Project/Activity Number:

Project/Activity Number: NC1100

Project/Activity Title: Rural Development, Work and Poverty in the North Central Region

Period Covered: 7-04-705

Date of This Report: 7-08-05

Annual Meeting Date(s): May 11, 2005

Participants:

Richard Goe: Kansas State University

Roman Keeney: Purdue University

Michael Schulman: North Caroline State

Cindy Anderson: Iowa State University

Chih Yuan Weng: Iowa State University

Linda Lobao: Ohio State University

Scott Loveridge: Michigan State University

Katherine Fennelly: University of Minnesota

Donna Hess: South Dakota State University

Gary Green: University of Wisconsin

Wendy Wintersteen: Administrative Advisory, Iowa State University

Cornelia Flora, Mary Emery: NCRCRD at Iowa State University

Adopted Agenda:

1. Introductions

2. Overview of the committee charge

3. Administrative Advisor Report

4. Committee reports

5. Discussion on research design

Discussion on audience for policy information
Work session to finalize research design, tasks, and timeline
Work session to plan the application process for at least one funding source
Assignment to follow up on additional funding sources
Conference call and meeting dates for the coming year

Next annual meet connected with 2006 RSS in Louisville.
We will get a committee meeting together at RSS
Conference call schedule: June 28 p.m. CDT and August 22 10 a.m. CDT

Strategies to improve communication via the website and the list serv

We will add Michael and Chh-Yaun Weng to the list
We will set up a web CT page or check with the grants’ office on their website.

Action plan

1. Read Rank’s book

2. Data base cooperation: Cindy, Linda, Richard and Gary

3. Coordinate the database at ISU.

4. Initial maps by mid-July

5. Case study methodology team/ proposal writing – Scott, Mary, Michael (Ann Tickemeyr comparative case studies of counties of Appalachia. Contact and call.

6. Send out a reminder to everyone on who will do what.

7. Work on NRI proposal this summer

13. Review of meeting results and assignments

14. Elect committee chair and discuss logistics: Cornelia Flora will serve as chair for the coming year.

Brief summary of Minutes of Annual Meeting:

The NC100 meeting in Chicago focused on refining the rationale for the study focus, identifying existing databases to support the work, determining the study design, and identifying key variables. The committee also worked on identifying possible funding sources and discussing strategies for a successful NRI proposal. The committee will work on the action plan and proposals for funding using a web-based work space. Initial mapping of the descriptive results will provide initial results in July. This information will be used to identify locations for case studies of communities that are working their way out of poverty successfully and those that are falling into further decline. A sub committee will work on developing the protocol for the survey and case study.

Key Discussions:

Administrative advisor report: Wendy Wintersteen went over the nature of NC committees which are designed to support collaborative research with specific objectives that lead to external funding and publications. She encouraged people to visit the NIMS website which houses the multi-state projects. NC region has about 110-120 projects. The funding stream requires 25% of the funding to support multi-state collaborative research projects. She also reported on the controversy regarding Hatch funding.

We also talked about the low level of funding for rural development and markets and trade. They have decided to alter funding for these to every other year, so there is more money each year for the one area. Neal expressed her concern about the lack of funding to support research related to the community and economic development issues that relate to needs and programs in the field. There is now a proposal for NIFA, an institute for fundamental research for plant and animal systems. The problem with this proposal is that it might put existing funding in jeopardy and would narrow the funding focus even more than the current NRI.

She hopes that we will be able to put into place a plan that can be presented to the Directors this summer. We need to have a plan and enthusiasm for the project. The Directors have a history of providing a small amount of funding to the Center, so we need to show how the Center adds value to the system.

Sources of Data: We are interested in place-based research while the ARMS data focuses on the individuals. ARMS does have some data on off-farm employment in 4 classifications and the connection to education levels. It also has info on spousal employment. There is something on the motivation for off-farm employment including access to healthcare/insurance.

We are interested in the intersection of community, employer, labor force and labor market. We need to identify rural labor markets and assign them to categories, select a stratified sample from each market, survey households based on the employers (over sampling minorities and poor people), and look at food security and other issues.

