Columbia Economic Review: Spring 2020

Columbia Economic Review

Spring 2020 Issue | Volume 12 Does Having a Disabled Child Affect Maternal Labor Supply?

Risk of Collusion in the FIFA World Cup Computer Adoption and Income Inequality An Analysis of the Market for Egg Donation Online Feature: Is Economics Right-Wing Biased?

Columbia Economic Review

Spring 2020 Issue | Volume 12 Risk of Collusion in the FIFA World Cup Computer Adoption and Income Inequality Does Having a Disabled Child Affect Maternal Labor Supply?

An Analysis of the Market for Egg Donation Is Economics Right-Wing Biased?

Spring 2020 Issue | Volume 12

COLUMBIA ECONOMIC REVIEW SPRING 2020 ISSUE | VOLUME 12

FACULTY ADVISOR Wouter Vergote, Ph.D.

EDITORIAL BOARD Editors-in-Chief: Ignacio Lopez Gaffney and Sinet Chelagat Managing Editor (Journal): Neel Puri Executive Editor (Online): Hallie Gruder Junior Editor (Online): Deyvani Goel Junior Editor (Online): Philip Jang Executive Director: Jenna Karp Publisher: Genevieve Spencer

Special thanks to Carlos Ochoa for providing the art for this issue.

STAFF EDITORS Bennet Bookstein Blake Jones Jeremy Liu Jeffrey Luo Charlie Lousie Munns Ariunzaya Oktyabri Edmund Qian Long Shen Nurassyl Shokeyev

ONLINE TEAM

OPERATIONS TEAM

Joseph Campbell Lenny Ciotti Linh Khanh Nguyen Hoang Nicolas Hortiguera Shreya Ganguly Alqaim Lalani Casey Li Andrew Li-Yang Liu Leon Lu Eshita Sangal Serdil Yalcin

Nathan Chen Phoenix Chen Reyna Choi Sybil Fu Yaxin Gao Surina Seehra Goel Kristina Hadjipetkov Jennifer Lee Donna Qi Vidhima Shetty Samantha Nicole Syme Cara Vo

Contents

Letter From the Editors

Submission Guidelines

11

The Intensive and Extensive Effects of Having a Disabled Child on Maternal Labor Supply

25

Quantifying the Risk of Collusion and Non-Competition in a 32-Team FIFA World Cup

Elizaveta Brover, Rebecca Schleimer

Sophia Cornell

41

Computer Adoption and Income Inequality

63

Lemons, Limes, and Eggs: An Economic Analysis of the Market for Egg Donation

Noah Talerman

Emily Malpass

91 Online Feature: Is Economics Right-Wing Biased?

Shreya Ganguly

Spring 2020 Environmental Policy Competition Spring 2020 High School Essay Competition

Letter from the Editors Dear Readers,

This past semester has been fraught, to say the least. What started as a tensely watched – albeit distant – news story became an ineluctable and frightening reality for those of us in Morningside Heights. Though the global pandemic has undoubtedly had a differential impact on the economic and physical well-being of individuals in the Columbia community, we have all had to cope with unanticipated class cancellations and the indefinite suspension of in-person campus activities.

Compounding individual tragedy is the dire global economic outlook.

The limited data we have at the time of publication suggests the United States – like the rest of the world – is in the throes of a sudden and dramatic downturn.

As unemployment claims climb to record highs, the increasingly frequent comparisons made to the Great Depression become more appropriate by the day.

It is under these circumstances that we present to you the Spring 2020 Issue of the Columbia Economic Review. It is only through the diligence and flexibility of our staff, editorial board, and advisor that we were able to publish a full issue. While the semester has been difficult, this has not discernibly affected our collective effort. We believe, unequivocally, that this edition represents the very best in undergraduate economics research. It demonstrates our capacity, as students, to make valuable contributions to the realm of academic inquiry.

This issue features rigorous work in a range of topics. Though there is no thematic connection, they share a common sensitivity to matters of contemporary importance in economics, politics, and society. Noah Talerman (University of Michigan - Ann Arbor) examines the relationship between income inequality and the adoption of computer technology by analyzing countries’ internet penetration rates. Emily Malpass (Harvard University) analyzes irregularities in the market for egg donation as a result of adverse selection, price controls, and consumer preferences. Sophia Cornell (Columbia University) investigates collusion and other forms of anti-competitive behavior in the World Cup. Elizaveta Brover and Rebecca Schleimer (University of Pennsylvania) examine the literature on childhood disability and its impact on maternal labor supply. Lastly, we present an article by Shreya Ganguly (Columbia University–Sciences-Po), a member of the Review’s online team, on the alleged right-wing bias in introductory economics courses.

L

While we encountered difficulties in the publication process, we overcame them with the guidance and support of faculty and student body members. In particular, we would like to acknowledge the invaluable assistance of Professor Wouter Vergote, our academic advisor, Lauren Close, the Program Manage at the Program for Economic Research (PER), and Dr. Sophia N. Johnson, Assistant Director of PER. Their continued involvement and encouragement make CER possible.

Lastly, we would like to offer a reflection on the coming decade. Even though significant uncertainties cloud the economic and social panorama, we know that there will be a time after this crisis. That time will give all of us – including us amateur economists – the opportunity to understand the event we are currently living through. While we may not know when that time will come, CER will continue to facilitate the probing academic work that makes sense of a complex world. We expect this crisis to be the topic of research for years to come, and we hope to begin that effort whenever sensible.

We would like to dedicate this journal in honor of those who have lost their lives to this pandemic, as well as those individuals working tirelessly to fight it and prevent further loss of life.

Sincerely, Ignacio Lopez Gaffney and Sinet Chelagat

Submission Guidelines General Information The Columbia Economics Review is the biannually circulated undergraduate economics journal of the Department of Economics at Columbia University. We are looking for seminar papers, research papers, theses, etc., on all fields related to economics. Our goal is to give undergraduates at Columbia and other universities throughout the world the opportunity to publish their research in a premier undergraduate academic journal.Papers should be emailed to econreview@columbia.edu. They can also be submitted online here: columbiaeconreview.com/submit/.

Please read our submission guidelines thoroughly before submitting a paper. We will not review your paper if requirements are not met. You will be contacted to re-submit under the requirements.

Eligibility & Guidelines Paper submissions must be from current undergraduate students or recent graduates.

The submissions should meet the following requirements: 1. The content of the paper (not including the bibliography and extra data tables) must not exceed 40 pages. It is the author’s responsibility to trim down their work prior to submitting it. 2. Include the author’s name and university, acknowledgements (if relevant), and image files of all graphics or tables used in the paper. All images should be included in the paper and separate files (e.g jpeg) should be submitted alongside the paper.

3. Any spreadsheets used should also have relevant data linked.

4. Not have already been published in other journals. By submitting, you give CER the sole right to publish the paper and make any edits that we see fit. Please do not submit to other academic journals.

5. All manuscripts should be submitted in PDF format with 1.5 line spacing. We strongly recommend manuscripts not exceed 40 pages (not including the bibliography and extra data tables). The suggested length includes reference lists, figures, and tables. Submit the .tex file if LaTeX was used). It is the author’s responsibility to condense the thesis prior to submitting the documents.

6. Please use 12-point Times New Roman or similar font. Margins should be 1.5 inches on the top, bottom, and sides.

7. Include an abstract of 100 or fewer words Deadlines Submissions are reviewed on a rolling basis. We encourage you to submit your work as soon as you have turned in the final version to your department.

Policies Any paper submitted to The Columbia Economics Review must NOT be under consideration for publication by any other journal. All submitted papers must represent original work and give proper credit to the work of other authors, as it pertains to the submitted paper. Plagiarism is unacceptable. If, upon review, the submitted paper is deemed to have anunacceptable amount of plagiarized material, the paper will no longer be eligible forconsideration. Thanks to the generous support of the Department of Economics at Columbia University, there is no submission fee for papers. We reserve the right to review or reject manuscripts that have been previously rejected by CER. Furthermore, CER reserves the right to reject papers without review.

Selection Process and Publication CER has a team of senior editors and editors who are selected for their academic accomplishments, superior writing skills and research experience. All submissions will be reviewed rigorously and we will select the papers best exemplifying strong analytic thought and originality. Please visit our website for past publications: www.columbiaeconreview.com We will notify selected authors after the final decisions have been made. We appreciate all contributions. If you have further questions, please contact us at econreview@columbia.edu.

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The Intensive and Extensive Effects of Having a Disabled Child on Maternal Labor Supply

Elizaveta Brover, Rebecca Schleimer University of Pennsylvania Abstract: This literature review surveys four prominent economics papers that address the effects of disabled children on maternal labor supply. According to Becker’s fertility model, disability in children affects the family’s utility function, which takes inputs of child quality, quantity of children, and consumption. We analyze the relationship between this model and the empirical findings of subsequent literature, finding that the extensive choice to work depends more on a mother’s marital status than on a child’s disability type.

Acknowledgment: We would like to thank our labor economics professor, Dr. Petra Todd, for inspiring us to research this topic and eventually submit it to undergraduate publications.

Introduction and Background Literature

The past decades have seen a rise in the number of documented cases of childhood disabilities. Houtrow et. al. observe that since 1980 the percentage of disabled children has more than doubled, from 3.8% to 7.9% of the U.S. population. The same paper suggests that mental health conditions among children have become more prevalent, with cases rising by 21% between 2000 and 2010. Coinciding with this increase in the incidence of childhood disabilities is a rise in the number of disabled children receiving cash benefits through the Supplemental Security Income (SSI) program. Low-income, disabled recipients of SSI benefits more than quadrupled in the period between 1990 and 2015 while in the same period the population of U.S. children only grew by 15%, from 64.2 million to 73.8 million. Though this marked increase is significant, furnishing an opportunity for academic inquiry, we must acknowledge that researchers do not have a comprehensive understanding of the sources of this growth. While it may be that the number of children with disabilities has in fact increased over time, we would be remiss to ignore the impact of changes in disability awareness and survey methods.

With so many families affected by disabilities, labor economists continue to study the long-run economic consequences of having a disabled child. This research is complicated by the dynamic nature of disabilities. Many disabilities vary in severity, both at onset, as well as over the duration of a child’s lifetime.

As a result of this variability, disabilities often impose differential costs over the course of a child’s life, in turn, affecting the labor supply of the child’s family Columbia Economic Review | 11

Elizaveta Brover, Rebecca Schleimer

members. Consequently, much of the research in this area relies on extensive longitudinal data so as to obtain a comprehensive view of the economic impact of childhood disability on family income and employment.

A significant strand of the literature focuses on the impact of disabled children on maternal labor supply. Multiple studies have demonstrated a negative correlation between poor child health and maternal labor supply. In one such study, Kuhlthau et. al. – using data from the 1994 National Health Interview Survey and a cross-sectional framework – found that the presence of a child in poor health was associated with reduced employment of both parents.

Similarly, Gould et. al. – using data from the Panel Study of Income Dynamics – found that a cross-sectional relationship is significant for some time-intensive or unpredictable medical conditions (e.g. autism, diabetes, various heart conditions) but not others.

Three recurring themes in the literature discussing maternal labor supply stood out to us. The first concerns the impact of socioeconomic and demographic effects on the endogeneity of childhood disability. For this, we chose to discuss the article by Hope Corman et al. (Eastern Economic Journal, 2005). For our second theme – that of the evolution of disabilities – we turned to the recent paper by Dara Lee Luca and Purvi Sevak (Mathematica, 2019).

For our third theme – the differential impact of disabled children on married and single mothers – we selected two papers published by Elizabeth T. Powers (American Economic Review, 2001, and The Journal of Human

12

Effects of Having a Disabled Child on Maternal Labor Supply

Resources, 2003). While each paper addresses these themes to varying degrees, this combination allows for a well-rounded discussion of the impact of disabled children on a mother’s labor supply.

We hypothesize that estimates of the impact of disabled children on maternal labor supply will vary depending on the type of disability, the empirical model used for estimation, and the choice of dataset. To test this hypothesis, we consider several external variables that often appear in the relevant literature such as marital status, income, and number of children. By aggregating the various conclusions (and the methods used to arrive at those conclusions) of the considered papers, we hope to develop a comprehensive framework to understand the impact of childhood disability on maternal labor decisions.

Theoretical Framework Gary Becker’s fertility model – which outlines the relationship between the cost of goods (pX) and the cost of children (pN) – furnishes the theoretical foundations of much of the literature around maternal labor supply. Becker’s model introduces the following constraints, relating quantity of goods consumed (X) to quantity of children (N): Budget constraint: pXX + pNN = I

Becker defines the net cost of children as “the present value of expected outlays plus the imputed value of the parents’ services, minus the present value of the expected money return plus the imputed value of the child’s services.” In this way, he treats children as an investment, with an associated future return dependent on the “quality” or the utility gained from the child.

When applied to the question at hand, Becker’s model gives us an ambiguous prediction. As the time and monetary costs of maintaining a disabled child are significantly higher than those for a healthy child, pN (Becker’s cost term) increases if a child has a disability. In theory, this would create an income effect, motivating mothers to work more hours to finance treatments for their disabled children. Yet, the increased time cost associated with a disabled child would also create a substitution effect, causing the mother to substitute away from working and towards childcare. We will see later that disability benefits often eliminate the income effect, making the choice to spend more time on childcare more palatable for mothers. This is especially pronounced for single mothers.

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Elizaveta Brover, Rebecca Schleimer

A more sophisticated version of the Becker model includes the time and resource costs described above:

By reference to these formulae, we can describe the differential effects of resource-costly and time-costly disabilities on labor supply choices. Using Becker’s terminology, a disability alters the quality production function of the child, causing higher time and resource expenditures to become less resilient to increases in quality. A resource-costly disability – which calls for an increase in monetary resources per child (RN) – would be offset by decreases in time spent per child (tc), consumption (X), price (pX), or increases in wage (w). Similarly, a time-costly disability – which calls for an increase in time spent on the child (tc) – would be offset by decreases in outlays on monetary resources per child (RN), consumption (X), price (pX), or increases in wage (w).

The first option of decreasing X could be achieved by substituting towards cheaper goods, thus lowering consumption in order to offset increased time or resource costs. A household could increase w by switching into full-time or other higher-paying positions, thus offsetting higher resource costs. Lastly, households could lower the amount of future children, or N. Below, we explore how empirical models treat these theoretical tradeoffs.

Defining Disability

Economists employ divergent definitions of disability. This inevitably causes them to adopt different data collection methods, influencing their eventual conclusions. The highly contingent nature of disability specifications stems from the sheer variety of early-onset disabilities. These differ in severity, level of incapacitation, and required amount of care.

Powers uses a multi-level definition of disability in her studies. In her 2001 article, “New Estimates of the Impact of Child Disability on Maternal Employment,” Powers relies on the learning-centered SIPP dataset, which 14

Effects of Having a Disabled Child on Maternal Labor Supply

defines a disabled child as someone who has “ever [had] a physical, mental, or other health condition that adversely affected their ability to learn.” She then expands this definition with a functional approach, evaluating children on their ability to “accomplish particular socially accepted, age-appropriate tasks.” The second dataset used by Powers (CPS) classifies learning disabilities, speech disorders, other health impairment lasting six months or more, vision impairment, hearing impairment, orthopedic impairment, serious emotional disturbance, mental retardation, deafness, and blindness in its definition of disability. Powers also gauges the variability of the “care burden posed by the child” depending on the severity of the child’s condition.

In her 2003 study, “Children’s Health and Maternal Work Activity: Estimates under Alternative Disability Definitions,” Powers focuses on three different definitions of disability. The first and second definitions come from SIPP datasets from 1992-2000 and focus on limitations in the “usual kind of activities done by most children their age” as a result of a “physical, learning or mental health condition.” Due to the differences in maternal work outcomes that result from the age of the disabled child, Powers distinguishes the first two definitions on the basis of age. She tailors the first definition to children under the age of six, and the second to children between the ages of six and twenty-one, this time using pooled data from the SIPP datasets dated 19911992. Additionally, the second definition incorporates learning disabilities, explicitly including limitations in completing normal school work “because of a physical, learning, or mental health condition.” To address the issue of self-report bias, Powers bases her third definition on the “receipt of therapy or diagnostic services for children under seven and other activity limitations of children 15 and older (including work limitations).”

Unlike Powers, Luca and Sevak define disability solely on a shifting list of formal diagnoses in the Fragile Families Study. This study uses a “wave” rubric to describe the development of disability in a child. Participants are asked in each period, or “wave”, if their child has a disability. If they respond in the affirmative, they are then asked to choose a specific disability from a given list. The questions and specific conditions vary somewhat from wave to wave.

Starting in the fourth wave, the operative question changes to “has a doctor or health professional ever told you that (CHILD) has any of the following health conditions?” This addition in later waves makes sense as it is often not possible to diagnose many conditions at an early age. Both Luca and Sevak use a list of some of the most common childhood disabilities, creating a dummy variable that equals one “if the primary caregiver responded ‘yes’ to the binary question in waves 2 and 3, and responded yes to any of the listed disabilities in wave 4 onward.” Therefore, Powers’s definition is more sensitive to the disabled child’s level of deficiency than Luca and Sevak’s. Recognizing this possible deficiency, Luca and Sevak take the variable severity of a disability factor into account when conducting their analysis and measuring the robustness of their conclusions.

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Elizaveta Brover, Rebecca Schleimer

Finally, Corman et. al.’s definition of disability combines elements of Powers’s and Luca et. al.’s definition. Much like Powers, Corman et. al. uses a set of general physical criteria that reflect the severity of a child’s condition. However, Corman et. al. restrict their definition of disability substantially by including only those who “weighed less than 4 pounds at birth” or “[were] at least 12 months old at follow-up and had neither walked or crawled.” They argue that these factors correspond to a serious health shock which leads to cases of “serious chronic health problems.” Similar to Luca and Sevak, Corman et. al adds a third criteria that accounts for the mother’s self-report at follow-up “that the child had a physical disability.” Of the four definitions introduced above, we find this one to be the most restrictive.

It is important to note that these definitions of disability make it difficult to differentiate between resource and time-expensive conditions, especially for the studies that rely on the Fragile Families Survey, which only has one dummy variable for the presence of an aggregate set of disabilities.

Therefore, we will collapse our concept of time-expensive and resourceexpensive disabilities, the assumption being that on aggregate, children’s disabilities require both a higher time cost tc and a higher resource cost RN. Comparison of Datasets

The scholars we consider mostly align in their use of extensive, longitudinal datasets so as to account for variations in maternal labor supply.

The exception to this is the data in Powers’ earlier “New Estimates” article, which she collects from the October 1992 Current Population Survey (CPS).

This dataset consists of a broad cross-sectional sample of 9,500 observations from two-parent families and 2,800 observations from single-parent families. While this sample is much larger than those used in the other studies, it does not contain longitudinal documentation of maternal working decisions and familial status. Powers modifies her method of data collection in her 2003 “Children’s Health” article, pooling multiple panels of data from the Survey of Income and Program Participation (SIPP), thus capturing both static and dynamic maternal working decisions under each of her three disability criteria.

