Levine (1991) constructs a theoretical model of stock markets, economic growth,
and several different taxes. While a capital gains tax, which is actually a transaction tax in the
model, is not found to be the most disruptive of the taxes, it does impede capital accumulation,
and thus economic growth. More examination of the relationship between transaction costs and
capital gains taxes will help determine if Levine's treatment of a capital gains tax as a securities
transaction tax is appropriate or if the behavior changes brought on by capital gains taxes
produce separate growth effects.
4.3 SOURCES OF GROWTH
There are also opportunities to improve our understanding of stock markets’ role in
economic growth. The channels for stock market development to affect economic growth still
need more study. Do more efficient stock markets help capital find more productive uses? Do
more efficient stock markets encourage more savings and capital accumulation? Do more
efficient stock markets do both? More efficient stock markets may be particularly important for
46
one or more of the following: producing information, monitoring investments, facilitating risk
management, mobilizing savings, and easing exchange of goods and services.
The research presented here adds to previous findings that stock market development is
important for economic growth by specifically looking at the efficiency of stock markets. It does
not, however, address how that efficiency translates into more economic growth. Levine and
Zervos (1998) consider three different channels through which stock markets might affect
economic growth. They find that stock market development increases productivity and capital
accumulation but has no effect on savings rates. Valerie Bencivenga, Bruce Smith, and Ross
Starr (1995) present a theoretical framework for the relationship between transaction costs and
capital accumulation. In their model, lower transaction costs lead to higher rates of savings, but
the composition of the savings, and subsequently the capital stock, change in ambiguous ways.
This theoretical finding is the opposite of the empirical findings of Levine and Zervos.
The stock market transaction cost estimates produced in this dissertation can provide a
more direct test of the findings of Bencivenga et al. It is possible that the theoretical results hold
when applied to transaction costs but get reversed when looking at other measures of stock
market development. For example, lower transaction costs may promote more savings, but a
wealth effect from increased economic growth may bring that rate back down.
Similarly, the transaction costs produced here can be used to help examine how
successful stock markets are at producing information, monitoring investments, facilitating risk
management, mobilizing savings, and easing exchange of goods and services. A critique of
stock markets' role in economic growth is that stock markets do not provide sufficient
information, diversification of risk, or corporate governance. Since stock markets are supposed
to do these things by reducing financial information, transaction, and contract enforcement costs;
47
then using measures of transaction costs will help to identify how well stock market perform
those functions.
4.4 GROWTH VOLATILITY
A transaction cost approach to stock market development can also improve our
understanding of how stock markets contribute to economic growth volatility. As John Maynard
Keynes once said, we are all dead in the long run. Stock markets may have positive long-run
effects on an economy but still be destabilizing in the short-run. Stock markets may help capital
find its most productive use, but that does not necessarily mean that it does so quickly or
painlessly. The ability to transfer ownership of assets does nothing to change either the nature of
the physical capital or the incentive for firms to acquire more physical capital (Pollin 2003, 529).
If highly active and efficient stock markets contribute to economic volatility, these negative
effects may outweigh the positive gains to economic growth. If, however, well-functioning stock
markets contribute to a stable short-run economic environment, then there is added reason to
promote stock markets with low trading costs.
The short-run effects of transaction costs are not addressed here. Much like with
economic growth, however, other researchers have examined the role of stock market size and
activity in economic volatility. The results have been a mix of stock markets dampening
volatility, to magnifying it, to doing nothing.
William Easterly, Roumeen Islam, and Joseph Stiglitz (2000) examine various causes of
growth volatility and find financial development to be a more important determinant than more
expected causes, like wage and price rigidity. The authors focus primarily on measures of bank
development but do find that stock market development, as measured by value traded, reduces
48
the probability of an economic downturn (204). The authors also find that banking development
is a stabilizing force on economic volatility for low levels of banking development (202). As a
banking sector develops more, however, banks tend to exacerbate economic volatility. The
authors do not report whether they find the same result with stock market development.
Another interesting finding from Easterly et al. (204) is that banking development
increases the probability of an economic downturn even though it reduces economic volatility.
Combined with the result on stock market development, this finding means that economies
respond differently depending on whether debt or equity is the more prominent finance vehicle.
Add this result to the finding of Arestis et al. (2001) that banking development exerts a much
larger influence on economic growth than stock market development, and there is an obvious
risk-reward tradeoff. A country could have more growth with bank development but a greater
chance of recessions than with stock market development.
Thorsten Beck, Mattias Lundberg and Giovanni Majnoni (2006, 1159) find the effect of
banking development on economic volatility depends on what caused the volatility in the first
place. Real sector shocks are dampened by a more developed banking system while monetary
shocks are magnified. This finding suggests a volatile monetary policy could have a magnified
effect in a country with a well-developed banking system. The authors control for stock market
capitalization but do not report how stock market development matters for growth volatility.
Piyapas Tharavanij (2007, 22) uses turnover ratio to look specifically at the role of stock
markets in business cycle volatility. Using panel data for 44 countries from 1975 to 2004, the
author finds more developed stock markets reduce economic fluctuations. Interestingly,
Tharavanij finds less support for banking development playing an important role in economic
growth volatility. Combining this result with the results from Arestis et al. (2001) that bank
49
development is much more important for economic growth than stock market development, there
is again a contrast between when and how the different types of financial development matter for
economic activity. This conflict can be better resolved using transaction costs to measure stock
market development.
4.5 DETERMINANTS OF TRANSACTION COSTS
The transaction cost estimates can be used to study a variety of effects stock markets
have on other economic activity. Perhaps equally interesting, however, is using them to study
what causes the efficiency of stock markets to differ across countries. In this case transaction
costs would go from being an independent variable to the dependent variable. Such a study
would further help guide policy makers in decisions about taxing, regulating, and configuring
stock markets.
Transaction taxes have already been shown to negatively affect economic growth. The
transaction costs produced here could also be used to see who bears the tax burden of transaction
taxes. If the demand for trading stock is inelastic, then transaction taxes would be expected to
increase the transaction cost by the amount of the tax. If, however, investors have other options
(such as other investments or buying the same stocks on overseas exchanges) then the demand
for investing can be expected to be elastic. If so, transaction taxes may have minimal effect on
transaction costs as the market maker or the exchange bear the burden of the tax.
These transaction cost estimates can also be used to determine which system, quote-
driven or order-driven, results in lower transaction costs. One practical difficulty in evaluating
the different systems is that most markets are now hybrid systems, with most orders being
matched in an order driven system but with market makers available to match trades when
50
needed. Adequately classifying the differences in markets may be difficult. Still, there are
variations in how markets operate so these estimates can be used to help determine which
variations are the most efficient. Armed with this information, exchanges could set up their
trading systems in the most efficient way.