Cindy reported on her data sets. She did a presentation on a demographics of the area. She has been working on data sets including on based on 70s to present county level data including indicators that relate to economic shifting looking at percentages of people in types of work and access to assistance. She also has one based on the 2000 data with has county-level, census tracks, and other levels. It would include some of the same variables, but this one would you use hierarchical models, so looking at the upswing in low-wage jobs to compare to counties with similar changes but without the upswing in low wage. We need to look at the local decision making to see what is going on. Down the road, it would be nice to do case studies of communities that have successfully navigated these changes. They hope to put out some demographic profiles this summer. Cindy is looking at data which has a threshold of 100,000. It provides a way to capture some of the rural areas. Big cluster analysis is based on commuting data. Soon they will also have access to commuting zones: labor market areas which are multi county. Commuting zones are considered to be a more reliable measure of the intersection of the household and workforce. There are two things – the geography which anyone can use. There are also some micro data for 1980 1990 which is accessible. For 2000 they released the geography, but not the mirco profiles. Cindy reported they are using PUMA which connects to micro levels. This is something that allows contextualizing with different levels to look at interaction among levels. We don’t see the response from employers and communities in this data just the impact: full or part-time, measures of inequality, type of work. We hope that this data base can help us contextualize the data. We are also interested in the notion of agency – how communities and employers respond. We hope to put out demographic profiles and some maps. We will try to get some maps for the Directors’ meeting

ERS has been working on labor market at the macro level.

Gary reported that he has been working on the question of, do manufacturing jobs matter? He has been working at the community/county level. The labor department has done some match surveys from employers and households. The employer data deals with hiring, turn over, lay offs, etc. He has done some analysis. In WI 15% of manufacturing jobs were lost, most of them are low-wage jobs – sort of counter intuitive to what we think is happening. The information does not include sub contractors/ contingent workers. Are communities and counties with higher levels of manufacturing doing better than those with lower levels? Also, he is doing some individual analysis with current population data. Does having a manufacturing job matter? We are seeing a decline in these jobs as good jobs – more low-wage and part time. The loss of manufacturing is uneven; some places like IA, Dakotas and WI have held on to many manufacturing jobs, but other states have lost more jobs. The bigger story is that the loss of these low-wage jobs is really restructuring rural manufacturing. They match data from the census is from a separate survey they started some time ago, but not all states participate. You match the data of employers with the data on workers. The plan is to do it annually. You can not get access to raw data, but you can get county-level data. What we are doing with county-level data is some spatial analysis as it tends to be highly concentrated. Ironically, some rural areas are emerging as highly concentrated in manufacturing, but the recession means that some of these areas lost jobs.

Three interesting sets of data: How could they inform each other? How do we address shifting impacts on people and places? How do we look at local agency? Could we merge these sets and then look at particular communities? Richard is looking at changes in underemployment by low earning and low hours and then looking at county level data and the commuting zones and different determinants of change. He is not including 1970. He is also interested in service industry, particularly business services that support other businesses. Does the growth of these firms impact economic development with higher paying jobs – do they become a growth node? Gary commented that the real question is whether these workers can move up. One finding is that they move up by changing employers. How constrained are they in rural areas by the opportunities in the area

Gary and Katherine are interested in the employer piece.

Michael we have been working on the individual level with focus groups and also looking at data on pre and post displacement, particularly elderly women workers. (March 2004 issue of Footnotes) May be related to community self-help for displaced workers. It shows disparities. There are a variety of paths – low wage work and migration -- like cleaning houses, rural kin reliance with people moving in together. Some go through GED and use the special funding for displaced workers which may also lead to out migration. In NC there is a very strong sense of place and community. People are bound to place and the oral community. The one stop is not really one stop. These lost workers are a segment of the population that doesn’t show up in county or national data.

Economic development. How do communities choose the strategies that they do to stimulate economic development? Time preference of poor for how long they are willing to wait for economic impacts – wealthier income categories want the jobs now. The poor would be willing to wait longer to get good jobs in the future.

Key – intersection of work and households through commuting zones.

Contextual analysis with the embedded layers. No employer data.

Can map global trends, but can’t map the responses of communities and employers.

