# Underemployment of college graduates: is doing anything better than doing nothing?

Michel Armel Ndayikeza

► To cite this version:

Michel Armel Ndayikeza. Underemployment of college graduates: is doing anything better than doing nothing?. Journal of Development Economics, 2025, 174, pp.103459. 10.1016/j.jdeveco.2025.103459. hal-04957036

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Distributed under a Creative Commons Attribution 4.0 International License
*Underemployment of college graduates: is doing anything better than doing nothing? *

Michel Armel Ndayikeza

Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000 Clermont-Ferrand, France †

January, 2025

**Abstract**

Across low-income countries, limited prospects for high-skill employment and poverty push numerous college graduates into jobs which do not require a college degree. These types of experiences may be advantageous or detracting for the new graduates. To examine this issue, we conducted a field experiment in Burundi which elicited preferences of employers with respect to low-skill job experience of recent college graduates. We estimate the impact of signaling various types of low-skill experiences, such as working as a phone credit sales agent, a waiter, a security guard and other positions that do not necessitate a college degree, on the hiring interest of employers in a high-skill job. Results indicate that mentioning a low-skill experience on a resume increases by approximately 10% the hiring interest of employers. Furthermore, employers prefer job seekers with low-skill experience rather than individuals with no experience at all, irrespective of the quality of the job seeker.

JEL Classification: C93, J23, J64, J71

Keywords: Low-skill Jobs; Underemployment; Labor Demand; Job Search; Field Experiment; Incentivized Resume Rating

<footnote>*The author gratefully acknowledges valuable feedback received from Francesca Marchetta, Vianney Dequiedt, Arcade Ndoricimpa, Edward Miguel, Prachi Jain, Supreet Kaur, Alain de Janvry, Elisabeth Sadoulet, Travis J. Lybbert, Daniel N. Posner, Jeremy Magruder, Jeff Weaver, Bertoli Simone, Ketki Sheth, Marcel Fafchamps, Maya Ranganath, Chelsea Downs, Amy Shipow, Jonathan Izudi, Richard Boso, John Gambo, Mulusew Gerbaba, as well as two anonymous referees. Special gratitude to Luisa Cefala, Nicholas Swanson, Chelysea Irakoze, Irvine Floréale Murame and the staff of Infinity Group for their support. This work was supported by the Agence Nationale de la Recherche of the French government through the program "France 2030" ANR-16-IDEX-0001 and the Center for Effective Global Action (CEGA) - Berkeley, CA, USA.</footnote>

<footnote>†Email: michel.ndayikeza@doctorant.uca.fr</footnote>
# 1 Introduction

Over the past two decades, enrollment in tertiary education has substantially increased in low-income countries, going from 4.5% to 9.3% in 2020 (WDI, 2022). However, a significant number of students graduate into low-skill jobs, meaning jobs that do not require a college degree<fnref n="1" />. This underemployment<fnref n="2" /> issue is of concern to policy makers as well as graduates themselves who invest important sums of money in higher education. Understanding whether or not post-graduation low-skill experience increases or diminishes the likelihood of obtaining a job that is commensurate with one's qualifications is an important question for determining optimal policy in improving the employment prospects of college graduates in developing countries.

In addition to obviously increasing income, soft skills could also signal to employers positive traits such as perseverance, and may improve matching by allowing individuals to better understand their career preferences. Such jobs may also increase appreciation for high-skill jobs and build discipline. On the other hand, low-skill experience may also be viewed as a negative signal by employers, who may infer lower ability from this information. People may avoid taking up a low-skill employment for fear of being trapped in a low equilibrium as the low-skill job reduces the time available to search for jobs that require a college degree. Furthermore, knowledge gained in college may decay during the low-skill employment period,

<footnote n="1">¹The latest estimates of underemployment in low-income countries for the general population, typically lower than underemployment of young college graduates, are 31% in Burkina Faso, 26% in The Gambia and 48% in Rwanda in 2023 (ILO, 2024). Underemployment is defined here as the share of people whose level of education is above the average requirement of their occupation among the labor force with at least a lower secondary education. Statistics on the number of recent college graduates affected by underemployment in low-income countries are currently not provided by the International Labour Organization.</footnote>

<footnote n="2">²Unless otherwise indicated, underemployment refers to persons whose highest level of education is above the educational requirements for their occupation.</footnote>
decreasing chances of obtaining a college-level job in the future.

The question of whether low-skill work sends a positive or negative signal to employers is pertinent not only in the context of Burundi but also in other developing countries, and notably in Sub-Saharan Africa (SSA), where the rising population of university graduates is not being met with commensurate job creation. Across SSA, 8 to 11 million youth enter the labor market annually, but only 3 million formal jobs are created each year (Banque Mondiale, 2023). Consequently, many graduates resort to low-skill jobs, navigating an environment where employment opportunities are scarce and often inadequate.

In a labor market where high-skill jobs and low-skill jobs are scarce, employers may value the tenacity and resilience shown by a graduate's decision to work in low-skill roles. Conversely, with many high-skill job offers, low-skill job experience may not be as favorably viewed. Here, employers might question why a graduate with a college degree would opt for a job that doesn't utilize their academic qualifications. This could be interpreted as a lack of ambition or ability, potentially leading to a decrease in the perceived value of the graduate's profile. Our study delves into what it means for a graduate to take up a low-skill job during this time in comparison to being unemployed<fnref n="3" /> from the standpoint of employers<fnref n="4" />.

To elicit preferences of employers with respect to low-skill experience, we conducted a variant of the Incentivised Resume Rating (IRR) experiment, introduced by Kessler et al. (2019) to address the issue of deception inherent in audit studies. We started with a group of

<footnote n="3">³We provide a discussion in appendix 6 on how realistic this comparison is in the African context, following a presentation of the current knowledge on young graduates' transition into the labor market, and provide some complementary information on school-to-work transition.</footnote>

<footnote n="4">⁴Understanding these preferences doesn't fully clarify whether it's more advantageous for recent tertiary education graduates to accept low-skilled positions or to seek high-skilled roles while remaining unemployed. The ideal way to address this would be through an experiment where individuals are randomly assigned to low-skilled jobs. However, conducting such an experiment would be ethically questionable.</footnote>
real resumes of students who were one month from finishing their bachelor studies at the
University of Burundi<fnref n="5" />, in the faculty of economics and management. By using graduates'
actual resumes, we obtained a variety of formats and other idiosyncrasies that reflect the
material that hiring managers typically review. These resumes were modified such that
the period since graduation corresponded to one year and they do not mention any post-
graduation experience. Starting from this pool of resumes, we created a new set of resumes
which was similar to the first one, except with the addition of low-skill experience. We used
data on the types of low-skill jobs past graduates have done after graduation to generate
types of low-skill jobs that we randomly populated the resumes with. Our implementing
partner, a well-established human resource (HR) firm, sent the resumes to various employers
in the country with whom it usually collaborates for recruitment, informing them that their
evaluations will be used to improve the quality of future matches. A total of 712 resumes
were evaluated by 37 employers, among the largest in the country, who rated them on a scale
of 1 to 10 with respect to hiring interest.

Results of the experiment show that resumes with low-skill experience received a higher score
on average compared to resumes devoid of any post-graduate experience, with a statistically
significant difference (*p* < 0.01). Moreover, results indicate a stochastic dominance of a job
search strategy which consists of signaling low-skill experience in comparison to presenting
a resume devoid of any post-graduate experience. The latter finding means that employers
prefer job seekers with low-skill experience rather than individuals with no experience at all,
irrespective of the quality of the job seekers as indicated by the score given to his or her

<footnote n="5">⁵We focus on students finishing their undergraduate studies since less than 10% of bachelor's graduates pursue master's studies (MENRS, 2024).</footnote>
resume. We analyze the heterogeneity of the low-skill experience treatment with respect to resume and evaluator characteristics. Main results are robust to different specifications and we benchmark the effect size against other effects identified in the literature. Post experiment interviews are consistent with experimental results and reveal that employers perceive people with low-skill experience as hard-working, disciplined and persevering individuals rather than individuals with financial difficulties, incompetent or less qualified compared to classmates.

This study contributes to the literature on the influence of career histories in securing a high-skill employment. Several experimental studies have delved into the impact of unemployment duration on the likelihood of obtaining a high-skilled position (Kroft et al., 2013; Eriksson and Rooth, 2014; Farber et al., 2019; Cahuc et al., 2021). Closer to this study is Nunley et al. (2017), which compares underemployment and employment, while this study compares periods of underemployment and unemployment. Nunley et al. (2017) found that underemployed college graduates face a 30% reduction in callback rates compared to their adequately employed counterparts. Relatedly, Farber et al. (2016) found that college-educated females engaged in "interim" jobs at a lower skill level than the job for which they are applying for are markedly less likely to receive callbacks for administrative support positions, while finding no relationship between callback rates and the duration of unemployment.

In a departure from these findings, Adermon and Hensvik (2022) found in the Swedish context that having "gig-experience" is more advantageous than being unemployed. Other experiments and audit studies have evaluated the value of gaining work experience during school via internships (Nunley et al., 2017; Kessler et al., 2019), summer jobs (Gelber et al.,
2016; Davis and Heller, 2020), or year-long employment (Baert et al., 2016; Le Barbanchon et al., 2023).

Conceptually, the signaling of low-skill experience can have either a positive or negative impact. Consequently, recent graduates might choose to exclude this information from their resumes, even if it could be advantageous. A study examining a similar strategic behavior among college graduates in Peru shows that, despite a correspondence study indicating that signaling that the job seeker is a laureate of a highly selective scholarship for poor and talented students increases callback rates by 20 percent, 92 percent of beneficiaries avoid listing this award when applying for jobs (Agüero et al., 2023). This behavior is consistent with beneficiaries perceiving a negative labor market return from sending a signal indicative of a lower social background. Similarly, Rivera and Tilcsik (2016) conducted an audit study to investigate the effect of social class signals (such as name, awards and personal interests) on entry into large US law firms. The authors found that law firms prefer higher-class men relative to lower-class men, lower-class women and even higher-class women<fnref n="6" />. In the context of this study, since low-skill experience is often a consequence of low income, it could be that employers deduce from such a signal that the job candidate is from a lower-class. Part of this research investigates this.

More generally, our work supplements previous research which has investigated the impact of different resume signals, including race (Bertrand and Mullainathan, 2004; Kline et al., 2022), marital status (Arceo-Gomez and Campos-Vazquez, 2014), religion (Valfort, 2020),

<footnote n="6">⁶The study suggests that higher-class in itself confers an advantage because of the elite culture and clientele of large law firms. In the case of higher-class women however, the class advantage is dampened by a negative stereotype that portrays them as less committed to full-time, intensive careers.</footnote>
resume mistakes (Sterkens et al., 2023) and gender (Bohren et al., 2022). Researchers
are typically interested in investigating preferences that employers do not indicate in job
adverts either because it is forbidden<fnref n="7" /> (some countries have prohibited discrimination in
recruitment based on race, religion, sexual orientation, etc)<fnref n="8" /> or because of social desirability
(for instance, employers might not be willing to admit in interviews that they prefer higher-
class employees).

In this chapter, we examine the value of low-skill jobs, which may signal varied skills to employers, influencing their hiring interest. Echoing this objective, there exists a body of literature that delves into the worth of skill signals made apparent to employers. Abebe et al. (2021) show that young people possess valuable skills that are unobservable to employers and that helping them signal these skills to employers generates large and persistent improvements in their labor market outcomes. In a similar vein, studies by Bassi and Nansamba (2022), as well as Carranza et al. (2022), underscore the idea that providing credible skill information can improve labor market outcomes. Overall, the existing literature has established that unemployment spells, work experience, and skill signals matter for securing employment.

