Social interaction, Internet access and stock market participation—An empirical study in China

Journal of Comparative Economics xxx (2015) xxx–xxx

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Social interaction, Internet access and stock market participation—An empirical study in China q Pinghan Liang, Shiqi Guo ⇑ Research Institute of Economics and Management, Southwestern University of Finance and Economics, China

a r t i c l e

i n f o

Article history: Received 17 February 2014 Revised 14 February 2015 Available online xxxx JEL classification: C31 D14 O16 Z13 Keywords: Social interaction Internet access Information Stock market participation

a b s t r a c t Liang, Pinghan, and Guo, Shiqi—Social interaction, Internet access and stock market participation—An empirical study in China Social interaction plays an important role in transmitting relevant information to potential investors. However, the informational role of social interaction might be affected by other information channels, which is to a large extent ignored in previous studies. Using a national representative household finance survey data covering more than 8000 Chinese households, we demonstrate that social interaction alone positively affects household stock market participation, but Internet access mitigates the influence of social interaction. In particular, among households with the access to Internet, sociable households in effect are associated with a 6 percentage-point decrease in the probability to participate in the stock market. This finding supports the substitution between Internet access and social interaction as information channels. Moreover, we also identify the social multiplier effect of social interaction: sociable households living in the communities with higher stock market participation rate are more likely to invest in stocks. Journal of Comparative Economics xxx (xx) (2015) xxx–xxx. Research Institute of Economics and Management, Southwestern University of Finance and Economics, China. Ó 2015 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved.

1. Introduction Information is key for decision-making. Individuals usually rely on their social networks, e.g., relatives, friendships, neighborhood, etc., to acquire information for making important decisions, such as searching for jobs (Granovetter, 1973), participating in criminal activities (Glaeser et al., 1996), voting (Katz and Lazarsfeld, 1955), and entering the stock market (Cohen et al., 2008). On the one hand, there is evidence that a stock market with wide participation improves the efficiency of resource allocation, facilitates financial development, and consequently causes economic growth (Levine, 1997, 2005). On

q The authors are grateful to the insightful comments from an anonymous referee as well as the editor. The valuable suggestions from Zaichao Du, Yi Huang, Han Li, Jingye Shi, Yan Yuan, Zhichao Yin, Ligang Zhong, as well as the attendees of 2013 China Household Finance Research Workshop (Chengdu), 2013 Doctoral Meeting on Quantitative Economics (Xiamen), Geneva-China Workshop on International Macroeconomics and Finance (2014), Ronald Coase Institute Workshop (2014). CHFS (2011) data are kindly provided by China Household Finance Survey and Research Center, Southwestern University of Finance. This paper is partially supported by the China Scholarship Council. All remaining errors are our own. ⇑ Corresponding author. E-mail addresses: [email protected] (P. Liang), [email protected] (S. Guo).

http://dx.doi.org/10.1016/j.jce.2015.02.003 0147-5967/Ó 2015 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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the other hand, Mankiw and Zeldes (1991), and Vissing-Jorgensen (2002) demonstrate that limited stock market participation is also closely linked to the equity premium puzzle (Mehra and Prescott, 1985): the observed long-run returns on stocks are much higher than returns on risk-free assets. Hence, the potential reliance of household financial decisions on information acquisition from the interaction with others might influence important aggregate outcomes. As many researchers observe (Shiller, 1989; Hong et al., 2004; Ivkovic and Weisbenner, 2007; Brown et al., 2008; Li, 2014), social interaction might serve as a channel to disseminate market-related information, consequently affecting household stock market participation. However, since the information transmitted by social interaction is usually biased (Shiller, 1984, 1990; Kaustia and Knupfer, 2012), making financial decisions based on information from social interaction might result in investors’ heterogeneous beliefs, consequently contributing to asset price bubbles (Scheinkman and Xiong, 2004; Hong and Stein, 2007) and large fluctuations of financial markets. Therefore, understanding how stock market participation decisions are affected by social interaction has important implications for macroeconomics and finance. Social interaction might exhibit two effects in affecting household financial decisions: informational effect and social multiplier effect. It is well-acknowledged that social interaction may serve as a mechanism for information exchange by means of word-of-mouth communication or ‘‘observational learning’’ (Ellison and Fudenberg, 1995). Specifically, word-of-mouth communication makes it easy and convenient for potential investors to learn about opening accounts, making transactions, etc., and to obtain relevant information by talking with experienced friends and neighbors. This is referred to as the informational effect of social interaction on stock market participations, which reflects an individual’s active use of information. On the other hand, if an individual’s behavior is affected by the behavior of neighbors, then a more sociable person would be influenced more by peers. Consequently, a sociable person in a high stock market participation community is more likely to participate in the stock market. In other words, social interaction increases the correlation between community-level stock market participation rates and individual participation. We call it the social multiplier effect of social interaction, which reflects that individuals are passively influenced by the average behavior (characteristics) of the community they live in. A novel point is that we explore the substitution between social interaction as an information channel and other information channels, e.g., Internet access. This also helps us to identify the informational effect of social interaction. We now live in an age of information explosion. Therefore, we need to allocate our limited attention and information-processing capacities to different information channels (Sims, 2003; Veldkamp, 2011). As an information channel, the Internet substantively changes our way of learning and acquiring information. If individual investors rely more on Internet to acquire information for participating the stock market, they have to reduce using the information from social interaction, e.g., word-of-mouth communication through channels other than the Internet. Therefore, social interaction and Internet access should substitute with each other in affecting stock market participation decisions. To examine the impacts of social interaction on stock market participation, we need the proxies for social interaction which do not cover the possible Internet social network. Therefore, we are interested in the face-to-face interaction and telephone interaction. China Household Finance Survey (CHFS) 2011, a national representative survey data designed for collecting detailed household financial information, enables us to fulfill this task. We use the amount spent on gifts to non-family members, as well as the expenditure on telephone communication, to measure the household sociability, respectively. Conditional on whether the level of sociability measured by gifts or communication expenditure is in the upper 50% or not, households are categorized into two types, ‘‘sociable’’ and ‘‘non-sociable’’, respectively. Moreover, we employ the community interview refusal rate to proxy for the community-level degree of social interaction. Our paper makes four contributions. First, our empirical results find support for the substitution between social interaction and Internet access as information channels. More specifically, among households without the Internet access, sociable households are 1.6% more likely to enter the stock market than those non-sociable ones. However, for households with access to the Internet, being sociable in effect is associated with a 6 percentage-point decrease in the probability to participate in the stock market. Therefore, the access of Internet mitigates the informational effect of social interaction. We think this result might shed light on the weakening informational effect of social interaction in the Internet Age in general. Second, after controlling for the informational effect of social interaction, we also demonstrate that for a household with the average Internet access level (22%), sociability does not significantly increase stock market participation. Hence, in contrast with Kaustia and Knupfer (2012), we support the view that negative returns might discourage market participation. Third, we provide evidence for the social multiplier effect of social interaction. We show that the effect of the communityaverage stock market participation rate on households’ stock market participation is influenced by the level of households’ sociability, i.e., sociable households are more likely to be associated with stock market participation if the neighbors participate in the stock market. Active social interaction (having above the median level sociability) raises the probability to participate in the stock market by 5.13% for the household living in high participation communities, but by only 0.67% for those living in low participation communities. Fourth, our paper also contributes to the understanding of stock market participation in China, the second largest economy in the world. By the end of 2011, Shanghai Stock Exchange, one of the two stock exchanges in China, became the world’s 6th largest stock market by market capitalization at US$2.3 trillion.1 With more than 140 million investor accounts, the stock market has become an important way for Chinese households to allocate financial assets and diversify risks. However, due to

1

http://en.wikipedia.org/wiki/Shanghai_Stock_Exchange.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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the lack of data, the previous studies on Chinese individual stock market participation only focus on the households’ behavior in certain coastal provinces (Li, 2006a), a few large cities (Li, 2006b; Zhou et al., 2011), or provincial-level aggregate behavior (Liu et al., 2013). To the best of our knowledge, our paper is the first to provide a comprehensive analysis of stock market participation decisions among Chinese households. The rest of this paper proceeds as follows: Section 2 reviews the existing literature. In Section 3, we describe our data set, explain the variables, and present our theoretical hypotheses. Section 4 presents the empirical results. Section 5 concludes.