Internet trading grew increasingly popular during the time period of these transaction cost
estimates. In the earliest years of the estimates, there was no internet trading. Internet trading
also grew in popularity at different rates in different countries. These transaction cost estimates
can be used to determine how big of an effect the internet had on the cost of trading stock.
4.6 CONCLUSION
The research produced here contributes to our base of economic knowledge in three
distinct steps. Step 1 is to produce the means. In Chapter 2, I produce a set of annual stock
market transaction cost estimates that spans 95 exchanges in 74 countries for up to 20 years. The
estimates can be aggregated by country or disaggregated by firm. While the estimates by
themselves are of limited value, they can facilitate future research into stock markets and their
economic effects.
In Chapter 3, I advance our understanding of how stock markets affect economic growth.
Stock markets develop by reducing financial information, transaction, and contract enforcement
costs. Previous empirical research has glossed over these theorized functions of stock markets
by focusing on proxy measures of stock market development that measure stock market size and
activity. Large stock markets with lots of activity probably reduce these costs better than stock
markets with less activity and lead to better economic growth but better economic growth also
leads to larger and more active stock markets. By replacing these proxy measures of stock
51
market development with measures of information, transaction, and contract enforcement costs, I
can better control for the simultaneity and omitted variable problems with stock markets and
economic growth and infer better policy prescriptions. The estimated effect sizes are small and
indicate the importance of stock markets may have faded since the earlier studies. The change in
findings over time suggests more study is need to test the durability of the relationship between
stock markets and economic growth.
In Chapter 4, I am able to make direct policy prescriptions based on these findings.
Previous research could only say that trading activity was good for economic growth but it could
not get more concrete than that. Based on the estimated effects produced here, a 0.5% roundtrip
tax on stock trades would be expected to decrease current economic growth by $5 to $17 billion
a year.
52
BIBLIOGRAPHY
Admati, A., & Pfleiderer, P. (1991). Sunshine trading and financial market equilibrium. Review
of Financial Studies, 4(3), 443-481.
Amihud, Y., & Mendelson, H. (1980). Dealership Market: Market Making with Inventory.
Journal of Financial Economics, 8(1), 31-53.
Anand, A., Tanggaard, C., & Weaver, D. (2009). Paying for market quality. Jounral of Financial
and Quantitative Analysis, 44(6), 1427-1457.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo and an
application to employment equations. The Review of Economic Studies, 58(2), 277-297.
Arellano, M., & Bover, O. (1995). Another look at the instrumental-variable estimation of error-
components models. Journal of Econometrics, 68(1), 29-52.
Arestis, P., Demetriades, P., & Luintel, K. (2001). Financial Development and Economic
Growth: The Role of Stock Markets. Journal of Money, Credit, and Banking, 33(1), 16-
41.
Atje, R., & Jovanovic, B. (1993). Stock Markets and Development. European Economic Review,
37(2-3), 632-640.
Bagehot, W. (1873). Lombard Street. A Description of the Money Market (2005 ed.). Fairford,
Glas: The Echo Library.
Batista, V., & Palmiero, T. (2002). The Euromoney Guide to World Equity Markets 2002.
London: Euromoney Books.
Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of
Banking and Finance, 28(3), 423-442.
Beck, T., Demirguc-Kunt, A., & Levine, R. (2000). A New Database on on the Structure and
Development of the Financial Sector. World Bank Economic Review, 14(3), 597-605.
Beck, T., Demirguc-Kunt, A., & Levine, R. (2001). Legal Theories of Financial Development.
Oxford Review of Economic Policy, 17(4), 483-501.
Beck, T., Lundberg, M., & Majnoni, G. (2006). Financial intermediary development and growth
volatility: Do intermediaries dampen or magnify shocks? Journal of Internatinoal Money
and Finance, 25(7), 1146-1167.
53
Bellelah, M., Boubaker, A., & Sebai, S. (2006). Estimation of Transaction Costs on the Tunisia
Stock Exchange: An Empirical Research Via a Tobit Model with Frictions. International
Journal of Business, 11(2), 89-106.
Bencivenga, V., & Smith, B. &. (1995). Transaction Costs, Technological Choice and
Endogenous Growth. Journal of Economic Theory, 67(1), 153-177.
Bentson, G., & Smith, C. (1976). A Transaction Cost Approach to the Theory of Financial
Intermediation. Journal of Finance, 32(2), 215-231.
Benveniste, L., Marcus, A., & Wilhelm, W. (1992). What's special about the specialist. Journal
of Financial Economics, 32(1), 61-86.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel
data models. Journal of Econometrics, 87(1), 115-143.
Brorsen, W. (1991). Futures Trading, Transaction Costs, and Stock Market Volatility. The
Journal of Futures Markets, 11(2), 153-163.
Chinn, M., & Ito, H. (2006). What matters for financial development? Capital controls,
institutions, and interactions. Journal of Development Economics, 81(1), 163-192.
Constantinides, G. (1984). Optimal Trading with Personal Taxes: Implications for Prices and
Abnormal January Returns. Journal of Financial Economics, 13(1), 65-89.
Davidson, P. (1998). Volatile Financial Markets and the Speculator. Economic Issues, 3(2), 1-18.
De Soto, H. (2000). The Mystery of Capital. New York, NY: Basic Books.
DeAngelo, H., & E, R. (1983). Anti-Takeover Charter Amendments and Stockholder Wealth.
Journal of Financial Economics, 11(1-4), 329-360.
Demirguc-Kunt, A., & Levine, R. (2001). Financial Structure and Economic Growth: A Cross-
Country Comparison of Banks, Markets, and Development. Cambridge, MA: MIT Press.
Dyl, E. (1977). Capital Gains Taxation and Year-End Stock Market Behavior. The Journal of
Finance, 32(1), 165-175.
Easterly, W., Islam, R., & Stiglitz, J. (2000). Shaken and Stirred: Explaining Growth Volatility.
(B. Pleskovic, & N. Stern, Eds.) Annual Bank Conference on Development 2000, pp. 191-
211.
Foster, M., & George, T. (1992). Anonymity in securities markets. Journal of Financial
Intermediation, 2(2), 168-206.
Garman, M. (1976). Market Microstructure. Journal of Financial Economics, 3(3), 257-275.
