Understanding Job Matching in Online Labor Marketseconseminar/seminar2017/Jinnai.pdf · Background...

Understanding Job Matchingin Online Labor Markets

Evidence from the World’s Largest

Crowd-sourcing Service

Yusuke Jinnai

International University of Japan (IUJ)

Background of online labor markets

I In online labor markets, potential employers and potentialworkers can meet with each other.

I Several companies organize such markets (crowd-sourcingservices), which have grown rapidly across the globe.

I Freelancer.com is the leading company, while oDesk andElance merged as Upwork in 2014.

(http://visual.ly/odesk-vs-elance-vs-freelancer)

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Background of online labor markets

I In online labor markets, employers need to hire workerswithout knowing the details about their skills andcharacteristics.

I Most of workers’ personal information, such as gender, age,and education, is unavailable.

I Some workers reveal their personal information, but it can befake.

I In order to encourage transactions, online labor marketsprovide several indicators of workers including onlinereputation (review) and job experience.

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Research questions and literature

I Question 1: What are the values of workers’ onlinereputation and other indicators?

I Related literature on online-review systemsI Resnick (2006) on eBay auctionI Anderson and Magruder (2012) on Yelp

I Question 2: Does bidders’ nationality matter in finding jobsin online labor markets?

I Related literature on labor-market discriminationI Goldin and Rouse (2000) on genderI Bertrand and Mullainathan (2004) on race

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Job matching in online labor markets

I Step 1: At Freelancer.com, an employer posts a job.I An employer provides project description and budget range.

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I Step 2: Workers bid for the job.I In response to the employer’s posted budget range, each bidder

proposes (1) how much it costs and (2) how many days ittakes to complete the project.

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I Step 3: The employer chooses one bidder.

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I Step 4: The worker completes the project.I Workers and employers can communicate through online about

the progress of the project.

I Step 5: The employer pays for the worker.I Employers can ask the worker to improve the output materials

(if needed).

I The company (Freelancer.com) that provides the online labormarket charges a commission (10% for basic members).

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How to hire a right person

I Which bidder would you choose, A or B?

Bidder Budget Deadline

A $100 3 daysB $100 3 days

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Bidder Budget Deadline ReputationA $100 3 days 4.0 / 5.0B $100 3 days 3.0 / 5.0

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Bidder Budget Deadline Reputation

A $120 3 days 4.0 / 5.0B $100 3 days 3.0 / 5.0

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I Question 1: What is the value of online reputation?

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I Which bidder would you choose (A, B, C, or D)?

Bidder Budget Deadline Reputation

A $120 3 days 4.0 / 5.0B $120 3 days 4.0 / 5.0C $120 3 days 4.0 / 5.0D $120 3 days 4.0 / 5.0

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I Which bidder would you choose (A, B, C, or D)?

Bidder Budget Deadline Reputation NationalityA $120 3 days 4.0 / 5.0 IndiaB $120 3 days 4.0 / 5.0 PakistanC $120 3 days 4.0 / 5.0 BangladeshD $120 3 days 4.0 / 5.0 U.S.

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I Question 2: Does nationality matter?

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Data obtained from Freelancer.com

I In the online labor market, all transactions can be observed byanyone in the market.

I Who posted jobs and what kind of jobs.I Who applied for those jobs and who won the bidding.I All characteristics of employers, bidders, and winners.

I In order to examine the hiring by employers, this studyrestricts the data to

I Matched projects.I Projects with two ore more bidders.I Transactions in $US.

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Data (1) by job category

I Most projects allow workers to complete all process online.

# Projects % ProjectsDesign, Media & Architecture 2,202 37.8Websites, IT & Software 2,171 37.3Writing & Content 593 10.1Mobile Phones & Computing 236 4.1Data Entry & Administration 225 3.9Engineering & Science 153 2.6Sales & Marketing 76 1.3Business, Accounting & HR 48 0.8Product Sourcing & Manufacturing 15 0.3Translation & Languages 14 0.2Local Jobs & Services 3 0.1Total 5,825 100%

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Data (2) by employer’s nationality

I Top 4 developed countries account for 61.4%.

