Background and Purpose

Relevant Terms

This study includes demographic and geographic terms that require precise definitions. The U.S. Department of Labor’s National Agricultural Workers Survey (NAWS) defines a migrant farmworker as “a person who reported jobs that were at least 75 miles apart or who reported moving more than 75 miles to obtain a farm job during a 12-month period” (Hernandez & Gabbard, 2018, p. 5). Farmworkers who do not travel more than 75 miles from a residence or “home base” are classified as non-migrant or settled farmworkers. Migrant farmworkers can be either domestic or international persons. Migrant farmworkers may or may not have formal authorization to work within the United States.

The U.S. Census Bureau defines counties within a binary metropolitan or nonmetropolitan framework. A county is designated as metropolitan if it includes an urbanized area of 50,000 or more people. A county is designated as nonmetropolitan if it does not include an urbanized area of 50,000 or more people (Cromartie, 2021).

The U.S. Census Bureau’s definition for rural is distinct from the county designation. Rural is any area that is not located within an urbanized area (a city population of 50,000 or more) or within in an urban cluster (a city population between 2,500 and 49,999) (Ratcliffe et al., 2016). Thus, a household can be rural (outside of an urban area) yet still reside within a metropolitan county. Likewise, a household can be located in an urban cluster yet still reside in a nonmetropolitan county.

Introduction

In order to understand the geography of migrant farmwork, it is first important to discuss the special barriers these workers face for vocational rehabilitation (VR) outreach. These barriers are directly related to the geographical and social context of this population, as well as the difficulties associated with locating and reaching these communities. Migrant farmwork communities are often out of view of the wider community, and a background of the special barriers associated with outreach efforts is necessary to understand the difficulties in locating and identifying these communities. Migrant farmworkers have unique barriers, in addition to common rural barriers. Common rural barriers include few or no providers through wide-ranging rural regions, low caseloads resulting in insufficient provider compensation, and fewer rural employment opportunities for clients (Ipsen et al., 2019).

Farmworkers retain additional barriers for VR. Over one-quarter (26%) of farmworkers in the United States are migrant, representing some 364,000 people (Nichols et al., 2014). Some demographic information is collected on migrant populations by worker interviews conducted through the U.S. Department of Labor’s National Agricultural Workers Survey (NAWS). NAWS’s data is derived from employers who consent to employee interviews with the U.S. Department of Labor, which may exclude non-consenting employers with high migrant employment (Ravuri, 2017). Data from NAWS interviews estimate that one in three migrant workers cross the Mexico-United States border during seasonal work, and almost one in five migrants (18%) are new international migrants within the previous year (Hernandez & Gabbard, 2018). Migrant farmwork often includes more hazardous work environments and higher health-related issues (Villarejo, 2003). Almost half (49%) of farmworkers do not possess United States citizenship, residency, or official visa work authorization, and a great majority of farmworkers (69%) in the United States were born in Mexico (Hernandez & Gabbard, 2018). Language barriers among farmworkers add additional barriers to vocational rehabilitation, as 77% of U.S. farmworkers identify Spanish as their primary language. Educational attainment is also a factor in vocational advancement for farmworkers, as eighth grade is the average level of education among this demographic. Most migrant farmworkers are separated from all nuclear family members (Hernandez & Gabbard, 2018). A migratory lifestyle, isolated social conditions, lack of education, language differences, and fear of arrest for unauthorized migrant farmworkers establish unique barriers for VR service. Such difficulties discourage procurement of vocational services, hindering economic advancement for migrant workers.

A critical barrier for migrant farmworkers is the isolation of this demographic from wider society. Incomplete demographics pertaining to migrant farmworkers have been a known issue for several decades. Migrant workers, by definition, live seasonally mobile work cycles, while demographic data only captures a moment in time. In 1962, the federal government established the United States’ Migrant Health Program (MHP) to provide health services to migrant farmworkers (National Center for Farmworker Health, 2021). MHP activities were thus established for domestic farmworkers and not foreign farmworkers, even though hundreds of thousands of farmworkers were foreign contractors (Villarejo, 2003).

