Presentation by Douglas Sutherland at the OECD Workshop on Spatial Dimensions of Productivity, 28-29 March 2019, Bolzano. More info: https://oe.cd/GFPBolzano2019

1. SPATIAL MOBILITY OF WORKERS:
EVIDENCE FROM THE U.S.
Damien Azzopardi, Fozan Fareed, Mikkel Hermansen, Patrick Lenain
and Douglas Sutherland
OECD Workshop on Spatial Dimensions of Productivity
28-29 March 2019, Bolzano

2. Outline
1. Motivation
2. About the Job-to-Job (J2J) Dataset
3. Rising Labour Market Regulation: Occupational Licensing
4. What gives rise to J2J Earnings Growth?
5. Can Occupational licensing help explain J2J developments?
6. Next steps

3. Labour market fluidity has slowed along with
productivity growth
8
9
10
11
12
13
14
15
16
17
18
1994 1999 2004 2009 2014
Gross job gains Gross job losses
%
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1994 1998 2002 2006 2010 2014 2018
Labour productivity growth
3
3.5
4
4.5
5
5.5
6
6.5
7
1994 1998 2002 2006 2010 2014 2018
Job-to-job flow rate
%
0
0.5
1
1.5
2
2.5
3
3.5
1994 1998 2002 2006 2010 2014 2018
Inter-State migration rate
%

4. • Job-to-Job (J2J) data comes from the U.S. Census Bureau
• It’s a matched employer-employee dataset that includes job-to-job flows at the state
level, by industry (NAICS), and across worker characteristics
• Coverage: Quarterly data from 2000 to 2017
• About 800+ million observations
About the Data
• Origin State and Industry to Destination State and Industry
• Origin State to Destination State by
• Worker demographics (Sex, Age, Education, Race and Ethnicity)
• Firm characteristics (Size/Age)
Job-to-Job (J2J) Flows

5. • Most job hirings are not for entry-level workers and are moves “up the job ladder”
• About 40-50% of the total hires between 2000-17 were J2J hires
Job-to-Job movements in U.S. are large
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
2000 Q3 2002 Q3 2004 Q3 2006 Q3 2008 Q3 2010 Q3 2012 Q3 2014 Q3 2016 Q3
ShareofEmployment
Hire Rate and Job-to-Job Hires Rate
Job-to-Job
Hires Rate
Hires Rate

6. • Worker reallocation is a driver of productivity
• During upswings, workers tend to move up the job ladder
• During downturns, job-to-job flows dry up and low productivity
workers lose their jobs
• Potential knowledge spillovers from reallocation of workers in
high productivity firms
Why are Job-to-Job Movements Important?

7. 0
5
10
15
20
25
30
35
% of labour force
Occupational licensing at the state level
Licensing has increased over time
US workers with a state license
But varies sizeably across states
Workers with a state license, 2013
Source: White House (2015); Kleiner and Vorotnikov (2017); BLS.
0
5
10
15
20
25
30
1950s 1960s 1970s 1980s 1990s 2000 2006 2008 2015 2016 2017 2018
% of labour force

8. Licensing across occupations
Source: BLS. 0 10 20 30 40 50 60 70 80
Food preparation and serving related
Computer and mathematical
Building and grounds cleaning and maintenance
Office and administrative support
Farming, fishing, and forestry
Production
Arts, design, entertainment, sports, and media
Sales and related
Construction and extraction
Transportation and material moving
Installation, maintenance, and repair
Management
Business and financial operations
Architecture and engineering
Total
Life, physical, and social science
Personal care and service
Community and social services
Protective service
Healthcare support
Education, training, and library
Legal
Healthcare practitioners and technical
% of employment
Workers with a license by occupation, 2018

9. Exploiting variation in occupational licensing
across states and industries
1) Database of licensed occupations from CareerOneStop
2) Merge onto employment statistics by state x industry x occupation
3) Compute (proxy) share of licensed workers by state-industry
State year Industry Occupation Employment Licensed
Florida 2017 Other services Hairdressers, Hairstylists, and Cosmetologists 22680 1
Florida 2017 Retail trade Hairdressers, Hairstylists, and Cosmetologists 2580 1
…
𝑶𝒄𝒄𝒖𝒑𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝒍𝒊𝒄𝒆𝒏𝒔𝒊𝒏𝒈 𝑭𝒍𝒐𝒓𝒊𝒅𝒂, 𝑶𝒕𝒉𝒆𝒓 𝒔𝒆𝒓𝒗𝒊𝒄𝒆𝒔, 𝟐𝟎𝟏𝟕 = 𝟎. 𝟏𝟖