Our research distinguishes itself from the aforementioned literature by concentrating on a
comparative analysis of underemployment and unemployment spells in the quest for high-
skilled jobs and by focusing on recent college graduates. Another distinguishing feature of
our study is its setting - we base our experiment in a low-income country, contrasting with

<footnote n="7">⁷For example, the US law prohibits discrimination based on race, national origin, gender, pregnancy, religion, disability, age, military service or affiliation, wealth, genetic information and citizenship status (Baert, 2018)</footnote>

<footnote n="8">⁸See for instance Bertrand and Mullainathan (2004) for racial discrimination, Valfort (2020) for discrimination based on religion and Drydakis (2009) for sexual orientation.</footnote>
the previous audit type of studies which are focused on high-income countries.<fnref n="9" />

Our analysis of the impact of low-skill experiences also feeds into the literature on qualification-related underemployment, a topic which has received little attention in the economic literature (Barnichon and Zylberberg, 2019) particularly the demand side of the problem (Brunello and Wruuck, 2021). The issue of underemployment has been mainly investigated from the perspective of high skilled workers rather than employers. It has been advanced that high skilled workers search for low-skill jobs in order to maximize chances of obtaining a job quickly by avoiding relatively tense competition for high-skill jobs (Barnichon and Zylberberg, 2019). The migration literature has also documented the issue of underemployment of high skilled workers who migrate to developed countries (Lo et al., 2019; Chiswick and Miller, 2009). We contribute to this literature by analyzing underemployment of recent college graduates from the perspective of employers.

Understanding preferences of employers with respect to low-skill experience is also important for policy. Previous research suggests that expectations about how workers will be treated in the labor market may affect their investment (Lang and Lehmann, 2012). In the specific case of low-skill jobs, preconceptions may affect their take-up or their signaling in interviews or on resumes. Hence, this study is of interest for organizations in charge of advising job seekers as well as job seekers themselves.

The rest of the paper proceeds as follows. The following section presents the context of the study. Section 3 provides details on the study design, and results are presented in Section

<footnote n="9">⁹In low-income countries, sending fictitious applications via email for experimental purposes is generally not feasible, as this is not the typical method of applying for jobs. Additionally, it is more challenging to find enough open job offers to achieve adequate statistical power.</footnote>
4. Section 5 concludes.

## 2 Context of the study

With a population of approximately 12 million people and a gross national income per capita of USD 800 (PPP), Burundi is currently ranked among the poorest countries in the world (World Bank, 2022). The majority of Burundians is young, with an estimated 65% of the population below the age of 25 (WPP, 2024). In what follows, we present the labor market and higher education contexts.

### The general labor market

As is generally the case in low-income countries, the level of unemployment in Burundi is low in absolute terms (2.8%)<fnref n="10" />, and slightly lower than average unemployment in low-income countries, estimated at 5.5% in 2022 (World Bank, 2023). However, unemployment is higher in urban areas compared to rural areas, with respective rates of 17.2% and 1.1%. Furthermore, unemployment increases with the level of education (see Table 1).

However, the Burundian labor market, like that of other developing countries, is better described by underemployment rather than unemployment. In fact, 53.4% of the employed are actually underemployed with respect to time, i.e. they work for less than 40 hours per week. Time-related underemployment<fnref n="11" /> is predominant in rural areas where the main economic activity is agriculture. In urban areas, it is estimated at 27.7%, with Bujumbura being the least affected (18.5%). Underemployment decreases with the level of education,

<footnote n="10">¹⁰Unless otherwise mentioned, labor market statistics come from the most recent survey of the national statistics institute of Burundi (INSBU, 2022).</footnote>

<footnote n="11">¹¹Qualification-related underemployment data is not available for Burundi. In the remainder of this section, underemployment refers to time-related underemployment.</footnote>
with a rate of 57.9% among people with no education and 25.4% among people with higher education, suggesting that relatively educated persons might be less willing to take on bad jobs.

<table><caption>Table 1: Summary statistics on education and the labor market in Burundi</caption><thead><tr><td>Level of education</td><td>Unemployment (%)</td><td>Underemployment (%)</td><td>Enrollment</td></tr></thead><tbody><tr><td>None</td><td>1.0</td><td>57.9</td><td>-</td></tr><tr><td>Primary (or Fundamental)</td><td>1.6</td><td>51.9</td><td>2 756 241</td></tr><tr><td>Secondary (or Post-Fundamental)</td><td>10.3</td><td>45.3</td><td>239 645</td></tr><tr><td>Higher education</td><td>18.2</td><td>25.4</td><td>63 428</td></tr></tbody></table>

Notes: (1) The unemployment rate measures the number of people who want to work but do not, even though they are available for work and are actively looking for work, as a proportion of the labor force. The rate used is the broad unemployment rate, that is, it includes persons who at the time of the survey were available for work but did not look for work and persons who looked for work but were not immediately available to work. (2) The underemployment rate is calculated with respect to the number of hours worked, using an official reference of 40 hours per week. (3) Enrollment by level of instruction has been calculated based on the fundamental and post-fundamental levels, whereas other statistics use the primary and secondary levels of education. Fundamental education begins at age 6 and lasts 9 years, after which students take a national exam for entry into post-fundamental education, which lasts 3 years. Before this reform was introduced in 2013, primary school lasted 6 years after which students took a national exam to enter secondary school which also lasted 6 years.

Sources: INSBU (2022), MENRS (2021a) and MENRS (2021b).

Arrival rates

Employers' preferences concerning low-skill job experience of recent college graduates may be influenced by the arrival rates of high-skill and low-skill jobs. When both arrival rates are low, meaning that the labor market is tight for all types of jobs, employers should value any type of experience over no experience at all. For instance, employers may interpret experience in low-skill jobs in such a labor market context as a sign of perseverance and adaptability. On the contrary, in a situation where the arrival rate of high-skill jobs is high and the arrival rate of low-skill jobs is low, employers should favor graduates who do not have low-skill job experience, interpreting the low-skill experience signal negatively. For instance, employers may interpret such a signal as lack of ambition or lower ability compared to classmates.

Low economic growth (an average of 1.7% of annual GDP growth over the past decade
(World Bank, 2023) and relatively high unemployment rate of individuals with higher education (18.2%)<fnref n="12" /> suggest that the arrival rate of high-skill jobs is low. As for low-skill jobs, discussions with recent university graduates highlight the difficulty in obtaining such positions. For instance, several of them reported joining a waiting list in order to become a security guard. In our survey of a cohort from the Faculty of Economics and Management at the University of Burundi, one year after completing their undergraduate studies, we found that only 18% had obtained a job requiring their qualifications.

## Education

For what concerns the higher education system, Burundi had 63 428 students in 2021 (a 73% increase since 2011) distributed over 49 institutions of higher learning (MENRS, 2021b). The University of Burundi, that we worked with for our experiment, is a public and tuition free university, and the largest higher learning institution in the country. Most of its campuses are located in Bujumbura where it attracts students mainly from poor backgrounds from all over the country.

Although the number of students in the higher education system (63 428) is considerably lower than the number of students in primary schools (2 756 241)<fnref n="13" />, government expenditure per student (PPP) in primary school was last estimated at USD 95 in 2013 compared to USD 2794 per student in tertiary education (UNESCO, 2023). In addition, the small number of students in higher education should not obscure the importance of this sector, as these

<footnote n="12">¹²As a comparison, unemployment of individuals with secondary education is estimated at 10.3%, 1.6% for those with primary education and 1% for persons with no education (INSBU, 2022).</footnote>

<footnote n="13">¹³Gross enrollment in tertiary education was estimated at 6.52% in 2022, in comparison to 103.9% for primary education (World Bank, 2024). Gross enrollment rate is the number of children enrolled in a certain education level as a percentage of the number of children who should be studying officially in that level. This indicator can be higher than 100% due to class repetition and early or late enrollment.</footnote>
young people are a particularly critical group. If they are not employed, they may engage
in activities that pose a risk to themselves and to society. Moreover, the number of students
should be assessed in relation to the jobs available. The government employs only 2.9%
of the working age population, private enterprises and associations 6.3% and NGOs 0.1%.
Households employ the remaining labor (90.7%), mainly in the agricultural sector, a sector
in which higher education graduates tend to refrain from engaging in.

3 Study design

Our experimental approach is a variant of the Incentivized Resume Rating (IRR) introduced
by Kessler et al. (2019), which involves incentivizing employers to evaluate resumes in the
absence of a job offer, thereby avoiding the deception issue associated with traditional audit
studies. However, instead of researchers engaging directly with employers as in Kessler et al.
(2019), a HR company we partnered with, Infinity Group (IG)<fnref n="14" />, sent printed resumes to
employers with whom they had a professional relationship<fnref n="15" />. Prior to evaluating the resumes,
IG informed the employers that their evaluations would be used to send them workers in
the future that correspond to preferences they have indicated<fnref n="16" />. Therefore, in our case, the
incentive for employers to indicate their true preferences is the promise of better matches in
the future.

A total of 800 resumes were sent to 40 employers for evaluation. Out of the 40 employers

<footnote n="14">¹⁴IG's services include human resources hiring and management, marketing, communication and project management. Since the company was created in 2018, it has provided its services to the corporate sector (Bank, Industry, Large construction companies), International NGOs and other organizations.</footnote>

<footnote n="15">¹⁵Meaning employers who had either hired a worker through IG or employed a worker under an IG contract.</footnote>

<footnote n="16">¹⁶We show the template of the letter that was sent to employers by IG in appendices A1.2 and A1.3, as well as the verbatim translation in appendix A1.4.</footnote>
that we targeted, 37 provided us with their evaluations and responded to follow-up questions.
Those who did not respond were not available in the period that our data collection partner,
IG, contacted them. Each employer was given 20 randomly selected resumes to evaluate. We
stratified the sampling such that each employer receives 10 resumes with low-skill experience,
10 resumes without low-skill experience, 10 resumes of males and 10 resumes of females. The
set of 20 resumes evaluated by each employer was randomly drawn without replacement such
that it does not contain duplicates. Consequently, for each individual evaluator, all resume
characteristics varied across the 20 resumes, ensuring that he or she could not identify our
treatments of interest.

We targeted employers who are among the largest in the country. This list of employers
includes Brarudi, an affiliate of Heineken International, multinational banks, NGOs,
manufacturing firms and services firms <fnref n="17" />. Nonetheless, the small sample of employers does
not allow a within group analysis. For instance, it would have been interesting to analyze
the results by sector of activity. However, the sample may be viewed as representative of
the largest employers in the country given that the study covered almost all the employers
in the database of the implementing partner (Approximately 90% of employers that work
with IG) and that the distribution of firms is skewed to the left with very few employers in
the formal sector (See details in the context section).

<footnote n="17">¹⁷The full list of employers who participated in the experiment is the following: Action Aid, Akeza.Net, Banque Burundaise de Commerce et d'investissement, Banque de Crédit de Bujumbura, Banque Commerciale du Burundi, Best imprimerie, Bi-Switch, Bicor, Brarudi, Clinique de l'œil, DHL, Ecobank, DIFO, Ercon, Finbank, FSCJ Microfinance, Groupement ADP-MD2P, Hope Design, International Rice Research Institute, Jimbere Magazine, Kenya Commercial Bank, Kaz'O'Zah, Liquides, Modern Dairy Burundi, Memisa, Metalusa, Mutualité Santé Plus, Play International, SOFEPAC, Savonor, Socabu, Socar AG, Socar Vie, Sogerbu, TwoFiveSeven Arts, Université du Lac Tanganyika and Zebra Electronics.</footnote>
## 3.1 Creating a pool of resumes

In March 2022, we surveyed economics and management graduates of 2021 from the University of Burundi with the main goal of collecting data on the kind of low-skill jobs they had been doing in the one year after finishing their undergraduates studies. Out of 139 students surveyed in a cohort of 203 students, 18% had done a high-skill job and 34% had done the following low-skill jobs after graduation: Phone credit sales agent (13), Data Entry Agent and Enumerator (7), Cashier (3), Waiter (3), Petty trader (3), Welder (2), Call center agent (1), Clothing salesperson (1), Milk seller (1), TukTuk driver (1), Security guard (1), Chicken trader (1), Driver (1), Photographer (1), and other low-skill jobs (8). The rest of the students (48%), had no experience whatsoever.

In May 2022, we hired a training firm<fnref n="18" /> to train students that were finishing their bachelor studies in June 2022 on how to make a resume. The training had a general objective of developing the participants’ skills on how to navigate the hiring process in a training session called “How to attract the recruiter’s attention?”. More specifically, the training aimed to teach participants the basic rules of making a good resume and touched on how to write a cover letter and how to succeed in a job interview. The methodological approach followed in the training was a participatory one with the use of an inductive method, starting with the trainer’s presentation, exercises and then discussions. The training took place in the computer room of the Faculty of Economics and Management of the University of Burundi over a period of 10 days, with 6 hours per day. In the end, 249 students benefited from this training and 248 of them provided their resumes<fnref n="19" />.

