National QWI qwi_national_wia Virtual RDC Flavio Stanchi Lars Vilhuber Cornell NSF-Census Research Network Cornell NCRN Project 30 January 2015 Labor Dynamics Institute, Cornell University, Ithaca NY CED2AR, Version 1.0 The Comprehensive Extensible Data Documentation and Access Repository 2.5 The Comprehensive Extensible Data Documentation and Access Repository 2.5 The Comprehensive Extensible Data Documentation and Access Repository 2.5 National Science Foundation (NSF) 1131848 NSF-Census Research Network - Cornell node Cornell Institute for Social and Economic Research Labor Dynamics Institute 30 January 2015 Revision 1322 Comprehensive Extensible Data Documentation and Access Repository. Codebook for the National QWI [Codebook file]. Cornell Institute for Social and Economic Research and Labor Dynamics Institute [distributor]. Cornell University, Ithaca, NY, 2013 qwi_national_wia National Quarterly Workforce Indicators national-qwi Cornell University. Labor Dynamics Institute. Labor Dynamics Institute John Abowd and Lars Vilhuber Ithaca, NY, USA National Science Foundation 0922005 and 0922494 Cornell University 16 January 2012 r2254 John M. Abowd and Lars Vilhuber, "National Quarterly Workforce Indicators, r2254," Cornell University, Labor Dynamics Institute [distributor], Ithaca, NY, USA, [Computer file], 2012 labor dynamics

The Quarterly Workforce Indicators are local labor market data produced and released every quarter by the United States Census Bureau. Unlike any other local labor market series produced in the U.S. or the rest of the world, the QWI measure employment flows for workers (accession and separations), jobs (creations and destructions) and earnings for demographic subgroups (age and sex), economic industry (NAICS industry groups), and detailed geography (county, Core-Based Statistical Area, and Workforce Investment Area, as well as experimental, unreleased block-level estimates). The current QWI data cover 47 states and about 98% of the private workforce in each of those states.

John Abowd and Lars Vilhuber have used the existing public-use data (and only those public-use data) to construct the first national estimates. The national estimates are an important enhancement to existing series because they include demographic and industry detail for both worker and job flows compiled from data that have been integrated at the micro-level by the Longitudinal Employer-Household Dynamics Program at the Census Bureau. The research paper (see below) compares the new estimates to national data published by the BLS from the Quarterly Census of Employment and Wages and the Business Employment Dynamics series.

United States of America National Jobs All private employment Imputation and aggregation Quarterly Workforce Indicatorshttp://lehd.ces.census.gov/data/ 3 The National QWI are constructed using only public-use data. No confidential data was used. The U.S. Census Bureau was not involved in the data creation process other than through their provision of the Quarterly Workforce Indicators. ldi@cornell.edu Please use the following language in published work that make use of this dataset: "The creation of the National QWI by John M. Abowd and Lars Vilhuber was made possible through NSF Grants #0922005 and #0922494. Access to the National QWI was made possible through NSF Grant #0922005 and #1131848 ." Please also cite Abowd and Vilhuber (2012) and use the bibliographic citation for the dataset provided in this document. The National Quarterly Workforce Indicators dataset is a research product, not an official U.S. Census Bureau product. Elements flaged with this access level can be released Elements flaged with this access level cannot be released The home page of the National QWI can be found athttp://www2.vrdc.cornell.edu/news/data/qwi-national-data/. John M. Abowd and Lars Vilhuber, "National estimates of gross employment and job flows from the Quarterly Workforce Indicators with demographic and industry detail," Journal of Econometrics, vol. 161, iss. 1, pp. 82-99, 2011.http://dx.doi.org/10.1016/j.jeconom.2010.09.008. John M. Abowd and Lars Vilhuber, "National estimates of gross employment and job flows from the Quarterly Workforce Indicators with demographic and industry detail (with color graphs)," Center for Economic Studies, U.S. Census Bureau, Working Papers 10-11, 2010.http://ideas.repec.org/p/cen/wpaper/10-11.html qwi_national_wia.dta 38340 190 Stata qwi_national_wia.sas7bdat 38340 190 SAS qwi_national_wia.csv 38340 190 CSV NAICS Sector 38340 0 North American Industry Classification System (NAICS) Sector. 00 All NAICS Sectors 1917 11 Agriculture, Forestry, Fishing and Hunting 1917 21 Mining, Quarrying, and Oil and Gas Extraction 1917 22 Utilities 1917 23 Construction 1917 31-33 Manufacturing 1917 42 Wholesale Trade 1917 44-45 Retail Trade 1917 48-49 Transportation and Warehousing 1917 51 Information 1917 52 Finance and Insurance 1917 53 Real Estate and Rental and Leasing 1917 54 Professional, Scientific, and Technical Services 1917 55 Management of Companies and Enterprises 1917 56 Administrative and Support and Waste Management and Remediation Services 1917 61 Educational Services 1917 62 Health Care and Social Assistance 1917 71 Arts, Entertainment, and Recreation 1917 72 Accommodation and Food Services 1917 81 Other Services (except Public Administration) 1917 Gender 38340 0 Gender code: 0 = Male and female 1 = Male 2 = Female 0 Male and female 12780 1 Male 12780 2 Female 12780 Age group 38340 0 Age group code (Workforce Investment Act). A00 Age 14-99 4260 A01 Age 14-18 4260 A02 Age 19-21 4260 A03 Age 22-24 4260 A04 25-34 4260 A05 Age 35-44 4260 A06 Age 45-54 4260 A07 Age 55-64 4260 A08 Age 65-99 4260 Year 38340 1993 2160 1994 2160 1995 2160 1996 2160 1997 2160 1998 2160 1999 2160 2000 2160 2001 2160 2002 2160 2003 2160 2004 2160 2005 2160 2006 2160 2007 2160 2008 2160 2009 2160 2010 1620 Quarter 38340 0 1 9720 2 9720 3 9720 4 9180 YYYYQq (year quarter) 38340 0 1993Q1 540 1993Q2 540 1993Q3 540 1993Q4 540 1994Q1 540 1994Q2 540 1994Q3 540 1994Q4 540 1995Q1 540 1995Q2 540 1995Q3 540 1995Q4 540 1996Q1 540 1996Q2 540 1996Q3 540 1996Q4 540 1997Q1 540 1997Q2 540 1997Q3 540 1997Q4 540 1998Q1 540 1998Q2 540 1998Q3 540 1998Q4 540 1999Q1 540 1999Q2 540 1999Q3 540 1999Q4 540 2000Q1 540 2000Q2 540 2000Q3 540 2000Q4 540 2001Q1 540 2001Q2 540 2001Q3 540 2001Q4 540 2002Q1 540 2002Q2 540 2002Q3 540 2002Q4 540 2003Q1 540 2003Q2 540 2003Q3 540 2003Q4 540 2004Q1 540 2004Q2 540 2004Q3 540 2004Q4 540 2005Q1 540 2005Q2 540 2005Q3 540 2005Q4 540 2006Q1 540 2006Q2 540 2006Q3 540 2006Q4 540 2007Q1 540 2007Q2 540 2007Q3 540 2007Q4 540 2008Q1 540 2008Q2 540 2008Q3 540 2008Q4 540 2009Q1 540 2009Q2 540 2009Q3 540 2009Q4 540 2010Q1 540 2010Q2 540 2010Q3 540 QWI: average employment 38340 0 279.806 114334409.266 1557892.319 6133444.348 QWI: Average employment: `(E + B)/2` Note: this variable is not present on the regular QWI, but it's derived from variables on the regular QWI. QWI: average FQ employment 37260 1080 196.445 97690818.555 1324389.545 