A. Version 2 Enhancements 

1. Revision of Raking

Phil Hardiman’s work on the EDE’s (which led to a news conference by his director and a piece in 

the San Francisco Chronicle!) resulted in a revision of our raking to ES-202 data. The original raking 

procedure attributed too much of the excess variation in net jobs flows (the difference between unraked EDE and the BLS ES-202 estimates) to sex – we now treat sex and age symmetrically. State 

partners have alerted us to other changes that need to be made in our processing, for which we are 

very grateful. Look for Version 2.3 to be distributed in the next few months. 

B. Version 3.0 Research and Development 

1. Disclosure Proofing

The old approach, Version 2, had two levels of protection: one at the cell and one at the table level. 

Version 2 currently protects individual cells by adding noise to all business-entity level micro data by 

a percentage determined by the number of establishments in a cell (county x SIC division). As we 

discussed in the January workshop, the amount of noise added depends on how many establishments are in a cell. LEHD adds a flag “significantly distorted to protect confidentiality” if the confidentiality-protected estimate differs from the actual estimate by more than a fixed percentage or 

there are fewer than a given number of establishment or employees. We do not do complementary 

suppression at the cell level because all the other estimates in the confidentiality-protected table 

are already protected. 

Version 2 currently protects tables by suppressing all estimates for any county or industry whenever BLS suppresses the month 1 employment estimate for the same county or industry. Version 2 

also rakes table-level estimates so that the LEHD beginning-of-quarter employment estimate for a 

given industry or county matches the month 1 estimate from the BLS CEW series. LEHD flow statistics are also raked for consistency. The new approach has to be approved by the Census 

Disclosure Review Board (DRB). We have asked them to consider the following: 

• .Elimination of the raking procedure 

• Modification of the table-level suppression as follows 

- release any table level estimates that are based on noise addition to the state LMI directors PROVIDED THAT the state LMI directors agree that whenever they publish any 

LEHD estimates, they will also publish the associated confidentiality flag. This confidentiality flag is the one you already receive with Version 2 data; so, this is not a new 

requirement. The state LMI directors have the final decision on what table-level suppressions to apply when they publish their data. 

• Permission for the release of Employment Dynamics Estimates for 

- More detailed geographic areas 

- More detailed industry classifications 

- Geographic classification x industry detail if its methodology meets Census Bureau data 

quality standards. The LEHD Program will perform the necessary data quality research, 

not the Census Disclosure Review Board.

I. State Employment Dynamics Estimates (EDE’s) 

Special points of interest: 

1. BLS has agreed that EDE estimates no 

longer need to be benchmarked to ES202 

data. We are working with the Census 

Disclosure Review Board to see what level of 

detailed industry and county level data we 

can provide without breaching confidentiality 

requirements. 

2. The successor/predecessor work 

(Entity Demography Editing) is being extended to all states. We have some preliminary results for the temporary help industry, 

about which we are requesting feedback from 

the Entity Demography Editing team. 

3. LEHD measures of human capital for 

businesses will be presented at an NBER 

conference this month at the Federal Reserve. Alan Greenspan will open the conference; Katharine Abraham is the discussant. 

4. The SSN editing work is being refined 

and extended 

Inside this issue: 

State Employment Dynamics 

Estimates I 

BLS-Census Prototype 

Expansion of the EDE II 

LEHD Infrastructure III 

Human Capital, Worker Flows 

and the Definition of a 

Business 

VI 

Demographic Survey Improvement V 

Geocoding VI 

Wage Record Editing VII 

Population Estimates 

Improvements VIII 

Bureau of the Census LEHD Newsletter 

We’d like to update you on what we’ve done about the key issues 

raised at the last workshop.Please remember that nothing in this is “official” Census Bureau data: this is 

an informal update on the LEHD program’s activities. 

