Financial Globalization, Propensity to Speculation and Valuation Segmentation

of Emerging Markets

Chin-Wen Hsin Yuan Ze University, Taiwan

This study focuses on testing market characteristics contributing to market segmentation.

Results indicate that for most markets the level of segmentation, in terms of valuation-ratio

deviation from the world market, tends to elevate during bearish world or local market. The

valuation differentials are also found to vary across industries. Pair-wise valuation ratio differences

across the sample emerging markets are computed. Overall results show that Asian markets exhibit

closest valuation ratio between each other, which may be attributable to the greater similarities in

terms of investor habit among Asian markets. The fixed effects model results indicate that those

markets that are more financially developed, more widely covered by analysts, less corrupted, and

with more capital expenditures are associated with less severe market segmentation. I also find

markets with investors with stronger individualism tend to yield greater valuation deviations with

the rest of the world market, though the result is only marginally statistically significant. Last, while

greater analyst coverage and less corruption reduce market segmentation, these factors become less

important as the world market becomes more financially globalized. To my best knowledge, these

results are new to the literature and add to the large literature on market integration.

Keywords: Financial globalization; Market segmentation; Propensity to speculation; Trust

JEL Classification: G12; G14; G15

*Please address correspondence to: Chin-Wen Hsin, College of Management, Yuan Ze University , 135 Far East Road, Taoyuan, Taiwan 333; E-mail: fncwhsin@saturn.yzu.edu.tw.

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1. Introduction

There have been many regulatory changes toward the openness of emerging markets around the world

since 1980s. This provides a unique opportunity to investigate their impacts on the integration of

financial markets (e.g., see Bekaert, 1995; Bekaert and Harvey, 1995, 1997, 1998, 2000; Kim and Singal,

2000; Henry, 2000; Lang, Lins, and Miller, 2003; Bailey, Karolyi, and Salva, 2006; Bekaert, Harvey and

Lundblad, 2007; Pukthuanthong and Roll, 2009; Bekaert, Harvey, Lundblad, and Siegel, 2011; Carrieri,

Chaieb and Errunza, 2013). Nonetheless, by various measures, world markets, especially emerging

markets, are found yet fully integrated. The literature has identified a range of various factors explaining

the cross-country variations in market integration, including the level of market development, political

risk, information quality, liquidity, credit ratings, and cross-listings (see Errunza, 1977; Errunza and

Losq, 1987; Bekaert, Harvey, and Lundblad; 2007; Nishiotis, 2004; Lang, Lins, and Miller, 2003; Bailey,

Karolyi, and Salva, 2006; Bekaert et al., 2011; Carrieri et al. 2013).

Among those studies, Carrieri, Chaieb and Errunza (2013) particularly focus on the role of implicit

barriers, those related to institutional, governance and information environments, in explaining why

markets deviate from full integration. As they quantify the implicit barriers, their evidence indicates that

less implicit barriers indeed serve as positive influence on market integration. Bekaert et al. (2011) apply

a valuation-based measure of market segmentation and also find certain non-regulatory factors,

specifically political risk and market development, are important. In a similar vein, this study focuses on

implicit factors in examining market segmentation in emerging markets. We test factors pertaining to

investor characteristics, including investor propensity to speculation and social culture, in explaining

market segmentation in emerging markets.

The primary interest of this study is to examine whether those factors related to investor

characteristics and non-financial country factors play a significant role in explaining market

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segmentation, and then whether financial globalization changes the role of those factors. This study

focuses on the following research issues on market segmentation.

First, this study examines the role of investor propensity to speculation and social culture in

explaining market segmentation. The characteristics of investors in emerging markets seem to coincide

with the socioeconomic characteristics identified by Kumar (2009) for being prone to investing

lottery-type stocks. Plus, the literature provides evidence that such poor diversification choices of

investors influence stock returns of the market (e.g., see Kumar, 2007, 2009; Bali et al., 2011). This

study borrows the framework of Bali et al. (2011) and measure investor propensity to speculation by

return premium associated with those stocks in a market. It is hypothesized that markets with

participants with greater propensity to speculation tend to dare to value stocks differently from the norm,

i.e., the global market. This then leads to greater market segmentation.

Meanwhile, the consideration of social culture as possible factors is inspired by recent studies

testing the influence of cultures on investor reactions to information and on stock pricing. For example,

Chui, Titman and Wei (2010) study the role of individualism on price momentum across international

markets, and they find that individualism is positively associated with trading volume, volatility and the

magnitude of price momentum profits. More recently, Pevzner, Xie, and Xin (2013) examine the role of

social trust in investor reactions to earnings announcement across 25 countries. They find that investor

reactions to earnings announcements are significantly higher in more trusting countries. The positive

effect of societal trust is more pronounced when a country's information environment is poorer. Their

results suggest that trust acts as a substitute for formal institutions. Considering that our segmentation

measure is earnings-based, the conclusion of the study by Pevzner et al. (2013) certainly points to the

important role of societal trust in market segmentation. If investors in a society of greater trust tend to

react to information more responsively, one would expect the market to be more efficient in terms of

driving prices to the norm, leading to a less segmented market.

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My second research objective is to test the role of de facto financial globalization along the

evolution of market segmentation of an economy. Doidge et al. (2013) recently find that growing

financial globalization tend to mitigate the influence of weaker institutions of local market on their

research subject - global IPO. Along a similar line, we conjecture that the interaction effect between

financial globalization and institution factors may be different from the interaction effect between

financial globalization and factors related to investor characteristics. In particular, when the world

financial globalization increases, it is hypothesized that the influence of poorer institution quality will

exhibit less impact on market segmentation, while at the same time the influence of investor

characteristics on market segmentation may be intact.

Third, the design of valuation-based SEG by Bekaert et al. (2011) provides a convenient platform

for us to analyze segmentation at industry level. It is expected that industries with their supply chains

more globally, such as information technology related industries, involved tend to expose to less degree

of market segmentation.

Last, this study examines market segmentation between neighboring economies, i.e., economies

domiciled on the same continent. Following the study by Bekaert et al. (2013), this study will apply a

revised bi-lateral measure of segmentation for this research purpose. The results expect to answer the

following questions regarding market segmentation, i.e., valuation ratio differences, between two

neighboring markets. The results will indicate whether countries in the same geographical region are less

segmented with each other.

To test for the above research questions, this study selects the market segmentation measure

proposed by Bekaert, Harvey, Lundblad, and Siegel (2011). The literature has offered various measures

of market integration/segmentation (e.g., see Baele, 2005; Bekaert and Harvey, 1995; Bekaert, Hodrick,

and Zhang, 2009; Carrieri, Errunza, and Hogan, 2007; Eiling and Gerard, 2007; Eun and Lee, 2009;

Pukthuanthong and Roll, 2009). Recently, Bekaert, Harvey, Lundblad, and Siegel (2011) propose a

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measure based on relative industry valuation ratios. The authors call it a de facto market segmentation

measure, indicating that it assesses the departure of actual valuation of one country from the world

market. Bekaert et al. point out that the market integration measures in the international finance

literature are usually derived from an international asset pricing model and therefore interpretation of the

measure is model dependent, even though the consensus on such pricing model is lacking. The

estimation of those measures also requires historical data and particular estimation method (see Bekaert,

Hodrick, and Zhang, 2009). In comparison, their proposed segmentation measure can be directly

observed at one point in time, with a frequency as high as monthly, and is easy to interpret.

In addition to the aforementioned advantage, this study selects this measure as it serves to evaluate

the progress of market integration at industry level. Considering the heterogeneities in investor clientele,

level of information asymmetry and growth opportunities across industries, this measure suits the

purpose of this study in investigating market integration at industry level. Moreover, this measure may

be modified to assess the deviation of valuation between any two countries. Such pair-wise measures

then provide a basis for this study to examine regional integration, one of the primary purposes of this

study.

The primary findings of this study include the following. First, this study computes the

segmentation measures across thirty emerging markets. The results indicate that for most markets the

level of segmentation, in terms of valuation difference from the world, becomes less serious over time.

The valuation ratio deviation also exhibits significant time-variation, in particular, in association with

market condition for many sample markets. Nonetheless, it is also worth noting that for some smaller

economies, the progress of market integration is slow and sometimes even shows going backward.

Second, this study computes an aggregated segmentation measure for industries. The result shows

that as expected for those industries, such as “Software & Computing Services”, exhibit the least

severity of segmentation, which is attributable to the fact that the supply chains of this industry in

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increasingly globalized. On the other hand, industries such as “Industrial Metals & Mining” exhibit on

average a much greater level of segmentation. This may be explained by the relatively localization of

such industry. This finding high lights how international activities involving real products affect the

valuation of financial securities internationally.

Third, this study extends the measure by Bekaert et al. (2011) to evaluating the pair-wise valuation

difference between two countries. The purpose is to examine whether there exists regional effects for

such valuation deviation. We compute those pair-wise valuation differences across the sample emerging

markets. Results show that the level of pair-wise segmentation varies across regions. Overall, Asian

markets exhibits closest valuation ratio between each other. This may be attributable to the greater

similarities in terms of investor habit among Asian markets.

Fourth, a time series analysis shows that most emerging markets demonstrate a downward trend of

market segmentation. This coincides with the trend of financial globalization of world markets. In

addition, most sample markets indicate that such valuation deviation tends to elevate during bearish

world or local market. That is, worse performing market actually promotes the segmentation of

emerging markets from the global standard.

