Job Search and Hiring with Two-sided Limited Information about

Workseekers’ Skills∗

Eliana Carranza† Robert Garlick‡ Kate Orkin§ Neil Rankin¶

September 25, 2018

Abstract

Firms typically make hiring decisions with limited information about workseekers’ skills and

productivity. Similarly, workseekers make job search and employment decisions with limited

information about their own skills and productivity. These information frictions on both the

demand and supply sides of the labor market can distort job search and hiring decisions, lowering

total employment, total earnings, and firm productivity. We test for the existence of information

frictions using a series of randomized controlled trials in urban South Africa. We directly

assess workseekers’ skills in six domains and experimentally vary whether workseekers receive

information about their own skills and whether they can signal this information to firms. We

find that giving information to workseekers has large effects on their beliefs about their skills,

no effects on their job search behavior, and limited effects on their employment outcomes.

Helping workseekers signal their skills to firms substantially increases their employment rate and

earnings. Employment effects do not differ substantially by measured skills or other plausible

correlates of productivity but are higher for workseekers who receive more positive reports

conditional on the skills. We conclude that information frictions exist on both sides of the labor

market but only the demand-side frictions appear important for labor market outcomes.

∗We are grateful for exceptional work by Emmanuel Bakirdjian, Nilmini Herath, Shelby Carvalho, Allegra Cock-burn, Lukas Hensel, Wim Louw, Svetlana Pimkina, Ashley Pople, Sri Ramesh, Caitlan Russell, Wendy Trott, andLaurel Wheeler as well as multiple staff at the Harambee Youth Employment Accelerator, the Abdul Latif JameelLatif Poverty Action Lab Africa Office (J-PAL Africa), the Universities of Cape Town (UCT), Duke, Oxford, andStellenbosch, the Centre for Study of African Economies. The project is conducted in collaboration with the WorldBank Jobs Group and Africa Gender Innovation Lab, and is partly funded by the Private Enterprise in Low IncomeCountries Consortium. The findings, interpretations, and conclusions in this paper are entirely those of the authors.This study has been approved by the Commerce Faculty Ethics in Research Committee at UCT, the Duke Institu-tional Review Board for the Protection of Human Subjects in Non-Medical Research (protocol # D0368) and theDepartment of Economics Research Ethics Committee at Oxford (protocol # ECONCIA15-055). The pre-analysisplan is available at https://www.socialscienceregistry.org/trials/1631.†World Bank, ecarranza@worldbank.org‡Duke University, robert.garlick@duke.edu§University of Oxford, kate.orkin@merton.ox.ac.uk¶University of Stellenbosch, neilrankin@sun.ac.za

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1 Introduction

Many countries face high and rising unemployment and underemployment, particularly for young

people (World Bank, 2018). This challenge occurs across both developed and developing countries in

Latin America, the Middle East, North Africa, South Europe, and sub-Saharan Africa. This poses

major economic, political and social challenges: periods of unemployment can permanently harm

youths’ employment trajectories and contribute to crime and political instability (Altonji et al.,

2015; Blattman and Annan, 2016; Freeman, 1999). At the same time, firms in many developing

countries have low labor productivity and low growth rates and report that they cannot find and

hire suitable workers (Hardy and McCasland, 2017; Hsieh and Olken, 2014).

Limited information about workseekers’ skills can contribute to this problem by distorting

labor supply, labor demand, and firm-worker matching, particularly but not exclusively for young

people. On the demand side, firms who observe noisy proxies for workseekers’ skills may hire fewer

workers and pay lower wages if they face some combination of concave production technology in

skill-adjusted labor use, minimum wages, firing costs, or risk aversion. On the supply side, noise

in workseekers’ beliefs about their own skills will distort their job search behavior and potentially

their employment outcomes, though the direction of these distortions is theoretically ambiguous.

Both demand- and supply-side distortions can reduce the quality of firm-worker matches, reducing

output and earnings even if there are no employment effects. Both the demand- and supply-side

distortions will be larger when formal education is weakly correlated with skills and for young

workseekers who cannot use prior work experience to certify their skills. The latter effect can lead

to inefficiently low hiring of young workseekers from a social welfare perspective, as firms do not

want to incur the cost of making first-time hires and allowing the market to learn about workseekers’

skills through experience.

We use a series of experiments in urban South Africa to test the existence of demand- and supply-

side information frictions. We assess roughly 7,000 young workseekers’ skills in six domains using

validated psychometric tests. We randomly assign workseekers to either not see their assessment

results, see their assessment results, or see their assessment results and be able to communicate

these results to firms. (In ongoing work, we also conduct an audit study that experimentally

manipulates how much information firms see about applicants’ skills.) We work with a nationally

recognised assessment and placement agency whose branding helps to give the assessment results

credibility with both workseekers and firms. We measure workseekers’ beliefs about their own skills,

beliefs about the labor market, search behavior, and employment outcomes before and three to six

months after the treatment.

We find evidence that information frictions exist, particularly on the demand side, and distort

labor market outcomes. Workseekers who learn their assessment results update their beliefs about

their own skills and beliefs about returns to search but do not substantially change their job

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search behavior or employment outcomes. Workseekers who learn their assessment results and can

share the results with firms update their beliefs in similar ways but also experience a 17% rise in

employment and 32% rise in earnings. Their search behavior is mostly unchanged, suggesting these

effects are driven by reductions in demand-side information frictions. We also explore heterogeneity

in the effect of information frictions on workseekers with different skill levels, which is theoretically

ambiguous. We find little evidence that the treatment effects differ by skill, by education, or by

prior work experience.

We build on a growing literature on information frictions in labor markets. One set of papers

documents patterns in panel data consistent with information frictions: the relationship between

wages and skills that are observed by the econometrician but unobserved by firms becomes stronger

with tenure, consistent with employers learning about skills over time (Altonji and Pierret, 2001;

Farber and Gibbons, 1996; Kahn and Lange, 2014; MacLeod et al., 2017). This part of the literature

tests for information frictions using earnings or wage data conditional on employment. This is less

informative in labor markets with high unemployment, informal employment, and self-employment.

In these markets, information frictions may distort the employment margin, stopping firms from

beginning to learn about workseekers’ skills over time. A more recent set of papers show that

experimentally providing more information about workseekers’ skills can change their job search

behavior and employment outcomes (Abebe et al., 2016; Bassi and Nansamba, 2017; Pallais, 2014).

Similarly, Alfonsi et al. (2017) argue that vocational training helps workseekers more than on-the-

job training because it carries a credential employers can observe.

Our work also relates to a literature on firm-side responses to information frictions. Firms using

skills assessments or algorithmic recommendations to make hiring decisions can raise fill rates,

raise productivity and lower turnover (Autor and Scarborough, 2008; Hoffman et al., 2018; Horton,

2017). This helps motivate the idea that information frictions can reduce the quality of firm-worker

matches in addition to any employment effects. Firms may also hire through networks (Beaman

et al., 2018; Ioannides and Loury, 2004), use reference letters as signals of productivity (Abel et al.,

2016), or use apprenticeship programmes as screening mechanisms to identify high-skilled workers

(Hardy and McCasland, 2017). These mechanisms may alleviate information frictions but all have

limitations. Networks can systematically exclude some types of workers and reference letters are

least useful for new labor market entrants.

We contribute to the literature in three ways. First, we separately vary workseekers’ information

about their own skills and their ability to signal this to firms, which allows us to show that demand-

side information frictions are more important than supply-side information frictions. Existing work,

other than Bassi and Nansamba (2017), studies only one side of the market. This is important

because the incidence of and optimal policy response to information frictions differs for demand- and

supply-side frictions. If only one side of the market faces information frictions, then market-based

or state provision of information will be more effective when targeted at that side of the market.

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If only one side of the market faces information frictions, agents on the other side may be able to

extract rents, either by workseekers misreporting their skills or firms failing to pay workseekers in

line with their skills (Adhvaryu et al., 2017).

Second, we measure the full decision process for workseekers, from job search through hiring to

retention. Existing work, other than Abebe et al. (2016) and Pallais (2014), studies downstream

outcomes and may miss the effect of information frictions on upstream outcomes. For example,

studying the effect of information frictions on earnings trajectories misses effects on employment

and on search behavior. We find that downstream supply-side decisions like job search are weakly

affected by information frictions but demand-side decisions like hiring are sensitive to information

frictions.1

Third, we study how the effect of information frictions differs by workseekers’ skills. The

distribution of effects is theoretically ambiguous and depends on specific modeling choices. We

estimate heterogeneous treatment effects of better information for workseekers with higher and

lower skills. We also provide workseekers with coarse assessment results (reporting their terciles,

not cardinal scores) and use a regression discontinuity design to compare treatment effects on

workseekers with similar skills who receive more or less positive reports. We find little evidence

of heterogeneity by skills other than a positive employment effect of crossing the threshold into

a higher tercile. This result, combined with little evidence of heterogeneity by prior experience

and education, shows that information frictions do not differentially affect high- and low-skilled

workseekers.

With these advances, we provide a more comprehensive description of labor markets with in-

formation frictions. Our finding that demand-side information frictions can be quantitatively im-

portant can help to inform the design of policies to improve labor market functioning. The class of

policies we study are relatively cheap and easy to implement. Most other policies which try to re-

duce youth unemployment, such as wage subsidies, vocational training, entrepreneurship training,

and public works programs are either expensive or do not robustly increase employment (Blattman

and Ralson, 2015; Card et al., 2015; McKenzie, 2017).

We work in a developing country context with high unemployment and with young workseekers.

We argue this is a particularly salient context and population to study. Formal education is weakly

correlated with measured skills and there are many small firms that cannot afford complex in-house

skills assessment systems (Hsieh and Olken, 2014; Pritchett, 2013). Information frictions may also

have particularly serious impacts for young workseekers, who cannot use past work experience

and reference letters as signals of employment (World Bank, 2018). We do not claim our results

generalize to all labor markets. But the indirect evidence of information frictions documented in

the US suggests that our results are relevant even in markets with lower unemployment.

1We work with a sample of workseekers that are actively searching at baseline. Hence we capture effects ofinformation frictions on intensive margin search but may understate effects on extensive margin search.

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We divide this paper into five substantive sections. We present a simple conceptual description

of a labor market with limited information in section 2. We describe the context in which we work,

our trial, and our sample in section 3. We report average treatment effects in section 4 and explore

heterogeneous treatment effects in section 5 motivated by the conceptual framework.