54
George, T., Kaul, G., & Nimalendran, M. (1991). Estimation of the Bid-Ask Spread and its
Components. Review of Financial Studies, 4(4), 623-656.
Griffin, J., Kelly, P., & Nardari, F. (2006, January 16). Measurement Short-Term International
Stock Market Efficiency. Retrieved July 25, 2011, from
http://69.175.2.130/~finman/Orlando/Papers/IntlInfoEff.pdf
Grossman, S., & Hart, O. (1980). Takeover Bids, the Free-Rider Problem, and the Theory of the
Corporation. Bell Journal of Economics, 11(1), 42-64.
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. New York,
NY: Oxford University Press.
Harris, R. (1997). Stock markets and development: A re-assessment. European Economic
Review, 41(1), 139-146.
Hau, H. (2006). The Role of Transaction Costs in Financial Volatility: Evidence from the Paris
Bourse. Journal of the European Economic Association, 4(4), 862-890.
Heston, A., Summers, R., & Aten, B. (2011). Penn World Table Version 7.0. Center for
International Comparisons of Production, Income and Prices at the University of
Pennsylvania.
Hu, S. (1998). The Effects of the Stock Transaction Tax on the Stock Market - Experience from
Asian Markets. Pacific-Basin Finance Journal, 6(3-4), 985-1000.
Jensen, M. (1993). The Modern Industrial Revolution, Exit, and the Failure of Internal Control
Systems. Journal of Finance, 48(3), 831-880.
Jones, C., & Seguin, P. (1997). Transaction Costs and Price Volatility. American Economic
Review, 87(4), 728-737.
Keynes, J. (1936). General Theory of Employment, Interest and Money. New York: Harcourt,
Brace and Company.
La Porta, R., Lopez-De-Silanes, F., & Shleifer, A. (2002). Government Ownership of Banks.
Journal of Finance, 57(1), 265-301.
La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. (1997). Legal Determinants of
External Finance. Journal of Finance, 52(3), 1131-1150.
Law, S., & Habibulla, M. (2009). The determinants of financial development: Institutions,
openness, and financial liberalization. South African Journal of Economics, 77(1), 45-58.
Lesmond, D., Ogden, J., & Trzcinka, C. (1999). A New Estimate of Transaction Costs. The
Review of Financial Studies, 12(5), 1113-1141.
55
Levine, R. (1991). Stock Markets, Growth, and Tax Policy. The Journal of Finance, 46(4), 1445-
1465.
Levine, R. (1997). Financial Development and Economic Growth: Views and Agenda. Journal
of Economic Literature, 35(2), 688-726.
Levine, R. (1998). The Legal Environment, Banks, and Long-Run Economic Growth. Journal of
Money, Credit, and Banking, 30(3), 596-613.
Levine, R. (1999). Law, Finance, and Economic Growth. Journal of Financial Intermediation,
8(1-2), 8-35.
Levine, R. (2006). Finance and Growth: Theory, Evidence, and Mechanism. In P. Aghion, & S.
Durlauf (Eds.), Handbook of Economic Growth, Volume 1A (pp. 865-934). Amsterdam:
North Holland.
Levine, R., & Renelt, D. (1992). A Sensitivity Analysis of Cross-Country Growth Regressions.
American Economic Review, 82(4), 942-63.
Levine, R., & Schmukler, S. (2006). Internationalization and Stock Market Liquidity. Review of
Finance, 10(1), 1-35.
Levine, R., & Zervos, S. (1998). Stock Markets, Banks, and Economic Growth. American
Economic Review, 88(3), 537-558.
Levine, R., Loayza, N., & Beck, T. (2000). Financial Intermediation and Growth: Causality and
Causes. Journal of Monetary Economics, 46(1), 31-77.
Madhavan, A. (1995). Consolidation, fragmentation, and the disclosure of trading information.
Review of Financial Studies, 8(3), 579-603.
Naes, R., Skjeltorp, J., & Odegaard, B. (2008, April). Liquidity at the Oslo Stock Exchange.
Retrieved July 25, 2011, from Social Science Research Network:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1158410
Naes, R., Skjeltorp, J., & Odegaard, B. (2011). Stock Market Liquidity and the Business Cycle.
Journal of Finance, 66(1), 139-176.
Odegaard, B. (2008, September). The (implicit) cost of equity trading at the Oslo Stock
Exchange. What does the data tell us? Retrieved July 25, 2011, from UiS Working
Papers in Economics and Finance:
http://www1.uis.no/ansatt/odegaard/uis_wps_econ_fin/uis_wps_2009_17_odegaard.pdf
O'Hara, M. (1995). Market Microstructure Theory. Oxford: Basil Blackwell Inc.
56
Plerou, V. G. (2005). Quantifying Fluctuations in Market Liquidity: Analysis of the Bid-Ask
Spread. Physical Review E, 71, 046131.
Pollin, R., Baker, D., & Schaberg, M. (2003). Securities Transaction Taxes for U.S. Financial
Markets. Eastern Economic Journal, 29(4), 527-558.
Roll, R. (1984). A Simple Measure of the Bid-Ask Spread in an Efficient Market. Journal of
Finance, 39(4), 1127-1140.
Roll, R. (1989). Price Volatility, International Market Links, and Their Implications for
Regulatory Policy. Journal of Financial Services Research, 3(2-3), 211-246.
Rousseau, P., & Wachtel, P. (2000). Equity Markets and Growth: Cross-Country Evidence on
Timing and Outcomes. Journal of Business and Finance, 24(12), 1933-1957.
Sala-I-Martin, X. (1997). I Just Ran Two Million Regressions. American Economic Review,
87(2), 178-183.
Shefrin, H., & Statman, M. (1985). The Disposition to Sell Winners Too Early and Ride Losers
Too Long: Theory and Evidence. The Journal of Finance, 40(3), 777-790.
Slemrod, J. (1982). The Effect of Capital Gains Taxation on Year-End Stock Market Behavior.
National Tax Journal, 35(1), 69-77.
Stiglitz, J. (1985). Credit Markets and Control of Capital. Jounral of Money, Credit, and
Banking, 17(2), 133-152.
Summers, L., & Summers, V. (1989). When Financial Markets Work Too Well: A Cautious
Case for a Securities Transaction Tax. Journal of Financial Services Research, 3(2-3),
261-286.
Tharavanij, P. (2007, September 9). Capital Market and Business Cycle Volatility. Retrieved
July 24, 2011, from Munich Personal RePEc Archive: http://mpra.ub.uni-
muenchen.de/5188/1/MPRA_paper_5188.pdf
Tobin, J. (1978). A Proposal for International Monetary Reform. Eastern Economic Journal,
4(3-4), 153-159.