Last, a fixed effects model is implemented to examine country factors responsible for market

segmentation. The results indicate that among our thirty emerging markets, those markets that have

larger market capitalization over GDP, more analyst coverage, less corruption, and more capital

expenditures are associated with less severity of market segmentation. As to the variables assessing

investor habits, neither the propensity to speculation nor the culture variables (trust and individualism)

are found to have significant impact on market segmentation. Among those variables pertaining to

investor culture, individualism demonstrates positive impact on market segmentation with marginal

significance. That is, markets with investors with stronger individualism tend to show greater valuation

deviations with the rest of the world market.

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This study also tests the hypothesis that financial globalization weakens the importance of country

institutional factors on market integration. Results show that while greater analyst coverage and less

corruption reduce market segmentation, such impact becomes increasingly unimportant as the world

market becomes more financially globalized. To my best knowledge, these results are new to the

literature and add to the large literature on market integration/segmentation.

2. Literature Review

2.1 Investor Preference for Stocks with Extreme Payoffs

Even though the classic asset pricing theories are built on the assumption that investors hold well

diversified portfolios, the literature has documented empirical evidence that investors are generally not

well-diversified (see e.g., Kelly, 1995; Barber and Odean, 2000; Goetzmann and Kumar, 2008). In view

of the evidence, researchers examine whether investors’ systematic portfolio choices that deviate from a

well-diversified scenario influence stock returns. For example, Kumar (2007), in his investigation of

such effect, finds that the poor diversification choices lead to a mispricing, a consequence from investor

sentiment, narrow risk framing, and asymmetric information. Kumar (2009) focuses on US stocks with

lottery-type payoffs. By identifying those stocks with lottery-type payoffs as those with high

idiosyncratic volatility, high idiosyncratic skewness, and low price, he finds that those lottery-type

stocks underperform in their future returns.

Bali et al. (2011) define lottery-type stocks by those having maximum one-day return (MAX) for

the past one month. The next-month return difference between the portfolio composed of stocks with the

largest MAX and the portfolio composed of stocks with the lowest MAX is the return premium

associated with lottery-type stocks. Their study finds significant premium associated with lottery-type

stocks in the US market, even after controls for size, book-to-market, momentum, short-term reversals,

liquidity, and skewness. That is, those stocks showing extreme positive returns in the prior month will

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yield lower return in the subsequent period. This result is consistent with the finding of Kumar (2009).

Bali et al. find that stocks with extreme positive payoffs tend be firms of small size, low price level, high

market beta, and high illiquidity in the US market. They also find the return distributions of those

high-MAX stocks mostly coincide with those by Kumar (2009). In addition, Bali et al. find that stocks to

deliver lottery-like payoffs in the portfolio formation month continue to exhibit this behavior in the

future. Their result suggests that poorly diversified investors prefer lottery-like payoffs and are willing

to pay more for those stocks. Such behavioral biases influence prices and hence future returns. This

again adds to the evidence that poor diversification choices of investors influence stock returns.

The literature offers theoretical reasons for pricing such extreme returns. For example, Barberis and

Huang (2008) proposed a model based on the cumulative prospect theory of Tversky and Kahneman

(1992), which indicates that it is the low probability, extreme return states that drive the results.

Brunnermeier, et al. (2007), in their optimal beliefs model, also suggest that it is low probability states

that drive the relevant pricing effects.

In the mean time, empirical literature also discusses some possible explanations. Some of the

studies emphasized the potential role of gambling in investment decisions (e.g., Shiller, 1989, 2000;

Shefrin and Statman, 2000; Statman, 2002; Barberis and Huang, 2008). Several studies suggest that the

preferences for lottery-type stocks simply reflect investors’ preference for return skewness (Walker and

Young, 2001), while the recent study by Bali et al. (2011) finds that the return skewness cannot explain

away return premiums associated with such preferences.

Kumar (2009), taking advantage of account data showing portfolio holdings and trades of a group

of individual investors at a brokerage house, identifies socioeconomic factors associated with preference

for investment in lottery-type stocks; namely, he finds that poor, young, less educated single men living

in urban areas, undertake nonprofessional jobs, and belong to certain minority groups invest more in

lottery-type stocks.

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The characteristics of investors at emerging markets seem to coincide with the socioeconomic

characteristics identified by Kumar (2009). Indeed, Hsin (2013) applies a similar method as Bali et al.

(2011) to 30 emerging markets, and finds significantly positive premium associated with those

lottery-type stocks for most markets. This study again borrow the framework of Bali et al. (2011) and

measure investor propensity to speculation by the return premium associated with those lottery-type

stocks in a market.

2.2 Financial Globalization

There have been many regulatory changes toward the opening of emerging markets in the recent

years. Such changes are anticipated to show impacts on stock returns. Among the related studies, some

find that liberalizations will lower cost of capital due to the participations of foreign capital; others argue

that foreign capital may also raise the stock market volatility due to the dramatic inflows and outflows of

hot money, while with no conclusive findings in this regard (Richards, 1996; Bekaert and Harvey, 2000;

Henry, 2000; Kim and Singal, 2000).

In the meantime, the world capital markets have observed a trend of growth in international asset

trades in recent years. The financial globalization, as presented by trade liberalization, less capital

account restrictions, decreasing transaction costs, and provisions of sophisticated financial products, are

expected to affect investors’ activities in the international financial markets, developed and emerging

markets alike (e.g., see Richards, 1996; Bekaert and Harvey, 2000; Henry, 2000; Kim and Singal, 2000;

Bekaert, Harvey and Lumsdaine, 2002; Bekaert, Harvey and Lundblad, 2006).

Indeed, if the regulation changes do not actually activate the exchanges of trades across boarders,

there will be no observed impacts on stock return dynamics. As specifically addressed by Bekaert,

Harvey and Lumsdaine (2002), it is the market integration, as a result of the regulatory liberalizations,

that counts as we examine any impact on the stock returns of emerging markets. As an emerging market

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become liberalized and increasingly integrated with the world markets, domestic investors start being

able to invest in foreign assets and foreign investors participating trades in domestic assets. The

consequence is then the assets of identical risk command the same expected return around the globe. In

the process, gradual or fast, the expected returns, volatilities and correlations with world factors all

change as a result.

This study requires a proxy for financial globalization. The literature of international finance uses a

number of measures for market openness. Most globalization measures used in the literature only assess

de jure openness, i.e., openness defined by relaxed regulation and laws, instead of de facto openness, i.e.,

what actually takes place in terms of globalized financial activities. As a result, those de jure openness

measures usually only exhibit cross-country variation but limited time variation in recent years. Lane

and Milesi-Ferretti (2007) construct estimates of external assets and liabilities for countries around the

world. They also devise a measure of financial globalization defined as the sum of external assets and

liabilities over GDP. Recent studies, including Kose, Prasad, Rogoff, and Wei (2009) and Doidge

Karolyi and Stulz (2013), applied their measure for their research analysis. This study will also employ

the measure, estimated at both country level and at global level.

3. Method and Data

3.1 Valuation-Based Measure of Market Segmentation

Bekaert, Harvey, Lundblad, and Siegel (2011) propose a measure based on relative industry

valuation ratios. In particular, each country is composed of K industries with each industry’s portfolio

weight equal to the relative market value of the industry in the country. The level of segmentation (an

inverse of integration) is then estimated by the weighted average of earnings yield differentials between

local and global market. That is, we have

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∑=

−=Nk

ktkwtkctkctc EYEYIWSEG

1,,,,,,, || (1)

In the above equation, EYc,k,t denotes the industry k’s earnings yield (i.e., the inverse of P/E ratio)

in country c; EYw,k,t is the corresponding industry’s earnings yield in global capital market; IWc,k,t

denotes the weight of industry k in country c relative to all industries within the same country, with the

weight being determined by relative market capitalization; and Nk is the number of industries. The

authors call it a de facto market segmentation measure, indicating that it assesses the departure of actual

valuation of one country from the world market. In comparison to other measures in the literature,

Bekaert et al. point out the advantages of this measure in that it does not rely on specific asset pricing

model and that the segmentation measure in equation (1) can be directly observed at one point in time,

with a frequency as high as monthly, and is easy to interpret.

Bekaert et al. (2011) however also recognize the weakness of the measure. From the derivation of

their measure, one can see that the valuation ratios incorporate any possible volatility of the shocks to

earnings growth rates and discount rates, while market integration does not have to impose restrictions

on those variables. The variation in financial leverage also affects the P/E. In addition, this measure,

though has the advantage of being observed at one point in time, also becomes sensitive to extreme

values and tends to be volatile over time, especially when there is only limited number of firms available

for calculation. In response to the above issues, Bekaert et al. mitigate those biases by incorporating

proper control variables in their regression analysis. This study selects this measure also for the purposes

of performing tests for market segmentation.

In addition, this study also calculates the industry-level segmentation. Equation (2a), SEGIc,k,t,

simply measures the earnings yield differential for industry k in country c during year t. The aggregated

measure is presented in equation (2b). In equation (2b), CWc,k,t denotes the market capitalization of

industry k in country c relative to the sum of market capitalization of same industry across all countries.

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|| ,,,,,, tkwtkctkc EYEYSEGI −= (2a)

∑=

−=Nc

ctkwtkctkctk EYEYCWSEGind

1,,,,,,, || (2b)

In addition, this measure may be modified to assess the deviation of valuation between any two

countries. Such pair-wise measures then provide a basis for this study to examine regional integration.

