The effect of financial goals and incentives onlabor.

An experimental test in an online market∗†

David B. Johnson and Justin Weinhardt

PRELIMINARY DRAFT

October 2, 2014

Abstract

Empirical studies investigating work motivation over time find people with fluctuating wageswork more on days when their wage rate is lower compared to when wages are higher. Theauthors of these studies theorize individuals use daily income goals and stop working once theyreach their goal. This study involves assignment and manipulation of financial goals in an onlinelabor market that is nearly frictionless. Workers can quit at any time and can start jobs postedby competing employers almost instantly. Results, with pooled data, indicate financial goalsdo not lead to workers stopping work once they reached their goals and there is no significantwage related crowding out. However, when we separate the sample by western and non-westernworkers, we find production by western workers is greatest in treatments with goals and lowincentives. This effect is absent in non-western workers.

JEL classification:

Keywords: Experiment; Online; Goal

∗ David Blake Johnson: University of Calgary, Department of Economics, 2500 University Dr NW, Suite 554 , Social

Sciences, Calgary, Alberta, Canada T2N 1N4. johnsod@ucalgary.ca. Justin Weinhardt: University of Calgary,

Department of Human Resources and Organizational Dynamics, Scurfield Hall 442, Calgary, Alberta, Canada T2N

1N4. justin.weinhardt@haskayne.ucalgary.ca†We are grateful to valuable comments provided by Tim Salmon, Alexi Thompson, Gary Fournier, and Rob Oxoby.All mistakes are our own.

1 Introduction

Several field studies investigate the influence of fluctuating wages on labor. Many of these studiesfind productivity does not increase with wages (e.g., Laisney et al., 1996). Instead, individuals worklonger when the wage rate is low (Camerer et al., 1997; Chou, 2002; Lynn, 2002; Fehr and Goette,2007).1 This set of findings is inconsistent with traditional theories of labor supply suggesting in-dividuals work longer when their wage rate is high (e.g., Lucas Jr and Rapping, 1969) or, in otherwords, when there is a greater “extrinsic” work motivation. On the other hand, goals introduced byemployers, experimenters, and individuals (Austin and Vancouver, 1996; Wu et al., 2008; Gomez-Minambres et al., 2012; Sun and Weinhardt, 2014) enhance production and even in cases where thegoals have no impact on the workers earnings.2 Such responses are thought to relate to “intrinsic”motivation and, when taken together, these findings suggest a “crowding out” effect of high wages.In economics and psychology, the crowding out theory posits tangible benefits and costs (e.g., mon-etary rewards and punishments or any motivation that is from outside the individual) can reduce anindividual’s intrinsic motivation (e.g., task enjoyment or any motivation from within the individual).

Several models explaining the observed crowding out phenomenon have been introduced with manypiggybacking off of Kahneman and Tversky (1979).3 For instance Falk and Knell (2004) present amodel where individuals set goals based upon the performance of their peers. Doing so enhancesindividual utility through self-enhancement and self-improvement.4 Falk and Knell (2004) go furtherand present survey evidence confirming the validity of their model. Wu et al. (2008) extend thisline of research by presenting an elegant theory explaining the underlying mechanisms behind theperformance gains. Put simply, gains relative to the goal (i.e., the reference point) enhance utilitywhile losses (i.e., deviations from the goal) are costly.

Our work most directly extends that of Gomez-Minambres et al. (2012) and Goerg and Kube (2013).Gomez-Minambres et al. (2012) builds upon Wu et al. (2008) by introducing exogenously assignedgoals (e.g., goals introduced by an employer). Such a model has similar properties to that of Wuet al. (2008) which lead to goals increasing productivity. Gomez-Minambres et al. (2012) provideexperimental evidence in support of their model. In treatments with goals that have no direct impacton subjects’ monetary earnings, productivity significantly increases. Moreover, and quite surprising,Gomez-Minambres et al. (2012) find no evidence of extrinsic crowding out, as the wage rate increasesproduction further. This is not a trivial result and is in stark contrast to the “crowding out” hy-pothesis. Goerg and Kube (2013) furthers this line of research with a real effort experiment findingthe use of personal goals increases worker production. This occurs if the goals are self-assigned orif selected by a principal.5 Moreover, much like previous work, these goals are effective even ininstances where they are not tied to monetary earnings.

