تراکم، شبکه های اجتماعی و روش های جستجوی شغل: نظریه و کاربرد تا مصر

We first develop a theoretical model in which individuals are embedded within a network of social relationships. We show that, conditional on being employed, the probability to find a job through social networks, relative to other search methods, increases and is concave with the size of the network. The effects are stronger for the uneducated. There is however a critical size of the network above which this probability decreases. We then test empirically these theoretical findings for Egypt using the 1998 Labor Market Survey. The empirical evidence supports the predictions of our theoretical model.

It is commonly observed that job seekers use their friends and relatives to find a job. The empirical evidence suggests that about half of all jobs are filled through personal contacts.1 One reason put forward is that it is the most efficient and the least costly job search method (Holzer, 1988). Another explanation is that it allows firms, which are unable to identify the characteristics of applicants because of adverse selection problem, to screen them.2 The focus of this paper is different. We analyze the acquisition and transmission of job information by job seekers through their friends and relatives, and in particular the effect of the size and quality of social networks on the probability to find a job. Calvó-Armengol (2004) and Calvó-Armengol and Zenou (2005) were the first to study the effect of the size of social network in a theoretical context. They use a job-matching model in which workers find jobs through social contacts. They show that more social contacts increase the probability to find a job, a standard result in the social network literature, especially in sociology (see e.g., Wasserman and Faust, 1994). We extend the model of Calvó-Armengol and Zenou (2005) in the case of a developing country by differentiating between low- and high-educated workers. However, we only focus on the transmission of job information through social network rather than on the matching process between firms and workers. Thus, we do not model the matching process, but examine the transmission of job information by comparing the success of using “friends and relatives” versus other search methods. The main contribution of our paper is to test empirically the predictions of the theoretical model of the effect of social network on the probability of finding a job in the case of a developing country. We develop a theoretical model in which individuals are embedded within a network of social relationships. We focus on social networks of individuals that have weak ties with each other and not necessarily strong ties because we are interested in capturing random encounters and personal contacts that might arise in denser areas.3 We distinguish between two types of workers: the low educated (illiterate and less educated) and the high educated. We assume that the low-educated workers only search using their social networks while high-educated workers use both formal and informal (i.e. networks) methods. By assuming that density does not affect the “job application or at the gate” method (i.e. living in a denser area does not improve your chance to get a job by this method, since even if on average you are more likely to pop in a firm with ads at its gate, there are more workers that will do the same thing),4 we show that the probability to find a job through friends and relatives increases and is concave with the network size. We also show that for very dense networks, i.e. large number of weak ties, this probability can even decrease. The intuition runs as follows. Denser areas expose people to more contacts (the size of the network of weak ties increases) so that each worker has more direct friends and therefore has more job information through these friends. As a result, since the probability to find a job directly (i.e. “job application or at the gate” method) does not increase with density, the probability to find a job using friends and relatives increases. The concavity stems from the fact that, if each worker has more friends, each of his/her friends has also more friends to transmit information to, which creates congestion. For very dense networks, this congestion can be so important that it outweighs the benefits of large networks so that the probability to find a job decreases. We also show that the probability to find a job through friends and relatives is affected by the unemployment rate which is a measure of the quality of the network. If unemployment rate increases, workers are more likely to have friends that are unemployed and so have less chance to get a job through their friends and relatives. To test this model we use population density as a proxy for the size of social network (weak ties relationships). Our conjecture is that in denser areas, the network of social relationships is larger so that the size of the network can reasonably be approximated by the population density of the area. This should be particularly true for social networks consisting exclusively of weak ties since it is more likely to encounter more random acquaintance in denser areas (such as cities) than in less dense areas (such as rural areas).5 Using the 1998 Egyptian Labor Market Survey, which is a nationally represented individual level data covering more than 20,000 individuals, we find that the probability to find a job through friends and relatives indeed increases and is concave with population density. We also find that the predicted probability to find a job can even decrease when the area becomes very dense, confirming one of the most surprising and controversial results of our theoretical model. This effect is stronger for the illiterate and the less educated workers. We then show that this probability is negatively affected by the quality of the social network measured by local unemployment rate. Most of the empirical literature on social networks is largely confined to U.S. and British studies – except for Addison and Portugal (2002) who analyze job search methods and outcomes in Portugal – and has mainly studied the relative efficiency of one search method versus the others. For example, Holzer, 1987 and Holzer, 1988 has shown that, in the U.S., for workers aged 16 to 23 years, friends and relatives, and direct applications without referral are not only the most frequently used search methods but also the most productive in generating offers and acceptances. In a similar way, Blau and Robins (1990), in an analysis of Equal Opportunity Pilot Project data for 1980, have shown that friends and relatives generate the most offers and acceptances per contact, while having the highest acceptance rate per offer. For the U.K., Gregg and Wadsworth (1996) find similar results. As stated above, our focus is different in that we study the impact of population density (as measured by the population per inhabited square kilometer) on the probability to find a job using social networks in Egypt. This country is a particularly well adapted case study since it has extremely large variations in terms of population densities. For example, Cairo has around 27,000 inhabitants per square kilometer whereas, for the least dense, Suez, it is only 46. Very few countries have such extreme differences. For instance, in the United States, the highest dense MSAs are: Jersey City, New York and Chicago which have respectively 4577, 2875 and 1243 inhabitants per square kilometer.6 To the best of our knowledge, this is the first paper that studies the relation between network size (as captured by population density) and the probability to find a job. There is also an important empirical literature on social networks in less developed countries. However, the focus is essentially on migration and on how migrants obtain information about jobs through friends and relatives (see for example Banerjee, 1981, Banerjee, 1983, Munshi, 2003 and Mazumdar, 1987). In fact, very few studies have analyzed the importance of social networks in finding a job in less developed countries (without migration).7 A notable exception is Assaad (1997) who found that, in Egypt, among construction workers, kinship ties and social networks matter less than their regional background in finding a job. In another paper, Assaad (1993) found that, in the informal sector, employers prefer to hire craftsmen to whom they have previous personal ties. The paper is organized as follows. In Section 2, we develop the theoretical model. In Section 3, we provide some empirical evidence on the link between population density and network size. Section 4 presents the data while the econometric model is presented in Section 5. In Section 6, we discuss the econometric results and discuss alternative theories. Section 7 concludes.

In this paper, we study the impact of the size and quality of social network on the probability to find a job using social networks. For that, we develop a theoretical model in which individuals (educated and uneducated) are embedded within a network of social relationships and firms only advertise their jobs using informal methods (e.g. help-wanted signs on their windows). What is crucial here is to obtain information about jobs. This can be done indirectly via an employed friend who does not need the job and transmits this information to his/her direct neighbors. We show that the probability to find a job through friends and relatives increases and is concave with population density. This effect is stronger for the uneducated than the educated. We also show that, beyond a certain size of the network, this probability decreases. Finally, the probability to find a job through friends and relatives decreases with local unemployment rate. We then test empirically these theoretical findings using Egyptian data. The empirical evidence supports the predictions of our theoretical model. The empirical findings indicate that conditional on being employed, the probability to have found a job through friends and relatives increases and is concave with population density. In addition, the evidence supports the seemingly surprising theoretical prediction that, above a certain size of the population density, predicted probability is reduced. This effect is stronger for the least educated workers. We also find that this probability is negatively affected by the local unemployment rate.