<footnote n="18">¹⁸Called *Cabinet MARC*.</footnote>

<footnote n="19">¹⁹The resumes used in our experiment were edited by the trainer but not the experimenters. This approach</footnote>
We subsequently randomly selected 200 resumes among the 248 that were collected and used, with consent, in the experimental approach. This strategy provided us with a starting pool of resumes that reflects idiosyncrasies that are realistic and that employers typically review. We then created three other copies of these initial resumes that are similar except for low-skill experience and gender. We thus obtained 200 resumes of males with low-skill experience, 200 resumes of males without low-skill experience, 200 resumes of females with low-skill experience and 200 resumes of females without low-skill experience<fnref n="20" />. Additionally, the duration of low-skill experience was randomized by introducing either a short duration (less than three months) or a long duration (between nine and twelve months) of low-skill experience.

Contrary to what is often done in audit studies, our experimental design started with multiple real resumes and manipulated only three characteristics (low-skill experience, duration of the low-skill experience and gender), rather than starting with a limited set of resume samples and randomizing numerous characteristics. This strategy mitigates the risk of inadvertently incorporating incompatible elements or uncommon combinations into the resume. For example, we excluded the resume of a woman with welding experience to avert potential skepticism as this would be uncommon. Moreover, names, contact information and other identifying information were hidden prior to sending the resumes for evaluation (See sample resume in appendices A1.7 and A1.8).

<footnote>reflects the reality, as young graduates often have their resumes reviewed by experienced adults and more importantly, the HR firms normally reviews resumes before sending them to employers.</footnote>

<footnote n="20">²⁰We illustrate the randomization process in appendix A1.1.</footnote>
## 3.2 The “low-skill treatment”

To test the preferences of employers with respect to low-skill experience against the counterfactual of doing nothing, the resumes were modified such that they appear to employers as corresponding to job candidates who have been on the market for a year. More specifically, given that these resumes were from students preparing to graduate, they naturally lacked any post-graduation experience. To create the desired comparison, we adjusted the resumes to indicate graduation in the previous year. This ensured that candidates without low-skill experience had a clear period of non-employment on their resumes. Thus, the comparison examines having low-skill experience since graduation versus having no professional activity during the same period.

Among the 800 resumes in our sample, 400 were “treated” with a low-skill experience, i.e we added a work experience section on the resume, randomly chosen from the list of different types of experiences, with more weight given to more common experiences. The other 400 resumes served as a control group. The low-skill experiences themselves were grouped in two categories. The first group consisted of experiences which lasted between one to three months, which we call short duration low-skill experiences. The second group consisted of experiences which lasted between 9 to 12 months, which we call long duration low-skill experiences. The starting dates of the jobs corresponded to actual starting dates obtained from our survey of past experiences of graduates. We show in appendices A1.7 and A1.8 a sample of resumes sent out for evaluation. The part that was added to half of the resumes is section 12 under the heading “Expérience”.
### 3.3 Resume evaluation and employers’ interviews

Since employers were aware that there would be no immediate recruitment following their evaluation, we were concerned about ensuring that their choices are as close as possible to their true preferences. We made sure in the letter that was sent to employers as well as in oral contacts that IG managers had with them that resume evaluators are aware that the value of the proposed incentive (future job candidates propositions from IG that correspond to indicated preferences) is positively correlated with the accuracy with which they report their preferences. No other incentive was provided and prior to evaluating the resumes, employers were not informed that we are interested in estimating the impact of low-skill experiences.

Employers were asked to rate resumes on a scale of 1 to 10, assuming that a job seeker with such a profile would accept the offer. The precise wording of the evaluation form (See appendix A1.5 for original form and appendix A1.6 for the English translation) was the following: “On a scale of 1 to 10, how interested are you in hiring this candidate? Evaluate only the quality of the candidate. Assume that the candidate would accept an offer if he/she received one.” Considering that the experiment invited employers to suggest up to three jobs that an individual with such a resume could be hired for, it is reasonable to assume that they would suggest jobs they think the job seeker would accept<fnref n="21" />.

#### Additional employer data

After evaluating the resumes, each employer was invited to respond to a questionnaire

<footnote n="21">²¹Employers also had the option to suggest a starting salary, but few chose to do so. Those who did not provide this information indicated that salary decisions were beyond their authority.</footnote>
designed to obtain further information on employers' perception of low-skill experience. The employers were asked to choose between employing an individual without post-graduation experience or an individual who has been doing different types of low-skill jobs, chosen among the 15 from our earlier survey. On the positive side, we asked whether employers think that people with low-skill experience have grit and discipline. On the negative side, we asked whether they think such individuals are of lower ability compared to their peers or if they think they have financial difficulties.

The order of the questions was randomized to take into account potential preview or priming effects (Kahneman et al., 1992). The first kind of randomization concerned the following question which was asked for 15 different types of low-skill jobs: “Would you hire a job candidate: (a) without experience in any type of employment or (b) with 12 months of experience in [...] after graduation”. For half of employers, choice (a) appeared first and for the other half choice (b) appeared first.

The second type of randomization was related to the idea that employers may value low-skill experience more or less depending on whether they are taking into account that such experiences may affect soft skills development. Hence, half of the employers were made to reflect on the importance of soft skills in their organization by asking them to respond to two questions: (1) How important are soft skills (such as communication skills, interpersonal skills and being on time) in your organization? (2) Which soft skills do you look for when making a hiring decision? After responding to these questions, they were asked to indicate their preference relative to low-skill experience. The other half of employers were first asked to indicate their preference relative to low-skill experience and then were asked to respond
to the two questions that relate to the importance of soft skills.

The third kind of randomization concerned attributes employers associate with low-skill experience. Employers had to score between 1 and 10 the following attributes: perseverance, hardworking, discipline, financial difficulties, incompetent and less qualified compared to classmates. The order in which the attributes appeared on the screen was different for each employer.

We provide the English translation of the questionnaire (originally in French) in Appendix A1.10. The set of choices was randomized with Stata and displayed on a tablet using SurveyCTO.

## 3.4 Data

The data on resume evaluations that we use in this study was collected between July and August 2022 in Burundi, on a sample of 37 employers and each employer evaluated 20 resumes<fnref n="22" />. Each resume was given a score which ranges from 1 to 10, corresponding to hiring interest for a specific job. The employer could rate a resume with respect to a maximum of 3 jobs an individual with the given profile could occupy in the organization. The list of proposed jobs are shown in Appendix A1.8.

## 3.5 Methodology for regression analysis

The regression analysis focuses initially on how low-skill experience affects hiring interest of employers using the simplest specification. The hiring interest is measured with a score

<footnote n="22">²²The final dataset we use has 712 observations instead of 740 observations for two reasons. First, 10 resumes were mistakenly not scored. Second, for 18 other resumes, individuals did not indicate either their year of birth, their marital status or where they went for high school. We show that our results are robust to including the 18 observations without controls, and by imputing averages for missing observations.</footnote>
ranging from 1 to 10, that employers gave to resumes when asked to evaluate whether they would be interested in hiring the candidate, supposing that the job offer would be accepted. Hence, our baseline specification is the following ordinary least square model:

$$resumescor_{i,m,j} = \alpha_0 + \alpha_1 LSExperience_i + \alpha_2 Gender_i + \alpha_3 Pages_i + \varepsilon_{i,m,j} \quad (1)$$

with $resumescor_{i,m,j}$ the score given to resume $i$ by a hiring manager $m$ for a specific job $j$, $LSExperience_i$ a variable equal to 1 if the resume mentions post-graduate low-skill experience and 0 if the resume does not indicate any post-graduate professional experience, $Gender_i$ a variable equal to 1 for a resume of a female individual and 0 for a resume of a male individual. Since the length of the resume increases when we add a low-skill experience section, we systematically control for this confounding effect by adding the variable $Pages_i$, which is the number of pages of resume $i$. $\varepsilon_{i,m,j}$ is an idiosyncratic error.

In the full specification (Equation (2)) we add control variables (vector $X$) and employer/evaluator fixed effects ($\phi_m$) to the baseline specification.

$$resumescor_{i,m,j} = \beta_0 + \beta_1 LSExperience_i + \beta_2 Gender_i + \beta_3 Pages_i + X\theta + \phi_m + e_{i,m,j} \quad (2)$$

$X$ includes the year of birth of the job candidate and whether the individual is married or not. We also control for whether the job candidate went to a secondary school in the economic capital Bujumbura<fnref n="23" />. $X$ also includes the number of extra curricular training programs

<footnote n="23">²³In field preparation meetings we had with HR experts from IG, they indicated that employers frequently</footnote>
indicated on the resume, the gender of the evaluator, the size of the evaluating organization measured in terms of the number of employees and the number of years since the organization started its activities in Burundi. We include employer fixed effects to allow employers to have different mean ratings. Given the design of the experiment, adding fixed effects and control variables should not affect our estimates of interest $\alpha_1$ and $\alpha_2$ in Equation (1) as these additional controls should be orthogonal to the treatments we introduced on the resumes, i.e. low-skill experience and gender. Furthermore, we analyze the heterogeneity of the low-skill experience and gender treatments along resume and evaluator characteristics.

# 4 Results

## 4.1 Descriptive statistics

Table 2 shows summary statistics for the dependent variable, treatment variables and independent variables grouped into resume characteristics and evaluator characteristics.

Although hiring managers had the option of providing up to three different evaluations for three different types of jobs, we provide in Table 2 statistics for the evaluations of the first job only. This is because the second and third evaluations contain substantial missing observations. We show that treatment effects become more noisy as we include the second and third score, which is consistent with evaluators paying relatively more attention to the first job proposed.

Table 2 shows that the average resume score is 4.6 and the scores range from 1 to 10.

<footnote>express demand for individuals who are fluent in French and English, and that those skills are not well taught outside the capital.</footnote>
Approximately half of resumes are resumes of females. We do not obtain a perfect 50-50 split because we did not obtain evaluations for all 800 resumes. The duration of low-skill experience variable was generated such that 25% of the resumes showed a duration of three months or less and 25% of resumes showed a period between 9 and 12 months. The resumes had between one and three pages. They showed that individuals were born between 1986 and 1998, considering that the normal age if the individual had not repeated a school year and had started primary school at age 6, was 1998. Approximately 4% of resumes are of married individuals and 15% are of individuals who went to a high school in Bujumbura. Most individuals mentioned two extracurricular training programs or less on their resumes and one individual mentioned 10 training programs. Approximately half of the 712 resumes were evaluated by women.

Even though we do not have evaluation data for all 800 resumes, we still have balance on almost all our covariates. These are balanced for the low-skill experience treatment except for the *number of pages* variable which is on average higher for the treatment group than the control group (Table 3), highlighting the need to control for the length of the resume when estimating the impact of the low-skill experience treatment<sup>24</sup>. Table A1.1 shows that when we split the low-skill treatment group into a high-duration group (9-12 months) and low-duration (0-3 months) group, we obtain less balance of covariates. In fact, this lack of balance concerns all covariates except *training, year of birth* and *married*. As a result, heterogeneity analysis of the *low-skill experience* treatment by its duration includes the list of covariates as controls but may be biased by unobservable confounders. To verify that we are comparing comparable groups for the heterogeneity analysis with respect to gender, we

<footnote n="24"><sup>24</sup>We show balance for the gender treatment in Table A1.2.</footnote>
<table><caption>Table 2: Summary Statistics</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean/Median</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td><em>Dependent variable</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Resume score</td><td>712</td><td>4.628</td><td>2.241</td><td>1</td><td>10</td></tr><tr><td><em>Treatment variables</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female (=1)</td><td>712</td><td>0.499</td><td>0.5</td><td>0</td><td>1</td></tr><tr><td>Low-skill experience</td><td>712</td><td>0.5</td><td>0.5</td><td>0</td><td>1</td></tr><tr><td>3 months or less of low-skill experience</td><td>712</td><td>0.243</td><td>0.429</td><td>0</td><td>1</td></tr><tr><td>9 months or more of low-skill experience</td><td>712</td><td>0.257</td><td>0.437</td><td>0</td><td>1</td></tr><tr><td><em>Resume characteristics</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of pages</td><td>712</td><td>1.949</td><td>0.26</td><td>1</td><td>3</td></tr><tr><td>Year of birth</td><td>712</td><td>1994</td><td>1.914</td><td>1986</td><td>1998</td></tr><tr><td>Married (=1)</td><td>712</td><td>0.044</td><td>0.204</td><td>0</td><td>1</td></tr><tr><td>High school in Bujumbura</td><td>712</td><td>0.15</td><td>0.358</td><td>0</td><td>1</td></tr><tr><td>Training</td><td>712</td><td>1.086</td><td>1.165</td><td>0</td><td>10</td></tr><tr><td><em>Evaluator characteristics</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gender of evaluator</td><td>712</td><td>0.486</td><td>0.5</td><td>0</td><td>1</td></tr><tr><td>Number of employees</td><td>712</td><td>149.513</td><td>340.436</td><td>6</td><td>2000</td></tr><tr><td>Years in Burundi</td><td>712</td><td>23.709</td><td>19.01</td><td>1</td><td>70</td></tr></tbody></table>

Notes: For *Year of birth*, we show the median, and show the mean for all the other variables. Training is the number of extra curricular training programs mentioned on resumes such as IT or leadership training, that individuals took before entering the job market. *Years in Burundi* is the number of years the organization has been operating in Burundi.
check for balance of covariates along the four groups that are generated by the interaction of the low-skill experience and gender treatments. This randomization test does not show any statistically significant difference among covariates except for the number of pages (See Table A1.3).

