Linear Regression Model. The model will take the following form: Yi = Xi β + δTi + ei Where: Yi is the outcome: earnings in quarters 5 through 8 after the random assignment quarter Xi is a vector of baseline covariates Ti is a binary variable equal to 1 if the individual is in the treatment group and 0 if the individual is in the control group ei is the individual-specific error term The following list includes the variables and categories within variables that the Independent Evaluator may consider including in the regression model. Some variables and/or categories may need to be eliminated or combined depending on the composition of Study Population Members. Independent Evaluator will also check for strong collinearity between variables and remove one variable from each collinear pair, if necessary. • Gender (binary variable for female versus male) • Age • Race/Ethnicity (categorical variable with expected categories of Black, Hispanic, White, Asian/Pacific Islander, Other, and Unknown) • Highest Level of Education (categorical variable with expected categories of less than high school, high school diploma or equivalent degree, and any college degree) • Citizen/Resident Alien Status (categorical variable with expected categories of citizen, resident alien-temporary protected status, and resident alien-not temporary protected status) • Number of Years Residing in the United States • Ever Worked in a Job for Pay in the United States (binary variable for yes versus no) • Employment Status at Intake (categorical variable with categories of employed full time at intake, employed part time at intake, not employed but worked in the US within the past 6 months, not employed and last worked more than 6 months ago or never in the US) • Earnings During Quarters 1 through 8 Prior to the Random Assignment Quarter (including zero earnings, based on DUA data) • Parent of Child Under Age 18 (binary variable for yes versus no) • Number of Adults in Household Including Participant • Household Receiving TAFDC at Intake (binary variable for yes versus no) • Household Receiving SNAP at Intake (binary variable for yes versus no) • Household Receiving Unemployment Benefits at Intake (binary variable for yes versus no) • Region (categorical variable with categories for each of the four regions in which EfA will take place) • Month of random assignment (e.g., month 1 through month 36) Operational efforts will be made to minimize the level of missing data on the covariates. Independent Evaluator will review JVS Intake Data to identify problems with missing data after the first EFA Program Enrollment Quarter and work with JVS to resolve problems. Data may still be missing for Study Population Members who either could not answer or chose not to answer a particular question. If covariates selected for the final model have missing data, Independent Evaluator will impute the relevant mean value of the covariate for the Treatment and Control Group in order to retain participants in the study sample.
Appears in 2 contracts