Although similar to both Gomez-Minambres et al. (2012) and Goerg and Kube (2013) our workfurthers current understanding of goals. Instead of an environment where subjects know they arein an experiment, we take advantage of a venue in which it is left ambiguous as to whether or notsubjects are in an experiment. Moreover, we use a task purposefully selected to be natural for thevenue. We find little evidence of a wage effect seen in Gomez-Minambres et al. (2012). Subjects

1And to some extent in Lynn (2002). In this case, the author observes a negative correlation between tipping andturnover in low volume restaurants but not in high volume restaurants.

2For a review see Locke and Latham (2002).3A general discussion of models is outside the scope of the current work but interested parties should see Koszegi andRabin (2006), Koch and Nafziger (2011), Gomez-Minambres (2012) and Hsiaw (2013).

4Self-enhancement increases the individual’s utility by making them feel better about themselves. On the other hand,self-improvement increases utility by giving the individual something to strive for which increases their utility byway of increasing their performance.

5There is a caveat here in that the size of the goal matters quite a bit as easily obtained goals lead to the workerbeing less productive.

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in treatments with a high wage do not significantly increase their productivity. Further, we findonly weak evidence supporting a goal hypothesis and this effect is only present with workers fromdeveloped western nations.

Our work does not contradict previous work but rather illustrates a need for a more nuanced viewof goals. Specifically, workers in our experiment have the opportunity to switch employers or quit toattend to a personal task. Moreover, workers may do so at any time. In other words, there is littlecost to quit. This is quite novel and most relevant in workplaces where employees act more like theirown bosses. In such cases, because of the lack of a concrete work schedule and/or a “boss”, it istherefore less surprising that goals have a relatively weak effect. Moreover, a larger take away of thepresent work is how the importance of behavioral aspects change as the labor market approachesa neoclassical frictionless labor market with homogenous workers. Ex-post this might seem quiteobvious; more complicated markets contain greater frictions which can alter behavior. Remove thesefrictions and behavior neatly follows many implications of neoclassical models.

2 Present Study

Ideally, to test the effects of varying wage rates on work hours, one needs a job where wages varyacross some temporal period (e.g., days) but relatively constant within a period. Further, individu-als have a choice regarding how long they work and these variables (i.e., rate of pay and amount oftime worked) must be observable. One job where the above conditions are met is New York City cabdrivers (Camerer et al., 1997). Specifically, cab drivers wages stay relatively constant throughoutthe day, but fluctuate between days, presumably because of changes in the weather. Moreover, mostNYC cab drivers are able to determine how long they work and the cab companies track this infor-mation. Camerer et al. (1997) find across three samples that wages and time worked are negativelycorrelated. Follow-up studies by Chou (2002) and Fehr and Goette (2007) discover similar effectsfor taxi drivers in Singapore and bike messengers in Zurich Switzerland.

To account for their empirical results, as well as those seen in later studies, Camerer et al. (1997)posits individuals work in reference to an income goal and that, once reached, causes individuals tostop working for the day. Essentially, because individuals reach their goal relatively quickly on highwage days they work less. Yet, before accepting the goal-based account as a universal, a rigoroustest with a more independent work environment and across cultures seems prudent.

To investigate the effect of time-varying wage rates on labor supply in a market with fewer frictionsand across cultures, we designed an online experiment allowing us to manipulate goals and wageswhile monitoring worker productivity. Specifically, to test the goal mechanism directly we assigna monetary goal to a random set of participants (Locke and Latham, 1990). If wages correlatenegatively with productivity for individuals in the monetary goal conditions, it suggests the influ-ential nature of monetary goals (Helson, 1964; Kahneman and Tversky, 1979). To contrast the goalcondition, we include a control condition where individuals are told to do-your-best. We select thiscontrol because research demonstrates specific goals lead to better performance than vague do-your-best goals (Locke and Latham, 1990). An additional advantage of the present study is that we areable to run the same experiment across dramatically different populations. Previous work is notdesigned to investigate how identical incentive schemes can have different effects across populations.In world where the outsourcing of interchangeable labor is becoming increasingly the norm, such aninvestigation is a worthy extension.

