metadata: authors: 0: author: Matsuda,Norihiko
1: author: Nomura,Shinsaku
count: Bangladesh
docna: 0: docna: Fast, easy, and cheap job matching : social
            networks in Bangladesh
docty: Policy Research Working Paper
subtopic: Labor Markets,Labor Policies,Educational Sciences,Rural Labor Markets
teratopic: Rural Development
abstracts: cdata!: This paper uncovers the reason why
            social networks are used in a job market. The data are
            novel: a nationally representative matched employer-employee
            data set in Bangladesh with detailed information, including
            direct measures of the use of social networks. The empirical
            analysis shows that compared with those who used open
            channels to find jobs, the employees who used social
            networks found jobs more easily, have lower observable
            abilities, and achieved lower employment outcomes
            conditional on observable and unobservable abilities. These
            results are robust whether firm-occupation fixed effects are
            controlled for or not. By comparing these findings with
            theoretical predictions, the paper concludes that social
            networks play the role as fast and easy but narrow-spectrum
            matching. That is, social networks allow job seekers to find
            jobs quickly and easily and thereby reduce search costs, but
            the types of jobs available from social networks are
            narrower than those from open channels. As a consequence,
            those who choose to use social networks are more likely to
            end up having mismatched jobs, that is jobs in which they
            cannot take advantage of their specialties. In the context
            of developing countries, a considerable number of poor job
            seekers may use social networks out of necessity even if the
            returns to finding good-match jobs through open channels are
            sufficiently high.
geo_reg_key: 517212,517191
repnme: repnme: Fast, easy, and cheap job matching : social
            networks in Bangladesh
display_title: Fast, easy, and cheap job matching :
            social networks in Bangladesh
listing_relative_url: /research/2017/06/27659083/fast-easy-cheap-job-matching-social-networks-bangladesh
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Labor Markets m1326488 855,Education m1327048 470,Efficient Labor Exchanges m1503881 2813,Jobs m1503931 2670
publishtoextweb_dt: 2017-06-20T00:00:00Z
historic_topic: Rural Development
no_of_pages: 55
author_keys: 0: author_key: 1468296
1: author_key: 782579
model_extractions: WPS8107 Policy Research Working Paper 8107 Fast, Easy and Cheap Job Matching Social Networks in Bangladesh _Norihiko Matsuda Shinsaku Nomura_ Education Global Practice Group June 2017
Policy Research Working Paper 8107

# **Abstract**

This paper uncovers the reason why social networks are used in a job market. The data are novel: a nationally representative matched employer-employee data set in Bangladesh with detailed information, including direct measures of the use of social networks. The empirical analysis shows that compared with those who used open channels to find jobs, the employees who used social networks found jobs more easily, have lower observable abilities, and achieved lower employment outcomes conditional on observable and unobservable abilities. These results are robust whether firm-occupation fixed effects are controlled for or not. By comparing these findings with theoretical predictions, the paper concludes that social networks play the role as fast and easy but narrow-spectrum matching. That is, social networks allow job seekers to find jobs quickly and easily and thereby reduce search costs, but the types of jobs available from social networks are narrower than those from open channels. As a consequence, those who choose to use social networks are more likely to end up having mismatched jobs, that is jobs in which they cannot take advantage of their specialties. In the context of developing countries, a considerable number of poor job seekers may use social networks out of necessity even if the returns to finding goodmatch jobs through open channels are sufficiently high.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted atsnomura@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Fast, Easy and Cheap Job Matching: Social Networks in Bangladesh<sup>_∗_</sup>

Norihiko Matsuda, _University of Wisconsin-Madison_<sup>_†_</sup> Shinsaku Nomura, _World Bank_<sup>_‡_</sup> _JEL Codes_ : J64, J63, J31 _Key words_ : Social networks, referrals, job search, labor markets

> _∗_ The authors are grateful to Yoko Nagashima and Iffath Sharif for their review and feedback. The findings, interpretations, and conclusions in this paper are entirely those of the authors and should not be attributed to the World Bank or its affiliated organizations.

> _†_ Corresponding author. Department of Agricultural and Applied Economics. Madison, WI. nmatsuda@wisc.edu

> _‡_ Education Global Practice. Washington, D.C. snomura@worldbank.org
# **1 Introduction**

There is a consensus in the literature that social networks are widely used in job markets across the world.<sup>1</sup> While there are various theories about the roles of social networks in labor markets, mixed empirical evidence does not allow the literature to reach a consensus about the roles.

This paper uncovers the role of social networks in a job market, i.e., why social networks are used, by testing theoretical predictions under major hypotheses in the literature. The hypotheses examined are information transmission, screening, peer effects, and nepotism hypotheses. Using nationally representative matched employer-employee data in Bangladesh, we comprehensively estimate empirical counterparts of the theoretical predictions based on different regression specifications including a firm fixed effect model and a firm-occupation fixed effect model.

We find that employees who used social networks found jobs more quickly and easily, have lower observable abilities and similar unobservable ones, have a higher turnover, earn less salary persistently across job tenure, and are seemingly less productive, compared to employees at either the same or different firms who found jobs through formal channels. These empirical results agree with the information transmission hypothesis where job information via social networks and via formal channels originates from different sources. On the other hand, the results reject the screening, peer, and nepotism hypotheses.

Given the correspondence between the empirical results and the predictions, we conclude that the role of social networks in the Bangladeshi labor market is fast and easy but narrow-spectrum matching, as suggested by Bentolila et al. (2010) in the case of the U.S. and Europe. That is, social networks help job seekers quickly and easily find jobs, but the types of jobs available from social networks are narrower than those available from open channels. By using open channels, job seekers can find good-match jobs although the search costs are higher since they have to go through longer search spells and apply for more vacancies.<sup>2</sup> By using social networks, on the other hand, they can reduce search costs while social networks may bring mismatched jobs. Given this trade-off, those for whom job search through open channels is not worthwhile choose to use social networks and accept mismatched jobs.

On the employer-side too, we find evidence that social networks are used as fast

> 1For example, Topa (2011); Ioannides and Datcher Loury (2004) for developed countries, and Beaman and Magruder (2012); Larsen et al. (2011); Heath (2016) for developing countries.

> 2Throughout this paper, a good-match job means a job in which a worker can exploit his specialty. A mismatched job is a job in which he cannot exploit his specialty.

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and easy job match. The employers who used social networks as a common mode of job advertisements filled vacancies in a shorter period and felt less difficulty in filling vacancies compared with the employers who did not so commonly use social networks. This finding suggests that social networks allow employers to quickly and easily fill vacancies. Social networks do not necessarily bring the best workers, but employers set lower wages for worse workers.

In developing countries, many job seekers may have insufficient financial sources for job search and be very responsive to search costs. In particular, there may be a substantial number of poor job seekers who cannot help but using social networks due to lack of credits even if their returns to finding good-match jobs through open channels are sufficiently high.

This paper relates to the broad literature on social networks in labor markets (Beaman, 2016; Topa, 2011; Ioannides and Datcher Loury, 2004 for excellent reviews). We contribute to it by providing solid empirical evidence showing that social networks serve as fast and easy but narrow-range job matching. This aspect of social networks in labor markets, as well as the negative consequence that network-based job search causes mismatches, has been largely missed in the literature. The aspect is studied only by Bentolila et al. (2010) and Pellizzari (2010), to the best of our knowledge. Most of the previous studies highlight other aspects, such as implied by the screening and the peer effects hypotheses, which lead to the positive consequence that social networks generally improve labor market efficiency and do not bring about job mismatches.

Our empirical findings that social networks use is associated positively with turnover and negatively with salary are exceptional. The literature mostly finds opposite results. For example, using novel datasets of nine large firms, most of which are based in the U.S. and all of which have employee referrals programs, Burks et al. (2015) show that referred workers have lower turnover and similar or higher earnings. Brown et al. (2016) use data of a single firm and observe that referred workers have lower turnover and higher wage. Simon and Warner (1992) and Kugler (2003) also identify positive associations between social networks use and wages in the U.S. Outside the U.S., Hensvik and Skans (2016) and Dustmann et al. (2016) identify the positive associations with wages in Sweden and Germany, respectively. Conducting experiments in an online job marketplace, Pallais and Sands (2016) observe that referred workers have lower turnover. By contrast, only a handful of papers find similar results to ours. Using datasets from the U.S. (the Multi-City Study of Urban Inequality 1992–1994) and Europe (the European Community Household Panel, ECHP), Bentolila et al. (2010) find that workers who found jobs through social networks earned less. Pellizzari (2010) uses the same European data 3
(the ECHP) as Bentolila et al. (2010) and identifies the negative association between social networks use and wages in some countries. In the case of Ethiopia’s cut flower farms, Mano et al. (2011) find that workers recruited through social networks received discounted wages at entry, which quickly disappeared with job tenure, however.

This paper also adds to the literature by providing clean and country-level evidence. First, our estimation results are robust to controlling for firm fixed effects and firmoccupation fixed effects. In other words, the associations of social networks use with salary, turnover, and other outcomes do not change no matter whether the referred workers are compared with the nonreferred at the same firms or at different firms. Controlling for firm fixed effects is possible only with matched employer-employee data. Second, we use direct measures of social networks use, which are based on responses of employees. Besides, we check the validity of the measures with employer-side information. We do not need to rely on indirect measures such as being neighbors (e.g., Schmutte, 2015) and having worked at the same firms previously (e.g., Hensvik and Skans, 2016). Lastly, our data are nationally representative while most of the empirical evidence in the literature builds on data from small geographical areas or specific firms.

# **2 Theories in the literature**

The literature has various theoretical models of social networks in labor markets (Beaman, 2016; Topa, 2011; Ioannides and Datcher Loury, 2004 for reviews). Although those models hypothesize different roles of social networks, or different reasons behind social networks use, most of the hypotheses can be categorized into the following: transmission, screening, peer effects, and nepotism hypotheses. We briefly explain each hypothesis and related literature. We summarize the theoretical predictions under each hypothesis in table 1.

First, the transmitting information hypothesis is that social networks play the role in transmitting more information about job vacancies. Building on this hypothesis, a large number of theoretical papers study labor market consequences of social networks use (e.g., Calv ́o-Armengol and Jackson, 2004, 2007; Ioannides and Soetevent, 2006). While most of these papers are dedicated to analyzing network-level outcomes such as neighborhood and ethnicity rather than worker-level analysis, they have individual-level predictions that referred workers experienced a shorter unemployment spell and earn more than nonreferred ones. The intuitions behind the predictions are that since referred workers are connected to more contacts on average and therefore receive more job 4
information, they find jobs more quickly and have higher reservation wage.<sup>3</sup> The predictions are summarized in column (1) of table 1. Empirically, Munshi (2003) and Beaman (2012) find supporting evidence. Importantly, the predictions require the assumption that the job information via social networks and via open channels originates from an identical underlying distribution of job vacancies. This assumption is common among the studies building on the transmitting information hypothesis.

Notably, Bentolila et al. (2010) make the distinct assumption that job information sources are different while they still view the primary role of social networks as transmitting vacancy information. To our knowledge, their paper is the only one having this assumption. The settings are as follows: there are two types of occupations; each worker has occupation-specific advantage in a randomly chosen occupation;<sup>4</sup> this advantage is observable to everyone; her social network is connected to one occupation, and she can find more easily a job at the connected occupation than a job at the unconnected occupation. If her specialized occupation agrees with the connected occupation, she searches for a job of her specialty. If they disagree, she may not bother searching for a job in her specialty but rather take up a job that she can find through the network. As a result, those who use networks are more likely to work for mismatched jobs, and hence they earn less on average. Column (2) in table 1 summarizes the predictions. Noteworthy is the negative association between wages and the use of social networks, which is unique to their models. Although their model is simplified to having two occupations, in our understanding, the key difference from the other models is that information via social networks is drawn from a different distribution than the one from which information via open channels is drawn. Thus, we call their assumptions about the role of social networks the different information transmission hypothesis.