<table><caption>Table 3: Balance tests for the *Low-skill experience* treatment</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">N</th><th colspan="2">(1)</th><th colspan="2">(2)</th><th>t-test</th></tr><tr><th>0</th><th>1</th><th>p-value</th></tr><tr><th></th><th></th><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>(1)-(2)</th></tr></thead><tbody><tr><td>Number of pages</td><td>356</td><td>1.916<br>[0.016]</td><td>356</td><td>1.983<br>[0.010]</td><td>0.001</td></tr><tr><td>Year of birth</td><td>356</td><td>1993.826<br>[0.103]</td><td>356</td><td>1993.854<br>[0.099]</td><td>0.845</td></tr><tr><td>Married (=1)</td><td>356</td><td>0.042<br>[0.011]</td><td>356</td><td>0.045<br>[0.011]</td><td>0.855</td></tr><tr><td>High school in Bujumbura</td><td>356</td><td>0.160<br>[0.019]</td><td>356</td><td>0.140<br>[0.018]</td><td>0.464</td></tr><tr><td>Training</td><td>356</td><td>1.098<br>[0.065]</td><td>356</td><td>1.073<br>[0.059]</td><td>0.773</td></tr><tr><td>Gender of evaluator</td><td>356</td><td>0.486<br>[0.027]</td><td>356</td><td>0.486<br>[0.027]</td><td>1.000</td></tr><tr><td>Number of employees</td><td>356</td><td>150.315<br>[18.058]</td><td>356</td><td>148.711<br>[18.053]</td><td>0.950</td></tr><tr><td>Years in Burundi</td><td>356</td><td>23.809<br>[1.013]</td><td>356</td><td>23.610<br>[1.003]</td><td>0.889</td></tr></tbody></table>

Standard errors in brackets

## 4.2 Regression results

We show in column (1) of Table 4 parameter estimates of our baseline model. We find that having a low-skill experience increases the hiring interest of employers and gender does not have a significant effect. Given our study design, these results are robust to the inclusion in the model of controls for resume characteristics (Column (3)), evaluator characteristics
(Column (4)) and evaluator fixed effects (Column (5)).<fnref n="25" />

<table><caption>Table 4: Impact of low-skill experience</caption><thead><tr><th>VARIABLES</th><th>(1)<br>Resume Score</th><th>(2)<br>Resume Score</th><th>(3)<br>Resume Score</th><th>(4)<br>Resume Score</th><th>(5)<br>Resume Score</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.458***<br>(0.141)</td><td>0.462***<br>(0.147)</td><td>0.469***<br>(0.150)</td><td>0.479***<br>(0.151)</td><td>0.473***<br>(0.153)</td></tr><tr><td>Female (=1)</td><td>-0.009<br>(0.135)</td><td>-0.009<br>(0.135)</td><td>-0.039<br>(0.129)</td><td>-0.038<br>(0.128)</td><td>-0.014<br>(0.128)</td></tr><tr><td>Number of pages</td><td></td><td>-0.061<br>(0.246)</td><td>-0.132<br>(0.239)</td><td>-0.196<br>(0.231)</td><td>0.108<br>(0.175)</td></tr><tr><td>Year of birth</td><td></td><td></td><td>0.088**<br>(0.037)</td><td>0.109***<br>(0.035)</td><td>0.032<br>(0.028)</td></tr><tr><td>Married (=1)</td><td></td><td></td><td>-0.086<br>(0.464)</td><td>-0.044<br>(0.446)</td><td>-0.250<br>(0.204)</td></tr><tr><td>High school in Bujumbura</td><td></td><td></td><td>0.023<br>(0.291)</td><td>0.077<br>(0.257)</td><td>-0.114<br>(0.159)</td></tr><tr><td>Training</td><td></td><td></td><td>0.153*<br>(0.088)</td><td>0.126<br>(0.077)</td><td>0.145**<br>(0.067)</td></tr><tr><td>Gender of evaluator</td><td></td><td></td><td></td><td>0.440<br>(0.574)</td><td></td></tr><tr><td>Number of employees</td><td></td><td></td><td></td><td>-0.001**<br>(0.000)</td><td></td></tr><tr><td>Years in Burundi</td><td></td><td></td><td></td><td>0.038**<br>(0.018)</td><td></td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td></tr><tr><td>Observations</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td></tr><tr><td>Adjusted R-squared</td><td>0.008</td><td>0.006</td><td>0.013</td><td>0.112</td><td>0.648</td></tr><tr><td>Resume characteristics</td><td>NO</td><td>NO</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td><td>NO</td></tr><tr><td>Evaluator FE</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td></tr></tbody></table>

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1

Referring to the most robust and our preferred specification in column (5), results show that mentioning a low skill experience on one's resumes increases the Likert score approximately by 0.47²⁶ and the impact is statistically significant at 1%. This result implies that, all else

<footnote n="25">²⁵Although some of the other covariates are significant, they cannot be given a causal interpretation. For instance, younger students tend to be born in the capital Bujumbura which induces a positive correlation between *Year of birth* and *High school in Bujumbura*. Had we not controlled for where the person went for high school, the coefficient on *Year of birth* would have been biased. Similarly, the data suggests that older, more established organizations in the market tend to employ women more in management positions, inducing a positive correlation between *Gender of evaluator* and *Years in Burundi*. Generally, we cannot rule out the presence of unobserved confounders while interpreting the effect of control variables.</footnote>

<footnote n="26">²⁶Which corresponds to approximately a 10% increase in comparison to the mean of resumes of males</footnote>
equal, a person with a low-skill experience is preferred to a person with no experience. If employers typically select the most competitive candidates, then the coefficient's magnitude is not crucial in this context. However, if employers lean towards delaying hiring until they find a sufficiently qualified candidate, then the extent of the change in the Likert score caused by low-skilled experience becomes important. The coefficient on *Low-skill experience* is relatively large compared to coefficients on other covariates. In fact, the magnitude of the effect of *Low-skill experience* is the largest among all independent variables. The coefficient on *Low-skill experience* is also in the range of coefficients found by Kessler et al. (2019) who examined the impact of low-skill jobs during college studies<sup>27</sup>.

### 4.2.1 Correspondence between resume scores and callback rates

Unlike the outcome indicator we use in this study, i.e. a resume score, audit studies typically employ the callback rate for interviews as an indicator of an employer's interest in a candidate. However, in the context of Burundi, it is not possible to implement such a study notably because employers typically require job applicants to submit documents such as a criminal record, diplomas and proof of residence in a particular city at the time of application. To approximate the results that might have been obtained had an audit study with the callback rate as the outcome variable been implemented, various callback thresholds are hypothesized. These thresholds may change contingent upon labor market conditions, employer behavior, and other factors.

<footnote>without low-skill experience.</footnote>

<footnote n="27"><sup>27</sup>Similarly to this study, Kessler et al. (2019) used a 10 points Likert score as a dependent variable and found, in the context of the US, the following impacts of resume signals: GPA (2.13), Top Internship (0.90), Second Internship (0.47), Work for Money Jobs during studies (0.12), Technical Skills (0.047), Female and White (-0.15), Male and Non-White (-0.17), Female and Non-white (-0.009). The effect of low-skill experience is similar to the effect of a second internship and is superior to the effect of “work for money” jobs during studies.</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: Distribution of resume scores</figcaption></figure>

We change the coding of the dependent variable by making it binary with a cutoff at the Likert point of 5. In fact, it could be that evaluators were using “system 1” (Morewedge and Kahneman, 2010) when scoring the resumes, meaning that, instead of making a fine distinction between the resumes, they tended to score the resumes above or below the cutoff of 5 depending on whether they are interested in the profile or not. In fact, Figure 1 shows that resume scores are concentrated around the score of 5.

Results from logistic regressions in Table 5, Column (4), suggest that, if the callback threshold corresponds to the Likert score of 5, individuals who show a low-skill experience on their resume would have a probability of being called back which is approximately 13 percentage points higher than individuals who do not show any experience. If the callback threshold were set at 7, where we observe another peak in the score distribution, we estimate
that including low-skill experience on one's resume would augment the callback probability by almost 5 percentage points (See Table A1.4).

<table><caption>Table 5: Impact of low-skill experience: binary dependent variable</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>OLS</th><th>OLS</th><th>Logistic</th><th>Logistic</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.108***<br>(0.031)</td><td>0.110***<br>(0.032)</td><td>0.109***<br>(0.031)</td><td>0.128***<br>(0.038)</td></tr><tr><td>Female (=1)</td><td>-0.015<br>(0.030)</td><td>-0.016<br>(0.029)</td><td>-0.015<br>(0.030)</td><td>-0.022<br>(0.033)</td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>0.28</td><td>0.28</td><td>0.28</td><td>0.28</td></tr><tr><td>Observations</td><td>712</td><td>712</td><td>712</td><td>712</td></tr><tr><td>Adjusted R-squared</td><td>0.009</td><td>0.526</td><td></td><td></td></tr><tr><td>Resume characteristics</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td></tr><tr><td>Evaluator FE</td><td>NO</td><td>YES</td><td>NO</td><td>NO</td></tr></tbody></table>

The dependent variable is equal to 1 for scores above 5 and equal to 0 otherwise.

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1

We report marginal effects at the mean for the logistic regressions.

These callback findings bear similarities to results from prior literature. For instance, Valfort (2020) observed that the callback rate for applicants with Muslim inherited affiliations was 6.7 percentage points lower than that of their Christian counterparts in France. In the context of the United States, Kline et al. (2022) found that distinctively Black names decreased employer contact probability by 2.1 percentage points relative to distinctively white names. Arceo-Gomez and Campos-Vazquez (2014) found that married women had a callback rate 2.8 percentage points lower than that of single women within the Mexican context. These results suggest that the callback threshold in the context of the present study would likely correspond to the score of 7 rather than 5.
### 4.2.2 Low-skill experience and quality of job seeker

The empirical evidence further suggests that signaling low-skill experience not only increases hiring interest on average, but also, irrespective of the candidate's quality as measured by the resume score, it is invariably more advantageous to signal low-skill experience than to present a resume devoid of any experience at all. This result is depicted in Figure 2, which illustrates the stochastic dominance of a job search strategy that incorporates the signaling of low-skill experience. The cumulative distribution function (CDF) of scores assigned to resumes exhibiting low-skill experience (in blue, plain line) is consistently beneath the CDF of scores associated with resumes that do not feature low-skill experience (in red, dashed line) across the full spectrum of the Likert scale.

