Gender Homophily across Types of Social Networks in Rural India By Claire Teter [email protected] Under the advisement of Matthew Jackson in the Stanford Department of Economics June 1, 2010 Many thanks to Geoffrey Rothwell and the economic department’s support through the undergraduate honors program, and to Mark Tendall for getting me started on this path in the economics summer research program. Thanks to Seema Jayachandran for her help researching the issue of caste. And of course, to Professor Jackson, without whom none of this research would have been possible. You have all been so patient and helpful, and I am endlessly thankful for your involvement. Keywords: Informal credit, gender preference, homosocial norm, social network formation
Teter 1 In this paper, I search for evidence that women in 75 Indian villages are somehow constrained in their ability to make instrumental social connections necessary to start or run businesses. I compare gender homophily across ten different types of social networks, and find it to be relatively high in both genders and in most all of networks. It appears that both genders are similarly discouraged from relationships with people of the other gender, but less so when there is a transaction involved than when the association is entirely social in nature. Women in higher castes do not appear to be any more constrained than their lower caste counterparts. There is evidence that a homosocial norm discourages other-­gender relationships, but it appears to be uniformly enforced across groups. 1. Introduction Social network analysis is not a traditional economic tool, but all transactions and opportunities are allowed by the social connections an individual has. Social network analysis helps to account for and explain such clearly economic forces as market friction, information asymmetry, and socioeconomic gradation. This paper uses social networking data to analyze how women may be constrained from forming their optimal social networks in business and entrepreneurial settings. In particular, does it appear that women are prevented by cultural norms from creating business connections that might be useful to them? For example, are women discouraged from making connections that might enable them to receive informal loans from men? This project uses data from Professor Matthew Jackson’s Indian village microfinance survey, collected in collaboration with teams at MIT and the Center for Microfinance in Chennai, India. The extensive dataset includes social networking surveys from 75 villages in rural India. Each participant was asked to identify people with whom they would associate for a number of different social and business transactions. This dataset is unique from other social networking datasets Teter 2 because it includes different types of social connections and indicates their directionality. That is, where most surveys may map only friendships or marriages or business partners, this data maps several overlapping types of social networks within the same groups of people. This project has organized ten such types of social networks. The dataset includes data on each individual’s demographic characteristics, which allows us to see what diverse criteria people use to form different types of social connections. The primary metric of interest in this project is homophily, the measure of group members’ tendency to associate with others who are like themselves. For this project I study gender homophily, or a gender’s penchant to prefer social connections with the same gender. I analyze differences in homophily across genders and type of social networks to inform the discussion of whether women are constrained by their ability to make social business connections. In particular, I seek to gain insight on these questions: First, do the women in these villages appear to have business networks that could somehow be constraining their ability to start companies? Second, women in higher castes appear to exhibit less financial literacy. It would appear that more traditional lifestyle has somehow constrained their access to this knowledge. Do their social connections indicate that this may be the case in these villages, or why? We may concern ourselves with two implications of these answers: is what we find either unfair, inefficient, or both? In short, I find that women are indeed constrained in what gender people they associate with, but appear to be no more so constrained than men. Teter 3 Furthermore, constraint does not appear to differ across castes. There does appear to be a homosocial norm that suggests people not associate with other genders, which may well be inefficient if it sometimes precludes advantageous social connections—however, it does not appear to be enforced differently for different groups, so I see no clear evidence of a fairness issue in this particular dataset. 2. Background This project investigates a range of smaller claims gleaned from social science literature that motivate its hypotheses. It will first be helpful to discuss the tools and measures I will use, and then the claims I aim to investigate. 