2 Conceptual Framework

We consider a labor market facing two-sided limited information: firms and/or workseekers may

imperfectly observe workseekers’ skills. We show that these information frictions can distort job

search decisions and job offer decisions, ultimately distorting employment, earnings, productivity,

and revenue. We first describe the full-information labor market and then introduce information

frictions on the supply and demand sides. We begin with a very simple static model and discuss

extensions at the end of this section.

On the supply side, each workseeker i has one heterogeneous attribute, skill Si, and homogeneous

time endowment, T . Based on these attributes, she makes two sequential decisions. Her first

decision is how to allocate time between search for formal work Wi and other activities Oi. Job

search yields utility UW (Si,Wi), discussed in more detail below, while time spent on other activities

yields utility UO(Si, Oi). These utility functions include pecuniary and non-pecuniary benefits and

costs such as wages, monetary costs of job search, and leisure.2 She then receives a job offer with

probability p(Si,W∗i ) with an associated wage Y (Si,W

∗i ). Her second decision is to accept or reject

the job offer.

In a full information world, she chooses W ∗i and O∗i = T −W ∗i to equate the marginal return

from job search and the marginal return from other activities

∂UW (Si,W∗i (Si))

∂Wi=∂UO (Si, Ti −W ∗i (Si))

∂Wi. (1)

If she receives a job offer, she accepts any offer with wage Y (Si,W∗i ) > Y (Si,W

∗i ). Her decision de-

cisions may depend on her heterogeneous skill Si in two ways. First, W ∗i (·) will be a non-degenerate

function of Si if skill and time are non-separable in the utility functions UW (·, ·) and/or UO(·, ·).For example, the return to job search may higher for workseekers with higher skills, in which case

the optimal time allocation to searching for formal work will be increasing in skill. Second, her

wage and/or reservation wage will depend on skill if firms’ pay a skill premium (discussed below)

or workseekers with higher skills have different outside options.

On the demand side, each firm f has a single fixed attribute, productivity Af . The firm produces

2The utility function UW (·, ·) can be interpreted as the reduced-form of three structural functions: a job productionfunction that maps skills and search time into a probability of securing a formal job, a wage function that maps skillsinto earnings conditional on securing a formal job, and a utility function over wage-financed consumption and timeallocated to other activities.

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output Qf using effective labor LEf and other inputs Kf with a constant elasticity of substitution

(CES) production function that depends on productivity Af . Firms face fixed output prices and

costs of capital. Effective labor LE =∑

i∈f Si is the sum of the skills of all workers employed by

the firm. Absent information frictions, firms’ optimal choice of labor satisfies p× ∂Qf (L∗,K∗)∂L = w,

where w is the wage. Workers are paid their marginal revenue product, so workers with higher

skills receive higher wages. This model gives rise to a threshold rule, where each firm hires workers

in a skill band that depends on the firm-specific technology Af .

In the full-information equilibrium, workseekers’ time allocation and firms’ input choices gen-

erate a wage for each skill level Y ∗(Si,Wi ∗ (Si)) in which all workseekers who receive job offers

accept them.

We now introduce supply-side information frictions. Workseekers observe a noisy proxy Si =

F (Si, εi), where εi captures the information friction. Workseekers allocate time between job search

and other activities based on Si but their payoffs from job search depend on Si.3 Hence, the new

indifference condition is

E[∂UW (Si,W

∗i (Si))

∂Wi

]=∂UO (Si, Ti −W ∗i (Si))

∂Wi(2)

where the expectation is taken over the distribution of εi. The optimal time allocations in conditions

(1) and (2) will be equal if both utility functions are linear in skill and time. Otherwise, the time

allocations will generally be different. If, for example, utility from job search is a concave function

of time allocated to job search, then information frictions will lower the optimal time allocation to

job search. This will in turn lower the formal employment rate and lower earnings.

This framework illustrates that the effect of information frictions will depend on the structure

of the utility functions. The framework does not generate an unconditional prediction about which

types of workseekers (in terms of skills) will be affected most by information frictions. The relative

effects on workseekers with different skills depend on the nature of information friction, captured

by F (·, εi), and on the structure of the utility functions. Even when the information friction is

identical for all workseekers, Si = Si + εi with E[εi|Si] = 0, a nonlinear utility function means that

the information friction may distort decisions differently at different levels of skills.

This structure still applies if skills are multidimensional, provded at least one skill is unobserved.

This structure still applies if workseekers can search in multiple sectors or using multiple strategies:

information frictions will then distort time allocations between search and other activities and

between different types of search.

Labor demand is determined by the decisions of profit-maximizing firms.We assume firm f faces

3With two-sided information frictions their payoff from job search will depend on ˜Si = G(Si, νi), where νi capturesthe information friction facing the firm. The wedge between search-relevant and wage-relevant skills remains, exceptin the special case where firms and workseekers face identical information frictions.

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produces output Qf using labor Lf and other inputs Kf with a constant elasticity of substitution

(CES) production function and firm-specific technology Af . Absent information frictions, firms’

optimal choice of labor satisfies p× ∂Qf (L∗,K∗)∂L = w, where w is the wage.4 We model heterogeneous

skills by specifying output as a function of effective labor LE =∑

i∈f Si, which is the sum of the

skills of all workers employed by the firm. Workers are still paid their marginal revenue product,

so workers with higher skills receive higher wages. This model gives rise to a threshold rule, where

each firm hires workers in a skill band that depends on the firm-specific technology Af .

Demand-side information frictions occur when firms cannot perfectly observe workseekers’ skills.

The concavity of the CES production means that the expected marginal revenue product from each

work seeker is lower than their expected skills:

E

[p×

∂Qf

(LE∗,K∗

)∂LE∗

]< p×

∂Qf

(E[LE∗] ,K∗)

∂LE∗ . (3)

If wages are completely flexible and all firms face information frictions, then wages will fall at each

level of skills. If there is some floor on wages then the lower wages may not fully offset the fall in

expected revenue and firms will reduce total labor demand.5 Concavity of the production function

is sufficient but not necessary to generate this result. The same result holds if firms are risk-averse,

which may be an appropriate assumption for small firms that cannot easily insure against negative

shocks from low-skilled workers damaging equipment or alienating customers.

This simple framework illustrates that information frictions on either the demand or supply side

of the labor market can generate lower employment and earnings. The framework also illustrates an

important distinction. Eliminating demand-side information frictions can change employment and

earnings without any change in workseekers’ behavior. Eliminating supply-side information frictions

can change employment and earnings only by changing workseekers’ behavior. Our experiment

and data collection are designed both to test for the existence of information frictions and to

differentiate demand- and supply-side frictions. Note that the framework does not deliver a clear

prediction about the incidence of distortions across the skill distribution. Workseekers with higher

skills can experience larger or small distortions under different parameterizations of the production

and utility functions. We view the incidence of distortions as an empirical question and test for

heterogeneous effects of improved information provision on workseekers with different skills.

This framework matches many features of countries with high youth unemployment. Formal

school qualifications are weakly correlated with measured skills and productivity so workseekers

do not learn about their skills during their schooling and cannot signal skills to firms through

4 We assume firms have no market power in the labor or product market so p and w do not depend on the firm’sinput choices. This assumption does not affect the core insights of the framework.

5This wage floor may reflect minimum wages or positive reservation wages from workseekers’ outside options.Even a wage floor at zero may distort hiring decisions if information frictions are very large and there is a positiveprobability that some workseekers will have negative marginal revenue products.

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schooling qualifications (Fedderke, 2005; Pritchett, 2013; Soderbom and Teal, 2004). In the specific

South African context, Lam et al. (2012) show that measured skills are weakly correlated with grade

progression in schools in low-income neighborhoods, consistent with the idea that years of schooling

are a weak signal of skill. Firms rely more on other signals, such as reference letters and referrals,

which exclude young workseekers who lack references from previous work and people without access

to referral networks (Beaman et al., 2018; Calvo-Armengol and Jackson, 2004; Dustmann et al.,

2015; Ioannides and Loury, 2004). Small and new firms, which cannot easily afford complex work

seeker assessment systems, risk making mistakes during hiring and hence grow more slowly and

struggle to challenge incumbents (Hsieh and Olken, 2014).

This framework can be easily generalized to relax a number of simplifying assumptions. We

focus on single-dimensional skills. But the core results are unchanged if skills are multidimensional,

provided at least one dimension is imperfectly observed. We focus on a single-dimensional measure

of search effort: time. But the core results are unchanged if we allow pecuniary costs of search and

perfect credit markets. With pecuniary credit constraints, distortions due to information frictions

are more difficult to characterize and depend on the joint distribution of true skills, noise, and

access to credit. We assume firms have no market power in the labor or product markets. Relaxing

this assumption does not change the prediction that information frictions distort search and hiring

but does make it harder to characterize welfare effects. Perhaps most importantly, we focus on

a static framework that does not allow firms or workseekers to learn. This framework can be

extended to a multiperiod model where firms observe workers’ skills after hiring them, as in Altonji

and Pierret (2001). As long as revelation is not instantaneous or firms incur hiring or firing costs,

the predictions of the framework are unchanged. This simplification is motivated by the fact that

learning cannot occur for unemployed workers and empirical work in the US shows that firms learn

slowly about employed workers’ skills (Arcidiaono et al., 2010; Lange, 2007).

3 Experiment and Setting

We implement a three-arm randomized controlled trial with 6,891 workseekers in South Africa’s

Gauteng province. The trial is directly designed to test predictions from the conceptual framework

in section 2.

We run the trial with the Harambee Youth Employment Accelerator, a large South African

NGO that provides assessment and placement services to large and medium-sized firms. Haram-

bee recruits young workseekers, assesses them, and selects a fraction of assessed workseekers for

further training and placement at corporate partners. Workseekers are eligible for recruitment and

assessment if they are age 18-29, have no criminal record, are South African citizens or have legal

permission to work in South Africa, have graduated from a high school in a disadvantaged neigh-

borhood, have not been formally employed in the past 12 months, and are actively searching for

8

work. Harambee recruits workseekers through advertising in traditional and social media, referrals,

and some door-to-door recruitment.

These criteria yield a sample of unemployed and underemployed youths from low-income homes

who have limited access to traditional signals of productivity: university qualifications, references

from prior employment, or family connections. This is exactly the sample that our framework

suggests should face information frictions. We deliberately focus on a theory-relevant population

rather than seeking a representative sample. We do not claim our results would generalize to the

broader population in South Africa or in other countries. We show in table 12 that respondents

in our sample have a similar employment rate to the province in which we work but have higher

education and are searching more actively. Our sample contains 62% women, perhaps because

employment rates conditional on education are higher for men in South Africa.