10. • Today two main objectives:
1. Exploit relatively new dataset to examine recent patterns of job-to-job flows
• Across states, industries, individual characteristics and firm characteristics
2. Investigate how regulatory impediments are affecting job-to-job flows
• Can Occupational Licensing Explain Job-to-Job Developments?
Research Objectives

11. Job-to-Job Mobility
Across States (Average 2010- 2017)
0%
1%
2%
3%
4%
5%
6%
7%
8%
ShareofEmployment
Job-to-Job Hire Rates

12. Job-to-Job Mobility
Across States (Average 2010- 2017)

13. Job-to-Job Mobility
Across Industries (Average 2010- 2017)
-15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Utilities
Manufacturing
Public Administration
Finance and Insurance
Wholesale Trade
Mining, Quarrying, and Oil and Gas Extraction
Transportation and Warehousing
Management of Companies and Enterprises
Real Estate and Rental and Leasing
Construction
Professional, Scientific, and Technical Services
Health Care and Social Assistance
Information
Educational Services
Other Services (except Public Administration)
Agriculture, Forestry, Fishing and Hunting
Retail Trade
Arts, Entertainment, and Recreation
Administrative, Support, Waste Management & Remediation Services
Accommodation and Food Services
Net % Change in J2J Mobility

14. Job-to-Job Mobility
Across Individual Characteristics
0% 3% 6% 9%
White Alone
Black or African American Alone
American Indian or Alaska Native Alone
Asian Alone
Native Hawaiian or Other Pacific Islander
Alone
Two or More Race Groups
Not Hispanic or Latino
Hispanic or Latino
Male
Female
Overall
Share of Employment
J2J Mobility Rate: Gender, Race and
Ethnicity
2001-2009
2010-2017
0% 3% 6% 9% 12% 15%
Educational attainment not available
(workers aged 24 or younger)
Bachelor's degree or advanced degree
Some college or Associate degree
High school or equivalent, no college
Less than high school
65-99
55-64
45-54
35-44
25-34
22-24
19-21
14-18
Share of Employment
J2J Mobility Rate: Age and Education

15. 0% 2% 4% 6% 8% 10% 12%
Firm Size Not Available For Public-Sector Firms
0-19 Employees
20-49 Employees
50-249 Employees
250-499 Employees
500+ Employees
Firm Age Not Available For Public-Sector Firms
0-1 Years
2-3 Years
4-5 Years
6-10 Years
11+ Years
Overall
Share of Employment
J2J Mobility Rate across Firm Age and Firm Size
2001-2009
2010-2017
Job-to-Job Mobility
Across Firm Characteristics

16. Baseline Econometric Model
∆log𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑜𝑑𝑥𝑦𝑡 = 𝐵1 𝑂L 𝑜𝑦𝑡 + 𝐵2 𝑂L 𝑑𝑦𝑡 + 𝐵3 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠 𝑜𝑑𝑥𝑦𝑡 +
𝐵4 𝑓𝑖𝑟𝑚𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠 𝑜𝑑𝑥𝑦𝑡 + 𝐵5 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑑 + µ 𝑜 + µ 𝑑 + µ 𝑥 + µ 𝑦 + µ 𝑡 + 𝜖 𝑜𝑑𝑥𝑦𝑡
J2JMobilityRate 𝑜𝑑𝑥𝑦𝑡
= 𝐵1 𝑂L 𝑜𝑦𝑡 + 𝐵2 𝑂L 𝑑𝑦𝑡 + 𝐵3 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠 𝑜𝑑𝑥𝑦𝑡 +
𝐵4 𝑓𝑖𝑟𝑚𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠 𝑜𝑑𝑥𝑦𝑡 + 𝐵5 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑑 + µ 𝑜 + µ 𝑑 + µ 𝑥 + µ 𝑦 + µ 𝑡 + 𝜖 𝑜𝑑𝑥𝑦𝑡

17. Who gains the most from job-to-job move?
Results from regression of job-to-job earnings growth on worker characteristics
All coefficients are significant at the 1% level. Separate estimations are used for gender-age; race/ethnicity; and education (N=5,396,500; 2,597,403; 4,236,895).
The estimated equations include controls for state unemployment, distance measures; state destination, state origin, industry destination, industry origin and time fixed effects.
Data period 2000q4-2017q1. Observations are weighted by number of job-to-job moves in each cell. Estimated log-differences are scaled by 100 and reported as %.
0 5 10 15 20
Male
Female
White
Black
American Indian
Asian
Other race
Not hispanic or latino
Hispanic or latino
Marginal earnings effect of job-to-job move (%)
0 10 20 30 40
Age 14-18
Age 19-21
Age 22-24
Age 25-34
Age 35-44
Age 45-54
Age 55-64
Age 65+
Less than high school
High school
Some college
Bachelor's or advanced degree
Marginal earnings effect of job-to-job move (%)

