INDIANABUSINESSREVIEW

Finding Work: New Career Pathways in an Evolving Labor Market Migration Trends and Population Change Between the Censuses demographic and economic perspectives, insights, and analysis since 1926 INDIANABUSINESSREVIEW Fall 2011 Volume 86, No. 3

Fall 2011 Volume 86, No. 3 Kelley School of Business Daniel C. Smith Dean Frank Acito Associate Dean of Information Technology Munirpallam Venkataramanan Associate Dean of Academic Programs Table of Contents 1Finding Work: New Career Pathways in an Evolving Labor Market Idie Kesner Associate Dean of Faculty and Research Philip L. Cochran Associate Dean of Indianapolis Programs Anne D. Auer Director of Marketing Indiana Business Research Center Jerry N. Conover Director and Publisher 8 Migration Trends and Population Change Between the Censuses Indiana Business Review Carol O. Rogers Executive Editor Rachel M. Justis Managing Editor Diane M. Probst Graphic Designer Flora A. Lewis Quality Control From the Editor Keen attention is paid to the latest job numbers often immediately followed by dismay at the continuing unemployment and lack of new jobs. Both articles in this issue of the IBR focus on jobs. Tim Slaper reveals a new way of approaching a career: pathway clusters. The principle is pretty simple: job seekers will seek and be most productive in jobs that are most similar to the ones they lost and the pathway cluster results in many possible job targets, not just one. And speaking of job loss, State Demographer Matt Kinghorn gives us a close-up at migration findings, taking us on a statewide journey across population-losing counties and population-gainers. The evidence seems clear that many of our industrial-based older communities have seen people move away after jobs were lost. Will we continue to see such patterns of migration, loss and gain? Matt is also working on our first set of Indiana and county population projections (by age, race and sex) for the 2010s; look for those in our Spring issue. www.ibrc.indiana.edu

Finding Work: New Career Pathways in an Evolving Labor Market Timothy F. Slaper, Ph.D.: Director of Economic Analysis, Indiana Business Research Center, Indiana University Kelley School of Business The manufacturing sector, and the automobile industry in particular, was under stress well before the advent of the Great Recession. Jobs for the Future

* Green and Growing Occupations Table 1 TABLE 1: Tri-State Top 15 Green and Growing Non-Automotive Occupation Postings and Expected Job Change to 2018 Miscellaneous Industries Not Elsewhere Classified Environmental Services Energy-Related Industries Non-Auto Manufacturing Rank Description HWOL Green Postings 1 10-Year Expected Growth 2 Industry Group 3 Postings-to- Employment Ratio 4 Mean Wage 5 1 Truck Drivers, Heavy and Tractor-Trailer 16,343 13.0% 1 : 10 $39,190 2 Customer Service Representatives 13,767 17.7% 1 : 13 $32,898 3 Marketing Managers 5,919 12.5% 1 : 2 $106,051 4 Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products 5,194 9.7% 1 : 8 $80,298 5 General and Operations Managers 4,038-0.1% 1 : 22 $108,057 6 Laborers and Freight, Stock, and Material Movers, Handlers 3,131-0.8% 1 : 75 $25,930 7 Automotive Specialty Technicians 2,745 4.7% 1 : 20 $37,297 8 Public Relations Specialists 1,826 24.0% 1 : 9 $51,630 9 Training and Development Specialists 1,794 23.3% 1 : 10 $53,051 10 Engineering Managers 1,638 6.2% 1 : 11 $109,392 11 Welders, Cutters, and Welder Fitters 1,574-1.6% 1 : 21 $35,842 12 Computer Software Engineers, Systems Software 1,377 30.4% 1 : 14 $81,926 13 Production, Planning, and Expediting Clerks 1,365 1.5% 1 : 20 $41,314 14 Aerospace Engineers 1,353 10.4% 1 : 1 $86,484 15 Heating and Air Conditioning Mechanics and Installers 1,323 28.1% 1 : 16 $45,441 1 Source: HWOL, Quarter 4, 2010; Green jobs total, N=131,248. 2 Source: BLS; Projections from 2008 to 2018 are for parent, six-digit SOC. HWOL and O * NET now report occupations at the eight-digit SOC detail. As a result, those occupations listed in this table are at the more detailed, eight-digit SOC while the projection figures are for the parent six-digit SOC. Hence the projection is for a group of similar occupations and not the specific occupation listed in the table. 3 Source: O * NET; O * NET categorizes green industries into 12 sectors. For the purposes of this report, the research team recast those 12 sectors into five categories (auto manufacturing being the fifth). 4 Source: IBRC using HWOL and BLS/OES data 5 Source: BLS. Mean wage calculated for the tri-state region using a weighted average.

