Does Information Improve the Experience of Pursuing Labor Migration? Evidence from a Field Experiment in Pakistan

Abstract

A large literature on international labor migration explores how to improve low-skilled migrants’ experience of pursuing and obtaining overseas employment. Much of this scholarship focuses on describing and mitigating difficult, and sometimes exploitative, conditions in the host country. Scholars have paid less attention to factors in home countries that may affect aspiring migrants’ experience of looking for overseas work. We help address this gap by conducting a field experiment in the high out-migration country of Pakistan to examine whether receiving new information about employment brokers and overseas opportunities improves aspiring migrants’ subjective and objective experience of the job-seeking process. After analyzing the effects of information on those who lack alternative sources of information, we report mixed findings. These highlight the need to think carefully about ways to improve aspiring migrants’ job-seeking experiences, especially with regard to the common assumption that if low-skilled migrants simply “knew more” — that is, had more relevant and accurate information — they would not allow themselves to have negative migration experiences; our findings suggest this is not necessarily always the case.

Keywords

recruitment labor migration Pakistan field experiment‌

Introduction

Low-skilled labor migration is commonly understood to be a challenging and often difficult experience. One of the negative aspects that has received widespread attention is the exploitative living and working conditions that labor migrants sometimes encounter in the host country (Buckley 2014; Lewis et al. 2015; Deshingkar 2019; Silvey and Parreñas 2020). As a result, scholars, policy makers, and activists have sought to improve labor migrants’ experience largely by identifying and studying destination countries’ markets, institutions, and visa regimes and other relevant regulations. They have also given some attention to the role of origin-country recruiters and job agents, who act as middlemen or brokers by connecting aspiring migrants to overseas jobs, and thus frequently have profound influence on migrants’ experience of overseas employment (Arif 2009; Morgan and Nolan 2011; Ghayur 2016; Babar 2021; Schewel 2021). For instance, since these brokers can establish a monopoly over employment information and resources, they sometimes introduce exploitative relationships into labor migration (Jones et al. 2014).

In this article, we focus on an important early step in the migration process, which has received comparatively less attention in the literature: aspiring low-skilled labor migrants’ experience of looking for overseas work before they leave their home country. Specifically, we examine whether providing aspiring migrants with new and relevant information about migration brokers and overseas employment opportunities improves their experience while they seek to leave home and undertake international labor migration.

To do so, we conducted a field experiment with aspiring migrants in a high out-migration city in Pakistan. Like other countries in the broader region, Pakistan makes up a large portion of the low-skilled migrants moving to the Gulf Cooperation Council (GCC) states for temporary jobs. In fact, Pakistan is the second largest supplier of temporary low-skilled labor migrants to the GCC, with the Kingdom of Saudi Arabia (KSA) being the most common destination followed by the United Arab Emirates. In the destination countries, most migrants are employed as construction workers, taxi and private drivers, or security guards. We consider our study's participants to be aspiring migrants because they were actively seeking opportunities to emigrate for such temporary jobs when they joined our study in July 2019.

During our experiment, we shared two different informational statements with randomly selected participants via mobile phone-based text messages. The first statement, or experimental treatment, gave participants information about the best job agent in their local area, or, more specifically, which agent in their neighborhood had recently connected the most aspiring migrants to overseas jobs. The second treatment provided participants with details of the most common overseas job appropriate for low-skilled labor migrants that was available at the time. Both treatments were randomized independently of one another, and each was given to half the participants. To make treatment and control group comparisons meaningful, participants in the control groups also received messages relevant to migration, but these provided generic information instead.

We measured participants’ experience of the migration process in two ways, capturing both objective and subjective aspects of the effort to obtain overseas employment. The objective outcome was whether participants gained more knowledge about the job contract they were likely to obtain, such as whether the employer offered accommodation and what the daily working hours were. The subjective outcome was whether participants were more confident about their chances of getting a job.

The results indicate that information about the best local job agents is associated with improvements in the objective outcome — more knowledge about the details of likely job contracts — when comparing treated participants to those in the control condition. While we find evidence of this result overall, analyses of heterogeneous effects indicate that the effect is driven by participants who had not talked to previous or current migrants and those who had not migrated before. In other words, the treatment effect holds among participants without two clear alternative sources of information. We also find that this information led participants to obtain more knowledge about contracts — that is, compared to themselves before receiving the information — but only if they had not migrated before. We additionally find null effects: first, information about the best local agents did not change participants’ confidence in their employment chances and, second, information about the modal available job opportunity did not affect participants’ knowledge of likely contracts nor their confidence in securing overseas employment.

Our positive findings suggest that providing new information to migrants can improve their experience, but only if they lack alternative personal sources of information. The results point to concrete and practical ways to heighten aspiring migrants’ satisfaction with the experience of obtaining overseas employment, increase their chances of actually migrating, and even possibly alter, or circumvent, the potentially exploitative relationship between job agents and aspiring migrants (Shrestha 2020). Yet, to be clear, we use this particular article to focus on improving aspiring migrants’ experience by improving their knowledge of the migration process and bolstering their confidence while engaging in this process.

Considering the positive and null findings together leads to the conclusion that not all new information affects migrants’ overseas job-seeking experience, and not every aspect of searching for a job overseas is affected. This broader conclusion helps illuminate some of the heterogeneity in the dynamics underlying migration processes. Recent theorizing about these processes has developed the aspiration-capabilities framework, which conceptualizes human mobility as the freedom to choose where to live (de Haas 2021). This freedom and choice, in turn, reflect the intersection of aspirations and capabilities in people. Aspirations to migrate take many forms (Carling and Collins 2018), but they can usefully — and simply — be thought of as the “conviction that migrating is preferable to staying” (Schewel 2021, 1622; see also Carling and Schewel 2018). Capabilities comprise not only resources and skills facilitating migration, but also the general well-being and opportunities that allow people to select where to live (Carling and Collins 2018; de Haas 2021; Schewel 2021; Karell 2022).

