1. Local Employment Dynamics: Partnership, Employment, and Public-Use Data Earlene Dowell and Heath Hayward LEHD Program Center for Economic Studies U.S. Census Bureau

2. Outline  Welcome and Introduction to LED  Basics of LED Data: LODES and QWI  QWI Explorer (Demo and Example Scenarios)  OnTheMap (Demo and Example Scenarios)  Advanced Scenarios/Coming Soon/Questions and Feedback 2

3. Local Employment Dynamics Partnership  Then:  Begun in late 1990s with a few states  Goal to generate new labor market statistics from existing records (UI and firm info)  Now:  53 partner states/territories  3 data products  4 web-based data tools  A culture of innovation and cost savings 3

4. What’s In a Partnership?  Sharing of costs (and data)  Breadth of expertise  Diversity of ideas and needs  National scale and local knowledge  But it requires commitment and maintenance… 4

5. Building on State Inputs  We combine state records with other admin/census/survey data from the Census Bureau and other Federal agencies  We can then create public statistics on:  Firms & Establishments  Jobs & Workers  By Firm and Person Characteristics  Without new respondent burden 5

6. Admin. Records & LED Infrastructure QCEW* Economic Survey Data Business Register UI* Wage Records Federal Records Demographic Census/Survey Data OPM* Public-Use Data Products… QCEW = Quarterly Census of Employment and Wages UI = Unemployment Insurance OPM = Office of Personnel Management Job data cover over 95% of private employment and most state, local, and federal jobs Data availability: 1990-2014, start year varies by state, rolling end date

7. Person Data Protecting Personal Information  Some records enter the Census Bureau with SSNs, some with other personally identifiable information  First, the SSN is replaced with a “protected identification key” (PIK).  The PIK is used for all further matching. 7

8. Costs & Benefits, Risks & Rewards  Universe-level data, but limited variables  Less expensive than surveys, but less control  More work cleaning the data, but if the data are being used elsewhere, then they are already pretty clean.  Large frame micro-data linkages allow innovative data products  Cutting-edge confidentiality protection is key 8

9. LED Data Products  Quarterly Workforce Indicators (QWI)  Employment, Job Creation, Job Destruction, Hires, Separations, Turnover, Earnings  By industry, county, and worker characteristics  LEHD Origin Destination Employment Statistics (LODES)  Employment and Workplace-Residence Connections  Detailed geography + firm/worker characteristics  Job-to-Job Flows (Beta)  Data being released over coming months 9

10. Data Product Infrastructure – Primary Unit of Analysis: Job  Association of: Worker–Employer–Year–Quarter  Workers can have multiple jobs within a quarter  “Primary Job” (greatest earnings) - not defined separately in QWI, but is in LODES/OnTheMap  In contrast, most other surveys and censuses are:  Household-based (ACS, CPS, Decennial), or  Employer-based (QCEW, Current Employment Statistics)  Advantage of job-based frame – can produce tabulations by both worker and firm characteristics 10

11. Core Data Input: UI Earnings Records  UI = Unemployment Insurance  Record of individual earnings for covered jobs  Administrative wage records, not UI claims data  Collected for operation of state UI program  UI benefits are based on historical earnings  Includes:  Total quarterly earnings for each job  Firm identifier = State UI account number (SEIN)  Worker identifier = Protected Identification Key (PIK)  Census identifier based on SSN 11

12. Job Coverage in UI Earnings Data  Most private sector jobs covered  For-profit and not-for-profit classified together (as per QCEW standard)  State and local government also in system, though some reporting inconsistencies  Federal worker data from Office of Personnel Management (OPM) not yet available in QWI (have been incorporated into LODES/OnTheMap)  Self-employed not available  Massachusetts data not available yet 12

13. Additional Data Inputs  UI wage records are linked to a variety of other data sources  Sources of establishment information:  Quarterly Census of Employment and Wages (QCEW)  Business Dynamics Statistics (BDS)  Sources of demographic information:  Decennial Census  Federal Tax Records  Social Security Administration Records  Other census and administrative records  This additional information enables tabulations by detailed worker and firm characteristics 13

14. Creating LODES Data: Sources  Confidential Data Sources:  UI Wage Records  QCEW  Other Censuses and Surveys  StARS  Public Use Data Sources  2000 Decennial (SF1, CTPP)  TIGER/Line Shapefiles  Previous year of OnTheMap LED Infrastructure Files

15. Creating LODES Data: Processing  Unlike QWI, much of the processing is performed at the national level, starting with the definition of Primary/Dominant Job.  Also unlike QWI, data for previous years are not reproduced with every production cycle – LODES is constrained by a “confidentiality budget”  One year is processed at a time because of the requirement of a previous year of data for the synthetic method.