Other Growing Occupations Table 2 Career Pathway Clusters * A key advantage of pathway clusters over other career transition resources is that the user is provided a set of many possible target occupations, rather than one at a time. The operating principle for the pathway cluster concept is that workers will seek, and be most productive in, occupations that are most similar to their current or former jobs. * * Requirements of the worker: Traits of the worker: The e e e h e e e he Occupational requirements: The e e e he h e e he e e e he e he * T e h e h e e he e h e e h h e e

TABLE 2: Tri-State Top 15 Non-Green Occupation Postings and Expected Growth to 2018 Rank Description HWOL Green Postings 1 10-Year Expected Growth 2 Postings-to- Employment Ratio 3 Mean Wage 4 1 Registered Nurses 23,415 22.2% 1 : 11 $60,750 2 Retail Salespersons 16,233 8.4% 1 : 23 $23,938 3 Occupational Therapists 14,728 25.6% 1 : 1 $68,962 4 Physical Therapists 12,620 30.3% 1 : 1 $73,557 5 First-Line Supervisors/Managers of Retail Sales Workers 12,003 5.3% 1 : 8 $38,589 6 Computer Systems Analysts 10,422 20.3% 1 : 4 $77,109 7 Executive Secretaries and Administrative Assistants 9,971 12.8% 1 : 10 $41,237 8 Web Developers 8,790 13.4% 1 : 2 $68,394 9 Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products 7,895 6.6% 1 : 17 $58,906 10 Medical and Health Services Managers 7,796 16.0% 1 : 4 $82,542 11 Computer Support Specialists 7,740 13.8% 1 : 6 $42,408 12 First-Line Supervisors/Managers of Office and Administrative Support Workers 7,662 11.0% 1 : 13 $48,399 13 Speech-Language Pathologists 7,352 18.5% 1 : 1 $72,655 14 Sales Agents, Financial Services 6,041 9.3% 1 : 4 $72,610 15 Office Clerks, General 5,970 11.9% 1 : 44 $26,764 Note: All occupations in this table fall within the Miscellaneous Industries Not Elsewhere Classified industry group. 1 Source: HWOL, Quarter 4, 2010; Green jobs total, N=131,248. 2 Source: BLS; Projections from 2008 to 2018 are for parent, six-digit SOC. HWOL and O * NET now report occupations at the eight-digit SOC detail. As a result, those occupations listed in this table are at the more detailed, eight-digit SOC while the projection figures are for the parent six-digit SOC. Hence the projection is for a group of similar occupations and not the specific occupation listed in the table. 3 Source: IBRC using HWOL and BLS/OES data 4 Source: BLS. Mean wage calculated for tri-state using a weighted average. e e e h * T he e e h he * T e h e Table 3 h h he e e e e e h e e e e e e he e e e e e e he e e ee e e Th e he e e ee e e h e e e e e e ee e e e e h he e e he he h e e e e e e e e ee e h e e e ee e h he e h h he h e e e e e e e e e e e e he e h h e he h e h e e The e e e h e e ee h e e he e e e e Finding and Closing the Skills Gap The h e e he e e e e e e h e Th he h e e h e e he e e he ee e e e e e e h e h e e e he he e e e e e h e e e e h e e