Second, the screening hypothesis is that social networks reduce uncertainty about a worker’s productivity or match quality by bringing hard-to-observe information. In the seminal paper of Montgomery (1991), workers are observationally equivalent, and the productivity of workers is ex ante unknown and revealed after employment.<sup>5</sup> Wages cannot be contingent on output, and workers live only for one period. As a role of social networks, the model assumes inbreeding, or homophily, of social structure: workers within a network are relatively homogeneous. Thus, firms are able to more precisely 3Calv ́o-Armengol (2004) and Calv ́o-Armengol and Zenou (2005), however, show non-monotonicity of unemployment probability with respect to network size.

> 4This occupation specific advantage can be interpreted in various ways. It may represent occupationspecific expertise, personal preference which leads to high future productivity, or any other types of match quality.

> 5The model holds whether workers know their productivity ex ante or not. Thus, it is possible to consider the productivity as match quality.

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infer the productivity of applicants who belong to networks of incumbent workers. The model predicts that referred workers earn more and have higher unobservable abilities than nonreferred workers conditional on observable abilities.<sup>6</sup> Hensvik and Skans (2016) provide empirical evidence supporting the predictions. Although the model of Montgomery (1991) does not have a prediction about wage growth since a worker lives for one period, we straightforwardly expect that wage growth is higher for referred workers since they have better abilities on average.

Another seminal paper is Simon and Warner (1992). Although their framework, applying Jovanovic’s (1979) matching model, is different from Montgomery (1991), they essentially assume the same role of social networks: social networks reduce the ex ante uncertainty about match quality. Since their model is dynamic, they have predictions about turnover and wage growth, which Montgomery (1991) does not have. The settings are the following: Infinitely-lived workers meet employers through either social networks or open markets; workers are homogeneous in the sense that their firm-specific productivity, or job-match quality, is independently and identically distributed; the productivity is ex post observable but is ex ante only partially observable with noise to both workers and employers; wages can be renegotiated, and workers can quit jobs; lastly, the role of social networks is modeled as that the noise is smaller when workers and employers meet through social networks than when they meet in open markets. In an equilibrium, entry wage is set equal to the expected productivity, and subsequent wage is equal to the actual productivity. The predictions are the following: (i) Entry wages are higher for those who find jobs through social networks than those who find them through open channels. (ii) Wage growth of nonreferred workers is higher. (iii) Referred workers have lower turnover than nonreferred ones.<sup>7</sup><sup>_,_8</sup> Brown et al. (2016) and Dustmann et al. (2016) obtain empirical results aligning with these predictions. The predictions under

> 6The original paper does not explicitly state the prediction about unobservable abilities, however.

> 7The reason for prediction (i) is that the nonreferred, whose productivity is less revealed at hire, have the option to quit jobs if their firm-specific productivity turns out low while they can enjoy high wage if the productivity turns out high. The reason for prediction (ii) is the following: wages from the subsequent period are set exactly equal to the true productivity, and only those whose productivity is higher than an identical threshold irrespective of being referred or not continue to work. The reason for prediction (iii) is that the probability that actual productivity turns out higher than the threshold conditional on an initial offer being accepted is higher for the referred than the nonreferred.

> 8These predictions hold in extended versions such as Dustmann et al. (2016) and Galenianos (2013). Dustmann et al. (2016) slightly formalize the original model and confirm that the same predictions are derived. Galenianos (2013) adds heterogeneity in firm’s productivity and allows firm’s investment in reducing the ex ante uncertainty particularly when hiring through an open labor market. Even in this case, the same predictions about entry wages, wage growth, and turnover are obtained conditional on firm’s productivity. However, unconditional association between social networks use and wage can be negative.

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the screening hypothesis are shown in column (3) of table 1.

Third, the peer effects hypothesis is that social networks are exploited to increase worker’s performance by peer effects. Kugler (2003) puts forth the model where moral hazard occurs. She models the role of social networks as that those who are referred by high-effort workers have disutility from shirking due to peer monitoring by their referrals. Put intuitively, peer monitoring lowers employer’s costs for monitoring.<sup>9</sup> Predictions are that referred workers earn more and have longer job tenure than nonreferred ones.<sup>10</sup> Different aspects of peer interactions, other than peer monitoring, are studied in other papers empirically. For example, Bandiera et al. (2013) examine how team productivity differs depending on incentive structures; Bandiera et al. (2005) examine how individuals respond to potential externalities to their peers; Pallais and Sands (2016) experimentally test whether referred workers are more productive when working with referrers and whether referred workers exert more effort when they know that their performance known to referrers affects referrers’ promotion. They do not have theoretical predictions relevant to our empirical analysis, however.<sup>11</sup> The predictions are summarized in column (4) of table 1.

Lastly, the nepotism hypothesis is that nepotism by incumbent workers and employers is the reason behind social networks use. Nepotism plausibly takes place in job markets if private returns of incumbent workers and employers to referral differs from firm’s returns to it. A few papers empirically find the existence of nepotism (Wang, 2013; Fafchamps and Moradi, 2015), but, to our knowledge, no theoretical models conceptualize the use of social networks as nepotism.<sup>12</sup> Nonetheless, we expect that under

> 9Another assumption on social networks in her model is that social networks are less efficient than the open market in terms of the encounter rates. While this assumption may not be plausible in some contexts, it is mainly for the convenience in having the two matching channels exist in an equilibrium. It does not drive main implications.

> 10The reason for the wage difference is that firms pay efficiency wages to referred workers while firms pay lower wages to nonreferred workers and incur shirking. The reason for the tenure difference is that non-referred workers end up being dismissed due to their shirking.

11Heath (2016) develops a model which incorporates moral hazard problems and produces predictions about observable abilities and wage growth. Her model is quite specific to the empirical context where a minimum wage regulation is binding. A firm may be reluctant to hire a low-ability worker if willingnessto-pay wage is below the minimum wage. In this case, if a low-ability worker has a high-ability worker in his network, he and the firm can get around the minimum wage restriction by bundling up the wages of him and the high-ability companion. This bundling of wages is the role of social networks in her model, and there are no peer effects. Thus, we do not categorize the model under the peer effects hypothesis although it is sometimes categorized so (e.g. Beaman, 2016). The model is also called as a moral hazard model and viewed as the same category as Kugler (2003) (e.g., Brown et al., 2016). However, we differentiate it from Kugler (2003) since the bundling to get around the minimum wage, which is the key role of social networks in the model, does not require moral hazard and applies to the case where worker’s output is perfectly observable.

> 12Goldberg (1982) puts forth a theory modeling nepotism. However, it focuses on firm-side implica-

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the nepotism hypothesis, referred workers experienced a shorter job search spell, have lower abilities and productivity.<sup>13</sup> We consider that implications on turnover and wages are theoretically unclear and depend on contexts. Column (5) in table 1 summarizes the predictions.

# **3 Data**

We use Bangladeshi matched employer–employee survey data collected in 2012 by the World Bank. The data are nationally representative of formal-sector employers and employees in manufacturing, commerce, finance, education, and public administration.<sup>14</sup> The survey randomly sampled establishments, and then sampled employees within the establishments. The sampling of establishments was stratified by industries and size.<sup>15</sup> Interviews were administered separately for employers and employees. Note that while the data represent the formal sector employees, they do not represent the whole labor force since the data do not include informal-sector employees or the unemployed labor force.

We restrict the sample as follows. We include only three industries: manufacturing, commerce, and finance, because the other industries: education and public administration, consist mostly of government or government-aided organizations for public services.<sup>16</sup> The three industries account for 48% and 71% of the establishments and employees, respectively, in the Bangladesh formal sector (Nomura et al., 2013). We exclude seasonal and day laborers and part-time and contract workers, and focus only on full-time permanent employees,<sup>17</sup> who constitute about 90% of the original sample. We also restrict the sample to males, who account for nearly 90% of the original sample. Lastly, we restrict the sample to those aged 40 and below, who constitute about 90% of the original sample. The reason for excluding older employees is that information at the timing of recruitment, such as initial salary, is crucial for our analysis and may be less accurate for older employees since they tend to have been recruited a longer time ago. After the above restrictions are applied, our sample has 339 establishments and 3,396 tions of nepotism and does not have predictions relevant to our study.

> 13In a Bangladeshi context, Mahmood and Absar (2015) suggests that there exists nepotism favoring relatives and friends in a labor market.

> 14See Nomura et al. (2013) for details about the data.

> 15The size is defined by the number of employees and consists of small (10–20), medium (21–70) and large (71+).

> 16Most schools in Bangladesh are government-aided privately-managed schools. The recruitment of full-time teachers is regulated by the government.

> 17Permanent workers are those who have an indefinite period of employment.

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# employees.

The data includes information about cognitive and non-cognitive skills that were assessed during the survey based on short tests and questions about language and math skills and personality traits. These skills are likely to be unobservable, or not easily observable, to employers at the timing of hiring. The non-cognitive skills include Big 5 personality traits, which we convert into a single index for our analysis.<sup>18</sup> Summary statistics of establishments are shown in table 2. Approximately, half the sample is manufacturing ( _N_ = 191), and the other half is evenly split into commerce and finance ( _N_ = 74 each). The proportions of small, medium and large establishments are respectively 48%, 29%, and 22%. 61% are single establishment firms. 69% use personal networks as a common mode of job advertisement.

The ways employees found their current jobs are presented in table 3. Social networks are the most prevalent channel to find jobs. 63% of employees found jobs through social networks. As for social network type, families and relatives, friends, and neighbors were dominant, used by 23%, 27%, and 12%, respectively. Political affiliations and school alumni were almost negligible, used by only 1 % each. Among formal and open channels, the most important is media advertisements, through which 33% of employees found jobs. The other formal methods such as public and private employment services and internet postings were used only by 4%. These statistics demonstrate that social networks of families/relatives, friends, and neighbors are the most common job search method in the formal sector and that media advertisements are dominant among the formal open methods.

Our measure of social networks use is based on employees’ responses as to whether they found a job through social networks or not. This measure, however, does not say whether an employee was referred or not. The data do not include information about referral. Thus, the use of social networks in this paper is not restricted to referrals but includes various forms such as passing on information.

Table 4 shows summary statistics of employees. An average employee is 24 years old and has 9.0 years of schooling and 4.3 years of tenure. The composition of entry occupations is that 6%, 20%, 61% and 14% of employees were recruited as managers, professionals, semi-skilled workers, and elementary workers, respectively. Only 4% have been promoted in current firms. Compared to those who did not use social networks to find jobs, those who did are younger and less educated, have slightly shorter tenure 18For Big 5, we firstly calculate the z-score for each of the five personality traits, i.e., openness, conscientiousness, extroversion, agreeableness, and emotional stability, using the sample mean and standard deviation. Then we construct Big 5 index as an equally weighted average of them, as Burks et al. (2015) do.

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and less earnings, are more likely to be at elementary occupations and less to be at professional occupations, and less often experienced promotions.

# **4 Estimation strategy**

Our overall strategy to uncover the role of social networks is to estimate differences in job search experience, abilities, and employment outcomes between referred and nonreferred workers and then compare empirical results to theoretical predictions. We run the following regression equation: where _i_ , _j_ , and _k_ denote employees, establishments, and entry occupation types, respectively; _Networkijk_ is a dummy indicating that employee _i_ found his job through social networks; _Xijk_ are controls. The coefficient _θ_ represents a difference associated with the use of a social network.

