Information Frictions and Observable Experience:

The New Employer Price Premium in an Online Market

Christopher T. Stanton

Harvard, NBER, and CEPR

Catherine Thomas

LSE, Centre for Economic Performance, and CEPR

May 2017∗

Abstract

This paper investigates the implications of being new to an unfamiliar market. In a setting

where buyers’ inexperience is observed—the online labor market oDesk.com—workers (suppli-

ers) submit higher wage bids (prices) to first-time employers (buyers). Treating each worker

as a differentiated product and separately estimating new and experienced employers’ demand

for workers reveals that applicants’ wage bids result both from higher markups and higher

costs when applying to jobs posted by new employers. Both effects can be attributed to new

employers’ relative lack of information about aspects of the market. An illustrative model with

learning demonstrates that first-time employers have less elastic demand than employers who

have hired in the market before because they attach more weight to information in job applica-

tions and less weight to their own imprecise priors. Workers’ higher costs may be attributed to

the additional transactions costs imposed as employers learn how to use the market. Both cost

and demand frictions act as barriers to employer entry and reduce the number of experienced

employers in the market. Even though new employers have less elastic demand, subsidizing

learning by charging lower relative fees to new employers would significantly increase total

market profits.

∗This paper is preliminary and incomplete, with comments welcome. We thank seminar participants at the AEAMeetings, the CEPR Workshop on Incentives, Management and Organisation, Harvard, LMU, LSE, Mannheim,NBER Summer Institute, Stanford, and Yale, along with Ajay Agrawal, Nava Ashraf, Ricard Gil, John Horton,Lisa Kahn, Bill Kerr, Ed Lazear, Arnaud Maurel, Luis Rayo, Yona Rubinstein, Scott Schaefer, Kathryn Shaw, andNathan Seegert for helpful comments.

1

1 Introduction

This paper investigates information frictions when buyers begin transacting in unfamiliar markets.

It presents empirical evidence that inexperienced buyers evaluate suppliers differently than their

experienced counterparts. The costs of doing business, during the search phase of a transaction or

after agreeing to a contract, also differ with buyer experience. Both demand side and supply side

considerations lead sellers to condition their supply on whether past use of the market is observed.

This phenomenon has particular relevance for settings in which a market maker can alter policy

or institutions to account for how observable history affects transactions and matching. The setting

examined in this paper is the online labor market oDesk.com (now called Upwork), where the buyers

are employers of remote labor services and the suppliers are workers who are located around the

world. The majority of applications come from outside the United States, while most employers are

American. Potential workers observe an employer’s past hiring history before bidding hourly wages

when applying to jobs. Data on worker’s applications and hourly wage bids reveal significant dif-

ferences depending on whether the employer has observable experience hiring. Although a number

of papers have been written about online labor markets, this is the first paper to consider informa-

tion frictions and gains from experience on the employer side of the market.1 These frictions are

important for understanding barriers to entry and trade and insight beyond oDesk can be gathered

from this setting just as insight from eBay generalizes beyond auctions.2

New employers in this market receive higher wage bids than employers who have made prior

hires on the site. That is, rather than a first-time buyer discount, a within-employer regression

shows that workers bid hourly wages that are about 5% higher if applying when the employer is

new compared to applying when the same employer with multiple prior hires. The majority of

first-time job posters leave the market without hiring and never return. It is possible that the

higher wage bids submitted to new employers deter buyers from gaining experience and learning

about the market. This paper examines the reason for the new employer price premium and builds

a model to assess implications for market use and profitability.

A model of labor demand and supply, estimated using data on offered and contracted wages over

the entire history of employers’ use of the market, shows that the new buyer wage bid premium is

1While several recent papers use oDesk data (Agrawal et al., 2013a and 2013b; Ghani, Kerr, and Stanton, 2014;Horton, 2017a; Horton, Kerr, and Stanton, 2017; Lyons, 2017; Pallais, 2014; and Stanton and Thomas, 2016), manyof these papers focus on how individual workers convey quality to potential employers. The closest work in spirit isHorton (2017b) who studies how employers react to the imposition of a minimum wage in the market. The minimumwage occurred after our sample ends.

2For examples and discussion, see Bajari and Hortacsu (2004), Cabral and Hortacsu (2010) and Lewis (2011).

2

due both to workers’ higher costs on the supply side and higher markups arising from differences

in demand. Both worker costs and wage elasticities of demand are found to differ across the two

employer groups. The raw gap in bids between inexperienced and inexperienced employers is about

12%, and demand differences explain 60% of the gap. Cost differences explain the remainder.

On the supply side, workers’ bids are assumed to be equilibrium price offers in a differentiated

products Bertrand game. When applying to an employer’s posted job, the worker anticipates the

costs she will face. She marks up this cost by an amount that reflects the employer’s expected wage

elasticity of demand given observable experience. Segmenting employers into those that are new

to the market and those that have more than one prior hire allows for estimation of the market

equilibrium for each group.

On the demand side, employers select only a single applicant or exit the market, enabling

mixed logit models for estimation. The primary difficulty is that correlation of wage bids and

unobserved worker and job-match quality that enters employers’ demand requires an instrument

that shifts workers’ bids. Because all contracts are paid in dollars but workers receive payment in

their local currency, exchange rates provide one candidate instrument. Fluctuations in the dollar

to local currency rate change the relative price of the worker’s online payment compared to offline

alternatives. The first instrument used is the log of the monthly average dollar to local currency

exchange rate, de-trended by currency. A second instrument provides a source of variation when

workers do not have exchange rate movement; this instrument uses variation in market ”tightness,”

captured through differences in the number of applicants to other jobs in the same category in a

week. We form the log applicant to opening ratio and then remove time and job category fixed

effects, leaving idiosyncratic variation in market tightness within job category and time and time

period.

Holding workers’ participation decisions fixed, these instruments are plausibly orthogonal to

employer demand. Both instruments are strong, and the residuals from a first stage regression on

the instruments serve as control functions to estimate elasticities (Petrin and Train 2010). However,

worker sorting into applications based on exchange rate or competition shocks is a potential concern.

Estimates with and without accounting for workers’ resume characteristics allow for a sensitivity

analysis of sorting and how it may change estimated demand elasticities. Under the assumption

that the change in estimated elasticities due to omitted variables is no larger than the change in

elasticities from omitting all observable characteristics, the difference in bids due to differences in

demand is at least 43% of the raw bid gap.

3

One important finding from the demand estimates is that employers’ valuations for the market

increase with experience overall even as they become more price-sensitive when choosing among

workers. A Oaxaca-Blinder decomposition shows that the majority of the increase in valuations is

through ”coefficients”, which we interpret as learning by doing. As a result, any information friction

that limits potential employers from gaining experience prevents realization of the gains from this

learning by doing.

The rest of the paper explores the reasons why new employers appear to have more elastic

demand and why they impose higher costs on workers. The main explanation considered is an

information friction that relates to employer learning about the market. The results related to

cost declines suggest that employers learn about how to use the market, reducing the costs they

impose on the workers they screen or employ. The findings related to higher equilibrium markups

for new employers are consistent with employers learning about the distribution of workers.3 The

distribution here means that each employer has a particular ”match” with using online work, and

the to-employer distribution of worker quality is centered on an unknown mean match. As a result,

workers may account for this employer uncertainty in matches when bidding.

From a worker’s perspective, she will be hired when the employer assesses that she is the best

applicant in the set of candidates. Using the language of discrete choice demand, this occurs

when the worker is the highest order statistic among the candidates. The spread between the

highest and second-highest order statistic (the best and next-best applicant) provides market power

and motivates higher markups. We demonstrate that the spread between the best and next-best

applicant is largest when employers are uncertain about the distribution of worker value. In the

illustrative model, employers receive noisy signals of worker quality, with the mean of the quality

distribution uncertain. Having sampled fewer applicants, inexperienced employers put more weight

on noisy signals from workers and less weight on their own prior. Because applicants’ signal are

given additional weight when the employer is new, the expected spread between the best and

next-best applicant is greater. Therefore, in the event that the worker has the highest expected

wage-adjusted quality among available workers, an inexperienced employer with greater uncertainty

about the distribution has a higher expected relative willingness to pay for that worker compared

3For related theory, see Lars Ljungqvist and Thomas Sargent’s textbook treatment of the job search model withan unknown offer distribution. The labor economics literature has extensively considered learning models over thecareer. See Gibbons and Farber (1996), Altonji and Pierret (2001), Lange (2007), Kahn (2013), and Kahn and Lange(2014). For empirical work in the context of learning one’s own type, see Miller (1984) and Arcidiacono, Aucejo,Maurel, and Ransom (2016). This paper is also related to the literatures in industrial organization and marketingabout how demand changes as consumers learn about product quality (see Erdem and Keane, 1996; Ackerberg,2003).

4

to alternatives. In other words, the inexperienced employer has less elastic demand with respect to

wage bids. This intuition is general beyond labor or matching environments and applies to other

contexts with unit demand: noise in the environment when evaluating products in goods markets

or applicants in labor markets has the potential to reduce price elasticities, increasing equilibrium

markups.

Several features of the data are consistent with information frictions that result from the resolu-

tion of uncertainty. The comparative statics from a search model with learning about an unknown

distribution can explain some otherwise unexpected results. As the precision of beliefs increases

with experience, the information value of an additional interview declines. The data show that

experienced employers conduct significantly fewer interviews; within-employer estimates show that

employers with five or more hires conduct, on average, 38% fewer interviews than when they were

inexperienced.

Second, as an employer becomes more certain about the distribution, the reservation value at

which he hires declines. When inexperienced employers hire after only a small number of interviews,

they forgo further learning opportunities. An employer would choose to forgo these opportunities

only if an early interview yielded an applicant that exceeds a high reservation value. Employers

who continue to search are likely to do so because they haven’t found a good match and will not

have updates about the distribution that are as favorable as those who match early. In the data,

the probability that an inexperienced employer hires and reports a successful job is falling with the

number of interviews conducted. One might expect that additional screening or search effort would

improve outcomes, but instead, and consistent with the comparative static, outcomes appear to

become worse for those who were revealed-preferred not to hire early.

Incomplete information about the market acts as an entry barrier that reduces the number of

transactions relative to a benchmark where new employers have the same information as experienced

employers. The oDesk revenue model at the time of the sample imposed a fee of 10 percent on all

transactions on the site. Using estimated differences in demand according to employer experience

and estimated differences in workers’ costs of interacting with employers in each group, we examine

whether a fee rate that varies with employer experience would increase transactions and generate

additional profits for the platform. Although inexperienced employers have less elastic demand with

respect to wage bids, oDesk could, nonetheless, generate higher platform revenues and profits by

charging lower fees to inexperienced employers. This is because of the large spillovers to future

demand of increasing the probability of making a first hire on the site. The short-term nature of

5

job contracts on the site means that no individual worker has an incentive to subsidize employer

learning because the benefits of doing so would accrue to the employer and the employer’s future

hires. In contrast, the platform itself does have some incentive to subsidize employer learning,

and offset part of the premium charged by workers to new employers, since it captures a share of

all future revenues that the employer generates in the market. This means that optimal platform

pricing partially internalizes the effect on wage bids from revealing employer inexperience.

The paper proceeds as follows: Section 2 provides details about the empirical setting and how

employers search in this market, introducing evidence on differential pricing by observable experi-

ence. Section 3 presents a model of workers’ bids and employers demand, then estimates demand by

employer experience levels, decomposing equilibrium wage bids into costs and markups. Section 4

provides evidence that a search model with learning can fit important moments of the data, lending

support to the hypothesized mechanism driving the result. Section 5 evaluates how changes in the

market affect hiring rates and profitability. Section 6 presents some robustness tests that address

alternative reasons for higher wage bids. Section 7 concludes.

2 The Setting: oDesk.com

2.1 How it works

oDesk.com is an online platform that allows employers to contract with remote workers—the sellers

of online labor services.4 The platform facilitates both search and matching for workers as well as

remote task and project management and payments. Work includes a range of jobs where output can

be delivered electronically, and the most frequently observed job categories are Web Development

and Administrative Support. Jobs tend to be short-term spot transactions and the majority of

postings require less than three months of work. Around 85 percent of the transactions in the

market span international borders and, therefore, constitute international labor services trade.