While the aspiration-capabilities framework conceptually distinguishes between aspirations and capabilities, they are not functionally independent (de Haas 2021). For example, increases in education — an increase in capabilities — can encourage aspirations to migrate by making individuals more confident that they could successfully establish a life elsewhere (de Haas 2021, 18; see also Schewel 2021 and Hagen-Zanker, Hennessey, and Mazzilli 2023). Thus, seen through the lens of the aspiration-capabilities framework, our findings highlight how information can improve the capabilities of individuals considering migration, which can subsequently influence their aspirations, just as the more commonly studied economic and social capital can (de Haas 2021; Schewel 2021). However, our findings also indicate that improving capabilities can be an uneven process — not all attempts to cultivate capabilities may succeed, perhaps leading to unanticipated changes in individuals’ migration aspirations.

The artcile proceeds as follows. The next section situates our project in the more specific literature discussing migrant precarity, including the role of information in potentially reducing this precarity. This section concludes with our hypotheses. We then explain the context of our study and outline our experimental design and data. Following this, we present our main findings before concluding with a discussion about future research, the implications for the aspiration-capabilities framework, and policy implications.

Information, Brokerage, and Migration

For many developing countries, emigration and labor export are important ways of achieving returns on human capital through remittances (Turner and Kleist 2013). This often occurs in the form of temporary migration to more developed countries for relatively low-skilled jobs, such as construction workers, private and taxi drivers, security guards, and salon workers. Research on the experience of low-skilled temporary labor migration has largely focused on migrants’ vulnerability, highlighting conditions in destination countries, the exploitation that emerges due to dependence on brokers in migrants’ home countries, and the role that migrants’ own networks can play (which can be both positive and negative).

The scholarship on low-skilled migrants’ experience in destination countries is vast, although for the most part it paints a negative picture by highlighting migrants’ vulnerabilities (e.g., Buckley 2014; Lewis et al. 2015; Deshingkar 2019; Silvey and Parreñas 2020). For example, some scholars have studied how the Kafala system in many Gulf countries harms migrants by restricting family reunification, tying them to a single employer, disallowing local marriages, and enforcing other mobility restrictions so that the migrants remain transient workers (Longva 1999; Esim and Smith 2004; Shah 2008; Silvey and Parreñas 2020). Other work has drawn attention to cases in which migrants are compelled to re-sign contracts on arrival in the destination country with a lower salary and other differences in terms and conditions than what was originally promised (Arif 2009).

Related scholarship on low-skilled labor migrants’ experience focuses on the brokering role that home-country job agents or brokers (terms we use interchangeably) play during aspiring migrants’ attempts to find overseas employment, including how these agents can additionally contribute to migrants’ vulnerability. Broadly speaking, individuals acting as brokers bridge “structural holes,” or gaps in social structure, thereby helping goods, information, and opportunities flow across these gaps and between other individuals, groups, and firms (Granovetter 1973; Stovel and Shaw 2012). In the case of countries with consistently high levels of low-skilled out-migration, foreign employers often liaise with job agents, paying the agents commissions for supplying workers (Segall and Labowitz 2017). As Williams et al. (2020) note about Nepal, the higher the logistical costs associated with the destination countries, the more prominent agents become. Agents help aspiring migrants meet logistical requirements that would be difficult for them to fulfill on their own, such as arranging housing, medical checks, and insurance.

In addition to logistical support, home-country job agents are frequently a valuable — and oftentimes sole — source of information for many aspiring migrants. This is particularly the case in rural areas where recruitment agencies are often not directly present (Morgan and Nolan 2011; Schewel 2021), in situations where migrants cannot learn about the specifics of jobs themselves due to language barriers (Babar 2021), and, more broadly, any time migrants’ own information sources are relatively limited (Stovel and Shaw 2012). These kinds of dependencies for support and information point to how brokering can become problematic. Namely, parties on either side of the gaps in social structure risk becoming overly reliant on brokers, giving brokers the opportunity to monopolize the flow of resources and information, which can be exploited for personal gain (Stovel and Shaw 2012).

While not all agents use their brokering position to treat migrants unfairly, the high risk for unscrupulous behavior has led some to characterize agents as another source of exploitation in migrants’ experience of the migration process. After all, agents’ primary focus is on personal profits rather than migrant well-being, which increases the migrants’ precarity (Deshingkar 2019). For instance, Arif (2009) finds that the actual cost of overseas migration in Pakistan is over 10 times higher than the official cost regulated by the government's Bureau of Overseas Emigration (BEOE). Eelens and Speckmann (1990) find similar patterns in Sri Lanka where, due to brokers facing a supply of labor migrants exceeding demand, recruitment for overseas employment essentially became a “buyers’ market.”

The risk that in-country brokers potentially pose to migrants raises the question of how low-skilled workers’ experience of obtaining overseas employment can be improved by altering their relationship with agents. This is the focus of our project. One potential approach for shifting this relationship is through better regulation in home countries. Such regulation often entails improved inspection of private-sector agencies and their practices, with the aim of reducing malpractice, fraud, and exploitative costs imposed upon migrants, as well as to elevate agencies offering better compliance (Kern and Müller-Böker 2015). However, there is evidence from countries like Pakistan that this is not the usual outcome. Instead, regulation often results in inefficiencies and corruption such that the state entities that are tasked with oversight of the job agents engage in bribery and bureaucratic delays themselves (Ghayur 2016; Babar 2021). Consequently, new problems emerge rather than existing ones being resolved.