It is important to note that our estimate of _θ_ , a difference associated with the use of social networks, is essentially descriptive, not causal. Therefore, if there is an unobservable factor that is associated with the use of social networks and a dependent variable and that is not endogenized in theoretical models, our estimate is unable to test theoretical predictions. To address this endogeneity problem, we have different specifications of the fixed effect, _γjk_ , and the controls, _Xijk_ , and draw conclusions that are consistent across the different specifications.<sup>19</sup> The three different specifications of the fixed effect, _γjk_ control for no fixed effects, firm fixed effects, and firm-occupation fixed effects, respectively. Therefore, the first specification estimates an association of social networks usage in the whole sample across firms, whereas the second and third specifications estimate an association, respectively, within firms and within firm-occupation cells. The third specification is not necessarily more preferable than the others. For example, if the use of social networks causes sorting of employees into different firms, estimates from the first specification are meaningful. But, if employee’s selection into firms is driven by an unobservable factor, the second and third specifications are more informative.<sup>20</sup> Hence, we consider that the three specifications

> 19It is worth noting that most of the previous studies rely on descriptive analyses. Exceptional papers exploiting exogenous variation in social networks use are Beaman (2012) and Pallais and Sands (2016). Besides, among the descriptive studies, only a handful of papers, such as Dustmann et al. (2016), provide estimates with and without controls for

> 20Since the theoretical predictions, except for those under the information transmission hypothesis, apply to within-firm differences in outcome variables, within-firm comparison based on the second and

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are all informative and complement each other.

A concern in our estimations is that they may suffer from attrition bias since our data do not include those who had been employed in the formal sectors but quit already. For example, if the use of social contacts is associated with turnover patterns, our estimates may have selection bias. We examine this attrition bias issue later.

# **5 Results**

## **5.1 Job search spell**

Table 5 presents differences in terms of last job search experience between employees who used social networks and those who did not. Those who used social networks found jobs in a significantly shorter spell. The difference not controlling for firm fixed effects is _−_ 24% (column (2)), and the within-firm and the within-firm-occupation difference are _−_ 20% and _−_ 22%, respectively (columns (3) and (4)). Besides, those who used social networks applied for fewer vacancies by 16–23% (columns (6)–(8)), which implies that a shorter search spell was not a result of more intensive search. These results show that those who used social networks found jobs more quickly and easily.

## **5.2 Observable abilities**

Table 6 presents differences in the observable abilities: years of schooling, age at hire, and parent’s education. The estimation results across the different specifications clearly show that those who used social networks have lower observable abilities compared with those who used formal channels. According to the estimates controlling for firm-occupation fixed effects, those who used social networks have significantly less schooling by 1.39 years (column (3)) and were younger by 0.61 year at hire (column (6)). Their fathers and mothers are less likely to have completed primary school education by 7% and 9% points, respectively (columns (9) and (12)).<sup>21</sup> third specifications is informative. The predictions under the same information transmission hypothesis may or may not be valid for within-firm differences, though.

> 21The result that those using social networks have less schooling could have been driven by the possibility that job seekers with poor family backgrounds tend to rely on networks. A regression of years of schooling with parents’ education being included as a control for family backgrounds, however, gives a very similar coefficient of the social network dummy in terms of magnitude and significance, which implies that the use of social networks is negatively associated with schooling conditional on similar family backgrounds.

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# **5.3 Unobservable abilities**

Table 7 presents estimates of differences in seemingly unobservable cognitive and noncognitive abilities: the math, language, and personality scores. While these abilities were made observable to econometricians by conducting an assessment in the survey, they are somewhat unobservable, or hard-to-observe, to employers during a recruitment process. The associations between social networks and the math and language scores are statistically insignificant conditional on the observable abilities, except for the regression of Big 5 without fixed effects (column (10)). The associations may also be economically insignificant. According to the estimations in columns (4) and (8), those who used social networks have higher math and language scores at most by 0.19 _σ_ and 0.24 _σ_ , respectively, based on the upper bound of the 95% confidence interval.<sup>22</sup> As for the Big 5 index, the within-firm-occupation difference is precisely estimated to be zero (column (12)) while the difference not controlling for firm-fixed effects may be economically significant. We interpret the above results as indicating that the workers who used networks and those who did not are precisely similar in terms of the unobserved abilities, especially within colleagues at the same occupations in the same firms.

# **5.4 Tenure**

The average job tenure of those who found jobs through social networks is shorter by 12– 20% (columns (2)–(4) in table 8). These estimates are significant at the 10% level, except for that controlling for firm fixed effects (column (3)). It is important to understand that this estimation compares the average tenure of current employees who had survived, not the expected tenure conditional on being hired. Thus, the estimation results do not mean that the expected tenure conditional on being hired is 19% shorter for the employees who used social networks than for those who did not use them. Nonetheless, the results are likely to suggest that the use of social networks is negatively associated with job tenure.

# **5.5 Entry salary and salary growth**

The regressions of entry salary, including bonuses, overtime, and other compensations, reported in table 9 show that those who used social networks received significantly lower entry salary by 7–9% conditional on the observable and unobservable abilities whether

> 22In the literature on impact evaluation of certain interventions, an effect size of about 0.2 on educational outcomes such as cognitive scores is considerable. However, in our context where descriptive associations, not impacts of a treatment, are examined, a 0.2 _σ_ difference in cognitive skills is not so considerable to the whole distribution of the skills.

12
firm and firm-occupation fixed effects are included or not (columns (2)–(4)).<sup>23</sup> To examine salary growth, we run the following equation: Job tenure as well as its interactions with individual characteristics such as abilities are included in _Xijk_ . The coefficient of the interaction between the network dummy and tenure, _ρ_ , represents a salary growth difference between those who used social networks and who did not. The coefficient of the network dummy, _θ_ , represents a difference in entry salary.

The estimation results are reported in table 10. First, the coefficient of the network dummy confirms that those who used social networks received significantly lower entry salary. The entry salary discount is 9–13% (columns (2)–(4)), which are similar to the estimates, 7–9%, reported in table 9. This similarity indicates robustness of the estimated entry salary discount. Second, the difference in annual salary growth is precisely estimated to be very small, if any, which implies that the salary discount does not dissipate with job tenure. According to the estimation controlling for firm-occupation fixed effects (column (4) in table 10), the salary discount initially starts at 11.8% and narrows only by 0.5% annually according to the point estimates. The salary gap persists at 9.3% after five years and at 6.8% after ten years.<sup>24</sup> In addition, this annual catch-up is much smaller than the annual growth, 6.7%, which is common to the both types of employees.

We conduct a different investigation on salary growth. Specifically, we run equation (1) with the dependent variable being a _current_ salary separately for a subsample of each tenure, and examine how the salary gap changes with tenure. The motivation for this analysis is to address the possibility that a salary trajectory may not be linear. The result is illustrated in figure 1. It shows that the salary gap is persistent at about 10% until 7 years of tenure. This persistent gap corroborates the finding that the earnings of those who used social networks do not catch up.

> 23The coefficient of the language score is negative. This is opposite our expectation, and we do not have a good explanation for it.

> 24According to a formal statistical test that takes into account the variances and covariance of the two coefficients, the hypothesis that the salary gap is zero after 10 years is rejected with the p-value being 0.0473. The corresponding p-value for 11 years is 0.0996.

13
# **5.6 Productivity: Promotion**

Our data do not have a direct measure of employee’s productivity. We use promotion history as a proxy of employee’s productivity, following Brown et al. (2016).<sup>25</sup> Regression results where the dependent variable is the dummy for having ever been promoted is reported in table 11. The promotion likelihood for those who used social networks is about 1–2% points lower in all the specifications although the estimates are not significant.<sup>26</sup> This result may suggest that those who used social networks are less productive although the result lacks statistical precision and also may suffer from measurement errors of productivity.

# **5.7 Robustness checks**

## **5.7.1 Attrition bias**

A concern in our results is that the estimates may have attrition bias. This is because our data do not include former employees who had already quit before the data collection and because our estimation on tenure suggests that turnover is correlated with the use of social networks. To examine if the attrition bias exists, we run the same estimations using the subsample of those who were hired in the last three years. Although the attrition may have occurred among those new workers, the subsample may suffer less from the potential attrition bias.

Appendix A presents all the estimation results using the subsample. Although those who used social networks still have shorter tenure by 9% than those who did not (column (4), table A1), we obtain basically the same results.

We examine the attrition bias issue in a different way. We estimate a difference between those who used networks and who did not, separately using a subsample of each tenure. If the attrition bias is in play, the estimate may differ with tenure. Since estimates for shorter tenure are less affected by the attrition, we particularly look into if estimates for shorter tenure are similar to the main results explained in the preceding subsections. Figure A1 demonstrates the estimation results. Although they lack precision, the estimates, particularly the ones for short tenure, look similar to the main results.

> 25Although promotion plausibly correlates with productivity, the obvious limitation is that promotion reflects only a subset of productivity and also correlates with different factors than productivity.

> 26Since the sample mean of the likelihood is 4% (table 4), this 1–2% points difference is substantial.

14
### **5.7.2 Other robustness checks**

We additionally conduct two robustness checks. First, since we restrict the sample to the employees aged 40 and below, we examine if our results are sensitive to the cutoff age by running the same estimations for the sample aged 60 and below. We obtain similar estimation results (appendix B).

Second, our main estimations use the single aggregated index of Big 5 rather than the five indexes of personality traits. As a robustness check, we run the same estimations using the five indexes individually and find similar results (Appendix C).<sup>27</sup>

# **6 Discussions**

The summary of the results, reported in column (6) of table 1, is that employees who used social networks found jobs more quickly and easily, have lower observable abilities and similar unobservable ones, have a higher turnover, earn less salary persistently through job tenure, and are seemingly less productive, compared to those who found jobs through formal channels. These results are robust whether firm fixed effects and firm-occupation fixed effects are controlled for or not. This robustness somewhat implies that our estimation results are not driven by unknown factors that are not incorporated in the hypothesized theories.

In the following subsections, we first compare these results with the theoretical predictions under the different hypotheses. Then, we discuss the role of social networks in the Bangladeshi labor market.

## **6.1 How the empirical findings agree with the theoretical predictions**

The same information transmission hypothesis disagrees with our results. Under the hypothesis, social networks increase job arrival rate and thereby reservation wage. Hence, wages of referred workers are predicted to be higher on average. This prediction is robust across different models.<sup>28</sup> Contrary to the prediction, however, our estimations strongly show that the use of social networks is negatively associated with wages.

> 27From a different perspective other than the robustness check, these estimations are interesting in that the estimations may inform us how each trait is associated with labor market outcomes.

> 28In the case that members in social networks increase, social networks can be negatively associated with wages in a short run after the increase. However, this temporary negative association with wage is not relevant to our results since there is no reason to believe that such increase in network members occurred across Bangladesh. Long-run implications correspond to our empirical context.

15
The screening hypothesis also contradicts our results. Under the hypothesis, since social networks bring hard-to-observe information and reduce uncertainty about productivity, referred workers are strongly predicted to be more productive, earn more, and have lower turnover, compared to nonreferred workers particularly within the same firms. However, our results controlling for firm-fixed effects reject the predictions on wage and turnover.

The peer effects hypothesis does not match the results either. Since social networks are exploited to increase workers’ productivity or extract workers’ effort, referred workers are more productive and earn more. They also have lower turnover since they do not shirk and therefore are not dismissed.<sup>29</sup> The estimation results imply the opposite, however.

As for the nepotism hypothesis, although the discrepancy between the predictions and the results is not strong, we interpret that the empirical results do not support it for two reasons. First, the empirical finding that referred workers earned about 10% less significantly than nonreferred ones conditional on abilities seems not to support that nepotism is in play although nepotism does not necessarily lead to patronees earning more than they would earn in the absence of nepotism. We consider that the 10% salary discount is too economically significant for nepotism to exist behind the use of social networks.<sup>30</sup> Second, if nepotism is in play, referred workers may have higher likelihood of promotion than nonreferred ones since nepotism may help promotion. However, the empirical analysis suggests referred workers faced lower likelihood of promotion than nonreferred ones conditional on abilities although this difference is not significant.

Lastly, the different information transmission hypothesis agrees with the empirical results well. Under the hypothesis, job search through social networks causes mismatch because “workers may sacrifice their productive advantage so as to find a job more easily” (Bentolila et al., 2010). Hence, referred workers are less productive, earn less, and have higher turnover. All of these predictions are supported by the empirical findings although the empirical result on productivity is not significant.

The employer-side information also supports the different information transmission hypothesis. According to table 12, the employers who use social networks as a common mode of job advertisements fill vacancies in a shorter period than those who do not (2.6 weeks vs 4.8 weeks). Also, the employers using social networks feel less difficulty in filling job vacancies. These findings suggest that social networks help employers find

> 29These predictions may be more relevant for within-firm estimates.