2. Literature review It is well-acknowledged that household stock market participation is much lower than would be predicted by the Consumption-based Asset Pricing Model and other models. Many studies have been conducted to investigate the determinants underlying household participation in the stock market. For instance, Vissing-Jorgensen (2003) shows that since it is easier for wealthier households to overcome the fixed costs of participating, participation strongly increases in wealth. Bernheim and Garrett (2003) and Hong et al. (2004) establish that the holding of stocks and other financial assets is increasing with household educational level. Hong et al. (2004) suggest that the race of households is linked with stock market participation. Guiso et al. (2004, 2008) have studied the effect of trusting behavior on portfolio choice decisions, and shown that countries with high generalized trust level exhibit on average high stock market participation rates because trust in others matters for the subjective expected return. As Grossman and Stiglitz (1980) illustrate, information plays a key role in financial markets. Bogan (2008) suggests that the Internet could mitigate three causes for low stock market participation: transaction costs, information costs, and limited access. He demonstrates that Internet/computer usage substantially raises U.S. households’ stock market participation rates by reducing transaction costs. Using a German panel household survey, Glaser and Klos (2013) suggest that the positive effect of Internet usage on stock market participation depends on households’ financial literacy. In China, access to the Internet to a large extent reflects the availability of stock market information. Hence, the penetration of the Internet should play an important role in influencing stock market participation of Chinese households.2 A novel part of this paper is that we consider Internet access as one information channel, and jointly examine the information role of Internet access and that of social interaction. Especially, we provide a hypothesis about the substitution relationship between these two information channels and empirically examine it. Manski (1993, 2000) distinguishes three elements of social interaction. The first is endogenous interaction, which captures that ‘‘the propensity of an agent to behave in some way varies with the behavior of the group’’. Specifically, endogenous interaction can take effect through mechanisms of word-of-mouth communication, common topic pleasure,3 and social norm.4 The second is contextual interaction (exogenous interaction), which shows ‘‘the propensity of an agent to behave in some way varies with exogenous characteristics of the group members’’. For example, the losses (gains) in stock investment of neighbors are likely to demonstrate stock investment unattractiveness (attractiveness). The third one is the correlated effect, which attributes the similar behaviors of the group members to their similarities in individual characteristics and institutional environment. However, it is notorious that empirically identifying social interaction is subject to a serious endogeneity problem—since households are not assigned randomly into communities, their unobserved characteristics may result in the behavioral consistency. Among the mechanisms underlying endogenous interaction, word-of-mouth communication represents a clear information channel. As Ellison and Fudenberg (1995) note, economic agents have to ‘‘rely on whatever information they have obtained via causal word-of-mouth communication’’. It is convenient for individuals to learn about opening accounts, making deals obtaining related information by talking with friends and neighbors who are experienced in stock market investment.5 By analyzing a cross-sectional data of U.S. investors, Hong et al. (2004) suggest that endogenous interaction provides individuals with a channel to obtain information and allow observational learning. Furthermore, Brown et al. (2008) use an instrumental variable approach to establish the observed causality between an individual’s decision to invest in the stock market and the average participation rate of his community (community effect). They suggest that word-of-mouth communication is the source of this ‘‘community effect’’. Using a field experiment conducted with a financial brokerage, Bursztyn et al. (2014) demonstrate that both learning effect and social norms underlie the social influence in financial decisions. Pool et al. (forthcoming) use the residential address history of fund managers to identify the role of information transmission among neighbors and correlated effect. In this paper, we confirm that a higher level of social interaction is associated with a higher probability to participate in the stock market. Moreover, we take an alternative perspective in addressing the magnitude 2 According to ‘‘China Statistical Bulletin of National Economic and Social Development 2010’’, there are 420 million Internet users in China in 2010, with the Internet penetration rate 31.8%. 3 Individuals might enjoy pleasures from the conversations based on common interests (Becker, 1991). For example, an investor can get pleasure from a conversation about stock issues with his peers who also own stocks (Hong et al., 2004). 4 The ‘‘social norm’’ mechanism is also called the ‘‘keeping up with the Joneses’’ effect (Abel, 1990; Brown et al., 2008). This phenomenon provides insights for many studies—the conformity model by Bernheim (1994), the consumption-based model with external habit formation by Campbell and Cochrane (1999), and the theoretical work based on competition on local resources of Demarzo et al. (2004). 5 Liu et al. (2013) distinguish word-of-mouth communication from observational learning in analyzing Chinese stock market participation. However, identifying these two models is not the priority of our paper.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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of the influence of sociability. Our finding that Internet access decreases the marginal effect of the word-of-mouth communication supports the view that social interaction acts as an informational channel, and illustrates the limitation of social interactions in the Internet Age. Previous studies also suggest that social interaction might be a biased information transmission channel. Shiller (1984, 1990) suspects that the spreading of investment success stories within social networks might partly explain the patterns of stock market fluctuations. Kaustia and Knupfer (2012) suggest that positive returns that local peers experienced encourage stock market entry, whereas negative returns do not make entry less likely. Using China’s provincial-level aggregate data, Liu et al. (2013) show that the word-of-mouth effect is stronger in the bull market. However, by using various proxies of social interaction, Li (2006a, 2006b) suggests that the substantial negative returns incurred on Chinese investors might be responsible for the negative effect of contextual interaction on stock market participation in China. Our paper finds evidence that social interaction discourages stock market participation when Internet access is available. This supports the negative effect of contextual interaction. Finally, our work is also related to the literature on social trust and social capital. Since social interaction and trust are two essential components of social capital (Durlauf and Fafchamps, 2004), they may mix together to influence households’ investment behavior. It is found that stock market investors tend to exhibit more generalized trust (Guiso et al., 2003). In addition, households with more reliable information should be less affected by the cultural stereotypes (Guiso et al., 2008). Following Guiso et al. (2008) and El-Attar and Poschke (2011) show that trust level affects the household asset allocation between housing and risky financial assets in Spain. Georgarakos and Pasini (2011) separate trust from sociability in determining stock market participation. They show that these two factors both have distinct and sizeable positive effects on stock market participation in European countries, and sociability is likely to partly balance the discouragement effect on stockholding induced by low generalized trust level. It is not the priority of this paper to distinguish the role of trust from that of sociability. However, from the perspective that sociability might be closely related to social trust level, either generalized or specified, our finding of the substitution between sociability and Internet access might complement the empirical results that the usage of modern communication devices might contribute to the decline of social capital in developing countries (Olken, 2009).

3. Data and hypotheses 3.1. Data description We use the data from the China Household Finance Survey 2011 (CHFS 2011), which was jointly conducted by the People’s Bank of China and the Southwestern University of Finance and Economics.6 This survey aims to be a national representative by collecting micro-level information about household finance. It employs a stratified three-stage probability proportion-to-size random sample design, and the respondents are representative of all the residences in China. The respondents would receive a small gift (value about $15) for answering the questionnaire. The average time to interview a household is 2 h. This survey was carried out in July and August, 2011, which covers the detailed financial information of 8438 households located in 320 communities across 25 of China’s provinces in the end of 2010. We drop 370 observations (4% of the total sample), including 346 households with negative or zero gross annual income and 24 households who invest in the companies where their family members are working or once worked. All the variables presented in Table 1 are from the questionnaires and are relevant indicators of the survey.7 The CHFS dataset includes information on each family member—Family Size takes values from 1 to 18—so we have 29,324 individual observations in total. Following Hong et al. (2004), we take the highest age and education level within the family as the household ‘‘age’’ and ‘‘education’’. Two education dummies, Middle Education (55.18%) and College Education (31.43%), are constructed, with the former representing junior high, high school or secondary/vocational school educational level, and the latter including college/vocational, undergraduate, master and PhD degrees. The dummy variable Finance takes the value of one (1.95%) if at least one family member works in the financial industry. Income (Log) is the logarithm of household gross annual income in 2010, while Wealth (Log) is the logarithm of household wealth.8 The dependent variable Stock equals to one for those households entering stock markets (opening an account in stock markets), and zero for the others. In the sample, 8.68% of the households participate in the stock market.9 We use three proxy variables to measure the level of social interaction (sociability): the interview response rate of a community, a household’s expenditures on cash-gift, and a household’s communication expenditure. Here we will explain these variables in detail. 6

See Gan et al. (2014) for detailed description about this dataset. For the English version of questionnaires, see http://chfs.swufe.edu.cn/upload/files/CHFS-English.pdf. 8 The household wealth is calculated as the total household assets in production and management projects, land and real estate, vehicles, other non-financial assets and financial assets. 9 The stock market participation rate is 11% in a household survey data of Guangdong Province in 2004 (Li, 2006a); 32% in a survey of urban residents of 15 Chinese cities in 2007 (Li and Guo, 2009); and 23% in a household survey covering Guangdong, Beijing and Shanghai in 2008 (Zhou et al., 2011). Given that CHFS covers a national representative sample of households including both urban and rural residents, this participation rate also looks plausible. 7

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx Table 1 Descriptions of variables. Variable

N

Min

Max

Explanation

Stock

8062

Mean 0.0868

Median 0

St. dev. 0.282

0

1

Community stock

8068

Binary: Whether household owns stock investment accounts Average stock participation rate in the community