2.1 Homophily The concept of homophily is concisely explained by Lazarsfeld and Merton (1954): “Birds of a feather flock together.” People are inclined to associate with others that share characteristics with themselves—though this can be any characteristic, especially useful types of homophily to consider are race, gender, education, or other measures commonly associated with inequality. Currarini, Jackson and Pin (2009, 2010) discuss homophily as a factor in friendship formation. They address three patterns of network formation: first, larger groups tend to have more same-­‐type ties and less other-­‐type ties. Second, larger groups have denser networks—that is, they have more ties per capita than smaller groups. Third, medium-­‐sized groups have the highest levels of homophily. They use these observations to model the formation of a friendship network, and Teter 4 determine that it is a function of both opportunity and type preference that causes these patterns of inbreeding homophily. That is, people are introduced to more people of a larger type, and when they are introduced to others, they still prefer their own type. Their findings hinge on the assumptions that each individual is exposed to new individuals at the same frequency regardless of group; this may not hold true in the Indian villages in the data in this project if women are somehow socially constrained. McPherson et al (2001) describe the history of homophily: the study of homophily arose largely as a response to desegregation of schools, to study homophily as an informal variety of segregation. Indeed, many studies find that adolescent social networks have high racial homophily indices. Even though institutional influences were largely removed, homophily remained as a “sticky” remnant of the previous institution. This discussion of homophily informs our investigation of gender homophily in rural India. What psychological or institutional influences can explain homophily levels in different types of social networks, or which do we expect to find but do not see in the data? 2.2 Sex Segregation A considerable amount of psychology research addresses sex segregation, but mostly considers it in the context of childhood development. Mehta and Strough (2009) discuss sex segregation throughout the life. They identify the “homosocial” norm, that people are expected to associate primarily with others of their gender. They note that this norm is stronger in those who are married, potentially as a Teter 5 mechanism to reduce marital jealousy, and that married people tend not to have other-­‐gender friends who are not also friends of their spouse. Ibarra (1992) seeks to investigate how men’s and women’s social structures differ in the workplace using social network data rather than the anecdotal survey data that preceded it. Much like the data used in this project, his data mapped different types of social connections between employees at a particular advertising firm. He grouped those types of social connections as “instrumental” (that is, strategically important to one’s work or career) or “expressive” (purely social) connections. He presents three findings relevant to this project: first, women and men tend to prefer men for instrumental connections, though women prefer other women for expressive connections. Second, men demonstrated higher homophily than women in all categories. Third, men hold more central positions in instrumental networks. He argues that these social network patterns prevent women from succeeding within a firm and enforce gender inequality. However, this was conducted in a specifically western and organized setting and may not be relevant to the villages in question. This workplace homophily could be caused by a combination of two forces: first, it could be a function of a simple homosocial norm—men simply prefer to work with men and women with women. Second, people may “point” towards those who are higher in the company and therefore have more resources and clout to assist them in instrumental connections. If men are higher on the company ladder, this will also cause inflated homophily in men and lower homophily in women. Teter 6 2.3 Gender and Caste in India The caste system in itself is an extremely complicated, non-­‐uniform one, and is unavoidable in discussions about gender equality in India. Liddle and Rama (1986) explain how the caste system of gender inequality formed synergistically, each reinforcing the other. They argue that the daughter’s dowry increased economic incentives to closely control the marriage and sexuality of women, as parents wished to keep property within their family or caste. Notably, they also mention that women in the highest castes were those that were most closely controlled—perhaps because their families had more to lose if their daughters strayed. Field et al find that women in higher castes exhibit higher rates of return on financial education, which suggests that women in higher castes and more traditional institutions know less about business. This could be due to either of two factors: women in higher castes are more tightly controlled (as Liddle discusses), or that business sense is superfluous in women when their families do not depend on a second income. The propriety of sex segregation would dictate fewer other-­‐gender connections, both expressive and instrumental, than in less traditional groups, or that women in general have less dense business networks than men. 3. Methodology and Hypotheses It is the goal of this project to consider the literature presented and determine whether its relevant findings or conjectures are reflected in this real-­‐
