Policy Sci (2006) 39:335–359 DOI 10.1007/s11077-006-9028-1
Does local access to employment services reduce unemployment? A GIS analysis of One-Stop Career Centers Pascale Joassart-Marcelli Æ Alberto Giordano
Received: 3 March 2006 / Accepted: 9 November 2006 / Published online: 16 December 2006 Springer Science+Business Media B.V. 2006
Abstract The paper uses Geographic Information System to investigate (1) the location of One-Stop Career Centers in Southern California, (2) their level of accessibility to unemployed workers of various demographic groups, (3) their proximity to employment opportunities, and (4) the effect of these spatial relations on Census tract unemployment. We build on the non-profit literature on accessibility to social service providers and on spatial mismatch research that emphasizes the gap between places of work and residence. We argue that One-Stops can play an important role in bridging this gap. We find that One-Stops are well positioned to serve the unemployed, although accessibility varies by race/ethnicity, age, and location. Access to One-Stops reduces local unemployment, particularly in neighborhoods with limited employment opportunities. This effect is larger for groups who experience limited mobility due to gender or race, such as black and female job seekers. Keywords Unemployment Æ One-Stop Æ Service provider Æ Accessibility Æ Mobility Æ Spatial mismatch Æ GIS Introduction One-Stop centers, funded by the Workforce Investment Act (WIA) of 1998, have been designed to bring together multiple agencies offering similar services, with the primary goal of facilitating employment and simultaneously helping both businesses and low-income workers. While this sort of consolidation presumably promotes partnerships across programs, increases funding opportunities, and raises the quality P. Joassart-Marcelli (&) Department of Economics, University of Massachusetts-Boston, 100 Morrissey Blvd., Boston, MA 02125, USA e-mail:
[email protected] A. Giordano Department of Geography, Texas State University, San Marcos, TX 78666, USA e-mail:
[email protected]
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of services, the benefits of streamlining may be limited if the resulting geographical concentration excludes a significant share of people in need. To the extent that access to these services varies across space, geography has become a critical but little understood element in determining the success of workforce development policies. This paper uses Geographic Information System (GIS) to investigate the location of One-Stop Career Centers and its spatial relationship to (1) the various demographic groups who may potentially use such services and (2) the jobs they may be eligible for. Specifically, we are interested in the effectiveness of One-Stop centers in decreasing neighborhood level unemployment, distinguishing between race/ethnicity, age, and gender, and controlling for local employment opportunities. Since One-Stops are designed to be a bridge between unemployed people and jobs, an evaluation of their location needs to focus on both job-seekers and employers. Using the multi-centric and economically segregated region of Southern California as a case study, we ask whether One-Stops can play a role in reducing the so-called ‘‘spatial mismatch’’ between jobs and place of residence, by promoting employment in areas of limited opportunities. In other words, do One-Stop centers create connections between people and jobs when these are geographically distant from each other? Two primary sources of literature address this question, but have not been systematically linked. First, recent research on the geography of non-profits and their ability to serve people in poverty has highlighted the fact that anti-poverty organizations are often weaker in poorer communities (Allard, 2004; Bielefeld & Murdoch, 2004; Joassart-Marcelli & Wolch, 2003). Although significant amount of resources are allocated to poor neighborhoods, these are insufficient to guarantee equal service to all people in need. Thus, poor people in wealthier areas often receive better social services. Variations across race and ethnicity are likely to exist, but have not been systematically investigated, partially because of limited data. This paper also borrows from another field of research associated with the spatial mismatch hypothesis. According to this hypothesis, the poor labor market outcomes of minority central city residents can be explained by their limited access to employment opportunities, which results primarily from a combination of employment decentralization and housing discrimination (Ihlanfeldt & Sjoquist, 1998; Kain, 1968). Despite recent development in the spatial mismatch literature, including improvement of accessibility measures (Mouw, 2000; Raphael, 1998) and greater attention to ethnicity, race, and gender (Johnston-Anumonwo, 1995; Preston & McLafferty, 1999), very little attention has been given to the role of institutions that can mediate the effect of spatial mismatch, with the exception of housing assistance programs (Joassart-Marcelli, 2006). We have very limited knowledge about the role of employment related nonprofits or public agencies in mediating the spatial mismatch. To address this issue, this paper focuses on the geographical nexus between employment opportunities and workforce development resources such as the OneStop Career Centers. We are interested in how jobs and agencies interact to shape the labor market outcome of various groups residing in the five-county Southern California region. We hypothesize that accessibility to One-Stop Career Centers is more likely to play a critical role in reducing unemployment in neighborhoods where jobs have been declining and employment opportunities are limited. In contrast, in areas where there is rapid job growth and high job density, One-Stop centers may not be necessary to promote employment. Moreover, we expect One-Stop Career Centers to play a more important role in reducing the unemployment rates of groups who face greater physical and social barriers to mobility, particularly when
