Labor economics Why is labor behaviorally interesting? Why is labor behaviorally interesting? Important in scale Important in scale People sell themselves (identity, appreciation) People sell themselves (identity, appreciation) Natural social comparison with others Natural social comparison with others Quality assurance problem + room for rationalization Quality assurance problem + room for rationalization Firms’ problem is endogenous sorting & incentive Firms’ problem is endogenous sorting & incentive Behavioral effects in labor markets: Behavioral effects in labor markets: “Gift exchange” and supra-marginal wages “Gift exchange” and supra-marginal wages Crowding out Crowding out Critique of the single-activity agency model Critique of the single-activity agency model Labor supply: Cabs Labor supply: Cabs

1. Too-high wages and unemployment Efficiency wages vs gift exchange supply demand Wage w Quantity Q Price P unemployment at w

Why are wages too high? Efficiency wages (Stiglitz et al) Efficiency wages (Stiglitz et al) Pay “too much” so workers have something to lose if they shirk Pay “too much” so workers have something to lose if they shirk Why don’t workers bid for jobs? Why don’t workers bid for jobs? Role for nepotism, social networks, “hiring bonusses” ($5k consulting firm “bounties”) Role for nepotism, social networks, “hiring bonusses” ($5k consulting firm “bounties”) “Gift exchange” (Akerlof-Yellen) “Gift exchange” (Akerlof-Yellen) Pay “too much” so workers reciprocate with high (uncontractible) effort Pay “too much” so workers reciprocate with high (uncontractible) effort Consistent with resistance to wage cuts (Bewley) Consistent with resistance to wage cuts (Bewley) Experimental evidence (Fehr et al, PJ Healy,…) Experimental evidence (Fehr et al, PJ Healy,…)

Moral hazard in contracting: Theory and experimental evidence Fehr setup: Firms offer w Firms earn 10e-w Workers choose e Workers earn w-c(e) No reputations (cf. PJ Healy)

Competition does not drive wages down…firms choose high wage offer workers & expect reciprocity

2. Crowding out Do extrinsic ($) incentives crowd out intrinsic motivation? Do extrinsic ($) incentives crowd out intrinsic motivation? Do puzzles for $ or no-$. After $ removed, no- $ group does more puzzles (Deci et al) Do puzzles for $ or no-$. After $ removed, no- $ group does more puzzles (Deci et al) Female tennis players: Play for fun as kids… Female tennis players: Play for fun as kids… …later on tour, quit after getting appearance fee Q: Is it a “strike” or permanent decrease in incentive? Q: Is it a “strike” or permanent decrease in incentive?

Benabou-Tirole REStud 03 Workers infer task difficulty or skill from wage offer (“overjustification”, “self-perception”, “looking glass self”) Workers infer task difficulty or skill from wage offer (“overjustification”, “self-perception”, “looking glass self”) Worker exerts effort 0,1, cost is c in [c *,c*] Worker exerts effort 0,1, cost is c in [c *,c*] Worker gets signal σ correlated with c Worker gets signal σ correlated with c Success pays V to agent, W to firm Success pays V to agent, W to firm Θ is probability of success given effort Θ is probability of success given effort Firm offers bonus b Firm offers bonus b Worker exerts effort c(σ,b) σ*(b) Worker exerts effort c(σ,b) σ*(b) Prop 1: In equilibrium Prop 1: In equilibrium Bonus is short-term reinforcer: b 1 σ*(b 2 ) Bonus is short-term reinforcer: b 1 σ*(b 2 ) Rewards are bad news: b 1 <b 2  E[c|σ 1,b 1 ] < E[c|σ 2,b 2 ] Rewards are bad news: b 1 <b 2  E[c|σ 1,b 1 ] < E[c|σ 2,b 2 ] Empirical leverage: Negative effect occurs only if firm knows more about task difficulty or worker skill than the worker knows Empirical leverage: Negative effect occurs only if firm knows more about task difficulty or worker skill than the worker knows

3.Critiques of standard agency model Standard model (one activity) Standard model (one activity) Firms pay wage package w=f+b(e+θ) Firms pay wage package w=f+b(e+θ) Workers choose hidden effort e Workers choose hidden effort e b is “piece rate”, θ is “luck” b is “piece rate”, θ is “luck” Risk-neutral firms earn Π (e)-w Risk-neutral firms earn Π (e)-w Risk-averse workers earn w-c(e)-var(w) Risk-averse workers earn w-c(e)-var(w) Tradeoff: Tradeoff: “High powered incentive” b increases motivation… “High powered incentive” b increases motivation… …but creates bad variance in wages …but creates bad variance in wages

Behavioral critiques Workers don’t know c(e) (prefs constructed) Workers don’t know c(e) (prefs constructed) U(W-r) depends on reference point U(W-r) depends on reference point Previous wages, wages of others Previous wages, wages of others Workers care about procedures or income source Workers care about procedures or income source Psychic income: meaning and appreciation Psychic income: meaning and appreciation Crowding out of intrinsic motivatoin Crowding out of intrinsic motivatoin Biases in separating e and θ Biases in separating e and θ Hindsight bias (agents should have known) Hindsight bias (agents should have known) Diffusion of responsibility in group production (credit-blame) Diffusion of responsibility in group production (credit-blame) Attribution error (blame agent skill, not situation difficulty) Attribution error (blame agent skill, not situation difficulty) Workers overconfident about luck or productivity Workers overconfident about luck or productivity

Zink et al (Neuron 04): Earned money more rewarding than unearned money

4. Labor supply Basic questions: Basic questions: Does supply rise with wage w? Does supply rise with wage w? Participation (days worked) vs hours Participation (days worked) vs hours A: Very low + supply elasticities for males A: Very low + supply elasticities for males …but most data from fixed-hours …but most data from fixed-hours Intertemporal substitution Intertemporal substitution Do workers work long hours during temporary wage increases (e.g. Alaska oil pipeline)? (Mulligan JPE 98?) Do workers work long hours during temporary wage increases (e.g. Alaska oil pipeline)? (Mulligan JPE 98?) Alternative: Amateur “income targeting” Alternative: Amateur “income targeting”

Cab driver “income targeting” (Camerer et al QJE 97)

Cab driver instrumental variables (IV) showing experience effect

Farber (JPE 04) hazard rate estimation: Do hrs worked or accumulated income predict quitting? Note: If workers are targetting, why isn’t the income distribution more spiky? Note: If workers are targetting, why isn’t the income distribution more spiky?

