Columbia Economics Review: Spring 2013

Columbia Economics Review Vol. III, No. II

a league of their own bend it like beca pass the bargaining chips pregaming the tequila crisis

Spring 2013

Lost and Found Is a Lost Generation Inevitable after a Recession?

Spring Spring 2013 2013

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8Bend Bend It Like It Like Becca Becca Gender Gender Inequality Inequality and Women’s and Women’s Soccer Soccer Success Success

Microeconomic MicroeconomicTheory Theory 16 16PassPass the the (Bargaining) (Bargaining) Chips Chips Carbon Carbon Emissions Emissions and the andBargaining the Bargaining Power Power of Trade of Trade Unions Unions and Firms and Firms

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Spring 2013

A League of Their Own Lebron James and the Impact of the Sports Superstar

Zachariah Reitano Columbia University

On Friday, November 6th, 2009, the New York Knicks played the Cleveland Cavaliers. Madison Square Garden was packed and the energy was reminiscent of the 1990s when Ewing and the Knicks battled 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 opposing 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 Madison 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 NBA The 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

The structure of the game of basketball lends itself to the creation of superstars. distance. Lastly, the MLS incorporates increased 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 resulting 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 Columbia Economics Review

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 talent 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 evident when two players with almost identical statistics and playing styles have different 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 questions, I examine the case of Lebron James.

Lebron James With the advances in media technology

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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 local university because their gym was exceeding 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 behind the success Cleveland achieved following 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 AllStar–Zydrunas Ilgauskas, who had only made one other All-Star game prior to Lebron entering the league, and Mo Williams, who never before nor would ever again, play in an All-Star game.

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Lebron’s Effect on the Cavaliers’ Home Attendance In the summer of 2010, Lebron became a free agent, and there was significant speculation on which team he would choose.

Lebron James, over the course of 7 seasons, 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-

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

In terms of home attendance, a lag effect 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 Irving. 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 maneuver 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 evaluated the 2009-2010 season (the year prior to Lebron’s departure) and then compared this to the 2010-2011 season (proxy for unobserved variable).

Lebron’s Effect on Opponents Attendance Over the course of years, as a team improves, it is unsurprising that attendance increases. There is an obvious, but not perfect correlation, between winning percentage and attendance. While Lebron was on the Cavaliers, they performed extremely well. Thus, one could argue that when the Cavaliers visited another well-performing team, the increased attendance was a consequence of the uncertainty 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 demand, we would have anticipated that attendance 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 factor in determining game attendance. This is far from reality.

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

Spring 2013 chairs or standing room). In Tables 5-7, we can see that if a team’s average attendance 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 percent of their entire season’s revenue from gate attendance. Hence, it was not the total attendance of the game that resulted in 2 percent of their entire season’s gate revenue, but exclusively the additional attendance (above average without Lebron) that netted over five hundred thousand dollars.

In total, Lebron’s incremental revenue equaled 4.3 million dollars. This represents just under a half of a percentage 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 average, 11.69 percent of the revenue for all away games (lower than average attendance increase because teams with lower ticket prices had higher average attendance). Overall, seventeen of thirty stadiums were above capacity when Lebron visited.

The Big Three’s Effect on the Heat’s Away Attendance In 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 attributed to a slight reduction in the average ticket price. The Big Three slightly outperformed 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, singlehandedly, was able to generate about the same incremental revenue when visiting opposing teams as Lebron, Wade and Bosh Columbia Economics Review

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

Why was Lebron able to generate almost 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 recognize proportional incremental benefit given the lack of dynamic pricing and the limit on the size of stadiums.

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.

Net Effect on Attendance: Cavaliers vs.

Heat The net effect, in terms of home attendance, is the net incremental revenue received 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 exercise 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 attendance, is the net incremental revenue received by all teams, except the Cavaliers 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 attendance of 19,200 during their away games.

During their first season without Lebron, they dropped to the worst traveling team. No team had a lower average away attendance than the Cavaliers in the 2010-2011 season. On the other

Spring 2013 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 highest average away attendance, which limited 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 capacity, 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 superstars, who were able to draw significant 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 interested in seeing fewer games. Given the limit on stadium seating, the home team suffers.