Do manufacturing jobs matter for rural areas? Survey matching employers and employees. Since the recession, what kinds of jobs are being lost – hirings, layoffs, and turnovers. The low wage, non-durable sector manufacturing jobs have been lost. What is left is the better paying jobs. Manufacturing jobs are not as good as they used to be. Huge losses in manufacturing occurs more in the traditional manufacturing places. More contingent jobs are left.

Major issue: is there social mobility. Harry Holtzer – low-wage workers can move up if they can change employers. In rural areas, how constrained are you by the opportunity structure? Do low-wage jobs provide experience to move up?

Displaced workers – pre and post displacement. Disparities by race, gender, and region. There is a variety of paths in post-displacement. Marginal employment and kin-based survivals strategies. GEDs being pushed through the displaced worker funds – low level service work in hospitals. Because of housing costs, people are bound to place. Lumbi have a very strong sense of place. With older women, it is more varied than the displaced worker CPS survey shows. One-stop service work is not one stop. They tell you where else to go.

Issues: Access to transportation, services, what the community can or cannot provide.

There is a new national survey of entrepreneurship which would allow on to identify minority owned firms. Marta Tienda at Princeton is one of the people involved. (Survey is only of Chicago per Michael’s follow up email.)

What is the non-profit density in these communities?

Can we identify success stories that have achieved growth? How can they keep the high wage jobs when loosing the low wage jobs?

Tenure in the community was a big indicators of willing to defer immediate results for good jobs. How are strategies formulated?

Does an influx of immigrant workers lead to an increase in main street businesses? Ladder – from wageworkers to small business owners.

Testable hypotheses – low wage manufacturing jobs are the ones being lost.

Causal mechanisms: What are the causal mechanisms that produce low wage jobs? Value chain in a firm and the division of labor. Manufacturing was once a high value added activity and now is a lower value added activity. Other nations can develop this capability rather quickly. Rural areas first targeted for low wage manufacturing in the 1970s. There is an another wave when these are being transferred to a lower wage environment. If workers can organize to protect there wages, they go up when the jobs cannot be moved. JIT delivery further disadvantages rural America.

Low wage manufacturing jobs are exiting rural areas faster than high wage manufacturing jobs.
Low wage jobs are increasingly in the service sector.
Rural counties with a higher dependence on import sensitive industries are more likely to have higher rates of underemployment and job loss.
Counties with high concentrations of women and immigrants would be more likely to have low wage jobs.
How does the labor market and demographic of the county insect in terms of well-being – race, sex and gender. The older the age of the labor force let go, the harder the opportunity of getting new work. Women are less likely to commute. The more embedded the workers, depending on opportunity structure, the less like to obtain employment elsewhere.
What happens to displaced immigrants? (relation to school system)
Communities with more immigrants will have a higher increase in small businesses than those that do not.
Counties that invest in big box stores are more likely to have low wage jobs and higher rates of SSI.
Tax structures

The notion of agency – labor unions, county supervisions, NGOs, dominant faith influences.

Discussion on audience for policy information

We also want to come up with some policy recommendations. If this is the case, how do you create career ladders? How do you move from a packing plant to air conditions to management, etc.? Are you unhireable because of race or ethnicity?

Work session to finalize research design, tasks, and timeline

Donna reported that the study had a very low proportion of Indian families, so they went out to 23 families. In the area where they collected data, the presence of the tribal college led them to be very optimistic about possibilities – hopefulness. How might distance education fit in with having a future with place-bound low-income rural people? There is a lot of overlap between the two projects for example access to transportation.

They were approached by a county commissioner to come down and do a study. The county has lost population over the last decades. There is a fairly large segment of middle-aged workers and old people. There is controversy over a signers’ community, especially from farmers. Their proposal is to support the county through tourism. They want to establish their own school system; they have an option to purchase the land. The county commissioner wants to know why people are so opposed. They will build a new community; they are largely upper class. What they are proposing might have an impact on the low-wage population. Farmers are worried about the odors.

Question: is it worthwhile to merge these data sets? How would we do that? Then we could do follow up studies and also look at the county commissioners’ notions of models, efficacy and ideas about working poor and economic development.

There is a new national survey… on entrepreneurship which would allow one to identify minority firms. Contact Marta Tienda at Princeton. Michael will follow up. (He reported that the data was only related to ethnic entrepreneurs in Chicago.)