> 30A literature on patronage in Bangladesh suggests that favoritism for relatives and friends is salient at workplaces (e.g., Mahmood and Absar (2015)). Such favoritism may help patronees get not only jobs but also high salary and promotion. However, this is not supported by our empirical findings, especially the economically significant wage discount conditional on abilities.

16
employees quickly and easily. The findings seem to agree with the information transmission hypothesis. On the other hand, the findings do not necessarily agree with the screening and the peer effect hypotheses since under these two hypotheses the employers using social networks may spend longer time on recruitment in order to carefully select applicants.<sup>31</sup>

# **6.2 The role of social networks**

Given the correspondence between the empirical results and the theoretical predictions, we conclude that the role of social networks in the Bangladeshi labor market is fast and easy but narrow-spectrum matching. That is, social networks help job seekers quickly and easily find jobs, but the types of jobs available from social networks are narrower than those available from open channels. By using open channels, they can find goodmatch jobs although the search costs are higher since they have to go through longer search spells and apply for more vacancies. By using social networks, they can reduce search costs while they may find only mismatched jobs. Given this trade-off, those for whom the extensive but expensive job search through open channels is not worthwhile choose to use social networks and end up having mismatched jobs. As a consequence, the jobs filled by social networks are more likely to be mismatched than those filled by open channels.

Table 13 provides additional evidence consistent with the above interpretation. The table shows main reasons why employees chose the jobs. Compared to those who did not use social networks, those who did are more likely to have chosen the jobs because of “no other offers” and “others’ recommendation” and less likely to have chosen because of “good prospect for career progression” and “good prestige of firm.” This result indicates that those who used social networks sacrifice better match (less “good prospect for career progression”) and have lower abilities like motivation and forthcoming labor market performance (less “good prospect for career progression,” less “good prestige of firm,” and more “no other offers”).<sup>32</sup> While Bentolila et al.’s (2010) model does not have predictions about observable abil-

> 31Table 12 provides another important implication. Since the social networks dummy we use is based on information from employees, our main results do not directly inform why employers use social networks. It is even possible that employers do not know whom of their employees used social networks. However, the employers who commonly use social networks have a significantly larger proportion of employees who found jobs through social networks: the proportion of such employees is 82.9% for the employers using social networks but 37.0% for the employers not using them. This result implies that employers actually use social networks and know whom of employees use social networks.

> 32These abilities seem not to be well captured by years of schooling, or the cognitive or non-cognitive skills.

17
ities, the negative association between social networks and observable abilities identified in our data stands with the above interpretation. It is plausible that the wage difference between a good-match job and a mismatch job increases with abilities. If so, the returns to searching jobs through open channels are higher for high-ability workers, and hence the use of social networks negatively correlates with observable abilities. Another possibility is that if wealth and abilities are positively correlated, lower-ability workers are more likely to be poor, lack credits, and be unable to afford an open-channel search.

Importantly, in the context of developing countries where a considerable number of people live on the margin of subsistence, a small difference in search costs may matter a lot, and more job seekers may use social networks to reduce search costs than in developed countries. Besides, if poor job seekers lack access to credits, they simply cannot use open channels but social networks even if the returns to finding good-match jobs through open channels is sufficiently high.

Table 14 somewhat suggests that poorer workers are more likely to use social networks to find jobs. The table shows correlations of social networks with worker’s characteristics in a slightly different way than tables 6 and 7. The negative correlations with parents’ education in table 14 may imply that poorer workers tend to use social networks since parents’ education somewhat represents worker’s wealth while it also correlates with worker’s ability.

# **7 Conclusions**

Social networks are widely used in job markets across the world. Yet the reason for the widespread use remains unclear. Using the novel data in Bangladesh, this paper provides new evidence about the role of social networks in labor markets.

The empirical results find that the employees who used social networks found jobs more quickly and easily, have lower observable abilities and similar unobservable ones, have a higher turnover, earn less salary persistently across job tenure, and are seemingly less productive, compared to the employees, at either the same or different firms, who found jobs through formal channels. Building on these findings, we draw the conclusion that social networks play the role as fast and easy but narrow-spectrum matching. That is, social networks help job seekers easily and quickly find jobs, but the types of jobs offered through social networks are narrower.

Social networks, as fast and easy but narrow-spectrum job matching, cause mismatches. If contacts of job seekers are unluckily at different occupations than their specialties, they face a trade-off between job match quality and search costs, and some 18
of them opt to use social networks and accept mismatched jobs. In the context of developing countries, a reduction in job search costs, primarily in the form of a reduction in search spells, may matter more than in developed countries. Besides, there may be a substantial number of job seekers who cannot help but using social networks because of limited access to credits even if an increase in earnings by finding a good-match job through open channels exceeds the reduction in search costs by using social networks.

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<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9<br>Tenure in years<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Coefficient of the social network dummy<br>-.6<br><!-- End of picture text --> Figure 1: Differences in current wages between those who used social networks and those who did not _Note_ . Plotted are estimates of _θ_ , the coefficient of the social network dummy, in the equation of log( _CurrentWage_ ) _ijk_ = _θNetworkijk_ + _β_<sup>_′_</sup> _Xijk_ + _γjk_ + _εijk_ , where _Xijk_ are years of schooling, age at hire, math and language z-scores, Big 5 index, and father’s and mother’s years of schooling, and _γjk_ is an establishment-entry occupation fixed effect. Occupations are grouped into manager, professional, semiskilled, and elementary. The regressions are run separately for a subsample of each year of tenure, except for 1-year and 9-year tenure. The subsamples of 0- and 1-year tenure are jointly used because their size is small. Due to the same reason, the subsamples of 9- and 10-year tenure are jointly used. The dash lines correspond to 95% confidence intervals. Standard errors are clustered within an establishment.

23
Table 1: Predictions and estimation results

||<br>Information|<br>transmission<br>**H**|**ypotheses**<br>|||**Data**|
|---|---|---|---|---|---|---|
|Outcome variables|Same<br>distribution|Different<br>distribution|Screening|Peer effects|Nepotism||
||(1)|(2)|(3)|(4)|(5)|(6)|
|a. Job search spell|−|−|||−|−|
|b. Observable abilities|||||−|−|
|c. Unobservable abilities|||+||−|0|
|d. Tenure|||+|+||−|
|e. Entry wage|+|−|+|+||−|
|f. Wage growth|||+/−|||0|
|g. Productivity||−|+|+|−|(−)|

_Note_ . Column “Data” indicates our estimation results. + and _−_ mean that use of social networks is, respectively, positively and negatively correlated. 0 means flat or no correlation. ( _−_ ) is negative but statistically insignificant. A blank cell means no or ambiguous prediction.

Table 2: Summary statistics: Establishments

||N|%|
|---|---|---|
|_Industry_|||
|Manufacturing|191|56|
|Commerce|74|22|
|Finance|74|22|
|_Size_|||
|Small (no. employees is 20 and less)|164|48|
|Medium (no. employees is 21-70)|99|29|
|Large (no. employees is 71 and above)|76|22|
|Single establishment frm|207|61|
|Social networks are a common mode of job advertisement|233|69|

_Note_ . _N_ = 339. “ _Personal networks are a common mode of job advertisement_ ” indicates that an establishment answered that personal networks are a primary or secondary mode of advertising job vacancies.

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Table 3: How jobs were found

||N|%|
|---|---|---|
|Social networks, including reference from somebody|2148|63|
|Family and relatives|773|23|
|Friends|931|27|
|Political afliation|19|1|
|School alumni|30|1|
|Same village or town|395|12|
|Media advertisement and posting|1107|33|
|Through school|3|0|
|Public employment services|17|1|
|Private employment services|79|2|
|Job fairs|13|0|
|Internet posting|29|1|
|Observations|3396||

_Note_ . This table is based on the question in the survey, ”How did you find this job?” If a response is “social networks,” the survey further asked the type of networks used.

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Table 4: Summary statistics: Employees

||All|Social net<br>use|works are<br>d?|
|---|---|---|---|
|||Yes|No|
|Age at hire|24.21|23.56|25.33|
||(4.89)|(5.05)|(4.40)|
|Schooing in years|9.01|7.19|12.15|
||(4.64)|(3.84)|(4.19)|
|Tenure in years|4.32|4.23|4.47|
||(3.35)|(3.36)|(3.34)|
|Earnings at hire (monthly, in Taka)|5745.66|4758.19|7445.25|
||(4213.63)|(2865.85)|(5443.01)|
|Earnings (monthly, in Taka)|9146.08|7448.94|12067.13|
||(7108.49)|(4418.45)|(9510.92)|
|_Occupation at hire_||||
|Manager|0.06|0.05|0.06|
||(0.23)|(0.22)|(0.23)|
|Professional|0.20|0.16|0.28|
||(0.40)|(0.37)|(0.45)|
|Semi-skilled worker|0.61|0.62|0.58|
||(0.49)|(0.49)|(0.49)|
|Elementary job|0.14|0.16|0.08|
||(0.34)|(0.37)|(0.28)|
|Search spell till fnding the current job (in weeks)|8.60|5.56|13.85|
||(12.94)|(5.91)|(18.76)|
|No. of vacancies applied for|3.60|2.28|5.87|
||(5.36)|(2.79)|(7.53)|
|Promoted ever in the current frm (1=yes)|0.04|0.02|0.06|
||(0.18)|(0.15)|(0.23)|
|Observations|3396|2148|1248|

_Note_ . Shown are means and standard deviations for permanent male employees aged 40 and below. Standard deviations are in parentheses. The second column is the statistics of those who found jobs through social networks, and the third is of those who did not. Earnings include bonuses, overtime, and other compensations.

26
Table 5: Job search spell and the no. of vacancies applied for: Comparing the employees who used social networks and those who did not

|||Log(searc|h spell)|||Log(no. a|pplications)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.437<sup>_∗∗∗_</sup>|-0.243<sup>_∗∗_</sup>|-0.202<sup>_∗∗_</sup>|-0.223<sup>_∗∗_</sup>|-0.428<sup>_∗∗∗_</sup>|-0.225<sup>_∗∗_</sup>|-0.203<sup>_∗∗_</sup>|-0.163|
||(0.086)|(0.104)|(0.096)|(0.094)|(0.099)|(0.097)|(0.092)|(0.111)|
|Schooing in years||0.069<sup>_∗∗∗_</sup>|0.045<sup>_∗∗∗_</sup>|0.022||0.043<sup>_∗∗∗_</sup>|0.023<sup>_∗∗∗_</sup>|0.023<sup>_∗_</sup>|
|||(0.011)|(0.010)|(0.015)||(0.012)|(0.008)|(0.012)|
|Age at hire||-0.013|-0.008|-0.010||0.013<sup>_∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|0.013<sup>_∗∗_</sup>|
|||(0.008)|(0.009)|(0.009)||(0.006)|(0.005)|(0.006)|
|Father’s pri.school||0.016|0.103|0.138<sup>_∗_</sup>||0.034|0.091|0.076|
|||(0.078)|(0.079)|(0.076)||(0.068)|(0.067)|(0.076)|
|Mother’s pri.school||0.045|0.126<sup>_∗_</sup>|0.061||0.021|0.125<sup>_∗∗_</sup>|0.137<sup>_∗∗_</sup>|
|||(0.072)|(0.066)|(0.056)||(0.061)|(0.055)|(0.067)|
|Math z-score||0.004|0.011|-0.012||0.046|0.029|0.021|
|||(0.034)|(0.033)|(0.044)||(0.042)|(0.035)|(0.041)|
|Language z-score||-0.101<sup>_∗∗_</sup>|-0.100<sup>_∗∗_</sup>|-0.060||0.015|-0.014|-0.031|
|||(0.042)|(0.050)|(0.054)||(0.057)|(0.058)|(0.061)|
|Big 5 index z-score||-0.005|0.134|0.079||-0.072|-0.113<sup>_∗∗_</sup>|-0.133<sup>_∗∗∗_</sup>|
|||(0.083)|(0.100)|(0.118)||(0.051)|(0.045)|(0.047)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.150|0.213|0.159|0.116|0.100|0.166|0.119|0.078|
|N|3396|3390|3390|3390|3396|3390|3390|3390|