Volume 1 Issue 2 April 2002 
Page 2 LEHD Newsletter 

“The outcome 

for ..our Local 

Workforce 

Investment Boards 

is a portfolio of 

never before 

available labor 

market measures 

at a level of 

geography never 

before 

available” (Deputy 

Governor of Illinois) 

The statistical analysis of the relation between residential location and work place, based 

on the Minnesota UI data, has been completed. The statistical model, which is fully 

integrated into the model for imputing work place, uses three different size classes of UI 

accounts (SEINs) as its initial cut of the data. Within each SEIN size class, the model 

computes great circle (not commuting) distances to each of the places of work for each 

person. Controlling for the size distribution of the work places, the model estimates the 

conditional probability that the individual works at each of the establishments within the 

SEIN as a function of the distance from the individual's residence to the work place. For 

the smallest SEIN size class, the effect of distance is to decrease the odds that an 

individual works at a particular site monotonically until the site is more than 25 miles 

away. For the smallest SEIN size class, sites more than 25 miles away have a negligible 

probability of being the correct work place. For the largest SEIN size class, the effect of 

distance is also monotonic but sites up to 50-75 miles away have non-negligible 

probabilities. The intermediate SEIN size class show a monotone relation to distance that 

dies out in the 25-50 mile range. 

We are now working on incorporating the effects of the wage distribution on the work place 

selection and refining the way we use these statistics in the work place imputation model. 

3. Graphical Delivery System 

Russ Marshall has asked for input from team partners on the delivery system. We are purchasing a collaborative software package. There are some other more expensive packages 

with greater features – but these features typically do not work correctly, or at all, through 

the firewall. The website for this is http://www.vbulletin.com/. You can see it in action on 

www.i-club.com (the Subaru site) under "forums." 

C. Entity Demography Editing (Successor/Predecessor Firm Research) 

The LEHD staff has worked with the Florida ES202 data (1994 –1998) using the initial 

guidelines discussed at the January state workshop. The goal is to use UI wage record data 

to shed light on the births/deaths/mergers and acquisitions of businesses (entity demography editing), with the goal of using the information for ES202 successor/predecessor editing. The state partners expressed particular interest on flows for temporary help services 

(7363) and personnel supply companies (7361). We have sent Florida a report; the other 

state partners will be receiving their reports within the next two months. 

We identify four categories of linkages: 

1. Predecessor firm dies, more than 5 workers and more than 80% of predecessor's 

employment moves to successor; 

2. Predecessor firm dies, and more than 5 workers but less than 80% of predecessor's 

employment moves to successor; 

3. Predecessor firm lives, more than 5 workers and more than 80% of predecessor's 

employment moves to successor; 

4. Predecessor firm lives, and more than 5 workers but less tan 80% of predecessor's 

employment moves to successor.

2. Assignment of Place of Work 
Key Findings 

• Total linkages identified by ES202 and UI data, and their concurrence: 

 - The ES202 data identify 10,449 linkages; the UI data almost four times as 

 many: 44,596. 

 - The files agree on 3, 274 of the links—although this may be due to data entry 

 errors on either the successor or predecessor field in the ES202 data. 

• The most commonly occurring industry links in the UI data: 

 - The industry with the most successor/ predecessor changes (category 1) is eating 

 and drinking establishments, followed by 8011 (Offices and Clinics of Doctors of 

 Medicine). All of these firms stay in the same industry, as would be expected. 

 - The industry into which most businesses are acquired (category 2) is industry 

 7363 (temporary help), followed by eating and drinking establishments. 

 - Almost no firms fall into category 3. 

 - The industry which absorbs large numbers of workers from other industries 

 (primarily 7363, 5810, 5411 and 5311) is temporary help services (7363). 

• The most commonly occurring industry links in the ES202 data: 

 - The industry with the most successor/ predecessor changes (category 1) is eating 

 and drinking establishments, followed by 8011. This is consistent with the UI 

 data. Just as with the UI data, all of the firms stay in the same industry, as would 

 be expected. 

 - The industry into which most businesses are acquired (category 2) is industry is 

 eating and drinking establishments. 

 - The role of industry 7363 is much less pronounced than in the UI data 

 - Almost no businesses fall into categories 3 and 4. 

• An analysis of industries 7361 and 7363: 

 - The main predecessor firms for industry 7363 are in eating and drinking estab-

 lishments, firms with no industry provided, 7363 itself, and construction firms. 

 - When the predecessor firm continues, and still sends large numbers of employees 

 to 7363, they are predominantly in industries 7363, 5810, 5411, 5311 and 

 7361. 

 - The main activity for industry 7361 is category 4: where a substantial portion of 

 employees from one firm move into a firm in industry 7361 

 - The most dominant industries sending employees into 7361 are 7363, 5810, 

7361 and 5411. 

D. Cornell Support Site 

Final specifications of the network layout, including firewall specifications, have been defined. 

