# Presentation: 394034

## Looking Ahead: Research and Development at LEHD 

- Erika McEntarfer
- Kristin Sandusky
- Fredrik Andersson
- LEHD, U.S. Census Bureau
- 2007 State Workshop
- Brookings Institution, Washington DC

## National QWIs with Universal Coverage: Coming Soon.

- New York & Ohio bring LEHD much closer to covering entire US.

- Self-employed

- Federal Workers

## National QWIs with Universal Coverage: Why it is important

- Brings us closer to our goal of longitudinal data on entire US workforce:
- • Examine employment flows across state lines.
- • Account for flows of workers in and out of self-employment.
- • Identify flows of workers in and out of the federal workforce.

## Overview of this session

- Two R&D projects in depth:
- • Self-Employed Workers
- • Daytime Population Estimates
- Overview of Other Projects.
- • Adapting the current production system to a national frame
- • Employer-to-Employer Flows
- • Integrating OPM federal worker file

## Two Current Research and Development Projects:

- • Integration of Self-Employed Workers into the QWI
- • Estimating the Daytime Population of the U.S.

## Expanding QWI Coverage:Including the Self-Employed

## Why are the Self-Employed Important?

*Large Group:*
- 		Over 9% of workforce has some ties to self-	employment
*Changing economy:*
- 		Increased numbers of Internet based 	businesses and contract workers
*General Interest:*
*	**	*Universal measures of entrepreneurship needed

## Finding Data on Self-Employed:From Business Data?

- Business Register
- 	Bureau’s sample frame for censuses and surveys of businesses.
- 	Data from tax reports to IRS for businesses
-     Data are annual.

## Moving from Annual Business Data to Quarterly Worker Data:

- First Key Challenge:  Who is Self-Employed?* *
***1. Partnerships and Corporations***
- 		Difficult to link to a set of individuals.
***2. Sole Proprietors***
- 	Data from IRS 1040 Schedule C – Business ID is SSN
- 	    *Not married and filing jointly*  - SSN is sole prop
- 	    *Married joint filers* - determine which spouse is self-	employed using supplemental data from SSA
*	**	*

## Moving from Annual Business Data to Quarterly Worker Data:

- Second  Key Challenge:  Who are Active Sole Proprietors Each Quarter?* *
*	**	*
- Must impute quarterly self-employment pattern
- Many possible sources of data for imputation model among household surveys

## Moving from Annual Business Data to Quarterly Worker Data:

- Third Key Challenge:  Measuring Earnings* *
*1. Annual Self-employment earnings sometimes available*
*2. In other cases, we know annual receipts (after removing spending on payroll and costs of good sold)*
- 3.  In these cases, must impute quarterly self-employment earnings
*	**	*

## An Early Look....

- Select two years that have supplemental SSA data:  1992 and 1997 (economic census years)
- Link via SSN (or PIK) to annual wage and salary data for a few states.
- Use linked data to answer some interesting questions

## Example 1:

- Will adding data on self-employment spells impact our measures of labor market transitions?

## What Fraction Leave Labor Force?With and Without Self-Employment

## Example 2:

- Among wage and salary workers, what fraction "try out" self employment?
- Among workers “trying out” self-employment, what fraction transition to full self-employment?
- How do these fractions vary by age?

## What Fraction “Try Out” Self-Employment?

## Among Workers Trying Out Self-Employment, What Fraction Move to Full Self-Employment?

## What Next?

- Work in progress but key steps:
- 	1. Identify who is self-employed each year
- 	2. Impute quarterly self-employment pattern
- 	3. For these, impute quarterly self-employment 	earnings when not available
- 	4.  Integrate information into existing data on 	employment histories
- Final Note:
- 	Adding Self-employed to QWIs may provide clearer picture of workforce dynamics, especially for certain groups.

