Gender Differences in the Provision of Job-Search Help

Abstract

The existing literature has well studied the use of social contacts in job search, including gender inequality, in using social contacts. What is missing is the perspective of social contacts who help others find jobs. Using a large data set from the 2012 China Labor-Force Dynamics Survey, this study reveals significant gender differences in the provision of job-search help. Compared with women, men are more likely to provide job-search help and especially show a greater likelihood of exerting direct influence on the hiring process. While women are gender neutral in their choice of help recipients, men display a selective preference for helping other men. This men’s advantage of providing job-search help, especially influence-based help, and men’s selective preference for helping other men, imply another prominent gender inequality in informal hiring in the labor market. This study suggests several theoretical propositions to explain the revealed gender differences in both “whether to help” and “whom to help,” providing a starting point for further research.

Keywords

employment gender job search labor market social networks

Sociologists have paid much attention to the use of social contacts in finding jobs and have explored the links between the use of social contacts and diverse labor market outcomes such as wages, occupational prestige, occupational choice, job loss, and turnover (Bentolila, Michelacci, and Suarez 2010; Bian, Huang, and Zhang 2015; Calvó-Armengol and Jackson 2004; Granovetter [1974] 1995; Lin 2001; Lin, Ensel, and Vaughn 1982; Marsden and Hurlbert 1988; McDonald, Benton, and Warner 2012; Montgomery 1992; Mouw 2003, 2006). There is a wealth of evidence in support of gender inequality in contact use in job search (Huffman and Torres 2002; McDonald 2011; McDonald, Lin, and Ao 2009; Reskin and McBrier 2000; Tian and Liu 2018). Women are generally disadvantaged in informal hiring and men have more social resources that translate into (better) jobs.

This literature has largely been from the perspective of job seekers, however. What is missing is the perspective of social contacts who help job seekers find jobs. We know little about who are more likely to become help providers and what characteristics they possess. Simply by looking at gender differences in social resources embedded in job seekers’ networks, we cannot obtain a complete picture of gender inequality in job searching processes. Suppose Person A has a network containing more social resources than Person B. If B’s social contacts are more willing to provide help, B may find a (good) job as easily or even more easily than A. Jobs generated by social contacts “cannot be explained simply by the resource content of networks” (Marin 2012, 181), and social contacts do not always help, “even if they are well positioned to do so” (Smith and Young 2017, 172). Embeddedness in networks with rich social resources does not equate to being able to use the potential resources in a job search “because contacts actively decide with whom and under what conditions they will share their resources” (O’Connor 2013, 594). It is thus important to ask who are more likely to help and how their characteristics influence the way they help.

A better understanding of actual helpers’ characteristics will shed light on how men and women differ in their activation and mobilization of social resources in the labor market. It is known widely that being a member of men-dominated networks significantly increases a person’s labor market opportunities (Drentea 1998; Hanson and Pratt 1991; Huffman and Torres 2002; McDonald and Day 2010; Mencken and Winfield 2000). The popular explanation is that men have more valuable social resources (Huffman and Torres 2002; McDonald 2011). Another plausible but understudied mechanism is that men may be more disposed to provide job-search help than women. This study fills this lacuna and investigates the gender difference in providing job-search help. In addition to the unequal ability of acquiring job-search help, the gender difference in the ability to actually produce job-search help for others is another important aspect of gender inequality. Helping others with their job search brings the helper confidence and a favorable reputation. It may also give rise to tangible benefits, as the person helped is likely to return the favor in the future.

This study recognizes that not all help is the same and that help can be at different levels. It pays special attention to influence-based help that entails more cost and effort on the part of the helper. Directly exerting influence on the employer is very different from simply providing information about a job opening. It entails a much higher level of commitment to helping others. Certain kinds of help providers are more likely to go out of their way to help. This study also investigates the gender difference in providing influence-based help.

Using a large dataset from the 2012 China Labor-Force Dynamics Survey (CLDS), this study finds that men are not only more disposed to provide job-search help but also tend to use more influence in their help than women, even after other factors are taken into account. Men and women also are different in choosing whom to provide influence-based help. While women are gender neutral in their choice of help recipients, men display a selective preference for helping other men. This men’s advantage of providing job-search help, especially influenced-based help, and men’s selective preference for helping other men, implies another prominent gender inequality in informal hiring in the labor market.

To explain these gender differences in the provision of job-search help, I propose two theoretical propositions. First, because of structural discrimination and cultural bias against women, women are less likely to provide job-search assistance, especially influence-based help, to others. Structurally, women have less access to workplace power, even when they occupy similar positions to men, thereby limiting their capacity to provide help. Culturally, it is perceived to be more “feminine” for women to abandon their autonomy and follow the ideal of feminine passivity, which suppresses their predilection for using their resources and power to help others. Second, as a result of gender-based homophily or homosocial reproduction, individuals are more likely to provide job-search assistance to others of the same gender. Among men helpers, this tendency is further reinforced by the biased cultural belief that men are more competent and thus more worth helping than women. Among women helpers, however, the homosocial reproduction tendency is offset by the cultural bias disadvantaging women job seekers. As a result, women are gender neutral in their choice of help recipients, but men display a strong selective preference for helping other men. These theoretical propositions provide a starting point for further research that can better flesh out the mechanisms underlying the gender differences in the provision of job-search help.

