OUTMIGRATION AS AN ECONOMIC INDICATOR: A CASE STUDY OF SOUTHWEST MINNESOTA By John C. Shepard, AICP, Southwest Regional Development Commission and Carrie Quast, Upper Minnesota Valley Regional Development Commission
The majority of counties in Southwest Minnesota have experienced continuous, ongoing population loss over the last thirty years. A large component of this population loss is outmigration. It is believed that this significant dislocation factor is disguising the current socioeconomic situation of many of our counties. This includes the skewing of several typical data evaluators such as unemployment and income. This report is an analysis of the issue of outmigration in Southwest Minnesota. The current socioeconomic situation in the region is described, with an emphasis on the significance of outmigration occurring in Southwest Minnesota. The analysis then considers how outmigration functions as an improved indicator of economic distress.
SOUTHWEST MINNESOTA BY THE NUMBERS Southwest Minnesota bridges the divide between the humid industrialized Midwest and the dry agricultural Great Plains. Counties in the northern and eastern part of the region have the rolling hills, lakes and forested valleys commonly thought of as Minnesota’s landscape, but large parts of the region are more Southwest Minnesota Year 2000 Population similar to the Dakotas and Iowa to the south Region County Population Region County Population and west. This tall6E Kandiyohi 41,203 6W Chippewa 13,088 grass prairie was 6E McLeod 34,898 6W Swift 11,956 densely settled 6E Meeker 22,644 6W Yellow Medicine 11,080 beginning 150 years 6E Renville 17,154 6W Lac qui Parle 8,067 ago, with a web of Mid-Minnesota Region 115,899 6W Big Stone 5,820 railroads serving small Upper Minnesota Valley 50,011 towns and family 8 Lyon 25,425 9 Blue Earth 55,941 farms. As Joseph A. 8 Nobles 20,832 9 Nicollet 29,771 Amato reminds us, 8 Redwood 16,815 9 Brown 26,911 “The history of 8 Cottonwood 12,167 9 Le Sueur 25,426 Minnesota mirrors the 8 Jackson 11,268 9 Martin 21,802 national story of 8 Pipestone 9,895 9 Waseca 19,526 migration… In 1910 8 Rock 9,721 9 Faribault 16,181 over half the people 8 Murray 9,165 9 Sibley 15,356 8 Lincoln 6,429 9 Watonwan 11,876 living in most counties Southwest Region 121,717 Region Nine 222,790 of southwestern Minnesota had been Southwest Minnesota Study Area 510,417 born outside the United Source: US Census
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States” (1996: 3). Over the 19th and 20th centuries, the development pattern matured, with some towns becoming small cities and others little more than memories. The demographic and economic profile of communities across the region is relatively homogenous on a national scale. No part of the region is classified as Metropolitan, although several counties have been classified as Micropolitan Areas1. Commercial agriculture is a major economic driver, inter-related with agricultural processing industries such as food manufacturing, meat packing, and increasingly ethanol and bio-diesel production. Four Economic Development Districts serve the area. Region 6E, the Mid-Minnesota Development Commission, serves four counties in the north-east part of the area. Region 6W, the Upper Minnesota Valley Regional Development Commission, serves five counties in the north-west part of the area. Region 8, the Southwest Regional Development Commission, serves nine counties in the southwestern corner of the area, and Region Nine Development Commission serves nine counties in the southeastern corner. Mankato, in Region 9, is the largest city with currently about 35,000 residents. Willmar, in Region 6E, has about 18,000 and Marshall in Region 8 has 13,000. Montevideo is the largest city in Region 6W with just more than 5,000 residents. Color maps are attached to this report illustrating these locations and data which follows. The majority of counties in Southwest Minnesota (in Regions 8 and 6W as well as select counties in Regions 6E and 9) have experienced continuous, ongoing loss of population over the last thirty years. In aggregate, Region 6E has gained population in each decade since 1970 and Region 9 has seen a mix of growth and contraction; Region 8 has lost population in each decade, and Region 6W would have without the addition of a new prison in Swift County in the 1990s.
Population Change Since 1970 The 1970s are sometimes referred to as a rural renaissance in the United States, but the decade was not necessarily good to Southwest Minnesota. Counties closer to the Twin Cities metropolitan area on the eastern side of the region generally grew, while those closer to the Dakotas on the western side of the region generally lost population. Kandiyohi and Meeker counties in Region 6E saw double digit growth in the 1970s, while Pipestone and Murray in Region 8 lost eight and nine percent of their population base respectively. This stagnation and modest decline gave way to severe declines in population during the 1980s. Only five of twenty-seven counties experienced positive population growth, with McLeod the only county to outpace the statewide average growth rate. Every county in Regions 8 and 6W lost population, with two counties suffering more than 20% loss.
1
Metropolitan Statistical Areas as defined by the US Office of Management and Budget (OMB). Blue Earth, Brown, Kandiyohi, Lyon, McLeod, Martin, Nicollet, and Nobles counties are classified as Micropolitan Areas. McLeod County is also classified as part of the Minneapolis-St. Paul-St. Cloud Combined Statistical Area (CSA).
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The familiar pattern of the 1970s reappeared in the 1990s—growth in the east and contraction in the west, with some locally significant variations. No county in Southwest Minnesota grew faster than the state average. Lac Qui Parle and Big Stone counties, both adjacent to South Dakota, saw 8-10% lower population in Census 2000. McLeod County had the largest absolute gain, while Swift County had the fastest growth with the onetime influx of residents to the correctional facility. Le Sueur and McLeod counties, both adjacent to the Minneapolis-St. Paul MSA, saw 8% growth rates. As Macht (2005) points out, employment centers in Willmar (Kandiyohi County) and Hutchinson (McLeod) add to growth spillover in Region 6E from the metro areas. Regional centers at Mankato (Blue Earth), New Ulm (Brown), Fairmont (Martin), and Marshall (Lyon) also impact regional growth patterns (SRF 2003, Macht and Ridgeway 2005).