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employment opportunities are limited. For instance, women and non-white populations are usually less mobile than other groups because of gender and racial dynamics and might enjoy greater benefits from having access to a One-Stop. The next section briefly describes the history and role of One-Stop centers and reviews the literature on accessibility to non-profits. The following section discusses the spatial mismatch hypothesis and develops a theoretical framework to address our research question. The ‘‘Data and methodology’’ section provides information regarding the data and methodology employed in this paper. In the ‘‘Accessibility to One-Stop centers’’ section, we analyze the geographic distribution of One-Stop Career Centers in Southern California and measure the access level of various demographic groups to these centers. The ‘‘The changing landscape of employment opportunities’’ section focuses on the relationship between One-Stops and employment opportunities. In the ‘‘Access to One-Stop centers and local unemployment rates’’ section, we estimate the relationship between access to One-Stop centers and local unemployment rates for various demographic groups, taking into account employment opportunities. We conclude with policy recommendations.
Workforce development, the One-Stop Career Centers, and access to services Until the late 1990s, workforce development efforts had been highly decentralized (Grubb et al., 1999; Pindus, Koralek, Martinson, & Trutko, 2000). The Job Training and Partnership Act of 1982 (JTPA) supported employment services (i.e., training, job placement, work experience development, referral) for economically disadvantaged youth and unskilled adult workers. States receiving federal funds were in charge of establishing service delivery areas and corresponding Private Industry Councils, which consisted primarily of members of the local private business sector, to oversee their activities. During the 1990s, coordination and integration of employment and training services increased. A majority of states established agencies, such as human resource investment councils and local workforce boards, to promote collaboration among various service providers. One-Stop centers were created to centralize a wide range of services to both job-seekers and employers. By 1998, 46 states had received federal grants to establish such centers (Martinson, 1999). The Workforce Investment Act (WIA), which was signed in 1998 and began its implementation in 2000, further promoted the integration of employment services by requiring the creation of One-Stop Career Centers in all states. Such centers are managed by Local Workforce Investment Boards, which like the earlier Private Industry Councils, represent the interests of private employers and, to a lesser extent, human service providers, vocational institutions, community colleges, and other employment service providers. While large variations exist across states, WIA brought together funding from the JTPA programs, the Employment Service, community colleges, Community Service Block Grants, Housing and Urban Development, the Department of Veteran’s Affairs, Welfare-to-Work Grants, and in a growing number of cases, the Temporary Assistance to Needy Families (TANF) work programs. In the process of consolidation, the scope of One-Stops also expanded. Although there is usually limited coordination between the workforce development system and state welfare agencies (Elliott, Sprangler, & Yorkievitz, 1998; Hyland, 1998; Martinson, 1999; Martinson & Holcomb, 2002; Nightingale, Jones, O’Brien, &
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Brennan, 1997), several states have recently developed contractual financial agreement where TANF funds are transferred to the local workforce development agency to provide services to welfare recipients. These funds are also supplemented by Welfare-to-Work grants that require workforce development agencies to provide services to people transitioning from welfare to work (Nightingale, O’Brien, Egner, Pindus, & Trutko, 2003). In 2004, only Wisconsin and Utah had fully integrated welfare-to-work and employment services into a common agency. In California, WIA and TANF are administered by two different departments under the same state agency (i.e., Health and Human Services). The TANF program known as California Work Opportunity and Responsibility to Kids (‘‘CalWORKs’’) is administered by the California Department of Social Services. The administration of the WIA program is overseen by the California Employment Development Department. Thus there are strong, although not fully integrated, relationships between TANF and WIA, with TANF funds allocated to One-Stops and TANF services provided in most One-Stop locations (Werner & Lodewick, 2004). Indeed most of the One-Stop or WorkSource