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Aggregation Bias in the Measurement of U.S. Global Value Chains
September 2024
Working Paper Number:
CES-24-49
This paper measures global value chain (GVC) activity, defined as imported content of exports, of U.S. manufacturing plants between 2002 and 2012. We assesses the extent of aggregation bias that arises from relying on industry-level exports, imports, and output to establish three results. First, GVC activity based on industry-level data underestimate the actual degree of GVC engagement by ignoring potential correlations between import and export activities across plants within industries. Second, the bias grew over the sample period. Finally, unlike with industry-level measures, we find little slowdown in GVC integration by U.S. manufacturers.
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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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Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?
January 2017
Authors:
Lars Vilhuber,
John M. Abowd,
Daniel Weinberg,
Jerome P. Reiter,
Matthew D. Shapiro,
Robert F. Belli,
Noel Cressie,
David C. Folch,
Scott H. Holan,
Margaret C. Levenstein,
Kristen M. Olson,
Jolene Smyth,
Leen-Kiat Soh,
Bruce D. Spencer,
Seth E. Spielman,
Christopher K. Wikle
Working Paper Number:
CES-17-59R
The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives.
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Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics
February 2016
Working Paper Number:
CES-16-10
We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau's Business Dynamics Statistics and Synthetic Longitudinal Business Database, highlighting accuracy and protection afforded by the method when compared to existing public-use tabulations (with suppressions).
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Evidence for the Effects of Mergers on Market Power and Efficiency
January 2016
Working Paper Number:
CES-16-43
Study of the impact of mergers and acquisitions (M&As) on productivity and market power has been complicated by the difficulty of separating these two effects. We use newly-developed techniques to separately estimate productivity and markups across a wide range of industries using confidential data from the U.S. Census Bureau. Employing a difference-in-differences framework, we find that M&As are associated with increases in average markups, but find little evidence for effects on plant-level productivity. We also examine whether M&As increase efficiency through reallocation of production to more efficient plants or through reductions in administrative operations, but again find little evidence for these channels, on average. The results are robust to a range of approaches to
address the endogeneity of firms' merger decisions.
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THE IMPACT OF LATINO-OWNED BUSINESS ON LOCAL ECONOMIC PERFORMANCE
January 2016
Working Paper Number:
CES-16-34
This paper takes advantage of the Michigan Census Research Data Center to merge limited-access Census Bureau data with county level information to investigate the impact of Latino-owned business (LOB) employment share on local economic performance measures, namely per capita income, employment, poverty, and population growth. Beginning with OLS and then moving to the Spatial Durbin Model, this paper shows the impact of LOB overall employment share is insignificant. When decomposed into various industries, however, LOB employment share does have a significant impact on economic performance measures. Significance varies by industry, but the results support a divide in the impact of LOB employment share in low and high-barrier industries.
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Micro Data and the Macro Elasticity of Substitution
March 2012
Working Paper Number:
CES-12-05
We estimate the aggregate elasticity of substitution between capital and labor in the US manufacturing sector. We show that the aggregate elasticity of substitution can be expressed as a simple function of plant level structural parameters and sufficient statistics of the distribution of plant input cost shares. We then use plant level data from the Census of Manufactures to construct a local elasticity of substitution at various levels of aggregation. Our approach does not assume the existence of a stable aggregate production function, as we build up our estimate from the cross section of plants at a point in time. Accounting for substitution within and across plants, we find that the aggregate elasticity is substantially below unity at approximately 0.7. Lastly we assess the sources of the bias of aggregate technical change from 1987 to 1997. We find that the labor augmenting character of aggregate technical change is due almost exclusively to labor augmenting productivity growth at the plant level rather than relative growth in capital intensive plants.
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The Impact of Plant-Level Resource Reallocations and Technical Progress on U.S. Macroeconomic Growth
December 2009
Working Paper Number:
CES-09-43
We build up from the plant level an "aggregate(d) Solow residual" by estimating every U.S. manufacturing plant's contribution to the change in aggregate final demand between 1976 and 1996. We decompose these contributions into plant-level resource reallocations and plant-level technical efficiency changes. We allow for 459 different production technologies, one for each 4- digit SIC code. Our framework uses the Petrin and Levinsohn (2008) definition of aggregate productivity growth, which aggregates plant-level changes to changes in aggregate final demand in the presence of imperfect competition and other distortions and frictions. On average, we find that aggregate reallocation made a larger contribution than aggregate technical efficiency growth. Our estimates of the contribution of reallocation range from 1:7% to2:1% per year, while our estimates of the average contribution of aggregate technical efficiency growth range from 0:2% to 0:6% per year. In terms of cyclicality, the aggregate technical efficiency component has a standard deviation that is roughly 50% to 100% larger than that of aggregate total reallocation, pointing to an important role for technical efficiency in macroeconomic fluctuations. Aggregate reallocation is negative in only 3 of the 20 years of our sample, suggesting that the movement of inputs to more highly valued activities on average plays a stabilizing role in manufacturing growth.
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Discretionary Disclosure in Financial Reporting: An Examination Comparing Internal Firm Data to Externally Reported Segment Data
September 2009
Working Paper Number:
CES-09-28
We use confidential, U.S. Census Bureau, plant-level data to investigate aggregation in external reporting. We compare firms' plant-level data to their published segment reports, conducting our tests by grouping a firm's plants that share the same four-digit SIC code into a 'pseudo-segment.' We then determine whether that pseudo-segment is disclosed as an external segment, or whether it is subsumed into a different business unit for external reporting purposes. We find pseudo-segments are more likely to be aggregated within a line-of-business segment when the agency and proprietary costs of separately reporting the pseudo-segment are higher and when firm and pseudo-segment characteristics allow for more discretion in the application of segment reporting rules. For firms reporting multiple external segments, aggregation of pseudo-segments is driven by both agency and proprietary costs. However, for firms reporting a single external segment, we find no evidence of an agency cost motive for aggregation.
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The Importance of Reallocations in Cyclical Productivity and Returns to Scale: Evidence from Plant-Level Data
March 2007
Working Paper Number:
CES-07-05
This paper provides new evidence that estimates based on aggregate data will understate the true procyclicality of total factor productivity. I examine plant-level data and show that some industries experience countercyclical reallocations of output shares among firms at different points in the business cycle, so that during recessions, less productive firms produce less of the total output, but during expansions they produce more. These reallocations cause overall productivity to rise during recessions, and do not reflect the actual path of productivity of a representative firm over the course of the business cycle. Such an effect (sometimes called the cleansing effect of recessions) may also bias aggregate estimates of returns to scale and help explain why decreasing returns to scale are found at the industry-level data.
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