We conduct six skills assessments with workseekers and randomly divide workseekers into four

groups:

1. A control group of 2,275 workseekers, who receive no feedback on their assessment results.

2. A private treatment group of 2,114 workseekers, who receive feedback on their assessment

results in two ways: a roughly one-hour group briefing with a psychologist and printed reports

showing their assessment results and briefly explaining the assessments. These reports contain

no identifying information about the work seeker and no branding (figure 1). This treatment

is designed to improve workseekers’ information about their own skills and hence test for

supply-side information frictions.

3. A public treatment group of 2,248 workseekers, who receive the same treatment as the private

group and 30 color-printed reports that contain the work seeker’s name and national identity

number and the brand of the agency and the World Bank (figure 2). This treatment is

designed to improve workseekers’ information about their own skills and their ability to

inform prospective employers about these skills by sharing the reports and hence test for

both supply- and demand-side information frictions. Workseekers in this group can scan or

photocopy their reports and contact the implementing agency to receive additional reports.

They can also direct prospective employers to https://www.assessmentreport.info/, which

provides more information about the skills assessments, sample questions, and research linking

these assessments to workplace productivity.

4. A placebo treatment group of 254 workseekers, who receive the same reports as members of

the public group but with their own assessment results omitted (figure 6). This treatment

is designed to test if any public treatment effect is driven by the information in the skills

assessments or some other feature of the reports. For example, employers may be more likely

to hire workseekers with reports because the branding makes applications more salient or

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because just attending the assessment process conveys a positive signal of workseekers’ dedi-

cation. This treatment group is deliberately small because it is designed to test a competing

mechanism rather than a core prediction of the conceptual framework.

All reports place workseekers in terciles, relative to the population of assessed workseekers,

rather than reporting their absolute scores. We piloted versions of the report with only absolute

scores and only relative scores. The absolute scores provided readers with less information, as they

could not easily anchor absolute scores to real outcomes. Providing both the absolute and relative

scores led to complicated reports that overwhelmed and confused users.

We assess workseekers’ skills in six domains. Detailed information on all six assessments is

available at https://www.assessmentreport.info, including sample questions.

1. Numeracy, which focuses on practical arithmetic and pattern recognition. We calculate a

single numeracy score using the inverse variance-weighted average of two numeracy assessment

scores. One assessment is developed by a South Africa-based adult education agency and is

registered with the South African Qualifications Authority. The other assessment is developed

by a large retail chain and used in their applicant screening process.

2. Literacy and communication, which focuses on comprehension of spoken and written passages.

The assessment is developed by a South Africa-based adult education agency and is registered

with the South African Qualifications Authority.

3. Concept formation, which measures fluid intelligence and has a similar design to the Ravens

Progressive Matrices test. The assessment is developed by a South Africa-based adult edu-

cation agency and is registered with the Health Professionals Council of South Africa.

4. Grit, which a self-reported measure of how much determination candidates show when they

work on challenging problems. Our assessment is adapted from the survey-based measure

discussed in Duckworth et al. (2007).

5. Focus, which measures a candidate’s ability to distinguish relevant from irrelevant information

in potentially confusing environments. Our assessment is a shortened and computerized

version of the widely-used Stroop Test (Stroop, 1935).

6. Planning, which measures how candidates behave when faced with complex, multi-step prob-

lems. Our assessment is adapted from a test proposed by Gneezy et al. (2010).

The first three assessments were developed and validated by South African psychologists. We

developed the final three assessments by piloting measures used by psychologists and behavioral

economists in other contexts, administering the assessments with a sample of more than 100 respon-

dents from our target population, verifying that the individual items are appropriately positively

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Figure 1: Sample Private Report

REPORT ON CANDIDATE COMPETENCIES-Personal Copy-

This report contains results from the assessments you took at Harambee in Phase 1 and Phase 2. These results can helpyou learn about some of your strengths and weaknesses and inform your job search.

You completed assessments on English Communication (listening, reading and comprehension) and Numeracy today inPhase 2. In Phase 1, you completed a Concept Formation assessment which asked you to identify patterns.

1. The Numeracy tests measure various maths abilities. Your score is the average of the two maths tests you did today at Harambee.

2. The Communication test measures English language ability through listening, reading and comprehension.3. The Concept Formation test measures the ability to understand and solve problems. Candidates with high scores

can generally solve complex problems, while lower scores show an ability to solve less complex problems.

You also did some games and questionnaires to measure your soft skills:4. The Planning Ability Test measures how you plan your actions in multi-step problems. Candidates with high

scores generally plan one or more steps ahead in solving complex problems. 5. The Focus Test looks at your ability to pick out which information is important in confusing environments.

Candidates with high scores are able to focus on tasks in distracting situations.6. The Grit Scale measures candidates’ determination when working on difficult problems. Candidates with high

scores spend more time working on the problems rather than choosing to pursue different problems.

Your results have been compared to a large group of young South African job seekers who have a matriccertificate, are from socially disadvantaged backgrounds and have been assessed by Harambee.

You scored in the MIDDLE THIRD of candidates assessed by Harambee for Numeracy, MIDDLE THIRD forCommunication, LOWER THIRD for Concept Formation, LOWER THIRD for Planning Ability, MIDDLETHIRD for Focus and TOP THIRD for the Grit Scale.

DISCLAIMERPlease note that this is a confidential assessment report and is intended for use by the person specified above. Assessment results are not infallible and may not be entirely accurate.

Planning Ability

Notes: This figure shows an example of the reports given to workseekers in the private treatment arm. The reportscontain the work seeker’s assessment results but no identifying information and no branding. This is spacefiller textused to align the position of the figures across pages. It contains no information and should be left hidden usingwhite text.

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Figure 2: Sample Public Report

REPORT ON CANDIDATE COMPETENCIESname.. surname..ID No. id..This report provides information on assessments conducted by Harambee Youth Employment Accelerator (harambee.co.za), a SouthAfrican organisation that connects employers looking for entry-level talent to young, high-potential work-seekers with a matric orequivalent. Harambee has conducted more than 1 million assessments and placed candidates with over 250 top companies in retail,hospitality, financial services and other sectors. Assessments are designed by psychologists and predict candidates’ productivity andsuccess in the workplace. This report was designed and funded in collaboration with the World Bank. You can find more informationabout this report, the assessments and contact details at www.assessmentreport.info. «name» was assessed at Harambee on 13September, 2016.

«name» completed assessments on English Communication (listening, reading, comprehension), Numeracy, and Concept Formation:1. The Numeracy tests measure candidates’ ability to apply numerical concepts at a National Qualifications Framework (NQF) level,

such as working with fractions, ratios, money, percentages and units, and performing calculations with time and area. This score isan average of two numeracy tests the candidate completed.

2. The Communication test measures a candidate's grasp of the English language through listening, reading and comprehension. Itassesses at an NQF level, for example measuring the ability to recognise and recall literal and non-literal text.

3. The Concept Formation Test is a non-verbal measure that evaluates candidates’ ability to understand and solve problems. Thosewith high scores are generally able to solve complex problems, while lower scores indicate an ability to solve less complexproblems.

«name» also completed tasks and questionnaires to assess their soft skills: 4. The Planning Ability Test measures how candidates plan their actions in multi-step problems. Candidates with high scores gener-

ally plan one or more steps ahead in solving complex problems. 5. The Focus Test assesses a candidate’s ability to distinguish relevant from irrelevant information in potentially confusing

environments. Candidates with high scores are generally able to focus on tasks in distracting surroundings, while candidates withlower scores are more easily distracted by irrelevant information.

6. The Grit Scale measures whether candidates show determination when working on challenging problems. Those with high scores generally spend more time working on challenging problems, while those with low scores choose to pursue different problems.

«name»’s results have been compared to a large benchmark group of young (age 18-34) South Africans assessed by Harambee.All candidates have a matric certificate and are from socially disadvantaged backgrounds. The benchmark group is 5,000 forcognitive skills and 400 for soft skills. «name» scored in the «tercile_num» THIRD of candidates assessed by Harambee for Numeracy, «tercile_lit» THIRD forCommunication, «tercile_cft» THIRD for Concept Formation, «tercile_tol» THIRD for Planning Ability, «tercile_troop»THIRD for Focus and «tercile_grit» THIRD for the Grit Scale.

DISCLAIMER: This is a confidential assessment report for use by the person specified above. The information in the report should only be disclosed on a “need to know basis” with the prior understanding of the candidate. Assessment results are not infallible and may not be entirely accurate. Best practice indicates that any organisation’s career management decisions should depend on factors in addition to these assessment results. Harambee cannot accept responsibility for decisions made based on the information contained in this report and cannot be held liable for the consequences of those decisions.

Notes: This figure shows an example of the reports given to workseekers in the public treatment arm. The reportscontain the assessment results, the workseeker’s name and national identity number, and the logo of the World Bankand the implementing agency. Each work seeker received 20 of these reports and guidelines on how to request morereports.

12

correlated with each other, and shortening the test by selecting the items that best predict the

average score.6 All assessments are conducted on desktop computers, so the assessment results

will be driven in part by candidates’ computer skills.

This design yields six cardinal skills measures. We divide candidates into terciles separately for

each skills measure and display these terciles on the reports. We use both the terciles and cardinal

scores in analysis, which we discuss in section 4.

We interact with workseekers over multiple stages:

1. Workseekers apply on date T1 to be assessed by Harambee, after being recruited.

2. Workseekers are invited to a first assessment on date T2. They complete the concept formation

assessment, a career placement tool (see https://www.shadowmatch.com for details), and

receive some basic job search training (e.g. how to write a CV). Surveys are completed at a

computer workstation. Candidates receive some basic training on how to use a mouse and

keyboard, but the computer skills required are very minimal.

3. Workseekers are invited to a second assessment on date T3. They complete the other five

assessments and a self-administered baseline survey completed at a computer workstation.

This asks questions about their demographics, employment, job search, beliefs about their

skills, self-esteem, beliefs about the labor market returns to skills and search effort, risk

preferences, and time preferences.

4. At the end of day T3, workseekers receive any treatment to which they have been assigned.

5. On day T3 +2 or T3 +3, workseekers are invited to participate in a short text message survey.

This surveys asks about perceived numeracy skills and their self-esteem.

6. Starting 2 months after T3, workseekers complete a detailed follow-up survey by phone. This

survey asks about their current and past employment, job search, beliefs about their skills,

and beliefs about the labor market returns to search effort. The date that we first attempt

to survey workseekers is randomized between 2 and 4 months.

The time lags between T1, T2, and T3 are determined by Harambee’s operations and may last

several month. The assessment dates are based on Harambee’s perception about labor demand

in specific neighborhoods. The time lags will be smaller if workseekers apply during periods of

high labor demand or live in neighborhoods where there is high labor demand. This recruitment

schedule means that some workseekers will complete stage 1 but not proceed to stage 2 or stage 3.