centers in Southern California, although primarily funded by WIA, have been in operation for many years and consist of evolving partnerships between non-profits, public agencies, and in some cases businesses. For example, non-profits such as Community Career Development Inc. and the Goodwill of Southern California, have been providing employment services in the region for over 30 years and are currently in charge of several One-Stops. As One-Stop Career Centers play an increasingly prominent role in welfare policy, questions regarding their ability to serve the most disadvantaged individuals remain. These concerns are fueled by the geographical concentration of employment services that accompanies the integration efforts discussed above and are likely to create physical barriers for those with limited mobility, such as people with disability and/or inadequate access to transportation. The growing literature on the importance of access to non-profits in determining the ability of such organizations to serve the poor provides a conceptual and empirical framework to address these questions. For instance, Joassart-Marcelli and Wolch (2003) analyze the location of antipoverty non-profits and its relationship to the distribution of poverty in Southern California. They show that although antipoverty service providers tend to locate in proximity to poor populations, they lack the financial resources to provide equal services to all. Thus non-profits are limited in their ability to alleviate fiscal disparities and reduce poverty concentration. Allard (2004) focuses on access to employment services for welfare recipients and find that service providers are concentrated in central areas but do not necessarily match the distribution of needs. Welfare populations residing in suburban areas have limited access to services, which creates a significant barrier to employment. Metzler and Giordano (2007) look at the location of One-Stops and Vocal Rehabilitation (VR) facilities in relation to people with disabilities across the United States and conclude that needs are better met in urban areas. While these studies investigate the location of service providers and their determinants, they do not systematically measure the effect that access to these services has on clients’ socio-economic outcomes, such as poverty and unemployment. They also fail to take into account the landscape of employment opportunities, which is likely to shape the demand for such services.
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Geographical access to employment opportunities Since Kain’s (1968) first formulation of the spatial mismatch hypothesis, a growing number of scholars have focused on the role of geographical access to employment opportunities on labor market outcomes of Blacks and other minority populations relative to Whites. Although no consensus has been reached, there is mounting evidence that accessibility to employment opportunities, either through proximity to jobs or better transportation, reduces the probability of being unemployed and increases wages (Mouw, 2000; Raphael, 1998; Stoll, 1999; Sultana, 2005). Debates about the validity of the spatial mismatch hypothesis remain, however, partly due to measurement and methodological issues (Coulton, 2003). Spatial mismatch has been usually measured by looking at commuting patterns (Holtzer, 1991; McLafferty & Preston, 1997; Sultana, 2005) or access to jobs (Immergluck, 1998; Raphael, 1998; Stoll, 1999). Although longer commutes suggests a spatial mismatch, it is unclear whether greater commuting times result from the modes of transportation used, residential choices, carpooling, and other components of worker’s daily trips (e.g., shopping, children’s school). Thus, the constraining aspect of commuting is hard to isolate. For example, it is often difficult to distinguish between suburban workers living in exclusive communities and commuting to job centers, and central city residents traveling to other central city or inner-ring locations. Clearly, although both types of workers can experience long commutes, they face very different levels of constraint and mobility. Several studies have found little variation in commuting time between ethnic and racial groups. Taylor and Ong (1995) suggest that the main difference is in access to private automobile which affords greater flexibility. An additional problem associated with commuting studies is the fact that they focus only on employed workers and thus cannot explain why others who are willing to work remain unemployed. The other approach to spatial mismatch consists of measuring the effect of local employment opportunities on labor market outcomes. Most studies use job density as an indicator of accessibility and usually focus on broad geographical areas such as central city and suburbs (Carlson & Theodore, 1997; Stoll, 1999). Another similar measure looks at the percent of workers who work in the area where they reside (Immergluck, 1998). Recently, more adequate measures of spatial accessibility have been developed that take into account distance to employment. For example, Raphael (1998), Mouw (2000), Ong and Blumenberg (1998), and Allard and Danziger (2003) use an access measure based on a distance decay function that assign a lower weight to jobs located farther from one’s place of residence. These measures, which can be more easily implemented in a GIS, are much more reliable than previous indicators. Several factors might influence the effect of employment accessibility on labor market outcomes. For instance, gender, race and ethnicity can influence employer preferences and geographical mobility within local labor markets. Thus, jobs that may appear proximate can be off-limit for certain groups. For instance, employers’ preferences towards minorities may be lower in neighborhoods with lower proportions of minority residents (Kirschenman & Neckerman, 1991). In fact, Coulton (2003) suggests that some firms move to suburban areas to avoid hiring minorities