5239820.185 QWI: average full-quarter (FQ) employment: `(F_{t-1} + F_t)/2` Note: this variable is not present on the regular QWI, but it's derived from variables on the regular QWI. QWI: FQ employment 37260 1080 175.089 97861311.012 1326172.167 5246023.225 The concept of full-quarter employment estimates individuals who are likely to have been continuously employed throughout the quarter at a given employer. An individual is defined as full-quarter employed if that individual has valid UI wage records in the current quarter, the preceding quarter, and the subsequent quarter at the same employer (SEIN). That is, in terms of the point-in-time definitions, if the individual is employed at the same employer at both the beginning and end of the quarter, then the individual is considered full-quarter employed in the QWI system. Full-quarter status is not defined for either the first or last quarter of available data. QWI: FQ accessions 37260 1080 0 12459478.916 151675.636 595422.887 Full-quarter employment is not a point-in-time concept. Full-quarter accession refers to the quarter in which an individual first attains full-quarter employment status at a given employer. Full-quarter employment refers to an estimate of the number of employees who were employed at a given employer during the entire quarter. An accession to full-quarter employment, then, involves two additional conditions that are not relevant for ordinary accessions. First, the individual (PIK) must still be employed at the end of the quarter at the same employer (SEIN) for which the ordinary accession is defined. At this point (the end of the quarter where the accession occurred and the beginning of the next quarter) the individual has acceded to continuing-quarter status. An accession to continuing-quarter status means that the individual acceded in the current quarter and is end-of-quarter employed. Next, the QWI system must check for the possibility that the individual becomes a full-quarter employee in the subsequent quarter. An accession to full-quarter status occurs if the individual acceded in the previous quarter, and is employed at both the beginning and end of the current quarter. QWI: FQ new hires 37260 1080 0 10026858.544 120134.043 474195.784 Full-quarter new hires. Accessions to full-quarter status can be decomposed into new hires and recalls. The QWI system accomplishes this decomposition by classifying all accessions to full-quarter status who were classified as new hires in the previous quarter as new hires to full-quarter status in the current quarter. Otherwise, the accession to full-quarter status is classified as a recall to full-quarter status. QWI: FQ separations 37260 1080 0 11703331.525 145434.791 570784.485 Full-quarter employment is not a point-in-time concept. Full-quarter separation occurs in the last full-quarter that an individual worked for a given employer. As previously noted, full-quarter employment refers to an estimate of the number of employees who were employed at a given employer during the entire quarter. An accession to full-quarter employment, then, involves two additional conditions that are not relevant for ordinary accessions. First, the individual (PIK) must still be employed at the end of the quarter at the same employer (SEIN) for which the ordinary accession is defined. At this point (the end of the quarter where the accession occurred and the beginning of the next quarter) the individual has acceded to continuing-quarter status. An accession to continuing-quarter status means that the individual acceded in the current quarter and is end-of-quarter employed. Next, the QWI system must check for the possibility that the individual becomes a full-quarter employee in the subsequent quarter. An accession to full-quarter status occurs if the individual acceded in the previous quarter, and is employed at both the beginning and end of the current quarter. Full-quarter separation works much the same way. One must be careful about the timing, however. If an individual separates in the current quarter, then the QWI system looks at the preceding quarter to determine if the individual was employed at the beginning of the current quarter. An individual who separates in a quarter in which that person was employed at the beginning of the quarter is a separation from continuing-quarter status in the current quarter. Finally, the QWI system checks to see if the individual was a full-quarter employee in the preceding quarter. An individual who was a full quarter employee in the previous quarter is treated as a full-quarter separation in the quarter in which that person actually separates. Note, therefore, that the definition of full-quarter separation preserves the timing of the actual separation (current quarter) but restricts the estimate to those individuals who were full-quarter status in the preceding quarter. QWI: accessions 38340 0 41.251 32716703.814 371648.689 1467814.611 An accession occurs in the QWI system when it encounters the first valid UI wage record for a job (an individual [PIK]-employer [SEIN] pair). Accessions are not defined for the first quarter of available data from a given state. The QWI definition of an accession can be interpreted as an estimate of the number of new employees added to the payroll of the employer (SEIN) during the quarter. The individuals who acceded to a particular employer were not employed by that employer during the previous quarter, but received at least one dollar of UI-covered earnings during the quarter of accession. QWI: separations 36791 1549 0 32495685.715 373501.149 1475641.134 A separation occurs in the current quarter of the QWI system when it encounters no valid UI wage record for an individual-employer pair in the subsequent quarter. This definition of separation can be interpreted as an estimate of the number of employees who left the employer during the current quarter. These individuals received UI-covered earnings during the current quarter but did not receive any UI-covered earnings in the next quarter from this employer. Separations are not defined for the last quarter of available data. QWI: worker reallocation rate 38340 0 0.0354 5.707 0.638 0.48 Gross worker flows are measured using the Worker Reallocation Rate: `W\R\R_{agkst} =\ (A_{agkst} + S_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `A_{agkst} \equiv` accessions (new hires plus recalls) `S_{agkst} \equiv` separations (quits, layoffs, other) `B_{agkst} \equiv` beginning-of-quarter employment `E_{agkst} \equiv` end-of-quarter employment `W\R\R` measures total accession and separation flows as a proportion of average employment over the quarter in the age, gender, industry and state. The `W\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `W\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `AR_{agkst} =\ A_{agkst}*(2/(B_{agkst} + E_{agkst}))` `SR_{agkst} =\ S_{agkst}*(2/(B_{agkst} + E_{agkst}))` Accessions and separations satisfy the net job flow (`JF_{agkst}`) identity: `JF_{agkst} \equiv \ E_{agkst} - B_{agkst} = A_{agkst} - S_{agkst}` QWI: job reallocation rate 38340 0 0.0227 1.387 0.244 0.158 Gross job flows are measured in similar fashion using the symmetric Job Reallocation Rate: `J\R\R_{agkst} =\ (JC_{agkst} + JD_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `JC_{agkst} \equiv` job creations `JD_{agkst} \equiv` job destructions `J\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of average employment over the quarter in the category. The gross job inflow and outflow rates, the Job Creation Rate (`JCR`) and Job Destruction Rate (`JDR`) , can be defined as additive components of the `J\R\R`: `JCR_{agkst} =\ JC_{agkst}*(2/(B_{agkst} + E_{agkst}))` `JDR_{agkst} =\ JD_{agkst}*(2/(B_{agkst} + E_{agkst}))` Gross job flow measures are defined at an establishment, not job, level. Let `B_{agjt}` be beginning-of-quarter employment for demographic group `ag` at establishment `j` in quarter `t`, and similarly let `E_{agjt}` be end-of-quarter employment for the same category and time period. Then: `JC_{agjt} \equiv \ max(E_{agjt} - B_{agjt}, 0)` `JD_{agjt} \equiv \ max(B_{agjt} - E_{agjt}, 0)` so that, as originally specified by Davis and Haltiwanger, job creations are the change in employment when employment is growing at the establishment and job destructions are the change in employment when employment is shrinking at the establishment. Net job flows also satisfy the identity `JF_{agkst} =\ JC_{agkst} - JD_{agkst}` QWI: excess reallocation rate (churning) 38340 0 0.000876 4.792 0.394 0.351 We define the excess reallocation measured using the symmetric Excess Reallocation Rate (see variables "qwi_wrr" and "qwi_jrr" for further information): `E\R\R_{agkst} =\ W\R\R_{agkst} -\ J\R\R_{agkst}` which measures the difference between gross worker flow and gross job flow rates, sometimes called the labor market ?churning? rate. The `E\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `E\R\R`?specifically, the Excess Inflow Rate (`EIR`) and the Excess Outflow Rate (`EOR`): `EIR_{agkst} =\ AR_{agkst} -\ JCR_{agkst}` `EOR_{agkst} =\ SR_{agkst} -\ JDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `EIR_{agkst} \equiv \ EOR_{agkst}`. QWI: accession rate 38340 0 0.0144 2.739 0.33 0.257 Gross worker flows are measured using the Worker Reallocation Rate: `W\R\R_{agkst} =\ (A_{agkst} + S_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `A_{agkst} \equiv` accessions (new hires plus recalls) `S_{agkst} \equiv` separations (quits, layoffs, other) `B_{agkst} \equiv` beginning-of-quarter employment `E_{agkst} \equiv` end-of-quarter employment `W\R\R` measures total accession and separation flows as a proportion of average employment over the quarter in the age, gender, industry and state. The `W\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `W\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `AR_{agkst} =\ A_{agkst}*(2/(B_{agkst} + E_{agkst}))` `SR_{agkst} =\ S_{agkst}*(2/(B_{agkst} + E_{agkst}))` Accessions and separations satisfy the net job flow (`JF_{agkst}`) identity: `JF_{agkst} \equiv \ E_{agkst} - B_{agkst} = A_{agkst} - S_{agkst}` QWI: separation rate 38340 0 0.0178 2.968 0.308 0.234 Gross worker flows are measured using the Worker Reallocation Rate: `W\R\R_{agkst} =\ (A_{agkst} + S_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `A_{agkst} \equiv` accessions (new hires plus recalls) `S_{agkst} \equiv` separations (quits, layoffs, other) `B_{agkst} \equiv` beginning-of-quarter employment `E_{agkst} \equiv` end-of-quarter employment `W\R\R` measures total accession and separation flows as a proportion of average employment over the quarter in the age, gender, industry and state. The `W\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `W\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `AR_{agkst} =\ A_{agkst}*(2/(B_{agkst} + E_{agkst}))` `SR_{agkst} =\ S_{agkst}*(2/(B_{agkst} + E_{agkst}))` Accessions and separations satisfy the net job flow (`JF_{agkst}`) identity: `JF_{agkst} \equiv \ E_{agkst} - B_{agkst} = A_{agkst} - S_{agkst}` QWI: job creation rate 38340 0 0.00635 1.222 0.133 0.111 Gross job flows are measured in similar fashion using the symmetric Job Reallocation Rate: `J\R\R_{agkst} =\ (JC_{agkst} + JD_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `JC_{agkst} \equiv` job creations `JD_{agkst} \equiv` job destructions `J\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of average employment over the quarter in the category. The gross job inflow and outflow rates, the Job Creation Rate (`JCR`) and Job Destruction Rate (`JDR`) , can be defined as additive components of the `J\R\R`: `JCR_{agkst} =\ JC_{agkst}*(2/(B_{agkst} + E_{agkst}))` `JDR_{agkst} =\ JD_{agkst}*(2/(B_{agkst} + E_{agkst}))` Gross job flow measures are defined at an establishment, not job, level. Let `B_{agjt}` be beginning-of-quarter employment for demographic group `ag` at establishment `j` in quarter `t`, and similarly let `E_{agjt}` be end-of-quarter employment for the same category and time period. Then: `JC_{agjt} \equiv \ max(E_{agjt} - B_{agjt}, 0)` `JD_{agjt} \equiv \ max(B_{agjt} - E_{agjt}, 0)` so that, as originally specified by Davis and Haltiwanger, job creations are the change in employment when employment is growing at the establishment and job destructions are the change in employment when employment is shrinking at the establishment. Net job flows also satisfy the identity `JF_{agkst} =\ JC_{agkst} - JD_{agkst}` QWI: job destruction rate 38340 0 0.00949 0.847 0.111 0.0763 Gross job flows are measured in similar fashion using the symmetric Job Reallocation Rate: `J\R\R_{agkst} =\ (JC_{agkst} + JD_{agkst})*(2/(B_{agkst} + E_{agkst}))` where `JC_{agkst} \equiv` job creations `JD_{agkst} \equiv` job destructions `J\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of average employment over the quarter in the category. The gross job inflow and outflow rates, the Job Creation Rate (`JCR`) and Job Destruction Rate (`JDR`) , can be defined as additive components of the `J\R\R`: `JCR_{agkst} =\ JC_{agkst}*(2/(B_{agkst} + E_{agkst}))` `JDR_{agkst} =\ JD_{agkst}*(2/(B_{agkst} + E_{agkst}))` Gross job flow measures are defined at an establishment, not job, level. Let `B_{agjt}` be beginning-of-quarter employment for demographic group `ag` at establishment `j` in quarter `t`, and similarly let `E_{agjt}` be end-of-quarter employment for the same category and time period. Then: `JC_{agjt} \equiv \ max(E_{agjt} - B_{agjt}, 0)` `JD_{agjt} \equiv \ max(B_{agjt} - E_{agjt}, 0)` so that, as originally specified by Davis and Haltiwanger, job creations are the change in employment when employment is growing at the establishment and job destructions are the change in employment when employment is shrinking at the establishment. Net job flows also satisfy the identity `JF_{agkst} =\ JC_{agkst} - JD_{agkst}` QWI: excess inflow rate 38340 0 -0.0459 2.397 0.197 0.176 We define the excess reallocation measured using the symmetric Excess Reallocation Rate (see variables "qwi_wrr" and "qwi_jrr" for further information): `E\R\R_{agkst} =\ W\R\R_{agkst} -\ J\R\R_{agkst}` which measures the difference between gross worker flow and gross job flow rates, sometimes called the labor market ?churning? rate. The `E\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `E\R\R`?specifically, the Excess Inflow Rate (`EIR`) and the Excess Outflow Rate (`EOR`): `EIR_{agkst} =\ AR_{agkst} -\ JCR_{agkst}` `EOR_{agkst} =\ SR_{agkst} -\ JDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `EIR_{agkst} \equiv \ EOR_{agkst}`. QWI: excess outflow rate 38340 0 -0.0308 2.395 0.197 0.176 We define the excess reallocation measured using the symmetric Excess Reallocation Rate (see variables "qwi_wrr" and "qwi_jrr" for further information): `E\R\R_{agkst} =\ W\R\R_{agkst} -\ J\R\R_{agkst}` which measures the difference between gross worker flow and gross job flow rates, sometimes called the labor market ?churning? rate. The `E\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `E\R\R`?specifically, the Excess Inflow Rate (`EIR`) and the Excess Outflow Rate (`EOR`): `EIR_{agkst} =\ AR_{agkst} -\ JCR_{agkst}` `EOR_{agkst} =\ SR_{agkst} -\ JDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `EIR_{agkst} \equiv \ EOR_{agkst}`. QWI: FQ worker reallocation rate 37260 1080 0.0263 1.155 0.284 0.155 Gross full-quarter worker flows are measured using the Full-Quarter Worker Reallocation Rate: `FW\R\R_{agkst} =\ (FA_{agkst} + FS_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FA_{agkst} \equiv` full-quarter accessions (new hires plus recalls) `FS_{agkst} \equiv` full-quarter separations (quits, layoffs, other) `F_{agkst} \equiv` full-quarter employment in period `t` `F_{agkst-1} \equiv` full-quarter employment in period `t-1` `FW\R\R` measures total accession and separation flows as a proportion of average full-quarter employment in the age, gender, industry and state. The `FW\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `FW\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `FAR_{agkst} = \ FA_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FSR_{agkst} = \ FS_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` Accessions and separations satisfy the net full-quarter job flow (`FJF_{agkst}`) identity: `FJF_{agkst} \equiv \ FA_{agkst} - FS_{agkst}` where the net change in full-quarter is defined as `FJF_{agkst} = F_{agkst} -\ F_{agkst-1}` QWI: FQ job reallocation rate 37260 1080 0.0207 0.998 0.202 0.115 Gross full-quarter job flows are measured using the symmetric Full-Quarter Job Reallocation Rate: `FJ\R\R_{agkst} =\ (FJC_{agkst} + FJD_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FJC_{agkst} \equiv` job creations `FJD_{agkst} \equiv` job destructions `FJ\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of full-quarter average employment in the category. The gross job inflow and outflow rates, the Full-Quarter Job Creation Rate (`FJCR`) and Full- Quarter Job Destruction Rate (`FJDR`) , can be defined as additive components of the `FJ\R\R`: `FJCR_{agkst} =\ FJC_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FJDR_{agkst} =\ FJD_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` QWI: FQ excess reallocation rate (churning) 37260 1080 -0.318 0.556 0.0822 0.0644 We define the full-quarter excess reallocation measured using the symmetric Full-Quarter Excess Reallocation Rate (see variables "qwi_fwrr" and "qwi_fjrr" for further information): `FE\R\R_{agkst} = FW\R\R_{agkst} - FJ\R\R_{agkst}` which measures the difference between full-quarter gross worker flow and full-quarter gross job flow rates, sometimes called the labor market ?churning? rate. The `FE\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `FE\R\R`?specifically, the Full-Quarter Excess Inflow Rate (`FEIR`) and the Full-Quarter Excess Outflow Rate (`FEOR`): `FEIR_{agkst} =\ FAR_{agkst} -\ FJCR_{agkst}` `FEOR_{agkst} =\ FSR_{agkst} -\ FJDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `FEIR_{agkst} \equiv \ FEOR_{agkst}`. QWI: FQ accession rate 37260 1080 0 0.807 0.151 0.0992 Gross full-quarter worker flows are measured using the Full-Quarter Worker Reallocation Rate: `FW\R\R_{agkst} =\ (FA_{agkst} + FS_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FA_{agkst} \equiv` full-quarter accessions (new hires plus recalls) `FS_{agkst} \equiv` full-quarter separations (quits, layoffs, other) `F_{agkst} \equiv` full-quarter employment in period `t` `F_{agkst-1} \equiv` full-quarter employment in period `t-1` `FW\R\R` measures total accession and separation flows as a proportion of average full-quarter employment in the age, gender, industry and state. The `FW\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `FW\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `FAR_{agkst} = \ FA_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FSR_{agkst} = \ FS_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` Accessions and separations satisfy the net full-quarter job flow (`FJF_{agkst}`) identity: `FJF_{agkst} \equiv \ FA_{agkst} - FS_{agkst}` where the net change in full-quarter is defined as `FJF_{agkst} = F_{agkst} -\ F_{agkst-1}` QWI: FQ separation rate 37260 1080 0 0.634 0.133 0.0664 Gross full-quarter worker flows are measured using the Full-Quarter Worker Reallocation Rate: `FW\R\R_{agkst} =\ (FA_{agkst} + FS_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FA_{agkst} \equiv` full-quarter accessions (new hires plus recalls) `FS_{agkst} \equiv` full-quarter separations (quits, layoffs, other) `F_{agkst} \equiv` full-quarter employment in period `t` `F_{agkst-1} \equiv` full-quarter employment in period `t-1` `FW\R\R` measures total accession and separation flows as a proportion of average full-quarter employment in the age, gender, industry and state. The `FW\R\R` is a symmetric growth rate designed to approximate the logarithmic change over the time period (one quarter). In addition, the `FW\R\R`, can be expressed as the sum of its inflow and outflow components, the distinct accession and separation rates are defined, respectively, as: `FAR_{agkst} = \ FA_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FSR_{agkst} = \ FS_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` Accessions and separations satisfy the net full-quarter job flow (`FJF_{agkst}`) identity: `FJF_{agkst} \equiv \ FA_{agkst} - FS_{agkst}` where the net change in full-quarter is defined as `FJF_{agkst} = F_{agkst} -\ F_{agkst-1}` QWI: FQ job creation rate 37260 1080 0.00495 0.698 0.11 0.0808 Gross full-quarter job flows are measured using the symmetric Full-Quarter Job Reallocation Rate: `FJ\R\R_{agkst} =\ (FJC_{agkst} + FJD_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FJC_{agkst} \equiv` job creations `FJD_{agkst} \equiv` job destructions `FJ\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of full-quarter average employment in the category. The gross job inflow and outflow rates, the Full-Quarter Job Creation Rate (`FJCR`) and Full- Quarter Job Destruction Rate (`FJDR`) , can be defined as additive components of the `FJ\R\R`: `FJCR_{agkst} =\ FJC_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FJDR_{agkst} =\ FJD_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` QWI: FQ job destruction rate 37260 1080 0.00792 0.486 0.0917 0.0482 Gross full-quarter job flows are measured using the symmetric Full-Quarter Job Reallocation Rate: `FJ\R\R_{agkst} =\ (FJC_{agkst} + FJD_{agkst})*(2/(F_{agkst} + F_{agkst-1}))` where `FJC_{agkst} \equiv` job creations `FJD_{agkst} \equiv` job destructions `FJ\R\R` measures total job creations and destructions (called job creations/destructions in the QWI and gross job gains/losses in the BED) as a proportion of full-quarter average employment in the category. The gross job inflow and outflow rates, the Full-Quarter Job Creation Rate (`FJCR`) and Full- Quarter Job Destruction Rate (`FJDR`) , can be defined as additive components of the `FJ\R\R`: `FJCR_{agkst} =\ FJC_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` `FJDR_{agkst} =\ FJD_{agkst}*(2/(F_{agkst} + F_{agkst-1}))` QWI: FQ excess inflow rate 37260 1080 -0.187 0.218 0.041 0.0324 We define the full-quarter excess reallocation measured using the symmetric Full-Quarter Excess Reallocation Rate (see variables "qwi_fwrr" and "qwi_fjrr" for further information): `FE\R\R_{agkst} = FW\R\R_{agkst} - FJ\R\R_{agkst}` which measures the difference between full-quarter gross worker flow and full-quarter gross job flow rates, sometimes called the labor market ?churning? rate. The `FE\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `FE\R\R`?specifically, the Full-Quarter Excess Inflow Rate (`FEIR`) and the Full-Quarter Excess Outflow Rate (`FEOR`): `FEIR_{agkst} =\ FAR_{agkst} -\ FJCR_{agkst}` `FEOR_{agkst} =\ FSR_{agkst} -\ FJDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `FEIR_{agkst} \equiv \ FEOR_{agkst}`. QWI: FQ excess outflow rate 37260 1080 -0.155 0.462 0.0412 0.0323 We define the full-quarter excess reallocation measured using the symmetric Full-Quarter Excess Reallocation Rate (see variables "qwi_fwrr" and "qwi_fjrr" for further information): `FE\R\R_{agkst} = FW\R\R_{agkst} - FJ\R\R_{agkst}` which measures the difference between full-quarter gross worker flow and full-quarter gross job flow rates, sometimes called the labor market ?churning? rate. The `FE\R\R` measures the rate of gross worker flow activity in each category in excess of the minimum rate required to account for the observed gross job reallocation. Separate inflow and outflow excess reallocation rates can be defined using the components of the `FE\R\R`?specifically, the Full-Quarter Excess Inflow Rate (`FEIR`) and the Full-Quarter Excess Outflow Rate (`FEOR`): `FEIR_{agkst} =\ FAR_{agkst} -\ FJCR_{agkst}` `FEOR_{agkst} =\ FSR_{agkst} -\ FJDR_{agkst}` where the additive and symmetric growth rate properties of the measure within categories continue to hold. Because of the net job flow identities, `FEIR_{agkst} \equiv \ FEOR_{agkst}`. QWI: FQ average monthly earnings 37260 1080 212.787 24174.711 2679.631 1831.52 Measuring earnings using UI wage records in the QWI system presents some interesting challenges. The earnings of end-of-quarter employees who are not present at the beginning of the quarter are the earnings of accessions during the quarter. The QWI system does not provide any information about how much of the quarter such individuals worked. The range of possibilities goes from one day to every day of the quarter. Hence, estimates of the average earnings of such individuals may not be comparable from quarter to quarter unless one assumes that the average accession works the same number of quarters regardless of other conditions in the economy. Similarly, the earnings of beginning-of-quarter workers who are not present at the end of the quarter represent the earnings of separations. These present the same comparison problems as the average earnings of accessions; namely, it is difficult to model the number of weeks worked during the quarter. If we consider only those individuals employed at the employer in a given quarter who were neither accessions nor separations during that quarter, we are left, exactly, with the full-quarter employees. The QWI system measures the average earnings of full-quarter employees by summing the earnings on the UI wage records of all individuals at a given employer who have full-quarter status in a given quarter, then dividing by the number of full-quarter employees. QWI: FQ average monthly earnings FQ accessions 37258 1082 174.506 26645.807 2063.091 1371.03 A full-quarter accession is an individual who acceded in the preceding quarter and achieved full-quarter status in the current quarter. The QWI system measures the average earnings of full-quarter accessions in a given quarter by summing the UI wage record earnings of all full-quarter accessions during the quarter and dividing by the number of full-quarter accessions in that quarter. QWI: FQ average monthly earnings FQ new hires 37253 1087 175.642 24698.907 2019.165 1323.248 Full-quarter new hires are accessions to full-quarter status who were also new hires in the preceding quarter. The average earnings of full-quarter new hires are measured as the sum of UI wage records for a given employer for all full-quarter new hires in a given quarter divided by the number of full-quarter new hires in that quarter. QWI: FQ average monthly earnings FQ separations 37259 1081 167.658 40096.338 2169.135 1638.36 Full-quarter separations are individuals who separate during the current quarter who were full-quarter employees in the previous quarter. The QWI system measures the average earnings of full-quarter separations by summing the earnings for all individuals who are full-quarter status in the current quarter and who separate in the subsequent quarter. This total is then divided by full-quarter separations in the subsequent quarter. Thus, the average earnings of full-quarter separations are the average earnings of full-quarter employees in the current quarter who separated in the next quarter. QWI: average quarters of inactivity before accession 38340 0 0.787 3.71 1.784 0.39 An accession occurs when a job starts; that is, on the first occurrence of a SEIN-PIK pair following the first quarter of available data. When the QWI system detects an accession, it measures the number of quarters (up to a maximum of four) that the individual spent nonemployed in the state prior to the accession. The QWI system estimates the number of quarters spent nonemployed by looking for all other jobs held by the individual at any employer in the state in the preceding quarters up to a maximum of four. If the QWI system does not find any other valid UI wage records in a quarter preceding the accession, it augments the count of nonemployed quarters for the individual who acceded, up to a maximum of four. Total quarters of nonemployment for all accessions is divided by accessions to estimate average periods of nonemployment for accessions. Average periods of nonemployment for new hires and recalls are estimated using exactly analogous formulas except that the measures are estimated separately for