The Minimum Wage and Productivity: A Case Study of California Strawberry Pickers

Transcription

1 The Minimum Wage and Productivity: A Case Study of California Strawberry Pickers Alexandra E. Hill University of California, Davis AliHill@ucdavis.edu October 26, / 20 Alexandra E. Hill

2 Motivation CAPITOL ALERT APRIL04,20169:47AM Jerry Brown signs $15 minimum wage in California 30 JANUARY 2017 REGULATORY > LEGISLATIVE Agriculture reacts to California's new overtime law CALIFORNIA MINIMUM WAGE AND AGING FARMWORKERS BY PHILIP MARTIN Todd Fitchette I Sep 12, 2016 U.S. I U.S. NEWS California Farmworkers to Get Overtime Pay After 8 Hours Under New Law Agricultural workers now must work 10 hours a day or 60 hours a week to earn that benefit 2 / 20 Alexandra E. Hill

3 Motivation. aid minimum wag e so " do only a "W e're being P t of work. minimum amoun 3 / 20 Alexandra E. Hill

4 Research Questions Piece Rate Wages and the Minimum Wage How does the minimum wage interact with piece rate wages for strawberry harvesters? Payment Scheme Productivity Effects What effect, if any, does an increase in the minimum wage have on worker productivity? Policy Implications What do these productivity effects mean in the face of rising minimum wages? Can producers mitigate these effects? 4 / 20 Alexandra E. Hill

5 Literature on Payment Schemes and Productivity Theoretical Literature: Asymmetric information and contract choice e.g. Rasmusen, 2001 and Bolton & Dewatripont, 2005 Heterogeneity and selection into payment schemes Gibbons, 1987; Lazear, 1986; Magnum, 1962; Robertson, 1960; and Stiglitz, 1975 Empirical Literature: Productivity gains from piece rate payments, relative to fixed wage Bandiera, Barankay, & Rasul, 2004; Lazear, 2000; Paarsch & Shearer, 1999; Pekkarinen & Riddell, / 20 Alexandra E. Hill

6 Literature on Payment Schemes in Agriculture Empirical Literature: Variation in local piece rate wages allows workers to sort according to their comparative advantage Newman & Jarvis, 2000 Variation in payment schemes allows workers to sort themselves based primarily on comparative advantage Foster & Rosenzweig, 1996 Piece rate payments increase effort and calories consumed Foster & Rosenzweig, / 20 Alexandra E. Hill

7 Contributions Theoretical: A model that combines fixed and piece rate wage schemes Utilizes heterogeneity in worker productivity to elicit effects of a wage change Benefit to this approach: All workers can face the same minimum wage increase. Empirical: Short-run effect of a minimum wage increase on worker productivity Examine persistence of the productivity effect and mitigating factors 7 / 20 Alexandra E. Hill

8 Theoretical Framework Low, Medium, and High Productivity Workers Facing a Pure Piece Rate Wage 8 / 20 Alexandra E. Hill

9 Theoretical Framework The Piece Rate and Minimum Wage 9 / 20 Alexandra E. Hill

10 Theoretical Framework Model The Piece Rate and a Minimum Wage Incease 10 / 20 Alexandra E. Hill

11 Data Buenaventura Ranch 211 acre berry farm in Watsonville Panel Data from Number of Workers 1 Timestamp Productivity Summary Worker clocks in, clocks out, goes on break, switches tasks, or drops off a box of strawberries 2 Payroll Data Payroll Summary Piece Rate wages, total boxes delivered, hours worked, and the hourly wage rate Records when workers need a minimum wage adjustment 11 / 20 Alexandra E. Hill

12 Minimum Wage Change and Worker Productivity Examples ~"'.~... ~ X 00 m- g V, Q) ~ co ~ C "' ~"' Minimum Wag e: $8.80/hr / Minimum Wage : $9.05/hr 30apr may2013 2Bmay2013 Day 11jun jun2013 Low Productivity Medium Productivity High Productivity 12 / 20 Alexandra E. Hill