Another possibility is to bypass the broker relationship, or, in other words, offer migrants a way to close information or resource gaps without agents. For example, a government agency or independent advocacy organization could provide migrants with alternative sources of information about the migration process and the conditions of the jobs they are likely to get. This way, migrants could make better informed decisions regarding migration while lowering their dependence on job agents (which could then result in an improved experience of migration). This strategy of information provision has been popular among states and international development agencies to ensure safe and orderly migration trajectories (Asis and Aguinas 2012). It is not clear, however, whether such information provision is always effective. For instance, Frantz (2014), studying the case of Jordan, finds that formal predeparture training given to small proportions of migrants did not cover their legal rights. In Cape Verde, Åkesson and Alpes (2019) find that state-provided predeparture programs were, in fact, ineffective due to low trust in state-sanctioned initiatives, especially in comparison to local brokers who elicited greater trust. Similarly, others discuss how, despite their heavy commissions, brokers are still considered more trustworthy, especially by rural migrants, as they often form part of migrants’ own social networks (Spaan 1994; Deshingkar 2019).

Thus, previous research indicates that providing novel sources of information to aspiring migrants before they leave their countries is not always an effective way to alter their experience in the destination countries, especially when the information comes from the state. Building on these findings, we examine the effect of information provision on aspiring migrants’ experience of looking for overseas employment while still in their home countries. That is, to support further understanding of the effects of information on migrants’ experiences in the destination country, we examine a prior stage: whether the provision of information affects aspiring migrants’ job search experience in their home countries. At the same time, since the literature provides mixed evidence that information provision improves migrants’ experiences in destination countries, we simply ask whether information provision can, at minimum, improve aspiring migrants’ experience of looking for jobs before departure.

We broadly expect that providing information will improve aspiring migrants’ experiences of seeking overseas employment. We specifically hypothesize and test two components of this experience (i.e., the outcomes of our study). The first is objective. Namely, we examine the knowledge that migrants obtain of likely job contracts. More knowledge of contracts will likely help migrants to make better decisions when, for example, comparing employment opportunities.

How might providing migrants with information help them obtain new knowledge (beyond the information we directly provide)? One way this can happen is through the “knowledge gap” process of curiosity and knowledge acquisition. During this process, an individual first encounters some stimuli which make salient a particular lack of knowledge (Lowenstein 1994; Litman 2005). Then, after realizing the deprivation of this knowledge — the knowledge gap — the individual comes to understand the importance of the missing knowledge, as well as what can be gained by obtaining related knowledge. This understanding motivates the individual to seek out and learn knowledge broadly related to the knowledge gap (Murayama 2022). The process is especially strong when the original stimuli comprise information that not only illuminates the knowledge gap, but is itself beneficial to the individual (Cogliati Dezza, Molinaro, and Verguts 2023). Building on these insights, we posit that providing new information to aspiring migrants will motivate them to seek out more knowledge about contracts. In our case, closing an information gap is not only likely to act as a stimuli making salient a knowledge gap, but also helps aspiring migrants realize that this kind of information exists and can be obtained by seeking it out from, for example, job agents.

Hypothesis 1:

All else equal, providing information to aspiring migrants will be associated, on average, with migrants subsequently obtaining new knowledge about likely job contracts, both compared to their earlier selves (within-subject) and compared to migrants who did not receive the information (between-subject).

The second component is subjective. Migrants’ subjective experience of the migration process — their perception of what is happening and likely to happen as they try to migrate — matters because it can influence their decisions. For example, Shrestha (2020) finds that some Nepalese migrants were more likely to migrate after they were informed of lower-than-expected mortality rates associated with overseas migration, which they tended to overestimate. In our study, we examine migrants’ perception of their chances of (eventually) securing an overseas job. We posit that receiving new information increases migrants’ confidence that they understand the often opaque migration process and, as a result, makes them feel they are more likely to get a job.

Hypothesis 2:

All else equal, providing new information to aspiring migrants will be associated, on average, with migrants having greater confidence in securing overseas employment, both compared to their earlier selves (within-subject) and compared to migrants without new information (between-subject).

While we expect information to have an overall, or average, positive effect on aspiring migrants’ objective and subjective experience of trying to migrate overseas, we are particularly interested in the heterogeneous effects of information provision for those who are less likely to have alternative personal sources of information. Aspiring migrants of course begin the migration process with some existing knowledge, although the amount varies greatly across individuals. One obvious source of alternative information is the internet, which scholars have found can provide a wealth of useful information to aspiring migrants (Pesando et al. 2021). However, in this paper, we focus on migrants’ personal social networks, largely because we are studying a context in which aspiring migrants’ low literacy rates strongly influence the efficacy of alternative sources of information.

Aspiring migrants’ social networks sometimes include friends or family members who have migrated before or who are currently overseas (Liu 2013). These networks often affect the decision to migrate at all, as well as the choice of destination (Dahinden 2005; Mckenzie and Rapoport 2007; Liu 2013; Zhang et al. 2020). Migrants tend to go to countries where they already know previous migrants (Epstein 2008; Haug 2008) because there are consequently better social support networks in the destination countries (Dahinden 2005). In addition, these networks provide both information and resources, which not only increases the number of people migrating but also lowers the costs associated with migration (Mckenzie and Rapoport 2007; Liu 2013).

We expect that aspiring migrants already interacting with previous or current migrants have an alternative and personal source of information and therefore would be less likely to be influenced by the information we provide. By contrast, those without these kinds of social ties are less likely to have alternative information sources and, consequently, be more likely to be influenced by our informational interventions. In terms of Granovetter's (1973) seminal framework on how social ties facilitate the flow of information and resources, our provision of information would matter the most when it could potentially ameliorate the consequences of “structural holes.” Namely, aspiring migrants connected to previous or current migrants can rely on these social ties to span these gaps in their networks and gain access to novel and useful knowledge. In these cases, our information would be redundant, acting like Granovetter's “strong ties” by reinforcing existing knowledge. However, for those aspiring migrants who do not have the social connections that can help bridge them to novel resources, our intervention could have an impact by bringing the novel information directly to them.

A similar logic applies to aspiring migrants’ personal migration history. Those who have migrated themselves in the past have a wealth of knowledge of their own, which makes it less likely that their experience will be affected by information provided by a new source, such as a government agency, independent organization, or researchers like us. Conversely, those who do not have this background are more likely to be influenced by the information we provide (or provided by others). We thus offer four more hypotheses that amend our two main hypotheses:

Hypothesis 1a:

The effect in H1 will be larger for those who do not personally know and chat with any previous migrants.

