About

Chris Clapp is an Assistant Instructional Professor at the Harris School of Public Policy at the University of Chicago. His research interests are in applied microeconomics, primarily focusing on public, urban, environmental, and labor economics. He also has work related to health and sports economics topics. Prior to his current position, he earned his Ph.D. in Economics from the University of Virginia and worked as a research associate for Compass Lexecon in Washington, DC. He previously served as an Assistant Professor at Florida State University. Clapp completed his undergraduate studies in Economics and English at Clemson University. He resides in Hyde Park with his wife, Susan, their son, and their dog, Boomer.

Research topics

• Business
• Finance
• Computer Science
• Geography
• Marketing
• Medicine
• Demographic economics
• Psychology
• Labour economics
• Economics
• Environmental health
• Psychiatry
• Physical therapy
• Environmental planning
• Data science
• Mathematics
• Developmental psychology
• Environmental science
• Database
• Statistics
• Regional science
• Economic growth
• Environmental resource management

Selected publications

• Assessing Vocational Rehabilitation Agency Capacity to Engage in Evidence-Based Decision Making

  Journal of Vocational Rehabilitation · 2025-09-22

  articleOpen access1st author

  Background: The Evidence-based Policy-Making Act of 2018 requires that Federal agencies use their data to develop statistical evidence to support policy and programmatic decisions. Objective: This study assessed Vocational Rehabilitation agency capacity to effectively use their data to inform evidence-based decision-making. Methods: The Capacity Survey assessed agency capacity in data management, data visualization and statistical analysis. The survey asked for details about (1) the availability of relevant software programs (e.g. SPSS for statistical analysis), and (2) staff expertise to use that software. Results: Results pointed to capacity gaps that would significantly hinder most agencies' application of even a simplified return on investment model. When examining statistical capacity, 60% of agencies responded "not applicable - staff do not have competence in the listed software packages (including SPSS, SAS, R, Python, Stata, and other)" and 71% of respondents said they lacked internal statistical capacity to analyze data using any of the listed programs. Conclusions: Results suggested that many state VR agencies lack internal capacity to meet requirements outlined in the Evidence-Based Policy-Making Act of 2018 or regulations related to reporting and performance accountability requirements. Potential solutions to overcome capacity deficits include expanding internal capacity, expanding agency/consultant partnerships, and building cross-agency collaborations.

  PublisherOA PDFDOI

• National Data: What Do We Learn?

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract The goal of this chapter is to consider some of the costs and benefitsassociated with using national data instead of state agency data (as was discussed in Chap. 3). We provide information ona list of national datasets available for research and discuss the costs and benefits of using national datasets. We discuss the implied differences in modeling between using national datasets and state agency datasets. The discussion implies that using agency data is the preferred approach.

  PublisherOA PDFDOI

• Simplifying the Model

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract Although the VR-ROI model provides a state-of-the-art approach for ROI analysis of VR, the model’s complexity can render interpreting and assessing the benefits of VR challenging. Moreover, the models are difficult to estimate and require advanced computational methods, statistical knowledge, programming skills, and computing resources. This complexity can make it prohibitive for VR agency staff to estimate and use such models to evaluate the ROI of VR programs in other states and time periods. Given these practical concerns, a critical issue is determining whether a simplified model and estimator can provide credible agency-specific ROI estimates. Focusing on the North Carolina program as a case study, we estimate the benefits and net present value (NPV) of VR from simpler models that are relatively easy to understand and can be estimated using standard statistical software packages on a laptop computer.

  PublisherOA PDFDOI

• Assessing Vocational Rehabilitation Agency Capacity to Engage in Evidence-Based Decision Making

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Introduction

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract This book presents the latest advances in models and data for evaluating the efficacy of vocational rehabilitation (VR)services provided to individuals with disabilities. For the first time, the VR-ROI (return on investment) model is used tosimultaneously compare short- and long-term labor market outcomes across multiple state agencies and four distinct disability groups. For each disability group, the book provides information about the return on investment, as measured bythe rate of return, for VR services. By offering this broad and in-depth evaluation in concert with intuitive explanations of the model and the estimation methodology, the book helps to bridge the gap between research and practice and to equip stakeholders with data-driven insights to enhance vocational rehabilitation programs for individuals with disabilities.

  PublisherOA PDFDOI

• Problems in Using Measures of Taxpayer Return on Investment to Evaluate Work Force Programs

  Rehabilitation Counseling Bulletin · 2025-07-31

  article1st author

  This article contrasts social and taxpayer return on investment measures of the vocational rehabilitation (VR) program in Virginia. To do this, we use the analyses in prior work which demonstrates substantial social return to Virginia’s VR program. Using this estimated model and administrative data on VR clients in Virginia, we simulate earnings that would be realized with and without VR service receipt by each client and estimate the costs of the services provided to each client. Then, given these simulation results, we compute the taxpayer return on investment. Since most VR recipients have a weak attachment to the labor market (i.e., relatively low employment rates and earnings), the relatively large estimated impact of VR on earnings translates into only a small impact on the taxpayer return. That is, the cost of VR is large relative to the lifetime changes in tax receipt. In particular, we estimate that only 29% of VR recipients have a positive taxpayer return.

  PublisherDOI

• Literature Review

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract Return on investment (ROI) analysis of state vocational rehabilitation (VR) agencies is a way to evaluate the efficacy of a VR program. Several different formulas can be used to make this comparison, but all compare program benefits with costs in some manner. For every dollar spent on services provided to a VR client, the ROI reports how many extra dollars (in present value terms) the client earns as a result. Although an ROI measure is straightforward to calculate given its components, credibly estimating program benefits and costs from available data can be difficult (King & O’Shea, 2003; Clapp et al., 2019). In this chapter, we review the empirical literature on VR program ROI. To do so, we first provide an overview of the basic conceptual issues involved in estimating VR program benefits and costs and, ultimately, the ROI of VR programs. Our aim is to highlight some of the key issues in ROI evaluations and how the approaches used in the VR literature have evolved over time, not to provide an exhaustive how-to guide. McGuire-Kuletz and Tomlinson (2015) and articles in the special issue introduced by Schmidt et al. (2019b) provide a more detailed guide to ROI analysis of VR programs.

  PublisherOA PDFDOI

• Evaluation of Vocational Rehabilitation Services

  Diversity and inclusion research · 2025-01-01

  bookOpen access1st authorCorresponding
  PublisherDOI

• Introduction to Rate of Return, Modeling, and Estimation

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract The goal of our work is to measure the effectiveness of vocational rehabilitation (VR) services by comparing benefits and costs. Relative to earlier work, we add the following important features: We use long-term data and distinguish between short-term and long-term effects. We allow effects to vary by disability type, service type, and state. We control for other demographic, socioeconomic, and disability characteristics. We use a structural model to estimate the relevant effects and control for endogeneity problems. In this chapter, we describe the model and data used to estimate the effectiveness of VR services and compute the return on investment of the VR program.

  PublisherOA PDFDOI

• Conclusions and Next Steps

  Diversity and inclusion research · 2025-01-01

  book-chapterOpen access1st authorCorresponding

  Abstract In this concluding chapter, we briefly summarize some of the key themes from this book and then discuss directions for future research and refinements of the model.

  PublisherOA PDFDOI

Frequent coauthors

• Steven Stern

  19 shared

• John V. Pepper

  11 shared

• Robert M. Schmidt

  9 shared

• Alec Brandon

  7 shared

• Kevin Willardsen

  Wright State University

  6 shared

• John A. List

  University of Chicago

  5 shared

• Saleh Mamun

  4 shared

• Robert Metcalfe

  4 shared