Many factors influence welfare recipients’ ability to find and retain employment. A number of studies have examined the presence of barriers and their relationship to employment outcomes among welfare recipients. These studies typically center on a single employment barrier—poor job skills, physical or mental health problems, domestic violence, or inadequate employment support systems such as childcare and transportation. A few studies emphasize the presence of multiple barriers to employment (Olson and Pavetti 1996; Speiglman et al. 1999; Danziger et. al. forthcoming).
In this report, we focus on transportation barriers and examine their relationship to two outcome measures—the employment rate and welfare recipients’ perceptions of their ease of travel. The first model presented in Table 33 uses logistic regression to model the employment rate as a function of age, education, the number of small children, race/ethnicity, unlimited access to a household car, ability to borrow a car, age of the vehicle, and residential location. The logistic regression takes the following functional form:
where Emp = working at the time of the survey; B = a set of 10 independent variables and : =
Policy Options Drivers Car Passengers
Transit Riders
Unlimited Transit Pass 13% 11% 20%
More Frequent Bus Service 14% 13% 15%
Emergency Lift Home From Work
5% 6% 0%
Shuttle Van Service To And From Work
64% 68% 63%
None 4% 3% 1%
Total 259 83 76
Source: Fresno Welfare Recipient Survey.
1 10 1 0 β µ α + =
ℜ
+ = k k EMPA Survey of Fresno County Welfare Recipients 70
Table 32: Definition of Variables Used in Logistic Analysis
Model 1 shows that holding constant for a number of characteristics related to the probability of employment, having unlimited access to automobiles is strongly associated with being employed. Additionally, having an older car reduces the likelihood of employment, but the ability to borrow cars is not a statistically significant predictor of employment. Additionally, living in a rural location within the county is negatively related to employment. Finally, the traditional human capital variables such as age and education are both positively associated with employment rates.
Variables Definition
Dependent Variables
Employment Employed at the time of the survey (1), job searching or unemployed at time of survey (0)
Difficult Travel Travel for employment or job search is difficult (1), travel for employment or job search is easy (0)
Independent Variables
Age Age of adult participant
Education H.S. degree or more (1), less than a H.S. degree (0) # of small children Number of children 5 and under
# of children Number of children under 18
Job search Searching for employment (1), not searching (0)
Asian Asian (1), Not Asian (0)
Black Black (1), Not Black (0)
Hispanic Hispanic (1), Not Hispanic (0)
Unlimited access to car Unlimited Access to Car (1), Limited or No Access to Car (0) Ability to borrow car Ability to borrow a car (1), unable to borrow car (0)
Old Car Car is 10 years or older (1), car is less than 10 years old (0)
Subsidy Receives a transportation subsidy (1), does not receive subsidy (0) Insure Biggest problem with owning a car is insurance costs (1),
insurance is not the biggest problem (0)
A Survey of Fresno County Welfare Recipients 71
Table 33: Transportation and Barriers to Employment
MODEL 1: Barriers to Employment
Parameter Std. Error Odds Ratio
Intercept -1.4579** 0.5874 Age 0.0214* 0.0129 1.022 Education 0.3616* 0.216 1.436 # of Small Children 0.0812 0.1243 1.085 Asian 1.1112** 0.4361 3.038 Black 0.3958 0.3165 1.486 Hispanic 0.5624** 0.2521 1.755 Unlimited Access to Car 1.178*** 0.2527 3.248 Ability to Borrow Car 0.0693 0.2525 1.072 Old Car -0.5565** 0.2593 0.573 Rural -0.4121* 0.2437 0.662 -2 Log Likelihood 648.5 Chi-Square (df) 40.9 (10) # of observations 458 *p<.10, **p<.05, ***p<.01
A Survey of Fresno County Welfare Recipients 72
The next two models presented in Table 34 also use logistic regression; these models predict ease of travel for all respondents and then separately for respondents with cars in their households.2 The model takes on a similar functional form to the previous model:
where Difficult = welfare recipients’ perception of the difficulty they have traveling for employment or job search; Bk = a set of 9 independent variables and : = the random error term. The findings show that once again, having unlimited access to cars is strongly associated with the perception of “easy” travel. Additionally, both models show that welfare recipients perceive their job search travel as much more difficult than their travel for employment. Asian welfare recipients appear to report more difficulty with their travel compared to other racial and ethnic groups. And finally, among welfare recipients with cars in their households, those who identify automobile insurance as a problem also report more difficulty with their travel.
In these models, a rural residential living location does not appear to influence welfare recipients’ ease of travel. This finding may be due to the lower employment rates among rural welfare recipients. In other words, those recipients with the greatest transportation difficulties may be least likely to find employment and, therefore, may be less likely to travel at all. The finding may also be due to rural welfare participants’ greater reliance on automobiles. Finally, the ease of travel among rural welfare participants may also be affected by the geographic location of their employment. Fifteen percent of all survey respondents live and work outside of the Fresno-Clovis metropolitan area. Only three percent of respondents live outside the metropolitan area and make the, perhaps, long commute into the Fresno-Clovis metropolitan area. Additionally, the receipt of public transportation subsidies also does not influence welfare recipients’ ease of travel, although the coefficient is positive. This finding is difficult to interpret since less than one quarter of welfare recipients receive these subsidies and those who receive assistance may also be the recipients with the greatest need.
2 The sample of transit users is too small to run a separate regression model for this subgroup.
1 9 1 0 β µ α + =
ℜ
+ = k k DifficultA Survey of Fresno County Welfare Recipients 73
Table 34: Ease of Travel
MODEL 2