Hypothesis 1b:

The effect in H1 will be larger for those who have not migrated before.

and

Hypothesis 2a:

The effect in H2 will be larger for those who do not personally know and chat with any previous migrants.

Hypothesis 2b:

The effect in H2 will be larger for those who have not migrated before.

We test our hypotheses by providing aspiring migrants with two kinds of information. One gives the study's participants information about the best-performing job agent in their neighborhood. The other provides reliable contract details about the modal job opportunity available. We explain these informational interventions in more detail below, after we introduce the study context.

Study Context: Pakistan

Pakistan, the fifth most populous country in the world, has had high temporary out-migration for the past few decades, largely due to a vast low-skilled labor force. This emigration is concentrated among the GCC countries because of geographical proximity and the large number of temporary employment opportunities. In 2013 alone, 623,000 Pakistanis left to go abroad; in 2015, nearly 96 percent of Pakistanis working abroad resided in the GCC countries (ILO 2015). We conduct our study in one high-out migration area of Pakistan, the neighboring and contiguous cities of Rawalpindi and Islamabad (henceforth Rawalpindi). “Pindi” is the third largest metropolitan region in Pakistan and, as a result of its geographic location, draws aspiring migrants from both the county's north and very populous Punjab regions. This makes the sampling population more sociodemographically diverse than other major cities in Pakistan.

In migrant-receiving countries like the GCC states, companies seeking foreign-born low-skilled workers typically sign contracts with recruitment agencies, including job agents, in sending countries (like Pakistan) to find individuals to fill their employment openings (Segall and Labowitz 2017). Many of these aspiring migrants reside in rural areas and must travel to urban centers to learn about overseas employment opportunities, liaise with recruitment agencies holding the contracts, and accomplish several requirements for migration, such as completing a medical check and obtaining a passport and visa. Doing so may require the assistance of neighbors, relatives, or local subcontracted job agents who guide them through the process (Arif 2009). Indeed, because most aspiring low-skilled labor migrants in Pakistan have no easy way to even learn about which opportunities for overseas employment exist, much less how to apply for jobs tendered in contracts held by a broker without actually working with the broker, they are effectively required to work with brokers and pay their requisite fees. In Pakistan, many of these brokers and (sub-)agents are officially licensed as overseas employment providers (henceforth OEPs, used alongside “agents” or “brokers”) by the BEOE and are legally allowed to charge aspiring migrants fees.

In sum, low-skilled labor migration from countries like Pakistan typically involves a series of steps involving the acquisition of novel information and development of new knowledge. Usually, these steps are thoroughly mediated and essentially monopolized by brokers. These steps also carry monetary costs. Consequently, aspiring migrants have an incentive to acquire accurate knowledge about brokers and migration processes and opportunities, search out and gain even more knowledge, and update their beliefs about their migration prospects, lest they take on more financial burdens for unlikely or unwanted outcomes. Moreover, while we focus on Rawalpindi and Pakistan, these mediation and financial dynamics are common across many countries with high levels of low-skilled labor migration (Arif 2009; Segall and Labowitz 2017; Schewel 2021).

Data and Study Design

Our study's participants are a sample of 450 aspiring migrants.¹ Since the study required individuals who were actively looking for opportunities to migrate overseas for low-skilled jobs, we recruited participants from outside OEP offices in Rawalpindi. Specifically, we used the BEOE website to construct a list of active agents and their neighborhoods of operation in Rawalpindi during early 2019. As agents tend to set up their offices close to well-known markets, this approach helped us identify clusters of agents around the city. We shortlisted clusters with a relatively high number of OEP offices for logistical simplicity, and our survey enumerators then visited randomly selected OEPs from these clusters. At the agents’ offices, and with the agents’ permission, the enumerators approached randomly selected migrants and asked about their willingness to participate in the study. They continued the recruitment effort until they had enrolled 450 participants.²

Recruiting participants from OEP offices was how we addressed the challenge that there exists no sampling frame of aspiring migrants in Pakistan. In other words, since it is not possible to know at the outset who all aspiring migrants are and then sample from this population, we needed to somehow first identify people trying to migrate, then select individuals from this group. While this strategy successfully yielded numerous study participants, it worked against obtaining positive and meaningful results. Namely, people already going to OEP offices likely have some nontrivial amount of information about OEPs and job opportunities, knowledge of contracts, and confidence in securing overseas employment. This penalizes the potential impact of our informational interventions — or, in terms of inferential modeling, it becomes harder to detect a statistically significant effect.

The structure of the study consisted of two face-to-face interviews with treatment (or control) interventions administered in between. The entry interview consisted of a structured survey lasting about 60 min, and took place shortly after participants were recruited into the study. This was during July and August of 2019. The exit interview, a shorter structured survey, occurred during February and early March 2020.³ Of the 450 individuals who expressed interest in the study, 417 completed the entry survey. Of these, 286 could be tracked down and interviewed for the exit survey.⁴ Importantly, as we discuss below, the participants who completed the exit survey are very similar, on average, to those who dropped out of the study with regards to sociodemographic attributes, migration history, and their relationships with current or previous migrants. This helps mitigate concerns about attrition in our study. All interviews took place in Urdu.

During the entry interview, we asked participants about several topics, including demographics, their migration history, and their relationships with current or previous migrants. Table 1 uses this information to describe those participants who eventually also participated in the exit interview. All participants were men and most were single (never married) (58.48%). We intentionally had an all-male sample as low-skilled female temporary migration from Pakistan is essentially nonexistent. The mean age was about 28. Most spoke Punjabi (77.62%) and identified their ethnicity as Punjabi (78.7%), which we use to code binary variables for speaking the majority language or not and for self-identifying as the majority ethnicity or not. About half had no formal education beyond secondary school (47.29%), and most were unemployed (75.45%) and came from households earning less than 60,000 Pakistani Rupees (about 390 USD in 2020). The majority had not spoken with someone they personally knew who had migrated before about the migration experience or process, nor had most migrated before themselves, as indicated by the means for these two binary variables.