18. 0 5 10 15 20
0-19 employees
20-49 employees
50-249 employees
250-499 employees
500+ employees
0-1 year
2-3 years
4-5 years
6-10 years
11+ years
Marginal earnings effect of job-to-job move (%)
What type of job-to-job move pays off?
Results from regression of job-to-job earnings growth on mobility characteristics
All coefficients are significant at the 1% level. Separate estimations are used for firm size and firm age (N=3,700,815; 3,413,698). The estimated equations include controls
for gender, age, state unemployment, distance measures; state destination, state origin, industry destination, industry origin and time fixed effects. Data period 2000q4-2017q1.
Observations are weighted by number of job-to-job moves in each cell. Estimated log-differences are scaled by 100 and reported as %.
Destination firm size
Destination firm age
0 5 10 15 20
Within industry move
Between industry move
Within state move
Neighbour state move
Non-neighbour state move
Marginal earnings effect of job-to-job move (%)

19. -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5
Destination state-industry
Origin state-industry
Effect on earnings growth of job-to-job move (%-point)
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Destination state-industry
Origin state-industry
Effect on job-to-job hire rate (%-point)
Preliminary results suggest occupational licensing is
associated with lower mobility and lower earnings growth
Effect on job-to-job hire rate
10%-point increase in share of licensed employment
Effect on job-to-job earnings growth
10%-point increase in share of licensed employment
All coefficients are significant at the 1% level, except origin state-industry for job-to-job hire rate. The estimated equations include controls for gender, age,
state unemployment, distance measures; state destination, state origin, industry destination, industry origin and time fixed effects (N=1,473,141).
Data period 2012q1-2017q1. Observations are weighted by number of job-to-job moves in each cell. Estimated log-differences are scaled and reported as %-point.

20. • Try to improve our measures of Occupational Licensing, or at least make sure results are
robust to other indicators
• Investigate the relationship between State proxies for productivity and labor mobility
Next Steps

21. EXTRA SLIDES

22. • Nurses need State-specific
licenses to practice
• Some States now recognise
licenses from other States
• Mutual recognition boosts
migration flows of medical
personnel between these States
• Some evidence of diversion
Occupational licensing can affect worker flows
-0.2
-0.1
0
0.1
0.2
Compact Non-compact All
Effect of joining the nurse licensing
compact on inter-State migration flows
Source: Amy Ghani (2018)

23. Job-to-Job Mobility
Across States (Average 2010- 2017)
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
Illinois
NewYork
NewJersey
Pennsylvania
Michigan
Connecticut
Maryland
Ohio
Virginia
Mississippi
Indiana
Missouri
Alabama
Louisiana
Wisconsin
Massachusetts
NewMexico
Kansas
WestVirginia
Arkansas
RhodeIsland
Iowa
Kentucky
Wyoming
NewHampshire
Maine
Nebraska
Delaware
Vermont
Minnesota
Idaho
Oklahoma
Hawaii
Montana
Utah
DistrictofColumbia
NorthDakota
California
Georgia
Arizona
Nevada
SouthCarolina
Tennessee
Oregon
NorthCarolina
Washington
Colorado
Florida
Texas
NetChangein#ofJ2JHires
Net Change in J2J Mobility
(Inflows- Outflows)

24. Job-to-Job Mobility
Across Industries (Average 2010- 2017)
0% 2% 4% 6% 8% 10%
Utilities
Public Administration
Educational Services
Manufacturing
Finance and Insurance
Management of Companies and Enterprises
Health Care and Social Assistance
Wholesale Trade
Information
Other Services (except Public Administration)
Professional, Scientific, and Technical Services
Average
Real Estate and Rental and Leasing
Transportation and Warehousing
Retail Trade
Arts, Entertainment, and Recreation
Mining, Quarrying, and Oil and Gas Extraction
Construction
Accommodation and Food Services
Agriculture, Forestry, Fishing and Hunting
Administrative and Support and Waste Management and Remediation…
Share of Employment
Job-to-Job Hire Rates

25. Job-to-Job Mobility
Across Industries (Average 2010- 2017)
-300000 -250000 -200000 -150000 -100000 -50000 0 50000 100000 150000 200000
Manufacturing
Health Care and Social Assistance
Wholesale Trade
Finance and Insurance
Construction
Professional, Scientific, and Technical Services
Transportation and Warehousing
Public Administration
Educational Services
Real Estate and Rental and Leasing
Management of Companies and Enterprises
Information
Mining, Quarrying, and Oil and Gas Extraction
Utilities
Other Services (except Public Administration)
Agriculture, Forestry, Fishing and Hunting
Arts, Entertainment, and Recreation
Retail Trade
Administrative and Support and Waste Management and Remediation Services
Accommodation and Food Services
Net Change in # of J2J Hires
Net Change in J2J Mobility
(Inflows - Outflows)