13 Preliminary Results Empirical Model Dependent Variable: Daily Mean Box Delivery Time (1) (2) (3) (4) Low-type (0.09) (0.09) (0.09) (0.18) Medium-type (0.09) (0.09) (0.09) (0.19) Min Wage Δ (0.21) (0.21) (0.20) (0.19) Min Wage Δ Low (0.22) (0.21) (0.20) (0.18) Min Wage Δ Med (0.22) (0.21) (0.20) (0.19) Time Trend N Y Y Y Day of Week N N Y Y Crew N N N Y N 21,964 21,964 21,531 21,531 F-stat R p < 0.01, p < 0.05, p < / 20 Alexandra E. Hill

14 Preliminary Results To Summarize: Significant productivity losses in the short-run for low-types only. 50 seconds - 1 minute slower delivering each flat. In this sample, low-types constitute roughly half of the pickers In the short-run, this is a substantial productivity loss for the workforce. Controlling for crew removes baseline significance of worker productivities. 14 / 20 Alexandra E. Hill

15 Current Work: New Data Sierra Farms 67 acre berry farm in Watsonville Panel Data from Wage rates differ from Buenaventura (both piece rate and minimum wages) 15 / 20 Alexandra E. Hill

16 Trends in Productivity Daily Picker Productivity for Buenaventura Daily Picker Productivity for Sierra Farm 01jul jan jul jan jul jul jan jul jan jul2016 Date Date 16 / 20 Alexandra E. Hill

17 Productivity and Minimum Wage Increases Daily Picker Productivity for Buenaventura Daily Picker Productivity for Sierra Farm 01jul jan jul jan jul jul jan jul jan jul2016 Date Date 17 / 20 Alexandra E. Hill

18 2013 Minimum Wage Changes LO 2013 Productivity Sierra piece rate: $1.70 BVR piece rate: $ apr2013 Sierra minimum wage increases from $8.25 to $ jun2013 I BVR minimum I wage increases I from $8.80 to I 9.0~ I Sierra _mimmum i;~~;~~~s I $9.05 I wt:i~ 01aug2013 Date 01oct dec Sierra 18 / 20 Alexandra E. Hill

19 Future Work Next Steps Defining Low, Medium, and High Productivity Workers Consider short and long-run effects Using differential wage changes at the ranches Effects of a change to the piece rate wage Other applications of these data 19 / 20 Alexandra E. Hill

20 Thank You! 20 / 20 Alexandra E. Hill

21 Supplemental Theoretical Framework Data Empirical Methodology Theoretical Model max U = w(q(e, s, θ)) c(e) λ[q(θ) q(e, s, θ)] e w(.) is the wage, q(.) is output, c(.) is the cost function e is effort, s is skill, θ are external factors (e.g. harvest abundance) q(θ) is the minimum output required to keep the job λ 0 Back to Theoretical Framework 20 / 20 Alexandra E. Hill

22 Supplemental Theoretical Framework Data Empirical Methodology Theoretical Model First Order Condition: 0 w q q e + λq e = c (1) Back to Theoretical Framework 20 / 20 Alexandra E. Hill

23 Supplemental Theoretical Framework Data Empirical Methodology Theoretical Model Piece Rate and the Minimum Wage: The pure piece rate wage is expressed as: w = r q The piece rate wage with a minimum wage is expressed as: w = max {w 0, r q} First Order Conditions : 0 λq e = c if w 0 r q ; λ 0 0 r q e + λq e = c if w 0 < r q ; λ 0 Back to Theoretical Framework 20 / 20 Alexandra E. Hill

24 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Table: Number of Workers in the BVR Data Year Total Observations Unique Farmworkers Unique Pickers , , ,240 1,128 1, ,500 1,398 1, ,463 1,544 1, ,808 2,422 1, ,220 2,594 1,527 Total 494,803 6,676 5,020 Back to BVR Data 20 / 20 Alexandra E. Hill

