Vol. III, No. II Spring 2013

Columbia Economics Review

a league of their ownbend it like becapass the bargaining chipspregaming the tequila crisis

Lost and Found

Is a Lost Generation Inevitable after a Recession?

Columbia Economics Review Columbia Economics Review

2 Spring 2013 Spring 2013 3

C O L U M B I A E C O N O M I C S R E V I E W T A B L E O F C O N T E N T S

Printed with generous support from the Columbia University Program for Economic Research

Licensed under Creative CommonsAttribution-NonCommercial-NoDerivs

3.0 Unported LicenseWe welcome your comments. To send a letter to the editor, please email: economics.columbia@gmail.com.

We reserve the right to edit and condense all letters.

2 0 1 3 - 2 0 1 4 E D I T O R I A L B O A R D

LEAD ASSOCIATE EDITOR

Sarika RamakrishnanASSOCIATE EDITORS

Anna EtraAndriano Henrique

David FroomkinZepeng GuanVictoria Steger

Julie Tauber

EXECUTIVE EDITORRui Yu

PUBLISHERDaniel Listwa

LAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFZhaokun (James) Ma

P U B L I C A T I O N I N F O R M A T I O N

Columbia Economics Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of student es-says, opinions, and research papers. CER also holds the Columbia Economics Forum, a speak-er series established to promote dialogue and encourage deeper insights into economic issues.

Columbia Economics | Program for Economic Research

CONTENT

Sports Economics4 A League of Their Own

Lebron James and the Impact of the Sports Superstar

8 Bend It Like BeccaGender Inequality and Women’s Soccer Success

Microeconomic Theory16 Pass the (Bargaining) Chips

Carbon Emissions and the Bargaining Power of Trade Unions and Firms

Monetary and Fiscal Policy

Labor Economics

Columbia Economics Review would like to thankits donors for their

generous support of the publication

22 Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

28 Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Recession?

OPERATIONSEXECUTIVE OFFICER

Rachel RosenPRESIDENT OF ALUMNI AFFAIRS

Maren Killackey

CONTRIBUTING ARTISTS

Grace TanGemma Gené

Ariel Rahimzada (Cover)WEB EDITORS

David FroomkinVictoria Steger

OPERATING OFFICERS

Carlos GarciaCindy Ma

Theodore MarinDaniel Morgan

MANAGING OFFICERS

Mitu BhattatiryErin Bilir

Marc LemannAllison Lim

Cem Zorlular

2 0 1 2 - 2 0 1 3 E D I T O R I A L B O A R D

PUBLISHER

Skanda AmarnathZhaokun Ma

TREASURER

Cem Zorlular

COPY CHIEF

Grace TanCOPY EDITORSDiana Ding

David FroomkinSarika Ramakrishnan

Hong Yi Tu YeSergio Villar

MANAGING EDITORS

Ben EckersleyDinorah Villalobos

Rui YuLAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFHadi Elzayn

SENIOR EDITORS

Ashwath ChennapanMatthew Chou

Diana DingEvan Munro

Hong Yi Tu YeSamantha Zeller

MEDIA/DESIGN EDITOR

Theodore MarinWEBMASTER

Carlos Garcia

CONTENTSENIOR EDITORS

Grant D’AvinoMatt Yeaton

Raph RubbardAsher Hecht-Bernstein

Justin Kahn

EVENTSENVIRONMENTAL POLICY

Ines Mouline (Outreach)Maureen Lackner (Content)

HEALTHCARE POLICY

Akshay Menon (Content)Varun Parmar (Outreach)

OUTREACH COORDINATORS

Sarika RamakrishnanRachel Rosen

COMPETITION WEBMASTER

Shruti KulkarniFORUM DIRECTOR

Mitu Bhattatiry

For a complete list of the papers cited by each of our authors, please visit

our website at columbiaeconreview.com

Opinions expressed herein do not necessarily reflect the views of Columbia University or Columbia Economics Review, its staff, sponsors, or affiliates.

Columbia Economics Review Columbia Economics Review

2 Spring 2013 Spring 2013 3

C O L U M B I A E C O N O M I C S R E V I E W T A B L E O F C O N T E N T S

Printed with generous support from the Columbia University Program for Economic Research

Licensed under Creative CommonsAttribution-NonCommercial-NoDerivs

3.0 Unported LicenseWe welcome your comments. To send a letter to the editor, please email: economics.columbia@gmail.com.

We reserve the right to edit and condense all letters.

2 0 1 3 - 2 0 1 4 E D I T O R I A L B O A R D

LEAD ASSOCIATE EDITOR

Sarika RamakrishnanASSOCIATE EDITORS

Anna EtraAndriano Henrique

David FroomkinZepeng GuanVictoria Steger

Julie Tauber

EXECUTIVE EDITORRui Yu

PUBLISHERDaniel Listwa

LAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFZhaokun (James) Ma

P U B L I C A T I O N I N F O R M A T I O N

Columbia Economics Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of student es-says, opinions, and research papers. CER also holds the Columbia Economics Forum, a speak-er series established to promote dialogue and encourage deeper insights into economic issues.

Columbia Economics | Program for Economic Research

CONTENT

Sports Economics4 A League of Their Own

Lebron James and the Impact of the Sports Superstar

8 Bend It Like BeccaGender Inequality and Women’s Soccer Success

Microeconomic Theory16 Pass the (Bargaining) Chips

Carbon Emissions and the Bargaining Power of Trade Unions and Firms

Monetary and Fiscal Policy

Labor Economics

Columbia Economics Review would like to thankits donors for their

generous support of the publication

22 Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

28 Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Recession?

OPERATIONSEXECUTIVE OFFICER

Rachel RosenPRESIDENT OF ALUMNI AFFAIRS

Maren Killackey

CONTRIBUTING ARTISTS

Grace TanGemma Gené

Ariel Rahimzada (Cover)WEB EDITORS

David FroomkinVictoria Steger

OPERATING OFFICERS

Carlos GarciaCindy Ma

Theodore MarinDaniel Morgan

MANAGING OFFICERS

Mitu BhattatiryErin Bilir

Marc LemannAllison Lim

Cem Zorlular

2 0 1 2 - 2 0 1 3 E D I T O R I A L B O A R D

PUBLISHER

Skanda AmarnathZhaokun Ma

TREASURER

Cem Zorlular

COPY CHIEF

Grace TanCOPY EDITORSDiana Ding

David FroomkinSarika Ramakrishnan

Hong Yi Tu YeSergio Villar

MANAGING EDITORS

Ben EckersleyDinorah Villalobos

Rui YuLAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFHadi Elzayn

SENIOR EDITORS

Ashwath ChennapanMatthew Chou

Diana DingEvan Munro

Hong Yi Tu YeSamantha Zeller

MEDIA/DESIGN EDITOR

Theodore MarinWEBMASTER

Carlos Garcia

CONTENTSENIOR EDITORS

Grant D’AvinoMatt Yeaton

Raph RubbardAsher Hecht-Bernstein

Justin Kahn

EVENTSENVIRONMENTAL POLICY

Ines Mouline (Outreach)Maureen Lackner (Content)

HEALTHCARE POLICY

Akshay Menon (Content)Varun Parmar (Outreach)

OUTREACH COORDINATORS

Sarika RamakrishnanRachel Rosen

COMPETITION WEBMASTER

Shruti KulkarniFORUM DIRECTOR

Mitu Bhattatiry

For a complete list of the papers cited by each of our authors, please visit

our website at columbiaeconreview.com

Opinions expressed herein do not necessarily reflect the views of Columbia University or Columbia Economics Review, its staff, sponsors, or affiliates.

Columbia Economics Review Columbia Economics Review

2 Spring 2013 Spring 2013 3

C O L U M B I A E C O N O M I C S R E V I E W T A B L E O F C O N T E N T S

Printed with generous support from the Columbia University Program for Economic Research

Licensed under Creative CommonsAttribution-NonCommercial-NoDerivs

3.0 Unported LicenseWe welcome your comments. To send a letter to the editor, please email: economics.columbia@gmail.com.

We reserve the right to edit and condense all letters.

2 0 1 3 - 2 0 1 4 E D I T O R I A L B O A R D

LEAD ASSOCIATE EDITOR

Sarika RamakrishnanASSOCIATE EDITORS

Anna EtraAndriano Henrique

David FroomkinZepeng GuanVictoria Steger

Julie Tauber

EXECUTIVE EDITORRui Yu

PUBLISHERDaniel Listwa

LAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFZhaokun (James) Ma

P U B L I C A T I O N I N F O R M A T I O N

Columbia Economics Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of student es-says, opinions, and research papers. CER also holds the Columbia Economics Forum, a speak-er series established to promote dialogue and encourage deeper insights into economic issues.

Columbia Economics | Program for Economic Research

CONTENT

Sports Economics4 A League of Their Own

Lebron James and the Impact of the Sports Superstar

8 Bend It Like BeccaGender Inequality and Women’s Soccer Success

Microeconomic Theory16 Pass the (Bargaining) Chips

Carbon Emissions and the Bargaining Power of Trade Unions and Firms

Monetary and Fiscal Policy

Labor Economics

Columbia Economics Review would like to thankits donors for their

generous support of the publication

22 Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

28 Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Recession?

OPERATIONSEXECUTIVE OFFICER

Rachel RosenPRESIDENT OF ALUMNI AFFAIRS

Maren Killackey

CONTRIBUTING ARTISTS

Grace TanGemma Gené

Ariel Rahimzada (Cover)WEB EDITORS

David FroomkinVictoria Steger

OPERATING OFFICERS

Carlos GarciaCindy Ma

Theodore MarinDaniel Morgan

MANAGING OFFICERS

Mitu BhattatiryErin Bilir

Marc LemannAllison Lim

Cem Zorlular

2 0 1 2 - 2 0 1 3 E D I T O R I A L B O A R D

PUBLISHER

Skanda AmarnathZhaokun Ma

TREASURER

Cem Zorlular

COPY CHIEF

Grace TanCOPY EDITORSDiana Ding

David FroomkinSarika Ramakrishnan

Hong Yi Tu YeSergio Villar

MANAGING EDITORS

Ben EckersleyDinorah Villalobos

Rui YuLAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFHadi Elzayn

SENIOR EDITORS

Ashwath ChennapanMatthew Chou

Diana DingEvan Munro

Hong Yi Tu YeSamantha Zeller

MEDIA/DESIGN EDITOR

Theodore MarinWEBMASTER

Carlos Garcia

CONTENTSENIOR EDITORS

Grant D’AvinoMatt Yeaton

Raph RubbardAsher Hecht-Bernstein

Justin Kahn

EVENTSENVIRONMENTAL POLICY

Ines Mouline (Outreach)Maureen Lackner (Content)

HEALTHCARE POLICY

Akshay Menon (Content)Varun Parmar (Outreach)

OUTREACH COORDINATORS

Sarika RamakrishnanRachel Rosen

COMPETITION WEBMASTER

Shruti KulkarniFORUM DIRECTOR

Mitu Bhattatiry

For a complete list of the papers cited by each of our authors, please visit

our website at columbiaeconreview.com

Opinions expressed herein do not necessarily reflect the views of Columbia University or Columbia Economics Review, its staff, sponsors, or affiliates.

Columbia Economics Review Columbia Economics Review

2 Spring 2013 Spring 2013 3

C O L U M B I A E C O N O M I C S R E V I E W T A B L E O F C O N T E N T S

Printed with generous support from the Columbia University Program for Economic Research

Licensed under Creative CommonsAttribution-NonCommercial-NoDerivs

3.0 Unported LicenseWe welcome your comments. To send a letter to the editor, please email: economics.columbia@gmail.com.

We reserve the right to edit and condense all letters.

2 0 1 3 - 2 0 1 4 E D I T O R I A L B O A R D

LEAD ASSOCIATE EDITOR

Sarika RamakrishnanASSOCIATE EDITORS

Anna EtraAndriano Henrique

David FroomkinZepeng GuanVictoria Steger

Julie Tauber

EXECUTIVE EDITORRui Yu

PUBLISHERDaniel Listwa

LAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFZhaokun (James) Ma

P U B L I C A T I O N I N F O R M A T I O N

Columbia Economics Review (CER) aims to promote discourse and research at the intersection of economics, business, politics, and society by publishing a rigorous selection of student es-says, opinions, and research papers. CER also holds the Columbia Economics Forum, a speak-er series established to promote dialogue and encourage deeper insights into economic issues.

Columbia Economics | Program for Economic Research

CONTENT

Sports Economics4 A League of Their Own

Lebron James and the Impact of the Sports Superstar

8 Bend It Like BeccaGender Inequality and Women’s Soccer Success

Microeconomic Theory16 Pass the (Bargaining) Chips

Carbon Emissions and the Bargaining Power of Trade Unions and Firms

Monetary and Fiscal Policy

Labor Economics

Columbia Economics Review would like to thankits donors for their

generous support of the publication

22 Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

28 Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Recession?

OPERATIONSEXECUTIVE OFFICER

Rachel RosenPRESIDENT OF ALUMNI AFFAIRS

Maren Killackey

CONTRIBUTING ARTISTS

Grace TanGemma Gené

Ariel Rahimzada (Cover)WEB EDITORS

David FroomkinVictoria Steger

OPERATING OFFICERS

Carlos GarciaCindy Ma

Theodore MarinDaniel Morgan

MANAGING OFFICERS

Mitu BhattatiryErin Bilir

Marc LemannAllison Lim

Cem Zorlular

2 0 1 2 - 2 0 1 3 E D I T O R I A L B O A R D

PUBLISHER

Skanda AmarnathZhaokun Ma

TREASURER

Cem Zorlular

COPY CHIEF

Grace TanCOPY EDITORSDiana Ding

David FroomkinSarika Ramakrishnan

Hong Yi Tu YeSergio Villar

MANAGING EDITORS

Ben EckersleyDinorah Villalobos

Rui YuLAYOUT EDITOR

Daniel Listwa

EDITOR-IN-CHIEFHadi Elzayn

SENIOR EDITORS

Ashwath ChennapanMatthew Chou

Diana DingEvan Munro

Hong Yi Tu YeSamantha Zeller

MEDIA/DESIGN EDITOR

Theodore MarinWEBMASTER

Carlos Garcia

CONTENTSENIOR EDITORS

Grant D’AvinoMatt Yeaton

Raph RubbardAsher Hecht-Bernstein

Justin Kahn

EVENTSENVIRONMENTAL POLICY

Ines Mouline (Outreach)Maureen Lackner (Content)

HEALTHCARE POLICY

Akshay Menon (Content)Varun Parmar (Outreach)

OUTREACH COORDINATORS

Sarika RamakrishnanRachel Rosen

COMPETITION WEBMASTER

Shruti KulkarniFORUM DIRECTOR

Mitu Bhattatiry

For a complete list of the papers cited by each of our authors, please visit

our website at columbiaeconreview.com

Opinions expressed herein do not necessarily reflect the views of Columbia University or Columbia Economics Review, its staff, sponsors, or affiliates.

A League of Their OwnLebron James and the Impact of the Sports Superstar

Zachariah Reitano

Columbia University

On Friday, November 6th, 2009, the New York Knicks played the Cleveland Cava-liers. Madison Square Garden was packed and the energy was reminiscent of the 1990s when Ewing and the Knicks bat-tled Jordan and the Bulls. Although the Knicks 2008-2009 season resulted in a 39 percent win rate, only slightly better than the Eastern Conference’s worst team, the Washington Wizards, this game held far greater weight than any early November regular season game had in decades: it was a mere 236 days before Lebron James would become a free agent.

The hope that at the end of the season Lebron would wear the blue and orange was on everyone’s mind. Local fans were rooting for the home team and an oppos-ing player. Each time Lebron touched the ball, the crowd held its breath, and each time he scored, increasing the Cavalier’s lead, they released exultant cheers. There was no denying Lebron’s effect on Madi-son Square Garden, but how far would his impact extend? What would be the ripple effect of Lebron’s decision on the NBA?

Superstars in the NBAThe structure of the game of basketball

lends itself to the creation of superstars. Consider the NBA in comparison with

the four other primary sports leagues in the United States. In the NFL, the best players only play half the time. Their physical appearances are mostly covered by equipment, and, for the most part, the only identifiable characteristics are the names on the backs of the jerseys and their numbers. In the NHL, players are equally obscured by protective gear. In the MLB, a player may be out of view for almost half the contest and, when seen, performs for brief moments and at a great

distance. Lastly, the MLS incorporates in-creased visibility of players for greater periods, but it has more players on each team playing at even greater distances from the fan, with their efforts often re-sulting in few goals and many ties. All of this limits the ability of American soccer players to demonstrate their talents, yet superstars of European football share a level of notoriety similar to that of NBA stars. A superstar’s presence can have a

ripple effect and increase the revenue of both his team and the opposing team in three primary ways. First, he can increase the overall quality of his team based solely on his performance. This increase in the team’s performance can strengthen and increase the fan base. Second, his tal-ent demands additional attention from the opposition, enabling his teammates to perform at a higher level – this can be considered a positive externality. Third, the superstar’s appeal, controlling for an increase in performance quality, draws additional attention from fans. This is ev-ident when two players with almost iden-tical statistics and playing styles have dif-ferent levels of popularity. In terms of the opposition, a superstar can increase gate revenue, television ratings, and the sale of NBA merchandise. The NBA does not share any local revenue (gate, local TV) but it evenly distributes income derived from the sale of merchandise and from national television contracts with the teams.

The question remains, how influential are the top superstars, and how large is the ripple effect? To answer these ques-tions, I examine the case of Lebron James.

Lebron James With the advances in media technology

and increased media coverage, no player has ever matched the attention received by Lebron James.

Lebron’s superstar status began before he took the SATs. In his junior year, he faced eligibility questions and appealed to enter the NBA draft early. Having his appeal rejected only served to increase his popularity. Lebron made the cover of Sports Illustrated before his senior year of high school under the headline “The Chosen One”. His high school team’s practices were forced to move to the lo-cal university because their gym was ex-ceeding its capacity. ESPN 2 and Pay-Per-

View televised his high school games, which were attended by celebrities like Shaquille O’Neal, with whom he later played. Lebron was the driving force be-hind the success Cleveland achieved fol-lowing his entry in the league. He played for Cleveland for seven years; in five of those years not a single team member was declared an All-Star. In two of those years, he played with only one other All-Star–Zydrunas Ilgauskas, who had only made one other All-Star game prior to Lebron entering the league, and Mo Wil-liams, who never before nor would ever again, play in an All-Star game.

Lebron’s Effect on the Cavaliers’ Home Attendance

In the summer of 2010, Lebron became a free agent, and there was significant spec-ulation on which team he would choose. Lebron James, over the course of 7 sea-sons, increased Cavalier attendance by an average of 71.75 percent compared to the year prior to his arrival5. Before Lebron, in 2002-2003, the average attendance at a Cleveland Cavaliers game was 11,496. During his tenure, they averaged 19,745. This translated into 131,745,626 million dollars of increased revenue6. Their aver-

The structure of the game of basketball lends itself to the

creation of superstars.

Columbia Economics Review Columbia Economics Review

4 Spring 2013 Spring 2013 5

A League of Their OwnLebron James and the Impact of the Sports Superstar

Zachariah Reitano

Columbia University

On Friday, November 6th, 2009, the New York Knicks played the Cleveland Cava-liers. Madison Square Garden was packed and the energy was reminiscent of the 1990s when Ewing and the Knicks bat-tled Jordan and the Bulls. Although the Knicks 2008-2009 season resulted in a 39 percent win rate, only slightly better than the Eastern Conference’s worst team, the Washington Wizards, this game held far greater weight than any early November regular season game had in decades: it was a mere 236 days before Lebron James would become a free agent.

The hope that at the end of the season Lebron would wear the blue and orange was on everyone’s mind. Local fans were rooting for the home team and an oppos-ing player. Each time Lebron touched the ball, the crowd held its breath, and each time he scored, increasing the Cavalier’s lead, they released exultant cheers. There was no denying Lebron’s effect on Madi-son Square Garden, but how far would his impact extend? What would be the ripple effect of Lebron’s decision on the NBA?

Superstars in the NBAThe structure of the game of basketball

lends itself to the creation of superstars. Consider the NBA in comparison with

the four other primary sports leagues in the United States. In the NFL, the best players only play half the time. Their physical appearances are mostly covered by equipment, and, for the most part, the only identifiable characteristics are the names on the backs of the jerseys and their numbers. In the NHL, players are equally obscured by protective gear. In the MLB, a player may be out of view for almost half the contest and, when seen, performs for brief moments and at a great

distance. Lastly, the MLS incorporates in-creased visibility of players for greater periods, but it has more players on each team playing at even greater distances from the fan, with their efforts often re-sulting in few goals and many ties. All of this limits the ability of American soccer players to demonstrate their talents, yet superstars of European football share a level of notoriety similar to that of NBA stars. A superstar’s presence can have a

ripple effect and increase the revenue of both his team and the opposing team in three primary ways. First, he can increase the overall quality of his team based solely on his performance. This increase in the team’s performance can strengthen and increase the fan base. Second, his tal-ent demands additional attention from the opposition, enabling his teammates to perform at a higher level – this can be considered a positive externality. Third, the superstar’s appeal, controlling for an increase in performance quality, draws additional attention from fans. This is ev-ident when two players with almost iden-tical statistics and playing styles have dif-ferent levels of popularity. In terms of the opposition, a superstar can increase gate revenue, television ratings, and the sale of NBA merchandise. The NBA does not share any local revenue (gate, local TV) but it evenly distributes income derived from the sale of merchandise and from national television contracts with the teams.

The question remains, how influential are the top superstars, and how large is the ripple effect? To answer these ques-tions, I examine the case of Lebron James.

Lebron James With the advances in media technology

and increased media coverage, no player has ever matched the attention received by Lebron James.

Lebron’s superstar status began before he took the SATs. In his junior year, he faced eligibility questions and appealed to enter the NBA draft early. Having his appeal rejected only served to increase his popularity. Lebron made the cover of Sports Illustrated before his senior year of high school under the headline “The Chosen One”. His high school team’s practices were forced to move to the lo-cal university because their gym was ex-ceeding its capacity. ESPN 2 and Pay-Per-

View televised his high school games, which were attended by celebrities like Shaquille O’Neal, with whom he later played. Lebron was the driving force be-hind the success Cleveland achieved fol-lowing his entry in the league. He played for Cleveland for seven years; in five of those years not a single team member was declared an All-Star. In two of those years, he played with only one other All-Star–Zydrunas Ilgauskas, who had only made one other All-Star game prior to Lebron entering the league, and Mo Wil-liams, who never before nor would ever again, play in an All-Star game.

Lebron’s Effect on the Cavaliers’ Home Attendance

In the summer of 2010, Lebron became a free agent, and there was significant spec-ulation on which team he would choose. Lebron James, over the course of 7 sea-sons, increased Cavalier attendance by an average of 71.75 percent compared to the year prior to his arrival5. Before Lebron, in 2002-2003, the average attendance at a Cleveland Cavaliers game was 11,496. During his tenure, they averaged 19,745. This translated into 131,745,626 million dollars of increased revenue6. Their aver-

The structure of the game of basketball lends itself to the

creation of superstars.

Columbia Economics Review Columbia Economics Review

4 Spring 2013 Spring 2013 5

age league rank in attendance improved from 29 to breaking the top 5.

In terms of home attendance, a lag ef-fect existed upon Lebron’s departure. The year after he left, the team only dropped from 2nd in attendance to 3rd. It was only the year after that they dropped to 19th, despite drafting another number 1 draft pick and potential superstar, Kyrie Ir-ving. It was well documented, and a prominent topic in the Cleveland press, that season ticket holders had to opt to pay for the first season after Lebron’s possible departure and well before the date of his “decision,” thus accounting for the “lag”. Nevertheless, this maneu-ver only benefitted the Cavaliers in terms of attendance and not teams hoping to see Lebron as a visiting player. Moreover, in terms of TV receipts, the NBA does not share any local revenue.

To evaluate the incremental revenue Lebron creates for his opposition, I evalu-ated the 2009-2010 season (the year prior to Lebron’s departure) and then com-pared this to the 2010-2011 season (proxy for unobserved variable).

Lebron’s Effect on Opponents Attend-ance

Over the course of years, as a team improves, it is unsurprising that attend-ance increases. There is an obvious, but not perfect correlation, between winning percentage and attendance. While Leb-ron was on the Cavaliers, they performed extremely well. Thus, one could argue that when the Cavaliers visited another well-performing team, the increased at-tendance was a consequence of the un-certainty hypothesis (doubt of outcome), resulting from the competitive balance in the league. Adhering strictly to this line of reasoning, assuming that competitive balance is the primary driver behind de-mand, we would have anticipated that at-tendance would not increase appreciably when the Cavaliers were “expected” to win (when they were playing the lower ranked teams in the league). Less doubt surrounding the outcome of a game should result in lowered attendance if competitive balance is the dominant fac-tor in determining game attendance. This is far from reality.

In the 2009-2010 season, Lebron’s posi-tive externality resulted in increasing opponents’ attendance by an average of 13.42 percent. The average capacity at an away stadium was 100.54 percent. Stadi-ums with “fixed” arena sizes increased in capacity when Lebron visited. Owner-ship created additional seating to capital-ize on the increased demand (e.g. fold out

chairs or standing room). In Tables 5-7, we can see that if a team’s average attend-ance was low, the percentage increase in attendance resulting from a visit from the Cavaliers rose significantly. In fact, the 76’ers saw an average 43.4 percent increase in their attendance (for 2 games) when they played the Cavaliers at home. This increased income represented 2 per-cent of their entire season’s revenue from gate attendance. Hence, it was not the to-tal attendance of the game that resulted in 2 percent of their entire season’s gate revenue, but exclusively the additional attendance (above average without Leb-ron) that netted over five hundred thou-sand dollars.

In total, Lebron’s incremental rev-enue equaled 4.3 million dollars. This represents just under a half of a per-centage point of the league’s revenue. Lebron’s incremental revenue from away games accounted for 1/250 of all league gate revenue. This may not seem like a large fraction, but consider that there are 30 teams, 82 games per season, and 1230 regular season games. Lebron’s marginal revenue accounted for, on aver-age, 11.69 percent of the revenue for all away games (lower than average attend-ance increase because teams with lower ticket prices had higher average attend-ance). Overall, seventeen of thirty stadi-ums were above capacity when Lebron visited.