Implications of Results and Future Research After 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 separately 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 Lebron’s margin, it was not nearly enough to compensate for the decrease in attendance 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 static. The resale of tickets may reflect the additional 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 revenue. 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 superstar’s impact on TV viewership and the expansion in NBA merchandise sales outweigh the loss in attendance? The difficulty is that these numbers are not easily 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 merchandise that outweighed the loss to the league in attendance, then one could argue that the concentration of talent has the potential 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 superstars are concentrated in particular teams, their combined star power will create a positive effect on revenue. However, 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 determine whether or not concentrating stars is beneficial to the league in the long run. The league must consider this when establishing its new collective bargaining 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 compensation 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 contribute to the level of fame achieved by Columbia Economics Review

7 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 approximately $30 million dollars in the same year. The majority of his income resulted from off-the-court compensation. With the increase in technology, the expanding capacity of advertisers to access and to communicate with potential customers, 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 importance. Where it was once thought that players could be counted on to follow the money offered by teams, it now

The question remains, how influential are the top superstars, and how large is the ripple effect? To answer these questions, I examine the case of Lebron James. 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 diminished salaries. If those forces conspire to weaken competitive balance or to diminish the value of the sport and the teams in the long run, then any new CBA must quantify those elements and create compensatory responses.

Competitive balance, to a certain extent, is a necessity for a sports league.

However, as we have shown, an individual 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 environment strikes the appropriate balance, the closer we can come to maximizing the full potential of the NBA.

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Spring 2013

Bend It Like Becca Gender 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 countries’ 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 fiftyone percent of any country’s population in a way that most would consider arbitrary and unjust. Second, gender inequality 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 relationship between gender inequality and sport? What can this tell us that the

Gender inequality unfairly limits the opportunities of fifty-one percent of any country’s population in a way that most would consider arbitrary and unjust.

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 understand underlying social attitudes. The universality of the rules of sports like soccer makes our conclusions widely applicable. The rules are the same everywhere in the world as well as in men’s and women’s soccer. Essentially, the homogeneity of conditions in sports like socColumbia Economics Review

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 applicability of the method in measuring the effect of gender inequality, or other hardto-measure factors in unrelated realms of study, may be interesting even if the reader has no interest in either gender inequality or sport.

However, studying women’s sports immediately 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 counterparts. 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 determinants 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 significant impact in analyses of men’s sports, but it is entirely possible that it would affect women’s sports.

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Figure 1: First vs. Second Principal Components for Robustnes Model suit. Today there are numerous amateur domestic leagues, and all but a few countries field women’s national teams.

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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 lPopW

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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 Soccer Hence, I focus on women’s soccer. In many parts of the world even today, soccer 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 participation 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 eventually the rest of the world, would follow

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each attempt to elaborate on this work, attempting to estimate the impact of gender inequality. However, both of these studies suffer from multicollinearity in their regression estimations. The correlation between development and gender inequality 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 development 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 inequality variables are only significant at the 10 percent level.

Furthermore, both of these studies suffer from relatively poor fit of their models. The R-squared value on Matheson and Congdon-Hohman (2011) primary estimation 0.8 was below 50 percent.

Neither study is able to make robust arguments about the importance of gender inequality in determining women’s soccer success. This study utilizes a novel approach in order to address the multicollinearity problem in the regression estimation, using principal component analysis and principal component regression. Through this approach, I am able to make convincing and robust arguments about the importance of gen-

Figure 3: First vs. Third Principal Components for Primary Model per game in attendance over its seventeen reflect wider issues of gender inequality games, for a total of 658,000 fans. This beyond sport. was larger than the average attendance of Determinants of Success in Sport English Premier League, the most popuIt is unlikely that gender inequality is lar men’s professional league, during the the sole determinant of differences in same year. Moreover, the final game of women’s national team quality. Torgler the Women’s World Cup saw attendance (2008) establishes that the determinants of 92,000, which to this day is the largest of women’s soccer success may be differcrowd ever to witness a women’s sportent than men’s. Hoffman, et al (2006) and ing event. The television audience, the Matheson and Congdon-Hohman (2011) most quickly growing and lucrative component 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 understand 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 semiprofessional and amateur women’s soccer. 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 Table I: Summary Statistic for Explanatory Variables Columbia Economics Review

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

Data To measure success of national teams, I use the number of points gained in match-

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. 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 autocorrelations 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 surprising, as most of the earlier studies mentioned suggest that the differential success of a given country’s soccer program, for men or women, is largely determined by macroeconomic factors and other components that do not change quickly over time. Because the length of my time series is somewhat short from the perspective of macroeconomic change, high autocorrelations 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, including the larger sample of countries will help us to at least partially avoid

Columbia Economics Review

sample selection bias in the study. Table I shows summary statistics for both the men’s and women’s teams’ points averaged 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 relationship 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 rankings. However, there are other variables needed to understand the differential performance in the women’s rankings. Otherwise, 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 women’s success in sports. This paper will argue that this is significant because of the pervasive gender inequality in many of these countries.

