Amanda E. Kowalski


Brief Biography

Amanda Kowalski, Associate Professor of Economics at the Yale University Department of Economics and Faculty Research Fellow at the National Bureau of Economic Research (NBER), is a health economist who specializes in bringing together theoretical models and econometric techniques to answer questions that inform current debates in health policy.

Professor Kowalski's recent research advances methods to analyze experiments and clinical trials with the goal of designing policies to target insurance expansions and medical treatments to individuals who will benefit from them the most. Applied to the Oregon Health Insurance Experiment, these methods show that among the individuals who entered a lottery for Medicaid, the individuals most likely to enroll in Medicaid were the individuals who had previously visited the emergency room the most. These individuals were also the most likely to increase their use of the emergency room upon gaining coverage. Her previous research has explored the impact of previous Medicaid expansions, the Affordable Care Act, the Massachusetts health reform of 2006, and employer-sponsored health insurance plans. She has also used cutting-edge techniques to estimate the value of medical spending on at-risk newborns.

Professor Kowalski has been honored with a CAREER Award from the National Science Foundation and the Yale Arthur Greer Memorial Prize for Outstanding Scholarly Publication or Research. Her research has received the HCUP Outstanding Article of the Year Award, the Garfield Economic Impact Award, the National Institute of Health Care Management Research Award, and the Zellner Thesis Award. The National Institutes of Health, the Robert Wood Johnson Foundation, and the W.E. Upjohn Institute have also supported her research, which has been published in peer-reviewed journals, including the American Economic Review, the Quarterly Journal of Economics, the Journal of Health Economics, and the Journal of Public Economics. Her research has also been featured in the popular press, including The New York Times, NPR, and The Wall Street Journal.

Professor Kowalski holds a PhD in economics from MIT and an AB in economics from Harvard. Before joining Yale, she held a post-doctoral fellowship in Health and Aging at the NBER. Her interest in health policy has led her to spend two years in Washington, DC, one as a research assistant in health and labor at the White House Council of Economic Advisers, and another as the Okun Model Fellow at the Brookings Institution. She spent the 2015-2016 academic year as a Visiting Associate Professor at the Stanford Institute for Economic Policy Research. She is currently spending the 2017-2018 academic year as a Visiting Associate Professor at the Princeton Department of Economics and as a Visiting Research Scholar at the Princeton Center for Health and Wellbeing. In September 2018, she will join the Department of Economics at the University of Michigan as the Gail Wilensky Professor of Economics.

Working Papers

"Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment." NBER Working Paper 24647. Latest Version: May 2018.

I aim to shed light on why emergency room (ER) utilization increased following the Oregon Health Insurance Experiment but decreased following a Massachusetts policy. To do so, I unite the literatures on insurance and treatment effects. Under an MTE model that assumes no more than the LATE assumptions, comparisons across always takers, compliers, and never takers can inform the impact of polices that expand and contract coverage. Starting from the Oregon experiment as the "gold standard," I make comparisons within Oregon and extrapolate my findings to Massachusetts. Within Oregon, I find adverse selection and heterogeneous moral hazard. Although previous enrollees increased their ER utilization, evidence suggests that subsequent enrollees will be healthier, and they will decrease their ER utilization. Accordingly, I can reconcile the Oregon and Massachusetts results because the Massachusetts policy expanded coverage from a higher baseline, and new enrollees reported better health.

"Censored Quantile Instrumental Variable Estimation with Stata" (with Victor Chernozhukov, Ivan Fernandez-Val, and Sukjin Han). NBER Working Paper 24232. Revise & resubmit, Stata Journal. Latest Version: January 2018. [Download Stata Command to Implement CQIV Below]

Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov, Fernandez-Val, and Kowalski (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifes application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, we describe the use of the cqiv command, and we provide empirical examples.