Umlauf, S. (1993). Transaction Taxes and the Behavior of the Swedish Stock Market. Journal of
Financial Economics, 33(2), 227-240.
Zhu, A., Ash, M., & Pollin, R. (2004). Stock Market Liquidity and Economic Growth: A Critical
Appraisal of the Levine/Zervos Model. International Review of Applied Economics,
18(1), 63-71.
57
APPENDIX A - TABLES
TABLE 1 – COUNTRY AND EXCHANGE TRANSACTION COST ESTIMATES
Country Exchange Avg
Cost
Min
Cost
Max
Cost Trend
OECD
Australia Australia 12.9% 6.5% 32.1% -0.41%
Austria Vienna 4.9% 1.7% 9.2% 0.28%
Belgium Euronext Brussels 4.7% 3.0% 8.0% 0.05%
Canada Toronto 9.2% 2.8% 22.0% -0.60%
Canada TSX 30.5% 13.0% 54.8% -0.40%
Chile Santiago 5.3% 3.0% 8.1% -0.19%
Czech Republic Prague 5.2% 1.6% 10.8% -0.13%
Denmark Copenhagen 4.7% 2.2% 6.4% -0.06%
Estonia Tallinn 6.0% 1.2% 16.6% -0.22%
Finland Helsinki 5.5% 2.3% 10.8% -0.32%
France Euronext Paris 5.8% 3.7% 8.1% 0.12%
Germany Frankfurt 5.2% 2.0% 15.0% 0.44%
Greece Athens 2.8% 0.5% 10.8% -0.21%
Hungary Budapest 7.3% 4.1% 12.7% -0.35%
Iceland Iceland 6.4% 1.2% 23.4% 1.02%
Ireland Dublin 11.9% 5.4% 20.6% 0.02%
Israel Tel Aviv 7.0% 3.3% 11.1% 0.11%
Italy Milan 0.8% 0.4% 1.0% -0.02%
Japan OTC 7.5% 2.4% 14.7% -0.32%
Japan Tokyo 2.0% 0.7% 3.8% -0.08%
Korea Korea 1.8% 1.0% 3.9% 0.01%
Korea KOSDAQ 2.8% 1.0% 9.6% -0.27%
Luxembourg Luxembourg 4.2% 2.6% 5.0% 0.09%
Mexico Mexico 5.8% 2.6% 8.4% -0.13%
Netherlands Euronext Amsterdam 3.8% 1.8% 9.5% 0.07%
New Zealand New Zealand 8.0% 5.8% 11.2% 0.47%
Norway Oslo 6.6% 2.4% 11.7% -0.22%
Poland Warsaw 4.7% 1.1% 20.1% 0.16%
Poland Warsaw Cont' 2.7% 1.0% 5.6% 0.06%
58
TABLE 1 – COUNTRY AND EXCHANGE TRANSACTION COST
ESTIMATES
Country Exchange
Avg
Cost
Min
Cost
Max
Cost Trend
OECD Continued
Portugal Euronext Lisbon 5.6% 3.5% 8.6% -0.12%
Slovakia Bratislavia 9.3% 2.9% 17.7% 0.10%
Slovenia Ljubljana 2.6% 1.4% 3.6% 0.06%
Spain Barcelona 15.6% 0.5% 46.7% 0.80%
Spain Madrid 7.3% 0.3% 16.6% -0.59%
Spain Madrid SIBE 1.7% 0.7% 2.6% -0.05%
Sweden Stockholm 6.9% 2.4% 16.0% -0.10%
Switzerland SIX Swiss 3.8% 1.6% 7.9% -0.25%
Turkey Istanbul 2.9% 1.5% 7.2% -0.17%
United Kingdom London 8.1% 6.1% 10.7% 0.10%
United States Amex 6.2% 2.3% 9.9% -0.07%
United States NASDAQ 3.2% 1.3% 6.5% -0.21%
United States NASDAQ_NMS 7.7% 0.3% 15.3% -0.61%
United States New York 1.9% 0.3% 6.6% -0.30%
United States Non-NASDAQ OTC 36.5% 8.9% 92.2% 3.34%
United States OTC Bulletin 23.2% 9.7% 46.3% 1.24%
Europe
Bulgaria Sofia 17.5% 15.6% 21.1% 0.84%
Croatia Zagreb 15.6% 4.0% 47.2% -2.06%
Cyprus Cyprus 13.2% 9.5% 22.3% 1.11%
Latvia Riga 9.3% 2.0% 22.6% -1.80%
Lithuania Lithuania 11.3% 4.3% 15.4% -1.25%
Malta Malta 4.1% 1.7% 5.8% 0.09%
Serbia Belgrade 10.0% 10.0% 10.0% NA
Asia
Bangladesh Dhaka 4.1% 3.7% 4.6% -0.05%
China Shanghai 0.5% 0.3% 1.4% 0.04%
China Shenzhen 0.6% 0.2% 1.5% 0.09%
Hong Kong Hong Kong 5.9% 2.2% 10.0% 0.26%
India Bombay 7.9% 0.4% 18.3% -0.74%
Indonesia Indonesia 15.3% 8.4% 36.5% -0.13%
Kazakhstan Kazakhstan 4.3% 4.3% 4.3% NA
Malaysia 2nd Board 10.8% 1.5% 57.2% 2.02%
Malaysia Kuala Lumpur 3.9% 1.8% 11.1% 0.29%
Malaysia Mesdaq 16.5% 4.3% 37.5% 6.91%
Philippines Philippines 14.2% 9.4% 19.2% -0.09%
59
TABLE 1 – COUNTRY AND EXCHANGE TRANSACTION COST
ESTIMATES
Country Exchange
Avg
Cost
Min
Cost
Max
Cost Trend
Asia Continued
Russia ICE 8.1% 1.0% 22.0% -0.38%
Russia Moscow 40.0% 8.0% 148.0% -3.54%
Russia RTS 28.8% 0.2% 63.2% -0.69%
Singapore Catalist 21.6% 3.0% 42.2% 3.33%
Singapore Singapore 9.3% 1.9% 16.8% 1.07%
Sri Lanka Colombo 13.1% 8.4% 21.1% -0.18%
Taiwan OTC 1.3% 0.9% 1.9% 0.03%
Taiwan Taiwan 1.0% 0.7% 1.3% -0.01%
Thailand Bangkok 5.9% 2.1% 20.2% 0.09%
Vietnam Hanoi OTC 1.4% 1.1% 2.0% -0.32%
Vietnam Hanoi STC 1.5% 1.2% 2.0% -0.33%
Vietnam Ho Chi Minh 0.9% 0.6% 1.2% -0.08%
South America
Argentina Buenos Aries 5.6% 3.6% 10.1% -0.23%
Brazil Sao Paulo 10.9% 5.4% 20.7% -0.96%
Colombia Bogota 5.7% 3.5% 9.5% -0.17%
Peru Lima 10.9% 6.9% 19.9% -0.47%
Venezuela Caracas 11.0% 4.3% 25.5% 0.55%
Middle East & Africa
Bahrain Bahrain 6.9% 4.3% 11.5% 0.76%
Egypt Cairo 3.5% 2.3% 6.2% -0.17%
Jordan Amman 2.9% 2.7% 3.4% 0.18%
Kenya Nairobi 9.0% 2.8% 19.1% -0.72%
Kuwait Kuwait City 4.4% 3.2% 6.7% 0.58%
Lebanon Beirut 9.1% 2.0% 26.0% -0.60%
Morocco Casablanca 4.7% 2.4% 6.8% -0.14%
Oman Muscat 6.1% 5.2% 7.1% 0.06%
Pakistan Karachi 9.3% 3.7% 17.3% -0.32%
Qatar Doha 2.1% 1.3% 2.9% 0.03%
Saudi Arabia Riyadh 1.6% 0.8% 3.7% -0.16%
South Africa Johannesburg 11.7% 6.7% 18.6% -0.10%
United Arab Emirates Abu Dhabi 5.0% 3.8% 7.8% 0.57%