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3 Methods

The experiment takes place on Amazon Mechanical Turk (AMT). AMT is an online labor marketwhere requesters pay workers to complete human intelligence tasks (HITs). Total compensationHITs depend on a flat fee (generally between 5 and 50 cents), and a bonus individually assigned bythe requester.6 Following the critiques presented by Levitt and List (2007) workers are not told theyare in an experiment until after they complete it.7 Workers are required to correctly answer Englishcomprehension questions prior to the start of the experiment. Workers failing one or more of theEnglish comprehension questions are not eligible to continue.8 The answers to these questions arerandomly generated and serve as the treatment assignment mechanism. This random assignmentallows all treatments to be posted at the same time, preventing autocorrelation in the treatments.The English comprehension questions are interspersed within the initial survey.9 After completingthe survey, workers complete 5 practice tasks and begin the experiment.

3.1 Task and Treatments

The task we use is particularly well suited for the market as it is a task that is difficult for a com-puter to do and would reasonably be needed for a firm trying to keep digital copies of “strange”documents. Workers transcribe pieces of an instruction manual from a 1996 Oldsmobile Cutlass(essentially a series of CAPTCHAs). Each of these pieces is a jpeg file so workers cannot copy andpaste in the answer. After five transcriptions, paying 2 cents per transcription, workers are paid apiece rate of two or five American cents for each transcription they complete. There is no ambiguityin the currency; workers know they are being paid in American currency because they are paidthrough Paypal. Additionally, competing HITs advertise wages in American currency (always toour knowledge in cents). Connection speed should not matter. The HIT was coded in HTML whichis essentially static.10 Workers complete as many transcriptions as they want (with the maximumbeing 100) and can quit anytime. Workers are only required to complete the first five transcriptions.The maximum number of transcriptions is capped at 100 mostly for ease. Subjects have computersof varying speed and we wanted to make sure load times would be reasonable even on mediocremachines with weak connectivity.11

Figure 1: Example Task

As discussed above, before seeing the treatment screen, workers complete five practice transcriptionspaying 2 cents each. The amount paid for practice questions is the same for all treatments. In thetreatment screen, workers view 1 of 2 texts assigning them an unenforced goal. In“Do Your Best”treatments (DYB), which serve as a control, workers view the text “Working for us your goal is to

6For an in-depth discussion of the bonus mechanism see Cooper and Johnson (2013).7Of course workers have the option of requesting their data to not be used; this is truthfully respected.8If this happens, workers are shown a screen that requests them to “return” the HIT. A returned HIT can be completedby a different worker and any worker who dropped an HIT is no longer eligible to participate. Other than being nolonger eligible to participate, returning a HIT does not harm the worker.

9A common complaint of AMT is that workers randomly enter text in hopes of bilking the requesters. Any workerdoing so, in our study, would likely answer the questions incorrectly and be kicked out of the experiment.

102 workers did lose connection but no others reported problems. These workers are paid based upon the number oftranscriptions they said they completed but are omitted from the analysis.

11We tested this with an older laptop and wifi connection which we gradually moved away from the wireless internetsource.

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just do your best and do as many sentences as you can” while in GOAL treatments workers aretold “Working for us your goal is to make $2”. Because we vary the piece rate we end up with 4treatments (DYB - 2 Cents, DYB - 5 Cents, GOAL - 2 Cents, and GOAL - 5 Cents).

Because our treatment relies on English comprehension we split the sample by national languagebut also present pooled results. Summary statistics for each of the treatments and split by WesternEnglish Speaking (WES) can be seen in Table 1. These treatments at a cursory glance maybeproblematic as they require turkers to be aware of the value of a dollar is. Normally this is a validcriticism but in the case of AMT it is unwarranted. AMT is well established and has a long historyof operating with the US dollar. Most, if not all, workers will be aware of this as well as the buyingpower of the dollar in their home country.

Table 1: Treatments

ALL WES Non - WES

DYB - 2 Cents 55 24 31

GOAL - 2 Cents 53 26 27

DYB - 5 Cents 64 25 39

GOAL - 5 Cents 62 32 30

ALL 234 107 127

The stakes of the experiment are not trivial - especially by AMT standards. With the bonus, workersin the five cents treatments could make up to 4 dollars and 95 cents (or around a half day’s work inIndia) in under an hour.12 Workers in the 2 cent treatments could top out at lower amount ($2.20)but this amount is still generous by AMT standards. All currency is in United States denominations.This is not probably does not create any confusion as AMT is an established labor market and theworking currency has been US dollars (or cents) since its inception.