Here, her dynamic measure of work activity comes from surveying mothers in nine “waves” (each around four months in length), recording labor force status, usual work hours, and the total number of children within the family at each interval.

Corman et al. and Luca and Sevak both use results from the Fragile Families and Child Wellbeing Study, a more extensive longitudinal dataset that follows a cohort of around 5,000 children born between 1998 and 2000 for a total of 15 years.” Corman et al., like Luca and Sevak, use data from baseline interviews with parents at the time of the child’s birth, as well as from subsequent interviews 12 to 18 months later.

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Effects of Having a Disabled Child on Maternal Labor Supply

Several familial factors are common to the datasets of each study. In Powers’s earlier paper, she writes that a child’s impairments “are important determinants of the care burden posed by the child but do not themselves directly interfere with parental labor supply.” In her second paper she revises this, writing that “the health impairments, in conjunction with the child’s characteristics, parental characteristics, and family and community supports (or lack thereof ) determine the overall caregiving burden on the parent.”

Therefore, in both of her articles, she accounts for these additional factors, including basic parental data. In her later study, Powers creates a much clearer divide between the survey results of female heads and married mothers.

Similarly, the Corman et al. dataset maintains a focus on certain details, such as “[1] labor force activity, [2] characteristics [e.g., health and human capital] of fathers as well as mothers, and [3] detailed information on parents’ relationship status, living arrangements and other children (together and with other partners).”

The primary difference, of course, between Powers’s dataset and the Fragile Families Survey is the fact that the longitudinal data in the latter is weighted to be “representative of births in U.S. cities with populations over 200,000.” Given the focus on urban areas, this dataset contains a larger share of unwed parents. Luca and Sevak use the same “rich information on parental labor market activity, household income and poverty, and safety net program participation,” again with an “oversample of unmarried parents.” One element separating the Corman et. al. and Luca and Sevak datasets from those of Powers is the distinction between “the existence of full biological siblings, mother’s children with other partners, and father’s children with other partners.” Each of these factors could have differential effects on maternal employment, and thus, play an important role in shaping their results and later analysis.

Comparison of Methodology and Preliminary Findings

We separate the studies based on their interaction with Becker’s fertility theory. First, we examine Luca and Sevak’s and Corman et al.’s papers, which use quantity and quality of children as well as family income to model mother’s labor supply in the presence of a disabled child. We then examine Powers’s papers, which do not explicitly engage with Becker’s fertility model.

Luca and Sevak and Corman et al. possess several methodological similarities, choosing, among other things, to include the child quantity and quality observations emphasized by Becker in their regressions. Additionally, both studies use data from the Fragile Families Study, making comparisons particularly apt. However, differences in measurement of quality factors, along with different choices with respect to included demographic factors, explain differences in their findings.

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Elizaveta Brover, Rebecca Schleimer

We start with Corman et. al’s paper, in which the model is as below: Mother’s labor supply

= f(child health,other measures of child quality,child

quantity,mother and father characteristics,city labor market

characteristics,state policy and economic environment, µ (unobservables)) Corman et al. recognize that some of the factors affecting child health may be endogenous (i.e. correlated with unobservable factors µ), leading to biased estimates of a mother’s labor supply. To address these concerns, the authors control for the availability of adoption and the quality of hospitals, finding that a maximum log likelihood estimation of the parameters are unbiased after doing so. At this point, the authors run a regression predicting mother’s labor supply with (1) child characteristics (child’s health as an aggregate of the at-birth measurements outlined in the disability definition section, child gender, existence of biological siblings, and existence of step-siblings), (2) parent relationship (cohabitating, rarely talking, or friends), (3) mother characteristics (age, schooling, Medicaid eligibility, race, immigrant status, religious status, health, employment status two years before birth), (4) father characteristics (age, schooling, Medicaid eligibility, race, health), and (5) local labor market characteristics (unemployment rate, average female earnings). The statistically significant regressors that the logit and tobit models selected were child being in poor health, father having children with other mothers, parents cohabitating, parents are friends, mother’s age, mother’s age squared, mother’s education, Medicaid eligibility, mother working two years before birth, father age, father race, and both of the labor market variables. Notably insignificant is the presence of other siblings in the nuclear family of the disabled child.

After running the regression, Corman et al. come to the conclusion that 54% of married mothers reduce the amount of hours worked – by an average of three hours a week – when they have a disabled child. However, when a child’s father has children with other women, the decline in the mother’s labor supply is smaller – an average of two hours a week. Similarly, the authors conclude that single mothers are 7% more likely to continue working than mothers in relationships.

Luca and Sevak use a similar method of measuring changes in maternal labor supply as a function of observables, using the model described below: Mother’s labor supply

= f(identified child disability,child and parental age, parent

relationship status,parental health,presence of siblings,local

labor market conditions,state welfare generosity index,

national macroeconomic trends index,unobservables) 18

Effects of Having a Disabled Child on Maternal Labor Supply

Luca and Sevak’s method differs from Corman et al.’s, as disability is described in terms of five “waves”: 2 before, and 3 after identification. Luca and Sevak then document the variables in the function outlined above in each wave in order to track their effect on labor supply. The regression predicted mother’s labor supply in each wave as a function of the following variables: (1) mother’s information (hours worked last week, hours worked last year, earnings last year), (2) child’s benefit receipt (supplemental security income, TANF, SNAP), and (3) household information (income, poverty, marital status of parents). The estimates (using the same definition as Corman et al.) suggest working mothers begin reducing hours worked (conditional on reporting positive hours) as early as two waves before the disability is identified. After identification of the disability, mothers are more likely to withdraw from the labor force at the extensive margin. Changes in mothers’ earnings are consistent with the changes in labor market activity, with earnings beginning to fall two waves before identification, but experiencing greater reductions after identification onset.

These results are consistent with the notion that symptoms of a disability may be present prior to identification, leading mothers to reduce work at the intensive margin.

Luca and Sevak then adjust the Fragile Families Study definition of disability, only including children with a disability that is likely to be permanent, which they define as disabilities that are “consistently reported in subsequent waves after the wave of onset.” Their regression produced the same set of significant regressors. Additionally, they note that household income falls and poverty increases significantly after onset, suggesting that “persistent disabilities are associated with greater changes in the household.” However, Luca and Sevak do not comment on endogeneity of child disability, noting that their model does not prove decreases in maternal labor supply are caused by disabled children. They even suggest that a reduction of maternal labor supply may lead to a higher probability of filing for disability benefits.

It is important to compare these findings to that of Corman et. al. As Corman et. al. only uses data at baseline to categorize the disability status of children – precluding analysis of persistent disabilities or health impairments that infants outgrow (e.g. infants that were born underweight) – we might resist the extension of their results to all families. On the other hand, Corman et al.’s model incorporated robust demographic estimators that Luca and Sevak omitted. For example, Luca and Sevak did not analyze the existence of father’s children with other women, or mother’s education, both of which contribute to Corman et al.’s conclusion that higher educated, single mothers are less likely to reduce the amount of hours worked than married mothers.

We then examined both of Powers’s studies. Both of these studies base their conclusions on limited inputs: the disability exhibited by the child, and basic characteristics of the mother. While elegant in their simplicity, these Columbia Economic Review | 19

Elizaveta Brover, Rebecca Schleimer

studies assume endogeneity of children’s disabilities without testing for it as Corman et al. does. Powers also discounts the possibility that external factors affect maternal labor supply in the presence of a child with disabilities. We document both of Powers’ models below: 2001: Mother’s labor supply = f(mother’s health,mother’s relationship

status, child disability status definitions,unobservables) 2003: Mother’s labor supply= f(mother’s employment,mother’s

relationship status,child disability studies definitions, unobservables)

Powers’s first study uses disability specifications from SIPP and CPS, including a SIPP one-stage model, CPS one-stage model, CPS two-stage linear probability model (LPM) and CPS two-stage probit LPM. The two-stage LPM simplifies the theoretical model by assuming that disability and employment are each discrete endogenous variables, which would make the disability coefficient estimates readily comparable with the one-stage model. The two-stage probit LPM estimates employment as a function of a simplified disability definition, which is useful when the disability variable indicates a continuous latent variable crossing a threshold. Across these four models, Powers estimates the presence of a disabled child reduces maternal employment by an average of 8 percent among married women, and by an average of 11 percent among single women. When maternal health is included in the specification, the estimates are substantially smaller for both wives and female heads of household.

Powers’s second study uses the three SIPP definitions explained in the Defining Disabilities section. Powers argues that by pooling SIPP data from 1991 to 1992 we can construct better estimates of the different decisions made by single and married women. Thus Powers conducts three analyses for each of the three definitions: first on pooled cross-section samples, then on one-year horizon samples, and finally. on two-year horizon samples. Overall, the findings across the three methods reveal more significant coefficients in the second definition than the first definition. This is to be expected, as the second definition is more comprehensive.

For married mothers, two disability coefficients are significant at standard confidence levels, even though they have positive estimated effects.

Powers’s conclusion – when using the second definition – furnish scant evidence that maternal labor supply is adversely affected by disabled children.

However, the findings for single mothers from the two-year-horizon analysis provide reasonable support for this hypothesis. In this scenario, all the disability definitions have a significant negative effect on hours worked and on the probability that initial nonworkers begin working. The third disability definition reduces the average two-year change in usual weekly hours by 3.3, 20

Effects of Having a Disabled Child on Maternal Labor Supply

reduces the probability that a single mother increases her usual hours worked over the two-year-period by 7.8 percentage points, and reduces the probability that a nonworker begins working by the end of the period by 18.9 percentage points.

While Powers uses the most sophisticated definition of disability, limitations of the CPS and SIPP datasets require cautious analysis of her results. Powers does not choose to include demographic characteristics available in the SIPP and CPS datasets. Instead, in the 2001 study, the author separates her analysis on the basis of maternal health, and in the 2003 study, focuses her analysis on single mothers. Although all of her definitions of disability were significant predictors of maternal labor supply, analysis of socioeconomic factors present in Luca and Sevak and Corman et al. cast doubt on the causal nature of Powers’s conclusions. For example, Corman et al. found that a mother’s employment status two years prior to having a child to be statistically significant, a variable omitted by Powers. Despite these limitations, the four studies we examined revealed consistent findings.

Comparison of Results

Though the examined papers differ in their methodological approach, their conclusions are remarkably similar vis-a-vis married women: they generally show a decrease in maternal labor supply as a result of having a disabled child. Luca and Sevak, who base much of their analysis on changes over time in maternal earnings, observe that at the intensive margin, mothers reduce their work week between one and six hours after their child develops symptoms.

Corman et al. find that married mothers decrease the amount they work by an average of three hours a week. Powers finds (in her 2001) study that married women decrease the amount they work by an average of 3.7 hours per week, and are 7-10% more likely to stop working. These measurements are consistent with our initial hypothesis that disabled children require additional time from their parents, with mothers typically assuming this burden in a nuclear family.

The literature on single mothers is similarly significant. In her 2003 study, Powers finds that on the extensive margin, non-working single mothers are 18.9% less likely to start working if her child is disabled, while working single mothers are 10% more likely to stop working if her child is disabled. On the intensive margin, Corman et. al. find that single mothers reduce the amount they work per week by an average of two hours (compared to three hours for married mothers).

Yet these studies do not agree on everything. While Powers (2001 and 2003) predicts a larger negative effect on labor supply for single mothers, Corman et. al. suggests the opposite is true. To begin to resolve this issue, we must look at why Powers finds a larger decrease in working hours among single mothers. In Powers’s 2003 article, she writes that under each of her definitions of disability.

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Elizaveta Brover, Rebecca Schleimer

“the estimated effect of disability is always more negative for female heads than wives.” However, using the dataset from the Fragile Families survey, the Corman et. al. and Luca and Sevak studies both observe that “poor child health decreases the likelihood of employment by over 11 percentage points among unmarried mothers, but appears to have no effect on employment among married mothers.” We might be able to interpret this difference as a result of a relief in time pressure because of the ability of a wife to split childcare with her husband. Alternatively, having an additional source of household income likely makes special needs daycare more affordable, allowing married mothers the freedom to work more hours than single mothers or “female heads.” Another possible explanation for this observation may be the higher likelihood that a single mother would qualify for welfare or disability transfer programs such as SSI. Largely consisting of a high proportion of unmarried parents, the Fragile Families study showed that “child SSI receipt increase[d] markedly with the identification of child disability and further increases with each wave after.”

Receipt of monthly cash transfers or other benefits might allow a single mother to “make up shortfalls” in earnings so she can take more time off from work to care for her time-costly, disabled child.

In addition to marital status, the literature includes different sociodemographic factors in their analyses. For example, Corman et. al. notes that maternal employment increases with education, and that mothers who had worked two years before having their disabled children were almost 40 percentage points more likely to continue working once their child was born.

Additionally, Corman et al. found that having biological siblings had no effect on the mother’s labor supply, while the child’s father having children with another woman was significant. In the 2001 study, Powers found that controlling for maternal health status resulted in a decline in the absolute value of the coefficients on labor supply for both wives and single women.

Additionally, Powers found that for married mothers, the third disability definition – receipt of therapy or other diagnostic services – constituted the largest point estimates.

Conclusion

It is interesting to note how the examined literature interacts with Becker’s model. Whether or not the models specified in our literature included income as a proxy for consumption (pXX + NRN), presence of other children (N), or information regarding mother’s working hours (T-tc), we find that having a child with disabilities has a negative effect on maternal labor supply. For Powers (who only considered the disability status of children), Luca and Sevak (who considered disability status of children and income), and Corman et. al. (who considered disability status of children, income, presence of other children, and mother’s working hours), there is evidence of an increase 22

Effects of Having a Disabled Child on Maternal Labor Supply

in tc as a result of the higher time needs of a disabled child. According to our theoretical framework, this would imply either a decrease in RN, X, pX, and N or an increase in w. We did not have data regarding the effect of an increase in tc on RN, X, pX, or w. At baseline, the disability-working hours tradeoff is statistically significant. This holds even for Powers’s model, which omitted many demographic factors. We observe that single mothers are more likely to decrease the amount they work, perhaps by decreasing consumption as well. We explain this by suggesting that single mothers with disabled children are more likely to qualify for benefits not reflected by income.

We suggest further research be done in examining the effects of children with disabilities on the other factors of Becker’s model . For example, related research could examine the tradeoff between RN and tc, perhaps as a function of disability type. Additionally, seeing that our research question does not only concern health, economics, and sociology, but also gender, another avenue for further research could be on the effects of children’s disability on ‘nontraditional’ families, such as families with two mothers or two fathers.

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References Becker, Gary S. “An Economic Analysis of Fertility.” In Demographic and

Economic Change in Developed Countries, 209–40. New York, NY:

Columbia University Press, 1960. https://www.nber.org/chapters/c2387. pdf.

Corman, Hope, Nancy Reichman, and Kelly Noonan. “Mothers and Fathers

Labor Supply in Fragile Families: The Role of Child Health.”

Eastern Economic Journal 31, no. 4 (2005): 601–16. https://www.jstor. org/stable/40326366?seq=1#metadata_info_tab_contents.

Gould, Elise. “Decomposing the effects of children’s health on mother’s labor

supply: is it time or money?”, Health Economics, Vol. 13, Issue

6. p. 525-541. https://onlinelibrary.wiley.com/doi/abs/10.1002/hec.891 Houtrow, Amy J, Kandyce Larson, Lynn M Olson, Paul W. Newacheck, and

Neal Halfon. “Changing Trends of Childhood Disability, 2001–2011.”

Pediatrics 134, no. 3 (September 1, 2014): 530–38. https://pediatrics. aappublications.org/content/pediatrics/134/3/530.full.pdf.

Kuhlthau, Karen, and James Perrin. “Child Health Status and Parental

Employment,” Arch Pediatric Adolescent Medicine 2001; p. 1346-1350, https://www.ncbi.nlm.nih.gov/pubmed/11732954.

Luca, Dara Lee, and Purvi Sevak. “Child Disability, Maternal Labor Supply and

Household Well-Being.” Mathematica Center for Studying Disability

Policy, September 2019, 1–41. https://drive.google.com/ file/d/1u2oHfXrhZhDY3eSV3Pfj3pxIljXJN5_v/view?usp=sharing.

Powers, Elizabeth T. “New Estimates of the Impact of Child Disability on

Maternal Employment.” American Economic Review 91, no. 2 (2001):

135–39. https://www.jstor.org/stable/2677747?seq=1#metadata_info_ tab_contents.

Powers, Elizabeth T. “Children’s Health and Maternal Work Activity: Estimates

under Alternative Disability Definitions.” The Journal of Human

Resources 38, no. 3 (2003): 522. https://www.jstor.org/sta ble/1558767?seq=1#metadata_info_tab_contents.

Social Security Administration, “Social Security Administration Agency

Financial Report Fiscal Year 2018,” p. 71, https://www.ssa.gov/fi nance/2018/Full%20FY%202018%20AFR.pdf.

24

Quantifying the Risk of Collusion and Non-Competition in a 32-Team FIFA World Cup

Sophia Cornell Columbia University Abstract: In the 2026 Men’s World Cup, FIFA plans to expand the field from 32 to 48 teams. The 48 teams will be split into 16 groups of three, with the top two teams from each group advancing to an elimination round. Much has been made of an alleged increase in collusion opportunities (i.e. situations where teams may agree to a mutually beneficial score) as a result of the new tournament design. This paper quantifies the risk of collusion and other forms of “non-competition” already present in the existing World Cup design, in which two teams advance from each group of four. It finds that the percent of groups who face a risk of collusion in the current design is surprisingly high, ranging in theoretical calculations from 8.2% to 28.0% depending on assumptions of competitive balance or imbalance. Empirical data from the past six World Cups (1998-2018) show that teams from 25.0% of groups had the possibility of colluding as they went into their last round of simultaneous games, and that 43.8% of groups faced either collusion or non-competition risk. The theoretical average probabilities for the same statistics in groups of three are higher at 34.2% and 54.4.%, respectively. Nevertheless, the surprisingly high risks of collusion and non-competition in groups of four provide new context to the fear that groups of three would introduce unprecedented collusion risk to the World Cup.

Introduction and Examples of Collusion

The FIFA Men’s World Cup began in 1930 with 13 teams. The number of teams expanded to 16 in 1934, to 24 in 1982, and to the current 32 in 1998.

Since 1998, the 32-team World Cup has consisted of eight groups of four teams.

Every team plays the other members of their group in “group play” or a “round robin.” A team earns three points for a win, one point for a tie, and zero points for a loss. At the end of the round robin, the two teams with the highest point scores from each group advance to an 16-team elimination bracket. If teams within a group have the same point scores, the first two tiebreakers are goal differential and then goals scored. The first-place finisher has the advantage of likely encountering a less-skilled opponent in the first round of elimination play.

In 2017, FIFA’s governing council voted unanimously to expand the field to 48 teams in the 2026 World Cup in North America. The field of 48 will be broken into 16 groups of three, with the top two teams from each group advancing to the elimination round. The change from groups of four teams to groups of three Columbia Economic Review | 25

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teams affects the opportunities for any two teams in a group to collude or to conspire to eliminate the third team. We will first take a look at a few examples of collusion that occurred in the past in a group with four teams.