TABLE 3: Summary of Career Pathway Clusters Cluster Name Cluster ID Number of Occupations Number of Green Information and Investigation 1 62 12 Health, Social and Personal Services 2 90 0 Production, Construction and Engineering 3 217 55 Engineering and Applied Technology 3a 75 26 Construction and Extraction, Equipment Operation, and Repair 3b 69 15 Design and Production 3c 73 14 Liberal Arts, Education and Human Relations 4 86 7 Business, Sales and Administration 5 105 15 Transportation and Public Services 6 97 20 Environmental Sciences and Food Service 7 74 15 Clusters are ordered based on their relative strength, or how tight the clusters are. Information and investigation was the strongest cluster. The environmental sciences and food service cluster, in contrast, had the weakest similarity scores. The number of occupations in a cluster does not speak to the cluster s relative strength or importance. Based on the six-digit SOC definitions of the 2009 vintage of O * NET. The 2010 eight-digit O * NET/SOC definitions have considerably more jobs classified as green. Source: Indiana Department of Workforce Development (IDWD) and the Indiana Business Research Center (IBRC) e e e he e h h he e e e he e e e he e e e e Figure 1 he e e e ee he e e he h e e e he The e e e e ee he he e e e h e e e e he The e he e e h e e e e he e e e e ee e e he e e e e h e The e e h he e e e h e he e e e ee e e e h e e e e e The e e h e e he e he e e e e e he h e he e e h he e e The e e he e e h h e e e e e e path e e e e e a a a h e t t p t e a ep e e t the e e e et ee e pat a at t a the The e ea h tea e ha e the eth that e t e a the p a ap ea e * T e a p e e e t e t ete e a a th the e e p e e e a a e a e e a ha a te t a the e at a a t a t e t ta e a a t e e p e t at a pa t a T the e te t p e the e t ate h e e e at a t a e e a e te t a e e t at a a e at a app e t e h p Th a ea ta a a a e t t p t e h t e ta e te a t athe a a ea e pa et ee e e t a ee t a t The e ea h tea e t ate the e t e e e e t a e e t the t p ta t e e e e e t a pat Th e e e the a t ap T p t e the the ap ea e h p epa at the path a te a e h pat a e pe t e the Figure 2 h FIGURE 1: Selected Occupations in the Same Cluster as an Industrial Machinery Mechanic Source: Indiana Business Research Center e e e

h t p t e p epa at t e ea e the th e The path a te eth e t p pat ht e t that a th te t p t e e e tha t p t e et ee te t th t a a t e pat e e pe t path a te a t a te a pe a t a t the e h a h h a a a t t e h a ha hea e t t t e e a e e a e h e e t ea e t e th te Research into Practice e e a p a e a t e pat a e e a e a p e t h that ea h pat ha e e a pt a that ea h e ha a e pe te a t t e t a e the pat t a t Table 4 p e t t a t pt e e te a t t e pat e th the at te a e t e The e e a p e a ha e e at e h t t p t e h h e e a p e e e t a pt e a ee pp t t e a a p e e e t a t th the a e path a te a e t e t a pat that h e a t the p e pat te e e e e t e t a t a e e e t t he pe a a e e at e a t t a t th th FIGURE 2: Trip Times to Transition from an Industrial Machinery Mechanic to Selected Occupations the te t a h e e e pe at a te e p t t e the te t t e hea a t a t t a e pe at h e the e at e t p t e the a e the e t e t at pat a e t e a a e that the t p t e ea e e the a te at e a ee t t pe e t pat th a te ha e a a t e The ate e p a ee e ta e a pe e e a pat h he te t t a t a t e t pat t ee pat eat e e a p a e e pt the t tate e ee p e a t pe e t a a p t Table 5 p e e t a a p ee a ee t a t a te at e tea a e e a p t he pe th e at e h t t p t e Source: Indiana Business Research Center Closing the Gap e the ha e p e t a a t e e p a e e t he p p a e e p t a path e a e a ee path t a e t e e e a t a t t a e pat a e e t the et a a e at pt a e t the ha e e te a p e a e a e e e t p e at a t a a at a p a ee a pat a e the ta ete pat e a TABLE 4: Sample Career Transitions from Automotive Occupations to In-Demand Green Occupations with Above Average Wages Occupation Auto Sector Occupation Pathway Cluster ID Occupation Destination Occupation Pathway Cluster ID Trip Time (Hours) Helpers Production Workers 3a Separating, Filtering, Clarifying, Precipitating, and Still 3b 370 Machine Setters, Operators, and Tenders Truck Drivers, Heavy and Tractor-Trailer 6 370 First-Line Supervisors/ Managers of Production and Operating Workers Source: IDWD and IBRC 5 First-Line Supervisors/Managers of Farming, Fishing, and Forestry Workers Computer-Controlled Machine Tool Operators, Metal and Plastic 5 360 3a 310 a a e t e e h e a a e e ea h e te