Migrant farmworkers have differing work patterns. A distinction in lifestyle exists between domestic and international migrants. Domestic migrants are evenly distributed between “follow-the-crop” lifeways and “shuttle” lifeways. Follow-the-crop farmworkers travel to different farms seasonally, following the harvesting patterns of different crops. Alternatively, shuttle migrants have a home in one place, and then shuttle at least 75 miles to a job before returning to their home base. International migrants are much more likely to be shuttle migrants (Hernandez & Gabbard, 2018). Within the context of international migrants, the home base for shuttle migrants is likely in Mexico, meaning the migrant farmworker spends part of the year in Mexico, shuttles to the United States for a job, and then returns to Mexico. In California, 91.4% of farmworkers were born in Mexico, and 16% are classified as migrant workers (Schenker et al., 2015). However, the seasonal nature of agricultural fieldwork—combined with the factor that many migrant workers cross the border back into Mexico seasonally—complicates any demographic figure.

Migrant farmworkers are at an increased risk of medical issues as compared to farmworkers who are not migrants. The U.S. Department of Agriculture’s Food and Nutrition Service provides a supplemental nutrition program for women, infants, and children (WIC). Data derived from WIC participants illustrates critical differences between migrant and non-migrant communities. The most significant distinction between migrant and non-migrant communities is the proliferation of homelessness among migrant workers. Among migrant households receiving WIC supplements, over half of children (51.5%) and almost half of infants (47.2%) are homeless, compared to 0.2% of children and infants among non-migrant WIC participants (Kline et al., 2020). Anemia, elevated blood pressure, obesity, and growth stunting are more prevalent among migrant children than with settled children in the United States. Ironically, many migrant farmworkers, who spend their days working agricultural fields, live in a state of food insecurity. Social and cultural isolation affects the children of migrant workers, with many children unable to receive primary health care (Nichols et al., 2014). Homeless children, families without health insurance, increased health risks, and manual labor are endemic among migrant communities.

Migrant farmworkers face additional health and safety issues. Agricultural work is physically demanding and dangerous, exposing migrant farmworkers to an increased risk of occupational injury (Villarejo, 2003). The Hispanic population accounts for 60% of work-related fatalities in the United States (Smith et al., 2006). The U.S. Bureau of Labor Statistics (2020) tabulated work associated with animal production and pig farming among the occupations with the highest incidence rates of workplace injury. Exposure to pesticides is a serious concern for farmworkers, and these workers have a heightened danger of exposure to poisonous chemicals (Nichols et al., 2014). In a survey of 150 farmworkers in Oregon, 34% reported having been sprayed by chemicals from planes and tractors (Farquhar et al., 2008). In 2020, rural meatpacking counties had more than ten times the cases of COVID-19 per capita as compared to other rural counties (Cromartie et al., 2020). Despite these risks, only 34% of migrant farmworkers have health insurance coverage (Hernandez & Gabbard, 2018). Unauthorized workers often do not report work-related injuries due to fear of deportation (Smith et al., 2006).

The purpose of this paper is to identify specific regions, states, and counties that are likely to contain communities of migrant farmworkers. The questions examined include (a) which states likely have significant migrant farmworkers, and (b) which counties likely have significant migrant farmworkers. Identifying the geography of migrant communities is the first step in improving VR outreach to the counties in greatest need.

Methodology

This study examined data sets that may indicate the geographic context of migrant farmworkers in the United States. VR practitioners can prioritize specific counties and regions with the highest rates of unemployment, poverty, Mexican-born populations, and non-English speaking residents to make inroads into migrant communities. Primary data sources for this study are derived from the U.S. Department of Agriculture’s Atlas of Rural and Small-Town America. The Atlas of Rural and Small-Town America is a product of the Economic Research Service (ERS) within the U.S. Department of Agriculture. The objective of ERS is to understand the demographics, economics, and social factors within rural communities at the county level. The data sources include demographic figures for over 3,000 counties and states from all 50 United States and Puerto Rico. However, this study limited the data coverage to the 50 United States and Washington, DC. Data in the Atlas of Rural and Small-Town America is derived from the Office of Management and Budget, the U.S. Department of Agriculture, the U.S. Census Bureau, and the U.S. Bureau of Labor Statistics. The Atlas of Rural and Small-Town America is accessible electronically (see Cromartie, 2021).