United Arab Emirates Dubai 3.5% 2.0% 6.8% -0.39%
Zimbabwe Zimbabwe 16.1% 5.5% 29.3% 1.05%
60
TABLE 2 – CORRELATIONS WITH TRANSACTION COST ESTIMATES
Country Exchange Capitalization Turnover Value
Traded
Argentina Buenos Aires 0.44 0.12 -0.01
Australia Australia -0.62 -0.52 -0.53
Austria Vienna 0.65 -0.56 -0.17
Bangladesh Dhaka -0.07 0.59 0.48
Belgium Euronext Brussels -0.30 0.16 -0.21
Brazil Sao Paulo -0.86 0.66 0.18
Canada Toronto -0.54 -0.80 -0.61
Canada TSX -0.15 -0.48 -0.24
Chile Santiago -0.47 -0.26 -0.40
China Shanghai -0.12 0.28 0.12
China Shenzhen 0.39 -0.33 0.08
Colombia Bogota 0.50 0.48 0.52
Croatia Zagreb -0.26 -0.62 -0.57
Czech Republic Prague 0.16 0.11 -0.05
Denmark Copenhagen -0.10 -0.26 -0.20
Estonia Tallinn 0.30 0.33 -0.14
Finland Helsinki -0.44 -0.88 -0.69
France Euronext Paris 0.40 0.38 0.49
Germany Frankfurt 0.44 0.27 0.59
Greece Athens -0.54 -0.42 -0.44
Hungary Budapest -0.37 -0.38 -0.33
Iceland Iceland -0.91 -0.20 -0.59
India Bombay -0.38 0.01 -0.14
Indonesia Indonesia 0.07 -0.01 0.00
Ireland Dublin 0.17 0.28 0.15
Israel Tel Aviv 0.39 -0.02 0.34
Italy Milan -0.83 0.32 -0.99
Japan OTC -0.07 -0.52 -0.55
Japan Tokyo -0.17 -0.54 -0.59
Kenya Nairobi -0.31 -0.92 -0.84
Latvia Riga -0.84 0.61 0.65
Lebanon Beirut -0.47 -0.25 -0.35
Lithuania Lithuania -0.87 0.70 0.19
Luxembourg Luxembourg -0.28 -0.41 -0.36
Malaysia 2nd Board -0.61 -0.60 -0.63
Malaysia Kuala Lumpur -0.40 -0.44 -0.48
61
TABLE 2 CONTINUED – CORRELATIONS WITH TRANSACTION COST
ESTIMATES
Country Exchange Capitalization Turnover Value
Traded
Malta Malta -0.25 -0.71 -0.73
Mexico Mexico -0.14 -0.25 -0.25
Morocco Casablanca -0.64 -0.35 -0.42
Netherlands Euronext Amsterdam -0.17 0.21 0.04
New Zealand New Zealand -0.58 0.27 -0.31
Norway Oslo -0.58 -0.44 -0.54
Pakistan Karachi -0.67 -0.09 -0.44
Peru Lima -0.69 0.68 0.39
Philippines Philippines -0.27 -0.39 -0.45
Poland Warsaw 0.33 -0.53 -0.38
Poland Warsaw Continuous -0.39 -0.06 -0.72
Portugal Euronext Lisbon -0.25 0.03 -0.02
Singapore Catalist -0.64 -0.35 -0.71
Singapore Singapore -0.74 -0.35 -0.79
Slovenia Ljubljana 0.46 -0.65 -0.19
South Africa Johannesburg -0.56 0.37 0.16
Spain Barcelona -0.81 -0.29 -0.86
Spain Madrid -0.40 -0.37 -0.31
Spain Madrid SIBE -0.50 -0.31 -0.39
Sri Lanka Colombo -0.55 0.29 -0.08
Sweden Stockholm -0.35 -0.37 -0.30
Switzerland SIX Swiss -0.77 -0.58 -0.78
Thailand Bangkok -0.48 -0.28 -0.55
Turkey Istanbul -0.51 -0.63 -0.56
United Kingdom London 0.03 0.45 0.45
United States Amex 0.34 0.53 0.50
United States NASDAQ -0.68 -0.50 -0.51
United States NASDAQ NMS -0.34 -0.34 -0.30
United States New York -0.85 -0.73 -0.73
United States Non-NASDAQ OTC 0.63 0.78 0.71
United States OTC Bulletin 0.70 0.78 0.74
62
TABLE 3 – CROSS COUNTRY REGRESSIONS OF STOCK MARKET
DEVELOPMENT
Capitalization Value Traded Turnover Ratio
Transaction Cost -0.108 -0.598** -0.458*
(0.354) (0.000) (0.022)
Shareholder Rights 0.192* -0.025 -0.195
(0.038) (0.864) (0.174)
Accounting Standards 0.024** 0.055** 0.029*
(0.004) (0.001) (0.044)
Legal Origin UK 0.285 0.571 0.270
(0.412) (0.110) (0.347)
Legal Origin French 0.392 0.108 -0.259
(0.235) (0.803) (0.477)
Legal Origin German 0.670 0.446 -0.162
(0.180) (0.418) (0.609)
Property Rights 0.002 -0.001 -0.004
(0.827) (0.872) (0.749)
Politics -0.141 -0.276 -0.149
(0.812) (0.817) (0.858)
Trade 0.007** 0.001 -0.006
(0.008) (0.771) (0.102)
Climate -0.606+ -0.768 -0.261
(0.099) (0.163) (0.585)
Constant -3.542** -6.206** -2.482+
(0.000) (0.000) (0.050)
Observations 31 31 31
Adjusted R-squared 0.620 0.681 0.372
F-stat 8.492 9.429 5.075
Robust p-values in parentheses
**p<0.01, *p<0.05, + p<0.1
63
TABLE 4 – FIXED EFFECTS REGRESSIONS OF STOCK MARKET DEVELOPMENT
Capitalization Value Traded Turnover Ratio
Transaction Cost -0.200** -0.355** -0.158+
(0.003) (0.004) (0.079)
Property Rights 0.006 0.007 0.001
(0.109) (0.501) (0.911)
Trade 0.010** 0.011** 0.000
(0.001) (0.006) (0.925)
Constant -2.383** -3.835** -1.367
(0.000) (0.000) (0.119)
Observations 408 408 410
R-squared 0.158 0.093 0.014
Number of countries 38 38 39
F-stat 7.831 6.491 1.190
Robust p-values in parentheses
**p<0.01, *p<0.05, +p<0.1
64
TABLE 5 – COUNTRIES IN THE CROSS-SECTION REGRESSIONS
Australia Germany Israel Pakistan Thailand
Austria Finland Japan Peru Taiwan
Belgium France Mexico Philippines United Kingdom
Brazil Greece Malaysia South Africa United States
Canada India Netherlands South Korea Zimbabwe
Chile Indonesia Norway Sweden
TABLE 6 – SUMMARY STATISTICS FOR CROSS-SECTION COUNTRIES