6For the first 23% of the workseekers we assessed, we used self-reported measures of control and flexibility insteadof the focus and planning assessments. The control scale is a self-reported measure of how candidates react whenconfronted with new and confusing problems. The flexibility scale is a self-reported measure of how candidatestackle problems with several potential solution approaches. We adapted both scales from an instrument developedby Hepner and Petersen (1982) and validated them for our target population.

13

Hence our sample at T3, relative to T1, is likely to contain fewer workseekers who are at the margin

of employment and unemployment (they get jobs in the meantime) and who are at the margin

between searching and discouragement (they withdraw from search). Our randomization is based

on workseekers who reach stage 3 so this attrition may affect the generalizability of our results but

not their internal validity.

We implemented the treatment for 84 days between 22 September 2016 and 13 April 2017. We

conducted the phone follow-up survey between 30 November 2016 and 5 September 2017.

Harambee invites some workseekers after date T3 for further training and placement at a corpo-

rate partner. These workseekers are selected based on strong performance in the concept formation

and numeracy assessments and their results in the career placement tool. Only a small fraction of

workseekers are invited for further training and placement and these invitations are independent

of treatment assignment.

We randomize treatment at the day level using a sequential blocking algorithm:

1. We divide the first 18 days of the trial intro 2 blocks of 9 days. We then randomly assign 3

days from each block to control, private treatment, and public treatment.

2. Near the end of block 2, we repeat the randomization process for a third block. We then

repeat this process near the end of blocks 3 to 9 for blocks 4 to 10.

3. If we cannot implement one day of the intervention (e.g. due to power failures or network

problems that mean the baseline cannot be administered) in block B, then we add another

day to block B+1. We then randomly assign one extra day from block B+1 to the same

treatment status as the dropped day from block B.

The first 7 blocks each contain 9 or 10 days. The last 3 blocks contain 6, 13, and 7 days as the

exact end date of the intervention was subject to ongoing negotiation with Harambee. All placebo

treatment days are in block 9. Table 13 shows that the treatment groups are balanced on key

baseline characteristics.

We report summary statistics for key baseline and follow-up variables in Tables 1 and 2. At

both baseline and endline, respondents are actively searching and expect to continue searching.

The baseline employment rate is 38% and, conditional on working, respondents earn an average

of USD90 per week and only 10% of them work full-time. The endline employment rate drops to

32% and employed workers now work slightly more hours and earn roughly the same. Very few

workers have any sort of written contract and 44% work for themselves or family members. This

clearly shows that most of our respondents are strongly attached to the labor market but have low

employment rates and are seldom in “high quality” jobs.

The attrition rates for the text message and phone surveys are respectively 18.6 and 4.1%.

Attrition does not differ by treatment arm, as we show in Appendix Table 15. Attrition does

14

Table 1: Baseline Summary StatisticsBaseline Variable # Obs Mean Std Dev. Min 10th pctile 90th pctile MaxAge 6891 23.6 3.3 18.0 19.8 28.3 35.3Male 6891 0.382 0.486 0 . . 1Completed diploma or degree 6891 0.167 0.373 0 . . 1Completed high school 6891 0.610 0.488 0 . . 1Self-esteem index 6891 0.192 .998 -3.598 -1.347 1.251 1.424# job applications in past 30 days* 6815 9.9 18.6 0 2 20 400Hours of job search in past 7 days* 6699 16.7 19.6 0 2 48 96# planned job applications in next 30 days* 6840 17.8 25.5 0 4 36 190Worked in the past 7 days 6891 0.378 0.485 0 . . 1Hours worked in past 7 days* 2508 13.9 15.0 0 3 40 72Total earnings in past 7 days* 2116 559.9 712.1 0 100 1400 4000Hourly wages in past 7 days 2076 64.3 84.9 0 7.5 160 500

This table shows summary statistics for selected baseline variables. Variables marked with * have been winsorized at the 99thpercentile. Earnings and wages are reported in South African Rands; 1 Rand ≈ USD0.16 in purchasing power parity terms.

Table 2: Endline Summary StatisticsEndline Variable # Obs Mean Std Dev. Min 10th pctile 90th pctile Max

Self-esteem index 6609 -0.056 0.858 -3.598 -1.17 .905 1.424Any job search activity in last 30 days 6608 0.918 0.274 0 . . 1Any job search activity in last 7 days 6608 0.692 0.462 0 . . 1# job applications submitted in last 30 days 6577 12.799 21.467 0 1 27 200Hours spent searching for work in past 7 days 6601 9.935 14.196 0 0 25 125Worked in past 7 days 6607 0.323 0.468 0 . . 1Worked since treatment 6607 0.690 0.462 0 . . 1Worked in 1st calendar month after treatment 6604 0.482 0.500 0 . . 1Worked in 2nd calendar month after treatment 6607 0.454 0.498 0 . . 1Hours worked for all jobs in past 7 days 2121 28.4 21.1 0 4 56 91Total earnings from all jobs in past 7 days* 2112 590.4 928.7 0 2 1500 5300Hourly wages from all jobs in past 7 days* 2097 31.216 52.981 0 .111 77.8 375Have written permanent job contract 2100 0.035 0.183 0 . . 1Employed in wage job 2100 0.563 0.496 0 . . 1Self-employed 2100 0.251 0.434 0 . . 1

This table shows summary statistics for selected endline variables. Variables marked with * have been winsorized at the 99thpercentile. Earnings and wages are reported in South African Rands; 1 Rand ≈ USD0.16 in purchasing power parity terms.

differ over some baseline characteristics: male respondents are more likely to attrite and, in the

text message survey, attrition is higher for those with lower numeracy and concept formation skills

(Appendix Table 15).

4 Average Treatment Effects

We estimate average treatment effects using the following regression model:

Yid = Privateid · β1 + Publicid · β2 + Sd + Xid · µ+ εid (4)

where Privateid and Publicid are treatment group indicators for work seeker i who completed the

intervention on date d, Sid is a vector of randomization block fixed effects, and Xid is a vec-

15

tor of control variables from the baseline survey and assessment process.7 Standard errors are

heteroskedasticity-robust and clustered by intervention date – the unit at which treatment is as-

signed. We report only intention-to-treat estimates and do not construct a measure of treatment

take-up such as using public reports with job applications, as our conceptual framework predicts

that both treatments can affect outcomes through multiple mechanisms.

Building on the conceptual framework in section 2, we test if the average effects of the public and

private treatment are separately equal to zero (β1 = 0 and β2 = 0), equal to each other (β1 = β2),

and jointly equal to zero (β1 = β2 = 0). We report p-values for these tests using the cluster-

and heteroskedasticity-robust standard errors. To account for false discovery rates in multiple

hypothesis testing, we also report the corresponding q-values that control the false discovery rate

with each family of outcomes, following Benjamini et al. (2006).8

4.1 Summary index results

We begin by estimating the public and private treatment effects on seven outcome families, three to

four months after treatment. We classify all outcomes into seven families and construct a summary

index for each family, as an inverse-covariance weighted average of the outcomes in the family

Anderson (2008). We examine beliefs about own skills, beliefs about returns to job search, beliefs

about future earnings, search effort, search effectiveness, employment status, and employment

quality.9. This gives us a general overview of the types of outcomes affected by treatment, and

guides our discussion of individual outcomes within these families in the next subsections.

We find that both treatments have large effects on beliefs about own skills, small effects on

beliefs about the returns to job search, and little effect on beliefs about earnings, search effort, search

effectiveness, and employment quality. The public treatment substantially increases employment

status but the private treatment does not. These results show that workseekers do face limited

information about their skills. Providing new information about their skills changes their beliefs

but only changes employment outcomes when they can communicate this information to firms. The

lack of results on search suggests that the key margin is changes in firm behavior, though we add

some caveats to this interpretation in subsection 4.3.

7We prespecified baseline controls for numeracy, communication, concept formation, and grit scores; the firstprincipal component of focus and planning scores; self-esteem score; expected numeracy score; expected tercile of thecommunication score; expected tercile of the concept formation score; age; gender; an indicator for having educationabove the sample median; and an indicator for working in the week before baseline. We selected these controls from alarger list by running a LASSO algorithm for each pre-specified outcome and including all controls that were selectedby the LASSO for at least 50 percent of the outcome measures (Tibshirani, 1996).

8We do not correct for adjust testing across outcome families. These measure different types of outcomes thatmay be affected in different ways by our treatments.

9We first standardize each outcome to have mean zero and standard deviation one–if necessary, we recode their signto make the direction of all outcomes uniform and replace missing values with zeroes. We then calculate a weightedaverage of the demeaned primary outcomes in each domain, where the weights equal the diagonal elements of theinverse covariance matrix. Each index has mean zero but their standard deviations vary; indices whose componentsare more strongly correlated will have lower variance.

16

Table 3: Effects of Public and Private Treatments on Outcome Indices(1) (2) (3) (4) (5) (6) (7)

Beliefs about . . . Search . . . Employment . . .Skill Search Earnings Effort Effective Status Quality

Public 0.706∗∗∗ 0.079∗∗ 0.002 0.011 0.030 0.132∗∗∗ 0.018(0.036) (0.025) (0.029) (0.031) (0.034) (0.025) (0.027)

Private 0.558∗∗∗ 0.045∗ 0.012 0.013 0.031 0.047 0.014(0.033) (0.026) (0.027) (0.033) (0.031) (0.029) (0.025)

# observations 6367 6367 6367 6367 6367 6367 6367# clusters 81 81 81 81 81 81 81R-squared 0.293 0.055 0.306 0.083 0.046 0.115 0.015Control mean 0.000 0.000 -0.000 -0.000 0.000 0.000 0.000

p: Pub = 0 0.000 0.003 0.950 0.739 0.369 0.000 0.515p: Pvt = 0 0.000 0.091 0.655 0.698 0.311 0.108 0.563p: Pub = Pvt 0.000 0.189 0.672 0.936 0.977 0.001 0.900p: Pub = Pvt = 0 0.000 0.010 0.871 0.918 0.527 0.000 0.769

Outcomes are expressed in standard deviations of summary indices (Anderson, 2008). All re-gressions include randomization block fixed effects and baseline values of the outcome; education;all assessment results; beliefs about numeracy, communications & concept formation results; age;gender; and employment status. Heteroskedasticity-robust standard errors clustered by treatmentday are shown in parentheses. Lower rows of the table show p-values for linear combinations ofcoefficients.