Provision of Job-Search Help

Research on the role of social contacts in job search mainly focuses on the receiving side of the help, examining who receives more help in job searching processes and the benefits resulting from the help. However, on the supplying side, potential help providers have agency in choosing whether and whom to help (Marin 2012; Smith 2005; Smith and Young 2017). For instance, Marin (2012) found that when the entry-level white collar workers she interviewed had opportunities to provide information about job openings to their social contacts, they did so only 27 percent of the time. Smith (2005) also found that the young and low-income African Americans she interviewed often are reluctant to help their contacts and forgo the opportunities to help others’ job searches. Similar data are not available for the Chinese labor market, but it is certain that those with social resources do not always provide help to their contacts.

While empirical studies on job-search helpers are scarce, there are a few notable exceptions. They are mostly based on qualitative research and interview data (Marin 2012; Smith 2005; Smith and Young 2017). These qualitative studies generated insightful knowledge about the decision-making process of individuals deciding whether to provide others with job-search assistance or not. They show that potential help providers are selective about whom to help and are concerned about their own reputation when helping others. Nevertheless, their samples are nonrandom and from relatively homogenous social groups. For instance, Smith’s work (2005) was based on in-depth interviews of low-income urban African Americans, while Marin (2012) used interview data from a sample of 37 insurance agents employed in a Toronto call center. The recent work by Smith and Young (2017) is based on 146 interviews with a nonrandom sample of workers at one Californian public sector employer. Quantitative studies based on survey data (Hamm and McDonald 2015; O’Connor 2013) also make important contributions to our knowledge about the provision of job-search help in much larger populations. For instance, using the 2002 and 2004 waves of the U.S. General Social Survey data, Hamm and McDonald (2015) investigated racial differences in providing job-search help in the United States and found that in general blacks offer more frequent job-search assistance to friends than do whites. Their study mainly focuses on the effect of race. Gender is not discussed and only appears in their analysis as a control variable. In their results, women generally provided less job-finding help than men, although the gender gap was not always significant. O’Connor (2013) surveyed approximately 350 Washington State residents on their provision of job-search help and identified their characteristics. While gender was not its major focus, the study revealed a significant gender gap and found that men are more likely to provide help than women. Nevertheless, it focused exclusively on solicited help provided to active job seekers and left out unsolicited help provided to nonsearchers. Nonsearch is very common in the labor market, however, and unsolicited help constitutes a large part of job-search assistance (Granovetter [1974] 1995; McDonald and Elder 2006; McDonald et al. 2016).

The lack of large-scale survey data is a major impediment to the research on the part help providers play in job searching processes, and data collection and analysis often “overlook or simplify their role” (O’Connor 2013, 593). The 2012 CLDS offers a rare opportunity for examining job-search helpers. It is a nationwide representative data set and contains rich information on individuals who have assisted others in finding jobs. While the wide use of social contacts in job-seeking activities has been well documented and studied in the Chinese context (Bian 1997; Bian and Huang 2015; Bian, Huang, and Zhang 2015; Shen and Bian 2018; Tian and Lin 2016; also see the review by Bian 2018), there has been no research from the perspective of job-search helpers, let alone on gender inequality in the provision of job-search help.

Gender Differences in “Whether to Help”

Two main characteristics of individuals may promote the provision of job-search help in general. One factor is socioeconomic status. Socioeconomic status is closely related to access to social resources, and individuals with higher socioeconomic status have better resources that can be used to help others’ job searches (De Graaf and Flap 1988; Lai, Lin, and Leung 1998; Lin 2000; Marsden and Hurlbert 1988). They also know better how to help job seekers than their lower-status counterparts (O’Connor 2013). Another factor is the level of activity in one’s social interactions. More active interactions with social connections give rise to more occasions to offer help. Socially active individuals should have more chances to mention job opportunities and to offer help to others in their networks.

I argue that there is gender inequality in help provision even after the two above factors have been taken into account. Even with the same socioeconomic status and level of social activity, men are still more likely to provide job-search help. Also, men are more likely to use influence in their help than their women counterparts. Scholars usually distinguish two major types of help, information and influence. While information is the most common way to help, influence reflects more effort and commitment made by the helper (Lin 2001). More often than not, information exchange between helpers and job seekers is spontaneous and not purposeful (McDonald and Elder 2006; McDonald et al. 2016; Tian and Liu 2018), so information may not be as good an indicator of the helper’s disposition to help as influence. The helper incurs more costs and risks her own reputation when trying to influence the hiring process directly by putting in a good word or vouching for the job seeker (Bian, Huang, and Zhang 2015; Lin 2001; Marsden 1994).

