Job Search and Job Finding in a Period of Mass Unemployment: Evidence from High-Frequency Longitudinal Data

Transcription

1 Job Search and Job Finding in a Period of Mass Unemployment: Evidence from High-Frequency Longitudinal Data by Alan Krueger and Andreas Mueller Discussion by Bob Hall NBER EF&G Meeting, New York Fed, February 4,

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3 Search effort Factor Relation to search effort Finding Sorting Prior information Learning High effort searchers find jobs more quickly, leaving the low effort ones Searchers find out about available jobs fairly easily at the outset of search and then spend time waiting for prospects to materialize Appears to be the opposite, but measurement problems may be the reason Strongly supported Appears to be strongly supported, but Searchers reduce effort after early results measurement problems may be the are unfavorable reason Wealth Unemployment insurance As wealth is depleted, search becomes more intense Once benefits are exhausted, search becomes more intensive. Appears to be strongly rejected but, measurement problems may be the reason Not considered 3

4 Reservation wage Factor Relation to reservation wage Finding Sorting Prior information Learning Wealth Unemployment insurance Low reservation wage searchers depart unemployment soon, leaving high reservation wage searchers. Wages are known at the outset, so there is no decline during unemployment Searchers cut reservation wages after learning that higher-wage jobs are not available. As wealth is depleted, reservation wages decline Benefits: Once exhausted, reservation wage declines. Rejected Strongly supported Rejected Rejected Not discussed but implicitly supported 4

5 Old finding, confirmed here Figure 2.1: UI weekly exit rate by UI duration 5

6 Two measures of search time Time diary for the day before the survey: 7.6 hours per week 6

7 Two measures of search time Time diary for the day before the survey: 7.6 hours per week Recall question for the week: 11.5 hours per week 6

8 Minutes per day of search Figure 3.1a: Time spent on job search (yesterday), in minutes per day Figure 3.1b: Time spent on job search (last 7 days), in minutes per day 7

9 Related finding in the CPS Bailar JASA, Journal of the American Statistical Assoc 1. Rotation Group Indices in the CPS for Two Periods, (T1) and (T2), for Selected Characteristics Characteristic Month in sample M ~~ Total population 16 and over Civilian labor force Ti T Employed Ti T Unemployed Ti T Hours worked per week 1-29 Ti T Ti T Ti T

10 Regressions of search time on unemployment duration Table 3.1a Linear regressions of time spent on job search (yesterday), with and without fixed effects Pooled Dependent varialbe: Fixed Fixed Week 1 crosssection time spent on job search, in mins. per day effects effects Unemployment duration, in weeks (0.104)** (0.072) (0.250)*** (0.313)*** Lapse (before November 8) (6.924) Exhausted UI (10.724) After extension of November (3.039)*** Log(weekly benefit amount) (12.570) (10.311)* Log(weekly previous wage) (7.284) (6.113)** Controlling for age, education, sex, race and ethnicity x x Dummies for day of week of diary x x x x 9

11 Marginal probit coefficients for probability of early UI exit Table 5.2 Probit models (marginal effects) for leaving UI early and receiving a job offer Left UI early (before March 14, 2010) Dependent Varia Explanatory Variables: (1) (2) (3) Time spent on job search, in hours per week (0.0006)*** (0.0005)*** (0.0005)*** ( Log(reservation wage ratio) (0.0255)* (0.0246)** (0.0252)** Cohort (0.0352) (0.0331) Cohort (0.0277) (0.0271) Cohort (0.0276) (0.0275) Cohort (0.0202)*** (0.0202)*** Cohort

12 Identification Search productivity: h i = α i + s i 11

13 Identification Search productivity: h i = α i + s i Exit benefit: h i 1 2 h2 i 11

14 Identification Search productivity: h i = α i + s i Exit benefit: h i 1 2 h2 i Search time cost: γ i s i s2 i 11

15 Identification, continued max α i + s i 1 s i 2 (α i + s i ) 2 γ i s i 1 2 s2 i 12

16 Identification, continued max α i + s i 1 s i 2 (α i + s i ) 2 γ i s i 1 2 s2 i FONC: 1 h i γ i s i = 0 12

17 Two-equation system Search productivity: h i = α i + s i 13

18 Two-equation system Search productivity: h i = α i + s i Optimal time allocation to search: h i = 1 γ i s i 13

19 Two dimensions of heterogeneity 4 High productivity High productivity Low productivity 2Exit haza rd Low search time cost 0.5 High search time cost Search time