4.2 Effects on Employment

The public treatment increases both the employment status index and each component of the index

(Table 4). Respondents are 5 percentage points more likely to have worked in the week before the

phone survey and at any point since the baseline, off bases of respectively 30 and 67% (columns 1

and 2). They worked 16 percent more hours in the preceding week (column 3). And they found

work soon after treatment: they are more likely to have worked in the first or second month after

treatment (columns 5 and 6).10 The private treatment effects are uniformly positive but always

smaller than the public treatment effects, seldom statistically significantly different to zero, and

often statistically significantly smaller than the public effects.

The public treatment does not affect the full employment quality index but does affect several

components (Table 5). The public treatment raises earnings in the past week by 32% and hourly

wages by 20% (columns 1 and 2). The public treatment has no effect on the formality of the job

(columns 3 and 4) and little effect on the probability of wage employment versus self employment

10Given variation in the length of time between baseline and phone survey, we only observe 3,230 workseekers in thesample four months after treatment, enabling us to calculate effects on employment one, two and three months aftertreatment. For the remainder, the phone survey is three months after baseline, which only enables us to calculateeffects on employment after one and two months. The effects after three months are similar in size to those after oneand two months but are not statistically significantly different from zero because the sample is smaller.

17

Table 4: Effects of Public and Private Treatments on Employment Quantity

(1) (2) (3) (4) (5) (6)Worked . . . Hours worked Worked in . . . month

past week since baseline in past week First Second Third

Public 0.052∗∗∗ 0.052∗∗∗ 1.339∗ 0.035∗∗∗ 0.058∗∗∗ 0.038(0.012) (0.010) (0.571) (0.011) (0.014) (0.022)

Private 0.011 0.031∗ 0.525 0.027 0.009 0.010(0.012) (0.013) (0.513) (0.013) (0.015) (0.021)

# observations 6365 6365 6356 6362 6365 3230# clusters 81 81 81 81 81 81R-squared 0.041 0.089 0.034 0.095 0.074 0.057Control group mean 0.309 0.671 8.794 0.465 0.437 0.403Control mean | employed 28.672

p: Pub = 0 0.000 0.000 0.021 0.002 0.000 0.082q: Pub = 0 0.001 0.001 0.014 0.002 0.001 0.031p: Pvt = 0 0.348 0.021 0.309 0.035 0.565 0.618q: Pvt = 0 0.771 0.142 0.771 0.142 0.792 0.792p: Pub = Pvt 0.002 0.097 0.152 0.457 0.001 0.210q: Pub = Pvt 0.005 0.193 0.236 0.354 0.004 0.267p: Pub = Pvt = 0 0.000 0.000 0.070 0.006 0.000 0.204q: Pub = Pvt = 0 0.001 0.001 0.044 0.007 0.001 0.109

All regressions include randomization block fixed effects and baseline values of the outcome; educa-tion; all assessment results; beliefs about numeracy, communications & concept formation results;age; gender; and employment status. Heteroskedasticity-robust standard errors clustered by treat-ment day are shown in parentheses. Lower rows of the table show p- and q-values for linearcombinations of coefficients. The q-values adjust for multiple testing over all outcomes in the samedomain following Benjamini et al. (2006).

or family employment (columns 5 and 6).11 The private treatment substantially increases earnings

but has no effect on formality or employment type.

The earnings and wage effects may occur at the extensive margin – more workseekers are

employed and hence have positive earnings – or the intensive margin – workseekers who would

have jobs anyway earn more. We show that these effects are separately identified using a simple

derivation. Setting earnings for the non-working equal to zero and restricting attention to the

11The outcome in column 5 is a binary variable equal to one if the respondent has wage employment and zero ifthe respondent has self employment, family employment, or no employment. The outcome in column 6 is equal toone if the respondent has self employment and zero otherwise.

18

Table 5: Effects of Public and Private Treatments on Employment Quality

(1) (2) (3) (4) (5) (6)In past week . . . Contract type . . . Employment type

earnings wages written permanent wage self

Public 49.635∗∗∗ 1.782∗ 0.020 0.004 0.024∗ 0.010(17.892) (0.830) (0.010) (0.003) (0.012) (0.008)

Private 43.855∗∗∗ 1.009 0.017 0.005 0.022 -0.012(18.183) (1.013) (0.009) (0.003) (0.011) (0.007)

# observations 6347 6333 6333 6333 6333 6333# clusters 81 81 81 81 81 81R-squared 0.027 0.022 0.014 0.004 0.023 0.021Control mean 155.163 8.731 0.120 0.009 0.172 0.081Control mean | employed 504.858 28.645 0.392 0.029 0.562 0.265

p: Pub = 0 0.007 0.035 0.055 0.266 0.049 0.187q: Pub = 0 0.116 0.211 0.211 0.500 0.211 0.454p: Pvt = 0 0.018 0.322 0.072 0.148 0.043 0.080q: Pvt = 0 0.158 0.210 0.158 0.177 0.158 0.158p: Pub = Pvt 0.789 0.465 0.771 0.710 0.903 0.001q: Pub = Pvt 1.000 1.000 1.000 1.000 1.000 0.014p: Pub = Pvt = 0 0.007 0.104 0.091 0.307 0.069 0.004q: Pub = Pvt = 0 0.038 0.167 0.166 0.241 0.152 0.038

All regressions include randomization block fixed effects and baseline values of the outcome; educa-tion; all assessment results; beliefs about numeracy, communications & concept formation results;age; gender; and employment status. Heteroskedasticity-robust standard errors clustered by treat-ment day are shown in parentheses. Lower rows of the table show p- and q-values for linearcombinations of coefficients. The q-values adjust for multiple testing over all outcomes in the samedomain following Benjamini et al. (2006).

control and public treatment groups, and we can write

E[Earn|Public = 1]− E[Earn|Public = 0]︸ ︷︷ ︸≡A

= E[Earn|Public = 1,Work = 1]− E[Earn|Public = 0,Work = 1]︸ ︷︷ ︸≡B

·Pr[Work = 1|Public = 1]︸ ︷︷ ︸≡C

+ E[Earn|Public = 0,Work = 1]︸ ︷︷ ︸≡D

· (Pr[Work = 1|Public = 1]− Pr[Work = 1|Public = 0])︸ ︷︷ ︸≡F

Term A is the average treatment effect on earnings, term B is the average treatment effect on

earnings conditional on working, term C is the level of employment in the public treatment group,

term D is the average earnings for employed members of the control group, and term F is the

treatment effect on employment. Term B is not identified in standard experiments, as introduce

bias when we condition on the potentially selected sample of working respondents. However, the

other four terms are identified, so we can recover B = (A−D · F )/C. If the earnings effect occurs

19

only at the extensive margin, then B = 0; if the earnings effect occurs only at the intensive margin,

then F = 0.

Applying this idea, we find that the average public treatment effect on weekly earnings for

workers employed in the public treatment group is 66 South African Rands (standard error 27),

equal to 12% of baseline weekly earnings for those with jobs. The effect for hourly wages is 0.8

South African Rands (standard error 1.2), equal to 2% of baseline hourly wages for those with

jobs. Taken together, these results show that the public treatment affects earnings by increasing

employment and hours but has little effect on hourly wages. To the extent that wages are a measure

of match quality, it does not appear that the public treatment is substantially improving match

quality.

4.3 Effects on Beliefs and Search

In this section we explore the mechanisms driving the employment effects. Our conceptual frame-

work predicts that the private treatment can change employment outcomes by changing workseekers

beliefs about their skills, hence changing their expected returns to search and/or expected earn-

ings, and hence changing their search effect and/or effectiveness. The public treatment can work

through the same mechanisms but may also change employment by changing firms’ responses to

workseekers’ search activities.

We first discuss treatment effects on respondents’ beliefs about their ability. We compare

workseekers’ beliefs three or four months after treatment, at the time of the phone survey, to their

actual test scores. Then we estimate effects on the proportion of respondents who place themselves

in the correct tercile on the six skills measures.12 Our results, in Table 6, show robust and large

increases in the accuracy of respondents beliefs about their skills that are slightly larger for the

public treatment. Both treatments increase the number of domains in which respondents place

themselves in the correct tercile for all assessments (column 1) and decrease the number of domains

in which they place themselves in a higher tercile or lower tercile (columns 2 and 3). Effects on

over-confidence are larger than on under-confidence, but this may be driven by the higher levels of

over- than under-confidence at baseline. The public effects are uniformly and significantly larger

than the private effects.

We find evidence suggesting that the larger impact of the public treatment may arise from

reinforcement of effects on beliefs when workseekers find work. For one test score, numeracy, we

collected the respondents’ beliefs about which tercile they fall into immediately after treatment

via SMS. In Table 7, we observe that immediately after treatment, the public and private treat-

ment effects on beliefs are nearly identical (column 1). The difference in treatment effects on

beliefs between public and private treatments only arises after three to four months (column 2).

12The measures range from 0 for those who are inaccurate on all skills to 3 for those who are accurate on all skills.

20

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test

ing

over

all

outc

om

esin

the

sam

efa

mily

follow

ing

Ben

jam

ini

etal.

(2006).

21

Our interpretation is that as the public reports increase likelihood of employment, workseekers

receive feedback that the information on the report is accurate thus reinforcing their beliefs.13 Nei-

ther treatment significantly changes overall self-esteem in either the text message or phone survey

(columns 3 and 4). Effects on beliefs about ability do not spill over onto general self-worth, either

immediately after treatment or three to four months later.

Table 7: Reconciling Differential Belief Effects(1) (2) (3) (4)

Correct numeracy belief in . . . Above-median self-esteemText survey Phone survey Text survey Phone survey

Public treatment 0.317 0.231 -0.003 0.002(0.015) (0.016) (0.014) (0.015)

Private treatment 0.333 0.203 0.010 -0.000(0.017) (0.016) (0.015) (0.017)

# respondents 5088 5088 0.4861 0.4861# clusters 81 81 81 81Control mean 0.400 0.404 0.487 0.481p: Pub = Pvt 0.219 0.039 0.414 0.896p: Pub effects equal 0.000 0.777p: Pvt effects equal 0.000 0.585p: Differences equal 0.014 0.480

All regressions include randomization block fixed effects and baseline values of the outcome; education; all assessment re-sults; beliefs about numeracy, communications & concept formation results; age; gender; and employment status. Sample isrestricted to respondents with valid responses from both survey rounds. Round-specific models are estimated as a system.Heteroskedasticity-robust standard errors clustered by treatment day are shown in parentheses. Lower rows of the table showp–values for linear combinations of coefficients.