Teter 7 world data set. To do this, I first look at the homophily differences among the various types of social networks averaged across all the data. I then consider deeper implications of these differences by analyzing the variations in homophily across groups. Hypothesis 1: There is a homosocial norm limiting other gender-­connections, but it is less strictly enforced when its opportunity cost is high. We first test for the existence of the expected homosocial norm, and for other evidence that women’s social networks are more constrained than men’s. H1.1: All types of social networks exhibit some level of homophily. While considering the costs and benefits of create a social connection, a person has to factor in both the gains of the relationship and how it will reflect on them from a moral or ethical standpoint. If a homosocial norm exists that discourages other-­‐gender relationships, I expect to see proportionately fewer of them. That is, if Mehta’s homosocial norm holds, then I expect to see a high averaged level of homophily across all types of social networks. H1.2: Sex segregation is higher in expressive networks than in instrumental networks. The networks present in the data can be categorized as expressive or instrumental: Teter 8 Variable Expressive visitcome visitgo nonrel Instrumental borrow krcome krgo Survey Question Who visits your house in his/her free time? In your free time, whose house do you visit? Name the 5 non-­‐relatives whom you speak to the most. If you suddenly needed to borrow Rs.50 for a day, who would you ask? Who would come to you if he/she needed to borrow kerosene or rice? Who would come to you if he/she needed to borrow kerosene or rice? Who do you trust enough, that if he/she needed to borrow Rs.50 for a lend day you would lend it to him/her? If you had a medical emergency and were alone at home, whom would medic you ask for help in getting to a hospital? giveadvice Who comes to you for advice? If you had to make a difficult personal decision, whom would you ask for helpdecision advice? An instrumental relationship must be formed for some pareto-­‐improving purpose outside of the pure utility of social interaction. That is, at least one party has something to gain, whether it be information, social capital, or the potential for a monetary transaction. It is debatable whether giveadvice, helpdecision, and medic are relevant to Ibarra’s discussion of men’s power in business, so I conduct all relevant tests with these networks both included and thrown out. If a person chooses not to establish a particular social connection due to any social norm (homosocial or otherwise), they will have a higher opportunity cost than they would with an expressive connection. In order to avoid violating the norm, they give up some concrete utility gain from the relationship. If a homosocial norm sways people away from creating other-­‐gender social connections, we would expect it to have a smaller effect on instrumental relationships since the opportunity cost of not creating the connection is higher. If the effect of the homosocial norm is Teter 9 indeed smaller in instrumental relationships, it would be expected that there would be lower homophily in those networks. Hypothesis 2: Women are constrained in their ability to create instrumental connections. Next I look for other patterns that would indicate constrained female social connections—in particular, we are interested in instrumental connections since they are the ones that enable entrepreneurial lending and business transactions. Women could be constrained in two ways. Women could be discouraged from forming business connections with anyone, or they could simply be discouraged or prevented from making business connections with men. H2.1: Women have high homophily in instrumental connections. If women are constrained by their inability to create business connections with men specifically, we will see that they have a high level of homophily in instrumental connections. H2.2: Women have less dense instrumental networks than expressive networks. This would be a stricter and more inefficient type of constraint that women cannot even transact with other women. Women may be discouraged from making instrumental connections regardless of gender. We can determine if this is the case by considering whether women simply have less business connections than men. If Teter 10 the number of connections per capita is not significantly different, we can conclude that women are not limited in making business connections of some type. H2.3: Men exhibit higher homophily when monetary exchange is involved than when it is not. It would certainly be evidence of constraint if men were unwilling to participate in monetary exchange with women. Since the ties in this data are directional and not necessarily mutual, it’s possible that a woman would think she could borrow money from someone who would, in fact, not be willing to participate in the transaction. For