Sorting of Motivated Agents_Evidence from Applicants to the German Police
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Sorting of Motivated Agents_Evidence from Applicants to the German Police

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NES 20th Anniversary Conference, Dec 13-16, 2012 ...

NES 20th Anniversary Conference, Dec 13-16, 2012
Sorting of Motivated Agents_Evidence from Applicants to the German Police (based on the article presented by Guido Friebel at the NES 20th Anniversary Conference).
Authors: Guido Friebel (Frankfurt, CEPR, IZA); Wiebke Homann (Frankfurt); Michael Kosfeld (Frankfurt, CEPR, IZA);
Bernard Richter (Frankfurt);
Gerd Thielmann (Federal Police University)

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    Sorting of Motivated Agents_Evidence from Applicants to the German Police Sorting of Motivated Agents_Evidence from Applicants to the German Police Presentation Transcript

    • Sorting of Motivated Agents: Evidence from Applicants to the German Police Guido Friebel (Frankfurt, CEPR, IZA) Wiebke Homann (Frankfurt) Michael Kosfeld (Frankfurt, CEPR, IZA) Bernard Richter (Frankfurt) Gerd Thielmann (Federal Police University) 20 Years of NES, Moscow, December 201216.01.2013 Guido Friebel 1
    • SORTING AND MOTIVATION16.01.2013 Guido Friebel 2
    • Sorting (self-selection)• Workers differ in their productivity• Guasch and Weiss (REStud, 1980): wage penalties for unproductive applicants• Lazear (Journal of Business, 1986): salaries attract less, piece-rates more productive workers• Lazear (AER, 2000): empirical evidence for sorting effects of incentives• Dohmen and Falk (AER, 2011): – experimental evidence for sorting by incentives, – multi-dimensional sorting 3
    • Sorting of motivated workers• Workers differ in motivation for a job or profession• Various sources of compensating differentials from a job: social concerns/altruism, outcome of the job/professional standards, organization’s “mission” Organizational efficiency can be increased by employing motivated workers• Francois (JPubE, 2008): public sector workers’ motivation, an argument for government provision of certain services• Besley and Ghatak (AER, 2005): agents’ motivation as a substitute to monetary incentives 4
    • Sorting of motivated agents, continued• Prendergast (AER, 2007): different types of motivation, sorting of extreme types• Delfgaauw and Dur (JEBO, 2008): tradeoff btw filling vacancies and getting motivated applicants• Kosfeld and von Siemens (Rand, 2011, JEEA, 2010): Team-orientation of workers• Friebel and Giannetti (Economic Journal, 2010): Creativity and realizing one’s ideas• …. 5
    • Testing sorting of motivated agents• Test by cross-sectional variation of compensation: imprecise• More direct test: measure profession-specific behavior?• Missions of organizations/professions, individual behavior: physicians are (by oath) committed to saving lives; teachers to educating children; firemen to prevent and fight fires• What measures to be used for empirical work? Find a profession that has clear behavioral requirements• How to exclude that behavior is not driven by experience/socialization, i.e., endogenously shaped? Look at applicants, not experienced workers 6
    • OUR STUDY: POLICE APPLICANTS16.01.2013 Guido Friebel 7
    • Our design• Run experiments with police applicants• Behavioral requirements of a police officer: – Trustworthy, otherwise no cooperation of citizens – Willing to spend ressources on norm-enforcement (go extra mile, consummate vs perfunctory peformance)• Control group: high-school students – State polices, as Hesse or Rhineland-Palatinate, hire only high-school students• Large sample• Internet-based trust and norm enforcement game16.01.2013 Guido Friebel 8