Projected Population Change Projections prepared by the Minnesota State Demographic Center shortly after the last U.S. Census were more optimistic about the prospects for population growth in rural Minnesota.2 Although several counties were projected to continue to loose population, these loses were seen to smooth out after 2010.
2
The 2002 projections are used throughout this report. The Demographic Center released updated population projections for counties on 6 June 2007, after this report went into production. The 2007 figures are generally lower than the 2002 estimates for the region under study.
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Still, no county in the area was projected to gain population at a rate faster than the State of Minnesota overall in any upcoming decade. More recent estimates from the U.S. Census Bureau are pessimistic. The most recent (March 2007) figures show gains in only three counties through 2006 (see discussion of migration below). The State Demographic Center projections are the best available figures at this time. Blue Earth County is home to the region’s largest city, Mankato, and is projected to continue as the largest county in the area with a modest 1.5% to 4.9% growth rate. Meeker County, adjacent to the Minneapolis-St. Paul-St. Cloud CSA, will consistently lead Southwestern Minnesota growth on a percentage basis. Big Stone County is projected to continue to have the smallest population in the area. Lac Qui Parle, Murray and Pipestone counties are seen continuing to vie for greatest percentage population loss. Southwest Minnesota Population Change by County Region 6W 9 9 6W 8 9 8 6E 6W 9 8 8 9 6E 6E 8 9 8 8 8 6E 8 9 6W 9 9 6W
County Big Stone Blue Earth Brown Chippewa Cottonwood Faribault Jackson Kandiyohi Lac Qui Parle Le Sueur Lincoln Lyon Martin McLeod Meeker Murray Nicollet Nobles Pipestone Redwood Renville Rock Sibley Swift Waseca Watonwan Yellow Medicine
1970 7,941 52,322 28,887 15,109 14,887 20,896 14,352 30,548 11,164 21,332 8,143 24,273 24,316 27,662 18,387 12,508 24,518 23,208 12,791 20,024 21,139 11,346 15,845 13,177 16,663 13,298 14,523
Region 6E Region 6W Region 8 Region 9 Southwest Minnesota
97,736 78,577 129,150 215,766 521,229
State of Minnesota
1980 1970-1980 7,716 -2.9% 52,314 0.0% 28,645 -0.8% 14,941 -1.1% 14,854 -0.2% 19,714 -6.0% 13,690 -4.8% 36,763 16.9% 10,592 -5.4% 23,434 9.0% 8,207 0.8% 25,207 3.7% 24,687 1.5% 29,657 6.7% 20,594 10.7% 11,507 -8.7% 26,929 9.0% 21,840 -6.3% 11,690 -9.4% 19,341 -3.5% 20,401 -3.6% 10,703 -6.0% 15,448 -2.6% 12,920 -2.0% 18,448 9.7% 12,361 -7.6% 13,653 -6.4% 107,415 78,270 125,329 217,222 528,236
3,806,103 4,075,970
9.0% -0.4% -3.0% 0.7% 1.3%
1990 1980-1990 6,285 -22.8% 54,044 3.2% 26,984 -6.2% 13,228 -12.9% 12,694 -17.0% 16,937 -16.4% 11,677 -17.2% 38,761 5.2% 8,924 -18.7% 23,239 -0.8% 6,890 -19.1% 24,789 -1.7% 22,914 -7.7% 32,030 7.4% 20,846 1.2% 9,660 -19.1% 28,076 4.1% 20,098 -8.7% 10,491 -11.4% 17,254 -12.1% 17,673 -15.4% 9,806 -9.1% 14,366 -7.5% 10,724 -20.5% 18,079 -2.0% 11,682 -5.8% 11,684 -16.9% 109,310 68,924 113,672 209,919 501,825
6.6% 4,375,099
1.7% -13.6% -10.3% -3.5% -5.3%
2000 1990-2000 5,820 -8.0% 55,941 3.4% 26,911 -0.3% 13,088 -1.1% 12,167 -4.3% 16,181 -4.7% 11,268 -3.6% 41,203 5.9% 8,067 -10.6% 25,426 8.6% 6,429 -7.2% 25,425 2.5% 21,802 -5.1% 34,898 8.2% 22,644 7.9% 9,165 -5.4% 29,771 5.7% 20,832 3.5% 9,895 -6.0% 16,815 -2.6% 17,154 -3.0% 9,721 -0.9% 15,356 6.4% 11,956 10.3% 19,526 7.4% 11,876 1.6% 11,080 -5.5% 115,899 69,537 112,449 214,532 512,417
6.8% 4,919,479
5.7% 0.9% -1.1% 2.2% 2.1%
2010 2000-2010 5,530 -5.2% 58,810 4.9% 27,310 1.5% 13,000 -0.7% 11,920 -2.1% 15,710 -3.0% 11,130 -1.2% 43,670 5.6% 7,480 -7.8% 27,300 6.9% 6,310 -1.9% 25,880 1.8% 21,110 -3.3% 37,490 6.9% 24,520 7.7% 8,720 -5.1% 31,860 6.6% 21,230 1.9% 9,440 -4.8% 16,620 -1.2% 17,020 -0.8% 9,670 -0.5% 16,450 6.7% 12,300 2.8% 20,430 4.4% 12,070 1.6% 10,800 -2.6% 122,700 69,540 111,800 221,750 525,790
11.1% 5,452,550
5.5% 0.0% -0.6% 3.3% 2.5%
2020 2010-2020 5,480 -0.9% 59,960 1.9% 28,460 4.0% 13,330 2.5% 12,070 1.2% 15,870 1.0% 11,340 1.9% 45,980 5.0% 7,300 -2.5% 28,920 5.6% 6,420 1.7% 26,310 1.6% 21,120 0.0% 39,780 5.8% 26,470 7.4% 8,630 -1.0% 33,190 4.0% 21,810 2.7% 9,270 -1.8% 17,050 2.5% 17,280 1.5% 9,870 2.0% 17,610 6.6% 12,900 4.7% 21,360 4.4% 12,290 1.8% 10,850 0.5% 129,510 71,220 113,450 228,760 542,940
9.8% 5,909,620
5.3% 2.4% 1.5% 3.1% 3.2%
2030 2020-2030 5,490 0.2% 60,910 1.6% 29,280 2.8% 13,650 2.3% 12,290 1.8% 16,050 1.1% 11,460 1.0% 47,680 3.6% 7,220 -1.1% 30,100 3.9% 6,540 1.8% 26,730 1.6% 21,230 0.5% 41,580 4.3% 27,890 5.1% 8,600 -0.3% 34,000 2.4% 22,250 2.0% 9,270 0.0% 17,450 2.3% 17,520 1.4% 10,070 2.0% 18,480 4.7% 13,370 3.5% 21,990 2.9% 12,460 1.4% 10,940 0.8% 134,670 72,660 115,230 233,970 556,530
3.8% 2.0% 1.5% 2.2% 2.4%
7.7% 6,268,220
5.7%
Source: US Census, Projections by MN Demographic Center
Aging of the Population Aging Americans are becoming a greater proportion of our national population. This is due in part to increased longevity as modern medicine extends our lifespan. A person born in 1970 could expect to live to age 70. We expect that a person born in 2010 will live to age 78 (US Census Bureau, Statistical Abstract). Also, as the baby boomers (those born from 1946 through 1964) mature, they leave an identifiable bulge in the nation’s population pyramids.