An employer who wants to purchase online labor services creates an account on the platform,

free of charge. To post a job opening, the employer must select the job category and expected

duration, give the job a title, and describe the work to be done as well as the skills needed. Once

the posting is in the system, potential employees learn about the job by searching on the site or

through automatic notification. As Figure 1 shows, the postings contain information about the

4Other prominent platforms included elance and Guru, the first of which merged with oDesk in 2014. The mergedcompany has recently changed its name to Upwork. The data used in the paper are from before the merger.

6

employer and job, clearly showing the employers’ experience in the market.

Figure 1: A Job Posting.

Interested workers then submit applications for the job posting and bid an hourly wage to work

on the specific job. Employers also have the option of searching worker profiles directly and inviting

applications from individual workers. Workers’ profiles, visible to potential employers, contain

information about their skills, education, prior offline work experience, and experience on oDesk

(see Figure 2). The country where they are located is also displayed prominently on the profile.

For workers that have experience on the site, their profile shows a summary feedback score out of

five, and all worker profiles include a requested hourly wage rate that the worker can change at any

time.

After receiving applications or initiating candidacies, employers can opt to interview any number

of workers for the job. If applicants agree to be interviewed, the interview usually takes place via

Skype. Whether an interview actually occurs is not recorded in the oDesk database, so the remainder

of the paper will refer to the intention to interview as an interview.5 An employer can choose to

hire an applicant with or without interviewing them first. Upon hiring, the employer can monitor

workers via software provided by oDesk, and oDesk manages all payments. When a job is complete,

the employer is asked for feedback about the worker and vice-versa. The employer is also asked

whether or not the job was completed successfully.

5The data record that an interview takes place whenever an employer sends an interview request to a worker,whether that worker was invited to apply or initiated the candidacy herself.

7

Figure 2: A Worker Profile.

Every employer’s job-specific search process is observed on each of their successive job postings.

For each posting, the data contain information about the complete applicant pool; which candidates,

if any, are interviewed; and which candidate, if any, is hired. This paper does not use information

about on-the-job monitoring, but does exploit information on feedback and indicators of job success.

2.2 Summary Measures by Employer Experience

The employers studied in this paper are those who first posted jobs on oDesk between January 2008

and June 2010. The sample consists of 82, 237 unique employers and, together, they posted 322, 864

jobs over the time period studied, receiving a total of over 5 million job applications. There are nine

job categories in the data. 38% of all job postings are in Web Development, the largest technical

category. The next largest technical job category, Software Development, contains 9% of the job

postings. Administrative Support is the largest non-technical category, with 17% of the postings.6

Employers are mostly located in the United States and include private individuals as well as

6Other categories are: Design and Multimedia; Writing and Translation; Sales and Marketing; Business Services;Networking and Information Systems; and Customer Service.

8

individuals hiring on behalf of established firms. Figure 3 presents the distribution of the number

of hires per employer throughout the period. 63% of employers make no hire at all, 16% make one

hire, and 17% of employers make five or more hires. Important for the analysis in this paper is

that the workers can observe employers’ previous experience on the platform. On each employer’s

job posting page, the number of past hires and total hours is prominently displayed (see Figure 1).

Workers can also observe any feedback employers have received on prior job postings.

Of the 119, 846 jobs posted by employers without having hired previously on the site, 61, 197

received at least one worker-initiated application, had a gap of at least one day before going on to

post another job, and survive filters for spam and arms-length hiring. For these jobs, the hiring

periods can be segmented by time and, throughout the paper, this subset of jobs is referred to as

the non-overlapping or sequential sample. This is the sample used in demand estimation. Each

of these restrictions is important for estimating demand. Some employers bring workers onto the

platform from offline, violating ”arms-length” hiring. Other accounts belong to spammers posing

as employers. But most importantly, some employers hire multiple workers simultaneously and do

so by posting a batch of jobs simultaneously. Batching makes it impossible to observe the choice set

for each opening. These restriction reduce the total number of postings from 322, 864 to 109, 8156.

Tables 1A and 1B present summary statistics about job postings, grouped by the number of

previous hires made by the relevant employer. Table 1A includes all job posts in the sample. Table

1B includes only the sequential job sample. These tables provide initial evidence that employer—

and worker—behavior differs according to the number of previous hires the employer has made on

the site.

The first row of Table 1A summarizes new employers’ behavior on the platform and how workers

interact with these employers. Employers without prior hiring experience receive an average of 18.42

applications prior to closing the job or making a hire. In the sequential sample, summarized in Table

1B, inexperienced employers receive an average of 25.49 applications per job. The mean hourly

wage bid for inexperienced employers in the overall sample is 10.15 USD, and in the sequential

jobs sample it is 10.24 USD. 44% of applications are from workers with no recorded feedback, and

34% of applicants have a current feedback score above 4.5 (termed good feedback) at the time of

application. Only a small fraction of applications were employer-initiated, which means the vast

majority were candidate-initiated. New employers conduct an average of 2.98 (3.64) interviews in the

full (sequential) sample. Column 8 shows that only 22% (16%) of openings posted by inexperienced

employers result in a hire. Columns 9 through 11 present data for those jobs for which a hire was

9

Figure 3: Number of Hires per Employer

0.2

.4.6

Den

sity

0 10 20 30Total Number of Hires

The unit of analysis is an employer. Total hires are censored at 30.

made. The mean hourly wage paid (Column 9) is slightly lower than the mean bid received; the

hired worker is more likely to have had good feedback, and less likely to have missing feedback,

than the typical applicant (Columns 10 and 11).

The subsequent rows in Tables 1A and 1B summarize the search processes of employers with

at least one prior hire. As employers gain experience, the number of applications per job tends

falls in the overall sample, with the largest decline taking place between having hired zero and one

prior worker (Column 2), but the number of applications increases in the sequential sample because

of the restriction on having worker-initiated applications. Column 4 shows that the mean hourly

bid declines with employer experience. For those with one prior hire, the average bids fall to 9.76

USD (9.85 USD in the sequential sample). After four or more previous hires, employers receive a

mean bid of 8.69 USD (9.05 USD in the sequential sample). Among employers with prior hires,

similar shares of applications come from workers with good feedback (Column 5) or who have no

observable feedback (Column 6) regardless of the number of past hires. Experienced employers

typically conduct fewer interviews than the inexperienced (Column 7) in the overall sample, and

employers with at least four prior hires conduct an average of 2.07 interviews. In the sequential

10

sample, the reverse is true; the number of interviews increases with experience.7 Column 8 shows

that experienced employers are far more likely than inexperienced employers to make a hire. For

employers with at least four prior hires, 57% ( 28%) hire a worker. The mean wage paid to the

hired worker also declines with employer experience (Column 9).

The data also provide some evidence that workers appear differentiated to employers, and it is

relatively rare for employers to hire the worker that submits the lowest hourly bid. Figure 4 shows

that the applicant hired was in the lowest decile of received bids less than 20% of the time after

controlling for worker’s observable characteristics.

Figure 4: Bid Decile of Employed Worker

0.0

5.1

.15

.2D

ensi

ty

0 2 4 6 8 10bidDecile

The sample is jobs where a hire is made. The figure shows the bid decile of the worker who is hired within the set

of applicants. Bid deciles are calculated from residuals after removing observable worker characteristics including

feedback, English skills, indicators for education, and country fixed effects.

2.3 Workers’ Hourly Bids and Hourly Wages

Table 2 examines the decline in hourly bids with employer experience shown in Table 1 Column 4 in

more detail. The coefficients show the percentage difference between the hourly wage bids received

7Later, it will be shown that this is due to selection. Within-employer, interviews fall in the sequential sample.

11

by employers after hiring one, two, three, four, or five plus workers and those employers who have

made no previous hires. To isolate changes in the information that workers are able to observe

when bidding, the sample includes only those jobs that are sequential.

Column 1 shows that, in the cross section, employers with one observable prior hire receive bids

that are two percent lower than employers who have no observable experience hiring. Employers

with five or more hiring spells receive bids that are four percent lower than employers with no

experience. Column 2 adds controls for worker resume characteristics and a third-order polynomial

in the number of characters in the job description. These controls are intended to allow for worker

sorting and for the possibility that the employer is learning about how to write an effective job

posting. The observed decline in average wage bids received by employers with five or more prior

hires is over three percent.

Subsequent columns of Table 2 add various further controls to account for other plausible ex-

planations for the cross-sectional decline in wages bid with employer experience. Columns 3 and

4 add employer fixed effects. A given employer receives bids that are nearly 5 percent lower with

five or more prior hires compared to bids received when he has no experience. Adding controls

for worker and job characteristics reduces the effect to about 4%, indicating sorting on observable

characteristics and employer experience rather than sorting simply on the identity of the employer.

Columns 5 and 6 remove employer fixed effects and add worker fixed effects. A given worker submits

lower bids to experienced employers, although the magnitudes of these reductions are smaller than

in the previous columns. Within worker, bids to employers on their fifth or more hiring spell are,

on average, about two percent lower than bids to employers with no observable experience. Panel

B of Table 2 segments the applications to employers with no previous hires into those from workers

with different prior interactions with inexperienced employers on the platform. The omitted group

is workers who have not previously applied to an inexperienced employer. The first three rows of

estimated coefficients present the difference in bids submitted to inexperienced employers between

the omitted group and, respectively: workers with prior applications to inexperienced employers;

workers who have previously been interviewed but not hired by an inexperienced employer; and

workers who have been hired by an inexperienced employer in the past. These three rows of es-

timated coefficients show that inexperienced employers receive the highest bids from workers who

have been hired by another inexperienced employer before. Columns 5 and 6 show that workers

with previous interactions submit bids that are around 9% lower than the first time they apply to an

inexperienced employer. Once they have been interviewed by an inexperienced employer (but not

12

hired), they submit bids that are 1% lower than those workers who have never applied to an inex-

perienced employer. Considered together, these findings are consistent with the extra costs created

by inexperienced employers being anticipated but then diminishing through experience of making

an application. Nonetheless, additional anticipated costs remain for those that have previous expe-

rience being hired by the inexperienced. The decline in wage bids received by employers with one,

two, three, four, or five or more previous hires remain in Panel B. These coefficients are the declines

relative to the bids received by inexperienced employers from workers who have not previously

applied to other inexperienced employers, and are much larger in magnitude than the comparable

estimates in Panel A. Table 3A shows that it is employer experience, rather than public employer

feedback, that is associated with lower bids. In these columns, the sample is limited to employers’

with no observable hiring experience and to employers with two or more prior hires. Indicators for

the employer having no observable feedback and for the employer having observable feedback of

4.5 or higher are interacted with the indicator for having two or more prior hires. Including these

controls does not change the main result that experienced employers receive lower bids. In fact, the

main point estimate measures the effect for experienced employers with bad feedback: these em-

ployers still receive bids that are significantly lower than inexperienced employers. The interactions

of employer experience and good employer feedback are, in most cases, a fraction of the main effect

and are insignificant. The main message from these regressions is that employer experience tends

to dominate any differences in how employer feedback evolves over time in explaining differences in

wage bids.

Panel B of Table 3 shows that the relationship between employer experience and lower bids

is robust to including controls for feedback that the employer has given to workers on prior jobs.

As in Panel A, the sample is employers with no prior hiring experience and employers with two

or more prior hires. Indicators for the employer having given no observable prior feedback and

for having given good observable prior feedback are interacted with an indicator for having two or

more prior hires. The significant negative coefficients on having experience remain when including

these controls. The estimated coefficients on these controls are positive in sign, which is consistent

with an interpretation that workers are price discriminating and attribute having left no feedback

or giving good feedback as positively correlated with employers’ willingness to pay.

13

3 A Model of Worker Supply and Employer Demand

The probability that an applicant is hired as a function of wages appears to differ by employer

experience. Figure 5 plots differences in hiring probabilities by employer experience for various job

categories. Both the intercept and the slope of the hiring probability function with respect to wage

bids appears to differ with employer experience.

Figure 5: Residual Hiring Probability as a Function of Residual Bids

24

68

10R

esid

ual W

age

Bid

.002 .004 .006 .008 .01Residual Prob of Hire

0 Prior Hires 2+ Hires

Admin Support

810

1214

1618

Res

idua

l Wag

e B

id

.004 .006 .008 .01 .012 .014 .016Residual Prob of Hire

0 Prior Hires 2+ Hires

Web Programming

510

1520

Res

idua

l Wag

e B

id

.004 .006 .008 .01 .012 .014 .016Residual Prob of Hire

0 Prior Hires 2+ Hires

Design + Multimedia

510

1520

25R

esid

ual W

age

Bid

.004 .006 .008 .01 .012 .014 .016Residual Prob of Hire

0 Prior Hires 2+ Hires

Software Dev.