Structural Discrimination

Men are expected to show a higher likelihood of providing job-search help, especially influence-based help, than women for intertwined structural and cultural reasons. For structural reasons, individuals of similar status and social activity still may have different capacities for offering job-search help. Gender-based discrimination and occupational segregation restrict women’s access to networks of power and influence (England 2010; Reskin 1993). Even with similar socioeconomic status, women may have less access to powerful contacts and possess fewer influential connections than their men counterparts (Ibarra 1997; McGuire 2002). Also, their connections with influential contacts are less intensive and thus less ready for use for instrumental purposes. Women often are isolated from informal social interactions in their work, which reduces the range and intensity of the connections they can establish (McDonald 2011). They encounter more difficulties in translating their organizational positions into usable network resources (Tian and Liu 2018).

In contrast, men’s work provides better opportunities for building useful social connections. They are better able to build larger and more diverse networks that offer greater access to job leads and hiring processes. The structural challenges for women to develop usable and influential connections in work settings may not only “hinder their future opportunities for informal recruitment and ultimately limit their earnings potential” (McDonald 2011, 1673) but may also hurt their ability to cumulate resources, especially influence, that can be utilized to help others’ job searches. Taken together, relative to men, women face more structural exclusion from workplace networks that control access to information, resources, and opportunities needed to enable job-search help (Elliott and Smith 2004; McGuire 2002). The gender difference in the provision of help reflects a structural inequality in accessing workplace power (Elliott and Smith 2004).

Cultural Bias

Cultural factors may also play a role. The ideal of feminine inferiority and passivity has long been found present across different cultures and social classes (Flora 1971; Lindsey 2016). As summarized by Stycos (1955, 29), “almost universally the woman is seen as inferior to the man, and a system of rationalization is typically constructed to justify the belief and the accompanying dearth of privileges for the female.” Under the influence of the deep-rooted belief in feminine inferiority and passivity, it is perceived more culturally appropriate for women to receive than initiate help, and this constrains women’s predisposition to provide job-search help to others.

Similar socio-cognitive bias (Ridgeway 1997) looms large in informal job matching processes and gives rise to gender disparities on the job market. Women are perceived to be less competent and committed at work than men. These widely shared cultural beliefs about gender systematically bias the behaviors and assessments of otherwise similar men and women (Ridgeway and Correll 2004). Women themselves may take in these stereotyped perceptions and thus undervalue their own social resources and influence. Within an organization, women’s opinions are not as well respected as those of their men counterparts. Women often are held to higher standards and placed under more scrutiny, which contributes to a greater aversion to taking risks. When they take the risk of helping others, they may incur more costs, such as greater damages to their own reputation and networks from botched referrals. All these depress women’s confidence in providing help, especially vouching for others. Women face greater difficulty materializing potential social resources and influence (Carli 1999) and turning them into instrumental use, and men may feel more comfortable about using their resources and influence in helping others’ job searches.

Hypothesis 1: Men are more likely to provide job-search help than women, other things being equal.

Hypothesis 2: Men are more likely to use influence in job-search help than women, other things being equal.

Gender Differences in “Which Gender to Help”

The choice faced by the help provider is not only whether to help, but also whom to help. Another potential gender difference lies in whether men and women choose to provide more help to others of a certain gender. There is no consensus in the existing literature. Some studies seem to suggest a gender-based selective preference, but they differ in which gender is more likely to be helped (Huffman and Torres 2002). These competing perspectives need to be adjudicated by empirical data.

Cultural Gender Bias

First, empirical studies on gender differences in informal job matching generally find that reliance on informal hiring methods disadvantages women job seekers (Fernandez and Mors 2008; McDonald and Day 2010; McDonald and Elder 2006; Reskin and McBrier 2000) and advantages men in receiving help in job searches (McDonald, Lin, and Ao 2009). Salient cultural beliefs often lead to the stereotyped assumption that women are less competent than men on a job due to more family obligations and less career commitment (Ridgeway 1997; Ridgeway et al. 1998). Helpers may make less effort to help women because they see less value and more risks in recommending women job seekers (Smith 2000; Son and Lin 2012). Consequently, women may receive significantly less help from social connections than similarly situated men. It is not only men but women also who may take in this bias against other women. Ridgeway (1997) suggested that women are not immune from these stereotyped beliefs, so like men, women may also provide more help to men than to women. Given the gender bias due to cultural beliefs that rank women below men, both men and women helpers are less willing to provide their help to women than men (McGuire 2002).

Hypothesis 3-1: Helpers (both men and women) are more likely to use influence when providing job-search help to men than to women.