### 4.2.3 Is there a case for gender discrimination?

The non-significance of the gender effect as shows Table 4 can be interpreted as a lack of gender-based discrimination at an early stage of the hiring process and for early career job candidates. However, this may not be the case for other stages of the hiring process, for example during interviews, or for relatively senior positions. Nonetheless, this result is surprising given that the average age in our sample is 28, in a country context where the median age of women at first union is 20.3 and median age of women at first birth is 21.5 (DHS, 2018)<fnref n="28" />. One might expect that employers would discriminate against women who are likely to be pregnant and have childcare responsibilities, as it has been observed in other contexts (Becker et al., 2019). However, the result is consistent with the literature that found that discrimination against women increases as the level of responsibility of the

<footnote n="28">²⁸Estimates from the latest demographic and health survey conducted between 2016 and 2017.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Cumulative distribution functions for resumes with and without low-experience</figcaption></figure>
occupation increases (Valfort, 2020). In fact, employers scored resumes with respect to
entry-level positions such as Accountant, Administrative Assistant, Teller, Financial Officer,
HR Assistant, Logistics Officer, Sales Agent, although some relatively small organizations
proposed relatively senior positions such as Administrative and Finance Director, Branch
Manager and Head of Accounting Department (refer to Table A1.8 for a comprehensive list).
Furthermore, results indicate that the positions matched to an applicant do not differ based
on gender or presence of low-skill experience on the resume.

4.2.4 Heterogeneity with respect to resume and evaluator characteristics

Our heterogeneity analysis shows that the effect of low-skill experience for males (0.59) is
higher than the general effect found previously (0.47) and is statistically significant at 1%
(See Column (1) of Table A1.5). Conversely, the effect of low-skill experience for females
(0.36) is lower than the average of both genders and statistically significant at 1%. However,
the difference of the effects for males and females (0.231) is not statistically significant.
Results further suggest that the longer the duration of the low-skill experience the more
hiring interest increases but the difference between the corresponding two coefficients is not
statistically significant (p=0.105). We do not find significant differences of the treatment
effects with respect to the year of birth of an individual, whether they are married or not,
whether they went to a high school in Bujumbura or not, the number of training programs
attended, the gender of the evaluator, the number of employees of the evaluating organization
nor the number of years the organization has been present in the country. It could be that
the latter dimensions of heterogeneity are not important in the assessment of the value of
low-skill experience. However, we cannot rule out that this absence of significance is due to
a lack of statistical power.

It would have been interesting to estimate the heterogeneity of the low-skill treatment with
respect to the types of low-skill experiences individuals typically do but since the design of
the experiment does not allow that, we have asked directly the question to hiring managers.
We discuss the corresponding results in sub-section 4.3 below.<fnref n="29" />

4.2.5 Robustness checks

In Table A1.6, we estimate the impact of the treatment variables while combining scores
given by the employers considering the first, the second and the third type of job for which
they indicated that they would be interested in hiring the profile being evaluated<fnref n="30" />. In
Column (1), we reproduce the main result obtained in Table 4 where the dependent variable
is the score attributed for the first type of job. In Column (2), the dependent variable is the
score assigned for the second type of job and in Column (3), the score given for the third
type of job. Column (4) shows results for the three types of jobs after pooling all the scores
in the dataset. The effect of low-skill experience is largest and most significant in Column
(1) and decreases in magnitude and in significance from column (2) to (4). These results are
consistent with evaluators being less attentive in their evaluations for the second and third
job compared to the first job, thus inducing attenuation bias in the estimates. Relatedly,
it could be argued that attention may wane from the first to the 20th resume evaluated.
However, one might contend that paying reduced attention to subsequent resumes or jobs
might more accurately mirror real-world practices, if employers’ hasty decisions reflect their

<footnote n="29"><sup>29</sup>We do not investigate how preferences of employers vary with respect to different types of low-skill job experiences, different fields of study of jobs seekers or different high-skill jobs being applied for because such an analysis requires a sample that is larger than the one used here. The limiting factor in expanding the sample size is finding enough employers to evaluate many resumes.</footnote>

<footnote n="30"><sup>30</sup>This additional complexity in the design was not mentioned earlier to simplify exposition.</footnote>
biases more than their meticulous evaluations. Be that as it may, our primary findings are based on the results for the first job, as this provides the most complete data across all employers.

We also conducted a robustness check where we added the 18 observations we deleted because of missing observations for control variables. We show in Table A1.7 that our results are robust to the inclusion of these and replacing missing observations with means.

## 4.3 Eliciting employers’ preferences directly

After evaluating resumes, the hiring managers were invited to express directly their preferences with respect to low-skill experience. Employers were asked to choose between hiring: (a) an individual without any type of post-graduate professional experience or (b) an individual with 12 months of low-skill experience after graduation. They were asked to respond to the question in the context of a college graduate who has been 12 months on the market. The types of low-skill jobs were randomly chosen from the set of experiences used in the IRR. The managers had the option of expressing their hiring interest with respect to up to five types of existing jobs in their organizations. Among 645 choices expressed, 75% were in favor of low-skill experience, which is consistent with the IRR results. More importantly, this qualitative data does not suggest that employers care about which type of low-skill experience a person has, as long as the alternative is, in a sense, staying at home.

Next, we asked employers to evaluate on a Likert scale how they perceive in general post-graduate experience in the low-skill jobs. We show their responses in Table 6. The results suggest that employers perceive job seekers with low-skill experience as perseverant,
hard-working and disciplined, rather than people with financial difficulties<fnref n="31" />, generally incompetent or relatively incompetent compared to classmates. The list includes purposely qualifiers that have a close meaning, such as perseverance and hard-working, to check the consistency of responses.

<table><caption>Table 6: Employers’ perception of low-skill experience</caption><thead><tr><th>Experience in low-skill jobs suggests that the job seeker is:</th><th>Mean of a 10 points Likert Scale</th></tr></thead><tbody><tr><td>Persevering</td><td>8.05</td></tr><tr><td>Is a hard worker</td><td>7.22</td></tr><tr><td>Has discipline</td><td>5.59</td></tr><tr><td>Has financial difficulties</td><td>3.51</td></tr><tr><td>Incompetent</td><td>2.27</td></tr><tr><td>Is less qualified compared to classmates</td><td>1.86</td></tr></tbody></table>

Note: The order of these attributes in the survey was randomized

In pre-survey interviews with out of sample employers, they insisted on the importance of developing soft skills of young job seekers such as communication skills, showing up on time at work (discipline) and others<fnref n="32" />. We therefore investigated whether low-skill jobs might be a way of developing such soft skills. We first asked employers about the importance of soft skills in their organization. We found that they value soft skills with an average of 7.5 of the Likert score. We then asked them to indicate which low-skill experiences teach soft skills that are directly relevant to their organization. Their answers are shown in Figure A1.9. The interpretation of the results is not straightforward, however, the figure suggests that employers might be valuing jobs that involve speaking to clients such as sales jobs more than jobs that do not require this skill such as enumerator and data entry jobs, while

<footnote n="31">³¹It is possible that low-skill experience could still signal some degree of "gettability". However, the fact that employers disagreed that low-skill experience mainly signals financial difficulties, goes against this idea.</footnote>

<footnote n="32">³²The literature also argues that soft skills, encompassing personality traits, preferences and motivations, are important for labor market success (Heckman and Kautz, 2012).</footnote>
showing importance for different kinds of low-skill jobs regardless of whether they are related
to positions they offer or not.

Does low-skill experience build soft skills? It could be that employers are making a mistake
by overvaluing low-skill experience, leading them to update their beliefs as the perceived
value of this signal changes. An indirect way to assess whether graduates effectively
accumulate desirable traits during low-skill experience is to examine whether employers
with more experience in the market perceive such candidates differently compared to more
recent employers in the market. This would be reflected in a correlation between the time
an organization has operated in the local market and the degree of agreement that low-
skill experience fosters perseverance and discipline, the two soft-skills central to this study.
However, we do not find a correlation<fnref n="33" /> between the time spent in the market and agreement
that low-skill experience builds perseverance, or between time in the market and agreement
that low-skill experience builds discipline<fnref n="34" />.

Lastly, it is reasonable to assume that employers would value low-skill experiences related
to the high-skill job they are offering. However, it is less certain whether they would value
unrelated low-skill experiences. Qualitative evidence suggests that they might. For instance,
a manager at a microfinance institution indicates that experience as a security agent is more
valuable than no experience at all when hiring for a credit analyst position. However,
whether these two experiences are related is debatable. In general, distinguishing between
low-skill jobs that are related or unrelated to high-skill jobs is not as straightforward as it
may seem. Nevertheless, the fact that employers widely agree that low-skill experience can

<footnote n="33"><sup>33</sup>Correlation coefficient of 0.06 with p = 0.711.</footnote>

<footnote n="34"><sup>34</sup>Correlation coefficient of 0.07 with p = 0.665.</footnote>
develop personality traits such as perseverance and discipline, as well as soft skills, regardless
of whether the experience is related to the high-skill job, supports the idea that low-skill
experiences are valuable regardless of their direct relevance to high-skill employment.

5 Conclusion

Using an experimental approach which avoids deception, this paper investigated preferences
of employers with regard to low-skill experience of recent college graduates. The focus was
on examining the impact of various types of low-skill experiences, such as working as a phone
credit sales agent, a waiter, a security guard and other positions that do not necessitate a
college degree, on the hiring interest of employers in a high-skill job. The signalling effects of
low-skill work on employers are crucial in the context of Burundi as well in other developing
countries where job creation lags behind the growing number of university graduates.

We find that individuals who mention a low-skill experience on their resume are more likely
to be hired than individuals who do not mention any post-graduate experience. This main
finding holds irrespective of the quality of the resume. Post-experiment interviews with
employers suggest that they value low-skill experiences because they signal grit, discipline
and a hard-working character rather than financial difficulties or relative incompetence.

Overall, this research could help recent college graduates and career services offices to
understand the importance of low-skill jobs as stepping stones to high-skill jobs. Additionally,
it may challenge societal perceptions about low-skill work and emphasize its value.
Considering that expectations of how employers will perceive different experiences can affect
their uptake and signaling, graduates should be informed that signaling such experiences to
potential employers may improve their chances of being hired.

In fact, it is crucial for young graduates to perceive a particular value of low-skill jobs. Underemployment was a significant factor contributing to the popular uprisings that occurred in many countries of the Arab world starting in 2010, as reported by the International Labour Organization (ILO, 2011). The sense of frustration among the youth was exacerbated by the fact that their parents had invested significant amounts of money in their education, with the hope of providing them with a better future, only to see them end up in low-skill jobs or no job at all. It is therefore important that young people who are forced to take on low-skill jobs do not feel alienated from the career they aspire to, especially in contexts where social security coverage, particularly unemployment insurance, is limited or does not exist.

An important direction for future research would be to examine how employers' preferences vary across different labor markets, particularly where high-skill and low-skill jobs are more or less scarce. We also highlight the need to collect detailed data on the time use of university graduates in low-income countries. Current standard school-to-work surveys sample university graduates in proportion to their share of the population, resulting in a small sample size of this group. It would be beneficial to oversample university graduates to enable thorough studies of this subcategory of young people, as they are at the forefront of governance and have a relatively high capacity to cause disruption if dissatisfied with their condition.
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# 6 Appendix

## School-to-work transition of young graduates

The school-to-work transition indicators of the International Labor Organization (ILO) provide a detailed breakdown of the transition of young people (individuals aged 15 to 29) into the labor market. There are two families of indicators: the stage of transition from school to work and the form this transition takes. Stage indicators classify young people into segments according to their stage of transition: (1) transitioned, (2) in transition, and (3) not yet in transition. The form indicators focus on the specific outcomes of those who have completed the transition, distinguishing between stable salaried employment, satisfactory self-employment or temporary employment (ILO, 2009).

*What do we know about the school-to-work transition of young graduates in Sub-Saharan Africa?*

Africa has a relatively high proportion of young people in transition compared to other regions of the world (ILO, 2023). The latest ILO data on transition, available for four countries only in Sub-Saharan Africa - Kenya, Rwanda, Senegal and Uganda -, reveal that in Kenya and Rwanda, almost half of young people are considered to be in transition. In Uganda, the ratio is closer to one in three, while in Senegal it is around one in four. In all these countries, between 40% and 45% of young people, a particularly high proportion, have not yet begun their transition. In Kenya and Rwanda, barely 10% of young people have made the transition (ILO, 2023).

