Thank you Elaine – very helpful

 

Alan E. Pisarski

6501 Waterway Drive

Falls Church Va. 22044

703 941-4257

alanpisarski@alanpisarski.com

From: ctpp-news-bounces@chrispy.net [mailto:ctpp-news-bounces@chrispy.net] On Behalf Of Murakami, Elaine
Sent: Wednesday, June 20, 2007 6:04 PM
To: ctpp-news maillist
Cc: Deb Niemeier; siim soot
Subject: [CTPP] CB Center for Economic Studies Discussion papers fromResearch Data Centers

 

As some of you know, we (the transportation data community) have not had much success using the CB’s Research Data Center program that is housed at the Center for Economic Studies. We attempted to use the RDC to examine early ACS results.   The RDCs provide researchers with access to confidential microdata and go through careful review before results are released to ensure that no confidential data are released. 

While glancing at the CES RDC website today, I noticed that several “discussion papers”  over the past few years (2005 through current) have included analysis using the 1990 and 2000 decennial census “long form” data to examine patterns of home-to-work.  2004 ACS data are included on the list of available data at the RDC.  Typically, these analyses focus on “labor market outcomes.” 

 

http://www.ces.census.gov/index.php/ces/1.00/cmshome

 

Here are a few that I found:

 

Hellerstein, Judith K;  David Neumark; and Melissa McInerney. “Spatial Mismatch or Racial Mismatch? “  CES 07-16.  June 2007.

 

Job density  and employment rates are compared between black and white population (no break-out for “Hispanic”),  The authors use the term “racial mismatch” to mean a “lack of jobs into which blacks are hired,”  in contrast to “spatial mismatch” that  argues that jobs are not located near to where people live contributing to lack of employment. They found that space alone plays a relatively minor role in low black male employment rates. They find that jobs that are “available to blacks” is more important, especially for workers with lower education.   

 

Wang, Qingfang “How does Geography Matter in Ethnic Labor Market Segmentation Process?  A Case Study of Chinese in the San Francisco CMSA”  CES-WP-07-09  March 2007

 

The authors defined Chinese residence and workplace concentrations in the San Francisco Bay area.  They found clear market segmentation by gender and job skill among the Chinese immigrants.  They assert that the housing market is limited for immigrant ethnic minorities, but that ethnic social networking will influence employment and therefore job location, beyond commute time considerations.  The pattern for Chinese immigrant men and women is very different, with men in skilled computer and electronics jobs, and women in lower skilled factory and assembly line jobs. 

 

Bayer, Patrick; Stephen L. Ross, and Giorgio Topa. “Place of Work and Place of Residence:  Informal Hiring Networks and Labor Market Outcomes.”  CES 05-23  October 2005.

 

The 1990 Census “long form” data was used to measure social interaction by comparing the propensity of individuals living in the same vs. nearby blocks to work in the same location.  They found that residing on the same block increased the probability of working together by over 33 percent.  When the characteristics of persons (age, education and presence of children) matched, these interactions were even stronger. 

 

Fu, Shine.  “Smart Café Cities:  Testing Human Capital Externalities in the Boston Metropolitan Area.”  CES 05-24 October 2005.

 

“Human Capital Externalities”  or “knowledge spillover” are benefits that accrue to workers from being close to a dense skilled labor market.  Most work done on these externalities have been at the macro-scale of metropolitan areas.  This effort examines microgeographic scale of externalities by using census tracts, block groups and blocks.  This paper used the 1990 Census long form data using worker and job characteristics and job location.  They found that these benefits are very localized within microgeographic scales. 

 

So, the good news is that other people besides the transportation community have benefited from the detailed PLACE OF WORK  geocoding in the Decennial Census “long form.”  What can we learn from this? To get projects approved in the CES RDC system, we would probably have better luck if we discussed our research in terms of economic productivity and labor market benefits, rather than benefiting transportation planning.  Also, we can expect that researchers on labor market outcomes will want to use ACS results on place of work in the future. 

 

Elaine Murakami