accessions who are also new hires as compared with accession who are recalls. QWI: average quarters of inactivity since separation 36720 1620 1.026 3.998 1.768 0.407 Analogous to the average number of periods of nonemployment for accessions prior to the accession, the QWI system measures the average number of periods of nonemployment in the state for individuals who separated in the current quarter, up to a maximum of four. When the QWI system detects a separation, it looks forward for up to four quarters to find valid UI wage records for the individual who separated among other employers in the state. Each quarter that it fails to detect any such jobs is counted as a period of nonemployment, up to a maximum of four. The average number of periods of nonemployment is estimated by dividing the total number of periods of nonemployment for separations in the current quarter by the number of separations in the quarter. Within-implicate variance for QWI: worker reallocation rate 38340 0 2.45e-06 1.927 0.00108 0.0134 The within-implicate variance for QWI `W\R\R` is: `V^{(l)}[\hat{W\R\R}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(W\R\R_{agkst}^{(l)}-\hat{W\R\R}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: job reallocation rate 38340 0 6.27e-09 0.00252 7.3e-05 0.000134 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: excess reallocation rate (churning) 38340 0 8.52e-08 1.921 0.000877 0.0132 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: accession rate 38340 0 1.73e-10 0.482 0.000308 0.00335 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: separation rate 38340 0 7.09e-07 0.481 0.000289 0.00337 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: job creation rate 38340 0 4.55e-08 0.0029 5.27e-05 0.000142 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: job destruction rate 38340 0 9.26e-08 0.00274 3.95e-05 0.000115 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: excess inflow rate 38340 0 2.49e-08 0.48 0.00022 0.00331 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: excess outflow rate 38340 0 6.7e-08 0.48 0.000219 0.00328 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{B_{agkst}^{(l)}+E_{agkst}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{B_{agkvt}^{(l)}+E_{agkvt}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ worker reallocation rate 37260 1080 9.91e-07 0.00357 6.68e-05 0.000119 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ job reallocation rate 37260 1080 1.29e-06 0.00224 5.52e-05 0.000103 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ excess reallocation rate (churning) 37260 1080 6.41e-08 0.00655 1.33e-05 5.21e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ accession rate 37260 1080 0 0.00245 3.43e-05 8.31e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ separation rate 37260 1080 0 0.00521 2.82e-05 8.39e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ job creation rate 37260 1080 2.8e-08 0.00279 3.14e-05 8.17e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ job destruction rate 37260 1080 2.81e-07 0.00209 2.51e-05 7.5e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ excess inflow rate 37260 1080 1.17e-08 0.000637 3.49e-06 1.04e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ excess outflow rate 37260 1080 8.3e-09 0.00661 3.69e-06 4.29e-05 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: FQ average monthly earnings 37260 1080 11.487 50544894.7 18787.306 334526.131 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: average monthly earnings FQ accessions 37258 1082 13.006 34610188.698 22520.412 365824.68 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: average monthly earnings FQ new hires 37253 1087 8.442 81647129.005 20460.585 518437.176 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: average monthly earnings FQ separations 37259 1081 11.276 116576100.983 54342.079 1241551.598 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: average quarters of inactivity before accession 38340 0 4.54e-05 0.057 0.00139 0.00209 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Within-implicate variance for QWI: average quarters of inactivity after separation 36720 1620 2.55e-05 0.0411 0.00135 0.00207 The within-implicate variance for a generic variable `X` is: `V^{(l)}[\hat{X}_{agkt}] =\ 1/49 sum_{AAs}\frac{(\frac{F_{agkst}^{(l)}+F_{agkst-1}^{(l)}}{2})(X_{agkst}^{(l)}-\hat{X}_{agkt}^{(l)})^2}{sum_{AAv}(\frac{F_{agkvt}^{(l)}+F_{agkvt-1}^{(l)}}{2})}` where `1/49` is the number of states minus 1. Between implicate variance for QWI: average employment 36423 1917 0 78133069213.445 171237747.072 1350092847.8 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: average FQ employment 35397 2943 0.076 58621888495.515 142035361.096 1138775593.101 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: FQ employment 35397 2943 0 67865822254.794 144830972.963 1179146466.343 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: FQ accessions 35397 2943 0 24084654975.334 15177192.869 245819835.473 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: FQ new hires 35397 2943 0 21754261848.141 15958281.898 256059987.765 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: FQ separations 35397 2943 0 16131657066.299 11467510.696 161860848.192 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: accessions 36423 1917 0 174959498147.445 192554501.67 2709970532.359 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between implicate variance for QWI: separations 34955 3385 0 46078995758.876 90663761.143 756663262.389 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: worker reallocation rate 38340 0 0 0.68 0.000623 0.00821 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: job reallocation rate 38340 0 0 0.0107 5.6e-05 0.000256 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: excess reallocation rate (churning) 38340 0 0 0.655 0.000487 0.00783 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: accession rate 38340 0 0 0.162 0.00018 0.00202 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: separation rate 38340 0 0 0.178 0.000167 0.00214 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: job creation rate 38340 0 0 0.0127 3.54e-05 0.000201 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: job destruction rate 38340 0 0 0.01 2.76e-05 0.000201 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: excess inflow rate 38340 0 0 0.165 0.000123 0.00199 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: excess outflow rate 38340 0 0 0.163 0.000121 0.00194 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ worker reallocation rate 37260 1080 1.08e-09 0.0258 5.27e-05 0.000295 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ job reallocation rate 37260 1080 8.18e-10 0.0136 4.42e-05 0.00022 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ excess reallocation rate (churning) 37260 1080 6.08e-11 0.0305 1.27e-05 0.000195 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ accession rate 37260 1080 0 0.014 2.61e-05 0.000166 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ separation rate 37260 1080 0 0.0244 2.33e-05 0.000195 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ job creation rate 37260 1080 2.95e-10 0.0154 2.41e-05 0.000159 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ job destruction rate 37260 1080 3.94e-10 0.0084 2.08e-05 0.000154 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ excess inflow rate 37260 1080 2.07e-11 0.0035 3.15e-06 3.02e-05 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ excess outflow rate 37260 1080 1.73e-11 0.0309 4.22e-06 0.000168 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: FQ average monthly earnings 37260 1080 0.497 8106200.181 3855.464 52895.787 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: average monthly earnings FQ accessions 37258 1082 0.35 7492231.113 5844.725 79730.171 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: average monthly earnings FQ new hires 37253 1087 0.344 9883075.134 6066.205 104978.996 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: average monthly earnings FQ separations 37259 1081 0.27 76141308.486 14387.779 568253.115 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: average quarters of inactivity before access 38340 0 1.83e-07 0.368 0.00141 0.00648 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Between-implicate variance for QWI: average quarters of inactivity after separat 36720 1620 2.52e-07 0.319 0.00134 0.0061 The between implicate variance for a generic variable `X` is: `B[\bar{X}_{agkt}] =\ 1/(M-1)sum_{l=1}^{100}(\hat{X}_{agkt}^{(l)} - \bar{X}_{agkt})^2` Total variation for QWI: worker reallocation rate 38340 0 5.22e-06 2.181 0.00171 0.0191 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: job reallocation rate 38340 0 1.75e-08 0.0116 0.000129 0.00034 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: excess reallocation rate (churning) 38340 0 1.88e-07 2.174 0.00137 0.0187 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: accession rate 38340 0 1.01e-09 0.546 0.000489 0.00476 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: separation rate 38340 0 1.55e-06 0.545 0.000456 0.00487 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: job creation rate 38340 0 1.17e-07 0.0138 8.84e-05 0.000289 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: job destruction rate 38340 0 2.38e-07 0.0128 6.73e-05 0.000279 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: excess inflow rate 38340 0 6.98e-08 0.544 0.000344 0.00472 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: excess outflow rate 38340 0 1.69e-07 0.543 0.000341 0.00463 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ worker reallocation rate 37260 1080 1.01e-06 0.028 0.00012 0.00037 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ job reallocation rate 37260 1080 1.71e-06 0.0152 9.97e-05 0.000291 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ excess reallocation rate (churning) 37260 1080 6.66e-08 0.0372 2.6e-05 0.000239 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ accession rate 37260 1080 0 0.0154 6.05e-05 0.000215 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ separation rate 37260 1080 0 0.0297 5.16e-05 0.000255 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ job creation rate 37260 1080 2.11e-07 0.0168 5.57e-05 0.000208 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ job destruction rate 37260 1080 5.57e-07 0.0103 4.6e-05 0.000207 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ excess inflow rate 37260 1080 1.26e-08 0.00379 6.65e-06 3.6e-05 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ excess outflow rate 37260 1080 8.45e-09 0.0376 7.94e-06 0.000208 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: FQ average monthly earnings 37260 1080 48.465 51395035.372 22662.047 346526.894 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: average monthly earnings FQ accessions 37258 1082 41.952 34811568.938 28394.361 401504.208 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: average monthly earnings FQ new hires 37253 1087 43.294 81789525.474 26557.121 556928.591 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: average monthly earnings FQ separations 37259 1081 37.298 193098116.011 68801.797 1688115.262 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: average quarters of inactivity before accessions 38340 0 0.000142 0.401 0.00281 0.0077 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Total variation for QWI: average quarters of inactivity after separations 36720 1620 0.000137 0.348 0.0027 0.0073 The total variation for a generic variable `X` is: `T[\bar{X}_{agkt}] =\ 1/M sum_{l=1}^{M}V^{(l)}[\hat{X}_{agkt}] + (M+1)/M B[\bar{X}_{agkt}]` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Standard error for QWI: worker reallocation rate 38340 0 0.00228 1.477 0.0258 0.0323 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: job reallocation rate 38340 0 0.000132 0.108 0.00896 0.00701 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: excess reallocation rate (churning) 38340 0 0.000433 1.474 0.0206 0.0307 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: accession rate 38340 0 3.18e-05 0.739 0.0142 0.0169 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: separation rate 38340 0 0.00124 0.738 0.0133 0.0167 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: job creation rate 38340 0 0.000342 0.117 0.00659 0.0067 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: job destruction rate 38340 0 0.000487 0.113 0.00568 0.00593 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: excess inflow rate 38340 0 0.000264 0.737 0.0103 0.0154 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: excess outflow rate 38340 0 0.000411 0.737 0.0103 0.0153 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ worker reallocation rate 37260 1080 0.001 0.167 0.00877 0.00654 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ job reallocation rate 37260 1080 0.00131 0.123 0.00787 0.00614 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ excess reallocation rate (churning) 37260 1080 0.000258 0.193 0.00369 0.00353 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ accession rate 37258 1082 0.000723 0.124 0.00577 0.00521 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ separation rate 37259 1081 0.00071 0.172 0.0052 0.00496 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ job creation rate 37260 1080 0.000459 0.13 0.00538 0.00517 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ job destruction rate 37260 1080 0.000746 0.102 0.0048 0.00478 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ excess inflow rate 37260 1080 0.000112 0.0616 0.00188 0.00176 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ excess outflow rate 37260 1080 9.19e-05 0.194 0.00188 0.0021 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: FQ average monthly earnings 37260 1080 6.962 7169.033 85.476 123.921 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: average monthly earnings FQ accessions 37258 1082 6.477 5900.133 84.951 145.528 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: average