Table 1.

Descriptive Statistics.

Statistic	N	Mean	SD	Min	Median	Max
Age	277	28.25	6.67	18	27	80
Primary language Punjabi	277	0.78	0.42	0	1	1
Punjabi ethnicity	277	0.79	0.41	0	1	1
No education beyond secondary school	277	0.47	0.5	0	0	1
Single (never married)	277	0.59	0.49	0	1	1
Currently unemployed	277	0.76	0.43	0	1	1
Household income < 60,000 PKR	182	0.62	0.49	0	1	1
Household size	277	7.09	2.5	3	7	20
Chat with previous migrant	286	0.23	0.42	0	0	1
Migrated before	277	0.08	0.28	0	0	1
Knowledge index	277	7.9	0.65	2	8	8
Knowledge index change (Δ)	277	0.14	1.03	−6	0	4
High job confidence	277	0.11	0.69	0	0	1
Job confidence change (Δ)	277	0.38	0.49	−1	0	1

Table 2.

Differences Between Groups.

	Nonattriters versus attriters		T1 versus No T1		T2 versus No T2
Statistic	ΔM	p	ΔM	p	ΔM	p
Age	−0.126	≥ .05	0.167	≥ .05	−0.877	≥ .05
Primary language Punjabi	0.002	≥ .05	−0.082	≥ .05	−0.032	≥ .05
Punjabi ethnicity	0.049	≥ .05	−0.038	≥ .05	0.029	≥ .05
No education beyond secondary school	0.013	≥ .05	−0.09	≥ .05	0.004	≥ .05
Single (never married)	−0.09	≥ .05	−0.081	≥ .05	−0.041	≥ .05
Currently unemployed	0.024	≥ .05	−0.088	≥ .05	0.051	≥ .05
Household income < 60,000 PKR	0.059	≥ .05	0.093	≥ .05	0.135	≥ .05
Household size	0.058	≥ .05	0.299	≥ .05	−0.042	≥ .05
Chat with previous migrant	−0.061	≥ .05	0.014	≥ .05	−0.035	≥ .05
Migrated before	0.019	≥ .05	0.002	≥ .05	−0.023	≥ .05
Treatment 1 (T1) exposure	−0.083	≥ .05
Treatment 2 (T2) exposure	−0.006	≥ .05
Knowledge index	0.021	≥ .05	−0.029	≥ .05	−0.024	≥ .05
High job confidence	−0.036	≥ .05	0.015	≥ .05	0.013	≥ .05

Note: This table shows the difference in means (ΔM) between nonattriters and attriters, those who received Treatment 1 (T1) and not, and those who received Treatment 2 (T2) and not. The table also shows the p-values obtained when using either t-tests or chi-square tests to test for differences between groups’ various attributes and treatment conditions. In the tests of the information index and belief in obtaining a job, the measures of the index and chance of obtaining a job come from the entry interview, that is, they are pretreatment.

Treatment and Control Conditions

Participants received the treatment or control conditions as informational messages sent to their phones via SMS text message in late October and early November 2019, about midway through the study. We opted to use phone-based text messages to administer the treatment and control conditions because of how commonplace (and inexpensive) SMS messaging is for interpersonal communication and information consumption in Pakistan, as well as in other similar contexts.⁵

We employed two treatment messages, allowing us to assess the effects of different kinds of information. The first treatment message (T1) told the respondent about the best performing OEP in the particular neighborhood where they were recruited into the study. It mentioned how many migrants that OEP had sent abroad in the last year — that is, in 2018 — and how many the average OEP in their neighborhood had sent in the same time period. The message also provided them with the name, address, and phone number of this best-performing OEP. The second treatment message (T2) provided respondents with information about the modal job contract available at that time. We obtained the information about high-quality OEPs and available job opportunities from a database maintained by the BEOE.⁶^,⁷ The database was publicly available on the BEOE's website, but its information was in English, making it inaccessible to most low-skilled aspiring migrants in Pakistan, including those in our study.

One motivation for our treatment statements was that we used all public (and factual) information available from the BEOE. In other words, we wanted to inform participants about things that were not only relevant to the migration process but also true and accessible to people not in the study, so as not to confer an unfair advantage. With these parameters in mind, the treatments had to be about OEPs’ performance and job opportunities — the main information available on the BEOE's website at the time.

The treatment and control messages were in Urdu. The English translations are as follows:

T1:

OEP Muhammad Tufail Butt has found 687 migrants jobs abroad last year. The average OEP in your area sent 98. Tufail Butt's contact information is as follows …

T2:

There are 5,258 open contracts for private drivers in KSA (Saudi Arabia) in the twin cities of Rawalpindi and Islamabad. The average salary for this job is 57,737 PKR per month, and most of these contracts receive accommodation, meals, health insurance, and plane ticket provided by the employer.

Control:

Did you know that one of the main functions of the Bureau of Emigration & Overseas Employment, based in Islamabad, is to advise the Federal Government on emigration procedures and policies?

The italicized text in T1 varied based on the neighborhood where the participant had been recruited. Each message was sent twice in the same day to maximize the chances of it being read.

After receiving the treatment messages, participants were asked “How helpful is this information?” with the following options listed: “very helpful”; “somewhat helpful”; “neither helpful nor unhelpful”; and “unhelpful.” We requested that respondents texted back their answers. For those in the control condition, we asked them to text back with “yes,” “no,” or “I’m not sure.” We asked these questions and requested responses to gain insight into whether individuals had read the messages. Of the participants who eventually participated in the exit interview, 52% replied to our queries about the treatment or control messages. To be clear, this does not mean that the other 48% did not read the messages; it means that we have strong evidence that over half of the sample did read the messages. We check the robustness of our main results by analyzing both the full sample of respondents and the subset of those who responded to the postmessage query.