Effects on workseekers’ beliefs about their returns to search effort are smaller but significant,

and similar for both treatment groups. We asked workseekers the number of applications they

expect to submit in next 30 days and how long they believe they will take to find a formal job

if they submit that many applications, as well as half and twice as many applications. We then

calculate the number of months the workseekers expect it will take for them to get a job, per job

application. The public treatment substantially reduces the expected number of months required to

get a job (Table 6 column 5), while the private treatment has a smaller and statistically significant

effect. (Effects on time to employment with more and fewer applications have the same signs but

are not precisely estimated.)

Neither treatment has any effect on any measure of expected earnings conditional on full-time

employment. The public treatment effects on beliefs are qualitatively consistent with the actual

treatment effects: the treatment increases both actual and expected employment over short time

horizons while having little effect on either actual or expected wages conditional on hours worked.

Consistent with our summary index results, we find no significant effects of either the public

or private treatment on any of the component measures of the job search effort and effectiveness

13Our evidence favors this explanation, but there are a number of other potential reasons. Workseekers may believethe information on the public reports is more credible because it is branded and color-printed. Or information on thepublic reports may be more salient as workseekers may continue to see the information on the reports as they attachthem to job applications. We cannot directly rule out these explanations but we provide some evidence against themin the appendices when we discuss the placebo reports.

22

indices (Appendix Tables 16 and 17). There are no large or statistically significant effects on

whether workseekers searched for jobs in the week prior to the phone survey, hours of search in the

last week, spending on search in the last week, the number of applications submitted in the last 30

days.14 Effects on all measures of search effectiveness are fairly precisely estimated zeros, including

on the number of employer responses of any form, number of offers received, the ratio of employer

responses to the number of applications, and the ratio of offers to number of applicants.

It is possible that by the time of the phone survey, some workseekers may have stopped searching

altogether or be searching less as a result, even if they had searched harder or more effectively

immediately after treatment. Indeed, in table 4 column 4, we showed a significant effect of the

public treatment on employment one month later (and potentially sooner). To test this idea, we

compare search and employment effects across workseekers surveyed in the first and second halves

of the survey period (Table 8). We find that search effects decrease over time, though the differences

are small and imprecisely estimated (columns 1 and 2). This result is robust to instrumenting the

survey date with the randomly assigned target date for the survey.

These results show that allowing workseekers to signal their skills to firms by providing them

with certified skill assessments substantially increases their employment rate and earnings. This

shows that demand-side information frictions can distort hiring decisions and reduce employment

and earnings. Providing information on its own does change work seeker beliefs but this has limited

to no effects on their job search effort or effectiveness or their employment outcomes. Supply-side

information frictions seem to result in only limited distortions in job search decisions and outcomes.

5 Heterogeneous Treatment Effects

5.1 Heterogeneity by Skill

Our conceptual framework predicts that employment effects may differ by skill level, though the

precise incidence of information frictions through the skill distribution depends depend on specific

modeling choices. We explore this hypothesis by constructing an index of the numeracy, commu-

nication, and concept formation scores following Anderson (2008). We then regress employment

outcomes on the skills index using a local linear regression, separately by treatment group, and

show the fitted values in figures 3, 4, and 5.

These results show a strong relationship between the skill index and all employment status

measures. The employment quantity index, hours worked in the preceding 7 days, employment in

the preceding 7 days and employment since treatment are weakly increasing in skill throughout most

of the distribution, though the relationship is sometimes decreasing in the tails, for all treatment

groups. In addition, there are public treatment effects for all these measures: all are higher for

14This may be partially because overall search levels are high. Even in the control group 70 percent of workseekerswere actively searching.

23

Figure 3: Effects of Public and Private Treatments on Employment by Skills

−.1

0.1

.2.3

Em

plo

ym

ent quantity

index

−1 −.5 0 .5 1 1.5Skills index

Control Private Public

−.1

0.1

.2.3

Em

plo

ym

ent qualit

y index

−2 −1 0 1 2Skills index

Control Private Public

Notes: This figure shows the fitted values from local linear regressions of selected employment outcomes on the skillsindex. Regressions are truncated at the 5th and 95th percentiles of the skills index. Vertical lines show the 33rd and67th percentiles of the skills index. Employment index construction is described in the text and follows Anderson(2008). This is filler text used to align figures. This is filler text used to align figures. This is filler text used to alignfigures. This is filler text used to align figures.

24

Figure 4: Effects of Public and Private Treatments on Employment by Skills

.25

.3.3

5.4

.45

Work

ed in last 7 d

ays

−1 −.5 0 .5 1 1.5Skills index

Control Private Public

.6.7

.8.9

Work

ed s

ince tre

atm

ent

−1 −.5 0 .5 1 1.5

Skills index

Control Private Public

Notes: This figure shows the fitted values from local linear regressions of selected employment outcomes on the skillsindex. Regressions are truncated at the 5th and 95th percentiles of the skills index. Vertical lines show the 33rd and67th of the skills index. This is filler text used to align figures. This is filler text used to align figures. This is fillertext used to align figures. This is filler text used to align figures. This is filler text used to align figures.

25

Figure 5: Effects of Public and Private Treatments on Employment by Skills

68

10

12

Hours

work

ed in last 7 d

ays

−1 −.5 0 .5 1 1.5Skills index

Control Private Public

150

200

250

300

Tota

l earn

ings in last 7 d

ays

−1 −.5 0 .5 1 1.5Skills index

Control Private Public

Notes: This figure shows the fitted values from local linear regressions of selected employment outcomes on the skillsindex. Regressions are truncated at the 5th and 95th percentiles of the skills index. Vertical lines show the 33rd and67th of the skills index. Hours and earnings are both reported for the preceding seven days, asked separately aboutmain and othe jobs and then combined, winsorized at their 99th percentiles, and set to zero for respondents who didnot work in the preceding seven days.

26

Table 8: Dynamic Effects and Survey Timing(1) (2) (3) (4)

Search Effort Search Effective Work Status Worked in past 7 daysPublic × Early 0.047 0.067 0.152∗∗∗ 0.052∗∗

(0.053) (0.044) (0.036) (0.018)Public × Late -0.042 -0.017 0.112∗∗ 0.054∗∗

(0.042) (0.036) (0.040) (0.018)Private × Early 0.058 0.066 0.047 0.017

(0.051) (0.044) (0.040) (0.021)Private × Late -0.049 0.007 0.047 0.007

(0.040) (0.035) (0.039) (0.016)Late Phone Survey 0.037 -0.020 0.018 0.012

(0.048) (0.033) (0.042) (0.018)# respondents 6367 6367 6367 6365# clusters 81 81 81 81R2 0.041 0.017 0.115 0.041p: Average Public = 0 0.453 0.191 0.000 0.000p: Public × Late = 0 0.381 0.127 0.000 0.006p: Public × Early = 0 0.328 0.644 0.007 0.003p: Average Private = 0 0.230 0.326 0.272 0.622p: Private × Early = 0 0.258 0.137 0.250 0.417p: Private × Late = 0 0.230 0.849 0.240 0.670

All regressions include randomization block fixed effects and baseline values of the outcome; education; all assessment re-sults; beliefs about numeracy, communications & concept formation results; age; gender; and employment status. Sample isrestricted to respondents with valid responses from both survey rounds. Round-specific models are estimated as a system.Heteroskedasticity-robust standard errors clustered by treatment day are shown in parentheses. Lower rows of the table showp–values for linear combinations of coefficients.

the public group than the private or control groups for most of the skills distribution. In contrast,

the relationship between the private and control groups varies over the distribution and is often

fairly similar. On earnings, there is a less strong relationship between skills and earnings. This is

consistent with a labor market where entry-level wages for workseekers with limited education and

little work experience may be fairly fixed regardless of work seeker ability.

However, there is little evidence of heterogeneous treatment effects: the gap between the public

and other groups is similar at all levels of the skills distribution. We test this idea more formally

by estimating models of the form

Yid = Privateid ·β1 +Publicid ·β2 +Privateid ·Highid ·γ1 +Publicid ·Highid ·γ2 +Sd+Xid ·µ+εid (5)

where Highid is an indicator equal to one for workseekers with above-median values of the skills index

and is included in the control vector Xid. We report the estimated coefficients of this regression in

columns 1 and 2 of Table 9. We gain find little evidence of heterogeneous treatment effects by skill.

5.2 Heterogeneity by Skill or Signal?

Treatment effects may differ by skill for two reasons. First, workseekers with different skills receive

different information about themselves and are able to send a different signal to firms. Second,

workseekers with different skills may use information and technology differently or face different

information frictions. For example, more skilled workseekers may be better at understanding the

27

Tab

le9:

Het

erog

eneo

us

Tre

atm

ent

Eff

ects

by

Skil

lan

dP

rod

uct

ivit

yM

easu

res

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Sta

tus

ind

exQ

uality

ind

exS

tatu

sin

dex

Qu

ality

ind

exS

tatu

sin

dex

Qu

ality

ind

exS

tatu

sin

dex

Qu

ality

ind

exP

ub

lic×

Hig

h-0

.034

0.0

15

-0.0

58

0.0

49

-0.0

39

0.0

86

0.0

36

0.0

05

(0.0

49)

(0.0

61)

(0.0

52)

(0.0

69)

(0.0

67)

(0.0

76)

(0.0

66)

(0.0

81)

Pu

blic

0.1

40∗∗∗

0.0

67∗

0.1

42∗∗∗

0.0

40

0.1

38∗∗∗

0.0

42

0.1

18∗∗∗

0.0

74∗∗

(0.0

34)

(0.0

37)

(0.0

34)

(0.0

39)

(0.0

33)

(0.0

31)

(0.0

26)

(0.0

34)

Pri

vate×

Hig

h-0

.040

0.0

72

-0.0

32

-0.0

29

-0.0

66

-0.0

89

-0.0

00

0.0

23

(0.0

44)

(0.0

60)

(0.0

54)

(0.0

67)

(0.0

61)

(0.0

58)

(0.0

84)

(0.0

97)

Pri

vate

0.0

78∗∗

0.0

27

0.0

64∗

0.0

66

0.0

82∗∗

0.0

98∗∗

0.0

59∗∗

0.0

59

(0.0

36)

(0.0

44)

(0.0

37)

(0.0

46)

(0.0

33)

(0.0

40)

(0.0

28)

(0.0

36)

Het

erogen

eity

mea

sure

Skill

ind

exE

mp

loyab

ilit

yin

dex

Base

lin

eem

plo

ym

ent

Deg

ree/

dip

lom

a#

resp

on

den

ts6367

6367

6367

6367

6367

6367

6367

6367

#cl

ust

ers

81

81

81

81

81

81

81

81

R2

0.0

81

0.0

15

0.0

82

0.0

15

0.0

81

0.0

15

0.0

81

0.0

14

All

regre

ssio

ns

incl

ud

era

nd

om

izati

on

blo

ckfi

xed

effec

tsan

db

ase

lin

evalu

esof

the

ou

tcom

e;ed

uca

tion

;all

ass

essm

ent

resu

lts;

bel

iefs

ab

ou

tnu

mer

acy

,co

mm

un

icati

on

s&

con

cep

tfo

rmati

on

resu

lts;

age;

gen

der

;an

dem

plo

ym

ent

statu

s.H

eter

osk

edast

icit

y-r

ob

ust

stan

dard

erro

rscl

ust

ered

by

trea

tmen

td

ay

are

show

nin

pare

nth

eses

.C

olu

mn

s1-4

show

inte

ract

ions

bet

wee

ntr

eatm

ent

ind

icato

rsan

din

dic

ato

rsfo

rab

ove-

med

ian

valu

esof

the

skill

ind

exor

late

nt

pro

bab

ilit

yof

emp

loym

ent

ind

ex.

