Download - The Motivation
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Job Chains and Welfare Gains from Employment: Testing an I-O Type
Labor Market Model
Joseph PerskyDaniel Felsenstein
Funded by the W.E. Upjohn Institute for Employment Research
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The Motivation
• What are the welfare and distributional effects of employment creation?
• What is a job worth?• No policy guidance: what kind of jobs to
promote? High wage or low wage? For locals or commuters? Service sector or manufacturing?
• No adequate model of employment creation in local economic development efforts
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A Job Chains Model of Local Labor Markets
• Assume unemployment and underemployment – slack in labor market
• Assume rigid wage structure• A new job, if filled by an employed worker,
opens up a chain• Workers move from job to job to improve their
welfare• New perspective on employment ‘multipliers’
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Horizontal Multipliers
Backward LinkagesSuppliers:30 Indirect Jobs
Light Bulbs Inc.
Forward LinkagesHousehold-serving:20 Induced Jobs
Supermarket Stores
Instrument Plant100 Direct Jobs
SciSource
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‘Horizontal’ Multipliers
InducedIndirect
Direct
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Job Chains and Vertical Multipliers
New Job in SciSourceExisting
Similar Job in OptiSourceExisting
Related Job in InstruSource
In-Migrant to LocalArea Ms. Black
Terminates Chain
Job Changer:Mr. Jones
Job Changer:Ms. Dee
Vacancies
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Job Chains and ‘Vertical’ MultipliersInduced
Chain Termination Job ChainsVacancies
IndirectDirect
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Chain Literature• Housing Market Studies—
– Lansing J.B., Clifton C.W. and Morgan J.N. (1969)– Emmi and Magnusson (1994,1995)
• Organizational Studies —– Parishes: White (1970) – Orchestras: Abbot and Hrycak (1990)
• Hermit Crabs– Chase, Weissburg and DeWitt (1988)
• Labor Market Flows– Schettkat (1996)
• Economic Development– Webster (1979)– Robson, Bradford and Daes (1999) – Persky and Felsenstein (1999)
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The Job Chains Model as an I-O Type Model • Job chains = production chains in I-O• Production chains: estimated by average ‘input
vector’ for each industry• Job chains: estimate ‘input vector’ for each
type of new job, by wage group.• Chain length=size of employment multiplier• Chains initiated by indirect and induced
activity, not just direct.
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Leontief Model of Job Chains
• (Q) : A square job flow matrix (origin-destination)
• qij, elements of Q which show the chance that a job vacancy of a j-type position is taken by a worker currently in an i-type position.
• tij elements of T matrix, probabilities that chain will terminate due to (1) unemployed, (2) out of labor force and (3) in-migrants.
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A Job Chains Matrix: Q and T matrices
New Wage Group Origin 1 2 3 4 5
Group 1 q11=41.1% 0.0% 0.0% 0.0% 0.0% Group 2 q21=25.0% q22=52.9% 0.0% 0.0% 0.0% Group 3 q31=4.8% q32=22.1% q33=46.6% 0.0% 0.0% Group 4 q41=2.2% q42=1.5% q43=18.5% q44=47.3% 0.0% Group 5 q51=0.0% q52=0.3% q53=2.4% q54=13.3% q55=34.5%
Column Sum 73.1% 76.8% 67.5% 60.6% 34.5%
New Wage Group
1 2 3 4 5 Unemployed t11=2.9% t12=3.8% t13=9.7% t14=15.8% t15=24.7%
Not-in-Labor Force t21=4.0% t22=3.8% t23=7.5% t24=13.5% t25=30.5% In-Migrant t31=20.1% t32=15.6% t33=15.4% t34=10.0% t35=10.2%
Column Sum 27.0% 23.2% 32.6% 39.3% 65.4%
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A Housing Chains Matrix
Destination: New Home
Origin: Old Home
High Quality Mid Quality Low Quality
High Quality 0.45 0.07 0.01
Mid Quality 0.38 0.39 0.09
Low Quality 0.09 0.35 0.38
Outside 0.08 0.19 0.52
Total 100.0 100.0 100.0
Based on Marullo (1985).