Richard, if we look at different types of communities and the decline in ag, mining, timber, so the places we are looking at have some diversification – are they connected to a value change where some connections are linked to higher wages. The problem is that moving to rural areas is usually a strategy to reduce wages.

Outsourcing for the region – using Forrestor’s formula. Tourism and personal services less likely to outsource. In the Dakotas there is more chance for out sourcing including call centers.

Part of the nesting piece is what is happening at the household level.

We offer the approach that looks at the linkage between commuting areas, what localities do to respond. We might survey two sets of responders: county commissioners, non profits, and employers. Look at networks of family support, churches, tribal colleges, and other sources of support.

MN has lost some of the non profit section. We can identify communities with manufacturing decline with match sample and look at what is going on locally to address the displacement of jobs. It will take us to the notion of community agency.

Can we identify successful communities with lower unemployment and less low-wage jobs and poverty? Can they keep high wage jobs when they lose low-wage jobs?

We need to look testable hypotheses: macro level changes and what makes for community success

some places people will just leave, but other places have reasons for embededness: reservations, small community with strong links. Sense of community, tenure in the community, community strategy link to outmigration.
If people have data on Latinos or businesses that employ Latinos, we can look at that.

People are interested in models of economic development that address low-wage jobs.

Katherine is interested in the entrepreneurship explosion on mainstreet where there are large immigrations of Latinos.

One piece will be the macro database probably on a county basis; The other piece might be the survey interviews we do with county commissioners

We might want to connect with the state associations of counties. We can see if they will help fund the survey.

Case studies: outliers: better and worse than the norm; fault line communities.

Farmer labor mobility – farmers are tied to place. In Indiana, farmers got jobs in manufacturing. Role of specific human capital that is not transferable to a new job at a new wage rate. It will be underemployment in the new job. What are the implications for community over time? Do they become a place of just one big farm? What is missing is the labor market side, and how transferable are skills across jobs. Technology curves steep. A lot of experience in one sector might not even qualify for entry level jobs. How these roles accept farm numbers.

Is it restructuring or disappearing of jobs?

The military is a major occupational ladder for achievement – less in North Central than in the South. It offers a less racialized occupational structure.

We need to show how it is different here. County questionnaires on models of development.

One official per county – 60%, and ICMA data provide annual surveys of many different topics. Very few rural counties included and a low response rate. Counties are doing more, and are the fastest growing general purpose government in terms of employment. Responding quickly to decentralization. (prior to 9/11) Post 9/11 may be different? Are they cutting back on social service activities?

County supervisor data. Need a graduate student to work with the data and pull the variables. No master data set – need to pull them all into one data set.

Data sets. Include some health outcome variables. Lois Wright Morton has some health outcomes Area resource file – mortality, National Center for Health Statistics. Higher rates of morbidity are associated with low wage workers. Health costs for employers and how employers deal with it. This is part of job quality. Stephan Goetz’ measure of what counties pay in health costs. Policy through rural caucus put pressure on pharmaceutical companies to serve low-income rural counties.

Do we have race/ethnicity and US born and foreign born Kathy will respond on hypotheses generation.

Primary variables of interest – we need maps on them

Read Mark Rank’s On Nation UnderPriviledged 2004 a recent book on poverty. Dependent variable: low wage earners. Economic structure and social safety net. Weak on laying out the economic part – just says there aren’t enough jobs and they are not good enough. People fall in and out of poverty several times during their life. Help spatialize.

Ship Cindy a description of data sets they are using.

Term: How do we conceptualize the working poor? Underemployed by: low hours, low wages, unemployment, contingent employment.

GET Gary’s maps of low-income work.

We need a sub group working on the methodology and connections for the survey. Scott, Neal, and Mary.

test the finding that most of the jobs that are lost are low wage Low-wage manufacturing jobs are exiting rural areas more rapidly than high wage manufacturing jobs (recent Amber Waves) We would have to define rural very carefully

Low-wage jobs are found increasingly in the service sector

Rural areas with value change are likely to have more high wage jobs with career ladders for people to move up.