*What do young university graduates do when they are not doing low-skill jobs?*
Standard school-to-work surveys conducted with the support of the International Labor
Organization do not provide an answer to this question. This is because the number of
university graduates surveyed is too low (usually below 3% for samples of around 1,000
individuals). While the ideal dataset would be a representative sample of recent university
graduates, the preparatory data collection, whose main purpose was to gather information
on the types of low-skill jobs recent graduates do, tracked 139 students from the Faculty of
Economics and Management at the University of Burundi who had completed their bachelor's
degree (fewer than 10% of students pursue a master's education). For this cohort, we find
that 34% of university graduates who have been on the market for about one year have
been working in low-skill jobs, 18% have done work related to their qualifications, and 48%
indicated that they haven't worked at all since graduating.

It is challenging to have a solid discussion on the time use of recent college graduates in the
absence of comprehensive survey data, but the quick survey we conducted and observations
on the ground suggest that many of the graduates, if not in low-skill jobs, are likely at
home, helping on the farm or in other household activities—activities they cannot include
on a resume. If young graduates are not in high-skill jobs or education, they could be
in low-skill jobs, in training, or volunteering. Thus, one might consider other interesting
comparisons beyond the one made in this study, such as comparing time spent on training
versus unemployment. However, it should be noted that training and volunteering are costly
and may not be accessible to many graduates who are liquidity-constrained. Furthermore, the
low-skill versus unemployment comparison is particularly pertinent to the study population,
i.e. university graduates, who could be tempted to wait for the job they studied for. Finally, it
seems reasonable to assume that, when available, training and high-skill employment should
typically be better options for recent college graduates compared to low-skill jobs. Therefore,
the less obvious comparison is between low-skill employment and unemployment.

A few determinants of a successful transition

In a review of studies on the school-to-work transition in developing countries, Nilsson
(2019) identifies various determinants of school-to-work in the African context. In particular,
research conducted in the context of Mali shows that young people with tertiary education
spend on average more time (6 years) to find their first job than primary and secondary
school graduates (3 years), but need less time to find a satisfactory job (9 years vs.12 years)
(Boutin, 2013). The review also points out that the current generation of young Africans
faces a different labour market from that of the previous generation when it comes to waiting
for jobs in the public sector. Whereas the well-educated of previous generations had relatively
easy access to employment in the public sector, the increase in their numbers combined with
economic crises and structural reforms has meant that this option is no longer effective.

A World Bank survey conducted in several developing countries, including Kenya and Ghana,
revealed that socio-emotional skills facilitate the transition from school to work (Valerio et al.,
2014). Workers who report a smoother transition from school to work have different socio-
emotional skills than those who took longer to find their first job: they tend to be more
conscientious, emotionally stable and persistent. In a way, this study examines the role of
job-seeker perseverance in the job search as reflected in their take-up of low-skill jobs.
<table><caption>Table A1.1: Balance tests for the *Duration of low-skill experience* treatment</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">N</th><th colspan="2">None</th><th colspan="2">3 months or less</th><th colspan="2">9 months or more</th><th>t-test</th><th>t-test</th><th>t-test</th></tr><tr><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>(1)-(2)</th><th>(1)-(3)</th><th>(2)-(3)</th></tr></thead><tbody><tr><td>Number of pages</td><td>356</td><td>1.916<br/>[0.016]</td><td>173</td><td>1.988<br/>[0.012]</td><td>183</td><td>1.978<br/>[0.017]</td><td>0.003</td><td>0.016</td><td>0.624</td></tr><tr><td>Year of birth</td><td>356</td><td>1993.826<br/>[0.103]</td><td>173</td><td>1993.769<br/>[0.147]</td><td>183</td><td>1993.934<br/>[0.135]</td><td>0.752</td><td>0.533</td><td>0.406</td></tr><tr><td>Married (=1)</td><td>356</td><td>0.042<br/>[0.011]</td><td>173</td><td>0.035<br/>[0.014]</td><td>183</td><td>0.055<br/>[0.017]</td><td>0.681</td><td>0.514</td><td>0.365</td></tr><tr><td>High school in Bujumbura</td><td>356</td><td>0.160<br/>[0.019]</td><td>173</td><td>0.104<br/>[0.023]</td><td>183</td><td>0.175<br/>[0.028]</td><td>0.083</td><td>0.663</td><td>0.055</td></tr><tr><td>Training</td><td>356</td><td>1.098<br/>[0.065]</td><td>173</td><td>1.156<br/>[0.091]</td><td>183</td><td>0.995<br/>[0.075]</td><td>0.608</td><td>0.323</td><td>0.171</td></tr><tr><td>Gender of evaluator</td><td>356</td><td>0.486<br/>[0.027]</td><td>173</td><td>0.538<br/>[0.038]</td><td>183</td><td>0.437<br/>[0.037]</td><td>0.266</td><td>0.283</td><td>0.058</td></tr><tr><td>Number of employees</td><td>356</td><td>150.315<br/>[18.058]</td><td>173</td><td>209.867<br/>[35.372]</td><td>183</td><td>90.896<br/>[9.006]</td><td>0.096</td><td>0.023</td><td>0.001</td></tr><tr><td>Years in Burundi</td><td>356</td><td>23.809<br/>[1.013]</td><td>173</td><td>27.757<br/>[1.554]</td><td>183</td><td>19.689<br/>[1.220]</td><td>0.030</td><td>0.013</td><td>0.000</td></tr></tbody></table>

Standard errors in brackets
<table><caption>Table A1.2: Balance tests for the *Gender* treatment</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">N</th><th colspan="2">(1)</th><th colspan="2">(2)</th><th>t-test</th></tr><tr><th>Male</th><th>Female</th><th>p-value</th></tr></thead><tbody><tr><td>Number of pages</td><td>357</td><td>1.950<br/>[0.013]</td><td>355</td><td>1.949<br/>[0.014]</td><td>0.988</td></tr><tr><td>Year of birth</td><td>357</td><td>1993.751<br/>[0.103]</td><td>355</td><td>1993.930<br/>[0.100]</td><td>0.213</td></tr><tr><td>Married (=1)</td><td>357</td><td>0.039<br/>[0.010]</td><td>355</td><td>0.048<br/>[0.011]</td><td>0.571</td></tr><tr><td>High school in Bujumbura</td><td>357</td><td>0.148<br/>[0.019]</td><td>355</td><td>0.152<br/>[0.019]</td><td>0.892</td></tr><tr><td>Training</td><td>357</td><td>1.039<br/>[0.059]</td><td>355</td><td>1.132<br/>[0.064]</td><td>0.286</td></tr><tr><td>Gender of evaluator</td><td>357</td><td>0.485<br/>[0.026]</td><td>355</td><td>0.487<br/>[0.027]</td><td>0.942</td></tr><tr><td>Number of employees</td><td>357</td><td>150.199<br/>[18.007]</td><td>355</td><td>148.823<br/>[18.104]</td><td>0.957</td></tr><tr><td>Years in Burundi</td><td>357</td><td>23.768<br/>[1.011]</td><td>355</td><td>23.651<br/>[1.005]</td><td>0.935</td></tr></tbody></table>

Standard errors in brackets
<table><caption>Table A1.3: Balance tests for the interaction of the gender and low-skill experience treatments</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">(1)</th><th colspan="2">(2)</th><th colspan="2">(3)</th><th colspan="2">(4)</th><th colspan="2">t-test</th><th colspan="2">t-test</th><th colspan="2">t-test</th></tr><tr><th>N</th><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>N</th><th>Mean/SE</th><th>(1)-(2)</th><th>(1)-(3)</th><th>(1)-(4)</th><th>(2)-(3)</th><th>(2)-(4)</th><th>(3)-(4)</th></tr></thead><tbody><tr><td>Number of pages</td><td>179</td><td>1.989<br>[0.014]</td><td>177</td><td>1.977<br>[0.016]</td><td>178</td><td>1.910<br>[0.023]</td><td>178</td><td>1.921<br>[0.023]</td><td>0.587</td><td>0.003</td><td>0.012</td><td>0.017</td><td>0.047</td><td>0.730</td></tr><tr><td>Year of birth</td><td>179</td><td>1993.698<br>[0.144]</td><td>177</td><td>1994.011<br>[0.136]</td><td>178</td><td>1993.803<br>[0.147]</td><td>178</td><td>1993.848<br>[0.146]</td><td>0.116</td><td>0.611</td><td>0.465</td><td>0.301</td><td>0.415</td><td>0.828</td></tr><tr><td>Married (=1)</td><td>179</td><td>0.039<br>[0.015]</td><td>177</td><td>0.051<br>[0.017]</td><td>178</td><td>0.039<br>[0.015]</td><td>178</td><td>0.045<br>[0.016]</td><td>0.594</td><td>0.991</td><td>0.784</td><td>0.602</td><td>0.795</td><td>0.793</td></tr><tr><td>High school in Bujumbura</td><td>179</td><td>0.128<br>[0.025]</td><td>177</td><td>0.153<br>[0.027]</td><td>178</td><td>0.169<br>[0.028]</td><td>178</td><td>0.152<br>[0.027]</td><td>0.515</td><td>0.289</td><td>0.529</td><td>0.682</td><td>0.982</td><td>0.666</td></tr><tr><td>Training</td><td>179</td><td>0.994<br>[0.074]</td><td>177</td><td>1.153<br>[0.092]</td><td>178</td><td>1.084<br>[0.092]</td><td>178</td><td>1.112<br>[0.091]</td><td>0.180</td><td>0.447</td><td>0.316</td><td>0.599</td><td>0.756</td><td>0.828</td></tr><tr><td>Gender of evaluator</td><td>179</td><td>0.492<br>[0.037]</td><td>177</td><td>0.480<br>[0.038]</td><td>178</td><td>0.478<br>[0.038]</td><td>178</td><td>0.494<br>[0.038]</td><td>0.830</td><td>0.791</td><td>0.959</td><td>0.960</td><td>0.790</td><td>0.751</td></tr><tr><td>Number of employees</td><td>179</td><td>149.575<br>[25.439]</td><td>177</td><td>147.836<br>[25.697]</td><td>178</td><td>150.826<br>[25.566]</td><td>178</td><td>149.803<br>[25.582]</td><td>0.962</td><td>0.972</td><td>0.995</td><td>0.934</td><td>0.957</td><td>0.977</td></tr><tr><td>Years in Burundi</td><td>179</td><td>23.849<br>[1.431]</td><td>177</td><td>23.367<br>[1.410]</td><td>178</td><td>23.685<br>[1.433]</td><td>178</td><td>23.933<br>[1.437]</td><td>0.811</td><td>0.936</td><td>0.967</td><td>0.874</td><td>0.779</td><td>0.903</td></tr></tbody></table>

Standard errors in brackets
<table><caption>Table A1.4: Impact of low-skill experience: binary dependent variable at 7</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>OLS</th><th>OLS</th><th>Logistic</th><th>Logistic</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.050**<br>(0.025)</td><td>0.050*<br>(0.025)</td><td>0.050**<br>(0.025)</td><td>0.048**<br>(0.024)</td></tr><tr><td>Female (=1)</td><td>-0.005<br>(0.029)</td><td>-0.004<br>(0.030)</td><td>-0.005<br>(0.029)</td><td>-0.006<br>(0.027)</td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>0.112</td><td>0.112</td><td>0.112</td><td>0.112</td></tr><tr><td>Observations</td><td>712</td><td>712</td><td>712</td><td>712</td></tr><tr><td>Adjusted R-squared</td><td>0.001</td><td>0.434</td><td></td><td></td></tr><tr><td>Resume characteristics</td><td>NO</td><td>YES</td><td>NO</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td></tr><tr><td>Evaluator FE</td><td>NO</td><td>YES</td><td>NO</td><td>NO</td></tr></tbody></table>

The dependent variable is equal to 1 for scores above 7 and equal to 0 otherwise.