"Politics, Hospital Behavior, and Health Care Spending" (with Zack Cooper, Eleanor N Powell, and Jennifer Wu). NBER Working Paper 23748. Latest Version: September 2017. [Slides]

Press Coverage: [The Economist] [Politico] [The Upshot - The New York Times]

This paper examines the link between legislative politics, hospital behavior, and health care spending. When trying to pass sweeping legislation, congressional leaders can attract votes by adding targeted provisions that steer money toward the districts of reluctant legislators. This targeted spending provides tangible local benefits that legislators can highlight when fundraising or running for reelection. We study a provision - Section 508 - that was added to the 2003 Medicare Modernization Act (MMA). Section 508 created a pathway for hospitals to apply to get their Medicare payment rates increased. We find that hospitals represented by members of the House of Representatives who voted "Yea" on the MMA were significantly more likely to receive a 508 waiver than hospitals represented by members who voted "Nay." Following the payment increase generated by the 508 program, recipient hospitals treated more patients, increased payroll, hired nurses, added new technology, raised CEO pay, and ultimately increased their spending by over $100 million annually. Section 508 recipient hospitals formed the Section 508 Hospital Coalition, which spent millions of dollars lobbying Congress to extend the program. After the vote on the MMA and before the vote to reauthorize the 508 program, members of Congress with a 508 hospital in their district received a 22% increase in total campaign contributions and a 65% increase in contributions from individuals working in the health care industry in the members' home states. Our work demonstrates a pathway through which the link between politics and Medicare policy can dramatically affect US health spending.

"How to Examine External Validity Within an Experiment." Working Paper. Latest Version: August 2016.

A fundamental concern for researchers who design and analyze experiments is that the experimental result might not be externally valid in another context. Researchers have traditionally attempted to assess external validity by comparing data from an experiment to other data. In this essay, I use insights from my recent work to show researchers how to begin the examination of external validity internally, within the data from a single experiment. My insights rely on overlooked information and minimal assumptions.

"Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments." NBER Working Paper 22363. Latest Version: July 2016. [Slides]

Press Coverage: [NPR Marketplace] [Yale Alumni Magazine]

I examine treatment effect heterogeneity within an experiment to inform external validity. The local average treatment effect (LATE) gives an average treatment effect for compliers. I bound and estimate average treatment effects for always takers and never takers by extending marginal treatment effect methods. I use these methods to separate selection from treatment effect heterogeneity, generalizing the comparison of OLS to LATE. Applying these methods to the Oregon Health Insurance Experiment, I find that the treatment effect of insurance on emergency room utilization decreases from always takers to compliers to never takers. Previous utilization explains a large share of the treatment effect heterogeneity. Extrapolations show that other expansions could increase or decrease utilization.

"Long-Term Impacts of Childhood Medicaid Expansions on Outcomes in Adulthood" (with David Brown and Ithai Lurie). NBER Working Paper 20835. Latest Version: June 2018. Under review. [PDF and Online Appendix] [BKL Calculator Appendix] [Slides]

Press Coverage: [Vox] [The Upshot - The New York Times] [The Upshot - The New York Times] [Connecticut Post] [Academy Health] [The New York Times] [Daily Tar Heel] [NBER Digest]

Video Presentation:

[Yale Law School Conference on the Law of Medicare and Medicaid at 50, November 2014] [Slides]

We use administrative data from the IRS to examine long-term impacts of childhood Medicaid eligibility on outcomes in adulthood at each age from 19--28. Greater Medicaid eligibility increases college enrollment and decreases fertility, especially through age 21. Starting at age 22, females have higher contemporaneous wage income, although male increases are imprecise. Together, both genders have lower mortality. These adults collect less from the earned income tax credit and pay more in taxes. Cumulatively from ages 19--28, at a 3% discount rate, the federal government recoups 67 cents of each dollar of its "investment" in childhood Medicaid.