TABLE 5: Sample Career Transitions from Automotive Occupations to In-Demand Non-Green Occupations with Above Average Wages Auto Sector Occupation Occupation Pathway Cluster ID Destination Occupation Occupation Pathway Cluster ID Trip Time (Hours) Team Assemblers 3c Extruding, Forming, Pressing, and Compacting 3c 130 Machine Setters, Operators, and Tenders Pipelayers 3b 170 Coin, Vending, and Amusement 6 310 Machine Servicers and Repairers Helpers Production Workers 3a Excavating and Loading Machine and 3b 200 Dragline Operators Extruding, Forming, Pressing, and Compacting 3c 205 Machine Setters, Operators, and Tenders Coin, Vending, and Amusement Machine Servicers and Repairers 6 330 Source: IDWD and IBRC a the e e a t p t e a h e p a that pat th the e e te e aph a at a e e e p e t p a e a a the e e e e e a e the ata p e e t the e at e e t at a t e at a p a at a h h a a e aph e e e e e p e t p a t t e h a e t t t ate the th a a t e e t e t t a e p e a e t a ea p t a e e t ha e a pe a t a e e hat e at a p a a e ea t the ap The e e e t a a a a e e a ee ha e at e ha e he p t a p a e e the t tate e ta e e p e t t a e a a at e pa the e e e p e t t t the t e Notes The ha e t ha ee a a at e e t e e e p e t a e e a a h a a h a the t ate pa t e Th p e t a pp te a a t the p e t a T a t at the epa t e t a The a ee path a a t p t e e ea h a te the a a e e ea h e te at a a e t e e h e a the e ea h a a the a a epa t e t e e e p e t ha e e ea h ep t a e e a e a a a e at e ha e The p ete ep t a a ee a e at e ha e ee a p The a at ee e e p the a ate ee pat * T t pat the ta e the ta a pat a e a e e e a ee * T ee h e e e t a e e h t a ta a a h e ee the p at * T a e a e pat e a et e te ep t ee ht e a p e ep t a pat a a ha p t a the t ta e e that pat the p t t e p e t at Th the e p t a et a a e a a e p p t a a et a a e the tate The a th a e e that a p t a e e t a t e pe a e the e t a h a e e epa t e t a t t the e e p the e t ep e e t a t e pe Th the p t t e p e t at a e at e ea e a ee e pet t The e a t a e a e e at h p et t hh e a a a p Th pe at p p e a t the T te h h a p e t that p e a pat t e pa e pat th a the h p t p t The T te p e e eta e a pe t that e e the e e a a t e a pat t ete e hethe t pat a e a at h e e the path a te app a h e a pat e pt ate e * T h h a e a et a a e e e a