_Note_ . The variable, _Social networks_ , is the dummy indicating that social networks were used to find jobs. The other controls included are entry year fixed effects and geographical division fixed effects. Occupations are grouped into manager, professional, semi-skilled, and elementary. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

27
Table 6: Observable abilities

||Sc|hooling (ye|ars)||Age at hir|e|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|-3.401<sup>_∗∗∗_</sup>|-2.788<sup>_∗∗∗_</sup>|-1.394<sup>_∗∗∗_</sup>|-1.511<sup>_∗_</sup>|<sup>_∗∗_</sup><br>-1.673<sup>_∗∗_</sup>|-0.610|
||(0.315)|(0.350)|(0.270)|(0.575|)<br>(0.680)|(0.913)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.215|0.135|0.072|0.066|0.062|0.047|
|N|3396|3396|3396|3396|3396|3396|
||Pri.|school: fat|her|Pri.|school: mot|her|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-0.092<sup>_∗∗_</sup>|-0.059<sup>_∗_</sup>|-0.071<sup>_∗∗_</sup>|-0.160<sup>_∗∗∗_</sup>|-0.087<sup>_∗∗_</sup>|-0.089<sup>_∗∗_</sup>|
||(0.043)|(0.034)|(0.035)|(0.041)|(0.034)|(0.039)|
|Schooing in years|0.031<sup>_∗∗∗_</sup>|0.026<sup>_∗∗∗_</sup>|0.022<sup>_∗∗∗_</sup>|0.032<sup>_∗∗∗_</sup>|0.030<sup>_∗∗∗_</sup>|0.018<sup>_∗∗_</sup>|
||(0.005)|(0.004)|(0.006)|(0.006)|(0.005)|(0.007)|
|Age at hire|-0.002|-0.004|-0.005|0.006<sup>_∗_</sup>|0.006<sup>_∗∗_</sup>|0.007|
||(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|(0.004)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.160|0.078|0.044|0.190|0.112|0.050|
|N|3396|3396|3396|3396|3396|3396|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

28
Table 7: Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.328<sup>_∗∗∗_</sup>|0.057|0.042|0.049|-0.416<sup>_∗∗∗_</sup>|0.107|0.120|0.066|
||(0.085)|(0.077)|(0.063|)<br>(0.073)|(0.088)|(0.087)|(0.076)|(0.089)|
|Schooing in years||0.107<sup>_∗∗∗_</sup>|0.101<sup>_∗∗_</sup>|<sup>_∗_</sup><br>0.109<sup>_∗∗∗_</sup>||0.129<sup>_∗∗∗_</sup>|0.117<sup>_∗∗∗_</sup>|0.148<sup>_∗∗∗_</sup>|
|||(0.010)|(0.012|)<br>(0.020)||(0.011)|(0.013)|(0.016)|
|Age at hire||-0.006|-0.002|-0.003||-0.006|-0.003|0.000|
|||(0.007)|(0.007|)<br>(0.009)||(0.006)|(0.006)|(0.006)|
|Father’s pri.school||0.071|0.056|0.069||0.269<sup>_∗∗∗_</sup>|0.164<sup>_∗∗∗_</sup>|0.114<sup>_∗_</sup>|
|||(0.083)|(0.056|)<br>(0.058)||(0.071)|(0.058)|(0.061)|
|Mother’s pri.school||0.061|0.042|0.015||0.149|0.120|0.168|
|||(0.090)|(0.115|)<br>(0.123)||(0.093)|(0.100)|(0.121)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.141|0.299|0.214|0.149|0.123|0.428|0.306|0.283|
|N|3396|3396|3396|3396|3396|3396|3396|3396|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.069<sup>_∗_</sup>|0.132<sup>_∗∗_</sup>|0.034|0.002|||||
||(0.039)|(0.052)|(0.038)<br>|(0.040)|||||
|Schooing in years||0.012|0.008|0.004|||||
|||(0.009)|(0.006)<br>|(0.006)|||||
|Age at hire||0.004|-0.002|-0.003|||||
|||(0.005)|(0.005)<br>|(0.005)|||||
|Father’s pri.school||0.075|-0.060<sup>_∗_</sup>|-0.051|||||
|||(0.058)|(0.032)<br>|(0.035)|||||
|Mother’s pri.school||0.008|0.015|0.002|||||
|||(0.066)|(0.038)<br>|(0.035)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.109|0.124|0.053|0.058|||||
|N|3390|3390|3390|3390|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

29
Table 8: Job tenure

|||Log (tenur|e in years)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.152<sup>_∗_</sup>|-0.166<sup>_∗_</sup>|-0.122|-0.197<sup>_∗_</sup>|
||(0.090)|(0.099)|(0.100)|(0.109)|
|Schooing in years||-0.008|-0.004|-0.009|
|||(0.008)|(0.009)|(0.010)|
|Age at hire||-0.010|-0.012<sup>_∗_</sup>|-0.005|
|||(0.006)|(0.007)|(0.006)|
|Father’s pri.school||-0.019|-0.042|-0.072|
|||(0.060)|(0.063)|(0.062)|
|Mother’s pri.school||0.040|0.018|0.049|
|||(0.052)|(0.059)|(0.072)|
|Math z-score||-0.020|0.034|0.035|
|||(0.043)|(0.056)|(0.058)|
|Language z-score||0.071|0.060|0.040|
|||(0.053)|(0.052)|(0.057)|
|Big 5 index z-score||0.066|0.085|0.158<sup>_∗_</sup>|
|||(0.055)|(0.082)|(0.085)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.013|0.024|0.018|0.026|
|N|3396|3390|3390|3390|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are establishment-entry occupation fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

30
Table 9: Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.298<sup>_∗∗∗_</sup>|-0.070<sup>_∗_</sup>|-0.065<sup>_∗_</sup>|-0.089<sup>_∗∗_</sup>|
||(0.048)|(0.042)|(0.038)|(0.043)|
|Schooing in years||0.057<sup>_∗∗∗_</sup>|0.061<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|
|||(0.006)|(0.009)|(0.008)|
|Age at hire||0.022<sup>_∗∗∗_</sup>|0.020<sup>_∗∗∗_</sup>|0.013<sup>_∗∗∗_</sup>|
|||(0.004)|(0.003)|(0.003)|
|Father’s pri.school||0.052|0.058<sup>_∗∗_</sup>|0.020|
|||(0.033)|(0.029)|(0.027)|
|Mother’s pri.school||0.043|0.056<sup>_∗_</sup>|0.016|
|||(0.035)|(0.033)|(0.037)|
|Math z-score||0.069<sup>_∗_</sup>|0.087<sup>_∗_</sup>|0.089<sup>_∗_</sup>|
|||(0.039)|(0.046)|(0.049)|
|Language z-score||-0.101<sup>_∗∗∗_</sup>|-0.098<sup>_∗∗_</sup>|-0.081<sup>_∗_</sup>|
|||(0.036)|(0.046)|(0.049)|
|Big 5 index z-score||0.021|-0.012|-0.082<sup>_∗∗_</sup>|
|||(0.045)|(0.032)|(0.036)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.174|0.391|0.387|0.220|
|N|3396|3390|3390|3390|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

31
Table 10: Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.358<sup>_∗∗∗_</sup>|-0.129<sup>_∗∗∗_</sup>|-0.086<sup>_∗∗∗_</sup>|-0.118<sup>_∗∗∗_</sup>|
||(0.050)|(0.036)|(0.030)|(0.032)|
|Social networks*tenure|0.011|0.011|0.004|0.005|
||(0.007)|(0.007)|(0.005)|(0.005)|
|Tenure in years|0.032<sup>_∗∗∗_</sup>|0.070<sup>_∗∗∗_</sup>|0.077<sup>_∗∗∗_</sup>|0.067<sup>_∗∗∗_</sup>|
||(0.006)|(0.017)|(0.016)|(0.023)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.222|0.493|0.520|0.377|
|N|3396|3390|3390|3390|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table 11: Productivity: Promotion

||||Dummy|for having|been prom|oted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.043<sup>_∗∗_</sup>|-0.023|-0.017|-0.025|-0.001|-0.026|-0.003|-0.015|
||(0.019)|(0.024)|(0.024)|(0.022)|(0.032)|(0.033)|(0.031)|(0.031)|
|Social networks*tenure|||||-0.009|0.002|-0.001|0.000|
||||||(0.007)|(0.007)|(0.007)|(0.007)|
|Tenure in years|0.016<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|0.014<sup>_∗∗∗_</sup>|0.021<sup>_∗∗∗_</sup>|0.001|0.003|-0.001|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.006)|(0.018)|(0.018)|(0.021)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.077|0.088|0.091|0.110|0.081|0.122|0.137|0.167|
|N|3396|3390|3390|3390|3396|3390|3390|3390|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

32
Table 12: Recruitment: Comparing the employers who use social networks and those who do not

||Employers u<br>networks as <br>mode o<br>advertise|se social<br>a common<br>f job<br>ment?|Dif. in<br>mean|
|---|---|---|---|
||Yes|No||
|% employees who used social networks|82.859|36.962|45.897<br>(3.978)<sup>_∗∗∗_</sup>|
|N|233|106||
|Weeks to fll vacancies|2.611|4.813|-2.201<br>(0.492)<sup>_∗∗∗_</sup>|
|N|232|105||
|Difculty in flling vacancies (normalized)|-0.225|0.297|-0.522<br>(0.136)<sup>_∗∗∗_</sup>|
|N|227|98||
|No. vacancies posted in the last 12 months|10.443|9.718|0.725<br>(4.046)|
|N|233|106||
|% vacancies flled in the last 12 months|96.523|97.340|-0.817<br>(1.827)|
|N|130|51||

_Note_ . Means are shown above. The figures in parentheses are robust standard errors for the differences in means. The variable, _Difficulty in filling vacancies_ , is based on responses to the question, “How difficult is it to fill vacancy?” asked for each occupation categories. The original answers are on the scale of 1–10 with 10 being the most difficult. We normalize them to have the zero means and the one standard deviations.

