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Empirical Distribution of the Plant-Level Components of Energy and Carbon Intensity at the Six-digit NAICS Level Using a Modified KAYA Identity
September 2024
Working Paper Number:
CES-24-46
Three basic pillars of industry-level decarbonization are energy efficiency, decarbonization of energy sources, and electrification. This paper provides estimates of a decomposition of these three components of carbon emissions by industry: energy intensity, carbon intensity of energy, and energy (fuel) mix. These estimates are constructed at the six-digit NAICS level from non-public, plant-level data collected by the Census Bureau. Four quintiles of the distribution of each of the three components are constructed, using multiple imputation (MI) to deal with non-reported energy variables in the Census data. MI allows the estimates to avoid non-reporting bias. MI also allows more six-digit NAICS to be estimated under Census non-disclosure rules, since dropping non-reported observations may have reduced the sample sizes unnecessarily. The estimates show wide variation in each of these three components of emissions (intensity) and provide a first empirical look into the plant-level variation that underlies carbon emissions.
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The Icing on the Cake: The Effects of Monetary Incentives on Income Data Quality in the SIPP
January 2024
Working Paper Number:
CES-24-03
Accurate measurement of key income variables plays a crucial role in economic research and policy decision-making. However, the presence of item nonresponse and measurement error in survey data can cause biased estimates. These biases can subsequently lead to sub-optimal policy decisions and inefficient allocation of resources. While there have been various studies documenting item nonresponse and measurement error in economic data, there have not been many studies investigating interventions that could reduce item nonresponse and measurement error. In our research, we investigate the impact of monetary incentives on reducing item nonresponse and measurement error for labor and investment income in the Survey of Income and Program Participation (SIPP). Our study utilizes a randomized incentive experiment in Waves 1 and 2 of the 2014 SIPP, which allows us to assess the effectiveness of incentives in reducing item nonresponse and measurement error. We find that households receiving incentives had item nonresponse rates that are 1.3 percentage points lower for earnings and 1.5 percentage points lower for Social Security income. Measurement error was 6.31 percentage points lower at the intensive margin for interest income, and 16.48 percentage points lower for dividend income compared to non-incentive recipient households. These findings provide valuable insights for data producers and users and highlight the importance of implementing strategies to improve data quality in economic research.
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Collaborative Micro-productivity Project: Establishment-Level Productivity Dataset, 1972-2020
December 2023
Working Paper Number:
CES-23-65
We describe the process for building the Collaborative Micro-productivity Project (CMP) microdata and calculating establishment-level productivity numbers. The documentation is for version 7 and the data cover the years 1972-2020. These data have been used in numerous research papers and are used to create the experimental public-use data product Dispersion Statistics on Productivity (DiSP).
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The Impact of Industrial Opt-Out from Utility Sponsored Energy Efficiency Programs
October 2023
Working Paper Number:
CES-23-52
Industry accounts for one-third of energy consumption in the US. Studies suggest that energy efficiency opportunities represent a potential energy resource for regulated utilities and have resulted in rate of return regulated demand-side management (DSM) and energy efficiency (EE) programs. However, many large customers are allowed to self-direct or opt-out. In the Carolinas (NC and SC), over half of industrial and large commercial customers have selected to opt out. Although these customers claim they invest in EE improvements when it is economic and cost-effective to do so, there is no mechanism to validate whether they actually achieved energy savings. This project examines the industrial energy efficiency between the program participants and non participants in the Carolinas by utilizing the non-public Census of Manufacturing data and the public list of firms that have chosen to opt out. We compare the relative energy efficiency between the stay-in and opt-out plants. The t-test results suggest opt-out plants are less efficient. However, the opt-out decisions are not random; large plants or plants belonging to large firms are more likely to opt out, possibly because they have more information and resources. We conduct a propensity score matching method to account for factors that could affect the opt-out decisions. We find that the opt-out plants perform at least as well or slightly better than the stay-in plants. The relative performance of the opt-out firms suggest that they may not need utility program resources to obtain similar levels of efficiency from the stay-in group.
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Technology Lock-In and Costs of Delayed Climate Policy
July 2023
Working Paper Number:
CES-23-33
This paper studies the implications of current energy prices for future energy efficiency and climate policy. Using U.S. Census microdata and quasi-experimental variation in energy prices, we first show that manufacturing plants that open when electricity prices are low consume more energy throughout their lifetime, regardless of current electricity prices. We then estimate that a persistent bias of technological change toward energy can explain the long-term effects of entry-year electricity prices on energy intensity. Overall, this 'technology lock-in' implies that increasing entry-year electricity prices by 10% would decrease a plant's energy intensity of production by 3% throughout its lifetime.