Populations of migrant farmworkers are, by nature of the migrant lifestyle, difficult to identify. However, the interlacing of various data points, including poverty and unemployment rates, coupled with ratios of non-English-speaking residences and ratios of Mexican-born residents provide a mechanism to identify regions, states, and counties that likely have migrant communities. It is important to consider that demographic data regarding non-English-speaking residents and Mexican-born residents is likely tabulating established residents of a community, as opposed to migrant farmworkers and their families. While these data points individually do not necessarily indicate a significant migrant farmworker force, a convergence of these points in a single county may indicate the presence of migrant communities. For instance, poverty rate alone does not indicate a county’s likelihood of containing migrant communities. However, the convergence of high poverty rates and high Mexican-born population rates within a single county might indicate a higher probability of migrant communities within a county. The counties with the highest unemployment rates (n = 100), the counties with the highest poverty rates (n = 100), the counties with the highest rates of Mexican-born residents (n = 100), and the counties with the highest rates of non-English speakers (n = 100) have been arranged to identify states, regions, and counties that likely have large numbers of migrant farmworkers. A State Average Score was developed to determine states that may have the most urgent outreach needs. The representation of nonmetropolitan counties within these data sources was also examined.

Results

State-Level Findings

Correlations exist between unemployment, poverty, county classification, non-English-speaking populations, and Mexican-born resident populations at the state level. State-level information was tabulated by examining the counties with the highest unemployment rates (n = 100), the counties with the highest poverty rates (n = 100), the counties with the highest ratio of Mexican-born citizens (n = 100), and the counties with the highest ratio of non-English speaking families (n = 100). These counties have been identified and tabulated by their respective states in Table 1. States that did not register a county in any of these categories are not listed. The State Average Score is the average number among these factors. Table 1 arranges the states in descending order by State Average Score.

Texas has the largest State Average Score (29.00). Texas includes 10 counties among the counties with the highest poverty rates, 13 counties among the counties with the highest unemployment rates, 46 counties among the counties with the highest Mexican-born resident population rates, and 47 counties among the counties with the highest non-English-speaking resident population rates. Thus, nearly half of all the counties in the nation that are included in this study’s listings of counties with the highest ratio of Mexican-born resident populations and non-English-speaking resident populations are in Texas.

California has the second highest State Average Score (10.00). California includes nine counties among the counties with the highest rates of unemployment, 13 counties among the counties with the highest rates of non-English-speaking resident populations, and 18 counties among the counties with the highest rates of Mexican-born residents, but California does not have any counties among the counties with the highest rates of poverty.

Mississippi has the third largest State Average Score (6.75). However, Mississippi does not include any counties among the counties with the highest rates of Mexican-born residents or the counties with the highest rates of non-English-speaking residents. This same scenario is true of Michigan, which has 13 counties among the counties with the highest unemployment rates, but Michigan does not have any counties among those counties with the highest rates of Mexican-born residents or the counties with the highest rates of non-English-speaking residents.

In contrast to states like Mississippi and Michigan, Kansas does not have any counties among the counties with the highest unemployment or the highest poverty, but Kansas includes five counties among the counties with the highest rates of non-English-speaking residents and eight counties among the counties with the highest rates of Mexican-born residents. Idaho is similar to Kansas, with six counties in Idaho registered among the counties with the highest ratio of Mexican-born residents and two counties among the counties with the highest ratio of non-English-speaking residents.

County-Level Findings

Data provides context at the county level. The counties of highest unemployment (n = 100) demonstrate demographic figures strongly contrasting corresponding figures derived from counties across the nation (Table 2).