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Chains by Attributes
Attributes
G-Chain(Leontieff – Production)
L-Chain(Labor market)
H-Chain(Housing)
Direction of Movement through chain
Goods – physical transformation (horizontal)
•Workers – moving up
•Vacancies reaching down
• Housing declining
• Households improving
Change in Stock (cohort) over Time
Depreciates Appreciates Depreciates
Driver of Movement Technology Job
OpportunitiesHousing
Opportunities
Stickiness in Movement through Chains
Low High Moderate
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Assumptions of Chain Models Assumptions of
ModelG-Chain
(Leontieff – Production)
L-Chain(Labor market)
H-Chain(Housing)
Model Focus Demand for Goods
Demand for Labor
Supply of Housing
Characteristic of Market
Highly Elastic Short Run Supply
Highly Elastic Short Run Supply
Inelastic Supply in the Short Run
Equilibrium Characteristics Excess Supply Excess Supply Excess Demand
Allocation via Proportions (Technology)
Probabilities (Recruiting Channels)
Proportions (Marketing Channels)
Role of Prices Fixed Fixed Variable
Welfare Gains via Quantity Change Job Change Quality Change and Asset Prices
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Chain Models: the Case Against
• Mechanistic, lacking in theoretical basis• No formal maximizing behavior by firms
and individuals• Little attention to short run price changes
as allocation method• Often assume markets don’t clear, no real
closure
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Chain Models: the Case For
• When markets are connected through institutional and structural links (wage agreements, banking regulations etc) not prices
• In the presence of fixed mark-ups: suppliers working with fixed prices (flat supply curve).
• When quantity changes represent welfare changes (firms receiving fixed mark-up, workers welfare gain through movement). Chains as modeling spread.
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The Job Chains Model
• Mechanics• Data• A supply-side example:
restricting access to employment
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1. Chain Lengths
• Mn = 1/(1- qnn)
• Mn-1 = [1/(1- q(n-1)(n-1))] * [ 1 + qn(n-1) Mn ]
• Mn-2 = [1/(1- q(n-2)(n-2)) * ( 1 + q(n-1)(n-2) Mn-1 + q(n(n-2) Mn )
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2. Welfare Gains (Efficiency Effects)
V/wj = wage gains of job changers + (wage- opportunity cost) of non-employed job recruits, as a share of new wages
Vj = imij [(kqki *(wi-wk ) + hthi *(wi- chi)].
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3. Distributional Effects
• A Rawlsian measure (R/w): gains to lowest groups, as a share of new wages
Rj = mnj h thn *(wn – chn)
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Opportunity Costs (w/c) for Unemployed, Out of Labor Force and In-
Migrants
Opp.Costs
1: $25.50 - $40.00 75%
2: $16.40 - $25.50 75%
3: $10.50 - $16.40 50%
4: $ 6.70 - $10.50 40%
5: $ 4.25 - $ 6.70 31%
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Data
• PSID 1987-1993 (heads and spouses only)
• 3500 distinct year-to-year job changes
• 1992 Real average wage gains for job changers
• Data for four regions and five earnings classes
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Flows into Job VacanciesDestination Job Group
Origin Job Group 1 2 3 4 51: $25.50 - $40.00 41.1% 0.0% 0.0% 0.0% 0.0%
2: $16.40 - $25.50 25.0% 52.9% 0.0% 0.0% 0.0%3: $10.50 - $16.40 4.8% 22.1% 46.6% 0.0% 0.0%4: $ 6.70 - $10.50 2.2% 1.5% 18.5% 47.3% 0.0%5: $ 4.25 - $ 6.70 0.0% 0.3% 2.4% 13.3% 34.5%Unemployed 2.9% 3.8% 9.7% 15.8% 24.7%Out of Labor Force 4.0% 3.8% 7.5% 13.5% 30.5%In-Migrant 20.1% 15.6% 15.4% 10.0% 10.2%Column Sum 100.0% 100.0% 100.0% 100.0% 100.0%
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Multiplier Effect
Class of New JobCreated Vacancies 1 2 3 4 51: $25.50 - $40.00 1.70 0.00 0.00 0.00 0.002: $16.40 - $25.50 0.90 2.12 0.00 0.00 0.003: $10.50 - $16.40 0.52 0.88 1.87 0.00 0.004: $ 6.70 - $10.50 0.28 0.37 0.66 1.90 0.005: $ 4.25 - $ 6.70 0.08 0.12 0.20 0.39 1.53
