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"original_study": {
"claim": {
"hypothesis": "Average working hours per worker in a state will be positively associated with carbon emissions.",
"hypothesis_location": "it is discussed in the abtsract and the introduction",
"statement": "State-level carbon emissions and average working hours have a strong, positive relationship, which holds across a variety of model estimation techniques and net of various political, economic, and demographic drivers of emissions. Specifically, they find that, over time, a 1 percent increase in average working hours per worker is associated with a 0.668 percent increase in emissions, holding all else constant.",
"statement_location": "Results section, p. 1862; also Table 4 model 1.",
"study_type": "Observational"
},
"data": {
"source": "carbon emissions: US Environmental Protection Agency (2016); \nworking hours, labor productivity, and employed population: US Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) database (2016); \nGDP, and manufacturing as a percentage: US Department of Commerce Bureau of Economic Analysis (2016); \ntotal population size: US Census Bureau 2016; \nstate’s energy production: EIA’s State Energy Database System (2016);\nworking-age population, and average household size: US Census Bureau’s American Community Survey (2017);",
"wave_or_subset": "2007-2013",
"sample_size": "50 states (350 total state-year observations)",
"unit_of_analysis": "US state",
"access_details": "carbon emissions: Currently, the EPA website has been updated and no longer includes access to these data. However, it is possible to access the files through the January 19, 2017, snapshot version of the webpage (https://19january2017snapshot.epa.gov/statelocalclimate/state-energy-co2-emissions_.html). The lead author of this study will share these data upon request;\nworking hours, labor productivity, and employed population: there's a link in references (https://www.bls.gov/ces/) but no other access details are mentioned; \nGDP, and manufacturing as a percentage: there's a link in references (https://www.bea.gov/itable/index.cfm) but no other access details are mentioned;\ntotal population size: there's a link in references (https://www.census.gov/en.html) but no other access details are mentioned; \nstate’s energy production: there's a link in references (https://www.eia.gov/state/seds/) but no other access details are mentioned; \nworking-age population, and average household size: there's a link in references (https://www.census.gov/programs-surveys/acs/data.html) but no other access details are mentioned;",
"notes": "All non-binary variables are transformed into logarithmic form. For such variables, the regression models estimate elasticity coefficients where the coefficient for the independent variable is the estimated net percent change in the dependent variable associated with a 1 percent increase in the independent variable. The working hours data cover all nonfarm private employees, but exclude public employees. The effect of working time on environmental outcomes is considered as a scale effect. The scale effect is measured as working time’s contribution to GDP. The authors test for the scale effect by disaggregating GDP into three parts: working hours, labor productivity, and employment to population ratio. In Model 1 they include both labor productivity and employed population percentage as the other components of GDP. Labor productivity is measured as GDP per hour of work. Employed population percentage is measured as the employed population of a state divided by its total population. The composition effect is measured net of GDP. All continuous variables are logged (ln). All models are calculated with AR(1) correction. All models contain unreported year-specific intercepts and unreported unit-specific intercept."
},
"method": {
"description": "The study evaluates the relationship between average working hours per worker and state-level carbon dioxide emissions across U.S. states, using panel data to test a positive association over time.",
"steps": "1. Collect the data.\n2. Transform all non-binary variables into logarithmic form. \n3. Specify a model to examine the scale effect of average weekly working hours per worker on state-level carbon dioxide emissions. \n4. Include control variables: GDP per hour, employed population percentage, total population, energy production, manufacturing as a percentage of GDP, average household size, and working-age population percentage (ages 15–64).\n5. Estimate two-way fixed effects models with both state-specific and year-specific intercepts\n6. Apply Prais–Winsten estimation with panel-corrected standard errors (PCSEs) to account for heteroskedasticity and contemporaneous correlation\n7. Correct for first-order autocorrelation (AR(1)) within panels.",
"models": "fixed effects regression (Prais-Winsten model with panel corrected standard errors)",
"outcome_variable": "carbon dioxide emissions",
"independent_variables": "working hours, GDP per hour, employed population percentage",
"control_variables": "total population, manufacturing, state's energy production, working-age, average household size",
"tools_software": "Stata version 13"
},
"results": {
"summary": "Results show a strong positive association between average working hours and state-level carbon emissions; a 1% increase in working hours corresponds to a 0.668% increase in emissions, holding other factors constant (SE = 0.179).",
"numerical_results": [
{
"outcome_name": "CO₂ emissions",
"value": "0.668",
"unit": "% (increase in emissions per 1 percent increase in average working hours)",
"effect_size": "not stated",
"confidence_interval": {
"lower": "not stated",
"upper": "not stated",
"level": "not stated"
},
"p_value": "< 0.001",
"statistical_significance": "true",
"direction": "positive"
}
]
},
"metadata": {
"original_paper_id": "10.1093/sf/soy014",
"original_paper_title": "Working Hours and Carbon Dioxide Emissions in the United States, 2007–2013.",
"original_paper_code": "not stated",
"original_paper_data": "not stated"
}
}
}
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