this reason, I consider male homophily. If male homophily is not higher in monetary instrumental networks than in non-­‐monetary instrumental, we can rule out this particular constraint. Hypothesis 3: Variation in instrumental network gender homophily arises from both the homosocial norm and from the hierarchy within a town. Individuals do not make the decisions about who to associate with based only on their gender. For instrumental connections, they will look to others who have the most clout and resources (social or financial) to get what they need from the relationship. Much of the observed homophily differences may be accounted for by women and women both pointing to men who are in power, rather than by a simple homosocial preference to associate with the same gender. The former of these explanations raises the question of whether it is active gender norms or residual gender power structures that are constraining female social networks. Teter 11 H3.1: Men inhabit the higher levels of the town hierarchy. Of course, these villages do not have the corporate organization that Ibarra’s advertising firm did. There may be no clear gender “on top” of the hierarchy. If men do not hold any more central positions in the village than women, then the structure of the hierarchy cannot affect gender homophily as imagined. I use another survey question for this test: “Name the local leaders living in the village.” If the proportion of men listed for this question is significantly higher than women, we can feasibly proceed on the assumption that they tend to be the dominant gender in the hierarchy. H3.2: The ratio of men’s to women’s homophily is higher in instrumental networks than in expressive networks. Ibarra suggests that men have most of the business clout, and therefore have little to gain from reaching out to women to make instrumental connections. Women, on the other hand, must seek men to make purposeful connections. If so, men are preferred instrumental connections because of their positions in the hierarchy rather than their gender. Imagine that each instrumental homophily index is positively correlated to the effect of the homosocial norm and the effect of the hierarchy, and that expressive connections are unaffected by hierarchy and thus are only correlated to the social norm. Compared to the homophily measure for the expressive norm, we expect that the instrumental homophily will have a lesser effect from the Teter 12 homosocial norm due to an increased opportunity cost (tested in H1.2). From there, the added effect of the hierarchy should increase male instrumental homophily and decrease female homophily as they both will tend to point to more men in positions of power. As male instrumental homophily rises and female instrumental homophily falls, the ratio of male to female should increase. If this ratio is significantly higher in instrumental than expressive networks, there is evidence that the presence of men in high positions is affecting the level of homophily. Hypothesis 4: Women in higher castes are more constrained than women in lower castes. Field et al’s finding that women in higher castes know less about business could be explained by two mechanisms. The first is that women in higher castes are more constrained by traditional religious and social norms. The second is that due to their higher socioeconomic status, their participation in business is not necessary to their families’ financial health. We test two hypotheses to determine if these hold in this context. H4.1: Women in non-­scheduled castes exhibit higher homophily than women in lower castes. If women in higher castes are subject to more stringent traditional ideals, they may be more affected by homosocial norms and thus have higher homophily than women in scheduled castes. I consider women to be of a “higher caste” if they Teter 13 are not coded as being in a scheduled caste, scheduled tribe, other backward caste, minority, or roman catholic. H4.2: Women in non-­scheduled castes have less dense business-­related networks. The second theory as to why upper caste women know less about finances is that higher castes tend to be richer and women don’t need to have much business savvy since their second income is not necessary. If business knowledge is unnecessary, so must be business-­‐oriented social connections. We will expect that if these women have no need for business knowledge, they will have less dense (that is, fewer connections per person) connections than they do in expressive networks. If the gap were due to propriety, we would expect that they would attempt to make more same-­‐gender instrumental connections to compensate. 