Homosocial Reproduction

It may not be a particular gender that receives more committed help. Instead, helpers may like to help others of the same gender, which is summarized in the gender-based homophily or homosocial reproduction thesis. Gender homophily suggests that people of the same gender have a greater chance of establishing close connections (McPherson, Smith-Lovin, and Cook 2001). The literature from the perspective of the job seeker often finds that job seekers are more likely to rely on others of the same gender as job contacts (Fernandez and Sosa 2005; Hanson and Pratt 1991; Ibarra 1997; McDonald 2011). Two mechanisms may explain this same-gender preference, choice, and induced homophily (McPherson, Smith-Lovin, and Cook 2001). Helpers are expected to prefer to help others of the same gender because of preferences for same-sex social relationships (choice homophily) and limited opportunities to interact with individuals of the other gender (induced homophily).

Homosocial reproduction stresses a similar mechanism. Coined by Moore (1962) and Kanter (1977), homosocial reproduction refers to “the tendency of people to select incumbents who are socially similar to themselves” (Rivera 2013, 377). The underlying idea is that social similarity facilitates smooth communication and mutual trust (Elliott and Smith 2004). Homosocial reproduction is a type of “in-group favoritism” and can apply to the provision of job-search help. Helpers may try to maintain relative social homogeneity in the organization by choosing to help those of the same gender. Helpers often categorize potential help recipients into in-groups and out-groups subconsciously and bias treatment of potential help recipients because of gender (Elliott and Smith 2004; Reskin 2002).

Hypothesis 3-2: Helpers are more likely to use influence when providing job-search help to others of the same gender.

Cultural Gender Biases and Homosocial Reproduction Combined: Selective Men and Neutral Women

Another possibility is that there is no uniform pattern in the choice of help recipients. Men and women may display differing patterns, when we consider the interplay between cultural gender biases and homosocial reproduction. Among men helpers, both gendered cultural bias and gender-based homosocial reproduction lead to a greater tendency to provide help to men job seekers. The two influences reinforce each other and generate among men helpers a strong selective preference for helping other men. In contrast, among women helpers, the preference to help other women resulting from the homosocial reproduction tendency is offset by the cultural bias that women job seekers are less competent. The two countervailing tendencies among women helpers render their choice of help recipients more gender-neutral. Taken together, men and women may significantly differ in choosing whom to provide help.

Hypothesis 3-3: Men are particularly likely to use influence to help men, while women are equally likely to help men and women.

In other words, among the four possible gender pairs including men helping men (men–men), men helping women (men–women), women helping men (women–men), and women helping women (women–women), the difference between the men–men pair and the men–women pair is significant while the difference between the women–men pair and the women–women pair is not.

Methods

I analyze a large data set from the 2012 CLDS conducted by the Centre for Social Science Survey at Sun Yat-sen University in China. The CLDS is the first national social survey targeted at the labor force in China. Thanks to a carefully designed sampling scheme, the CLDS data are “nationally representative” (Wang, Zhou, and Liu 2017, 85). The sample is drawn from all 29 provinces (including municipalities and autonomous regions) in China except Hainan, Tibet, Hong Kong, Macao, and Taiwan. The CLDS strictly implements a multistage “probability-proportional-to-size sampling (PPS),” and population size; administrative units and socioeconomic status (SES) are used as main stratification variables (see Wang, Zhou, and Liu 2017 for details).¹ More information about the CLDS can be found at http://css.sysu.edu.cn, and the data are available at http://css.sysu.edu.cn/Data/Main (accessed November 2016).

Dependent Variables

The first dependent variable looks at general attempts individuals have made to help others’ job searches and is captured by the question “Did you inform anyone of a job opportunity in the past year?” This variable is binary, with 1 indicating yes and 0 no. The second dependent variable measures more committed job-search help. Those who had provided help were further asked this follow-up question: “Regarding the person you most recently helped, besides mentioning this job opportunity, did you directly recommend this person to anyone involved in hiring?” Positive responses, coded as 1, indicate the use of influence in job-search help, while negative responses are coded as 0, suggesting no further help provided beyond information.

Independent Variables

There are three types of independent variables. The first set is about the characteristics of all individuals surveyed—both helpers and nonhelpers—such as their socioeconomic status, social activity, and main demographic attributes including gender and age. Socioeconomic status is captured by education and income. Education is the highest level of education received measured on a seven-point scale, with 1 indicating no education, 2 elementary school, 3 junior high school, 4 senior high school, 5 junior college, 6 university, and 7 postgraduate education. Income is the amount of annual income (in Chinese Yuan) received last year. Social activity is the number of individuals the respondent keeps contact with (excluding working relationships) on an average day, measured on an eight-point scale (1 = 0 people, 2 = 1-2 people, 3 = 3-4 people, 4 = 5-9 people, 5 = 10-19 people, 6 = 20-49 people, 7 = 50-99 people, and 8 = 100 or more people). Gender is a binary variable, with man coded as 1 and woman as 0. Age is measured in years. The effect of age may not be perfectly linear but inverted U–shaped, so I also include a quadratic term of age.

