The Impact of Hiring Quotas on Employment, Wages, and the Skill Premium: Evidence from Saudi Arabia Jennifer Peck and Conrad Miller Swarthmore College, UC Berkeley (Haas)
October 2015
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Introduction Quotas and affirmative action policies are used in many countries to favor members of disadvantaged or underrepresented groups. I
Improve employment outcomes among targeted groups
I
Impose costs on other workers and firms
Empirically difficult to measure the overall effects: I
Clear quotas are relatively rare
I
Policies often apply only to a small subset of firms or industries
I
Spillovers make it difficult to identify aggregate effects
! We look at effects of Nitaqat nationalization policy in Saudi Arabia
Introduction
Background
Data
Empirical Strategy
Results
Introduction Objectives
Spillovers to non-treated firms and non-targeted workers are likely very important in evaluating the effects of quotas: I
Employment effects
I
Competitive effects
I
Wages
Previous work on firm-level effects of Nitaqat: I
Increase in Saudi hiring of 96,000 workers
I
50% increase in firm exit
I
Significant decrease in average firm size
I
Suggestive evidence of poaching from other firms
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Introduction Objectives
1. How are firms meeting their Saudization targets? I I I I
Hire unemployed Saudis Poach experienced Saudis from other firms Reduce Saudi turnover Reduce expatriate employment
2. What does this mean for the composition of Saudi private sector employment? I I I
Occupation mix Human capital Firm-level wage premia
3. How did market-level outcomes respond? I I I
Wages Total employment Firm exit
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Background: Nitaqat Program Structure
The Nitaqat program is an ongoing quota policy started in 2011 by the Saudi Ministry of Labor to incentivize firms to replace foreign workers with unemployed Saudis I
Requires private-sector firms to achieve employment quotas for Saudi nationals
I
One of the world’s largest quota-based labor market policies
I
Broad scope: applies to firms across all industries Clearly-defined and well-enforced
I
I
Quotas were firm, compliance closely monitored, and sanctions for non-compliant firms were triggered automatically
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Background: Nitaqat Program Structure Overview I
All private-sector firms were sorted into categories based on 41 industry and 4 size groups
I
Saudization percentage quotas announced for each industry by size cell
I
Firms assigned to color bands based on Saudization percentage relative to cell quotas I
I
For example, cutoffs for medium-sized construction firms were: Red: 0-2% Yellow: 2-6% Green: 6-28% Platinum: 28+%
Sanctions imposed on firms in Yellow and Red bands I
I
Included limitations on visa issuance and foreign recruitment assistance Automatically triggered through integration of Nitaqat database and visa renewal system
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Data Overview Merge Nitaqat program data with employee records from the General Organization for Social Insurance (GOSI) I
Nitaqat program data from the Ministry of Labor (weekly) [2011-2013] I Entity-level employment measures and color band assignments I Nitaqat firms employ over 95% of Saudis in the private sector workforce
I
Engagement-level data from GOSI (monthly) [2009-2014] I Information for Saudis on: job characteristics (occupation, salary, location, start and end dates) worker characteristics (age, gender, education) I I
8.9 million observations (worker x firm x position x salary) 2.8 million Saudi workers
Introduction
Background
Data
Empirical Strategy
Results
Data Worker Types
For each firm define three groups of workers: 1. Retained workers from previous month 2. Workers hired from other firms I
Appear in GOSI records of another firm since 2009
3. Workers hired from outside private sector labor force I
Do not appear in any available GOSI records since 2009
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Empirical Strategy
The analysis uses three main empirical tools to identify the program effects: 1. Regression Kink Design (RKD) ! Incentive to hire Saudis is increasing with baseline distance below the quota cutoff ! Examine how outcomes vary with quota distance
2. Firm and Worker Wage Effects ! Compare firms in Red and Yellow color bands to Green firms in the same industry by size cell
3. Market-level analysis using local labor markets approach (in progress)
Introduction
Background
Data
Empirical Strategy
Results
Empirical Strategy: RKD Incentive to increase Saudization is increasing with distance below the Green/Yellow cutoff quota
−50
Required Percentage Change for Compliance 0 50
Full Compliance Benchmark
50
0
−50 Distance from Cutoff
−100
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Empirical Strategy: Firm Wage Effects We estimate firm-specifc wage premia using worker-firm wage decomposition methods (Abowd, Kramarz, and Margolis 1999; Card, Heining, and Kline 2013) I
Additional amount a firm would pay for the same worker relative to reference firm
I
Estimate the following: ln(earnings)it =
↵i |{z}
worker effect I
I I
I
+
J(i,t)
| {z }
firm effect
+Xit0 + ⌘iJ(i,t) +✏it | {z } match effect
Time-varying controls: month dummies and age cubic (linear omitted) Match effect: assumed to be random effect Firm effects only identified within “connected set”–we use largest connected set ( 90% of employment)
Identification derives from workers moving across firms
Introduction
Background
Data
Empirical Strategy
Results
Results 1. How are firms meeting their Saudization targets? I I I I
Hire unemployed Saudis Poach experienced Saudis from other firms Reduce Saudi turnover Reduce expatriate employment
2. What does this mean for the composition of Saudi private sector employment? I I I
Occupation mix Human capital Firm-level wage premia
3. How did market-level outcomes respond? I I I
Wages Total employment Firm exit
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
How are firms meeting their quotas? Total Employment and New Employment
Nitaqat corresponded with large increases in total employment and new private sector labor market entrants. Total Private Sector Saudi Employees, Monthly 2,000
Employees (Thousands)
1,500
1,000
500
0 2009
2010 Female
Male
2011
2012
2013
2014
2015
Total
New Employees, Firm Transitions, and Exits, Monthly 80
Employee (Thousands)
60
40
20
0 2009
2010 Entrant
Transition
2011 Exit
2012
2013
2014
2015
Introduction
Background
Data
Empirical Strategy
Results
How are firms meeting their quotas? Overall change in percentage was positive for yellow firms and negative for green firms:
-15
Change in Saudization Percentage -10 -5 0 5 10
15
Change in Saudization Percentage
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
How are firms meeting their quotas? Largest source of improvement for Yellow firms was hiring previously unemployed workers: Chg. in Saudization percentage from market entrants -15 -10 -5 0 5 10 15
Change in Saudization Percentage: New Entrants
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
How are firms meeting their quotas?