A. The Disgrace of Gijón

The most infamous example of collusion in a World Cup occurred in 1982 in a game between West Germany and Austria that is now known as the Disgrace of Gijón. At the time, two points were awarded for a win, one point for a tie, and a zero point for a loss. The two teams from the group of four with the most points would advance. Algeria and Chile had already played each other in their final game of group play. The only remaining game was between Austria and West Germany. The standings before the final game appear in Table 1.

The mutually beneficial result—which would allow both Austria and West Germany to advance—was a West German victory over Austria with a goal differential of one or two points. In the first ten minutes of play, the West Germans scored a goal. Satisfied with the result of 1-0 , both teams sat back and avoided attacking each other for the rest of the game. Fans of both teams, and especially of Algeria, were furious. The local newspaper ran coverage of the game in its crime section. The standings after the game appear in Table 2.

One might ask why Austria was willing to collude on the 1-0 result if it meant finishing second rather than first in their group. One explanation is that West Germany was considered to be the stronger side. A more attacking strategy in soccer leads to more goals but also leaves a team more vulnerable to counterattacks and to giving up goals. In other words, had Austria played to win, they would have been more likely to lose. In general, teams may prefer to guarantee that they will advance out of group play rather than to fight for a 26

Risk of Collusion and Non-Competition in a 32-Team FIFA World Cup

first-place spot. By not playing to win, they can rest their best players, prevent injuries, and ensure that they will avoid an embarrassing departure in the round robin games.

B. France vs Denmark

After the Disgrace of Gijón, FIFA ruled that the last two games in a group would be played simultaneously. Although this is damaging to TV numbers—viewers who may have otherwise watched both games must now choose one—it significantly reduces the risk of collusion. Nevertheless, opportunities for collusion still exist. The most recent example occurred in the 2018 Men’s World Cup. The two simultaneous games would be played between France and Denmark and between Peru and Australia. The standings for the group before the final simultaneous games appear in Table 3.

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If France beat Denmark and Australia beat Peru, Australia could finish with four points and advance as long as its final goal differential was more positive than Denmark’s.

In order to ensure that they both advanced, France and Denmark could collude to tie, which would provide them with respective finishes of seven and five points, enough to allow both teams to advance to the elimination round regardless of the result in the simultaneous game between Peru and Australia. In reality, the two teams played as expected and produced the only 0-0 tie of the 2018 tournament with Denmark largely refraining from attacking. In its write up of the game, The Guardian noted the audience’s “loud jeers” and deemed the game “a travesty.”

Motivation

In 2019, a mathematician at New York University named Julien Guyon published a paper called “Will Groups of 3 Ruin the World Cup?” In the paper, he found that the probability that a group of three teams would face the opportunity for collusion ranged from 5.9% to 52.7%, depending on assumptions of relative strengths of teams in a group and match schedule. A group of three in which each team is equally talented would face the opportunity for collusion 41.9% of the time. At first glance, these numbers appear astonishingly high. Guyon’s paper led to an uproar from researchers, reporters, and pundits who suggested that FIFA’s proposed shift to groups of three would introduce an unprecedented level of collusion into the World Cup. However, his paper did not consider the existing opportunities for collusion in groups of four: i.e., what percent of groups in previous World Cups have had the opportunity to collude. Without an understanding of the baseline risk in groups of four, the threat posed by groups of three cannot be properly evaluated.

This paper intends to quantify the empirical and theoretical collusion risk in groups of four (Section IV) so as to compare it with the theoretical risk found by Guyon for groups of three (Section VI). It also recognizes that collusion is not the only way that games can be less than fully competitive, thereby diminishing the excitement of the World Cup. To continue the comparison between group sizes of three and four teams, the paper will also quantify and compare the probability of games that do not have collusion risk but may be less competitive for other reasons (Section V).

Collusion Risk in Groups of Four, with Two Teams Advancing A. Notation Each World Cup group has Team A, Team B, Team C and Team D. We define Team A and Team B as two teams who have not yet played each other in the first four games. The following is an example schedule for a round robin. The order of the first four games is irrelevant to collusion considerations. The final two games in each group, which are played simultaneously, are highlighted.

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The opportunity for collusion occurs before the final two games, which will always be denoted as taking place between Team A and Team B and between Team C and Team D. The standings with all points accumulated in the first four games, or up until the simultaneous games, will be denoted penultimate standings, or PS. These standings are not ordered by point value, but rather from Team A to Team D, so that the first two teams listed will play each other, and the final two teams listed will play each other. This will also be denoted PS: (NA, NB, NC, ND) with NX as the number of points Team X has earned before the two simultaneous games.

B. Types of Collusion

A game with a risk of collusion is defined in this paper as a game in which two teams may implicitly or explicitly agree on a result that advances both teams from group play to the detriment of another team in the group that could have advanced. This definition mirrors the definition used by Guyon in order to allow for a fair comparison of results.

Each of the four already decided games has three potential outcomes (one team wins, the other team wins, they tie), and the results of each game are (theoretically) independent.1 As a result, there are 34=81 potential outcomes from the first four games. The 81 potential outcomes were examined for possible collusion risk, and three types of collusion emerged. • Type I collusion: Team A and Team B tie to ensure that both advance

• Type II collusion: Team A and Team B tie to ensure that both advance.

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• Type III collusion: Team A agrees to lose to Team B to ensure that both advance. This is an example of “aggravated collusion,” in which the teams arrange to win and lose rather than tie.

C. Empirical Probability of Collusion in World Cups since 1998

Since 1998, the World Cup has had its current structure of eight groups of four teams, with the top two teams advancing. We can therefore examine six World Cups for collusion risk in groups of four. Data was collected from the match statistics available at Fifa.com. The results appear in Table 7. Groups that faced a suspicion of collusion are marked with their PS. The groups that intentionally or coincidentally ended up with the result predicted by collusion strategy (RPCS) are highlighted in red.

Of the 48 groups in the past six World Cups, 12 groups, or 25%, were suspected of having colluded going into their last two games. Five groups have ended up with the RPCS. Notably, both instances of Type III collusion, which leads to the best possible finish for both teams, have resulted in the RPCS. The fact that only three of the remaining ten groups that had the possibility of colluding ended up with the RPCS is meaningful—it suggests that teams have a strong incentive to finish first even when collusion is possible. The percent of groups with each type of collusion risk that ended in the RPCS appear in Table 8.

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D. Theoretical Collusion Risk in Groups of Four with Two Teams Advancing 1. Methodology

These three types of collusion account for 16 of the 81 possible outcomes. The 16 cases are expanded in Table 9. In Type II collusion, each PS is repeated because it can happen in two different ways. We denote the probability that Team A beats Team B as pAB and the probability that Team B beats Team A as pBA. The probability that they tie is therefore TAB = 1-pAB - pBA. The probability of a given PS is the multiplied probabilities of the game results that lead to the PS. For example, the probability of PS: (6,4,1,0) is pACpADTBCPBD. The probability of PS: (4,4,1,1)1 is pACTADTBCPBD and the probability of PS:(4,4,1,1)^2 is TACPADPBCTBD. The total probability of collusion risk is calculated by summing the probabilities of each of the 16 situations.

The probability that one team will beat another is an unknown quantity, but we can model it by assuming different levels of competitive balance within a group. A group can have perfect competitive balance, in which each team is equally likely to beat another. A group can also be imbalanced or strongly imbalanced. In a given group of four, let us assume that one team is strong, two teams are medium-strength, and one team is weak. Imbalance 1 is defined as the situation in which the two medium-strength teams have already played each other before the simultaneous games, while Imbalance 2 is defined as a situation in which the two medium teams have not yet played each other before the simultaneous games. Strong Imbalance 1 and Strong Imbalance 2 are Columbia Economic Review | 31

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defined analogously. Further research could model groups of four in which all four teams have different strengths.

Realistic (but arbitrary) win probabilities for each case have been copied from Guyon’s analysis in order to allow for the most direct comparison of results. Table 10 shows the probability that the row team beats the column team in each assumption of competitive strength.

2. Results

The probabilistic risks of collusion under different assumptions of competitive balance appear in Table 11. Let pC denote the probability that there is the suspicion of collusion in a given group. Therefore pC(8)=100(1-(1-pC)8) is the probability that there is collusion in at least one of the eight groups and E[NC ]=8(pC) is the expected number of groups that will face the suspicion of collusion.

Table 11 shows that the risk of collusion in groups of four varies from 8.2% to 28.0% depending on assumptions of competitive balance. The empirical probability of collusion risk in a group, which was 25%, is in line with these theoretical estimates. Table 12 shows that groups of four have a small risk of aggravated collusion. he empirical probability of aggravated collusion risk was 4.2%, which is also in line with the theoretical estimates.

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Risk of Collusion and Non-Competition in a 32-Team FIFA World Cup

The risk of collusion is worst in a strongly imbalanced group where the two medium-strength teams do not play before the final simultaneous games, and lowest in a strongly imbalanced group where the two medium-strength teams do not play before the final simultaneous games. Adding teams to the World Cup would make it more likely for groups to be strongly imbalanced because the addition of sixteen weaker teams who were previously deemed unworthy of the World Cup would add lower-end outliers. However, a World Cup with 48 teams that maintained a group size of four would also necessitate uneven advancement into the elimination round, in which either the eight worst two-seeds fail to advance or the eight best three-seeds advance. This uneven advancement would introduce uncertainty across groups, which would significantly decrease the risk of collusion.

Risk of Non-Competition in Groups of Three and Four A. Definition

The paper also considers games that do have the risk of collusion, but which may be less competitive because a) neither team needs to win to advance out of group play, b) neither team can mathematically advance, or c) one team does not need to win to advance and the other team cannot mathematically advance. Such games are defined as having a risk of “non-competition.�

Collusion is bad because it damages the integrity of the World Cup, but it is also bad because it leads to less exciting games. After the France-Denmark game, fans complained that the game was unfair to Australia, but also because it was boring. Non-competitive games, in which teams have less of an incentive to try to win, have the same impact. If a team does not need to win to advance, it may bench its superstars or refrain from aggressive play, which angers fans who watch the World Cup to see players and coaches give everything they have to the pursuit of victory. In a group of four, there are two types of non-competition risk, which occur at PS: (6,6,0,0) and PS: (6,0,3,3).

Type I non-Competition: Any result between A or B ensures they both advance. Neither C nor D can mathematically advance, so they have nothing to play for. (This is known as a dead rubber.) As a result, Type I non-competition leads to two non-competitive games.

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Sophia Cornell Type II non-Competition: A does not need to win to advance. B cannot mathematically advance.

B. Empirical Probability of Non-Competition in Groups of Four

The same methodology was used as in Section IV. C. In the last six World Cups, Type I non-competition and Type II non-competition have occurred in 9 of 48, or 18.8% of the groups. These results appear in Table 18.

Because Type I non-competition results in two, rather than one, noncompetitive games, this number does not fully capture the risk of Type I non-competition. This paper accepts the underestimate in order to maintain the convention of measuring risk by group rather than by game, but further research could compare groups of three and four by percent of affected games, rather than affected groups.

C. Theoretical Risk of Non-Competition in Groups of Four

Using the same methodology as in Section IV. D., we find the theoretical probability of non-competition. The results appear in Table 16.

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These results show that, depending on the assumption of competitive balance, the risk that a group will have at least one non-competitive game ranges from 10.4% to 26.0%. The risk is generally higher with more imbalance because more imbalance leads to fewer ties and makes Type I and Type II non-competition more likely. The empirical probability of groups with non-competition risk, which was 18.8%, fits within the theoretically predicted range.

D. Theoretical Probability of Non-Competition in Groups of Three

Groups of three have only one preliminary standing with risk of non-competition. This case is shown in Table 18, which shows the standings before the final game between Team X and Team Y, where Team Z is the team that does not play in the final game. In the final game between Team X and Team Y, neither team needs to win to advance.

Using the methodology described in Guyon’s paper, we find the probability of the above case depending on the competitive balance in a group of three and the relative strength of the team that does not play in the last group-play game. These probabilities appear in Table 19 as pNC, or the probability of non-competition.

Comparison of Risk in Groups of Three and Four

Table 18 summarizes the probability of different types of risk in a group size of four. Recall that pC is the probability that a group will have the potential for collusion, p*C is the probability a group will have the potential for aggravated collusion, and pNC is the probability a group will have at least once game at risk of non-competition. Table 18 also displays the average risk across the assumptions of competitive balance, not including perfect balance, which is unlikely to occur in a real tournament. This average is an imperfect measurement because not all competitive balances are equally likely, but it allows for easier prima facie comparisons. Finally, Table 18 includes a measure for “Total Risk,” denoted pC+NC. The risk of collusion and of non-competition were defined to be mutually exclusive, so pC+NC is simply equal to pC + pNC. Again, this is an imperfect statistic, since the two risks are not equally bad. Collusion is unfair and boring; non-competition is simply boring.

Table 19 reports the same statistics for groups of three. The probabilities in the first two rows were taken from Julien Guyon’s analysis. The probability of Columbia Economic Review | 35

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non-competitive risk was calculated for each assumption of competitive balance using his methodology and assumptions of competitive balance. Table 19 also reports the averages and the statistic pC+NC, which approximates total risk in groups of three.

Collusion risk is worse in groups of three than groups of four, but not exorbitantly so. On average, 34.2% of three-team groups will face the possibility of collusion while only 19.2% of four-team groups will do the same.

Furthermore, certain assumptions of competitive balance in a group of three lead to “worst-case scenarios� that are unrivaled in groups of four. A strongly imbalanced group of three in which the team that does not play in the last game is the weakest team has a 52.7% probability of having the suspicion of collusion, which is much worse than 28%, the worst-case scenario in a group of four. The types of collusion possible in groups of three may also be more malevolent than those possible in groups of four. The risk of aggravated collusion is more than two and a half times more likely in a group of three than in a group of four.

The risk of non-competition is comparable in groups of three and four, which have respective probabilities of 20.2% and 18.0%. Since a group with PS: (6,6,0,0) has two separate games at risk of non-competition, the risk of non-competition in a group of four is an underestimate, and groups of four are probably more likely to suffer from non-competition issues outside of collusion.

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Risk of Collusion and Non-Competition in a 32-Team FIFA World Cup

Overall, an average group of three has a 54.4% chance of collusion or non-competition risk while an average group of four has an only 37.1% risk of the same. (The empirical probability of total risk in groups of four was 43.8%.)

This difference in total risk shows that groups of three have a higher probability for games that will upset fans, either through suspicion of being fixed or through a lack of incentives for teams to win. Furthermore, a group size of three leads to several unfairness issues that are not present in groups of four. While any team in a group of four can be the victim of collusion, in a group of three, only the team that does not play the last game can be the victim of collusion.

Conclusion

This paper sought to quantify the probability of collusion risk in a tournament structure in which two teams advance from groups of four. Under various assumptions of competitive balance, we expect between 8.2% to 28.0% of groups to face the risk of collusion. Empirically, 25.0% of groups in the past six World Cups faced the risk of collusion and 43.8% of groups of four faced either collusion or non-competition risk. The average theoretical probabilities for the same statistics in groups of three are higher at 34.2% and 54.4%, respectively. Further, the asymmetric nature of the schedule means that the risk of being the victim of collusion is, in a group of three, concentrated on the one unluckily scheduled team.

Although the risk of collusion is indeed higher in groups of three, this paper finds that substantial collusion risk is already tolerated in the World Cup. This is interesting for two reasons. The first is that many teams with the opportunity for collusion choose not to collude. Of the 12 games with the opportunity to collude in the past six World Cups, only five resulted in the RPCS, or the result predicted by collusion strategy. This suggests that teams given the incentive to collude often choose not to do so, either because of a strong incentive to finish first in the group or a sense of pride and integrity. The second is that decades of a fairly high level of collusion risk has not yet seemed to alienate fans from the World Cup. The most recent World Cup was viewed by over three and a half billion people and brought in an estimated $6.1 billion in revenue. By seeking to expand the field, FIFA is gambling that the additional revenue and profit created by the additional teams (estimated at $1 billion and $640 million respectively) will outweigh the anger and lack of excitement caused by the expected increase in collusion risk.

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Notes The lack of independence will be accounted for when we introduce probabilities of each team winning, which will make certain cumulative outcomes more likely or unlikely.

1

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and Is Dumb and Weird and Only an Organization as Dumb and Weird

as FIFA Could Possibly Think It Was a Good Idea.Https://Twitter.Com/

UpshotNYT/Status/1006192308151349248 ….” Tweet. @natesilver538

(blog), June 11, 2018. https://twitter.com/natesilver538/status/1006193 852963835904?lang=en.

Wiggins, Brandon. “The New Format for the World Cup Has a Flaw That

Could Encourage Collusion among Teams.” Business Insider. Accessed

December 21, 2019. https://www.businessinsider.com/new-world-cup- format-collusion-scandals-2018-6.

“World Cup Collusion on the Rise,” June 27, 2018. https://www.theaustralian. com.au/sport/football/world-cup-collusion-to-become-more-common/ news-story/e840082a1b6fda8bf33281e330193e3f.

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Computer Adoption and Income Inequality

Noah Talerman University of Michigan Abstract: This paper examines the relationship between income inequality and computer technology adoption. I utilize data on the income share of the top one percent of earners as a measure of income inequality and data on the percentage of individuals using the Internet as a measure of computer technology adoption for 122 countries from the years 1990 to 2017. The main hypothesis is that a negative relationship exists between the income share of the top one percent of earners and the internet penetration rate. As Internet usage increases, individuals in lower income brackets gain greater access to technologies and tools to bolster their skillset and subsequently earn higher incomes. In my main regression, I use the public availability of a web browser as an instrument to measure the internet penetration rate and correlate this instrument with a given country’s distance from the United States. The results indicate that a significant negative relationship exists between the income share of the top one percent and the internet penetration rate; however, this relationship is highly dependent on a given country’s distance to the United States.

Introduction

My research project develops an understanding of the relationship between the adoption of computer technology and income inequality within countries. The recent era of globalization has resulted in an unprecedented flow of goods and services between countries. One category of these goods, namely technological advances, has greatly influenced the way in which international citizens learn, consume media, and produce work. In addition to increasing efficiency, technology provides its users with the ability to learn new skills and subsequently increase their personal welfare. As computer technology becomes more widespread following the growth in globalization, citizens who earn less will have the ability to broaden their professional opportunities and potentially increase their personal income.

Technological progress has long been believed to positively influence long run growth. The Solow Model explains that a country’s economy will converge to the same steady state equilibrium in the long run even with short run changes in capital accumulation or labor (Solow 1956). The only factor that produces long run growth is technological progress, or what Solow calls an Columbia Economic Review | 41

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an increase in total factor productivity. Using this knowledge, many countries have dedicated large amounts of financial resources to research and development (R&D). Other countries, however, rely on obtaining technological advances through the diffusion of ideas and trade of physical technology.

