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Labor Market Segmentation and the Distribution of Income: New
Evidence from Internal Census Bureau Data
August 2023
Working Paper Number:
CES-23-41
In this paper, we present new findings that validate earlier literature on the apparent segmentation of the US earnings distribution. Previous contributions posited that the observed distribution of earnings combined two or three distinct signals and was thus appropriately modeled as a finite mixture of distributions. Furthermore, each component in the mixture appeared to have distinct distributional features hinting at qualitatively distinct generating mechanisms behind each component, providing strong evidence for some form of labor market segmentation. This paper presents new findings that support these earlier conclusions using internal CPS ASEC data spanning a much longer study period from 1974 to 2016. The restricted-access internal data is not subject to the same level of top-coding as the public-use data that earlier contributions to the literature were based on. The evolution of the mixture components provides new insights about changes in the earnings distribution including earnings inequality. In addition, we correlate component membership with worker type to provide a tacit link to various theoretical explanations for labor market segmentation, while solving the problem of assigning observations to labor market segments a priori.
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Managing Employee Retention Concerns: Evidence from U.S. Census Data
February 2023
Working Paper Number:
CES-23-07
Using Census microdata on 14,000 manufacturing plants, we examine how firms man age employee retention concerns in response to local wage pressure. We validate our measure of employee retention concerns by documenting that plants respond with wage increases, and do so more when the employees' human capital is higher. We doc ument substantial use of non-wage levers in response to retention concerns. Plants shift incentives to increase the likelihood that bonuses can be paid: performance target transparency declines, as does the use of localized performance metrics for bonuses. Furthermore, promotions become more meritocratic, ensuring key employees can be promoted and retained. Lastly, decision-making authority at the plant-level increases, offering more agency to local employees. We find evidence consistent with inequity aversion constraining the response to local wage pressure, and document spillovers in both wage and non-wage reactions across same-firm plants.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
CES-22-49
Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current
expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand.
First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation.
The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations.
Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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Using Small-Area Estimation (SAE) to Estimate Prevalence of Child Health Outcomes at the Census Regional-, State-, and County-Levels
November 2022
Working Paper Number:
CES-22-48
In this study, we implement small-area estimation to assess the prevalence of child health outcomes at the county, state, and regional levels, using national survey data.
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The Transformation of Self Employment
February 2022
Working Paper Number:
CES-22-03
Over the past half-century, while self-employment has consistently accounted for around one in ten of the United States workforce, its composition has changed. Since 1970, industries with high startup capital requirements have declined from 53% of self-employment to 23%. This same time period also witnessed declines in 'hometown' local entrepreneurship and the probability of the self-employed being among top earners. Using 2016 data, we show that high startup capital requirements are linked with lower profitability at small scales. The transition away from high startup capital industries appears most closely linked to changes in small business production functions and less due to advantageous reallocation to other opportunities, growth in returns-to-scale among large businesses, or a worsening of financing conditions and debt levels.
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Combining Rules and Discretion in Economic Development Policy: Evidence on the Impacts of the California Competes Tax Credit
June 2021
Working Paper Number:
CES-21-13
We evaluate the effects of one of a new generation of economic development programs, the California Competes Tax Credit (CCTC), on local job creation. Incorporating perceived best practices from previous initiatives, the CCTC combines explicit eligibility thresholds with some discretion on the part of program officials to select tax credit recipients. The structure and implementation of the program facilitates rigorous evaluation. We exploit detailed data on accepted and rejected applicants to the CCTC, including information on scoring of applicants with regard to program goals and funding decisions, together with restricted access American Community Survey (ACS) data on local economic conditions. Using a difference-in-differences approach, we find that each CCTC-incentivized job in a census tract increases the number of individuals working in that tract by over two ' a significant local multiplier. We also explore the program's distributional implications and impacts by industry. We find that CCTC awards increase employment among workers residing in both high income and low income communities, and that the local multipliers are larger for non-manufacturing awards than for manufacturing awards.
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Whose Job Is It Anyway? Co-Ethnic Hiring in New U.S. Ventures
March 2021
Working Paper Number:
CES-21-05
We explore co-ethnic hiring among new ventures using U.S. administrative data. Co-ethnic hiring is ubiquitous among immigrant groups, averaging about 22.5% and ranging from 2% to 40%. Co-ethnic hiring grows with the size of the local ethnic workforce, greater linguistic distance to English, lower cultural/genetic similarity to U.S. natives, and in harsher policy environments for immigrants. Co ethnic hiring is remarkably persistent for ventures and for individuals. Co-ethnic hiring is associated with greater venture survival and growth when thick local ethnic employment surrounds the business. Our results are consistent with a blend of hiring due to information advantages within ethnic groups with some taste-based hiring.
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Entrepreneurial Teams: Diversity of Skills and Early-Stage Growth
December 2020
Working Paper Number:
CES-20-45
We use employer-employee linked data to track the employment histories of team members prior to startup formation for a full cohort of new firms in the U.S. Using pre-startup industry experience to measure skillsets, we find that startups that have founding teams with more diverse collective skillsets grow faster than peer firms in the same industries and local economies. A one standard deviation increase in teams' skill diversity is associated with an increase in five-year employment (sales) growth of 16% (10%) from the mean. The effects are stronger among startups in innovative industries and among startups facing greater ex-ante uncertainty. Moreover, the results are robust to a variety of approaches to address the endogeneity of team composition. Overall, our results suggest that teams with more diverse collective skillsets adapt their strategies more successfully in the uncertain environments faced by (innovative) startup firms.
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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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Do Short-Term Incentives Affect Long-Term Productivity?
March 2020
Working Paper Number:
CES-20-10
Previous research shows that stock repurchases that are caused by earnings management lead to reductions in firm-level investment and employment. It is natural to expect firms to cut less productive investment and employment first, which could lead to a positive effect on firm-level productivity. However, using Census data, we find that firms make cuts across the board irrespective of plant productivity. This pattern seems to be associated with frictions in the labor market. Specifically, we find evidence that unionization of the labor force may prevent firms from doing efficient downsizing, forcing them to engage in easy or expedient downsizing instead. As a result of this inefficient downsizing, EPS-driven repurchases lead to a reduction in long-term productivity.
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