Volume 1 Issue 2 Page 3 

The most 

dominant 

industries 

sending 

employees into 

7361 are 

industries 

7363, 5819, 

7361 and 5411 
1. Low Wage Work 

A Russell Sage/Rockefeller Foundation/HHS sponsored research program is studying 

the labor force dynamics of low-wage workers using the LEHD individual characteristics, 

employer characteristics, and employment history files from cooperating partner states 

(CA, FL, IL, MD, MN, NC, TX). The project is continuing to develop sets of descriptive 

tables using different sets of assumptions to define low-wage workers. New findings 

since the January workshop include: 

Staying with the same firm is not the best way to “grow out” of low-wage work. The 

returns to job tenure are the same for low-wage and non-low-wage workers. 

Changing jobs is an important source of wage growth for low-wage workers. The 

average job change generates a percentage wage change of about .31 to .33 

for low income workers; -.03 to -.08 for non-low-wage workers. 

Changing jobs hurts some non low-wage workers – particularly non-whites and older 

workers 

The distance between place of residence and high wage firms work is an important 

factor in explaining the ability of workers to move out of low wage work. 

Current work is focused on refining measures of low-wage work using CPS, ACS and 

decennial Census information, as well as place of residence data.. 

Availability: Interim report to states will be finished by August 2002. 

2. Demand for Older Workers

NIA sponsored research on the demand for older workers has begun. We are focusing 

on linking in the 5500 file on pension benefits, examining labor demand for older workers, and examining workforce composition in the nursing home industry 

Availability: Interim report to states will be provided August 2002 

3. Displaced Workers 

This is a side-by-side comparison of person-incidence of displacement in the CPS Displaced Worker Supplement and administrative data. The results are favorable -showing 

a slightly lower incidence in the administrative data. This will influence the final version 

of any analysis on displaced workers. A more formal research program using individuals 

who are both CPS respondents and are observed in the administrative data is being 

Page 4 LEHD Newsletter 

Staying with 

the same firm 

is not the best 

way to “grow 

out” of lowwage work. 

The returns to 

job tenure are 

the same for 

low-wage and 

non-low-wage 

workers 

II. BLS-ETA Census Prototype Expansion of the EDE 

A. Inclusion of Minnesota, New Jersey, North Carolina 

and Pennsylvania 

The program has received data from Minnesota and North Carolina. Minnesota has received EDE’s; North Carolina has received edited UI wage records and will shortly receive EDE’s. The LEHD Program also received data from Pennsylvania in late April. An 

MOU is in progress in New Jersey. 
The LEHD is awaiting additional ETA funding before expansion is possible. 

B. Inclusion of Additional States 

III. LEHD Infrastructure 

A. Individual Characteristics, Employer Characteristics, 

and Work Histories. 

The LEHD Program, as a part of its Title 13 mandate and under NSF, NIA and the Sloan 

Foundation support, creates, maintains and enhances data products that permit the integration of Census Bureau demographic products (surveys like the SIPP, CPS, and ACS), 

administrative data (Federal tax information, state UI wage records, ES-202 records) and 

Census Bureau economic data (Business Register, Economic Censuses, and Economic 

Surveys). These data products are then used directly to improve the Census Bureau’s 

demographic censuses and surveys and the economic census and surveys. 

Availability: ongoing. 

B. SIPP, CPS, SSA Integration. 

Continuing Title 13 research projects study the quality of the administrative data integration into the SIPP and CPS. 

Availability: report available upon request 

C. Business Register, Economic Censuses and Survey Integration. 

Continuing Title 13 research projects study the quality of the integration of the business 

units defined by the Business Register, Economic Censuses, and Economic Surveys with 

the Census Bureau’s demographic products. This research involves the testing of different methods of exact (identification number) integration and statistical integration (using 

name and address information). UI and ES-202 data to firm-level data files collected by 

the Census Bureau. The business files we integrate include: the Economic Censuses collected every five years, the Business Register, and data collected from various less extensive establishment surveys such as the Business Expenditure Survey (which collects detailed capital spending data among non-manufacturers). In addition, we have integrated 

in Compustat data on large employers. 

Availability: report available upon request 

Volume 1 Issue 2 Page 5 

LEHD ...directly 

improves the 

Census 

Bureau’s 

demographic 

censuses and 

surveys, and 

the economic 

census and 

surveys 
A. Measuring Workforce Composition 

The combination of UI data with ES-202 and Census Bureau business data provide us 

with more detail on each worker’s place of employment and allow us to draw a very vivid 

picture of the collection of workers attached to each firm. Both enhancements to existing data create the potential for new areas of research that should be of interest to policy makers and businesses themselves as well as to the academic community. 