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Southwest Minnesota Estimates of Natural Increase 2000 - 2005 Births Region County - Deaths Region County 6E Kandiyohi 921 6W Big Stone 6E McLeod 1,097 6W Chippewa 6E Meeker 330 6W Lac qui Parle 6E Renville (65) 6W Swift Mid-Minnesota Region 2,283 6W Yellow Medicine Upper Minnesota Valley 8 8 8 8 8 8 8 8 8
Cottonwood Jackson Lincoln Lyon Murray Nobles Pipestone Redwood Rock Southwest Region
(38) (92) (170) 467 (59) 624 (41) (59) (30) 602
9 9 9 9 9 9 9 9 9
Blue Earth Brown Faribault Le Sueur Martin Nicollet Sibley Waseca Watonwan Region Nine Southwest Minnesota State of Minnesota
Source: Minnesota State Demographic Center
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Births - Deaths (175) (79) (188) (21) (41) (504) 1,170 62 (268) 572 (41) 1,013 232 362 164 3,266 5,647 155,072
Southwest Minnesota is leading the State and Nation in this trend. The state’s population aged 65+ is projected to exceed the share of the youngest population, aged 0-14, by 2030. This has already happened in Region 6W, and will be the case in the rest of the area by 2020. New entrants to the workforce (as a share of all residents) are declining statewide and in each region, as the population aged 15-24 contracts over the longterm. As well, the
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share aged 25-44—those most likely to be having children and starting families—is also projected to decline statewide after a peak in 2000. Even with the bulge of the baby boomers moving through the older working age group (age 45-64), this group will remain the most numerous statewide. However, in every region of Southwest Minnesota the older working age group will be most numerous in 2010, eventually surpassed by the most aged group by 2030. The greatest changes in age cohorts are often seen as particularly large (or small) birth years move through the population pyramids (such as the baby boomers noted above). Changes in the total population of an area can also be affected by the age profile of the area. Relatively fewer residents of child-bearing age and/or relatively more aging residents is often indicative of future Southwest Minnesota Unemployment Rates declines in growth rates. The US Census Bureau estimates that Minnesota overall is benefiting from a natural increase,with almost twice as many births as deaths since the 2000 census. In Southwest Minnesota, there are only about 1/5 more births than deaths counted. More particularly, over half of the counties in the area are experiencing more deaths than births.
Labor Force Trends It is fairly clear that population in Southwest Minnesota has been stagnate or contracting overall. The labor force, on the other hand, has been growing, if only below the growth rate for Minnesota overall. Some growth in the labor force is tied to population growth in trade centers (e.g. Willmar, Mankato) and closer to the Twin Cities metropolitan areas (Le Sueur, Meeker counties). More generally, greater numbers of people are participating in the local labor force. Projections after this decade indicate an expectation that this growth trend is not generally sustainable.
County Big Stone Blue Earth Brown Chippewa Cottonwood Faribault Jackson Kandiyohi Lac qui Parle Le Sueur Lincoln Lyon Martin McLeod Meeker Murray Nicollet Nobles Pipestone Redwood Renville Rock Sibley Swift Waseca Watonwan Yellow Medicine Region 6E Region 6W Region 8 Region 9 State of Minnesota United States
1990 1995 2000 2005 4.2 4.9 4.1 4.6 3.7 3.2 2.8 3.4 4.2 3.8 3.7 4.3 4.9 5.2 4.9 4.1 6.2 7.0 4.6 4.4 5.0 5.2 3.6 4.8 4.4 4.8 3.0 3.4 4.6 3.5 3.2 4.2 3.4 3.5 3.3 3.9 5.8 4.5 3.8 5.5 4.3 5.1 3.5 3.6 4.3 3.4 2.8 3.3 4.5 4.5 3.4 4.2 4.2 3.5 3.2 4.1 6.5 6.2 4.2 5.1 4.9 5.6 3.7 4.1 3.8 2.9 2.7 3.2 3.5 4.0 2.9 3.3 6.4 3.4 3.3 3.7 3.2 3.1 3.1 3.9 6.1 5.0 3.9 4.9 2.6 2.5 2.6 3.0 5.0 5.2 3.2 4.2 4.7 4.6 3.7 4.6 3.8 3.6 3.0 4.4 3.5 4.3 3.2 4.1 4.3 5.1 4.1 4.2 5.1 4.4 4.3 4.3 4.8 5.6
4.2 4.7 4.1 3.8 3.7 5.6
3.5 4.1 3.2 3.2 3.1 4.0
4.4 4.3 3.6 4.0 4.1 5.1
While below-average growth in the Source: MN DEED
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labor force certainly does not indicate a healthy economy, unemployment rates are at most equivocal on this measure. Annual average unemployment rates in the region vary somewhat county-to-county, from a high of 5.5% in LeSueur County (R9) to a low of 3.0% in Rock County (R8) in 2005. Looking at 5-year increments since 1990, only once did a county exceed the national rate by more than one percentage point. In recent years, unemployment in Southwest Minnesota has tended echo statewide trends. Southwest Minnesota Labor Force Change by County Region 6W 9 9 6W 8 9 8 6E 6W 9 8 8 9 6E 6E 8 9 8 8 8 6E 8 9 6W 9 9 6W
County Big Stone Blue Earth Brown Chippewa Cottonwood Faribault Jackson Kandiyohi Lac qui Parle Le Sueur Lincoln Lyon Martin McLeod Meeker Murray Nicollet Nobles Pipestone Redwood Renville Rock Sibley Swift Waseca Watonwan Yellow Medicine
Region 6E Region 6W Region 8 Region 9 Southwest Minnesota State of Minnesota
1990 2,797 29,183 13,370 6,193 5,750 7,629 5,430 18,915 3,885 11,568 3,003 12,533 10,911 16,546 9,805 4,318 15,148 9,637 4,733 7,812 7,887 4,669 7,032 4,672 8,999 5,579 5,149 53,153 22,696 57,885 109,419 243,153