The unit of analysis is a job application. Wage bids and hiring probabilities are residualized within each job category

using the worker resume data, a spline for the application number, and a linear time trend. Points are taken from a

polynomial smoothing function of the residual hiring indicator on the residual bid.

To capture these differences systematically, we develop a parsimonious, estimable model of the

probability that employer i selects applicant j on a job in which the employer has experience e.8 The

hiring probability, pije, is a function of: the characteristics an employer observes about applicant

j, her wage bid, characteristics of the job, and the composition of other applicants. The goal of

the model is to estimate whether differences in demand drive the observed differences in bids to

employers of different experience. The e subscript indicates that the hiring probability function

may differ with past experience.

8We abuse notation and use e to stand for employer experience when it also indexes job openings. Employersmay have multiple job openings at the same experience level and this is accounted for in estimation.

14

The remaining bid gap by employer experience, once conditioning for systematic differences

in demand, can be attributed to differences in workers’ costs. A worker’s cost, cije, captures the

worker’s outside option (opportunity cost of work) or hassle costs from working with or applying to

a particular employer. Separate identification of workers’ opportunity costs from the hassle costs

is difficult, but indexing costs by employer experience isolates relative cost differences between

segments of employers.

3.1 A Worker’s Optimal Bid

When choosing the wage to bid for a job posted by employer i with experience level e, worker j’s

objective is

maxlogwije

pije︸︷︷︸Pr(Hire)

× exp (logwije − log(1 + τ))︸ ︷︷ ︸Wages

+ (1− pije)× cije︸︷︷︸Cost

where logwije is the log of worker j’s to-employer wage bid inclusive of ad-valorem platform fees, τ.

Accounting for oDesk’s fee, the wage received by the worker if hired iswije(1+τ)

= exp (logwije − log(1 + τ))

and the employer pays wije. If worker j is not hired, she receives cije, her ”net” outside option. The

”net” outside option reflects that cost differences on the job or from applying to the job will be

reflected in this cost.9 When choosing bids, the worker trades off a lower probability of being hired

with a higher wage.

Each worker’s first order condition is given by

∂pije∂ logwije

(wije

(1 + τ)− cije

)+ pije

wije(1 + τ)

= 0. (1)

The system of equations containing the first order condition for each applicant determines equilib-

rium bids in a Bertrand Nash game in prices (here, in wages). Solving for the optimal bid gives

w∗ije = cije (1 + τ)

(1 + pije/

∂pije∂ logwije

)−1. (2)

This says that the bid is related to three objects: cije (1 + τ), which passes through costs and the

ad-valorem platform fee; the employer’s demand function, pije, which captures hiring probabilities;

9The objective could alternatively be written:

maxlogwij

pije × exp (logwije − log(1 + τ)− cijH) + [1− pije]× cijO

where cijH is a differential hassle cost from on-the-job work associated with being hired by employer i and cijO isthe outside wage for worker j. The first order condition in this case makes clear that only cije = cijH + cijO can beidentified.

15

and∂pije

∂ logwije, the semi-elasticity of the hiring probability with respect to the wage bid. The term(

1 + pije/∂pije

∂ logwije

)−1is the markup over costs. The bid equation can also be rearranged to give

workers’ costs

cije =wije

(1 + τ)

(1 + pije/

∂pije∂ logwije

). (3)

Because bids are observed, knowledge of pije and∂pije

∂ logwijeallow bid differences by experience to be

decomposed into cost differences and demand differences.

3.2 Employer Demand

Because the goal of the model is to understand how hiring probabilities vary on postings with

different levels of employer experience, the unit of analysis is a job posting. For these initial

purposes, the set of job openings and applications is treated as given and workers are assumed to

be available when they initiate an application.10 In later exercises on platform profitability, employer

decisions to post additional jobs will be considered.

A job opening arises because an employer has tasks requiring a single worker. After posting a

job, applications arrive. Denote the set of applicants to an opening by Jie. For each applicant to

the job, the employer observes the following: quality, qje, which may be weighted differently with

experience; µi, employer-specific heterogeneity that shifts the value of hiring on the platform; and

εij, an idiosyncratic Type-1 extreme value shock for each alternative. The employer’s task is to

choose the worker with the highest quality per unit of wage. That is, the employer chooses the

worker that maximizes

maxj∈{Jie,0}

qje exp (µi) exp (εij)

(wije)αe .

The employer’s choice set includes the option to hire no worker, captured by the addition of 0. For

the outside or no-hire option, 0, the ratioqje exp(µi)

(wije)αe is normalized to equal 1 for all employers.

This setup is quite general and captures many possible changes with experience. For example,

10This assumption is reasonable, requiring only that the probability that a worker receives two offers over atime interval ∆ is sufficiently small. Similar justifications are used when formulating many stochastic processes. Forexample, if, from the worker’s perspective, job offers follow a Poisson process, then the probability of receiving a joboffer in the interval (t, t+ ∆) is λ∆ + o (∆) , and the probability of two offer arrivals in (t, t+ ∆) is o (∆).

Although declined job offers are not observed, this assumption on arrival of offers seems to be reasonable in thedata. The observable arrival rate of interview requests fits this assumption. When the worker-day is the unit ofanalysis, only 3.6% of the worker-days in the sample have more than one interview request arriving; 0.6% of theworker days have more than two interview requests arriving. A post-candidacy survey asks employers for reasons whyparticular workers were not hired and workers for reasons why they exited the active candidate set. In some cases,employers or workers explicitly report a realized scheduling conflict. We drop cases of reported scheduling conflictsor when workers refuse invited applications.

16

learning-by-doing is captured because the mean value of using the platform is allowed to change

through the e subscript on quality. If bid differences are due to noise that muddles inexperienced

employers’ assessment of applicants, learning that improves signal precision is captured (as illus-

trated later) through experience-specific sensitivity to price, αe. Employer-specific heterogeneity

for the platform is captured through µi. The distribution of µ is assumed to be mean 0; variation

around the mean captures differences in the value of hiring on the platform relative to the no-hire

option.

This setup for the employer’s objective, after taking logs, gives an easily interpretable conditional

logit demand function. The probability that employer i with experience e chooses worker j is the

probability that

log (qje) + µi + εij − αe log (wije) ≥ log (qke) + µi + εik − αe log (wike) (4)

for all k ∈ {Jie, 0}. Conditional on observing mui and qje, the probability of choosing applicant j

is:

pije = exp (log (qje) + µi + εij − αe log (wije)) /(1 + ΣJi

j exp (log (qje) + µi + εij − αe log (wije))). (5)

We further specify that

log (qje) = Xjeβe

where the matrix Xje contains a rich set of resume, worker, and job characteristics, including a

constant term. The parameter vector βe varies with employer experience.

Several normalizations are required. Two location normalizations are (i) the systematic payoff

from the no hire option is log (1) and (ii) E (µ) = 0. The scale normalization is the standard

conditional logit normalization of the variance of ε.

The presence of µi also relaxes a well-known limitation of standard conditional logit models,

the independence of irrelevant alternatives or IIA assumption. Inclusion of µi allows for different

substitution patterns between the no-hire option and the available candidates in the choice set.

If Xje and µi were fully observed in the data, variation in prices, characteristics, and choices by

different levels of experience would identify βe and αe. Because µi is not observed, only the variance

of µ is identified from longitudinal data on employer choice. For example, if many employers persis-

17

tently hire when faced with low-quality workers submitting high bids, while many other employers

do not hire when high-quality workers with low bids are available, then the variance of µ would be

substantial. On the other hand, if the common parameters βe and αe do a good job of predicting

the sequence of employer hiring, the variance of µ would be smaller.

The remaining difficulty is handling worker quality that is not observed in the data. Suppose

that log quality in the data is log (qje) = Xjeβe but the employer observes log (qje) = Xjeβe + υj.

If υj is correlated with log bids, this will result in inconsistent estimates of price elasticities. The

next section details a strategy to account for price endogeneity.

3.3 Demand Estimation

Instruments

To address concerns about the endogeneity of wage bids, we use an instrumental variables strategy

that exploits changes in the dollar to local currency exchange rate for individual workers and their

competitors from other countries. Workers are paid in their local currency for offline work but

are paid in dollars for their work on oDesk. Frictions limiting exchange rate pass through to local

wages mean that offline opportunities are likely to adjust more slowly to exchange rates than online

transactions. In fact, we discovered this potential source of variation when talking to employers

who mentioned the frequency which exchange rate calculators appeared in the screenshots taken

by oDesk’s monitoring software. When the dollar appreciates relative to the local currency (one

dollar provides fewer local currency units), workers’ wage offers when bidding to jobs are predicted

to increase.

To see this how this variation affects workers’ bids, assume that cije is denominated in the

local currency and bids are denominated in dollars. Costs in the local currency must be trans-

lated into dollars for the purposes of submitting bids, so the worker’s optimal bid is w∗ije =

cije(DL

)θ(1 + τ)

(1 + pije/

dpijed logwije

)−1. The dollar to local currency exchange rate is D

Land the pa-

rameter θ captures several deviations from complete pass through: i) not all exchange rate variation

is reflected in local cost differences, ii) part of a worker’s consumption may become cheaper through

imports, and iii) the incidence of exchange rate variation is split between workers and employers.

The worker’s log bid becomes

log (wije) = θ log

(D

L

)+ log (cije) + log (1 + τ)− log

(1 + pije/

∂pije∂ logwije

). (6)

18

To deal with different mean exchange rates across currencies and to remove secular trends, we

use de-trended log(DL

)by currency and remove the currency’s time series mean. Figure 6 illustrates

the time-series variation in mean residual log bids and de-trended exchange rates for a India alone

(top, left) and India versus other large countries.

Figure 6: Mean Residual Log Bids and De-trended Exchange Rates.

-.1-.0

50

.05

.1.1

5E

xcha

nge

1.96

1.98

22.

02M

ean

Log

Bid

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Exchange

India Time Series

-.02

0.0

2.0

4.0

6In

v E

xcha

nge

Diff

-.1-.0

50

.05

.1Lo

g B

id D

iff

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Diff Exch. Diff

India v Philippines

-.1-.0

50

.05

.1In

v E

xcha

nge

Diff

-.1-.0

50

.05

.1Lo

g B

id D

iff

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Diff Exch. Diff

India v Pakistan

-.15

-.1-.0

50

.05

.1In

v E

xcha

nge

Diff

-.2-.1

0.1

.2Lo

g B

id D

iff

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Diff Exch. Diff

India v Bangladesh-.2

-.10

.1.2

Inv

Exc

hang

e D

iff

-.05

0.0

5.1

.15

Log

Bid

Diff

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Diff Exch. Diff

India v Ukraine

-.2-.1

0.1

Inv

Exc

hang

e D

iff

-.05

0.0

5.1

Log

Bid

Diff

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

Bid Diff Exch. Diff

India v Russia

The top left panel plots mean residual log bids against the log of the US Dollar to Indian Rupee exchange rate

after removing a time trend. The remaining panels plot log bid differences between India and other countries (left

y-axis) and the log other currency to Indian exchange rate (right y-axis).

While exchange rate movements are plausibly exogenous to demand on oDesk, there are two

additional concerns. First, not all workers have exchange rate variation. This is not a problem

for comparing relative price differences across workers, some of whom are subject to exchange rate

variability. But in this case there is no price variation against the outside option of not hiring. To

address this, we build in a second instrument that overcomes the latter limitation.

The second instrument takes the average number of applicant arrivals (in the first 24 hours

after posting) to other jobs in the same category in the same week. Job category and week fixed

effects are netted out, making this an instrument that varies the extent of competition across job

19

categories, holding fixed average competition in the category and average competition at the time.

Second, there is likely to be sorting on the instruments that influences which workers participate

in the market. The extent to which sorting is a problem for the analysis is assessed through

sensitivity of the estimates to observable worker characteristics that would also be expected to

affect sorting patterns.

Taking equation (6) to data, hiring probabilities and costs as a function of υj, the worker-level

unobservable, are not available, but the instruments are cost and competition shifters that are

independent of this term. This allows us to use the control function approach of Petrin and Train

(2010), putting instruments Zje and characteristics Xje in a first stage regression of the form

log(wije) = γ0 + Zjeγ1e +Xjeγ2e + νije. (7)

Both instruments have a substantive and statistically significant effect on workers’ wage bids.