33
Table 13: Main reason for having chosen the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|2.194|-0.075|1.967|0.298|-3.493|1.681|
||(2.210)|(2.848)|(3.953)|(6.851)|(3.129)|(6.271)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.058|0.073|0.015|0.036|0.049|0.059|
|N|3394|3388|3394|3388|3394|3388|
|Control mean|19.359|19.359|11.718|11.718|32.621|32.621|
||Good pro|spect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-13.431<sup>_∗∗∗_</sup>|-8.901<sup>_∗_</sup>|-0.465|-0.251|-2.470<sup>_∗∗_</sup>|-2.770|
||(3.193)|(4.791)|(0.368)|(0.253)|(1.174)|(1.761)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.076|0.066|0.023|0.022|0.047|0.084|
|N|3394|3388|3394|3388|3394|3388|
|Control mean|21.619|21.619|0.516|0.516|4.914|4.914|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|6.645<sup>_∗∗∗_</sup>|7.690<sup>_∗∗∗_</sup>|9.053<sup>_∗∗∗_</sup>|2.329|||
||(1.475)|(2.716)|(2.841)|(2.604)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.046|0.049|0.049|0.046|||
|N|3394|3388|3394|3388|||
|Control mean|0.721|0.721|8.533|8.533|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

34
Table 14: Who used social networks?

|||Social netw|ork dummy||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Schooing in years|-0.045<sup>_∗∗∗_</sup>|-0.050<sup>_∗∗∗_</sup>|-0.029<sup>_∗∗∗_</sup>|-0.025<sup>_∗∗∗_</sup>|
||(0.003)|(0.003)|(0.003)|(0.004)|
|Age at hire|-0.004<sup>_∗∗_</sup>|-0.006<sup>_∗∗∗_</sup>|-0.005<sup>_∗∗∗_</sup>|-0.005<sup>_∗∗∗_</sup>|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Father’s pri.school|-0.014|-0.011|-0.039<sup>_∗_</sup>|-0.034|
||(0.025)|(0.025)|(0.021)|(0.023)|
|Mother’s pri.school|-0.081<sup>_∗∗∗_</sup>|-0.081<sup>_∗∗∗_</sup>|-0.041<sup>_∗∗_</sup>|-0.037|
||(0.022)|(0.022)|(0.021)|(0.023)|
|Math z-score|-0.011|-0.012|0.003|0.001|
||(0.014)|(0.014)|(0.014)|(0.016)|
|Language z-score|-0.007|-0.004|0.002|-0.019|
||(0.015)|(0.015)|(0.014)|(0.016)|
|Big 5 index z-score|0.066<sup>_∗∗∗_</sup>|0.061<sup>_∗∗_</sup>|0.030|0.027|
||(0.024)|(0.024)|(0.020)|(0.021)|
|Entry occupation fxed efect||X|||
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.288|0.299|0.098|0.056|
|N|3390|3390|3390|3390|

_Note_ . All specifications control for geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

35
# **A Appendix: Robustness check on attrition bias**

36
<!-- Start of picture text --> Log of search spell Log of no. of applications<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Tenure in years Tenure in years<br>(a) Job search spell (b) No. of vacancies applied for<br>Years of schooling Father completed pri.school<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Tenure in years Tenure in years<br>(c) Years of schooling (d) Father’s completion of pri. education<br>Mother completed pri.school Log of entry wage<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Tenure in years Tenure in years<br>(e) Mother’s completion of pri. education (f) Log of entry salary<br>Dummy for being ever promoted<br>1 2 3 4 5 6 7 8 9<br>Tenure in years<br>(g) Promotion<br>Figure A1: Robustness check—Comparison at each year of job tenure<br>1 2<br>1<br>0<br>0<br>-1<br>-1<br>-2<br>-2<br>Coefficient of the social network dummy -3 Coefficient of the social network dummy<br>-3<br>6 .5<br>4 0<br>2<br>-.5<br>0<br>-2 -1<br>Coefficient of the social network dummy Coefficient of the social network dummy<br>-4 -1.5<br>1 1.5<br>.5 1<br>0 .5<br>-.5 0<br>Coefficient of the social network dummy Coefficient of the social network dummy<br>-1 -.5<br>1<br>.5<br>0<br>-.5<br>Coefficient of the social network dummy<br>-1<br><!-- End of picture text --> _Note_ . Plotted are estimates of _θ_ in the equation of _yijk_ = _θNetworkijk_ + _β_<sup>_′_</sup> _Xijk_ + _γjk_ + _εijk_ . In figures (a), (b), (f), and (g), _Xijk_ are years of schooling, age at hire, math and language z-scores, Big 5 index, and the dummies for father’s and mother’s completion of primary education. In figures (d) and (e), _Xijk_ are years of schooling and age at hire. In figure (c), _Xijk_ are not included for. The regressions are run separately for a subsample of each year of tenure, except for 1-year and 9-year tenure. The subsamples of 0- and 1-year tenure are jointly used because their size is small. Due to the same reason, the subsamples of 9- and 10-year tenure are jointly used. The dash lines are 95% confidence intervals. Standard errors are clustered within an establishment.

37
Table A1: Robustness check using the subsample with tenure being 3 years and less—Job tenure

|||Log (tenur|e in years)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.083|-0.039|-0.040|-0.091<sup>_∗∗_</sup>|
||(0.065)|(0.054)|(0.041)|(0.040)|
|Schooing in years||-0.009|-0.010|-0.003|
|||(0.007)|(0.008)|(0.009)|
|Age at hire||0.001|-0.004|-0.001|
|||(0.006)|(0.006)|(0.006)|
|Father’s pri.school||0.080|-0.036|-0.065|
|||(0.082)|(0.072)|(0.075)|
|Mother’s pri.school||0.087|0.128<sup>_∗∗_</sup>|0.172<sup>_∗∗∗_</sup>|
|||(0.054)|(0.055)|(0.056)|
|Math z-score||-0.068<sup>_∗_</sup>|-0.015|0.022|
|||(0.038)|(0.027)|(0.020)|
|Language z-score||0.113<sup>_∗∗_</sup>|0.103<sup>_∗∗_</sup>|0.048|
|||(0.054)|(0.048)|(0.047)|
|Big 5 index z-score||0.014|-0.043|-0.085|
|||(0.051)|(0.068)|(0.066)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.015|0.063|0.041|0.048|
|N|1745|1744|1744|1744|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are establishment-entry occupation fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

38
Table A2: Robustness check using the subsample with tenure being 3 years and less—Job search spell and the no. of vacancies applied for

|||Log(searc|h spell)|||Log(no. ap|plications)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.464<sup>_∗∗∗_</sup>|-0.322<sup>_∗∗∗_</sup>|-0.236<sup>_∗_</sup>|-0.318<sup>_∗_</sup>|-0.434<sup>_∗∗∗_</sup>|-0.257<sup>_∗∗∗_</sup>|-0.232<sup>_∗∗∗_</sup>|-0.241<sup>_∗∗_</sup>|
||(0.084)|(0.106)|(0.122)|(0.165)|(0.101)|(0.088)|(0.088)|(0.099)|
|Schooing in years||0.059<sup>_∗∗∗_</sup>|0.047<sup>_∗∗∗_</sup>|0.041<sup>_∗∗_</sup>||0.038<sup>_∗∗∗_</sup>|0.020|0.035<sup>_∗∗_</sup>|
|||(0.012)|(0.010)|(0.019)||(0.014)|(0.015)|(0.018)|
|Age at hire||-0.012|-0.010|-0.010||0.018<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.029<sup>_∗∗∗_</sup>|
|||(0.010)|(0.012)|(0.011)||(0.006)|(0.005)|(0.008)|
|Father’s pri.school||0.091|0.188<sup>_∗∗_</sup>|0.182<sup>_∗∗_</sup>||0.055|0.129|0.100|
|||(0.080)|(0.084)|(0.085)||(0.108)|(0.125)|(0.153)|
|Mother’s pri.school||-0.007|0.049|0.014||-0.004|0.053|0.107|
|||(0.062)|(0.066)|(0.069)||(0.062)|(0.062)|(0.075)|
|Math z-score||-0.001|-0.043|-0.059||0.052|0.025|0.051|
|||(0.051)|(0.063)|(0.093)||(0.046)|(0.048)|(0.061)|
|Language z-score||-0.112<sup>_∗∗_</sup>|-0.105|-0.124||-0.021|-0.046|-0.139|
|||(0.045)|(0.068)|(0.095)||(0.065)|(0.078)|(0.098)|
|Big 5 index z-score||0.039|0.182<sup>_∗_</sup>|0.095||-0.064|-0.140<sup>_∗∗_</sup>|-0.203<sup>_∗∗∗_</sup>|
|||(0.090)|(0.110)|(0.132)||(0.058)|(0.061)|(0.070)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.129|0.176|0.103|0.095|0.093|0.153|0.115|0.108|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . The variable, _Social networks_ , is the dummy indicating that social networks were used to find jobs. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

39
Table A3: Robustness check using the subsample with tenure being 3 years and less— Observable abilities

||S|chooling (years)||Age at hi|re|
|---|---|---|---|---|---|
||(1)|(2)<br>(3)|(4)|(5)|(6)|
|Social networks|-3.444<sup>_∗∗∗_</sup>|-2.846<sup>_∗∗∗_</sup><br>-1.521<sup>_∗∗_</sup>|<sup>_∗_</sup><br>-1.553<sup>_∗∗_</sup>|-1.695<sup>_∗_</sup>|-0.339|
||(0.349)|(0.416)<br>(0.406)|(0.738)|(0.928)|(1.491)|
|Firm fxed efect||X||X||
|Firm-occ fxed efect||X|||X|
|R-squared|0.237|0.151<br>0.079|0.034|0.029|0.004|
|N|1745|1745<br>1745|1745|1745|1745|
||Pri.|school: father|Pri. s|chool: mo|ther|
||(7)|(8)<br>(9)|(10)|(11)|(12)|
|Social networks|-0.047|-0.030<br>-0.041<br>|-0.184<sup>_∗∗∗_</sup>|-0.124<sup>_∗∗_</sup>|-0.096<sup>_∗∗_</sup>|
||(0.039)|(0.037)<br>(0.041)|(0.047)|(0.050)|(0.044)|
|Schooing in years|0.035<sup>_∗∗∗_</sup>|0.027<sup>_∗∗∗_</sup><br>0.025<sup>_∗∗∗_</sup>|0.032<sup>_∗∗∗_</sup>|0.023<sup>_∗∗_</sup>|0.017|
||(0.005)|(0.006)<br>(0.007)|(0.007)|(0.009)|(0.011)|
|Age at hire|-0.006|-0.006<br>-0.005|-0.001|0.003|-0.001|
||(0.005)|(0.006)<br>(0.008)|(0.004)|(0.005)|(0.006)|
|Firm fxed efect||X||X||
|Firm-occ fxed efect||X|||X|
|R-squared|0.163|0.058<br>0.033|0.184|0.089|0.053|
|N|1745|1745<br>1745|1745|1745|1745|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

40
Table A4: Robustness check using the subsample with tenure being 3 years and less— Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.293<sup>_∗∗∗_</sup>|0.090|0.171|<sup>_∗_</sup><br>0.235<sup>_∗_</sup>|-0.463<sup>_∗∗∗_</sup>|0.060|0.061|-0.001|
||(0.103)|(0.105)|(0.09|2)<br>(0.122)|(0.079)|(0.076)|(0.066)|(0.071)|
|Schooing in years||0.115<sup>_∗∗∗_</sup>|0.113<sup>_∗_</sup>|<sup>_∗∗_</sup><br>0.133<sup>_∗∗∗_</sup>||0.134<sup>_∗∗∗_</sup>|0.116<sup>_∗∗∗_</sup>|0.146<sup>_∗∗∗_</sup>|
|||(0.014)|(0.01|5)<br>(0.027)||(0.014)|(0.017)|(0.021)|
|Age at hire||-0.006|0.00|2<br>-0.003||-0.011<sup>_∗_</sup>|-0.005|-0.002|
|||(0.011)|(0.01|2)<br>(0.013)||(0.007)|(0.009)|(0.007)|
|Father’s pri.school||-0.015|0.14|0<br>0.171<sup>_∗∗_</sup>||0.210<sup>_∗∗_</sup>|0.183<sup>_∗∗_</sup>|0.136|
|||(0.111)|(0.09|4)<br>(0.082)||(0.087)|(0.088)|(0.089)|
|Mother’s pri.school||-0.005|-0.08|9<br>-0.180||0.158|0.047|0.072|
|||(0.138)|(0.17|0)<br>(0.174)||(0.123)|(0.117)|(0.149)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.125|0.270|0.21|3<br>0.169|0.149|0.440|0.293|0.276|
|N|1745|1745|1745|1745|1745|1745|1745|1745|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.063<sup>_∗_</sup>|0.098|0.043|-0.032|||||
||(0.038)|(0.063)|(0.057)|(0.065)|||||
|Schooing in years||0.007|0.001|-0.008|||||
|||(0.009)|(0.008)|(0.007)|||||
|Age at hire||0.002|-0.005|-0.006|||||
|||(0.007)|(0.007)|(0.006)|||||
|Father’s pri.school||0.053|-0.056|-0.061|||||
|||(0.084)|(0.062)|(0.063)|||||
|Mother’s pri.school||-0.003|-0.003|-0.001|||||
|||(0.084)|(0.056)|(0.047)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.122|0.128|0.028|0.029|||||
|N|1744|1744|1744|1744|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