2000 1990-2000 2,657 -5% 13% 33,035 9% 14,636 6,703 8% 6,103 6% 6% 8,113 5,867 8% 21,937 16% 3,975 2% 21% 13,959 3,216 7% 14,135 13% 3% 11,224 19,191 16% 11,743 20% 4,740 10% 16% 17,553 10,613 10% 5,077 7% 8,446 8% 8,641 10% 5,006 7% 16% 8,192 5,438 16% 11% 10,023 4% 5,808 5,519 7% 61,512 24,292 63,203 122,543 271,550
2,314,975 2,691,709
16% 7% 9% 12% 12%
2010 2000-2010 2,760 4% 8% 35,740 7% 15,720 7,130 6% 6,430 5% 4% 8,410 6,170 5% 24,530 12% 4,020 1% 15% 15,990 3,320 3% 15,160 7% 3% 11,550 21,770 13% 13,110 12% 4,790 1% 10% 19,300 11,340 7% 4,990 -2% 8,960 6% 8,920 3% 5,230 4% 14% 9,330 6,010 11% 10% 11,030 7% 6,190 5,630 2% 68,330 25,550 66,390 133,260 293,530
16% 3,112,800
11% 5% 5% 9% 8%
2020 2010-2020 2,620 -5% -1% 35,470 0% 15,660 7,090 -1% 6,370 -1% -2% 8,210 6,100 -1% 25,070 2% 3,790 -6% 4% 16,650 3,340 1% 15,120 0% -3% 11,170 22,870 5% 13,660 4% 4,570 -5% 2% 19,690 11,570 2% 4,770 -4% 9,000 0% 8,750 -2% 5,250 0% 7% 9,950 6,190 3% 2% 11,210 0% 6,220 5,450 -3% 70,350 25,140 66,090 132,210 293,790
3% -2% 0% -1% 0%
16% 3,287,100
6%
Source: US Census, Projections by DEED
We can look at changes in the makeup of the labor force for some clues to these trends. As we saw with the population overall, older workers are assuming a larger share of the workforce. We are already seeing a significant shift in the number of workers age 45-64. Fewer New Entrants to the workforce (age 24 and under) and young family earners will be available to replace retiring workers. Outmigration as an Economic Indicator
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Per Capita Income Income figures from the Bureau of Economic Analysis are also equivocal on Southwestern Minnesota’s economic health. The State of Minnesota has consistently had a higher per capita income than the national average, while the region has consistently had lower per capita income than the state average. With a higher share of the population entering the workforce, one might expect earned income to increase. However, growth in income will depend greatly on the character of the jobs created and retained. If we look at five-year snapshots since 1980, no county in Southwest Minnesota exceeded the per capita income figure for the State of Minnesota overall, except for once (1985). In 2005, county per capita income ranged from 70% to 88% of Minnesota per capita income, and 75% to 95% of the national per capita income figure (BEA REIS). Southwest Minnesota Per Capita Income Region 6W 9 9 6W 8 9 8 6E 6W 9 8 8 9 6E 6E 8 9 8 8 8 6E 8 9 6W 9 9 6W
County Big Stone Blue Earth Brown Chippewa Cottonwood Faribault Jackson Kandiyohi Lac qui Parle Le Sueur Lincoln Lyon Martin McLeod Meeker Murray Nicollet Nobles Pipestone Redwood Renville Rock Sibley Swift Waseca Watonwan Yellow Medicine Minnesota
1970 3,207 3,445 3,575 3,543 3,622 3,537 3,912 3,363 3,188 3,573 2,990 3,585 3,917 3,591 3,607 3,445 3,186 3,594 3,651 3,438 3,634 3,891 3,423 3,226 3,632 3,563 3,187 4,039
1975 5,223 5,578 5,508 5,731 5,919 6,026 6,751 5,669 5,493 5,648 4,769 5,347 6,313 5,789 5,367 6,195 5,475 6,437 5,956 5,383 6,356 7,074 6,145 5,801 6,050 5,943 5,204 6,223
1980 7,562 8,873 8,878 8,744 9,326 8,968 9,351 8,586 9,319 8,967 7,729 9,184 9,702 9,320 8,616 9,089 8,013 9,177 8,205 8,570 9,050 9,568 8,039 7,502 9,020 9,675 8,945 10,256
1985 11,705 12,503 12,837 12,544 16,752 13,270 12,525 12,201 12,100 12,909 11,599 12,838 13,658 13,585 12,163 12,412 11,999 12,633 12,250 12,626 12,495 12,534 12,352 11,172 13,300 12,327 12,037 15,166
1990 15,310 16,141 17,109 17,227 16,168 16,433 16,224 16,548 15,958 16,554 14,973 16,655 17,498 17,234 15,986 15,829 16,390 17,596 15,047 16,394 16,755 17,433 15,423 15,148 16,475 15,824 15,850 19,891
1995 15,707 20,555 20,216 18,371 18,007 18,414 17,682 19,886 16,227 20,460 15,652 19,759 20,099 21,951 17,677 17,620 19,716 18,364 17,168 18,873 18,458 18,917 16,522 15,836 19,180 18,254 17,290 24,078
2000 22,328 25,910 25,597 24,695 23,248 23,573 22,761 26,777 23,584 27,533 22,451 25,956 26,117 26,676 23,274 23,722 26,544 23,187 23,856 23,849 23,486 24,574 21,985 20,163 23,399 22,854 22,871 32,017
2005 29,005 31,602 30,964 30,443 30,937 32,732 30,323 32,085 29,829 29,813 29,359 31,838 32,668 30,713 28,702 30,404 31,760 29,637 31,597 29,193 27,012 30,721 27,673 25,986 28,115 29,574 29,596 37,290
Source: Bureau of Economic Analysis, REIS
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As per capita income does take more into account than simple wages, transfer payments such as Social Security and Medicare will account for a significant amount of income in areas with an aging population. Farm income and program payments, which can vary greatly year-to-year, also should be taken into account in an agricultural region. In Minnesota, 70% of personal income in 2005 came from earnings, while in Murray County (Region 8) for example, earnings account for only 61% of personal income. Transfer payments made up 12% of personal income in Minnesota, and 19% of income in Murray County. Farm income made up less than 1% of income in Minnesota, but over 14% of income in Murray County.
