Intervention(s)
Inequality in access to high-quality teachers is an important driver of student socioeconomic achievement gaps (Ajzenman et al., 2023). Public education is fundamental to equal opportunity for students of different socioeconomic backgrounds. Yet, in many countries, the widespread problem of teacher sorting (Jackson 2009) threatens this role: low-income students are more likely to attend schools with less qualified teachers and understaffed schools, thus exacerbating potential achievement gaps (Sass et al. 2012). This problem is not only detrimental in terms of equity but also in terms of efficiency: the sorting of candidates leads teacher assignment systems to be congested and, ultimately, does not optimize teachers’ well-being as they risk their chances of securing a job vacancy (Ajzenman et al., 2023).
Governments worldwide try to tackle this problem with monetary incentives: paying extra bonuses for teachers that work in schools that are typically understaffed and undersubscribed in the application processes. The problem is that they end up being very expensive and, many times, ineffective. Thus, strategies informed by the insights of behavioral economics have been growing in several developing countries, such as Peru and Ecuador (Ajzenman et al., 2023, Ajzenman et al., 2020).
Given the financial constraints, Ecuador cannot offer financial incentives and thus, since 2019, has been conducting zero-cost interventions to fight teacher sorting. This is a large-scale project implemented by the government of Ecuador in partnership with academics (PI) and education experts from the Inter-American Development Bank with experience in the topic and the specific country (one of our coauthors is an education expert based in Ecuador).
Objective
From a policy point of view, the objective is to motivate teachers to apply and work in understaffed schools. These are institutions identified by the government of Ecuador as schools that have teacher shortages and serve relatively poor students and thus are urgently in need. As a by-product, we hope this project will reduce congestion (that is, too many teachers applying only to a few vacancies) and thus increase the likelihood that every teacher will receive a job offer.
From a research point of view, the objective is to use insights from behavioral economics that have been tested in other settings (in the lab, for instance) in a real-world, policy-relevant setting. We know that there are specific strategies that work to motivate altruistic individuals to perform altruistic or pro-social actions. For instance, priming altruism (reminding individuals that they are altruistic individuals if they are) before making a decision triggers giving more money to charity (Kessler et al., 2018). We want to test this strategy in the teachers' application process context.
Hypotheses and/or research questions
Unlike other professions, a crucial motivation for many teachers is not material or extrinsic. Instead, they value service, helping others, and helping those that need them more (Watt et al., 2012). Indeed, in Ecuador, many teachers tend to show high levels of altruism (Ajzenman et al., 2020). However, when making decisions, individuals may be inattentive and even forget their true motivations for conducting an action.
Our hypothesis is, therefore, that reminding teachers that they are altruistic (in case they are) before they decide where to apply will help them consider their altruistic motivations and thus apply to schools where they could have the largest impact (schools that serve poorer populations and that are understaffed). This hypothesis was partially validated in a similar experiment in Peru (Ajzenman et al., 2023).
Context
In Ecuador teachers apply to vacancies through a centralized online system. After registering they enter a platform that remains open for a few days, where they select area and location (for instance, primary school, Quito) and see a list of available vacancies. They can choose (and rank) up to 5 vacancies. When they are ready, they submit. After the application window is closed, the government allocates teachers to vacancies using an algorithm (deferred acceptance) with takes into account teachers scores in the qualifying exams and teachers' preferences to prioritize. If the five vacancies selected by a teacher were offered to someone else (with better qualifications), the teacher will receive no offers. (See Ajzenman et al., 2020 for the details of the process).
The platform contains vast information about each vacancy (for instance location, type of school) and school so that teachers can make an informed decision. Some schools/vacancies are considered a "priority" for the government and thus are labeled with an icon next to a label that indicates that teachers that it is a vacancy where the teacher can have a high social impact. This is because those are understaffed schools that typically serve lower-income populations and, as evidence shows (Aaronson et al., 2007, Marotta, 2019), teachers are particularly effective in those contexts. The schools that have the labeled are selected by the government, according to their needs every year.
Procedure
The intervention is very simple. Half of the participants (randomly chosen) will be the control group and half the treatment group.
Control group: before the platform displays the vacancies to rank, teachers will see a message on the platform showing the "priority" icon with the following text: "Some schools need committed teachers, such as you. On the platform you will see that some institutions have an icon like the one below next to their names. Working in those institutions, you can trigger a larger social change and impact on the life of your students" [This message is the standard message that the government would use in the absence of any intervention]
Treatment group: before the platform displays the vacancies to rank, teachers will see a message on the platform showing the "priority" icon with the following text: "Some schools need committed teachers, such as you. On the platform you will see that some institutions have an icon like the one below next to their names. Working in those institutions, you can trigger a larger social change and impact on the life of your students" [This message is the standard message that the government would use in the absence of any intervention]. Besides, they will see the following text "Do you see yourself as a committed teacher, who would like to cause the largest impact in the life of your students? YES/NO" and a note "Your answer will be only used for informative purposes and will not affect your score or your school assignment".
The additional message in the treatment group is designed to induce teachers to think about their own preferences and motivations. Do they really care about having social impact and changing the world? Answering a question (even if the answer is irrelevant) is a way of helping them to consciously think about what they care. If they care about having a social impact (as many do), the treatment should increase applications to the most in need schools.
IMPORTANT: In a previous study in the same country (Ajzenman et al., 2020), we realized (after the experiment was in place) that many teachers cannot be "treated" because a) there are no disadvantaged schools to choose from in their locations/area of specialization, b) all the schools in their locations/area of specialization are disadvantaged. Therefore, we will present the results using the full sample (in Appendix) but mainly the "compliers" sample, which includes all the teachers that could choose schools in locations where there was at least one disadvantaged and one nondisadvantaged school. We will follow exactly what we did in the other paper.
IMPORTANT: Given the many applicants, the government divided the application into two groups (separated by three weeks). We are using both groups for the experiment.
References
Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago public high schools. Journal of Labor Economics, 25(1), 95-135.
Ajzenman, N., Elacqua, G., Marotta, L., & Olsen, A. (2020). Order effects and employment decisions: Experimental evidence from a nationwide program. IZA Working Paper
Ajzenman, N., Bertoni, E., Elacqua, G., Marotta, L., & Méndez Vargas, C. (2023). Altruism or money? Reducing teacher sorting using behavioral strategies in Peru. Forthcoming in the Journal of Labor Economics
Jackson, C. K., & Bruegmann, E. (2009). Teaching students and teaching each other: The importance of peer learning for teachers. American Economic Journal: Applied Economics, 1(4), 85-108.
Kessler, J. B., & Milkman, K. L. (2018). Identity in charitable giving. Management Science, 64(2), 845-859.
Marotta, L. Teachers’ contractual ties and student achievement: The effect of temporary and multiple-school teachers in brazil. Comparative Education Review, 63(3), 2019
Sass, T. R., Hannaway, J., Xu, Z., Figlio, D. N., & Feng, L. (2012). Value added of teachers in high-poverty schools and lower-poverty schools. Journal of Urban Economics, 72(2-3), 104-122.
Watt, H. M., Richardson, P. W., Klusmann, U., Kunter, M., Beyer, B., Trautwein, U., & Baumert, J. (2012).
Motivations for choosing teaching as a career: An international comparison using the fit-choice scale.
Teaching and teacher education, 28(6), 791–805.