20 What the econometrician sees Exit haza rd Search time

21 Wage-setting typology from Models of Wage Formation Hall-Krueger Commitment to ignore counteroffers Wage offer customized to worker Interruption to alternating offer bargaining likely? Diamond paradox Posted wage Wage tightly linked to conditions Wage less responsive to conditions 3 16

22 Reservation wage and unemployment duration Table 4.1 Reservation wage ratio by duration of unemployment Less All 5-9 than 5 durations weeks weeks Feldstein & Poterba (1984): All Job Losers and Leavers Feldstein & Poterba (1984): Job Losers Krueger & Mueller: Cross-section (1st week) Krueger & Mueller: Longitudinal estimate weeks weeks weeks weeks 50 + weeks Note: Survey weights are used. Universe: Unemployed; no job offer yet accepted; age Feldstein and Poterba's (1984) estimates are from a sample of 2,228 unemployed from the May 1976 Current Population Survey. 17

23 Probability of acceptance when there is a threshold at W = R Acce eptance probabili ty Ratio of wage offer to reservation wage 18

24 Table 6.1a Hourly offered wage below and above hourly reservation wage Hourly offered wage < Hourly offered wage >= hourly reservation wage hourly reservation wage W R matters Accepted 50.5% 74.1% Not accepted 23.6% 10.3% Undecided 25.9% 15.7% N Table 6.1b Hourly offered wage below and above hourly reservation wage (full time offers only) Hourly offered wage < hourly reservation wage Hourly offered wage >= hourly reservation wage Accepted 44.4% 73.8% Not accepted 24.2% 11.4% Undecided 31.4% 14.8% N

25 W R matters somewhere near 0 Table 6.2 Marginal effects from probit model for accepting a job offer, conditional on receiving an offer (1) (2) (3) (4) Hourly offered wage Hourly reservation wage (lagged) (0.098)** (0.061)*** (0.095)** Hourly offered wage Hourly reservation wage (lagged) * Part time job offer (0.127)* (0.126)* Hourly offered wage Hourly previous wage (0.101) (0.070) Hourly offered wage Hourly previous wage * Parttime job offer (0.126) Parttime job offer (0.079)*** (0.079)* (0.080)*** Log(lagged hourly reservation wage) (0.122) (0.127) 20

26 Discontinuity in probit coefficient on Chance of Accepting Job Figure 6.1: Effect of Alternative Cutoffs for Reservation Wage Relative to Offered Wage Notes: Figure shows the effect of varying the reservation wage threshold on job acceptance. Specifically, if the reservation wage is R and the offered wage is W, a binary variable was 21

27 Figure 5.1: Cumulative Probability of Receiving at Least One Job Offer Offers are rare 22

28 And most are accepted 61 percent 23

29 And most are accepted 61 percent Hardly any job-seekers see more than one offer 23

30 And most are accepted 61 percent Hardly any job-seekers see more than one offer This puts a lot of tension on job-seekers beliefs about the offer distribution they don t learn much about the distribution while searching 23

31 And most are accepted 61 percent Hardly any job-seekers see more than one offer This puts a lot of tension on job-seekers beliefs about the offer distribution they don t learn much about the distribution while searching One-armed bandit model not relevant 23

32 Search effort Factor Relation to search effort Finding Sorting Prior information Learning High effort searchers find jobs more quickly, leaving the low effort ones Searchers find out about available jobs fairly easily at the outset of search and then spend time waiting for prospects to materialize Appears to be the opposite, but measurement problems may be the reason Strongly supported Appears to be strongly supported, but Searchers reduce effort after early results measurement problems may be the are unfavorable reason Wealth Unemployment insurance As wealth is depleted, search becomes more intense Once benefits are exhausted, search becomes more intensive. Appears to be strongly rejected but, measurement problems may be the reason Not considered 24

33 Reservation wage Factor Relation to reservation wage Finding Sorting Prior information Learning Wealth Unemployment insurance Low reservation wage searchers depart unemployment soon, leaving high reservation wage searchers. Wages are known at the outset, so there is no decline during unemployment Searchers cut reservation wages after learning that higher-wage jobs are not available. As wealth is depleted, reservation wages decline Benefits: Once exhausted, reservation wage declines. Rejected Strongly supported Rejected Rejected Not discussed but implicitly supported 25