Accordingly, the following quantity is computed:

∑=

−=Nk

ktkctkctkcctcc EYEYIWSEGpair

1,,2,,1,,2,1,2,1 || (2c)

The measure in equation (2c), borrowed from the study by Bekaert et al. (2013), estimates the bilateral

segmentation between two countries, where IWc1,c2,k,t is the relative market capitalization of industry k

and Kc1,c2,t is the number of industries for country-pair c1-c2 at time t. IWc1,c2,k,t is calculated as the

combined market capitalization of industry k in both countries divided by the combined market

capitalization of all industries in both countries (see Bekaert et al., 2013). This measure is used for

regional analysis of market segmentation.

3.2 Measure of de facto Financial Globalization

The literature offers a variety of measures for market openness or globalization (e.g., see Kose,

Prasad, Rogoff, and Wei (2009)). Lane and Milesi-Feretti (2007) devise a measure of financial

globalization by the ratio of gross external assets and liabilities over the GDP. The primary advantage of

this measure is that the series, being an annual one, has time-series variation, and more importantly it

captures the extent to which financial globalization actually takes place (they call it de facto openness).

That is, most other openness measures only assess the degree of openness based on explicit barriers by

the laws and regulations, while investors may still do not do the global finance given the de jure

openness. Doidge, Karolyi and Stulz (2013), in their study on the relative decrease of IPOs in the U.S.,

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apply the data set complied by Lane and Milesi-Feretti (2007) and construct a measure of financial

globalization. Their measure is calculated by summing up the U.S. dollar denominated value of external

assets and external liabilities across world markets and then dividing the sum by the GDP across the

world markets.

This study also borrows the extended data set from the website of Lane and Milesi-Ferretti and

constructs similar financial globalization measure. The current database provided by Lane and

Milesi-Ferretti updates and extends the External Wealth of Nations Mark II database (see Lane and

Milesi-Ferretti (2007)), and now contains data for the period 1970-2011 for 188 countries. Doidge et al.

(2013) applies the measure which each year summing up the assets/liabilities and dividing by the world

GDP. This study also calculates country-generic world financial globalization annual series (FGlobalw,t),

as in the study of Doidge et al. (2013), in addition to a group of country-specific financial globalization

series (FGlobali,t) as follows.

∑∑

=

=

+=

nctc

nctitc

tw GDP

ExtLiabExtAssetsFGlobal

,1,

,1,,

,

)( (3a)

tc

tctctc GDP

ExtLiabExtAssetsFGlobal

,

,,,

+= (3b)

Doidge et al. (2013) explain their use of a “world” financial globalization measure, instead of

country-level measure, that it could avoid creating a mechanical relation with global IPO activity for the

country, and that the “world” measure captures the degree to which the whole world capital markets are

interconnected. The world measure indeed provides information on the evolution of financial

globalization for the world markets as a whole, while the county-level measure assesses the de facto

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financial globalization as a result explicit or latent barriers for each country. Considering the purpose of

this study, we apply both world and country-level financial globalization measures.

In comparison to the study by Bekaert et al. (2011), they applied three measures of de jure market

openness (capital account openness, equity market openness and regulatory openness) and a de facto

trade openness (ratio of the sum of exports and imports over GDP). This study adds the above de facto

financial globalization measure and will also examine its interaction effect with other country-level

factors on market segmentation.

3.3 Measures of Alternative Implicit Barriers – A Human Side of Story

Recent studies in the finance literature have started examining the role of social cultures in pricing

stocks (e.g., Diamond andVerrecchia,1991; Kim and Verrecchia, 1991, 1994; Harris andRaviv,1993;

Kandel and Pearson, 1995; Bamber et al., 2000; Bailey et al., 2006; Kumar, 2009; Chiu et al., 2010;

Pevzner, et al., 2013). Under similarly liberalized regulations or even similar institutions, investors may

well react differently to corporate information, which then lead to different pricing behavior across

world markets. This study examines a group of factors, pertaining to the characteristics of investors

across countries, which may affect how investors process firm information, incorporate the information

in pricing, and therefore the level of market segmentations.

Investor Propensity to Speculation

This study borrows the framework of Bali et al. (2011) and develops a measure for investor

propensity to speculation for each market based on the return premium for portfolios composed of

extreme positive returns in the previous month. In particular, we follow Bali et al. and define ‘MAXi,t’ as

the maximum daily return for stock i during month t, i.e.,

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)max( ,, diti RMAX = , d =1, Dt,

where Dt is the number of trading days in month t.

Portfolios are then formed each month by sorting stocks based on their maximum daily return

(MAX) for the past one month. The next-month return difference between the portfolio composed of

stocks with the largest MAX and the portfolio composed of stocks with the lowest MAX is the return

premium associated with lottery-type stocks. This premium differs across countries and vary over time,

indicating the magnitude of local investors willing to pay for such stocks.

A net risk premium attributable to having high MAX is estimated by controlling for other factors

affecting the cross-sectional returns. A standard Fama and MacBeth (1973) approach is performed by

including the market-beta (BETA), firm size (SIZE), book-to-market ratio (BM) and illiquidity measure

(ILLIQ), and price level (P) (see Bali et al., 2011; Kumar, 2009).

The market beta (BETA) of stock i in month t is estimated by applying a modified market model,

including the lead and the lag of the market portfolio return, in addition to the current market portfolio

return in the regression (see Scholes and Williams, 1977). The market beta is estimated with daily return

data for the quarter before the end of month t, and will be re-estimated each month using a quarterly

moving window. Firm size (SIZE) is measured by the natural logarithm of the market value of equity.

For each month t, we compute a firm’s book-to-market ratio (BM) by following Fama and French (1992)

and using the market value of its equity at the end of the prior year’s fiscal year end, which for most

countries should be December, and the book value of common equity for the firm’s latest fiscal year

ending in previous calendar year. Illiquidity is measured by the logarithm of turnover ratio for each

stock i in month t. The price level (P) of stock i during month t is simply the average daily unadjusted

price for stock i during month t.

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The control variables are included to measure the return premium attributable to MAX by running a

Fama-MacBeth type of cross-sectional regressions for each month t:

1,2,,71,,6

1,,51,,41,,31,,21,,1,0, lnlnln

+−−

−−−−−

+++

+++++=

titittit

tittittittittittti

MAXbLIQbPbMBbSIZEbBETAbMAXbbR

ε (4)

where Ri,t+1 is the realized return on stock i in month t+1. The average slope coefficient on maximum

daily return (MAX), b1,t, expects to be negative, and my previous results conform to this hypothesis for

most emerging markets. This suggests that stocks paying extreme returns tend to suffer from a negative

return during the following month. The magnitude of the coefficient b1 indicates the excessive price

paid by investors. Accordingly, the measure of investor propensity to speculation for each country is

proxied by the premium that investors are willing to pay for those lottery-type stocks is estimated as

follows.

Ct

Ct bPREFXtm ,1−= (5)

Cultural Dimensions of Investors - Trust, Hierarchy and Individualism

Intuitively, social culture expects to influence capital market participants’ investment decisions,

including how investors react to corporate information release and how investors trade around a news

event. International financial markets certainly serve a natural platform for testing the culture effect on

the aggregate market pricing behavior. Whether a market, on its aggregation, is segmented from world

market certainly depends on how investors incorporate the information in pricing.

This study follows prior researches including, La Porta, Lopez-de-Silanes, Shleifer, and Vishny

(1997), Guiso, Sapienza and Zingales (2008b), and Pevzner, Xi and Xin. (2013) and captures a country’s

level of societal trust, hierarchy and individualism by its citizens’ response to questions in World Values

Surveys (WVS). This study will calculate three dimensions of culture measures of trust, hierarchy and

individualism by closely following the procedure of Pevzner et al. (2013). The World Values Surveys

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(WVS) were undertaken in six waves from 1981 to 2014.

The societal trust is assessed based on the following question from the WVS: “Generally speaking,

would you say that most people can be trusted or that you need to be very careful in dealing with

people?”. There are two possible answers: (1) Most people can be trusted; and (2) You can never be too

careful when dealing with others. There can be alternative answers such as 'agree with both', 'agree with

none' and so on. We recode the response to this question to ‘1’ if the response is that “most people can

be trusted” and ‘0’ otherwise and then calculate the mean of the response in each country year as the

measure of societal trust. WVS also calculates a trust index for each country based on the following

formula: 100+(% of participants who respond “most people can be trusted”) – (% of participants who

respond “can't be too careful”) to the ‘trust’ question (see below). This trust index however is time

invariant. The index values are downloadable directly from WVS website.

To measure national attitudes toward hierarchy, we will use the following question from the WVS:

“People have different ideas about following instructions at work. Some say that one should follow one's

superior's instructions even when one does not fully agree with them. Others say that one should follow

one's superior's instructions only when one is convinced that they are right. With which of these two

opinions do you agree?” There are two possible answers: (1) Should follow instructions; and (2) Must

be convinced first. We will recode the response to ‘1’ if the answer is (1) and ‘0’ otherwise. The average

response in each country- year measures the degree of hierarchy in the economy.

To measure individualism this study uses the following WVS question: “How would you place your

views on this scale? (1) Incomes should be made more equal (2) We need larger income differences as

incentives for individual effort.” ‘1’ means the survey participant completely agree with statement

(1), ’10’ means agreeing completely with statement (2), and any number in between indicate the level of

agreement between (1) and (2). The response is rescaled with ‘0’ indicating completely agreeing with

statement (1) and ‘1’ indicating completely agreeing with statement (2). The average of the rescaled

- 17 -

response in each country year is the measure of individualism.