We randomly and independently assigned treatment such that half the respondents randomly received T1, and half randomly received T2. The “Treatment 1 (T1)” and “Treatment 2 (T2)” rows of the “Non-Attriters versus Attriters” column of Table 2 show that attriters did not differ, on average, from nonattriters with regard to treatment conditions. This further mitigates concern about attrition. The “T1 versus No T1” and “T2 versus No T2” columns of Table 2 present the results of pretreatment balance tests based on the participants who completed the exit survey. They show that in terms of the sociodemographic and migration-related attributes described earlier, the individuals treated with T1 were not significantly different, on average, than those not treated with T1. The same was true of the group treated with T2 and the group not treated with T2.

Outcomes

We analyze the effect of the treatments on two sets of outcomes. The first set captures an objective aspect of the migration experience and is used to test the first set of hypotheses (i.e., H1, H1a, and H1b). It was motivated by insights we gained during pre-study interviews, in which respondents often lamented receiving little information about the job they were being recruited for.

If respondents said that they had been given some information about a specific job posting that the OEP had in mind for them, we asked about eight possible specific aspects of this potential contract. The aspects were: whether the employer (via the job contract) offered accommodation, meals, the plane ticket, a medical test (required for the visa), health insurance, and/or local transportation when overseas; what the daily working hours were; and the total recruitment cost. We chose these aspects as most, if not all, of these should have been known by OEPs because they are usually specified in the job contracts OEPs manage. Indeed, they were reported for every job contract in the BEOE's online database.

From participants’ answers about whether they were given details contained in job contracts, we constructed one of the response variables in the first set of outcomes, knowledge index. This variable is a count of the number of contract aspects that the respondent said they had learned about. We also constructed a second response variable in the first set of outcomes, knowledge index change (Δ), which is a within-subject measure of the change in the knowledge index from the entry interview (pretreatment) to the exit interview (posttreatment). The former response variable allows us to measure the difference between respondents who were treated versus not while the latter variable captures the difference-in-differences by comparing the within-individual change for the treated versus control group.

The second set of outcomes pertains to the subjective experience of the migration process and corresponds to the second set of hypotheses (i.e., H2, H2a, and H2b). Respondents were asked about their own perceived likelihood of getting a job in the next 3 months, measured on a 0 to 3 ordinal scale. For those who chose 3 (very likely), we coded the first response variable in this second set, high job confidence, as a 1 versus 0 to analyze the between-subject difference at exit. Building on this variable, we also constructed a second response variable in this set of outcomes, job confidence change (Δ), which captures the within-subject difference over time. The bottom panel of Table 1 summarizes these four response variables. Table 2 shows that, in terms of participants’ knowledge index and their confidence of obtaining a job at the time of the entry interview, there was no significant difference, on average, between participants who completed the study and those who dropped out. We also find no evidence of a difference between those who received T1 and those who did not (and completed the exit interview), nor between those who received T2 and those who did not (and completed the exit interview).

When analyzing how the treatments affect the outcomes, we consider two heterogeneous treatment effects. Specifically, we examine how the effects are conditioned by talk with previous migrant, an indicator variable capturing whether the participant personally knows and has recently spoken with someone who has migrated in the past or is currently a migrant, and migrated before, another indicator variable, denoting whether the participant himself has migrated in the past or not. As mentioned earlier, Table 1 reports the distributions of these two variables used to examine heterogeneous effects. Table 2 shows that, with regard to these two variables, the group of participants who completed the study did not significantly differ from the group of those participants who did not complete the study. The statistics presented in Table 2 also indicate that distributions of these variables were not significantly different between the groups of treated and control participants for either T1 or T2.

Estimation

For between-subject analyses, we use the following basic specification to estimate the treatment effects:

Y_{i} = α + β T_{i} + β T_{i} x_{i} + ε_{i}

where

Y_{i}

is the outcome variable,

T_{i} = {0, 1}

is an indicator of treatment assignment for individual i, and

x_{i}

is the independent variable we interact the treatment with to explore possible heterogeneous treatment effects.

For within-subject analyses, we use:

Y_{i} = α + β T_{i t} + β T_{i t} x_{i t} + ε_{i t}

where

Y_{i}

is the change in Y pretreatment and posttreatment for individual i and

t = {0, 1}

indicates entry and exit interview, respectively. Other variables are the same as specified above.

Results

Tables 3 and 4 present the results of testing our first set of hypotheses, which focus on whether providing information to migrants leads them to obtain new knowledge about likely job contracts. The first test involves T1, giving migrants information about high-quality local OEPs. We see in Table 3 that while this information does not increase participants’ knowledge compared to their pretreatment selves (Table 3, Model 1), the treated participants do have more knowledge of likely contracts compared to the nontreated participants (Table 3, Model 2). These contrasting results are likely due to how individuals’ level of knowledge changes and the structure of the analytical comparisons. Namely, while participants, on average, gain new knowledge between the entry and exit interviews, it is relatively little in terms of the actual knowledge index. Indeed, the mean change in the index is 0.14 (see Table 1). Consequently, the models do not estimate a significant effect when used to examine the change undertaken by participants, as shown in the first model. However, when the changes within the treated and nontreated groups are compared, the difference between groups is significant at conventional levels.

Table 3.

Effects of Agent Information on Objective Outcomes.