    • Good cops, bad cops:Police image in different countries „GfK Trustindex Summer 2010“: three professions people have most trust in: firemen, physicians and policemen 9
    • What is a good policeman?• NYPD: “in partnership with the community, we pledge to: • Maintain a higher standard of integrity than is generally expected of others because so much is expected of us. • Value human life, respect the dignity of each individual and render our services with courtesy and civility. • Fight crime by preventing it and by aggressively pursuing violators of the law.• German Police: • „Polizei, Dein Freund und Helfer“ • „to avert danger for public security and order“ Trustworthiness Willingness to enforce norms 10
    • The experiment• State police: Hesse, Rhineland-Palatinate• Applicants are pre-screened• Then, various tests, physical, psychological, IQ• College• Our applicants are not even pre-screened, they have just filled in their application• Police applicants and non-applicants: contacted with same letter• Through Police, schools• Several thousands of people are invited 11
    • The experiment• Trust, norm enforcement game• Anonymous log in, experiment, questionnaire• 630 police applicants (not pre-screened yet), roughly 14% return rate• Around 960 high-school students from 42 high schools, roughly 20% return rate• 430 students from Goethe• Points won in game may be paid out in Euros: average pay out of 150 Euro, 27 drawn randomly 12
    • Experimental Set-up: Treatments Player A Player B Trustor Trustee Police1. Police treatment: applicants Students Goethe University High school2. Control treatment: students
    • Demographics/sum stats of high school students versus police applicants16.01.2013 Guido Friebel 14
    • THE TRUST GAME16.01.2013 Guido Friebel 15
    • Trust gameNote: Any transfer from A to B is tripled: Initial Transfer is efficient!16.01.2013 Guido Friebel 16
    • Students‘ (A player‘s) beliefs about trustworthiness• A person’s trust increases if the other party has behaved in a kind way earlier, e.g. donating money to an NGO before the trust game starts (Albert et al., 2007; Fehrler, 2010)• The social status or social role of people in society can work as a signal about a positive type and therefore the other person’s belief about trustworthiness is raised (Yamagishi, 1998) trustors believe police applicants’ Do trustworthiness to be higher than the control group’s?
    • Students‘ beliefs about trustworthinessT(x,y) = expected back transfer when 50, 100 was sent N Frequency (in %) Δ in Type Police Control Police Control perc.points treatment treatment treatment treatment T0, 0 52 74 24.09 34.58 - 10.49** T100, 0 45 51 20.91 23.83 2.92 T0, 200 29 31 13.18 14.49 1.31 T100, 200 91 58 41.82 27.10 14.72*** Sum 217 214 Two-tailed chi-square test. Statistical significance: *p<.10, **p<.05, ***p<.01
    • Trustors’ transfers to police applicants and to control group Type of trustee Control Police treatment treatment Average transfer level 52.53 46.96 Standard deviation 36.71 37.87 Number of 217 214 observations• Mann–Whitney U-test; p* = 0.0606, one-tailed 19
    • Results: Transfer behavior N Frequency (in %) Δ in Transfer Police Control Police Control percentage treatment treatment treatment treatment points EUR 0 53 68 24.42 31.78 -7.36* EUR 50 100 91 46.08 42.52 3.56 EUR 100 64 55 29.49 25.7 3.79 Sum 217 214The results are reported for two-tailed chi-square test. Statistical significance:*p<.10, **p<.05, ***p<.01. 20
    • Results: Back transfer Trustor sends EUR 50 Trustor sends EUR 100 Police Control Police Control treatment treatment treatment treatmentMean average back 79.80 73.18 157.34 145.83transferStandard deviation 40.18 44.32 81.98 88.92Number of observations 708 1,115 708 1,115• EUR 50 case: Mann–Whitney U-test; p*** = 0.0013, two-tailed• EUR 100 case: Mann–Whitney U-test; p*** = 0.0056, two-tailed
