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Climate Change, The Food Problem, and the Challenge of Adaptation through Sectoral Reallocation
September 2021
Working Paper Number:
CES-21-29
This paper combines local temperature treatment effects with a quantitative macroeconomic model to assess the potential for global reallocation between agricultural and non-agricultural production to reduce the costs of climate change. First, I use firm-level panel data from a wide range of countries to show that extreme heat reduces productivity less in manufacturing and services than in agriculture, implying that hot countries could achieve large potential gains through adapting to global warming by shifting labor toward manufacturing and increasing imports of food. To investigate the likelihood that such gains will be realized, I embed the estimated productivity effects in a model of sectoral specialization and trade covering 158 countries. Simulations suggest that climate change does little to alter the geography of agricultural production, however, as high trade barriers in developing countries temper the influence of shifting comparative advantage. Instead, climate change accentuates the existing pattern, known as 'the food problem,' in which poor countries specialize heavily in relatively low productivity agricultural sectors to meet subsistence consumer needs. The productivity effects of climate change reduce welfare by 6-10% for the poorest quartile of the world with trade barriers held at current levels, but by nearly 70% less in an alternative policy counterfactual that moves low-income countries to OECD levels of trade openness.
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Import Competition and Firms' Internal Networks
September 2021
Working Paper Number:
CES-21-28
Using administrative data on U.S. multisector firms, we document a cross-sectoral propagation of the import competition from China ('China shock') through firms' internal networks: Employment of an establishment in a given industry is negatively affected by China shock that hits establishments in other industries within the same firm. This indirect propagation channel impacts both manufacturing and non-manufacturing establishments, and it operates primarily through the establishment exit. We explore a range of explanations for our findings, highlighting the role of within-firm trade across sectors, scope of production, and establishment size. At the sectoral aggregate level, China shock that propagates through firms' internal networks has a sizable impact on industry-level employment dynamics.
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Business-Level Expectations and Uncertainty
December 2020
Working Paper Number:
CES-20-41
The Census Bureau's 2015 Management and Organizational Practices Survey (MOPS) utilized innovative methodology to collect five-point forecast distributions over own future shipments, employment, and capital and materials expenditures for 35,000 U.S. manufacturing plants. First and second moments of these plant-level forecast distributions covary strongly with first and second moments, respectively, of historical outcomes. The first moment of the distribution provides a measure of business' expectations for future outcomes, while the second moment provides a measure of business' subjective uncertainty over those outcomes. This subjective uncertainty measure correlates positively with financial risk measures. Drawing on the Annual Survey of Manufactures and the Census of Manufactures for the corresponding realizations, we find that subjective expectations are highly predictive of actual outcomes and, in fact, more predictive than statistical models fit to historical data. When respondents express greater subjective uncertainty about future outcomes at their plants, their forecasts are less accurate. However, managers supply overly precise forecast distributions in that implied confidence intervals for sales growth rates are much narrower than the distribution of actual outcomes. Finally, we develop evidence that greater use of predictive computing and structured management practices at the plant and a more decentralized decision-making process (across plants in the same firm) are associated with better forecast accuracy.
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Measuring the Effect of COVID-19 on U.S. Small Businesses: The Small Business Pulse Survey
May 2020
Working Paper Number:
CES-20-16
In response to the novel coronavirus (COVID-19) pandemic, the Census Bureau developed and fielded an entirely new survey intended to measure the effect on small businesses. The Small Business Pulse Survey (SBPS) will run weekly from April 26 to June 27, 2020. Results from the SBPS will be published weekly through a visualization tool with downloadable data. We describe the motivation for SBPS, summarize how the content for the survey was developed, and discuss some of the initial results from the survey. We also describe future plans for the SBPS collections and for our research using the SBPS data. Estimates from the first week of the SBPS indicate large to moderate negative effects of COVID-19 on small businesses, and yet the majority expect to return to usual level of operations within the next six months. Reflecting the Census Bureau's commitment to scientific inquiry and transparency, the micro data from the SBPS will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence from the Clean Air Act and Satellite-Based Measures of Air Quality
January 2020
Working Paper Number:
CES-20-02
Racial differences in exposure to ambient air pollution have declined significantly in the United States over the past 20 years. This project links restricted-access Census Bureau microdata to newly available, spatially continuous high resolution measures of ambient particulate pollution (PM2.5) to examine the underlying causes and consequences of differences in black-white pollution exposures. We begin by decomposing differences in pollution exposure into components explained by observable population characteristics (e.g., income) versus those that remain unexplained. We then use quantile regression methods to show that a significant portion of the 'unexplained' convergence in black-white pollution exposure can be attributed to differential impacts of the Clean Air Act (CAA) in non-Hispanic African American and non-Hispanic white communities. Areas with larger black populations saw greater CAA-related declines in PM2.5 exposure. We show that the CAA has been the single largest contributor to racial convergence in PM2.5 pollution exposure in the U.S. since 2000 accounting for over 60 percent of the reduction.