	Knowledge index Δ	Knowledge index	Knowledge index Δ	Knowledge index	Knowledge index Δ	Knowledge index
	(1)	(2)	(3)	(4)	(5)	(6)
T1 (best OEP)	0.196 (0.129)	0.164* (0.082)	0.229 (0.148)	0.223* (0.093)	0.234^‡ (0.134)	0.179* (0.086)
Talk with previous migrant			0.136 (0.226)	0.223 (0.142)
Talk with previous migrant × T1			−0.164 (0.311)	−0.286 (0.196)
Migrated before					0.545^‡ (0.325)	0.197 (0.200)
Migrated before × T1					−0.480 (0.471)	−0.179 (0.290)
Constant	0.044 (0.088)	7.820** (0.056)	0.018 (0.098)	7.777** (0.062)	0.000 (0.092)	7.803** (0.059)
Treated: average y	0.240	7.984	0.240	7.984	0.240	7.984
Control: average y	0.044	7.820	0.044	7.820	0.044	7.820
Observations	258	263	258	263	258	263
R ²	0.009	0.015	0.010	0.025	0.020	0.019

Note: ^‡ p < .1; * p < .05; ** p < .01.

Table 4.

Effects of Contract Information on Objective Outcomes.

	Knowledge index Δ	Knowledge index	Knowledge index Δ	Knowledge index	Knowledge index Δ	Knowledge index
	(1)	(2)	(3)	(4)	(5)	(6)
T2 (modal contract)	0.019 (0.129)	−0.007 (0.082)	0.018 (0.147)	−0.029 (0.094)	0.046 (0.135)	−0.005 (0.086)
Chat with previous migrant			0.067 (0.223)	0.039 (0.141)
Chat with previous migrant × T2			0.004 (0.311)	0.098 (0.198)
Migrated before					0.459 (0.328)	0.110 (0.196)
Migrated before × T2					−0.291 (0.473)	0.005 (0.294)
Constant	0.126 (0.092)	7.901** (0.058)	0.111 (0.105)	7.892** (0.066)	0.086 (0.096)	7.890** (0.062)
Treated: average y	0.145	7.894	0.145	7.894	0.145	7.894
Control: average y	0.126	7.901	0.126	7.901	0.126	7.901
Observations	258	263	258	263	258	263
R ²	0.0001	0.00003	0.001	0.004	0.009	0.002

Note: ^‡ p < .1; * p < .05; ** p < .01.

We obtain similar results when considering participants’ discussions with previous or current migrants. We find no within-subject change (Table 3, Model 3), but, in the between-subjects analysis, participants who receive information about the best local OEPs have more knowledge about contracts compared to those who do not receive the OEP information (Table 3, Model 4). However, this positive effect occurs only among those participants who had not been in communication with previous or current migrants. This indicates that our provision of information has an effect only on those participants who are not getting information from migrants in their own social networks.⁸

When we examine how the treatment effect differs across participants’ migration histories, we get slightly different results. We find that receiving information about high-quality local OEPs does lead participants to acquire new knowledge about likely contracts (i.e., a within-subject change), but only for those migrants who had not migrated before (Table 3, Model 5). The base coefficient on “migrated before” is also positive and statistically significant, indicating that those who have migrated before are associated with obtaining new knowledge, regardless of their treatment status. Following the explanation of our first two models’ results, these findings suggest that participants who have not migrated before increase their knowledge to a relatively larger degree than other participants, whether treated or not.⁹ In addition, and consistent with the preceding results, we find that those who receive the treatment and have not migrated before consequently have more knowledge about available jobs compared to untreated participants (Table 3, Model 6).¹⁰

In contrast to the first informational message (T1), we find that T2, information about the modal available job, has no effect on participants’ knowledge about likely contracts. Table 4 shows that the estimated effect of the treatment is never statistically significant at conventional levels, and, in some cases, is even negative. There is no evidence of an effect even when considering participants’ communication with previous or current migrants or when considering their migration histories. Thus, we find some evidence in support of our first hypothesis: providing participants with some kinds of information — namely, about the best-performing local OEP (T1) — is associated with more knowledge about likely job contracts, and primarily when comparing participants with the OEP information to those without it. In addition, our findings indicate that this kind of information has heterogeneous effects. It affects participants with social ties and experience associated with low levels of information, specifically, those not in communication with previous or current migrants (supporting H1a) and those who had not migrated before (supporting H1b).

Next, the tests of our second set of hypotheses examine whether providing aspiring migrants with information affects a subjective aspect of the migration experience — namely, their confidence in securing overseas employment. Overall, we find that it does not. Table 5 shows that telling participants about the high-quality local OEPs (T1) does not increase their confidence, whether analyzing within-subject change or between-subject differences. Outside of our treatments, we do find evidence that participants’ communication with previous or current migrants is associated with an increase in confidence (a within-subject change; see Table 5, Model 3), and that migrating previously is associated with greater confidence (Table 5, Models 5 and 6).

Table 5.

Effects of Agent Information on Subjective Outcomes.

	Job confidence Δ	Job confidence	Job confidence Δ	Job confidence	Job confidence Δ	Job confidence
	(1)	(2)	(3)	(4)	(5)	(6)
T1 (best OEP)	0.020 (0.084)	0.035 (0.060)	0.044 (0.096)	0.030 (0.068)	0.008 (0.087)	0.041 (0.062)
Chat with previous migrant			0.243^‡ (0.146)	0.105 (0.104)
Chat with previous migrant × T1			−0.153 (0.202)	−0.006 (0.144)
Migrated before					0.427* (0.203)	0.336* (0.145)
Migrated before × T1					0.129 (0.293)	−0.071 (0.210)
Constant	0.101^‡ (0.058)	0.360** (0.041)	0.054 (0.064)	0.339** (0.046)	0.063 (0.060)	0.331** (0.043)
Treated: average y	0.121	0.395	0.121	0.395	0.121	0.395
Control: average y	0.107	0.360	0.107	0.360	0.107	0.360
Observations	263	263	263	263	263	263
R ²	0.0002	0.001	0.012	0.009	0.044	0.033

Note: ^‡ p < .1; * p < .05; ** p < .01.

Similarly, Table 6 presents results indicating that informing participants about the modal available job opportunity (T2) does not increase their confidence in securing an overseas job. It also shows results suggesting that confidence is generally greater among participants who communicate with previous or current migrants (a between-subject difference; see Table 6, Model 4) and among participants who have migrated before (Table 6, Models 5 and 6). Overall, these results do not offer evidence in support of our second set of hypotheses.