"What Do Longitudinal Data on Millions of Hospital Visits Tell Us About Public Health Insurance as a Safety Net for the Young and Privately Insured? NBER Working Paper 20887. Latest Version: January 2015. [Slides][Data Appendix]

Young people with private health insurance sometimes transition to the public health insurance safety net after they get sick, but popular sources of cross-sectional data obscure how frequently these transitions occur. We use longitudinal data on almost all hospital visits in New York from 1995 to 2011. We show that young privately insured individuals with diagnoses that require more hospital visits in subsequent years are more likely to transition to public insurance. If we ignore the longitudinal transitions in our data, we obscure over 80% of the value of public health insurance to the young and privately insured.


"Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform" (with Jonathan T. Kolstad). Journal of Health Economics. February 2016. Vol. 47. 81-106. (Older Version: NBER Working Paper 17933. ) [Pre-publication PDF][Online Appendix][Slides][Download Problem Set on This Paper Below]

Press Coverage: [Yale Daily News] [Boston Herald] [Washington Examiner] [New York Times Economix Blog]

Video Presentations on Health Reform and the Labor Market:

[Brookings Event on The Future of U.S. Health Care Spending, April 2014] [Slides]

[Federal Reserve Bank of Chicago Conference on the Affordable Care Act and the Labor Market, March 2014] [Slides]

We model the labor market impact of the key provisions of the national and Massachusetts "mandate-based" health reforms: individual mandates, employer mandates, and subsidies. We characterize the compensating differential for employer-sponsored health insurance (ESHI) and the welfare impact of reform in terms of "sufficient statistics." We compare welfare under mandate-based reform to welfare in a counterfactual world where individuals do not value ESHI. Relying on the Massachusetts reform, we find that jobs with ESHI pay $5,350 less annually, approximately the cost of ESHI to employers. Accordingly, the deadweight loss of mandate-based health reform was approximately 2% of its potential size.

"Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care." Journal of Business and Economic Statistics. January 2016. Vol. 34, No. 1: 107-117. (Job Market Paper) (Older Version: NBER Working Paper 15085. ) [Pre-publication PDF][Online Appendix] [Data Appendix][Download Stata Command to Implement CQIV Below]

Awarded the Zellner Thesis Award

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.

"Estimating the Tradeoff Between Risk Protection and Moral Hazard with a Nonlinear Budget Set Model of Health Insurance." International Journal of Industrial Organization. November 2015. Vol. 43: 122-135. (Older Version: NBER Working Paper 18108. ) [Pre-publication PDF][Slides][Online Appendix][Data Appendix]

Insurance induces a tradeoff between the welfare gains from risk protection and the welfare losses from moral hazard. Empirical work traditionally estimates each side of the tradeoff separately, potentially yielding mutually inconsistent results. I develop a nonlinear budget set model of health insurance that allows for both simultaneously. Nonlinearities in the budget set arise from deductibles, coinsurance rates, and stoplosses that alter moral hazard as well as risk protection. I illustrate the properties of my model by estimating it using data on employer sponsored health insurance from a large firm. Within my empirical context, the average deadweight losses from moral hazard substantially outweigh the average welfare gains from risk protection. However, the welfare impact of moral hazard and risk protection are both small relative to transfers from the government through the tax preference for employer sponsored health insurance and transfers from some agents to other agents through a common premium.

"Quantile Regression with Censoring and Endogeneity" (with Victor Chernozhukov and Ivan Fernandez-Val). Journal of Econometrics. May 2015. Vol. 186: 201-221. (Older Versions: NBER Working Paper 16997. arXiv identifier 1104.4580. Boston University Department of Economics Working Paper 2009-012.) [Pre-publication PDF][Download Stata Command to Implement CQIV Below]

In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are nonadditive in the unobservables. The first stage estimates a nonadditive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a nonadditive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. Our analysis covers two-stage (uncensored) quantile regression with nonadditive first stage as an important special case. We illustrate the computation and applicability of the CQIV estimator with a Monte-Carlo numerical example and an empirical application on estimation of Engel curves for alcohol.