Migration Components of population change include natural increase—the difference between births and deaths which we examined above—and net migration—the difference between people moving in and moving out. In Southwest Minnesota, net migration has tended to mean outmigration. Across the Midwest and Great Plains, people were leaving rural areas for most of the 20th century as changes in the agricultural and manufacturing sectors reduced demands for labor. More recently some communities have stabilized this trend. As the Minnesota State Demographic Center pointed out after the 2000 census, “For the first time in many decades, migration made a substantial contribution to the state’s population growth” (McMurray 2002). However, this was only true in eight of the 27 counties in Southwest Minnesota. In one of these counties, as noted above, inmates at the new prison were added to the residential population, throwing the census count off trend. Martin County, along the Iowa border, had the largest net out-migration, followed by Lyon, Pipestone, Yellow Medicine and Murray counties, all clustered against the South Dakota border. More recent estimates by the State Demographer are even less optimistic about migration and population growth in the region. Five counties demonstrated positive net migration from 2000 to 2005. LeSueur and Blue Earth counties have attracted the greatest positive net migration this decade. Nicollet, McLeod and Meeker also attracted in-migrants. This could be expected as the Twin Cities expand into this exurban area. A significant number of people have left the region in the first half of this decade. The largest outmigration since 2000 has occurred from Lyon, Nobles, Redwood and Kandiyohi counties. Looking more closely at estimates by the U.S. Census Bureau, we see that the region is benefiting from more international migrants staying than leaving (a net positive gain) while losing many more domestic or internal migrants than it gained. This is certainly the case for the four counties with greatest overall outmigration. While some of the domestic migration in Lyon County can be attributed to the comings and goings of college students at Southwest Minnesota State University in Marshall, all of the counties experiencing the greatest negative net migration have significant manufacturing and food processing operations. This sector has suffered statewide since 2001. Where in
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some places job loss leads to high unemployment rates, it would appear that laborers are leaving Southwest Minnesota. Southwest Minnesota Net Migration
County R6W Big Stone R9 Blue Earth R9 Brown R6W Chippewa R8 Cottonwood R9 Faribault R8 Jackson R6E Kandiyohi R6W Lac qui Parle R9 Le Sueur R8 Lincoln R8 Lyon R9 Martin R6E McLeod R6E Meeker R8 Murray R9 Nicollet R8 Nobles R8 Pipestone R8 Redwood R6E Renville R8 Rock R9 Sibley R6W Swift R9 Waseca R9 Watonwan R6W Yellow Medicine R6E R6W R8 R9 Southwest Minnesota State of Minnesota
1990 - 2000 2000 - 2005 Population Net Population Net Change Migration Change Migration (465) (73) (325) (150) 1,897 (255) 2,553 1,383 (73) (483) (356) (418) (140) (46) (307) (228) (527) (315) (325) (287) (756) (271) (695) (427) (409) (334) (93) (1) 2,442 577 284 (637) (857) (479) (444) (256) 2,187 1,113 2,360 1,788 (461) (97) (364) (194) 636 (560) (477) (944) (1,112) (1,107) (820) (41) 2,868 1,253 1,744 647 1,798 1,327 772 442 (495) (495) (308) (59) 1,695 19 1,678 665 734 (113) (279) (903) (596) (543) (398) (357) (439) (334) (719) (660) (519) (372) (383) (318) (85) (174) (180) (150) 990 668 28 (204) 1,232 1,365 (527) (506) 1,447 808 25 (337) 194 (241) (348) (512) (604) (506) (497) (456) 6,589 613 (1,223) 4,613 10,592 544,380
2,785 261 (2,965) 251 332 258,056
2,417 (2,100) (3,143) 4,425 1,599 285,612
134 (1,596) (3,555) 1,897 (3,120) 130,540
Source: Minnesota State Demographic Center
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When Vikings Leave Thirteen students made up the last graduating class of the Milan “Vikings” in 1990. The entire school housed about 100 children from K-12. The school didn’t close its doors in 1990. It combined with three other small towns to form the Lac qui Parle Valley School district. The Milan school building housed grades 4-6 for both Milan and nearby Appleton until 2007, when it was decided that all the elementary children from Milan would go to Appleton and the Milan school building would close and be sold. Milan is vibrant town of 320, working very hard to create economic opportunities. But when the students leave Milan to go to college, there are few reasons for them to return. Photo by UMVRDC
OUTMIGRATION AS AN ECONOMIC DISTRESS FACTOR Socioeconomic distress is the focus of numerous public and private interests, from the United Nations to local economic and community development organizations. We seek to understand the underlying health of communities, to provide the most efficient and effective actions to alleviate distress and build paths to future prosperity. Much as physicians rely on medical charts to diagnose illness, researchers and service providers rely on socioeconomic indicators to diagnose distress in our communities. This report focuses more specifically on efforts by the U.S. Department of Commerce’s Economic Development Administration (EDA). Federal Indicators of Economic Distress The Federal Public Works and Economic Development Act of 1965, as amended (PWEDA), sets forth the framework of the U.S. Department of Commerce’s Economic Development Administration public works and economic development funding and technical assistance [42 USC §3121 et seq]. The first paragraph of Title 42, Chapter 38 of the United States Code states Congress’ finding that:
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(1) there continue to be areas of the United States experiencing chronic high unemployment, underemployment, outmigration, and low per capita incomes, as well as areas facing sudden and severe economic dislocations because of structural economic changes, changing trade patterns, certain Federal actions (including environmental requirements that result in the removal of economic activities from a locality), and natural disasters;
