Search and Matching Frictions for Day Labourers

Search and Matching Frictions for Day Labourers

Pakistan hosts the 9th largest labour force in the world, with 73% of its workers employed in the informal economy. A majority of households living in extreme poverty in South Asia receive income from casual day labor, characterised as temporary jobs typically lasting 1 to 5 days, in construction, agriculture or manual labor. In Pakistan, casual labor is a dominant form of labor in the construction industry in urban areas and agricultural work in rural areas. In the casual labor market, day laborers find jobs through social connections or by going to a “labor stand”, a spot market where low-skilled laborers wait each morning for employers looking to hire. Low-income poor households typically search for these jobs. Furthermore, women are disproportionately engaged in these jobs, with male workers constituting 78% of the overall workforce. Not only this, but Pakistan is found to have one of the highest gender pay gaps in the world; the bottom 1% of wage earners are mostly women.

The majority of low-wage employment is found through social networks (Hofreiter and Banha, 2019). This pattern exists throughout developed and developing countries, with 60-70% of low-income countries like the Philippines and Bangladesh finding work through their connections (Caria, Franklin, and Witte, 2020; Witte, 2021; Beaman and Magruder, 2012; Delcuvellerie, Anwar, Vyborny, Field and Garlick, 2019). One possible question is why employers rely heavily on hiring from their social networks. There are potential benefits to the firm. Networks may alleviate market failures, such as smoothing information frictions, reducing search costs, and addressing moral hazards through reputational effects (Beaman and Magruder, 2012; Witte, 2021; Kugler, 2003; Calvo-Armengol, 2004; Mortensen and Vishwanath, 1994; Galeotti and Merlino, 2009). This observed hiring pattern could also be a selection story, as individuals connected to employers may be more productive. There may also be private returns to the manager from hiring through their network, such as social preferences for employing those within their in-group or enjoyment from working with familial or personal connections. However, within-social network hiring has high welfare costs, as marginalized groups, lower-income individuals, and those with smaller networks disproportionately bear the costs (Witte, 2021; Beaman, Keleher, and Magruder, 2018).

Casual day laborers in Pakistan face high job search and matching costs, market inefficiencies and informational labor market frictions. In addition, informal workers face further challenges, such as poor working conditions, wage disparity, and discrimination in the hiring process based on gender and socio-cultural characteristics. All of these increase dependence of casual day laborers on informal social networks. Our ongoing survey collected data from 763 construction workers from urban areas of Lahore. Our data suggest that social networks (64 percent) constitute workers’ most common method of finding work (Figure 1). Workers also tend to rely on labor stands to find work.

Figure 1: Methods laborers use to find work.

Working in partnership with several construction firms in Pakistan that hire laborers on a short-term basis, Christina Brown and Maryiam Haroon’s study collects data on demographics, contractual arrangements, payment mechanisms, social ties, monitoring and reporting system, job search time and process, negotiation process or bargaining power and work experience of casual laborers involved in unskilled tasks. In future, the researchers plan to randomly vary aspects of the employer-employee interaction to explain the mechanism of social networks in hiring decisions.

Findings suggest that employers hiring casual laborers have little to no information about workers’ productivity, indicating that hiring decisions are purely based on social connections rather than the quality of workers. In figure 2, we show a positive correlation between the social connectedness of an individual, to the amount of monthly income they earn, which depicts that individuals who have more significant social connections notably get more days of work and therefore earn higher monthly incomes.

Figure 2: Relationship between laborer connectedness and monthly income

We also find some common characteristics shared among our sample of workers with weaker social networks. For example, they are generally less educated, younger, belong to lower castes, and are from more rural areas. These similarities affirm the driving force behind this research: the marginalized group, which could be high productivity and potential, is underemployed and overlooked because of employers’ preferential hiring within their social networks. In particular, the marginalization of low-skill workers along these lines has been long-rooted in Pakistan’s labour market, and social-network hiring is an important factor perpetuating this.

Although stronger socially connected workers earn higher monthly incomes, it also contributes to creating a number of distortions in the market. For example, not all workers looking for work are able to find work throughout the entirety of the month, largely affecting their monthly incomes. Based on the study’s pilot survey findings, the average worker manages to find work for 18 days each month and spends an extra eight days unsuccessful in the search process, often at labour stands. On average, workers earn PKR 6,940 (approximately USD 173) per month. The workers forgo a large fraction of their time looking for work and are unable to work. This disparity is magnified for less connected workers, which has long-term consequences for the welfare of these individuals.

In addition, findings from our pilot do not indicate that those who are more productive in terms of the number of bricks laid receive more days of work or have higher incomes. Figure 3 shows no relationship between our measure of worker productivity and the average income they are receiving. This pushes back against the standard economic notion that wages should be equal to marginal products. Presumably, this is a result of information frictions in this market about worker productivity or employer valuation of criteria other than productivity.

Figure 3: Relationship between Worker Productivity and Monthly Income

Moreover, this study addresses the important question of measuring the productivity of workers and highlighting the incentives that managers have to hire high-productivity workers. This research utilises partnerships with firms to develop a methodology for measuring laborer output in the construction sector. Measuring on-the-job productivity is often a challenge in many sectors, which has led many labor market experiments to use artificial job tasks where it is easier to quantify productivity, but the task is different from what the laborer would traditionally be doing for employment. However, using real tasks/jobs are crucial to be able to answer a number of key questions in the information economics of labor and personnel (e.g. “What information do employers have about applicants’ productivity?”, “How do employees credibly signal their quality?”, “What do managers know about the productivity of their team?”).

The pilot study’s findings show that information asymmetry about workers proved to be a hiring barrier for contractors. Workers’ productivity on site on a given day was measured, and this information was randomly shared with their respective contractors. Figure 4 shows a negative relationship between productivity and social connectedness which depicts that when followed up months later, these same labourers were now spending 30% less time each month looking for work, which notably cut down on their search costs and afforded them more time able to work and earn. This is a significant cost saving for these laborers and means they can spend more time working.

Figure 4: Relationship between laborer productivity and connectedness

Managers were highly responsive to our bonus incentives as when provided bonuses to allocate workers to jobs; they assigned high-productivity workers to jobs offering bonus payments. This suggests that the utility managers receive from hiring through their social connections can be overcome if the incentives are strong enough as managers have a sense of who are more productive laborers and have the scope to assign more productive individuals to high-return jobs.

This insightful study aims to contribute to policy implications to improve labor market outcomes and the functioning of marginalized and socially-disconnected workers and low-wage employees.

In the long run, the study’s results will inform a larger policy and research agenda by designing a matching market for laborers and employers. The goal is to eventually set up a platform in partnership with the government through which employers can find laborers. In addition, a unique dataset will be made publicly available along with a set of survey instruments documenting the job search process and productivity information of casual day laborers from a developing country. This data will be useful for a broad array of researchers studying topics including network formation, informalized job search, and short-term casual labor markets in developing countries. This data is unique as it documents the actual worker level productivity for a given day in a real task setting which is not documented earlier in the literature. The data will also be useful for policy targeting social welfare programs by the provincial and federal governments, as they currently do not collect any data on this employment sector.

Christina Brown, Assistant Professor, Department of Economics, University of Chicago
Maryiam Haroon, Postdoc, Development Innovation Lab, University of Chicago

Mahbub ul Haq Research Centre at LUMS

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