The Big Three’s Effect on the

Heat’s Away AttendanceIn July 2010, Lebron James appeared on

national television and announced that he would be taking his talents to South Beach. When the Lebron, Wade, and Bosh joined forces on the Heat, the effect on attendance was large and reminiscent of the impact witnessed with Lebron on the Cavaliers. The overall incremental revenue decreased, but this can be attrib-uted to a slight reduction in the average ticket price. The Big Three slightly out-performed Lebron in terms of average percentage increase with an average of 13.93 percent. The number of stadiums above capacity was slightly higher and accounted for an average of 9.9 percent of revenue for that game. Comparing each statistic, which is broken down in Table 10, key insights can be identified. The trends remained, but the percentage increases were not significantly higher than the increases seen with Lebron alone (Table 14-16). Lebron, singlehand-edly, was able to generate about the same incremental revenue when visiting op-posing teams as Lebron, Wade and Bosh

combined. There seems to be a point of diminishing returns with the coalescing of the top talent.

Why was Lebron able to generate al-most the equivalent amount of revenue as the Big Three? Even if the excitement was tripled, or even exponentially expanded, with the combination of the individual superstars, teams were unable to recog-nize proportional incremental benefit given the lack of dynamic pricing and the limit on the size of stadiums.

Net Effect on Attendance: Cavaliers vs. Heat

The net effect, in terms of home attend-ance, is the net incremental revenue re-ceived by both the Cavaliers and the Heat following Lebron’s decision. As discussed before, the Cavaliers experienced a clear lag effect after Lebron’s departure in the 2010-2011 season, probably due to the ex-ercise of season ticket options before “the decision.” However, even after acquiring Kyrie Irving (#1 draft pick), the Cavalier’s home attendance dropped significantly in the 2011-2012 season. The calculated lost incremental revenue is $4,517,796. This number would have been higher if the 2011-2012 season was not shortened by a lockout.

The net effect, in terms of away at-tendance, is the net incremental revenue received by all teams, except the Cava-liers and the Heat, following Lebron’s decision. This number totaled a loss of $5,410,455.48. It is larger than the net revenue on home attendance, and it only considers Lebron’s final year on the Cavaliers and first year on the Heat. The effect is dramatic. The Cavaliers with Lebron were the second best traveling team in the league, averaging an attend-ance of 19,200 during their away games. During their first season without Leb-ron, they dropped to the worst trave-ling team. No team had a lower aver-age away attendance than the Cavaliers in the 2010-2011 season. On the other

hand, the Heat became the number one traveling team with an average of 19,447 (247 higher than when Lebron was on the Cavaliers). Notably, the Heat, prior to Lebron’s arrival, had the fourth high-est average away attendance, which lim-ited the incremental revenue that Lebron could create.

Dwyane Wade was able to draw crowds by himself, as was Lebron in Cleveland. Nevertheless, stadiums have a finite ca-pacity, and the NBA has only recently started to implement dynamic pricing; thus, the incremental revenue created, in terms of gate revenue, is far less when su-perstars, who were able to draw signifi-cant crowds singlehandedly, join forces. The combination of superstars may have significant deleterious effects. Not only is the competitive balance of the league altered, but also the small market teams, who may have depended on playing both Lebron and Dwyane Wade (remember their incremental revenue contributed 2 percent and 1 percent of a teams season gate revenue, respectively), now lose a significant source of income. This could create a vicious cycle.

If these players are more concentrated, then fans may have a greater desire to see those teams play, but they will be inter-ested in seeing fewer games. Given the limit on stadium seating, the home team suffers.

Implications of Results and Future Re-

searchAfter calculating the net effect of the

home and away attendance for both the Cavaliers and Heat, it was noted that the NBA was losing money due to Lebron’s move. This is because Lebron and Wade were able to attract fans to the arena sepa-rately that was not matched by what they could do together. When they combined forces, although they were able to achieve an average attendance that exceeded Leb-ron’s margin, it was not nearly enough to compensate for the decrease in attend-ance that resulted when the Cavaliers traveled without Lebron. A stadium has a finite number of seats (for the most part), so if Lebron would sell out a stadium on his own, the addition of Wade would only increase the excitement but little else: the gate revenue would remain stat-ic. The resale of tickets may reflect the ad-ditional revenue that a team would have received if they could increase supply to reflect demand at the average original ticket price, or it could represent the price a ticket could fetch with the institution of dynamic pricing.

It is apparent that the combination of

superstars reduces the revenue received by the league, solely considering gate rev-enue. However, this information alone is insufficient to state that the league does not benefit as a whole. It simply raises the question: does the exponential effect created by the combination of a super-star’s impact on TV viewership and the expansion in NBA merchandise sales outweigh the loss in attendance? The dif-ficulty is that these numbers are not eas-ily accessible. However, the NBA would be able to calculate this and the result has massive implications. If the combination of superstars resulted in an increase in revenue through television and merchan-dise that outweighed the loss to the league in attendance, then one could argue that the concentration of talent has the poten-tial to be better for the league, even if it comes at the expense of achieving a more competitively balanced league. This is a short-term benefit that may or may not outweigh the potential long-term harm the loss of competitive balance may have on the league.

Sustainability is a key issue. If su-perstars are concentrated in particular teams, their combined star power will create a positive effect on revenue. How-ever, increasing the concentration of stars per team decreases the variance of the number teams that have a realistic chance to win the championships. This, in turn, creates a negative effect on revenue. Thus the magnitude of each effect will deter-mine whether or not concentrating stars is beneficial to the league in the long run. The league must consider this when establishing its new collective bargain-ing agreements (CBA). However, there may be a limit to the extent to which the league can control for these factors.

Lebron James, Dwyane Wade, and Chris Bosh all agreed to take a decrease in com-pensation in order to accommodate each other’s salary and not violate the league mandated salary cap. As a result, Udonis Haslem, Mike Miller, Shane Battier, and Ray Allen all accepted smaller contracts in order to play with the “Big Three.” The salary cap, a mechanism used by the NBA to achieve competitive balance, does not appear to have had its intended effect. It was noted that basketball lends itself to the creation of superstars because of the inherent traits of the sport: players are far more visible, they play a greater proportion of the game, they are a greater percentage of the team on the court, the fans are extremely close to the action, and one player can dominate the game as in no other team sport. All these factors con-tribute to the level of fame achieved by

these players; this is particularly evident in their ability to acquire sponsorships.

Herein lies the difficulty for the NBA. Michael Jordan’s annual salary, early in his career, was approximately $3 million. However, he earned a total of approxi-mately $30 million dollars in the same year. The majority of his income resulted from off-the-court compensation. With the increase in technology, the expand-ing capacity of advertisers to access and to communicate with potential custom-ers, and the growing awareness about the ways potential customers establish their brand preferences, superstars have the capacity to earn far more off the court than ever thought possible.

There exists an ecosystem in which the salary that a team is able to provide no longer serves as the strong incentive it once was in determining where stars play. Meaning, all of the rules intended to allow smaller market teams to keep their players by allowing them to increase player salaries have decreased in im-portance. Where it was once thought that players could be counted on to fol-low the money offered by teams, it now

seems that financial forces beyond the control of owners and the league are in play. Players have the ability to work among themselves to craft situations that they perceive could deliver benefits that outweigh the losses sacrificed in dimin-ished salaries. If those forces conspire to weaken competitive balance or to dimin-ish the value of the sport and the teams in the long run, then any new CBA must quantify those elements and create com-pensatory responses.

Competitive balance, to a certain ex-tent, is a necessity for a sports league. However, as we have shown, an indi-vidual player can have a dramatic effect on demand. To what extent the league is dependent on either is up for debate, but this debate is the key to achieving long-term prosperity for the league. The closer we can come to finding what envi-ronment strikes the appropriate balance, the closer we can come to maximizing the full potential of the NBA. ▪

There exists an ecosystem in which the salary that a team

is able to provide nolonger serves as the strong

incentive it once was in deter-mining where stars play.

The question remains, how influential are the top super-

stars, and how large is theripple effect? To answer these questions, I examine the case

of Lebron James.

Columbia Economics Review Columbia Economics Review

6 Spring 2013 Spring 2013 7

age league rank in attendance improved from 29 to breaking the top 5.

In terms of home attendance, a lag ef-fect existed upon Lebron’s departure. The year after he left, the team only dropped from 2nd in attendance to 3rd. It was only the year after that they dropped to 19th, despite drafting another number 1 draft pick and potential superstar, Kyrie Ir-ving. It was well documented, and a prominent topic in the Cleveland press, that season ticket holders had to opt to pay for the first season after Lebron’s possible departure and well before the date of his “decision,” thus accounting for the “lag”. Nevertheless, this maneu-ver only benefitted the Cavaliers in terms of attendance and not teams hoping to see Lebron as a visiting player. Moreover, in terms of TV receipts, the NBA does not share any local revenue.

To evaluate the incremental revenue Lebron creates for his opposition, I evalu-ated the 2009-2010 season (the year prior to Lebron’s departure) and then com-pared this to the 2010-2011 season (proxy for unobserved variable).

Lebron’s Effect on Opponents Attend-ance

Over the course of years, as a team improves, it is unsurprising that attend-ance increases. There is an obvious, but not perfect correlation, between winning percentage and attendance. While Leb-ron was on the Cavaliers, they performed extremely well. Thus, one could argue that when the Cavaliers visited another well-performing team, the increased at-tendance was a consequence of the un-certainty hypothesis (doubt of outcome), resulting from the competitive balance in the league. Adhering strictly to this line of reasoning, assuming that competitive balance is the primary driver behind de-mand, we would have anticipated that at-tendance would not increase appreciably when the Cavaliers were “expected” to win (when they were playing the lower ranked teams in the league). Less doubt surrounding the outcome of a game should result in lowered attendance if competitive balance is the dominant fac-tor in determining game attendance. This is far from reality.

In the 2009-2010 season, Lebron’s posi-tive externality resulted in increasing opponents’ attendance by an average of 13.42 percent. The average capacity at an away stadium was 100.54 percent. Stadi-ums with “fixed” arena sizes increased in capacity when Lebron visited. Owner-ship created additional seating to capital-ize on the increased demand (e.g. fold out

chairs or standing room). In Tables 5-7, we can see that if a team’s average attend-ance was low, the percentage increase in attendance resulting from a visit from the Cavaliers rose significantly. In fact, the 76’ers saw an average 43.4 percent increase in their attendance (for 2 games) when they played the Cavaliers at home. This increased income represented 2 per-cent of their entire season’s revenue from gate attendance. Hence, it was not the to-tal attendance of the game that resulted in 2 percent of their entire season’s gate revenue, but exclusively the additional attendance (above average without Leb-ron) that netted over five hundred thou-sand dollars.

In total, Lebron’s incremental rev-enue equaled 4.3 million dollars. This represents just under a half of a per-centage point of the league’s revenue. Lebron’s incremental revenue from away games accounted for 1/250 of all league gate revenue. This may not seem like a large fraction, but consider that there are 30 teams, 82 games per season, and 1230 regular season games. Lebron’s marginal revenue accounted for, on aver-age, 11.69 percent of the revenue for all away games (lower than average attend-ance increase because teams with lower ticket prices had higher average attend-ance). Overall, seventeen of thirty stadi-ums were above capacity when Lebron visited.

The Big Three’s Effect on the

Heat’s Away AttendanceIn July 2010, Lebron James appeared on

national television and announced that he would be taking his talents to South Beach. When the Lebron, Wade, and Bosh joined forces on the Heat, the effect on attendance was large and reminiscent of the impact witnessed with Lebron on the Cavaliers. The overall incremental revenue decreased, but this can be attrib-uted to a slight reduction in the average ticket price. The Big Three slightly out-performed Lebron in terms of average percentage increase with an average of 13.93 percent. The number of stadiums above capacity was slightly higher and accounted for an average of 9.9 percent of revenue for that game. Comparing each statistic, which is broken down in Table 10, key insights can be identified. The trends remained, but the percentage increases were not significantly higher than the increases seen with Lebron alone (Table 14-16). Lebron, singlehand-edly, was able to generate about the same incremental revenue when visiting op-posing teams as Lebron, Wade and Bosh

combined. There seems to be a point of diminishing returns with the coalescing of the top talent.

Why was Lebron able to generate al-most the equivalent amount of revenue as the Big Three? Even if the excitement was tripled, or even exponentially expanded, with the combination of the individual superstars, teams were unable to recog-nize proportional incremental benefit given the lack of dynamic pricing and the limit on the size of stadiums.

Net Effect on Attendance: Cavaliers vs. Heat

The net effect, in terms of home attend-ance, is the net incremental revenue re-ceived by both the Cavaliers and the Heat following Lebron’s decision. As discussed before, the Cavaliers experienced a clear lag effect after Lebron’s departure in the 2010-2011 season, probably due to the ex-ercise of season ticket options before “the decision.” However, even after acquiring Kyrie Irving (#1 draft pick), the Cavalier’s home attendance dropped significantly in the 2011-2012 season. The calculated lost incremental revenue is $4,517,796. This number would have been higher if the 2011-2012 season was not shortened by a lockout.

The net effect, in terms of away at-tendance, is the net incremental revenue received by all teams, except the Cava-liers and the Heat, following Lebron’s decision. This number totaled a loss of $5,410,455.48. It is larger than the net revenue on home attendance, and it only considers Lebron’s final year on the Cavaliers and first year on the Heat. The effect is dramatic. The Cavaliers with Lebron were the second best traveling team in the league, averaging an attend-ance of 19,200 during their away games. During their first season without Leb-ron, they dropped to the worst trave-ling team. No team had a lower aver-age away attendance than the Cavaliers in the 2010-2011 season. On the other

hand, the Heat became the number one traveling team with an average of 19,447 (247 higher than when Lebron was on the Cavaliers). Notably, the Heat, prior to Lebron’s arrival, had the fourth high-est average away attendance, which lim-ited the incremental revenue that Lebron could create.

Dwyane Wade was able to draw crowds by himself, as was Lebron in Cleveland. Nevertheless, stadiums have a finite ca-pacity, and the NBA has only recently started to implement dynamic pricing; thus, the incremental revenue created, in terms of gate revenue, is far less when su-perstars, who were able to draw signifi-cant crowds singlehandedly, join forces. The combination of superstars may have significant deleterious effects. Not only is the competitive balance of the league altered, but also the small market teams, who may have depended on playing both Lebron and Dwyane Wade (remember their incremental revenue contributed 2 percent and 1 percent of a teams season gate revenue, respectively), now lose a significant source of income. This could create a vicious cycle.

If these players are more concentrated, then fans may have a greater desire to see those teams play, but they will be inter-ested in seeing fewer games. Given the limit on stadium seating, the home team suffers.

Implications of Results and Future Re-

searchAfter calculating the net effect of the

home and away attendance for both the Cavaliers and Heat, it was noted that the NBA was losing money due to Lebron’s move. This is because Lebron and Wade were able to attract fans to the arena sepa-rately that was not matched by what they could do together. When they combined forces, although they were able to achieve an average attendance that exceeded Leb-ron’s margin, it was not nearly enough to compensate for the decrease in attend-ance that resulted when the Cavaliers traveled without Lebron. A stadium has a finite number of seats (for the most part), so if Lebron would sell out a stadium on his own, the addition of Wade would only increase the excitement but little else: the gate revenue would remain stat-ic. The resale of tickets may reflect the ad-ditional revenue that a team would have received if they could increase supply to reflect demand at the average original ticket price, or it could represent the price a ticket could fetch with the institution of dynamic pricing.

It is apparent that the combination of

superstars reduces the revenue received by the league, solely considering gate rev-enue. However, this information alone is insufficient to state that the league does not benefit as a whole. It simply raises the question: does the exponential effect created by the combination of a super-star’s impact on TV viewership and the expansion in NBA merchandise sales outweigh the loss in attendance? The dif-ficulty is that these numbers are not eas-ily accessible. However, the NBA would be able to calculate this and the result has massive implications. If the combination of superstars resulted in an increase in revenue through television and merchan-dise that outweighed the loss to the league in attendance, then one could argue that the concentration of talent has the poten-tial to be better for the league, even if it comes at the expense of achieving a more competitively balanced league. This is a short-term benefit that may or may not outweigh the potential long-term harm the loss of competitive balance may have on the league.

Sustainability is a key issue. If su-perstars are concentrated in particular teams, their combined star power will create a positive effect on revenue. How-ever, increasing the concentration of stars per team decreases the variance of the number teams that have a realistic chance to win the championships. This, in turn, creates a negative effect on revenue. Thus the magnitude of each effect will deter-mine whether or not concentrating stars is beneficial to the league in the long run. The league must consider this when establishing its new collective bargain-ing agreements (CBA). However, there may be a limit to the extent to which the league can control for these factors.

Lebron James, Dwyane Wade, and Chris Bosh all agreed to take a decrease in com-pensation in order to accommodate each other’s salary and not violate the league mandated salary cap. As a result, Udonis Haslem, Mike Miller, Shane Battier, and Ray Allen all accepted smaller contracts in order to play with the “Big Three.” The salary cap, a mechanism used by the NBA to achieve competitive balance, does not appear to have had its intended effect. It was noted that basketball lends itself to the creation of superstars because of the inherent traits of the sport: players are far more visible, they play a greater proportion of the game, they are a greater percentage of the team on the court, the fans are extremely close to the action, and one player can dominate the game as in no other team sport. All these factors con-tribute to the level of fame achieved by

these players; this is particularly evident in their ability to acquire sponsorships.

Herein lies the difficulty for the NBA. Michael Jordan’s annual salary, early in his career, was approximately $3 million. However, he earned a total of approxi-mately $30 million dollars in the same year. The majority of his income resulted from off-the-court compensation. With the increase in technology, the expand-ing capacity of advertisers to access and to communicate with potential custom-ers, and the growing awareness about the ways potential customers establish their brand preferences, superstars have the capacity to earn far more off the court than ever thought possible.

There exists an ecosystem in which the salary that a team is able to provide no longer serves as the strong incentive it once was in determining where stars play. Meaning, all of the rules intended to allow smaller market teams to keep their players by allowing them to increase player salaries have decreased in im-portance. Where it was once thought that players could be counted on to fol-low the money offered by teams, it now

seems that financial forces beyond the control of owners and the league are in play. Players have the ability to work among themselves to craft situations that they perceive could deliver benefits that outweigh the losses sacrificed in dimin-ished salaries. If those forces conspire to weaken competitive balance or to dimin-ish the value of the sport and the teams in the long run, then any new CBA must quantify those elements and create com-pensatory responses.

Competitive balance, to a certain ex-tent, is a necessity for a sports league. However, as we have shown, an indi-vidual player can have a dramatic effect on demand. To what extent the league is dependent on either is up for debate, but this debate is the key to achieving long-term prosperity for the league. The closer we can come to finding what envi-ronment strikes the appropriate balance, the closer we can come to maximizing the full potential of the NBA. ▪

There exists an ecosystem in which the salary that a team

is able to provide nolonger serves as the strong

incentive it once was in deter-mining where stars play.

The question remains, how influential are the top super-

stars, and how large is theripple effect? To answer these questions, I examine the case

of Lebron James.

Columbia Economics Review Columbia Economics Review

6 Spring 2013 Spring 2013 7

Bend It Like BeccaGender Inequality and Women’s Soccer Success

Matthew Yeaton

Columbia University

The aim of this paper is to understand the impact of gender inequality in the differential success of various coun-tries’ women’s soccer programs over the past decade. Gender inequality is an important topic in the study of modern economies. Although gender inequality has many adverse effects, it creates two main problems. First, gender inequality unfairly limits the opportunities of fifty-one percent of any country’s population in a way that most would consider arbi-trary and unjust. Second, gender inequal-ity places constraints on GDP and GDP growth due to labor inefficiency such that countries experience losses in the real economy. Dollar and Gatti (1999) assert that if one interprets gender inequality as evidence of either prejudice or market failure (or both), the gap between males and females is effectively a distortionary tax that has a negative impact on GDP and economic growth. This problem is well studied, and many others such as Boserup (1970) and Duflo (2010) have gone on to characterize and quantify the actual impact empirically. Hence, there are clear arguments for reducing gender inequality on the grounds of both social justice and economic growth. However, the impacts of gender inequality are not always obvious and in certain arenas,

such as sports, estimating the negative impact of gender inequality can become particularly challenging.

Why might we be interested in the re-lationship between gender inequality and sport? What can this tell us that the

GDP-focused arguments cannot? For that matter, why study sports? Why study women’s sports? Sports are largely a manifestation of culture, so studying the relationship between gender inequality and sports can help us to better under-stand underlying social attitudes. The universality of the rules of sports like soc-cer makes our conclusions widely appli-cable. The rules are the same everywhere in the world as well as in men’s and women’s soccer. Essentially, the homo-geneity of conditions in sports like soc-

cer across countries means that gender inequality’s isolated impact will be clear. If we can establish that gender inequality has an impact on the success of women’s soccer programs we will have established that gender inequality has an impact on the sport as a whole. Hence, wider appli-cability of the method in measuring the effect of gender inequality, or other hard-to-measure factors in unrelated realms of study, may be interesting even if the reader has no interest in either gender in-equality or sport.

However, studying women’s sports im-mediately poses a set of difficulties. In particular, much of the existing research on sports and economics has focused on men’s sports. This is due to the relative popularity of men’s sports and sports leagues compared to their female coun-terparts. It is also because of the relative dearth of available data with which to study women’s sports. As such, women’s sports are not particularly well studied. Further, it is entirely possible that the de-terminants of on-field success in men’s sports are different than the determinants of success in women’s sports. I would not expect gender inequality to have a signifi-cant impact in analyses of men’s sports, but it is entirely possible that it would af-fect women’s sports.

In order to do a rigorous analysis of the subject on a cross-country scale, I will examine an international sports event. There are only two options that provide the necessary international scale: soccer and the Olympics. The Olympics suffers from certain regional proclivities and interests (i.e. the inclusion of baseball or curling), while soccer does not. Soccer is the world’s most truly international sport.

Women and SoccerHence, I focus on women’s soccer. In

many parts of the world even today, soc-cer is a sport dominated by men. In fact, FIFA held the first women’s World Cup in 1991, almost sixty years after the first men’s World Cup. However, the sport is popular among women around the world. Murray (1996) and Williams (2002) note that there has been female participa-tion essentially since the beginning of the sport, and that as early as the 1930s women’s soccer leagues were formed in countries like Italy and Germany. Italy was the first country to form a national women’s soccer team, in 1950. The rest of northern and western Europe, and even-tually the rest of the world, would follow

suit. Today there are numerous amateur domestic leagues, and all but a few coun-tries field women’s national teams.

While women’s soccer still lags behind

men’s soccer in popularity, the sport does draw a high viewership for its largest events. The 1999 Women’s World Cup in the United States averaged about 38,000

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Figure 1: First vs. Second Principal Components for Robustnes Model

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Figure 2: First vs. Second Principal Components for Primary Model

Gender inequality unfairly limits the opportunities of

fifty-one percent of any coun-try’s population in a way that most would consider arbitrary

and unjust.

Columbia Economics Review Columbia Economics Review

8 Spring 2013 Spring 2013 9

Bend It Like BeccaGender Inequality and Women’s Soccer Success

Matthew Yeaton

Columbia University

The aim of this paper is to understand the impact of gender inequality in the differential success of various coun-tries’ women’s soccer programs over the past decade. Gender inequality is an important topic in the study of modern economies. Although gender inequality has many adverse effects, it creates two main problems. First, gender inequality unfairly limits the opportunities of fifty-one percent of any country’s population in a way that most would consider arbi-trary and unjust. Second, gender inequal-ity places constraints on GDP and GDP growth due to labor inefficiency such that countries experience losses in the real economy. Dollar and Gatti (1999) assert that if one interprets gender inequality as evidence of either prejudice or market failure (or both), the gap between males and females is effectively a distortionary tax that has a negative impact on GDP and economic growth. This problem is well studied, and many others such as Boserup (1970) and Duflo (2010) have gone on to characterize and quantify the actual impact empirically. Hence, there are clear arguments for reducing gender inequality on the grounds of both social justice and economic growth. However, the impacts of gender inequality are not always obvious and in certain arenas,

such as sports, estimating the negative impact of gender inequality can become particularly challenging.

Why might we be interested in the re-lationship between gender inequality and sport? What can this tell us that the

GDP-focused arguments cannot? For that matter, why study sports? Why study women’s sports? Sports are largely a manifestation of culture, so studying the relationship between gender inequality and sports can help us to better under-stand underlying social attitudes. The universality of the rules of sports like soc-cer makes our conclusions widely appli-cable. The rules are the same everywhere in the world as well as in men’s and women’s soccer. Essentially, the homo-geneity of conditions in sports like soc-

cer across countries means that gender inequality’s isolated impact will be clear. If we can establish that gender inequality has an impact on the success of women’s soccer programs we will have established that gender inequality has an impact on the sport as a whole. Hence, wider appli-cability of the method in measuring the effect of gender inequality, or other hard-to-measure factors in unrelated realms of study, may be interesting even if the reader has no interest in either gender in-equality or sport.

However, studying women’s sports im-mediately poses a set of difficulties. In particular, much of the existing research on sports and economics has focused on men’s sports. This is due to the relative popularity of men’s sports and sports leagues compared to their female coun-terparts. It is also because of the relative dearth of available data with which to study women’s sports. As such, women’s sports are not particularly well studied. Further, it is entirely possible that the de-terminants of on-field success in men’s sports are different than the determinants of success in women’s sports. I would not expect gender inequality to have a signifi-cant impact in analyses of men’s sports, but it is entirely possible that it would af-fect women’s sports.

In order to do a rigorous analysis of the subject on a cross-country scale, I will examine an international sports event. There are only two options that provide the necessary international scale: soccer and the Olympics. The Olympics suffers from certain regional proclivities and interests (i.e. the inclusion of baseball or curling), while soccer does not. Soccer is the world’s most truly international sport.