3.4 Institutions and Market Development Variables

Corporate Governance, Institutional Factors and Information Environment

The international finance literature has applied a variety of county-specific variables to evaluate the

level of institutions quality, which expects to affect how investors process corporate information and

price stocks. This class of variables generally assesses both the hardware (infrastructure) and software

(laws, governance, disclosure and enforcement standards) of the corporate environment. The former

may be directly measured, while the latter are usually indexed and sometimes obtained through survey.

Among those candidate variables, the mostly widely used variables are those from the following

series of studies: LaPorta, Lopez-de-Silanes, Shleifer, and Vishny, 1997, 1998; LaPorta,

Lopez-de-Silanes, and Shleifer, 2006; and Djankov, LaPorta, Lopez-de-Silanes, and Shleifer, 2008.

Those values are mostly time invariant, as they are collected through survey during one particular

historical year. Also widely cited are indexes from the ICRG (International Country Risk Guide) with

values mostly available at monthly frequency. In addition, researchers also compute scores to assess

corporate information quality/efficiency based on company-level information (e.g., Collins, Kothari,

Shanken and Sloan, 1994; Durnev, Morck, Yeung and Zarowin, 2003; Leuz, Nanda and Wysocki, 2003).

Also note that researches classify these variables differently. For example, Doidge et al. (2013)

name all of those factors related to law, governance, disclosure and enforcement standards as

‘institutions’, while Carrieri et al. (2013) classify implicit barriers into three categories: institutional

factors, corporate governance, and information quality. In fact, it is difficult and unnecessary to draw a

clear line among those variables in terms of their functions. For the purpose of this study, we will refer

more recent studies (e.g., Bekaert et al., 2011, 2013; Doidge et al., 2013; Carrieri et al., 2013; Wantanabe

et al., 2013) and employ the following as candidate proxies to explain market segmentation. Some of

- 18 -

those candidate variables expect to be correlated and some may drop from final testing.

The extent to which a country provides a commercial legal environment with better protections to

shareholders will influence how stocks are valued. Cross-country variation in this regard will then lead

to market segmentation. LLSV (1998) has devised a popular index, anti-director rights index, which

assesses legal protections for minority investors. DLLS (2008) has later revised the index. This revised

“anti-director rights index” is included in this study.

In addition, LLS (2006) devise a disclosure requirements index through a survey of attorneys in

year 2000. This disclosure index assesses the level of institution requirements on disclosing insider

information asymmetry of evaluates the requirements for prospectus.

Since LLSV (1997, 1998), common law countries are considered as having better institutions. As in

many studies in the literature, we employ a common law dummy indicating whether the country has a

legal origin of English common law. In comparison to other institution variables, this variable has the

advantage of being clearly exogenous.

ICRG provides index scores on Economics, Finance and Political Risk at monthly frequency. There

are further sub-components under each category. This study selects the following ICRG indexes as

candidate variables to explain market segmentation. The selections are particularly in view that Bekaert

et al. (2011) find political risk shows significant relationship with their market segmentation measure.

Accordingly, we use the following ICRG indexes: Corruption, Law and Order, and Investment Profile.

All of the above are sub-components of ICRG political risk.

Openness – Financial and Trade Market

This study employs variables proxy for financial market openness as well as trade market openness

as candidate variables to explain market segmentation. For financial market openness, we follow

Bekaert et al. (2011) and use capital account openness and equity market openness.

- 19 -

The capital account openness by Chinn and Ito (2008) is applied in the study. This index is based

on the presence of multiple exchange rates, restrictions on current account transactions, restrictions on

capital account transactions and the requirement of the surrender of export proceeds. KAOPEN is

normalized between 0 and 1. The higher value means greater openness in capital account transactions.

For trade market openness, we refer to the liberalization date in Wacziarg and Welch (2003) to

create a liberalization dummy, and also employ the widely used trade sector (exports of imports of

goods and services) over GDP of the country. The former is considered as a de jure trade openness

indicator and the latter is a de facto one.

Market Development

A well-developed financial market expects to have its securities priced efficiently and conforming

to the global norm. This study uses the following variables to measure the level of financial development

in an economy. Most of them are widely used in the literature.

The first included is the ratio of equity market capitalization over GDP. Second, I will evaluate

market liquidity using the equity market turnover (ratio of equity market trading volume in value over

total market capitalization). The third measure is private credit as a ratio of GDP. A higher ratio of

private credit represents greater resources offered to private sector.

3.5 Data

This study assembles a sample of emerging markets based on those defined by Morgan Stanley

Capital International and those considered in the studies of Morck et al. (2000) and Bekaert and Harvey

(2000). This study selects those emerging markets in the sample mainly due to their data availability,

including the sufficiency of firm-level data provided by Datastream and Worldscope. Our sample

initially covers 32 emerging markets from Europe, America, Africa and Asia. Note that Hong Kong and

- 20 -

Singapore are also included for their having market characteristics similar to emerging markets and their

emerging market history during our sample period. Considering limited data availability during earlier

years from Datastream, we apply a sample period extending from January 1980 to December 2013.

However, the main results rely on data from 1990 to 2013, as the emerging market data are limited prior

to 1990.

Only common stocks listed on the major exchange of the country with data available from

Datastream and Worldscope are included. That is, stocks must have a type of instrument indicator equal

to ‘Equity’. Sampled stocks should be domestically incorporated based on their home country and traded

in local currency. The prices of suspended stocks will be dropped from the sample. I also exclude the

initial six months’ trading data for those newly listed or re-listed stocks. Daily prices including dividends

(RI) are used for estimating MAX, and weekly data will be used for estimating the idiosyncratic risk

(R-square) measures. To enter the final sample, stocks must have return data available (after filtering)

for at least 120 days in the sample year. The company-level accounting data are collected from

Worldscope.1

For computing the segmentation measures, we closely follow the data treatment of Bekaert et al.

(2011). Accordingly, this study applies an industry classification yielding 38 different industries (see

Bekaert et al., 2011). Monthly measures of segmentation, SEG, are estimated for 30 emerging

economies, after Jordan and Sri Lanka are dropped from the analysis due to their lack of industry-level

data. The monthly equity industry portfolio data are collected from Datastream and the company-level

data are from Worldscope for the sample period between 1980 and 2013. Annual average of those

1 This study imposes a number of filters for those price data collected from Datastream. The sample includes only stocks listed on primary exchanges of the country and traded in local currency. Those leading and trailing zeros in the Datastream return series are set to missing values. To address issues on coding errors of Datastream data, I implement a filter for reversals in the data that could be caused by incorrect stock prices. In particular, I set Rt and Rt-1 to missing if |Rt| > 200% or |Rt-1| > 200% and Rt-1 + Rt < 50%. We further winsorize the top and bottom 0.1% of the final sample of stock returns. The study by Ince and Porter (2006) presents a detailed discussion on the treatment of coding errors in Datastream and provides possible solutions. To enter the sample, stocks must have available return data for at least 120 days in the sample year. This study will exclude country-years where fewer than 10 firms have available data.

- 21 -

monthly SEG measures are computed for later cross-sectional analysis.

Datastream uses the Industry Classification Benchmark (ICB) framework for industry classification,

and offers industry earnings yields for most countries. Datastream calculates these industry earnings

yields by adding lagged 12-month non-negative firm earnings across firms in a given industry and

country and then dividing the aggregated earnings by the aggregated market value of component firms

in the industries. For those markets without industry earnings yields or industry market capitalization

data available, we compute the corresponding quantities based on the above method of Datastream.

Equation (1) also requires a set of global industry earnings yields, which are available from

Datastream’s global industry portfolios.

All the index data will be converted into US dollars. Most of the macroeconomic data for sampled

markets are obtained from the World Bank database (WDI-online), FRED, and Datastream. ICRG data

are available from PRS Group at monthly frequency. World Values Survey database is used to collect

those social culture data (trust, hierarchy, and individualism), which are available from World Values

Survey Association (www.worldvaluessurvey.org). Those country-level variables are summarized in

Table 1.

[ Insert Table 1 about here]

4. Results and Analysis

4.1 Market Segmentation across Emerging Markets

The valuation-ratio based segmentation measures are computed for our sample emerging markets. Table

2 reports the results. As most emerging markets only have better complete data starting year 1990, this

table reports the results from 1990 to yield a fair comparison across markets.

[ Insert Table 2 about here]

Panel A lists the time series mean value while Panel B lists the median values over the 23 year

- 22 -

period. First column of each panel lists the results for the full period, from 1990 to 2013. Column 2 to

Column 6 then list the 5-year sub-period results, showing the progress of market integration over time.

Greater values of the segmentation measure indicate larger valuation ratio deviation from the global

industry norm, suggesting a greater level of segmentation from the world market. The full period results

find that among the sample emerging markets, Hong Kong has experienced the least level of market

segmentation, with a median value of 0.0191, while Russia posits as a market most segmented from the

world, show a median value of 0.0588 and Venezuela comes as a close second, showing a median value

at 0.0506. Later this study will test the country characteristics that contribute to the market

segmentation.

The five-year sub-period results show that the segmentation measure indeed varies significantly

over time. A time-series analysis later will reveal whether such valuation-ratio differentials change with

market conditions.

4.2 Variation of Market Segmentation across Industries

This study further computes the segmentation measure by industry using equations (2b) and (2c).

Table 3 lists the results across 38 ICB industries. The evidence finds interestingly that the level of

market segmentation varies across industries. Note that most studies in the literature assess the degree of

market integration at aggregated market level. The results in Table 3 clearly suggest that it is important

to consider industry factors when judging the degree of market integration at country level.

[ Insert Table 3 about here]

We find that emerging market stocks in the “Software & Computer Services” industry have

experienced closest valuation with the world market, showing a median segmentation measure of 0.013.