0.0869

0.0357

0.123

0

0.609

Social interaction and Internet access variables Response 8068 Cash-gift 6029

0.920 0.500

1 1

0.136 0.500

0.302 0

1 1

Communication

8068

0.503

1

0.500

0

1

Internet

8052

0.223

0

0.416

0

1

Cash-gift  Internet Communication  Internet

6021 8052

0.0977 0.118

0 0

0.297 0.323

0 0

1 1

Households characteristics Income (Log) 1st Quintile of income distribution

8068 8068

10.11 0.200

10.30 0

1.341 0.400

1.386 0

14.91 1

2nd Quintile of income distribution

8068

0.201

0

0.401

0

1

3rd Quintile of income distribution

8068

0.199

0

0.399

0

1

4th Quintile of income distribution

8068

0.200

0

0.400

0

1

5th Quintile of income distribution

8068

0.200

0

0.400

0

1

Wealth (Log) Education

8058 7999

11.96 4.370

12.22 4

1.839 1.814

1.609 1

16.57 9

Primary education

7999

0.134

0

0.341

0

1

Middle education

7999

0.552

1

0.497

0

1

College education

7999

0.314

0

0.464

0

1

Age Travel

8068 8017

56.23 0.443

56 0

17 0

112 1

House Family size Children

8067 8068 8068

0.912 3.493 0.552

1 3 0

0.284 1.551 0.755

0 1 0

1 18 9

Old

8068

0.382

0

0.678

0

3

Unemployed

8068

0.119

0

0.404

0

6

Finance

8068

0.0195

0

0.138

0

1

Job category 1 Job category 2

8068 8068

0.322 0.0876

0 0

0.467 0.283

0 0

1 1

Job category 3 Job category 4

8068 8068

0.264 0.000992

0 0

0.441 0.0315

0 0

1 1

Job category 5 Job category 6 Job category 7

8068 8068 8068

0.0357 0.00483 0.284

0 0 0

0.186 0.0694 0.451

0 0 0

1 1 1

Personal traits Risk attitude

7940

3.847

4

1.233

1

5

Risk loving

8068

0.132

0

0.339

0

1

15.15 0.497

Response rate of the community Binary: Whether the ratio of cash-gift expenditure to total family income is above the sample median Binary: Whether the ratio of communication expenditure to total family income is above the sample median Binary: Whether household not only owns computers but also use Internet as an information source Interaction term of Cash-gift and Internet Interaction term of Communication and Internet Gross annual household income taking log Binary: Whether household income lies within the range of lowest 20%. (Baseline group) Binary: Whether household income lies within the range of 20–40% Binary: Whether household income lies within the range of 40–60% Binary: Whether household income lies within the range of 60–80% Binary: Whether household income lies within the range of highest 20% Household wealth taking log Categorical: Highest education level within the household. (1 = never attended school. . .9 = PhD) Binary: Whether the highest education level within a household is Never Attended School or Primary School. (Baseline group) Binary: Whether the highest education level within a household is junior high, high school or secondary/vocational school Binary: Whether the highest education level within a household is college/vocational, undergraduate degree, masters degree or PhD Highest age within a household Binary: Whether household have expenditure on travel last year Binary: Whether household owns houses Number of family numbers Number of children within the household (younger than 16) Number of old people within the household (older than 65) Number of unemployed people within the household Binary: Whether its family members work in the financial industry Binary: Whether is employed in a work unit Binary: Whether is in individual or privately owned business, entrepreneur Binary: Whether farming at home Binary: Whether was once retired, and is now reengaged by original work unit as needed Binary: Whether is freelance Binary: Whether belongs to other job categories Binary: Whether the job category is not recorded Categorical: Which investment to choose. (1 = high risk, high return projects;. . .5 = not willing to take on any risks) Binary: Whether to choose High risk, high return or Slightly higher risk, lightly higher return (continued on next page)

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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Table 1 (continued) Variable

N

Mean

Median

St. dev.

Min

Max

Risk neutral

7940

0.260

0

0.439

0

1

Risk averse

7940

0.606

1

0.489

0

1

Seatbelt

7960

1.840

2

0.849

1

3

Traffic rule

8004

1.282

1

0.653

1

4

Expectation

7969

2.084

2

0.825

1

5

Donation

7781

0.499

0

0.500

0

1

Regional characteristics Urban Rural Western

8068 8068 8068

0.611 0.389 0.229

1 0 0

0.488 0.488 0.420

0 0 0

1 1 1

Central Eastern Public security

8068 8068 8056

0.303 0.468 2.503

0 0 2

0.460 0.499 0.887

0 0 1

1 1 5

Community Community Community Community Community Community

8068 8068 8068 8068 8068 8068

10.65 12.71 0.0350 4.371 56.23 0.222

10.60 12.62 0.0238 4.292 56.95 0.167

0.699 0.940 0.0394 1.006 5.415 0.214

8.768 10.45 0 2.389 41.95 0

12.70 15.17 0.205 7.638 71.11 0.854

Community stock (Exclusive)

8062

0.0868

0.0357

0.123

0

0.622

Community Stock Category

8068

0.193

0

0.824

1

1

320

0.155

0.100

0.168

0

0.838

6029

0.143

0

0.578

1

1

8068

0.116

0

0.585

1

1

income (Log) wealth (Log) unemployment education age internet

Response  community Internet Cash-gift  Community Stock Category Communication  Community Stock Category

Explanation projects. (Baseline group) Binary: Whether to choose Average risk, average return projects Binary: Whether to choose Slightly lower risk, slightly lower return projects or Not willing to take on any risks Categorical: Whether to normally wear seatbelts while driving. (1 = Yes; 2 = It depends; 3 = No) Categorical: Whether to follow traffic rules and wait for the stop light when crossing the road. (1 = always comply. . .4 = rarely obey) Categorical: In what way to expect that China’s economy is more likely to change in the next three to five years. (1 = Much better. . .5 = Much worse) Binary: Whether ratio of Wenchuan earthquake donation to total income is above the sample median Binary: Whether in urban areas. (Baseline group) Binary: Whether in rural areas Binary: Whether in eastern region. (Baseline group) Binary: Whether in central region Binary: Whether in western region Categorical: Comments on local public security. (1 = extremely good. . .5 = very poor) Average income of the community taking log Average wealth of the community taking log Average unemployment rate of the community Average education level of the community Average age of the community Proportions of the households not only owning computers but also using Internet as an information source within the community Average stock participation rate of other households within the community Categorical: Takes the value of 1 if no households owns stocks in the community he lives (45.5%). For the remaining communities whose stock market participation rates are positive, we divide them into two groups by their median and assign them the values of 0 (28.4%) and 1 (26.2%) respectively Interaction term of Response and Community Internet Interaction term of Cash-gift and Community Stock Category Interaction term of Communication and Community Stock Category

We think the interview response rate of a community might be a plausible proxy of the community-level extent of social interaction. During the survey stage, most interviewers are introduced to the respondents by the local social workers who have lived in the neighborhood for a long period. And only if an interviewer is refused at least three times by a household would that household be replaced by another in the sample pool. Hence, it is reasonable to say that the non-cooperative household in the survey has little interaction with the neighbors in the local community;10 a high refusal rate of a community thus reflects a low level of social interaction (or trust) within that community. The overall refusal rate of CHFS is 11.6%, with 16.5% in urban areas and 3.2% in rural areas, which is quite low in comparison with other major surveys.11 The variable Response, which represents the interview response rate of the community, varies 10 The payments are constant in all areas, a potential challenge is that the wealthy households might refuse to respond if they feel the compensation is not worthy of the effort. In Table 2 we could observe that the community level response rate is negatively correlated with wealth and income. However, the highest urban household wealth in the data is 1 billion RMB ($160,000,000), and the highest rural household wealth amounts to 100,000,000 RMB ($16,000,000), thus it is unlikely that the value of compensation is the main driving force underlying refusal behavior. 11 The overall refusal rate of China Health and Retirement Longitudinal Survey (CHARLS) in 2008 is 15.2–20.7% in the urban sample and 10.1% in the rural sample, respectively. The Survey of Consumer Finance (SCF, USA) in 2007 has the refusal rates of 32.2% and 67.3% respectively in the AP sample and the listed sample. The refusal rates of Consumer Expenditure Survey (CES, USA) in 2005 are 25.5% in the interview sample and 29% in the diary sample. And the Survey of Household Income and Wealth (the SHIW, Italy) in 2008 has an overall refusal rate of 43.9%.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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from 0.3 to 1. In effect, 52% of surveyed households live in the communities with no interview refusals. It has been debated that some personal traits may be associated with sociability as well as the propensity to participate in the stock market (Hong et al., 2004). In this sense, the use of response rate as a proxy for social interaction avoids such potential endogenous problems because it measures the overall social interaction level of a community which is exogenous for households. The expenditure on cash-gift reflects a household’s pattern of favor exchange in Chinese society and it has been employed by many researchers to measure the size of household’s social network in China (Yang et al., 2011; Ma and Yang, 2011). In the survey, the respondents answered the value of cash (or cash equivalents) given to non-family members for weddings and funerals (including birthday gifts) in 2010. Since the expenditure on cash-gift is largely affected by household income and local culture, we use the ratio of cash-gift expenditure to total family income. Among the 6029 households with records of cash-gift expenditure, dummy Cash-gift equals to one if the ratio of cash-gift expenditure to total family income is above the sample median (3%). The expenditure on communication is recorded as the expenditure a household spent on telephone and Internet fees last month. The main focus of this paper about social interactions is on its information role, which might be captured by the expenditure on communication. It equals to one if the ratio of communication expenditure to total family income is above the sample median (0.3%). Consequently, among those defined as high communication expenditure households, 2462 households (49.98% of the urban sample) live in the urban areas and 1598 households (50.86% of the rural sample) stay in rural areas. Since the expenditure on Internet is included, this measure might be positively correlated with the usage of Internet. However, as Table 2 shows, the correlation coefficient between this variable and our measure of Internet usage is only 0.03, but the correlation coefficient between this and Cash-gift is 0.23. Hence, we think this variable is a plausible measure of sociability.12 We consider both computer ownership and Internet usage to measure Internet access. In the survey, the respondents are asked separately whether they own computers (Question C8001), and whether they use Internet as the main source of information (Question A4001).13 The dummy variable Internet equals to one if a household not only owns computers but also uses the Internet as the main source of information, and zero for other cases. 22.3% of households actively use Internet to acquire information. To overcome the potential endogeneity problems arising from omitted variables, we make several attempts. Firstly, since individual’s characteristics can be related with both active social interaction and stock market participation, we control for respondent’s personal attitudes and traits. We take two dummies with respect to risk attitude—Risk Neutral and Risk Averse— with the benchmark group of risk lovers. As Hong et al. (2004) suggest, sociable people may be bolder and possibly more willing to invest in stocks without full knowledge about the stock market. To control for prudence and rashness, we use Traffic Rule and Seatbelt to measure whether the individual follows traffic rules, and whether he normally wears seatbelts while driving. Optimism is also associated with social interaction and may influence stock market participations as well (Hong et al., 2004). Based on CHFS questionnaires, we use Expectation to be a proxy for optimism. In addition, we also control for Donation to Wenchuan Earthquake, which reflects an individual’s other regarding preferences. Secondly, a household with little extra work time may not have sufficient time to invest in stocks, and could only acquire information by interacting with neighbors and friends. Therefore, we include the number of children, old and unemployed members, whether they spent money on travel last year and whether they own houses (Hong et al., 2004; Zhou et al., 2011) to measure a household’s free time. Finally, the level of social interactions might have regional characteristics (Durlauf and Fafchamps, 2004). These regional characteristics might be representative of local culture, and can also affect economic outcomes such as stock market participation (Guiso et al., 2006). Therefore, we include dummy variables Central and Eastern to represent the central and eastern regions in China, dummy variable Rural and other relevant variables such as household’s comments on local public security, average community income level, and local unemployment rate.14 All the variables are listed and described in details in Table 1.