41
Table A5: Robustness check using the subsample with tenure being 3 years and less— Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.355<sup>_∗∗∗_</sup>|-0.103<sup>_∗∗∗_</sup>|-0.083<sup>_∗_</sup>|-0.079<sup>_∗∗_</sup>|
||(0.046)|(0.038)|(0.045)|(0.036)|
|Schooing in years||0.065<sup>_∗∗∗_</sup>|0.076<sup>_∗∗∗_</sup>|0.053<sup>_∗∗∗_</sup>|
|||(0.006)|(0.008)|(0.009)|
|Age at hire||0.029<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.015<sup>_∗∗∗_</sup>|
|||(0.004)|(0.004)|(0.004)|
|Father’s pri.school||0.034|0.022|0.027|
|||(0.042)|(0.039)|(0.036)|
|Mother’s pri.school||0.029|0.092<sup>_∗∗_</sup>|0.011|
|||(0.037)|(0.041)|(0.042)|
|Math z-score||0.068<sup>_∗_</sup>|0.071<sup>_∗∗_</sup>|0.049<sup>_∗_</sup>|
|||(0.035)|(0.036)|(0.028)|
|Language z-score||-0.105<sup>_∗∗_</sup>|-0.090<sup>_∗_</sup>|-0.036|
|||(0.042)|(0.046)|(0.042)|
|Big 5 index z-score||0.028|0.006|-0.020|
|||(0.040)|(0.039)|(0.036)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.174|0.489|0.521|0.289|
|N|1745|1744|1744|1744|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

42
Table A6: Robustness check using the subsample with tenure being 3 years and less— Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.477<sup>_∗∗∗_</sup>|-0.312<sup>_∗∗∗_</sup>|-0.328<sup>_∗∗∗_</sup>|-0.257<sup>_∗_</sup>|
||(0.169)|(0.119)|(0.113)|(0.143)|
|Social networks*tenure|0.055|0.085<sup>_∗_</sup>|0.100<sup>_∗∗_</sup>|0.063|
||(0.062)|(0.045)|(0.044)|(0.048)|
|Tenure in years|0.026|0.047|-0.001|0.004|
||(0.068)|(0.130)|(0.115)|(0.112)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.179|0.511|0.555|0.312|
|N|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table A7: Robustness check using the subsample with tenure being 3 years and less— Promotion

||||Dummy|for havin|g been pr|omoted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.028|-0.021|-0.016|-0.015|-0.053|-0.075|-0.061|-0.069|
||(0.022)|(0.025)|(0.021)|(0.023)|(0.052)|(0.059)|(0.040)|(0.054)|
|Social networks*tenure|||||0.010|0.022|0.019<sup>_∗_</sup>|0.023|
||||||(0.014)|(0.016)|(0.011)|(0.016)|
|Tenure in years|0.002|0.001|0.015<sup>_∗∗_</sup>|0.016<sup>_∗_</sup>|-0.005|-0.022|-0.018|-0.035|
||(0.005)|(0.006)|(0.007)|(0.008)|(0.014)|(0.016)|(0.014)|(0.024)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.016|0.034|0.045|0.072|0.017|0.050|0.054|0.087|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

43
Table A8: Robustness check using the subsample with tenure being 3 years and less— Main reason for choosing the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|3.901|-0.870|-3.737|-5.390|-5.892|1.052|
||(2.742)|(3.543)|(6.337)|(8.460)|(5.814)|(8.817)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.063|0.034|0.016|0.077|0.016|0.073|
|N|1744|1743|1744|1743|1744|1743|
|Control mean|21.558|21.558|14.974|14.974|32.320|32.320|
||Good pr|ospect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-9.517<sup>_∗∗∗_</sup>|-5.888|-0.027|-0.062|-0.343|-0.186|
||(3.329)|(5.701)|(0.083)|(0.053)|(1.167)|(1.574)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.049|0.055|0.003|0.001|0.084|0.028|
|N|1744|1743|1744|1743|1744|1743|
|Control mean|16.472|16.472|0.102|0.102|4.819|4.819|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|7.388<sup>_∗∗∗_</sup>|8.589<sup>_∗∗_</sup>|8.227<sup>_∗∗∗_</sup>|2.756|||
||(1.990)|(3.850)|(2.681)|(3.078)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.039|0.059|0.038|0.055|||
|N|1744|1743|1744|1743|||
|Control mean|1.178|1.178|8.578|8.578|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

44
# **B Appendix: Robustness check on cutoff age to restrict the sample**

Table B1: Robustness check using the sample of the aged 60 and below—Job search

|||Log(searc|h spell)|||Log(no. ap|plications)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.465<sup>_∗∗∗_</sup>|-0.274<sup>_∗∗∗_</sup>|-0.229<sup>_∗∗_</sup>|-0.225<sup>_∗∗_</sup>|-0.452<sup>_∗∗∗_</sup>|-0.242<sup>_∗∗_</sup>|-0.226<sup>_∗∗_</sup>|-0.187<sup>_∗_</sup>|
||(0.087)|(0.105)|(0.099)|(0.093)|(0.098)|(0.093)|(0.089)|(0.105)|
|Schooing in years||0.065<sup>_∗∗∗_</sup>|0.038<sup>_∗∗∗_</sup>|0.018||0.048<sup>_∗∗∗_</sup>|0.026<sup>_∗∗∗_</sup>|0.027<sup>_∗∗_</sup>|
|||(0.011)|(0.010)|(0.013)||(0.012)|(0.008)|(0.011)|
|Age at hire||-0.009|-0.003|-0.004||0.007<sup>_∗∗_</sup>|0.010<sup>_∗∗∗_</sup>|0.008<sup>_∗∗_</sup>|
|||(0.006)|(0.006)|(0.006)||(0.004)|(0.003)|(0.004)|
|Father’s pri.school||0.034|0.130<sup>_∗_</sup>|0.150<sup>_∗∗_</sup>||0.039|0.089|0.062|
|||(0.079)|(0.078)|(0.073)||(0.064)|(0.060)|(0.067)|
|Mother’s pri.school||0.026|0.103<sup>_∗_</sup>|0.029||0.023|0.129<sup>_∗∗_</sup>|0.141<sup>_∗∗_</sup>|
|||(0.066)|(0.060)|(0.049)||(0.056)|(0.054)|(0.062)|
|Math z-score||0.005|0.012|-0.014||0.036|0.021|0.014|
|||(0.034)|(0.033)|(0.042)||(0.040)|(0.032)|(0.038)|
|Language z-score||-0.101<sup>_∗∗_</sup>|-0.086<sup>_∗_</sup>|-0.058||0.007|-0.006|-0.029|
|||(0.041)|(0.048)|(0.050)||(0.054)|(0.053)|(0.059)|
|Big 5 index z-score||0.001|0.144|0.081||-0.064|-0.113<sup>_∗∗_</sup>|-0.135<sup>_∗∗∗_</sup>|
|||(0.087)|(0.107)|(0.121)||(0.049)|(0.046)|(0.050)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.166|0.223|0.147|0.108|0.115|0.182|0.131|0.094|
|N|3763|3757|3757|3757|3763|3757|3757|3757|

_Note_ . The variable, _Social networks_ , is the dummy indicating that social networks were used to find jobs. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

45
Table B2: Robustness check using the sample of the aged 60 and below—Observable abilities

||Sc|hooling (ye|ars)||Age at hir|e|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|-3.515<sup>_∗∗∗_</sup>|-2.851<sup>_∗∗∗_</sup>|-1.382<sup>_∗∗∗_</sup>|-1.146<sup>_∗_</sup>|-1.304<sup>_∗_</sup>|-0.070|
||(0.311)|(0.338)|(0.263)|(0.667)|(0.782)|(0.891)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.222|0.146|0.095|0.059|0.067|0.071|
|N|3763|3763|3763|3763|3763|3763|
||Pri.|school: fat|her|Pri.|school: mo|ther|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-0.092<sup>_∗∗_</sup>|-0.054<sup>_∗_</sup>|-0.062<sup>_∗_</sup>|-0.157<sup>_∗∗∗_</sup>|-0.085<sup>_∗∗∗_</sup>|-0.081<sup>_∗∗_</sup>|
||(0.042)|(0.033)|(0.034)|(0.040)|(0.033)|(0.040)|
|Schooing in years|0.031<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.024<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|0.024<sup>_∗∗∗_</sup>|
||(0.004)|(0.004)|(0.006)|(0.005)|(0.005)|(0.007)|
|Age at hire|-0.002|-0.004<sup>_∗_</sup>|-0.004<sup>_∗_</sup>|0.002|0.001|0.000|
||(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|
|Firm fxed efect||X|||X||
|Firm-occ fxed efect|||X|||X|
|R-squared|0.167|0.099|0.065|0.189|0.118|0.062|
|N|3763|3763|3763|3763|3763|3763|

_Note_ . The dependent variables in columns (7)–(9) are the dummy indicating that the father completed primary education while those in columns (10)–(12) are the dummy for mother’s primary education. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

46
Table B3: Robustness check using the sample of the aged 60 and below—Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.328<sup>_∗∗∗_</sup>|0.069|0.057|0.060|-0.433<sup>_∗∗∗_</sup>|0.102|0.109|0.044|
||(0.084)|(0.070)|(0.062|)<br>(0.070)|(0.088)|(0.078)|(0.067)|(0.079)|
|Schooing in years||0.103<sup>_∗∗∗_</sup>|0.101<sup>_∗∗_</sup>|<sup>_∗_</sup><br>0.111<sup>_∗∗∗_</sup>||0.127<sup>_∗∗∗_</sup>|0.116<sup>_∗∗∗_</sup>|0.143<sup>_∗∗∗_</sup>|
|||(0.010)|(0.010|)<br>(0.016)||(0.010)|(0.012)|(0.014)|
|Age at hire||0.004<br>|0.002<br>|0.002<br><br>||-0.002<br>|-0.002<br>|0.001<br>|
|||(0.005)|(0.005|)<br>(0.006)||(0.003)|(0.004)|(0.004)|
|Father’s pri.school||0.096|0.075|0.081||0.266<sup>_∗∗∗_</sup>|0.161<sup>_∗∗∗_</sup>|0.103<sup>_∗_</sup>|
|||(0.078)|(0.055|)<br>(0.058)||(0.070)|(0.059)|(0.059)|
|Mother’s pri.school||0.043|0.022|0.005||0.133|0.109|0.145|
|||(0.085)|(0.110|)<br>(0.122)||(0.084)|(0.090)|(0.110)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.138|0.298|0.226|0.166|0.127|0.431|0.318|0.288|
|N|3763|3763|3763|3763|3763|3763|3763|3763|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.063<sup>_∗_</sup>|0.123<sup>_∗∗_</sup>|0.030|-0.005|||||
||(0.038)|(0.051)|(0.042)<br>|(0.043)|||||
|Schooing in years||0.011|0.007|0.002|||||
|||(0.008)|(0.006)<br>|(0.004)|||||
|Age at hire||0.003|-0.001|-0.000|||||
|||(0.004)|(0.004)<br>|(0.004)|||||
|Father’s pri.school||0.077|-0.054<sup>_∗_</sup>|-0.044|||||
|||(0.057)|(0.030)<br>|(0.032)|||||
|Mother’s pri.school||0.006|0.021|-0.002|||||
|||(0.068)|(0.037)<br>|(0.038)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.107|0.121|0.046|0.054|||||
|N|3757|3757|3757|3757|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

47
Table B4: Robustness check using the sample of the aged 60 and below—Job tenure

|||Log (tenur|e in years)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.134<sup>_∗_</sup>|-0.120|-0.068|-0.135|
||(0.080)|(0.093)|(0.098)|(0.115)|
|Schooing in years||-0.004|-0.004|-0.009|
|||(0.008)|(0.009)|(0.010)|
|Age at hire||0.001|0.003|0.007|
|||(0.005)|(0.006)|(0.006)|
|Father’s pri.school||-0.061|-0.092|-0.115<sup>_∗_</sup>|
|||(0.064)|(0.068)|(0.064)|
|Mother’s pri.school||0.046|-0.007|0.036|
|||(0.053)|(0.056)|(0.070)|
|Math z-score||-0.011|0.033|0.036|
|||(0.045)|(0.058)|(0.062)|
|Language z-score||0.076|0.065|0.061|
|||(0.058)|(0.059)|(0.073)|
|Big 5 index z-score||0.053|0.060|0.114|
|||(0.060)|(0.082)|(0.085)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.014|0.022|0.011|0.020|
|N|3763|3757|3757|3757|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are establishment-entry occupation fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