4 Results

Over ninety percent of workers are in the USA (45 %) or India (46 %). The remainder hail froma variety of nations but primarily developed western ones. As such, it comes as no surprise thatour workers are much more heterogeneous than usual laboratory subjects. The gender of workers isevenly split; 49 % of workers indicated they are female. We gather information regarding the ageof workers, education and income. The average age of workers is 34 years. The modal income andeducation of workers is between $12,500 and $25,000 per year and a bachelors degree.13

Average production (the number of transcriptions completed) is in Table 2. The distribution ofproduction by treatment and by Western English Speaking nations14 (WES) can be found in theappendix (Figures 2, 3, and 4). The differentiation between WES and non-WES workers is es-sentially differentiating between high and low ability workers. Workers from WES nations spendabout 15 seconds less per transcription on HIT (t = −1.91) .15 Moreover, workers from non-WES

12National Sample Survey Organisation. (2011). Employment and Unemployment in India, 2009-10, NSS Sixty SixthRound. Report No. 537. New Delhi: Department of Statistics, Government of India. Retrieved February 1, 2013,from http://mospi.nic.in/Mospi New/upload/NSS Report No 537.pdf (Login Required).

13We present modes instead of averages here because workers are asked to select an appropriate category rather thanenter a number. Of course averages are available upon request.

14Australia, Canada and the United States of America.15This is rough approximation as it includes time spent on the survey.

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Table 2: Production by Treatment and English

ALL WES Non-WES

DYB - 2 Cents 60.60 58.92 61.90(4.34) (6.69) (5.78)

GOAL - 2 Cents 62.81 70.00 55.89(4.91) (6.79) (6.94)

DYB - 5 Cents 58.89 64.16 55.51(4.18) (6.68) (5.36)

GOAL - 5 Cents 57.70 55.88 59.58(3.97) (5.72) (5.57)

ALL 59.86 61.93 58.13(2.15) (3.21) (2.9)

Standard deviations in parentheses.

nations spend over five and a half minutes more on the HIT than workers from WES nations (−2.81).

Initially, with pooled data we find no significant treatment effects. However when we separate byWES, we find Goal - 2 Cents production of workers in WES countries to be greater than similarworkers in DYB - 2 Cents (p = 0.126) and Goal - 5 Cents (p = 0.057). Identical methods withnon-WES nations’ residents result in no significant differences.

In Table 3 we estimate worker production and whether or not they completed all the possible tran-scriptions as a function of the treatment, income, and whether or not they live in a WES nation.Models 1 through 3 are tobits with the dependent variable being the number of transcriptions com-pleted; models 4 through 6 are probits with the dependent variable being equal to 1 if the workercompleted all 100 transcriptions.16 We find no significant treatment effects in the regressions withpooled data (models 1 and 4) but workers from a WES nation are more productive and are morelikely to complete the all 100 transcriptions. Workers reporting higher incomes complete fewer tran-scriptions but this primarily being driven by workers from WES nations; when we remove them fromthe sample the coefficient estimate on income switches sign and is not significantly different from zero.

Returning now to productivity we find a single modest treatment effect occurring in Goal - 2 centsand only when we separate the sample by WES. Workers in non-WES nations exhibit no change inproductivity across all treatments. This result is echoed in the probit models (4, 5, and 6) whereonly the workers in WES nations have higher likelihood of completing all 100 transcriptions.17

16To save space, we do not present marginal effects here. However, they can be found in the appendix. Additionally,alternative models (e.g., simple regression and ordered probits with a bin size of ten) and “kitchen sink” models arealso tested. Results did not change significantly.

17Increasing the sample size may be worthwhile endeavor here and may result in significant differences. However weargue these differences, except in the case of the Goal -2 cents in WES workers, would not be economically significant- especially after controlling for income and nation of origin. Since only one treatment is large in magnitude andsignificant, increasing the sample size would almost certainly only shrink standard errors down to the point wherewe are reporting significant “zeros.”

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Table 3: Empirical Results

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Tobit Tobit Tobit Probit Probit Probit

DYB - 2 Cents 4.12 5.17 2.99 0.10 0.13 0.06(8.45) (12.74) (11.15) (0.25) (0.37) (0.35)

DYB - 5 Cents 2.71 13.66 -5.02 0.01 0.23 -0.19(8.00) (12.65) (10.46) (0.25) (0.36) (0.34)

GOAL - 2 Cents 8.90 22.56† -3.46 0.40† 0.7** 0.08(9.42) (14.55) (12.08) (0.25) (0.35) (0.36)

Income -2.73* -3.58* 0.88 -0.06 -0.06 -0.03(1.59) (1.86) (3.14) (0.05) (0.05) (0.11)