3.2. Theoretical hypotheses Previous studies in the U.S. and China have established the positive effect of social interactions on stock market participation (Hong et al., 2004; Li, 2006a, 2006b). However, the mechanisms behind it are still ambiguous. First, the lack of data and indicators make it difficult to identify contextual interaction, as well as the different mechanisms of endogenous 12 As an anonymous referee points out, mobile could be used to connect to the Internet to make online surfing, which might also be related to Internet access. According to the 26th Statistical Report about China’s Internet Development issued by CNNIC (China Internet Network Information Center, http://www.cnnic. net.cn/) on 2010, there were 277 million mobile internet users in the mid of 2010, more than one third of them were students, the majority of them were under 30, and the main function of mobile internet was instant communication (61.5%), which is a clear kind of social interactions, while the commercial use of mobile internet was rare (6.1% for online payment through mobile). On the other hand, 77% users only used the online surfing functions of mobile in the leisure time. Therefore, in 2010, the year the survey was conducted, mobile was not the main access to Internet for the heads of households, and the role of mobile internet as a financial information channel for individual investors was also limited. 13 A4001 explicitly asks the main sources of information acquisition, the options include newspaper, mobile, TV, radio, Internet, relatives/friends/colleagues, etc. 14 We did not add dummies for province in order to avoid multicollinearity.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

8

Stock Stock Response Cash-gift Communication Internet Income (Log) Wealth (Log) Education Age Risk Attitude Seatbelt Traffic Rule Donation Expectation *

1 0.1954* 0.1064* 0.0504* 0.3378* 0.2538* 0.2813* 0.2829* 0.0870* 0.1764* 0.0671* 0.00440 0.1357* 0.1441*

Response

Cash-gift

Communication

Internet

Income (Log)

Wealth (Log)

Education

Age

Risk attitude

Seatbelt

Traffic rule

Donation

Expectation

1 0.0643*

1

1 0.0995* 0.00360 0.2322* 0.1889* 0.1828* 0.2174* 0.0853* 0.0865* 0.0957* 0.00500 0.1089* 0.1454*

1 0.2271* 0.1314* 0.3533* 0.1365* 0.1489* 0.0457* 0.0367* 0.0494* 0.00330 0.0700* 0.0606*

1 0.0300* 0.3467* 0.0451* 0.0224* 0.1030* 0.0529* 0.0261* 0.00390 0.2027* 0.000300

Indicates a 0.05 significance level of correlation coefficient.

1 0.3253* 0.3185* 0.4329* 0.3010* 0.2932* 0.1056* 0.0271* 0.2111* 0.1823*

1 0.4594* 0.4338* 0.1485* 0.1755* 0.1009* 0.0176 0.0407* 0.1068*

1 0.3519* 0.0894* 0.1628* 0.1164* 0.0374* 0.1613* 0.1131*

1 0.2127* 0.2003* 0.1019* 0.0221* 0.2672* 0.1842*

1 0.2653* 0.0636* 0.0315* 0.0948* 0.0998*

1 0.0952* 0.0217 0.1017* 0.0444*

1 0.1262* 0.1064* 0.0223*

1 0.0688* 0.0397*

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

Table 2 Correlations of main variables.

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

9

interactions. Second, social interaction may interact with other factors. For instance, Hong et al. (2004) show the peer effects to be stronger for the households who are white, educated and wealthy. On the one hand, individuals might actively use their social networks as an instrument to acquire information, hence social interactions serve as an information channel. This is the informational effect of social interactions. Informational effects imply that individuals’ sociability positively contributes to stock market participation. On the other hand, individuals might be influenced by the behavior/characteristics of peers in their social networks, hence social interactions may also serve as a channel through which peers influence an individual’s behavior. This is the social multiplier effect of social interactions. This means that sociability expands the influence of peers on individual behavior, so a sociable individual is more likely to be influenced by the neighborhood participation decisions. We first address the informational effect of social interactions. Recent theories on individual information choice (Sims, 2003; Veldkamp, 2011) suggest that households are subject to information-processing capacity constraint and have to allocate attention among different information sources. Therefore, as an information channel, the role of social interactions should be negatively affected by the usage of other information channels. In effect, the possible substitution relationship between other information channels and social interactions could be considered as evidence in favor of the informational effect of social interactions. Therefore, our first hypothesis highlights the substitution effect between sociability and Internet access. Hypothesis 1. As two channels to transmit stock market information, both Internet access and social interaction can increase stock market participation, but they substitute for each other. Informational effects of social interactions capture the role of word-of-mouth communication mechanisms within endogenous interactions. The effect of contextual interactions on Chinese stock market participation, however, is ambiguous. On the one hand, both the cases of gains and losses are likely to produce demonstration effects, which will promote and discourage stock market investments, respectively. On the other hand, the absence of the investment performance of an individual’s neighbors in data causes difficulty in measuring the effect of contextual interactions. In effect, traditionally it is difficult to identify these different mechanisms underlying social interactions. However, if we could control for the effect of word-of-mouth communication, we could reasonably believe that what we observe is the aggregate effect of the remaining mechanisms. As Li (2006a, 2006b) suggests, contextual interactions negatively affect stock market participation in China since most individual investors lost money in the stock market, and only the negative experience in the stock market is demonstrated to the neighbors. According to CHFS 2011, only 10% of households who own a stock market account gain from investment. In the year information was collected (2010), the stock market index in Shanghai Exchange decreased 470 points (14%). And the index in the end date of 2010 was 2808.08 points, significantly lower than the historical height of 6124 points on October 16, 2007. Moreover, most individuals have loss aversion (Kahneman and Tversky, 1979). Therefore, the negative effect of contextual interactions is likely to dominate any other remaining positive effects of social interactions, including the common topic pleasure, social norm and the demonstration effect created by the cases of gains. Hence, we set up our second hypothesis regarding the effects of other mechanisms. Hypothesis 2. The above ‘‘substitution relationship’’ causes social interactions to discourage stock market participation when Internet access is available. Our third hypothesis focuses on the social multiplier effect. It is shown that an individual is more likely to participate in the stock market provided that a higher fraction of individuals in the neighborhood are stock market investors (Brown et al., 2008). Hong et al. (2004) and Brown et al. (2008) both find that if there are more social interactions, there is a stronger relationship between community-level variables and individual participation decisions. Hence, frequent social interactions exacerbate the influence of neighbors on individual behavior, which could be referred to as ‘‘social multiplier effect’’ in terms of Hong et al. (2004). In this paper, we examine the magnitude of social multiplier effect on the individual level. According to Manski (1993), this effect could also be driven by common topic pleasure and social norm, in additional to word-of-mouth communication.15 Hence we state our Hypothesis 3 as follows: Hypothesis 3. There exists the ‘‘social multiplier effect’’—a sociable household is more likely to invest in stocks if he lives in a community with higher participation rates. Hypothesis 3 predicts that the marginal effect of social interactions on stock market participation is greater in a high-participation community. The social multiplier effect implies that individuals will also be influenced by the peers through social interactions. Hence, social interactions could also serve as a channel of receiving influences.16 15

Brown et al. (2008) actually take a broader definition of ‘‘word-of-mouth interactions’’, which is in effect equivalent to endogenous interactions here. As an anonymous referee points out, there is the possibility that Internet access also serves as a channel through which other people’s behavior affect individual investor. However, in our data we could not categorize the usage of Internet between acquiring information and receiving influence from the peers. 16