Congress clearly intended that outmigration be used as an indicator of economic distress. Section 206(1) [42 USC §3146] directs the Secretary to consider relative need of eligible areas based on: (A) the severity of the rates of unemployment in the eligible areas and the duration of the unemployment; (B) the income levels and the extent of underemployment in eligible areas; and (C) the outmigration of population from eligible areas and the extent to which the outmigration is causing economic injury in the eligible areas;
While there are also provisions for areas facing economic adjustment, other parts of the Act concentrate more heavily on employment and income. The rules under which EDA implements the PWEDA spell out more specific criteria for distress. The final rule specified the method to calculate Per Capita Income and Unemployment rates to determine if a county has "economic distress" [13 C.F.R. §301.3(a)(1) ]: (i)
An unemployment rate that is, for the most recent twenty-four (24) month period for which data are available, at least one (1) percent greater than the national average unemployment rate; (ii) Per capita income that is, for the most recent period for which data are available, eighty (80) percent or less of the national average per capita income; or (iii) A “Special Need”, as determined by EDA.
Substantial outmigration or population loss is included in the definition of a special need, and EDA’s investment rate rules do contain a provision to consider “outmigration of population and the extent to which such outmigration is causing economic injury in the Region” [13 C.F.R. §301.4(b)(1)(i)(A)(3) ]; however, nowhere else in Part 301 is migration mentioned. In fact, the term is only found twice more in passing through the rest of the Title.
Scan of Research Population, employment and income are commonly used as indicators of economic wellbeing. They are comfortable to use and easily available for many different geographic areas in long-term time series. It is fairly simple to count people at rest, jobs and figures reported on income taxes. Easy does not always mean accurate or reliable. Policy researchers at the Center for the New West took a close look at the use of these and other socioeconomic indicators in the EDA-Denver region, as part of larger project sponsored by the Ford Foundation
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considering the entire Great Plains region including Minnesota’s tall-grass prairies (Shepard, Muehlbauer, and Higgs). Considering a variety of socioeconomic measures, they noted that traditional indicators by themselves give an incomplete view of the situation of the region: • Population measures are quickly out-of-date and do not account for seasonal housing or people who maintain multiple residences. The historic political units of cities, counties, and states seldom match economic units. • Employment (and unemployment) measures alone are incomplete, poorly accounting for job quality, multiple job holders, underemployment, and the growing importance of self-employment. • Income measures are perhaps the least understood of these three indicators. Per Capita Income figures vary widely between the decennial Census and BEA figures. Perhaps most problematic, PCI can not account for the dramatic difference in cost of living within and between states and regions. EDA has struggled over the years with conflicting needs to accurately measure distress. The Supplementary Information published in the Federal Register (71, No. 187, Sept. 27, 2006) on EDA’s final rule also acknowledges comments regarding each of the criteria discussed in this report (56659). In particular, “EDA received approximately 100 identical or nearly identical comments on §301.3(a)(4)(i) [specifying use of] the most recent American Community Survey (“ACS”) published by the U.S. Census Bureau”. The ACS is being phased into use as a replacement for the long form of the decennial census. The purpose and methodology of the ACS has been widely criticized (see for example, MacDonald 2006). Because of timing and disclosure issues, the ACS is unlikely to provide accurate information at a local level for small, rural places without substantial additional federal investments. The existing measures are far from perfect. University of North Carolina faculty conducted a systematic study of persistent outmigration, population loss and regional economic distress (Feser and Sweeney 1998, 2003). They state that “there is a fundamental difference between (a) OPL [outmigration/ population loss] and (b) unemployment and income in terms of their relationship to economic well-being” (1998: i). While many classical economists see out-migration and population loss as a positive effect as workers leave to seek gainful employment, these researchers note that out-migration also drains communities of critical human capital, and places significant fiscal pressure on local units of government. This left-right punch can put otherwise resilient communities down for the count. Feser and Sweeney mapped out portions of the country with sustained out-migration where distress would not be indicated using traditional economic indicators alone. Examining the characteristics of the counties experiencing long-term population loss, compared to unemployment and income distress, they developed a proxy measure which “isolates regions that are most likely to be passed over by traditional indicators” (1998: 48). This statistical analysis identified severe outmigration in portions of Southwest Minnesota and nearby areas of northern Iowa and the Dakotas. “More than half of the plains [commuter] zones were OPL-distressed in 1998, compared to 14 percent of the zones in the [Mississippi] Delta and 11 percent of zones in Appalachia” (2003: 52).
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Using the OPL measure, Feser and Sweeney also found that migrants from areas suffering severe outmigration/population loss are more likely to be highly skilled, wealthier and younger than migrants from other areas (1998: 54). This process removes the “best and brightest” from distressed areas that need them the most. As well, communities faced with long-term outmigration are reluctant to invest in capital facilities, leaving fewer people who are less able to pay more for less. Population, employment and income are relatively easy to count; however, they do not necessarily alone provide an accurate assessment of the socioeconomic situation in a region. Migration data provide additional nuance that can help identify long-term economic distress.