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Who's Most Exposed to International Shocks? Estimating Differences in Import Price Sensitivity across U.S. Demographic Groups
March 2023
Working Paper Number:
CES-23-13R
Differences in consumption patterns across demographic groups mean that international price shocks differentially affect such groups. We construct import price indexes for U.S. households that vary by age, race, marital status, education, and urban status. Black households and urban households experienced significantly higher import price inflation from 1996-2018 compared to other groups, such as white households and rural households. Sensitivity to international price shocks varies widely, implying movements in exchange rates and foreign prices, both during our sample and during the Covid-19 pandemic, drove sizable differences in import price inflation ' and total inflation ' across households.
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The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing
June 2020
Working Paper Number:
CES-20-18
This paper estimates stochastic frontier energy demand functions with non-public, plant-level data from the U.S. Census Bureau to measure the energy efficiency gap and energy price elasticities in the food processing industry. The estimates are for electricity and fuel use in 4 food processing sectors, based on the disaggregation of this industry used by the National Energy Modeling System Industrial Demand Module. The estimated demand functions control for plant inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. Own price elasticities range from 0.6 to -0.9 with little evidence of fuel/electricity substitution. The magnitude of the efficiency estimates is sensitive to the assumptions but consistently reveal that few plants achieve 100% efficiency. Defining a 'practical level of energy efficiency' as the 95th percentile of the efficiency distributions and averaging across all the models result in a ~20% efficiency gap. However, most of the potential reductions in energy use from closing this efficiency gap are from plants that are 'low hanging fruit'; 13% of the 20% potential reduction in the efficiency gap can be obtained by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industry; ranging from a low of 0.4% to a high of 5.7%.
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Misallocation or Mismeasurement?
February 2020
Working Paper Number:
CES-20-07
The ratio of revenue to inputs differs greatly across plants within countries such as the U.S. and India. Such gaps may reflect misallocation which hinders aggregate productivity. But differences in measured average products need not reflect differences in true marginal products. We propose a way to estimate the gaps in true marginal products in the presence of measurement error. Our method exploits how revenue growth is less sensitive to input growth when a plant's average products are overstated by measurement error. For Indian manufacturing from 1985'2013, our correction lowers potential gains from reallocation by 20%. For the U.S. the effect is even more dramatic, reducing potential gains by 60% and eliminating 2/3 of a severe downward trend in allocative efficiency over 1978'2013.
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Housing Booms and the U.S. Productivity Puzzle
January 2020
Working Paper Number:
CES-20-04
The United States has been experiencing a slowdown in productivity growth for more than a decade. I exploit geographic variation across U.S. Metropolitan Statistical Areas (MSAs) to investigate the link between the 2006-2012 decline in house prices (the housing bust) and the productivity slowdown. Instrumental variable estimates support a causal relationship between the housing bust and the productivity slowdown. The results imply that one standard deviation decline in house prices translates into an increment of the productivity gap -- i.e. how much an MSA would have to grow to catch up with the trend -- by 6.9p.p., where the average gap is 14.51%. Using a newly-constructed capital expenditures measure at the MSA level, I find that the long investment slump that came out of the Great Recession explains an important part of this effect. Next, I document that the housing bust led to the investment slump and, ultimately, the productivity slowdown, mostly through the collapse in consumption expenditures that followed the bust. Lastly, I construct a quantitative general equilibrium model that rationalizes these empirical findings, and find that the housing bust is behind roughly 50 percent of the productivity slowdown.
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Addressing Data Gaps:
Four New Lines of Inquiry in the 2017 Economic Census
September 2019
Working Paper Number:
CES-19-28
We describe four new lines of inquiry added to the 2017 Economic Census regarding (i) retail health clinics, (ii) management practices in health care services, (iii) self-service in retail and service industries, and (iv) water use in manufacturing and mining industries. These were proposed by economists from the U.S. Census Bureau's Center for Economic Studies in order to fill data gaps in current Census Bureau products concerning the U.S. economy. The new content addresses such issues as the rise in importance of health care and its complexity, the adoption of automation technologies, and the importance of measuring water, a critical input to many manufacturing and mining industries.
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