Table 6.

Effects of Contract Information on Subjective Outcomes.

	Job confidence Δ	Job confidence	Job confidence Δ	Job confidence	Job confidence Δ	Job confidence
	(1)	(2)	(3)	(4)	(5)	(6)
T2 (modal contract)	−0.039 (0.084)	−0.026 (0.060)	−0.029 (0.095)	0.020 (0.068)	−0.036 (0.087)	−0.028 (0.062)
Chat with previous migrant			0.188 (0.144)	0.209* (0.102)
Chat with previous migrant × T2			−0.046 (0.202)	−0.205 (0.143)
Migrated before					0.454* (0.196)	0.251^‡ (0.140)
Migrated before × T2					0.097 (0.295)	0.113 (0.211)
Constant	0.130^‡ (0.06)	0.389** (0.042)	0.088 (0.068)	0.343** (0.048)	0.085 (0.062)	0.364** (0.044)
Treated: average y	0.091	0.363	0.091	0.363	0.091	0.363
Control: average y	0.130	0.389	0.130	0.389	0.130	0.389
Observations	263	263	263	263	263	263
R ²	0.001	0.001	0.011	0.017	0.044	0.032

Note: ^‡ p < .1; * p < .05; ** p < .01.

Discussion and Conclusion

How does information influence low-skilled migrants’ experience of seeking overseas employment, particularly their knowledge and perceptions of the migration process? Drawing on data from a field experiment with aspiring migrants in Rawalpindi, Pakistan, we find that some kinds of information affect some aspects of the migration experience before migrants leave their home country. Specifically, information about high-quality OEPs, or job agents, in migrants’ local area is associated with greater knowledge about likely job contracts when comparing informed migrants to those who do not receive the information. While we find evidence of this result overall, analyses of heterogeneous effects indicate that it occurred among participants who did not talk to previous or current migrants and among those who had not migrated before themselves — in other words, among those without two obvious alternative personal sources of information. We additionally find that information about the best-performing local OEPs led migrants to obtain more knowledge about contracts — that is, compared to themselves before receiving the information — but only if they had not migrated before. This latter result supports the broader conclusion that providing new information to migrants will be more likely to confer positive benefits on those without alternative sources of information.

Our findings also support another broad conclusion: not all information affects migrants’ migration experience, and not every aspect of searching for a job overseas is affected. Our results indicate that information about the modal available job opportunity does not change participants’ knowledge about likely contracts, whether or not migrants have alternative sources of information. In addition, neither information about the best local OEPs nor about common job opportunities affects participants’ knowledge about contracts or their confidence in securing overseas employment. Of course, these null findings could be due to aspects of the study, such as participant attrition. However, the participants who dropped out during the study are comparable to those who remained in many observable ways, as discussed in detail in earlier sections. Consequently, we believe an important insight from the study is a reminder that information comes in many forms and the migration process comprises many elements, and these forms and elements have diverse, complex relationships.

Future research can build on these findings to shed further light on the interplay between different kinds of information and aspiring migrants’ capabilities and aspirations (Carling and Collins 2018; de Haas 2021), including how this interplay is conditioned by migrants’ own sources of information. One useful starting point is differentiating between information that is novel to migrants — akin to the information we provide participants — and information that corrects migrants’ existing but inaccurate knowledge (e.g., Shrestha 2020). Another possibility, building on both our positive and null findings and aiming to advance the aspiration-capabilities framework (de Haas 2021), entails investigating why capabilities for migration do or do not respond to more information or kinds of economic, human, and social capital. That is, why might aspiring migrants’ capabilities fail to develop in expected ways, and how would this affect their aspirations? Our examination of dissimilar informational statements provides a foundation for this kind of research on heterogeneity in the aspiration-capability dynamics of migration processes.

A third option for future research, drawing on our positive findings that some information improves some knowledge, and thus aspiring migrants’ capabilities, is whether this development of capabilities helps individuals to migrate abroad and have (hopefully) better experiences in the host country than they otherwise would have had. Furthermore, could providing information to a large population of aspiring migrants, and subsequently changing their capabilities, have broader consequences by shifting the structures that constrain the migration of some groups while facilitating the migration of others (de Haas 2021, 26)? Our study was not designed to examine these types of long-term effects of information-provision, but it does provide a starting point for tracing how concrete and straightforward interventions into aspiring migrants’ lives could not only alter the long-term experience of international labor migration but also the structures that shape these experiences.

We conclude with remarks on what our findings suggest for policy. In brief, Pakistan's BEOE, as well as analogous governmental and nongovernmental entities in comparable settings, should develop accessible, efficient, and easily comprehensible communication that shares the information they already have with aspiring migrants. Recall that the information we provided to study participants was available on the BEOE's website, yet it was in English and difficult to find, especially for users who are not technologically savvy. We simply obtained the information, identified which parts were relevant to which participants based on their locations, translated these parts into Urdu, and proactively shared them with participants. Our study's clearest policy implication, then, is not necessarily for agencies to collect difficult-to-obtain types of information or implement programs cultivating certain knowledge, but simply to share the large amount of information they already have with those who are most likely to benefit.

Footnotes

Acknowledgements

We thank Peter Bearman, Hannah Brückner, Katherine Stovel, Nathalie Williams, members of the Research & Empirical Analysis of Labor Migration program, and participants at the annual meetings of the European Political Science Association (2022 and 2023) for helpful comments and suggestions. We also thank two anonymous reviewers and the IMR editorial team. We are indebted to Waseem Janjua, Wasif Naqvi, and MYMACOM for their assistance with implementation.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research and Empirical Analysis of Labor Migration, under a grant from the New York University Abu Dhabi, with Peter Bearman from Columbia University acting as Program Director (Grant No. RR2-K-015).

ORCID iDs

Daniel Karell

Rabia Malik

Notes

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