Following the conclusion of World War II, rapid growth in globalization has resulted in an increasing movement of technological goods and ideas at the country level (Levchenko et al. 2013). Many countries previously without access to complex technologies, such as China and India, are now some of the largest importers and exporters of technological goods. Increasing trade and investments in technology have resulted in citizens across the globe to continue adopting these technologies. Personal computers, which allow for the presentation of data and information, continue to become a more prominent resource for obtaining knowledge. Access to computer technology allows global citizens to pursue a higher level of education, and thus find more success in moving into higher skill and higher paying occupations. Furthermore, higher levels of computer technology adoption among citizens may decrease the income gap between top and bottom earners.

This research work examines the relationship between a country’s income inequality and internet usage as a measure of computer technology adoption. I choose internet usage as a measure of computer technology adoption because the internet provides access to the majority of tools and information that can effectively increase an individual’s knowledge or skill set. I further assume that only owning a physical computing device will not produce a measurable effect on an individual’s human capital and subsequent income. In order to examine this relationship, I utilize data on the income share of the top one percent and the internet penetration rate for 122 countries from 1990 to 2017. I then run both ordinary least squares (OLS) and instrumental variable (IV or 2SLS) regressions to examine the coefficient for the independent variable of interest— the internet penetration rate.

In addition to the main independent and dependent variables, I include both an instrument for the internet penetration rate and an instrumented interaction term between the internet penetration rate and the observed country’s distance from the United States. The instrument devised is a dummy variable that assigns a value of one to observations that occurred within years of the public availability of a web browser. This instrument is interacted with the observed country’s distance from the United States in order to add country variation. Distance to the United States acts as a proxy for the time it takes for a given country to adopt public web browser technology. I assume that the majority of the knowledge and hardware required to introduce internet technology originated in the United States. Unlike other possible measures that may add country variation to the instrument, like GDP per capita, distance from the United States reflects the probable outward spread of the infrastructure 42

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needed to provide the public of a given country with access to a web browser.

The IV regression of interest, including both the instrumented internet penetration rate and the instrumented interaction term, reveals a significant negative relationship between the main variables of interest.

A significant negative relationship is found between the income share of a given country’s top one percent of earners and the internet penetration rate.

However, this relationship is heavily dependent on a given country’s distance from the United States. Countries with a distance measure of over 5,590 kilometers exhibit a significant positive relationship between the two main variables of interest. The robustness and economic significance of these results lose strength when country-fixed effects and new controls are added to the regression.

Much of the existing literature on the relationship between personal welfare and computer technology can be divided into two groups. The first discusses this relationship through education. The second focuses on the relationship between income and technology through a country’s comparative advantage. While most existing research provides a discussion on this relationship at the country or individual level, this paper attempts to contribute discussion towards the relationship between income inequality and internet adoption as it relates to globalization.

Chinn and Fairlie (2006) examine the potential causes for differing penetration rates of computer and internet technology in 161 countries from 1999 to 2004. The authors determine that factors such as income, human capital, youth dependency ratio, telephone density, legal quality, and banking sector development are associated with technology penetration rates. In addition, these factors do not differ significantly between developed and developing countries. By examining the relationship between income inequality and the internet penetration rate, my research expands on the discussion introduced by Chinn and Fairlie. My paper examines the effect of differing internet and computer technology penetration rates on income inequality as a measure of a country’s personal welfare.

Research conducted by Malamud (2010) and Bulman (2016) show that computer technology has ambiguous effects on the development of human capital and educational success respectively. Malamud illustrates, using data from a government trial in Romania, that children granted access to computer technology earned significantly lower Math and English school grades but earned higher scores for computer skills. The results presented by Bulman exhibit the ambiguous effect of information and communications technology (ICT) investment in schools as well as the use of computers at home on education. Both of these studies attempt to examine the relationship between computer adoption and educational attainment. My paper will expand on these results by providing discussion for the impact of internet technology on a country’s income inequality.

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The second group of existing literature on the relationship between technology and income presents results on how acquiring new skills impacts an individual’s ability to obtain higher income. BÊnabou (2004) illustrates that in an industry in which production technology has a greater substitutability between tasks, the cost of skill disparities is reduced. As a result, individuals incur relatively fewer costs when acquiring new skills. He further explains that countries that have main exports in industries with a greater skill flexibility have a potential for lower income inequality as it costs less for individuals to acquire the necessary skills. This study provides an important foundation for the main argument I present in this research paper. I provide evidence that internet technology may be a crucial element in acquiring new skills. In countries in which these new skills provide avenues for higher paying occupations, income inequality should decrease with relation to higher internet penetration.

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Empirical Methodology

To analyze the impact of computer adoption on income inequality, I first define an accurate measure of computer adoption for each country observed. In this paper, I represent computer adoption using the percentage of individuals using the internet, denoted by Nc , in country c. I also add a country’s distance from the United States in thousands of kilometers, denoted Dc, as a control. I then estimate the following equation: IEt = α + β1 Nct + β2Dc + εct

(1)

The variable on the left-hand side of the equation, , is the income share of the top one percent of earners in which c indexes countries and t indexes years.

The main hypothesis is that a negative relationship exists between and Nct; increased internet usage will have a negative effect on the income share of the top one percent of earners. I include the control-specific variable Dc as a country’s internet technology penetration may be influenced by its trade with the United States. A country’s distance from the United States acts as a proxy for the measurement of the trade of internet technology and ideas with the United States. The slope of the regression, β1, signifies the relationship between Nct and while the intercept is represented by α. The error term in observation for country c at time period t is represented by εct.

I also utilize country fixed effects to control for unobserved variables at a country and time period level: IEt = α + β1 Nct + β2Dc + δc + εct

(2)

This panel specification allows for the analysis of the effect of countries’ internet use on income inequality while controlling for factors that differ across countries and years. Here δc represents the country fixed effects of country c. The value of δc will affect the intercept for the country c’s regression; however, the slope, β,will remain the same across all countries and time periods.

One concern that arises is the possibility for reverse causality between income inequality and the percentage of individuals using the internet in each country. According to research done by Brueckner and Lederman (2018), greater income inequality may have a positive effect on the economic growth of poor countries, though in contrast, may have a negative effect on the economic growth of high- and middle-income countries. As a result, poorer countries that experience an increase in income inequality may invest in more information and communications technologies (ICT), increasing the availability of the internet.

Oppositely, high- and middle-income countries that experience an increase in income inequality will invest in less ICT, potentially decreasing the availability of the internet.

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Possible omitted variable bias is a second concern. Factors such as a country’s GDP per capita may be correlated with both the country’s percentage of internet users and income inequality. To handle potential reverse causality and omitted variable bias, I develop an instrument for the percentage of internet users by country using the public availability of web browser technology.

The potential for reverse causality and omitted variable bias creates a useful opportunity to implement an instrumental variable. By definition, an instrumental variable must only affect the outcome variable, here the income share of the top one percent of earners in each country for a given year, through its effect on the explanatory variable, here the percentage of internet users in each country. This means that the instrumental variable must have a statistically significant correlation with the percentage of internet users in each country and no correlation with the error term. In this paper, the dummy variable takes on a value of zero or one depending on whether an observation occurred before or after the availability of a public web browser; it acts as the instrument for the percentage of internet users in each country.

In 1993, the National Center for Supercomputing Applications (NCSA) released Mosaic 1.0. Mosaic was one of the first web browsers to provide easy access to websites and is often credited with instigating the internet boom of the 1990s (NCAC 2019). The computer software known as a web browser allows personal computer users to peruse the internet and receive educational information from public websites. Today, the most common web browsers used are Google Chrome, Mozilla Firefox, Safari, and Microsoft Edge. The variable for the availability of a public web browser will be titled post-mosaic, or PMt, where t indexes years. Observations following the release of NCSA’s Mosaic web browser will receive a value of one for PMt while years prior to the release will receive a value of zero. While the development of Mosaic was officially halted in 1997, its release presented personal computer users with web browser software. Thus, any observation following 1993 receives a value of one for PMt.

In order to justify the validity of the instrumental variable, PMt must satisfy the two conditions of relevance and exogeneity. An instrumental variable satisfies relevance if it is correlated with the explanatory variable. Exogeneity is satisfied if the instrument is not correlated with the error term, meaning the instrument affects the outcome variable only through its effect on the explanatory variable. Below, I display a regression between variables Nct, the percentage of a country’s population that is using the internet, and my instrument PMt. The regression equation used is expressed as: Nct = α + βPMt + δc + εct

(3)

Using this cross-sectional regression equation, we can observe the effect of the instrument PMt on the explanatory variable Nct. The variable δc represents the 46

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country fixed effects and accounts for all unobserved factors that differ across countries. δc is added to the intercept α to obtain a country specific intercept.

The slope β of the regression, which represents PMt ’s effect on Nct, remains the same for all countries in this cross-sectional regression. The error term is denoted by εct.

Table 1 displays the results of the regression of Nct on PMt with country fixed effects. The slope coefficient (β) value of 32.40 indicates that upon holding unobserved factors across countries constant, the presence of a publicly available web browser increases a given country’s internet penetration rate by 32.40 percent. This result satisfies the relevance criteria for our instrumental variable PMt as there is a statistically significant positive correlation between Nct and PMt.

To satisfy the exogeneity condition, PMt must be uncorrelated with the error term in the main regression for observation: PMt may only affect income inequality through its effect on the internet penetration rate Nct. The question at hand is whether the introduction of Mosaic and other web browsers has any statistically significant effect on income inequality that is unaccounted for by a country’s internet usage rate. Earnings by large technology companies such as the NCSA (developers of Mosaic) and now Google, Microsoft, and others may impact a country’s income inequality.

Employee income at these companies may positively affect the income share of the top one percent of earners in a given country.

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We first explore the question of whether the incomes of these employees are affected by the sale of web browser technology. In the case of NCSA and Mosaic, the state-federal partnership likely did not provide employees with salaries that would impact the United States’ income inequality.

The effect of Mosaic on the income equality among most countries observed is dependent on their respective internet penetration rates. Today, modern technology companies and web browser providers such as Google and Microsoft are some of the world’s most valuable companies. As a result, the income of many employees may have a positive effect on income inequality in countries where these companies are present.

The large revenues generated by providing public web browsers do not wholly lend to PMt ’s direct positive effect on . For example, Google’s advertising service Google Ads serves as the firm’s main revenue stream, accounting for $24.1B of $27.77B in revenue in Q3 2018 (Rosenberg 2019).

Microsoft’s Internet Explorer held most of the web browser market share until its steep decline in 2010. Like Google, Microsoft’s revenue largely stems from its other services including the Windows operating system and its Azure cloud offerings. In most cases the revenue generated by web browser technology does not significantly affect the incomes, and subsequent income inequality, within each country. Thus, PMt is uncorrelated with the error term in my regression.

Realistically, the instrument PMt varies across country observations as well as years. After all, countries adopted public web browser technologies across different years. However, the dummy variable constructed does not vary across countries. To achieve country variation, I interact the instrument with the country’s distance from the United States, giving us new instrumental variable regression equations: IEt = α + β1Nct + β2(Nct * Dc) + εct IEt = α + β1Nct + β2(Nct * Dc) + δc + εct

(4) (5)

Equation (4) depicts the interaction term between Nct and Dc while Equation (5) also includes country fixed effects. I run both of these regressions with Nct instrumented by PMt, and the interaction term Nct * Dc instrumented by PMt * Dc. These are the main regressions of interest, and their results are explained in depth in the section titled “Empirical Results.”

Data Sources and Summary Statistics

The main outcome variable, and the main focus for the analysis in this project, is the income share of the top 1% of earners in each observed country and year. This measure provides a means to observe the effect of the explanatory variable on income inequality. My main regression equation, given by Equation 48

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(2), denotes income inequality by . The data for the top 1% of earners by country comes from the World Inequality Database (WID), which provides free online access to data on the evolution of global income and wealth distribution. The database provides yearly observations of income and wealth inequality among 124 countries. For many of the countries provided, observations begin in 1990 and are updated annually.

Table 2 displays the countries with the highest mean values since 1990. The incomes of the top 1% of earners make up the largest share in these respective countries. The table also reports the minimum and maximum values of for these countries. Table 3 displays the countries with the lowest mean values since 1990, where the top 1% of earners make up the smallest share in these respective countries.

Chart 1 displays mean values for all countries and years observed in the dataset. As illustrated by the chart, mean global drops from 14.89% in 1990 to 9.16% in 1991. Beginning in 1992, each subsequent year experiences a rise in the mean global until it reaches 14.33% in 1996. Mean global remains between 14% and 15% for all years following 1996. This general observation indicates the importance of isolating the internet penetration rate Nct on . If year and country fixed effects are not controlled for in the main regression, then the relationship between and Nct would likely reflect the trend displayed by Chart 1.

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A country’s internet penetration rate, Nct, is the explanatory variable for the main regression. As explained in this section, Nct is instrumented by the public availability of a web browser denoted by PMt. Data on Nct comes from the International Telecommunication Union (ITU) database, which collects data from national telecommunications and Information and Communication Technologies (ICT) authorities. The ITU provides a measure of the internet penetration rate for 240 countries from 1960 to 2017. Note that the yearly mean observation for years prior to 1990 is zero, indicating that there was either no available internet or no available observations of internet users during these years. Thus, I will utilize observations for Nct from 1990 to 2017.

Table 4 displays the five countries with highest observed mean Nct values from 1990 to 2017. All five countries report a very similar mean ranging from 55% to 59%, reach a maximum Nct of at least 93.95%, and display a similar trend in growth of internet users. Chart 2 illustrates this positive trend among the top five countries. Iceland, Norway, Sweden, Denmark, and the Netherlands, all members of the OECD, experience a large increase in the internet penetration rate from around 10% in 1996 to 50% in 2001. This large 50

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and rapid increase in internet users provides robust data to further explore the relationship between income inequality and internet usage within these specific countries. Table 5 reports the countries with the lowest observed mean Nct values from 1990 to 2017. All of these countries have a Nct upper bounded by 6%, and each report a Nct value of 0% until 1996 (not shown in Table 5).

Empirical Results

Table 5 displays the main results of the estimation equations for ordinary least squares (OLS) regressions and instrumental variable (IV) regressions outlined by Equation (1) and Equation (2). The dependent variable, , and the main independent variable of interest, Nct, are measured as percentages. represents the income share of the top one percent of a given country c’s population during year t, Nct represents the internet penetration rate in a given country c during year t, and Dc measures the distance from the United States by thousands of kilometers. This conversion from the dataset’s units of kilometers to thousands of kilometers is performed in order to receive larger coefficients that are easier to interpret for each regression. Column (1) through Column (4) utilize an OLS regression which means the regression and interpretation of these results does not include the instrumental variable PMt. In contrast, Columns (5) Columbia Economic Review | 51

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through (8) utilize an IV regression, the regression and interpretation of these results does include the instrumental variable PMt.

Column (1) presents the OLS regression illustrated by Equation (1).

There is a significant negative relationship between the income share of the top one percent and the internet penetration rate at the one percent level. These results exhibit that, on average across the 122 countries and 27 years observed, a one percent increase in the internet penetration rate results in a 4.73 percent decrease in the income share of a country’s top one percent of earners holding distance to the United States constant. This result is promising because it indicates that there exists the potential for a causal negative effect between income inequality and internet usage. However, rushing to this conclusion would be ignoring other unobserved factors that affect income inequality as well as the discussion of reverse causality between internet usage and income inequality presented in the section title “Empirical Methodology.�

Column (2), the results of the panel regression denoted by Equation (2), introduces country fixed effects to the OLS regression. The inclusion of country fixed effects implies that we are now observing the effects of internet usage on income inequality within each country as opposed to across countries. Utilizing this technique accounts for omitted variable bias between countries and allows us to compare observations within each country over time. The results in Column (2) display a significant positive relationship between the income share of the top one percent of earners and the internet penetration rate at the one percent level. We can interpret the results presented by Column (2) by saying that a one percent increase in the internet penetration rate results in a 1.35 percent increase in the income share of a given country c holding distance from the United States constant.

We may be able to attribute the significant positive relationship between the income share of the top one percent and the internet penetration rate to the variations in income inequality and internet usage over time. Chart 1 reports the fluctuations in the mean income share of the top one percent of earners across countries from 1990 to 2017. Beginning in the year 1992, there is a steep increase in the mean income share of the top one percent of earners until the variable of interest reaches a relative plateau in 1997. This relatively large increase in the mean income share of the top one percent of earners coincides with the relatively large increase in the mean internet penetration rate. Thus, when observing the relationship between income share of the top one percent and the internet penetration rate within countries over time, we should expect a positive relationship. In order to further explore the relationship of interest we need to examine regressions that include variables that affect the income share of the top one percent through the internet penetration rate These regressions are the instrumental variable regressions.

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Column (3) and Column (4) report the results from two OLS regressions that almost mirror those reported by Column (1) and Column (2) respectively, however, Column (3) and Column (4) include an interaction term between variables Nct and Dc. Thus, the effect of the internet penetration rate on the income share of the top one percent is different given different values for the distance to the United States. These columns illustrate the OLS results from Equation (4) and Equation (5) and can be simply interpreted by saying, on average, a one percent change in the internet penetration rate results in a β2+ β3 * Dc change in the income share of the top one percent of earners.

Column (3) includes the interaction term between variables Nct and Dc, and indicates a significant positive relationship between and Nct at the five percent level, holding the interaction term and distance to the United States constant. In addition, there is a significant negative relationship between

and the interaction term, Nct * Dc, at the one percent level on average holding internet usage and distance constant. While the numerical interpretation of these results is relatively obscure, the observation that a country’s distance from the United States has a negative effect on the relationship between a country’s income share of the top one percent and the internet penetration rate deserves further analysis.

Column (3) presents that holding the internet penetration rate constant, a 1,000 kilometer increase in a given country’s distance from the United States decreases the income share of the top one percent by 0.87 percent on average.

In other words, the results of this OLS regression illustrate that a larger distance from the United States has a greater dampening effect on the positive relationship between and Nct. As a given country’s distance from the United States increases, it can be loosely estimated that this country will take a longer time to adopt computer and internet technology. This estimation is made under the assumption that the majority of the infrastructure and hardware required to introduce internet technology exists in the United States. As a result, countries that are relatively far from the United States may not have successfully adopted internet technology at the citizen level until the late 1990s to early 2000s. If this is the case, these countries and years would account for observations within the data that contain a relative increase in measures of the internet penetration rate, and a relative decrease in the measures of the income share of the top one percent.

As illustrated by Chart 1, there was a steady increase in the mean income share of the top one percent of earners across countries during the years 1992 to 1997. Under the assumption that countries further from the United States would experience a lag in adopting necessary internet technology, these countries would have experienced a relative increase in the internet penetration rate following the relative global increase in the income share of the top one percent. Observations for these countries following the year 1997 could include Columbia Economic Review | 53

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relatively high values for the internet penetration rate or Nct and relatively low values for the income share of the top one percent or .