Women and SoccerHence, I focus on women’s soccer. In

many parts of the world even today, soc-cer is a sport dominated by men. In fact, FIFA held the first women’s World Cup in 1991, almost sixty years after the first men’s World Cup. However, the sport is popular among women around the world. Murray (1996) and Williams (2002) note that there has been female participa-tion essentially since the beginning of the sport, and that as early as the 1930s women’s soccer leagues were formed in countries like Italy and Germany. Italy was the first country to form a national women’s soccer team, in 1950. The rest of northern and western Europe, and even-tually the rest of the world, would follow

suit. Today there are numerous amateur domestic leagues, and all but a few coun-tries field women’s national teams.

While women’s soccer still lags behind

men’s soccer in popularity, the sport does draw a high viewership for its largest events. The 1999 Women’s World Cup in the United States averaged about 38,000

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GDPpc

labRat

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materMortRatW

lPopW

pubHlthSpndPctGDPsecEdRat

ptsM

Component 1

Comp

onen

t 2

Figure 1: First vs. Second Principal Components for Robustnes Model

−0.6 −0.4 −0.2 0 0.2 0.4 0.6

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adolesFertility

HDI

labPartRat

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lPopW

secEdRattemp

ptsM

Component 1

Component 2

Figure 2: First vs. Second Principal Components for Primary Model

Gender inequality unfairly limits the opportunities of

fifty-one percent of any coun-try’s population in a way that most would consider arbitrary

and unjust.

Columbia Economics Review Columbia Economics Review

8 Spring 2013 Spring 2013 9

per game in attendance over its seventeen games, for a total of 658,000 fans. This was larger than the average attendance of English Premier League, the most popu-lar men’s professional league, during the same year. Moreover, the final game of the Women’s World Cup saw attendance of 92,000, which to this day is the largest crowd ever to witness a women’s sport-ing event. The television audience, the most quickly growing and lucrative com-ponent of sports-related monetization, was over 40 million in the United States alone, comparable to viewership of a World Series or NBA Finals game. While the United States competed in this game, the significance of the magnitude of these numbers still stands. One need only glance at the men’s soccer industry to un-derstand how lucrative women’s soccer could be, but to date, the world has not seen a successful women’s professional league, despite the prevalence of semi-professional and amateur women’s soc-cer. According to FIFA (2012), ``soccer for young girls in many parts of the world is often considered a solely recreational activity due to cultural barriers, social mores and the lack of any financial hope for a future in the game.’’ These barriers

reflect wider issues of gender inequality beyond sport.

Determinants of Success in SportIt is unlikely that gender inequality is

the sole determinant of differences in women’s national team quality. Torgler (2008) establishes that the determinants of women’s soccer success may be differ-ent than men’s. Hoffman, et al (2006) and Matheson and Congdon-Hohman (2011)

each attempt to elaborate on this work, attempting to es-timate the impact of gender inequality. However, both of these studies suffer from multicollinearity in their re-gression estimations. The correlation between devel-opment and gender inequal-ity is well documented, for instance by Cuberes (2011). However, Matheson and Congdon-Hohman (2011) did not address this problem, resulting in the insignificance of the coefficients of the de-velopment variables for both the men’s and the women’s rankings. Hoffman, et al (2006) dealt with the problem by excluding variables, but even so, the gender inequal-ity variables are only signifi-cant at the 10 percent level. Furthermore, both of these studies suffer from relatively poor fit of their models. The R-squared value on Mathe-son and Congdon-Hohman (2011) primary estimation was below 50 percent.

Neither study is able to make robust arguments about the importance of

gender inequality in determining wom-en’s soccer success. This study utilizes a novel approach in order to address the multicollinearity problem in the regres-sion estimation, using principal compo-nent analysis and principal component regression. Through this approach, I am able to make convincing and robust arguments about the importance of gen-

der inequality in determining success in women’s international soccer, while at the same time fitting a better model as compared to the earlier studies.

DataTo measure success of national teams, I

use the number of points gained in match-

es over time. In soccer, winning teams receive three points, drawing teams one,

and losing teams zero. For both men’s and women’s teams points data, the au-tocorrelations are extremely high over the 2003-2011 period. For the women’s points, the autocorrelations are typically larger than 99.5 percent. For the men’s points, the values of the autocorrelations are slightly lower, but still larger than 95 percent. This is not particularly surpris-ing, as most of the earlier studies men-tioned suggest that the differential suc-cess of a given country’s soccer program, for men or women, is largely determined by macroeconomic factors and other com-ponents that do not change quickly over time. Because the length of my time series is somewhat short from the perspective of macroeconomic change, high autocorrela-tions are expected. Hence, to help keep the sample of countries included in the study large (since some countries are not included in the rankings until later years in the sample period), I have averaged the points of both the men’s and women’s teams over the period. Furthermore, in-cluding the larger sample of countries will help us to at least partially avoid

sample selection bias in the study. Table I shows summary statistics for both the men’s and women’s teams’ points aver-aged over the period 2003-2011.

There are a few things to understand from this brief glance at the data alone. First, there appears to be some relation-ship between the men’s and women’s rankings. Hence, I expect that some of the same variables that explain the men’s rankings explain the women’s rank-ings. However, there are other variables needed to understand the differential per-formance in the women’s rankings. Oth-erwise, I would expect a nearly perfect relationship between the two. These data supports the findings of Matheson and Congdon-Hohman (2011) who found that Muslim religious affiliation reduces wom-en’s success in sports. This paper will ar-gue that this is significant because of the pervasive gender inequality in many of these countries.

ModelsI begin with a basic estimation before

moving on to our primary models. The basic estimation equation is as follows:

−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

adolesFertilityHDI

labPartRat

materMort lPopWsecEdRat

ptsM

1st Principal Component

3rd Principal Component

Figure 3: First vs. Third Principal Components for Primary Model −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8

−0.8

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adolesFertilityHDI

labPartRat

materMort lPopWsecEdRat

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2nd Principal Component

3rd Principal Component

Figure 4: Second vs. Third Principal Components for Primary Model

This allows us to draw the general conclusion that gen-der equality is very important in determining success of wom-

en’s national soccer teams, and perhaps approximately

as important as development, population size, and soccer

tradition and affinity.

Table I: Summary Statistic for Explanatory Variables

Columbia Economics Review Columbia Economics Review

10 Spring 2013 Spring 2013 11

per game in attendance over its seventeen games, for a total of 658,000 fans. This was larger than the average attendance of English Premier League, the most popu-lar men’s professional league, during the same year. Moreover, the final game of the Women’s World Cup saw attendance of 92,000, which to this day is the largest crowd ever to witness a women’s sport-ing event. The television audience, the most quickly growing and lucrative com-ponent of sports-related monetization, was over 40 million in the United States alone, comparable to viewership of a World Series or NBA Finals game. While the United States competed in this game, the significance of the magnitude of these numbers still stands. One need only glance at the men’s soccer industry to un-derstand how lucrative women’s soccer could be, but to date, the world has not seen a successful women’s professional league, despite the prevalence of semi-professional and amateur women’s soc-cer. According to FIFA (2012), ``soccer for young girls in many parts of the world is often considered a solely recreational activity due to cultural barriers, social mores and the lack of any financial hope for a future in the game.’’ These barriers

reflect wider issues of gender inequality beyond sport.

Determinants of Success in SportIt is unlikely that gender inequality is

the sole determinant of differences in women’s national team quality. Torgler (2008) establishes that the determinants of women’s soccer success may be differ-ent than men’s. Hoffman, et al (2006) and Matheson and Congdon-Hohman (2011)

each attempt to elaborate on this work, attempting to es-timate the impact of gender inequality. However, both of these studies suffer from multicollinearity in their re-gression estimations. The correlation between devel-opment and gender inequal-ity is well documented, for instance by Cuberes (2011). However, Matheson and Congdon-Hohman (2011) did not address this problem, resulting in the insignificance of the coefficients of the de-velopment variables for both the men’s and the women’s rankings. Hoffman, et al (2006) dealt with the problem by excluding variables, but even so, the gender inequal-ity variables are only signifi-cant at the 10 percent level. Furthermore, both of these studies suffer from relatively poor fit of their models. The R-squared value on Mathe-son and Congdon-Hohman (2011) primary estimation was below 50 percent.

Neither study is able to make robust arguments about the importance of

gender inequality in determining wom-en’s soccer success. This study utilizes a novel approach in order to address the multicollinearity problem in the regres-sion estimation, using principal compo-nent analysis and principal component regression. Through this approach, I am able to make convincing and robust arguments about the importance of gen-

der inequality in determining success in women’s international soccer, while at the same time fitting a better model as compared to the earlier studies.

DataTo measure success of national teams, I

use the number of points gained in match-

es over time. In soccer, winning teams receive three points, drawing teams one,

and losing teams zero. For both men’s and women’s teams points data, the au-tocorrelations are extremely high over the 2003-2011 period. For the women’s points, the autocorrelations are typically larger than 99.5 percent. For the men’s points, the values of the autocorrelations are slightly lower, but still larger than 95 percent. This is not particularly surpris-ing, as most of the earlier studies men-tioned suggest that the differential suc-cess of a given country’s soccer program, for men or women, is largely determined by macroeconomic factors and other com-ponents that do not change quickly over time. Because the length of my time series is somewhat short from the perspective of macroeconomic change, high autocorrela-tions are expected. Hence, to help keep the sample of countries included in the study large (since some countries are not included in the rankings until later years in the sample period), I have averaged the points of both the men’s and women’s teams over the period. Furthermore, in-cluding the larger sample of countries will help us to at least partially avoid

sample selection bias in the study. Table I shows summary statistics for both the men’s and women’s teams’ points aver-aged over the period 2003-2011.

There are a few things to understand from this brief glance at the data alone. First, there appears to be some relation-ship between the men’s and women’s rankings. Hence, I expect that some of the same variables that explain the men’s rankings explain the women’s rank-ings. However, there are other variables needed to understand the differential per-formance in the women’s rankings. Oth-erwise, I would expect a nearly perfect relationship between the two. These data supports the findings of Matheson and Congdon-Hohman (2011) who found that Muslim religious affiliation reduces wom-en’s success in sports. This paper will ar-gue that this is significant because of the pervasive gender inequality in many of these countries.

ModelsI begin with a basic estimation before

moving on to our primary models. The basic estimation equation is as follows:

−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

adolesFertilityHDI

labPartRat

materMort lPopWsecEdRat

ptsM

1st Principal Component

3rd Principal Component

Figure 3: First vs. Third Principal Components for Primary Model −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

adolesFertilityHDI

labPartRat

materMort lPopWsecEdRat

ptsM

2nd Principal Component

3rd Principal Component

Figure 4: Second vs. Third Principal Components for Primary Model

This allows us to draw the general conclusion that gen-der equality is very important in determining success of wom-

en’s national soccer teams, and perhaps approximately

as important as development, population size, and soccer

tradition and affinity.

Table I: Summary Statistic for Explanatory Variables

Columbia Economics Review Columbia Economics Review

10 Spring 2013 Spring 2013 11

pi = β0 + βXi + εiwhere pi is country’s women’s pro-

gram’s points, Xi is a vector of explana-tory variables for each country, i, and εi is the country-specific error term. I begin by defining Xi in line with intuition and the previous literature, and so I include GDP per capita, the logarithm of the popula-tion of the country, the squared deviation from the ideal temperature of 14 degrees Celsius, points of the men’s program, and an indicator for gender inequality.

In this simple model, GDP per capita is a proxy for the level of economic de-velopment in a country, which is related to the leisure time available to develop-ing athletes in addition to infrastructure like youth leagues and stadiums in which to play. Further, because the citizens of wealthier, developed countries tend to be wealthier and have more leisure time, there may be larger financial remunera-tion in developed countries. This is true even, and perhaps especially, for women

where there is not an efficient internation-al market for soccer labor. As such, these women must depend on national federa-tions (like the United States Soccer Feder-ation in the United States or the Football Association in England) or universities to support them while they train. Popula-tion is included because more populous countries have a larger pool of potential athletes to draw from. Deviation from the ideal temperature is included as soccer is largely played outdoors, so I can assume that athletes in countries with more mod-erate temperatures will have more time during the year to train than those in very hot or very cold countries. The points of the men’s team are included to try to capture soccer tradition or overall inter-est in the country. However, it is likely that these earlier variables are at least somewhat related to men’s rankings (as indicated in the literature), and as such I will likely have some multicollinearity in estimation of the model. The last is a gen-

der inequality variable. A few have been used in the literature, including the gen-der inequality index, ratio of women’s to men’s earnings, and ratio of women’s to men’s enrollment rates in secondary schools. All of these appear to be valid candidates for capturing gender inequal-ity in a country for the simple model.

There is a significant problem in us-ing a linear model to estimate the im-pact of gender inequality on women’s soccer success. All of the earlier studies that included a gender inequality indica-tor exhibited what seem to be symptoms of multicollinearity in their regression results when using OLS, even after I ig-nore the relatively smaller multicollinear-ity problem resulting from including the men’s teams’ points. I know that more developed countries tend to have lower levels of gender inequality, and vice versa. This is well supported by both the literature and our data. For example, the pairwise correlation between the human

development index and the gender in-equality index, another composite index constructed by the UNDP, is larger in magnitude than 85 percent. Other meas-ures of gender inequality, like secondary education ratio between women and men and adolescent fertility rate (among oth-ers) have slightly lower pairwise correla-tions with HDI, but still average around 75 percent in magnitude. If I use GDP per capita, a less precise measure of develop-ment as suggested in the models above, the pairwise correlations are slightly lower, but still extremely high, hovering around 50-60 percent in magnitude. This is unacceptable in any model that hopes to be estimated by OLS, and I predict that our estimates of this model will also suf-fer from multicollinearity.

Multicollinearity results from having high correlations between two or more explanatory variables in a multiple re-gression model like the one above. It can manifest itself in a few ways, all of them important in economic interpretation of data. Most notably, it tends to obscure the results of b for individual predictors since two or more of the explanatory fac-

tors co-move. It also causes the standard errors of the b’s to be unnaturally high, and hence in many cases, to appear in-significant, even though joint tests of sig-nificance of the affected variables (such as the F-test) will successfully reject the null hypothesis that all of their coeffi-cients are zero. It also causes the model to be extremely sensitive to both variable selection and sample selection; both coef-ficients and standard errors can fluctuate quite dramatically even through random sampling of segments of the full data or swapping two variables that should be capturing the same effect. This makes any discussion of the impact of a par-ticular explanatory variable pointless, as I am unable to assess the validity of ei-ther its coefficient or its significance. As noted above, the earlier studies that tried to incorporate gender inequality as an explanatory variable suffered from this problem.

MethodologiesIn order to use this model effectively,

we will use principal component regres-sion. Instead of directly regressing the dependent variable against the explana-

tory variables, we will regress the de-pendent variable against the principal components of the explanatory variables. Under the assumptions of OLS, the use of principal component analysis will help us to deal with the collinearity of the data discussed above. Further, the use of prin-cipal component analysis may help us to better understand the underlying rela-tionships and comovements in the data.

As per Jolliffe (2002), the principal com-ponent is given by:

nYTp = nXT

pWp (4)where nYT

p = (y1,y2,…, yp) is the princi-pal component, nXT

p is the mean-centered transpose of the data matrix, and pWp is the matrix resulted from singular value decomposition of the data matrix, pXn1, as shown in equation (5):

pXn = pWpΣnVTn (5)

where pWp is the matrix of eigenvec-tors of the covariance matrix pXnXT

p, pΣn is a rectangular diagonal matrix with the diagonal composed of elements of R+, and nVn is the matrix of of eigenvectors of pXnXT

p. Essentially, principal component analy-

sis uses a particular orthogonal transfor-−0.5

0

0.5 −0.5

0

0.5

−0.5

0

0.5

Component 2

ptsMlPopW

HDIsecEdRat

labPartRat

adolesFertilitymaterMort

Component 1

Comp

onen

t 3

1 2 3 4 5 6 70.4

0.6

0.8

1

Cumulative Variance Explained (%)

Principal Component

1 2 3 4 5 6 70

0.2

0.4

0.6

Variance Explained (%)

Cum. Var. Expl.Var. Expl.

Figure 6: Percent Variance Explained by Principal Components of the Primary Model

Figure 5: First Three Principal Components for PrimaryModel

Columbia Economics Review Columbia Economics Review

12 Spring 2013 Spring 2013 13

pi = β0 + βXi + εiwhere pi is country’s women’s pro-

gram’s points, Xi is a vector of explana-tory variables for each country, i, and εi is the country-specific error term. I begin by defining Xi in line with intuition and the previous literature, and so I include GDP per capita, the logarithm of the popula-tion of the country, the squared deviation from the ideal temperature of 14 degrees Celsius, points of the men’s program, and an indicator for gender inequality.

In this simple model, GDP per capita is a proxy for the level of economic de-velopment in a country, which is related to the leisure time available to develop-ing athletes in addition to infrastructure like youth leagues and stadiums in which to play. Further, because the citizens of wealthier, developed countries tend to be wealthier and have more leisure time, there may be larger financial remunera-tion in developed countries. This is true even, and perhaps especially, for women

where there is not an efficient internation-al market for soccer labor. As such, these women must depend on national federa-tions (like the United States Soccer Feder-ation in the United States or the Football Association in England) or universities to support them while they train. Popula-tion is included because more populous countries have a larger pool of potential athletes to draw from. Deviation from the ideal temperature is included as soccer is largely played outdoors, so I can assume that athletes in countries with more mod-erate temperatures will have more time during the year to train than those in very hot or very cold countries. The points of the men’s team are included to try to capture soccer tradition or overall inter-est in the country. However, it is likely that these earlier variables are at least somewhat related to men’s rankings (as indicated in the literature), and as such I will likely have some multicollinearity in estimation of the model. The last is a gen-

der inequality variable. A few have been used in the literature, including the gen-der inequality index, ratio of women’s to men’s earnings, and ratio of women’s to men’s enrollment rates in secondary schools. All of these appear to be valid candidates for capturing gender inequal-ity in a country for the simple model.

There is a significant problem in us-ing a linear model to estimate the im-pact of gender inequality on women’s soccer success. All of the earlier studies that included a gender inequality indica-tor exhibited what seem to be symptoms of multicollinearity in their regression results when using OLS, even after I ig-nore the relatively smaller multicollinear-ity problem resulting from including the men’s teams’ points. I know that more developed countries tend to have lower levels of gender inequality, and vice versa. This is well supported by both the literature and our data. For example, the pairwise correlation between the human

development index and the gender in-equality index, another composite index constructed by the UNDP, is larger in magnitude than 85 percent. Other meas-ures of gender inequality, like secondary education ratio between women and men and adolescent fertility rate (among oth-ers) have slightly lower pairwise correla-tions with HDI, but still average around 75 percent in magnitude. If I use GDP per capita, a less precise measure of develop-ment as suggested in the models above, the pairwise correlations are slightly lower, but still extremely high, hovering around 50-60 percent in magnitude. This is unacceptable in any model that hopes to be estimated by OLS, and I predict that our estimates of this model will also suf-fer from multicollinearity.

Multicollinearity results from having high correlations between two or more explanatory variables in a multiple re-gression model like the one above. It can manifest itself in a few ways, all of them important in economic interpretation of data. Most notably, it tends to obscure the results of b for individual predictors since two or more of the explanatory fac-

tors co-move. It also causes the standard errors of the b’s to be unnaturally high, and hence in many cases, to appear in-significant, even though joint tests of sig-nificance of the affected variables (such as the F-test) will successfully reject the null hypothesis that all of their coeffi-cients are zero. It also causes the model to be extremely sensitive to both variable selection and sample selection; both coef-ficients and standard errors can fluctuate quite dramatically even through random sampling of segments of the full data or swapping two variables that should be capturing the same effect. This makes any discussion of the impact of a par-ticular explanatory variable pointless, as I am unable to assess the validity of ei-ther its coefficient or its significance. As noted above, the earlier studies that tried to incorporate gender inequality as an explanatory variable suffered from this problem.

MethodologiesIn order to use this model effectively,

we will use principal component regres-sion. Instead of directly regressing the dependent variable against the explana-

tory variables, we will regress the de-pendent variable against the principal components of the explanatory variables. Under the assumptions of OLS, the use of principal component analysis will help us to deal with the collinearity of the data discussed above. Further, the use of prin-cipal component analysis may help us to better understand the underlying rela-tionships and comovements in the data.

As per Jolliffe (2002), the principal com-ponent is given by:

nYTp = nXT

pWp (4)where nYT

p = (y1,y2,…, yp) is the princi-pal component, nXT

p is the mean-centered transpose of the data matrix, and pWp is the matrix resulted from singular value decomposition of the data matrix, pXn1, as shown in equation (5):

pXn = pWpΣnVTn (5)

where pWp is the matrix of eigenvec-tors of the covariance matrix pXnXT

p, pΣn is a rectangular diagonal matrix with the diagonal composed of elements of R+, and nVn is the matrix of of eigenvectors of pXnXT

p. Essentially, principal component analy-

sis uses a particular orthogonal transfor-−0.5

0

0.5 −0.5

0

0.5

−0.5

0

0.5

Component 2

ptsMlPopW

HDIsecEdRat

labPartRat

adolesFertilitymaterMort

Component 1

Comp

onen

t 3

1 2 3 4 5 6 70.4

0.6

0.8

1

Cumulative Variance Explained (%)

Principal Component

1 2 3 4 5 6 70

0.2

0.4

0.6

Variance Explained (%)

Cum. Var. Expl.Var. Expl.

Figure 6: Percent Variance Explained by Principal Components of the Primary Model

Figure 5: First Three Principal Components for PrimaryModel

Columbia Economics Review Columbia Economics Review

12 Spring 2013 Spring 2013 13

mation to turn our potentially correlated explanatory variables into a set of lin-early independent vectors called ``prin-cipal components.’’ We can think of this as finding a new orthogonal basis for the space of our explanatory variable that preserves the ``structure’’ of the data in the sense of maintaining variance. This new basis is constructed to help us iden-tify the most important gradients in the data. The first principal component is in the direction of maximum variance in the data, the second principal component is in the direction of maximum variance such that it is orthogonal to the first, and as such will be in the direction of second most variance, etc. We are in effect ``ro-tating’’ our data to identify the directions of maximum variance. Note that, as men-tioned above, the principal components are simply a linear combination of the original variables, albeit chosen such that the first component contains more infor-mation than the second, the second more than the third, and so on for all p compo-nents.

Because the principal components are sensitive to the relative scaling of the vec-tors in the data matrix, and can give mis-leading results if not mean-centered, we have standardized the explanatory vari-ables before implementing the method.

There are several advantages to prin-cipal component analysis that we will be able to utilize in the context of this study. First and most important is the or-thogonality of the principal components. Hence, assuming normality of the data, we have independence. This eliminates the multicollinearity problem discussed above. Further, because principal com-ponent analysis by design creates the coordinates of the new basis in order of importance, we can project our original

data onto this new basis and can visual-ize what was originally high dimension-al data in the space of R2 or R3. This is a significant advantage in helping us to interpret the meaning of principal com-ponents.

Results: Principal Component Analy-sis

As previously mentioned, principal component analysis is sensitive to the rel-ative scaling and centering of the data, so I standardize the data so that each of the original variables now has a mean of zero and a variance of one. Principal compo-nent analysis is also sensitive to the inclu-sion of outliers in the data, but our earlier treatment of outliers using Winsorization should have rectified this issue.

I use HDI as a proxy for development, as this will give more full coverage of hu-man development level than simply GDP per capita, and it will be easier to interpret than including a number of development indicators, but with a minimal loss of in-formation. As noted above, I will include the logarithm of population, squared de-viation from ideal temperature, Men’s FIFA points. Instead of choosing just one gender inequality proxy, I will include four: labor force participation ratio, sec-ondary education ratio, adolescent fertil-ity rate, and maternal mortality rate.

One of the benefits of principal com-ponent analysis is that I can project the original variables onto the space of the first few components in order to make meaningful interpretations. I can do this using the eigenvectors of the principal components, which can be seen in Table I. When the eigenvectors of the principal components are discussed in the context of the original explanatory variables, they are called scoring coefficients, and these coefficients are used to interpret projec-tions of the original data into reduced dimension spaces of the new orthogonal basis.

Figure 1 shows the original explana-tory variables and original data points projected onto the space of the first two principal components. The data form a ``cone’’ in the space of these compo-nents, and I note that there are many countries with high values of both the first and second components, with rela-tively fewer that have high values of just one or the other, and even fewer that have low values of both. Once I look at the projection of our original explanatory variable vectors, I see that the develop-ment variables, and the some portion of the gender inequality variables related to development have high magnitudes in the first component. Hence, I can think of

the first component as the development component, which generally embodies the various aspects of human develop-ment from our set of variables. It is in effect extracting development from each of our variables, so that later components can extract other elements. Since princi-pal component analysis constructs each component to be orthogonal to all others, any later components will therefore be independent of this one under normality. Hence, it is consistent that in the develop-ment component I see very large positive values of HDI, and very large negative values of adolescent fertility and mater-nal mortality, two very development-fo-cused measures of gender inequality, and moderate positive values of secondary education ratio. There is also a moderate positive value of men’s FIFA points in the development component. This also falls in line with our earlier assumption that the men’s FIFA points were related to the level of development in a country. By ex-tracting the component of men’s points determined by development, I will be able to better understand what portion of men’s points is related to soccer tradition and national interest in the sport.

The second component is dominated by population and men’s FIFA points. I think of this as the tradition and size com-ponent. I understand from this that the interaction of population and tradition together is more important than either of these separately, and in fact is enor-mously important overall, as the second principal component. This makes sense intuitively as well. For example a large population in a country without an estab-lished soccer tradition, such as India, is not predictive of women’s team success. At the same time, a small population in a country with a longstanding tradition and small population, like Portugal, is not sufficient either.

Interpreting Figure 2 which shows the projection of data and original explanato-ry variables projected onto these compo-nents, I see that there are many countries that have both high development levels, and large populations with strong tradi-tions. I predict that both of these varia-bles will have a positive impact on wom-en’s FIFA points.The third component, in Figures 3 and 4 is most interesting for the purposes of this paper. This component is dominated by labor force participation ratio, with moderate magnitudes of sec-ondary education rate and men’s FIFA points. I can think of this as the gender equality component. It is intuitive that I would see high levels of both labor force participation ratio and secondary educa-Table II: Principal Component Regres-

sion Results

tion ratio, but the component is broader than gender equality alone.

