Public Engagement

  • Faculty


    Richard Hendra teaches various statistics and methods classes at the School for Public Engagement and has been on the part-time faculty since 1998. Most recently, he has taught Quantitative Methods, Advanced Quantitative Methods and Quantitative Research Design. In the past he has taught Data Management and Presentation and Economics. Hendra has served on several dissertation and advisory committees. He was among the first cohort to graduate from the New School’s Ph.D. in Public Policy program and also did his Masters in the Urban Policy Analysis at the New School. He graduated Summa Cum Laude in Economics from the College of New Jersey.

    Richard is a Senior Research Associate at MDRC where he has played central roles in the quantitative analysis on a range of welfare, asset building, and workforce development projects. He is currently the Principal Investigator as well as design and impact lead for the WorkAdvance project, a four-site sector based training initiative being evaluated with a random assignment design. He is also Principal Investigator on a study of microlending in the US and leads the impact analysis for the Subsidized Transitional Employment Project, a multisite study sponsored by the Department of Health and Human Services. Hendra also led the impact analysis and data collection on the 16-site Employment Retention and Advancement (ERA) project. He was also the lead impact analyst for a similar project in the UK (the UK Employment Retention and Advancement project) and, earlier, conducted analyses for several welfare reform waiver projects. Hendra is involved in numerous corporate statistical and data management initiatives at MDRC and serves as the methodological reviewer for many of MDRC’s projects. He has also worked in corporate analytics using behavioral and attitudinal targeting methods to increase marketing efficiency. He is currently a co-chair of the Employment and Training panel at APPAM.

    Research Interests:

    Employment Policy, Welfare Policy, Data Mining, Causal Inference

    Current Courses:

    Advanced Quant Methods (Spring 2020)

    Quantitative Research Design