3.3 Metrics To measure homophily, I use the Inbreed Homophily Index (IHI) presented by Currarinis, Jackson and Pin. The index is calculated as follows: There are Ni individuals in a group of N total individuals, for i=1,…k different groups. In this case, N1 is the number of men in a village and N2 is the number of women. wi= Ni/N is the proportion of a population that is in group i. The most basic homophily index Hi measures the number of ties to others in group i (si) over the total number of same and different ties with group i (si + di). That is, Hi = si/(si+di). Teter 14 If an individual’s choice of whether to connect with another person is independent of the second person’s type, then the proportion of same ties they make will average out to the proportion of those of the same type in their group. That is, if individuals do not care about the type of the people they connect with at all then Hi = wi. This case is referred to as baseline homophily. If Hi>wi, they prefer others of the same type and exhibit inbreeding homophily. If Hi<wi, they prefer people of other types and exhibit heterophily. The Inbreeding Homophily Index (IH) accounts for the size of the group: IH = (Hi-­‐wi)/(1-­‐wi) If IH=0, we see that individuals show no discrimination based on type and there is baseline homophily. If IH is positive, there is inbreeding homophily. If IH<0, there is Heterophily. 3.4 Data Each individual answered the survey questions listed above with a list of individuals. The responses were translated into unique identification numbers assigned to the individuals. I transformed this data by counting the number of same-­‐
type and other-­‐type connections for each gender, in each village and for each of the ten social networks. I then calculated the IH of each group. This resulted in a dataset of 75 villages times 2 genders times 10 social networks, or 1500 separate homophily indices to consider. A problem arises in that the gender of the person cited as a connection is only known if that person was surveyed as well. Only about half of households were Teter 15 surveyed, and participants were not selected randomly; households were surveyed if they had a woman of working age (around 16 to 65 years old) who could potentially be a participant in the sponsoring microfinance program coming to the village. As such, a disproportionate number of ties that exist but are left out of the data must be to men (though ties to especially young and old women were also left out). This will result in a conservative estimate of si and wi for men and an inflated measure of si and wi for women. Furthermore, the relationships exhibit a shockingly low level of reciprocity. When asked to name their relatives, those surveyed mutually cited one anther in less than 10% of connections in nearly every village. One would expect for other relationships to be somewhat non-­‐reciprocal, but naming one’s relatives should be an objective and mutual task. It could be that people ran out of patience with the survey, forgot those they cared less about, or defined family in different ways. In any case, one must consider the connections represented in the data to be a subset of all actual connections. 4. Analysis 4.1 The Homosocial Norm Below is a graph of the average IH indices for men and women in each type of social network. First, average homophily is well above zero with the exception of women asking others for help with a personal decision. This would suggest that Mehta’s homosocial norm exists in this cultural context—that is, there is strong Teter 16 support of hypothesis 1.1. The only time that a group appears to prefer the other gender is that women prefer to ask men for help with personal decisions. 1 0.8 0.6 0.4 female male 0.2 0 -­‐0.2 -­‐0.4 Using a simple difference of two means test, the difference in IH between men and women is significant with 95% confidence in every type of social network except krgo (“Who would come to you if he/she needed to borrow kerosene or rice?”). Men exhibit higher homophily than women in every case except krcome (Who comes to you to borrow kerosene and rice?). Perhaps this is because women tend to be the ones using kerosene and rice, or maybe men just like to be neighborly towards women. One could interpret this across-­‐the-­‐board gender difference in two ways: men care more about the homosocial norm and self-­‐enforce to a greater extent, or men are held to a higher homosocial norm by their wives and female family. There is no clear way to distinguish between the two given this data. Teter 17 Separating the types of social networks into expressive and instrumental yields the following average IH values: Including helpdecision, giveadvice, and medic Instrumental Expressive Male 0.688 0.880 (0.265) (0.138) Female 0.440 0.665 (0.381) (0.274) t-­‐value 16.137* 13.872* Omitting helpdecision, giveadvice, and medic Instrumental Expressive Male 0.725 0.880 (0.262) (0.138) t-­‐value1 16.442* 12.248* t-­‐value 11.174* Female 0.640 0.665 1.431 (0.248) (0.274) t-­‐value 5.378* 13.872* In support of hypothesis 1.2, homophily is significantly higher on average for expressive relationships. If helpdecision, giveadvice, and medic are omitted, the difference is not significant for women. Men do have strictly and significantly higher homophily for instrumental networks than women, whether or not the borderline connections are omitted. This could be due to a lessening of the homosocial norm when it is understood that there is some secondary purpose to the relationship. 