Danielle Li

Assistant Professor of Business Administration

Danielle Li is an Assistant Professor of Entrepreneurship. Her research focuses on the determinants of productivity in settings where innovation and talent is important—how do we design organizations that recognize and encourage high impact ideas?  

Her current work examines these issues in the context of life sciences research and pharmaceutical development.  In one set of projects, Professor Li is working with the U.S. National Institutes of Health to examine and improve their peer review process.  In another strand of research, she studies how new technologies such as online job testing and performance management software impact firm HR practices and worker productivity.  

Professor Li graduated from Harvard College and worked at the Poverty Action Lab in Udaipur, India prior to earning her Ph.D. in Economics from MIT. 

Danielle Li is an Assistant Professor of Entrepreneurship. Her research focuses on the determinants of productivity in settings where innovation and talent is important—how do we design organizations that recognize and encourage high impact ideas?  

Her current work examines these issues in the context of life sciences research and pharmaceutical development.  In one set of projects, Professor Li is working with the U.S. National Institutes of Health to examine and improve their peer review process.  In another strand of research, she studies how new technologies such as online job testing and performance management software impact firm HR practices and worker productivity.  

Professor Li graduated from Harvard College and worked at the Poverty Action Lab in Udaipur, India prior to earning her Ph.D. in Economics from MIT. 

Journal Articles

  1. Big Names or Big Ideas: Do Peer-Review Panels Select the Best Science Proposals?

    Danielle Li and Leila Agha

    This paper examines the success of peer-review panels in predicting the future quality of proposed research. We construct new data to track publication, citation, and patenting outcomes associated with more than 130,000 research project (R01) grants funded by the U.S. National Institutes of Health from 1980 to 2008. We find that better peer-review scores are consistently associated with better research outcomes and that this relationship persists even when we include detailed controls for an investigator’s publication history, grant history, institutional affiliations, career stage, and degree types. A one–standard deviation worse peer-review score among awarded grants is associated with 15% fewer citations, 7% fewer publications, 19% fewer high-impact publications, and 14% fewer follow-on patents.

    Keywords: Patents; Research; Entrepreneurship; Forecasting and Prediction; Innovation and Invention; Business and Government Relations; United States;

    Citation:

    Li, Danielle, and Leila Agha. "Big Names or Big Ideas: Do Peer-Review Panels Select the Best Science Proposals?" Science 348, no. 6233 (April 24, 2015): 434–438. View Details

Working Papers

  1. Discretion in Hiring

    Mitchell Hoffman, Lisa B. Kahn and Danielle Li

    Who should make hiring decisions? We propose an empirical test for assessing whether firms should rely on hard metrics such as job test scores or grant managers discretion in making hiring decisions. We implement our test in the context of the introduction of a valuable job test across 15 firms employing low-skill service sector workers. Our results suggest that firms can improve worker quality by limiting managerial discretion. This is because, when faced with similar applicant pools, managers who exercise more discretion (as measured by their likelihood of overruling job test recommendations) systematically end up with worse hires.

    Keywords: Selection and Staffing; Management Practices and Processes;

    Citation:

    Hoffman, Mitchell, Lisa B. Kahn, and Danielle Li. "Discretion in Hiring." Harvard Business School Working Paper, No. 15-055, October 2015. View Details
  2. Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules

    Pierre Azoulay, Joshua S. Graff Zivin, Danielle Li and Bhaven N. Sampat

    We quantify the impact of scientific grant funding at the National Institutes of Health (NIH) on patenting by pharmaceutical and biotechnology firms. Our paper makes two contributions. First, we use newly constructed bibliometric data to develop a method for flexibly linking specific grant expenditures to private-sector innovations. Second, we take advantage of idiosyncratic rigidities in the rules governing NIH peer review to generate exogenous variation in funding across research areas. Our results show that NIH funding spurs the development of private-sector patents: a $10 million boost in NIH funding leads to a net increase of 2.3 patents. Though valuing patents is difficult, we report a range of estimates for the private value of these patents using different approaches.