Table 4 provides details about the first stage regression. The first two columns provide estimates

for inexperienced employers, with and without country specific trends as controls, while Columns 3

and 4 provide estimates for experienced employers. Panel A in Table 4 includes the detailed worker

resume data in the first stage while Panel B does not. In all cases, F statistics are extremely large,

indicating instrument strength. A comparison of Panels A and B provides some evidence of sorting

on the instrument. Under the null of no sorting, the estimated parameters would be statistically

indistinguishable.11 Although we do not perform a formal statistical test, the parameters appear to

differ. As a consequence, we estimate later models with and without the resume data to compare

how estimated elasticities change.

Estimating Choice Parameters

With the employer’s problem defined and a strategy to control for unobserved characteristics that

may be correlated with bids, we do the following to form choice probabilities. First, we segment

job openings based on whether the employer has zero prior hires or more than one prior hire.12

Second, we estimate equation (7) separately segment by segment. Third, we take the residuals

from estimating equation (7) to form control functions, where the control function is CFije = νije.

11Panel C offers a comparison with Panel A when worker fixed effects are included.12We experimented with using any prior hires or more than 1 prior hire. Signs were similar but separation only

occurred after conditioning on additional hires.

20

Fourth, fix a value of µi and, conditional on the value of µi, form choice probabilities

pije = exp (Xjeβe + µi + εij − αe log (wije) + ψCFije) /(1 + ΣJi

j exp (Xjeβe + µi + εij − αe log (wije) + ψCFije)). (8)

We then form the likelihood, which is defined over sequences of employer choices. The probability

of a sequence is the product of the individual choice probabilities for the alternative selected (y = j).

But because µi is not observed, it must be integrated out of the likelihood. The marginal likelihood

is then

L = Πi

∫Πe (pije)

y=j f (µi) dµi.

The e subscript on the product in the integrand is a slight abuse of notation; the product of

probabilities is taken over all openings posted while having no prior hires or having two or more

prior hires even if multiple openings exist at the same experience level. Gauss-hermite quadrature

is used to perform the integration and sandwich-form standard errors are reported.

3.4 Demand Results

Table 5 presents the results for the demand estimation with parameters differing by employer expe-

rience segment. The first column shows estimates for employers who have not hired before on the

site, using data from 61, 194 postings. Column 2 presents deviations (that is, parameter estimates

on interactions with experience) from the inexperienced segment estimates for employers on their

third or greater job post. This segment contains 38, 441 openings. The important results to note

relate to differences in estimated coefficients for the two groups. As is apparent from a comparison

across columns, αe, the estimated price coefficient, differs substantially between segments.

Our preferred specification contains country-group specific time trends for the largest countries in

the sample. Results with these group-trends are in Columns 3 and 4 and Columns 7 and 8. Columns

7 and 8 add employer random effects. Comparing the results in Column 3 to the interaction term

in Column 4, the estimated price coefficient gets more negative (more elastic) for the experienced

employer segment.13

These estimated parameters imply differences in markups. The mean own-bid elasticity for

13The elasticity in the conditional logit model is (1− pije)αe. In models with random effects, the mean elasticityis∫

(1− pije)αedF (µ) .

21

inexperienced employers is −4.527. The experienced employer segment is more elastic, with an

estimated own-bid elasticity of −5.952. Despite inexperienced employers’ lower probability of hiring,

the estimated average mark up over cost for inexperienced employers is 28.4 percent, compared to

20.2 percent for experienced employers. This reflects the differing elasticities across these segments

of employers.

Following the construction of the equilibrium bids in Section 3.1, the observed bids and estimated

markups allow for an estimate of costs. These estimated costs also differ by employer experience.

The mean cost of working for an inexperienced employer is 7.25 USD per hour (before the oDesk

fee) compared to 6.93 USD per hour in the experienced sample. This suggests that the additional

expected hassle cost of applying to an inexperienced employer is about 0.32 USD per hour, and, at

the mean, this represents a reduction in estimated costs of about 4 percentage points.

We further decompose Xβ + µ, what we term the log productive value of hiring, into its com-

ponent parts. The mean log productive values are provided for each segment toward the bottom

of Table 5. Using the logic of the Oaxaca-Blinder decomposition, the difference in log productive

values due to differences in the characteristics of applicants and jobs is(XE − XI

)βI , which is the

difference in characteristics for the experienced and inexperienced segment weighted by the inex-

perienced parameter estimates. The difference in log productive values due to different parameters

is XE (βE − βI) .14 Finally, the difference due to employer heterogeneity calculates the mean of the

posterior distribution of µ for the experienced employer sample and compares this mean to the

overall mean of the random effects distribution.15 Much of the difference is due to the change in

coefficients rather than to characteristics or selection based on heterogeneity. Note that the positive

change in coefficients is offset by an increase in price-sensitivity, so interpreting these results in light

of the employers’ objective function requires scaling by the added price sensitivity. Even after that

scaling, overall valuation for the platform increases with experience. The results in this table suggest

that both demand and supply side effects play a significant role in the new employer bid premium.

In the preferred estimates in Columns 7 and 8, around 60% of the premium can be attributed to

the higher markups set by workers who anticipate new employers to be have relatively inelastic

demand, and the remainder is due to the higher expected costs of applying to new employers.

14The parameter βE is estimated using only the experienced segment, but βI is estimated from employers who arepresent in both the experienced and inexperienced segment. It is possible that the difference in parameters is duesolely to selection and the random effects do not account fully for changing composition. To test this, we evaluatethe likelihood under each set of parameters for the sample of inexperienced employers who eventually transition tobecome experienced. For this subset of inexperienced employers, the parameters βI fit the data significantly betterthan the parameter βE . The predicted probability of the chosen alternative improves by 8 percentage points underβI .

15The posteriors are calculated using standard techniques given in Train (2003).

22

To check whether sorting concerns or omitted variables are driving the results, Panel A presents

the findings for the demand model that includes worker resume characteristics while Panel B ex-

cludes these variables from qje. In each case, omitting workers’ characteristics results in parameter

estimates that are closer to zero, indicating that if the demand estimates are biased, the direction

of bias is likely to be toward inelastic demand. To get a sense for how this affects sensitivity of

the estimates, assume that the change in parameter estimates with respect to any omitted variable

or worker sorting is at most proportional to the change between Panels A and B. Then the 8.2%

markup difference falls to 5.4%, about 43% of the total difference in bids across segments.

4 Employer Learning about Market Value

4.1 Learning and Changing Markups

Why does an employer appear to have more elastic demand after gaining experience in the mar-

ket? This section develops an illustrative model to provide intuition. Rather than assuming that

employers know µi, their value for the market, with certainty, they are instead assumed to enter

the market with a prior belief about µi and learn about the true value after hiring or evaluating

applicants.16 Because the employer hires only the best applicant, the spread in the employer’s

assessment between the best and next-best candidate is the decision-relevant summary measure for

an applicant’s market power. The illustration here shows that the spread between the highest and

second highest order statistic is increasing in the employer’s uncertainty about µi.

The act of gaining experience in the market is assumed to provide information because each job

application sends the employer a noisy signal of worker-employer specific match quality, mij. The

signal mij is modeled as

logmij = ηij + ξij, (9)

where ηij is a random variable with employer-specific mean E (ηij) = µi that is orthogonal to

common quality qj. The term µi is a permanent, employer-specific term that captures average

valuation for the market, and ηij is potentially valuable match quality centered around µi. A noise

term, ξij, means that ηij and µi are not realized with certainty. It is assumed that the employer does

16For related theory on experience goods and pricing, see Shapiro (1983) and Bergemann and Valimaki (2006).Israel (2005) is an example of how most empirical applications are structured; he examines how consumer behaviorchanges over time as service quality of an auto insurer is realized. Benabou and Gertner (1993) consider learning fromprices with the goal to resolve aggregate uncertainty; different predictions are derived here because the uncertaintyis idiosyncratic.

23

not know µi when he enters the market for the first time, but updates about it from interactions

with workers.

A convenient way to parameterize employer uncertainty and learning about µi is a normal-

normal learning model (Farber and Gibbons, 1996; Altonji and Pierret, 2001; Kahn and Lange,

2014). Suppose new employers believe the distribution of µi is normal, with prior mean and variance

µi0 and σ2µ0. The random variable ηij is also normally distributed with mean µi and variance σ2

η.

On their first job posting, an employer receives a set of signals mij, and uses his prior about

µi and the signals received to evaluate each applicant’s employer-specific match quality. When the

prior is imprecise, i.e. when σ2µ is large, a Bayesian employer attaches more weight to the signal

and less weight to his prior beliefs. Since each signal contains noise as well as information about

true employer-specific match quality, the variance of estimated worker qualities is increasing in

σ2µ. The variance of the estimated worker qualities is denoted σ2

η. This variance determines the

extent to which workers appear differentiated to the employer and, hence, determines the employer’s

willingness to pay for the most preferred worker over the second most preferred.

Understanding that each signal provides information about the employer-specific µi, each worker

interaction also allows the employer to update his beliefs about µi and σ2µ. Because σ2

η falls with

experience, in expectation it affects employer i’s willingness to pay for the most preferred worker.

In the normal-normal problem set out in Appendix 2, the expected difference between the highest

and second highest order statistic is inversely related to σ2η. This means that conditional on being

the highest order statistic, the expected spread between oneself and the second best alternative is

increasing with σ2η. Hence, an experienced employer, with a smaller σ2

η, is more likely to conclude

that the second ranked candidate is more substitutable for the preferred candidate. In the event

that any given worker in question is the preferred candidate, there is more competition from the

second-most preferred worker and, hence, a more elastic demand. Equation (2) shows that when

workers anticipate that employer demand is less elastic, they will submit a higher wage bid.

We do not estimate this learning process directly but the flexibility of the model likely captures

many of the central features. For example, allowing the coefficients on price and quality to differ

with experience allows for a different scaling of information relative to the (fixed) error variance.

This is isomorphic to a declining error variance through learning that holds fixed the coefficients,

although our approach has the added benefit of allowing full flexibility. This flexibility also captures

changing valuations with experience.

24

4.2 Evidence that New Employers Learn about Market Value

The model set out here generates predictions consistent with the empirical result in Section 3 that

workers’ bids to experienced employers contain higher markups. This subsection presents several

analyses that provide support for the learning mechanism described in the model.

4.2.1 Employers’ Number of Interviews Falls with Experience

Table 1 showed that the number of interviews falls on average on successive job postings. If em-

ployers are using interviews to both look for the best applicant for the job and also to learn about

their own value for the market, then the marginal benefit of an interview is higher when employers

know relatively little about their value for the market. It is therefore optimal for inexperienced

employers to interview more applicants. Table 6 presents the results of a regression of (1+ the log

of) the number of interviews conducted on each job by the number of previous hires made. The

first column does not include employer fixed effects and subsequent columns include combinations

of employer fixed effects, controls for qualitative opening features and fixed effects for expected job

duration, and controls for the mean log bid on the opening. Even with different levels of controls,

in all specifications with employer fixed effects, the number of interviews decreases, at a decreasing

rate, on successive jobs. The predicted number of interviews falls by more than 40 percent after

five prior hires.

4.2.2 Employer Outcomes

If an employer’s search process can be modeled as an optimal stopping problem where the employer

hires the first interviewed applicant whose expected value exceeds a threshold, the relevant threshold

will be higher when the employer is also using interviews to learn about the market. This is because

the more information an interview conveys, the greater the benefit of a marginal interview. An

implication of using interviews to learn is that as an employer interviews more candidates, the

threshold stopping value for hiring falls as the marginal learning value declines (Kohn and Shavell,

1974). A further implication is that new employers who hire after conducting a small number of

interviews must have found an applicant with a very high expected value early on in their search

process. An employer who interviews many applicants before hiring will likely end up hiring an

applicant with a lower expected value to them. Under the standard model in which the employer

is not using interviews to learn about the distribution of worker value, the threshold value remains

25

constant with the number of interviews. In this case, the expected value of the hired applicant

is independent of the number of interviews conducted before hiring. Under this alternative, an

employer who had to interview more applicants before hiring was simply unlucky in his or her

interviewee choices compared to an employer who found a worker whose expected value exceeded

the threshold level early on in the process.