16. Creating LODES Data: Confidentiality Protection  Two main processes are taking place to protect OnTheMap data:  Noise Infusion – Applied to workplace totals.  Synthetic Data Methods – Applied to residential locations. For more information, see: http://lehd.ces.census.gov/led/datatools/doc/OTMSyntheticData%2005262009- jma.pdf http://lehd.ces.census.gov/led/datatools/doc/OTMSyntheticData%2005262009- jma.pdf http://lehd.ces.census.gov/led/datatools/doc/SyntheticDataDiagram%2006082009_JM A.pdf

17. Releasing LODES Data  Public Data Release has two file types:  OD  Connects a home block with a work block.  Gives one count of jobs for each home-work block pair and for each combination of year, job type, and segment variables.  RAC/WAC  Provides totals on residence/workplace side only.  Gives one total for each worker/job characteristic for each combination of year, job type, and segment variables.

18. Releasing LODES Data Jobs are classified by:  Whether the job is a worker’s primary/dominant job.  Whether the job is in the private sector.  Whether the job is sourced from OPM*. The six Job Types are:  All Jobs, Primary Jobs, All Private Jobs, Private Primary Jobs, All Federal Jobs*, and Federal Primary Jobs.* * The Federal Job Types are only broken out in the raw LODES data. Only four Job Types are available in OnTheMap Note: A job in LODES are defined as Beginning of Quarter Employment, which means the worker was employed by the same employer in both the current (2nd) and previous (1st) quarter.

19. Releasing LODES Data  Labor Market Segment  Jobs are aggregated by 10 “segments” determined by a worker’s/firm’s characteristics.  The 10 segments are:  All Workers  Workers by Age (29 or younger; 30-54; 55 or older)  Workers by Earnings ($1,250/mo or less; $1,251/mo to $3,333/mo; greater than $3,333/mo)  Workers by Firm’s Industry (Goods Producing; Trade, Transportation, and Utilities; All Other Services)  OD Data is reported for each segment.  Segments are similar to but not the same as characteristics.

20. Releasing LODES Data: A note about the Industry segments  Industry Segments are build from NAICS Industry Sectors * (“2-digit”)  Goods Producing:  NAICS 11, 21, 23, 33-33  Trade, Transportation, and Utilities:  NAICS 22, 42, 44-45, 48-49  All Other Services  NAICS 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81, 92 * See http://www.census.gov/eos/www/naics/ for more info.http://www.census.gov/eos/www/naics/

21. Releasing LODES Data Characteristics: (available only in RACAC d  3 Age Characteristics – Comparable to Age Segments  3 Earnings Characteristics – Comparable to Earnings Segments  20 Industry Characteristics (NAICS Sectors)  6 Race Characteristics  2 Ethnicity Characteristics  4 Educational Attainment Characteristics  2 Sex Characteristics

22. Releasing LODES Data  OD vs. RAC/WAC: What’s Available?  OD  Count for Year × Job Type × Segment  Residence Area and Workplace Area Specified  RAC/WAC  Count for Year × Job Type × Segment × Characteristic  Only one of Residence Area or Workplace Area Specified  Example  OD: Count for 2011, All Jobs, 29 or younger, with home and work block specified.  RAC: Count for 2004, Primary Jobs, Goods Producing Industries, $1250/mo or less, with only home block specified.

23. Releasing LODES Data: Downloadable Data  Main vs. Aux  To make it easier for users to limit the amount of data they need to download, the files have been split by state of employment and state of residence.  Main: Jobs of workers who are employed in the state and reside in the state.  Aux: Jobs of workers who are employed in the state and reside out of state.