Look at causal mechanisms to generate hypotheses

Technology and automation are variables

Enhance disadvantage of rural because of time and transport issues

What industries have higher transferability – the map of out sourcing – we already have that

Rural counties with high outsourcing risk are more likely to have higher under employment and job losses

Rural counties that invest in big box strategies are more likely to have more low-wage jobs

Katherine Newman - % of kids working at mcdonalds had a huge positive impact – we don’t talk about the working poor, we talk about the poor on welfare

Would we find something like that with walmart in rural areas.

How does collective bargaining impact wage rates. Walmart jobs are no easily transferable – but the internet might threaten that.

2. Rural counties with high outsourcing risk are more likely to have higher under employment and job losses

Rural counties with higher sensitive higher rates of under employment and job loss
Import sensitive work would have higher participation by women and minorities.
Communities with high rates of in migration will see an increase in small business, particularly related to those ethnic markets. Labor is moving in to jobs defined as marginal by the majority, but this is a creative group that will lead to new business need to use county employment stats for establishment size. Maybe also look at tax information/impact
Social service transfer: low-wage jobs will increase demand for and costs of social services – medcaid, public assistance
Agency as demonstrated by NGOs and county officials, type of faith will impact % of low-wage jobs - look at fault lines like two counties adjacent to one another with different faith-based groups have different ways of addressing poverty; fair wage movement how do demographics intersect with well being related to gender, race, age the greater the embededness of the person the less likely to migrate the greater the opportunity structure the less likely to migrate out racializing the workforce – we have no obligation to them as they are not counted as part of the community higher the rate of decline in school population, the more welcoming to migrants how employment opportunities and displacement impact the future of farming

8. Labor markets that have options will see less out migration

9. Jobs are just disappearing – the opportunity set has shrunk

Katherine can test the hypothesis that people in counties with declining school population are more welcoming to migrants

11. Opportunity structure, embeddedness, population loss, wage structures

People in rural area have multiple income sources – the ratio of people killed in Afghanistan and Iraq – twice as many as expected because of multiple income sources – this is an indicator of diversified incomes. Penn state did a study of rural income sources.
Military is the most highly diversified, but there is ceiling for minorities and women; also a ladder for achievement
Labor market commuting and what happens to the workers.
We have access to national data sets. We can start interviews with county people in our region.
Richard – national data set which can be integrated into Cindy’s – analyze looking at under employment identify factors or determinants of why some places do better in same or similar situations
First level of agency at the county level to look at their models of economic or workforce development.

Linda: NRI grant in 2001 survey of county officials use of economic development and social services and they have repeated that survey. They have not analyzed the data from 2005 yet. Casey is interested as a strategy to learn how they use data. First set, decentralization is going on. Response rate over 60%. ICMA provides annual survey data. Interesting data but low response with few rural. Counties were reporting more; employment growth faster than any other form of govt. Counties are a good level of government to learn about responses to decentralization. They also asked questions about entrepreneurship, taxes, fees, advertising nationally for business, workforce development. There was a smaller survey in 99 in Ohio. They don’t have any more funding to work on the data – there is no master level data set to look at the most recent data. We would have to figure out how to access this with the folks who are part of the collaborative effort. Linda will send out the first survey. They have a paper in Economic Development Quarterly and also perhaps with rural soc.

One issue is different levels of analysis. Cindy is looking at historical county-level. We also want to look at the data based on commuting zones and at industrial change and at why the working poor is an emerging issue? We have more heterogeneity in this region: tribal, Latino, other immigrants. And communities that are doing well and not so well and these connect to the other categories. Spatial inequality: city centered approach and cross national approach. We are missing the middle. Arguing conceptually to apply the concepts across scales.

If we go to NSF or another funding source we could ask for funding to support merging the databases so we can learn something about the impact of local decision making. We can look at the data to determine which counties are doing better and looking at some hypotheses related to that data.

Look at the models that the County officials are doing in regard to economic development. What people used to do was human capital upgrading versus attracting industry. The theoretical model says you will see human capital, no growth or attracting industry. Usually, if they are doing one they are doing all. Flora and Flora found that if you do self development, you likely to it all. Today counties may see themselves as more responsible for economic development than previously.

When we do this descriptive study, then we can go in and do these in-depth case studies.

National health interview survey. Does CDC have county-level data? Diabetes or asthma rates and maybe other rates. Donna will get us some data. Louis Wright Morton also have some data on health. One of the reasons people become poor is health issues. The area resource file has mortality figures, accidents, etc. and it is over time. National Center for Health Statistics. Health outcomes Also, health costs for employers and impact on low-wage workers.

