|Explanation||This is a generic metric. The actual estimation depends on the availability of outcome data from the program. Employment programs may include: Job training programs, job placement programs, programs that provide direct employment to participants.|
Data requests for programs seeking to improve employment should include the following items.
- Number of individuals who receive employment services from the program.
- Identify any specific population served by the program (e.g. formerly incarcerated, disability, mentally ill, etc.).
- Number of individuals who find employment after or during the program.
- Average or detailed wages before and after program (hourly or annual wages).
- Average or detailed length of employment after program.
- Number or percentage of participants’ type of employment (e.g. full-time or part-time), or number of hours/day worked (if possible before and after program).
- Typical educational level of participants.
- If training is provided, request curriculum or diploma type and delivery characteristics of program (e.g. length and intensity).
Use the following examples of procedures and assumptions as starting point:
- Create separate estimations (metrics) for each subpopulation served. For example, by educational level, type of jobs (if this varies significantly across participants), formerly incarcerated, etc.
- Create separate estimations (metrics) for each level of employment. For example, part-time and full-time, by wage levels or type of jobs if wages vary significantly across participants.
- Create separate estimations (metrics) for participants who receive a raise in wages due to program. If data is not available but the impact is plausible, use 1.1%, which the average for the Twin Cities metropolitan area as reported by the U.S. Bureau of Labor Statistics over the 12-month period ended December 2017.
- When program data is available: # participants who find employment due to the program = [(# who find employment)/(# participants)] – (Employment rate of low-income population).
- When the available data is not sufficient to compute the effectiveness of the program, we use a 50% effectiveness rate. Regarding an alternative measure of the chance of having a job post-program in the absence of the program, Heinrich, Mueser, Troske, Jeon, & Kahvecioglu (2013) estimate that the probability of finding a job for participants in public job training programs included in the Work Investment Act (WIA) is between 50% and 60%.
- The employment rate of low-income population in the Philips neighborhood in Minneapolis, [66%], (Minnesota Compass, 2018).
- We assume employment last for one year, unless specific data is provided by the program. The number of years of benefits (annual earnings) after the training period is determined by evidence from data or literature and usually covers between 6 months and 5 years (Council of Economic Advisers, 2016). Evidence from research literature (Card, Kluve & Weber, 2017) categorizes short-term impacts of less than a year post-program, medium-term impacts of 1-2 years post program, and long-term impacts of more than 2 years post-program.
- We assume full-time employment implies 2,080 hours of paid work per year.
- Net increase in earnings: Based on the population served by the program. Typical counterfactual earning levels for when pre-program earnings are not known include:
- Average annual earnings of employed low-income individuals or without a high school diploma: [$13,500], estimated using ACS Census data (U.S. Census Bureau, 2016) for the Twin Cities metropolitan area.
- Average annual earnings of individuals with high-school diploma or GED: [$24,300], estimated using ACS Census data (U.S. Census Bureau, 2016) for the Twin Cities metropolitan area.
- When pre and post-program wages are known: Net increase in wages = ($ average annual post-training earnings) – ($ average annual pre-training earnings). Note: discount to present value if benefits are assumed to last for more than 3 years.
|References||Card, D., Kluve, J., & Weber, A. (2017). What works? A meta-analysis of recent active labor market program evaluations. National Bureau of Economic Research. Working Paper 21431. Retrieved from: http://www.nber.org/papers/w21431|
Council of Economic Advisers. (2016). Active labor market policies: Theory and evidence for what works. [Issue Brief]. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/page/files/20161220_active_labor_market_policies_issue_brief_cea.pdf
Heinrich, C. J., Mueser, P. R., Troske, K. R., Jeon, K.S., & Kahvecioglu, D. C. (2013). Do public employment and training programs work? IZA Journal of Labor Economics, 2(1), 6.
Minnesota Compass. (2018). Twin Cities region neighborhood profile data: Phillips community. Retrieved from http://www.mncompass.org/profiles/communities/minneapolis/phillips
U.S. Census Bureau. (2016). American Community Survey 5-year estimates – public use microdata sample, 2012-2016. Generated using Public Use Microdata Area (PUMA) in the Seven-county Twin Cities Metropolitan Area. Retrieved from http://factfinder.census.gov