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1
<table><caption>Table A1.5: Heterogeneity of gender and low-skill experience treatments</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr><tr><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.589***<br>(0.182)</td><td></td><td>144.422<br>(117.177)</td><td>0.498***<br>(0.161)</td><td>0.464***<br>(0.165)</td><td>0.391*<br>(0.202)</td><td>0.584***<br>(0.155)</td><td>0.449***<br>(0.163)</td><td>0.332<br>(0.272)</td></tr><tr><td>Female (=1)</td><td>0.101<br>(0.134)</td><td>-0.014<br>(0.126)</td><td>-25.231<br>(102.715)</td><td>0.002<br>(0.132)</td><td>-0.009<br>(0.136)</td><td>-0.037<br>(0.181)</td><td>-0.067<br>(0.138)</td><td>-0.031<br>(0.139)</td><td>0.132<br>(0.159)</td></tr><tr><td>Female*Low-skill</td><td>-0.231<br>(0.194)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3 months or less of low-skill experience</td><td></td><td>0.326<br>(0.201)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>9 months or more of low-skill experience</td><td></td><td>0.612***<br>(0.168)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low-skill*Yearofbirth</td><td></td><td></td><td>-0.072<br>(0.059)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female*Yearofbirth</td><td></td><td></td><td>0.013<br>(0.052)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low-skill*Married</td><td></td><td></td><td></td><td>-0.565<br>(0.360)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female*Married</td><td></td><td></td><td></td><td>-0.349<br>(0.384)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low-skill*HighSchoolinBujumbura</td><td></td><td></td><td></td><td></td><td>0.066<br>(0.283)</td><td></td><td></td><td></td><td></td></tr><tr><td>Female*HighSchoolinBujumbura</td><td></td><td></td><td></td><td></td><td>-0.042<br>(0.273)</td><td></td><td></td><td></td><td></td></tr><tr><td>Low-skill*Training</td><td></td><td></td><td></td><td></td><td></td><td>0.076<br>(0.120)</td><td></td><td></td><td></td></tr><tr><td>Female*Training</td><td></td><td></td><td></td><td></td><td></td><td>0.018<br>(0.133)</td><td></td><td></td><td></td></tr><tr><td>Low-skill*MaleEvaluator</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.215<br>(0.290)</td><td></td><td></td></tr><tr><td>Female*MaleEvaluator</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.056<br>(0.264)</td><td></td><td></td></tr><tr><td>Low-skill*NumberofEmployees</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.000<br>(0.000)</td><td></td></tr><tr><td>Gender*NumberofEmployees</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.000)</td><td></td></tr><tr><td>Low-skill*YearsinBurundi</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.006<br>(0.008)</td></tr><tr><td>Female*YearsinBurundi</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.007<br>(0.006)</td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td><td>4.342</td></tr><tr><td>Observations</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td><td>712</td></tr><tr><td>Adjusted R-squared</td><td>0.648</td><td>0.649</td><td>0.648</td><td>0.648</td><td>0.647</td><td>0.647</td><td>0.110</td><td>0.110</td><td>0.111</td></tr><tr><td>Resume characteristics</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Evaluator FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td><td>NO</td><td>NO</td><td>NO</td></tr></tbody></table>

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1
<table><caption>Table A1.6: Impact of low-skill experience: three types of jobs</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Resume Score for first type of job</th><th>Resume Score for second type of job</th><th>Resume Score for third type of job</th><th>Resume Score for all types of jobs</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.473***<br>(0.153)</td><td>0.287***<br>(0.100)</td><td>0.275*<br>(0.145)</td><td>0.388***<br>(0.103)</td></tr><tr><td>Female (=1)</td><td>-0.014<br>(0.128)</td><td>-0.033<br>(0.172)</td><td>-0.062<br>(0.224)</td><td>-0.025<br>(0.128)</td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>4.342</td><td>4.397</td><td>4.102</td><td>4.311</td></tr><tr><td>Observations</td><td>712</td><td>410</td><td>277</td><td>1.399</td></tr><tr><td>Adjusted R-squared</td><td>0.648</td><td>0.752</td><td>0.741</td><td>0.684</td></tr><tr><td>Resume characteristics</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>NO</td><td>NO</td></tr><tr><td>Evaluator FE</td><td>YES</td><td>YES</td><td>YES</td><td>YES</td></tr></tbody></table>

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1

<table><caption>Table A1.7: The impact of low-skill experience: taking into account missing observations</caption><thead><tr><th rowspan="2">VARIABLES</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th><th>Resume Score</th></tr></thead><tbody><tr><td>Low-skill experience</td><td>0.035<br>(0.128)</td><td>0.023<br>(0.125)</td><td>0.020<br>(0.126)</td><td>0.023<br>(0.125)</td></tr><tr><td>Female (=1)</td><td>0.451***<br>(0.150)</td><td>0.459***<br>(0.151)</td><td>0.464***<br>(0.150)</td><td>0.436***<br>(0.155)</td></tr><tr><td>Mean dependent<br>(Male, Without low-skill experience)</td><td>4.333</td><td>4.333</td><td>4.333</td><td>4.333</td></tr><tr><td>Observations</td><td>730</td><td>730</td><td>730</td><td>730</td></tr><tr><td>Adjusted R-squared</td><td>0.006</td><td>0.007</td><td>0.054</td><td>0.643</td></tr><tr><td>Resume characteristics</td><td>NO</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>Evaluator characteristics</td><td>NO</td><td>NO</td><td>YES</td><td>NO</td></tr><tr><td>Evaluator FE</td><td>NO</td><td>NO</td><td>NO</td><td>YES</td></tr></tbody></table>

Standard errors are clustered at the employer level

*** p<0.01, ** p<0.05, * p<0.1

Note: From columns (2) to (4), missing observations for Year of birth, Maried and High school in Bujumbura are replaced with means.
<table><caption>Table A1.8: Types of high-skill jobs suggested by resume evaluators</caption><tr><td>Accountant</td><td>Financial Analyst</td></tr><tr><td>Accounting and Financial executive</td><td>Monitoring and Evaluation</td></tr><tr><td>Accounting and HR Manager</td><td>Grant Accountant</td></tr><tr><td>Accounting Department Executive</td><td>Head of Accounting Department</td></tr><tr><td>Accounting Internship</td><td>Head of Credit Department</td></tr><tr><td>Administration and Finance Assistant</td><td>Head of Operations</td></tr><tr><td>Administration Assistant</td><td>HR Assistant</td></tr><tr><td>Administration Executive</td><td>HR Assistant</td></tr><tr><td>Administrative and Financial Director</td><td>Human Resources Clerk</td></tr><tr><td>Administrative and Financial Intern</td><td>Internal Auditor</td></tr><tr><td>Administrative Manager</td><td>Inventory Manager</td></tr><tr><td>Administrative Secretary</td><td>Journalist</td></tr><tr><td>Assistant Accountant</td><td>Local Purchasing Agent</td></tr><tr><td>Assistant Manager</td><td>Logistics</td></tr><tr><td>Automotive Underwriter</td><td>Logistics Manager</td></tr><tr><td>Bank Operation Officer</td><td>Logistics Officer</td></tr><tr><td>Billing and Collection Department</td><td>Logistics/security Department</td></tr><tr><td>Branch Manager</td><td>Logistics/Security Manager</td></tr><tr><td>Business Banker</td><td>Management and Warehouse Department</td></tr><tr><td>Teller</td><td>Management Control Department</td></tr><tr><td>Claims Department</td><td>Marketing Agent</td></tr><tr><td>Collection officer</td><td>Marketing/Commercial Officer</td></tr><tr><td>Collection Service</td><td>Miscellaneous Operations Officer</td></tr><tr><td>Collections Controller</td><td>Order Manager</td></tr><tr><td>Commercial Attache</td><td>Personnel Manager</td></tr><tr><td>Commercial Department Executive</td><td>Production Agent</td></tr><tr><td>Communication Officer</td><td>Production Manager</td></tr><tr><td>Community Manager</td><td>Program Manager</td></tr><tr><td>Cooperative Facilitator</td><td>Project Manager</td></tr><tr><td>Credit Analyst</td><td>Public Relations</td></tr><tr><td>Credit Department</td><td>Sales Agent</td></tr><tr><td>Credit Officer</td><td>Sales Manager</td></tr><tr><td>Credit Risk Officer</td><td>Sales Representative</td></tr><tr><td>Customer Service Agent</td><td>Sales Representative Internship</td></tr><tr><td>Customer Service and Marketing</td><td>Team Leader</td></tr><tr><td>Data Entry in Accounting</td><td>Technical Sales Executive</td></tr><tr><td>Direct Sales Representative</td><td>Treasury Custody</td></tr><tr><td>Finance Officer</td><td>Underwriter trainee</td></tr></table>

Note: All the high-skill jobs listed by employers require an undergraduate degree within their organization. The degree requirement criterion is the only one used to distinguish between low-skill and high-skill jobs in this study.
<figure><img src="image_4.png" /><figcaption>Figure A1.1: Resume modification</figcaption></figure>

*Note:* This figure illustrates the randomization process. From an initial set of real 200 resumes, we generated four groups of resumes (A, B, C and D) that are similar except for gender and low-skill experience. Each employer was invited to evaluate a total of 20 resumes drawn from all 4 groups by randomly selecting 5 resumes in each group. The randomization algorithm included an instruction to not select twice a resume from the same person.
Figure A1.2: Template of the letter sent to employers (Page 1)

<figure><img src="image_5.png" /></figure>

Bujumbura, le 30 mai 2022

A Monsieur/Madame le Directeur Général de ....................................

à Bujumbura

Objet : Etude des préférences des organisations partenaires/collaborateurs

Réf. : ....../IG/MK/05/2022

Madame/Monsieur le Directeur Général,

Je vous présente mes compliments et sollicite votre participation à une évaluation que nous conduisons sur les préférences des employeurs burundais. Cette évaluation permettra à INFINITY GROUP de vous fournir à l'avenir et au besoin, des travailleurs correspondant aux préférences que vous aurez indiquées.

L'insertion professionnelle des jeunes ! Une équation à « n » inconnues que l'Etat, le Système Educatif, les Pourvoyeurs d'Emplois – acteurs des Secteurs tant Public que Privé, les Bailleurs de Fonds Internationaux, etc... tentent de résoudre par tous les moyens. Les Jeunes se plaignent de ne pas avoir suffisamment accès à l'emploi, alors que les Employeurs potentiels se plaignent de n'avoir pas une main d'œuvre, adéquatement préparée pour le milieu professionnel. INFINITY GROUP souhaite apporter sa pierre à la résolution de ce problème sur base d'une théorie de changement selon laquelle : Si les pourvoyeurs d'emplois communiquent mieux leurs besoins en ressources et participent à la mise à niveau des jeunes à travers leur responsabilité sociale et que les capacités des jeunes sont renforcées et adaptées aux besoins du marché, alors les jeunes auront un meilleur accès au marché du travail burundais.

En effet, dans le cadre de l'amélioration continue de nos services et pour mieux préparer nos formations à l'endroit de certains lauréats, potentiels employés de demain, nous souhaitons mieux connaître les profils de candidats qui vous intéressent le plus. Nous vous demandons ainsi d'évaluer les 20 CVs en annexe sur une
Figure A1.3: Template of the letter sent to employers (Page 2)

échelle de 1 à 10 en utilisant la fiche d'évaluation également en annexe. Pour ce lot spécifique de CVs, il s'agit de profils de candidats ayant terminé leur Baccalauréat en Sciences Economiques et de Gestion en mai 2021 à l'Université du Burundi. Après avoir terminé l'évaluation des CVs, nous vous demandons de répondre à quelques questions supplémentaires pour nous aider à mieux comprendre les besoins de votre **entreprise/organisation/institution**. Cette activité prend environ 20 minutes. Plus vous évaluerez soigneusement les CVs, mieux nous pourrons vous proposer les profils adéquats. Il serait préférable que les CVs soient évalués par un haut cadre qui participe habituellement dans les décisions d'embauche afin d'augmenter la précision de nos recommandations.

En espérant une suite favorable, je vous prie d'agréer, **Madame/Monsieur** le Directeur Général, l'expression de ma haute considération.

Irvine Floréale Murame
Managing Director

CPI:

<figure><img src="image_6.png" /></figure>

A Monsieur / Madame l'ADGA

<figure><img src="image_7.png" /></figure>

A Monsieur / Madame le DRH

<figure><img src="image_8.png" /></figure>
Figure A1.4: Verbatim translation of the letter sent to employers

Dear ...,

I would like to offer you my compliments and ask for your participation in an evaluation we are carrying out on preferences of Burundian employers. This evaluation will enable INFINITY GROUP to provide you with workers corresponding to the preferences you have indicated.