Migration Trends and Population Change Between the Censuses Matt Kinghorn: Demographer, Indiana Business Research Center, Kelley School of Business, Indiana University Data from the 2010 Census show that Indiana added more than 400,000 residents in the last 10 years to reach a total population of 6.48 million. a a pe e t th ate a tpa e e h h a a h the e a a t e p te a p p at ea e e the a e pe the a e t the e a e e a e et the a e t t e a e t th a a t e ha a et t at e e t e the e a e a t e the e e e e et the at a ea e the p p at e e th tha eath t t a tate e a a ha e a et at e e t a t e a e t the a ta a e e the e e the h e ata t ta p p at ha e a e p ta t a a et ea the t a a p e a ea at h p p at e p t the e t t the at e the the ha e a e ep e e tat e ea e a a a ea ta t The e e a e a t a e e e the e a t e t a e t Th a t e e a e a a p p at h t the e e t p e t ha e th a e pha at ate the e e a a a at pa e a e a the e aph t t a t t a the tate a e at t e a ha t e t e a e the p a e at e e t a ea the tate Population Change across the State a a p p at th e the pa t e a e t ta t th the a ap a e et a ea The t e e e h a a p p at th e the pa t e a e a pe p e a pe e t ea e Th e a te pe e t the tate t ta th Th ap th ea that the tate p p at Posey e ea e t ate e t a a a The t et a ea ha e the tate p p at ea e pe e t t pe e t Source: IBRC, using U.S. Census Bureau data FIGURE 1: Indiana Population Change by County, 2000 to 2010 Vermillion Gibson Blackford Vanderburgh Lake Newton Benton Warren Vigo Sullivan Knox Daviess Martin Pike Warrick Porter Jasper Fountain Parke Clay LaPorte White Tippecanoe Montgomery Putnam Greene Spencer Owen Dubois Starke Pulaski Carroll Boone Marshall Cass Hendricks Perry Morgan Orange St. Joseph Clinton Fulton Howard Hamilton Marion Johnson Brown Monroe Lawrence Crawford Kosciusko Wabash Miami Tipton Jackson Washington Harrison Elkhart Grant Hancock Bartholomew Floyd Scott Clark Noble Whitley Huntington Rush Decatur Jennings Wells Delaware Madison Shelby LaGrange Henry Jefferson Increases More than 20% (5) 10% to 20% (8) 0% to 9.9% (50) Declines Allen Adams Jay Randolph Wayne Fayette Union Ripley Steuben DeKalb Franklin Dearborn Ohio Switzerland -0.01% to -3 (16) Less than -3% (13) a a e t e e h e a a e e ea h e te

a a the a e et p ta tat t a a ea a t a e a e a th e a e t t a e tpa e the tate a pe e t th e the e a e ea h e a the e t e that e h a a t a a e a a t p p at e e Th pa t a the a e th h a ath th e t a a ea t e t a a a he e et p ta a p ta a ea h a a p t pe e t a a h pe e t pe e t a pe e t e pe e t e pe e t h pe e t a e e pe e t t p p at a a a t e a e e e t e the a t e a e h e t p p at ee Figure 1 a t pe e t e t t pe e t a a a h t pe e t ha the ha pe t e e a a a t e The t e th h p p at e t e e e t e th pe ea h e the tate e a te t t e a e e a t The e t e e e e a t a e h h e pe e t e e t a pe e t e pe t e a a a e t t e a e t a t a e e e t t ea h a t ta p p at a pe e t ea e th e t a a a e t p pe e t t e e t h e e h te t e pe e t t ea h e t e pe e t t t e e t h e t a e e a ha e e t p p at a pa t the tate t et a ea that a e hea e a a t a t th ha t t e p te e h t ha th Many of the mid-sized communities that long formed much of Indiana s industrial backbone saw signi cant population decline. e e th e a e p te a pe e t p p at th h e a th e t e pe e t Components of Indiana s Population Change p at t a t th h at a ea e the e e e et ee the e th a eath a at e the a t e a e a a ha h e th tha eath h h a te pe e t the tate t ta p p at th t e a a t e e e a a a a h e te e a p t e at a ea e e the a t e a e et at a a e p p at th a t ea a e p ea a a t e a e p p at et th h at e the a t e a e e a that t e t he p p at a e th pe h h ea that the at a ea e t e a a e e h t et a et t at e e t th p p at th e e a a t e the a ap et a ea ha the eate t e e et at ee Figure 2 a t t ha the tate a e t p t e et at at h e e t e the e a e Th e a t e a eat a e p a e e t h h ha a et at e e t a t a e t e at e e ate t a at ate pe e e t a e pe t e t the tate e h he t at ate e e the e a ap et t e a t pe e e t e t a h t a t e e e he e the tate a h e h h ate at a a a t e the e et a ea ha h h ate et at a a t the a e a ea a te t th e t a a the thea t e the tate a e a t a h t a ha h h a et t at e the a t e a e h e e a e p ea the the ha the tate a t e t e the a ap et a ea the a a t e th te tate te e te t ha et at e the a t ea a e t a a a a e t e ea h ha et t at ate a e e e t pe a t ha the a e t a te et t p p at e the e a e at h e e t e t eph t a a e t h e a a p te a et at e e t et ee a Th a a e e e a