    • NORM ENFORCEMENT 22
    • Enforcing norms through privately costly actions• Charness and Rabin (QJE, 2002), Fehr and Schmidt (QJE, 1999), Fehr and Gächter (JEP, 2000)• Are police applicants willing to spend more of their own resources on norm enforcement?• If yes: When do they punish, reward more intensively?• What can be inferred about motives?• C-Player (police or control group) decides according to strategy method how many points to allocate• Can spend up to 160 points on rewards or penalties• Payoff of A, B affected: 2* transfer (of C) 23
    • Norm enforcement game Outcome of the game between A, B: C‘s decision to reward or punish16.01.2013 Guido Friebel 24
    • Total points spent by C: Police applicants are more willing to enforce norms Police Applicants 42 Control Group 36 points = 16%16.01.2013 Guido Friebel 25
    • Predictions about Transfers of C; Police v control Situation Payoffs Efficiency Equ Reci Predicted ality procity Actions A0, B0 A100, B100 Low Yes n.a. A: punish or nothing B: nothing A50, B100 A150, B150 Medium Yes Yes A: reward, punish, nothing B: reward A100, B200 A200, B200 High Yes Yes A: reward B: reward A50, B0 A50, B150 Medium No No A: reward, punish, nothing B: punish A100, B0 A0, B200 High No No A: reward16.01.2013 Guido Friebel B: punish 26
    • Points spent by Player C on Player A (positive: reward, negative punishment) Police Applicants vs. Control Group: A50B0: 21.5 vs. 19.8 A100B0: 39 vs. 3316.01.2013 Guido Friebel 27
    • Points spent by C on Player B (positive: reward, negative punishment) Police Applicants vs Control Group: A50B0: -41 vs -35 A100B0: -40 vs 3516.01.2013 Guido Friebel 28
    • Frequencies16.01.2013 Guido Friebel 29
    • Results of regressions, clustered on person-level controlling for gender, age, income Total Reward PunishmentSituation CG Pol CG Pol CG PolA100,B200 27 23 25 21 0 0A50,B100 -5 -4 -5 -4 0 0A0,B0 -22 -24 -15 -17 -7 -7A100,B0 -23 -25 12 14 -35 -38A50,B0 -36 -44 0 -4 -35 -4016.01.2013 Guido Friebel 30
    • Differences between police applicants and control group• Police applicants spend more of their resources on enforcement• Differences pronounced for high efficiency (A100, B200), but at low levels• Differences very pronounced in rewards for A when A is cheated by B (A50,B0 and A100,B0), equality or reward for good intention matters• Differences very pronounced in punishment for B when A is cheated by B (A50,B0 and A100,B0)16.01.2013 Guido Friebel 31
    • Summary• Selection matters: police applicants seem quite different from their peers (high school students)• Belief in police applicants‘ trustworthiness is higher• Trust measured in transfers is higher (but magnitude is smaller than beliefs)• Trustworthiness is higher• Police applicants are willing to spend more resources on rewarding and penalizing others 32
    • BACKUP16.01.2013 Guido Friebel 33
    • Regression, Transfer behaviorTable 5: Results of the ordered logit regression analysisDependent variable: students’ transfer All All All AllModel (1) (2) (3) (4)Police treatment 0.278* 0.282* 0.279* 0.219 * (0.179) (0.179) (0.180) (0.185)Female -0.056 0.119 0.030 (0.181) (0.192) (0.191)No private contact with Police -0.040 -0.124 -0.227 (0.182) (0.187) (0.201)Taking risks…- in general 0.311*** 0.299*** (0.054) (0.080)- by financial matters -0.036 (0.066)- by trusting foreign people 0.236*** (0.057)Additional controls No No Yes YesPseudo R-squared 0.0026 0.0028 0.0512 0.0800N 431 431 423 423The results are reported for an ordered logit regression; the dependent variable is students’ transfer in the role of thetrustor. Additional controls include age, taking risk by car driving, by sports & leisure, by career and by health. Robuststandard errors are in parentheses. Statistical significance: *p<.10, **p<.05, ***p<.01.