Figure 3 shows the projection onto the first and third principal components, and there is a bit of a cone in the data focused in the first quadrant, confirming the ear-lier discussion of the strong relationship between development and gender equali-ty. Figure 4 shows the projection onto the second and third principal components. There is no distinctive data shape here, but it is interesting to note that the devel-opment variables are nearly invisible due to having almost no representation in these components, further evidence that I am successfully extracting the various elements of interest. Furthermore, while men’s FIFA points did have some posi-tive value in the gender inequality com-ponent, it is nearly twice as large in the tradition and size component. I remain confident in our interpretations of the second and third components discussed previously.

Figure 5 shows the projection onto the space of the first three components. I can see that the variables generally cluster in the direction of one of the components

discussed previously, which supports our interpretation of the components and justification for the use of the method as a solution for multicollinearity in general.

Results: Principal Component Regres-sion

While the principal components are ordered in descending explanatory pow-er, it is with respect to the data, not the dependent variable: while I can suggest hypotheses about the impact of these components on the data, I must actually regress the components against the data in order to draw conclusions about their significance. Typically, one does not use the full set of principal components when conducting a principal component re-gression, but rather uses a subset of the most important principal components.

Essentially the eigenvalues denote the stretch of the transformation in the direc-tion of the corresponding eigenvector, and in this case, I can use the relative magnitude of the eigenvalues as a way to understand the variance explained by the principal component. The principal com-ponents are ordered by the amount of ex-planatory power they provide. However,

I must decide when to stop including components. Another way of thinking about this is by seeing the actual variance explained by each component. Figure 6 shows this as well as the cumulative variance explained by the principal com-ponents. From this figure, the first three principal components, the development component, the tradition and size com-ponent, and the gender equality compo-nent, explain over 80 percent of the vari-ance in the data, I choose to include these first three principal components for use in the principal component regression.

Table II shows the results of estimation of the principal component regression us-ing OLS with heteroskedasticity-robust standard errors using the first three prin-cipal components as independent varia-bles. The first component is generally the development component, the second is the tradition and size component, and the third is the gender inequality component. I note first that all three coefficients are significant at the 1 percent level and that their corresponding p-values are incred-ibly small. This is already a large advan-tage compared to the simple linear model described above. The coefficients of our principal components are as I predicted: the development component is positive, the tradition and country size component is positive, and the gender inequality component is positive.

ConclusionI have a model with significant, inter-

pretable coefficients, and a fit approxi-mately as good as, and often better than, the simple linear model. I can say with some confidence that this model and im-plementation is better than the simple linear models used in the previous lit-erature. Further, I see that although the development component embodies about twice the variance in the data as the gen-der inequality component, their coeffi-cients are very close in magnitude. This allows us to draw the general conclusion that gender equality is very important in determining success of women’s national soccer teams, and perhaps approximately as important as development, population size, and soccer tradition and affinity. This is something that no previous study has been able to say with a high level of certainty, and this too provides an advan-tage over previous work on the subject. Robustness checks have confirmed my results, and the assumptions of our pri-mary model appear to have been met: all of which is reassuring in confirming the validity of our study. ▪

Columbia Economics Review Columbia Economics Review

14 Spring 2013 Spring 2013 15

mation to turn our potentially correlated explanatory variables into a set of lin-early independent vectors called ``prin-cipal components.’’ We can think of this as finding a new orthogonal basis for the space of our explanatory variable that preserves the ``structure’’ of the data in the sense of maintaining variance. This new basis is constructed to help us iden-tify the most important gradients in the data. The first principal component is in the direction of maximum variance in the data, the second principal component is in the direction of maximum variance such that it is orthogonal to the first, and as such will be in the direction of second most variance, etc. We are in effect ``ro-tating’’ our data to identify the directions of maximum variance. Note that, as men-tioned above, the principal components are simply a linear combination of the original variables, albeit chosen such that the first component contains more infor-mation than the second, the second more than the third, and so on for all p compo-nents.

Because the principal components are sensitive to the relative scaling of the vec-tors in the data matrix, and can give mis-leading results if not mean-centered, we have standardized the explanatory vari-ables before implementing the method.

There are several advantages to prin-cipal component analysis that we will be able to utilize in the context of this study. First and most important is the or-thogonality of the principal components. Hence, assuming normality of the data, we have independence. This eliminates the multicollinearity problem discussed above. Further, because principal com-ponent analysis by design creates the coordinates of the new basis in order of importance, we can project our original

data onto this new basis and can visual-ize what was originally high dimension-al data in the space of R2 or R3. This is a significant advantage in helping us to interpret the meaning of principal com-ponents.

Results: Principal Component Analy-sis

As previously mentioned, principal component analysis is sensitive to the rel-ative scaling and centering of the data, so I standardize the data so that each of the original variables now has a mean of zero and a variance of one. Principal compo-nent analysis is also sensitive to the inclu-sion of outliers in the data, but our earlier treatment of outliers using Winsorization should have rectified this issue.

I use HDI as a proxy for development, as this will give more full coverage of hu-man development level than simply GDP per capita, and it will be easier to interpret than including a number of development indicators, but with a minimal loss of in-formation. As noted above, I will include the logarithm of population, squared de-viation from ideal temperature, Men’s FIFA points. Instead of choosing just one gender inequality proxy, I will include four: labor force participation ratio, sec-ondary education ratio, adolescent fertil-ity rate, and maternal mortality rate.

One of the benefits of principal com-ponent analysis is that I can project the original variables onto the space of the first few components in order to make meaningful interpretations. I can do this using the eigenvectors of the principal components, which can be seen in Table I. When the eigenvectors of the principal components are discussed in the context of the original explanatory variables, they are called scoring coefficients, and these coefficients are used to interpret projec-tions of the original data into reduced dimension spaces of the new orthogonal basis.

Figure 1 shows the original explana-tory variables and original data points projected onto the space of the first two principal components. The data form a ``cone’’ in the space of these compo-nents, and I note that there are many countries with high values of both the first and second components, with rela-tively fewer that have high values of just one or the other, and even fewer that have low values of both. Once I look at the projection of our original explanatory variable vectors, I see that the develop-ment variables, and the some portion of the gender inequality variables related to development have high magnitudes in the first component. Hence, I can think of

the first component as the development component, which generally embodies the various aspects of human develop-ment from our set of variables. It is in effect extracting development from each of our variables, so that later components can extract other elements. Since princi-pal component analysis constructs each component to be orthogonal to all others, any later components will therefore be independent of this one under normality. Hence, it is consistent that in the develop-ment component I see very large positive values of HDI, and very large negative values of adolescent fertility and mater-nal mortality, two very development-fo-cused measures of gender inequality, and moderate positive values of secondary education ratio. There is also a moderate positive value of men’s FIFA points in the development component. This also falls in line with our earlier assumption that the men’s FIFA points were related to the level of development in a country. By ex-tracting the component of men’s points determined by development, I will be able to better understand what portion of men’s points is related to soccer tradition and national interest in the sport.

The second component is dominated by population and men’s FIFA points. I think of this as the tradition and size com-ponent. I understand from this that the interaction of population and tradition together is more important than either of these separately, and in fact is enor-mously important overall, as the second principal component. This makes sense intuitively as well. For example a large population in a country without an estab-lished soccer tradition, such as India, is not predictive of women’s team success. At the same time, a small population in a country with a longstanding tradition and small population, like Portugal, is not sufficient either.

Interpreting Figure 2 which shows the projection of data and original explanato-ry variables projected onto these compo-nents, I see that there are many countries that have both high development levels, and large populations with strong tradi-tions. I predict that both of these varia-bles will have a positive impact on wom-en’s FIFA points.The third component, in Figures 3 and 4 is most interesting for the purposes of this paper. This component is dominated by labor force participation ratio, with moderate magnitudes of sec-ondary education rate and men’s FIFA points. I can think of this as the gender equality component. It is intuitive that I would see high levels of both labor force participation ratio and secondary educa-Table II: Principal Component Regres-

sion Results

tion ratio, but the component is broader than gender equality alone.

Figure 3 shows the projection onto the first and third principal components, and there is a bit of a cone in the data focused in the first quadrant, confirming the ear-lier discussion of the strong relationship between development and gender equali-ty. Figure 4 shows the projection onto the second and third principal components. There is no distinctive data shape here, but it is interesting to note that the devel-opment variables are nearly invisible due to having almost no representation in these components, further evidence that I am successfully extracting the various elements of interest. Furthermore, while men’s FIFA points did have some posi-tive value in the gender inequality com-ponent, it is nearly twice as large in the tradition and size component. I remain confident in our interpretations of the second and third components discussed previously.

Figure 5 shows the projection onto the space of the first three components. I can see that the variables generally cluster in the direction of one of the components

discussed previously, which supports our interpretation of the components and justification for the use of the method as a solution for multicollinearity in general.

Results: Principal Component Regres-sion

While the principal components are ordered in descending explanatory pow-er, it is with respect to the data, not the dependent variable: while I can suggest hypotheses about the impact of these components on the data, I must actually regress the components against the data in order to draw conclusions about their significance. Typically, one does not use the full set of principal components when conducting a principal component re-gression, but rather uses a subset of the most important principal components.

Essentially the eigenvalues denote the stretch of the transformation in the direc-tion of the corresponding eigenvector, and in this case, I can use the relative magnitude of the eigenvalues as a way to understand the variance explained by the principal component. The principal com-ponents are ordered by the amount of ex-planatory power they provide. However,

I must decide when to stop including components. Another way of thinking about this is by seeing the actual variance explained by each component. Figure 6 shows this as well as the cumulative variance explained by the principal com-ponents. From this figure, the first three principal components, the development component, the tradition and size com-ponent, and the gender equality compo-nent, explain over 80 percent of the vari-ance in the data, I choose to include these first three principal components for use in the principal component regression.

Table II shows the results of estimation of the principal component regression us-ing OLS with heteroskedasticity-robust standard errors using the first three prin-cipal components as independent varia-bles. The first component is generally the development component, the second is the tradition and size component, and the third is the gender inequality component. I note first that all three coefficients are significant at the 1 percent level and that their corresponding p-values are incred-ibly small. This is already a large advan-tage compared to the simple linear model described above. The coefficients of our principal components are as I predicted: the development component is positive, the tradition and country size component is positive, and the gender inequality component is positive.

ConclusionI have a model with significant, inter-

pretable coefficients, and a fit approxi-mately as good as, and often better than, the simple linear model. I can say with some confidence that this model and im-plementation is better than the simple linear models used in the previous lit-erature. Further, I see that although the development component embodies about twice the variance in the data as the gen-der inequality component, their coeffi-cients are very close in magnitude. This allows us to draw the general conclusion that gender equality is very important in determining success of women’s national soccer teams, and perhaps approximately as important as development, population size, and soccer tradition and affinity. This is something that no previous study has been able to say with a high level of certainty, and this too provides an advan-tage over previous work on the subject. Robustness checks have confirmed my results, and the assumptions of our pri-mary model appear to have been met: all of which is reassuring in confirming the validity of our study. ▪

Columbia Economics Review Columbia Economics Review

14 Spring 2013 Spring 2013 15

Pass the (Bargaining) ChipsCarbon Emissions and the Bargaining Power of Trade Unions and Firms

Christopher Evans

Cambridge University

This paper looks at the bargaining problem and creates a model that allows a social welfare-conscious government to transfer bargaining power between the trade union and firm, therefore directly affecting the level of pollution-intensive capital present within the economy. In doing so, I will address the social welfare issues of externalities caused by the firm and its bargaining with trade unions. The focus will primarily be on carbon dioxide (CO2) as it is the basis of many scientific studies of pollution. As govern-ments implement schemes to cap carbon emissions, scrap pollutant capital, and promote greener technologies — all un-der the pressure created by global warm-ing—this paper focuses on whether trade unions are obstructing these goals. I also investigate how governments can change the unions’ bargaining power to reduce these externalities. I will conclude that in a world with “highly polluting” capital, the government transfers the bargaining power to the firm such that the firm can deflate wages and employ more labor and less pollution-intensive capital. Us-ing an altered pollution function which is “strongly decreasing,” I find the oppo-site results: more power is transferred to the trade union, and the inflated wages force the firm to employ more capital.

Trade unions began growing and ex-panding in number since the industri-alization of Europe, as workers became afraid that they lacked the skills to per-form most jobs. This situation shifted the bargaining power from the workers to the firm, which was able to mistreat

workers and underpay them. The power of the trade union stemmed from collec-tive bargaining, the practice of collecting individual workers’ power into one cen-tral entity, allowing them to bargain over wages and employment more effectively. The recent decrease in union membership

lends credence to my analysis, based on the concurrent increase in disutility from inflation in the past decades. Indeed, the government would prefer to shift power to the firms and away from unions, thus leading to a decrease in membership. The sectors of the economy which have great-er union membership include mining and quarrying, public administration and de-fense, education, and manufacturing (BIS 2010). Discussion focuses on the more pollutant sectors such as manufacturing.

Related LiteratureThe manufacturing industry is one of

the most pollutant sectors of the econo-my due to its high level of capital produc-tion which requires high energy input. In most countries, the energy supply is based fuels that are relatively cheap but highly polluting, such as coal. Schip-per, et al. (2001) study carbon emissions across time for different countries and sectors of the economy and find that the 13 highly industrialized countries of the International Energy Agency produced a large proportion of their carbon emis-sions through the use of machinery in sec-tors such as manufacturing, and the Na-tional Institute for Environmental Studies (2002) finds that developing countries see a similar problem of industrial pollution as the governments prioritize economic

development over the long term effects of pollution.

Forster (1973) states, “When pollution is accounted for, the economy tends to a lower capital stock than when pollution is ignored.” He uses a simplified pollu-tion function where the marginal utility of pollution is negative and strictly de-creasing and capital has a positive and increasing effect on pollution. The idea that capital causes pollution is generally accepted, but whether an additional unit of capital has a greater increased effect on pollution than the last unit of capital is still contested. To ensure the model is simple I ignore ef-fects of pollution on production but al-low for variation of labor over time—a standard assump-tion in the literature which Forster does not use.

Ehrenberg and Smith’s (1982) text-book presents a simplified model of trade unions where their function can be expressed through shifting the labor supply curve to the left and subse-quently causing an increase in wages. This can be thought of as a closed shop1. Legality issues aside, this model as-sumes unions facing monopsony possess a supply curve.

When formulating a function that the trade union wishes to maximize many economists such as Akerlof (1969), Corden (1981), and Oswald (1979) started with an extensive form of Cobb-Douglas called a Stone-Geary function. These quasi-concave in-creasing preferences can consequently be viewed as a set of convex indiffer-ence curves. These curves intersect with the firm’s isoprofit function, creating a contract curve on which the bargaining

1 A closed shop is an agreement where the employer agrees to hire union mem-bers only, thus reducing the total supply of labor available. It has been deemed il-legal in the UK under the Trade unions and labor relations act of 1992

solution sits. This utility function in the literature is of the form U=U(w,N) where unions bargain over wages ‘w’ and em-ployment ’N’.

Carruth and Oswald (1987) extend the findings from Lindbeck and Snower (1984), with a model employing an ‘in-sider-outsider’ approach. An insider is a worker who is a member of the trade un-ion while an outsider is a non-member. This adaptation comes about through the assumption that “once all members of a labor group have jobs, that group no long-er attaches any weight to its employment goal,” a “potentially serious deficiency”

in the Lindbeck and Snower model. Con-sequently, the utility curves are kinked and the trade union demands labor up to a certain level and then continues to put pressure on wages. “The reason for this disparity is that firms incur labor turno-ver costs when they replace insiders by outsiders.” (Lindbeck and Snower 1984) Carruth and Oswald find that trade un-ions will let in outsiders when product demand rises above a level where all of the insiders are already employed. This approach is too extensive and intricate to be imposed on this paper’s economic

model. Carruth and Oswald’s paper sug-gests the importance of considering alter-natively shaped utility functions, but they do not consider that trade unions may wish to benefit employed outsiders so as to recruit labor into the union. Bargain-ing over wages and employment with the firm depends on trade union power, so the union will wish to maximize the employment level to enhance its bargain-ing power. A further critique by Doiron (1995) shows that empirical data contra-dicts the insider-outsider approach.

With the set of utility curves defined by the trade unions’ preferences and the set

of isoprofit curves for the firm, the bargaining model can be complet-ed. The contract curve is the set of Pareto optimal al-locations; the so-lution to the bar-gaining problem is the point where these two utility curves intersect. Economists have been research-ing and debat-ing over the ap-propriate way to view bargaining between the firm and union. The Nash bargaining a p p r o a c h — a n extensive form of Nash’s axiomatic bargaining solu-tion— is the fa-vored solution over the alterna-tive hypothesis offered by Rubin-stein.

Nash (1950) in-troduced the case of a two player

bargaining game that includes “monop-oly versus monopsony.” He presents a bargaining problem that consists of a feasible set S and a disagreement point d. Using von Neumann-Morgenstern utilities, Nash constructs a bargaining solution as a unique pair of utility levels taken from (S,d), where S is compact and convex and strictly dominates d (Rubin-stein, Safra and Thomson 1992). Nash’s paper presented axioms which were used by Osborne (2004), Rubinstein et al (1992) and Binmore et al (1986), and which can be seen in the appendix (A.1).

The idea that capital causes pollution is generally ac-

cepted, butwhether an additional unit of capital has a greater in-creased effect on pollution than the last unit of capital

is still contested.

Columbia Economics Review Columbia Economics Review

16 Spring 2013 Spring 2013 17

Pass the (Bargaining) ChipsCarbon Emissions and the Bargaining Power of Trade Unions and Firms

Christopher Evans

Cambridge University

This paper looks at the bargaining problem and creates a model that allows a social welfare-conscious government to transfer bargaining power between the trade union and firm, therefore directly affecting the level of pollution-intensive capital present within the economy. In doing so, I will address the social welfare issues of externalities caused by the firm and its bargaining with trade unions. The focus will primarily be on carbon dioxide (CO2) as it is the basis of many scientific studies of pollution. As govern-ments implement schemes to cap carbon emissions, scrap pollutant capital, and promote greener technologies — all un-der the pressure created by global warm-ing—this paper focuses on whether trade unions are obstructing these goals. I also investigate how governments can change the unions’ bargaining power to reduce these externalities. I will conclude that in a world with “highly polluting” capital, the government transfers the bargaining power to the firm such that the firm can deflate wages and employ more labor and less pollution-intensive capital. Us-ing an altered pollution function which is “strongly decreasing,” I find the oppo-site results: more power is transferred to the trade union, and the inflated wages force the firm to employ more capital.

Trade unions began growing and ex-panding in number since the industri-alization of Europe, as workers became afraid that they lacked the skills to per-form most jobs. This situation shifted the bargaining power from the workers to the firm, which was able to mistreat

workers and underpay them. The power of the trade union stemmed from collec-tive bargaining, the practice of collecting individual workers’ power into one cen-tral entity, allowing them to bargain over wages and employment more effectively. The recent decrease in union membership

lends credence to my analysis, based on the concurrent increase in disutility from inflation in the past decades. Indeed, the government would prefer to shift power to the firms and away from unions, thus leading to a decrease in membership. The sectors of the economy which have great-er union membership include mining and quarrying, public administration and de-fense, education, and manufacturing (BIS 2010). Discussion focuses on the more pollutant sectors such as manufacturing.

Related LiteratureThe manufacturing industry is one of

the most pollutant sectors of the econo-my due to its high level of capital produc-tion which requires high energy input. In most countries, the energy supply is based fuels that are relatively cheap but highly polluting, such as coal. Schip-per, et al. (2001) study carbon emissions across time for different countries and sectors of the economy and find that the 13 highly industrialized countries of the International Energy Agency produced a large proportion of their carbon emis-sions through the use of machinery in sec-tors such as manufacturing, and the Na-tional Institute for Environmental Studies (2002) finds that developing countries see a similar problem of industrial pollution as the governments prioritize economic

development over the long term effects of pollution.

Forster (1973) states, “When pollution is accounted for, the economy tends to a lower capital stock than when pollution is ignored.” He uses a simplified pollu-tion function where the marginal utility of pollution is negative and strictly de-creasing and capital has a positive and increasing effect on pollution. The idea that capital causes pollution is generally accepted, but whether an additional unit of capital has a greater increased effect on pollution than the last unit of capital is still contested. To ensure the model is simple I ignore ef-fects of pollution on production but al-low for variation of labor over time—a standard assump-tion in the literature which Forster does not use.

Ehrenberg and Smith’s (1982) text-book presents a simplified model of trade unions where their function can be expressed through shifting the labor supply curve to the left and subse-quently causing an increase in wages. This can be thought of as a closed shop1. Legality issues aside, this model as-sumes unions facing monopsony possess a supply curve.

When formulating a function that the trade union wishes to maximize many economists such as Akerlof (1969), Corden (1981), and Oswald (1979) started with an extensive form of Cobb-Douglas called a Stone-Geary function. These quasi-concave in-creasing preferences can consequently be viewed as a set of convex indiffer-ence curves. These curves intersect with the firm’s isoprofit function, creating a contract curve on which the bargaining

1 A closed shop is an agreement where the employer agrees to hire union mem-bers only, thus reducing the total supply of labor available. It has been deemed il-legal in the UK under the Trade unions and labor relations act of 1992

solution sits. This utility function in the literature is of the form U=U(w,N) where unions bargain over wages ‘w’ and em-ployment ’N’.

Carruth and Oswald (1987) extend the findings from Lindbeck and Snower (1984), with a model employing an ‘in-sider-outsider’ approach. An insider is a worker who is a member of the trade un-ion while an outsider is a non-member. This adaptation comes about through the assumption that “once all members of a labor group have jobs, that group no long-er attaches any weight to its employment goal,” a “potentially serious deficiency”

in the Lindbeck and Snower model. Con-sequently, the utility curves are kinked and the trade union demands labor up to a certain level and then continues to put pressure on wages. “The reason for this disparity is that firms incur labor turno-ver costs when they replace insiders by outsiders.” (Lindbeck and Snower 1984) Carruth and Oswald find that trade un-ions will let in outsiders when product demand rises above a level where all of the insiders are already employed. This approach is too extensive and intricate to be imposed on this paper’s economic

model. Carruth and Oswald’s paper sug-gests the importance of considering alter-natively shaped utility functions, but they do not consider that trade unions may wish to benefit employed outsiders so as to recruit labor into the union. Bargain-ing over wages and employment with the firm depends on trade union power, so the union will wish to maximize the employment level to enhance its bargain-ing power. A further critique by Doiron (1995) shows that empirical data contra-dicts the insider-outsider approach.

With the set of utility curves defined by the trade unions’ preferences and the set

of isoprofit curves for the firm, the bargaining model can be complet-ed. The contract curve is the set of Pareto optimal al-locations; the so-lution to the bar-gaining problem is the point where these two utility curves intersect. Economists have been research-ing and debat-ing over the ap-propriate way to view bargaining between the firm and union. The Nash bargaining a p p r o a c h — a n extensive form of Nash’s axiomatic bargaining solu-tion— is the fa-vored solution over the alterna-tive hypothesis offered by Rubin-stein.

Nash (1950) in-troduced the case of a two player

bargaining game that includes “monop-oly versus monopsony.” He presents a bargaining problem that consists of a feasible set S and a disagreement point d. Using von Neumann-Morgenstern utilities, Nash constructs a bargaining solution as a unique pair of utility levels taken from (S,d), where S is compact and convex and strictly dominates d (Rubin-stein, Safra and Thomson 1992). Nash’s paper presented axioms which were used by Osborne (2004), Rubinstein et al (1992) and Binmore et al (1986), and which can be seen in the appendix (A.1).

The idea that capital causes pollution is generally ac-

cepted, butwhether an additional unit of capital has a greater in-creased effect on pollution than the last unit of capital

is still contested.

Columbia Economics Review Columbia Economics Review

16 Spring 2013 Spring 2013 17

The current firm-union bargaining models use Nash’s generalized model, where ui is the utility curve of player i and d is the payoff when they fail to reach an agreement (the disagreement point). One of Nash’s axioms for the model, symmetry, implies that bargaining power is equal between the firm and the union, a fairly unrealistic assumption which I drop. The government then chooses this bargaining power by manipulating poli-cies to maximize its objective function. In this case the bargaining power is affected by the government’s preferences over the firm’s pollution output, characterized by CO2 emissions. The introduction of bar-gaining power θ, where allows greater manipulation and realism of the standard model.

The ModelAssumption 1. The trade union gains

utility from wages and labor with equal weighting. Hence, it wishes to maximize U(w,L) given by (3.1) U(w,L)=wL.

Assumption 2. The firm wishes to maxi-mize profits, denoted \pi, with respect to the choice variables K,L,w where . The rental price of capital, r, is exogenous. For simplicity, the firm produces one unit at a price normalized to one with a pro-duction function f(K,L)=KL. This gives a profit function for the firm of:

π(K,L,w,r)=KL-wL-rKUsing the Nash bargaining solution,

I use the standard model with the disa-greement point d omitted for simplicity.

Here I maximize the log of the profit function, and simplify further. The differ-entiation and first order condition can be seen in the appendix (A.3 - A.8).

The critical points are: L*=r* and w*=θ K*Assumption 3. The government places

weight on the effect of pollution on social welfare. Then the government maximizes G (δ) with respect to the bargaining pow-er θ: G(δ)=(1-δ)[π^*+U^*]-P(K)δ.

AnalysisEquation (3.6) shows that although

trade unions do not care directly for K, as it is not present in their utility func-tion, they are aware of the effects of capi-tal and the firm’s bargaining power over wages. When their bargaining solution is reached the labor employed depends on the rental price of capital (3.5) and wag-es depend on bargaining power and the level of capital (3.6). These conclusion are economically plausible, indeed if wages were high the firm would choose to sub-

stitute in capital for labor.Equation (3.7) represents the govern-

ment’s preferences over the economy and social welfare. This depletion in social welfare is caused by pollution from use of capital goods, where signifies how strongly the government values social welfare. The government wishes to maxi-mize its own function with respect to θ, the bargaining power. This however is highly dependent on the pollution func-tion P(K).

