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  • 标题:Jobs Versus the Environment: An Industry-level Perspective
  • 本地全文:下载
  • 作者:Morgenstern, Richard D. ; Pizer, William A. ; Shih, Jhih-Shyang
  • 期刊名称:Journal of Food Distribution Research
  • 印刷版ISSN:0047-245X
  • 出版年度:2000
  • 期号:SUPPL
  • 出版社:Food Distribution Research Society
  • 摘要:The possibility that workers could be adversely affected by environmental policies imposed onheavily regulated industries has led to claims of a "jobs versus the environment" trade-off by bothbusiness and labor leaders. The present research examines this claim at the industry level for four heavilypolluting industries: pulp and paper mills, plastic manufacturers, petroleum refiners, and iron and steelmills. By focusing on labor effects across an entire industry, we construct a measure relevant to theconcerns of key stakeholders, such as labor unions and trade groups.We decompose the link between environmental regulation and employment into three distinctcomponents: factor shifts to more or less labor intensity, changes in total expenditures, and changes in thequantity of output demanded. We use detailed plant-level data to estimate the key parameters describingfactor shifts and changes in total expenditures. We then use aggregate time-series data on industry supplyshocks and output responses to estimate the demand effect.We find that increased environmental spending generally does not cause a significant change inindustry-level employment. Our average across all four industries is a net gain of 1.5 jobs per $1 millionin additional environmental spending, with a standard error of 2.2 jobs—an insignificant effect. In theplastics and petroleum sectors, however, there are small but significantly positive effects: 6.9 and 2.2jobs, respectively, per $1 million in additional expenditures. These effects can be linked to favorablefactor shifts—environmental spending is more labor intensive than ordinary production—and relativelyinelastic estimated demand.
  • 关键词:Jobs-environment trade-off; distribution of environmental costs; translog cost function
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