## The Construction of Daytime Employment Estimates Using LEHD Data

- Fredrik Andersson
- February 2, 2007

## The Composition of the Night and Daytime Population in D.C.

- 571,000

- 983,000 (+73%)

## The Variation Matters!

- The expansion and contraction of the population during the 24 hours of a day has important implications for many planning purposes:
- - Transportation
- - Community
- - Economic
- - Disaster and relief operations

## Purpose of Presentation

- “To demonstrate how data from the LEHD program can be integrated with survey data to provide estimates of high geographic resolution of the daytime working population”
- The methodology addresses two issues:
- How to identify daytime employment in LEHD Data
- How to deal with non-covered sectors in LEHD data
- Some preliminary empirical results

## The Census Bureau’s Daytime Population Project

- Census Bureau produced estimates of the 2000 daytime population at the county and place level
- Daytime population = Residents + incommuters – out commuters
- The Daytime Population Project’s aim is to enhance existing estimates in several ways:
- Broadening the scope of the daytime population
- Creating estimates at the tract level
- Produce estimates in inter-decennial years
- Improve estimates of the daytime working population
- Initial demonstration for four states: Illinois, Missouri, North Carolina and South Carolina

## The Daytime Working Population

- Workers constitute a key component of the daytime population:
- - in terms of its sheer size
- - in terms of its intra-daily geographic mobility
- No single ideal data
- Decennial too infrequent
- ACS do not provide estimates by detailed geography
- Administrative LEHD data do not identify *when *workers are employed
- Definition: *Worked last week, arriving at work between hours of 5 am to noon, who are 16+ years old and not enrolled in school or college level*

## Methodology Overview

- - Integrate 2000 Decennial Long Form and LEHD data
- - Use statistical relationships between daytime employment status and observable LEHD characteristics in 2000 to impute daytime employment status for the broader LEHD population in different periods
- Adjust daytime employment estimates for non-covered sectors using survey data benchmarks

## Combining LEHD data with Decennial Information

- For approximately one in six of all workers with positive earnings in LEHD 2000Q1 we can obtain additional information from the Decennial
- About 80-85% are “Decennial Employed”
- About 60-65% are “Decennial Daytime Employed”

## Substantial variation in Daytime Employment

-  Calls for a more sophisticated imputation methodology

## Imputation model

- Statistical model to impute daytime employment status at the person level conditional on:
- Age categories
- Earnings categories
- Gender
- County of employment
- Employment status
- Industry
- Possible to aggregate daytime population estimates to any geographical level

## Preliminary Daytime Worker Estimates (in thousands)

## Next Steps

- From prototype to production
- Phase 1: Developing proof of concept
- Phase 2: Development of prototype estimates
- Phase 3: Production?
- Methodology development
- Flow approach
- Extending the daytime population definition
- Data dissemination tools
- Integration of daytime worker estimates in the QWIs/OnTheMap?

## Other Current Data Innovations and Research at LEHD

- • Integration of federal workers in the LEHD data infrastructure
- • Employer-to-Employer Flows
- • Development of national frame for QWI production

## Adding Federal Workers

- LEHD will soon receive employment and wage data on federal workers from the U.S. Office of Personnel Management (OPM).
- • This is a transactional file of personnel actions (hires, promotions, transfers, terminations, etc.) for federal workers.
- Integrating OPM file into QWIs.
- • Develop a data infrastructure from OPM data that mimics that of the underlying UI wage records and ES202 firm data.
- • Geography information on file is limited.

## Employer-to-Employer (EE) Flows

- QWIs currently provide separations and hires.
- Clearly many of these flows are movements between jobs.
- Would like to characterize job paths of workers from job to job.
- What we find:
- • Almost 4% of workers experience an EE flow every quarter.
- • About 30% of separations are direct flows into another job.
- • EE flows are highly procyclical.
- • EE flows are associated with strong earnings gains, particularly for younger workers but for mid-age workers as well.
- Next steps:
- Research possible integration of EE flows into QWI.

## Redesign of Production System to National Frame

- Current production system is based on state-based data universe.
- Moving to a national frame poses a number of R&D challenges.
- 	Tracking of workers across states - OnTheMap.
- Tracking of workers across UI and non-UI employment universes.
- Increased computational burden – more workers, more establishments.

## Conclusions and Next steps:

- Addition of new partner states and new data sources will bring us much closer to our goal of constructing a national longitudinal database on the US workforce.
- Next steps toward that goal:
- •  Integration of federal and self-employed workers into production.
- •  Adapting production system to national data frame.