Summary Statistics: Cross-Section Data 1990-1998
Economic Growth Transaction Costs Turnover Ratio
Accounting
Standards
Descriptive Statistics
Mean 0.021 0.079 0.572 68.781
Maximum 0.058 0.260 2.951 83
Minimum -0.010 0.010 0.075 24
Standard Deviation 0.017 0.057 0.531 12.886
Observations 29 29 29 25
65
TABLE 7 – INSTRUMENTAL VARIABLES CROSS-SECTION REGRESSIONS
Dependent Variable: Growth of Real GDP per Capita
Instrumental Variable: Transaction Costs
(1) (2) (3) (4)
Turnover Ratio 0.121 0.112 0.064 0.049
(0.929) (0.957) (0.970) (0.891)
Private Credit -0.038 -0.034 -0.018 -0.012
(0.915) (0.960) (0.963) (0.951)
Average Years of Schooling 0.052 0.049 0.027 0.022
(0.966) (0.970) (0.961) (0.908)
Initial Level of Real GDP per Capita -0.023 -0.021 -0.014 -0.013
(0.949) (0.969) (0.920) (0.822)
Government Consumption
-0.005
(0.989)
International Trade
0.014
(0.937)
Inflation
-0.030
(0.951)
Black Market Premium
Constant 0.186 0.185 0.073 0.124
(0.923) (0.960) (0.970) (0.874)
Observations 29 29 29 29
R-squared -19.594 -16.548 -4.158 -2.107
C-stat 5.140 3.711 3.108 5.135
(0.023) (0.054) (0.078) (0.023)
LM 0.173 0.102 0.567 1.805
0.677 (0.750) (0.452) (0.179)
F-stat . . . .
(0.975) (0.972) (0.843) (0.582)
F-stat First Stage 1.65 1.44 1.33 1.69
(0.194) (0.248) (0.286) (0.178)
Bootstrapped p values in parentheses
** p<0.01, * p<0.05, + p<0.1
66
TABLE 8 – COUNTRIES IN 4 PERIODS, 5-YEAR AVERAGES PANEL DATA
Argentina Denmark Ireland Latvia Portugal
Australia Egypt Iceland Morocco Russian Federation
Austria Spain Israel Mexico Saudi Arabia
Belgium Estonia Italy Malta Singapore
Bulgaria Finland Jordan Malaysia Slovak Republic
Bahrain France Japan Netherlands Slovenia
Brazil United Kingdom Kenya Norway Sweden
Canada Greece Korea New Zealand Thailand
Switzerland Hong Kong Kuwait Oman Turkey
China Croatia Lebanon Pakistan United States
Colombia Hungary Sri Lanka Peru Venezuela
Cyprus Indonesia Lithuania Philippines South Africa
Czech Republic India Luxembourg Poland Zimbabwe
Germany
TABLE 9 – SUMMARY STATISTICS FOR 4 PERIODS, 5-YEAR AVERAGES PANEL
DATA
Summary Statistics: Panel Data, 1990-2009
Economic
Growth
Transaction
Costs
Turnover
Ratio
Descriptive
Statistics
Mean 2.5 6.9 68.3
Maximum 10.8 34.4 354.8
Minimum -6.0 0.3 0.3
Std. Dev. 2.2 4.7 57.0
Observations 187 187 182
67
TABLE 10 – INSTRUMENTAL VARIABLES FIXED-EFFECTS REGRESSIONS
Dependent Variable: Growth of Real GDP per Capita
Instrumental Variable: Transaction Costs
(1) (2) (3) (4)
Turnover Ratio 8.377 17.754 8.212 6.083
(0.278) (0.521) (0.249) (0.167)
Private Credit -4.742 -5.839 -4.764 -3.989+
(0.119) (0.340) (0.115) (0.058)
Initial Level of Real GDP per Capita 3.357 11.876 3.748 3.444
(0.626) (0.527) (0.613) (0.517)
Average Years of Schooling -20.130 -40.718 -19.893 -17.657
(0.372) (0.515) (0.358) (0.252)
Government Consumption
34.818
(0.476)
International Trade
-0.006
(0.843)
Inflation
-21.562
(0.419)
Observations 142 139 142 142
R-squared -8.327 -36.176 -7.989 -4.228
Number of countries 48 47 48 48
C-stat 5.981 7.093 6.638 6.663
(0.014) (0.008) (0.010) (0.010)
LM 1.241 0.400 1.406 1.987
(0.265) (0.529) (0.236) (0.159)
F-stat 0.853 0.281 0.713 1.132
(0.500) (0.923) (0.71) (0.349)
F-stat First Stage 1.70 1.99 1.40 1.53
(0.156) (0.088) (0.231) (0.190)
Robust p values in parentheses
** p<0.01, * p<0.05, + p<0.1
68
TABLE 11 – CROSS-COUNTRY REGRESSIONS
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Transaction Costs -0.108 -0.083 -0.071 -0.160
(0.405) (0.559) (0.639) (0.451)
Accounting Standards -0.002* -0.002 -0.002* -0.002+
(0.022) (0.115) (0.032) (0.063)
Legal Origin – UK 0.008 0.013 0.005 0.010
(0.613) (0.540) (0.778) (0.574)
Legal Origin – France -0.003 -0.003 -0.010 -0.001
(0.808) (0.875) (0.521) (0.956)
Legal Origin – Scandinavian 0.025 0.030 0.019 0.028
(0.200) (0.182) (0.395) (0.235)
Private Credit 0.019+ 0.018 0.014 0.021
(0.099) (0.141) (0.258) (0.147)
Average Years of Schooling 0.010 0.003 0.003 0.025
(0.802) (0.952) (0.950) (0.637)
Initial Level of Real GDP per
Capita -0.009 -0.004 -0.006 -0.012
(0.403) (0.765) (0.607) (0.327)
Government Consumption
-0.020
(0.428)
International Trade
0.009
(0.337)
Inflation
0.026
(0.870)
Black Market Premium
Constant 0.211** 0.217** 0.168* 0.204*
(0.001) (0.001) (0.040) (0.024)
Number of Countries 25 25 25 25
Adjusted R-squared 0.321 0.370 0.349 0.298
Wald Chi-Sq 11.84 13.28 11.32 8.64
(0.158) (0.150) (0.254) (0.472)
Joint Significance P-value (0.196) (0.520) (0.229) (0.405)
Bootstrapped p values in parentheses