24. Releasing LODES Data: Downloadable Data

25.  LODES data is available on the LED website: http://lehd.ces.census.gov/data/#lodes http://lehd.ces.census.gov/data/#lodes  LODES data is also available for bulk download through the following http site: http://lehd.ces.census.gov/data/lodes/ http://lehd.ces.census.gov/data/lodes/  See OnTheMap Data Technical Document (http://lehd.ces.census.gov/data/lodes/LODES7/LODESTe chDoc7.0.pdf) for more detail.http://lehd.ces.census.gov/data/lodes/LODES7/LODESTe chDoc7.0.pdf

26. QWI Measures  32 indicators on:  Employment  Counts of jobs (Individual)  Hiring and Separation counts and rates (Individual)  Job Creation and Destruction (Firm)  Earnings  Average earnings for selected job histories  Total earnings  Some indicators are special-purpose measures used in calculation of various rates  Files and applications organized by state 26

27. QWI Aggregation Levels: Firm  Firm-level characteristics:  Based on national-level firm, sourced from Business Dynamics Statistics (BDS)  Firm Age (years)  0-1, 2-3, 4-5, 6-10, 11+  Firm Size (employees)  0-19, 20-49, 50-249, 250-499, 500+  Firm Age and Size available only for private ownership  Reduced detail on geography/industry tabulations (3- and 4- digit NAICS are only available for state-level totals) 27

28. QWI Aggregation Levels: Establishment  Establishment-level characteristics:  Geography  State totals  County, Metro, Workforce Investment Board (WIB) areas  Industry  All industries  NAICS Sectors, Sub-sectors (3-digit), Industry groups (4-digit)  Ownership  All (Public + private)  Private-only  All crossings of these characteristics reported (with a few exceptions for firm age and firm size) 28

29. QWI Aggregation Levels: Employee Age/Sex  Age (years)  14-18, 19-21, 22-24, 25-34, 35-44, 45-54, 55-64, 65- 99  Sex  Male, Female  We use the age categories specified in the Workforce Investment Act (WIA)  Data comes from a variety of sources (Decennial Census, surveys and administrative records) 29

30. QWI Aggregation Levels: Employee Education  Education categories:  Less than high school  High school or equivalent, no college  Some college or Associate degree  Bachelor’s degree or advanced degree  Educational Attainment Not Available (age 24 or younger)  Valid only for individuals age 25 and up  Reflects person’s maximum education level  Crossed by Sex in QWI tabulations  Sourced from decennial census where available; otherwise, imputed using multinomial logit model 30

31. QWI Aggregation Levels: Employee Race/Ethnicity  Tabulated according to categories defined by the Office of Management and Budget:  Race  White alone  African-American or Black alone  Asian or Pacific alone  Native Hawaiian or Other Pacific Islander alone  American Indian or Alaska Native alone  Two or More Races  Ethnicity  Hispanic or Latino  Not Hispanic or Latino  Race and Ethnicity are cross-tabulated in public QWI data  Use data from Decennial Census where available; otherwise, impute using Census file provided from Social Security Administration (SSA) 31

32. Concept: Employment History  Jobs are linked across time  Diagram illustration:  Diagram Legend:  Reference quarter t  Earlier quarters (-), Later quarters (+)  RED: positive earning  BLACK: zero earning  COMBINED: earning in ANY ONE of the quarter  GREY: quarters not referenced 32 Measure-5-4-3-2 t +1+2+3+4+5

33. Overview: Employment Measures Measure-5-4-3-2t+1+2+3+4+5 Count of Jobs (Flow Employment) EmpTotal Beginning-of-quarter Employment Emp End-of-quarter Employment EmpEnd Full-quarter (Stable) Employment EmpS Full-quarter (Stable) Employment, previous quarter EmpSpv 33

34. Overview: Earnings Measures for Employment Counts Measure-5-4-3-2t+1+2+3+4+5 Average Monthly Earnings for Beginning-of-quarter Jobs EarnBeg$ Average Monthly Earnings for Full-quarter Jobs EarnS$ Total Reported Earnings Payroll$ 34  All income amounts reported for UI wages  Mix of full-time and part-time jobs (not adjusted for hours)  Average earnings are based on quarterly wage record, divided by 3 (monthly estimate)

35. Overview: Worker Flows Measures – Accessions Measure-5-4-3-2t+1+2+3+4+5 All Hires (Accessions) HirA New Hires HirN Recalls HirR End-of-quarter Hires HirAEnd Full-quarter Hires HirAS New Hires into Full-quarter Employment HirNS 35