Models I begin with a basic estimation before moving on to our primary models. The basic estimation equation is as follows:

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Component 1 Figure 5: First Three Principal Components for PrimaryModel pi = β0 + βXi + εi where pi is country’s women’s program’s points, Xi is a vector of explanatory 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 population 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 development in a country, which is related to the leisure time available to developing 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 remuneration in developed countries. This is true even, and perhaps especially, for women

where there is not an efficient international market for soccer labor. As such, these women must depend on national federations (like the United States Soccer Federation in the United States or the Football Association in England) or universities to support them while they train. Population 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 moderate 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 interest 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 genColumbia Economics Review

der inequality variable. A few have been used in the literature, including the gender 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 inequality in a country for the simple model.

There is a significant problem in using a linear model to estimate the impact of gender inequality on women’s soccer success. All of the earlier studies that included a gender inequality indicator exhibited what seem to be symptoms of multicollinearity in their regression results when using OLS, even after I ignore the relatively smaller multicollinearity 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

Spring 2013 development index and the gender inequality index, another composite index constructed by the UNDP, is larger in magnitude than 85 percent. Other measures of gender inequality, like secondary education ratio between women and men and adolescent fertility rate (among others) have slightly lower pairwise correlations with HDI, but still average around 75 percent in magnitude. If I use GDP per capita, a less precise measure of development 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 suffer from multicollinearity.

Multicollinearity results from having high correlations between two or more explanatory variables in a multiple regression 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 insignificant, even though joint tests of significance of the affected variables (such as the F-test) will successfully reject the null hypothesis that all of their coefficients are zero. It also causes the model to be extremely sensitive to both variable selection and sample selection; both coefficients 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 particular explanatory variable pointless, as I am unable to assess the validity of either 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.

Methodologies In order to use this model effectively, we will use principal component regression. Instead of directly regressing the dependent variable against the explana-

13 tory variables, we will regress the dependent 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 principal component analysis may help us to better understand the underlying relationships and comovements in the data.

As per Jolliffe (2002), the principal component is given by: YTp = nXTpWp (4) n where nYTp = (y1,y2,…, yp) is the principal component, nXTp 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): X = pWpΣnVTn (5) p n where pWp is the matrix of eigenvectors of the covariance matrix pXnXTp, pΣn is a rectangular diagonal matrix with the diagonal composed of elements of R+, and nVn is the matrix of of eigenvectors of X X Tp. p n Essentially, principal component analysis uses a particular orthogonal transfor-

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

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14 mation to turn our potentially correlated explanatory variables into a set of linearly independent vectors called ``principal 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 identify 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 ``rotating’’ our data to identify the directions of maximum variance. Note that, as mentioned above, the principal components are simply a linear combination of the original variables, albeit chosen such that the first component contains more information than the second, the second more than the third, and so on for all p components.

Because the principal components are sensitive to the relative scaling of the vectors in the data matrix, and can give misleading results if not mean-centered, we have standardized the explanatory variables before implementing the method.

There are several advantages to principal component analysis that we will be able to utilize in the context of this study. First and most important is the orthogonality of the principal components.

Hence, assuming normality of the data, we have independence. This eliminates the multicollinearity problem discussed above. Further, because principal component analysis by design creates the coordinates of the new basis in order of importance, we can project our original

Table II: Principal Component Regression Results

Spring 2013 data onto this new basis and can visualize what was originally high dimensional data in the space of R2 or R3. This is a significant advantage in helping us to interpret the meaning of principal components.

Results: Principal Component Analysis As previously mentioned, principal component analysis is sensitive to the relative 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 component analysis is also sensitive to the inclusion 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 human 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 information. As noted above, I will include the logarithm of population, squared deviation from ideal temperature, Men’s FIFA points. Instead of choosing just one gender inequality proxy, I will include four: labor force participation ratio, secondary education ratio, adolescent fertility rate, and maternal mortality rate.

One of the benefits of principal component 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 projections of the original data into reduced dimension spaces of the new orthogonal basis.