Column (4) illustrates the panel results from Equation (3). The regression denoted by Equation (3) includes the interaction term between the internet penetration rate and distance from the United States in thousands of kilometers, as well as country fixed effects. The relationship between the main variable of interest, , and the interaction term is negative but insignificant.

The regression measures the effects of the internet penetration rate and the distance from the United States through the internet penetration rate within each country. However, the distance from the United States variable will not change, each country observed is always the same distance from the United States. The inclusion of country fixed effects in the regression equation allows us to compare a country with itself, across varied years. While the coefficient of the interaction term is insignificant in Column (4), the coefficient for the internet penetration rate (0.0165) is significantly positive and larger than the respective coefficient in Column (3) (0.0135). The 0.003 increase in the coefficient of Nct is most likely due to the negative but insignificant measure for the interaction term. A given country’s distance from the United States has a measurable damping effect on the relationship between Nct and .

Columns (5) through (8) report the results for the instrumental variable regression equations. The main instrument utilized is a novel dummy variable that represents the public availability of a web browser. Using the release of Mosaic in 1993 as the introduction of the first publicly available web browser, years following 1993 receive a value of one for the instrumental variable and years before 1993 receive a value of zero.

Column (5) reports the results of the regression given by Equation (1) and includes the internet penetration rate instrumented by PMt as the main independent variable and as the main dependent variable. The results exhibit a significant positive relationship, on average, between the income share of the top one percent and the internet penetration rate across countries. We can interpret these results by saying that, on average, a one percent increase in the internet penetration rate increases the income share of the top one percent by 6.9 percent. This result is relatively large (0.069) and of the opposite sign when compared to the coefficient obtained in the equivalent OLS regression (-0.047).

One hypothesis for the significant change in the main coefficient of interest again involves the observations from the time trend for mean income share of the top one percent using all 122 countries. In the regression that produces the results in Column (5), the instrumental variable changes value from zero to one for all countries in the year 1994. As illustrated from Chart 1, the mean income share of the top one percent rose from 10.1% in 1993 to 11.4% in 1994. The regression results in Column (5) explore the relationship between the income share of the top one percent and our instrument through its impact on the 54

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percentage of the population that uses the internet. This relatively large jump in mean income share of the top one percent occurs during the years when the instrument changes in value.

Columns (5) and (6) display the IV regression results in which Nct is instrumented by the PMt or post-mosaic instrument however the instrument does not vary across countries in these regressions. These regressions reflect the assumption that every country had public access to web browsers beginning in the year 1994. In order to account for the variation of web browser availability between countries, Equations (4) and (5) include an interaction term between our instrument PMt and a given country’s distance from the United States or Dc. The results from these IV regressions are displayed in Columns (7) and (8) respectively.

Column (7) displays the results from the IV regression that includes an interaction term between Dc and the instrumental variable PMt. The coefficients of interest, both for the instrumented percentage of individuals using the internet and the instrumented interaction term, are both significant at the one percent level. The percentage of individuals using the internet has a coefficient of -0.232 and the interaction term has a coefficient of 0.0415. We can attempt to interpret these results by explaining that if a country has a distance from the United States of zero kilometer, a one percent increase in the percentage of individuals using the internet corresponds to a 23% decrease in the income share of the top one percent on average. Holding this one percent increase in the percentage of individuals using the internet constant, a 1,000 kilometer increase in a country’s distance from the United States will dampen the negative relationship between and Nct by 4.15%. Column (8) adds country fixed effects to the instrumental variable regression represented by Column (7). The results in Column (8) display a significant positive relationship between and Nct at the five percent level. The results reflect the relationship between and Nct within countries, and may experience a similar upward bias due to the time trend of the mean income share of the top one percent discussed earlier.

The results present in Column (7) display a significant and large negative relationship between the internet penetration rate and income share of the top one percent. However, this negative relationship is met with a positive coefficient for the interaction term. The instrumented interaction term provides variation across countries in terms of adopting web browser technology which may lessen the instruments overemphasis on the changes in the income share of the top one percent during the years 1993 and 1994.

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Robustness

To strengthen the results displayed by the IV regression that instrumented both the main independent variable Nct and the interaction term between Nct and Dc, I added several controls to the regression. The results of adding these controls are displayed in Table 7. The results of the instrumental variable regression displayed in Column (7) of Table 6 does not include country fixed effects. As a result, these results are potentially biased by various unobservable factors that vary across countries. One such example is GDP per capita.

Members of countries with a lower GDP per capita possess less income that they can spend on technology that provides them with internet access. If two countries have the same value for Nct and Dc, we would expect them to have the same estimated value for according to Table 6. However, there are a multitude of other factors that affect a given country’s income inequality. I will now discuss the impact of additional controls and inclusion of country fixed effects on the main IV regression.

In Table 7, Column (1) presents the results of the IV regression with the inclusion of the logarithm of GDP per capita as a control. The results illustrate a significant negative relationship between the income share of the top one percent and the internet penetration rate with a significant positive relationship between the income share of the top one percent and the interaction term. With the control, the main regressor of interest, Nct, displays a coefficient of -0.124 and the interaction term between Nct and Dc displays a coefficient of 0.0232.

Both coefficients share the same sign but at a smaller magnitude than the IV regression without the control. These results provide support for the negative relationship displayed in the IV regression without controls as the addition of a GDP per capita variable illustrates that withholding this variable produced a downward bias on the coefficients of interest but did not drastically alter the results.

Like the results of the IV regression in Table 6, the results displayed in Column (1) of Table 7 can be interpreted by stating the internet penetration rate 56

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rate has a significant effect on the income share of the top one percent of earners. However, a significant amount of this negative affect is explained by a country’s proximity to the United States. The impact of the distance to the United States variable as well as the impact of the logarithm GDP per capita control on the main coefficient of interest indicates that an analysis of more controls and the inclusion of country fixed effects are necessary before we can make the claim for a causal relationship.

Column (2) of Table 7 displays the results for the IV regression with instruments for Nct and the interaction term between Nct and Dc and an Organization for Economic Cooperation and Development (OECD) membership control variable. This control consists of a dummy variable in which a value of one is assigned to countries that are OECD member countries and a value of zero is assigned to countries that are not members. Again, the results display a significant negative coefficient for Nct and a significant positive coefficient for the interaction term.

The results of the IV regression with the inclusion of the OECD member control provides further evidence for the existence of a significant negative relationship between the internet penetration rate and the income share of the top one percent. The positive and significant coefficient for the interaction term suggests that a country’s distance from the United States has a significant effect on the negative relationship. Countries located in closer proximity to the United States are estimated to experience the largest negative relationship between and Nct. In Column (2) the coefficient for Nct is -0.124 and the coefficient for the interaction term is 0.0232. Using these values we can Columbia Economic Review | 57

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make the claim that a one percent increase in the internet penetration rate will have a negative effect on the income share of the top one percent of earners until the observed country’s distance from the United States is greater than 5,391 kilometers. At distances greater than 5,391, the relationship between and Nct becomes positive.

Column (3) presents the results of the inclusion of an internet censorship control variable in the IV regression. This variable utilizes data from the OpenNet Initiative (ONI) database. ONI is a partnership between several institutions that share the common goal of analyzing and exposing global Internet surveillance and filtering. I utilized data from their filtering dataset, which includes the internet censorship ratings across five categories for 74 countries. These categories are titled political, social, Internet tools and conflict/ security and each country receives a rating between zero and four for each category. A rating of zero corresponds to “No evidence of filtering” while a rating of four corresponds to “Pervasive filtering.”

For this project I created the Internet censorship dummy utilizing observations within the Internet tools category because this category contains software offered on the Internet that may be utilized to increase an individual’s skills or knowledge. I believe that the remaining four categories did not contain Internet related content that could affect the income of individuals. Thus, the dummy variable for Internet censorship is constructed by assigning a value of one to countries that exhibit a rating greater than zero in the Internet tools category. If a country is given a score of zero in the Internet tools category then this country is assigned a value of zero for the internet censorship dummy.

Column (3) illustrates that with the inclusion of the Internet censorship control, the coefficient for Nct becomes positive and insignificant. The coefficient for the Internet censorship dummy is positive and significant at the one percent level.

One may be tempted to interpret this as a positive relationship between the income share of the top one percent and Internet censorship, holding a country’s percentage of Internet users constant. However, there is potential for omitted variable bias because countries that choose to implement measures of Internet censorship are likely to implement other restrictions on citizens that may have a positive effect on the income share of the top one percent.

Economic Significance

Interpreting the significance of the results displayed by the main IV regression requires greater analysis than just describing how various changes in Nct affect . One cannot look at Column (7) in Table 6 and immediately conclude that there is a significant negative relationship between the income share of the top one percent and the internet penetration rate. As illustrated by the interaction term between Nct and Dc, the negative relationship between

and Nct is closely tied to a country’s distance from the United States. Column (7) 58

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in Table 6 displays a coefficient of -0.232 for the regressor Nct, and a coefficient of 0.0415 for the interaction term. Thus, a one percent increase in the internet penetration rate results in a change of -0.232 + 0.0415Dc in a country’s income share held by the top one percent. This expression indicates that the relationship between and Nct depends on the country’s distance from the United States.

To further explore the results, I will discuss several values for the distance from the United States Dc and how these values alter the relationship between our main dependent and independent variables. Using the expression from the previous paragraph, we can determine the exact value for Dc in which the relationship between and Nct shifts from negative to positive. Setting the expression equal to zero results in a value of roughly 5.59 for Dc. This illustrates that a one percent change in the internet penetration rate is associated with a negative change in the income share of the top one percent when Dc < 5.59.

Note that because Dc is measured in thousands of kilometers, a value of 5.59 represents a distance of 5,590 kilometers. Table 8 displays countries in the dataset that are less than 5,590 kilometers from the United States.

Table 8 is not a comprehensive list of all the countries that are within 5,590 kilometers of the United States, but rather a list of the countries both in the dataset and within 5,590 kilometers of the United States. The list provides a general indication of what additional countries would be included in the 5,590 kilometer radius. Countries in Central America, the northern region of South America, and Western Europe exist within this radius and, as a result, would possess a negative relationship between the income share of the top one percent and the internet penetration rate as described by the regression results.

Interpreting the results as a significant and negative causal effect of Nct on for these countries is problematic for two reasons. First, the non-inclusion of country fixed effects in the main regression of interest allows for the potential Columbia Economic Review | 59

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of omitted variable bias. It is possible that there are a multitude of other factors that have an effect on the income share of the top one percent other than the percentage of individuals using the Internet. Second, the distance measurement used in the regression is a bilateral measurement utilizing the countries’ most populous cities. The United States contains many large cities that have the ability to exchange ideas and technology with neighboring countries and as a result, the methods used to calculate bilateral distance may not be a perfect measurement of a country’s close proximity to the United States.

Column (8) in Table 6 displays the results of the main IV regression of interest with the addition of country fixed effects. The addition of country fixed effects is an attempt to account for the potential omitted variable bias introduced by the IV regression without country fixed effects. By examining the relationship between and Nct within countries, we control for all other variables that may differ across countries. Column (8) illustrates a significant positive coefficient, at the five percent level, for the main variable of interest, Nct, and an insignificant negative coefficient for the interaction term.

These results raise a concern with the robustness and applicability of the main results. While the results displayed in Column (7) provide relatively large and significant coefficients for both Nct and the interaction term, the results following the inclusion of fixed effects provide indication that there exist various factors that differ across countries and influence the results. These factors are not being accounted for in the main analysis of the relationship between the income share of the top one percent and the internet penetration rate.

The bilateral distance measurement used in the CEPII dataset is calculated using the two countries’ largest cities in terms of population and the great circle formula. While the measurement seems intuitively correct for smaller countries that contain one to three large cities, the measurement loses predictive power when assessing a country’s distance to the United States. With large cities and technology hubs in both the eastern and western regions of the United States, there are many countries that reside in close proximity to a major city in the United States. However, the bilateral distance data does not account for the relatively short distance between, for example, the cities of Tokyo and Seattle. Instead, countries that have adopted internet technology and public web browsers as a result of frequent trade with the United States are assigned a bilateral distance that may be too large. If this is the case, the results given

by Column (7) of Table 6 present an underestimation of the coefficient for the interaction term. A given country’s distance from the United States may in fact have a larger impact on the instrumented relationship between the income share of the top one percent and the internet penetration rate. Without a more accurate measure of a country’s distance to one of the many large technologically advanced cities in the United States, the results presented by the main regression of interest lose substantial economic significance.

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Conclusion

Computer and internet technology have altered how people communicate, learn, and acquire new skills. The proliferation of internet access due to globalization allows individuals to bolster their skill set. As a result, access to these internet tools may provide lower-income individuals with an opportunity to increase their current income. I hypothesize that an increase in the availability of information and tools due to internet access should decrease income inequality across countries. To examine the relationship between a country’s income inequality and internet proliferation, I utilize data on the income share of the top one percent of earners and the internet penetration rate across 122 countries from 1990 to 2017. My results show a significant negative relationship between the income share of the top one percent and the internet penetration rate; however, this negative relationship is largely dependent on a country’s distance from the United States. Countries located over 5,590 kilometers from the United States experience a significant positive relationship between the two main variables of interest upon holding the internet penetration rate constant.

After including country fixed effects to account for omitted variable bias in the main regression, the results do not remain as robust. The relatively recent introduction of internet technology globally provides ample opportunity to explore internet usage with other variables of interest. However, exploring the relationship between income inequality and internet technology has proven to be difficult due to a multitude of unobserved factors that differ across countries which also affect income inequality. A smaller randomized trial on individuals— perhaps one that randomly provides subjects with vouchers for internet technology—may offer better grounded results in examining the relationship between internet usage and personal welfare. Such a study would shed light upon the possible causal effect of the internet on acquiring new skills that would improve personal welfare.

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References “Actualites Du CEPII.” CEPII, http://www.cepii.fr/cepii/en/bdd_modele/ bdd.asp.

Benabou, Roland. “Inequality, Technology, and the Social Contract.” 2004, doi:10.3386/w10371.

Brueckner and Lederman. “Inequality and Economic Growth: The Role of

Initial Income.” 2018 Bulman, George, and Robert Fairlie. “Technology and Education: Computers,

Software, and the Internet.” 2016, doi:10.3386/w22237.

Chinn, Menzie, and Robert Fairlie. “ICT Use in the Developing World:

An Analysis of Differences in Computer and Internet Penetration.”

2006, doi:10.3386/w12382.

“Committed to Connecting the World.” ITU, https://www.itu.int/en/Pages/ default.aspx.

“Internet Free Expression Timeline.” National Coalition Against Censorship, 18

Nov. 2019, https://ncac.org/resource/a-selective-timeline-of-the-inter net-and-censorship.

Levchenko, Andrei A., and Jing Zhang. “The Global Labor Market Impact of

Emerging Giants: A Quantitative Assessment.” SSRN Electronic

Journal, 2013, doi:10.2139/ssrn.2320645.

Malamud, Ofer, and Cristian Pop-Eleches. “Home Computer Use and the

Development of Human Capital.” 2010, doi:10.3386/w15814.

“ONI Methodology, Tools, and Data FAQ.” ONI Methodology, Tools, and

Data FAQ | OpenNet Initiative, https://opennet.net/oni-faq.

Rosenberg, Eric. “How Google Makes Money (GOOG).” Investopedia,

Investopedia, 4 Dec. 2019, https://www.investopedia.com/articles/ investing/020515/business-google.asp.

Solow, Robert M. “A Contribution to the Theory of Economic Growth.”

The Quarterly Journal of Economics, vol. 70, no. 1, 1956, p. 65., doi:10.2307/1884513.

“The Source for Global Inequality Data.” WID, https://wid.world/.

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Lemons, Limes, and Eggs: An Economic Analysis of the Market for Egg Donation

Emily Malpass Harvard University Abstract: According to the American Medical Association, “Egg donation is a process in which a fertile woman donates an egg, or oocyte, to another woman to help her conceive.” This paper analyzes the irregularities in the market for egg donation caused by adverse selection and price controls. The paper contends that the market for egg donation is bisected into a clinic market, which offers flat compensation packages for eggs with generic traits, and an agency market, which offers trait-specific compensation and advanced vetting services. The bisection of the market emerges from the information asymmetries between buyers and sellers, the price controls imposed on the clinic market, and the preferences of potential buyers. Overall, the buyer-seller information asymmetries, combined with the price floor and ceiling on the market, produce a clinic market dominated by low-quality eggs and an agency market of high-quality eggs.

Acknowledgements: I would like to thank Claudia Goldin and Ayushi Narayan for their feedback and assistance.

I

n 1983, Australian physicians announced that the first “test tube baby” had been born through in vitro fertilization (IVF). Since Louisa Brown’s birth, the use of IVF services has skyrocketed: in the United States, approximately 1.3% of children are born using IVF per annum; in Europe, the number hovers at approximately 2% (Dobson, 2006). The success of IVF has spawned a set of associated procedures, broadly described as assisted reproductive technology (ART), that address maternal, paternal, or combined infertility and sterility.

Though less widely discussed than sperm donation or surrogacy, egg donation constitutes one form of third-party assisted reproduction. In instances of maternal infertility, women can purchase donated eggs, which are then inseminated and implanted in the would-be mother’s uterus via the same process as IVF or surrogacy. The American Medical Association summarizes egg donation succinctly: “Egg donation is a process in which a fertile woman donates an egg, or oocyte, to another woman to help her conceive” (Leonard, 2019).

The process of donating an egg is complex because of the individualized procedure, the rapid changes in the fertility field, and the lack of standardization

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across clinics and countries. First, a potential donor â&#x20AC;&#x201C; normally a healthy woman aged twenty to thirty â&#x20AC;&#x201C; connects with a couple, a clinic, or an agency.

Should the donor connect with a couple or agency, she is preliminarily screened, then introduced by the intermediary to a clinician. Second, the clinic performs a battery of genetic screenings and psychological tests. Approximately 10% of donors successfully pass the testing stage and progress to collection, though most are compensated for attending the initial consultations. Third, the donor undergoes a course of hormonal therapy, then a short outpatient procedure to collect her eggs. Fourth, the eggs are implanted in a recipient or stored for later use while they are advertised to potential couples. Finally, the donor is compensated for her services and can choose to restart the process again (see Graphic 1) (Robertson, 2006).

The compensation component of egg donation raises an interesting set of ethical, medical, and economic problems. The market for egg donation emerged shortly after the advent of the procedure, when the Cleveland Clinic pioneered an Oocyte Donation Program in 1987. In the initial program, the Cleveland Clinic paired anonymous donors with prospective parents, compensating the donors for their participation in the program with approximately $1,200. Within a decade, dozens of clinics launched comparable programs that offered an average of $2,000 per donation (Robertson, 2006).

The number of prospective donors and recipients has now outpaced the growth of clinic capacity and compensation, with almost 24,000 cycles of donation performed in the U.S. in 2019 alone (Society for Assisted Reproductive Technology, 2019). At present, the American Medical Association (AMA) 64

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reports that 93% of American fertility centers offer egg donation services for an average compensation of $8,000.