FIGURE 2: Net Migration Rates per 100 Residents by County, 2000 to 2010 Posey Vermillion Gibson Blackford Vanderburgh Lake Newton Benton Warren Vigo Sullivan Knox Pike Jasper Fountain Parke Warrick Porter Clay Daviess Source: Indiana Business Research Center Spencer White Greene LaPorte Tippecanoe Montgomery Putnam Owen Pulaski Martin Dubois Starke Carroll St. Joseph Hendricks Lawrence Perry Morgan Marshall Fulton Cass Clinton Boone Monroe Orange Crawford Howard Tipton Hamilton Marion Brown Johnson Kosciusko Jackson Washington Harrison Elkhart Wabash Miami Hancock Floyd Whitley Grant Madison Shelby Bartholomew Scott LaGrange Noble Huntington Rush Wells Increases More than 10 (7) 0 to 10 (23) Declines Steuben Allen Delaware Randolph Henry Decatur Jennings Clark Jefferson Ripley DeKalb Adams Jay Wayne Fayette Union Franklin Ohio Switzerland 0 to -4 (39) Dearborn -4 to -8 (19) Less than -8 (4) a the e e t e e a e that at ha a e a p t e t t t the tate p p at th a a ha a et at e tha e e t the a t ata Behind the Numbers The e at e a e a t e t h he e a e the t a h at at ate a e he p t a e the h e t Figure 3 h the tate ha h h ate et at the t a the t a e p e t that a a a a a a t e e t at e a e the a t e a e t et e e t e t a he pe t t the at ate the t a e p t the at a e te at tat t a a e a a a the t p tate et at e e e h a a a a e e hth th ea e th a et e tha t e t the the e the a a t a e e a t e the a t e a e et e e the t a e p e t the tate e the e a e at a ate ea e e e e t pe The et t the t a e p a a t The ate et at tape th a e a a e a e a et at tate the p p at et ee the a e a t at e e e ate th ea h e e a e p The e a a ht et t e e t the t a e p et the tate et e t et at the a e p p at ata the te a e e e e e the e e t e ta pa e h that h pe e t a t t the tate e e t ea a e ee Figure 4 Th a a e tha a a e t e e h e a a e e ea h e te

t t e eate tha a the tate a pe e t a t t a a a e h e t h a a e aph e pe e t the tate a t a e the e t pa e t pe e t the th pe e t the e t a pe e t the thea t a a the a e t e p e t a t a a et ee a t the th a the t p e t at e th pe e t a a a t hea e t the h e tate a a the e a e t e t at a a a t the the tate e t a a the pe e t a a t a t t the th t ta t pe e t the pe e t a a a t ta e the e t p t h pe p e e h at pa e ha e t e at a at e p e that t e e a a et a t a e t e e te t e the a t e at t e a e h e the a e a e te e e p e t e ate e at at at pa e a e a a ea he p t t ate the e e e t a t Indiana s Large Metro Areas t the ate t e t p at e the e ea e tha pe e t pe p e that a e h t ta e e e e tha e a t e the e the h a e ate ea The e a t e ta e at t e th a a a e t et a ea ee ea e a t ha the tate a e t et t at the a t e a e a a e t ha the th a e t et t Figure 5 p e e t the at pa e a e the e t a t a p p a FIGURE 3: Indiana Net Migration Rates by Age, 2000 to 2010 Migration Rate (per 100 residents) 10 8 6 4 2 0-2 -4-6 -8 Source: Indiana Business Research Center Illinois Ohio Kentucky Michigan Florida Midwest (other) South (other) West Northeast Age in 2010 FIGURE 4: Migration to and from Indiana by Region, 2001 to 2008 In-Migrants from Out-Migrants to 0% 10% 20% 30% Note: Data are unavailable for 2004 Source: IBRC, using Internal Revenue Service data a a e e e a