Proposition 1. When the government optimizes according to its preferences— that include a pollution function which is strongly increasing— the optimal so-lution is a transfer of bargaining power toward the firm. This increase in bargain-ing power θ reduces the level of pollut-ant capital employed due to the ability to deflate wage.

ProofI begin at (4.1) with a strongly increas-

ing pollution function (A.9). Here α and β are pollution scalars that allow the func-tion to react suitably: P(K)=αK+βK2

Function (4.1) has the property that the relationship between capital and pol-lution is positive and has an increasing marginal rate of pollution with respect to capital. This function has the same properties as the pollution function used in Forster (1973). I do not include the as-sumption of pollution causing an adverse effect on the marginal product of the fac-tors of production. I then substitute the strongly increasing pollution function (4.1) into the government welfare func-tion G (δ) (3.7):

G(δ)=(1-δ)[LK-rK-wL+wL]-(αK+βK2)δI substitute in the critical values (3.5,3.6)

found from maximizing (3.1) and (3.2) for the firm and trade union respectively. This gives a simplified version of the gov-ernment function:

G(δ,θ)=-(α(w/θ)+β(w/θ)2)δI now maximize this welfare function

(4.3) with respect to bargaining power θ to find the optimal level for the govern-ment, denoted θ*: θ*= -2w(β/α)

Taking α and β as arbitrary positive constants gives a direct relation between bargaining power and the wage rate. As such, under pollution intensive capital (4.1) I substitute the critical value (3.6) w*=θK* to find the optimal level of capi-tal: θ*= -2θ*K*.

Rearranging: K*= -1/2Substituting capital into the govern-

ment’s optimal level of θ*, the transfer of bargaining power toward the firm causes the level of capital to decrease. Therefore because capital employed must be posi-tive, the level of capital at the optimal θ*

is a corner solution at zero, proving the transfer of bargaining power has a nega-tive relationship with the level of pollut-ant capital employed and reaches an op-timal level of zero. This is due to (4.4) as an increase in bargaining power toward the firm deflates wages, thus making the substitution of labor for capital more ap-pealing.

Proposition 2. When the government maximizes its preferences including a pollution function which is strongly de-creasing, the optimal solution is a trans-fer of bargaining power toward the un-ion. The increase in bargaining power toward the union is directly related to the wage rate with the inflationary pressure on wages causing the firm to substitute in capital for labor. Therefore there is a positive relationship between the trade union’s bargaining power and capital employed.

ProofThe proof relies on using previous re-

sults of (3.1-3.7). The proof begins at (4.7)

where there is a strongly decreasing pol-lution function (A.11). I substitute this function into (3.7) and maximize the gov-ernment’s welfare G(δ) with respect to the bargaining power θ, where α and β are pollution scalars that allow the func-tion to react suitably. I do not include the assumption of pollution causing an adverse effect on the marginal product of the factors of production. I then sub-stitute the strongly decreasing pollution function (4.7) into the government wel-fare function (3.7): P(K)=α K+β(1/K)

G(δ)=(1-δ) [LK-rK-wL+wL]- [αK+β (1/K)]δ

Substituting in the critical values (3.5,3.6) found from maximizing (3.1) and (3.2) gives a simplified version of the gov-ernment function to be maximized.

I now maximize this welfare function (4.9) with respect to bargaining power, and find the optimal level for the govern-ment, denoted θ*

Taking α and β as arbitrary positive

constants again gives a direct relation between bargaining power and the wage rate. As such, assuming intensive capital (4.7) provides critical value (3.6) w*=θK* to find how capital reacts to a change in the bargaining power, θ: θ*=θ*K*

Rearranging: K*=1At this optimum, capital is strongly in-

creasing, therefore proving that the opti-mal transfer of bargaining power under a strongly decreasing pollution function has a positive relationship on the level of low pollutant capital employed until capital reaches its corner solution. This means that the firm employs capital as the only factor of production. This is be-cause the transfer of bargaining power toward the trade union causes inflation-ary wages, proven in (4.10), which force the firm to substitute in capital for labor.

Equations (4.4) and (4.10) represent the optimum level of θ if the government is maximizing total utility of the society as a whole. Take special note that the optimal bargaining power does not rely directly on labor.

DiscussionIn the case of a strongly increasing

pollution function, the critical point ex-pressed in (4.4) represents capital that causes pollution (4.1). Since capital is highly pollutant and the government is rational and gains a disutility over pol-lution it will transfer all of the bargaining power toward the firm. This is because if the government does not, then the trade union is still bargaining for wages and la-bor. This will inflate the cost and quantity of labor supplied causing an increase in the cost of production. This increase in the cost of labor will cause the firm to sub-stitute in capital for labor. This capital is inherently pollutant due to the pollution function. Therefore as the government wishes to reduce pollution it will give all of the bargaining power to the firm to stop the creation of this inflationary wage level. Once the firm has all of the bargain-ing power, it is free to choose the level of capital and labor that will maximize its own profit function. This maximization will therefore lead to a socially optimal

level. The result in (4.4) suggests the op-timum bargaining power is negatively related to the wage rate, proving that it is the unions bargaining over wages and not their bargaining over labor that criti-cally affects bargaining power and sub-stitution for capital. This has led us to believe that Carruth and Oswald’s (1987) ‘insider-outsider’ approach may not be unrealistic as first thought.

For a strongly decreasing pollution function, the critical point in (4.10) rep-resents cleaner capital due to technologi-cal progress (4.7). An increase in wages therefore has an increased effect on op-timum bargaining power, such that it is desirable for the government to transfer all power to the trade union. This would result in increased wages, forcing the firm to invest in more capital stock. This transfer is characterized by (4.12) which shows that capital is strongly increasing until reaching the corner solution of ex-clusive use of capital in production. This full transfer of power would only be ap-propriate if capital produced no pollu-tion, however, as governments and firms invest into more alternatives to pollutant energy this could be a more realistic pros-pect. Equation (4.10) is more characteris-tic of a developed economy.

Some further assumptions allow the model to be implemented, and the find-ings have useful real-world implications. The first pollution function (4.1) signifies highly pollutant capital, much like those found in a developing world. This would allow governments to view whether un-ionization is a good mechanism to allow on a policy level if they are concerned with pollution. This would be most effec-tive if a trade union is not presently in-cumbent in a developing country, which is in the earlier stages of industrializa-tion, thus preventing the trade union to form and starting to accumulate great power over the government. This would give free reign for the firm to maximize their profits.

In this model, the government’s objec-tive is to maximize total welfare of the economy, taking into account the bar-gaining between the firm and the un-ion while subtracting the externalities caused. The model produces two stark results dependant on the pollutant na-ture of capital. The proof of Proposition 1 and Proposition 2 present us with a direct relationship between bargaining power and wages, allowing us to analyze how the government should act if it was pollu-tion conscious and how transferring bar-gaining power will have a direct effect on wages. The propositions not only provide

us with a direct relationship between the wage rate and bargaining power but also their effect on capital (4.6, 4.12). This re-lationship allows the government to ob-serve in greater depth how a change to the bargaining power will have direct and indirect relationships that will affect the ratio of capital and labor employed and thus alter the structure of the economy.

Concluding Remarks

There are many weaknesses to this sim-plistic model that need to be addressed and researched further. The main argu-ment assumes capital investment is eas-ily substituted in for labor when wages are too high. However, the level of capital stock is not only likely to be decided at an earlier date than when the bargaining has commenced, it is also a long term in-vestment with binding rental contracts. This would mean it could not be easily substituted. Additionally, substituting labor for capital is unrealistic for many capital-intensive industries such as min-ing or energy production, and the corner solution of proposition 1 is not attainable. Another weakness is the ease of transfer-ring bargaining power. Bargaining power is difficult to measure and implementing policy to affect it directly is also difficult. Any policies seen to be going after the trade union or firms specifically could be potentially harmful to the government in charge and the overall economy. Poli-cies concerning the trade union would aim at breaking up collective bargaining and would likely lead to unemployment. Therefore the government may value this above the threat of pollution.

Additionally, many concepts can be taken and further explored to provide greater insight into this and similar prob-

When the government opti-mizes according to its prefer-

ences— that include apollution function which is strongly increasing— the

optimal solution is a transfer of bargaining power toward

the firm.

Assuming that the govern-ment can freely modify the

bargaining power and the firm can easily substitute in capital

for labor, then knowledge of the pollutant nature of

capital can be used to predict which of the firm or the un-ions will have greatest bar-

gaining power.

Columbia Economics Review Columbia Economics Review

18 Spring 2013 Spring 2013 19

The current firm-union bargaining models use Nash’s generalized model, where ui is the utility curve of player i and d is the payoff when they fail to reach an agreement (the disagreement point). One of Nash’s axioms for the model, symmetry, implies that bargaining power is equal between the firm and the union, a fairly unrealistic assumption which I drop. The government then chooses this bargaining power by manipulating poli-cies to maximize its objective function. In this case the bargaining power is affected by the government’s preferences over the firm’s pollution output, characterized by CO2 emissions. The introduction of bar-gaining power θ, where allows greater manipulation and realism of the standard model.

The ModelAssumption 1. The trade union gains

utility from wages and labor with equal weighting. Hence, it wishes to maximize U(w,L) given by (3.1) U(w,L)=wL.

Assumption 2. The firm wishes to maxi-mize profits, denoted \pi, with respect to the choice variables K,L,w where . The rental price of capital, r, is exogenous. For simplicity, the firm produces one unit at a price normalized to one with a pro-duction function f(K,L)=KL. This gives a profit function for the firm of:

π(K,L,w,r)=KL-wL-rKUsing the Nash bargaining solution,

I use the standard model with the disa-greement point d omitted for simplicity.

Here I maximize the log of the profit function, and simplify further. The differ-entiation and first order condition can be seen in the appendix (A.3 - A.8).

The critical points are: L*=r* and w*=θ K*Assumption 3. The government places

weight on the effect of pollution on social welfare. Then the government maximizes G (δ) with respect to the bargaining pow-er θ: G(δ)=(1-δ)[π^*+U^*]-P(K)δ.

AnalysisEquation (3.6) shows that although

trade unions do not care directly for K, as it is not present in their utility func-tion, they are aware of the effects of capi-tal and the firm’s bargaining power over wages. When their bargaining solution is reached the labor employed depends on the rental price of capital (3.5) and wag-es depend on bargaining power and the level of capital (3.6). These conclusion are economically plausible, indeed if wages were high the firm would choose to sub-

stitute in capital for labor.Equation (3.7) represents the govern-

ment’s preferences over the economy and social welfare. This depletion in social welfare is caused by pollution from use of capital goods, where signifies how strongly the government values social welfare. The government wishes to maxi-mize its own function with respect to θ, the bargaining power. This however is highly dependent on the pollution func-tion P(K).

Proposition 1. When the government optimizes according to its preferences— that include a pollution function which is strongly increasing— the optimal so-lution is a transfer of bargaining power toward the firm. This increase in bargain-ing power θ reduces the level of pollut-ant capital employed due to the ability to deflate wage.

ProofI begin at (4.1) with a strongly increas-

ing pollution function (A.9). Here α and β are pollution scalars that allow the func-tion to react suitably: P(K)=αK+βK2

Function (4.1) has the property that the relationship between capital and pol-lution is positive and has an increasing marginal rate of pollution with respect to capital. This function has the same properties as the pollution function used in Forster (1973). I do not include the as-sumption of pollution causing an adverse effect on the marginal product of the fac-tors of production. I then substitute the strongly increasing pollution function (4.1) into the government welfare func-tion G (δ) (3.7):

G(δ)=(1-δ)[LK-rK-wL+wL]-(αK+βK2)δI substitute in the critical values (3.5,3.6)

found from maximizing (3.1) and (3.2) for the firm and trade union respectively. This gives a simplified version of the gov-ernment function:

G(δ,θ)=-(α(w/θ)+β(w/θ)2)δI now maximize this welfare function

(4.3) with respect to bargaining power θ to find the optimal level for the govern-ment, denoted θ*: θ*= -2w(β/α)

Taking α and β as arbitrary positive constants gives a direct relation between bargaining power and the wage rate. As such, under pollution intensive capital (4.1) I substitute the critical value (3.6) w*=θK* to find the optimal level of capi-tal: θ*= -2θ*K*.

Rearranging: K*= -1/2Substituting capital into the govern-

ment’s optimal level of θ*, the transfer of bargaining power toward the firm causes the level of capital to decrease. Therefore because capital employed must be posi-tive, the level of capital at the optimal θ*

is a corner solution at zero, proving the transfer of bargaining power has a nega-tive relationship with the level of pollut-ant capital employed and reaches an op-timal level of zero. This is due to (4.4) as an increase in bargaining power toward the firm deflates wages, thus making the substitution of labor for capital more ap-pealing.

Proposition 2. When the government maximizes its preferences including a pollution function which is strongly de-creasing, the optimal solution is a trans-fer of bargaining power toward the un-ion. The increase in bargaining power toward the union is directly related to the wage rate with the inflationary pressure on wages causing the firm to substitute in capital for labor. Therefore there is a positive relationship between the trade union’s bargaining power and capital employed.

ProofThe proof relies on using previous re-

sults of (3.1-3.7). The proof begins at (4.7)

where there is a strongly decreasing pol-lution function (A.11). I substitute this function into (3.7) and maximize the gov-ernment’s welfare G(δ) with respect to the bargaining power θ, where α and β are pollution scalars that allow the func-tion to react suitably. I do not include the assumption of pollution causing an adverse effect on the marginal product of the factors of production. I then sub-stitute the strongly decreasing pollution function (4.7) into the government wel-fare function (3.7): P(K)=α K+β(1/K)

G(δ)=(1-δ) [LK-rK-wL+wL]- [αK+β (1/K)]δ

Substituting in the critical values (3.5,3.6) found from maximizing (3.1) and (3.2) gives a simplified version of the gov-ernment function to be maximized.

I now maximize this welfare function (4.9) with respect to bargaining power, and find the optimal level for the govern-ment, denoted θ*

Taking α and β as arbitrary positive

constants again gives a direct relation between bargaining power and the wage rate. As such, assuming intensive capital (4.7) provides critical value (3.6) w*=θK* to find how capital reacts to a change in the bargaining power, θ: θ*=θ*K*

Rearranging: K*=1At this optimum, capital is strongly in-

creasing, therefore proving that the opti-mal transfer of bargaining power under a strongly decreasing pollution function has a positive relationship on the level of low pollutant capital employed until capital reaches its corner solution. This means that the firm employs capital as the only factor of production. This is be-cause the transfer of bargaining power toward the trade union causes inflation-ary wages, proven in (4.10), which force the firm to substitute in capital for labor.

Equations (4.4) and (4.10) represent the optimum level of θ if the government is maximizing total utility of the society as a whole. Take special note that the optimal bargaining power does not rely directly on labor.

DiscussionIn the case of a strongly increasing

pollution function, the critical point ex-pressed in (4.4) represents capital that causes pollution (4.1). Since capital is highly pollutant and the government is rational and gains a disutility over pol-lution it will transfer all of the bargaining power toward the firm. This is because if the government does not, then the trade union is still bargaining for wages and la-bor. This will inflate the cost and quantity of labor supplied causing an increase in the cost of production. This increase in the cost of labor will cause the firm to sub-stitute in capital for labor. This capital is inherently pollutant due to the pollution function. Therefore as the government wishes to reduce pollution it will give all of the bargaining power to the firm to stop the creation of this inflationary wage level. Once the firm has all of the bargain-ing power, it is free to choose the level of capital and labor that will maximize its own profit function. This maximization will therefore lead to a socially optimal

level. The result in (4.4) suggests the op-timum bargaining power is negatively related to the wage rate, proving that it is the unions bargaining over wages and not their bargaining over labor that criti-cally affects bargaining power and sub-stitution for capital. This has led us to believe that Carruth and Oswald’s (1987) ‘insider-outsider’ approach may not be unrealistic as first thought.

For a strongly decreasing pollution function, the critical point in (4.10) rep-resents cleaner capital due to technologi-cal progress (4.7). An increase in wages therefore has an increased effect on op-timum bargaining power, such that it is desirable for the government to transfer all power to the trade union. This would result in increased wages, forcing the firm to invest in more capital stock. This transfer is characterized by (4.12) which shows that capital is strongly increasing until reaching the corner solution of ex-clusive use of capital in production. This full transfer of power would only be ap-propriate if capital produced no pollu-tion, however, as governments and firms invest into more alternatives to pollutant energy this could be a more realistic pros-pect. Equation (4.10) is more characteris-tic of a developed economy.

Some further assumptions allow the model to be implemented, and the find-ings have useful real-world implications. The first pollution function (4.1) signifies highly pollutant capital, much like those found in a developing world. This would allow governments to view whether un-ionization is a good mechanism to allow on a policy level if they are concerned with pollution. This would be most effec-tive if a trade union is not presently in-cumbent in a developing country, which is in the earlier stages of industrializa-tion, thus preventing the trade union to form and starting to accumulate great power over the government. This would give free reign for the firm to maximize their profits.

In this model, the government’s objec-tive is to maximize total welfare of the economy, taking into account the bar-gaining between the firm and the un-ion while subtracting the externalities caused. The model produces two stark results dependant on the pollutant na-ture of capital. The proof of Proposition 1 and Proposition 2 present us with a direct relationship between bargaining power and wages, allowing us to analyze how the government should act if it was pollu-tion conscious and how transferring bar-gaining power will have a direct effect on wages. The propositions not only provide

us with a direct relationship between the wage rate and bargaining power but also their effect on capital (4.6, 4.12). This re-lationship allows the government to ob-serve in greater depth how a change to the bargaining power will have direct and indirect relationships that will affect the ratio of capital and labor employed and thus alter the structure of the economy.

Concluding Remarks

There are many weaknesses to this sim-plistic model that need to be addressed and researched further. The main argu-ment assumes capital investment is eas-ily substituted in for labor when wages are too high. However, the level of capital stock is not only likely to be decided at an earlier date than when the bargaining has commenced, it is also a long term in-vestment with binding rental contracts. This would mean it could not be easily substituted. Additionally, substituting labor for capital is unrealistic for many capital-intensive industries such as min-ing or energy production, and the corner solution of proposition 1 is not attainable. Another weakness is the ease of transfer-ring bargaining power. Bargaining power is difficult to measure and implementing policy to affect it directly is also difficult. Any policies seen to be going after the trade union or firms specifically could be potentially harmful to the government in charge and the overall economy. Poli-cies concerning the trade union would aim at breaking up collective bargaining and would likely lead to unemployment. Therefore the government may value this above the threat of pollution.

Additionally, many concepts can be taken and further explored to provide greater insight into this and similar prob-

When the government opti-mizes according to its prefer-

ences— that include apollution function which is strongly increasing— the

optimal solution is a transfer of bargaining power toward

the firm.

Assuming that the govern-ment can freely modify the

bargaining power and the firm can easily substitute in capital

for labor, then knowledge of the pollutant nature of

capital can be used to predict which of the firm or the un-ions will have greatest bar-

gaining power.

Columbia Economics Review Columbia Economics Review

18 Spring 2013 Spring 2013 19

lems. Looking for a more complex utility curve for the trade union using differ-ent trade union models is a good start-ing point for developing the arguments presented; Manning (1987) summarizes examples from other economists. For ex-ample, the ‘right to manage’ assumes the firm chooses the level of employment and the trade union bargains over the wage only. In addition to this, the isoprofit curve of the firm can also be investigated further. This paper contains a profit func-tion that included constant returns to scale; there is dispute over whether this exists or whether decreasing or increas-ing returns to scale should be included instead. Additionally, a more realistic and useful approach concerning the cost of capital would be to include an increas-ing time dependant variable, which could signify the price depreciation that capital faces as well as the increased upkeep cost. This in turn would alter the ratio of factors of production.

Aside from differences in the modeling of preferences of the trade union and the firm, other alterations could be made to further enhance the model and assump-tions. Forster’ s (1973) pollution function, much like this one, does not include any variables that display the negative effect pollution has on the work force. Pollu-tion such as air pollution and water pol-lution could cause a worker to miss work or work at a slower rate due to illness; this will reduce productivity of labor. Furthermore, a model such as Oswald’s (1979) deals with wage determination in a setting with competing trade unions that follow each other’s actions. Oswald calls this the ‘jealousy effect’. Generalizing the bargaining problem to include more than one trade union could also provide insight into more readily applicable con-clusions.

The purpose of this paper was to ex-plore more closely the transfer of bar-gaining power between the trade union and the firm when under observation by a pollution conscious government. As-suming that the government can freely modify the bargaining power and the firm can easily substitute in capital for labor, then knowledge of the pollutant nature of capital can be used to predict which of the firm or the unions will have greatest bargaining power. From (4.4) and Proposition 1 it is clear that an in-crease in the bargaining power of the firm will cause a decrease in wages and thus an increase in labor employed; this is an intuitive result. However, a more striking result is that if capital is highly pollutant then the government would transfer all

bargaining power toward the firm, creat-ing a corner solution at zero for capital purchased. Even though the union is bar-gaining over wages and labor, one would think the bargaining for labor would deem it optimal to transfer power toward the trade union as the substitution of la-bor for pollutant intensive capital is the government’s desired goal. However, the government transfers the power toward the firm to deflate wages and increase employment. Nonetheless, labor is not a direct determinant of bargaining power, as unlike wages, it does not appear in the bargaining power function. This leads to further questioning and scrutiny of this model and its implications toward the logistics of collective bargaining. The simplistic nature of this model creates a need for further research into alternative models, as well as an extensive evalua-tion, before any strong conclusions can be drawn.

A. AppendixA.1 Nash’s Bargaining ProblemThe standard model of the Nash bar-

gaining problem:

This is subject to and for both i.

An extension of this model which we will be adopting can be seen below. Here θ represents bargaining power.

This is subject to and for both i.

A.2 Solving the bargaining problemIf we label the equation (3.4) D then we

have three first order conditions omitting *s until the end:

Thus giving us two critical points of: L*=r* and w*=θK*

A.3 Properties of the pollution functionHighly pollutant capital: P(K)=αK+βK2

Less pollutant capital: P(K)=αK+β(1/K)

A.4 Differentiation of the government function including the pollution function

Below is the government’s welfare function that they wish to maximize, with δ being the degree to which they dislike pollution: G(δ)=(1-δ)[π*+U*]-P(K)δ

Inserting in our values for π, U, and P(K) first gives us, omitting asterisks:

G(δ,θ)=(1-δ)[LK-rK-wL+wL]-(αK+βK2)δ

Using proof (A.6) and (A.7) to substi-tute and simplify the expression:

Thus giving us: θ*=-2w(β/α)

Using our less polluting function (A.10) gives us: G(δ,θ)=(1-δ)[LK-rK-wL+wL]-[αK+β(1/K)]δ

Which gives us:

▪

Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

Asher Hecht

Columbia University

Mexico has a long history of currency troubles. Since 1945, it has made six at-tempts at fixing the exchange rate of the peso, each ending in crisis. Most followed identical scripts: fixing the exchange rate led to economic prosperity, real apprecia-tion of the currency, and accumulation of deficits. Each episode ended with some external shock that forced Mexico to de-value the peso.

The most interesting and instructive of these currency crises is that which triggered the Tequila Crisis in 1994. The events leading to the devaluation of the peso and eventually to the abandonment of a currency band included civil upris-ing, political assassinations, and a contro-versial presidential election. Moreover, the effects of this currency devaluation were not confined to Mexico, but spread through much of South America. The de-valuation of the peso and subsequent the subsequent widespread economic disas-ter prompted many economists to study this episode to gain insights into the dy-namics of currency crises.

This paper analyzes the events leading up to the floating of the peso, with spe-cial attention paid to the role of expecta-tions of market participants and feedback loops between policymakers and finan-cial markets. The complexity of this crisis makes it difficult to defend a one-model

approach, but by accommodating vari-ous models we gain insight into both the fundamental origins of the crisis and the channels of feedback that affected it.

Models of Currency CrisesThe bedrock of “first-generation” mod-

els of currency crises is Krugman (1979), in which unsustainable policy results unavoidably in a balance-of-payments crisis. In Krugman’s model, the protec-tion of the fixed exchange rate regime is a subordinated part of a policy package: the exchange rate will only be held if it does not conflict with other policy objec-tives. For example, a central bank could

be required to finance the government’s budget deficit by acquiring newly issued debt at the rate issued by the government. Thus, there is a policy tension between maintaining the fixed exchange rate and accomplishing the primary policy objec-tive of financing a fully monetized deficit.

The central bank finances the deficit by selling off foreign reserves. Denoting the real deficit as d and the stock of foreign currency reserves as R, under a fixed ex-change rate we have:

𝑅𝑅𝑅𝑅�̇�𝑡𝑡𝑡 = −𝑑𝑑𝑑𝑑 (1)Since demand for money is constant and

is a function of the international interest rate L(i*) the additional domestic money released by the central bank to finance the deficit is not demanded by the public. Under purchas-ing power parity (PPP) this excess money cannot affect prices, and thus only the ex-change of domestic for foreign currency en-sures equilibrium. Given the fixed-exchange mandate, the central bank is required to sell off foreign reserves one-for-one to finance the deficit. Equation (1) shows one of the essen-tial features of currency crises, that some ex-ogenously determined policy creates a drain on reserves over time. The most important insight of Krugman’s model is that reserves will plummet, having come under a specu-lative attack, and that at this exact moment the government must switch from a fixed ex-

Most followed identical scripts: fixing the exchange

rate led to economic prosper-ity, real appreciation of the currency, and accumulation

of deficits. Each episode ended with some external shock that forced Mexico to devalue the

peso.

Columbia Economics Review Columbia Economics Review

20 Spring 2013 Spring 2013 21

lems. Looking for a more complex utility curve for the trade union using differ-ent trade union models is a good start-ing point for developing the arguments presented; Manning (1987) summarizes examples from other economists. For ex-ample, the ‘right to manage’ assumes the firm chooses the level of employment and the trade union bargains over the wage only. In addition to this, the isoprofit curve of the firm can also be investigated further. This paper contains a profit func-tion that included constant returns to scale; there is dispute over whether this exists or whether decreasing or increas-ing returns to scale should be included instead. Additionally, a more realistic and useful approach concerning the cost of capital would be to include an increas-ing time dependant variable, which could signify the price depreciation that capital faces as well as the increased upkeep cost. This in turn would alter the ratio of factors of production.