    Keywords: economics of science; Patenting; academic reserach; NIH; Knowledge Spillovers; Patents; Research; Government and Politics;

    Citation:

    Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. "Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules." Harvard Business School Working Paper, No. 16-056, October 2015. View Details
  3. Expertise vs. Bias in Evaluation: Evidence from the NIH

    Danielle Li

    Evaluators with expertise in a particular field may have an informational advantage in separating good projects from bad. At the same time, they may also have personal preferences that impact their objectivity. This paper develops a framework for separately identifying the effects of expertise and bias on decision making and applies it in the context of peer review at the US National Institutes of Health (NIH). I find evidence that evaluators are biased in favor of projects in their own area, but that they also have better information about the quality of those projects. On net, the benefits of expertise tend to dominate the costs of bias. As a result, policies designed to limit reviewer biases may also reduce the quality of funding decisions.

    Keywords: Prejudice and Bias; Performance Evaluation; Experience and Expertise;

    Citation:

    Li, Danielle. "Expertise vs. Bias in Evaluation: Evidence from the NIH." Harvard Business School Working Paper, No. 16-053, October 2015. View Details
  4. School Accountability and Principal Mobility: How No Child Left Behind Affects the Allocation of School Leaders

    Danielle Li

    The move toward increased school accountability may substantially affect the career risks that school leaders face without providing commensurate changes in pay. Since effective school leaders likely have significant scope in choosing where to work, these uncompensated risks may undermine the efficacy of accountability reforms by limiting the ability of low-performing schools to attract and retain effective leaders. This paper empirically evaluates the economic importance of principal mobility in response to accountability by analyzing how the implementation of No Child Left Behind (NCLB) in North Carolina affected principal mobility across North Carolina schools and how it reshaped the distribution of high-performing principals across low- and high-performing schools. Using value-added measures of principal performance and variation in pre-period student demographics to identify schools that are likely to miss performance targets, I show that NCLB decreases average principal quality at schools serving disadvantaged students by inducing more able principals to move to schools less likely to face NCLB sanctions. These results are consistent with a model of principal-school matching in which school districts are unable to compensate principals for the increased likelihood of sanctions at schools with historically low-performing students.

    Keywords: Leadership; Corporate Accountability; Education; North Carolina;

    Citation:

    Li, Danielle. "School Accountability and Principal Mobility: How No Child Left Behind Affects the Allocation of School Leaders." Harvard Business School Working Paper, No. 16-052, October 2015. View Details
  5. Cheaper by the Dozen: Using Sibling Discounts at Catholic Schools to Estimate the Price Elasticity of Private School Attendance

    Susan Dynarski, Jonathan Gruber and Danielle Li

    The effect of vouchers on sorting between private and public schools depends upon the price elasticity of demand for private schooling. Estimating this elasticity is empirically challenging because prices and quantities are jointly determined in the market for private schooling. We exploit a unique and previously undocumented source of variation in private school tuition to estimate this key parameter. A majority of Catholic elementary schools offer discounts to families that enroll more than one child in the school in a given year. Catholic school tuition costs therefore depend upon the interaction of the number and spacing of a family’s children with the pricing policies of the local school. This within-neighborhood variation in tuition prices allows us to control for unobserved determinants of demand with a fine set of geographic fixed effects, while still identifying the price parameter. We use data from 3700 Catholic schools, matched to restricted Census data that identifies geography at the block level. We find that a standard deviation decrease in tuition prices increases the probability that a family will send its children to private school by one half percentage point, which translates into an elasticity of Catholic school attendance with respect to tuition costs of -0.19. Our subgroup results suggest that a voucher program would disproportionately induce into private schools those who, along observable dimensions, are unlike those who currently attend private school.

    Keywords: Price; Religion; Entrepreneurship; Education;

    Citation:

    Dynarski, Susan, Jonathan Gruber, and Danielle Li. "Cheaper by the Dozen: Using Sibling Discounts at Catholic Schools to Estimate the Price Elasticity of Private School Attendance." Harvard Business School Working Paper, No. 16-054, October 2015. View Details