On the other hand, stocks in the “Leisure Goods” industry and the “Industrial Metals & Mining”

industry, showing a median segmentation measure respectively at 0.051 and 0.041, have experienced

- 23 -

greatest valuation differentials from the world stocks in the same industry. A possible explanation is that

firms in the “Software & Computer Services” industry usually involve supply chains distributed across

world markets, which expects to lead to close ties in terms of valuation across international markets. A

further analysis on industry factor expects to reveal more details.

4.3 Valuation Differentials across Regional Markets

This study extends the measure of Bekaert et al. and develops a measure assessing the valuation

differentials between two markets, as shown in equation (2c). This pair-wise measure computes the

valuation differential from one market (benchmark market) to another. The numbers will be different

between two same markets as the weight is determined by the industry weight of the benchmark market.

[ Insert Table 4 about here]

Table 4 reports the results by region. The column country is the benchmark market in each panel.

Panel A lists the results for markets in South America. Among those South American markets,

Venezuela is found to exhibit greatest valuation differential from other markets in the same region. In

fact, Table 2 shows that Venezuela is also one of the markets most segmented from the world. The pair

showing the closest valuation between each other is Chili and Colombia with a median measure equal to

0.012.

Panel B reports the results for nine Asian markets. The largest valuation differential occurs for the

pair of China and Indonesia, with a segmentation value equal to 0.055. The closest valuation pair is,

interestingly, between Taiwan and China, showing a segmentation measure at 0.010. Note that these two

markets are different in terms of industry structure, market capitalization, and corporate governance.

However, these two markets have close trade relationships, and more interestingly, they share similar

culture and investor habits.

Panel C lists the results for European emerging markets, which do not reveal particular pair

- 24 -

showing widest valuation differentials. The pair with closest valuation differentials is Czech Republic

and Ireland, showing a segmentation measure at 0.011.

Panel D lists the results for countries in middle east and Africa. The results in this panel distinct

from other regions in that many pair are found to have large valuation differentials. Among those pair

markets mutually segmented to a high degree, we have Egypt and Peru, Morocco and Israel, Russia and

Israel, and Israel and Egypt, all showing a mutual segmentation measure as high as 0.07.

4.4 Time Series Analysis of Valuation Differentials from World Market

The level of market segmentation expects to change with institutional or culture factors of a market and

does not expect to vary at high frequencies. The version of market segmentation by Bekaert et al.

however is based on valuation differentials from the world market, which may well respond to market

conditions. I perform a time series regression for those computed monthly segmentation measures and

results are reported in Table 5.

[ Insert Table 5 about here]

For most sample markets, the determinant showing strongest impact on the monthly valuation

differentials is time. There are about half of the sample markets revealing their degrees of market

segmentation to decrease over time. More interestingly, there are eighteen out of thirty markets showing

their levels of market segmentation tend to become aggravated during bearish world or local market

conditions. That valuation differentials become widened during bear market is consistent with the claim

that investor pricing behavior responds to market condition. This is also consistent with the large

literature arguing that investor sentiment affects stock pricing (Baker and Wurgler, 2006).

4.5 Cross-Market Analysis of Capital Market Segmentation

The primary interest of this study is to examine what country factors contributing to market

- 25 -

segmentation. We are particularly interested in whether those implicit factors related to investor

characteristics play a significant role in explaining market segmentation. Emerging market investors are

usually characterized as being more speculative. A possible scenario is that even two liberalized

economies enjoy the same perfect information environment or quality institutions, the habitual or social

differences may drive market participants of these two economies to price securities with different

functional forms.

In comparison to the study by Bekaert et al. (2011), this study focuses on the following research

issues on market segmentation. First, this study particularly examines the role of investor propensity to

speculation and social culture (Trust and Individualism) in explaining market segmentation. It is

expected that markets with participants more prone to investing in securities with extreme payoffs tend

value stocks greatly different from the norm, i.e., the global market. This then leads to greater market

segmentation. Meanwhile, if investors in a society of greater trust tend to react to information more

responsively (see Pevzner et al., 2013), one would expect the market to be more efficient in terms of

driving prices to the norm, leading to a less segmented market. On the other hand, the study by Chui,

Titman and Wei (2010) suggest that momentum anomaly tends to become more pronounced in markets

with investors showing higher individualism. This then suggests that a market with investors of high

individualism is expected to yield greater valuation differentials from the global market.

Another focus of this study is to test the role of de facto financial globalization, FGlobal, along the

evolution of market segmentation of an economy. Doidge et al. (2013) recently find that growing

financial globalization tend to mitigate the influence of weaker institutions of local market on their

research subject - global IPO. Along similar line, this study conjectures that the institutional factors may

yield less impact on market segmentation when markets become increasingly globalized. In particular,

when the world financial globalization increases, the influence of poorer institution quality will exhibit

less impact on market segmentation.

- 26 -

The results of the study by Bekaert et al. (2011) indicate that political risk and the level of market

development explain the market segmentation. However, this study makes the following differences in

this part of empirical test. First, the test is performed over an extended sample period, particularly

including the 2008 financial crisis. Second, we add variables characterizing market participants,

including investor propensity to speculation, Trust and Individualism. Third, we include the de facto

financial globalization measure, FGlobal, in the regression and further test for possible interactive

effects with other variables. Any identified significant relationship will also be new findings.

To answer the preceding research questions, I perform a fixed effects regression for SEG for all

sample markets on those candidate factors discussed in the previous section. The results are reported in

Table 6. Panel A reports the results for the full period from 1990 to 2013, while Panel B reports those for

later years, i.e., from 2000 to 2013. Column (1) and Column (3) list the regression results for the

primitive model, while Column (2) and Column (4) list the results when considering the inter-active

effects with world financial globalization.

[ Insert Table 6 about here]

The evidence shows that those markets with greater market capitalization over GDP, greater

financial coverage, less corruption, better exchange rate stability and greater capital expenditures tend to

be less segmented from the world market. These results conform to the findings in the existing literature,

which generally finds that better financial market development, greater information disclosure, better

corporate governance, and greater growth options tend to promote market integration with the world.

Nonetheless, those factors characterizing investor habit of a market, including propensity to speculation,

Trust and Individualism, do not show statistically significant association with the market segmentation

measure. Among those three variables, Individualism exhibits positive relationship, though only

showing a p-value around 0.13, with market segmentation. This is similar to the finding by Chui, Titman

and Wei (2010) on price momentum for world markets.

- 27 -

This study also tests the hypothesis that financial globalization weakens the importance of country

institutional factors on market integration. Column (4) shows that the regression coefficients for

IBES_coverage and Corruption are significantly negative but their inter-active effects with world

financial globalization are both significantly positive. This means that while greater analyst coverage

and less corruption reduces market segmentation, such impact becomes increasingly unimportant as the

world market becomes more financially globalized. To my best knowledge, these results are new to the

literature of international market integration.

5. Conclusion

This study focuses on testing market characteristics contributing to market segmentation, including

factors pertaining to investor characteristics, namely investor propensity to speculation and social

culture. This study also examines whether financial globalization changes the role of those institutional

factors in explaining market segmentation. The market segmentation measure proposed by Bekaert,

Harvey, Lundblad, and Siegel (2011) is selected for the study. This measure essentially assesses the

valuation differentials between local stocks and world stocks in the same industry.

The primary findings include the following. First, the results indicate that for most markets the level

of segmentation, in terms of valuation-ratio deviation from the world, becomes less serious over time.

The valuation ratio deviation also exhibits significant time-variation with market condition for most

sample markets.

Second, the segmentation measure for industries shows that the industry of Software & Computing

Services exhibit the least severity of segmentation, while industries such as “Industrial Metals &

Mining” exhibit on average a much greater level of segmentation. Such finding high lights how

international activities involving real products affect the valuation of financial securities internationally.

Third, those pair-wise valuation ratio differences across the sample emerging markets are computed.

- 28 -

Results show that the level of pair-wise segmentation varies across regions. Overall, Asian markets

exhibits closest valuation ratio between each other. This may be attributable to the greater similarities in

terms of investor habit among Asian markets.

Fourth, a time series analysis shows that most emerging markets demonstrate a downward trend of

market segmentation. In addition, most sample markets indicate that such valuation deviation tends to

elevate during bearish world or local market.

Last, the fixed effects model results indicate that those markets that are more financially developed,

more widely covered by analysts, less corrupted, and with more capital expenditures are associated with

less severity of market segmentation. As to the variables assessing investor habits, individualism

demonstrates positive impact on market segmentation with marginal significance. That is, markets with

investors with stronger individualism tend to show greater valuation deviations with the rest of the world

market. Results also show that while greater analyst coverage and less corruption reduce market

segmentation, such impact becomes increasingly unimportant as the world market becomes more

financially globalized. To my best knowledge, these results are new to the literature and add to the large

literature on market integration/segmentation.

- 29 -

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Table 1 Summary Statistics by Country This table reports country-level median values of firm-year observations. Panel A reports median value for firm-level variables and Panel B reports median value for country-level variables. The sample period is from 1988 to 2013. The definitions of variables are described in Appendix. All continuous variables are winsorized at the 1% and 99% levels.

Country Exchange # of Firms

MV (US$ in Million)

Turnover %

Analyst Coverage Beta M/B VOL Price ROA

% Leverage

% Cap.