Aside from differences in the modeling of preferences of the trade union and the firm, other alterations could be made to further enhance the model and assump-tions. Forster’ s (1973) pollution function, much like this one, does not include any variables that display the negative effect pollution has on the work force. Pollu-tion such as air pollution and water pol-lution could cause a worker to miss work or work at a slower rate due to illness; this will reduce productivity of labor. Furthermore, a model such as Oswald’s (1979) deals with wage determination in a setting with competing trade unions that follow each other’s actions. Oswald calls this the ‘jealousy effect’. Generalizing the bargaining problem to include more than one trade union could also provide insight into more readily applicable con-clusions.

The purpose of this paper was to ex-plore more closely the transfer of bar-gaining power between the trade union and the firm when under observation by a pollution conscious government. As-suming that the government can freely modify the bargaining power and the firm can easily substitute in capital for labor, then knowledge of the pollutant nature of capital can be used to predict which of the firm or the unions will have greatest bargaining power. From (4.4) and Proposition 1 it is clear that an in-crease in the bargaining power of the firm will cause a decrease in wages and thus an increase in labor employed; this is an intuitive result. However, a more striking result is that if capital is highly pollutant then the government would transfer all

bargaining power toward the firm, creat-ing a corner solution at zero for capital purchased. Even though the union is bar-gaining over wages and labor, one would think the bargaining for labor would deem it optimal to transfer power toward the trade union as the substitution of la-bor for pollutant intensive capital is the government’s desired goal. However, the government transfers the power toward the firm to deflate wages and increase employment. Nonetheless, labor is not a direct determinant of bargaining power, as unlike wages, it does not appear in the bargaining power function. This leads to further questioning and scrutiny of this model and its implications toward the logistics of collective bargaining. The simplistic nature of this model creates a need for further research into alternative models, as well as an extensive evalua-tion, before any strong conclusions can be drawn.

A. AppendixA.1 Nash’s Bargaining ProblemThe standard model of the Nash bar-

gaining problem:

This is subject to and for both i.

An extension of this model which we will be adopting can be seen below. Here θ represents bargaining power.

This is subject to and for both i.

A.2 Solving the bargaining problemIf we label the equation (3.4) D then we

have three first order conditions omitting *s until the end:

Thus giving us two critical points of: L*=r* and w*=θK*

A.3 Properties of the pollution functionHighly pollutant capital: P(K)=αK+βK2

Less pollutant capital: P(K)=αK+β(1/K)

A.4 Differentiation of the government function including the pollution function

Below is the government’s welfare function that they wish to maximize, with δ being the degree to which they dislike pollution: G(δ)=(1-δ)[π*+U*]-P(K)δ

Inserting in our values for π, U, and P(K) first gives us, omitting asterisks:

G(δ,θ)=(1-δ)[LK-rK-wL+wL]-(αK+βK2)δ

Using proof (A.6) and (A.7) to substi-tute and simplify the expression:

Thus giving us: θ*=-2w(β/α)

Using our less polluting function (A.10) gives us: G(δ,θ)=(1-δ)[LK-rK-wL+wL]-[αK+β(1/K)]δ

Which gives us:

▪

Pregaming the Tequila CrisisThe Role of Structural Imbalances and Expectation in Currency Crises

Asher Hecht

Columbia University

Mexico has a long history of currency troubles. Since 1945, it has made six at-tempts at fixing the exchange rate of the peso, each ending in crisis. Most followed identical scripts: fixing the exchange rate led to economic prosperity, real apprecia-tion of the currency, and accumulation of deficits. Each episode ended with some external shock that forced Mexico to de-value the peso.

The most interesting and instructive of these currency crises is that which triggered the Tequila Crisis in 1994. The events leading to the devaluation of the peso and eventually to the abandonment of a currency band included civil upris-ing, political assassinations, and a contro-versial presidential election. Moreover, the effects of this currency devaluation were not confined to Mexico, but spread through much of South America. The de-valuation of the peso and subsequent the subsequent widespread economic disas-ter prompted many economists to study this episode to gain insights into the dy-namics of currency crises.

This paper analyzes the events leading up to the floating of the peso, with spe-cial attention paid to the role of expecta-tions of market participants and feedback loops between policymakers and finan-cial markets. The complexity of this crisis makes it difficult to defend a one-model

approach, but by accommodating vari-ous models we gain insight into both the fundamental origins of the crisis and the channels of feedback that affected it.

Models of Currency CrisesThe bedrock of “first-generation” mod-

els of currency crises is Krugman (1979), in which unsustainable policy results unavoidably in a balance-of-payments crisis. In Krugman’s model, the protec-tion of the fixed exchange rate regime is a subordinated part of a policy package: the exchange rate will only be held if it does not conflict with other policy objec-tives. For example, a central bank could

be required to finance the government’s budget deficit by acquiring newly issued debt at the rate issued by the government. Thus, there is a policy tension between maintaining the fixed exchange rate and accomplishing the primary policy objec-tive of financing a fully monetized deficit.

The central bank finances the deficit by selling off foreign reserves. Denoting the real deficit as d and the stock of foreign currency reserves as R, under a fixed ex-change rate we have:

𝑅𝑅𝑅𝑅�̇�𝑡𝑡𝑡 = −𝑑𝑑𝑑𝑑 (1)Since demand for money is constant and

is a function of the international interest rate L(i*) the additional domestic money released by the central bank to finance the deficit is not demanded by the public. Under purchas-ing power parity (PPP) this excess money cannot affect prices, and thus only the ex-change of domestic for foreign currency en-sures equilibrium. Given the fixed-exchange mandate, the central bank is required to sell off foreign reserves one-for-one to finance the deficit. Equation (1) shows one of the essen-tial features of currency crises, that some ex-ogenously determined policy creates a drain on reserves over time. The most important insight of Krugman’s model is that reserves will plummet, having come under a specu-lative attack, and that at this exact moment the government must switch from a fixed ex-

Most followed identical scripts: fixing the exchange

rate led to economic prosper-ity, real appreciation of the currency, and accumulation

of deficits. Each episode ended with some external shock that forced Mexico to devalue the

peso.

Columbia Economics Review Columbia Economics Review

20 Spring 2013 Spring 2013 21

change rate to a floating one.To illustrate this, it is easiest to examine

first what happens under the floating-rate re-gime. After reserves are exhausted, the gov-ernment will be forced to abandon the fixed exchange rate and will no longer be able to use the mechanism illustrated in Equation (1). Instead prices will adjust to accommodate the

currency devaluation. The rate of inflation will be equal to the devaluation due to PPP, and thus in the steady-state of the floating re-gime we have:

(2)Equations (1) and (2) show what hap-

pens during the fixed and flexible phases respectively. Equation (2) requires that

inflation be positive and therefore im-plies a discrete increase in the nominal interest rate at precisely the time that the fixed-rate is abandoned, causing a decrease in money demand. Under the assumption of perfect foresight and per-fect capital mobility, Krugman rules out a discrete jump in the exchange rate at

any time, because any perfectly foresee-able change in the exchange rate would represent an infinitely arbitragable profit opportunity. Thus, at the time when the exchange rate regime switches there can be no change in the exchange rate itself. However, given the different demands for money implied by equations (1) and (2) there will be a loss of reserves at the switch time.

(3)Equation (3) determines the amount

of reserves that will be lost up until the switch time (T). After time T, there will be a run against the currency and re-serves will be driven down to zero. This is the most important result of the Krug-man model: there can be , under perfect foresight ,a gradual depletion of reserves followed by a perfectly predicted specu-lative attack. Additionally, at this time T, the fixed exchange rate is precisely equal to what the exchange rate would be un-der a flexible rate policy.

The Krugman model is particularly useful in examining currency crises, be-cause it predicts a common pattern: the steady depletion of reserves followed by a run that completely depletes foreign exchange reserves and forces the central bank to abandon a fixed exchange rate.

The Role of ExpectationsExpanding on Krugman’s work, Ob-

stfeld (1986) pioneered “second-gener-ation” models of currency crises by in-corporating the role of expectations and their interaction with policy-making de-cisions. It is not necessary for the sake of this paper to expand on the particulars of Obstfeld’s model, but his main contri-bution is the proof of multiple equilibria resulting from self-fulfilling expecta-tions. One key distinction between first and second-generation models is the role of policy chosen by the central bank. In Krugman (1979), the government ex-ogenously determines macroeconomic policy; the central bank merely follows a mandate. However, in later endogenous-policy models, the policymakers respond optimally to macroeconomic shocks.

The essential feature of Obstfeld’s mod-el is that there can be multiple equilibria that depend on investors’ expectations and that the actions of investors are in-formed by risks determined by financial markets that may or may not reflect the underlying macroeconomic fundamen-tals. The expectations of market partici-pants, which are volatile and subject to sudden and arbitrary shifts, thus play an integral role in determining whether or not fixed exchange rate systems can

survive. The implications of Obstfeld’s model are important in understanding and appreciating the unpredictability of balance-of-payments crises and draw at-tention to the instability that high degrees of capital mobility cause. However, while this model, and those like it, adds a layer of depth to the analysis of currency cri-ses, it offers no insight as to what causes the shift in investor sentiment as it can vary on a case-by-case basis.

The Consequences of Capital Mobility and Investors’ Expectations

Calvo and Mendoza (1996) introduce a model that they assert fits the Mexican cri-

sis well. Their approach blends aspects of both first and second-generation models as it results in a Krugman-like speculative attack but is precipitated by investors’ expectations about future bailouts of banks. Calvo and Mendoza propose a scenario in which there are large capital inflows channeled through banks. The central bank is implicitly desig-nated as the lender of last resort in cases of panic and crisis, and as such depositors lack strong incentives to closely monitor the char-acteristics of bank loans. Particularly, there is a maturity mismatch between long-term loans and short-term deposits, and as such the cen-tral bank acquires short-term obligations in excess.

Source: The International Monetary Fund (IMF), International Financial Statistics

Source: The International Monetary Fund (IMF), International Financial

Columbia Economics Review Columbia Economics Review

22 Spring 2013 Spring 2013 23

change rate to a floating one.To illustrate this, it is easiest to examine

first what happens under the floating-rate re-gime. After reserves are exhausted, the gov-ernment will be forced to abandon the fixed exchange rate and will no longer be able to use the mechanism illustrated in Equation (1). Instead prices will adjust to accommodate the

currency devaluation. The rate of inflation will be equal to the devaluation due to PPP, and thus in the steady-state of the floating re-gime we have:

(2)Equations (1) and (2) show what hap-

pens during the fixed and flexible phases respectively. Equation (2) requires that

inflation be positive and therefore im-plies a discrete increase in the nominal interest rate at precisely the time that the fixed-rate is abandoned, causing a decrease in money demand. Under the assumption of perfect foresight and per-fect capital mobility, Krugman rules out a discrete jump in the exchange rate at

any time, because any perfectly foresee-able change in the exchange rate would represent an infinitely arbitragable profit opportunity. Thus, at the time when the exchange rate regime switches there can be no change in the exchange rate itself. However, given the different demands for money implied by equations (1) and (2) there will be a loss of reserves at the switch time.

(3)Equation (3) determines the amount

of reserves that will be lost up until the switch time (T). After time T, there will be a run against the currency and re-serves will be driven down to zero. This is the most important result of the Krug-man model: there can be , under perfect foresight ,a gradual depletion of reserves followed by a perfectly predicted specu-lative attack. Additionally, at this time T, the fixed exchange rate is precisely equal to what the exchange rate would be un-der a flexible rate policy.

The Krugman model is particularly useful in examining currency crises, be-cause it predicts a common pattern: the steady depletion of reserves followed by a run that completely depletes foreign exchange reserves and forces the central bank to abandon a fixed exchange rate.

The Role of ExpectationsExpanding on Krugman’s work, Ob-

stfeld (1986) pioneered “second-gener-ation” models of currency crises by in-corporating the role of expectations and their interaction with policy-making de-cisions. It is not necessary for the sake of this paper to expand on the particulars of Obstfeld’s model, but his main contri-bution is the proof of multiple equilibria resulting from self-fulfilling expecta-tions. One key distinction between first and second-generation models is the role of policy chosen by the central bank. In Krugman (1979), the government ex-ogenously determines macroeconomic policy; the central bank merely follows a mandate. However, in later endogenous-policy models, the policymakers respond optimally to macroeconomic shocks.

The essential feature of Obstfeld’s mod-el is that there can be multiple equilibria that depend on investors’ expectations and that the actions of investors are in-formed by risks determined by financial markets that may or may not reflect the underlying macroeconomic fundamen-tals. The expectations of market partici-pants, which are volatile and subject to sudden and arbitrary shifts, thus play an integral role in determining whether or not fixed exchange rate systems can

survive. The implications of Obstfeld’s model are important in understanding and appreciating the unpredictability of balance-of-payments crises and draw at-tention to the instability that high degrees of capital mobility cause. However, while this model, and those like it, adds a layer of depth to the analysis of currency cri-ses, it offers no insight as to what causes the shift in investor sentiment as it can vary on a case-by-case basis.

The Consequences of Capital Mobility and Investors’ Expectations

Calvo and Mendoza (1996) introduce a model that they assert fits the Mexican cri-

sis well. Their approach blends aspects of both first and second-generation models as it results in a Krugman-like speculative attack but is precipitated by investors’ expectations about future bailouts of banks. Calvo and Mendoza propose a scenario in which there are large capital inflows channeled through banks. The central bank is implicitly desig-nated as the lender of last resort in cases of panic and crisis, and as such depositors lack strong incentives to closely monitor the char-acteristics of bank loans. Particularly, there is a maturity mismatch between long-term loans and short-term deposits, and as such the cen-tral bank acquires short-term obligations in excess.

Source: The International Monetary Fund (IMF), International Financial Statistics

Source: The International Monetary Fund (IMF), International Financial

Columbia Economics Review Columbia Economics Review

22 Spring 2013 Spring 2013 23

In this model, deposit and interest rates are the same in equilibrium and are the same as in the Krugman model: i* before T and i* |π afterwards. There is an influx of investment of the amount Z that is facilitated by banks that extend infinite maturity loans financed by instant maturity deposits. A bank run oc-curs when depositors foresee devaluation and want to exchange their domestic assets for foreign ones. The Central Bank, as the de facto lender of last resort, is forced to issue base money in exchange for the banks’ port-folio of assets. Thus, the result is similar to the Krugman model, except that the govern-ment deficits are augmented by the interest on the banks’ loans: 𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗ + 𝜋𝜋𝜋𝜋) = 𝑑𝑑𝑑𝑑 − 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖∗ (4)Similarly, the loss of reserves at time T

is: ∆𝑅𝑅𝑅𝑅 = 𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗) − 𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗ + 𝜋𝜋𝜋𝜋) + 𝑍𝑍𝑍𝑍 > 0 (5)It follows that Z magnifies the fall in re-

serves at T for sufficiently small values of i*. The same level of depletion (-d) occurs prior to time T, but in this model ∆R is larger and thus T is smaller. Ultimately, the expectation of an impending banking crisis speeds up the collapse of foreign reserves and reinforces a downward spiral.

Investors’ herding behavior is the second major feature of Calvo and Mendoza’s model. The details are beyond the scope of this pa-per, but Calvo and Mendoza demonstrate that if investors are optimizing a mean-variance portfolio strategy, then highly diversified port-folios can lead to calamitous results. Inves-tors are highly sensitive to news concerning the quality of investments in a given country, even if the news is “small” and not given with absolute certainty. Additionally, the more di-versified an investor is, the less incentive he has to learn about the actual quality of invest-ments in a given country. Together, these re-sults create an investment landscape in which news or rumors about a country cause sudden

and severe capital flight, even if the underly-ing fundamentals remain largely unchanged.

Calvo and King (1998) note that the same model of investor behavior has particularly dangerous implications in a case where the debt of a country is mainly composed of short-term obligations. If investors demand full repayment before rolling over debt, this could put strain on the government’s ability to finance the debt and necessitate devalua-tion.

The BuildupThe Tequila Crisis was particularly

surprising because Mexico appeared to be on the verge of emerging as a stable and robust economy in the early 1990s. Despite five previous attempts at creat-ing a stable fixed exchange rate system, there was significant confidence, both within Mexico and in the global commu-nity, that this time was different. Much of this belief came from confidence in the Pacto. The Pacto was a comprehensive at-tempt at structural reform that created a social “pact” between the government, workers, and firms. These groups met regularly to make macroeconomic deci-sions concerning prices, wages, and the exchange rate. There was a general opti-mism that President Carlos Salinas had freed Mexico from its sordid history of economic calamity. In addition to inter-nal reforms, Mexico was realigning itself with the global economic community and embracing globalization. The Salinas ad-ministration restructured their foreign debt through the Brady Plan, reduced the budget deficit, curbed inflation, eliminated trade barriers, and privatized government-controlled industries. The North American Free Trade Agreement (NAFTA) was ratified on January 1, 1994 and was expected to solidify Mexico’s

position as a stable contributor to the world economy. However, between 1988 and 1994 GDP growth was somewhat un-even, indicating that Mexico’s economy was still adjusting to the large structural shifts.

The real appreciation of the peso and the mounting current account deficit were Mexico’s central economic prob-lems. Prior to the end of 1994, Mexico had a crawling peg exchange rate system in which the upper limit of the exchange rate band was raised daily by a predeter-mined amount. However, these adjust-ments were small and did not do enough to account for the real appreciation of the peso. The real appreciation was fueled by higher inflation in Mexico than in the United States (after adjusting for any de-preciation), increasing the purchasing power of pesos. This meant that prices of Mexican goods rose relative to those from the United States. This, in turn, drove up the current account deficit as imports bal-looned by 400 percent from 1987 to 1994 while export growth lagged behind.

The mounting current account deficit and real appreciation of the peso did not go unnoticed, and the idea of devaluing the peso was actively discussed. Dorn-busch and Werner (1994) suggested a devaluation of 20 percent in March 1994.

They argued that the stabilization strat-egy pursued by Salinas had left Mexico vulnerable, with firms facing high real interest rates, increasing nonperform-ing loans, and real appreciation fueling a mounting current account deficit. How-ever, other economists argued that Mexi-co was simply adjusting to a new equilib-rium induced by the structural reforms of the Pacto and other policies. Regardless, no one expected the collapse of the peso

Source: The International Monetary Fund (IMF), International Financial Statistics

at the end of 1994 to play out as it did. The consensus worst-case-scenario was that Mexico would devalue its currency, cut the current account deficit, and con-tinue its positive economic trajectory af-ter a period of adjustment.

The Crash: Political Shocks and a Col-lapsing Economy

The most probable explanation for the devaluation of the peso (and the one cited by the Bank of Mexico) is a series of po-litical shocks that began in 1994. At the beginning of 1994, the Zapatistas in the Southern province of Chiapas declared war on the Mexican government and be-gan an armed uprising. This raised con-siderable doubts about the political stabil-ity of Mexico that were only heightened by the 1994 election. However, investors seemed not to react dramatically to the news of the uprising, as the political fu-ture of Mexico seemed to be stable. Luis Donado Colosio was the ruling party’s candidate, considered the overwhelming favorite to win the election. On March 23, Colosio was assassinated. Financial panic ensued, as investors rapidly pulled funds out of Mexico, forcing the Bank of Mexico to intervene heavily in order to keep the value of the peso fixed. The result was a massive draining of international re-serves, almost $11 billion in a four-week period.

Reserve depletion was not the only ad-verse effect of the assassination, as inter-est rates spiked and the peso depreciated. The majority of Mexico’s debt at this time was in the form of cetes, short-term bonds similar to US Treasury Bills. The 28-day cetes auctioned after the assassination had an interest rate of 16.4 percent while those offered in February were sold at 9.5 percent (Whitt 1996). The Mexican gov-ernment also utilized the flexibility of the exchange rate band as it allowed the peso to approach the top of the band reflecting a depreciation of roughly 8 percent from January to March.

Following the assassination, investor confidence in Mexico’s stability continu-ally wavered. Late June saw an attempted resignation from the Minister of Interior, Jorge Capizo, who was supposed to over-see the election, as well as the kidnapping of a prominent businessman. Elections proceeded without major incident in Au-gust, and the ruling party candidate, Er-nesto Zedillo, won by a convincing mar-gin. However, the turmoil did not abate, and in late September one of the highest officials of the ruling party, Jose Francisco Ruiz Massieu, was assassinated. Finally, in mid-November, Deputy Attorney Gen-

eral Mario Ruiz Massieu, brother of the deceased, accused the ruling party of or-chestrating the assassination.

Foreign currency flooded out of Mexico as international reserves shrunk to $12 billion, from $26 billion in January, put-ting even more pressure on the currency peg. On December 20, 1994 the govern-ment decided to devalue the peso by 15 percent to roughly four pesos per U.S. dollar. However, this was only a tempo-rary measure, and international reserves continued to rush out of Mexico. $4.5 bil-lion in reserves were withdrawn the fol-lowing day, on December 21, the largest single-day movement of the year. On De-cember 22, Mexico was forced to let the peso float, and it jumped to 5.3 pesos per dollar by the end of December. The re-sult was nothing less than financial crisis. Credit markets dried up, the peso plum-meted, and interest rates increased sharp-ly. Mexico had lost its reputation as a safe

and viable place to invest, and the ensu-ing financial crisis spread to other parts of South America.

The Role of DebtLeading up to the floating of the peso,

Mexico’s biggest policy response to the increased pressure on the currency was to shift the composition of their debt. After the Colosio assassination in March, Mexico began to offer larger and larger quantities of dollar-denominated debt in the form of tesobonos. The goal was to ease investors’ worries about in-vesting in peso-denominated assets and assuming risk associated with currency devaluation. However, by November 1994, peso-denominated cetes comprised only 25 percent of Mexico’s debt, down from 75 percent in December 1993, while tesobonos accounted for 70 percent of the Mexican government’s securities.

The switch to tesobonos from cetes was

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

Source: The International Monetary Fund (IMF), International Financial Statistics

The Krugman model is par-ticularly useful in examining

currency crises, because it predicts a common pattern: the steady depletion of re-

serves followed by a run that completely depletes foreign

exchange reserves and forces the central bank to abandon a

fixed exchange rate.

Columbia Economics Review Columbia Economics Review

24 Spring 2013 Spring 2013 25

In this model, deposit and interest rates are the same in equilibrium and are the same as in the Krugman model: i* before T and i* |π afterwards. There is an influx of investment of the amount Z that is facilitated by banks that extend infinite maturity loans financed by instant maturity deposits. A bank run oc-curs when depositors foresee devaluation and want to exchange their domestic assets for foreign ones. The Central Bank, as the de facto lender of last resort, is forced to issue base money in exchange for the banks’ port-folio of assets. Thus, the result is similar to the Krugman model, except that the govern-ment deficits are augmented by the interest on the banks’ loans: 𝜋𝜋𝜋𝜋𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗ + 𝜋𝜋𝜋𝜋) = 𝑑𝑑𝑑𝑑 − 𝑍𝑍𝑍𝑍𝑖𝑖𝑖𝑖∗ (4)Similarly, the loss of reserves at time T

is: ∆𝑅𝑅𝑅𝑅 = 𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗) − 𝜋𝜋𝜋𝜋(𝑖𝑖𝑖𝑖∗ + 𝜋𝜋𝜋𝜋) + 𝑍𝑍𝑍𝑍 > 0 (5)It follows that Z magnifies the fall in re-

serves at T for sufficiently small values of i*. The same level of depletion (-d) occurs prior to time T, but in this model ∆R is larger and thus T is smaller. Ultimately, the expectation of an impending banking crisis speeds up the collapse of foreign reserves and reinforces a downward spiral.

Investors’ herding behavior is the second major feature of Calvo and Mendoza’s model. The details are beyond the scope of this pa-per, but Calvo and Mendoza demonstrate that if investors are optimizing a mean-variance portfolio strategy, then highly diversified port-folios can lead to calamitous results. Inves-tors are highly sensitive to news concerning the quality of investments in a given country, even if the news is “small” and not given with absolute certainty. Additionally, the more di-versified an investor is, the less incentive he has to learn about the actual quality of invest-ments in a given country. Together, these re-sults create an investment landscape in which news or rumors about a country cause sudden

and severe capital flight, even if the underly-ing fundamentals remain largely unchanged.

Calvo and King (1998) note that the same model of investor behavior has particularly dangerous implications in a case where the debt of a country is mainly composed of short-term obligations. If investors demand full repayment before rolling over debt, this could put strain on the government’s ability to finance the debt and necessitate devalua-tion.

The BuildupThe Tequila Crisis was particularly

surprising because Mexico appeared to be on the verge of emerging as a stable and robust economy in the early 1990s. Despite five previous attempts at creat-ing a stable fixed exchange rate system, there was significant confidence, both within Mexico and in the global commu-nity, that this time was different. Much of this belief came from confidence in the Pacto. The Pacto was a comprehensive at-tempt at structural reform that created a social “pact” between the government, workers, and firms. These groups met regularly to make macroeconomic deci-sions concerning prices, wages, and the exchange rate. There was a general opti-mism that President Carlos Salinas had freed Mexico from its sordid history of economic calamity. In addition to inter-nal reforms, Mexico was realigning itself with the global economic community and embracing globalization. The Salinas ad-ministration restructured their foreign debt through the Brady Plan, reduced the budget deficit, curbed inflation, eliminated trade barriers, and privatized government-controlled industries. The North American Free Trade Agreement (NAFTA) was ratified on January 1, 1994 and was expected to solidify Mexico’s

position as a stable contributor to the world economy. However, between 1988 and 1994 GDP growth was somewhat un-even, indicating that Mexico’s economy was still adjusting to the large structural shifts.

The real appreciation of the peso and the mounting current account deficit were Mexico’s central economic prob-lems. Prior to the end of 1994, Mexico had a crawling peg exchange rate system in which the upper limit of the exchange rate band was raised daily by a predeter-mined amount. However, these adjust-ments were small and did not do enough to account for the real appreciation of the peso. The real appreciation was fueled by higher inflation in Mexico than in the United States (after adjusting for any de-preciation), increasing the purchasing power of pesos. This meant that prices of Mexican goods rose relative to those from the United States. This, in turn, drove up the current account deficit as imports bal-looned by 400 percent from 1987 to 1994 while export growth lagged behind.