"Adverse Selection and an Individual Mandate: When Theory Meets Practice" (with Martin Hackmann and Jonathan T. Kolstad). American Economic Review. March 2015. Vol. 105, No. 3: 1030-66. (Older Version: NBER Working Paper 19149. ) [Pre-publication PDF] [Slides] [Data and Programs] [Online Appendix][Download Problem Set on This Paper Below]

Press Coverage: [SHADAC Issue Brief] [SHARE Webinar with Slides] [Politico]

Awarded the NIHCM Research Award

Awarded the Yale Greer Prize (highlighted as part of research portfolio)

We develop a model of selection that incorporates a key element of recent health reforms: an individual mandate. Using data from Massachusetts, we estimate the parameters of the model. In the individual market for health insurance, we find that premiums and average costs decreased significantly in response to the individual mandate. We find an annual welfare gain of 4.1% per person or $51.1 million annually in Massachusetts as a result of the reduction in adverse selection. We also find smaller post-reform markups.

"The Early Impact of the Affordable Care Act State-by-State" Brookings Papers on Economic Activity, Fall 2014:277-333. (Older Version: NBER Working Paper 20597. ) [Pre-publication PDF] [BPEA Slides] [Full Slides] [Video Summary by Justin Wolfers] [Interactive Map] [Data and Programs] [Online Appendix]

Press Coverage: [Washington Post Blog] [Wall St Journal Blog] [Forbes] [Motley Fool] [Vox]

Radio Interview on Early Impact of the ACA:

[Top of Mind with Julie Rose, July 2015]

I examine the impact of state policy decisions on the early impact of the ACA using data through the first half of 2014. I focus on the individual health insurance market, which includes plans purchased through exchanges as well as plans purchased directly from insurers. In this market, at least 13.2 million people were covered in the second quarter of 2014, representing an increase of at least 4.2 million beyond pre-ACA state-level trends. I use data on coverage, premiums, and costs and a model developed by Hackmann, Kolstad, and Kowalski (2013) to calculate changes in selection and markups, which allow me to estimate the welfare impact of the ACA on participants in the individual health insurance market in each state. I then focus on comparisons across groups of states. The estimates from my model imply that market participants in the five direct enforcement states that ceded all enforcement of the ACA to the federal government are experiencing welfare losses of approximately $245 per participant on an annualized basis, relative to participants in all other states. They also imply that the impact of setting up a state exchange depends meaningfully on how well it functions. Market participants in the six states that had severe exchange glitches are experiencing welfare losses of approximately $750 per participant on an annualized basis, relative to participants in other states with their own exchanges. Although the national impact of the ACA is likely to change over the course of 2014 as coverage, costs, and premiums evolve, I expect that the differential impacts that we observe across states will persist through the rest of 2014.

"The Impact of Health Care Reform on Hospital and Preventive Care: Evidence from Massachusetts" (with Jonathan T. Kolstad). Journal of Public Economics. December 2012. Vol. 96. 909-929. (Older Version: NBER Working Paper 16012. ) [Pre-publication PDF][Slides]

Press Coverage: [NBER Digest] [Knowledge@Wharton] [Newsweek] [The Economist Online] [NPR WBUR CommonHealth]

Video Presentations of Massachusetts Research:

[Yale Panel on the Economy and the Election, November 2012] [Slides]

[ISPS Health At Yale, September 2013] [Slides]

In April 2006, Massachusetts passed legislation aimed at achieving near-universal health insurance coverage. The key features of this legislation were a model for national health reform, passed in March 2010. The reform gives us a novel opportunity to examine the impact of expansion to near-universal coverage state-wide. Among hospital discharges in Massachusetts, we find that the reform decreased uninsurance by 36% relative to its initial level and to other states. Reform affected utilization by decreasing length of stay and the number of inpatient admissions originating from the emergency room. When we control for patient severity, we find evidence that preventable admissions decreased. At the same time, hospital cost growth did not increase.