1 These are the t-­‐values for the difference of means test between the IH values in that column or row. Teter 18 4.2 Gender Equality in Instrumental Networks On that vein, homophily of women in instrumental networks is actually the lowest of the genders and types regardless of whether you include the borderline connections—a clear indication that hypothesis 2.1 does not hold. Women may be constrained in terms of with whom they make instrumental connections (their IH is still positive), but they are less constrained than they are in an expressive setting. Looking at the density of the social networks (that is, the mean number of ties per capita): Including helpdecision, giveadvice, and medic Instrumental Expressive Male 1.104 1.432 (0.258) (0.341) Female 1.120 1.459 (0.261) (0.319) t-­‐value -­‐1.315 -­‐1.142 Omitting helpdecision, giveadvice, and medic Instrumental Expressive Male 1.167 1.432 (0.277) (0.341) Female 1.192 1.459 (0.272) (0.319) t-­‐value -­‐1.468 -­‐1.142 t-­‐value 19.556* 20.969* t-­‐value 12.867* 13.585* Women do have significantly more dense expressive connections than instrumental connections, but so do men. This would indicate that people simply need fewer instrumental connections in these villages, not that women are not able to make as many connections. In terms of density, we see no evidence of constraint. Hypothesis 2.2 holds, but loses its meaning considering that men have the same difference in density. Teter 19 It is possible that women are constrained in settings that are uniquely monetary. The only network connections that involve monetary exchange are borrow and lend. If we look only at average IH for borrow and lend, and krcome and krgo (the other connections where a transaction occurs): 1 0.9 0.8 0.7 0.6 0.5 female 0.4 male 0.3 0.2 0.1 0 borrow lend krgo krcome Men do prefer men more strongly when a transaction involves money (as do women, but only slightly), in support of hypothesis 2.3. This may just be because more women perform household duties that require kerosene and rice and are more likely to go seeking it (this would explain the difference in male IH for krgo and krcome), but in any case it is worth noting that 85% of the people men will lend to are also men. This is only 50 rupees, but one could imagine that a greater amount could only inflate the effect. This would certainly be a stumbling block to any woman seeking funding for a business. Teter 20 4.3 Hierarchy versus Gender Preference Men were identified more often than women as local leaders, inclusive of duplicates for each individual. Many people were listed more than once: One man in village 65, for instance, was listed 332 times. If we look simply at the identity of everyone listed but count each only once, women are listed as leaders more often than men. Even if we assume that every person not surveyed (if they were not surveyed, we do not know their gender) is male, there are still more women “leaders” than men. Male Female Not surveyed Total With duplicates 20724 Without duplicates 7571 42.8% 43.1% 13030 9424 26.9% 53.7% 14700 557 30.3% 3.2% 48454 17552 Individual men were each listed by significantly more people than women—
86% of those listed more than once were men. It may be that women are leaders, but men are more important, obvious ones. Clearly this is an imperfect measure and leaves much ambiguity as to the structure of the hierarchy in these villages. If one does choose to continue on the assumption that men hold higher positions in the town hierarchy, I consider the ratio of men’s to women’s homophily in instrumental versus expressive networks. If this ratio is higher in instrumental networks, as reasoned in section 3.1, then there is evidence that the hierarchy is exerting some effect on the choice of male instrumental partners. Teter 21 Simply taking the ratio of IHmen/IHwomen is unwieldy since the two homophily indices often take opposite signs and grow quite large with values of IHwomen that lie close to zero. Instead I consider (IHmen+1)/(IHwomen+1). This still captures the difference between the two, but recalibrates the inbreeding homophily index so that 1 indicates baseline homophily. Including helpdecision, giveadvice, and medic Instrumental Expressive t-­‐value 1.283 1.157 -­‐5.761** (0.517) (0.209) Omitting helpdecision, giveadvice, and medic Instrumental Expressive t-­‐value 1.085 1.157 4.385** (0.281) (0.209) If helpdecision, giveadvice, and medic are included in the data, then hypothesis 3.2 holds: the ratio of male to female homophily is higher in instrumental networks, suggesting the presence of a hierarchy effect. However if we leave these non-­‐transaction relationships out, the ratio is higher in expressive networks. That doesn’t rule out the presence of a hierarchy effect, it just indicates that it is not as strong as the opportunity cost of the homosocial norm. Clearly, it is difficult to make any strong statements as to the difference in hierarchical and gender preferential effects in this homophily data. 4.4 Upper Caste Restrictions To further explore the causes of Field et al’s observation that women in higher castes know less about business and financial literacy, we first observe whether women in higher castes appear to be more constrained by a homosocial Teter 22 norm. I do not consider each individual’s caste because most of the villages only contain a few members of “upper” castes, and enforcement of a social norm requires a critical mass of members to enforce it. I instead regress the IH for each group on the proportion of non-­‐scheduled