e t at a t et a e ea e the t a e e t a t a ha a te t et t e a t a et e e t a e e e t a e t e that t ha the at ha a te t th a a e t a a t e a a a ea the t ha a et a e a t e a e e e the e a e e e he e the a e a t a a tea t e e t e the a e Figure 6 a Figure 7 h e h t e e e the a e t e p e t a t the e a a ea et ee a e tha ha a a t a t e e e e he e the eate a ap a e et a ea a t the ata the pe e t e e he e e e the tate te e t a t a t e e t e a e t e t the th pe e t t ta a t tha a the e te tate pe e t a t h a a e t e e p e the a e t a e t The p t the h a t te e tha ha a a t t a e t et ee a the pe e t a t t a e t a e the a a p t the h a et e a pe e t a te t e a t ea a e t pe e t e th the a a pa t the h a h e pe e t e the tate e t ta e the eate et a ea the pe e t a e t a t hea e t the th a pe e t e ate e e he e the e t Figure 8 p e a e e a p e h the t a e a a ea pa t a a h h at t e e t e the e t a a a FIGURE 5: Marion County and Lake County Net Migration Rates by Age, 2000 to 2010 Migration Rate (per 100 residents) 40 30 20 10 0-10 -20-30 Source: Indiana Business Research Center a t e a a a h e a e e t the t a e p e t a ha a te t t at e e t a e a e The at at e te a a t a e a a th h t a a at e tha ha the a t t the e t e et ee a Age in 2010 Marion County (Indianapolis) Lake County (Gary) FIGURE 6: Migration to and from Marion County by Region, 2001 to 2008 Indianapolis-Carmel MSA Indiana (Other) Midwest Northeast South West Note: Data are unavailable for 2004 Source: IBRC, using Internal Revenue Service data In-Migrants from Out-Migrants to 0% 10% 20% 30% 40% 50% 60% a e a the t the e pe t e et a ea e the e a t a t h e p e that a a t ta et at e e e e a t the th ee a e et a ea t e et ee a the ata h that the e a a a t the p t the h a t e e a a e t e e h e a a e e ea h e te

FIGURE 7: Migration to and from Lake County by Region, 2001 to 2008 Chicago MSA (Illinois Portion) Chicago MSA (Indiana Portion) Indianapolis-Carmel MSA Indiana (Other) Midwest (Other) Northeast South West Note: Data are unavailable for 2004 Source: IBRC, using Internal Revenue Service data the tate t ta at t th a ea a at ea t a a a ha a p e at e ha e th the e a at et a ea t the e e ea ata h a et e e t t the tate the e a ea th t th a a ha e ha a h e e e et at a t e a e The Impact of Job Losses p e t e a p a a p ta t e at pa e The e t p at e ate that pe e t h t ta e e a e t ate e p e t e Th e e t pe e t ta e e e e t a e the a t t e a e h t a a e p e t ha e ea t ea hethe p t e e at e ha e t p a a e a a t the tate a a et at e e t ate a a e ha t e e the pa t e a e ta e the tate a e e tha pa In-Migrants from Out-Migrants to 0% 20% 40% 60% the th the e e p t a a t t a t a a he e a the a t e a e a t ea a a t a e e tha ha the e a a t e a e e e the t e e t e e h t ate Th t a t the e p e t t e e e e ea h a a et at a a t e a e a a e tha the The e e ha e h t a a ea the a ea t e t a a a pa t a ha a e t the e e a e h e t a the a a t e th et t at a p p at e e e the e a e th h a the t e ha e ha a a e pe e e e at t t e a a a e t et a e e the t p a at t e the e a ea e the t e a the tate the e t t e a the a a t e p e t e e tha pe e t e the a t e a e t a the th t p p at a ha a et t e e t Figure 9 h th a a a e ha a et t at e e t at ea e e a e p th pa t a ha p t et ee the a e t The t e e t at a t ea a t FIGURE 8: Net Migration Rates by Age for Select High-Growth Suburban Counties, 2000 to 2010 Migration Rate (per 100 residents) 120 100 80 60 40 20 0-20 -40 Source: Indiana Business Research Center Age in 2010 Hendricks County (Plainfield) Porter County (Portage) Harrison County (Corydon) a a e e e a