"Health Reform, Health Insurance, and Selection: Estimating Selection into Health Insurance Using the Massachusetts Health Reform" (with Martin Hackmann and Jonathan T. Kolstad). American Economic Review: Papers & Proceedings. May 2012. Vol. 102, No. 3: 498-501. (Older Version: NBER Working Paper 17748. )

We implement an empirical test for selection into health insurance using changes in coverage induced by the introduction of mandated health insurance in Massachusetts. Our test examines changes in the cost of the newly insured relative to those who were insured prior to the reform. We find that counties with larger increases in insurance coverage over the reform period face the smallest increase in average hospital costs for the insured population, consistent with adverse selection into insurance before the reform. Additional results, incorporating cross-state variation and data on health measures, provide further evidence for adverse selection.

"The Role of Hospital Heterogeneity in Measuring Marginal Returns to Medical Care: A Reply to Barreca, Guldi, Lindo, and Waddell" (with Douglas Almond, Joseph J. Doyle, and Heidi Williams). Quarterly Journal of Economics. November 2011. Vol. 126, No. 4: 2125-2131. [Pre-publication PDF]

Response to comment on "Estimating Marginal Returns to Medical Care: Evidence from At-Risk Newborns"

In Almond, Doyle, Kowalski and Williams (2010), we describe how marginal returns to medical care can be estimated by comparing patients on either side of diagnostic thresholds. Our application examines at-risk newborns near the very low birth weight threshold at 1500 grams. We estimate large discontinuities in medical care and mortality at this threshold, with effects concentrated at "low-quality" hospitals. While our preferred estimates retain newborns near the threshold, when they are excluded the estimated marginal returns decline, although they remain large. In low-quality hospitals, our estimates are similar in magnitude regardless of whether these newborns are included or excluded.

"Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns" (with Douglas Almond, Joseph J. Doyle, and Heidi Williams). Quarterly Journal of Economics. May 2010. Vol. 125, No. 2: 591-634. [Online Appendix] (Older Version: NBER Working Paper 14522. ) [Download Problem Set on This Paper Below]

Awarded the HCUP Outstanding Article of the Year Award

Awarded the Garfield Economic Impact Award

We estimate marginal returns to medical care for at-risk newborns by comparing health outcomes and medical treatment provision on either side of common risk classifications, most notably the "very low birth weight" threshold at 1500 grams. First, using data on the census of US births in available years from 1983-2002, we find evidence that newborns with birth weights just below 1500 grams have lower one-year mortality rates than do newborns with birth weights just above this cutoff, even though mortality risk tends to decrease with birth weight. One-year mortality falls by approximately one percentage point as birth weight crosses 1500 grams from above, which is large relative to mean one-year mortality of 5.5% just above 1500 grams. Second, using hospital discharge records for births in five states in available years from 1991-2006, we find evidence that newborns with birth weights just below 1500 grams have discontinuously higher costs and frequencies of specific medical inputs. We estimate a $4,000 increase in hospital costs as birth weight approaches 1500 grams from above, relative to mean hospital costs of $40,000 just above 1500 grams. Taken together, these estimates suggest that the cost of saving a statistical life of a newborn with birth weight near 1500 grams is on the order of $550,000 in 2006 dollars.

"State Health Insurance Regulations and the Price of High-Deductible Policies" (with William J. Congdon and Mark H. Showalter). Forum for Health Economics & Policy. 2008. Vol. 11: Iss. 2 (Health Care Reform), Article 8.

Press Coverage: [Wall Street Journal]

This study examines the impact of state health insurance regulations on the price of high-deductible family and individual polices in the nongroup market. We use a unique and rich data set on actual insurance policies sold through a large Internet health insurance distributor to examine the impact of various regulations on policy prices, controlling for policy characteristics, demographic characteristics of the purchasers, and state-level demographics. We also use data from a single major insurance firm that provided offer prices for a family policy from a set of randomly selected zip codes. Both datasets suggest a strong statistical relationship between regulation and insurance prices.

Selected Work in Progress

"Returns to Medicare Spending: Evidence From Variation Across Physicians." (with Joseph J. Doyle and Heidi Williams). Latest Version: Coming soon.