The counties with the highest rate of unemployment (n = 100) have an unemployment rate that is nearly double the county average unemployment rate across the nation (n = 3,198). The starkest distinction between the counties with the highest rates of unemployment as compared to all the counties across the United States regards the average ratio of non-English-speaking residents. The average proportion of non-English-speaking residents in counties with the highest ratio of unemployment (n = 100) increases nearly 200% as compared to the average ratio of non-English-speaking residents in all the counties of the nation (n = 3,194). Another significant distinction is that the average ratio of Mexican-born residents in the counties with highest unemployment (n = 100) increases 159.90% over the average ratio of Mexican-born residents across all counties in the United States (n = 3,194). The representation of nonmetropolitan counties increases by 3.52% among the counties with the highest ratio of unemployment (n = 100) as compared to the representation of nonmetropolitan counties across the nation (n = 3,147).

Table 3 identifies the nonmetropolitan counties (n = 10) with the highest ratio of Mexican-born residents. These counties have a median Mexican-born population of 28.17% (compared to 1.92% among all counties), a median unemployment rate of 12.15% (compared to 6.75% among all counties), and a median poverty rate of 30.13% (compared to 19.41% across all counties). Presidio County, Texas has the highest ratio of residents born in Mexico (36.85%), and a poverty rate that is also the eleventh highest in the nation (57.66%). While the median unemployment rate is higher in these counties as compared to the nation, unemployment rates in Garza County, Texas, Clark County, Idaho, and Hansford County, Texas are below 7.00%.

Discussion

Farmworkers and migrant populations have significant barriers for VR professionals—a primary barrier being the difficulty in locating and contacting these communities. Migrant communities are often isolated from other communities, and the farmworkers live transitory lives, rendering their population figures and geographic locations difficult for agencies like the U.S. Census, the U.S. Bureau of Labor Statistics, and the U.S. Department of Agriculture to monitor. Nevertheless, VR can use available data to identify local counties that likely have high numbers of migrant workers. It is important to consider that demographic data regarding Mexican-born residents and non-English-speaking residents is not a measurement of migrant communities; these demographic figures are likely registering established residents as opposed to transient migrant farmworkers. However, these figures also hint at what may be happening under the surface.

This study used various factors, that independently, may not indicate the geography of migrant communities; but taken together, these factors indicate a high degree of probability that a state or county has a significant migrant workforce. While a county’s high rate of poverty alone may not indicate a local migrant workforce, a county that has a high poverty rate, a high proportion of Mexican-born residents, and a high proportion of non-English speakers may indicate that there is a migrant workforce, especially (but not only) if that county is categorized as nonmetropolitan.

Many states show a convergence of demographic data that indicate significant numbers of migrant farmworkers. Texas and California have a significant number of counties that meet the criteria for high unemployment, high populations of Mexican-born residents, and high numbers of non-English-speaking residents. It is important to remember that the data show that high proportions of Mexican-born residents and non-English-speaking residents are not just a demographic for the states that border Mexico. Kansas has a substantial non-English-speaking population, with five counties (Finney, Ford, Hamilton, Seward, and Stevens) registered among the counties with the highest rates of non-English speaking residents. Kansas also contains eight counties (Finney, Ford, Grant, Hamilton, Haskell, Seward, Stevens, and Wichita) among the counties with the highest rates of residents born in Mexico. Washington State, in the Pacific Northwest, registered five counties (Adams, Douglas, Franklin, Grant, and Yakima) among the counties with the highest ratio of Mexican-born residents, as well as two counties (Adams and Franklin) registered among the counties with the highest ratios of non-English-speaking residents in the country. Idaho also contains multiple counties among the counties with the highest ratios of Mexican-born (Clark, Gooding, Jerome, Lincoln, Minidoka, and Power) and non-English-speaking residents (Clark and Jerome). Every state has counties with higher proportions of Mexican-born or non-English-speaking residents, and these counties might be considered by local VR agencies interested in outreach to migrant communities.