The second set of independent variables is about help recipients. For those who had experience of helping others, the CLDS asked them follow-up questions about key characteristics of those they helped, including their gender, age, and education. These three variables are measured in the same way as those discussed above.

The third set of independent variables is about the relationships between help providers and recipients, including tie strength, search signal, and gender patterns. I control for tie type and search signal, as they may be relevant to the likelihood of influence-based help. It is often believed that people are more motivated or even obligated to help their strong ties (Bian 1997; Bian, Huang, and Zhang 2015; Kim and Fernandez 2017; Marin 2012; Obukhova 2012). People may also be more likely to go to great lengths to help others if this help is solicited (i.e., there is an explicit search signal), because people usually do not want to be intrusive (Marin 2012). The tie between the helper and the helped is captured by this question: “Regarding the person you most recently helped, which item below best describes the relationship between you and that person? That person is (1) a family member, (2) a friend, (3) an acquaintance, (4) a friend of friends (including a friend of family members, a family member of friends, and a friend of friends), and (5) someone you barely know.” I generate a binary variable, tie strength, with 1 indicating a strong tie and 0 a weak tie. The strong tie category contains (1) and (2), while the weak tie category combines (3), (4), and (5).

Search signal measures whether the help is solicited and is captured by the question: “Regarding the person you most recently helped, how did you know that person was looking for a job?” The responses fall into three categories: (1) “That person had no job,” (2) “That person had a job but told me he or she was looking for a new job,” and (3) “That person had a job and had never told me that he or she was looking for a new job.” I keep the three categories and generate three dummy variables. Some help is explicitly sought, whereas other help is unsolicited and takes place when the help recipient does not engage (at least not actively) in a job search (Granovetter [1974] 1995; Hanson and Pratt 1991; McDonald and Elder 2006; McDonald et al. 2016).

Gender patterns are measured in two ways. First, I create a dummy variable named same gender, with 1 indicating that the help provider and the help recipient are of the same gender and 0 indicating different genders. Second, I create a set of dummy variables including the men–men pair (men helping men), men–women pair (men helping women), women–men pair (women helping men), and women–women pair (women helping women).

Considering the literature on the use of social contacts in the Chinese labor market (Bian 1997; Bian and Huang 2015; Bian, Huang, and Zhang 2015; Shen and Bian 2018; Tian and Lin 2016; Tian and Liu 2018), I also control for state ownership of the respondent’s workplace and the respondent’s Chinese Communist Party (CCP) membership. The state-owned sector has more rigid and bureaucratic procedures for hiring, so social contacts may be used less frequently to acquire jobs. The CCP membership is commonly seen as an important indicator of one’s social resources in China. The state ownership and CCP membership variables are both binary.²

Since both dependent variables are binary, I choose logistic regression in the analyses.³ I define P as the probability of the binary dependent variable equal to 1 and let P be modeled using a logit link function. The model for estimating help provision is specified as follows:

\begin{array}{l} L o g [P / (1 - P)] = β_{0} + β_{1} G e n d e r + β_{2} A g e + β_{3} E d u c a t i o n \\ + β_{4} I n c o m e + β_{5} A c i t i v i t y + β_{6} C o n t r o l s + ε \end{array}

where P is the probability of an individual helping others, βs are the coefficients of the explanatory variables and ε is the error term. This model is applied to the whole sample (all individuals surveyed) to examine what factors promote the likelihood of providing job-search help. Table 1 shows descriptive statistics about all variables used in the analyses of help provision.

Table 1:

Descriptive Statistics for Variables Used in Estimating Help Provision (n=5,448)

Variables	Mean	SD	Minimum	Maximum
Help provision	0.160	0.367	0	1
Gender (man)	0.506	0.500	0	1
Age	49.298	16.257	17	94
Education	3.100	1.426	1	7
Income (in thousands)	23.291	30.953	0	1,000
Social activity	3.279	1.369	1	8
CCP membership	0.122	0.327	0	1
State ownership	0.109	0.312	0	1

NOTE: SD = standard deviation; CCP = Chinese Communist Party.

The model for estimating the use of influence in helping others is specified as follows:

\begin{array}{l} L o g [P / (1 - P)] = β_{0} + β_{1} G e n d e r + β_{2} A g e + β_{3} E d u c a t i o n \\ + β_{4} I n c o m e + β_{5} A c t i v i t y + β_{6} G e n d e r 2 + β_{7} A g e 2 \\ + β_{8} E d u c a t i o n 2 + β_{9} T i e + β_{10} S i g n a l + β_{11} G e n d e r + β_{12} C o n t r o l s + ε \end{array}

where p is the probability of the helper using influence in their help, βs are the coefficients of the explanatory variables, and ε is the error term. This model is applied to the subsample of all individuals who helped with others’ job searches. There are three sets of independent variables. The first set (Gender, Age, Education, Income, and Activity) refers to the characteristics of the help provider, the second set (Gender2, Age2, and Education2) includes the characteristics of the help recipient, and the third set (Tie, Signal, and Gender) contains the help provider-recipient relational variables. Table 2 presents descriptive statistics about all variables used in the estimation of the use of influence.