Chg. in Saudization pct from market entrants (Sec.) -15 -10 -5 0 5 10 15
New Entrants: HS-
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Chg. in Saudization pct from market entrants (Univ.) -15 -10 -5 0 5 10 15
Consistent with overall trends, most of these workers were HS-educated or below: New Entrants: College+
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Introduction
Background
Data
Empirical Strategy
Results
How are firms meeting their quotas? Yellow firms also less likely to experience turnover than Green firms: Chg. in Saudization percentage from exits -15 -10 -5 0 5 10 15
Change in Saudization Percentage: New Entrants
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
How are firms meeting their quotas? Hiring experienced workers actually most common among Green firms: Chg. in Saudization percentage from previously employed -15 -10 -5 0 5 10 15
Change in Saudization Percentage: Previously Employed
20
10 0 -10 Initial Percentage Distance from Cutoff
-20
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Results 1. How are firms meeting their Saudization targets? I I I I
Hire unemployed Saudis Poach experienced Saudis from other firms Reduce Saudi turnover Reduce expatriate employment
2. What does this mean for the composition of Saudi private sector employment? I I I
Occupation mix Returns to human capital Firm-level wage premia
3. How did market-level outcomes respond? I I I
Wages Total employment Firm exit
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Occupations and Wages Aggregate Wages
Period saw aggregate decrease in average wages Wages within occupation and education groups increasing
1000
Normalized Annual Average Saudi Wages
0
500
Avg Wage Education Controls Occupation Controls Educ & Occ Controls
-500 -1000
I
2009
2010
2011
2012
2013
2014
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Occupations and Wages Occupational Distribution
Higher growth in low-skill occupations over Nitaqat period: Emp. Level by Skill Percentile
0
0
.005
5.0e+04
.01
1.0e+05
.015
1.5e+05
.02
2.0e+05
Emp. Share by Skill Percentile
0
20
40 60 Occupation Skill Rank July 2011
80
October 2012
100
0
20
40 60 Occupation Skill Rank July 2011
80
October 2012
100
Introduction
Background
Data
Empirical Strategy
Results
Occupations and Wages Skill Distribution
Large increase in employment of HS-educated workers:
.8
Monthly Saudi Employment by Education
0
.2
.4
.6
Diploma+ High School Secondary Elementary/Illiterate
2009
2010
2011
2012
2013
2014
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Occupations and Wages Returns to Education
Decreasing wages among HS-educated workers: Skill Premium
1.6
6000
1.8
7000
2
Average Wages
1
3000
1.2
4000
1.4
5000
College High School
2009
2010
2011
2012
2013
2014
2009
2010
2011
2012
2013
2014
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Occupations and Wages Firm Wage Premia
Wage premia higher at Green firms, but evidence that they rose at Yellow firms relative to conterfactual:
-2
Firm wage premia (post-Nitaqat) -1.5 -1 -.5 0 .5 1
1.5
Firm Wage Effects Post-Nitaqat
50
40
30 20 10 0 -10 -20 -30 Initial Percentage Distance from Cutoff
-40
-50
Introduction
Background
Data
Empirical Strategy
Results
Results 1. How are firms meeting their Saudization targets? I I I I
Hire unemployed Saudis Poach experienced Saudis from other firms Reduce Saudi turnover Reduce expatriate employment
2. What does this mean for the composition of Saudi private sector employment? I I I
Occupation mix Returns to human capital Firm-level wage premia
3. How did market-level outcomes respond? I I I
Wages Total employment Firm exit
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Next Steps Goal: estimate aggregate effects of the quota on Saudi employment and wages using a local labor markets approach ! National-level quotas generate variation in Nitaqat intensity across local labor markets 1. Geographic I
I
Treat commuting zones as separate markets to identify the effects of an economic shock on local markets Vary size of CZ to identify level of spillovers
2. Estimated I
I
Use pre-quota career histories to identify distinct labor markets by location, industry, occupation, gender, etc. Firm by occupation pairs (nodes) are linked if any worker has worked at both jobs
Introduction
Background
Data
Empirical Strategy
Results
Next Steps Local Labor Markets: Geographic
Quota Distance by City: Share of Baseline Saudi Employment (60 minute commuting zones)
!
Conclusion
Introduction
Background
Data
Empirical Strategy
Next Steps Local Labor Markets: Estimated
Use GOSI career data to construct job partitions: Labor Market Partitioning: Schmutte (2015)
Results
Conclusion
Introduction
Background
Data
Empirical Strategy
Results
Conclusion
Summary 1. How are firms meeting their Saudization targets? I I I
Mostly by hiring previously unemployed Saudis Most of these were HS-educated or less Yellow firms also more likely to retain existing workers
2. What does this mean for the composition of Saudi private sector employment? I I I I
Largest increases in low-wage occupations Driving decrease in wages for HS-educated workers Increase in skill premium Some evidence that Yellow firms catching up in terms of firm-specific wage premia
3. How did market-level outcomes respond? I
Coming soon!