monthly earnings FQ new hires 37253 1087 6.58 9043.756 79.032 142.519 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: average monthly earnings FQ separations 37259 1081 6.107 13895.975 103.87 240.862 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: average quarters of inactivity before accessions 38340 0 0.0119 0.634 0.044 0.0297 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Standard error for QWI: average quarters of inactivity after separations 36720 1620 0.0117 0.59 0.0427 0.0295 The standard error for a generic variable `X` is defined as `sqrt(T[\bar{X}_{agkt}])`, where `T[\bar{X}_{agkt}]` is the total variation of `X`. Degrees of freedom for QWI: worker reallocation rate 38333 7 199.126 478663330.69 17936.4 2464165 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: job reallocation rate 38333 7 199.127 166208627.306 8008.951 853704.344 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: excess reallocation rate (churning) 38333 7 199.162 1103453827.566 43432.739 5812229.374 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: accession rate 38333 7 199.132 135523497.889 10667.066 812291.8 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: separation rate 38333 7 199.155 1334789100.772 42804.719 6877620.437 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: job creation rate 38333 7 199.135 32503782.344 4446.182 200303.632 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: job destruction rate 38333 7 199.147 3316711.755 2660.392 49478.403 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: excess inflow rate 38333 7 199.12 756660324.265 32057.641 4012550.359 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: excess outflow rate 38333 7 199.127 1656612910.46 60418.766 8657345.947 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ worker reallocation rate 37260 1080 199.146 8763468.356 2602 66058.746 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ job reallocation rate 37260 1080 199.152 5555295.596 2795.345 63092.76 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ excess reallocation rate (churning) 37260 1080 199.155 3105738.011 2312.04 33698.601 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ accession rate 37258 1082 199.126 21085406.843 3521.556 144786.681 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ separation rate 37259 1081 199.164 10958063.249 2848.254 85268.219 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ job creation rate 37260 1080 199.132 12519718.384 2884.432 91256.354 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ job destruction rate 37260 1080 199.166 11323445.995 2785.923 87566.98 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ excess inflow rate 37260 1080 199.155 5291065.584 2600.111 51697.574 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ excess outflow rate 37260 1080 199.151 4269951.278 2434.568 40939.274 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: FQ average monthly earnings 37260 1080 199.188 1097031.851 2362.209 28546.712 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: average monthly earnings FQ accessions 37258 1082 199.146 2442075.107 1645.242 28072.384 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: average monthly earnings FQ new hires 37253 1087 199.096 9492998.925 2530.636 80738.818 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: average monthly earnings FQ separations 37259 1081 199.142 3667346762.939 100841.042 18999490.72 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: average quarters of inactivity before accessions 38340 0 199.118 495768.542 670.547 5512.114 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Degrees of freedom for QWI: average quarters of inactivity after separations 36720 1620 199.142 640694.405 535.242 4113.44 Degrees of freedom for a generic variable `X` are defined as: `df[\bar{X}_{agkt}] =\ (M-1)(1 + 1/(M+1) \frac{1/M sum_{l=1}^M V^{(l)} [\hat{X}_{agkt}]}{B[\bar{X}_{agkt}]})^2` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`. Effective missing data rate for QWI: worker reallocation rate 38340 0 0 0.936 0.257 0.277 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: job reallocation rate 38340 0 0 0.936 0.263 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: excess reallocation rate (churning) 38340 0 0 0.92 0.257 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: accession rate 38340 0 0 0.933 0.257 0.277 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: separation rate 38340 0 0 0.923 0.256 0.277 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: job creation rate 38340 0 0 0.932 0.261 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: job destruction rate 38340 0 0 0.926 0.261 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: excess inflow rate 38340 0 0 0.938 0.257 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: excess outflow rate 38340 0 0 0.936 0.257 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ worker reallocation rate 37260 1080 2.38e-05 0.927 0.257 0.275 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ job reallocation rate 37260 1080 3e-05 0.925 0.259 0.275 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ excess reallocation rate (churning) 37260 1080 4.01e-05 0.923 0.258 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ accession rate 37258 1082 1.53e-05 0.936 0.257 0.276 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ separation rate 37259 1081 2.13e-05 0.919 0.259 0.275 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ job creation rate 37260 1080 1.99e-05 0.933 0.258 0.276 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ job destruction rate 37260 1080 2.09e-05 0.919 0.26 0.275 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ excess inflow rate 37260 1080 3.07e-05 0.923 0.258 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ excess outflow rate 37260 1080 3.42e-05 0.925 0.258 0.279 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: FQ average monthly earnings 37260 1080 6.79e-05 0.909 0.256 0.278 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: average monthly earnings FQ accessions 37258 1082 4.53e-05 0.927 0.266 0.284 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: average monthly earnings FQ new hires 37253 1087 2.29e-05 0.949 0.278 0.294 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: average monthly earnings FQ separations 37259 1081 1.16e-06 0.929 0.263 0.283 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: average quarters of inactivity before acces 38340 0 0.000102 0.939 0.278 0.289 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`. Effective missing data rate for QWI: average quarters of inactivity after separa 36720 1620 8.92e-05 0.929 0.286 0.287 The effective missing data rate for a generic variable `X` is defined as: `MR[\bar{X}_{agkt}] =\ \frac{B[\bar{X}_{agkt}]}{T[\bar{X}_{agkt}]}` where `V^{(l)}[\hat{X}_{agkt}]` and `B[\bar{X}_{agkt}]` are respectively the within implicate variance and the between implicate variance of `X`, and `T[\bar{X}_{agkt}]` is the total variation of `X`.