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

João de Faria (EPGE-FGV) Labor Economics - 2014 20 de junho de 2014 1 / 30

Table of Contents

1 Introduction and Motivation

2 Data and Design

3 Empirical Framework

4 Results

5 Conclusion


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


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


Table of Contents


2 Data and Design


4 Results

5 Conclusion


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.


Data and Design


Data and Design


Data and Design


Table of Contents


2 Data and Design


4 Results

5 Conclusion


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.


Empirical Framework



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]


Empirical Framework



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


Table of Contents


2 Data and Design


4 Results

5 Conclusion


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Results


Table of Contents


2 Data and Design


4 Results

5 Conclusion


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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A Present a Cao Labor