36. Overview: Worker Flows Measures – Separations 36 Measure-5-4-3-2t+1+2+3+4+5 Separations Sep Beginning-of-quarter Separations SepBeg Separations from Full-quarter Employment SepS Separations from Full-quarter Employment, next quarter SepSnx

37. Overview: Earnings for Worker Flow Measures 37 Measure-5-4-3-2t+1+2+3+4+5 Average Monthly Earnings for Hires to Full-Quarter Employment EarnHirAS $ Average Monthly Earnings for New Hires to Full-Quarter Employment EarnHirNS $ Average Monthly Earnings for Separations from Full-Quarter Employment EarnSepS $  All are based full-quarter counts, which are less biased by jobs that began or ended part-way through the quarter  Average earnings are based on quarterly wage record, divided by 3 (monthly estimate)

38. Hiring Rate 38 Measure-5-4-3-2t+1+2+3+4+5 HirAEnd Emp EmpEnd

39. Separation Rate 39 Measure-5-4-3-2t+1+2+3+4+5 Emp EmpEnd SepBeg

40. Turnover Rate 40 Measure-5-4-3-2t+1+2+3+4+5 HirAS SepSnx EmpS

41. Overview: Firm-Based Measures: Flows, Creations, Destructions MeasureDescriptionCategory FrmJbGnJob Creation (Gain) Creations FrmJbGnSFull-quarter Job Creation FrmJbLsJob Destruction (Loss) Destructions FrmJbLsSFull-quarter Job Destruction FrmJbCNet Job Flows Flows FrmJbCSNet Full-quarter Job Flows HirAEndReplReplacement Hires HirAEndReplRReplacement Hire Rate 41

42. Measuring Firm-Level Worker Flows  Firm job flows display dynamics at the establishment level  Job creation  Establishments that grow over the quarter  Establishment births  Job destruction  Establishments that shrink over the quarter  Establishment deaths  Net Job Change = Job Creation – Job Destruction 42

43. Firm Job Flow Measures (1 of 2)  Calculated at establishment level  Job Gain (FrmJbGn)  Difference between End-of-quarter and Beginning-of-quarter employment (EmpEnd - Emp)  zero if negative  Job Loss (FrmJbLs)  Difference between Beginning-of-quarter and End-of-quarter employment (Emp - EmpEnd)  zero if negative  Net Job Flows (FrmJbC)  Difference between End-of-quarter and Beginning-of-quarter employment (EmpEnd - Emp)  Can be positive (net job creation) or negative (net job destruction) 43 -5-4-3-2 t +1+2+3+4+5 Emp EmpEnd

44. Firm Job Flow Measures (2 of 2)  Full-Quarter measures are defined similarly:  Full-Quarter Job Creation (FrmJbGnS)  Difference between Full-Quarter employment (EmpS - EmpSpv)  zero if negative  Full-Quarter Job Destruction (FrmJbLsS)  Difference between Full-Quarter employment (EmpSpv - EmpS)  zero if negative  Full-Quarter Net Job Flows (FrmJbCS)  Difference between Full-Quarter employment (EmpS - EmpSpv)  Can be positive (net job growth) or negative (net job destruction) 44 -5-4-3-2 t +1+2+3+4+5 EmpSpv EmpS

45. Replacement Hiring 45

46. QWI Estimates: Source of Replacement Hires 46 Replacement Hires Data: QWI pooled across all available states

47. Firm Job Flows: Be Careful about Aggregation  Note that for worker demographic categories (such as age and sex), the published net job flows for the subcategories will sum to the margin  But for gross Job Creation and gross Job Destruction, this is not true  (Job Creation for men) + (Job Creation for women) does not equal (total Job Creation)  Why? Consider this example: A job could be created at a firm and filled by a woman, while another job at the same firm is destroyed, previously filled by a man  Job Creation and Job Destruction should be 0, since these are defined at the firm level. Summing across characteristics would produce the wrong totals.  QWI Explorer has built-in rules to prevent these incorrect aggregations MenWomenIncorrect Total (sum across characteristics) Correct Total Job Creation0110 Job Destruction1010 Net Job Flows+100 47

48. Noise Infusion (“Fuzzing”)  Why infuse noise into data?  Reduce the amount of cell suppression while preserving confidentiality and analytic validity  Properties of noise  Every data item is distorted by a minimum amount  For a given workplace, data are always distorted in the same direction, by the same percentage in every period and release of QWIs  When aggregated, the effects of the distortion cancel out for the vast majority of the estimates  QWI statistics are flagged when the value is significantly distorted  See infrastructure document, section 6, for more details 48