Figure 1 shows the original explanatory 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 components, and I note that there are many countries with high values of both the first and second components, with relatively 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 development 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 Columbia Economics Review

the first component as the development component, which generally embodies the various aspects of human development 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 principal 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 development component I see very large positive values of HDI, and very large negative values of adolescent fertility and maternal mortality, two very development-focused 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 extracting 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 component. I understand from this that the interaction of population and tradition together is more important than either of these separately, and in fact is enormously important overall, as the second principal component. This makes sense intuitively as well. For example a large population in a country without an established 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 explanatory variables projected onto these components, I see that there are many countries that have both high development levels, and large populations with strong traditions. I predict that both of these variables will have a positive impact on women’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 secondary 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-

Spring 2013 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 earlier discussion of the strong relationship between development and gender equality. 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 development 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 positive value in the gender inequality component, 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 Regression While the principal components are ordered in descending explanatory power, 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 regression, but rather uses a subset of the most important principal components.

Essentially the eigenvalues denote the stretch of the transformation in the direction 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 components are ordered by the amount of explanatory power they provide. However,

15 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 components. From this figure, the first three principal components, the development component, the tradition and size component, and the gender equality component, explain over 80 percent of the variance 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 using OLS with heteroskedasticity-robust standard errors using the first three principal components as independent variables. 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 incredibly small. This is already a large advantage 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.

Conclusion I have a model with significant, interpretable coefficients, and a fit approximately as good as, and often better than, the simple linear model. I can say with some confidence that this model and implementation is better than the simple linear models used in the previous literature. Further, I see that although the development component embodies about twice the variance in the data as the gender inequality component, their coefficients 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 advantage over previous work on the subject.

Robustness checks have confirmed my results, and the assumptions of our primary model appear to have been met: all of which is reassuring in confirming the validity of our study.

â–Ş

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Pass the (Bargaining) Chips Carbon 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 governments implement schemes to cap carbon emissions, scrap pollutant capital, and promote greener technologies — all under the pressure created by global warming—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. Using an altered pollution function which is “strongly decreasing,” I find the opposite 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 expanding in number since the industrialization of Europe, as workers became afraid that they lacked the skills to perform most jobs. This situation shifted the bargaining power from the workers to the firm, which was able to mistreat

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.

workers and underpay them. The power of the trade union stemmed from collective bargaining, the practice of collecting individual workers’ power into one central entity, allowing them to bargain over wages and employment more effectively.

The recent decrease in union membership Columbia Economics Review

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 greater union membership include mining and quarrying, public administration and defense, education, and manufacturing (BIS 2010). Discussion focuses on the more pollutant sectors such as manufacturing.

Related Literature The manufacturing industry is one of the most pollutant sectors of the economy due to its high level of capital production which requires high energy input.

In most countries, the energy supply is based fuels that are relatively cheap but highly polluting, such as coal. Schipper, 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 emissions through the use of machinery in sectors such as manufacturing, and the National Institute for Environmental Studies (2002) finds that developing countries see a similar problem of industrial pollution as the governments prioritize economic

Spring 2013 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 pollution function where the marginal utility of pollution is negative and strictly decreasing 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 effects of pollution on production but allow for variation of labor over time—a standard assumption in the literature which Forster does not use.

Ehrenberg and Smith’s (1982) textbook presents a simplified model of trade unions where their function can be expressed through shifting the labor supply curve to the left and subsequently causing an increase in wages.

This can be thought of as a closed shop1.

Legality issues aside, this model assumes 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 StoneGeary function. These quasi-concave increasing preferences can consequently be viewed as a set of convex indifference 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 members only, thus reducing the total supply of labor available. It has been deemed illegal 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 employment ’N’.

Carruth and Oswald (1987) extend the findings from Lindbeck and Snower (1984), with a model employing an ‘insider-outsider’ approach. An insider is a worker who is a member of the trade union 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 longer attaches any weight to its employment goal,” a “potentially serious deficiency”

in the Lindbeck and Snower model. Consequently, 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 turnover costs when they replace insiders by outsiders.” (Lindbeck and Snower 1984) Carruth and Oswald find that trade unions 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 Columbia Economics Review

17 model. Carruth and Oswald’s paper suggests the importance of considering alternatively 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. Bargaining 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 bargaining power. A further critique by Doiron (1995) shows that empirical data contradicts 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 completed. The contract curve is the set of Pareto optimal allocations; the solution to the bargaining problem is the point where these two utility curves intersect.

Economists have been researching and debating over the appropriate way to view bargaining between the firm and union. The Nash bargaining approach—an extensive form of Nash’s axiomatic bargaining solution— is the favored solution over the alternative hypothesis offered by Rubinstein.

Nash (1950) introduced the case of a two player bargaining game that includes “monopoly 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 (Rubinstein, 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).

