Upon arrival in a new location, migrants collect information from a variety of sources: their own resources, job postings, and informal networks of friends and neighbors, including those who have moved to the destination from the same place of origin. The information flow seems particularly strong among those who have originated from the same place. The success of previous migrants in the destination market affects perceptions of the economic value of migration, which, in turn, influences the decision of others to move and, more directly, the labor market performance of those who move. To assess empirically these nonmarket interactions, the authors of this study use micro data of employment status from Bangkok, Thailand, to examine whether the population size (the relative share of migrants from a particular area in a population) and efficiency (the estimated employment probability among a group of migrants from a particular area) of previous migrants affect employment prospects of recent migrants from the same area.
A Simple Framework
The analysis begins with a discussion of migrant information acquisition behavior. The authors construct a simple framework that tries to answer the two fold question of whether migrants are influenced by previous migrants from the same area, and, if so, whether they are influenced by competition in the same market or by learning that enhances the job search. The first question assumes that the labor market for migrants is segmented between groups of different origins. However, if there is no such segmentation, the total labor supply in the destination will affect the labor market equilibrium and the probability of finding a job. Thus, a large population of previous migrant and native labor forces will have a negative effect on the employment probability of newly arrived migrants. On the other hand, a large pool of previous migrants may increase the employment probability of recent migrants, e.g., migrants from the same area may help more recent migrants find jobs, have better information on where to find a job, and have already gone through a successful job search.
In this type of empirical exercise, the challenge is to identify origin group-specific externalities against group-specific unobserved fixed factors and/or common shocks correlated among the group members. For example, the quality of education may be higher in a particular region; thus, those who come from that region are more likely to be employed. Since this factor affects both previous and recent migrants, the presence of positive correlation occurs between the two groups. The authors therefore control for such effects as well as for time specific “common shocks” and origin province-specific year shocks such as agricultural production and labor-demand fluctuations and weather conditions that affect employment opportunities in rural areas.
The authors use the Labor Force Survey conducted by Thailand’s National Statistical Office (NSO) in the years 1994 to 1996. The survey provides rich information on employment, such as working hours, payment types, wages, and fringe benefits. It also collected detailed data on migrants such as origin, length of stay in destination, and reasons for migration. The survey identifies the length of residence in the destination up to nine years.
The authors used this data to calculate:
1. The normalized share of migrants from a particular province in the total population of migrants in Bangkok at a given point in time, and
2. The share of the employed that moved from a particular province among migrants from the same province in Bangkok at a given point of time. They also calculate the employment probability among people from a particular region and analyze the effects of the two network variables on the employment probabilities of recent migrants.
All the estimation results include three fixed effects: (1) origin-province fixed effects, (2) origin-province year-specific effects, and (3) round-year effects. With the first two effects, both unobserved common fixed factors and correlated shocks specific to origin provinces are controlled for to avoid spurious inference of external origin-specific network effects. With the time-specific fixed effects, labor demand as well as macro-economic shocks are controlled.
It does appear that a positive informational-scale economy effect dominates the labor-market substitution effect. However, the prediction that currently employed migrants can improve employment probability for newly coming migrants was not supported. However, this specification does not reflect the possibility of interdependence between migrants’ population size and their efficiency in the labor market.
Other results suggest that previous migrants are substitutes for recent migrants from the same origins in the Bangkok labor market. With a large pool of previous migrants, it becomes difficult for recent migrants to find a job. Another implied result is that if new migrants are interacted with more efficient workers (those with jobs), the likelihood of finding a job is greater. This finding supports the main hypothesis that employed migrants who have stayed for a certain period are a key information source for newcomers.
Finally, the efficiency of previous migrants alters the negative substitution effect of the migrants’ population size, i.e., once most previous migrants are employed in Bangkok, their population size enhances employment opportunities for new arrivals.
The authors check the robustness of the key results in two ways by testing whether more experienced migrants become more efficient information sources and how individual attributes alter the efficiency of information acquisition from previous migrants’ networks. While the estimates were not significant enough to reach any conclusion on the above hypotheses, they did show that education, age, and gender do not matter in learning from networks.
This paper shows that an external effect exists in the channels from previous migrants to newly arrived migrants of the same province of origin. In the Bangkok labor market, the employment probability of recent migrants is affected by both the population size and efficiency of previous migrants originating from the same region. A large population size of previous migrants per se decreases employment opportunities among new migrants, while more employment among previous migrants raises the employment probability of new migrants. The above results imply that previous and recent migrants are substitutable in the Bangkok labor market and that the key information source for recent migrants is those who are currently employed.
More interestingly, when previous migrants are highly efficient, a large population size of previous migrants enhances employment prospects among newly arrived migrants. Only when most previous migrants are employed in the market does the size of the local network exhibit information scale economies.