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

4. Empirical results 4.1. Descriptive statistics In Table 2, we present the correlations of the main variables. First of all, the three sociability variables are correlated with each other. The correlation between Cash-gift and Communication is 0.23, while their correlations with Response are weak— the coefficients are 0.1 and 0.004 respectively. It seems all of them capture different aspects of social interactions. It is noteworthy that Response is measured on the community level while Cash-gift and Communication are measured on household level, so it can be misleading to simply look at their correlations. We also observe weak correlations between the social variables and individual traits variables. Second, Stock is highly correlated with Internet, with the correlation coefficient 0.34. Stock is negatively correlated with sociability variables, with the coefficients 0.2, 0.11 and 0.05. Also, Stock is positively correlated with Income, Wealth and Education, and negatively correlated with Age and Risk Attitude. Fig. 1 provides a simple tabulation of average stock market participation across different groups, which allows us a rough look at the overall stylized facts about participation in China. From Fig. 1-1 to -6, we divide the sample into high and low groups regarding Income, Education, Internet access, Cash-gift, Communication and Response, with the blue bar representing the ‘‘high’’ group and red bar the ‘‘low’’ group. For each indicator, we make further comparisons of urban–rural areas and eastern–central–western regions to capture the relationship of stock market participation and regional imbalanced development in China. There are considerable regional differences in stock participation rates. The participation rates fall from the coastal areas (the eastern) to inland (the central and western). This still holds when we look at urban and rural subsamples separately. For urban–rural comparisons, urban areas have higher participation than rural areas. As shown in Fig. 1-2, the high education households and high income households are much more likely to participate in the stock market. Stock market participation also increases in access to the Internet, both for the region and urban–rural division. Take, for example, the eastern households. The participation rate is 5.9% for those without Internet access, and 30% for those with Internet access. Again this confirms the significant correlation coefficient between Internet access and stock market participation in Table 2. However, in line with the negative correlations between sociability variables and stock market participation reported in Table 2, stock market participation is higher in the low social interaction group in Fig. 1. This is in sharp contrast with the views that social interactions are associated with high stock market participation (Hong et al., 2004; Brown et al., 2008). Previous studies in China (Li, 2006a, 2006b) suggest that the widespread losses in the stock market investment in China discourage stock market investment due to the contextual interaction mechanism. Here we propose another explanation that the high correlation between Internet access and stock market participation ‘‘crowds out’’ the positive effect of social interaction because, as information channels, Internet and sociability substitute with each other—as the Hypothesis 1 indicates—in increasing stock market participation.

Rural

East Middle West

East Middle West

East Middle West

Total

Urban

Rural Bottom 50% cashgift

.4 .3 .2 .1 0

Stock Market Participation Rate

.25 .2 .15 .1 .05 0

East Middle West

Urban

0

.05

.1 East Middle West

East Middle West

East Middle West

Total

Urban

Rural

Top 50% communication

Bottom 50% communication

East Middle West

East Middle West

Total

Urban

Rural without Internet access

1-6 Variation of Stock Market Participation

.2

.25

across respond rates

.15

.15

.2

across communication expenditures

East Middle West

with Internet access

Bottom 50% education

1-5 Variation of Stock Market Participation Stock Market Participation Rate

.05

.1

.15

.2

across cashgift expenditures

0

Stock Market Participation Rate

East Middle West

Total Top 50% education

1-4 Variation of Stock Market Participation

Top 50% cashgift

East Middle West

.1

Rural Bottom 50% income

.05

East Middle West

Urban Top 50% income

across Internet access

0

East Middle West

Total

across education

Stock Market Participation Rate

East Middle West

Stock Market Participation Rate

.25 .2 .15 .05 .1 0

Stock Market Participation Rate

across income

1-3 Variation of Stock Market Participation

1-2 Variation of Stock Market Participation

1-1 Variation of Stock Market Participation

East Middle West

East Middle West

East Middle West

Total

Urban

Rural

Top 50% respond rates

Bottom 50% respond rates

Fig. 1. Stock market participation across different groups. Notes: High Internet access group refers to the households owning computers and use the Internet to collect information; low Internet access group refers to other households. The numbers of observations of these two groups in the whole sample, rural sub-sample and urban sub-sample are 1792 and 6260; 175 and 2959; 1617 and 3301 respectively.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

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4.2. Informational effect of social interactions To examine the impacts of social interactions and Internet access on households’ stock market participation, as well as the substitution relation between these two information channels, we employ the following specification:

Stocki ¼ a þ b1 Sociabilityi þ b2 Sociabilityi  Interneti þ b3 Internet i þ cIndiv iduali þ dHouseholdi þ uCommunityi þ e;

ð1Þ

The marginal effect of sociability on stock market participation could be assessed by estimating b1 + b2 ⁄ Interneti. Similarly, the marginal effect of Internet access is represented by b3 + b2 ⁄ sociabilityi. Hypothesis 1 predicts that the sign of b1 and b3 are positive because Sociability and Internet both could promote stock market participation. However, substitutions between these two channels imply that the sign of b2 would be negative. Individuali refers to the personal traits of the survey responder within household i,17 e.g., risk attitude, prudence and rashness, etc. Householdi represents household i’s characteristics, including income, wealth, educational level, family size and family members. Communityi measures the characteristics of the community household i living, e.g., the average community indicators, rural–urban and regional variables. We control these personal traits and household and community characteristics in order to alleviate the omitted variable problem. The baseline results are presented in Table 3. The regressions from column (1) to (4) are based on household-level observations, where we use Cash-gift (Communication) to be the proxies of sociability in Column (1) and (2) (Column (3) and (4)). Since the dependent variable is binary, we run both OLS and Probit regressions, with the marginal effects reported in the latter case.18 All the standard errors are adjusted as the cluster-robust standard errors (Rogers, 1994) clustering on the community level because the community characteristic variables only vary with communities. Due to the missing values of variables, the numbers of observations are somewhat different across columns. In columns (2) and (4), we replace Income (Log) with four dummies corresponding to the second, third, forth and fifth quintiles of the income distribution, and take further controls for individual traits, household burdens and job categories. Finally, the regression in column (5) is based on 320 community observations, with the dependent variable the community stock market participation. And Social interaction and Internet access are measured by the Response and Community Internet. Other community characteristics are also controlled. We focus on the informational effect first. The coefficients of Internet are large and statistically significant under 1% level in all specifications. For those non-sociable households whose sociability level is in the bottom 50% (e.g., sociabilityi = 0), having the access to Internet is associated with a 7-percentage-points-higher probability of stock market participation in the Probit regression, and a 17-percentage-points-higher probability in the OLS regression. For the households without Internet access, e.g., Interneti = 0, being sociable is only associated with a 2% point increase in the likelihood to participate in the stock market. The coefficients of Sociability  Internet are significantly negative at 1% level in all specifications, indicating a remarkable substitution relationship between sociability and Internet access. Take the OLS result in column (2) as an example: among those non-sociable households, those with Internet access have a 13.8% higher participation rate than those without; however, for the sociable households, Internet access only leads to a 6.1% increase in probability (0.1375  0.0764 ⁄ 1 = 0.0611).19 For the households without Internet access, the difference in stock market participation rate between sociable and non-sociable households is 1.6%; but for households with the access to the Internet, being sociable in effect is associated with a 6-percentage-points (0.016  0.076 ⁄ 1 = 0.06) decrease in the probability to participate in the stock market! This supports our Hypothesis 1. Although the two information channels substitute for each other, because the marginal effect of Internet access is always positive while that of sociability could be negative, we could say that access to the Internet dominates sociability in increasing stock market participation. As to the aggregate effect of social interactions, for a household with the average Internet access level (22%), sociability does not substantially increase stock market participation effectively.20 As suggested by Li (2006a, 2006b), the two most influential factors of social interaction on stock market participation are word-of-mouth communication and contextual interaction. And contextual interaction is the only possible element that may absorb the positive effect of word-of-mouth communication by spreading the loss cases of stock market investment. Hence, we support Hypothesis 2 about the negative effect of contextual interactions on Chinese households’ stock market participation decisions. However, when compared with Internet access, social interactions have only a limited role in increasing stock market participation. Given that only 22 percent of Chinese households have access to the Internet, there is still room for social interactions to increase stock market participation. Similar results are demonstrated in column (5) that uses the community-level observations. We replace Sociabilityi in Eq. (1) with the community response rate, and Interneti with the Community Internet. For communities with the sample average response rate (92%), a ten-percentage-point increase in Community Internet is associated with a 2.8 percentage point increase (10% ⁄ (0.77  0.53 ⁄ 92%) = 2.8%) in the community stock market participation rate; but for communities with no interview refusals (191 communities in this case), a ten-percentage-point increase in the community Internet access level is only associated with a 2.4-percentage-point increase (10% ⁄ (0.77  0.53 ⁄ 100%) = 2.4%) in community stock market participation 17

It is worth noting that the responder is not necessarily the person who makes the decision on stock market participation. We also run the Logit regression, the results of which are similar and are presented in Appendix Table 3. 19 Considering the magnitude of interaction effect in nonlinear models is not equal to the marginal effect of the interaction term (Ai and Norton, 2003), we only interpret the interaction terms of OLS regressions numerically. Though differences exist in the coefficients between the results of OLS and Probit regressions, their signs and statistical significance are the same for most cases. 20 The aggregate impacts of social interaction on stock market participation (b1 + b2 ⁄ Interneti) from column (1) to (4) are 0.25%, 1.18%, 0.04%, 0.92%, 0.83%, 1.62%, 0.77% and 1.62%, respectively. 18

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

12

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

Table 3 Substitution relationship between social interaction and internet access. Cash-gift

Communication

(1)

(2)

OLS

Probit ***

OLS **

Response

(3) Probit ***

(4)

OLS *

Probit ***

(5)

OLS ***

Probit *

OLS **

Sociability

0.0228 (0.0062)

0.0185 (0.0088)

0.0164 (0.0062)

0.0155 (0.0092)

0.0174 (0.0056)

0.0232 (0.0084)

0.0119 (0.0062)