Compensated donations naturally create a marketplace for oocytes. The prospective donors function similarly to “sellers” in a monopolistically competitive market. Each individual offers a unique product – her genetically distinct eggs – at different quantities, with some women offering one egg and others offering dozens. The “buyer” – the infertile recipient of the egg – sources the product through intermediaries such as fertility clinics, egg banks, and agencies. Though few authors have analyzed the market in aggregate, several authors have employed an economic analysis to examine the buyer-seller dynamics in the market. Authors have applied economic tools to assess the premiums paid for donor education (Levine, 2010), the risk-aversion of donors (Karol, 2016), the impact of disclosure on willingness to sell (Albert-Sherar, 2016), and the variance in donation success across clinics and countries (Wang, 2012). Thus, although few authors offer overviews of the market in economic terms, many have applied economic lenses to decompose elements of the buyer-seller exchanges.

This paper contributes to the existing literature by analyzing the irregularities in the oocyte market caused by adverse selection and price controls.

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The paper contends that the market is bisected into a clinic market, which offers flat compensation packages for eggs with generic traits, and an agency market, which offers trait-specific compensation and advanced vetting services. The bisection of the market emerges from information asymmetries between buyers and sellers, the price controls imposed on the clinic market, and the preferences of potential buyers. Overall, the buyer-seller information asymmetries, combined with the price floor and ceiling on the market, produce a clinic market dominated by low-quality eggs and an agency market of highquality eggs. In the following sections, I will describe the market, break down the agency premium, decompose the responsiveness of donors to shifts in the base compensation rate, and analyze the dynamics of the market at large.

Data The datasets on the markets for egg donation are scattered across dozens of websites, data indexes, and organizations. To compile comprehensive statistics over periods of time, I aggregated data from a number of sources.

For datasets relating to United States egg donation numbers, I relied on the Center for Disease Control’s Assisted Reproduction Technology Annual Report. The ART Annual Report extends to 1998, but remains consistently formatted within the 2005-2016 range. To compile the data, I used the tab “Clinic Data Tables” and searched for the terms “Fresh Donor Egg Total Cycles” (FshDnrEggTotCycles) and “Thawed Donor Egg Total Cycles” (ThwDnrEggTotCycles). Combined, the sum of the two numbers represent the total number of eggs donated per year in each clinic. I collapsed the clinics by state name into one column per year per state. All other numbers on the United States – population per year, median income – come from the U.S. Census’ datasets for the corresponding years. For datasets on the United States’ regulatory environment in 2016, I used a number of legal and state-based sources, including the International Fertility Law Group and The American Bar Association.

For datasets relating to European egg donation, no published international tables exist to quantify either the number of egg donations per year or the compensation rates. However, Human Reproduction, a science and medicine journal, has compiled and published a list of assisted reproduction procedures in Europe from 1998 to 2014. I manually aggregated the data from each volume of the journal from 2003 through 2014. For all other quantitative metrics on Europe (median income, population), I relied on the OECD or European Union published statistics.

Compiling data on the compensation rate offered to egg donors in Europe required more in-depth research across a number of platforms. To compile the dataset, I manually searched a set of fertility websites that advertise for donors and recipients, including the San Diego Fertility Center, Egg Donation Friends, Fertility Road, and Fertility Treatment Abroad. In 66

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addition to these fertility tourism websites, I relied on anecdotal evidence from The National Center for Biotechnology Information, The Journal of the American Medical Association, Reproductive Biomedicine and Society, Time Magazine, and The New York Times. A number of these sources offered concrete statistics on egg donation in Europe, including the average donor compensation for specific regions. When possible, I cross-checked the statistics in two ways: first, I scaled the cost of the implantation and fertilization procedure to the expected percentage of the cost of the procedure given to the donor; second, I confirmed the findings of the fertility tourism websites against published articles or other secondary sources. For all statistics on European compensation, I use the base year of 2016, which corresponds to the ART Annual Report used for the American statistics.

The Market I begin by diagramming a hypothetical market for donor eggs. Each recipient demands one egg and sets a maximum payment price. Similarly, each donor offers to sell one egg after setting a minimum sale price. Horizontally summed, the aggregate of each buyer-seller combination produces a roughly downwardsloping demand curve and upward sloping supply curve. In a perfectly functioning market, each buyer should be able to find a seller willing and able to provide the product at the given price point.

In the market for donor eggs, there are two basic consumers: one who seeks standard traits (a donor in good health without genetic defects), and another who seeks specific traits (a healthy donor that approximates her ethnicity or background). To service both of these consumers, the market has split into two segments: one which sources generic eggs, and another which vets trait-specific eggs. This dual market is displayed in Graphic 2.

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The generic egg market, which I call the “clinic market,” offers a cheap and efficient method to purchase an egg for women seeking generally accessible donor traits. Donors sell their eggs to the clinic, the provider of the extraction and implantation services, in exchange for a flat compensation package. The clinic re-sells the egg to recipients with minimal additional information about the donor, possibly including only a general description of her ethnicity and age.

Thus, the clinic market offers a base price of P*, which includes the cost of the medical procedure and the cost of compensation for the donor.

For buyers seeking oocytes with specific traits, the clinic market produces uncertainty, long waiting lines, and product shortages. These problems occur for two reasons: 1.

Adverse Selection: The donor may conceal, exaggerate, or lie about certain elements of her background. Since clinics perform only a routine health check, the recipient fears that she will receive a low-quality egg or an egg that differs from the one advertised. Without a way to confirm that the donor has the requested traits, the recipient faces a substantial amount of risk and uncertainty in the clinic market.

2.

Shortages: The specific composition of traits that the recipient requests may be unavailable. There are numerous anecdotes about lengthy waiting times for eggs that match the specific trait requirements of the recipient (Roberston, 2006). These shortages are indicative of a flaw in the ability of the market to match sellers and buyers, possibly due to the fractionalized regulatory environment or the price controls placed on compensation packages.

Instead of enduring a long and uncertain waiting period, many of the recipients seeking specific traits opt to exit the clinic market. These trait-specific buyers turn to a second market, which I dub the “agency market,” governed by a set of intermediaries that scout and vet donors, then match them with recipients. This segment of the market displays a standard upward sloping supply curve and downward sloping demand curve, since traits are priced to accurately reflect their market value. This market clears with relative efficiency by offering staggered compensation to donors based on the demand for and scarcity of their traits. The market equilibrates at a price of P* + P, the price of the standard clinic fee (P*) coupled with the markups charged by agencies for trait specificity (T) and vetting services (A). In the third section, I will offer an analysis of agencies’ vetting services and trait pricing.

The Base Price: What Determines P*?

P*, the base price charged by clinics and agencies, is roughly equivalent to the cost of the procedure and the cost of compensation for the donor [P* = (Cost of Procedure + Cost of Compensation)]. The cost of the procedure paid by the recipient reflects the healthcare coverage, regulatory environment, and cost of

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the equipment and services [Cost of Procedure= Regulatory Coefficient (Cost of Equipment) â&#x20AC;&#x201C; Healthcare Coverage]. In this section, I decompose the components of P* and analyze the effects that shift the costs.1 Cost of Procedure Equipment and Services

The procedure hovers at a cost of $18,000 to $20,000 for one successful course of implantation. This fee includes the screening services that the clinic provides to verify the health of the donor, the cost of short-term medical insurance for the donor, the medication and equipment used throughout the procedure, and the time of the practicing clinicians. Notably, this fee excludes the compensation for the donorâ&#x20AC;&#x201D;the full procedure, including compensation fees, is priced at $25,000 to $30,000 (Egg Donor America, 2017).

Healthcare Coverage

The high cost of the procedure indicates that a factor driving the buy-side should be the existence of healthcare coverage for IVF and egg donation. In states that require IVF insurance coverage, more women should demand treatment because the incidence of the payment falls on the insurer instead of the recipient. Graphic 8 depicts the presence of laws that mandate insurance coverage for IVF and ART in the United States in 2016.

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The presence of healthcare coverage laws undoubtedly increases the number of IVF procedures (not just egg donations) performed within a state.

The states with the highest per capita number of IVF cycles are also the states that mandate or offer healthcare coverage for the procedure. Within the standard market analysis framework, this result makes intuitive sense: when the cost falls, demand for the procedure rises, and the quantity supplied increases to equilibrate. Graphic 9 depicts the total number of cycles of IVF performed within a state in 2016 relative to the existence of healthcare coverage laws for the procedure; Graphic 10 depicts the total number of IVF cycles per capita performed within a state relative to its healthcare regulation. To quantify the correlation shown in Graphic 9 and 10, Table 1 shows the association between the number of IVF cycles total and per capita and the existence of healthcare coverage requirements, controlled for the growth rate, median income, and unemployment rate in 2016. States with healthcare provisions display an average of a 0.0004 increase in the per capita donation rate, which corresponds to an increase of 6,712 in the total number of IVF procedures performed per state (P < 0.01; P < 0.05).

In states with high demand for the procedure, a larger number of women donate eggs. Graphic 11 depicts the donation per capita rates in states

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with healthcare coverage laws and those without. Sure enough, the states that record the highest per capita donation rates tend to correlate with the outliers in IVF treatments. In order, the top 7 states ranked by per capita donation rates record the highest number of IVF treatments. To quantify the correlation, Table 2 displays a regression of the donation rate, controlled for the growth rate, median income per state, and unemployment rate. The association between the number of donations in a state and the existence of healthcare coverage laws for IVF indicates that states with healthcare provisions record 576 additional egg donations per annum (P < 0.027).

The analysis of healthcare raises an implicit question of endogeneity. Based on these correlations, it is tempting to assume that the existence of healthcare coverage causes a greater number of egg donations to occur. However, in practice, the only states that experience the political pressure to include egg donation in their insurance provisions are the ones that already performed high numbers of donations. In truth, the explanations most likely form a positive cycle of egg donation in a state: an established network of clinics performs donations without state healthcare coverage laws; the political pressure from potential donors and recipients prompts the state to require coverage for donations; the demand for fertility treatment rises due to the low cost of the procedure; and more clinics open in the state to meet the increasing demand.

Regulatory Coefficient (Anonymity, Parenthood Rights, and Disclosure)

The quintessential legal horror story of egg donation involves the genetic mother or father of an ART child attempting to claim parental rights over the offspring. In a multitude of complex legal cases, the parents of a child born from egg donation fight a legal parenthood claim made by the donor. Thus, couples pay premium prices to receive donations made in states that abide by strict anonymity regulations and have firm legal parenthood guarantees. I refer to this premium for anonymity and legal parenthood as a â&#x20AC;&#x153;regulatory coefficient,â&#x20AC;? which reflects the legal risk of egg donation.

In the United States, anonymity regulations fall into three broad categories: 1) states which have explicit provisions outlining donor anonymity and parental rights, 2) states which abide by the federal guidelines for donor anonymity and parental rights, and 3) states which have no explicit guidelines for donor anonymity and parental rights.

Graphic 3 depicts the breakdown of states by their anonymity regulations; Graphics 4 and 5 show the per capita and total donation rates of the states, relative to their anonymity provisions. In Graphic 5, California is not shown to preserve the scale of the graph â&#x20AC;&#x201C; it is a significant outlier with a donation amount of 6,000 per annum and falls in column three (3).

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Graphic 3: This graphic displays the United States, color-coded by anonymity regulation by state.

0 (gray) = no anonymity regulation 1 (light blue) = abides by federal guidelines 2 (blue) = has regulation on sperm donation, but not egg donation 3 (dark blue) = specific state-level regulation on egg donation (Notably, to perform the regressions on Table 3, the coding for sperm donation regulation was changed to 1, and state-level regulation to 2, and federal guidelines was changed to 3.)

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The states that include additional anonymity shields, especially those that abide by the strict federal guidelines, register higher per capita and total donations. The two states with secondary protections for egg donors – Vermont and California – register disproportionately high numbers of donors relative to their neighboring states and the country at large. Vermont has a per capita donation rate of 0.0032, significantly higher than New Hampshire’s rate of 0 donations per capita (no clinics in the state) and 5 times higher than Maine’s rate. California records well over 6,000 donations per year and boasts the highest number of clinics, agencies, and transfer programs in the U.S. To quantify the visual association, Table 3 displays the correlation between anonymity and legal parenthood rights and the number of donations per capita and total in a state.

Compounding this evidence, in Europe, states with two-sided anonymity options record the highest number of donations. Anonymity regulation in Europe also falls into three categories: 1) countries which do not allow anonymous donations of any kind, 2) countries which allow entirely anonymous donations on the part of the donor and the recipient, and 3) countries which allow anonymous donations while keeping donor records that are available at the request of the child at a certain age. The anonymity regulations by country are shown on Graphic 6; the per capita donation rate graphed against the existence of anonymity regulation is shown in Graphic 7.

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The states that allow for two-sided anonymous donations record significantly higher proportions of donations than their peers. Since parents value permanent and reliable anonymity, the states with records available upon the request of the child (or, occasionally, a doctor or the government) record fewer donations than their fully anonymous peers. The top 10 most frequented donation locations, the locations that offer the most lucrative compensation packages, and the locations that charge the least for the donation procedure universally allow for two-sided anonymous donations. Depicted in Table 4, for a given increase in the stringency of anonymity provisions, a country registers a 0.0109 increase in the per capita donation rate and a 2,927 increase in the total number of donations. Notably, the correlation lacks significance with a P < 0.2; the confidence interval similarly accounts for the entire change.

The anonymity provisions may factor into the base rate, with donors paying a premium to receive the procedure in a location with anonymity guarantees. At the very least, anonymity influences the demand for the procedure, with potential recipients flocking to regions that guarantee long-term, two-sided anonymity. This analysis poses the same question of endogeneity as the examination of healthcare coverage: anonymity provisions may emerge in response to existing demand for the procedure, further boosting the already-high demand. Thus, though no causal arrow can be drawn between anonymity and the quantity of procedures, the correlation undoubtedly stands.

Cost of Compensation

Clinics offer a flat compensation to donors that hovers between $7,000$9,000 per cycle, which is offered in a trait-blind fashion regardless of donors’ education, ethnicity, or associated factors. The compensation package has remained relatively stable since 2000, despite the increase in demand for the procedure and inflation and cost of living increases.

Far from being a market-based compensation rate, the compensation price reflects a floor and ceiling imposed by The American Society for Reproductive Medicine (ASRM) and the Society for Assisted Reproductive Technology (SART). Since the emergence of the market, the ASRM and SART have held the position that “reasonable” compensation for gamete donors is ethically permissible. However, following a series of high-profile advertisements offering six-digit sums for egg donors, in 2000 the ASRM and SART defined “reasonable” as “payments to women providing oocytes [that are] fair and not so substantial that they become undue inducements that will lead donors to discount risks.” Based on comparable market rates for sperm donation, the ASRM and SART recommended that payment remain between $5,000 and $10,000 per donation cycle in the United States (Bayefsky, 2016; ASRM, 2000; SART, 2000; Covington, 2007).

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In 2011, Lindsay Kamakahi, a prospective egg donor, filed a lawsuit against the organizations alleging that they had created a price-fixing scheme that violated the Sherman Antitrust Act (Krawiec, 2014). In response to the Kamakahi complaint, the ASRM and SART revised their position on the price cap and floor, claiming that the regulations were nothing more than guidelines for conduct. As a result, though the ASRM and SART guidelines continue to set the standard for compensation packages, clinics can theoretically deviate above and below the guidelines without a penalty. Nevertheless, in lieu of tangible legal consequences, clinics that willingly and repeatedly exceed the cap may risk their accreditation with the ASRM and SART, which would effectively prevent the AMA and state medical board from licensing them to practice. Notably, since agencies are not medical practitioners, they are certified by neither the SART or the ASRM, operating entirely outside the price constraints imposed on clinics.

As a result, the “guidelines” produce a powerful, long-term mechanism to ensure that compensation by clinics remains low. Overall, the guidelines produced a loose price floor and ceiling on the market by (a) anchoring donor expectations to the $5,000 to $10,000 bound, (b) creating legal mechanisms for the clinics to price coordinate, and (c) establishing soft costs to incentivize outlier clinics to remain within the cap. Consequently, the price floor and ceiling can be accurately characterized as “loose” – the majority of clinics abide by the guidelines for the average donor, but occasional donors may receive outsized compensation (Robertson, 2006; SART, 2007; Jayne, 2019).

As a result, donor payment by clinics hovers within this zone of compensation, averaging a $7,000-$9,000 base rate regardless of the qualities of the donor. As a crucial note, this compensation does not reflect variance within a single clinic (i.e. In Clinic A, some women are paid $6,000 and others $10,000, which averages to the compensation rate of $8,000). In practice, the median compensation per clinic is similar to the average, reflecting a lack of variance among the compensation awarded to individual donors. The justification for this lack of variance will be discussed in subsequent sections.

Responsiveness: How Responsive Are Women to Changes in Compensation?

Based on standard price theory, changes in the average compensation package should incentivize more women to donate in the clinic market. Though not a component of P*, in this section I analyze the responsiveness of donors to changes in their expected compensation.

In the United States, the porous nature of interstate boundaries means that prices charged across the U.S. are relatively standardized. First, there are few arbitrage opportunities in the market for fresh donor eggs. Since donors have to commute to the fertility clinic 7-10 times to undergo the correct courses of screenings, hormone therapy, and check-ups, women are unlikely to donate

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across large regions due to the high cost of long-distance travel. Second, frozen donor eggs, which theoretically could be shipped across state boundaries, are almost exclusively sold through clinics and egg banks. The clinic price, as has been noted previously, is usually a flat fee well within the bounds of the ASRM. As a result, in the market at large, the vast majority of donors receive the average price charged within their geographic region (SART, 2007).

However, by fortunate coincidence, two neighboring states – Louisiana and Arkansas – have banned the sale of human embryos, capping compensation for donors at the price of the procedure. Louisiana and Arkansas record some of the lowest per capita egg donation rates in the country, clocking in at only slightly above the states that lack egg donation clinics (New Hampshire, Wyoming, Maine, and Alaska). Though the majority of states in the Deep South are in line with the U.S. average, Texas and Georgia record significantly higher per capita donation rates, while Louisiana and Arkansas record disproportionately low rates. Graphic 12 depicts the variance in per capita donation rates across the Deep South. On average, taking the small sample of Southern states that surround Louisiana and Arkansas, offering compensation at the average rate for a region doubles the donation rate per capita donation.

Louisiana and Arkansas’ average per capita donation rate of 0.0012 is approximately half that of the neighboring Oklahoma, Mississippi, and Alabama, which hover at approximately 0.0023.

The European market offers a far better cost comparison than the U.S. due to its constellation of national regulations. Graphic 13 depicts the national donation rates in Europe alongside the average compensation packages offered to donors for a single cycle. As hypothesized, the correlation between donation rates and compensation is positive. As a second effect, countries appear to be grouped by per capita income and change in income. The states that are significant outliers on the graph – Spain, Czech Republic, Cyprus, Greece – were all severely affected by the 2009 financial crisis. These countries record median incomes significantly below the European average, offer compensation packages well above the European median, and conduct an outsized proportion of donations relative to their populations. Indeed, these countries’ outsized share of the egg donor market appears to reflect a financial motivation for donation.