Do they quit because of hours or $? Getting tired is a stronger regularity than targetting Note: Which has more measurement error, hours or $? Big tip experiment!

Farber is puzzled… Why do drivers lose $? Why do drivers lose $? A: Experienced do not A: Experienced do not Why be puzzled? Results were predicted… Why be puzzled? Results were predicted… Calendar date effects? Let’s look… Calendar date effects? Let’s look…

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Interviews: Great idea! Q: From passage above, do you think others did interviews too? Were they more or less systematic than Farber’s? (his are admittedly “not systematic”) Q: From passage above, do you think others did interviews too? Were they more or less systematic than Farber’s? (his are admittedly “not systematic”)

Farber on experience effect “Overall”? “Overall”? TRIP sample only TRIP sample only CBLT have 2 other samples CBLT have 2 other samples

Alan Krueger 6/26/03 NYTimes column Now their findings are being debated. First, Gerald S. Oettinger of the University of Texas at Austin published a paper in the Journal of Political Economy on the daily work decisions of food and beverage vendors at a major-league baseball stadium. The vendors were independent contractors, required to work until the seventh inning, but they could choose which games to work. Vendors make more when the number of fans is high and the number of other vendors is low. Professor Oettinger found that vendors were more likely to go to work when the expected payoff was higher -- for example, on days when a larger crowd was expected because of a pivotal game or a quality opponent. The decision of whether to work at all on a high-payoff day -- as opposed to how much to work -- was not considered in the cabdriver study. A: YES IT WAS. PERHAPS KRUEGER DID NOT READ OUR PAPER. Now their findings are being debated. First, Gerald S. Oettinger of the University of Texas at Austin published a paper in the Journal of Political Economy on the daily work decisions of food and beverage vendors at a major-league baseball stadium. The vendors were independent contractors, required to work until the seventh inning, but they could choose which games to work. Vendors make more when the number of fans is high and the number of other vendors is low. Professor Oettinger found that vendors were more likely to go to work when the expected payoff was higher -- for example, on days when a larger crowd was expected because of a pivotal game or a quality opponent. The decision of whether to work at all on a high-payoff day -- as opposed to how much to work -- was not considered in the cabdriver study. A: YES IT WAS. PERHAPS KRUEGER DID NOT READ OUR PAPER.

And most recently, my Princeton colleague Henry S. Farber revisited the question of cabdrivers, studying a different set of drivers. He found that cabdrivers quit after they work a lot of hours and grow weary. How much they have earned to that point has little or nothing to do with their decision. Moreover, the amount the drivers earn varies substantially from day to day, suggesting that their target income levels, if they have them, fluctuate wildly. He suggests that the earlier findings possibly resulted from reporting errors in the data: because daily wages were derived by dividing total revenue by hours worked, any mistake in reported hours would cause a mistake in the opposite direction in the calculated wage, inducing a negative correlation between wages and hours worked. And most recently, my Princeton colleague Henry S. Farber revisited the question of cabdrivers, studying a different set of drivers. He found that cabdrivers quit after they work a lot of hours and grow weary. How much they have earned to that point has little or nothing to do with their decision. Moreover, the amount the drivers earn varies substantially from day to day, suggesting that their target income levels, if they have them, fluctuate wildly. He suggests that the earlier findings possibly resulted from reporting errors in the data: because daily wages were derived by dividing total revenue by hours worked, any mistake in reported hours would cause a mistake in the opposite direction in the calculated wage, inducing a negative correlation between wages and hours worked. A: REPORTING ERRORS ARE NOT ENOUGH BECAUSE WE USED IV ESTIMATION. MUST BE REPORTING ERRORS *AND* SPECIFIC- DATE SHOCKS TO LABOR SUPPLY. A: REPORTING ERRORS ARE NOT ENOUGH BECAUSE WE USED IV ESTIMATION. MUST BE REPORTING ERRORS *AND* SPECIFIC- DATE SHOCKS TO LABOR SUPPLY.

Big tip experiment Prediction of reference-dependent model: Prediction of reference-dependent model: A large windfall will lead to lower labor supply A large windfall will lead to lower labor supply Example: Give drivers a big surprising tip… Example: Give drivers a big surprising tip… predict they will quit early (or street musicians etc) predict they will quit early (or street musicians etc) Tip must not be an indication of wage shift Tip must not be an indication of wage shift Do it? Only if it would convince true-believer labor economists Do it? Only if it would convince true-believer labor economists Thaler asked Kevin Murphy: Thaler asked Kevin Murphy: “They might just go home to celebrate” “They might just go home to celebrate” Implication: Some labor economists will not commit to reputational bets about whether theories are true Implication: Some labor economists will not commit to reputational bets about whether theories are true

Goette and Huffman: Early windfall increases work, then decreases

A misleading abstract No test of intertemporal substitution No test of intertemporal substitution What are “problems with conception and measurement”? What are “problems with conception and measurement”?