WES 10.97† 0.34*(6.99) (0.20)

Constant 66.84*** 75.83*** 64.53*** -0.64*** -0.43 -0.56*(7.55) (11.78) (11.08) (0.23) (0.32) (0.34)

Obs 233 107 126 233 107 126Log L -910.071 -393.520 -513.942 -138.524 -65.429 -71.995

Subject Pool ALL WES Non-WES ALL WES Non-WES

Lower Limit 2 2 0 NA NA NAUpper Limit 70 37 33 NA NA NA

Standard Errors in parentheses. ***: p < .01, **: p < .05, *: p < .1 and †: p < .15. Lower

and Upper Limit are the number of workers who completed the 5 and 100 transcriptions.

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5 Discussion and Conclusions

We find no evidence of an increase in productivity in any of the 5 cent piece rate treatments. Thisresult, while somewhat surprising, is consistent with much of the previous literature (Gneezy andList, 2006; Kube et al., 2006; Hennig-Schmidt et al., 2010) and in contrast to Gomez-Minambreset al. (2012). Regarding our treatment, we find limited evidence in support of a negative correla-tion between time spent working and wage/piece rate. The treatment effect is only significant ifthe sample is separated and each sub-sample estimated independently. This results in an odd butnon-trivial outcome where there are significant treatment and income effects in one sample that areabsent in the other.

Because income is insignificant in non-WES nations, income differences across the populations donot explain the results (poor people in non-WES nations are as productive as comparatively richerpeople in the same block of nations). Within the realm of WES nations, our results present ad-ditional evidence in support of the papers mentioned in the introduction. Positive wage shocks inlabor markets where workers set their own working hours, reduces worker productivity. How thisextends to labor markets where workers have set hours and/or wages based upon their time at work,we are somewhat agnostic to. However, we posit the following: within the lab, there exists a normto not allow subjects to quit the experiment. While natural to most work environments it is farfrom universal.

For instance, Gomez-Minambres et al. (2012) allow subjects to goof off on the internet, and thisoption has much relevance in the world outside the lab, but it does not replicate environments whereworkers have almost literally any outside option they wish. What this means, is that goals can bequite effective in workplaces where workers have set hours and high job search costs. Relax bothof these traits and ex-post it becomes less surprising that goals have little impact on productivity.This is especially interesting because it demonstrates that as labor markets move in the direction ofa neoclassical firm (with homogeneous labor and no search costs ), the behavioral aspects becomeless important. It follows that as the environments become more complicated and move away froma neoclassical firm, the behavioral aspects becoming increasingly important.

The lack of significance in the treatment effect in pooled and non-WES sample also presents a lim-itation researchers should consider before running an experiment on AMT. Read and Loewenstein(1995) suggest and present evidence of a diversification bias. An interesting but problematic twist ofthis bias is also present in AMT: workers have multiple HITs they are able to complete at any giventime. Consequently, workers may select to both maximize their monetary earnings and also varythe portfolio of the HITs they complete. Such a bias would be more pronounced in populations lessskilled in English. Presumably, workers in these populations glean less from instructions and learnby doing rather than the instructions and if they are not good at the task, they quit. In these cases,their stopping point would depend on a time per HIT threshold which would be independent of ourtreatment. This explains why workers from non-WES nations are not impacted by the treatments;they simply are not good enough at the task for the goals to matter.

However, such a bias requires the AMT labor market to also be more in the direction of “real” labormarket as it has a search feature which absent in the lab. This introduces another interpretationof our results: workers in WES nations are treating AMT as a real labor market where they canquit their job at anytime. This would be consistent with the observation that workers from WESnations are significantly richer (t= -7.35) and are more likely to be working on AMT for primaryor secondary source of income (t=-2.37). This presents evidence to the contrary that turkers fromWES nations are on AMT for more recreational purposes.