18 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 policies 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 bargaining power θ, where allows greater manipulation and realism of the standard model.

The Model Assumption 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 maximize 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 production function f(K,L)=KL. This gives a profit function for the firm of: π(K,L,w,r)=KL-wL-rK Using the Nash bargaining solution, I use the standard model with the disagreement point d omitted for simplicity.

Here I maximize the log of the profit function, and simplify further. The differentiation 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 power θ: G(δ)=(1-δ)[π^*+U^*]-P(K)δ.

Analysis Equation (3.6) shows that although trade unions do not care directly for K, as it is not present in their utility function, they are aware of the effects of capital 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 wages 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-

Spring 2013 stitute in capital for labor.

Equation (3.7) represents the government’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 maximize its own function with respect to θ, the bargaining power. This however is highly dependent on the pollution function P(K).

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

Proof I begin at (4.1) with a strongly increasing pollution function (A.9). Here α and β are pollution scalars that allow the function to react suitably: P(K)=αK+βK2 Function (4.1) has the property that the relationship between capital and pollution 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 assumption of pollution causing an adverse effect on the marginal product of the factors of production. I then substitute the strongly increasing pollution function (4.1) into the government welfare function G (δ) (3.7): G(δ)=(1-δ)[LK-rK-wL+wL]-(αK+βK2)δ I 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 government function: G(δ,θ)=-(α(w/θ)+β(w/θ)2)δ I now maximize this welfare function (4.3) with respect to bargaining power θ to find the optimal level for the government, 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 capital: θ*= -2θ*K*.

Rearranging: K*= -1/2 Substituting capital into the government’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 positive, the level of capital at the optimal θ* Columbia Economics Review

is a corner solution at zero, proving the transfer of bargaining power has a negative relationship with the level of pollutant capital employed and reaches an optimal 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 appealing.

Proposition 2. When the government maximizes its preferences including a pollution function which is strongly decreasing, the optimal solution is a transfer of bargaining power toward the union. 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.

Proof The proof relies on using previous results of (3.1-3.7). The proof begins at (4.7)

When the government optimizes according to its preferences— that include a pollution function which is strongly increasing— the optimal solution is a transfer of bargaining power toward the firm.

where there is a strongly decreasing pollution function (A.11). I substitute this function into (3.7) and maximize the government’s welfare G(δ) with respect to the bargaining power θ, where α and β are pollution scalars that allow the function 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 substitute the strongly decreasing pollution function (4.7) into the government welfare 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 government function to be maximized.

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I now maximize this welfare function (4.9) with respect to bargaining power, and find the optimal level for the government, 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*=1 At this optimum, capital is strongly increasing, therefore proving that the optimal 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 because the transfer of bargaining power toward the trade union causes inflationary 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.

Discussion In the case of a strongly increasing pollution function, the critical point expressed 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 pollution 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 labor. 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 substitute 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 bargaining 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 optimum 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 critically affects bargaining power and substitution 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) represents cleaner capital due to technological progress (4.7). An increase in wages therefore has an increased effect on optimum 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 exclusive use of capital in production. This full transfer of power would only be appropriate if capital produced no pollution, however, as governments and firms invest into more alternatives to pollutant energy this could be a more realistic prospect. Equation (4.10) is more characteristic of a developed economy.

Some further assumptions allow the model to be implemented, and the findings 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 unionization is a good mechanism to allow on a policy level if they are concerned with pollution. This would be most effective if a trade union is not presently incumbent in a developing country, which is in the earlier stages of industrialization, 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 objective is to maximize total welfare of the economy, taking into account the bargaining between the firm and the union while subtracting the externalities caused. The model produces two stark results dependant on the pollutant nature 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 pollution conscious and how transferring bargaining power will have a direct effect on wages. The propositions not only provide

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19 us with a direct relationship between the wage rate and bargaining power but also their effect on capital (4.6, 4.12). This relationship allows the government to observe 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

Assuming 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.

There are many weaknesses to this simplistic model that need to be addressed and researched further. The main argument assumes capital investment is easily 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 investment 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 mining or energy production, and the corner solution of proposition 1 is not attainable.

Another weakness is the ease of transferring 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. Policies 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-

20 lems. Looking for a more complex utility curve for the trade union using different trade union models is a good starting point for developing the arguments presented; Manning (1987) summarizes examples from other economists. For example, 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 function that included constant returns to scale; there is dispute over whether this exists or whether decreasing or increasing returns to scale should be included instead. Additionally, a more realistic and useful approach concerning the cost of capital would be to include an increasing 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 assumptions. 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. Pollution such as air pollution and water pollution 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 conclusions.