The counties with the highest rates of unemployment also show higher rates on average of Mexican-born residents and non-English-speaking residents. VR agencies may find that local counties with higher unemployment rates also have other factors that potentially identify a migrant farmworker community, including higher proportions of non-English speakers and Mexican-born residents.

Case studies indicate avenues that mitigate the many barriers associated with VR services to migrant communities. Farmworkers and migrant communities represent a significantly underserved population in the United States, and many of the people in these communities are in substantial need of economic and vocational opportunity. VR agencies can make inroads with these communities by adapting social and cultural perspectives appropriate to these communities. Breeding et al. (2005) utilized outreach workers to interact with migrant farmworkers in eight Kentucky counties. These outreach workers were present in migrant communities, regularly attended social gatherings, met with community leaders, and conducted visits with families and households. The outreach workers provided Spanish-language informational brochures regarding VR and medical access. They also provided community members with transport to medical appointments and assisted households with essential materials. Outreach workers then interacted with a Kentucky Migrant Vocational Rehabilitation Program (KMVRP) patient services coordinator. The patient services coordinator scheduled appointments with the Office of Vocational Rehabilitation (OVR) counselors. Over a three-year period, 1,700 migrant farmworkers received VR information and 105 referrals were made to OVR. Landon et al. (2019) also encouraged VR providers to develop personal relationships within a rural or isolated community.

The Farm Worker Family Health Program (FWFHP) in Moultrie, Georgia provides an example from medical outreach that can be applied to VR (Nichols et al., 2014). FWFHP is a two-week service-learning program conducted by students and faculty at Emory University’s Nell Hodgson Woodruff School of Nursing, Georgia State University’s departments of physical therapy and psychology, and the University of Georgia College of Pharmacy, as well as dental hygiene students and faculty from Clayton State University and Darton College. FWFHP provides medical services during the two-week program in Colquitt County’s summer school for children of migrant workers. FWFHP is an example of a successful and continuous program by which universities, collaborating with local clinics and schools, can provide services otherwise not rendered to migrants and their families (Nichols et al., 2014). A similar program utilizing students and faculty from psychology, counseling, and social services can provide comparable levels of attention to local migrant populations during a scheduled season.

Successful VR services to migrant communities require specific resources. The most significant barrier may be the initial geographic barrier. However, distance and geography can be overcome if VR offices hire Spanish-language outreach workers who are visible and available in migrant communities. Informational sheets and brochures printed in Spanish can be distributed at social events and in churches, and VR services can be advertised in Spanish radio and newspaper media. VR also has the potential to expand its services to reach migrant workers. VR offices may produce occupational work safety information in Spanish and disseminate these materials into migrant communities. VR may also facilitate occupational information sessions within migrant communities. Trust between vocational offices and potential migrant clients can build by providing transportation to medical appointments and assisting with household essentials purchases.

Conclusions

Migrant populations in the United States face significant barriers to economic and vocational rehabilitation, including living in communities that are isolated from public view, as well as the transient lifestyle endemic to migrant farmwork. Most farmworkers in the United States were born in Mexico, and most speak Spanish. Farmworkers also experience social concerns, including the fear of deportation and isolation from family. Many migrant women and children are homeless. However, VR can utilize existing data to identify local counties and regions that may have migrant communities. The first step is the identification of local counties that likely have migrant communities. Texas and California have many counties that are among the highest rates of Spanish-speaking residents and the highest rates of Mexican-born residents in addition to high levels of unemployment. Kansas, Washington, and Idaho are examples of states that do not border Mexico, yet have significant numbers of Spanish-speaking residents and Mexican-born residents. Counties with higher proportions of Mexican-born residents or non-English speakers exist in almost every state. Outreach to migrant communities by committed outreach workers has potential to improve VR outreach to migrant workers. Spanish-language outreach workers can provide information at social events and in cultural activities within the migrant communities. VR services to migrant communities can positively influence the families of those in greatest need of safe, stable employment.

Author Note

The contents of this manuscript were developed under a grant, the Vocational Rehabilitation Technical Assistance Center for Quality Employment, H264K200003, from the U.S. Department of Education. However, those contents do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal government.