B. Technological Investment and the Demand for Skill 

This project characterizes the distribution of “human capital” of the economies of several states and shows how this distribution changes between the economic census 

years of 1992 and 1997. The human capital measure used here is a composite of each 

worker’s “skill” combined with their labor market experience. We find that, on average, 

the within-firm mean of this composite human capital rises for the entire Illinois economy (and for each major industry group within the economy) between 1992 and 1997. 

We distinguish high human capital industries (such as Finance, Insurance, and Real 

Estate) from low human capital industries (retail, for example) and show which industries show highest growth in human capital. The integrated data are then used to examine the link between changes in use of technology by firms and these changes in human 

capital. We find that the demand for skilled labor is positively related to capital intensity 

(stronger positive relationship in manufacturing than in other sectors) and that increased spending on newer technologies (such as computer software and data processing) increases the demand for skilled workers (principally in the non-manufacturing sector). 

C. The Relationship Between Worker Skill and Firm Competitiveness 

This project explores which worker traits are most strongly connected to labor productivity (defined as sales per worker) at a business and to the market value of the firm. For 

this project, we use a much richer characterization of the human capital of each business. For example, we explore the impact of workers’ experience (both in the labor market and at the specific business) separately from our measure of workers’ skill. This 

allows us to identify the precise worker traits that help make a business more productive. For example, we find that firms with workers who are more diverse in terms of labor market experience are, all else equal, more productive. 

IV. Human Capital, Worker Flows and the Definition of a 

Business 

Page 6 LEHD Newsletter 

We distinguish 

high human 

capital 

industries..from 

low human 

capital 

industries and 

show which 

industries show 

highest growth 

Median Human Capital by Age of

Business

-0.15

-0.1

-0.05

0

0.05

0.1

Under 2

years

2-5 years 6-10 years 11+ years

1992 1997
We are working with the Sloan Foundation and selected Sloan Industry Centers 

(Finance, Trucking, Steel, Software, Semiconductors, and Retail Food) to develop more 

insights into why firms choose high skill (or low skill) workforces. Our workshop (April 

18/19) was attended by almost all partner states, and will result in a proposal to the 

Sloan Foundation to fund this work. LEHD, the partner states, and the Sloan Centers 

will request three year funding for a book and a conference to fund industry specific 

studies to understand firms’ choice of workforce. If funded, the report and associated 

book will have the following structure for each industry 

1. Why we care/understanding the issues for each industry 

2. Gathering basic facts along different dimensions (EDE’s by industry) 

• Age (young/old) 

• Experience 

• Sex/Race (if latter possible) 

• Immigrant 

• Skill level (Education/Occupation/Firm Job Tenure) 

All of these could be interacted with: starting wage level,as well as firm characteristics such as turnover, worker loss, entry, exit, productivity and location. 

3. Describing Firm Strategies 

• High road/low road 

• Location 

• Managerial Choice 

• Heterogeneity 

4. Reasons for choice 

• Regulation 

• Competition 

• Design factors 

• Entry/exit 

4. Consequences 

• Safety 

• Income inequality 

• Turnover 

• Unemployment 

• Firm productivity, growth, survival, and relocation 

• Career ladders 

5. Implications for workforce and economic development 

• Education and training needs for businesses and workers 

• Placement services 

• Economic development 

• Industrial extension (Sharing best practices in Human Resources) 

• Cross-state migration in response to economic slowdown and growth 

• Business cycles 

• New/emerging job flows 

D. Sloan Foundation Workshop and Grant Proposal 

Volume 1 Issue 2 Page 7 

The report and 

associated 

book will 

describe a 

specific 

industry 

structure. Each 

state will 

receive 

customized 

tables for 

comparison 

02468

101214

1995-09 

1995-11 

1996-01 

1996-03 

1996-05 

1996-07 

1996-09 

1996-11

1997-01

1997-03 

1997-05 

1997-07 

1997-09 

1997-11 

1998-01 

Month

Crashes/Mill VMT

All Crashes Large Crashes

Trucking: Higher Pay: 

Fewer Crashes 
A. SIPP Earnings and Work History Improvements 

LEHD SIPP research is currently focused on ways to improve the SIPP using administrative data. We have successfully matched respondents in the 1984, 1990, 1991,1992, 