Under the hypothesized learning process, then, inexperienced employers who hired after a small

number of interviews are likely to have hired a worker of higher value to them, since this worker

had a higher expected value when the hire was made. Table 7, Panel A, shows that inexperienced

employers who interview fewer candidates are more likely to hire, more likely to report having had

a successful hire, and more likely to give good feedback to the employed worker. Inexperienced

employers who hire after one interview are 5% more likely to report success and 4% more likely to

give good feedback after controlling for the hourly wage paid to the hired worker than inexperienced

employers who conduct six or more interviews, as shown in Columns 4 and 5. These results are

consistent with the hypothesis that employers who hire after one interview, forgoing the learning

value of additional interviews, must have been lucky in finding an interviewee with a high expected

value (and a high actual value) to them.

Columns 6 to 9 of Table 7, Panel A, examine a similar prediction for employers who go on

to post a second job. Using the variation in interviews on the first job, these results show that

employers who conduct more interviews on the first job are also less likely to report success or give

workers good feedback on the second job posting (although these findings are often insignificant).

The interpretation of these results is that, having already used the interviews conducted while

inexperienced to learn about the distribution of market value, these employers commence the second

search process when the learning value of a marginal interview is lower. Hence, an interviewee must

exceed a lower threshold expected value in order for the employer to hire that interviewee. Hired

workers, on average, live up to expectations and those workers that are hired having met a lower

threshold value in expectation turn out, ex post, to be of lower value on average.

Table 7 Panel B repeats the analysis in Panel A but includes an additional control that goes

some way to controlling for unobserved employer characteristics. This control is a group fixed effect

for all employers who share the following actions: They post in the same detailed job category, they

either all make one first interview request or all make simultaneous first interview requests; their

first interview from the same applicant or employer initiated candidacy category; and the residual

made by the first interviewee after removing country fixed effects is within the same 5% bucket of

26

the residual bid distribution. When this control is included, the negative relationship between the

number of initial interviews and job outcomes that was seen in Panel A mostly remains. Overall,

there is some evidence that inexperienced employers who conduct fewer interviews have better first-

and second-job outcomes.

5 Counterfactual Estimates: Platform Fees.

We now turn to the issue of platform fees, with a goal to understand whether a different fee

structure would improve platform profits and alter the mix of employers in the market. Since its

founding up through the end of the data period, the oDesk fee was constant at 10% of wages.

The ad-valorem structure results in complicated closed-form solutions for the optimal fee structure.

Analyzing the problem using specific (fixed) fees that do not depend on the actual wage helps to

clarify the tradeoffs from setting different fees. After building this intuition, we perform simulations

of platform profitability with different ad-valorem fees. Much of the intuition from the fixed-fee

case extends to simulations with an ad-valorem fee schedule.

5.1 Intuition about Different Fees by Experience

The platform’s objective is to maximize total profits, and it can do so with different fees on inexpe-

rienced and experienced employers. To denote specific fees, call the fee on inexperienced employers

tI and the fee on experienced employers tE. Let HI be an indicator for an employer hiring while

inexperienced and HE be an indicator for hiring while experienced. Wages for the inexperienced

and experienced segment are wI and wE. In this setup, employers only have the opportunity to hire

in the experienced segment once they have hired while inexperienced. The platform’s problem is

maxtI ,tE

Pr (HI |wI)× [tI + tE × Pr (HE|HI (wI) , wE)] .

where Pr (HI |wI) is the probability that an inexperienced employer hires given wages wI and

Pr (HE|HI (wI) , wE) is the probability an experienced employer hires as a function of wages wE

conditional on the first hire, HI (wI). Notice that the platform does not set wages, only fees, but

wages that employers face will vary with platform fees due to pass-through.

This analysis is relatively simple in the absence of selection concerns. Adding uncertainty and

selection makes the problem more interesting. When users are uncertain about platform valuation

and some uncertainty is resolved through hiring, Pr (HE|HI (wI) , wE) may change depending on

27

how employers’ beliefs about the platform evolve. That Pr (HE|HI (wI) , wE) specifically conditions

on HI (wI) and the wage paid captures the possibility that experienced hiring may be affected by the

identity of the marginal inexperienced employer. Variation in wages, induced by different platform

fees, induces variation in the identity of the marginal employer.

This formulation so far says nothing about how beliefs evolve with employer experience. This

leaves the learning process free, allowing models with myopic or anticipated learning. In the actual

calculations, the estimated parameters are used, meaning limited foresight is assumed about the

systematic evolution of valuations.17

Using HI as shorthand for Pr (HI |wI) and HE as shorthand for Pr (HE|HI (wI) , wE) , the first

order conditions are:

HI + tI∂HI

dwI

∂wI∂tI

+ tEHE∂HI

dwI

∂wI∂tI

+ tEHI∂HE

∂HI

∂HI

dwI

∂wI∂tI

= 0

HE ×HI + tE∂HE

∂wE

∂wE∂tE

HI = 0

The solution to the system of equations sets the fee for experienced employers equal to the

monetary value of the optimal markup for a monopolist with zero marginal cost:

t∗E = − HE

∂HE∂wE

∂wE∂tE

. (10)

The fee for the inexperienced is:

t∗I = − HI

∂HIdwI

∂wI∂tI

− t∗EHE − t∗EHI∂HE

∂HI

. (11)

The first term in t∗I is the standard static markup for the segment of inexperienced employers.

This markup is reduced by the latter two terms. The second term includes the future value of fees

for those hiring in the experienced segment, adjusting tI downward to account for the spillover to

future demand. The final term is of particular interest, which accounts for composition effects. The

expression ∂HE∂HI

incorporates how the marginal employer induced to hire will change the likelihood

of future hiring.

17 If employers anticipate that they will learn about their individual valuations for the platform, initial hiring mayreflect employers’ recognition of an option to no longer use the platform if the initial experience is unsuccessful. Onthe other hand, myopic employers who have low expectations of platform valuation may require inducement if theoption value of gaining information is not recognized.

28

5.2 Simulation of Profits under Different Ad-Valorem Fees

We simulate how employer hiring evolves under different fees and compute the associated platform

profits. Simulating employer hiring at different levels of fees makes use of the demand results

to characterize hiring probabilities. The estimated distribution of employer-specific heterogeneity

characterizes selection given the parameters. We simulate employer hiring using a grid of different

fees and look for the cell that results in the largest profit improvement.

The following steps are used in the simulation. First, inexperienced employers are assigned draws

from the distribution of random effects. Consistent with the random effects hypothesis, these draws

are orthogonal to eventual hiring behavior and the applicant set. Then for each ad-valorem fee pair,

(τI , τE) , simulated profits are constructed according to the following procedure: 1) Log wage bids to

inexperienced employers are calculated, where pass-through of the fee is computed according to the

worker’s first order condition for setting bids. 2) Inexperienced employers hire or not based on the

choice probabilities calculated from the demand model. 3) For those inexperienced employers who

hire, we iterate the following steps until convergence: a) A set of employers posts additional jobs. b)

Given the openings posted, elasticities are calculated and log wage bids including fees and workers’

markups are determined. c) Given wage bids, the expected surplus from posting additional jobs

is computed. d) Employers rationally choose to post additional jobs if the expected surplus from

an opening, accounting for wage bids and fees, is greater than a threshold. e) Given this threshold

rule and surplus, recompute the set of experienced employers posting subsequent jobs. If the set is

stable, terminate the loop. If not, go back to a). The loop in 3) involves re-calculating markups

and log wage bids conditional on the set of experienced employers. The threshold to continue

posting additional jobs is calculated to match the empirical transition rate between inexperienced

and experienced employers under the 10% uniform fee. 4) Profits are then calculated based on

hiring probabilities from the model and the fee-rate associated with the chosen bid. Employers

who become experienced are assumed to have a present value of 2.5 jobs while in the experienced

sample.

Table 8 gives the results. Panel A analyzes the change in platform profits relative to the current

fee structure, while Panel B provides the fraction of employers who ever transition to posting jobs

in the experienced segment. The main result is that the optimal fee on both segments is higher than

the 10% fee charged on oDesk at the time of the data. However, the optimal fee on the inexperienced

segment is lower than the optimal fee on the experienced segment. Profits are estimated to increase

by about 44% with a 20% fee on the inexperienced segment and a 30% fee on the experienced

29

segment. However, this would reduce the fraction of employers transitioning to become experienced

from 18.7% to 11.0%; the higher fees make up for the reduction in volume. Platform size would be

reduced, but per-transaction profitability would increase.

This analysis does, however, have some limitations. It does not account for other platforms

competitive response or entry of competing marketplaces. These considerations may reduce optimal

fees. In addition, the analysis abstracts away from tailored offers that deviate from this fee structure.

Here offers are assumed to be based on only a single segment, but additional segmentation or non-

linear schemes may be possible for the platform to price discriminate. Finally, the analysis does not

change the utility of hiring for inexperienced employers that anticipate hire fees when the become

experienced. Including forward looking behavior would require additional inducement to hire while

inexperienced.

6 Robustness Analysis

6.1 Different Application Rates

It is possible that the extent of competition on a job posting changes with employer experience, and

workers might submit lower wage bids when they anticipate a more competitive market. For varia-

tion in anticipated market competitiveness to explain the bid premium to inexperienced employers,

workers must anticipate that the job postings by experiecned employers are more competitive. Ta-

ble 1 showed that inexperienced employers in a sequential sample received a smaller number of

applicants in total, suggesting that, on average, competition might be greater for employers’ later

jobs.

To examine this possibility, Table 9 repeats the analysis from Table 2 but includes controls

for the log arrival rate of applicants within the first 24 hours of posting the job. Note that the

regressions already include a spline in the application number and bidders, at the time, could

observe the number of prior applicants. This additional regressor removes the effect of expected

future competition on bids. The faster the rate, the lower are all bids received by the employer.

However, including this control does not change the main finding from Table 2 that experienced

employers receive significantly lower bids.

30

6.2 Omitted Employer Characteristics

Because the order of interview requests is observed in the data, it is possible to examine whether

inexperienced employers’ early actions on a job posting are correlated with later choices and out-

comes. If this were the case, some of the results presented so far may be driven by unobserved,

predetermined, employer characteristics that shape employers’ search processes rather than their

experience and learning in the market. If ex-ante differences were driving, say, the number of inter-

views conducted or whether the employer eventually hires on the opening, one would expect that

these differences would be observed in the characteristics of who the employer interviews. Build-

ing on the analysis in Table 7B that controls for early actions in looking at job outcomes, Figure

7, Panel A, examines the distribution of the hourly wage bids of the worker selected for the first

interview based on the employer’s eventual action. The split in Panel A is based on whether the

employer does more or fewer than 5 total interviews. The figure plots the residual of a regression of

the log of the hourly wage on job category and year-month fixed effects. A comparison of the two

distributions shows very little difference in the choice of first interviewee for employers who go on

to interview few or many applicants.

Panel B of Figure 7 repeats this exercise by whether the employer hires on the first job. These

comparisons cast doubt on the hypothesis that inexperienced employers’ eventual differences in

interviewing or hiring behavior are driven by unobservable differences in information or preferences

at the time of the initial job posting or at the time of selecting the first interview candidate. Even

more important, the overlap in wage bid on the initial interview request sent by employers suggests

that workers are not able to segment inexperienced employers based on the eventual number of

interviews that they conduct or by the probability that they will hire.

7 Conclusion

This paper documents that potential employers on oDesk.com who have no prior experience hiring

in this labor market receive higher wage bids from workers compared to similar employers with

observable hiring experience. Employers who have made at least five prior hires receive bids that

are, on average, around 5% lower than employers who have not previously hired. This finding is

robust to controlling for employer, worker and for employer and worker fixed effects (not shown

here). The wages of the workers hired are also lower for experienced employers. The analysis

presented in the paper shows that workers’ optimal bids to inexperienced employers are higher

31

Figure 7: Residual Hourly Bids by First Applicant Selected for Interview

Panel A: Log Bids for employers who conduct fewer than five and five or more interviews on the first posting.

Panel B: Log bids for employers who don't hire and who hire on the first posting.

0.2

.4.6

.8

kd

en

sity log

HrlyR

ate

Resid

-4 -2 0 2 4Log Residual Bid

No Hire Made Hire Made

0.2

.4.6

.81

kd

en

sity log

HrlyR

ate

Resid

-4 -2 0 2 4Log Residual Bid

<5 interviews 5+ interviews

because of two channels: one on the demand side and one on the supply side.