Table 2:

Descriptive Statistics for Variables Used in Estimating Using Influence (n=874)

Variables	Mean	SD	Minimum	Maximum
Using influence	0.434	.499	0	1
Help provider
Gender (man)	.572	.495	0	1
Age	39.735	12.195	18	84
Education	3.780	1.335	1	7
Income (in thousands)	28.660	37.683	0	1000
Social activity	3.484	1.440	1	8
CCP membership	.131	.337	0	1
State ownership	.154	.362	0	1
Help recipient
Gender (man)	.586	.493	0	1
Age	31.962	9.946	14	71
Education	3.709	1.248	1	7
Provider–recipient relationship
Strong tie	.739	.439	0	1
Search signal
No job	.680	.467	0	1
Working with search signal	.276	.448	0	1
Working without signal	.044	.205	0	1
Same gender	.783	.413	0	1
Gender pair type
Man–man pair	.471	.499	0	1
Man–woman pair	.102	.300	0	1
Woman–man pair	.114	.317	0	1
Woman–woman pair	.313	.464	0	1

NOTE: SD = standard deviation; CCP = Chinese Communist Party.

Gender Differences in Job-Search Help Provision

I first compare the average profiles of the group who provided job-search help and the group who did not. From Figure 1 that shows the comparison, we can see that on average the helper group is more likely to be men, younger, better educated, wealthier, and more active in social interactions.

Figure 1:

Comparisons of Job-Search Helpers and Non-Helpers

I employ logistic regression to estimate the effects of these independent variables on the provision of job-search help. Results are presented in Table 3. Different model specifications (Models 1-4) produce consistent results. We can see that gender, age, socioeconomic status (such as education and income), and social activity all have significant effects on help provision. Individuals of higher socioeconomic status, such as those who are better educated and wealthier, are more likely to provide job-search help to others. Individuals who maintain more active interactions with their social contacts are also more likely to help. In addition, age has an inverted U-shaped effect on help provision, and middle-aged individuals are most likely to help others. Consistent with hypothesis 1, men are more likely to help than women, even after other variables are controlled. The odds of a man providing job-search help are almost 26 percent (e^.230 – 1) higher than those of a woman, when all other variables are held constant. When all other explanatory variables are held at their mean values, the probability of a man providing help is 17.4 percent, in comparison to a probability of 14.6 percent for women.

Table 3:

Logistic Regression of Providing Job-Search Help (n=5448)

Variable	Model 1	Model 2	Model 3	Model 4
Gender (man)	.284^*** (.081)	.230^** (.081)	.230^** (.081)	.230^** (.081)
Age	.070^*** (.01)	.074^*** (.018)	.074^*** (.018)	.080^*** (.018)
Age squared	−.0013^*** (.0002)	−.0013^*** (.0002)	−.0013^*** (.0002)	−.0014^*** (.0002)
Education	.147^*** (.033)	.145^*** (.033)	.149^*** (.034)	.182^*** (.036)
Income (in thousand)	.007^*** (.001)	.006^*** (.001)	.006^*** (.001)	.006^*** (.001)
Social activity		.148^*** (.028)	.149^*** (.028)	.152^*** (.028)
CCP membership			−.056(.132)	−.030(.132)
State ownership				−.364^** (.121)
Constant	−2.830^*** (0.405)	−3.325^*** (0.414)	−3.363^*** (0.421)	−3.596^*** (0.432)
R ²	.117	.123	.123	.125

NOTE: CCP = Chinese Communist Party. Numbers in parentheses are standard errors. From two-tailed tests, ^*P < .05, ^**P < .01, ^***P < .001.

Next, I focus on the group of helpers to see what factors facilitate the use of influence in their help. I estimate the use of influence by different model specifications and the results are presented in Table 4.

Table 4:

Logistic Regression of Using Influence in Job-Search Help (n=874)

Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Help provider
Gender (man)	.404^** (.151)	.458^* (.188)	.409^** (.152)	.383^* (.152)	.380^* (.145)
Age	.024(.032)	−.001(.035)	.018(.032)	.027(.032)	.022(.032)	.021(.032)	.024(.032)
Age squared	−.0003(.0004)	−.0001(.0004)	−.0002(.0004)	−.0003(.0004)	−.0002(.0004)	−.0002(.0004)	−.0003(.0004)
Education	−.149^* (.069)	−.077(.088)	−.149^* (.069)	−.150^* (.069)	−.149^* (.069)	−.149^* (.069)	−.150^* (.069)
Income (in thousand)	.002(.002)	.003(.002)	.002(.002)	.002(.002)	.002(.002)	.002(.002)	.002(.002)
Social activity	.060(.049)	.071(.051)	.058(.058)	.057(.049)	.061(.049)	.060(.049)	.058(.050)
CCP membership	−.171(.241)	−.124(.224)	−.206(.242)	−.153(.241)	−.158(.241)	−.153(.242)	−.134(.242)
State ownership	−.193(.219)	−.204(.223)	−.208(.219)	−.190(.220)	−.184(.219)	−.177(.219)	−.175(.220)
Help recipient
Gender (man)		.045(.186)
Age		.017(.049)
Age squared		−.0002(.0007)
Education		−.127(.087)
Strong tie			−.253(.170)
Search signal
No job				.346(.211)			.348(.212)
Working with signal				.501^* (.225)			.504^* (.226)
Working without signal				Ref.			Ref.
Same gender					.192(.179)
Gender pair type
Man-man pair						Ref.	Ref.
Man–woman pair						−.266^* (.131)	−.266^* (.128)
Woman–man pair						−.553^* (.238)	−.535^* (.239)
Woman–woman pair						−.421^* (.172)	−.399^* (.172)
Constant	−.489(.747)	−.219(1.019)	−.150(.790)	−.907(.855)	−.619(.757)	−.006(.753)	−.448(.862)
R ²	.101	.118	.110	.115	.107	.120	.125

NOTE: CCP = Chinese Communist Party. Numbers in parentheses are standard errors. From two-tailed tests, ^*P < .05, ^**P < .01, ^***P < .001.

Model 1 only contains the characteristics of the helper. When other variables are controlled, men are significantly more likely to use influence to help others, supporting hypothesis 2. This gender difference is consistently significant across different model specifications. Age and activity in social contacts show no significant effects on the use of influence. Two socioeconomic status variables diverge in their effects. Education has a significantly negative effect, while income shows no effect. Hence, among help providers, more educated individuals are actually less likely to exert influence on the employer and are instead more likely to simply provide information.

Model 2 further incorporates key characteristics of the help recipient including age, gender, and education. After controlling for the characteristics of the help provider, none of these variables show any significant effect. The insignificant effect of the help recipient’s gender does not support hypothesis 3-1. Men do not always receive more influence-based help, in comparison with women.

Next I explore the relational effects. Model 3 examines whether the strength of the tie between the help provider and the recipient affects the use of influence. Tie strength shows no effect on the use of influence; a helper does not discriminate against weak ties in using influence to help. While a helper may have more motivations to go out of her way to help a strong tie due to affective bonds, using influence on behalf of a weak tie actually has several advantages. Using influence to help weak ties tends to entail less conflict of interest; it is often less problematic to recommend a weak tie to the employer than a strong tie such as a family member. Also, since people have far more weak ties than strong ties, the greater pool of weak ties implies that a weak tie should be a better fit for the job than a strong tie. Hence, a weak tie is expected to perform better in the job so recommending a weak tie is less risky. Taken together, strong ties and weak ties have their own comparative advantages in mobilizing influence-based help, and consequently the effect of tie strength does not show significance overall.

Model 4 investigates the effects of search signals and examines whether solicited help facilitates the use of influence. A helper is most likely to use influence to help those who are currently working but clearly intend to change jobs. On the one hand, these job seekers have given the helper a clear signal that they welcome help. On the other hand, having a job already shows that they are “workable” and competent, which gives the helper confidence in vouching for them.

Model 5 assesses the effect of same-gender preference on the use of influence in job-search help. The effect of helping the same gender is not significant, which lends no support to hypothesis 3-2. There is no empirical evidence in support of general same-gender preference (i.e., men prefer helping men and women prefer helping women).

Finally, I compare the four types of gender pairs (men helping men, men helping women, women helping men, and women helping women) in model 6. Model 7 further controls for search signals that are found to have significant effects on providing influence-based help. Models 6 and 7 generate consistent results. After other variables are accounted for, the likelihood of men using influence to help other men is the highest, followed by men helping women, women helping other women, and finally women helping men. I also test the difference between every two pairs. While the difference between the men–men pair and the men–women pair is significant, the difference between the women–men pair and the women–women pair is not. As suggested by hypothesis 3-3, women show no gender bias in their choice of the helped, but men display a significant selective preference for helping other men. Moreover, the difference between the men–men pair and the other three pairs are all statistically significant. Men helping men is significantly more common than all the other gender pairs.

Taken together, the results reject a uniform preference for helping men (hypothesis 3-1) and a uniform preference for helping the same gender (hypothesis 3-2). The results support hypothesis 3-3; that is, men and women differ in choosing whom to provide help—while women are gender-neutral in their choice, men display a strong selective preference for helping other men. Figure 2 displays the gender patterns with respect to the use of influence in job-search help. When all other explanatory variables are held at their mean values, the probability of a man helper using influence in his help is 52.2 percent, whereas the probability of a woman helper using influence is 39.2 percent. In terms of the comparison among gender pairs, the probability of a man helper using influence to help another man is 53.6 percent while that of him helping a woman is only 45.6 percent. This 8 percent difference is statistically significant. In contrast, the probability of a woman helper using influence to help a man is 37.0 percent and that of her helping another woman is 40.0 percent. The 3 percent difference is not statistically significant.