** p<0.01, * p<0.05, + p<0.1
69
TABLE 12 – FIXED-EFFECTS REGRESSIONS, 4 5-YEAR PERIODS, 1990-2009
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Transaction Costs -0.172* -0.186** -0.183* -0.225**
(0.016) (0.004) (0.010) (0.004)
Private Credit -2.909** -2.837** -2.708** -3.170**
(0.002) (0.003) (0.003) (0.001)
Initial Level of Real GDP per Capita 3.300+ 4.114** 1.858 3.090
(0.085) (0.008) (0.368) (0.117)
Average Years of Schooling -7.700+ -8.556* -7.655 -6.877
(0.095) (0.031) (0.103) (0.144)
Government Consumption
2.722
(0.335)
International Trade
0.020*
(0.020)
Inflation
24.067+
(0.061)
Constant -0.712 -2.669 11.786 -109.724+
(0.956) (0.837) (0.413) (0.059)
Observations 160 155 160 160
R-squared 0.211 0.227 0.230 0.228
Number of Countries 61 58 61 61
F-stat 4.520 9.540 5.338 9.502
(0.003) (0.000) (0.000) (0.000)
Robust p values in parentheses
** p<0.01, * p<0.05, + p<0.1
70
TABLE 13 – COUNTRIES IN 3 PERIODS, 5-YEAR AVERAGES PANEL DATA
Argentina Egypt Iceland Morocco Russian Federation
Australia Spain Israel Mexico Saudi Arabia
Austria Estonia Italy Malta Singapore
Belgium Finland Japan Malaysia Slovak Republic
Bulgaria France Kenya Netherlands Slovenia
Brazil United Kingdom Korea Norway Sweden
Canada Greece Kuwait New Zealand Thailand
Switzerland Hong Kong Lebanon Pakistan Turkey
China Croatia Sri Lanka Peru United States
Colombia Hungary Lithuania Philippines Venezuela
Czech Republic Indonesia Luxembourg Poland South Africa
Germany India Latvia Portugal Zimbabwe
Denmark Ireland
TABLE 14 – SUMMARY STATISTICS FOR 3 PERIODS, 5-YEAR AVERAGES PANEL
DATA
Summary Statistics: Panel Data, 1993-2007
Economic Growth Transaction Costs Turnover
Ratio
Descriptive Statistics
Mean 3.0 7.0 68.9
Maximum 11.0 27.3 329.3
Minimum -4.2 0.3 0.3
Std. Dev. 2.4 4.8 59.1
Observations 146 146 146
71
TABLE 15 – FIXED-EFFECTS REGRESSIONS, 3 5-YEAR PERIODS, 1993-2007
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Transaction Costs -0.212+ -0.226* -0.228* -0.326**
(0.054) (0.037) (0.041) (0.001)
Private Credit -1.483 -1.526 -1.150 -2.517
(0.417) (0.398) (0.527) (0.138)
Initial Level of Real GDP per Capita 4.838+ 5.694* 3.174 4.234+
(0.080) (0.037) (0.281) (0.095)
Average Years of Schooling -3.891 -5.043 -4.065 -0.661
(0.610) (0.463) (0.600) (0.928)
Government Consumption
2.306
(0.631)
International Trade
0.025+
(0.097)
Inflation
101.195**
(0.001)
Constant -31.055* -32.930* -16.501 -492.895**
(0.034) (0.045) (0.252) (0.000)
Observations 127 125 127 127
R-squared 0.195 0.197 0.215 0.267
Number of Countries 58 57 58 58
F-stat 5.592 3.878 6.009 9.519
(0.001) (0.004) (0.000) (0.000)
Robust p values in parentheses
** p<0.01, * p<0.05, + p<0.1
72
TABLE 16 – CROSS-COUNTRY REGRESSIONS WITH TURNOVER RATIO
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Turnover Ratio 0.009 0.008 0.010 0.009
(0.149) (0.117) (0.143) (0.166)
Private Credit 0.011+ 0.011* 0.009 0.012
(0.059) (0.032) (0.151) (0.154)
Average Years of Schooling 0.024 0.019 0.020 0.025
(0.255) (0.309) (0.359) (0.280)
Initial Level of GDP per Capita -0.012* -0.008 -0.011+ -0.012*
(0.032) (0.153) (0.064) (0.049)
Government Consumption
-0.021*
(0.026)
International Trade
0.005
(0.286)
Inflation
0.005
(0.919)
Black Market Premium
Constant 0.091** 0.120** 0.070* 0.091**
(0.000) (0.000) (0.016) (0.000)
Observations 29 29 29 29
Adjusted R-squared 0.315 0.460 0.308 0.287
Wald Chi Squared 16.19 28.29 19.50 13.78
(0.003) (0.000) (0.002) (0.017)
p-values in parentheses, standard errors derived from bootstrapping
** p<0.01, * p<0.05, + p<0.1
73
TABLE 17 – FIXED-EFFECTS REGRESSIONS WITH TURNOVER RATIO
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Turnover Ratio 0.182 0.250 0.175 0.189
(0.496) (0.337) (0.514) (0.481)
Private Credit -3.001** -2.946** -2.909** -2.879**
(0.001) (0.001) (0.001) (0.001)
Initial Level of Real GDP per Capita 2.728 3.466+ 2.093 2.851
(0.211) (0.080) (0.370) (0.196)
Average Years of Schooling -4.437 -5.072 -4.301 -5.343
(0.352) (0.254) (0.378) (0.318)
Government Consumption
2.507
(0.395)
International Trade
0.009
(0.363)
Inflation
-10.009
(0.394)
Constant -3.774 -6.102 1.450 42.540
(0.798) (0.679) (0.928) (0.429)
Observations 155 150 155 155
R-squared 0.138 0.151 0.143 0.143
Number of countries 61 58 61 61
F-stat 3.870 5.316 3.256 4.778
Robust p values in parentheses
** p<0.01, * p<0.05, + p<0.1
74
TABLE 18 – SYSTEM DIFFERENCE ESTIMATOR REGRESSIONS
Dependent Variable: Growth of Real GDP per Capita
(1) (2) (3) (4)
