1. LABOR ECONOMICS Lecture 3: Labor Econometrics II: An Example Prof. Saul Hoffman Université de Paris 1 Panthéon-Sorbonne March, 2013

2. EMPLOYMENT EFFECTS OF THE 2009 MINIMUM WAGE INCREASE: EVIDENCE FROM STATE COMPARISONS OF AT-RISK WORKERS Saul D. Hoffman and Chenglong Ke October, 2012 2

3. Overview Focus on July, 2009 Min Wage Increase from $6.55 to $7.25 Natural Experiment Approach Taking advantage of state min wages > Fed Sets of comparisons – very simple! Between-state for at-risk workers (DID) Within-state for at-risk v not-at-risk (DID) Between and within combined (DIDID) to control for OVB With and without regression adjustments Data CPS 4-5 months before and 4-5 months after July 2009 increase. Individuals with state variation in min wage (arguably exogenous) 3

4. Quick Literature Review - The Elusive Negative Effects of the Minimum Wage Evidence – much more mixed than intro textbooks suggest Aggregate Time Series (through 1980s) - Brown et al Panel (State-level) – Neumark/Wascher Fast Food Industry- Katz/Krueger; Card/Krueger Demographic Group - Deere/Murphy/Welch Quasi-Experimental - Card/Krueger; Hoffman/Trace Newest Contributions - Dube, Lester, Reich 4

5. Our Approach - Methods Focus: State + demographics States fall into three groups classified by MW increase betw Feb/Mar & Nov/Dec, 2009 $6.55 $7.25: 24 states; 2 special cases (DC & NV) No increase: 16 states (State > Fed; no after Jan 1) Partial increase (.04¢ -.35¢) – 9 states Compare employment rates before and after July 2009 increase Feb/Mar v Nov/Dec using CPS Focus on Full Increase v No Increase Groups defined by education/age 5

6. States – Whos Where? Control = no increase. – New England (ME NH VT MA RI CT) – Midwest (OH MI IA) – West (CO NM AZ WA OR CA HW) – Most have laws that exogenously increase MW automatically by formula (CPI, etc) Partial Increase (mostly excluded in analysis) – FL NY NY PA DE AK MO IL MT Treatment = Full Increase – all the others Below will see that the groups appear reasonably similar to one another 6

7. Methods (cont.) - Comparisons Between-State: DID B = (E j2 T – E j1 T ) - (E j2 C – E j1 C ), where j is some at- risk group Within-State: DID w = (E j2 T – E j1 T ) - (E k2 T – E k1 T ), where k is some not at-risk group Pseudo Effects: DID B for group k; DID w for control states DIDID(1) = DID Bj - DID Bk =(E J T -E J C ) - (E k T -E k C ) – controls for T v C economy-wide changes (for group k) DIDID(2) = DID w T - DID w C = (E J T -E k T ) – (E J C -E k C ) – controls for j v k economy-wide changes (in C states) 7

8. Methods (cont.) - Regression Counterpart DID B : E ist = β 0 + β 1 TRT ist + β 2 Time2 ist + λ[TRT ist x Time2 ist ] + μ ist λ = treatment effect = DID estimator DID w : same, except for sample & definition of TRT Add Covariates: E ist = β 0 + β 1 TRT ist + β 2 Time2 ist + λTRT ist x Time2 ist + Z ist δ + μ ist Z = Race, gender, Hispanic Warning: DIDID regression is tricky to set up. 8

9. Data & Samples CPS, Feb/Mar & Nov/Dec 2009 Age 16-59; N= 60-70,000 per pair of months At-risk groups: Age 16-19 (not in coll) Not HS Grad, Age 20-59; same, males only Not At-risk Male, Age 30-49, At Least Some College Sample weights – exactly reproduce Civ. LFPRs 9

10. Table 1. Sample Means (Weighted), Individuals Age 16-59, Feb/Mar 2009, by Subsequent Minimum Wage Increase Control States (No Increase in Min. Wage) Treatment States (Full Increase Only) Age37.4337.26 Black0.0710.163 Hispanic0.2000.132 Male0.4980.493 Not HS graduate0.1680.178 College graduate0.2720.242 Employment rate0.6880.696 Number of Observations57,17067,616 10

11. Table 2. Between-State DID Estimates of Impact of 2009 Minimum Wage Increase on Employment of At-Risk Groups Age 16-19 Not HS Grad (Age 20-59) Treatment Before0.25750.5448 After0.23320.5413 Difference -0.0243**-0.0035 Control Before 0.23420.5441 After 0.21610.5368 Difference -0.0181**-0.0073 DID B (T-C) Difference-0.00620.0038 Elasticity-0.2260.065 11

12. Table 3. Within-State DID and DIDID Estimates of Impact of 2009 Minimum Wage Increase on Employment Rate of At-Risk Groups Males, Age 30- 49, At Least Some College Age 16-19 Not HS Grad (Age 20-59) Treatment Difference -0.0097**-0.0243**-0.0035 DID W -0.01460.0062 Control Difference 0.0006-0.0181-0.0073 Pseudo DID W -0.0188*-0.0079 DIDID (T-C).0041.0141 Elasticity.149.242 Note: DIDID also equals diff betw DID B for teens (=-.062; see Tbl 2) and DID B for adult males (-.01003) =.0041 12

13. Table 4. Regression Estimates, Minimum Wage Effect, Teens Model (Control Variables) Between-State Estimate Within-State Estimate DIDID 1. No Covariates -.0062 (.0122) -.0146 (.0090).0041 (.0133) 2. Demog. Traits -.0033 (.0117) -.0170* (.0090) -.0003 (.0133) 3. Demog. Traits and State Fixed Effects.0005 (.0116) -.0166* (.0089).0008 (.0132) Note: Model 1 results same as means DID and DIDID 13

14. Wrap-Up Neg Empl Effects of Min Wage still hard to find Very simple & direct methodology – no question that this is what is in the CPS data Multiple credible comparisons, with and w/o covariates As always – not definitive, but suggestive 14