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and/or central city residents. Ethnic networks also play a critical role in providing information about jobs, and to the extent that these networks are geographically constrained, can limit the effective size of one’s local labor market. Research on ethnic employment networks in Los Angeles, for instance, shows that Latino immigrants are more likely to be employed in neighborhoods where greater proportion of residents—and to some extent employers—are also Latinos (Parks, 2004; Waldinger, 1996). Geographers have also emphasized the fact that gender constraints the range of locations in which women operate. Women’s household responsibilities often reduce their ability to commute and restrict their job-search areas (Hanson & Pratt, 1995; MacDonald, 1999; Villeneuve & Rose, 1988; Wyly, 1998). As a result, women are more likely to suffer negative consequences from spatial mismatch (Blumenberg, 2004; Browne, 2000; Gilbert, 1998; Parks, 2004; Thompson, 1997). While several researchers have highlighted to role of social networks in promoting greater access to employment opportunities (Coulton, 2003; Gilbert, 1998; Henly, 1999), few have focused on the role of employment service providers in bridging the gap between places of residence and work. To the extent that spatial barriers to employment include geographically limited social networks, local organizations that create bridges between employers and job seekers can improve accessibility. It is on the role of these intermediary institutions on the relationship between neighborhood employment accessibility and unemployment rates that this paper focuses. Based on the literature reviewed above, we expect these organizations to have the greatest impact in neighborhoods where economic conditions are most adverse (i.e. declining jobs and limited low-skill employment opportunities) and for people who experience greater mobility constraints (i.e., minorities and women).
Data and methodology The first part of the paper uses GIS to measure and describe accessibility to OneStops and employment opportunities for various demographic groups. In the second part of the paper, we use multivariate regression analysis to investigate the effect of accessibility on unemployment. To build the GIS necessary for this type of analysis, we relied on a number of data sources. Data on One-Stops were collected from the US Department of Labor’s Service Locator website (www.servicelocator.org) in May 2004. The site provides data on each individual center based on reports provided by states. One-Stops are designated Comprehensive or Affiliate. Given the lack of consistency across states in how OneStop centers are classified, we ignore these differences in this paper. Satellite centers which are usually much smaller and provide very limited services were excluded from the data. The service locator website provides the name and address of each office. The address was used to georeference the office using ESRI’s ArcGIS 8.3 and street data from the ESRI Data and Maps CD (2003 edition). The software was unable to georeference about a third of the addresses, so this task was completed manually. In addition, all automatically georeferenced offices were double-checked for accuracy: about 10% were not located correctly by the software and had to be regeoreferenced manually. Overall, the dataset contains over 3,300 One-Stop. For this
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analysis, we used the 103 One-Stops located in the five counties of the Southern California region.1 Our analysis of accessibility identifies the demographic and socio-economic characteristics of people living in Census tracts located in proximity to One-Stops, compared to those in other areas. This analysis uses the 2000 Census of Population and Housing (SF3) to obtain information on race, ethnicity, income, employment, household type, public assistance use, education, poverty and transportation by Census tract. Instead of using more conventional measures of accessibility that would measure the presence of a One-Stop within a tract or within a pre-set boundary around each tract, we created a GIS-based index of accessibility that weights One-Stops based on their distance from each tract.2 Weights were computed using a distance decay function that gives lower weights to One-Stops located in tracts at greater distances. For each of the 3,373 tracts in the Southern California region, we computed the following: Ai ¼ RjOj ðcekdij Þ
ð1Þ