1993, and 1996 SIPP panels to extracts from the Master Earnings File that were prepared by the Social Security Administration. These administrative records provide LEHD 

with a secondary source of information on job tenure, date of labor market entry, number of jobs held, and annual earnings. Differences between the administrative data and 

SIPP survey responses are being studied in order to determine how the SIPP survey instrument and the editing and imputation process can be improved. Current projects 

include the development of an improved system for integrating information given about 

a job over time and an investigation of measurement error in the self-reported earnings 

measures. The first project will produce more accurate measures of job tenure and the 

second will provide evidence on the statistical properties of a key income variable. Long 

term projects include the integration of Census Bureau firm data to jobs reported in the 

SIPP and the study of the relationship between type of employer and type of 

worker. The characterization of the firm/worker relationship will provide important additional information to those studying outcomes which are the result of the interaction of 

employer and employee decisions such as health insurance coverage, retirement decisions, and returns to education.

B. CPS Research 

With the conversion of March CPS collection to a computerized instrument in March 

1995, aggregate wages increased by 6% relative to independent estimates. LEHD research, based on integrated earnings records form the Social Security Administration, 

demonstrated that a substantial portion of this increase occurred because of greater 

reporting of underground wages and of misclassified self-employment income. Interestingly, no similar change was noticed in SIPP although the same instrument change occurred in 1996. 

C. SIPP/SSA/CBO Public Use Data Project 

LEHD’s Title 13 mandate includes researching the feasibility of creating a public use file 

that combines some SIPP variables with federal information from the Social Security 

Administration on employment histories and earnings from the SSA master earnings 

and benefit files. This research is being done in collaboration with the SIPP branch at 

the Census Bureau SSA, and the CBO. The Census Bureau, CBO, SSA and IRS have 

agreed to investigate the masking approach described in the John Abowd and Simon 

Woodcock chapter of the book “Confidentiality, Disclosure and Data Access: Theory and 

Practical Applications for Statistical Agencies”. 

V. Demographic Survey Improvements 

Page 8 LEHD Newsletter 

The 

characterization of 

the firm/worker 

relationship will 

provide important 

additional 

information to 

those 

studying...health 

insurance coverage, 

retirement 

decisions and 

returns to 

education 
VI. Geocoding 

Volume 1 Issue 2 Page 9 

We are able to 

get “good” 

geocodes for 

about 80% of 

addresses 

LEHD is prototyping the development of an Address Master File. When complete LEHD’s 

Address Master File will provide a list of unique business and residential addresses contained in all of LEHD's files complete with current and historical geocoding references. 

The relevant input files include the ES-202, the Census Business Register (economic 

census and survey frame), the Census Bureau’s Master Address File (demographic census and survey frame), and frame updates provided by current survey products, in particular the American Community Survey. We are building this file to support the geocoding needs of the Employment Dynamics Estimates. This work will provide insights into 

how much LEHD can improve address frames and reduce field operation costs. Our approach to building this file reflects an important paradigm shift from building survey and 

census frames based on the geocoding of mailing addresses to the building of frames 

based on the latitude and longitude locations of physical structures. We will treat these 

structures as geographic features that possess attributes such as physical and mailing 

addresses, and usage characteristics such as business, residential, group quarters, or 

multipurpose. Units contained within these structures will possess the same set of attributes. This approach facilitates future simultaneous updating and geocoding of address 

frames with satellite imaging. 

We are integrating our input files by using a series of software products, including Group1 

Code1 and Vality, that sanitize, standardize, and unduplicate all addresses. In addition to 

the basic address information, i.e. street address, city, state, and zip, the final data set will 

also include detailed geocoding information (political and statistical geocodes as well as 

latitude and longitude measures) and a series of identifiers. We plan to maintain historical geocodes in order to provide researchers the ability to separate the impact of geopolitical changes from socioeconomic changes. Furthermore, historical usage characteristics will provide researchers the ability to measure the changeover time in the usage characteristics of a physical location. 

The production of the Address Master File (AMF) will aid in a number of other economic 

and demographic research projects at LEHD. By using the AMF in conjunction with LEHD's 

other data files, researchers will be able to determine the business and demographic 

composition of detailed geographic areas and will be able to create tabulations based on 

custom geography, and generate spatial regression models. In addition, the detailed geographic information produced by the AMF is one of the inputs into Version 3 EDE estimates. 