Colu

mn

s1-4

show

inte

ract

ion

sb

etw

een

trea

tmen

tin

dic

ato

rsan

din

dic

ato

rsfo

rw

ork

ing

at

base

line

or

havin

ga

un

iver

sity

deg

ree/

dip

lom

aat

base

lin

e.

28

information, may convey information about themselves better in interviews, or use the reports more

effectively. We call these two effects respectively treatment heterogeneity effects and treatment effect

heterogeneity.

Identifying treatment heterogeneity effects requires comparing workseekers with similar skills

who receive different information. We use a regression discontinuity to compare workseekers at the

margin of being placed in different terciles.15 We stack the two cutoffs so each workseeker may be

used twice in the analysis. Given the low power of regression discontinuity designs, these results

should be treated with caution.

In the public group, crossing a threshold increases the employment status index, probability

of current employment, and current earnings, though the first two effects are only marginally

statistically significant (Table 10). There are no similar effects for the private or control groups.

We see no effects on any measures of search behavior. These results are consistent with a role for

treatment heterogeneity effects, in which workseekers who can send more positive reports to firms

benefit more.

Table 10: Effect of Treatment Heterogeneity by SkillIndex of employment . . . In past 7 days . . .status quality worked earnings

Public 0.147 -0.018 0.058 103.2(0.085) (0.073) (0.037) (37.7)

p: Pub = 0 0.084 0.805 0.117 0.006# observations 4496 4496 4322 4476# clusters 81 81 81 81Private -0.016 -0.129 -0.042 -92.0

(0.061) (0.127) (0.029) (67.9)p: Pvt = 0 0.793 0.310 0.148 0.176# observations 4228 4228 4040 4216# clusters 81 81 81 81Control -0.075 0.021 -0.004 -20.3

(0.088) (0.063) (0.045) (39.0)p: Control = 0 0.394 0.739 0.929 0.603# observations 4550 4550 4368 4542# clusters 81 81 81 81

Outcomes in columns 1 - 2 are expressed in standard deviations of domain-level indices (Anderson, 2008). Results are obtainedby stacking the top-middle and middle-low tercile cutoffs for the numeracy assessment, recentering each cutoff at zero, andestimating discontinuities separately by treatment group. All results are generated using local linear regressions with triangularkernels and the robust bandwidth estimator from Calonico et al. (2014). Heteroskedasticity-robust standard errors clusteredby treatment day are shown in parentheses. Lower rows of the table show p-values for linear combinations of coefficients.

Identifying treatment effect heterogeneity requires comparing workseekers with different skills

who receive the same information. We thus compare workseekers in the top and bottom half of

each tercile. We focus only on numeracy scores for consistency with the previous analysis, though

results are again robust to including other scores. We find little evidence of either public or private

treatment effect heterogeneity: there are no significant differences in treatment effects between

workseekers with higher or lower skills, within a tercile.

15We only use the numeracy assessment for this exercise because it is the least coarse score. Results are similarwhen we add the grit and concept formation scores, which are less coarse than the communication score. When weuse all three scores, we stack all six cutoffs.

29

Table 11: Treatment Effect Heterogeneity by SkillIndex of employment . . . In past 7 days . . .status quality worked earnings

Public 0.105 0.012 0.041 53.6(0.047) (0.046) (0.021) (37.8)

Public * High -0.002 0.002 -0.003 4.3(0.059) (0.065) (0.027) (42.5)

Private -0.024 -0.020 -0.024 13.8(0.050) (0.042) (0.022) (35.2)

Private * High 0.067 0.056 0.040 60.9(0.056) (0.067) (0.026) (45.0)

High 0.001 -0.005 0.007 -22.4(0.045) (0.039) (0.021) (28.4)

# respondents 6367 6367 6365 6347# clusters 81 81 81 81R-squared 0.105 0.010 0.038 0.013Control mean 0.000 -0.000 0.309 152.9Control mean | employed 504.9p: Pub = 0 0.025 0.775 0.062 0.127p: Pub × High = 0 0.973 0.975 0.912 0.920p: Pvt = 0 0.631 0.634 0.275 0.695p: Pvt × High = 0 0.232 0.403 0.124 0.176

Outcomes in columns 1 - 2 are expressed in standard deviations of domain-level indices (Anderson, 2008). All regressions includerandomization block fixed effects and baseline values of the outcome; education; all assessment results; beliefs about numeracy,communications & concept formation results; age; gender; and employment status. Heteroskedasticity-robust standard errorsclustered by treatment day are shown in parentheses. Lower rows of the table show p-values for linear combinations of coefficients.

5.3 Heterogeneity by Other Potential Productivity Correlates

Even if treatment effects differ only weakly by skill, they may differ with other covariates that

either predict workplace productivity or are perceived by firms to predict workplace productivity.

We estimate the parameters of equation (5) replacing Highid with indicators for working at baseline,

having a university degree or diploma at baseline, and being above the median value of a latent

employability index. We construct the latter index by regressing endline employment on a vector

of baseline covariates using the control group observations and using linear predictions as the

employability index. We select the baseline covariates using a LASSO algorithm and, following

Abadie et al. (2014) we use out-of-sample estimation in the control group to avoid overfitting.

We show in Table 9 that there is little evidence of heterogeneity in treatment effects across any

of these measures. None of the interaction terms is large or statistically significantly different from

zero. We repeat this exercise for a longer list of prespecified baseline markers of heterogeneity –

age, gender, baseline employment history, other education measures, and time and risk preferences

– and do not find robust evidence of heterogeneity. (Results available on request.)

6 Conclusion

Firms typically make hiring decisions with limited information about workseekers’ skills and produc-

tivity. Similarly, workseekers make job search and employment decisions with limited information

about their own skills and productivity. These information frictions on both the demand and supply

30

sides of the labor market can distort job search and hiring decisions, lowering total employment, to-

tal earnings, and firm productivity. We study information frictions using a randomized controlled

trial and longitudinal survey in urban South Africa. South Africa is marked by extremely high

unemployment, particularly amongst youths, and a weak education system. This suggests that

information frictions may be particularly important in this market.

We directly assess workseekers’ skills and experimentally vary whether workseekers receive in-

formation about their own skills and whether they can signal this information to firms. We find

that giving information to workseekers has large effects on their beliefs about their skills, some early

effects on their job search behavior (which do not persist) and limited effects on their employment

outcomes. Helping workseekers signal their skills to firms substantially increases their employment

rate and earnings, driven partly by increased search effort but also by workseekers being more able

to signal skills to firms. We conclude that information frictions exist on both sides of the labor

market but only the demand-side frictions appear important for labor market outcomes.

Our results contribute to a growing literature on information frictions in the labor market.

We add to a small body of research that directly manipulates information about workseekers’

skills. Relative to this literature, we separately vary workseekers’ information about their own

skills and their ability to signal this to firms, which allows us to separate supply- and demand-side

information frictions. We provide more detailed measures of job search and employment quality

than most research in this area, which allows us to show that changes in search behavior do not

explain information-induced changes in employment outcomes. Our findings show that information

frictions can substantially distort individual workseekers’ employment outcomes. In ongoing work,

we build on this to test if information frictions reduce total labor demand or simply help some

workseekers at others’ expense. This ongoing programme of work will more thoroughly characterize

the employment and welfare effects of information frictions on both sides of the labor market.

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A Extra Tables and Figures

Table 12: Comparison between our population and nationally representative statistics

South Africa Gauteng Gauteng 18-29 Our SampleAge 28.914 31.461 23.776 23.646

(19.606) (19.099) (3.326) (3.299)Male 0.489 0.504 0.512 0.382Currently Employed 0.290 0.375 0.346 0.378Currently searching 0.102 0.151 0.302 0.984< Grade 12 education 0.737 0.612 0.429 0.011Grade 12 education 0.184 0.255 0.449 0.610> Grade 12 education 0.072 0.120 0.116 0.379

National statistics are calculated from the Quarterly Labour Force Surveys (QLFS), averaging over all 2016 waves. Calculationsuse post-stratification weights supplied by Statistics South Africa

Table 13: Balance Test Statistics

Baseline variable F-test statistic p-value

From clustered SEs From permutation testHours searched in past 7 days 0.01 0.988 0.98Worked in past 7 days 1.75 0.181 0.226Male 1.47 0.236 0.306Grit assessment score 2.91 0.061 0.096Flexibility assessment score 1.02 0.382 0.55Control assessment score 2.58 0.107 0.232Concept formation score 0.00 0.999 0.996Literacy assessment score 0.83 0.442 0.504Numeracy assessment score 0.51 0.602 0.64Self-esteem scale 2.51 0.088 0.124Notes: This table shows balance tests constructed by regressing each of the selected baseline characteristics on indicators for the public and private treatment groups and testing if these coefficients are jointly zero. The tests are conducted using heteroskedasticity-robust standard errors clustered by treatment date and by permuting the treatment assignments within blocks.

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Table 14: Post-Treatment Survey Attrition Rate by Treatment Group

(1) (2)Text Message Survey 1st Phone Survey

Control group 0.170 0.040(0.013) (0.006)

Public treatment group 0.177 0.039(0.011) (0.004)

Private treatment group 0.182 0.044(0.010) (0.004)

Placebo treatment group 0.142 0.047(0.032) (0.026)

p: Control = Pvt. 0.481 0.632p: Control = Pub. 0.670 0.855p: Pvt. = Pub. 0.785 0.388p: Control = Pvt. = Pub. 0.778 0.681p: Control = Plc. 0.414 0.787p: Pvt. = Plc. 0.238 0.888p: Pub. = Plc. 0.297 0.746p: Control = Pvt. = Pub. = Plc. 0.641 0.841# observations 6891 6891# clusters 84 84

Notes: This table shows the fraction of each treatment group that does not complete each follow-up survey round.Heteroskdasticity-robust standard errors clustered by treatment date are shown in parentheses.