Exp. % Argentina Buenos Aires 118 66.992 9.23 0.00 0.74 0.82 0.061 0.99 5.49 28.07 3.87 Brazil São Paulo 398 187.102 5.73 0.00 0.73 0.58 0.078 5.70 7.65 38.49 4.94 Chile Santiago 258 181.916 4.08 0.00 1.05 1.29 0.046 0.71 6.43 23.24 4.58 China Shanghai 983 357.148 248.42 0.00 1.08 2.34 0.054 0.92 3.86 22.51 4.43 Colombia Bogota 82 269.461 7.77 0.00 0.99 0.85 0.056 1.77 5.36 18.42 2.62 Czech Republic Prague 182 65.732 0.36 0.00 0.52 0.60 0.063 29.97 4.76 21.06 5.67 Egypt Cairo & Alexandria 167 105.508 37.83 0.00 0.78 1.37 0.060 3.76 9.32 10.45 2.44 Greece Athens 451 30.102 26.22 0.00 0.88 0.92 0.074 2.61 3.14 36.53 0.38 Hong Kong Hong Kong 1537 80.935 23.95 0.00 0.60 0.99 0.069 0.10 4.57 21.01 2.69 Hungary Budapest 90 47.740 34.15 1.00 0.68 1.00 0.062 7.81 5.49 21.80 6.34 India NSE 1689 51.573 30.26 0.00 0.93 1.04 0.074 1.41 6.65 47.44 4.53 Indonesia Indonesia 538 34.915 20.80 0.00 0.85 0.98 0.084 0.06 5.54 37.04 3.95 Ireland Dublin 106 78.049 19.08 2.58 0.59 1.34 0.058 1.40 4.97 24.24 3.33 Israel Tel Avia 848 41.264 13.55 0.00 0.94 1.21 0.060 2.61 4.11 34.34 2.14 South Korea Korea 1382 71.581 158.86 0.00 0.79 0.72 0.069 8.03 4.42 49.35 3.31 Malaysia Bursa Malaysia 1044 34.274 25.53 0.00 1.01 0.83 0.058 0.34 4.55 27.71 2.66 Mexico BMV 300 301.020 13.71 1.00 0.82 0.87 0.059 1.40 6.54 27.98 4.41 Morocco Casablanca 68 217.876 6.20 1.17 0.88 2.01 0.037 73.18 7.17 12.14 2.54 Pakistan Karachi 363 32.399 13.88 0.00 0.90 1.02 0.065 0.60 8.36 36.98 4.08 Peru Lima 197 23.100 9.89 0.00 0.97 0.35 0.071 0.74 7.66 21.89 4.22 Philippines Philippine 345 38.113 8.57 0.00 0.80 0.84 0.076 0.05 4.55 24.74 3.11 Poland Warsaw 945 31.225 25.44 0.00 0.85 1.15 0.069 2.58 4.34 18.14 3.61 Portugal Lisbon 185 73.833 15.71 0.00 0.84 0.98 0.051 5.88 3.91 47.18 2.94 Russian Federation MICEX 256 374.226 4.31 0.00 0.74 1.04 0.077 0.79 5.87 30.65 5.48 Singapore Singapore 759 60.069 22.25 0.00 0.95 1.07 0.066 0.19 5.06 21.00 3.09 South Africa Johannesburg 962 64.622 15.04 1.00 0.77 1.30 0.070 0.69 7.65 16.92 4.81 Sri Lanka Colombo 309 11.393 12.07 0.00 1.26 1.31 0.068 0.43 6.86 26.26 3.94 Taiwan Taiwan 1015 146.143 149.03 0.00 0.93 1.32 0.057 0.62 4.51 15.78 2.95 Thailand Thailand 754 35.088 51.49 0.00 0.67 1.13 0.059 0.32 6.48 29.09 3.68 Turkey Istanbul 461 71.470 439.97 1.00 0.82 1.35 0.074 2.72 7.04 21.13 3.34 Venezuela Caracas 12 -- -- -- -- -- -- -- -- -- --

Table 1—Continued Panel B: Country-Level Characteristics

Country GDP per capita

Trade (% of GDP)

Market Cap (% of GDP)

GDP Growth %

Investment Growth % Legal Individualism Masculinity Turnover % Private Debt

(% of GDP)

Good Government

Index Argentina 4234 16.3 2.3 3.1 5.3 0 46 56 12.2 15.5 9.0 Brazil 2801 19.2 14.9 3.8 4.7 0 38 49 46.8 44.3 9.6 Chile 3059 56.3 9.4 5.5 9.0 0 23 28 11.1 71.3 14.0 China 346 38.9 34.3 9.3 11.1 0 20 66 147.4 109.1 9.4 Colombia 1404 33.2 1.4 4.1 11.1 0 13 64 9.5 33.4 10.0 Czech Republic 6996 117.9 9.2 2.5 3.0 0 58 57 47.0 53.1 13.6 Egypt 767 52.2 5.9 4.8 4.9 0 25 45 22.2 36.3 8.5 Greece 9845 49.9 14.7 2.7 4.3 0 35 57 39.1 52.4 11.9 Hong Kong 16721 251.7 149.3 6.1 6.4 1 25 57 49.4 149.2 13.2 Hungary 3477 85.8 13.5 2.9 2.9 0 80 88 73.8 44.3 13.0 India 354 17.4 37.6 5.5 7.3 1 48 56 84.6 28 9.7 Indonesia 660 50.4 10.5 6.4 8.5 0 14 46 42.3 30.4 9.5 Ireland 14114 82.5 20.0 4.4 4.4 1 70 68 40.5 106.8 13.0 Israel 12244 112.0 26.1 4.8 3.3 1 54 47 56.4 76.3 12.1 South Korea 7905 61.2 112.1 7.4 7.3 0 18 39 174.1 89.8 12.0 Malaysia 2853 152.5 41.1 6.7 8.8 1 26 50 32.5 111.7 11.4 Mexico 3591 36.8 8.6 4.1 6.9 0 30 69 31.1 20.4 11.6 Morocco 1068 56.0 3.0 4.6 4.1 0 46 53 9.9 45.3 11.7 Pakistan 406 33.3 14.0 4.8 4.3 1 14 50 94.6 24.4 7.0 Peru 1457 35.4 2.7 4.9 6.2 0 16 42 9.3 22 10.4 Philippines 778 61.5 10.1 4.5 5.9 0 32 64 22.5 30.7 11.0 Poland 4472 60.7 5.9 4.3 6.4 0 60 64 47.6 27.5 12.8 Portugal 9123 61.8 15.2 2.6 2.6 0 27 31 48.0 129.8 14.0 Russian Federation 3427 53.4 18.8 3.9 5.9 0 39 36 45.6 21.2 9.8 Singapore 15324 340.9 71.2 7.9 9.7 1 20 48 51.6 96.3 15.4 South Africa 3170 53.2 58.3 3.0 4.4 0 65 63 36.5 132.4 12.2 Sri Lanka 539 68.0 2.0 5.3 6.6 1 -- -- 14.5 28.7 10.0 Taiwan 9821 93.7 70.6 8.8 7.6 0 17 45 194.1 143.2 13.4 Thailand 1775 78.2 42.8 5.6 6.5 1 20 34 81.7 112.2 9.6 Turkey 2771 35.1 32.5 5.0 8.0 0 37 45 138.5 18 10.0 Venezuela 3367 50.4 0.4 2.6 3.0 0 12 73 10.0 17.9 7.0

Table 2 Time-Varying Market Segmentation across Emerging Markets This table reports the values of market segmentation measures, computed based on 38 ICB industries, for 30 emerging markets during the period between 1990 and 2013. Five-year sub-period measures are also computed to reveal the time variations of market segmentation. Panel A lists the time series mean values and Panel B lists the corresponding median values.

Panel A. Time Series Mean Values of Segmentation Measures

Country Total Period Sub-1 Sub-2 Sub-3 Sub-4 Sub-5

1990-2013 1990-1995 1996-2000 2001-2005 2006-2010 2011-2013

Argentina 0.0374 0.0229 0.0274 0.0593 0.0337 0.0440 Brazil 0.0350 - 0.0567 0.0511 0.0217 0.0184 Chili 0.0257 0.0411 0.0193 0.0205 0.0194 0.0246 China 0.0510 0.0510 0.0813 0.0495 0.0244 0.0473 Colombia 0.0326 0.0223 0.0475 0.0335 0.0290 0.0259 CzRep 0.0328 0.0359 0.0513 0.0258 0.0203 0.0325 Egypt 0.0475 - 0.0301 0.0675 0.0455 0.0424 Greece 0.0374 0.0516 0.0200 0.0254 0.0317 0.0681 HongKong 0.0215 0.0307 0.0209 0.0162 0.0165 0.0209 Hungary 0.0299 0.0404 0.0229 0.0326 0.0210 0.0361 India 0.0253 0.0195 0.0288 0.0379 0.0181 0.0217 Indonesia 0.0296 0.0193 0.0391 0.0387 0.0284 0.0193 Israel 0.0794 0.0249 0.1246 0.1235 0.0514 0.0315 Ireland 0.0267 0.0321 0.0182 0.0206 0.0390 0.0196 Korea 0.0317 0.0192 0.0446 0.0427 0.0287 0.0225 Malaysia 0.0216 0.0171 0.0267 0.0226 0.0225 0.0192 Mexico 0.0406 0.0770 0.0409 0.0261 0.0218 0.0227 Morocco 0.0213 0.0059 0.0192 0.0215 0.0269 0.0247 Pakistan 0.0449 0.0196 0.0710 0.0556 0.0355 0.0337 Peru 0.1369 0.0306 0.3680 0.1176 0.0631 0.0426 Philippines 0.0284 0.0393 0.0231 0.0255 0.0242 0.0275 Poland 0.0346 0.0741 0.0304 0.0246 0.0265 0.0379 Portrugal 0.0256 0.0300 0.0138 0.0226 0.0251 0.0425 Russia 0.3896 - 1.2913 0.0987 0.0428 0.0748 Singapore 0.0245 0.0263 0.0202 0.0213 0.0289 0.0262 S Africa 0.0232 0.0174 0.0268 0.0343 0.0190 0.0171 Taiwan 0.0225 0.0236 0.0178 0.0270 0.0241 0.0178 Thailand 0.0376 0.0318 0.0464 0.0485 0.0355 0.0199 Turkey 0.0388 0.0550 0.0401 0.0329 0.0315 0.0274 Venezuela 0.1803 0.0409 0.0193 0.0885 0.5821 0.1103