    • ALLE DREI TABELLEN ZAHLEN CHECKENTransfers as C; Police v controllittle or no difference when no trust = equal outcome Person, situation N, Transfer Std. dev. N, Transfer Std. dev. Mann CG Pol Whitn. To A, if (A0,B0) 976 -5.25 (16.27) 630 -4.55 (18.36) 0.41 To B, if (A0,B0) 976 4.28 (11.61) 630 5.53 (13.36) 0.05** A, (A50,B100) 976 6.39 (12.48) 630 9.22 (15.61) 0.0*** B, (A50,B100) 976 9.31 (15.39) 630 12.18 (19.13) 0.0*** A, (A100, B200) 976 9.18 (17.68) 630 11.77 (20.40) 0.02** B, (A100, B200) 976 11.70 (20.85) 630 13.59 (23.97) 0.265 A, (A50,B0) 976 19.80 (19.11) 630 21.57 (20.70) 0.05** B, (A50,B0) 976 -34.98 (32.99) 630 -40.94 (34) 0.0*** A, (A100, B0) 976 33.18 (27.81) 630 39.10 (32.3) 0.0*** B, (A100, B0) 976 -35.25 (38.68) 630 -39.18 (40) 0.03**16.01.2013 Guido Friebel 35
    • Police reward trust and trustworthiness higher for equal outcomes Person, situation N, Transfer Std. dev. N, Transfer Std. dev. Mann CG Pol Whitn. A, (A50,B100) 976 6.39 (12.48) 630 9.22 (15.61) 0.0*** B, (A50,B100) 976 9.31 (15.39) 630 12.18 (19.13) 0.0*** A, (A100, B200) 976 9.18 (17.68) 630 11.77 (20.40) 0.02** B, (A100, B200) 976 11.70 (20.85) 630 13.59 (23.97) 0.265 A, (A50,B0) 976 19.80 (19.11) 630 21.57 (20.70) 0.05** B, (A50,B0) 976 -34.98 (32.99) 630 -40.94 (34) 0.0*** A, (A100, B0) 976 33.18 (27.81) 630 39.10 (32.3) 0.0*** B, (A100, B0) 976 -35.25 (38.68) 630 -39.18 (40) 0.03**16.01.2013 Guido Friebel 36
    • Police reward trust of A more (when B cheats), and punish B more intensively Person, situation N, Transfer Std. dev. N, Transfer Std. dev. Mann CG Pol Whitn. A, (A50,B0) 976 19.80 (19.11) 630 21.57 (20.70) 0.05** B, (A50,B0) 976 -34.98 (32.99) 630 -40.94 (34) 0.0*** A, (A100, B0) 976 33.18 (27.81) 630 39.10 (32.3) 0.0*** B, (A100, B0) 976 -35.25 (38.68) 630 -39.18 (40) 0.03**16.01.2013 Guido Friebel 37
    • Total transfers, Rewards and punishments, clustered on person-level controlling for gender, age, income Total Reward PunishmentSituation CG Pol CG Pol CG PolA100,B200 27 23 25 21 0 0A50,B100 -5 -4 -5 -4 0 0A0,B0 -22 -24 -15 -17 -7 -7A100,B0 -23 -25 12 14 -35 -38A50,B0 -36 -44 0 -4 -35 -4016.01.2013 Guido Friebel 38
    • Total transfers, Rewards and punishments, clustered on person-level controlling for gender, age, income D ependent V ar abl i e O ver lEfect al f R ewar Efect d f Puni hm ent Efect s f D eci on C ase si Cons ant ( 100 B200) t A 22.946*** 20.281*** 2.665 ( 252) 5. ( 347) 5. ( 560) 3. D eci i 2 ( 50 B100) s on A - 625*** 4. - 923*** 4. .298 ( 660) 0. ( 622) 0. ( 190) 0. D eci i 3 ( 0 B0) s on A - 867*** 22. - 600*** 15. - 267*** 7. ( 197) 1. ( 002) 1. ( 409) 0. D eci i 4 ( 100 B0) s on A - 675*** 23. 12.616*** - 291*** 36. ( 569) 1. ( 013) 1. ( 961) 0. D eci i 5 ( 50 B0) s on A - 520*** 39. - 601*** 2. - 919*** 36. ( 372) 1. ( 938) 0. ( 838) 0. D um m y f bei a P olce A pplcant or ng i i 1.982 3.516*** - 534* 1. ( 281) 1. ( 165) 1. ( 785) 0. C ontrol V ari e abl G ender 0.354 0.192 0.162 ( 166) 1. ( 070) 1. ( 725) 0. A ge - 142 0. 0.036 - 178 0. ( 279) 0. ( 287) 0. ( 190) 0. Log ofIncom e 0.311 0.218 0.092 ( 647) 0. ( 606) 0. ( 442) 0. R² 0.1318 0.1006 0.3450 O bs vat ons er i 1559 1559 155916.01.2013 Guido Friebel 39