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Re-engineering Key National Economic Indicators
July 2019
Working Paper Number:
CES-19-22
Traditional methods of collecting data from businesses and households face increasing challenges. These include declining response rates to surveys, increasing costs to traditional modes of data collection, and the difficulty of keeping pace with rapid changes in the economy. The digitization of virtually all market transactions offers the potential for re-engineering key national economic indicators. The challenge for the statistical system is how to operate in this data-rich environment. This paper focuses on the opportunities for collecting item-level data at the source and constructing key indicators using measurement methods consistent with such a data infrastructure. Ubiquitous digitization of transactions allows price and quantity be collected or aggregated simultaneously at the source. This new architecture for economic statistics creates challenges arising from the rapid change in items sold. The paper explores some recently proposed techniques for estimating price and quantity indices in large scale item-level data. Although those methods display tremendous promise, substantially more research is necessary before they will be ready to serve as the basis for the official economic statistics. Finally, the paper addresses implications for building national statistics from transactions for data collection and for the capabilities and organization of the statistical agencies in the 21st century.
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Statistics on the Small Business Administration's Scale-Up America Program
April 2019
Working Paper Number:
CES-19-11
This paper attempts to quantify the difference in performance, of 'treated' (program participant) and 'non-treated' (non-participant) firms in SBA's Scale-Up initiative. I combine data from the SBA with administrative data housed at Census using a combination of numeric and name and address matching techniques. My results show that after controlling for available observable characteristics, a positive correlation exists between participation in the Scale-Up initiative and firm growth. However, publicly available survey results have shown that entrepreneurs have a variety of goals in-mind when they start their businesses. Two prominent, and potentially contradictory ones are work-life balance and greater income. That means that not all firms may want to grow and I am unable to completely control for owner motivations. Finally, I do not find a statistically significant relationship between participation in Scale-Up and firm survival once other business characteristics are accounted for.
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Why are employer-sponsored health insurance premiums higher in the public sector than in the private sector?
February 2019
Working Paper Number:
CES-19-03
In this article, we examine the factors explaining differences in public and private sector health insurance premiums for enrollees with single coverage. We use data from the 2000 and 2014 Medical Expenditure Panel Survey-Insurance Component, along with decomposition methods, to explore the relative explanatory importance of plan features and benefit generosity, such as deductibles and other forms of cost sharing, basic employee characteristics (e.g., age, gender, and education), and unionization. While there was little difference in public and private sector premiums in 2000, by 2014, public premiums had exceeded private premiums by 14 to 19 percent. We find that differences in plan characteristics played a substantial role in explaining premium differences in 2014, but they were not the only, or even the most important, factor. Differences in worker age, gender, marital status, and educational attainment were also important factors, as was workforce unionization.
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The Management and Organizational Practices Survey (MOPS): Collection and Processing
December 2018
Working Paper Number:
CES-18-51
The U.S. Census Bureau partnered with a team of external researchers to conduct the first-ever large-scale survey of management practices in the United States, the Management and Organizational Practices Survey (MOPS), for reference year 2010. With the help of the research team, the Census Bureau expanded and improved the survey for a second wave for reference year 2015. The MOPS is a supplement to the Annual Survey of Manufacturing (ASM), and so the collection and processing strategy for the MOPS built on the methodology for the ASM, while differing on key dimensions to address the unique nature of management relative to other business data. This paper provides detail on the mail strategy pursued for the MOPS, the collection methods for paper and electronic responses, the processing and estimation procedures, and the official Census Bureau data releases. This detail is useful for all those who have interest in using the MOPS for research purposes, those wishing to understand the MOPS data more deeply, and those with an interest in survey methodology.
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Hiring through Startup Acquisitions:
Preference Mismatch and Employee Departures
September 2018
Working Paper Number:
CES-18-41
This paper investigates the effectiveness of startup acquisitions as a hiring strategy. Unlike conventional hires who choose to join a new firm on their own volition, most acquired employees do not have a voice in the decision to be acquired, much less by whom to be acquired. The lack of worker agency may result in a preference mismatch between the acquired employees and the acquiring firm, leading to elevated rates of turnover. Using comprehensive employee-employer matched data from the US Census, I document that acquired workers are significantly more likely to leave compared to regular hires. By constructing a novel peer-based proxy for worker preferences, I show that acquired employees who prefer to work for startups ' rather than established firms ' are the most likely to leave after the acquisition, lending support to the preference mismatch theory. Moreover, these departures suggest a deeper strategic cost of competitive spawning: upon leaving, acquired workers are more likely to found their own companies, many of which appear to be competitive threats that impair the acquirer's long-run performance.
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