Figure 2:

Probability (with 95% Confidence Intervals) of Using Influence in Job-Search Help: Cross-Gender Comparisons

Conclusions

It is not only access to social contacts per se that influences labor market outcomes such as obtaining a good job, but we should ask also whether these contacts actually provide help. Not all social contacts are equally likely to help, and the extent of the help varies. Seeing from the perspective of the individuals who have helped others’ job searches, this study produces new insight into the social profile of the helpers and especially the gender differences in the provision of job-search help. Some characteristics of the job-search helpers are clearly distinguishable. They are more likely to be men, middle-aged, high in socioeconomic status, and active in social interactions.

Further, significant gender differences are identified with respect to the use of influence in job-search help. Compared with women, men show a greater likelihood of using influence in their help. For intertwined structural and cultural reasons, men more often translate their potential resources and influence into actual use in the labor market. Social institutions and cultural norms may depress women’s willingness to use influence in their help. Women less often establish intensive and instrumental relationships with influential connections because of structural segregation in the labor market and within the workplace (England 2010; Ibarra 1997; McDonald 2011; McGuire 2002; Reskin 1993). Gender-based cultural beliefs that undervalue women’s competency and influence are also prevalent in society (Ridgeway 1997; Ridgeway and Correll 2004). These biased cultural beliefs encourage men to utilize influence while discouraging women to do so. Certain behavior is deemed more gender appropriate for one gender than the other, and it may be deemed more acceptable and legitimate for men to exert influence on hiring than women (Carli 1999).

Gender inequality is further aggravated by the differential tendency between men and women helpers in the choice of help recipients. While men helpers show a selective preference for using influence on behalf of other men, women do not show a significant gender-based preference. Among men helpers, the biased belief that women are less competent is further strengthened by the homosocial reproduction tendency, whereas the bias is counteracted by the homosocial reproduction tendency among women helpers. Consequently, the bias against women job seekers is particularly stronger among men than among women. Men helping men is the most typical pattern and occurs significantly more often than any other gender pairs.

This study sheds new light on gender differences in benefits associated with informal job matching. The relative lack of employment benefits associated with informal job matching among women is often attributed to the distinctive features of their networks (Ibarra 1997; McDonald, Lin, and Ao 2009; McGuire 2000, 2002; Smith 2000). It has long been found that social networks tend to be segregated on the basis of gender characteristics, which results from both structural segregation and gender homophily (Bielby and Baron 1986; Campbell 1988; Huffman and Torres 2002; McPherson, Smith-Lovin, and Cook 2001; Mouw 2003; Reskin 1993). Men are more likely to be located in men-dominated networks, and most studies show that men-dominated networks are relatively resource-rich. This study highlights another advantage of men-dominated networks—men contacts are more inclined to provide instrumental help than women contacts. It is worth noting that both men and women benefit from men contacts, but to different degrees. Men contacts provide more help to other men than to women.

Taken together, women face a double disadvantage in informal job matching. In comparison with her man counterpart, a woman job seeker’s network usually consists of more women who are not as instrumentally helpful as men. Even if the woman job seeker is embedded in a men-dominated network, she would not benefit as much as her man counterpart due to men’s selective preference for helping other men.

This study is not without its limitations. While I suggest some structural and cultural mechanisms underlying the revealed gender differences, more empirical research is needed to flesh out and more directly assess these proposed mechanisms. As suggested by O’Connor (2013, 600), more qualitative research based on in-depth interviews about “the decisions women and men make about whether they are able to help and their impressions of the kinds of help that would benefit particular job seekers” would be particularly helpful in this regard.

Last but not least, although this analysis is conducted in the Chinese context based on a data set from China, the findings here may well apply to other societies in general. In most societies, there are similar structural and cultural barriers to women’s accumulation and utilization of resources and power. For instance, women have less access to influential social contacts that can be turned into job-search help, even if they occupy similar positions as men. Women are constrained by widely shared cultural beliefs that undervalue their competence and influence, and they face greater difficulties in exerting influence that convey authority and power. I predict that in many societies we would observe similar gender inequality in the provision of job-search help. I thus welcome future research on other societies to assess the generalizability of my results.

Footnotes

Acknowledgements

I thank the Social Sciences and Humanities Research Council of Canada for supporting this research and the Center for Social Science Survey at Sun Yat-sen University for providing the data.

This research was funded by an Insight Grant from the Social Sciences and Humanities Research Council of Canada.

Notes

ORCID iD

Min Zhou

Min Zhou is an associate professor of sociology at the University of Victoria. He received his PhD from Harvard University. He is broadly interested in the processes and consequences of various forms of global social change. He has published articles on global economic networks, international organizations, global public opinions, and transnational social movements. His recent projects investigate the role of social networks in political activities, labor markets, and health in China.

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