In a 2017 survey by The European Society of Human Reproduction and Embryology (ESHRE), 40% of Greek donors, 52% of Russian donors, and 28% of Ukrainian donors reported giving for purely financial reasons, compared to under 15% in Belgium, Finland, and France (ESHRE, 2017). Without these outliers, the remaining states appear to cluster in a solid group, with a clear positive correlation between income and donation. Graphic 14 depicts the compensation packages (Euros) and per capita donation rates of the European states without the five outliers.

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As a methodological note, the correlation between change in median income during the financial crisis and the rate of egg donation is neither significant nor positive. I performed several regressions on the relationship between the per capita donation rate and the change in the growth rate and median income for both the United States and Europe. To capture a lag time between the recession and donation, I performed the analysis over a variety of periods, regressing changes in 2009 income and growth against egg donation rates from 2009-2014. For the United States, I relied on state-by-state statistics for median income and growth and included a variable to account for state fixed effects. Tables 5 and 6 display the results of the regressions.

The lack of a robust correlation is unsurprising. First, during a recession, demand for donations should decrease to reflect the financial strain and uncertainty placed on potential parents. As a result, the compensation packages during periods of recession may not be sufficient to entice donors to offer their eggs. Second, egg donation is highly personal, meaning that changes in macro-level variables might not produce tangible effects on the handful of women intending to donate eggs. When stated clearly, the hypothesis contains a note of absurdity: women are not more likely to subject themselves to invasive medical procedures after state GDP falls by a certain number of percentage points. In future analyses, more personal economic variables – the number of foreclosures and bankruptcies in a state – might be better proxies than changes in median income.

Thus, I draw my analysis of income groupings and donations in Europe from anecdotal evidence. All five outlier countries displayed modest egg donation industries prior to the financial crisis; all five exhibited rapid growth in the number of procedures after 2009. Directly following the financial crisis, a number of newspaper stories in Spain and Cyprus, as well as a set of sociological and anthropological articles, discussed the emergence of egg trafficking. Nahmen (2016) describes and interviews dozens of women shipped from Romania to Spain to donate eggs, many of whom cited the financial crisis as the primary impetus for their donation. In the United States, numerous anecdotes abound of women who donated eggs to pay off college loans or manage their mortgage payments (Talmage, 2013). Though no statistical correlation exists between the macro-level economic indicators and per capita donation, the conclusion that personal financial strain prompts women to donate appears intuitively obvious and anecdotally supported.

The Markup: What is P?

P* reflects the flat rate charged for egg donation by clinics. However, a certain number of trait-specific recipients – women looking for rare or in-demand characteristics – turn to the secondary market dominated by agencies. The agencies offer trait-specific compensation to donors and charge recipients staggered prices that reflect the differing market values of the eggs.

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Overall, the market equilibrates to an average of P* + P, a markup that reflects the vetting services of the agency (A) and the compensation bonus for the donors’ traits (T). The P term can be staggeringly large compared to the base price, ranging up to approximately six figures.

Crucially, only the agencies can employ a staggered pricing scheme due to their ability to mitigate adverse selection risk. With just clinics, donors, and recipients, the market struggles to mitigate the risk of adverse selection. The egg donor knows far more about her genetic and phenotypic profile than the recipient and, as a result, has great leeway to lie, mislead, or misrepresent herself. While some characteristics – hair color, ethnicity, college degree – can be confirmed with a routine background check, other genetic traits, like a family history of mental illness, are far more difficult for the recipient to verify. This information asymmetry threatens to produce a classic case of adverse selection.

In the unregulated market for eggs, prudent recipients should refuse to buy anonymously donated eggs, fearing a rigged trade in which they receive a suboptimal egg. Without trait-based compensation, women with genetically desirable characteristics may refuse to donate their eggs, feeling that they are being shortchanged for their product by the flat market rate. By contrast, women with less highly demanded traits may continue to donate, pricing their own eggs at the market rate or below. The end result: as women with in-demand traits exit the market, the market becomes a “market for lemons” filled with undesirable or low-quality eggs.

At the very least, unable to certify the quality of the eggs, recipients should decline to compensate donors for their specific characteristics. Clinics adhere to this model, performing routine screenings for genetic defects that disqualify the donor from undergoing the procedure, but little in the way of secondary vetting. Since clinics perform the bare minimum of due diligence prior to receiving eggs, they make little effort to stagger their prices on the buyside or sell-side. According to a 2007 SART survey, 86% of responding clinics stated that they paid donors a flat fee as compensation. When asked more specifically about phenotypic and genotypic traits, 80% of the clinics reported that they offer no compensation bonuses based on characteristics, including prior fertility history, education, and ethnicity. By offering flat compensation, the clinic disincentivizes donors from lying about background characteristics, since the donors stand to gain nothing from the deception.

Due to the adverse selection problem in the free market, recipients are willing to pay agencies a premium price (P*+ P) to accurately scout and vet donors. Recipients delegate the task of conducting thorough behavioral and genetic screening to an agency, paying the intermediary a price for its services and the donor a price for her traits. The donor receives the flat clinic sum (P*) when she undergoes the medical procedure to donate. Prior to this compensation, she receives a payment from the agency for her traits (T), the 78

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total markup price (P) minus the agency’s fee (A). The recipient foots the bill for the procedure and compensates the agency in a lump-sum payment for the donor egg and the vetting services (P). Since the traits will not be compensated without outside vetting due to the adverse selection risk, the P term accounts for both the vetting and the trait differential for the buyer.

With intermediaries, the market adjusts for the adverse selection risk through price equilibration. Compared to the clinics, the agencies vary their prices (P* + P) to reflect the stringency of the vetting services and the desirability of the donor’s traits. Agencies operate in two methods: first, they offer targeted ads to incentivize women with premium genetic characteristics to donate to an egg bank or clinic program; second, they facilitate directed donations, scouting a donor to fit the specifications of a couple. The primary difference in the service fee (A) between the two forms of agencies results from the ability of the recipient to supervise the vetting process. In directed donation, the recipient is able to monitor the agency throughout the screening process; when using a headhunting agency, the recipient relinquishes the ability to do so. As a result, the recipient offers a higher sum to the agency for the ability to perform s upervised vetting, compensating agencies that perform directed donations at a higher rate than those that simply scout donors.

The agencies pay premium trait-based compensation (T) to entice donors with specific in-demand genetic characteristics to enter the market. In a 2016 study by Keehn, Holwell, and Klitzman, the authors found that 34% of providers explicitly advertised compensation bonuses for women with specific traits, while another 15% referenced “preferred” or “in demand” traits. In addition to markups for prior donations, 18% of agencies offered additional compensation for education level; 12% for ethnicity; 12% for athletic or creative ability; 4% for physical appearance; and 2% for test scores. Notably, the agency’s pricing scheme reflects its own desire for certainty: women are compensated at lower rates for factors that the agency cannot verify (test scores) or ones that the agency cannot market uniformly to couples (physical appearance) than ones that the agency can confirm (college degree or ethnicity).

Crucially, the agency derives a portion of its prestige from its stock of high quality donors in the aggregate. The recipient or clinic compensates the agency for its expertise in scouting specific characteristics and rewards the agency for enforcing uniform standards. As a result, agencies explicitly seek out the most common in-demand traits. Between 2008 and 2012, researchers at a Manhattan fertility clinic administered questionnaires in which patients were asked to weigh the importance of egg donor traits (Hess, 2014). The study found that recipients sought donors who physically resembled them, but, with increasing frequency, requested donors who were healthy, athletic, and intelligent. The premium placed on donor intelligence, in particular, increased over the course of the study. In 2008, only 18% of donors prioritized the Columbia Economic Review | 79

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intellect of the donor; by 2012, well over half of donors weighed intelligence as one of the most important characteristics in a donor. The fertility clinic’s evidence is shockingly coherent with Keehn et al’s study: the factors referenced by parents on the questionnaire correspond in order of preference to the percentages of clinics offering compensation for each trait.

In practice, the agencies seem aware of the premiums placed on the resumes of donors. Aaron Levine of Georgia Institute of Technology aggregated the advertisements from student newspapers around the country and compared the compensation rates with the average of the college’s SAT scores.

Levine found that “Holding all else equal…each increase of 100 SAT points in the average for a university increased the compensation offered to egg donors at that school by $2,350” (Levine 2010). Overall, he interpreted the numbers to reflect a premium placed on highly educated donors by agencies sourcing eggs for couples. The coherence of the Manhattan fertility clinic, the Keehn et al., and the Levine studies indicates that the intermediaries within the market are responsive and efficient.

However, by delegating to the agency, the couple invites a second form of moral hazard: the agency can lie about the eggs it supplies or fail to conduct thorough background checks. Indeed, the risk of the agency misrepresenting its product or failing to perform due diligence on its donors is exacerbated by the profit incentive of the company. Though the donor may receive a flat compensation through the intermediary for her unvetted eggs, the agency can stagger its pay scale to correspond with the alleged qualities of the entirely anonymous egg. The only controlling bound on the agency is that it plays an iterative game, while the average donor plays a single game. Should an agency misrepresent itself on verifiable characteristics – say, the ethnicity of the egg – the recipient can easily discover the lie and report the lie. Since the agency depends on positive ratings and accreditations, the provider is disincentivized from lying by the knowledge that the recipient can inflict tangible harm on the company. Notably, this game structure does not prevent the agency from misrepresenting secondary donor traits, like the presence of a college degree or a healthy family history.

An individual egg donor is bound by no such iterative game structure – she can lie, receive her payment, and vanish. To prevent this, the agency often offers significantly higher compensation after the first successful round of donation to incentivize donors to foster a long-term relationship. In Keehn, Holwell, Klitzman’s study of compensation, the authors found that 64% of websites offered up-front additional compensation for women with a history of successful donation (Keehn, 2016). For all other traits, well under one fifth of intermediaries offered additional compensation, implying that the agencies and clinics valued the prior history of donation consistently and similarly. As expected, the agencies pay the most for donors playing a repeated game and structure incentives to prompt more women to donate repeatedly.

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Thus, in one portion of the market, the price is pinned at P*, reflecting the flat compensation offered to donors by clinics. In another portion of the market, a set of buyers offer extreme compensation differentials based on the traits of the donor and the vetting services of the agency. The equilibrium point in this secondary market is P* + P, with P being the price of vetting (A) and trait compensation (T). Since the secondary market prices products accurately, the supply of eggs slopes upwards and the demand slopes downwards, reflecting the ability of buyers to pay and sellers to donate.

A Market for Limes: The Clinic Market vs. The Agency Market Why do women donate in the P* market as opposed to the P* + P market? To address this question, I first analyze the motivations for women to donate eggs.

One set of women are willing to donate their eggs for purely altruistic reasons. These women have a low reservation wage – the compensation rate at which they decline to donate – because they are entirely price inelastic, donating regardless of compensation. In general, these women are relatives or friends of the recipient, meaning these donations tend to be non-anonymous, exclusive, and directed. In the case of anonymous altruistic donation, the donors are normally women who have undergone IVF for their personal fertility, but banked too many eggs to use individually. After using the desired quantity of their own eggs, these women often donate their eggs to a clinic or egg bank to enable other women to use them.

In contrast to the altruists, another set of donors offer their eggs only because of the compensation package. Women who provide their eggs primarily for the compensation tend to lack outside sources of income. As a result, they are typically students or recent immigrants who are young, childless, and unmarried. Women donating for profit-based reasons also tend to be more willing to donate in states and countries that allow for fully anonymous, closed-record donation. Finally, profit-seeking donors gravitate towards markets with high and stable demand for donations and above-average compensation packages. Exemplifying these characteristics, in Spain, where more egg donations occur than in the United States, over one quarter of donors are students and two-thirds of donors are not fully employed; 40% admit to donating for purely financial reasons (Carney, 2017). Notably, profit seeking donors do not all receive high compensation packages. Only those with particularly in-demand traits outpace the market, normally by finding an agency to vouch for their traits and connect them with a clinic or couple.

As a result, in practice, the P* market contains a combination of high- and low-quality eggs. On one hand, altruists – familial donors or women donating spare eggs – tend to donate exclusively to clinics. These women seek no additional profit for their eggs and, as a result, choose the fastest path to performing the donation. Instead of dealing with the matching process of an Columbia Economic Review | 81

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intermediary, they donate directly to the clinic and allow the clinic to allocate their eggs to a lucky recipient. In terms of quality, altruists are a mixed bag of donors, roughly representative of the population at large. On average, they may skew towards the slightly wealthier end of the spectrum due to the high cost of the IVF procedure and the exclusivity of the egg donor market at large. On the other hand, the P* market contains the eggs from women rejected from or shortchanged by donor agencies. The profit-seekers who donate in the general egg market find themselves receiving below-average compensation when their traits are considered in the secondary agency market. They view the flat price of the clinic as a markup and, therefore, jump at the opportunity to donate in a trait-blind fashion. Crucially, the clinic makes no distinction between altruists and profit seekers, nor does it collect any form of statistics on the motivations of donors.

The P* + P market contains a varying quality of eggs, but tends to sell eggs that receive sums higher than the P* market. Using the prior justification, profit-seekers who would receive compensation below P* in the agency market decline to sell to agencies. The only women who would donate to an agency at a price below P* must be unaware of the prevailing market rate in the clinic market. On the upper end of the P* + P market are women who would refuse to enter the market without an inducement significantly exceeding the clinic rate.

These women are profit-seekers who display in-demand traits, like advanced college degrees or rare ethnic compositions.

To appropriate Akerlof ’s famous terminology, the market for clinics hovers one step above the market for lemons (Akerlof, 1970). The high-quality eggs – Akerlof ’s “creampuffs” – are almost exclusively found in the agency market; the low-quality eggs – Akerlof ’s “lemons” – can be found primarily in the clinic market. However, due to the presence of altruists, the clinic market also offers an unknown number of average-quality eggs. As a result, the clinic market can be characterized as a “market for limes”: there are many low-quality options, but a number of average options that balance out the market.

Without the altruists who are being willingly shortchanged, the clinic market would entirely collapse, containing only eggs rejected from the agency donor pool.

Conclusion

Egg donor websites are filled with little truisms geared to incentivize women to donate eggs: “Believe, retrieve, conceive,” “It was my privilege to help someone have their miracle,” “Parenthood requires love, not DNA.” One website featured a pink and blue rendition of Uncle Sam pointing at the virtual reader: “We want YOU to donate an egg.” The reality of the market for donor eggs is that few women voluntarily offer their gametes to others. The majority of women in the market for eggs are family members performing directed 82

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donations, or profit-seekers looking for a fast eight grand. The slim size of the stock of egg donors, coupled with the steadily increasing number of infertile women searching for trait-specific matches, produces the existing dysfunction of the market.

The bisection of the market into a low-quality, low-cost clinic market and a high-quality, high-cost agency market reflects the difficulty of regulating non-traditional markets. The clinic market, charging flat costs and offering no trait compensation, abides by the ethical strictures imposed by the accreditation industries. In the process, it exhibits pronounced shortages of high-quality donors, forcing women with trait-specific preferences to seek outside options. In imposing ethical standards on clinic compensation, the regulating organizations created a secondary market that thrives on the undue inducements and trait-based discrimination that they endeavored to eliminate.

The agency market validates concerns over exploitation in the clinic market: the agencies lure women into the market with targeted advertisements and staggering promises of money, frequently understating the risk of the procedure and the psychological consequences of donating. And yet, at the same time, they are necessary for the functioning of the broader market, effectively responding to the threat of adverse selection that exists in the unvetted clinic market. In the same way that car accrediting agencies emerged in the used car market, agencies appeared in the egg donation market to ensure that customers receive the correct quality and quantity of eggs.

Thus, the question emerges: what can be done? First, the Society for Assisted Reproductive Technology and the American Society for Reproductive Medicine needs to reexamine the economic underpinnings of the market. The price cap and floor on the clinic market ensures that the agency market exists; without the caps, clinics might develop vetting services to compensate donors for their traits. The clinic is a less exploitative alternative to the agency because it has a medical obligation to perform risk disclosure and accurately assess the health of the donor. Second, state governments should reassess their anonymity and healthcare provisions. With a larger pool of egg donors, the financial inducements might stabilize at a lower equilibrium than the current $8,000 price tag. The regulatory environment of the state profoundly impacts the willingness of recipients and donors to undergo the procedure. Third, the matching formula for the recipient and donor should be reimagined to more accurately maximize the social surplus. In the age of dating apps and job search platforms, matching technology has improved exponentially, while clinics and agencies still offer the same localized, fragmented matching services as decades prior. The egg donor databases should be standardized, nationalized, and digitized to allow for the optimal matches to occur. Overall, governments, clinics, and agencies should cooperate to ensure that egg donation remains ethical, legal, efficient, and affordable.

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Tables

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In Column 1-3, this table displays a regression of the total number of IVF procedures and the existence of healthcare coverage in 2016. In Column 4-6, it displays a regression of the total number of IVF procedures per capita and the existence of healthcare coverage in 2016. The data for the total number of IVF procedures comes from the ART Annual Report (2016). The data on the growth rate, median income, and unemployment rate come from the Census Bureau and the National Bureau of Labor Statistics. Data on healthcare provisions is coded as 0 = No coverage; 1 = Coverage.

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

This table displays a regression of the total number of egg donations and the existence of healthcare coverage in 2016. The data for the total number of egg donation procedures comes from the ART Annual Report (2016). The data on the growth rate, median income, and unemployment rate come from the Census Bureau and the National Bureau of Labor Statistics. Data on healthcare provisions is coded as 0 = No coverage; 1 = Coverage.

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Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In Column 1-4, this table displays a regression of the per capita donation rate with anonymity provisions in 2016. In Column 4-6, it displays a regression of the per capita number of donations with the existence of anonymity provisions in 2016. The data for the total number of egg donation procedures comes from the ART Annual Report (2016). The data on the growth rate, median income, and unemployment rate come from the Census Bureau and the National Bureau of Labor Statistics. Data on anonymity provisions is coded as 0 = None; 1 = State-Level Sperm Regulation Only; 3 = State-Level Egg Donation Regulation; 2 = Abides by Federal Regulation.

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In Column 1-2, this table displays a regression of the per capita donation rate with anonymity provisions in 2014 for select European countries. In Column 3-5, it displays a regression of the per capita number of donations with the existence of anonymity provisions in 2016. The data for the total number of egg donation procedures comes from the Journal of Human Reproduction. The data Columbia Economic Review | 85

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on the growth rate, median income, and unemployment rate come from the OECD and European Commission. Data on anonymity provisions is coded as 0 = Egg Donation Banned; 1 = Two-Way Anonymity With Disclosure; 2 = Two-Way Anonymity Without Disclosure.

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In Colum 1-3, this table displays a regression of the total number of egg donations in 2009, 2010, and 2011 with the change in median income for 2009, controlled against the unemployment rate in 2009. In Column 5-7, this table displays a regression of the change in the number of donations 2009, 2010, and 2011 with the change in median income for 2009, controlled against the unemployment rate in 2009. The data come from the ART Annual Report, the Bureau of Labor Statistics, and the Census. Notably, the growth rate could not be included as a control variable due to its cross-correlation with median income.