The mounting current account deficit and real appreciation of the peso did not go unnoticed, and the idea of devaluing the peso was actively discussed. Dorn-busch and Werner (1994) suggested a devaluation of 20 percent in March 1994.

They argued that the stabilization strat-egy pursued by Salinas had left Mexico vulnerable, with firms facing high real interest rates, increasing nonperform-ing loans, and real appreciation fueling a mounting current account deficit. How-ever, other economists argued that Mexi-co was simply adjusting to a new equilib-rium induced by the structural reforms of the Pacto and other policies. Regardless, no one expected the collapse of the peso

Source: The International Monetary Fund (IMF), International Financial Statistics

at the end of 1994 to play out as it did. The consensus worst-case-scenario was that Mexico would devalue its currency, cut the current account deficit, and con-tinue its positive economic trajectory af-ter a period of adjustment.

The Crash: Political Shocks and a Col-lapsing Economy

The most probable explanation for the devaluation of the peso (and the one cited by the Bank of Mexico) is a series of po-litical shocks that began in 1994. At the beginning of 1994, the Zapatistas in the Southern province of Chiapas declared war on the Mexican government and be-gan an armed uprising. This raised con-siderable doubts about the political stabil-ity of Mexico that were only heightened by the 1994 election. However, investors seemed not to react dramatically to the news of the uprising, as the political fu-ture of Mexico seemed to be stable. Luis Donado Colosio was the ruling party’s candidate, considered the overwhelming favorite to win the election. On March 23, Colosio was assassinated. Financial panic ensued, as investors rapidly pulled funds out of Mexico, forcing the Bank of Mexico to intervene heavily in order to keep the value of the peso fixed. The result was a massive draining of international re-serves, almost $11 billion in a four-week period.

Reserve depletion was not the only ad-verse effect of the assassination, as inter-est rates spiked and the peso depreciated. The majority of Mexico’s debt at this time was in the form of cetes, short-term bonds similar to US Treasury Bills. The 28-day cetes auctioned after the assassination had an interest rate of 16.4 percent while those offered in February were sold at 9.5 percent (Whitt 1996). The Mexican gov-ernment also utilized the flexibility of the exchange rate band as it allowed the peso to approach the top of the band reflecting a depreciation of roughly 8 percent from January to March.

Following the assassination, investor confidence in Mexico’s stability continu-ally wavered. Late June saw an attempted resignation from the Minister of Interior, Jorge Capizo, who was supposed to over-see the election, as well as the kidnapping of a prominent businessman. Elections proceeded without major incident in Au-gust, and the ruling party candidate, Er-nesto Zedillo, won by a convincing mar-gin. However, the turmoil did not abate, and in late September one of the highest officials of the ruling party, Jose Francisco Ruiz Massieu, was assassinated. Finally, in mid-November, Deputy Attorney Gen-

eral Mario Ruiz Massieu, brother of the deceased, accused the ruling party of or-chestrating the assassination.

Foreign currency flooded out of Mexico as international reserves shrunk to $12 billion, from $26 billion in January, put-ting even more pressure on the currency peg. On December 20, 1994 the govern-ment decided to devalue the peso by 15 percent to roughly four pesos per U.S. dollar. However, this was only a tempo-rary measure, and international reserves continued to rush out of Mexico. $4.5 bil-lion in reserves were withdrawn the fol-lowing day, on December 21, the largest single-day movement of the year. On De-cember 22, Mexico was forced to let the peso float, and it jumped to 5.3 pesos per dollar by the end of December. The re-sult was nothing less than financial crisis. Credit markets dried up, the peso plum-meted, and interest rates increased sharp-ly. Mexico had lost its reputation as a safe

and viable place to invest, and the ensu-ing financial crisis spread to other parts of South America.

The Role of DebtLeading up to the floating of the peso,

Mexico’s biggest policy response to the increased pressure on the currency was to shift the composition of their debt. After the Colosio assassination in March, Mexico began to offer larger and larger quantities of dollar-denominated debt in the form of tesobonos. The goal was to ease investors’ worries about in-vesting in peso-denominated assets and assuming risk associated with currency devaluation. However, by November 1994, peso-denominated cetes comprised only 25 percent of Mexico’s debt, down from 75 percent in December 1993, while tesobonos accounted for 70 percent of the Mexican government’s securities.

The switch to tesobonos from cetes was

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

Source: The International Monetary Fund (IMF), International Financial Statistics

The Krugman model is par-ticularly useful in examining

currency crises, because it predicts a common pattern: the steady depletion of re-

serves followed by a run that completely depletes foreign

exchange reserves and forces the central bank to abandon a

fixed exchange rate.

Columbia Economics Review Columbia Economics Review

24 Spring 2013 Spring 2013 25

an interesting policy maneuver designed to quell investors’ worry and simultane-ously ease Mexico’s debt burden. The interest rates on tesobonos was roughly 6-8 percent less than that on cetes, and so Mexico saw a drastic reduction in its interest payments. However, Mexico had also assumed exchange rate risk, as now a devaluation of the peso would greatly harm Mexico itself. This sent a clear sig-nal to the global community that Mexico

did not plan to devalue the currency. The large spread between cetes and tesobonos implied that the market expected a de-valuation, and yet still the Mexican gov-ernment made no significant attempts to tighten monetary policy or pursue other plans to reinforce the currency peg. The government tied its hands by issuing so many tesobonos. Doing so eliminated the possibility of using devaluation as a poli-cy tool; devaluation would now signal to investors that the situation was beyond repair.

Issuing tesobonos absent any other poli-cy action was a critical mistake. The eco-nomic condition of Mexico continued to deteriorate, and a current account deficit of over $20 billion and over $25 billion of foreign-owned dollar denominated-debt loomed. Mexico eventually had no choice but to devalue the peso. This inconsisten-cy in policy may have contributed to the disastrous outcome. Mexico’s failure to anticipate the necessity of devaluing its currency put it in a difficult position, and when it was finally forced to devalue the currency, the loss of investor confidence resulted in a massive stampede of funds out of the country.

Reconciling Models with FactsOne of the puzzling aspects of the Te-

quila Crisis was that while the pattern of reserve losses at first glance looks consist-ent with a Krugman model of speculative attack, there was no fiscal deficit that pro-duced a strain on reserves. In fact, Mexico was close to running a balanced budget.

However, this does not necessarily mean that there was not some structural

imbalance that contributed to the drain-ing of reserves. As discussed earlier, Mexico’s large current account deficit produced a strain on reserves. Mexican officials, even after the crisis, insisted that the current account was not the origin of the problem, as it was the result of private capital inflow for investment rather than fiscal deficits or wanton monetary expan-sion (Whitt 1996). This line of thinking justifies the current account deficit by im-plying that once the investment projects are completed, exports will rise and bal-ance the current account. Thus, it is pos-sible that had there not been any adverse shocks to Mexican credibility, the current account situation would have been recti-fied. However, Dornbusch and Werner (1994) argue vehemently that this struc-tural adjustment was not forthcoming.

Additionally, close inspection of the flow of reserves in 1994 reveals that this episode did not exhibit a steady loss of reserves followed by some massive run on reserves at a single point in time.

Reserves appear to deplete suddenly at the end of March and then again in November and December, aligning per-

fectly with the Colosio assassination, the accusations of corruption by Mario Ruiz Massieu, and the devaluation itself. Thus, it is likely that the Tequila Crisis was not caused by something as simple and fore-told as a Krugman-like speculative attack but rather was triggered by these events and investors’ reactions to them. The pattern of reserve losses and their timing make models like those of Obstfeld (1986) seem like more plausible candidates for explaining the origins of the Tequila Crisis. The auction of cetes directly af-

ter the Colosio assassination gives some credence to the idea that this may have been a self-fulfilling crisis. None of the economic fundamentals of the Mexican economy had changed from February to April, yet the interest rate on cetes spiked by nearly 7 percent. This can be inter-preted as a risk premium that investors demanded to justify the perceived riski-ness of Mexican securities and perhaps an expected devaluation. It could follow, as outlined earlier in Section 2, that this higher cost of debt increased financial strain on Mexico and contributed to the collapse of the peso thus illustrating self-fulfilling expectations of devaluations. It is unlikely that the increased interest rate on the cetes was the sole cause of the crisis, but it was undoubtedly a contrib-uting factor. If there had been no hike in the interest rate after the assassination, the Mexican government would not have expanded its issuance of tesobonos so ag-gressively and the entire process would have played out very differently. While a single cause for the crisis is not read-ily apparent, it is easy to see that multiple equilibria may arise from the feedback

loop between investors and policy-mak-ers.

The sudden and extreme episode of reserve withdrawals supports the model of herd-like investors proposed by Calvo and Mendoza (1996). Such a model offers a good explanation for the final investor flight that forced Mexico to abandon the new currency peg and accept a floating currency. There was no change to the Mexican economy’s fundamentals after the 15 percent devaluation of the peso, but the single largest loss of reserves

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

occurred the following day. The global investor community rejected the devalu-ation as a tool that was being used to sta-bilize the economy and rather interpreted it as a signal of weakness.

In this framework, it is easy to under-stand why investors with so many oppor-tunities for capital allocation around the world decided to switch their portfolio composition away from Mexican assets even if the macroeconomic fundamentals had not changed. Investors did not want to be the last ones trampled on as capi-tal fled Mexico. This widespread attitude resulted in a self-reinforcing downward spiral on December 22. While investors may have not been consciously wary of a bailout of the banks by the government, this model points to the broader fact that investors may make decisions based on some general consensus of the future tra-jectory of a country’s financial condition. The expectations mechanism certainly has a self-fulfilling aspect, but it is rein-forced by the speed with which investors move capital around the world and the lack of incentives they have in determin-ing whether to react to shifts in financial markets or underlying macroeconomic fundamentals.

ConclusionStudying the peso crisis of 1994 reveals

a highly complex interaction of political shocks, policy decisions, and investor re-actions. Such complexity means that any single explanation will undoubtedly leave some aspect of the ordeal unaccounted for. However, close study of the timing of events and the policies pursued gives some insight into the factors that made

seemingly benign currency appreciation spiral into a financial crisis of global scale.

The most striking aspect of this crisis is that it is not clear that it was precipitated entirely by structural imbalances; the mac-roeconomic fundamentals of Mexico lead-ing up to the crisis do not seem to align with the end result. This gap between fundamentals and outcomes appears to

be caused by the power of expectations. The political upheaval surrounding the election triggered a series of events that rattled investor sentiment. However, it is not clear that the most likely path of pol-icy ever changed, and as such investors were mainly shaken by general concern rather than anything endogenous to the economic situation.

This crisis demonstrated that expecta-tions about the future of a country could have dire consequences for economic sta-bility, even if the expectations do not re-flect economic fundamentals. In a world with so many investment opportunities, it does not take much for a “fall from grace” to occur, and once it does, a self-fulfilling spiral can follow. The landscape of in-

ternational investing, which hinges more on news and expectations than economic fundamentals, is a by-product of high degrees of capital mo-bility and a defining factor of our world economy. By stud-ying events like the Tequila Crisis we can gain insight into how to prevent such disas-trous episodes from repeat-ing themselves. This crisis, in particular, makes it clear that appreciating currencies can present a very real prob-lem that only worsens if left unchecked over time. It also shows that policy responses need to be coordinated and consistent over time in order not to send mixed signals to financial markets. Ultimately, whether the financial crisis could have been avoided by any policy package is uncer-

tain, given the severity of the political shocks, but a close study of the Tequila Crisis is necessary in order to understand fully the powerful role that investors’ ex-pectations play in a time of crisis. ▪

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

The real appreciation of the peso and the mounting cur-rent account deficit were

Mexico’s central economic problems.

Columbia Economics Review Columbia Economics Review

26 Spring 2013 Spring 2013 27

an interesting policy maneuver designed to quell investors’ worry and simultane-ously ease Mexico’s debt burden. The interest rates on tesobonos was roughly 6-8 percent less than that on cetes, and so Mexico saw a drastic reduction in its interest payments. However, Mexico had also assumed exchange rate risk, as now a devaluation of the peso would greatly harm Mexico itself. This sent a clear sig-nal to the global community that Mexico

did not plan to devalue the currency. The large spread between cetes and tesobonos implied that the market expected a de-valuation, and yet still the Mexican gov-ernment made no significant attempts to tighten monetary policy or pursue other plans to reinforce the currency peg. The government tied its hands by issuing so many tesobonos. Doing so eliminated the possibility of using devaluation as a poli-cy tool; devaluation would now signal to investors that the situation was beyond repair.

Issuing tesobonos absent any other poli-cy action was a critical mistake. The eco-nomic condition of Mexico continued to deteriorate, and a current account deficit of over $20 billion and over $25 billion of foreign-owned dollar denominated-debt loomed. Mexico eventually had no choice but to devalue the peso. This inconsisten-cy in policy may have contributed to the disastrous outcome. Mexico’s failure to anticipate the necessity of devaluing its currency put it in a difficult position, and when it was finally forced to devalue the currency, the loss of investor confidence resulted in a massive stampede of funds out of the country.

Reconciling Models with FactsOne of the puzzling aspects of the Te-

quila Crisis was that while the pattern of reserve losses at first glance looks consist-ent with a Krugman model of speculative attack, there was no fiscal deficit that pro-duced a strain on reserves. In fact, Mexico was close to running a balanced budget.

However, this does not necessarily mean that there was not some structural

imbalance that contributed to the drain-ing of reserves. As discussed earlier, Mexico’s large current account deficit produced a strain on reserves. Mexican officials, even after the crisis, insisted that the current account was not the origin of the problem, as it was the result of private capital inflow for investment rather than fiscal deficits or wanton monetary expan-sion (Whitt 1996). This line of thinking justifies the current account deficit by im-plying that once the investment projects are completed, exports will rise and bal-ance the current account. Thus, it is pos-sible that had there not been any adverse shocks to Mexican credibility, the current account situation would have been recti-fied. However, Dornbusch and Werner (1994) argue vehemently that this struc-tural adjustment was not forthcoming.

Additionally, close inspection of the flow of reserves in 1994 reveals that this episode did not exhibit a steady loss of reserves followed by some massive run on reserves at a single point in time.

Reserves appear to deplete suddenly at the end of March and then again in November and December, aligning per-

fectly with the Colosio assassination, the accusations of corruption by Mario Ruiz Massieu, and the devaluation itself. Thus, it is likely that the Tequila Crisis was not caused by something as simple and fore-told as a Krugman-like speculative attack but rather was triggered by these events and investors’ reactions to them. The pattern of reserve losses and their timing make models like those of Obstfeld (1986) seem like more plausible candidates for explaining the origins of the Tequila Crisis. The auction of cetes directly af-

ter the Colosio assassination gives some credence to the idea that this may have been a self-fulfilling crisis. None of the economic fundamentals of the Mexican economy had changed from February to April, yet the interest rate on cetes spiked by nearly 7 percent. This can be inter-preted as a risk premium that investors demanded to justify the perceived riski-ness of Mexican securities and perhaps an expected devaluation. It could follow, as outlined earlier in Section 2, that this higher cost of debt increased financial strain on Mexico and contributed to the collapse of the peso thus illustrating self-fulfilling expectations of devaluations. It is unlikely that the increased interest rate on the cetes was the sole cause of the crisis, but it was undoubtedly a contrib-uting factor. If there had been no hike in the interest rate after the assassination, the Mexican government would not have expanded its issuance of tesobonos so ag-gressively and the entire process would have played out very differently. While a single cause for the crisis is not read-ily apparent, it is easy to see that multiple equilibria may arise from the feedback

loop between investors and policy-mak-ers.

The sudden and extreme episode of reserve withdrawals supports the model of herd-like investors proposed by Calvo and Mendoza (1996). Such a model offers a good explanation for the final investor flight that forced Mexico to abandon the new currency peg and accept a floating currency. There was no change to the Mexican economy’s fundamentals after the 15 percent devaluation of the peso, but the single largest loss of reserves

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

occurred the following day. The global investor community rejected the devalu-ation as a tool that was being used to sta-bilize the economy and rather interpreted it as a signal of weakness.

In this framework, it is easy to under-stand why investors with so many oppor-tunities for capital allocation around the world decided to switch their portfolio composition away from Mexican assets even if the macroeconomic fundamentals had not changed. Investors did not want to be the last ones trampled on as capi-tal fled Mexico. This widespread attitude resulted in a self-reinforcing downward spiral on December 22. While investors may have not been consciously wary of a bailout of the banks by the government, this model points to the broader fact that investors may make decisions based on some general consensus of the future tra-jectory of a country’s financial condition. The expectations mechanism certainly has a self-fulfilling aspect, but it is rein-forced by the speed with which investors move capital around the world and the lack of incentives they have in determin-ing whether to react to shifts in financial markets or underlying macroeconomic fundamentals.

ConclusionStudying the peso crisis of 1994 reveals

a highly complex interaction of political shocks, policy decisions, and investor re-actions. Such complexity means that any single explanation will undoubtedly leave some aspect of the ordeal unaccounted for. However, close study of the timing of events and the policies pursued gives some insight into the factors that made

seemingly benign currency appreciation spiral into a financial crisis of global scale.

The most striking aspect of this crisis is that it is not clear that it was precipitated entirely by structural imbalances; the mac-roeconomic fundamentals of Mexico lead-ing up to the crisis do not seem to align with the end result. This gap between fundamentals and outcomes appears to

be caused by the power of expectations. The political upheaval surrounding the election triggered a series of events that rattled investor sentiment. However, it is not clear that the most likely path of pol-icy ever changed, and as such investors were mainly shaken by general concern rather than anything endogenous to the economic situation.

This crisis demonstrated that expecta-tions about the future of a country could have dire consequences for economic sta-bility, even if the expectations do not re-flect economic fundamentals. In a world with so many investment opportunities, it does not take much for a “fall from grace” to occur, and once it does, a self-fulfilling spiral can follow. The landscape of in-

ternational investing, which hinges more on news and expectations than economic fundamentals, is a by-product of high degrees of capital mo-bility and a defining factor of our world economy. By stud-ying events like the Tequila Crisis we can gain insight into how to prevent such disas-trous episodes from repeat-ing themselves. This crisis, in particular, makes it clear that appreciating currencies can present a very real prob-lem that only worsens if left unchecked over time. It also shows that policy responses need to be coordinated and consistent over time in order not to send mixed signals to financial markets. Ultimately, whether the financial crisis could have been avoided by any policy package is uncer-

tain, given the severity of the political shocks, but a close study of the Tequila Crisis is necessary in order to understand fully the powerful role that investors’ ex-pectations play in a time of crisis. ▪

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool

The real appreciation of the peso and the mounting cur-rent account deficit were

Mexico’s central economic problems.

Columbia Economics Review Columbia Economics Review

26 Spring 2013 Spring 2013 27

Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Reces-sion?

Inna Grinis

Cambridge University

Youth unemployment has been one of the principal concerns of the Great Re-cession. In the UK, for instance, between 2008 and 2010, 74 percent of the decrease in employment fell on workers aged 16 to 24, but as a group they represented less than 20 percent of the working age popu-lation (Bell & Blanchflower, 2010).

In the second quarter of 2012, youth unemployment rates were more than twice as high as overall rates for most European countries (Table 1). Data from the period show that the ratio of youth unemployment to total unemployment varies widely across countries. Germany has the lowest ratio (1.5) followed by the Netherlands (1.8) and Denmark (1.9). Hence, young workers seem to fare bet-ter in countries using the “flexicurity model”, a combination of labor market flexibility with generous welfare provi-sions. However, large disparities exist, and aggregate data is rather ambiguous in identifying the countries where young workers have been more affected by the recession. For instance, although Italy and Sweden both have a youth overall unemployment ratio of about three, their respective youth unemployment rates are 34 percent and 23.4 percent.

Table 1 reports the same statistics for the 4th quarter of 2007, i.e., just before the

beginning of the 2008 recession (OECD). Surprisingly, the ratios remain roughly constant, even though absolute rates are much lower. This observation suggests that the key problem with young work-ers might arise not only from the reces-sion but also from country specific labor markets.

Furthermore, recessions are not only problematic to unemployed young peo-ple. By constraining their choice sets and forcing them to engage on suboptimal

paths, such as job/skill mismatches, reces-sions can also have significant, long-term negative effects on those “lucky” youths that do find jobs. The subpar labor mar-ket experiences that these young workers receive in their early years will continue to influence their careers years after re-cessions end, putting them at a disadvan-tage relative to those graduating in more prosperous times and thereby producing a “lost generation.”

In section 1, I present micro-level data studies and theories from labor econom-ics to understand why entering the labor market in a recession does not simply leave “temporary blemishes”, but rather “permanent scars” (D. T. Ellwood, 1982).

It is important to realize that the ef-ficiency of policies aimed at promoting youth employment crucially depends on successful job/skill matches. Job/skill mismatches are not only inefficient but also unstable and may therefore lead to higher youth unemployment rates. In section 2, I use data from Eurostat to test the hypothesis that job/skill mismatches play an important role in explaining cross-country variation in youth unem-ployment rates. Mismatch turns out to be significant in most fields of study, sug-gesting that the “lost generation” effects can be lessened by addressing the mis-

The subpar labor market experiences that these young workers receive in their early years will continue to influ-ence their careers years after recessions end, putting them

at a disadvantage relative to those graduating in more prosperous times and thereby

producing a “lost genera-tion.”

match issue. Section 3 first investigates why job/

skill mismatches occur, and then outline a theoretical benchmark that could be a good starting point for thinking about how both sides of the labor market could be coordinated on a large scale in order to produce an efficient, stable matching of young workers and jobs.

Section 1: Beyond Aggregate meas-ures: long-term effects of graduating in a recession

Recently, there has been a renewed in-terest among labor economists in inves-tigating the long-term career effects of economic conditions at the time of gradu-

ation. Availability of large panel datasets has enabled researchers to follow the same individuals for many years after their graduation and to test whether or not “early labor market experiences fore-tell future ones” (D. T. Ellwood, 1982).

In 2012, L.B. Kahn received the “EALE Labour Economics Prize” for her article: “The Long-Term Labor Market Conse-quences of Graduating from College in a Bad Economy”, in which she investigates the impact of the early 1980s recession on American white male college graduates using the National Longitudinal Survey of Youth. Her findings buttress the “lost generation” hypothesis: a 1 percentage point increase in the national unem-

ployment rate at the time of graduation1 translates into an initial wage loss of 6 to 7 percent per year. The effect is dimin-ishing, but significant and persistent: 15 years after graduation the wage loss is still 2.5 percent per year. Another per-verse consequence is lower occupational attainment, implying that young workers have difficulties switching into better jobs once the recession is over.

In 1991, Beaudry and DiNardo empiri-cally establish that whole worker cohorts can be disadvantaged if, at the time they enter the labor market, the average start-ing wage is lower and the national unem-ployment rate higher. Different theoreti-

1 She uses the overall unemployment rate as an indicator of the severity of a recession.

Columbia Economics Review Columbia Economics Review

28 Spring 2013 Spring 2013 29

Lost and FoundIs a Lost Generation of Young Workers an Evitable Outcome of Reces-sion?

Inna Grinis

Cambridge University

Youth unemployment has been one of the principal concerns of the Great Re-cession. In the UK, for instance, between 2008 and 2010, 74 percent of the decrease in employment fell on workers aged 16 to 24, but as a group they represented less than 20 percent of the working age popu-lation (Bell & Blanchflower, 2010).

In the second quarter of 2012, youth unemployment rates were more than twice as high as overall rates for most European countries (Table 1). Data from the period show that the ratio of youth unemployment to total unemployment varies widely across countries. Germany has the lowest ratio (1.5) followed by the Netherlands (1.8) and Denmark (1.9). Hence, young workers seem to fare bet-ter in countries using the “flexicurity model”, a combination of labor market flexibility with generous welfare provi-sions. However, large disparities exist, and aggregate data is rather ambiguous in identifying the countries where young workers have been more affected by the recession. For instance, although Italy and Sweden both have a youth overall unemployment ratio of about three, their respective youth unemployment rates are 34 percent and 23.4 percent.

Table 1 reports the same statistics for the 4th quarter of 2007, i.e., just before the

beginning of the 2008 recession (OECD). Surprisingly, the ratios remain roughly constant, even though absolute rates are much lower. This observation suggests that the key problem with young work-ers might arise not only from the reces-sion but also from country specific labor markets.

Furthermore, recessions are not only problematic to unemployed young peo-ple. By constraining their choice sets and forcing them to engage on suboptimal

paths, such as job/skill mismatches, reces-sions can also have significant, long-term negative effects on those “lucky” youths that do find jobs. The subpar labor mar-ket experiences that these young workers receive in their early years will continue to influence their careers years after re-cessions end, putting them at a disadvan-tage relative to those graduating in more prosperous times and thereby producing a “lost generation.”

In section 1, I present micro-level data studies and theories from labor econom-ics to understand why entering the labor market in a recession does not simply leave “temporary blemishes”, but rather “permanent scars” (D. T. Ellwood, 1982).

It is important to realize that the ef-ficiency of policies aimed at promoting youth employment crucially depends on successful job/skill matches. Job/skill mismatches are not only inefficient but also unstable and may therefore lead to higher youth unemployment rates. In section 2, I use data from Eurostat to test the hypothesis that job/skill mismatches play an important role in explaining cross-country variation in youth unem-ployment rates. Mismatch turns out to be significant in most fields of study, sug-gesting that the “lost generation” effects can be lessened by addressing the mis-

The subpar labor market experiences that these young workers receive in their early years will continue to influ-ence their careers years after recessions end, putting them

at a disadvantage relative to those graduating in more prosperous times and thereby

producing a “lost genera-tion.”

match issue. Section 3 first investigates why job/

skill mismatches occur, and then outline a theoretical benchmark that could be a good starting point for thinking about how both sides of the labor market could be coordinated on a large scale in order to produce an efficient, stable matching of young workers and jobs.