Why do we care?

Increased prevalence in our region
Externalized costs of low wages
Improving quality of life is on the agenda of many governors of our states
We hypothesize that local and state actions can make a difference.
We are convinced it is a place issue as well as an individual issue
There is a gap where policy is focusing and where the displaced and restructured population of working poor is located.
To retain population, must have alternatives rather than out-migration or low wage work. (meth use, militia formation)
Need to identify strategies for viable and sustainable communities.
Stress on families caused by multiple job holding makes parenting difficult and increases juvenile delinquency and health problems
Need to improve the quality of jobs that are there, as there is potential labor shortage. Job quality issues.
Long standing sociological issue: a spatial mismatch – over emphasis on persistent poverty, which blames the victim. By looking at the “deserving poor”, it can become a structural issue.
If there is only low wage off farm work, we cannot keep farmers on the land.
Critical base of rural development
Polarization of classes and society is a problem in rural areas, and low wage work contributes to it.
Globalization and rural competitiveness.
Upper Midwest has had such a low level of racial diversity, and low wage work is related to an influx of immigrant workers.

Primary variables of interest:

What is happening in terms of our primary variables of interest? What are the descriptors for the primary variables? Are we losing high-wage jobs at faster rates than low-wage jobs of vica versa. Labor utilization studies. Underemployed, low-wage work, - how we conceptualize that is pretty important. Underemployment by low wages compared to education and under employed by hours, contingent or intermittent employment. Which of the measures seem most robust in measuring and which are best at finding the outliers. Gary has done some work on this. There is poverty and then there is low-income work. We are more interested in the low-income work – the deserving poor.

Labor utilization framework: low hours worked per job (can’t sort out voluntary from involuntary), low earnings – employed below the poverty threshold. Or slightly above the poverty level. We are interested on how place makes a difference, using the county level of analysis. Unemployment – lack of work. Seasonal work. Part time work. Poverty rates of families, individuals, households, income inequality, related economic well-being variables. We are interested in the change over time. Moving out or falling in. (Thus we can identify the exceptions). Geni co-efficient of income, housing costs
Industrial structure – high paying and low paying manufacturing and service jobs; import sensitivity (risk of outsourcing) – changes over time.
Diabetes and asthma
Social safety net expenditures SSI and public assistance. % receiving AFDC/TANF over the poverty population
Place variables, Beale code (urban influence), amenity code
Demographic variables: out migration, educational levels, race, gender, immigration

Agency variables

1. Percent that vote (City-county data book)

2. Unionized labor

3. State level variables – unionization, right to work laws, anti-corporate farm laws and degree of toughness, barriers to entrepreneurship, state minimum wages, degree of local ability to see the rules and regulations under which they operate. Home rule.

Cases:

What would we look at in terms of case studies? Issues in response to low- wage rates? What are the responses to low income job development? Self employment not well remunerated, as a multiple incomes streams.

1. NOT look at no growth nor high out migration (hypothesis: high rates of out-migration keeps up the wages of those remaining

2. What are the community strategies that resulted in being above or below the mean?

3. Inventory resources that are there – presence of college, other assets

4. How balanced were community strategies. View of their own capacity as a community.

Linda suggested we look at Mark Rank’s new book, One Nation Underprivileged, 2004. . 66% of adults will fall into poverty at some time in their life. Two sets of factors the economic structure and the safety net. He is weak on economic - not enough jobs and not good jobs. It’s short term, not long-term. People fall in and out of it.

Katherine will look at the data sets in terms of her interests in immigrants and language differences.

Discussion on funding sources

Sources of funding

1. Joyce – explore it

2. Upjohn (small grants -- $15,000)

3. NRI – Neal contact Pat Hipple (due in December)

4. Health dimensions – NIH

5. Russell Sage from their low wage program

6. NSF

7. Anne E. Casey (may contribute a portion)

8. Kellogg re. Policy

Potential partners:

1. AFL/CIO (Institute for Work in America

2. NACo and NADO

Work session to plan the application process for at least one funding source

We will work on an NRI proposal for December

Action Plan