Notes 1 There may be a geographic coefficient needed to equalize for price differentials across different geographic regions. Both the cost of compensation and the cost of the procedure should be scaled by a geographic coefficient that reflects the cost of living in a specific region. The SART 2007 annual survey of clinic compensation noted that price variance in the U.S. market predominately reflected differences in the regional cost of living, with the West Coast and East Coast paying higher compensation packages than the Midwest (Covington, 2007). Aside from this geographic coefficient, the primary fluctuations in the base rate of the procedure reflect healthcare subsidies and compensation differentials. In a mathematical form, the geographic coefficient can be represented by controlling for the consumer price index in each state.

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Nahmen, Michal Rachel. “Romanian IVF: a brief history through the ‘lens’

of labour, migration and global egg donation markets.” Reproductive

Biomedicine and Society 2 (June 2016): 79-87, https:// doi.org/10.1016/j.rbms.2016.06.001.

National Summary Report. “FINAL CUMULATIVE OUTCOME PER EGG

RETRIEVAL CYCLE.” SART, 2016. https://www.sartcorsonline.com/ rptCSR_PublicMultYear.aspx?ClinicPKID=0#donor-fresh-egg.

Paraskou A, George BP. The market for reproductive tourism: an analysis with

special reference to Greece. Glob Health Res Policy. 2017;2:16.

Published 2017 Jun 12. doi:10.1186/s41256-017-0037-8.

Pavone, Vincenzo. “50% Of European Egg Donation Happens in Spain.

Why?” International Medical Travel Journal, 1 Aug. 2018, www. imtj.com/articles/50-european-egg-donation-happens-spain-why/.

Posner, Eric. “It’s Not Donating. It’s Selling: Fertility clinics’ feeble justifications

for fixing the price of human eggs.” Slate, July 29, 2015, https://slate. com/news-and-politics/2015/07/egg-donation-price-fixing-lawsuit-did- fertility-clinics-conspire-to-limit-payments-to-women.html.

Pulitzer Center. “It’s Not Altruism, It’s Selling.” Pulitzer Center, 16 Feb. 2017, pulitzercenter.org/reporting/its-not-altruism-its-selling.

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Rao, Sushil. “The More Beautiful the Female Egg Donor, the Higher the Price

They Can Command: Hyderabad News.” The Times of India,

May 9, 2017. timesofindia.indiatimes.com/city/hyderabad/the-more- beautiful-the-female-egg-donor-the-higher-the-price-they-can- command/articleshow/58587401.cms.

Roberston, John A. “Compensation and egg donation for research.” Fertility

and Sterility 86, no. 6 (December 2006): 1573-1575. https:// doi.org/10.1016/j.fertnstert.2006.08.084.

Schaefer, Louisa. “Germany’s Egg Donation Prohibition Is Outdated, Experts

Say,” Deutsche Welle, December 12, 2007. https://www.dw.com/en/ger manys-egg-donation-prohibition-is-outdated-experts-say/a-2999675-1.

Schneider, Jennifer, et al. “Breast Cancer in Young Egg Donors: A Call for

Follow-up, Research, and Transparency on the Long-Term Risks of

Ovarian Stimulation.” The Center for Bioethics & Culture, www.cbc-

network.org/pdfs/Schneider_Lahl_%20Kramer-Breast_Cancer_in_ Young_Egg_Donors-EARLY_VERSION.pdf.

Sharon N. Covington, M.S.W.,a and William E. Gibbons, M.D.,b, “What

is Happening to the Price of Eggs,” Society for Assisted Reproduction,

2007. https://www.fertstert.org/article/S0015-0282(07)00069-6/pdf.

Talmadge, Stephanie. “College Females Turn to Egg Donations to Fund

School.” USA Today, Gannett Satellite Information Network, 11 July

2013, www.usatoday.com/story/news/nation/2013/07/11/egg-dona tions-funding-college-payments/2509997/.

The Hastings Center, “PRESS RELEASE: Fertility industry offers big money to

recruit “desirable” egg donors at top universities,” The Hastings Center,

March 23, 2010. https://www.thehastingscenter.org/for-media/press-re

leases/press-release-03-24-10-fertility-industry-offers-big-money-to-re cruit-desirable-egg-donors-at-top-universities/.

Trappe, Heike. “Assisted Reproductive Technologies in Germany: A Review of

the Current Situation.” In: Kreyenfeld M., Konietzka D. (eds),

Childlessness in Europe: Contexts, Causes, and Consequences.

Demographic Research Monographs (A series of the Max Planck

Institute for Demographic Research). 2017. https://link.springer.com/ chapter/10.1007/978-3-319-44667-7_13#citeas.

Tuller, David. “Payment Offers to Egg Donors Prompt Scrutiny.” The New York

Times, The New York Times, 10 May 2010, www.nytimes. com/2010/05/11/health/11eggs.html.

Urist, Jacoba. “How Much Should a Woman Be Paid for Her Eggs?” The

Atlantic, Atlantic Media Company, November 10, 2017. www.theatlan tic.com/health/archive/2015/11/how-much-should-a-woman- be-paid-for-her-eggs/414142/.

Woodriff, Molly, et al. “Advocating for Longitudinal Follow-up of the Health

and Welfare of Egg Donors.” Fertility and Sterility, U.S. Nation

al Library of Medicine, Sept. 2014, www.ncbi.nlm.nih.gov/pmc/articles/ PMC4416474/.

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Online Feature Is Economics Right-Wing Biased?

Shreya Ganguly Columbia University

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n November 2011, a group of Harvard students walked out of Gregory Mankiw’s Economics 101 class in protest of its alleged conservative bias. In an open letter, they wrote: “A legitimate academic study of economics must include a critical discussion of both the benefits and flaws of different economic simplifying models.”1 According to its protesters, Mankiw’s introductory economics class failed to offer a sufficiently broad and critical view of the subject. Its omissions would have far-reaching consequences. The findings of economic analysis have direct policy implications, and Mankiw’s students often go on to play major roles in global financial systems. Indeed, these systems are increasingly plagued by economic inequality in the 21st century.

Mankiw’s reply to the protests came in the form of a New York Times op-ed. He maintained that his course material was a “broad survey of mainstream economics,” no different from the offerings of other introductory classes on the subject.2 A Harvard Crimson editorial echoed his views, agreeing that “Professor Mankiw’s curriculum sticks to the basics of economic theory without straying into partisan debate.”3 It reminded protesters that alternative schools of thought, like Marxism, were the domain of social theory, not economic theory.

What sparked the protest, then, wasn’t Mankiw’s personal conservatism, but the discipline of economics itself. The field is frequently accused of being an intellectual cover for right-wing ideology. Indeed, economics can appear to be fundamentally conservative: its basic axioms take self-interested consumers and profit-maximizing firms for granted, and its conclusions often overestimate the efficiency of free markets. The association between economics and conservatism is undeniable. An Allgood et al. study finds that economics majors are more likely to vote for and seek membership in right-wing parties, relative to those in other majors.4

Yet, economics aspires to be a rigorous scientific discipline that deals in objective truths, without intrinsic bias. To quote Keynes, “[t]he theory of economics does not furnish a body of settled conclusions immediately applicable to policy. It is a method rather than a doctrine, an apparatus of the mind, a Columbia Economic Review | 91

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technique for thinking, which helps the possessor to draw correct conclusions.”

Introductory economics classes thus separate positive and normative analysis, how things are and how they ought to be, and firmly plant themselves in the camp of the former. But can economics be a value-free enterprise? And more importantly, should it even try?

It is inevitable that values inform the most “objective” of economic research, to some degree. Edward S. Herman argues that bias has been institutionalized in the subject; ideological influence can creep into the discipline in the choice of problem to study, in the predetermination of correct answers and, at worst, in the manipulation of mathematical tools to muster support for a priori truths, in service of specific political and economic interests.5 At the very least, value judgements answer the question of what is worth studying, and where attention and resources should be diverted. For example, Zubin et al. find that an economist’s research area is correlated with their political leanings. Macroeconomists and financial economists are more right-leaning on average, while labor economists tend to be left-leaning.6 Complete objectivity is a myth even in the most rigorous of disciplines.

But as the Harvard Crimson editorial points out, Econ 101 deals only in the basic vocabulary of economic theory. Where, then, have values crept in?

It is true that the introductory class serves as nothing more than an academic grounding, laying the foundation for further, more nuanced study. We reduce complex phenomena to simple models, observe effects in isolation before incorporating more factors, and make certain assumptions to relax them in the future: ceteris paribus, consumers have rational preferences, information is complete, and so on. These models are the basic building blocks that later classes build upon, to achieve increasing complexity and realism. Simplified, idealized models are a necessary pedagogical starting point. It is a given that they are far removed from reality, in most instances — the problem lies in failing to acknowledge them as such.

As Noah Smith notes in a Bloomberg article, a huge gap exists between introductory economics courses and what professional economists do today.

Cognitive and behavioral approaches ditch the homo economicus altogether, using insights from psychology and sociology towards a more realistic, complex understanding of economic decision-making. With the advent of information technology and complex statistical methods, economic research today makes much greater use of data and empirical observation than Econ 101 would seem to imply. Economists are no longer what David Card calls "mathematical philosophers.” They favor a quasi-experimental, evidence-based approach instead.7

One of Econ 101’s infamous conclusions is that minimum wage policies increase unemployment. The basic supply-and-demand model says that wages adjust so that the demand for labor matches its supply; in the free market, 92

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everyone who wants a job has a job. Theoretically, a price floor makes it unprofitable for companies to retain workers with lower productivity than what is required at the price floor, and so minimum wage hikes should result in a flood of low-wage workers being laid off. Yet, empirical economics suggests otherwise: numerous economists have studied a wide range of minimum wage hikes, and concluded that in many cases, the short-run effect on employment has been very small. 8

This doesnâ&#x20AC;&#x2122;t make the theory wrong, but it does highlight its incompleteness. The model only provides a partial view of what goes on in labor markets. In reality, employment likely depends on a lot more than just wage levels, such as predictions of future wages, long-term employment relationships, and many other variables too complex to fit into the neat, simplified world of Econ 101. But only a fraction of students stays on to discover these complexities. For many, Econ 101 is the only exposure they receive to formal economics.9 The inadequacy of this exposure is unsurprising; Physics 101, too, strips itself down to be manageable for newcomers to the discipline. But the key difference is that students of Physics 101 are significantly less assured of their understanding of the field as a whole after taking the introductory course. As for what students of Econ 101 do, Gawker put it best:10 â&#x20AC;&#x153;Look, economists: we, the internet commenters, are going to be honest with you here. We donâ&#x20AC;&#x2122;t have time to be out there reading Columbia Economic Review | 93

Shreya Ganguly

‘empirical studies’ which may or may not prove or disprove various Econ 101 FACTS that we all know to be FACTS from taking or perhaps reading an internet thread about Econ 101.

SOCIALISM Doesn’t WORK because people are lazy—IT’S ECON 101. UNIONS are DETRIMENTAL to business—IT’S ECON 101. TAXES are BAD because people EARNED their money—IT’S ECON 101. REGULATION makes things WORSE because the FREE MARKET is the perfect method of distribution— IT’S ECON 101.”

Perhaps the problem lies not in the content of the undergraduate economics curriculum, but in its structure. While most economics departments offer elective courses in “alternative” schools of thought, like behavioral economics, they exclude them from their core requirements, making their study largely optional. Staple introductory classes like Greg Mankiw’s scarcely mention behavioral breakthroughs, and when they do, it is as little more than an afterthought.11 The organization of the economics curriculum reflects its priorities, belying a value judgement of what is worth studying — what we designate as ‘fundamental’ matters.

What could a potential alternative look like? Harvard professor Raj Chetty has ideas. An empirical economist, he seeks to introduce real-world issues to students of economics as early as possible. Empirical research relevant to major political questions, such as the roots of economic and racial inequality, is prioritized over theoretical supply-and-demand curves in a supplement to Econ 101 known as Econ 1152. The material taught in the course is relatively recent, with 11 out of its 12 required readings released after 2010.12

An openness towards new perspectives and a newfound focus on real-world observations rather than abstract, mathematical theory could draw in a new breed of students, injecting some much-needed diversity into economics departments. Taught at Harvard in Spring 2019 to 375 students, 49% of them women, Chetty’s course saw greater female enrollment than any other undergraduate Economics course at the university.13

Calling Econ 101 a value-free enterprise has only allowed undeclared and unarticulated values to creep in, with dangerous consequences. The findings of economics influence civic behavior, shape public policy and determine the priorities of our economic system. Its students often play major roles in government and financial institutions around the world. In the aftermath of the 2008 crisis, it may be tone-deaf and unproductive to harp on about the power of unregulated free markets, even in introductory courses. It is time to adopt a more ethical approach — we need to admit that economics cannot be value-free, and then openly discuss the values we want it to be based upon.

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References 1. “An Open Letter to Greg Mankiw.” Critical Quarterly 54, no. 2 (2012): 10–11. https://doi.org/10.1111/j.1467-8705.2012.02050.x.

2. Mankiw, N. Gregory. “Know What You're Protesting.” The New York Times. The New York Times, December 4, 2011. https://www.nytimes. com/2011/12/04/business/know-what-youre-protesting-economic-view. html 3. “Stay in School: Opinion: The Harvard Crimson.” Stay in School | Opinion. Accessed December 3, 2019. https://www.thecrimson.com/article/2011/11/3/ec-walkout-occupy/.

4. Allgood, Sam Anthony, William Bosshardt, Wilbert Van Der Klaauw, and Michael W. Watts. “Is Economics Coursework, or Majoring in Economics, Associated with Different Civic Behaviors?” SSRN Electronic Journal, 2010. https://doi.org/10.2139/ssrn.1619503.

5. Edward S. Herman, “The Institutionalization of Bias in Economics,” Media, Culture & Society 4, no. 3 (1982): pp. 275-291, https://doi. org/10.1177/016344378200400307) 6. Jelveh, Zubin, Bruce Kogut, and Suresh Naidu. “Political Language in Economics.” SSRN Electronic Journal, 2014. https://doi.org/10.2139/ ssrn.2535453.

7. Smith, Noah. “How to Restore Faith in Economics.” BloombergQuint.

Bloomberg Quint, March 16, 2017. https://www.bloombergquint.com/onweb/how-to-restore-faith-in-economics.

8. Zipperer, Ben. “Gradually Raising the Minimum Wage to $15 Would Be Good for Workers, Good for Businesses, and Good for the Economy: Testimony before the U.S. House of Representatives Committee on Education and Labor.” Economic Policy Institute. Accessed December 3, 2019. https:// www.epi.org/publication/minimum-wage-testimony-feb-2019/ 9. Smith, Noah. “Most of What You Learned in Econ 101 Is Wrong.” November 24, 2015. https://www.bloomberg.com/opinion/articles/2015-11-24/ most-of-what-you-learned-in-econ-101-is-wrong 10. Nolan, Hamilton. “Flaws in ‘Economics 101’ Arguments Pose Grave Risk to Internet Commenters.” Gawker. file://localhost/Accessed December 3, 2019. https/::gawker.com:flaws-in-economics-101-arguments-pose-graverisk-to-i-1744397091%3Fsidebar_promotions_icons=testingoff&utm_expid=66866090-67.e9PWeE2DSnKObFD7vNEoqg.1&utm_referrer=http/::gawker.com:flaws-in-ec 0, Accessed December 3, 2019.

11. Haldar, Antara. “Economics: The Discipline That Refuses to Change.” The Atlantic. Atlantic Media Company, December 14, 2018. https://www.theatlantic.com/education/archive/2018/12/why-do-econ-classes-barely-mention-behavioral-economics/578092/

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12. Matthews, Dylan. “The Radical Plan to Change How Harvard Teaches Economics.” Vox. Vox, May 22, 2019. https://www.vox.com/the-highlight/2019/5/14/18520783/harvard-economics-chetty 13. “Training the Next Generation.” Big Data to Solve Economic and Social Problems | Opportunity Insights. Accessed December 3, 2019. https://opportunityinsights.org/course/

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Columbia Economic Review | 97

Spring 2020 Environmental Policy Competition Since 2018, China and the United States have been in an escalating trade war, imposing tariffs on each other’s goods to promote buying goods made in one’s own country. While the economic effects of the trade war have been well studied, the environmental effects have not been explored in depth. In June 2018, Erik Solheim, the Executive Director of the UN Environment Programme at the time, argued that an economic slowdown caused by the trade war would be “very bad for the environment because you waste resources rather than using them effectively. It will make the spread of environmental technologies less fast. And of course it will keep more people in poverty for a longer period of time.”

In our annual Environmental Policy Competition, the Columbia Economic Review and Columbia Economics Society invited teams of undergraduates to explore whether or not Solheim is right. Teams made proposals about how to minimize specific environmental impacts of the trade war. In doing so, participants delved into current technologies, government policies, and infrastructure.

Our first-place winners are Bernadette Gostelow (Columbia University) and Maximilian Lim (Sciences Po & Columbia University), who wrote a paper titled “Pulling Back the Veil of Ignorance: A case study of the effects of the US-China trade war on Brazilian soy production and its social and environmental effects.”

Their paper examines the impact of the US-China trade war on soy production; they focus on how the trade war encourages an environmentally harmful level of soy production in Brazil. Gostelow and Lim describe the deforestation occurring in Brazil, make detailed environmental and sociopolitical projections for the future, and provide policy suggestions. Of particular note is their suggestion for an “Environmental social credit rating system.”

We thank all of our participants for their thoughtful and creative engagement with such an important environmental issue!

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Spring 2020 High School Essay Competition With over 400 cities in the United States implementing a plastic bag tax or a plastic bag ban, there has been a surge in discussion about the environmental and economic effects of these policies. What are the positive ramifications of these policies? Are there any negative repercussions? Do the positive economic and environmental ramifications of these taxes/bans outweigh any possible negative repercussions?

In our Spring 2020 High School Essay Competition, the Columbia Economic Review invited high school students to explore these questions. In doing so, students used data from areas with and without plastic bag policies, plus thought critically about how such policies could be applicable to other wasteful products.

Our first-place winner is Andrew Yan, who currently attends Princeton High School and will be attending Columbia in the fall. Yan examines both plastic bag bans and taxes, using Chicago as a case study. Yan presents a nuanced and thoughtful argument in favor of a plastic bag tax.

We thank all of our participants for offering such in-depth analyses of this important and current concern! We look forward to providing another opportunity for high school students to think critically about economic issues though our Fall 2020 High School Essay Competition.

Founded in 2009 as the first undergraduate economic journal in the United States, the Columbia Economic Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of research papers in our print journal. We further strive to engage individuals on campus, locally, and globally through speaker series, symposia, competitions, and other events established to promote dialogue and encourage deeper insights on economic issues. CER is sponsored by the Program for Economic Research (PER) at Columbia University, and is entirely led, organized, and operated by undergraduate students at Columbia University across a range of academic disciplines.

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