The preliminary work presented at the January workshop reported on the results of geocoding in Illinois. We identified “good” geocodes as those we could code to rooftop or 

zip+4. The results by employment size and by location are detailed below. We are able to 

get “good” codes for about 80% of addresses. Research is continuing into how much 

value we can add to the geocoding by including other sources of data – particularly 

ABI/INFORM 

ES-202 Address coding using the best address (physical or mailing)

Geography: Six Chicago Metro Counties Geography: State of Illinois

Coding Type Records Percent Records Percent

Good 178274 87.9% 248333 76.9%

Bad 24650 12.1% 74537 23.1%
EIN Employment Size

Coding Type 1-4 05-09 10-19 20-49 50-99 100-249 250-499 500-999 1000+ Unknown

Good 77110 28695 20184 15132 6152 4122 1076 441 255 25107

Bad 9166 4051 3149 2476 1053 749 246 108 80 3572

Percent 89.38% 87.63% 86.50% 85.94% 85.39% 84.62% 81.39% 80.33% 76.12% 87.54%

The Census Bureau produces annual estimates of the foreign born population using 

decennial census long form data and the Current Population Survey (CPS). Both data 

sources possess advantages and disadvantages; the census provides large sample 

sizes, but is only conducted once every ten years while the CPS is conducted monthly, 

but is a relatively small survey. The LEHD Program has data resources that may allow 

the Census Bureau to produce more timely and detailed estimates of the foreign born 

population. 

In order to compare census data with UI data, we need to first make the measures 

consistent. In column two of each of the tables, we calculate the number of workers 

that live in each state for whom Employment Status Recode (ESR) is equal to 1, 2, 4, 

or 5. In columns three, four, and five in both tables we present various definitions of 

employment for the UI data. In column three we select all workers that had a job in 

that state with a covered employer at any time during 1999. In Column four we only 

include workers that had at least a job in the first quarter or in the first and second 

quarter. Finally, in the last column we select workers that were employed in at least the 

first and second quarter. In general it appears that the census worker totals agree most 

closely with the second definition of employment on the UI. However, for the foreign 

born, the first definition is generally closer. 

VI. Geocoding (continued) 

VIII. Population Estimates 

Page 10 LEHD Newsletter 

VII. Wage Record Editing 

Sensitivity analysis has been performed on Texas and North Carolina files, and updated 

parameters used for all current and future processing. Revision of parameter allowed 

for a typical increase in contingent match rates by two percentage points. Texas files 

have been re-processed using the new parameters, and are ready for shipment pending 

the evaluation of an update algorithm. North Carolina has been processed, and is ready 

for shipping (expected shipping date: third week of April). Florida files are currently in 

processing, and California files, originally processed in July 2001, will be re-processed 

using the new parameters. 

An economic analysis of the wage record edit is expected to be ready in draft form by 

mid-May, contingent on EDE processing of edited California Files. 

The LEHD 

Program ..may 

allow the 

Census Bureau 

to produce 

more timely 

and detailed 

estimates of 

the foreign born 

population 

Number of Immigrant Workers by State 
Lars Vilhuber’s joint work with Audra Bowlus has been accepted at two international conferences. 

Work by John Abowd, John Haltiwanger, Julia Lane, Paul Lengermann, Kevin McKinney and Kristin Sandusky 

(together with Ron Jarmin and Kristin McCue of the Center for Economic Studies) on the relationship between firm 

choice of human capital and firm productivity and market outcomes is to be presented at the National Bureau of 

Economic Research/ Conference in Research in Income and Wealth April 27. Alan Greenspan will open the meeting. Katharine Abraham is the discussant. 

Work by John Abowd, John Haltiwanger, Julia Lane and Kristin Sandusky on the relationship between technology 

adoption and human capital will be presented at the Society of Labor Economists Annual meeting.in May 

Work by Simon Woodcock will be presented at the Canadian Economics Association in June. 

Paul Lengermann is expecting to graduate in June 2002, and has accepted a position with the Federal Reserve 

Board of Governors here in Washington. 

Martha Stinson is expecting to graduate in June 2002, and has accepted a position with LEHD. 