Total Job Multiplier 3.48 3.48 2.73 2.28 1.53
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Efficiency and Distributional Effects
Wage Group of Initial New Job1 2 3 4 5
V/w 0.43 0.42 0.56 0.62 0.69
Per job
Share to Job Changers 0.52 0.37 0.21 0.10 0
R/w Per initial new job:
Dollars per yr to Lowest-R 397 550 960 1,888 7,202
Dollars per yr to Low 4,654 4,303 6,600 10,582 7,202
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Efficiency Effect: Sensitivity Analysis
1 2 3 4 51 Basic Assumptions 0.43 0.42 0.56 0.62 0.69
2 .75 in-migs; .25 all others 0.41 0.39 0.47 0.54 0.62
3 .75 all in-migrants 0.51 0.51 0.57 0.63 0.67
4 .25 all non job-changers 0.74 0.72 0.74 0.74 0.75
Alternative Opportunity Cost Assumptions
Wage Group of Initial New Job
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A Supply-Side Example of the Job Chains Model
Simulation of three scenarios. New employment created but restricted:
1. Full local restriction: only locals can take jobs: no in-migrants
2. First round local restriction: no in-migrants on first round of hiring
3. First round restriction to local non-employed: only locals (local unemployed or out of labor force) can take jobs on first round
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Base Case Summary
New Wage Group 1 2 3 4 5 Total Job Multiplier 3.48 3.48 2.73 2.28 1.53 V/w 0.43 0.42 0.56 0.62 0.69 R*/w 0.07 0.10 0.22 0.49 0.58
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Case 1: Full Local Restriction
• Exclude in-migrants from T-matrix• Recalibrate Q and T matrix• Recalculate Leontief inverse• Using new multipliers and q’s recalculate
efficiency measure (V/w)• Using new multipliers and q’s recalculate
distributional measure (R/w)
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Case 1: Full Local Restriction – Chain Lengths, Welfare Gains and
Distribution
New Wage Group 1 2 3 4 5 Total Job Multiplier 6.08 5.34 3.59 2.62 1.63 V/w 0.56 0.52 0.58 0.63 0.69 R*/w 0.15 0.21 0.36 0.63 0.69
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Case 2: First Round Local Restriction
• Exclude in-migrants from T-matrix for first round only
• Use Resulting vacancies and Base Q and T matrices
• Recalculate efficiency measure (V/w)• Recalculate distributional measure (R/w)
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Case 2: First Round Local Restriction: – Chain Lengths,
Welfare Gains and Distribution
New Wage Group 1 2 3 4 5 Total Job Multiplier 4.10 3.94 3.04 2.43 1.59 V/w 0.47 0.45 0.58 0.63 0.69 R*/w 0.08 0.11 0.25 0.55 0.64
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Case 3: First Round Restriction to Local Non-employed
• Limit first and only round to local non-employed
• All chain lengths are thus 1.0• Calculate efficiency measure (V/w)• Calculate distributional measure (R/w)
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Case 3: First Round Restriction to Local Non-employed – Chain Lengths, Welfare Gains and
Distribution
New Wage Group 1 2 3 4 5
Total Job Multiplier 1.00 1.00 1.00 1.00 1.00 V/w 0.25 0.25 0.50 0.60 0.69 R*/w 0.00 0.00 0.00 0.60 0.69
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Comparing the Results
Compared to the standard case: • Full local restriction: longest chains, greater
efficiency, and more trickle down• First round local restriction: modest increases in
chain length, efficiency and trickle down• First round restriction to local non-employed: no
chains, lower efficiency and some (non-trickle down) distributional gains
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Tentative Policy Conclusions
• Full local restriction: dramatic returns, but unpalatable politically
• First round local restriction: politically feasible, but only modest expected gains
• First round restriction to local non-employed: politically feasible, but low return on high-end placements, and only modest gains on low-end placements