# Projects % ProjectsU.S. 2,158 37.1Australia 700 12.1Canada 365 6.3U.K. 343 5.9India 271 4.7Singapore 125 2.2U.A.E. 85 1.5Israel 80 1.5Germany 79 1.4Saudi Arabia 70 1.2- - - - - - - - - -Total 5,825 100%

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Data (3) by bidder

I Data entry and administration jobs attract many workers.

# Pr. % Pr. Ave.#BiddersDesign, Media & Architecture 2,202 37.8 22.9Websites, IT & Software 2,171 37.3 17.5Writing & Content 593 10.1 18.0Mobile Phones & Computing 236 4.1 19.2Data Entry & Administration 225 3.9 24.4Engineering & Science 153 2.6 12.6Sales & Marketing 76 1.3 11.1Business, Accounting & HR 48 0.8 12.3Product Sourcing & Manufacturing 15 0.3 13.9Translation & Languages 14 0.2 13.5Local Jobs & Services 3 0.1 10.7Total 5,825 100% 19.5

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Data (4) by bidder’s nationality

I Top 3 developing countries account for 64.7%. All of themare English-speaking South Asian countries.

# Bidders % BiddersIndia 46,652 42.3Pakistan 17,320 15.7Bangladesh 7,375 6.7U.S. 4,419 4.0Vietnam 2,731 2.4China 2,505 2.3Romania 2,278 2.1Ukraine 1,866 1.7U.K. 1,632 1.5Philippines 1,362 1.2- - - - - - - - - -Total 110,261 100%

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Data (5) by winner’s nationality

I Bidders from India and Pakistan occupy less among winners.

I Bidders from U.S. and U.K. occupy more among winners.

# Bidders % Bidders # Winners % WinnersIndia 46,652 42.3 1,853 32.5Pakistan 17,320 15.7 818 14.3Bangladesh 7,375 6.7 384 6.7U.S. 4,419 4.0 306 5.4Vietnam 2,731 2.5 211 3.7China 2,505 2.3 148 2.6Romania 2,278 2.1 139 2.4Ukraine 1,866 1.7 137 2.4U.K. 1,632 1.5 101 1.8Philippines 1,362 1.2 85 1.5- - - - - - - - - - - - - - - -Total 110,261 100% 11,734 100%

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Model: mixed logit

Suppose that employer i = 1, 2, ..,N hires bidder j = 1, 2, ..., J fora specific job.

Assume the utility that employer i derives from choosing bidder j isgiven as follows:

Uij = Xijβi + εij ,

where Xij represents observed characteristics related to employer iand bidder j .

βi captures different tastes, across employers, toward bidder’snationality.

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Model: mixed logit

The probability that employer i hires bidder j is, for any j ′ 6= j ,

Pij = Pr(Uij > Uij ′)

= Pr(Xijβi + εij > Xij ′βi + εij ′)

= Pr(εij ′ − εij < Xijβi − Xij ′βi ).

By assuming that εij is an i.i.d. type 1 extreme value, theconditional probability of employer i choosing bidder j is given by

Lij(βi ) =exp(Xijβi )∑Jj=1 exp(Xijβi )

.

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Model: mixed logit

The unconditional probability of the observed choice is thus,

Pij(θ) =

∫Lij(βi )f (β|θ)dβ,

where the density of β is denoted as f (β|θ) with θ as theparameter of the distribution. Therefore, the likelihood of themodel is given by,

L(θ) =N∏i=1

J∏j=1

Pij(θ)yij ,

where yij = 1 if employer i hires bidder j and 0 otherwise. As aresult, the log likelihood of the model is given as,

LL(θ) =N∑i=1

J∑j=1

yij lnPij(θ).

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Model: mixed logit

LL(θ) is approximated through simulation as follows.

SLL(θ) =N∑i=1

J∑j=1

yij ln P̂ij(θ),

where

P̂ij(θ) =1

R

R∑r=1

Lij(βri ),

and βri is the rth (r = 1, 2, ...,R) draw, for employer i , fromf (β|θ). The maximum simulated likelihood estimator is the valueof θ that maximizes SSL(θ).