VIII. Population Estimates (continued) 

Staff Notes 

Volume 1 Issue 2 Page 11 

Total Population and Workers by State (in thousands)

Census 2000 Unemployment Insurance (1999)

State Population Workers Workers (1) Workers (2) Workers (3)

California 33,052 14,713 16,807 13,514 12,521

Florida(1998) 15,593 7,017 7,983 6,381 5,735

Illinois 12,097 5,795 6,866 5,686 5,363

Maryland 5,162 2,615 2,597 2,065 1,846

Minnesota 4,784 2,550 2,946 2,374 2,187

Texas 20,290 9,264 10,784 8,833 8,201

Foreign Born Population and Workers by State (in thousands)

Census 2000 Unemployment Insurance (1999)

State Population Workers Workers (1) Workers (2) Workers (3)

California 9,070 4,579 4,700 3,807 3,558

Florida(1998) 2,777 1,409 1,428 1,145 1,037

Illinois 1,573 855 832 692 656

Maryland 559 333 290 224 199

Minnesota 280 150 178 130 119

Texas 3,059 1,540 1,487 1,204 1,124
LEHD Program 

US Census Bureau 

♦ 4700 Silver Hill Road 

Washington DC 20233 

Phone: 301-457-8305 

Fax: 301-457-8430 

Email: 

Ronald.c.prevost@census.gov 

jlane@ui.urban.org 

♦ John_abowd@cornell.edu 

Bureau of the Census 

LEHD Project Teams

LEHD 

Project Team Lead Team Members Milestones 

Labor Market Measures Oriane Cassell, MN Phil Hardiman, CA; Becky Rust, FL 1. State partners submit to Oriane suggested measures by 2/28 Wage record Edit Lars Vilhuber, Census Bob Cottrell, NC; Mustapha Hammida, MN; Becky 

Rust, FL 

1. Bob and Becky will develop examples of potential uses for LEHD data (with SSN edits) in WIA performance measures; 2. Census partners will develop position paper for presentation to Census Policy Committee; 3. Mustapha will investigate the usability of LEHD SSN algorithms (Lars is contact) in state environment 

Liaison with ES202 Policy Council Rick Clayton, BLS; Jay Mousa, MN; Becky Rust, 

FL 

1. Examine alternatives for incorporation of results from entity demography editing into ES202 data file; 2. Investigate revision of BLS disclosure standards 

Entity Demography Editing Bryce Stephens, Census Kathie Hughes, FL; Mary Mechenes, IL; George 

Putnam, IL 

1. Develop sensitivity threshold for measures; 2. Develop alternative measures for implementation; 3. Identify data elements for return to states 

Disclosure Proofing John Abowd, Census Bureau Rick Clayton, BLS; Phil Hardiman, CA 1. Investigate role of ownership and industry code in NAICS, implications for data processing 

and product development 

State Access to Simulation Data John Abowd, Census Bureau Waleed Almousa, IL; Mustapha Hammida, MN; 

Phil Hardiman, CA; Sonya Williams, NC 

1. Build infrastructure for state access to include state hardware/software requirements and training; 2. Develop data mining, data diagnostics and other statistical analysis capabilities 

Geocoding Firm Entity Rick Clayton, BLS; Ron Prevost, Census; Bureau 

George Putnam, IL 

Pat Arnold, MD; Bob Cottrell, NC; Dorothy Gattis, TX; Mary Ann Regan, PA; Becky Rust, FL 

1. Finalize longitude/latitude methodology; 2. Conduct pilot study for geocoding of state file; 3. Investigate alternative sources for automated geocoding coding; 4. Examine approaches to geocode updates; 5. Coordinate with GIS Apps. Project team 

GIS Applications Rick Clayton, BLS; Ron Prevost, Census; Bob 

Cottrell, NC 

Pat Arnold, MD; Dorothy Gattis, TX; Mary Ann Regan, PA; Becky Rust, FL 

1. Provide to Census states' ALMIS Database geog file; 2. Investigate alternative approaches to the use of secondary data (ES202-based and Wage Record-based) for use in GIS apps; 3. Identify implications of disclosure proofing for use of secondary data in GIS apps to maximize geographic specificity; 4. Coordinate with Product Development Project team 

Product Development, Marketing Julia Lane, Census; Bureau Mary Ann Regan, PA Pat Arnold, MD; Dorothy Gattis, TX; Russ Marshall, Census; George Putnam, IL; Becky Rust, 

FL; Sonya Williams, NC 

1. Develop product portfolio for product dissemination; 2. Develop LEHD marketing pamphlets for Census and States; 3. Design and implement Ferret-based data dissemination capabilities from Census to States; 4. Coordinate development of dissemination capabilities with Simulated Data Project team; 5. Design and implement Ferret-based information dis-