The professional integration of young people! An equation with "n" unknowns that the country, the Education System, the Job Providers - both in the Public and Private sectors, International Financial Organizations, etc...are doing their utmost to solve the problem. Young people complain that they do not have sufficient access to employment, while potential Employers complain that they do not have a workforce that is adequately prepared for the workplace. INFINITY GROUP wants to help solve this problem, based on the theory of change that: If job providers better communicate their resource needs and participate in the upskilling of young people through their social responsibility and young people's capacities are strengthened and adapted to market needs, then young people will have better access to the Burundian labor market.

In fact, as part of the continuous improvement of our services, and to better prepare our training courses for certain graduates, potential employees of tomorrow, we'd like to know more about the candidate profiles you are most interested in. We therefore ask you to evaluate the 20 CVs attached on a scale of 1 to 10, using the attached evaluation form. For this specific batch of CVs, candidates have completed their Bachelor in Economics and Management in May 2021 at the University of Burundi. After completing the CV assessment, we ask you to answer a few additional questions to help us better understand the needs of your company/organization/institution. This activity takes about 20 minutes. The more carefully you evaluate the CVs, the better we will be able to suggest suitable profiles. It would be preferable for the CVs to be evaluated by a senior executive who is usually involved in hiring decisions, to increase the accuracy of our recommendations.

We look forward to hearing from you. Please accept, Madam/Sir, the assurances of my highest consideration.

Irvine Floréale Murame
Managing Director
Figure A1.5: Evaluation form used by employers (Original)

<figure><img src="image_9.png" /></figure>

## OUTIL D'EVALUATION DE CVs

INFINITY GROUP réalise une étude qui vise à améliorer l'adéquation entre les travailleurs et les entreprises et autres employeurs du Burundi. Nous vous demandons d'évaluer sur une échelle de 1 à 10 les CV joints de jeunes diplômés en économie et gestion de l'Université du Burundi. Vos choix seront utilisés pour vous fournir des recommandations de travailleurs qui pourraient convenir à votre organisation. Plus vous évaluerez les CV avec soin, plus nous serons en mesure de trouver des candidats appropriés pour votre organisation. Les noms des candidats ainsi que de leurs référents ont été rendus anonymes pour des raisons de confidentialité.

Nom de l'évaluateur de CVs

Nom de l'organisation

Titre de l'évaluateur de CVs

Date

Intitulé du poste ou des postes qui pourrait entier
être occupé(s) par un détenteur d'un diplôme de
Baccalauréat en Economie et Gestion sans
expérience connexe.

Poste 1

Poste 2

Poste 3

## SCORE DE CHAQUE CV

Sur une échelle de 1 à 10, quel intérêt portez-vous à l'embauche de ce candidat ?

(Encerclez le chiffre correspondant : 1 est "Pas du tout intéressé" et 10 est "Très intéressé").

N'évaluez que la qualité du candidat. Supposez que le candidat accepterait une offre s'il en recevait une.

<figure><img src="image_10.png" /></figure>

Quel montant recommanderiez-vous comme salaire mensuel de départ si le candidat est offert le poste ?

Poste 1

Poste 2

Poste 3
<figure><img src="image_11.png" /><figcaption>Figure A1.6: Evaluation form used by employers (English translation)</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure A1.7: A sample of the resumes sent for evaluation (Page 1)</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure A1.8: A sample of the resumes sent for evaluation (Page 2)</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure A1.9: Ranking of low-skill jobs by employers</figcaption></figure>

Source: Authors’ computations using the present study’s survey data.
<table><caption>Figure A1.10: Questionnaire for employers</caption><thead><tr><th>Field</th><th>Question</th><th>Answer</th></tr></thead><tbody><tr><td>note_start</td><td>(After CV evaluation) We'll now ask you a series of questions designed to understand your preferences in relation to the people you'd like to recruit. This interview should last about 10 minutes. If at any point you don't hear or understand the question, please ask me to clarify. Your answers will be treated in the strictest confidentiality.</td><td></td></tr><tr><td colspan="3">TO BE COMPLETED BY THE CONSULTANT BEFORE STARTING THE FACE-TO-FACE INTERVIEW</td></tr><tr><td>q101 (required)</td><td>Organization name</td><td></td></tr><tr><td>q102 (required)</td><td>Address</td><td></td></tr><tr><td>q103 (required)</td><td>Organization's economic sector</td><td></td></tr><tr><td colspan="3">QUESTIONS TO ASK THE RESPONDENT</td></tr><tr><td>q201</td><td>In what year did the organization start its activities in Burundi?</td><td></td></tr><tr><td>q202</td><td>Number of employees</td><td></td></tr><tr><td colspan="3">QUESTIONS TO BE ASKED > Group2_1</td></tr><tr><td>q204</td><td>Can you provide rough estimates (in percentages) of employees' field of study?</td><td></td></tr><tr><td>q205</td><td>Economics and Management</td><td></td></tr><tr><td>q206</td><td>Law</td><td></td></tr><tr><td>q207</td><td>Geography</td><td></td></tr><tr><td>q208</td><td>History</td><td></td></tr><tr><td>q209</td><td>Literature</td><td></td></tr><tr><td>q210</td><td>Civil Engineering</td><td></td></tr><tr><td>q211</td><td>Other Engineering Studies</td><td></td></tr><tr><td>q212</td><td>IT</td><td></td></tr><tr><td>q213</td><td>Sport</td><td></td></tr><tr><td>q214</td><td>Chemistry</td><td></td></tr><tr><td>q215</td><td>Physics</td><td></td></tr><tr><td>q216</td><td>Biology</td><td></td></tr><tr><td>q217</td><td>Psychology</td><td></td></tr><tr><td>q218</td><td>Mathematics</td><td></td></tr><tr><td>q219</td><td>Medicine</td><td></td></tr><tr><td>q220</td><td>Public Health</td><td></td></tr><tr><td>q221</td><td>Clinical Sciences</td><td></td></tr><tr><td>q222</td><td>Without</td><td></td></tr><tr><td colspan="3">EMPLOYER PREFERENCES 1</td></tr><tr><td>q301 (required)</td><td>Has your organization hired or does it plan to hire graduates in Economics and Management?<br>Management without experience? (Consider first permanent positions, temporary positions and then internships).</td><td>1 Yes<br>0 No</td></tr><tr><td>q302 (required)</td><td>For which position?<br>Question relevant when: selected($q301), '1')</td><td></td></tr><tr><td>q303</td><td>Answer the following questions by referring to the position indicated above.<br>Question relevant when: selected($q301), '1')</td><td></td></tr><tr><td colspan="3">EMPLOYER PREFERENCES 1 > First type of position</td></tr><tr><td>q304</td><td>For each pair of candidate profiles, which one would you hire?</td><td></td></tr><tr><td>q316_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a photographer after undergraduate studies.</td></tr><tr><td>q307_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a cashier after undergraduate studies.</td></tr><tr><td>q317_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a welder after undergraduate studies.</td></tr><tr><td>q311_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.</td></tr></tbody></table>
<table><thead><tr><th>Field</th><th>Question</th><th>Answer</th></tr></thead><tbody><tr><td>q312_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a security guard after undergraduate studies.</td></tr><tr><td>q305_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a telephone credit sales agent after undergraduate studies.</td></tr><tr><td>q310_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a Tuktuk driver after undergraduate studies.</td></tr><tr><td>q313_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a small retailer after undergraduate studies.</td></tr><tr><td>q306_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a call center agent after undergraduate studies.</td></tr><tr><td>q319_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as an enumerator agent after undergraduate studies.</td></tr><tr><td>q315_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a driver after undergraduate studies.</td></tr><tr><td>q308_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a clothing salesperson after undergraduate studies.</td></tr><tr><td>q314_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a chicken trader after undergraduate studies.</td></tr><tr><td>q318_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.<br>2 with 12 months' experience as a data entry agent after undergraduate studies.</td></tr><tr><td>q309_1 (required)</td><td>Would you hire a job candidate :</td><td>1 without experience in any kind of job.</td></tr></tbody></table>
<table><tr><td></td><td></td><td>2 with 12 months' experience as a milk salesman after undergraduate studies.</td></tr><tr><td>q320 (required)</td><td>Is there any other position for which your organization has already hired or plans to hire Economics and Management graduates?<br/>(First consider permanent positions, temporary positions and then internships).</td><td>1 Yes<br/>0 No</td></tr><tr><td>Field</td><td>Question</td><td>Answer</td></tr><tr><td></td><td>Question relevant when: selected($q301), '1')</td><td></td></tr><tr><td>q321 (required)</td><td>Which one?<br/>Question relevant when: selected($q320), '1')</td><td></td></tr><tr><td>q322</td><td>Answer the following questions by referring to the position indicated above.<br/>Question relevant when: selected($q320), '1')</td><td></td></tr><tr><td colspan="3">EMPLOYER PREFERENCES 2</td></tr><tr><td>q401</td><td colspan="2">In the following section, we ask you to describe in general how you perceive the post-graduate experience of college graduates into low-skill jobs (jobs which do not require a college degree).</td></tr><tr><td>q404 (required)</td><td>Experience in "low-skill jobs" suggests that the job seeker is disciplined.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.<br/>4 4.<br/>5 5. Neutral<br/>6 6.<br/>7 7.<br/>8 8.<br/>9 9.<br/>10 10. Totally agree</td></tr><tr><td>q402</td><td>Experience in "low-skill jobs" suggests that the job seeker is incompetent.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.<br/>4 4.<br/>5 5. Neutral<br/>6 6.<br/>7 7.<br/>8 8.<br/>9 9.<br/>10 10. Totally agree</td></tr><tr><td>q407 (required)</td><td>Experience in "low-skill jobs" suggests that the job seeker is less qualified than his or her classmates.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.<br/>4 4.<br/>5 5. Neutral<br/>6 6.<br/>7 7.<br/>8 8.<br/>9 9.<br/>10 10. Totally agree</td></tr><tr><td>q403 (required)</td><td>Experience in "low-skill jobs" suggests that the job seeker is perseverant.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.<br/>4 4.<br/>5 5. Neutral<br/>6 6.<br/>7 7.<br/>8 8.<br/>9 9.<br/>10 10. Totally agree</td></tr><tr><td>q405</td><td>Experience in "low-skill jobs" suggests that the job seeker is a hard worker.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.<br/>4 4.<br/>5 5. Neutral<br/>6 6.<br/>7 7.<br/>8 8.<br/>9 9.<br/>10 10. Totally agree</td></tr><tr><td>q406 (required)</td><td>Experience in "low-skill jobs" suggests that the jobseeker has financial difficulties.</td><td>1 1. Strongly disagree<br/>2 2.<br/>3 3.</td></tr></table>
<table><thead><tr><th>Field</th><th>Question</th><th>Answer</th></tr></thead><tbody><tr><td></td><td></td><td>4 4.<br>5 5. Neutral<br>6 6.<br>7 7.<br>8 8.</td></tr><tr><td>q408</td><td>Do any of the following experiences teach soft skills that are directly relevant to your organization? Select them if so.</td><td>9 9.<br>10 10. Totally agree<br>1 Credit sales agent<br>telephone<br>2 Call center agent<br>3 Cashier<br>4 Clothing salesman<br>5 Milk seller<br>6 TukTuk driver<br>7 Server<br>8 Security agent<br>9 Small retailer<br>10 Chicken dealer<br>11 Driver<br>12 Photographer<br>13 Welder<br>14 Data entry agent<br>15 Enumerator</td></tr><tr><td>q409 (required)</td><td>How important are soft skills in your organization?</td><td>1 1. Not at all important<br>2 2<br>3 3.<br>4 4.<br>5 5.<br>6 6.<br>7 7.<br>8 8.<br>9 9.<br>10 10. Very important</td></tr><tr><td>q410 (required)</td><td>Which soft skills are you looking for when making a hiring decision?</td><td></td></tr><tr><td colspan="3">RESPONDENT INFORMATION</td></tr><tr><td>q501</td><td>Respondent job title</td><td></td></tr><tr><td>q502</td><td>Number of years with the organization.</td><td></td></tr><tr><td>q503</td><td>Phone number</td><td></td></tr><tr><td>q504</td><td>Email</td><td></td></tr><tr><td>q505</td><td>Type<br>Select without asking</td><td>1 Male<br>0 Female</td></tr><tr><td>q506</td><td>Age</td><td></td></tr><tr><td>note_end</td><td>Thank you for your time and participation in our survey.</td><td></td></tr></tbody></table>