On the demand side, inexperienced employers’ hiring probabilities are less elastic to the wage

bids offered because, at greater levels of uncertainty about match quality, the variance of the highest

expected order statistic of the quality distribution is larger. A marginal bid increase has a smaller

effect on the probability of being hired, resulting in higher optimal mark ups. Estimating market

demand for both inexperienced and experienced employers yields estimated wage elasticities of -4.5

and -6.0, respectively.

On the supply side, inexperienced employers are likely to create higher costs for workers when

applying for the job or when hired. For example, inexperienced employers conduct more interviews

and are less likely to hire, creating hassle costs for the workers with less expected benefit. Inexpe-

rienced employers are also more likely to need help understanding the mechanics of the site. These

greater costs appear to be passed through to inexperienced employers in higher wage bids.

32

Making the assumption that observed wage bids are the equilibrium outcome of a differentiated

products Bertrand game allows us to decompose the wage bid difference into these demand and

supply side effects. Differences in equilibrium markups due to differences in the wage elasticity of

demand account for an estimated 60% of the wage bid gap between inexperienced and experienced

employers. The residual—or 40% of the gap—can be attributed to differences in workers’ costs.

Further empirical findings suggest that experience on the site leads to learning-by-doing even as

employers resolve uncertainty. Frictions while inexperienced prevent employers from moving along

the learning curve to better use the platform.

Showing that both demand and supply differ by inexperienced and experienced segments allows

us to find the platform profit-maximizing fee that the platform should charge each segment of

employers. On the one hand, inexperienced employers’ more inelastic demand would lead oDesk

to impose a higher fee on these employers but, on the other hand, a lower fee on inexperienced

employers will increase the total number of transactions on the site in the future, as these employers

are more likely to remain in the market. The net effect is that oDesk could increase profits by setting

a fee of 30% to experienced employers and a lower fee of 20% to inexperienced employers. Both

fees are higher than the current 10% fee.

Whether or not potential trading partners have experience transacting in a market is observable

in many kinds of markets, including many online product markets. This paper finds evidence that

employer (buyer) experience is a salient characteristic for workers (suppliers) in the oDesk labor

market because experience affects both the nature of employer demand and the cost of worker

supply.

Appendix

Appendix 1: Data Details and Cleaning

Appendix Table 1 gives details about the detailed resume data used in the estimation sample. The

estimation sample is a subset of the data contained in Table 1A.

The following restrictions are used to clean job openings. The estimation sample restricts to

openings that have at least 1 day elapsed between the next posting and the last posting. This allows

for at least a single cycle of applicants from different time zones to arrive to the different jobs while

eliminating batched hiring for which available applicants may blend across jobs. The estimation

sample also drops jobs on which the employer hires a worker from a previous engagement. Many

33

jobs also appear to originate from bringing an offline relationship onto the platform. Filtering these

jobs involves requiring that at least one application be worker-initiated while the total number of

candidates must be greater than 5. Any job from an employer who sends over 100 interview

requests on the first job or who sends 60 interview requests on the job is omitted. These are likely

to be fake jobs posted by spammers. Finally, any job posted by mistake is dropped.

The following restrictions are used to clean applications. First, applications from workers invited

who later report they are unavailable are dropped. Applications are also dropped if the employer

reports obvious spam.

Appendix 2: Learning Illustration

This appendix expands on the illustrative model outline given in Section 4.1. The signal observed

by the employer, mij,is modeled as

logmij = ηij + ξij, (12)

where ηij is a random variable with employer-specific mean (µi) and ξij is an idiosyncratic error

component such that η is not realized with certainty. The term µi is a permanent, employer-specific

term that captures average valuation for the market. To parameterize uncertainty, it is assumed

that the employer does not know µi with certainty but it is learned about through experience.

Suppose employers believe the distribution of µi is normal, with prior mean and variance µie=0

and σ2µe=0. The random variable ηij is also normally distributed with mean µi and variance σ2

η. In

this notation, µi is the actual data generating process, and beliefs have experience subscripts, so

µie is the mean belief about µi for an employer with experience level e.

As a Bayesian employer gains experience, beliefs about µi change, and the interpretation of the

signal becomes less varied. To see this, start by considering what happens to overall uncertainty

about the market valuation, µi, when an employer with experience e interacts with one more worker

and observes logmij. In this case, the distribution of the posterior mean, µi(e+1), is

µi(e+1)|µie, σ2µe ˜ N

((logmij)σ

2µe + µie

(σ2η + σ2

ξ

)(σ2η + σ2

ξ + σ2µe

) ,σ2µe

(σ2η + σ2

ξ

)σ2η + σ2

ξ + σ2µe

), (13)

and this becomes the distribution of the prior for an employer with experience e+ 1.

Note that the variance from examining e prior candidates isσ2µe(σ2

η+σ2ξ)

σ2η+σ

2ξ+e×σ2

µe. To simplify the prob-

34

lem, we will assume that µi is revealed after the first hire. It is straightforward to model learning

dynamics when ξij is resolved but η and µ are indistinguishable.

From this recursive expression for the distribution of beliefs at any experience level e, it is

possible to solve the inexperienced employers’ filtering problem to determine expected log match

quality from the noisy match quality signal logmij. Employers care only about that part of logmij

which comes through ηij, as ξij is irrelevant for worker output. Thus, two factors are relevant for

the decision: ηij and µi. The best estimate of the productivity relevant components of the match

effect at experience level e is

ηije = E(ηij| logmij, µie, σ

2µe

)=

logmij

(σ2η + σ2

µe

)+ µieσ

2ξ

σ2η + σ2

µe + σ2ξ

, (14)

which says that the weight employers put on the signal logmij is the ratio of productive variance(σ2η + σ2

µe

)to total variance

(σ2η + σ2

µe + σ2ξ

). The weight on the prior is 1 minus the productive

to total variance ratio. The weight on logmij is increasing with the variance of the prior belief

about µie,as∂(σ2

µe+σ2η)/(σ2

η+σ2ξ+σ

2µe)

∂σ2µe

=σ2ξ

(σ2η+σ

2ξ+σ

2µe)

2 > 0. In the normal-normal version of the learning

problem, σ2µe declines with experience and experienced employers therefore put more weight on the

prior about µie relative to the weight on the noisy signal logmij. This is because the total variance

around µie declines, and optimality requires that additional weight is placed on the prior mean

because it is more precise.

When forming bids, because demand is unitary, each candidate anticipates that they will be

hired only in the event that they are the highest order statistic in the employer’s choice set after

adjusting for wage bids. The variance of the conditional expectation of worker-employer specific

match quality itself determines the expected order statistics in the employer’s choice set Ji. The

variance of the conditional expectation given µie declines with experience, and the fact that σ2ηe falls

with experience affects employer i’s willingness to pay for the most preferred worker and alters the

optimal markup for all workers. In the normal-normal problem, the expected difference between

the highest and second highest order statistic is inversely related to σ2ηe.

18 Conditional on being

the highest order statistic, the expected spread between oneself and the second best alternative

is increasing with σ2ηe. That is, an inexperienced employer, with a larger σ2

ηesubstitutable for the

preferred candidate. In the event that the worker in question is the preferred candidate, there is

less competition from the second-most preferred worker and, hence, a more inelastic demand.

18This is found to be true in simulations with normally distributed random variables, however, we are not awareof a rigorous proof.

35

References

[1] Ackerberg, Daniel. 2003. ”Advertising, learning, and consumer choice in experience good mar-

kets: an empirical examination.” International Economic Review, 44(3), 1007-1040.

[2] Agrawal, Ajay, Christian Catalini and Avi Goldfarb. 2011. ”Friends, Family, and the Flat

World: The Geography of Crowdfunding.” NBER Working Paper No.16820.

[3] Agrawal, Ajay and Avi Goldfarb. 2008. ”Restructuring Research: Communication Costs and

the Democratization of University Innovation.” American Economic Review, 98(4), 1578-1590.

[4] Agrawal, Ajay, Nicola Lacetera, and Elizabeth Lyons. 2012. ”How Do Online Platforms

Flatten Markets for Contract Labor?” Working Paper.

[5] Agrawal, Ajay, John Horton, Nicola Lacetera, and Elizabeth Lyons. 2013. ”Digitization and

the Contract Labor Market: A Research Agenda.” in “The Economic Analysis of the Digital

Economy’ edited by Avi Goldfarb, Shane Greenstein and Catherine Tucker.

[6] Altonji, Joseph, and Charles Pierret. 2001. ”Employer Learning and Statistical Discrimina-

tion.” The Quarterly Journal of Economics, 116(1), 313-350.

[7] Arcidiacono, Peter, Esteban Aucejo, Arnaud Maurel, and Tyler Ransom. 2016. ”College Attri-

tion and the dynamics of information revelation.” Working Paper

[8] Bajari, Patrick and Ali Hortacsu. 2004. ”Economic Insights from Internet Auctions.” Journal

of Economic Literature, 42(2), 457-486.

[9] Benabou, Roland and Robert Gertner. 1993. ”Search with Learning from Prices: Does In-

creased Inflationary Uncertainty Lead to Higher Markups.” Review of Economic Studies, 60(1),

69-94.

[10] Bergemann, Dirk and Juuso Valimaki. 2006. ”Dynamic Pricing of New Experience Goods.”

Journal of Political Economy, 114(4), 713-743.

[11] Cabral, Luis, and Ali Hortacsu. 2010. ”The dynamics of seller reputation: Evidence from

ebay*.” The Journal of Industrial Economics, 58.1, 54-78.

[12] Erdem, Tulin, and Michael Keane. 1996. ”Decision-making Under Uncertainty: Capturing

Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets.” Marketing Science,

15(1), 1-20.

36

[13] Farber, Henry and Robert Gibbons. 1996. ”Learning and Wage Dynamics.” The Quarterly

Journal of Economics, 111(4), 1007-1047.

[14] Ghani, Ejaz, William Kerr, and Christopher Stanton. 2014. ”Diasporas and outsourcing:

Evidence from oDesk and India.” Management Science, 60(7),1677-1697.

[15] Horton, John. 2010. ”Online Labor Markets.” Internet and Network Economics: 515-522.

[16] Horton, John. 2017a. ”The Effects of Algorithmic Labor Market Recommendations: Evidence

from a Field Experiment.” Journal of Labor Economics, 35(2).

[17] Horton, John. 2017b. ”Price Floors and Employer Preferences: Evidence from a Minimum

Wage Experiment.” Working Paper.

[18] Horton, John, William R. Kerr, and Christopher Stanton. 2017. ”Digital Labor Markets and

Global Talent Flows.” NBER Working Paper No. 23398.

[19] Israel, Mark. 2005. ”Services as Experience Goods: An Empirical Examination of Consumer

Learning in Automobile Insurance.” The American Economic Review, 95(5), 1444-1463.

[20] Kahn, Lisa. 2013. ”Asymmetric Information between Employers.” American Economics

Journal: Applied Economics. 5(4): 165-205.

[21] Kahn, Lisa and Fabian Lange. 2014. ”Employer Learning, Productivity, and the Earnings

Distribution: Evidence from Performance Measures.” Review of Economic Studies , 81.4:

1575-1613.

[22] Lange, Fabian. 2007. ”The Speed of Employer Learning.” Journal of Labor Economics, 25(1),

1-35.

[23] Lewis, Gregory. 2011. ”Asymmetric Information, Adverse Selection and Online Disclosure: The

Case of eBay Motors.” American Economic Review, 101(4), 1535-1546.

[24] Lyons, Elizabeth. 2017. ”Team Production in International Labor Markets: Experimental Ev-

idence from the Field.” American Economic Journal: Applied Economics, forthcoming July,

2017.

[25] Miller, Robert. 1984. ”Job matching and occupational choice.” Journal of Political Economy,

92(6), 1086-1120.

37

[26] Pallais, Amanda. 2014. ”Inefficient Hiring in Entry-Level Labor Markets.” American Economic

Review, 104(11): 3565-3599.

[27] Petrin, Amil and Kenneth Train. 2010. ”A Control Function Approach to Endogeneity in

Consumer Choice Models.” Journal of Marketing Research, 47(1): 3-13.

[28] Stanton, Christopher and Catherine Thomas. 2016. ”Landing the First Job: The Value of

Intermediaries in Online Hiring.” The Review of Economic Studies, 83(2), 810-854.

[29] Shapiro, Carl. 1983. ”Optimal Pricing of Experience Goods.” The Bell Journal of Economics,

14(2), 497-507.

