Projects

In addition to my personal research, I've also had the opportunity to work on a variety of projects, some of which I describe briefly below.  

Redesigning the Longitudinal Business Database

I worked with a team of economists and programmers at the Census Bureau to redesign the Longitudinal Business Database (LBD), building on the work of Jarmin & Miranda (2002). The LBD is one of the most widely requested datasets in the Federal Statistical Research Data Center network where approved projects access confidential Census Bureau microdata. The LBD contains annual information on the universe of non-farm employer business establishments in the US starting in 1976. The redesigned LBD was also used to improve the Business Dynamics Statistics (BDS), a public-use data product that reports the stock and flows of establishments, firms, and employment by establishment and firm characteristics such as geography, industry, age, and size. We made many improvements to the underlying data infrastructure including the integration of historical County Business Patterns microdata, improved matching and birth/death retiming  algorithms, the integration of vintage consistent industry codes, and the creation and use of multiplicative noise factors that allow for more granular tabulations. In addition to improving the data, we created a robust set of documentation available to microdata users on approved projects and released a working paper that summarizes each step of the processing. The redesigned LBD provides the flexibility to continually improve both the microdata and public-use statistics and will serve as the foundation for research on businesses and workers for years to come.   

Measuring the Dynamics of Innovative Firms

As part of my work at the Census Bureau I have the opportunity to work with and improve measures of firm dynamics in the Business Dynamics Statistics (BDS). The BDS data provide flows of establishments, firms, and employment by a number of interesting dimensions including firm age, firm size, industry, and geography. These data are an important source of information about trends in startup activity over time as well as the shifting distribution of activity across firm age and size groups. 

In addition to working to improve the core measures of dynamics in the BDS data, I also work to introduce new measures focused on the dynamics of innovative businesses. This measurement agenda is described in detail in Goldschlag and Perlman (2017). Reflective of the fact that innovative activity has many dimensions, we aim to produce measures based on both the inputs (STEM workers, R&D spending) and outputs (patents, trademarks, copyrights) of the innovation process. These types of statistics will not only show the number of firms and employment associated with firms engaged in different types of innovative activity (e.g. granted patents), but also job creation, job destruction, entry and exit associated with those businesses.  

Identifying High Tech Industries

Modern market economies are characterized by the reallocation of resources from less productive less valuable activities to more productive more valuable ones. Businesses in the High Technology sector play a particularly important role in this regard, introducing new products and services that impact the entire economy. Tracking the performance of this sector is therefore of primary importance especially in light of recent evidence that suggests a slowdown in business dynamism in this sector. Identifying businesses in the High Tech sector is a nontrivial exercise. There are a number of ways to identify High Tech industries including input methodologies such as the OECD R&D measures (Hatzichronoglou 1997) or STEM concentration measures (Hecker 2005) and output methodologies such as the Census "advanced technology products"  (NSF 2002) or R&D intensive products (Hatzichronoglou 1997). 

One of the interesting properties of the STEM methodology is that it can be replicated using different years of industry-occupation employment data. I've developed a GitHub repository with an iPython notebook (and necessary data) that uses 2012 and 2014 industry-occupation matrices to identify High Tech industries in those years.  

Leveraging Big Data to Measure Innovation

The UMETRICS project was initiated to partner with science agencies and research institutions to improve outcome measurements for science investments. The project created standardized measures of the impact of federal grants by combining university administrative records, publication data, patent data, and federal tax information. The result was a rich and novel data source for investigating a variety of questions related to R&D, innovation, the innovation labor force, and the impact of federal grant spending.  

By matching the UMETRICS data to Census microdata on businesses and individuals we are able to paint a more complete picture of the impacts of federally funded research. Using these new matched data, we are able to characterize the flow of research-trained individuals out of universities and into the labor force. We are also able to quantify the impact of grant spending and input purchasing activity on local economies. (See Zolas et al. 2015, Goldschlag, Jarmin, Lane, and Zolas (2017), and Goldschlag, Lane, Weinberg, and Zolas (2017).)

DISCLAIMER: This site is maintained by Nathan Goldschlag. Opinions expressed here do not reflect the official views of the U.S. Census Bureau or any other public or private organization.