Outmigration as a Distress Factor in Southwest Minnesota Unemployment and Per Capita Income may not give a true picture of economic distress in rural areas. In Southwest Minnesota, the picture they paint is not particularly useful to understanding the socioeconomic situation. The region lost about 2% of its population between 1970 and 2000, although it is projected to grow 9% by 2030. This may not look that bad, but consider that the State of Minnesota grew by 29% between 1970 and 2000, and is projected to grow by another 27% by 2030. Clearly, Southwest Minnesota is not sharing in this greater prosperity. Unemployment Unemployment statistics do not by themselves indicate that the region is in distress. Cottonwood County in Region 8 is the only county in Southwest Minnesota that met the EDA unemployment distress criteria of 1% over the US average, looking at 5-year snapshots since 1990. Cottonwood County’s population declined over 17% in the 1980s, and the county experienced worse than average negative net migration in the 1990s and in this decade. While mostly an agricultural county, the county seat of Windom has a significant manufacturing presence with a Toro assembly plant and beef rendering facility. In 2003, 27% of the county’s workforce was employed in Manufacturing, compared to 13% statewide. In 1995 and 2000, Cottonwood County’s PCI was also less than 80% of the national average, but recovered to 90% of US average by 2005. In this case, it would appear the indicators did work, identifying distress during the 1990s and indicating recovery since. However, the story on the ground is more complicated. In 2001, Cottonwood County participated in a Labor Force Assessment conducted by the Minnesota Department of Economic Security (now part of DEED) and the Southwest Regional Development Commission (SRDC). This study indicated significant issues with underemployment—part-time workers who would work more hours if available. This would suggest that the unemployment statistic is artificially reduced because workers are picking up part-time jobs rather than wait for a regular full-time position. Underemployment is typical in many rural areas, and is often a function of salaries
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offered. In 2006, average weekly wages in Cottonwood County—in fact through-out DEED’s Southwest Minnesota Planning Area—were below the statewide average for every major industry measured (DEED QCEW). Unemployment alone is not a useful economic indicator in the region. Income Income figures are much less consistent than unemployment. Again looking at five-year intervals, but further back to 1970, as many as 15 counties or as few as one would have qualified as income distressed (per capita income less than 80% of the US average). While sources of income differ county-to-county across the region, much of Southwest Minnesota is dependent on farm income which itself can vary widely depending on weather and commodity markets. Most recently, in 2005, Sibley County in Region 9, Renville County in Region 6E, and Swift County in Region 6W had 80% or less than US average PCI. Swift County does have a long-term history of below-average per capita income, and as discussed before, the new prison clouds population-based statistics. Renville County would have been considered income distressed in 1995 and 2000, and has consistently suffered outmigration, but not necessarily at greater levels than other agricultural counties in the region. Over 20% of PCI in Renville County in 2005 was from dividends, interest and rents, while 20% was from transfer payments, almost twice the state average. Sibley County lost 3% of its population from 1970-2000, but is projected to gain 20% by 2030 as the Twin Cities’ metropolitan growth reaches down the Minnesota Valley. It is not clear that counties that qualify as income distressed in terms of per capita income are any more or less distressed than other similar counties in Southwest Minnesota. Income alone is not a useful economic indicator in the region. Outmigration As Feser and Sweeney described, outmigration draws away those residents most vital to starting new businesses and creating new wealth. This fundamentally limits and reduces a community’s ability to recover and grow in the future. It is important to understand the distress this causes, to accurately address basic needs. Martin County in Region 9 suffered the largest net negative migration of any county in the region during the 1990s, having lost 10% of its population from 1970-2000. The county is projected to lose an additional 3% of its population by 2030, although local officials are concerned that Census figures undercount recent growth. The labor force has had flat to modest growth and the unemployment rate has typically been below the national annual average—4.3% in 2006, compared to 4.0% in Minnesota and 4.6% in the United States overall. The county attracts more workers from across the Iowa line than residents who commute out-of-state. Fairmont, the county seat on I-90, is a regional trade center with a population of over 10,000 residents that has seen modest contraction in population, under the county rate. Martin County also had the region’s second highest per capita income—$32,668 in 2005, the most recent year available from BEA. Many
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essential factors are in place that would seem to point towards better than average chances of economic success. Unemployment and income statistics simply fail to reflect the forces prompting residents to leave the county. Lyon County in Region 8 suffered the second largest outmigration in Southwest Minnesota in the 1990s. This occurred even as the county population grew by 2.5% overall and the labor force grew by 13% in the last decade of the 20th century. Neighboring counties Pipestone (Region 8), Yellow Medicine (Region 6W) and Murray (Region 8) had the next largest losses from outmigration in the 1990s. Since 2000, outmigration has taken the greatest toll on regional centers in Nobles (Worthington), Redwood (Redwood Falls), and Kandiyohi (Willmar) counties. Traditional indicators don’t indicate distress. Unemployment levels in Lyon County have stayed consistently below the state level. Per capita income has ranged fairly consistently between 85%-90% of the US average. Lyon County is estimated to have the largest negative net migration again since 2000. As discussed above, some of the migration statistics may be attributed to the Southwest Minnesota State University campus in Marshall—students are typically easier to track leaving than moving in. Some of the current statistic may be attributed to the difficulty in estimating population change between each decennial census, which if implemented fully and completely the American Community Survey is supposed to fix. Still, given the complete count of Census 2000, there is every reason to believe that Lyon County and its neighbors continue to loose migrants today. Marshall, the county seat, currently is estimated at about 13,000 population, and is home to the international headquarters of The Schwan Food Company. Other primary employers include U.S. Bancorp Business Equipment Finance Group, Southwest Minnesota State University, and Archer Daniels Midland’s wet-mill corn processing plant. As discussed previously, major manufacturing and food processing firms have faced economic challenges since 2001 that have created uncertainty in Minnesota’s labor markets—seen here in outmigration rather than unemployment indicators. Another group of about 15 counties has also continued to experience significant outmigration since 2000. Watonwan County in Region 9 southwest of Mankato has lost twice as many people to outmigration this decade as in all of the last. Yellow Medicine County in Region 6W north of Marshall, had a 500 person loss due to outmigration in the 1990s, and has lost almost as many people this decade. Pipestone County in Region 8 between Marshall and Sioux Falls has lost over 350 people to outmigration, although with the new Suzlon Rotor assembly plant opening in 2006, the City of Pipestone is experiencing a housing crunch. Many of these counties have an economic base more grounded in agriculture, but many also have fairly diverse economies. What is pushing people to leave Martin County, Lyon County, and other similar communities in Southwest Minnesota? If unemployment and per capita income are even, yet people are migrating away, there is likely a hidden source of economic distress not currently captured by existing socioeconomic indicators. Outmigration—more people leaving than moving in—indicates long-term economic distress in the region.