Transaction Costs -0.205 -0.243 -0.066 -0.105
(0.191) (0.101) (0.696) (0.218)
Private Credit -1.011 -0.962+ -0.565 -0.380
(0.296) (0.077) (0.314) (0.615)
Initial Level of Real GDP per Capita 0.192 0.372 0.357 0.120
(0.702) (0.218) (0.425) (0.769)
Average Years of Schooling 1.588 -0.296 0.183 1.380
(0.328) (0.931) (0.901) (0.288)
Government Consumption
0.456
(0.845)
International Trade
0.002
(0.750)
Inflation
-305.685
(0.151)
Constant 3.075 6.141 1.281 1,408.857
(0.397) (0.532) (0.730) (0.152)
Observations 160 155 160 160
Number of countries 61 58 61 61
F-stat 0.797 1.810 0.447 0.975
(0.532) (0.125) (0.813) (0.441)
Overidentification Test (p-value) (0.051) (0.101) (0.001) (0.991)
Serial Correlation Test (p-value) (0.204) (0.177) (0.225) (0.275)
p values in parentheses
** p<0.01, * p<0.05, + p<0.1
75
APPENDIX B - FIGURES
GRAPHS OF TRANSACTION COST ESTIMATES AND PERIOD AVERAGES FOR
SELECT COUNTRIES AND EXCHANGES
FIGURE 1 FIGURE 2
FIGURE 3 FIGURE 4
0%
2%
4%
6%
8%
10%
12%
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
Argentina
Buenos Aires
0%
5%
10%
15%
20%
25%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Canada
Toronto
0%
10%
20%
30%
40%
50%
60%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Canada
TSX
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
China
Shanghai
76
FIGURE 5 FIGURE 6
FIGURE 7 FIGURE 8
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%19
97
19
99
20
01
20
03
20
05
20
07
20
09
Pe
rce
nt
Year
China
Shenzhen
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
19
97
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
Croatia
Zagreb
0%
2%
4%
6%
8%
10%
12%
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Czech Republic
Prague
0%
1%
2%
3%
4%
5%
6%
7%
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Denmark
Copenhagen
77
FIGURE 9 FIGURE 10
FIGURE 11 FIGURE 12
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Estonia
Tallinn
0%
2%
4%
6%
8%
10%
12%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Hong Kong
Hong Kong
0%
2%
4%
6%
8%
10%
12%
14%
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Hungary
Budapest
0%
5%
10%
15%
20%
25%
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
Per
cen
t
Year
Iceland
Iceland
78
FIGURE 13 FIGURE 14
FIGURE 15 FIGURE 16
0%
5%
10%
15%
20%
25%
30%
35%
40%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Indonesia
Indonesia
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Japan
Tokyo
0%
2%
4%
6%
8%
10%
12%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Malaysia
Kuala Lumpur
0%
10%
20%
30%
40%
50%
60%
70%
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
Per
cen
t
Year
Malaysia
2nd Board
79
FIGURE 17 FIGURE 18
FIGURE 19 FIGURE 20
0%
5%
10%
15%
20%
25%
30%
35%
40%
20
04
20
05
20
06
20
07
20
08
20
09
Per
cen
t
Year
Malaysia
MESDAQ
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Mexico
Mexico
0%
2%
4%
6%
8%
10%
12%
14%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Norway
Oslo
0%
5%
10%
15%
20%
25%
19
91
19
94
19
97
20
00
20
03
20
06
20
09
Per
cen
t
Year
Philippines
Philippines
80
FIGURE 21 FIGURE 22
FIGURE 23 FIGURE 24
0%
5%
10%
15%
20%
25%19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Poland
Warsaw
0%
1%
2%
3%
4%
5%
6%
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Poland
Continuous
0%
20%
40%
60%
80%
100%
120%
140%
160%
19
97
19
99
20
01
20
03
20
05
20
07
Per
cen
t
Year
Russia
Moscow
0%
5%
10%
15%
20%
25%
19
98
20
00
20
02
20
04
20
06
20
08
Per
cen
t
Year
Russia
Interbank Currency Exchange
81
FIGURE 25 FIGURE 26
FIGURE 27 FIGURE 28
0%
10%
20%
30%
40%
50%
60%
70%19
97
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
Russia
Russian Trading System
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
19
97
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
Slovenia
Ljubljana
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
South Korea
Korean
0%
2%
4%
6%
8%
10%
12%
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
South Korea
KOSDAQ
82
FIGURE 29 FIGURE 30
FIGURE 31 FIGURE 32
0%
5%
10%
15%
20%
25%19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
Per
cen
t
Year
Sri Lanka
Colombo
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Sweden
Stockholm
0%
5%
10%
15%
20%
25%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
Thailand
Bangkok
0%
1%
2%
3%
4%
5%
6%
7%
19
90
19
93
19
96
19
99
20
02
20
05
20
08
Per
cen
t
Year
United States
NYSE
83
FIGURE 33
0%
5%
10%
15%
20%
25%
30%
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
20
09
Per
cen
t
Year
Venezuela
Caracas