Panel B. Time Series Median Values of Segmentation Measures

Country Total Period Sub-1 Sub-2 Sub-3 Sub-4 Sub-5

1990-2013 1990-1995 1996-2000 2001-2005 2006-2010 2011-2013

Argentina 0.0312 0.0243 0.0257 0.0436 0.0310 0.0443 Brazil 0.0250 - 0.0304 0.0548 0.0219 0.0177 Chili 0.0202 0.0212 0.0165 0.0200 0.0200 0.0247 China 0.0393 0.0470 0.0801 0.0423 0.0228 0.0515 Colombia 0.0258 0.0197 0.0469 0.0250 0.0247 0.0258 CzRep 0.0261 0.0394 0.0384 0.0243 0.0189 0.0281 Egypt 0.0404 - 0.0222 0.0702 0.0466 0.0314 Greece 0.0311 0.0460 0.0171 0.0234 0.0292 0.0506 HongKong 0.0191 0.0263 0.0140 0.0159 0.0168 0.0210 Hungary 0.0265 0.0466 0.0196 0.0261 0.0221 0.0362 India 0.0206 0.0186 0.0241 0.0332 0.0146 0.0212 Indonesia 0.0249 0.0184 0.0275 0.0352 0.0263 0.0189 Israel 0.0444 0.0248 0.1264 0.0978 0.0506 0.0310 Ireland 0.0217 0.0289 0.0174 0.0172 0.0208 0.0149 Korea 0.0258 0.0195 0.0355 0.0408 0.0268 0.0233 Malaysia 0.0192 0.0159 0.0241 0.0221 0.0215 0.0195 Mexico 0.0309 0.0721 0.0383 0.0271 0.0236 0.0171 Morocco 0.0218 0.0057 0.0215 0.0224 0.0233 0.0230 Pakistan 0.0326 0.0170 0.0618 0.0437 0.0261 0.0353 Peru 0.0568 0.0302 0.3737 0.0589 0.0483 0.0472 Philippines 0.0249 0.0382 0.0213 0.0271 0.0215 0.0241 Poland 0.0268 0.0617 0.0275 0.0240 0.0247 0.0401 Portrugal 0.0224 0.0266 0.0132 0.0200 0.0256 0.0426 Russia 0.0588 - 0.0782 0.0626 0.0343 0.0768 Singapore 0.0199 0.0212 0.0181 0.0201 0.0206 0.0196 S Africa 0.0217 0.0158 0.0268 0.0347 0.0199 0.0147 Taiwan 0.0212 0.0248 0.0113 0.0257 0.0217 0.0175 Thailand 0.0313 0.0276 0.0468 0.0449 0.0278 0.0185 Turkey 0.0306 0.0427 0.0400 0.0293 0.0241 0.0263 Venezuela 0.0506 0.0368 0.0180 0.0815 0.1074 0.1147

Table 3 Market Segmentation by Industry Classifications This table reports the segmentation measures by industry. I compute the earnings yield difference for every industry in every sampled emerging market, which are then weighted sum across countries for each industry. The sample period used for this calculation is from 1990 to 2013. Both mean and median values over the sample period are reported here.

Industry Mean Median Aerospace & Defense 0.039 0.034 Automobiles & Parts 0.044 0.036 Banks 0.024 0.021 Beverages 0.035 0.022 Chemicals 0.034 0.019 Construction & Materials 0.025 0.018 Electricity 0.023 0.017 Electronic & Electrical Equipment 0.042 0.014 Equity Investment Instruments 0.058 0.023 Food & Drug Retailers 0.038 0.021 Food Producers 0.028 0.016 Forestry & Paper 0.031 0.017 General Industrials 0.026 0.024 General Retailers 0.019 0.017 Gas, Water & Multiutilities 0.029 0.023 Health Care Equipment & Services 0.036 0.023 Household Goods & Home Construction 0.032 0.013 Industrial Engineering 0.022 0.018 Industrial Metals & Mining 0.054 0.041 Industrial Transportation 0.073 0.025 Leisure Goods 0.082 0.051 Life Insurance 0.041 0.038 Media 0.030 0.019 Mining 0.060 0.025 Nonequity Investment Instruments 0.030 0.030 Nonlife Insurance 0.035 0.029 Oil Equipment & Services 0.046 0.038 Oil & Gas Producers 0.020 0.017 Personal Goods 0.033 0.022 Pharmaceuticals & Biotechnology 0.054 0.041 Software & Computer Services 0.015 0.013 Support Services 0.043 0.017 Technology Hardware & Equipment 0.030 0.022 Fixed Line Telecommunications 0.027 0.021 Mobile Telecommunications 0.030 0.030 Tobacco 0.025 0.021 Travel & Leisure 0.024 0.020

Table 4 Pair-wise Segmentation Measures by Region This table reports the pair-wise segmentation measures between countries within the same region. The results are computed based on the market value of column country (relative to the row country). Reported are the median values of the measures over the sample period from 1990 to 2013. Panel A. America

Argentina Brazil Chili Colombia Mexico Venezuela Argentina - 0.042 0.038 0.047 0.020 0.113 Brazil 0.041 - 0.037 0.029 0.022 0.110 Chili 0.032 0.034 0.025 0.012 0.053 Colombia 0.037 0.031 0.020 - 0.021 0.062 Mexico 0.038 0.030 0.023 0.027 - 0.051 Venezuela 0.065 0.079 0.063 0.057 0.049 - Panel B. Asia

China HongKong Indonesia Korea Malaysia Philippines Singapore Taiwan Thailand China - 0.028 0.055 0.045 0.039 0.027 0.023 0.016 0.037 HongKong 0.025 - 0.043 0.024 0.029 0.034 0.024 0.019 0.029 Indonesia 0.023 0.029 - 0.029 0.021 0.021 0.020 0.015 0.025 Korea 0.023 0.024 0.028 - 0.023 0.015 0.025 0.024 0.032 Malaysia 0.023 0.024 0.026 0.031 - 0.025 0.018 0.016 0.033 Philippines 0.029 0.032 0.043 0.028 0.027 - 0.026 0.023 0.029 Singapore 0.024 0.021 0.034 0.027 0.019 0.027 - 0.021 0.032 Taiwan 0.010 0.022 0.021 0.028 0.012 0.013 0.022 - 0.039 Thailand 0.024 0.031 0.036 0.035 0.033 0.029 0.032 0.033 - Panel C. Europe

CzRep Greece Hungary Ireland Poland Portugal CzRep - 0.031 0.030 0.011 0.020 0.020 Greece 0.031 - 0.027 0.034 0.026 0.026 Hungary 0.030 0.025 - 0.032 0.019 0.025 Ireland 0.030 0.027 0.035 - 0.033 0.025 Poland 0.029 0.032 0.024 0.032 - 0.030 Portugal 0.022 0.024 0.024 0.021 0.025 - Panel D. Mid-East, Africa and Other Areas

Egypt India Israel Morocco Pakistan Peru Russia S.Africa Turkey Egypt - 0.035 0.063 0.024 0.028 0.074 0.042 0.034 0.036 India 0.021 - 0.032 0.017 0.024 0.048 0.033 0.031 0.035 Israel 0.070 0.043 - 0.043 0.048 0.060 0.038 0.044 0.068 Morocco 0.035 0.031 0.077 - 0.035 0.040 0.032 0.026 0.034 Pakistan 0.031 0.029 0.055 0.014 - 0.048 0.053 0.022 0.033 Peru 0.039 0.059 0.041 0.037 0.039 - 0.039 0.050 0.046 Russia 0.024 0.059 0.076 0.046 0.058 0.024 - 0.048 0.063 S.Africa 0.019 0.033 0.038 0.018 0.020 0.044 0.028 - 0.027 Turkey 0.033 0.036 0.045 0.030 0.041 0.058 0.046 0.039 -

Table 5 Analysis of Time-Varying Valuation Deviation from the World across Emerging Markets This table reports the time series regression results for the sample emerging markets. The dependent variable is the monthly segmentation measure and the explanatory variables include time, world market return, lagged world market return, local market return, lagged local market return and bMAX(-1), which is used as a proxy for the degree of investors preference for extreme payoffs in the market. The sample period is from 1988 to 2013, with most markets running with sufficient data starting from 1990.