0.0224 (0.0093)

0.1316*** (0.0412)

Sociability  Internet

0.0923*** (0.0211)

0.0306** (0.0132)

0.0764*** (0.0215)

0.0287** (0.0139)

0.1168*** (0.0193)

0.0318*** (0.0102)

0.0890*** (0.0196)

0.0284*** (0.0108)

0.5355*** (0.1265)

Internet

0.1615*** (0.0176)

0.0770*** (0.0103)

0.1375*** (0.0175)

0.0670*** (0.0105)

0.1921*** (0.0190)

0.0733*** (0.0093)

0.1619*** (0.0193)

0.0642*** (0.0096)

0.7772*** (0.1165)

Middle education

0.0063 (0.0068)

0.0411* (0.0228)

0.0039 (0.0067)

0.0261 (0.0218)

0.0080 (0.0056)

0.0447** (0.0193)

0.0023 (0.0049)

0.0307 (0.0188)

College education

0.0551*** (0.0106)

0.0813*** (0.0223)

0.0569*** (0.0114)

0.0561*** (0.0213)

0.0464*** (0.0087)

0.0768*** (0.0187)

0.0515*** (0.0096)

0.0562*** (0.0183)

Risk neutral

0.0761*** (0.0138)

0.0472*** (0.0090)

0.0712*** (0.0138)

0.0447*** (0.0091)

0.0646*** (0.0121)

0.0373*** (0.0076)

0.0615*** (0.0120)

0.0368*** (0.0075)

Risk averse

0.0861*** (0.0129)

0.0610*** (0.0084)

0.0787*** (0.0127)

0.0554*** (0.0085)

0.0754*** (0.0110)

0.0504*** (0.0066)

0.0711*** (0.0110)

0.0486*** (0.0070)

Age

0.0007*** (0.0003)

0.0005** (0.0003)

0.0020*** (0.0005)

0.0016*** (0.0004)

0.0008*** (0.0002)

0.0007*** (0.0002)

0.0017*** (0.0004)

0.0014*** (0.0003)

Family size

0.0090*** (0.0023)

0.0119*** (0.0029)

0.0173*** (0.0036)

0.0160*** (0.0039)

0.0080*** (0.0018)

0.0107*** (0.0024)

0.0152*** (0.0027)

0.0144*** (0.0031)

Wealth (Log)

0.0220*** (0.0028)

0.0253*** (0.0037)

0.0258*** (0.0035)

0.0318*** (0.0044)

0.0176*** (0.0022)

0.0220*** (0.0028)

0.0189*** (0.0026)

0.0268*** (0.0036)

Income (Log)

0.0115*** (0.0030)

0.0128*** (0.0032)

0.0094*** (0.0027)

0.0140*** (0.0031)

2nd Quintile of income distribution

0.0085 (0.0088)

0.0095 (0.0182)

0.0097 (0.0067)

0.0104 (0.0146)

3rd Quintile of income distribution

0.0166* (0.0098)

0.0113 (0.0149)

0.0123 (0.0081)

0.0206* (0.0120)

4th Quintile of income distribution

0.0191 (0.0124)

0.0204 (0.0151)

0.0132 (0.0119)

0.0309** (0.0132)

5th Quintile of income distribution

0.0455*** (0.0162)

0.0441*** (0.0152)

0.0474*** (0.0150)

0.0519*** (0.0130)

Seatbelt

0.0000 (0.0052)

0.0013 (0.0048)

0.0005 (0.0041)

0.0013 (0.0037)

Traffic rule

0.0024 (0.0051)

0.0000 (0.0052)

0.0015 (0.0040)

0.0018 (0.0045)

Expectation

0.0193*** (0.0050)

0.0119*** (0.0043)

0.0166*** (0.0042)

0.0093*** (0.0035)

Donation

0.0221*** (0.0080)

0.0235*** (0.0082)

0.0175*** (0.0067)

0.0177*** (0.0066)

Children

0.0239*** (0.0063)

0.0177** (0.0073)

0.0208*** (0.0049)

0.0154*** (0.0057)

Old

0.0226*** (0.0086)

0.0229*** (0.0088)

0.0171** (0.0071)

0.0184** (0.0073)

Unemployed

0.0066 (0.0091)

0.0051 (0.0100)

0.0004 (0.0073)

0.0007 (0.0080)

Travel

0.0135** (0.0067)

0.0058 (0.0069)

0.0159*** (0.0059)

0.0102* (0.0058)

House

0.0365** (0.0167)

0.0549*** (0.0169)

0.0111 (0.0130)

0.0388** (0.0151)

Public security

0.0062 (0.0039)

0.0098** (0.0039)

0.0066** (0.0033)

0.0088*** (0.0032)

Finance

0.0903** (0.0373)

0.0241 (0.0172)

0.0898** (0.0352)

0.0194 (0.0150)

0.0497*** (0.0153)

0.0351*** (0.0105)

0.0454*** (0.0132)

0.0320*** (0.0088)

Community income (Log)

0.0578*** (0.0154)

0.0459*** (0.0104)

0.0495*** (0.0130)

0.0388*** (0.0086)

0.0041 (0.0084)

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

13

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx Table 3 (continued) Cash-gift

Communication

(1)

(2)

(3)

Response (4)

(5)

OLS

Probit

OLS

Probit

OLS

Probit

OLS

Probit

OLS

Community unemployment

0.1106 (0.1077)

0.1161 (0.0917)

0.0891 (0.1115)

0.0785 (0.0956)

0.1384 (0.0917)

0.0841 (0.0727)

0.1351 (0.0950)

0.0446 (0.0765)

0.1158 (0.0820)

Rural

0.0021 (0.0187)

0.0439** (0.0196)

0.0062 (0.0197)

0.0262 (0.0203)

0.0041 (0.0155)

0.0434*** (0.0166)

0.0036 (0.0166)

0.0260 (0.0172)

0.0049 (0.0106)

Eastern

0.0025 (0.0111)

0.0045 (0.0124)

0.0006 (0.0107)

0.0014 (0.0116)

0.0063 (0.0095)

0.0033 (0.0106)

0.0021 (0.0093)

0.0010 (0.0102)

0.0023 (0.0077)

Central

0.0022 (0.0090)

0.0079 (0.0121)

0.0007 (0.0088)

0.0070 (0.0118)

0.0018 (0.0075)

0.0087 (0.0101)

0.0016 (0.0074)

0.0075 (0.0101)

0.0012 (0.0072)

Community wealth (Log)

0.0230*** (0.0069)

Community education

0.0186*** (0.0068)

Community age

0.0011 (0.0008)

Job categories

No

Constant

0.8951*** (0.1715)

Observations R-squared

5902 0.1904

No

Yes

Yes

0.8405*** (0.1673) 5902

5642 0.2092

No

No

0.7439*** (0.1455) 5642

7850 0.1949

Yes

Yes

0.7168*** (0.1453) 7850

7439 0.2119

No 0.5835*** (0.1026)

7439

320 0.7036

Notes: All the standard errors are adjusted as the cluster-robust standard errors clustering on the community level and the t-values are reported in the parentheses. * Represent the significance level of 0.1. ** Represent the significance level of 0.05. *** Represent the significance level of 0.01.

rate. For communities with no access to internet (97 communities), a 10% increase in Community Response rate is associated with a 1.3% increase (10% ⁄ (0.13  0.53 ⁄ 0) = 1.3%) in average community participation; but the same magnitude of increase in response rate is associated with only 0.13% (10% ⁄ (0.13  0.53 ⁄ 22%) = 0.13%) for communities with the average Internet availability. Other variables, including risk attitude, education, income and wealth, all significantly influence stock market participation. Take the Probit results in column (2), for example. Other things being equal, compared with a risk lover, the probability to participate drops by 4.5% (5.5%) if the household is risk neutral (risk averse). Apart from the findings that stock market participation increases in income and education, we find evidence for income and education ‘‘thresholds’’ of participation. Compared with primary school education households, Middle Education does not promote participation significantly, but households with College Education are 5.6% more likely to invest in stocks at 0.01 significance level. Households whose incomes lie in the middle three quintiles are not significantly different with the lowest quintile in participation, but those in the highest quintile have a 4.4 percent higher probability of participating significantly. Table 3 also suggests that household structure affects stock market participation. Generally speaking, the probability of participation increases by 0.2% for an additional year in the household’s highest age and decreases by 1.6% for an additional household member. Family burden also matters. Owning a house and having an additional old member lower the participation by 5.5% and 2.2%, while an additional child is associated with a 1.8-percentage-points-higher probability of participation. Among the five proxies for personality traits, only Expectation and Donations have significant influences. The coefficients of regional variables imply a considerable difference of stock market participation rates across China, especially between rural and urban areas, and among the communities with different average income levels. Though we attempt to alleviate the endogeneity problem caused by omitted variables by controlling for various personal traits and household characteristics, it is still possible that a household decided to invest in the stock market in the first place, and then bought computers and acquired information via the Internet. Therefore, we replace Interneti in Eq. (1) with the community average Internet access. The results are reported in Table 4, with the only difference from Table 3 that the Internet and its interaction terms are replaced by Community Internet and the corresponding interaction terms. Table 4 shows that the Internet access measured by the community average still influences the stock market participation significantly in all eight specifications. The substitution relationship between Internet access and social interactions remains, and the coefficient of the interaction term are still significant in OLS regressions. Take the OLS results in column (2) for example: a 10% increase in the community average Internet access—a move from 90th to 95th percentile of its distribution—is associated with a 3-percentage-pints increase in the probability of participation for non-sociable households, but only with Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