where Ai is the One-Stop accessibility of tract i, Oj is the number of One-Stops in tract j, c and k are parameters of the distance decay function,3 and dij is the distance between the centroids of tract i and tract j. If residents of one tract have multiple OneStops within relatively close distance the index will have a value larger than one. Accessibility to employment opportunities is based on a similar function. In this case, the parameters k is set at a higher value to reflect the fact that people are usually willing to travel greater distances to work.4 The number of One-Stops in Eq. 1 is replaced by the number of jobs. Since people using One-Stops are more likely to qualify for lower-skill jobs, we computed a separate measure for low-skill jobs. To measure skill-level, we use data from the Census Transportation Planning Package (part 2) that provide tract-level information about jobs by occupation, which can in turn be used to estimate skill requirements.5 Following Raphael (1998), we also 1 The Southern California region includes the counties of Los Angeles, Orange, Riverside, San Bernardino and Ventura, which make up the Los Angeles consolidated metropolitan statistical area (CMSA). 2 Distance between two tracts is measured as the Euclidian distance (straight line) between the tracts’ population-weighted centroı¨ds. 3
Since we have no data on trips between places of residence and employment service providers, we were unable to estimate the parameters of the distance decay function. Research on non-profits and social service providers suggests that people are usually not willing to travel long distances to nonprofits and that the use of facilities declines sharply after 3 miles and is almost null after 10 miles (Bielefeld, Murdoch, & Waddell, 1997). Based on these finding we set c = 1.284 and k = –0.25. This implies that a unit located at 1 mile gets a weight of 1, at 3 miles 0.60, at 5 miles 0.37, at 10 miles 0.11, and at 15 miles 0.03. 4 Parks (2004) uses a gravity model to estimate the distance-decay parameter k for Southern California workers based on actual commuting patterns in 2000. She estimates k at 0.058. We use her estimate for this paper. This implies that a job located at 1 mile gets a weight of 0.95, at 5 miles 0.75, at 10 miles 0.56 and at 20 miles 0.33. 5 Lower-skill jobs were those in occupations where the average educational attainment was less than a high-school degree. Based on data from the 5% 2000 Public Use Micro Sample for the five counties of Southern California, these include food preparation and serving, cleaning services, personal services, farming and fishing, construction, installers and repairers of machine and equipment, machine operators, and transportation.
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computed an accessibility measure that uses the change in jobs between 1990 and 2000. This measure better reflects job openings and vacancies than total jobs do. Data on 2000 tract-level employment and 1990–2000 changes came from the Southern California Association of Governments (SCAG).6 In addition, we computed a measure of labor market competition to control for the number of lower-skilled people potentially competing for the jobs accessible to each given tract. This competition measure relies on the same formula as the previous indices, using the population over 25 with less than a high-school degree as the main variable. We use these variables to model the relationship between accessibility to OneStops and census tract unemployment rate, distinguishing between race/ethnicity, gender, and age groups, and controlling for employment opportunities. Our final population, after excluding tracts with missing variables, includes 3,319 tracts. Building on Raphael (1998), we regress accessibility to low-skill jobs and to new jobs on tract-level unemployment rates, and include access to One-Stop Career Center as an additional determinant of unemployment. As typical in the spatial mismatch literature, we control for the quality of the labor force by including variables reflecting the percent of adults without high-school degree. We also take into account the potentially adverse conditions of neighborhoods, including official poverty, percent receiving Public Assistance, percent families with children headed by single mother, racial/ethnic composition, which previous research has shown to negatively affect employment probability (Coulton, 2003). Since accessibility is likely to be shaped by access to transportation (Blumenberg, 2004), we also include a variable that measures the percent of workers who are using a car to work. Table 1 provides brief descriptions, sources, and summary statistics for the variables used in the model. In addition to these variables, we include two interaction terms to measure the relationship between access to One-Stops and access to (1) new jobs and (2) low-skill jobs. Interaction terms, which are simply the product of two independent variables (say X and Z), measure the amount of change in the effect of X on the dependent variable when Z changes by one unit (Aiken & West, 1991). In this case, these terms allow us to see how tract-specific labor market conditions shape the relationship between access to One-Stop centers and unemployment. In other words, it allows us to test the hypothesis that One-Stop centers are more effective at reducing unemployment in places where jobs are scarce and the spatial mismatch is greater. The mediating role of institutions on spatial mismatch has not been studied systematically in the literature. We run the regression models to see how these variables predict unemployment for the entire labor force, as well as for specific demographic groups defined by race/ ethnicity, gender, and age. Since Census tracts do not have equal population sizes and labor force participation rates, tracts are weighted using the number of people in the labor force for the group under consideration in each regression.