Section 1: Beyond Aggregate meas-ures: long-term effects of graduating in a recession

Recently, there has been a renewed in-terest among labor economists in inves-tigating the long-term career effects of economic conditions at the time of gradu-

ation. Availability of large panel datasets has enabled researchers to follow the same individuals for many years after their graduation and to test whether or not “early labor market experiences fore-tell future ones” (D. T. Ellwood, 1982).

In 2012, L.B. Kahn received the “EALE Labour Economics Prize” for her article: “The Long-Term Labor Market Conse-quences of Graduating from College in a Bad Economy”, in which she investigates the impact of the early 1980s recession on American white male college graduates using the National Longitudinal Survey of Youth. Her findings buttress the “lost generation” hypothesis: a 1 percentage point increase in the national unem-

ployment rate at the time of graduation1 translates into an initial wage loss of 6 to 7 percent per year. The effect is dimin-ishing, but significant and persistent: 15 years after graduation the wage loss is still 2.5 percent per year. Another per-verse consequence is lower occupational attainment, implying that young workers have difficulties switching into better jobs once the recession is over.

In 1991, Beaudry and DiNardo empiri-cally establish that whole worker cohorts can be disadvantaged if, at the time they enter the labor market, the average start-ing wage is lower and the national unem-ployment rate higher. Different theoreti-

1 She uses the overall unemployment rate as an indicator of the severity of a recession.

Columbia Economics Review Columbia Economics Review

28 Spring 2013 Spring 2013 29

cal explanations exist. Analyzing them should help to understand why the “lost generation” effect occurs and how to make it evitable.

Human Capital Theory (G. Becker, 1967) suggests that the labor market re-wards more workers with greater human capital accumulation. Such human capi-tal encompasses both observed (educa-tion, experience) and unobserved (abil-ity, motivation) characteristics. Labor economists use the Mincerian wage equa-tion (1974) to summarize and test the re-lationship between earnings and human capital accumulation. They often assume a constant log-linear relationship among wages, education, and experience:

in which εit is i.i.d. and uncorrelated with the regressors, and δ’xit a vector of characteristics (age, gender,...)

However, in recessions, new entrants to the labor market face fewer vacancies and more competition from relatively ex-perienced workers. This may force young graduates to accept positions that are less-skilled than the ones they are quali-fied for (vertical mismatch), or jobs in a

field that is different from the one of their studies (horizontal mismatch). In either case, they won’t be fully employing their skills, and the return to their education will be lower.

Furthermore, the return to experience is probably also not uniform over time. In any job, workers acquire general skills and job-specific skills. Job-specific skills acquired on the suboptimal path may not be rewarded once the worker try to re-engage onto the optimal path.

These observations suggest that the standard Mincerian equation should be rewritten as follows:

in which mismatchit measures the de-gree of mismatch between the education of a worker and his occupation at time t, and a t subscript has been added to the return on experience.

The “lost generation” effect can also be explained within a signaling model of ed-ucation (Spence 1973, Stiglitz 1975) where education has no productivity enhancing role per se. According to agency and risk theories (Lazear 1981, Lazear and Moore 1984), wages are lower in periods when workers are less likely to voluntarily quit

their jobs, a phenomenon which often occurs at the beginning of their careers. Recessions amplify this effect by making young workers even more afraid of los-ing their jobs and thus more willing to ac-cept wages below their marginal produc-tivities. However, this initial lower wage may not only depress all future earnings by becoming the basis for further wage negotiations, but they also send a false signal about the true productivity of a worker when he tries to find a better job.

Section 2: Identifying mismatch as an

important determinant of cross-country variation in youth unemployment rates

A direct way of testing the impact of job/skill mismatch on young workers would be to estimate the second Mince-rian equation above. Unfortunately, this would also require an important amount of micro-level data to which access is re-stricted.

Nevertheless, Eurostat has made pub-licly available summary statistics on the proportion of young workers (25-34 years old) with tertiary education experiencing education and occupation mismatch be-tween 2003 and 2007. This data is disag-gregated by fields of study and makes it possible to test the hypothesis that the

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Figure 1: Education/occupation mismatch (ISCED 5-6) of persons aged 25-34 by study field (2003-2007)

Education Humanities and Arts Social sciences, business and lawScience, mathematics and computing Engineering, manufacturing and construction Health and WelfareServices

Source: EUROSTAT

proportion of job/skill mismatches help to explain cross-country varia-tion in youth unemployment rates.

The dependent variable is the field-specific youth unemployment rate. I am also controlling for educational at-tainment and cross-equation country-specific error correlation by running a seemingly unrelated regression (SUR) system:

in which F is the field of study and is allowed to be correlated across fields for a given country . is signifi-cant at the 0.1 percent level in five of the seven fields of study (Table 2) and has the expected positive sign in six of them. In Health and Welfare for in-stance, a 1 percentage point increase in mismatch translates into a 0.248 percentage point increase in youth unemployment rate.

Services is the only exception to this rule. According to Figure 1, it has much larger mismatch rates than all other fields, ranging from 14.3 percent for Latvia to 85 percent for Cyprus. Fur-thermore, Services is probably the sector which requires the least amount of specif-ic education, making job/skill mismatch less problematic.

The economic and statistical signifi-cance of education and occupation mis-match suggests that countries with high mismatch rates could greatly benefit from achieving more efficient matching of their college graduates. Such an initia-tive could alleviate the “lost generation” effect directly (through decreased job/skill mismatches) and indirectly (through decreased youth unemployment rates).

Section 3: Making It Evitable: Central-ized Coordinated Matching Mechanism

“The labor market suffers from asymmetric information, coordination, and collective ac-tion failures.” (Prof. D. Autor, 2008)

In practice, job/skill mismatches occur because either the worker with the right skills for a specific vacancy is absent from the labor market in the first place or be-cause they simply cannot find each other.

Although some growing evidence for the first pattern has emerged (Sahin, Song, Topa, and Violante 2012), I will concentrate on the second one.

There currently exist several initiatives in European countries aimed at achieving more efficient matching of young work-ers and jobs. For example the European Youth Guarantee provides young people

an apprenticeship, a job, or further train-ing within the four months following the beginning of the unemployment period. The German Occupation Orientational Program allows young workers to try more than three different internship po-sitions to help them gauge their abilities and make the right career choices.

The problem with most initiatives is that they have focused on counseling and informing young people. Even though some schemes provide internships or try to incentivize employers directly with fi-nancial rewards, they do not coordinate both sides of the labor market and are usually in small-scale.

However, Autor’s quotation suggests that there is a large scope for government intervention to eliminate the job/skill mismatch by lowering the cost of infor-mation and addressing adverse selection

and coordination failures. In this section, I would like to present a

theoretical framework that could be used to start thinking about how both sides of the labor market may be coordinated on a large scale. It comes from the literature on Mechanism Design for which the 2012 Nobel Prize in Economics was awarded.

Suppose we have a set of young un-employed workers with different char-acteristics (e.g., education levels, grade records, and languages spoken) and a set of firms with different job vacancies. For simplicity, assume that wages are not ne-gotiable and that they are part of the va-cancy description, along with other char-acteristics such as contract type, work hours, location, promotion perspectives, bonuses, and pension plans.

Within this framework, a central au-thority that makes all the information on workers and vacancies freely available to all participants. It also fixes a deadline by which young workers have to submit a preference ranking for vacancies they have applied to. Firms have to submit a similar preference ranking for applicants.

The advantage of having one central-ized labor market intermediary is that firms would have access to a larger pool of potential employees, while workers could choose from a larger set of posi-tions corresponding to their educational backgrounds. Moreover, by concentrat-ing all the information in one central body, the costs of job search and adver-tisement could be significantly lowered.

Another perverse consequence is lower occupational attain-

ment, implying that young workers have difficulties switching into better jobs once the recession is over.

Columbia Economics Review Columbia Economics Review

30 Spring 2013 Spring 2013 31

cal explanations exist. Analyzing them should help to understand why the “lost generation” effect occurs and how to make it evitable.

Human Capital Theory (G. Becker, 1967) suggests that the labor market re-wards more workers with greater human capital accumulation. Such human capi-tal encompasses both observed (educa-tion, experience) and unobserved (abil-ity, motivation) characteristics. Labor economists use the Mincerian wage equa-tion (1974) to summarize and test the re-lationship between earnings and human capital accumulation. They often assume a constant log-linear relationship among wages, education, and experience:

in which εit is i.i.d. and uncorrelated with the regressors, and δ’xit a vector of characteristics (age, gender,...)

However, in recessions, new entrants to the labor market face fewer vacancies and more competition from relatively ex-perienced workers. This may force young graduates to accept positions that are less-skilled than the ones they are quali-fied for (vertical mismatch), or jobs in a

field that is different from the one of their studies (horizontal mismatch). In either case, they won’t be fully employing their skills, and the return to their education will be lower.

Furthermore, the return to experience is probably also not uniform over time. In any job, workers acquire general skills and job-specific skills. Job-specific skills acquired on the suboptimal path may not be rewarded once the worker try to re-engage onto the optimal path.

These observations suggest that the standard Mincerian equation should be rewritten as follows:

in which mismatchit measures the de-gree of mismatch between the education of a worker and his occupation at time t, and a t subscript has been added to the return on experience.

The “lost generation” effect can also be explained within a signaling model of ed-ucation (Spence 1973, Stiglitz 1975) where education has no productivity enhancing role per se. According to agency and risk theories (Lazear 1981, Lazear and Moore 1984), wages are lower in periods when workers are less likely to voluntarily quit

their jobs, a phenomenon which often occurs at the beginning of their careers. Recessions amplify this effect by making young workers even more afraid of los-ing their jobs and thus more willing to ac-cept wages below their marginal produc-tivities. However, this initial lower wage may not only depress all future earnings by becoming the basis for further wage negotiations, but they also send a false signal about the true productivity of a worker when he tries to find a better job.

Section 2: Identifying mismatch as an

important determinant of cross-country variation in youth unemployment rates

A direct way of testing the impact of job/skill mismatch on young workers would be to estimate the second Mince-rian equation above. Unfortunately, this would also require an important amount of micro-level data to which access is re-stricted.

Nevertheless, Eurostat has made pub-licly available summary statistics on the proportion of young workers (25-34 years old) with tertiary education experiencing education and occupation mismatch be-tween 2003 and 2007. This data is disag-gregated by fields of study and makes it possible to test the hypothesis that the

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Figure 1: Education/occupation mismatch (ISCED 5-6) of persons aged 25-34 by study field (2003-2007)

Education Humanities and Arts Social sciences, business and lawScience, mathematics and computing Engineering, manufacturing and construction Health and WelfareServices

Source: EUROSTAT

proportion of job/skill mismatches help to explain cross-country varia-tion in youth unemployment rates.

The dependent variable is the field-specific youth unemployment rate. I am also controlling for educational at-tainment and cross-equation country-specific error correlation by running a seemingly unrelated regression (SUR) system:

in which F is the field of study and is allowed to be correlated across fields for a given country . is signifi-cant at the 0.1 percent level in five of the seven fields of study (Table 2) and has the expected positive sign in six of them. In Health and Welfare for in-stance, a 1 percentage point increase in mismatch translates into a 0.248 percentage point increase in youth unemployment rate.

Services is the only exception to this rule. According to Figure 1, it has much larger mismatch rates than all other fields, ranging from 14.3 percent for Latvia to 85 percent for Cyprus. Fur-thermore, Services is probably the sector which requires the least amount of specif-ic education, making job/skill mismatch less problematic.

The economic and statistical signifi-cance of education and occupation mis-match suggests that countries with high mismatch rates could greatly benefit from achieving more efficient matching of their college graduates. Such an initia-tive could alleviate the “lost generation” effect directly (through decreased job/skill mismatches) and indirectly (through decreased youth unemployment rates).

Section 3: Making It Evitable: Central-ized Coordinated Matching Mechanism

“The labor market suffers from asymmetric information, coordination, and collective ac-tion failures.” (Prof. D. Autor, 2008)

In practice, job/skill mismatches occur because either the worker with the right skills for a specific vacancy is absent from the labor market in the first place or be-cause they simply cannot find each other.

Although some growing evidence for the first pattern has emerged (Sahin, Song, Topa, and Violante 2012), I will concentrate on the second one.

There currently exist several initiatives in European countries aimed at achieving more efficient matching of young work-ers and jobs. For example the European Youth Guarantee provides young people

an apprenticeship, a job, or further train-ing within the four months following the beginning of the unemployment period. The German Occupation Orientational Program allows young workers to try more than three different internship po-sitions to help them gauge their abilities and make the right career choices.

The problem with most initiatives is that they have focused on counseling and informing young people. Even though some schemes provide internships or try to incentivize employers directly with fi-nancial rewards, they do not coordinate both sides of the labor market and are usually in small-scale.

However, Autor’s quotation suggests that there is a large scope for government intervention to eliminate the job/skill mismatch by lowering the cost of infor-mation and addressing adverse selection

and coordination failures. In this section, I would like to present a

theoretical framework that could be used to start thinking about how both sides of the labor market may be coordinated on a large scale. It comes from the literature on Mechanism Design for which the 2012 Nobel Prize in Economics was awarded.

Suppose we have a set of young un-employed workers with different char-acteristics (e.g., education levels, grade records, and languages spoken) and a set of firms with different job vacancies. For simplicity, assume that wages are not ne-gotiable and that they are part of the va-cancy description, along with other char-acteristics such as contract type, work hours, location, promotion perspectives, bonuses, and pension plans.

Within this framework, a central au-thority that makes all the information on workers and vacancies freely available to all participants. It also fixes a deadline by which young workers have to submit a preference ranking for vacancies they have applied to. Firms have to submit a similar preference ranking for applicants.

The advantage of having one central-ized labor market intermediary is that firms would have access to a larger pool of potential employees, while workers could choose from a larger set of posi-tions corresponding to their educational backgrounds. Moreover, by concentrat-ing all the information in one central body, the costs of job search and adver-tisement could be significantly lowered.

Another perverse consequence is lower occupational attain-

ment, implying that young workers have difficulties switching into better jobs once the recession is over.

Columbia Economics Review Columbia Economics Review

30 Spring 2013 Spring 2013 31

Many commercial Internet job boards, like Monster.com for instance, suffer from an adverse selection problem, be-cause firms believe that those who decide to post their CVs on such websites are below average quality and do not dis-close full information about themselves. In the case of young workers, a central-ized mechanism could easily solve this “lemons” problem by providing certified information on all students, such as their scores, peer-rankings, courses taken, etc.

Furthermore, the mechanism allows for open days, interviews, and advising ser-vices before the deadline.

At the deadline, the central authority would collect both preference lists and use an algorithm, for instance the de-ferred acceptance algorithm discovered by Gale and Shapley, in order to produce a stable and a Pareto optimal matching.

Of course, at the end, if there are fewer job vacancies than workers, which is like-ly to happen in a recession, some of them will be left unmatched. These unmatched people will have to be redirected towards either further education in other fields where job prospects are better or toward general-skills training through which they could continue to acquire skills that may be rewarded in any job they may take on in the future.

In practice, such mechanisms are of-ten called clearinghouses and have been used, for instance, in the U.S., to allocate new doctors to hospitals (National Resi-dent Matching Program). However, as Roth notes, “the lessons learned from the rather special market for American medical interns may generalize to a much wider variety of entry level labor markets and other matching processes.” (Roth and Sotomayor 1990)Conclusion

In this brief study, I first try to under-stand from the existing literature the

main causes of the “lost generation” ef-fect. It seems that bad early labor market experiences such as job/skill mismatch and unemployment can have a perverse long-term impact on young workers. While unemployment depreciates young graduates’ recently acquired human capi-tal, job/skill mismatch engages them onto suboptimal career paths from which they have difficulties escaping later on. Reces-sions increase both unemployment and mismatches, putting the unlucky genera-tion graduating at that time at a persis-tent disadvantage.

Most of the current policies aimed at helping young people have two main objectives: increasing youth employabil-ity through policies such as redirecting them towards education or training and promoting youth employment. However, very few initiatives realize that their ef-ficiency crucially depends on successful job/skill matches. As I investigate in sec-tion 2, job/skill mismatches and youth un-

employment rates are closely interlinked. This suggests that eliminating mismatch could not only make the “lost generation” effect evitable for young workers but also make it less severe for the young popula-tion as a whole by significantly lowering youth unemployment rates.

In the last section I outline a central-ized coordinated matching mechanism that could be a useful starting point for thinking about how both sides of the la-bor market could be more efficiently co-ordinated on a large scale.

Further research shall investigate how such a centralized coordinated match-ing mechanism could work in practice, and how it should be designed in order to avoid problems like congestion or un-raveling and become attractive to both young graduates and firms. Indeed, the success of this mechanism will depend on the willingness of workers and firms to adopt it. ▪

The economic and statistical significance of education and

occupation mismatch sug-gests that countries with high mismatch rates could greatly benefit from achieving more efficient matching of their

college graduates.

Columbia Economics Review

32 Spring 2013

C O L U M B I A E C O N O M I C S R E V I E W

Environmental Policy CompetitionWe are proud to announce that the winners of this year’s envi-ronmental policy competition are: Maria Brustos, Mara Freilich, Emily Kirkland, and Peter Vail of Brown University.

The annual competition, co-sponsered by the Earth Institute, was created to inspire undergraduate students to think critically about the complexities involved in devising United States climate change policy. Each year, the competition is open nation-wide to groups of 2-5 students, each of which is challenged to think about three dimensions of climate change–the physical drivers behind climate change itself, the conceptual frameworks in economics for analyzing issues related to climate change and emissions re-ductions, and finally, effective policymaking strategies to combat the problem.

Special thanks to our judges Dr. Gernot Wagner, Dr. Klaus Lack-ner, and Mr. Michael B. Gerrard.

Keep Up to Date on CERFollow us on twitter @EconReview and like us on Facebook for all the latest economics-related news and to stay informed about CER’s events.

Check out our Youtube channel Columbia CER to discover many videos of lectures from a wide variety of distinguished experts in the field.

Many commercial Internet job boards, like Monster.com for instance, suffer from an adverse selection problem, be-cause firms believe that those who decide to post their CVs on such websites are below average quality and do not dis-close full information about themselves. In the case of young workers, a central-ized mechanism could easily solve this “lemons” problem by providing certified information on all students, such as their scores, peer-rankings, courses taken, etc.

Furthermore, the mechanism allows for open days, interviews, and advising ser-vices before the deadline.

At the deadline, the central authority would collect both preference lists and use an algorithm, for instance the de-ferred acceptance algorithm discovered by Gale and Shapley, in order to produce a stable and a Pareto optimal matching.

Of course, at the end, if there are fewer job vacancies than workers, which is like-ly to happen in a recession, some of them will be left unmatched. These unmatched people will have to be redirected towards either further education in other fields where job prospects are better or toward general-skills training through which they could continue to acquire skills that may be rewarded in any job they may take on in the future.

In practice, such mechanisms are of-ten called clearinghouses and have been used, for instance, in the U.S., to allocate new doctors to hospitals (National Resi-dent Matching Program). However, as Roth notes, “the lessons learned from the rather special market for American medical interns may generalize to a much wider variety of entry level labor markets and other matching processes.” (Roth and Sotomayor 1990)Conclusion

In this brief study, I first try to under-stand from the existing literature the

main causes of the “lost generation” ef-fect. It seems that bad early labor market experiences such as job/skill mismatch and unemployment can have a perverse long-term impact on young workers. While unemployment depreciates young graduates’ recently acquired human capi-tal, job/skill mismatch engages them onto suboptimal career paths from which they have difficulties escaping later on. Reces-sions increase both unemployment and mismatches, putting the unlucky genera-tion graduating at that time at a persis-tent disadvantage.

Most of the current policies aimed at helping young people have two main objectives: increasing youth employabil-ity through policies such as redirecting them towards education or training and promoting youth employment. However, very few initiatives realize that their ef-ficiency crucially depends on successful job/skill matches. As I investigate in sec-tion 2, job/skill mismatches and youth un-

employment rates are closely interlinked. This suggests that eliminating mismatch could not only make the “lost generation” effect evitable for young workers but also make it less severe for the young popula-tion as a whole by significantly lowering youth unemployment rates.

In the last section I outline a central-ized coordinated matching mechanism that could be a useful starting point for thinking about how both sides of the la-bor market could be more efficiently co-ordinated on a large scale.

Further research shall investigate how such a centralized coordinated match-ing mechanism could work in practice, and how it should be designed in order to avoid problems like congestion or un-raveling and become attractive to both young graduates and firms. Indeed, the success of this mechanism will depend on the willingness of workers and firms to adopt it. ▪

The economic and statistical significance of education and

occupation mismatch sug-gests that countries with high mismatch rates could greatly benefit from achieving more efficient matching of their

college graduates.

Columbia Economics Review

32 Spring 2013

C O L U M B I A E C O N O M I C S R E V I E W

Environmental Policy CompetitionWe are proud to announce that the winners of this year’s envi-ronmental policy competition are: Maria Brustos, Mara Freilich, Emily Kirkland, and Peter Vail of Brown University.

The annual competition, co-sponsered by the Earth Institute, was created to inspire undergraduate students to think critically about the complexities involved in devising United States climate change policy. Each year, the competition is open nation-wide to groups of 2-5 students, each of which is challenged to think about three dimensions of climate change–the physical drivers behind climate change itself, the conceptual frameworks in economics for analyzing issues related to climate change and emissions re-ductions, and finally, effective policymaking strategies to combat the problem.

Special thanks to our judges Dr. Gernot Wagner, Dr. Klaus Lack-ner, and Mr. Michael B. Gerrard.

Keep Up to Date on CERFollow us on twitter @EconReview and like us on Facebook for all the latest economics-related news and to stay informed about CER’s events.

Check out our Youtube channel Columbia CER to discover many videos of lectures from a wide variety of distinguished experts in the field.

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C O L U M B I A E C O N O M I C S R E V I E W

CER is currently accepting pitches for its upcoming issue. You are encouraged to submit your article proposals, aca-demic scholarship, senior seminar papers, editorials, art and photography broadly relating to the field of economics. You may submit multiple pitches. CER accepts pitches under 200 words, as well as complete articles.

Your pitch or complete article should include the following information:1. Name, school, year, and contact information (email and phone number)2. If you are submitting a pitch, please state your argument clearly and expand upon it in one brief paragraph. List

sources and professors/industry professionals you intend to contact to support your argument. Note that a full source list is not required.

3. If you are submitting a completed paper, please make sure the file is accessible through MS Word.

Pitches will be accepted throughout the fall and are due by January 15; contact us for the exact date. Send all pitches to economics.columbia@gmail.com with the subject “CER Pitch- Last Name, First Name.” If you have any questions regarding this process, please do not hesitate to e-mail us at economics.columbia@gmail.com. We look forward to reading your submissions!

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Columbia Economics Review is interested in your article proposals,

senior theses, seminar papers, editorials, art and photography.

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Come join the Columbia Economics Review for a conversation with Professor Michael Woodford!

Prof. Woodford’s talk will be “Forward Guidance as a Tool of Monetary Policy” and there will be a Q&A session after the event. Refreshments and snacks will be served.

Michael Woodford’s primary research interests are in macroeco-nomics theory and monetary policy. He has written extensively about the microeconomic foundations of the monetary transmis-sion mechanism, the role of interest rates in inflation determina-tion, consequences of imperfect information for macroeconomics adjustment, rules for the conduct of monetary policy, central-bank communication policy, and interac-tions between monetary and fiscal policy.

Find out more about Professor Woodford’s research at http://www.columbia.edu/~mw2230/.

Michael Woodford is the John Bates Clark Professor of Political Economy at Columbia University. His first academic appointment was at Columbia in 1984, after which he

held positions at the University of Chicago and Princeton University, before return-ing to Columbia in 2004.

E V E N T D E T A I L S

“Forward Guidance as a Tool of Monetary Policy”with Michael Woodford

October 24th 6:00 - 7:30 PMLocation: International Affairs Building, Room 1027

Economic Forum

Columbia Economics Review1022 IAB, 420 West 118th St, New York NY 10027 | (609) 477-2902 | ColumbiaEconReview.com | economics.columbia@gmail.com

C O L U M B I A E C O N O M I C S R E V I E W

CER is currently accepting pitches for its upcoming issue. You are encouraged to submit your article proposals, aca-demic scholarship, senior seminar papers, editorials, art and photography broadly relating to the field of economics. You may submit multiple pitches. CER accepts pitches under 200 words, as well as complete articles.

Your pitch or complete article should include the following information:1. Name, school, year, and contact information (email and phone number)2. If you are submitting a pitch, please state your argument clearly and expand upon it in one brief paragraph. List

sources and professors/industry professionals you intend to contact to support your argument. Note that a full source list is not required.

3. If you are submitting a completed paper, please make sure the file is accessible through MS Word.

Pitches will be accepted throughout the fall and are due by January 15; contact us for the exact date. Send all pitches to economics.columbia@gmail.com with the subject “CER Pitch- Last Name, First Name.” If you have any questions regarding this process, please do not hesitate to e-mail us at economics.columbia@gmail.com. We look forward to reading your submissions!

G U I D E L I N E S

Columbia Economics Review is interested in your article proposals,

senior theses, seminar papers, editorials, art and photography.

Call for Submissions

U P C O M I N G E V E N T

Come join the Columbia Economics Review for a conversation with Professor Michael Woodford!

Prof. Woodford’s talk will be “Forward Guidance as a Tool of Monetary Policy” and there will be a Q&A session after the event. Refreshments and snacks will be served.

Michael Woodford’s primary research interests are in macroeco-nomics theory and monetary policy. He has written extensively about the microeconomic foundations of the monetary transmis-sion mechanism, the role of interest rates in inflation determina-tion, consequences of imperfect information for macroeconomics adjustment, rules for the conduct of monetary policy, central-bank communication policy, and interac-tions between monetary and fiscal policy.

Find out more about Professor Woodford’s research at http://www.columbia.edu/~mw2230/.

Michael Woodford is the John Bates Clark Professor of Political Economy at Columbia University. His first academic appointment was at Columbia in 1984, after which he

held positions at the University of Chicago and Princeton University, before return-ing to Columbia in 2004.

E V E N T D E T A I L S

“Forward Guidance as a Tool of Monetary Policy”with Michael Woodford

October 24th 6:00 - 7:30 PMLocation: International Affairs Building, Room 1027

Economic Forum

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