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CONCLUSIONS AND RECOMMENDATIONS Economic indicators such as unemployment and income may be useful tools in some regions, but not generally in the rural heartland, and specifically not in rural Southwestern Minnesota. The majority of counties in Southwest Minnesota have experienced, and are expected to continue experiencing, ongoing, long-term population loss. Outmigration—more people leaving than moving into the area—is a significant component of population change in the region. Outmigration is creating significant economic dislocation in Southwest Minnesota, and should be considered as a distress factor when evaluating the region’s socioeconomic situation. Outmigration takes away those residents a community needs most to create a vibrant future—the young people, professionals, and those with skills in demand for new growth. Currently, outmigration disguises the economic condition of much of the area, by skewing typical data evaluators such as unemployment and income. Considering outmigration as an indicator of distress would create a more complete picture of regional economic dynamics in areas where unemployment and income figures fail to provide paths to future prosperity. The data demonstrate that outmigration is an appropriate indicator of economic distress and should be used as a factor in investment policy. Further research is an appropriate next step to more fully understand the distress caused by outmigration. Action items should be documented and evaluated to provide better direction on policies and projects which will reverse, or at least stem, outmigration from distressed rural communities. Improved collection and dissemination of state and federal sources of socioeconomic data is an ongoing challenge. Fiscal constraints demand that agencies focus on essential information. Changing social norms also present a moving target in how people respond to surveys and their methodology. Projects such as the American Community Survey must be structured to take into account the needs of all of the nation, both urban and rural. Policy makers and private investors can only make informed decisions with accurate and timely information.
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REFERENCES Amato, Joseph A. To Call It Home: The New Immigrants of Southwestern Minnesota. Marshall, MN: Crossings Press, 1996. “Cottonwood County 2001 Labor Force Assessment” Minnesota Department of Economic Security, November 2001. Feser, E.J. and S.H. Sweeney. “Out-Migration, Depopulation, and the Geography of U.S. Economic Distress.” International Regional Science Review; Vol. 26, No. 1: 38-67, January 2003. Feser, E.J. and S.H. Sweeney. “Out-migration, Population Decline, and Regional Economic Distress.” University of North Carolina-Chapel Hill, December 1998. [EDA No. 99-07-13792] Local Area Unemployment Statistics (LAUS). Minnesota Dept. of Employment and Economic Development (DEED). http://www.deed.state.mn.us/lmi/unemployment.htm . MacDonald, Heather. “The American Community Survey: Warmer (More Current), but Fuzzier (Less Precise) than the Decennial Census.” Journal of the American Planning Association, Vol. 72, No. 4: 491-503, Autumn 2006. Macht, Cameron. “18-County Southwest Minnesota Regional Profile Project Report.” Minnesota Dept. of Employment and Economic Development (DEED). February 2005. Macht, C. and J. Ridgeway. “On the Rebound: Southwest and South Central Minnesota Employment Gains Driven by Regional Centers.” Minnesota Employment Review. June 2005. McMurry, Martha. “Migration a major factor in Minnesota’s population growth.” Minnesota State Demographic Center, July 2002. McMurry, Martha. “Migration Profiles for Minnesota Public Use Microdata Areas.” Minnesota State Demographic Center, June 2004. McMurry, Martha. “Migration Trends in Minnesota, 2000 to 2005.” Minnesota State Demographic Center, December 2006. Minnesota Land Management Information Center (LMIC) geographic data. Minnesota State Demographic Center. “Minnesota Population Projections 2000-2030.” October 2002. Shepard, J.C. “Grassroots Response from the Great Plains.” Forum for Applied Research and Public Policy 9: 101-105, Winter 1994. Shepard, J.C., C.B. Murphy, L.D. Higgs and P.M Burgess. A New Vision of the Heartland: The Great Plains in Transition. A Report to the Ford Foundation and the Aspen Institute. Denver: Center for the New West, March 1992. Shepard, J.C., M. Muehlbauer and L. Higgs. “Measuring Distress: Economic Indicators and the Great Plains.” Denver: Center for the New West, August 1992. [EDA No. 05-06-02503] SRF Consulting Group, Inc. “Trade Centers of the Upper Midwest, 2003 Update.” Minnesota Dept. of Transportation, 2003. US Bureau of Economic Analysis, Regional Economic Information System (REIS). http://www.bea.gov/regional/reis/ . US Census Bureau. Statistical Abstract of the United States, 26th Edition (CD-ROM) 2007. US Census Bureau. U.S. Census of Population and Housing, 1970, 1980, 1990, 2000.
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US Census Bureau, Population Division. “Cumulative Estimates of the Components of Population Change for Counties of Minnesota”, 2007. Wood, Lawrence E. “Trends in National and Regional Economic Distress: 1960-2000.” Appalachian Regional Commission, April 2005.
About the Authors John C. Shepard, AICP, is Development Planner with the Southwest Regional Development Commission (SRDC) in Slayton, Minnesota. Prior to joining SRDC, he worked with counties in Colorado, Montana, and North Dakota in local land use planning and economic development. Shepard has earned degrees in urban and regional planning from the University of Illinois and University of Colorado. He is certified as an Economic Development Finance Professional, and is a member of the American Institute of Certified Planners. Carrie Quast is GIS Planner with the Upper Minnesota Valley Regional Development Commission (UMVRDC) in Appleton, Minnesota. She provides geographic information systems (GIS) services to local units of government in Region 6W. Quast is a graduate of the University of Minnesota Duluth and has a Masters degree in GIS from Saint Mary’s University of Minnesota.
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