caste members in a village (pgeneral), omitting the homophily of men since women are the demographic of interest. If people of higher castes more strictly enforce the homosocial norm, we expect a significant if slight increase in average homophily as the proportion of upper caste population grows. pgeneral Ntotal R-­squared 1 0.126 2 0.132 (0.085) (.084) 0.003 0.0006** (0.0002) 0.0138 The proportion of upper caste population may have a slight upward affect on average homophily, but the difference in not significant with 90% confidence and the r-­‐squared value is particularly low—that is, hypothesis 4.1 is plausible but not sufficiently supported. Attempting to control for the size of the village, which may conceivably indicate level of modernity in a village, does not make the coefficient significant. This data cannot sufficiently support that upper caste women in this sample are any more concerned with homosocial norms than are other women. Next we test whether upper caste women simply have no need for business knowledge (and by extension, business connections). If this is the case, we expect that upper caste women have less dense instrumental networks than other women. Teter 23 Again, I regress the proportion of upper caste people on the density (ties per person) for all female groups IH indices. pgeneral Ntotal R-­squared 1 0.263** 2 .275** (.073) (.072) 0.0008** (.0001) 0.02 0.0445 The coefficient on pgeneral is significant, but it’s positive-­‐ the opposite of what was expected. Hypothesis 4.2 does not hold, so it would appear that the upper caste women in these villages are not any more constrained than their lower-­‐caste counterparts—they may even be more connected. It is worth noting that every village in the study was being considered as a place to start a microfinance program, meaning even the women in upper castes lived in these relatively poor places. Even though we see no wealth effect here, it could just be that none of the upper caste women were rich enough that they didn’t need to work. 5. Conclusion There are many other factors that determine whether women are able to start businesses—allocation of property rights and time spent on household production, to name two—but the structure of any person’s social network is indicative of their ability to access resources they need. The women in these particular villages will have easier access to those resources as a more formal credit market moves into their villages, loaning only to them and not to their husbands. Teter 24 The choice to lend only to women is one of efficiency and fairness, but as far as this data show there is no issue of fairness between men and women. Both genders are affected by a homosocial norm that sways them towards associating with their own gender. It is unclear whether the enforcement of the norm is internal or external, so we cannot tell whether people make same-­‐gender acquaintances due to their moral compass or their fear of their spouses’ and families’ disapproval. Men seem to be more strongly affected by this norm than women in any type of relationship, and as a result exhibit higher average homophily. In theory, that means women were pointing at men who were not pointing back (the relationship was not necessarily reciprocated). It may be that men are willing to participate in other-­‐gender associations less often than women, but I believe the more likely explanation is that they were biased against listing women in the survey. If women correctly identify who would be willing to associate with them, then they are certainly no more constrained in their ability to make connections by the homosocial norm than men are. In instrumental relationships that might be useful for business, both genders are less restricted in their choice of associates than they are in social situations. Also, women in non-­‐scheduled castes seemed to be no more constrained than women in lower castes; homophily and network density were statistically indistinguishable in villages with higher non-­‐scheduled caste population. Of course, people don’t choose their associates based only on their gender. Another major factor could be the economic or political hierarchies in the villages. Men tend to be leaders in these places and we expect that people will want to Teter 25 associate with leaders to gain access to resources, so male homophily should inflate and female homophily should deflate. The test I performed was unable to tease out the difference between the effect of the hierarchy and the effect of the gender preference; further study should use more sophisticated centrality methods to place each individual within his town’s hierarchy and more thoroughly analyze how people point “up” the ladder. The existence of a homosocial norm appears to affect both genders equally—
so we see no evidence of a fairness concern. The norm may, however, cause an efficiency drag since people are discouraged (if slightly) from potentially advantageous other-­‐gender connections. The introduction of a formal credit market should diminish any negative effects of this cultural norm, and generally making business and entrepreneurial funding more accessible for both genders.
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