6 Since 1990 and 2000 Census tract do not always match perfectly, several steps were required to allocate 1990 jobs to 2000 tracts in order to compute accurate levels of change. When tracts were split, we allocated 1990 jobs based on population. When tract were merged, we also merged jobs. Data on changes in Census tract boundary definitions between 1990 and 2000 came from the Census Bureau (1990, 2000).
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5.55% 10.45%
7.07%
39.25%
10.44%
3.07%
15.58%
5.94%
21.03%
Percent Black
Percent Latino
Percent Asian
Percent other race
Official poverty rate
Percent households receiving public assistance Percent families headed by single female
3,319
249,359
Access to low-skill jobs (100,000)
N
80,220
Access to new jobs (100,000)
322,967
3.79
Access to One-Stops
Competition lower-skill
10.72%
89.73%
199,262
135,271
58,028
3.19
21.37%
28.93%
Percent adults with less than high-school degree Percent using car to work
11.78%
2.06%
12.60%
28.10%
12.89%
4.78%
7.83%
Unemployment rate
SD
Mean
Variable
Table 1 Summary statistics, definitions, and data sources
Number of workers with no-high school degree potentially competing for jobs accessible to tract residents (see Eq. 1)
Percent of labor force (16 years and older) unemployed in 1999 Percent of population non-Latino black Percent of population Latino (all races) Percent of population non-Latino Asian Percent of population not identified in racial and ethnic groups listed above Percent of population with household income below official poverty threshold Percent of households receiving public assistance Percent of family households with children under 18 headed by single female Percent adults (25 and older) with no high-school diploma Percent of workers (16 and older) using a car for transportation to work Number of one-stops accessible to tract residents (see Eq. 1) Number of net new jobs (1990–2000) accessible to tract residents (see Eq. 1) Number of 2000 jobs in low-skill occupations accessible to tract residents (see Eq. 1)
Definition
US Department of Labor Service Locator (2004) SCAG (1990, 2000) tract-level employment data Census of Transportation Planning Package (2000) Census PUMS (2000) Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Census SF3 (2000)
Data source
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We first run the regression using ordinary least squares (OLS). However, because we suspect the presence of spatial autocorrelation, we also run maximum likelihood (ML) spatial lag and spatial error models using GeoDa software (2004). Relevant statistical tests and model comparisons are discussed in the result section.