14

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

Table 4 Results using community average Internet access as proxy. Cash-gift

Communication

(1)

(2)

OLS

Probit ***

(3)

OLS

Probit ***

(4)

OLS

Probit ***

OLS

Probit *

Sociability

0.0371 (0.0081)

0.0165 (0.0134)

0.0278 (0.0082)

0.0154 (0.0141)

0.0213 (0.0061)

0.0162 (0.0120)

0.0116 (0.0066)

0.0160 (0.0129)

Sociability  Internet

0.1556*** (0.0361)

0.0358 (0.0298)

0.1284*** (0.0330)

0.0414 (0.0297)

0.1004*** (0.0350)

0.0090 (0.0274)

0.0583* (0.0320)

0.0110 (0.0262)

Internet

0.3677*** (0.0507)

0.1515*** (0.0314)

0.3042*** (0.0495)

0.1140*** (0.0302)

0.3289*** (0.0482)

0.1175*** (0.0294)

0.2605*** (0.0460)

0.0833*** (0.0277)

Middle education

0.0107 (0.0068)

0.0431* (0.0238)

0.0016 (0.0067)

0.0285 (0.0222)

0.0155*** (0.0055)

0.0466** (0.0204)

0.0011 (0.0049)

0.0330* (0.0195)

College education

0.0601*** (0.0110)

0.0910*** (0.0232)

0.0629*** (0.0116)

0.0656*** (0.0218)

0.0518*** (0.0088)

0.0862*** (0.0198)

0.0580*** (0.0093)

0.0652*** (0.0189)

Risk neutral

0.0794*** (0.0145)

0.0499*** (0.0095)

0.0739*** (0.0144)

0.0466*** (0.0095)

0.0711*** (0.0127)

0.0405*** (0.0079)

0.0661*** (0.0124)

0.0391*** (0.0078)

Risk averse

0.1016*** (0.0137)

0.0737*** (0.0087)

0.0908*** (0.0133)

0.0654*** (0.0087)

0.0938*** (0.0120)

0.0632*** (0.0069)

0.0848*** (0.0116)

0.0583*** (0.0072)

Age

0.0003 (0.0003)

0.0002 (0.0003)

0.0018*** (0.0005)

0.0014*** (0.0004)

0.0004* (0.0002)

0.0004* (0.0002)

0.0016*** (0.0004)

0.0013*** (0.0004)

Family size

0.0057** (0.0023)

0.0090*** (0.0028)

0.0153*** (0.0035)

0.0144*** (0.0039)

0.0053*** (0.0018)

0.0084*** (0.0023)

0.0138*** (0.0026)

0.0134*** (0.0031)

Wealth (Log)

0.0230*** (0.0028)

0.0264*** (0.0037)

0.0263*** (0.0035)

0.0334*** (0.0045)

0.0189*** (0.0022)

0.0233*** (0.0029)

0.0197*** (0.0027)

0.0288*** (0.0038)

Income (Log)

0.0146*** (0.0030)

0.0150*** (0.0033)

0.0136*** (0.0027)

0.0176*** (0.0032)

2nd Quintile of income distribution

0.0110 (0.0088)

0.0065 (0.0185)

0.0132* (0.0069)

0.0083 (0.0150)

3rd Quintile of income distribution

0.0157 (0.0097)

0.0090 (0.0151)

0.0139* (0.0080)

0.0198 (0.0123)

4th Quintile of income distribution

0.0125 (0.0119)

0.0212 (0.0152)

0.0078 (0.0114)

0.0330** (0.0133)

5th Quintile of income distribution

0.0514*** (0.0163)

0.0446*** (0.0154)

0.0617*** (0.0145)

0.0567*** (0.0130)

Seatbelt

0.0000 (0.0050)

0.0013 (0.0047)

0.0013 (0.0040)

0.0011 (0.0037)

Traffic rule

0.0046 (0.0050)

0.0017 (0.0052)

0.0041 (0.0039)

0.0004 (0.0045)

Expectation

0.0208*** (0.0051)

0.0139*** (0.0043)

0.0189*** (0.0043)

0.0112*** (0.0035)

Donation

0.0204** (0.0079)

0.0230*** (0.0082)

0.0174*** (0.0066)

0.0186*** (0.0066)

Children

0.0237*** (0.0062)

0.0182** (0.0072)

0.0210*** (0.0047)

0.0161*** (0.0055)

Old

0.0235*** (0.0088)

0.0224** (0.0090)

0.0182** (0.0073)

0.0197*** (0.0075)

Unemployed

0.0062 (0.0094)

0.0033 (0.0102)

0.0002 (0.0073)

0.0019 (0.0081)

Travel

0.0157** (0.0066)

0.0081 (0.0068)

0.0201*** (0.0059)

0.0134** (0.0057)

House

0.0337** (0.0159)

0.0562*** (0.0168)

0.0096 (0.0127)

0.0423*** (0.0150)

Public security

0.0050 (0.0040)

0.0079** (0.0040)

0.0047 (0.0034)

0.0069** (0.0033)

Finance

0.0929** (0.0386)

0.0259 (0.0179)

0.0951** (0.0370)

0.0211 (0.0158)

0.0177 (0.0115)

0.0185* (0.0105)

0.0176* (0.0103)

0.0186** (0.0094)

Community income (Log)

0.0192* (0.0112)

0.0232** (0.0100)

0.0171* (0.0100)

0.0201** (0.0087)

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

15

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx Table 4 (continued) Cash-gift

Communication

(1) OLS

(2) Probit *

(3)

(4)

OLS

Probit

OLS

Probit

OLS

Probit

Community unemployment

0.0601 (0.1152)

0.1626 (0.0931)

0.0279 (0.1173)

0.1179 (0.0970)

0.1065 (0.1001)

0.1210 (0.0748)

0.0838 (0.1012)

0.0758 (0.0786)

Rural

0.0227 (0.0193)

0.0360* (0.0187)

0.0242 (0.0200)

0.0212 (0.0195)

0.0202 (0.0162)

0.0386** (0.0155)

0.0212 (0.0169)

0.0233 (0.0165)

Eastern

0.0039 (0.0111)

0.0085 (0.0121)

0.0016 (0.0109)

0.0043 (0.0114)

0.0049 (0.0097)

0.0058 (0.0106)

0.0011 (0.0096)

0.0005 (0.0103)

Central

0.0011 (0.0090)

0.0107 (0.0120)

0.0024 (0.0088)

0.0094 (0.0118)

0.0004 (0.0078)

0.0104 (0.0103)

0.0002 (0.0077)

0.0089 (0.0103)

Job categories

No

No

Yes

Yes

No

No

Yes

Yes

Constant

0.5654*** (0.1227)

Observations

5906

R-squared

0.1838

0.5603*** (0.1261) 5906

5646 0.2038

0.4760*** (0.1116) 5646

7858 0.1806

0.4697*** (0.1137) 7858

7446

7446

0.2009

Notes: All the standard errors are adjusted as the cluster-robust standard errors clustering on the community level and the t-values are reported in the parentheses. * Represent the significance level of 0.1. ** Represent the significance level of 0.05. *** Represent the significance level of 0.01.

a 1.76-percentage-points increase for sociable households. The effect of social interactions remains the same magnitude for all specifications. To check the robustness of our results, we explore two variations regarding the definition of stock market participation. First, we require the investment amount to be greater than 10,000 RMB (about 1660 USD, one third of the sample median household income level). This might preclude the inactive small individual investors. In this case, 64 additional households are assigned to the non-participation group. Second, we think perhaps making frequent deals is information-demanded, instead of just owning stock investment accounts. So we pay attention to the number of stocks holding and take 95 households who own stock accounts, but do not hold any stocks, out of the participation group. The corresponding results are provided in Appendix Table 4, and our findings in Table 4 remain regardless of these variations. 4.3. Social multiplier effect of social interactions The social multiplier effect refers to the idea that social interactions act as a channel through which other community members influence individual decisions. As our Hypothesis 3 suggests, the marginal effect of social interactions on stock market participation should be greater in a high-participation community. To examine this effect, we use the Community Stock (Exclusive) variable, which is calculated as the average participation rate of the other households in the community. Therefore, similar to the approach in Hong et al. (2004), in Table 5 we add Community Stock (Exclusive) in Columns (1) and (3) with the same control variables as those in columns (2) and (4) of Table 3, and replace Community Stock (Exclusive) with the interaction term of Sociability and Community Stock Category in columns (2) and (4).21 The Community Stock Category variable takes the value of 1 for the households who live in the communities with zero participation rate (3668 households), and 0 and 1 respectively for those in non-zero participation communities with average participation below (2287 households) and above (2113 households) the median level. The results about Sociability, Internet and Community stock participation are shown in Table 5, and the results of other variables are provided in Appendix Table 5. Table 5 provides support for the community effect suggested by Hong et al. (2004) and Brown et al. (2008). It shows that the average participation rate of the other households within the community is significantly positively associated with the household’s stock market participation. As revealed in results of the Probit regression, a 10-percentage-points rise in Community Stock (Exclusive)—a move from 50th to 75th percentile of its distribution—increases a household’s participation probability by 1.9–2.2% in columns (1) and (3). Households living in high participation communities are more likely to invest in stocks. Now we look at the social multiplier effect of social interactions. The results in columns (2) and (4) of Table 5 suggest this effect. For instance, the OLS results in column (2) show that when Internet is held constant at its average level, active social interactions raise the probability to participate in stock market by 5.13 if the household lives in a high participation 21 The Community Stock Category variable is not included to avoid the multicollinearity problem—the correlations between Sociability  Community Stock Category and Community Stock Category are 0.70 and 0.69 respectively using cash-gift and communication expenditures as sociability proxies.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