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Table B5: Robustness check using the sample of the aged 60 and below—Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.304<sup>_∗∗∗_</sup>|-0.071<sup>_∗_</sup>|-0.067<sup>_∗∗_</sup>|-0.081<sup>_∗∗_</sup>|
||(0.049)|(0.038)|(0.033)|(0.036)|
|Schooing in years||0.059<sup>_∗∗∗_</sup>|0.062<sup>_∗∗∗_</sup>|0.038<sup>_∗∗∗_</sup>|
|||(0.005)|(0.008)|(0.006)|
|Age at hire||0.018<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|0.011<sup>_∗∗∗_</sup>|
|||(0.003)|(0.003)|(0.003)|
|Father’s pri.school||0.062<sup>_∗_</sup>|0.062<sup>_∗∗_</sup>|0.020|
|||(0.033)|(0.029)|(0.030)|
|Mother’s pri.school||0.064<sup>_∗_</sup>|0.069<sup>_∗∗_</sup>|0.041|
|||(0.033)|(0.032)|(0.038)|
|Math z-score||0.065|0.086<sup>_∗_</sup>|0.094<sup>_∗_</sup>|
|||(0.040)|(0.048)|(0.051)|
|Language z-score||-0.105<sup>_∗∗∗_</sup>|-0.093<sup>_∗∗_</sup>|-0.086<sup>_∗_</sup>|
|||(0.036)|(0.046)|(0.050)|
|Big 5 index z-score||0.005|-0.030|-0.093<sup>_∗∗_</sup>|
|||(0.045)|(0.036)|(0.042)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.207|0.410|0.417|0.299|
|N|3763|3757|3757|3757|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

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Table B6: Robustness check using the sample of the aged 60 and below—Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.351<sup>_∗∗∗_</sup>|-0.112<sup>_∗∗∗_</sup>|-0.072<sup>_∗∗_</sup>|-0.083<sup>_∗∗∗_</sup>|
||(0.049)|(0.034)|(0.030)|(0.025)|
|Social networks*tenure|0.008|0.005|-0.001|-0.000|
||(0.006)|(0.006)|(0.005)|(0.006)|
|Tenure in years|0.030<sup>_∗∗∗_</sup>|0.073<sup>_∗∗∗_</sup>|0.082<sup>_∗∗∗_</sup>|0.076<sup>_∗∗∗_</sup>|
||(0.003)|(0.016)|(0.014)|(0.016)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.223|0.493|0.523|0.407|
|N|3763|3757|3757|3757|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table B7: Robustness check using the sample of the aged 60 and below—Promotion

||||Dummy|for havin|g been pr|omoted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.028|-0.021|-0.016|-0.015|-0.053|-0.075|-0.061|-0.069|
||(0.022)|(0.025)|(0.021)|(0.023)|(0.052)|(0.059)|(0.040)|(0.054)|
|Social networks*tenure|||||0.010|0.022|0.019<sup>_∗_</sup>|0.023|
||||||(0.014)|(0.016)|(0.011)|(0.016)|
|Tenure in years|0.002|0.001|0.015<sup>_∗∗_</sup>|0.016<sup>_∗_</sup>|-0.005|-0.022|-0.018|-0.035|
||(0.005)|(0.006)|(0.007)|(0.008)|(0.014)|(0.016)|(0.014)|(0.024)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.016|0.034|0.045|0.072|0.017|0.050|0.054|0.087|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

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Table B8: Robustness check using the sample of the aged 60 and below—Main reason for having chosen the job

||Good|salary|Good|location|Go<br>co|od work<br>nditions|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|1.393|-0.351|1.940|-0.501|-2.753|2.062|
||(2.190)|(2.830)|(3.708)|(6.318)|(2.818)|(5.623)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.064|0.082|0.030|0.052|0.045|0.054|
|N|3761|3755|3761|3755|3761|3755|
|Control mean|20.157|20.157|12.380|12.380|31.604|31.604|
||Good pro|spect for|Releva|nt to my|Good|prestige of|
||career pr|ogression|edu|cation||frm|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Social networks|-12.834<sup>_∗∗∗_</sup>|-7.790|-0.378|-0.228|-2.467<sup>_∗∗_</sup>|-2.702|
||(3.440)|(5.175)|(0.347)|(0.225)|(1.103)|(1.776)|
|Abilities||X||X||X|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.072|0.070|0.036|0.044|0.041|0.076|
|N|3761|3755|3761|3755|3761|3755|
|Control mean|21.456|21.456|0.512|0.512|4.791|4.791|
||Recomm<br>oth|ended by<br>ers|No oth|er ofers|||
||(13)|(14)|(15)|(16)|||
|Social networks|6.510<sup>_∗∗∗_</sup>|7.642<sup>_∗∗∗_</sup>|8.588<sup>_∗∗∗_</sup>|1.870|||
||(1.413)|(2.432)|(2.647)|(2.400)|||
|Abilities||X||X|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.053|0.060|0.048|0.048|||
|N|3761|3755|3761|3755|||
|Control mean|0.760|0.760|8.340|8.340|||

_Note_ . This table presents linear probability models analyzing main reasons why employees chose the jobs. The dependent variable is the binary dummy for the main reason being the one indicated in a corresponding column header. The survey asked employees which of listed reasons was the main reason. The row _Control mean_ shows the sample means of the dependent variables among the employees who did not use social networks. The row _Abilities_ indicates whether the observable and unobservable characteristics (years of schooling, age at hire, and father’s and mother’s years of schooling, math and language z-scores, and Big 5 index) are included or not. All specifications include geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Coefficients, standard errors, and control means are multiplied by 100 and should be interpreted as percentage points. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

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# **C Appendix: Big 5 and social networks**

.

Table C1: Comparing individual Big 5 personality between the employees who used social networks and who did not

||Conscien|tiousness|Emotion|al stability|Agreea|bleness|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|0.138|0.047|0.042|-0.009|-0.009|-0.072|
||(0.130)|(0.097)|(0.061)|(0.048)|(0.063)|(0.078)|
|Schooing in years||0.024<sup>_∗∗_</sup>||0.015||0.006|
|||(0.011)||(0.015)||(0.020)|
|Age at hire||0.002||0.008||-0.014|
|||(0.005)||(0.007)||(0.010)|
|Father’s pri.school||-0.083||-0.029||0.022|
|||(0.098)||(0.065)||(0.060)|
|Mother’s pri.school||0.009||0.024||-0.028|
|||(0.054)||(0.062)||(0.083)|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.038|0.040|0.022|0.043|0.031|0.057|
|N|3386|3386|3346|3346|3340|3340|
||Extra|version|Open|ness|||
||(7)|(8)|(9)|(10)|||
|Social networks|0.086|-0.055|0.017|0.091|||
||(0.063)|(0.109)|(0.077)|(0.064)|||
|Schooing in years||-0.013||-0.014|||
|||(0.020)||(0.013)|||
|Age at hire||-0.002||-0.006|||
|||(0.009)||(0.010)|||
|Father’s pri.school||-0.156<sup>_∗_</sup>||-0.004|||
|||(0.090)||(0.051)|||
|Mother’s pri.school||-0.091||0.097<sup>_∗_</sup>|||
|||(0.101)||(0.050)|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.042|0.060|0.028|0.039|||
|N|3377|3377|3383|3383|||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

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Table C2: Individual Big 5 personality: Job search spells, no. of applications, tenure, entry salary

||Log(s<br>sp|earch<br>ell)|Log(<br>applica|no.<br>tions)|Log(te<br>yea|nure in<br>rs)|Log(<br>sal|entry<br>ary)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.242<sup>_∗∗_</sup>|-0.223<sup>_∗∗∗_</sup>|-0.232<sup>_∗∗_</sup>|-0.168|-0.161|-0.184<sup>_∗_</sup>|-0.069|-0.085<sup>_∗∗_</sup>|
||(0.098)|(0.083)|(0.101)|(0.118)|(0.098)|(0.106)|(0.042)|(0.040)|
|Schooing in years|0.066<sup>_∗∗∗_</sup>|0.021|0.041<sup>_∗∗∗_</sup>|0.022<sup>_∗_</sup>|-0.007|-0.007|0.056<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|
||(0.011)|(0.014)|(0.012)|(0.012)|(0.008)|(0.011)|(0.006)|(0.008)|
|Age at hire|-0.012<sup>_∗_</sup>|-0.009|0.014<sup>_∗∗_</sup>|0.014<sup>_∗∗_</sup>|-0.010|-0.007|0.023<sup>_∗∗∗_</sup>|0.013<sup>_∗∗∗_</sup>|
||(0.007)|(0.008)|(0.006)|(0.006)|(0.007)|(0.007)|(0.004)|(0.003)|
|Father’s pri.school|0.029|0.135<sup>_∗_</sup>|0.040|0.077|-0.017|-0.064|0.057<sup>_∗_</sup>|0.022|
||(0.082)|(0.077)|(0.069)|(0.073)|(0.060)|(0.059)|(0.033)|(0.027)|
|Mother’s pri.school|0.030|0.053|0.030|0.132<sup>_∗_</sup>|0.052|0.055|0.046|0.021|
||(0.067)|(0.058)|(0.058)|(0.071)|(0.054)|(0.069)|(0.035)|(0.040)|
|Math z-score|0.017|-0.008|0.062|0.031|-0.022|0.030|0.075<sup>_∗_</sup>|0.096<sup>_∗_</sup>|
||(0.034)|(0.041)|(0.041)|(0.042)|(0.043)|(0.050)|(0.042)|(0.049)|
|Language z-score|-0.091<sup>_∗∗_</sup>|-0.049|0.014|-0.020|0.072|0.029|-0.096<sup>_∗∗∗_</sup>|-0.082<sup>_∗∗_</sup>|
||(0.037)|(0.046)|(0.051)|(0.057)|(0.052)|(0.054)|(0.035)|(0.040)|
|_Big 5 personality traits_|||||||||
|Conscientiousness|-0.074<sup>_∗∗_</sup>|-0.020|-0.024|-0.072<sup>_∗∗_</sup>|0.020|0.058|-0.029|-0.044<sup>_∗_</sup>|
||(0.036)|(0.035)|(0.028)|(0.029)|(0.023)|(0.038)|(0.018)|(0.026)|
|Emotional stability|-0.020|-0.037|-0.055<sup>_∗_</sup>|-0.057<sup>_∗_</sup>|0.042<sup>_∗_</sup>|0.082<sup>_∗∗_</sup>|0.026<sup>_∗∗_</sup>|0.034<sup>_∗∗_</sup>|
||(0.031)|(0.031)|(0.030)|(0.033)|(0.023)|(0.032)|(0.013)|(0.015)|
|Agreeableness|0.026|0.098<sup>_∗_</sup>|-0.028|-0.024|-0.029|-0.035|0.007|-0.021|
||(0.036)|(0.055)|(0.036)|(0.036)|(0.020)|(0.032)|(0.015)|(0.013)|
|Extraversion|0.021|0.006|0.060<sup>_∗_</sup>|-0.008|0.045|0.061<sup>_∗_</sup>|0.031|0.009|
||(0.032)|(0.038)|(0.033)|(0.022)|(0.033)|(0.037)|(0.021)|(0.018)|
|Openness|0.063<sup>_∗∗_</sup>|0.039|-0.027|0.023|0.000|-0.006|0.005|-0.057<sup>_∗∗∗_</sup>|
||(0.026)|(0.036)|(0.042)|(0.051)|(0.035)|(0.046)|(0.020)|(0.017)|
|Firm-occ fxed efect||X||X||X||X|
|R-squared|0.225|0.137|0.176|0.085|0.032|0.040|0.398|0.252|
|N|3315|3315|3315|3315|3315|3315|3315|3315|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

53