49. QWI Status Flags Each data item in the QWI is assigned a status flag. Status flags indicate why data items are missing, or whether they are significantly distorted:  -2 No data available in this category for this quarter  -1 Data not available to compute this estimate  1 OK, fuzzed value released  5 Value suppressed because it does not meet US Census Bureau publication standards  9 Data significantly distorted, distorted value released  10 Aggregate of cells, no significant distortion  11 Aggregate of cells not released because component cells do not meet US Census Bureau publication standards  12 Aggregate of cells, some of which have significantly distorted data *Note that suppression does not mean zero 49

50. LEHD Processing: Weighting  QWI Beginning-of-Quarter Employment is benchmarked against QCEW Mon1 employment  Firm-level weights (within bounds) are applied to adjust employment towards Mon1 employment  Secondary weights are applied to match statewide private-only employment  Weights are calculated at ECF stage, applied at QWI 50

51. Choosing Among LED Data Products Data Product Why Choose It?Potential Drawbacks QWIYou need employment, hires, separations, turnover, or earnings by detailed industry or person characteristics, quarterly time resolution, or a relatively short data lag No geography below county; no residential information LODESYou need employment for detailed or customized geography, or you need the residential patterns of the workforce Annual time resolution; less detailed firm/person characteristics; significant data lag (temporary) J2JYou need to understand transitions of workers among jobs Data product still under development* *But feedback is welcome. 51

52. Choosing Data 1 When should I be interested in using LED data compared to other available statistics? Suppose I’m primarily interested in Employment Do I need the latest national estimate available? Current Employment Statistics (CES) Employment by industry - ‘the payroll survey’ Current Population Survey (CPS) Employment status and demographics - ‘the household survey’ Some sub-state geographies are available concurrently through Local Area Unemployment Statistics (LAUS) ? 52

53. But suppose I need either sub-national employment data or statistics by detailed industry: Quarterly Census of Employment and Wages (QCEW) Employment by detailed industry, sub-state geography and better employment coverage (6-month lag) Quarterly Workforce Statistics (QWI) Employment by detailed industry, sub-state geography, and worker demographics (age, sex, education, race) and fewer cell suppressions than the QCEW (9-month lag) American Community Survey (ACS) Employment status by more sub-state geographies than CPS/LAUS (9-month lag) LODES/OnTheMap Employment at the block-level (>1 year lag) County Business Patterns (CBP) Employment at the zipcode-level (>1 year lag) Choosing Data 2 ? 53

54. Suppose I’m primarily interested in Hires/Separations/Turnover Do I need the most current national data (1 month lag) or do I want to differentiate between quits and layoffs? Job Openings and Labor Turnover Survey (JOLTS) Do I need sub-national data (state/county), data by worker demographics, or for detailed industries? Quarterly Workforce Statistics (QWI) Choosing Data 3 54

55. Suppose I’m primarily interested in Wages State and Regional Wage Information by Occupation? Occupational Employment Statistics (OES) Wages by Detailed Industry and Geography? Quarterly Census of Employment and Wages (QCEW) Wages by Detailed Industry and Geography and by Worker Demographics? Starting Wages for New Hires by Industry and Geography? Quarterly Workforce Statistics (QWI) Choosing Data 4 55

56. Choosing Data 5 Suppose I’m primarily interested in Commuting Transportation mode, time to work, work at home? American Community Survey (ACS) Commuting Data Commuting for Detailed/Custom Areas or Multiple Jobholders? LEHD Origin-Destination Employment Statistics (LODES) 56

57. Enough Slides! 57 Let’s open up the LED Applications and get our hands on the data

58. Quarterly Workforce Indicators (QWI) 58 Detailed workforce dynamics, by worker characteristics and firm characteristics Popular uses: Local workforce demographics Local industry workforce trends Workforce turnover, job creation and destruction

59. Can see workforce composition by detailed firm characteristics Such as what share of the workforce at startup firms is female? Quarterly Workforce Indicators (QWI) 59

60. Job-to-Job Flows  Types of questions that can be answered:  How did the growth and decline in construction jobs in the last decade impact the ability of low- wage workers to move to better jobs?  Where are North Dakota’s oil boom workers coming from?  Download data from http://lehd.ces.census.gov/data/j2j_beta.html http://lehd.ces.census.gov/data/j2j_beta.html 60