FIGURE 9: Net Migration Rates by Age for Select Out-Migration Counties, 2000 to 2010 Migration Rate (per 100 residents) 30 20 10 0-10 -20-30 -40 Source: Indiana Business Research Center e e t ea a a t t e h t the th e a t a a t e the a ap et a ea a e tha pe e t a t t a t ta e a a ee Figure 10 a t a the t e t at a t a e t e e h a ph e e h a e e a e t e The e t p e t e a e a e e tha pe e t a e t t a t et ee a Age in 2010 Howard County (Kokomo) Wayne County (Richmond) a e e t at e a e a e t t e the tate t e a e th a the e pe e e the a t e a e h e e e t ea t ha t t e t the e t e e a t that e p p at ha e a h t the e a p e a a t e t p p at a e t e tha e e t t a t t e a the p p at e e the a t e a e a t e t e tha e e t a e e the a a e pe e e h h e e t at the t the t e e e e ha p the e a e h e the e t e a ha e a pa t the tate e ee t ee a t e pe a e e e the About the Data th a t e t e e t eth e e e t e t ate et at T ta et at e t ate e e a ate a e a eth th app a h et at the e e e et ee the t ta at a ea e a e a ea et ee the a e a the t ta p p at ha e e the a e pe the p p at ha e e ee the at a ea e ta e the the e e e et at The at a ea e e t ate e p t e e the e ea a a p p at e t ate p a The e e t ate a e a e th a eath ata e te e t the a a tate FIGURE 10: Destination of Migrants from Howard County and Wayne County, 2001 to 2008 Indianapolis-Carmel MSA Conclusion The e e at t e ha e p ta t p at the tate the t e ta e a a ha the p ea a t t t e e a ha tate the e t thea t t ee t p p at e the a e ea e et ee a e e a th th e t t e ea h e the h p p at e e pe e t e h ha a a t e a e a ea the a a a e a ap e pa t t t at h e a e Indiana (Other) Midwest Northeast South West Note: Data are unavailable for 2004 Source: IBRC, using Internal Revenue Service data Howard County (Kokomo) Wayne County (Richmond) 0% 10% 20% 30% 40% 50% 60% a a e t e e h e a a e e ea h e te

epa t e t ea th a a t e t ate at a ea e a a t a e th t e the e pe e pe at ate e t ate e e a ate a a ate eth e th app a h a e pe ea a ate a e app e t the e p p at t t e t ate a e pe te p p at ea h a e p The e e pe te p p at e app ate hat the p p at e a e h t the e a at e the ea pe a eath e e the e p p at ha e The e e e et ee the e pe te p p at a the a t a e t e e the et at e t ate tate a t pe e ta e a ate e e a ate ta t ata a p p at e the e ea ata the e t at a t e the te a e e e e e The e ata a e te that the e the at e ta e a the epe e t T e te a a a t a e t e a et t e t e ea a ate a e e t e e t t ea h Th ea the ata a the e e t a e e t e t e et ee a t e that t e et That a th e the e t ata a a a e e a a a at t e a the e t at e The ata th a t e e ea h ea et ee a th the e ept h h a a a e Notes The a ap a e e e a t a e h a a t a a he t e t e e at e t ate the p p at at tate a e e a a t e t a a t e e e e e t ta p p at e e the e e e t t e ta Th a t e t h te e e e e a t e et at t et t at t e The e ea e a e t e e e th a a ee a ap the e e e at tat a a e apt ap a e e a a h p at a p e t ha e a a InContext t te t a a e a a t e a p a a e e e a