Download - A Present a Cao Labor
The Transitional Costs of Sectoral Reallocation:
Evidence from the Clean Air Act and the Workforce
W. Reed Walker
João de Faria
20 de junho de 2014
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Table of Contents
1 Introduction and Motivation
2 Data and Design
3 Empirical Framework
4 Results
5 Conclusion
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Introduction and Motivation
Environmental policies pertaining to air pollution have been estimated
to have large health bene�ts. However, they also come with costs:
"jobs vs. environment"
I Workers are reallocated away from newly regulated industriesI They �nd jobs elsewhere, but what if transition is costly?
The article uses (newly available) longitudinal data on workers and
�rms to estimate the e�ects on workers' reallocation stemming from
the 1990 Clean Air Act ammendments
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Introduction and Motivation
The analysis relates to a large literature on labor market adjustment to
external factors such as trade, immigration and other inovations or
regulations
Four important departures from the existing literature:I A focus not only on employment measures or in manufacturing
industries (Greenstone (2002), Kahn & Mansur (2010), Walker (2011))I The use of longitudinal data on workers to overcome limitations in
industry-level wage and employment dataI The use of a new, plant-level data set from the Environmental
Protection AgencyI The analysis of how workers and labor markets may have been
adjusting to sector speci�c shocks
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Table of Contents
1 Introduction and Motivation
2 Data and Design
3 Empirical Framework
4 Results
5 Conclusion
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Data and Design
Air pollution regulation in the US is coordinate under the Clean Air
Act (CAA)
Each county is designated annualy as being in attainment or out ofattainment of national air quality standards
I Polluting plants in nonattainment areas face greater regulatoryobligations (mainly emissions limits)
In 1990, an Ammendment to the CAA led to the largest increase in
number of nonattainment designations since 1978.
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Data and Design
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Data and Design
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Data and Design
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Table of Contents
1 Introduction and Motivation
2 Data and Design
3 Empirical Framework
4 Results
5 Conclusion
João de Faria (EPGE-FGV) Labor Economics - 2014 20 de junho de 2014 10 / 30
Empirical Framework
The main especi�cation for the empirical analysis is
Yjcst = η1[Nρc × Pρs × 1(τt > 0)] + χjcs + nct + pst + Φjt + εjcst (1)
c is the county nonattainment status:
cε(Attain,NonAttain)
s is the sectoral polluter status:
sε(PM10, ozone, both PM10 and ozone, neither PM10 or ozone)
There are two time periods:
τε(Pre,Post)
Then, Nρc × Pρs × 1(τt > 0) equals 1 for those plants that emit
pollutant ρ in counties designated as nonattainement for pollutant ρin the years after nonattainment went into place.
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Empirical Framework
The main especi�cation for the empirical analysis is
Yjcst = η1[Nρc × Pρs × 1(τt > 0)] + χjcs + nct + pst + Φjt + εjcst (1)
The full DDD speci�cation (Inbems/Wooldridge) in this case would
be:
Yjcst = η1[Nρc × Pρs × 1(τt > 0)]
+β1[Nρc × Pρs ] + β2[Nρ
c × 1(τt > 0)] + β3[Pρc × 1(τt > 0)]+λ1N
ρc + λ2P
ρs + λ31(τt > 0)
+εjcst
And consequently the DDD estimator:
η1 = [YNρc ,P
ρs ,t=1 − YNρ
c ,Pρc ,t=0]− [YNonNρ
c ,Pρs ,t=1 − YNonNρ
c ,Pρc ,t=0]
−[YNρc ,NonP
ρs ,t=1 − YNρ
c ,NonPρs ,t=0]
João de Faria (EPGE-FGV) Labor Economics - 2014 20 de junho de 2014 11 / 30
Empirical Framework
The main especi�cation for the empirical analysis is
Yjcst = η1[Nρc × Pρs × 1(τt > 0)] + χjcs + nct + pst + Φjt + εjcst (1)
In practice, the estimated regression is:
Yjcst =∑M
k=−m ηk1
[Nρc ×Pρs × 1(τt = k)] +χjcs +nct +pst + Φjt + εjcst
(with m and M usually 5 and 10, respectively)
The identifying assumption being:
E[εjcst × [Nρc × Pρs × 1(τt > 0)]|χjcs , nct , pst ,Φjt ] = 0
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Table of Contents
1 Introduction and Motivation
2 Data and Design
3 Empirical Framework
4 Results
5 Conclusion
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Results
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Table of Contents
1 Introduction and Motivation
2 Data and Design
3 Empirical Framework
4 Results
5 Conclusion
João de Faria (EPGE-FGV) Labor Economics - 2014 20 de junho de 2014 29 / 30
Conclusion
The article makes two primary contributions:
From a policy standpoint, it estimates the (high) reallocative costs of
environmental policy;
I The average worker in a newly regulated plant experiences an earningsloss of around 20 % in PDV - in aggregate, almost $ 5.4 billion inforegone earnings
I However, as usual, estimates are derived in a partial equilibriumframework.
From a methodological standpoint, it highlights the importance of
longitudinal data and of matching employer-employee information;
I And because of that, it sheds light on how both �rms and workersrespond to gradual, regulatory changes.
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