[30] Train, Kenneth. 2003. ”Discrete Choice Methods with Simulation.” Cambridge University

Press.

38

Table 1A: Summary Statistics

Postings where a Hire is made

Previous

Hires

Number of

Job Openings

Number of

Candidates

Share of

Employer-

Initiated

Candidates

Mean Wage

Bid

Share with

Good

Worker

Feedback

Share with

Missing

Worker FB

Number of

Interviews

Probability a

Hire is Made

Mean Wage

Bid of Hired

Worker

Share with

Good Worker

Feedback

Share with

Missing

Worker FB

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

0 119846 18.42 7.9% 10.15 34% 44% 2.98 0.22 9.54 38% 39%

(25.28) (7.20) (5.62) (0.41) (6.91)

1 32535 14.71 8.1% 9.76 37% 40% 2.47 0.49 9.16 40% 36%

(24.74) (7.02) (4.70) (0.50) (6.80)

2 22278 14.17 8.3% 9.31 37% 40% 2.32 0.52 9.05 41% 36%

(27.95) (6.96) (4.72) (0.50) (6.74)

3 16820 13.94 7.8% 9.32 37% 40% 2.17 0.54 8.88 40% 36%

(26.74) (7.02) (4.20) (0.50) (6.76)

4+ 131385 13.78 7.6% 8.69 36% 42% 2.09 0.57 8.54 40% 37%

(30.47) (7.01) (4.60) (0.50) (6.81)

Table 1B: Summary Statistics for Sequential Openings

Postings where a Hire is made

Previous

Hires

Number of

Job Openings

Number of

Candidates

Share of

Employer-

Initiated

Candidates

Mean Wage

Bid

Share with

Good

Worker

Feedback

Share with

Missing

Worker FB

Number of

Interviews

Probability a

Hire is Made

Mean Wage

Bid of Hired

Worker

Share with

Good Worker

Feedback

Share with

Missing

Worker FB

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

0 61197 25.49 2.1% 10.24 33% 45% 3.64 0.16 9.50 36% 41%

(27.26) (7.00) (5.84) (0.37) (6.70)

1 10177 27.75 2.0% 9.85 35% 41% 4.04 0.31 9.17 38% 37%

(29.66) (6.87) (5.65) (0.46) (6.57)

2 6221 29.43 1.8% 9.35 35% 42% 3.95 0.30 8.90 38% 38%

(36.99) (6.82) (5.74) (0.46) (6.51)

3 4527 28.30 1.8% 9.60 35% 41% 3.88 0.31 8.88 37% 37%

(31.32) (6.89) (5.26) (0.46) (6.47)

4+ 27693 31.19 1.7% 9.05 35% 42% 4.06 0.28 8.72 38% 38%

(39.84) (6.91) (6.00) (0.45) (6.59)

Notes: Sample period is from January 2008 to June 2010. Wage bids are winsorized at the 99th percentile. For details on sample composition, see Appendix 1. Standard

deviations are given in parentheses.

Table 2: Log Wage Bids Decline with Observable Employer Experience

OLS OLS

Employer Fixed

Effects

Employer Fixed

Effects

Worker Fixed

Effects

Worker Fixed

Effects

(1) (2) (3) (4) (5) (6)

On posts after making 1 hire -0.0231*** -0.0184*** -0.0200*** -0.0165*** -0.00469*** -0.00632***

(0.00384) (0.00337) (0.00502) (0.00443) (0.000779) (0.000763)

2 hires -0.0252*** -0.0202*** -0.0301*** -0.0245*** -0.00796*** -0.00908***

(0.00613) (0.00553) (0.00683) (0.00609) (0.000962) (0.000942)

3 hires -0.0258*** -0.0210*** -0.0329*** -0.0283*** -0.00857*** -0.0101***

(0.00570) (0.00495) (0.00694) (0.00609) (0.00112) (0.00111)

4 hires -0.0442*** -0.0351*** -0.0443*** -0.0357*** -0.0116*** -0.0130***

(0.00678) (0.00593) (0.00796) (0.00700) (0.00124) (0.00122)

5+ hires -0.0424*** -0.0339*** -0.0480*** -0.0396*** -0.0180*** -0.0195***

(0.00442) (0.00395) (0.00667) (0.00589) (0.000667) (0.000654)

Detailed Worker and Job Controls No Yes No Yes No Yes

Observations 3,016,148 3,016,115 3,016,115 3,016,115 3,016,115 3,016,115

R-Squared 0.051 0.483 0.557 0.550 0.609 0.851

Notes: Dependent variable is the log of the hourly wage bid. The sample is limited to worker-initiated applications on sequential job openings.

Robust standard errors are clustered by employer. All specifications contain a spline for the applicant's arrival order, detailed job category fixed

effects, monthly time fixed effects, and expected duration by hours-per-week fixed effects. Specifications with detailed worker and job controls also

include the following: a third-order polynomial in the number of characters in the job description, a dummy for good reported English skills, a

dummy for a BA or higher degree, a dummy for having no prior work experience, a dummy for agency affiliation and its interaction with having no

prior work experience, the number of prior jobs, the log of the wage on the last hourly job, and an indicator that no last wage is displayed when the

worker is experienced.

Table 3: Log Wage Bids Decline with Observable Employer Experience

OLS OLS

Employer Fixed

Effects

Employer Fixed

Effects

Worker Fixed

Effects

Worker Fixed

Effects

(1) (2) (3) (4) (5) (6)

Panel A: Interactions with Feedback Employer Has Received

On posts after making 2+ hires -0.0370*** -0.0256*** -0.0217*** -0.0185*** -0.0136*** -0.0142***

(0.00363) (0.00656) (0.00634) (0.00565) (0.00405) (0.00398)

2+ hires and no observable employer feedback -0.00696 -0.0166*** -0.0128** -0.00250 -0.00342

(0.00619) (0.00634) (0.00559) (0.00379) (0.00373)

2+ hires and good observable employer feedback -0.00182 -0.00635 -0.00516 0.00141 6.96e-05

(0.00726) (0.00771) (0.00679) (0.00445) (0.00437)

Detailed Worker and Job Controls No Yes No Yes No Yes

Observations 2,731,683 2,731,683 2,731,683 2,731,683 2,731,683 2,731,683

R-Squared 0.501 0.578 0.572 0.632 0.862 0.866

Panel B: Interactions with Feedback Employer Has Left for Workers

On posts after making 2+ hires -0.0375*** -0.0445*** -0.0534*** -0.0413*** -0.0159*** -0.0165***

(0.00330) (0.00988) (0.0109) (0.00997) (0.00530) (0.00521)

2+ hires and no worker feedback given 0.0161 0.0175* 0.0116 0.00158 0.000595

(0.0101) (0.0100) (0.00927) (0.00548) (0.00539)

2+ hires and good worker feedback given 0.0131 0.00480 0.00278 0.00315 0.00323

(0.0116) (0.0117) (0.0107) (0.00657) (0.00646)

Detailed Worker and Job Controls No Yes No Yes No Yes

Observations 2,731,683 2,731,683 2,731,683 2,731,683 2,731,683 2,731,683

R-Squared 0.501 0.578 0.572 0.632 0.862 0.866

Notes: Dependent variable is the log of the hourly wage bid. The sample is limited to worker-initiated applications on sequential job openings. Robust standard errors

are clustered by employer. All specifications contain a spline for the applicant's arrival order, detailed job category fixed effects, monthly time fixed effects, and

expected duration by hours-per-week fixed effects. Specifications with detailed worker and job controls contain the same additional controls as detailed in notes to

Table 2.

Table 4: First Stage Regression of Log Hourly Bids on Exchange Rate and Arrivals Instruments

Sample

Inexperienced

Employers

Inexperienced

Employers, Add

Country Time

Trends

Experienced

Employers

Experienced

Employers, Add

Country Time

Trends

(1) (2) (3) (4)

Panel A: Parameter Estimates from Models Including Detailed Resume Data

Log Dollar to Local Currency Exchange Rate, de-trended 0.0839*** 0.0836*** 0.179*** 0.115***

(0.00696) (0.00699) (0.00984) (0.00990)

Residual Log Applicants per Job Opening -0.109*** -0.0900*** -0.120*** -0.101***

(0.00359) (0.00363) (0.00425) (0.00436)

Number of Observations 1,559,848 1,559,848 1,175,033 1,175,033

R-Squared 0.600 0.601 0.638 0.639

F Statistic on Excluded Instruments 302.6 225.2 308.6 186.1

Panel B: Parameter Estimates from Models Excluding Resume Data

Log Dollar to Local Currency Exchange Rate, de-trended 0.0589*** 0.0637*** 0.158*** 0.104***

(0.00737) (0.00739) (0.0106) (0.0106)

Residual Log Applicants per Job Opening -0.125*** -0.102*** -0.156*** -0.126***

(0.00380) (0.00384) (0.00452) (0.00464)

Number of Observations 1,559,848 1,559,848 1,175,033 1,175,033

R-Squared 0.542 0.543 0.579 0.580

F Statistic on Excluded Instruments 315.2 224.5 379.4 225.8

Panel C: Benchmark Estimates from Models Including Resume Data and Worker Fixed Effects

Log Dollar to Local Currency Exchange Rate, de-trended 0.109*** 0.105*** 0.135*** 0.124***

(0.00573) (0.00575) (0.00867) (0.00875)

Residual Log Applicants per Job Opening -0.0245*** -0.0287*** -0.0136*** -0.0270***

(0.00267) (0.00268) (0.00341) (0.00343)

Number of Observations 1,559,846 1,559,846 1,175,035 1,175,035

R-Squared 0.868 0.868 0.870 0.870

Notes: First stage regression coefficients with robust standard errors in parentheses. The inexperienced sample is employers on their

first job post. The experienced sample is employers who have hired 2 or more previous workers. The first instrument is the log of the

average monthly dollar to local currency exchange rate after removing a currency-specific linear trend. The second instrument uses the

average number of applications arriving per job opening in the first 24 hours for other jobs in that week and job category cell. After

taking logs, the instrument is what remains after removing week and job category fixed effects. Indicators that each instrument is

missing or invariant within country are also included. All models contain a calendar time trend, job category fixed effects, a spline with

4 knots for applicant order (knots correspond to pagination after sorting by arrival time), an indicator that the application was employer

initiated, and 8 country-group fixed effects. The last country group includes many countries with small application shares. Models in

Panel A also include the following applicant characteristics: a dummy for good reported English skills, a dummy for a BA or higher

degree, a dummy for having no prior work experience, a dummy for agency affiliation and its interaction with having no prior work

experience, the number of prior jobs, and the log of the wage on the last hourly job. See Appendix Table 1 for details and summary

statistics on the resume data. Models in Panel C add worker fixed effects to the models in Panel A and omit time-invariant worker

characteristics.

Table 5: Demand Model Estimates, Elasticities, Costs, and Markups

(1) (2) (3) (4) (5) (6) (7) (8)

Inexperienced

Employer

Baseline

Experienced

Employer

Interaction

Inexperienced

Employer

Baseline, With

Country-Group

Trends

Experienced

Employer

Interaction

Inexperienced

Employer

Baseline, Add

Random

Effects

Experienced

Employer

Interaction,

Add Random

Effects

Inexperienced

Employer

Baseline, Add

Random

Effects and

Trends

Experienced

Employer

Interaction,

Add Random

Effects and

Trends

Panel A: Parameter Estimates from Demand Models Including Detailed Resume Data

Experienced Employer Parameters are Additive Interaction Terms Relative to Inexperienced Employer Baseline

Log Hourly Bid -2.951 -2.517 -3.984 -2.15 -3.446 -1.933 -4.558 -1.447

(0.189) (0.444) (0.789) (0.963) (0.0449) (0.516) (0.100) (0.131)

Standard Deviation of Random Effects 1.438 (0.516) 1.436

(0.030) (0.026)

Panel B: Parameter Estimates from Demand Models Excluding Resume Data

Log Hourly Bid -1.696 -1.872 -2.705 -1.504 -2.048 -1.471 -3.167 -0.87

(1.911) (1.868) (0.224) (0.279) (0.043) (0.094) (0.134) (0.103)

Standard Deviation of Random Effects 1.44 1.438

(0.024) (0.027)

Panel C: Valuations, Elasticities, and Costs from Estimates in Panel A

Mean Own-Price Elasticity -2.932 -5.416 -3.959 -6.077 -3.422 -5.33 -4.527 -5.952

Mean Markup, Pre oDesk-Fee 0.518 0.226 0.338 0.197 0.413 0.231 0.284 0.202

Mean Implied Cost (USD, Pre-Fee) $6.13 $6.79 $6.96 $6.96 $6.59 $6.77 $7.25 $6.93

Mean Wage Bid (Pre oDesk-Fee) $9.31 $8.33 $9.31 $8.33 $9.31 $8.33 $9.31 $8.33

Percentage of Mean Bid Difference Due to Markups 182.26% 100.11% 122.40% 60.81%

Mean Log Productive Value (XB+ E(mu)) 0.72 5.55 2.84 6.80 1.36 5.19 3.65 6.37

Change in XB Due to Characteristics -0.55 -0.71 -0.56 -0.73

Change in XB Due to Coefficients 5.38 4.67 4.22 3.28

Change in Log Prod Value Due to Buyer Heterogeneity 0.17 0.17

Notes: There are 1,633,546 applications to 61,194 job openings in the inexperienced segment and 1,175,035 applications to 38,441 job openings in the experienced segment.