61. Job-to-Job flows 61

62. Job-to-Job flows 62

63. QWI: Combining More than One Indicator to Create New Insights 63

64. QWI: Combining More than One Indicator to Create New Insights 64

65. QWI: Combining More than One Indicator to Create New Insights 65

66. Public Data Tools  All tools are free and available 24/7.  Live Demonstrations of  QWI Explorer  LED Extraction Tool (QWI)  OnTheMap  OnTheMap for Emergency Management 66

67. Web Addresses for Tools  QWI Explorer  http://qwiexplorer.ces.census.gov/ http://qwiexplorer.ces.census.gov/  LED Extraction Tool  http://ledextract.ces.census.gov/ http://ledextract.ces.census.gov/  OnTheMap  http://onthemap.ces.census.gov/ http://onthemap.ces.census.gov/  OnTheMap for Emergency Management  http://onthemap.ces.census.gov/em.html http://onthemap.ces.census.gov/em.html 67

68. What Questions Can LED Answer? Which industries in my region are hiring older workers? Younger workers? Workers without a high school diploma? What do these jobs pay? 68

69. Questions Where do the workers employed downtown live? What share of workers employed in my community also live there? What share of workers with a short commute have a college degree? 69

70. Questions Where did ND’s oil and gas workers come from (industry/geography)? Where did MI’s auto workers go to (industry/geography)? 70

71. QWI Explorer 32 Quarterly Workforce Indicators Flexible Pivot Table/Chart interface Data on detailed interactions between firms and workers include employment, employment change (individual and firm), and earnings Analyze/report by worker demographics: age, earnings, race, ethnicity, educational attainment, and sex Analyze/report by firm characteristics: NAICS classification (sector, 3, 4), firm age, and firm size Quarterly data very current (9-12 months old) 49 states available (plus DC, MA coming soon) 71

72. OnTheMap: Where workers live, and where they work This map shows LODES data of where residents of Vancouver, Washington work Popular uses: local economic development business site selection emergency planning 72

73. OnTheMap Where do workers live? Where do residents work? What are the commuter flows of a particular area? Analyze/report by worker demographics: age, earnings, race, ethnicity, educational attainment, and sex Analyze/report by firm characteristics: NAICS Sector, firm age, and firm size 2002-2011 annual data 49 states available (plus DC, MA coming soon) User-selected areas Based on Census Blocks Disclosure protection Flexible Inputs/Outputs Recognized by United Nations as a major U.S. statistical innovation 73

74. OnTheMap: Block- level employment detail 74

75. Hurricanes, Floods, Winter Storms Disaster Areas Wildfires Demographic & Economic Data Comprehensive Reports Real-time Data Updates Easy-to-use & Interoperable Historical Event Archive Flexible Analyses & Visualizations New Public Data Service for Emergency Preparedness & Response OnTheMap for Emergency Management

76. 76 Hurricane Sandy - October 25, 2012 76

77. Real World Examples Some brief examples of from our users… 77

78. LODES: An Examination of Maryland Enterprise Zones 78

79. LODES: An Examination of Maryland Enterprise Zones 79

80. QWI: Education and Employment in Utah 80

81. QWI: A Comparison of I-95 and I-270 Corridors 81

82. Kansas City, MO – Earnings Tax  Civic Council of Greater Kansas City  1% Earnings Tax on gross compensation for all those living or working in KCMO  In 2010 & 2011, ballot challenges to the tax were brought to voters  LODES and QWI from LED helped the community focus on “issues and outcomes” and showed “tax and benefits are shared with non-KCMO residents.” 82

83. Takeaways  The LED Partnership provides unique data products and tools at a relatively low cost  LED data products (QWI, LODES, J2J) can give insight into local and regional economies and labor markets  LED’s web tools provide free, 24/7 access to a basic analytical platform for the data 83

84. Thank You!  Local Employment Dynamics  lehd.ces.census.gov  Contact  Patrick.Hayward@census.gov Patrick.Hayward@census.gov  Earlene.KP.Dowell@census.gov Earlene.KP.Dowell@census.gov  Tools  QWIExplorer.ces.census.gov  LEDExtract.ces.census.gov  OnTheMap.ces.census.gov  OnTheMap.ces.census.gov/em.html 84