Accessibility to One-Stop Centers In 2004, there were 103 One-Stop Career Centers reported in the five county region of Southern California, with the majority (i.e., 69 centers) located in Los Angeles County. Figure 1 illustrates the geographic distribution of One-Stops in the region and the corresponding census-tract level measure of accessibility discussed above. Accessibility is highest in the core of Los Angeles County, progressively declining as we move south into Orange County and the inland area in and around the cities of San Bernardino and Riverside. It is very low in exurban areas and in Ventura County. Figure 2 shows census tract in Southern California classified into four categories based on their level of accessibility to One-Stops and their rate of unemployment. The highest rates of unemployment are found in the central part of the city of Los Angeles and in its adjacent cities to the East and South. Other large unemployed populations are found in and around the cities of San Bernardino, Riverside, and Santa Ana—the three largest older suburban cities of the region, as well as in many exurban locations in the northern part of Los Angeles, Ventura, and San Bernardino
Fig. 1 One-Stop Location and Accessibility, Southern California 2003
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Fig. 2 Relationship between Unemployment and Access to One-Stops
counties and East of Riverside. The map allows us to identify four types of areas: those with high unemployment and high access to One-Stops, those with high unemployment but low access to One-Stops, those with low-unemployment and low access to One-Stops, and those with low-unemployment but high access to OneStops.7 The most problematic imbalances are shown in the darkest shade which represents areas where unemployment is high and access to One-Stops is limited. This occurs primarily in areas outside of the core of Los Angeles County, in the older suburbs east of the city of Los Angeles and following the interstate corridor towards Riverside and San Bernardino. The opposite imbalance occurs in the low-unemployment cities around Los Angeles, where accessibility to One-Stop is high despite the limited needs. This is particularly true in places like Santa Monica, Manhattan Beach, Beverly Hills, Pasadena, San Marino, and Cerritos. While access to One-Stops seems to be greater in areas of high unemployment, there may be variations by race and ethnicity, gender, and age. Table 2 summarizes average accessibility and odds ratios for different demographic groups. These averages are computed by weighting each tract’s level of accessibility by the share of people in the demographic group under consideration residing in the tract. The odds ratios are simply the ratio of average accessibility of each group over accessibility for 7
Average tract-level unemployment rate is 7.88%. Thus, we define high unemployment areas as those where unemployment is above 7.88%, and low unemployment area as those with unemployment rate at or below 7.88%. Similarly, we define high (low) access area as those where access to One-Stop is greater than (less or equal to) the median access value of 3.77.
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Table 2 Average accessibility to One-Stops
All labor force All employed All unemployed White Black Latinos Asians Male Female 16- to 21-year-old 22- to 29-year-old 30- to 44-year-old 45- to 64-year-old 65 and older
Average
Odds ratio
SD
3.52 3.48 4.24 2.84 6.04 4.77 4.30 4.24 4.23 3.95 4.50 4.40 4.14 3.60
1.00 0.99 1.20 0.81 1.72 1.35 1.22 1.21 1.20 1.12 1.28 1.25 1.18 1.02
3 2.97 3.35 2.61 3.68 3.38 3.19 3.34 3.35 3.22 3.37 3.40 3.35 3.23
the entire labor force. Ideally unemployed people would have higher access to OneStops than the employed population. Moreover, every unemployed group would have similar access to employment services. While the data suggest that on average accessibility to One-Stops is 20% higher for the unemployed than for the employed population, they also indicate that there are important variations according to race/ ethnicity and age. White unemployed members of the labor force have lower access to One-Stops than Blacks and to a lesser extent Latinos and Asians. This is likely a result of residential segregation whereby whites, even those who are unemployed and poor, live in areas separate form other demographic groups. Unemployed whites tend to reside in suburban neighborhoods where access to One-Stops is more constrained. In contrast, Blacks, particularly those who are unemployed, are more likely to live in the central and southern part of Los Angeles county and other clusters where access to One-Stops is higher. Asians and Latinos, although experiencing lower levels of residential segregation than Blacks, are often concentrated in older suburban parts of the region, East and South of Los Angeles where One-Stops are also highly concentrated. Another interesting finding reported in Table 2 is the variation by age. We observe a sort of ‘‘inverted U’’ relationship whereas accessibility is low for younger unemployed workers, increases for adults in their twenties through forties, and declines again for older workers. This could be problematic to the extent that the groups with lowest access to One-Stops (i.e., youth and elderly) usually suffer from lower mobility. No significant differences are observed between genders. However, as we show below, the same level of accessibility has different implications for men and women, as the latter tend to be less mobile and must rely more heavily on locally available resources. To get a better picture of those who are geographically left-out of the network of One-Stop centers, we calculate the number and proportions of unemployed people in each of the demographic groups listed in Table 2 who have limited access to OneStop centers. These are unemployed people who live in tracts where the level of accessibility is less than one and thus do not have access to a One-Stop center in
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Table 3 Unemployed workers by level of access to One-Stops
All unemployed White Black Latinos Asians Female Male 16–21 year-old 22–29 year-old 30–44 year-old 45–64 year-old 65 and older
Number in group with access to One-Stops >1
Number in group with access to One-Stops