25 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Table: Summary of (All) Worker Retention Years in Data Frequency Percent 1 4, , Back to BVR Data 20 / 20 Alexandra E. Hill

26 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Table: Summary of Picker Retention Years in Data Frequency Percent 1 3, Back to BVR Data 20 / 20 Alexandra E. Hill

27 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Average Box Delivery Time by Day 01jan jan jan jan2016 Date Back to BVR Data 20 / 20 Alexandra E. Hill

28 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Year Rate per flat ($) Daily Flats Delivered Hourly Rate ($/hr) Mean Min/Max Mean Min/Max Mean Min/Max (.01) 1.73 (.14) 1.79 (.17) 1.87 (.14) 2.39 (.58) 1.85 (.21) 1.90 (.09) Min: 1.60 Max: 2.00 Min: 1.60 Max: 2.00 Min: 1.60 Max: 2.15 Min: 1.75 Max: 2.15 Min: 1.75 Max: 3.00 Min: 1.75 Max: 3.00 Min: 1.85 Max: (28.96) (25.68) (24.04) (21.41) (24.50) (18.37) (23.54) Min: 1 Max: 167 Min: 1 Max: 189 Min: 1 Max: 151 Min: 1 Max: 122 Min: 1 Max: 156 Min: 1 Max: 138 Min: 1 Max: (5.57) (5.34) (5.42) (6.07) (6.44) (6.42) (5.17) Min: 0 Max: 60 Min: 0 Max: Min: 0 Max: 120 Min: 0 Max: 150 Min: 0 Max: 175 Min: 0 Max: Min: 0 Max: 82.5 Back to BVR Data 20 / 20 Alexandra E. Hill

29 Supplemental Theoretical Framework Data Empirical Methodology Hourly Wage Rate by Crew, 2013 Crew # Mean SD Min Max (5.71) (6.27) (6.45) (6.18) (5.44) (5.99) (5.52) (6.65) (5.09) (5.65) (5.83) (5.78) (5.97) (4.77) (5.58) (4.63) Back to Paper 2 20 / 20 Alexandra E. Hill

30 Supplemental Theoretical Framework Data Empirical Methodology Example Crew Productivity on 6/01/2013 (a) Example Low Productivity Crew (b) Example High Productivity Crew Back to Paper 2 20 / 20 Alexandra E. Hill

31 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: BVR Data Table: Percent of Workers Receiving the Minimum Wage Adjustment Year At Least 1 Time % At Least 3 Times % 5+ % Back to BVR Data 20 / 20 Alexandra E. Hill

32 Supplemental Theoretical Framework Data Empirical Methodology Example Low-type Productivity (a) Week Before Minimum Wage Δ (b) Week After Minimum Wage Δ Back to Data 20 / 20 Alexandra E. Hill

33 Supplemental Theoretical Framework Data Empirical Methodology Example Med-type Productivity (a) Week Before Minimum Wage Δ (b) Week After Minimum Wage Δ Back to Data 20 / 20 Alexandra E. Hill

34 Supplemental Theoretical Framework Data Empirical Methodology Example High-type Productivity (a) Week Before Minimum Wage Δ (b) Week After Minimum Wage Δ Back to Data 20 / 20 Alexandra E. Hill

35 Supplemental Theoretical Framework Data Empirical Methodology Supplemental: Empirical Model y it = β 0 + β 1 t + β 2 L i + β 3 M i + β 4 dow t + β 5 (L i dow t ) +β 6 (M i dow t ) + β 7 crew i + δ 1 Post t + δ 2 (L i Post t ) +δ 3 (M i Post t ) + it y it is the average time to deliver a box for worker i on day t t is a time trend, L i and M i indicated whether the worker is a low-type or high-type dow t is a factor variable that indicates the day of the week Post is a binary indicator equal to one after the minimum wage change Back to Results 20 / 20 Alexandra E. Hill