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References

Austin, James T, and Jeffrey B Vancouver (1996) ‘Goal constructs in psychology: Structure, process,and content.’ Psychological bulletin 120(3), 338

Camerer, Colin, Linda Babcock, George Loewenstein, and Richard Thaler (1997) ‘Labor supply ofnew york city cabdrivers: One day at a time.’ The Quarterly Journal of Economics 112(2), 407–441

Chou, Yuan K (2002) ‘Testing alternative models of labour supply: Evidence from taxi drivers insingapore.’ The Singapore Economic Review 47(01), 17–47

Cooper, David J, and David B Johnson (2013) ‘Ambiguity in performance pay: An online experi-ment.’ Available at SSRN 2268633

Falk, Armin, and Markus Knell (2004) ‘Choosing the joneses: Endogenous goals and referencestandards.’ The Scandinavian Journal of Economics 106(3), 417–435

Fehr, Ernst, and Lorenz Goette (2007) ‘Do workers work more if wages are high? evidence from arandomized field experiment.’ The American Economic Review pp. 298–317

Gneezy, Uri, and John A List (2006) ‘Putting behavioral economics to work: Testing for gift exchangein labor markets using field experiments.’ Econometrica 74(5), 1365–1384

Goerg, Sebastian J, and Sebastian Kube (2013) ‘Goals (th) at work.’ Max Planck Institute forResearch on Collective Goods

Gomez-Minambres, Joaquın (2012) ‘Motivation through goal setting.’ Journal of Economic Psychol-ogy 33(6), 1223–1239

Gomez-Minambres, Joaquın, Brice Corgnet, and Roberto Hernan Gonzalez (2012) ‘Goal setting andmonetary incentives: When large stakes are not enough.’ Technical Report

Helson, Harry (1964) Adaptation-level theory. (Harper & Row)

Hennig-Schmidt, Heike, Abdolkarim Sadrieh, and Bettina Rockenbach (2010) ‘In search of workers’real effort reciprocitya field and a laboratory experiment.’ Journal of the European EconomicAssociation 8(4), 817–837

Hsiaw, Alice (2013) ‘Goal-setting and self-control.’ Journal of Economic Theory 148(2), 601–626

Kahneman, Daniel, and Amos Tversky (1979) ‘Prospect theory: An analysis of decision under risk.’Econometrica: Journal of the Econometric Society pp. 263–291

Koch, Alexander K, and Julia Nafziger (2011) ‘Self-regulation through goal setting*.’ The Scandi-navian Journal of Economics 113(1), 212–227

Koszegi, Botond, and Matthew Rabin (2006) ‘A model of reference-dependent preferences.’ TheQuarterly Journal of Economics pp. 1133–1165

Kube, Sebastian, Michel Andre Marechal, and Clemens Puppe (2006) ‘Putting reciprocity to work-positive versus negative responses in the field.’ University of St. Gallen Economics DiscussionPaper

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Levitt, Steven D, and John A List (2007) ‘What do laboratory experiments measuring social pref-erences reveal about the real world?’ The journal of economic perspectives pp. 153–174

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Locke, Edwin A, and Gary P Latham (1990) A theory of goal setting & task performance. (Prentice-Hall, Inc)

(2002) ‘Building a practically useful theory of goal setting and task motivation: A 35-year odyssey.’American psychologist 57(9), 705

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Lynn, Michael (2002) ‘Turnover’s relationships with sales, tips and service across restaurants in achain.’ International Journal of Hospitality Management 21(4), 443–447

Read, Daniel, and George Loewenstein (1995) ‘Diversification bias: Explaining the discrepancy invariety seeking between combined and separated choices.’ Journal of Experimental Psychology:Applied 1(1), 34

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A Figures

Figure 2: Productivity of Workers

Figure 3: Productivity of Non - WES Workers

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Figure 4: Productivity of WES Workers

B Marginal Effects

Table 4: Marginal Effects from Table 3

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6Tobit Tobit Tobit Probit Probit Probit

DYB - 2 Cents 4.12 5.17 2.99 0.04 0.05 0.02(8.63) (13.19) (11.3) (0.09) (0.14) (0.11)

DYB - 5 Cents 2.71 13.66 -5.02 0.00 0.09 -0.06(8.32) (13.11) (10.7) (0.08) (0.14) (0.1)

Goal - 2 Cents 8.9 22.56* -3.46 0.14 0.27** 0.03(8.88) (13.62) (11.64) (0.09) (0.13) (0.12)

Income -2.73* -3.58* 0.88 -0.02 -0.02 -0.01(1.6) (1.93) (3.28) (0.02) (0.02) (0.03)

WES 10.97 0.12*(6.89) (0.07)

OBS 233 107 126 233 107 126Subject Pool ALL WES Non-WES ALL WES Non-WES

Lower Limit 2 2 0 NA NA NAUpper Limit 70 37 33 NA NA NA

Standard Errors in parentheses. ***: p < .01, **: p < .05, *: p < .1 and †: p < .15. Lower

and Upper Limit are the number of workers who completed the 5 and 100 transcriptions.