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Model: mixed logit

Willingness to pay (WTP) for each of bidder’s attribute, such asonline reputation and work experience, is computed as the ratio ofan attribute’s parameter to the price parameter, as follows.

WTP = − βkβprice

,

where βk is the coefficient of bidder’s kth attribute and βprice isthe coefficient of the price variable, which is the budget proposedby bidders.

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Summary statistics (All jobs)

I There is a huge variation across job categories.

Mean S.D.Proposed budget (US$) 314.8 962.7Proposed completion days 5.9 7.6Reputation (0-5) 4.1 1.7Experience (0-10) 4.6 2.6Job completion rate (0-100) 74.6 32.6On budget completion rate (0-100) 84.2 34.9On time completion rate (0-100) 81.7 34.4Bidder hired again rate (0-100) 14.7 12.8N 110,293

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Summary statistics (Data entry & admin. jobs)

I Much smaller variation within data-entry jobs.

Mean S.D.Proposed budget (US$) 117.6 189.8Proposed completion days 3.1 3.9Reputation (0-5) 3.5 2.2Experience (0-10) 3.4 2.7Job completion rate (0-100) 63.8 41.5On budget completion rate (0-100) 70.3 44.8On time completion rate (0-100) 68.8 44.2Bidder hired again rate (0-100) 12.7 13.3N 5,368

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Summary statistics (Data entry & admin. jobs)

I Top 3 developing countries account for 63.0%; the number is64.7% for all jobs.

Mean S.D.India 0.31 0.46Bangladesh 0.21 0.41Pakistan 0.11 0.31U.S. 0.05 0.21Philippines 0.04 0.19Romania 0.02 0.15Indonesia 0.02 0.13Egypt 0.02 0.13Sri Lanka 0.01 0.12Serbia 0.01 0.11- - - - - - - - - -Total 5,368

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Results

Mean Mean S.D.Proposed budget -0.009*** India -0.511** 0.022

(0.002) (0.238) (0.904)Proposed completion days 0.071** Bangladesh -0.816** 0.697

(0.030) (0.370) (0.971)Reputation (0-5) 0.720 Pakistan -0.211 0.0927

(0.621) (0.286) (1.161)Experience (0-10) 0.369*** U.S. -0.769 1.598

(0.052) (1.832) (1.936)Job completion rate (1-100) 0.023*** Philippines 0.875** 0.097

(0.009) (0.430) (1.123)On budget completion rate (0-100) 0.084* Romania -0.445 2.104

(0.045) (1.983) (2.252)On time completion rate (0-100) -0.004 Indonesia 0.121 0.120

(0.012) (0.551) (1.218)Bidder hired again rate (0-100) 0.014** Egypt 0.146 0.028

(0.007) (0.517) (0.850)Sri Lanka -1.089 0.127

(1.092) (2.157)Serbia -0.242 0.880

(1.184) (1.883)N 5,323* p < 0.10, ** p < 0.05, *** p < 0.01 Standard errors in parentheses

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Results: Willingness to pay (US$)

I Employers are willing to pay $41.05 for one-unit increase inworkers’ experience and $2.50 for one-unit increase in jobcompletion rate.

I Employers are willing to pay less for Indian and Bangladeshworkers and more for Filipino workers.

WTP WTPProposed completion days 7.73 India -56.95Reputation (0-5) 80.12 Bangladesh -90.84Experience (0-10) 41.05 Pakistan -23.53Job completion rate (0-100) 2.50 U.S. -85.65On budget completion rate (0-100) 9.32 Philippines 97.43On time completion rate (0-100) -0.45 Romania -49.51Bidder hired again rate (0-100) 1.56 Indonesia 13.47

Egypt 16.20Sri Lanka -121.23Serbia -26.93

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Conclusion

I This study uses data from an online labor market to estimatethe WTP for bidders’ attributes including reputation andnationality.

I The results show that once more detailed information is takeninto account, overall reputation does no increase theprobability of winning a job.

I Employers are willing to pay for bidders’ experience in thesame job category as well as job completion rate.

I Employers are also willing to pay more for the workers fromthe Philippines but less for those from India and Bangladesh.

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