Published on Development Impact

No train, (because of) no gain? Under-training by employers in spot labor markets: Guest post by Nicholas Swanson

This page in:

This is the 17th in this year’s series of posts by PhD students on the job market.

Labor markets in low- and middle-income countries are often organized via short-term informal spot contracts: in many cases, contracts last only a few days, and workers are re-matched with different employers frequently. These spot markets are the primary source of employment for hundreds of millions of workers. While these contracts offer incredible flexibility, in our paper, we explore a possible downside to short-term employment relationships that is consistent with a longstanding theory in labor economics: that employers might not want to invest in training workers in “general skills” – productive skills that are transferable across multiple employers – because those employers do not end up “appropriating” the benefits from training.

In our paper, we test whether there is under-investment in general skills training in agricultural labor markets in Burundi, and whether this leads to the under-adoption of an improved agricultural technology. We hypothesize that agricultural employers (farmers) desire to adopt this technology and could do so if the village had sufficient skilled labor. However, agricultural employers do not train workers because they cannot capture the returns from the training–i.e., they cannot guarantee the ability to re-hire those workers during planting time in the future. To test this hypothesis, we run two experiments in agricultural labor markets in Burundi.

Our experiments take place in rural Burundian villages in collaboration with the country office of the NGO One Acre Fund (1AF). In these villages, one can characterize farmers as employers (smallholder farmers with relatively more land who farm and hire laborers to work on their farms) and laborers (also smallholder farmers but with less land who farm their own lands and also work for others). A typical village in this context might comprise more than one-hundred households, almost all farming, among which at least one quarter might also hire farm labor, and at least one quarter might offer labor to neighboring farms.

We focus on farmers’ decisions of whether to adopt row planting--an agricultural planting technique that has been shown to increase yields substantially, and which the NGO 1AF has promoted adoption of for around a decade among its clients in Burundi. Employers in our context are likely to be members of  1AF and, therefore, receive training in the technique by the NGO. Laborers, by contrast, are less likely to be members of the NGO, and at baseline, many do not know how to row plant and consequently have not adopted row planting. Adoption, even among the employers who know the techniques, remains limited -– while many employers adopt the techniques on some fields, many do not adopt the technique on all of their fields. Time is often cited as a limiting factor for further adoption: row planting is more time-intensive than broadcasting seeds, and farmers perceive high returns to planting quickly after harvest. Given the labor intensity of the techniques, most employers in our context cite a lack of laborers who know how to do the techniques well in their villages as limiting further adoption. Few employers report ever training laborers, with most employers stating that if they trained a laborer, they wouldn’t get any benefit since she would be in high demand by other employers or become less available, opting to use the techniques on her own fields.

Our first experiment tests whether these large spillovers from training exist. Specifically, in the “Spillover Experiment”, we generate an exogenous shock in training and then measure who captures the returns. To do this, we work in 80 villages, approximately half of which are assigned to a control and half to a treatment status. In all villages, we recruit around 20 trainer-employers to identify 20 trainees – laborers in the village they would be willing to hire who did not know row planting. In treated villages, we offer financial incentives to these trainer-employers to train their laborers in row planting and observe the general equilibrium response in the labor market. To measure the spillovers from the training, in every village we sample around 20 “spillover employers” – farmers in the village who regularly hire labor but who do not train laborers themselves.  In total, we sample more than 3600 households falling into these 3 categories (trainer-employers, trainees and spillover employers) to measure who captures the returns to training.

If employers train, who gains?

We find that providing financial incentives induces farmers to train, and this training is effective in upgrading the skills of workers. Trainees in treated villages are employed for 3.5 more days doing the trained techniques during the planting season (from a baseline of 0.5 days) but not solely by working for the farmer who trained them. We find that partly the interaction with the trainer during training may have operated by changing the beliefs of laborers about the value of the technology: at baseline, many laborers state that they do not believe the techniques would be profitable on their fields, but after being trained, many adopt them.

Employers who train the workers hire 46% more days of labor for row planting and adopt row planting on 19% more fields. There is also a large labor market spillover to other hiring farmers in the same village. Specifically, employers in training villages, uninvolved in the training, also hire 55% more days of labor to work on their fields doing the techniques taught in training and adopt row planting on 24% more fields.

Does the fact that there are these large spillovers from training itself limit farmers’ incentives to train workers? To test this idea more explicitly, we conduct a second field experiment – the “Contract Experiment” – with 200 other farmers.  To half of these farmers, we offer a conditional cash payment to their laborer if the laborer returns and works for the farmer during the planting season, making it more likely that that laborer will return to work for the farmer at planting time. We then offer the 200 farmers an opportunity to train their laborer at a training event that we hold. Farmers and their hired laborers who receive the experimental contract are 50 percentage points more likely to attend the training event for 3 hours or more (the estimated minimal amount of time needed to train), which is consistent with the idea that farmers would train if the labor market were structured so that they could capture more benefits from training.


Policy Implications

Our project has several interesting implications for policy. First, it points to the structure of casual agriculture labor markets as a factor in explaining the under-adoption of an improved agricultural technology. Second, also related to technology adoption and reflecting the mounting evidence that social learning is not frictionless, these findings suggest that policies incentivizing those with information to diffuse it may be a critical complementary approach. Finally, our project points to a possible coordination problem in the market – farmers may be more willing to train if every other farmer trains, so that they are less worried that their laborer will get poached. This suggests a potentially important role for policies that help coordinate such actions.

Nicholas Swanson is a PhD candidate in Economics at UC Berkeley. The paper is co-authored with Luisa Cefala, a postdoctoral scholar at the Haas School of Business (UC Berkeley), Pedro Naso, a postdoctoral scholar at the Swedish University of Agricultural Sciences, and Michel Ndayikeza, a job market candidate in Economics at the University of Clermont Auvergne, CERDI, and an Assistant Lecturer at the University of Burundi. Twitter: @ nickgswans

Join the Conversation

The content of this field is kept private and will not be shown publicly
Remaining characters: 1000