Experienced employer columns report additive interaction terms relative to inexperienced employer baseline. Robust standard errors calculated using the sandwich form in

parentheses. Decomposition of changes due to coefficients calculated as X_e(B_e-B_i) and decomposition due to changes in resume and job characteristics as (X_e-X_i)*B_i. The

change due to heterogeneity uses the posterior of the predicted random effect for each employer and takes the mean of the posterior for experienced employers.

Table 6: Log interviews per job opening fall with hiring experience

DV: Log Number of Interviews +1 OLS Employer Effects Employer Effects Employer Effects Employer Effects

(1) (2) (3) (4) (5)

One previous hire 0.163*** -0.138*** -0.139*** -0.140*** -0.140***

(0.0100) (0.0180) (0.0179) (0.0180) (0.0179)

Two previous hires 0.136*** -0.218*** -0.218*** -0.220*** -0.219***

(0.0125) (0.0220) (0.0220) (0.0220) (0.0220)

Three previous hires 0.143*** -0.248*** -0.247*** -0.249*** -0.248***

(0.0142) (0.0252) (0.0252) (0.0252) (0.0251)

Four previous hires 0.110*** -0.291*** -0.289*** -0.294*** -0.292***

(0.0170) (0.0290) (0.0290) (0.0290) (0.0290)

Five or more previous hires 0.121*** -0.376*** -0.373*** -0.378*** -0.376***

(0.0103) (0.0236) (0.0236) (0.0236) (0.0236)

Mean Log Bid -0.0816*** -0.0849***

(0.0194) (0.0194)

Constant 2.357*** 1.468*** 1.448*** 1.660*** 1.646***

(0.0820) (0.0886) (0.0886) (0.0996) (0.0995)

Includes job duration fixed effects and third

order polynomial of job description length No No Yes No Yes

Observations 109,748 109,748 109,748 109,748 109,748

R-Squared 0.021 0.658 0.659 0.658 0.659

Notes: Robust standard errors are clustered by employer. All specifications contain year-month fixed effects as well as job category and job

duration fixed effect.

Table 7: Productivity and Search Effort on the First and Second Jobs, for Employers who Interview on First Job

Hires a Worker

Hires and

Reports Success

Hires with

Good Feedback

Hires and

Reports

Success, Wage

Control

Hires with

Good Feedback,

Wage Control

Reports Success,

Sequential

Openings

Gives Good

Feedback,

Sequential

Openings

Reports Success,

Sequential

Openings and

Wage Control

Gives Good

Feedback,

Sequential

Openings and

Wage Control

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Panel A: Specifications Without Buyer Group Fixed Effects

2-5 Interviews 0.025*** 0.010** 0.007 -0.025** -0.043*** -0.011 -0.040* -0.012 -0.041*

(0.005) (0.004) (0.005) (0.013) (0.014) (0.021) (0.023) (0.021) (0.023)

6-10 Interviews 0.032*** 0.008 0.012** -0.048*** -0.035** -0.005 -0.012 -0.003 -0.010

(0.007) (0.005) (0.005) (0.015) (0.017) (0.025) (0.028) (0.025) (0.028)

11+ Interviews -0.038*** -0.032*** -0.028*** -0.051** -0.036 -0.062** -0.051 -0.057* -0.043

(0.007) (0.005) (0.005) (0.022) (0.025) (0.030) (0.032) (0.030) (0.032)

Includes Buyer Group Fixed Effects No No No No No No No No No

Mean of DV 0.246 0.140 0.124 0.662 0.667 0.642 0.665 0.642 0.665

Observations 39,085 37,383 36,214 7,895 6,730 3,330 2,866 3,329 2,866

R-Squared 0.057 0.064 0.051 0.092 0.046 0.100 0.056 0.106 0.064

Panel B: Including Buyer Group Fixed Effects

2-5 Interviews -0.018*** -0.014*** -0.016*** -0.023 -0.043* -0.043 -0.041 -0.043 -0.041

(0.006) (0.005) (0.005) (0.019) (0.024) (0.038) (0.042) (0.038) (0.041)

6-10 Interviews -0.017** -0.019*** -0.016*** -0.050** -0.037 -0.030 0.006 -0.028 0.009

(0.008) (0.006) (0.006) (0.021) (0.028) (0.044) (0.052) (0.044) (0.052)

11+ Interviews -0.085*** -0.060*** -0.056*** -0.060** -0.037 -0.072 -0.024 -0.068 -0.020

(0.008) (0.006) (0.007) (0.030) (0.037) (0.054) (0.058) (0.054) (0.057)

Includes Buyer Group Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.246 0.140 0.124 0.662 0.667 0.642 0.665 0.642 0.665

Observations 39,082 37,380 36,211 7,895 6,730 3,329 2,865 3,328 2,865

R-Squared 0.163 0.160 0.151 0.311 0.291 0.444 0.457 0.447 0.462

First Job Outcomes Second Job Outcomes, for Employers who Hire on Second Job

Notes: Panel B includes fixed effects for groups of employers who interview workers with similar characteristics on the first job. Standard errors clustered by detailed job category x time. All

specifications contain fixed effects for expected duration of job, time, and detailed job category.

Table 8: Platform Profits and Repeat Job Posting for Different Fee Schedules

Panel A: Percent Change in Profits Relative to 10% Uniform Fee

Inexperienced \ Experienced Fee 10% 15.0% 20.0% 25.0% 30.0% 35.0%

7.5% -6.5% 11.0% 22.0% 28.6% 31.0% 31.2%

10.0% 0.0% 16.9% 28.2% 34.6% 37.6% 36.7%

15.0% 9.5% 24.8% 33.6% 40.0% 43.5% 43.5%

20.0% 13.0% 26.2% 35.4% 40.4% 44.4% 42.9%

25.0% 13.2% 24.9% 32.7% 37.6% 40.2% 41.0%

30.0% 10.4% 20.1% 27.7% 31.7% 34.8% 35.3%

35.0% 4.8% 13.8% 20.2% 23.9% 26.3% 27.1%

Panel B: Fraction of Employers Posting 2 or More Jobs

Inexperienced \ Experienced Fee 10% 15.0% 20.0% 25.0% 30.0% 35.0%

7.5% 19.9% 19.0% 17.8% 16.4% 14.9% 13.5%

10.0% 18.7% 17.9% 16.8% 15.5% 14.1% 12.8%

15.0% 16.4% 15.7% 14.7% 13.7% 12.5% 11.5%

20.0% 14.1% 13.6% 12.8% 11.9% 11.0% 10.1%

25.0% 12.1% 11.7% 11.0% 10.3% 9.6% 8.8%

30.0% 10.4% 10.0% 9.5% 8.9% 8.3% 7.7%

35.0% 8.8% 8.5% 8.1% 7.6% 7.1% 6.7%

Notes: Simulations use the parameters from the last 2 columns of Table 5. Inexperienced employers are first assigned draws from the distribution of random

effects. Simulated profits are then constructed according to the following procedure for each pair of fees: 1) Log wage bids to inexperienced employers are

calculated, where pass-through of the fee is computed according to the worker's first order condition for setting bids. 2) Inexperienced employers hire or not

based on the choice probabilities calculated from the demand model. 3) Iterating until convergence, log wage bids including fees and the worker's markup

are calculated for experienced employers who post jobs. The set of experienced employers posting subsequent jobs is calculated using the realized random

draws, the expected surplus given wage bids, and a threshold offset for posting new jobs. Markups and log wage bids are recomputed given the set of

experienced employers. The threshold in step 3) is computed to match the empirical transition rate in the data under the 10% uniform fee. Profits are then

calculated based on hiring probabilities from the model and the fee-rate associated with the chosen bid. Employers who become experienced are assumed

to have a present value of 2.5 jobs while in the experienced sample.

Table 9: Log Wage Bids controlling for the arrival rate of job applicants

OLS OLS

Employer Fixed

Effects

Employer Fixed

Effects Worker Fixed Effects Worker Fixed Effects

(1) (2) (3) (4) (5) (6)

Panel A

On posts after making 1 hire -0.0130*** -0.00951*** -0.0135*** -0.0109** -0.00225*** -0.00376***

(0.00380) (0.00336) (0.00495) (0.00439) (0.000776) (0.000760)

2 hires -0.0137** -0.0103* -0.0220*** -0.0177*** -0.00516*** -0.00620***

(0.00645) (0.00586) (0.00689) (0.00617) (0.000960) (0.000941)

3 hires -0.0161*** -0.0125*** -0.0266*** -0.0230*** -0.00624*** -0.00767***

(0.00555) (0.00485) (0.00678) (0.00597) (0.00111) (0.00110)

4 hires -0.0314*** -0.0240*** -0.0365*** -0.0292*** -0.00853*** -0.00984***

(0.00661) (0.00582) (0.00776) (0.00684) (0.00123) (0.00122)

5+ hires -0.0309*** -0.0241*** -0.0396*** -0.0327*** -0.0152*** -0.0167***

(0.00431) (0.00386) (0.00653) (0.00577) (0.000659) (0.000648)

Log Applicant Arrivals in First 24 Hours -0.0906*** -0.0831*** -0.0910*** -0.0834*** -0.0272*** -0.0278***

(0.00240) (0.00222) (0.00277) (0.00252) (0.000604) (0.000585)

Detailed Worker and Job Controls No Yes No Yes No Yes

Control for Job Applicant Arrival Rate Yes Yes Yes Yes Yes Yes

Observations 3,015,891 3,015,891 3,015,891 3,015,891 3,015,891 3,015,891

R-Squared 0.487 0.561 0.552 0.610 0.851 0.856

Notes: Dependent variable is the log of the hourly wage bid. The sample is limited to worker-initiated applications on sequential job openings. Robust standard errors are clustered

by employer. All specifications contain a spline for the applicant's arrival order, detailed job category fixed effects, monthly time fixed effects, and expected duration by hours-per-

week fixed effects. Specifications with detailed worker and job controls also include the following: a third-order polynomial in the number of characters in the job description, a

dummy for good reported English skills, a dummy for a BA or higher degree, a dummy for having no prior work experience, a dummy for agency affiliation and its interaction with

having no prior work experience, the number of prior jobs, the log of the wage on the last hourly job, and an indicator that no last wage is displayed when the worker is

experienced.

Table A1: Details about Resume Data in Estimation Sample

Variable Mean Std. Dev. Min Max

Number of Prior Jobs 7.07 13.33 0 281

Indicator for No Prior Jobs 0.34 0.47 0 1

Log of Hourly Rate on Last Job 1.23 1.18 -0.40 3.66

Feedback Score (Including Zeros) 2.47 2.26 0 5

Feedback Squared 11.21 10.78 0 25

Feedback Cubed 51.74 52.32 0 125

Prior Experience and Zero Feedback 0.10 0.30 0 1

Self-reported Good English Skills 0.90 0.30 0 1

BA or Higher Degree 0.35 0.48 0 1

Agency Affiliate 0.34 0.47 0 1

Agency Affiliate x No Prior Jobs 0.07 0.26 0 1

Notes: This table provides summary measures for the detailed resume data used in estimation. In the

demand estimation, fixed effects for country groups, job category, a spline for applicant order, an

indicator for an employer-initiated application, and a time trend are also included. The log of the

hourly rate on the last job is set to zero for inexperienced applicants.