Argentina Brazil Chili China Colombia Ch.Rep Egypt Greece HKong Hungary India Indonesia Israel Ireland Korea log(time) 1.641 -7.756 0.496 -3.477 1.903 -4.775 -12.593 10.012 -1.172 0.651 -0.045 0.005 -4.474 -0.941 0.455 t-stat 5.166 -8.362 3.098 -4.875 3.705 -4.383 -5.343 3.797 -6.676 2.079 -0.279 0.024 -3.403 -3.043 2.244 p-val 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.039 0.781 0.981 0.001 0.003 0.026

Rw -0.006 0.070 -0.033 -0.045 -0.132 0.124 0.089 -0.118 0.032 -0.054 -0.032 -0.072 -0.219 0.000 -0.013 t-stat -0.122 1.541 -2.264 -0.964 -3.586 1.508 1.357 -2.039 1.642 -2.037 -1.344 -2.707 -1.340 -0.006 -0.536 p-val 0.903 0.125 0.025 0.336 0.001 0.136 0.178 0.043 0.102 0.043 0.180 0.007 0.182 0.995 0.592

Rw(-1) -0.041 0.054 -0.013 -0.010 -0.127 0.145 0.083 -0.139 0.036 -0.038 -0.049 -0.053 -0.217 0.058 0.018 t-stat -0.924 1.141 -0.979 -0.185 -2.965 1.792 1.202 -1.214 1.984 -1.371 -2.050 -2.081 -1.372 1.233 0.791 p-val 0.356 0.256 0.329 0.853 0.004 0.077 0.232 0.227 0.048 0.172 0.041 0.038 0.171 0.218 0.430

Rmrf 0.008 -0.019 0.006 -0.019 0.049 -0.049 -0.048 0.053 -0.042 0.008 -0.002 -0.005 0.089 -0.091 -0.018 t-stat 0.384 -0.904 0.463 -0.861 3.150 -1.307 -1.290 1.754 -3.603 0.715 -0.261 -0.477 0.747 -2.749 -1.660 p-val 0.702 0.367 0.644 0.390 0.002 0.195 0.200 0.082 0.000 0.475 0.795 0.634 0.456 0.006 0.098

Rmrf(-1) 0.019 -0.032 -0.009 -0.013 0.039 -0.024 -0.074 0.084 -0.046 0.008 -0.003 -0.009 0.059 -0.116 -0.024 t-stat 1.106 -1.442 -0.676 -0.605 1.778 -0.674 -1.804 0.993 -3.381 0.694 -0.322 -0.841 0.560 -3.083 -2.193 p-val 0.270 0.151 0.499 0.546 0.079 0.502 0.074 0.322 0.001 0.488 0.748 0.401 0.576 0.002 0.029

bmax(-1) -0.002 -0.002 -0.001 -0.004 0.001 0.003 -0.006 0.005 -0.002 -0.001 0.001 0.000 0.002 -0.003 -0.004 t-stat -1.062 -0.693 -0.969 -1.189 1.185 1.129 -2.352 1.433 -2.642 -0.892 0.729 -0.143 0.445 -2.385 -1.586 p-val 0.289 0.489 0.334 0.236 0.239 0.263 0.021 0.154 0.009 0.374 0.466 0.887 0.657 0.018 0.114 adj-R2 0.031 0.457 0.078 0.058 0.308 0.062 0.263 0.237 0.223 0.045 0.025 0.073 0.028 0.181 0.049 nobs 249 176 234 242 103 86 99 148 282 200 271 275 252 312 312

Malaysia Mexico Morocco Pakistan Peru Philippines Poland Portugal Russia Singapore S. Africa Taiwan Thailand Turkey log(time) -0.035 -5.245 -2.051 -0.576 -27.190 -1.516 0.506 6.410 15.459 0.069 -0.110 -0.396 -7.394 -2.744 t-stat -0.376 -8.579 -2.108 -0.563 -8.235 -5.584 1.320 19.867 16.135 0.343 -0.951 -3.665 -13.121 -6.978 p-val 0.707 0.000 0.040 0.574 0.000 0.000 0.188 0.000 0.000 0.731 0.342 0.000 0.000 0.000

Rw -0.014 -0.040 -0.012 -0.005 0.226 -0.049 0.035 -0.033 -0.039 -0.071 0.008 -0.038 0.021 -0.012 t-stat -1.141 -0.919 -0.788 -0.071 1.045 -2.753 1.222 -1.436 -0.580 -2.551 0.453 -2.839 0.544 -0.370 p-val 0.255 0.359 0.434 0.943 0.298 0.006 0.223 0.154 0.563 0.011 0.651 0.005 0.587 0.712

Rw(-1) -0.002 -0.044 0.003 -0.068 0.123 -0.034 0.029 -0.041 0.012 -0.077 -0.004 -0.037 -0.006 0.010 t-stat -0.131 -0.974 0.215 -0.683 0.563 -1.726 0.915 -1.839 0.177 -2.883 -0.192 -2.970 -0.238 0.323 p-val 0.896 0.331 0.831 0.495 0.574 0.085 0.361 0.069 0.860 0.004 0.848 0.003 0.813 0.747

Rmrf -0.020 0.011 -0.004 -0.016 -0.131 -0.005 -0.032 0.007 -0.044 -0.015 -0.014 0.004 -0.030 -0.024 t-stat -1.895 0.474 -0.308 -0.360 -1.155 -0.462 -2.114 0.436 -1.438 -1.273 -1.335 0.712 -1.079 -2.649 p-val 0.059 0.636 0.759 0.719 0.249 0.644 0.036 0.663 0.153 0.204 0.183 0.477 0.283 0.009

Rmrf(-1) -0.021 0.012 -0.006 -0.008 -0.194 -0.007 -0.032 0.009 -0.073 -0.016 -0.008 0.005 0.007 -0.022 t-stat -1.815 0.502 -0.376 -0.172 -1.578 -0.678 -1.757 0.547 -2.586 -1.389 -0.739 0.860 0.360 -2.408 p-val 0.070 0.616 0.708 0.864 0.116 0.499 0.080 0.586 0.011 0.166 0.460 0.390 0.720 0.017

bmax(-1) 0.000 0.001 -0.001 -0.004 0.000 0.002 -0.002 0.001 0.004 -0.002 -0.001 0.000 0.000 0.002 t-stat 1.177 0.224 -1.362 -0.891 -0.017 1.452 -0.918 1.173 2.440 -1.551 -0.568 0.698 -0.072 0.955 p-val 0.240 0.823 0.179 0.374 0.987 0.148 0.360 0.244 0.016 0.122 0.571 0.486 0.943 0.341 adj-R2 0.099 0.346 0.192 -0.020 0.333 0.206 0.060 0.070 0.638 0.634 0.217 -0.002 0.068 0.423 nobs 312 285 62 194 196 283 213 212 110 113 312 312 308 138

Table 6 Fixed Effects Analysis of Market Segmentation of Emerging Markets This table reports the fixed effects model results for the market segmentation measures with the country-level characteristics, including market capitalization, GDP per capita, GDP growth rate, turnover ratio of stock market, IBES coverage, contract viability, common law dummy, beta-MAX as proxy for investor propensity to speculation, culture variables (Trust and Individualism), capital expenditure, exchange rate stability, EWN financial globalization measure and firm Herfindahl as control. The basic sample period is from 1990 to 2013 to avoid inconsistent data availability across sample markets. Panel A reports the results for the period starting from year 1990 while Panel B reports those starting from year 2000. Panel A. 1990 - 2013 Panel B. 2000 - 2013 Market Characteristics (1) (2) (3) (4) Coef std-err p-value Coef std-err p-value Coef std-err p-value Coef std-err p-value Log (Market Cap) -3.031 2.395 0.207 -3.037 2.351 0.197 -3.891 1.434 0.007 -4.206 1.410 0.003 Log (GDP per capita) -3.983 6.618 0.548 -4.011 6.620 0.545 -1.625 3.224 0.615 -0.867 3.390 0.798 Turnover 0.099 0.052 0.057 0.099 0.051 0.055 0.066 0.026 0.013 0.063 0.028 0.024 Fin-Globalization 0.014 0.010 0.161 0.013 0.010 0.162 0.007 0.004 0.056 0.005 0.004 0.206 IBES coverage -0.041 0.025 0.100 -0.037 0.061 0.548 -0.028 0.010 0.006 -0.085 0.037 0.024 FGlobal*IBES -0.002 0.018 0.923 0.021 0.012 0.079 Corruption -0.006 0.004 0.098 -0.008 0.010 0.459 -0.005 0.003 0.096 -0.019 0.010 0.064 FGlobal*Corruption 0.001 0.004 0.864 0.005 0.003 0.083 Common Law -0.003 0.009 0.740 -0.003 0.009 0.746 -0.001 0.004 0.807 0.000 0.004 0.979 Prop to speculation 0.001 0.009 0.940 0.001 0.008 0.923 0.017 0.013 0.194 0.016 0.012 0.199 Trust -0.042 0.061 0.486 -0.042 0.063 0.504 0.018 0.018 0.333 0.012 0.019 0.505 Individualism 0.149 0.110 0.174 0.149 0.110 0.175 0.062 0.040 0.128 0.061 0.041 0.137 FX Stability 0.001 0.002 0.633 0.001 0.002 0.642 -0.002 0.001 0.186 -0.002 0.001 0.068 Patents 0.000 0.000 0.449 0.000 0.000 0.479 0.000 0.000 0.497 0.000 0.000 0.451 Capital Expenditure 0.052 0.565 0.927 0.066 0.529 0.901 -0.562 0.254 0.028 -0.478 0.282 0.091 GDP growth -0.002 0.002 0.347 -0.002 0.002 0.322 0.001 0.001 0.271 0.001 0.001 0.229 Firm Herfindahl -0.041 0.023 0.078 -0.040 0.025 0.105 -0.010 0.013 0.463 -0.003 0.014 0.819 Adjusted R-squared 0.157 0.152 0.2154 0.2238 F-statistic 2.859 2.694 3.724 3.658 Prob(F-statistic) 0.000 0.000 0.000 0.000 Nobs(country*year) 360 360 259 259