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Table 15: Post-Treatment Survey Attrition Rate by Baseline Characteristics

(1) (2)Text Message Survey 1st Phone Survey

Numeracy score -0.029∗∗∗ 0.003(0.006) (0.003)

Literacy score 0.009 0.004(0.006) (0.003)

Concept formation score -0.019∗∗∗ 0.002(0.006) (0.003)

Grit score -0.002 -0.007∗∗

(0.005) (0.003)Other scores 0.001 -0.002

(0.004) (0.003)Perceived numeracy score -0.000 -0.000

(0.000) (0.000)Perceived literacy score 0.014 -0.003

(0.010) (0.004)Perceived concept formation score 0.010 -0.003

(0.009) (0.004)Self-esteem index 0.006 0.002

(0.004) (0.002)Completed at most high school -0.008 -0.003

(0.012) (0.005)Age -0.002 0.001

(0.001) (0.001)Male 0.048∗∗∗ 0.014∗∗∗

(0.010) (0.005)Worked in last 7 days -0.005 -0.001

(0.008) (0.005)Missing (Other scores) -0.027 -0.027∗∗∗

(0.041) (0.004)Missing (Perceived numeracy score) -0.008 -0.036∗∗∗

(0.101) (0.011)Missing (Perceived literacy score) -0.005 0.051

(0.069) (0.039)Missing (Perceived concept formation score) 0.011 -0.035∗

(0.056) (0.020)Constant 0.171∗∗∗ 0.029

(0.056) (0.028)

F: All coefficients jointly zero 5.3 6.5p: All coefficients jointly zero 0.000 0.000Adj R2 0.014 0.005# observations 6891 6891# clusters 84 84Control group mean 0.170 0.040

Notes: This table shows the relationship between not completing each survey round and pre-specified baseline covari-ates in the full sample. Heteroskdasticity-robust standard errors clustered by treatment date are shown in parentheses.*; **; and *** denote significance at the 10; 5; and 1 percent levels respectively.

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Table 16: Effects of Public and Private Treatments on Search Effort(1) (2) (3) (4)

Any search Hours of search Apps submitted Spending on searchlast 7 days last 7 days last 30 days last 7 days

Public -0.02 0.252 0.624 3.001(0.014) (0.368) (0.684) (4.058)

Private -0.006 -0.31 0.078 1.02(0.014) (0.362) (0.648) (4.801)

# observations 6366 6350 6336 6350# clusters 81 81 81 81R-squared 0.031 0.024 0.062 0.036Control mean 0.695 11.083 11.891 122.81p: Public = 0 0.152 0.496 0.364 0.462p: Private = 0 0.702 0.393 0.904 0.832p: Public = Private 0.243 0.137 0.394 0.657p: Public = Private = 0 0.293 0.326 0.599 0.748

All regressions include randomization block fixed effects and baseline values of the outcome; education; all assessment results;beliefs about numeracy, communications & concept formation results; age; gender; and employment status. Heteroskedasticity-robust standard errors clustered by treatment day are shown in parentheses. Lower rows of the table show p- and q-values forlinear combinations of coefficients. The q-values adjust for multiple testing over all outcomes in the same domain followingBenjamini et al. (2006).

Table 17: Effects of Public and Private Treatments on Search EffectivenessNumber of Number of Number of responses Number of offers

employer responses offers per app per apps submittedPublic 0.049 0.007 0.001 0.001

(0.047) (0.017) (0.004) (0.002)Private 0.041 0.017 -0.006 0.002

(0.042) (0.017) (0.004) (0.003)# observations 6351 6350 5732 5731# clusters 81 81 81 81R-squared 0.021 0.011 0.010 0.009Control mean 0.772 0.182 0.097 0.026p: Public = 0 0.298 0.677 0.779 0.624p: Private = 0 0.298 0.677 0.779 0.624p: Public = Private 0.863 0.568 0.095 0.772p: Public = Private = 0 0.488 0.610 0.200 0.759

All variables are measured over the 30 days prior to endline. All regressions include randomization block fixed effects andbaseline values of the outcome; education; all assessment results; beliefs about numeracy, communications & concept formationresults; age; gender; and employment status. Heteroskedasticity-robust standard errors clustered by treatment day are shownin parentheses. Lower rows of the table show p- and q-values for linear combinations of coefficients. The q-values adjust formultiple testing over all outcomes in the same domain following Benjamini et al. (2006).

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B Placebo Treatment

Firms might respond to the existence or branding of the assessment results, rather than the actual

results. For example, the branding might make applications more salient in a large pool or the

report might be taken as a signal of the workseekers’ effort. We test this explanation with a

placebo treatment in which workseekers can inform firms about the assessments and assessment

process but not their own assessment results (Figure 6. We include only three treatment days and

254 workseekers in this treatment group because it is not our main focus. This means the placebo

treatment effects are imprecisely estimated, but we are still able to rule out some possible effects.

Table 18: Public, Private, and Placebo Treatment Effects on Indices

(1) (2) (3) (4) (5) (6) (7)Beliefs about . . . Search . . . Employment . . .

Skill Search Earnings Effort Effective Status QualityPublic treatment 0.720∗∗∗ 0.080∗∗∗ 0.002 0.029 0.025 0.123∗∗∗ 0.079∗∗

(0.037) (0.026) (0.029) (0.031) (0.028) (0.025) (0.030)Private treatment 0.570∗∗∗ 0.046∗ 0.012 0.013 0.013 0.058∗∗ 0.062∗

(0.034) (0.027) (0.028) (0.033) (0.028) (0.027) (0.032)Placebo treatment -0.055 0.127∗∗ 0.004 0.050 -0.006 0.043 -0.036

(0.065) (0.049) (0.064) (0.062) (0.045) (0.046) (0.063)# respondents 6609 6609 6609 6609 6609 6609 6609# clusters 84.000 84.000 84.000 84.000 84.000 84.000 84.000R-squared 0.298 0.056 0.305 0.140 0.028 0.084 0.020Control group mean 0.000 0.000 -0.000 0.000 0.000 0.009 0.006p: Public = 0 0.000 0.003 0.952 0.346 0.379 0.000 0.011p: Private = 0 0.000 0.087 0.666 0.704 0.642 0.031 0.054p: Placebo = 0 0.399 0.010 0.950 0.424 0.897 0.350 0.566p: Public = Private 0.000 0.197 0.681 0.589 0.707 0.020 0.622p: Public = Private = 0 0.000 0.010 0.879 0.631 0.670 0.000 0.024p: Public = Placebo 0.000 0.363 0.973 0.730 0.549 0.094 0.095p: Public = Placebo = 0 0.000 0.001 0.997 0.556 0.664 0.000 0.030

All regressions include randomization block fixed effects and baseline values of the outcome; education; all cognitiveand noncognitive assessment results; beliefs about all cognitive assessment results; age; gender; and employmentstatus. Heteroskedasticity-robust standard errors clustered by treatment day are shown in parentheses. Lower rowsof the tables show p- and q-values for linear combinations of coefficients. The q-values adjust for multiple testingover all outcomes in the same family following Benjamini et al. (2006). *; **; and *** denote significance at the 10;5; and 1 percent levels respectively.

We report the placebo treatment effects on indices in Table 18 and compare them to public

treatment effects. We find small and statistically insignificant effects on beliefs about skill, beliefs

about earnings, search effort, and search effectiveness, employment status, and employment quality.

We can reject equality with the public treatment effect for the first and last two indices. The only

treatment effect that exceeds 0.05 standard deviations is on beliefs about returns to search. This

is driven by a large increase in the number of applications workseekers plan to submit and a large

decrease in the length of time they expect to take to get a job. Neither effect is precisely estimated

or statistically significant at conventional levels. We conclude that there is little evidence to support

the mechanisms underlying the placebo report.

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Figure 6: Sample Placebo Report

REPORT ON ASSESSMENT PROCESSname.. surname..ID No. id..

This report provides information on assessments conducted by Harambee Youth Employment Accelerator(harambee.co.za), a South African organisation that connects employers looking for entry-level talent to young, high-potential work-seekers with a matric or equivalent. Harambee has conducted more than 1 million assessments and placedcandidates with over 250 top companies in retail, hospitality, financial services and other sectors. Assessments aredesigned by psychologists and predict candidates’ productivity and success in the workplace. This report was designedand funded in collaboration with the World Bank. You can find more information about this report, the assessments andcontact details at www.assessmentreport.info. «name» was assessed at Harambee on «date».

«name» completed assessments on English Communication (listening, reading, comprehension), Numeracy, and ConceptFormation:

1. The Numeracy tests measure candidates’ ability to apply numerical concepts at a National Qualifications Framework (NQF) level, such as working with fractions, ratios, money, percentages and units, and performing calculations with time and area. This score is an average of two numeracy tests the candidate completed.

2. The   Communication   test   measures   a   candidate's   grasp   of   the   English   language   through   listening,   reading   andcomprehension. It assesses at an NQF level, for example measuring the ability to recognise and recall literal and non­literal text.

3. The Concept  Formation Test   is  a  non­verbal  measure  that  evaluates  candidates’  ability   to  understand and solveproblems. Those with high scores are generally able to solve complex problems, while lower scores indicate an abilityto solve less complex problems.

«name» also completed tasks and questionnaires to assess their soft skills:

4. The Planning Ability Test measures how candidates plan their actions in multi-step problems. Candidates with high scores generally plan one or more steps ahead in solving complex problems.

5. The Focus Test assesses a candidate’s ability to distinguish relevant from irrelevant information in potentiallyconfusing environments. Candidates with high scores are generally able to focus on tasks in distracting surroundings,while candidates with lower scores are more easily distracted by irrelevant information.

6. The Grit Scale measures whether candidates show determination when working on challenging problems. Those with high scores generally spend more time working on challenging problems, while those with low scores choose to pursue different problems.

DISCLAIMER: This is a confidential assessment report for use by the person specified above. The information in the report should only be disclosed on a “need to know basis” with the prior understanding of the candidate. Harambee cannot accept responsibility fordecisions made based on the information contained in this report and cannot be held liable for the consequences of those decisions.

Notes: This figure shows an example of the reports given to workseekers in the placebo treatment arm. The reports contain noassessment results, the workseeker’s name and national identity number, and the logo of the World Bank and the implementingagency. Each work seeker received 20 of these reports and guidelines on how to request more reports.

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