"Medicaid Expansions and Health Spending Growth." (with Mikhail Golosov. Latest Version: Coming soon.

Call for Randomization

"The Use and Evaluation of Experiments in Health Care Delivery"

Video Presentation:

[Healthcare Beyond Obamacare: Bridging Policy to Practice, Yale Alumni Conference in Portland, September 2015] [Slides]

Stata Commands

"MTEBINARY: Stata Module to Compute Marginal Treatment Effects (MTE) with a Binary Instrument." First Version: December 2016. (with Ljubica Ristovska and Yen Tran). [Link to MTEBINARY at RePEc]

The simplest way to install this command is to type the following at the Stata command prompt:

ssc install mtebinary

You can also download the .ado and .sthlp files [Download CQIV Stata ado file][Download CQIV Stata help file], and then copy them into your personal ado directory [How to find your personal ado directory].

If you are updating from a previous version, type "net uninstall mtebinary" at the Stata prompt before installing.

"CQIV: Stata Module to Perform Censored Quantile Instrumental Variable Regression." First Version: December 2010. Latest Version: January 2018. (with Victor Chernozhukov, Ivan Fernandez-Val, and Sukjin Han). [Link to CQIV at RePEc]

The simplest way to install this command is to type the following at the Stata command prompt:

ssc install cqiv

You can also download the .ado and .sthlp files [Download CQIV file as zip file], and then copy them into your personal ado directory [How to find your personal ado directory].

If you are updating from a previous version, type "net uninstall cqiv" at the Stata prompt before installing.

Problem Sets

Problem Set on "Adverse Selection and an Individual Mandate: When Theory Meets Practice." First Version: January 2016. Latest Version: October 2016. (Developed with Austin Schaefer, Jack Welsh, and Megan Wilson).

This problem set is designed for graduate students and advanced undergraduates. It does not require Stata or Excel.

Download the PDF file with instructions.

Answer key available, instructors only please. Email Amanda Kowalski.

Problem Set on "Mandate-Based Health Reform and the Labor Market: Evidence from the Massachusetts Reform." First Version: May 2012. Latest Version: December 2016. (Developed with Toby Chaiken, Jonathan Kolstad, and Megan Wilson).

This problem set is designed for graduate students and advanced undergraduates. It requires Stata and Excel.

Download Stata, Excel, and Word Files as a zip file. The Word file contains instructions.

Answer key available, instructors only please. Email Amanda Kowalski.

Problem Set on "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns." First Version: February 2012. Latest Version: October 2016. (Developed with Tiffany Fan and Michael Cunetta).

This problem set is designed for undergraduates. It requires Stata.

Download Stata and Word Files as a zip file. The Word file contains instructions.

Answer key available, instructors only please. Email Amanda Kowalski.

Research Team

I am currently hiring full-time research assistants to start in summer 2019. Research assistant positions are posted in the following locations:

Full-Time Research Positions For College Graduates

Part-Time Research Positions for Yale Undergraduates

Summer 2017 Research Team at Princeton (from left): Tory Do, Bailey Flanigan, Pauline Mourot, Matthew Tauzer, Ljubica "LJ" Ristovska, Amanda Kowalski.

Summer 2016 Research Team at NBER Summer Institute (from left): Jennifer Wu, Yen Tran, Rebecca McKibbin, Matthew Tauzer, Samuel Moy, Ljubica "LJ" Ristovska, Rae Staben, Amanda Kowalski.

Summer 2015 ISPS Health Research Team (from left): Amanda Kowalski, Edward Kong, Megan Wilson, Cindy Zheng, Samuel Moy, Rebecca McKibbin, Maggie Zhou, Jennifer Wu, Stuart Craig (not pictured: Aigerim Kabdiyeva, Saumya Chatrath).

Summer 2013 Research Team (from left): William Bishop, Martin Hackmann, Kate Archibald, Tiffany Fan, Amanda Kowalski, Gerardo Ruiz Sanchez.

Last Updated February 2018| Email Amanda Kowalski