26. 50.0
52.0
54.0
56.0
58.0
60.0
62.0
64.0
66.0
68.0
70.0
2001
Q1
2002
Q1
2003
Q1
2004
Q1
2005
Q1
2006
Q1
2007
Q1
2008
Q1
2009
Q1
2010
Q1
2011
Q1
2012
Q1
2013
Q1
2014
Q1
2015
Q1
2016
Q1
2017
Q1
Percent
Employment to Population Ratio
Source: Bureau of Labour Statistics

27. 1%
2%
3%
4%
5%
6%
7%
8%
2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2
ShareofEmployment
Job to Job Hires (Continuous Employment versus Brief
Nonemployment)
Job-to-Job Hires Job-to-Job Hires (Continuous Employment) Job-to-Job Hires (Brief Nonemployment)

28. 12.6% 12.5%
10.3%
11.1% 11.4%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
2000-2003 2004-2007 2008-2011 2012-2015 2016-2017
Average Hire Rate
5.6% 5.5%
4.1%
4.9%
5.4%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
2000-2003 2004-2007 2008-2011 2012-2015 2016-2017
Average J2J Hire Rate
12.5%
11.9%
10.3% 10.3% 10.6%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
2000-2003 2004-2007 2008-2011 2012-2015 2016-2017
Average Separation Rate
6.5%
5.9% 5.9%
5.0% 4.8%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
2000-2003 2004-2007 2008-2011 2012-2015 2016-2017
Average Separations to
Persistent Nonemployment

29. 9% of the J2J flows from California were to other states. Rest were within California.
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
California
Michigan
Texas
Ohio
Wisconsin
Florida
Minnesota
Utah
Indiana
Georgia
Washington
Arizona
NewYork
North…
Pennsylvania
Maine
Alabama
Illinois
Colorado
Tennessee
Oregon
Oklahoma
Louisiana
Hawaii
Nevada
South…
Arkansas
Massachusetts
Missouri
Kentucky
Montana
Idaho
Nebraska
SouthDakota
Virginia
Iowa
NewMexico
Mississippi
Connecticut
NewJersey
Kansas
Vermont
Maryland
WestVirginia
New…
Delaware
RhodeIsland
Wyoming
NorthDakota
Districtof…
Out of State Mobility

30. *Net % J2J Change= (J2J Inflows- J2J Outflows)/J2J Outflows
-15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Utilities
Manufacturing
Public Administration
Finance and Insurance
Wholesale Trade
Mining, Quarrying, and Oil and Gas Extraction
Transportation and Warehousing
Management of Companies and Enterprises
Real Estate and Rental and Leasing
Construction
Professional, Scientific, and Technical Services
Health Care and Social Assistance
Information
Educational Services
Other Services (except Public Administration)
Agriculture, Forestry, Fishing and Hunting
Retail Trade
Arts, Entertainment, and Recreation
Administrative, Support, Waste Management & Remediation Services
Accommodation and Food Services
Net % Change in J2J Mobility*

32. Net Change in Job-to-Job Flows per State
Net change= J2J flows into state X from other states - J2J hires from state X to other states
Green states are the ones which get more J2J hires into them

33. Where are Texas Workers Going?
Job to Job Flows from Texas to Other States

34. Where are Texas Workers Going?
Job to Job Flows into Texas from Other States
About 90% of total job-to-job hires in Texas are “Within State”

35. Where are Texas Workers Going?
Net Flow of Job-to-Jobs for Texas
Net flow for State X= People to Texas from State X- People from Texas to State X
Green states are the ones where J2J gains for Texas are coming from

36. *Real GDP/ Total # of Employed People
0 0.2 0.4 0.6 0.8 1 1.2
Real estate and rental and leasing
Mining
Government
Utilities
Information
Finance and insurance
Wholesale trade
Manufacturing
Agriculture, forestry, fishing, and hunting
Management of companies and enterprises
Professional, scientific, and technical services
Construction
Transportation and warehousing
Other services, except government
Arts, entertainment, and recreation
Retail trade
Health care and social assistance
Administrative and waste management services
Accommodation and food services
Educational services
Productivity Average 2015-2017
Productivity
Across Sectors (Average 2015- 2017)

37. Productivity
Across States (Average 2015- 2017)
0
0.05
0.1
0.15
0.2
0.25
ME
MS
ID
VT
AR
SC
MT
KY
AL
MO
WV
WI
FL
KS
TN
UT
MI
IN
SD
NV
AZ
OH
NH
IA
NC
RI
NE
NM
GA
LA
PA
OK
OR
MN
IL
VA
ND
CO
MA
TX
NJ
WY
WA
MD
NY
HI
CT
CA
DE
DC
PRODUCTIVITY