16

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx

Table 5 The ‘‘Social Multiplier Effect’’ of stock market participation. Cash-gift

Communication

(1)

(2)

OLS Sociability Sociability  Internet

PROBIT

OLS *

PROBIT

***

0.0161 (0.0060)

0.0151 (0.0090)

0.0300 (0.0076)

0.0681*** (0.0216)

0.0267* (0.0138)

0.1059*** (0.0228) 0.1494*** (0.0181)

Internet

0.1245*** (0.0172)

0.0637*** (0.0101)

Community stock (Exclusive)

0.5370*** (0.0566)

0.2164*** (0.0402)

Sociability  Community stock category

(3)

***

0.0446*** (0.0077)

(4)

OLS

PROBIT *

OLS **

PROBIT ***

0.0113 (0.0060)

0.0213 (0.0091)

0.0323 (0.0077)

0.0422*** (0.0144)

0.0798*** (0.0191)

0.0258** (0.0106)

0.1301*** (0.0204)

0.0412*** (0.0107)

0.0706*** (0.0101)

0.1469*** (0.0183)

0.0608*** (0.0090)

0.1815*** (0.0197)

0.0692*** (0.0091)

0.5118*** (0.0488)

0.1882*** (0.0357) 0.0576*** (0.0071)

0.0617*** (0.0068)

0.0049 (0.0098)

0.0609*** (0.0077)

0.0022 (0.0093)

Notes: All the standard errors are adjusted as the cluster-robust standard errors clustering on the community level and the t-values are reported in the parentheses. The other control variables are the same as those in columns (2) and (4) of Table 3. * Represent the significance level of 0.1. ** Represent the significance level of 0.05. *** Represent the significance level of 0.01.

community; however, the increase is only 0.67% for those living in low participation communities. Active social interactions in effect are associated with 3.79% lower probability of participation for those living in zero participation communities! This supports our Hypothesis 3 that the marginal effect of social interactions is higher for those households in high participation communities. Our result is in line with Hong et al. (2004), which demonstrates that among American communities with low participation rates, social interactions reduce stock market participation by 0.5%. Besides, the aggregate effects of social interactions in these two columns are 0.19%, 1.42%, 0.37% and 1.13%, respectively; also close to the previous result.

4.4. Analyses of Sub-samples China is a country with more than 1.3 billion residents, thus wide differences across the country exist. In this subsection, as a robustness check, we investigate the informational effect and social multiplier effect of stock participation in groups with different demographic characteristics, e.g., urban–rural areas, high-low income, and high-low educational level. We firstly run the OLS regression for different subsamples with the same controls as the specification reported in columns (2) and (4) of Table 4, and then test if the coefficients in different subsample pairs are equivalent using ‘‘seemingly unrelated estimation’’ (Zellner, 1962).22 The main results and the comparisons of coefficients with Chi-square statistics are present in Table 6, with the results of other variables provided in Appendix Table 6-1, -2 and -3.23 As revealed in Table 6, overall our basic results remain in different subsamples. Social interactions and Internet access increase stock market participation. The coefficients of their interaction term are always negative, supporting the substitution relationship between them. The marginal effect of social interaction increases in community participation rates. However, the informational effect of social interactions varies with subsamples. For the comparison of urban–rural subsamples (Table 6-1), social interactions have a significantly greater effect for rural households.24 Access to the Internet is also associated with higher probability for stock market participation in rural areas than that in urban, though not statistically significant. It implies that social interactions remain an effective channel of information transmission in rural China and that there is more room for increasing rural stock market participation. In Table 6-2, households are divided into high and low annual income groups according to the sample median level 29602.5 RMB (about 4852 USD). The influence magnitudes of Sociability and Internet, as well as the two interaction terms are all smaller for low income households than high income households. It implies the limited roles of information channels for low income households to participate in the stock market, as well as the possibility that apart from the information channel, low income families may face other restrictions on participating in the stock market. As to educational level, we divide households into the high-low education groups by the sample median (high school education) in Table 6-3. The coefficients 22 We also try the dummy variables for subsamples and put its interaction terms with other variables into the regression models, which allows us to estimate the coefficients of different sample pairs at the same time. The results are similar. 23 The methodology of generating Sociability, Donation and income distribution dummies is that their original values are compared with the subsample medians before the they are assigned with the binary values. Take the Sociability dummies of the urban subsample for example, we compare the ratio of cashgift and communication expenditures to household income with the urban subsample median level, and then divide urban households into high and low sociability groups. 24 Due to the missing values of variables, the numbers of observations in regressions are less than the numbers of households in different subsamples.

Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

17

P. Liang, S. Guo / Journal of Comparative Economics xxx (2015) xxx–xxx Table 6 Comparisons of coefficients in different subsamples. Cash-gift

Table 6-1 Sociability Sociability  Internet Internet Sociability  Community stock category Number of observations

Table 6-2 Sociability Sociability  Internet Internet Sociability  Community stock category Number of observations

Table 6-3 Sociability Sociability  Internet Internet Sociability  Community Stock Category Number of observations

Communication

Urban

Rural

Chi-square

Urban

Rural

Chi-square

0.0155 0.0934*** 0.1329*** 0.0585*** 3641

0.0789*** 0.1662* 0.2275** 0.0784*** 2001

8.07*** 0.65 1.06 0.85

0.0181* 0.1092*** 0.1524*** 0.0619*** 4596

0.0586*** 0.1651* 0.2650** 0.0683*** 2814

5.78** 0.41 1.08 0.14

High income

Low income

Chi-square

High income

Low income

Chi-square

0.0308*** 0.1508*** 0.1789*** 0.0820*** 3151

0.0289*** 0.1109*** 0.1608*** 0.0428*** 2491

0.02 0.62 0.21 8.33***

0.0362*** 0.1586*** 0.1935*** 0.0840*** 3769

0.0323*** 0.0392 0.0579** 0.0546*** 3641

0.07 12.97*** 7.60*** 3.70*

High education

Low education

Chi-square

High education

Low education

Chi-square

0.0280 0.1344*** 0.1609*** 0.0682*** 2589

0.0207** 0.0360* 0.0967*** 0.0348*** 3095

0.19 4.31** 2.92* 5.36**

0.0245 0.1167*** 0.1549*** 0.0715*** 3204

0.0364*** 0.0238 0.0958*** 0.0544*** 4268

0.54 4.30** 2.08 1.79

Notes: The results are from OLS regressions for different subsamples with the other control variables the same as those in columns (2) and (4) of Table 4. The Chi-square statistics are obtained from ‘‘Seemingly unrelated estimation’’ (Zellner, 1962). * Represent the significance level of 0.1. ** Represent the significance level of 0.05. *** Represent the significance level of 0.01.

of regressions suggest that high education households rely more on Internet access to make stock market participation decisions, and their behavior is rarely correlated with the frequency of social interactions. It might be that households with higher educational levels are better at processing and analyzing information, which makes them put more attention on the public and more precise Internet access.

5. Conclusions We use a national representative sample of Chinese households to investigate the informational effect and the social multiplier effect of social interactions on stock market participation in China. We find that as two information channels, both access to the Internet and social interactions increase stock market participation, but they substitute with each other. This suggests that the usage of modern communication devices (Internet) might crowd out the informational effect of social interactions. The marginal effect of social interactions falls if the household has access to the Internet. In addition, after controlling for the substitution between social interactions and access to the Internet, the remaining mechanisms of social interactions discourage participation as a whole. It confirms the negative effect of the contextual interaction—the negative demonstration effect of the widespread loss cases in stock investment—on stock market participation in China. We also show that the marginal effect of social interactions on stock market participation is greater in high participation communities, which supports the social multiplier effect of social interactions. The above findings are robust in different subsamples, with the coefficients of these information channels varied. A large body of literature has focused on the role of social interactions in promoting stock market participation. (Hong et al., 2004; Li, 2006a, 2006b; Brown et al., 2008). Our study focuses on the information role of social interactions. Although social interactions are dominated by other channels (here the access to Internet) in acquiring information, they still have room to take effect because of the low Internet penetration rate in China, especially in rural areas. Because the first wave of CHFS only provides the cross-sectional data, even though we use several attempts to control for the possible omitted variables problem, we still could not completely rule out the endogeneity problem. Establishing a clearer causal relationship between the informational effect of social interactions and stock market participation is left for future research. Internet access almost becomes a necessity in the Internet Age, and many people nowadays seek social interactions online. In particular, there is an important tendency that online interaction substitutes for face-to-face social interaction. Therefore, we are aware that the Internet access of households not only facilitates accurate information processing (such as information from the official news), but also leads to the online social networking which will bring both information from word-of-mouth and the social multiplier effect of the online social networking. Even though the current paper focuses on the Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003

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Please cite this article in press as: Liang, P., Guo, S. Social interaction, Internet access and stock market participation—An empirical study in China. Journal of Comparative Economics (2015), http://dx.doi.org/10.1016/j.jce.2015.02.003