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C Online Appendix

C.1 Instructions

Phase 1: Before we begin we would like to gather some information regarding our workers (you).Please complete this short survey. When you are finished, please click the NEXT button.

What is your gender?

What is your age?

What country do you currently live in?

Which of the following best describes your highest achieved education level?

Over the weekend, Bob watched two football games. In the box below below, type the number offootball games that Bob watched over the weekend. Be sure to use a numeric character!

What is the total income of your household?

Why do you complete tasks in Mechanical Turk? Please check any of the following that applies:

Next year, Jack and Jill are planning on visiting Disneyland. Jill has been to Disneyland many timeswhile Jack has never been. In the box below below, type how often Jack has been to Disneyland.Be sure to use a numeric character!

We are crowdsourcing the transcription of an instruction manual of the 1996 Oldsmobile cutlass.You will be given a series of photocopies depicting short texts from the manual. Your job is to typethese short bits of text into the provided text box. We will spot check your work to make sure it isof sufficient quality. You will be paid 2 cents for each of these practice transcriptions. After which,you will start the actual task.

Please click the NEXT button to begin the practice messages. YOU MUST COMPLETE THESEPRACTICE MESSAGES TO BE PAID.

You have now completed the practice questions.

Thank you for completing the practice assignments. You will again be given sentences that werescanned from a book. We need you to type out each sentence exactly. You will be paid {2 cents, 5cents} for every sentence that you type.

$2 - Working for us your goal is to make $ 2 dollars.

DYB - Working for us your goal is to just do your best and do as many sentences as you can.

In order to be sure you are reading the instructions, please input the number X in the input boxbelow.

Please note that you can quit at any time by clicking the quit button. Once/if you quit, you will begiven some brief instructions and then be told to submit the HIT. Your performance will be recorded

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to pay you and with your consent may be used for research in the future.

The following study investigated the influence financial goals and financial incentives had on moti-vation to work and the decision to stop working. The reason we did not inform you in the beginningthat this was a study was because we wanted you to treat the work task as a real Amazon MechanicalTurk job. Therefore, we are able to understand how people decide to continue working and stopworking in a real economic marketplace. Previous research shows that when people have a smallerfinancial incentive but a difficult goal, they will work longer. We are trying to understand why thiseffect occurs.

Survey Description and Consent Form: This study is conducted by a team of researchers at theHaskayne School of Business, the University of Calgary. The principal investigator is Dr. JustinWeinhardt, Assistant Professor at the Haskayne School of Business. This research protocol is ap-proved by the Conjoint Faculties Research Ethics Board (CHREB). The purpose of this research isto investigate and better understand the factors affecting decision making under different conditions.Your participation in this study is voluntary and you may refuse to participate altogether or maychoose to withdraw from the study at any time.

In the experiment, you were asked type out various sentences. You were paid based on your perfor-mance. You will now be asked additional questions, consisting of general background, personalityand demographic data. The collected data will be kept on a password protected computer drive,stored in a secured location and accessible only by the researchers for research purposes, includingthe publication of scientific papers. Participation is completely voluntary, anonymous and confiden-tial. No one except the principal investigator and the research team will be allowed to see any of theanswers to the questions. No personal identifying data will be collected in this study. There are nonames collected and attached to the responses. Only group information will be summarized for anypresentation or publication of results. In case you withdraw from this study, the data collected tothe point of withdrawal will be deleted. There were no foreseeable risks, harms, or inconveniencesassociated with your participation in this study. The only cost on your part is the time you willspend for participating in this survey.

Your responses may be used for research purposes, but only with your consent. Regardless of whetheror not you want your responses used for research purposes, you will be paid. If you agree with theterms of this study, please press USE MY DATA FOR RESEARCH. If you do not agree for yourdata to be used in a research project, please click DO NOT USE DATA FOR RESEARCH.

In the following survey, you will be presented with a number of different decision problems and fillout a scale regarding personality and mental focus. If you have questions about this study, you cancontact Dr. Justin Weinhardt, the principal investigator, at justin.weinhardt@haskayne.ucalgary.ca.If you have concerns about your rights as a research participant, you may contact the University ofCalgary Ethics Resource Officer through email: cfreb@ucalgary.ca or telephone: 403-210-9863.

USE MY DATA FOR RESEARCH

DO NOT USE MY DATA FOR RESEARCH

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