The purpose of this paper was to explore more closely the transfer of bargaining power between the trade union and the firm when under observation by a pollution conscious government. Assuming 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 increase 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

Spring 2013 bargaining power toward the firm, creating a corner solution at zero for capital purchased. Even though the union is bargaining 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 labor 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 evaluation, before any strong conclusions can be drawn.

A. Appendix A.1 Nash’s Bargaining Problem The standard model of the Nash bargaining problem: This is subject to for both i.

and

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

This is subject to for both i.

and

A.2 Solving the bargaining problem If 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 function Highly pollutant capital: P(K)=αK+βK2 Less pollutant capital: P(K)=αK+β(1/K)

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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 substitute 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:

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Pregaming the Tequila Crisis The 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 attempts 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 appreciation of the currency, and accumulation of deficits. Each episode ended with some external shock that forced Mexico to devalue 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 uprising, political assassinations, and a controversial presidential election. Moreover, the effects of this currency devaluation were not confined to Mexico, but spread through much of South America. The devaluation of the peso and subsequent the subsequent widespread economic disaster prompted many economists to study this episode to gain insights into the dynamics of currency crises.

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

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

Most followed identical scripts: fixing the exchange rate led to economic prosperity, real appreciation of the currency, and accumulation of deficits. Each episode ended with some external shock that forced Mexico to devalue the peso.

Models of Currency Crises The bedrock of “first-generation� models of currency crises is Krugman (1979), in which unsustainable policy results unavoidably in a balance-of-payments crisis. In Krugman’s model, the protection 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 objectives. For example, a central bank could Columbia Economics Review

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 objective 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 exchange rate we have:

đ?‘…đ?‘…đ?‘…đ?‘…đ?‘Ąđ?‘Ąđ?‘Ąđ?‘ĄĚ‡ = −đ?‘‘đ?‘‘đ?‘‘đ?‘‘

(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 purchasing power parity (PPP) this excess money cannot affect prices, and thus only the exchange of domestic for foreign currency ensures 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 essential features of currency crises, that some exogenously 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 speculative attack, and that at this exact moment the government must switch from a fixed ex-

22 change rate to a floating one.

To illustrate this, it is easiest to examine first what happens under the floating-rate regime. After reserves are exhausted, the government 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

Spring 2013 currency devaluation. The rate of inflation will be equal to the devaluation due to PPP, and thus in the steady-state of the floating regime we have: (2) Equations (1) and (2) show what happens during the fixed and flexible phases respectively. Equation (2) requires that

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inflation be positive and therefore implies 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 perfect capital mobility, Krugman rules out a discrete jump in the exchange rate at

Spring 2013 any time, because any perfectly foreseeable 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 reserves will be driven down to zero. This is the most important result of the Krugman model: there can be , under perfect foresight ,a gradual depletion of reserves followed by a perfectly predicted speculative attack. Additionally, at this time T, the fixed exchange rate is precisely equal to what the exchange rate would be under a flexible rate policy.

The Krugman model is particularly useful in examining currency crises, because 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 Expectations Expanding on Krugman’s work, Obstfeld (1986) pioneered “second-generation” models of currency crises by incorporating the role of expectations and their interaction with policy-making decisions. It is not necessary for the sake of this paper to expand on the particulars of Obstfeld’s model, but his main contribution is the proof of multiple equilibria resulting from self-fulfilling expectations. One key distinction between first and second-generation models is the role of policy chosen by the central bank. In Krugman (1979), the government exogenously determines macroeconomic policy; the central bank merely follows a mandate. However, in later endogenouspolicy models, the policymakers respond optimally to macroeconomic shocks.

The essential feature of Obstfeld’s model is that there can be multiple equilibria that depend on investors’ expectations and that the actions of investors are informed by risks determined by financial markets that may or may not reflect the underlying macroeconomic fundamentals. The expectations of market participants, 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

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Source: The International Monetary Fund (IMF), International Financial survive. The implications of Obstfeld’s model are important in understanding and appreciating the unpredictability of balance-of-payments crises and draw attention 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 crises, 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 designated as the lender of last resort in cases of panic and crisis, and as such depositors lack strong incentives to closely monitor the characteristics of bank loans. Particularly, there is a maturity mismatch between long-term loans and short-term deposits, and as such the central bank acquires short-term obligations in excess.

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

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Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool 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 occurs 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’ portfolio of assets. Thus, the result is similar to the Krugman model, except that the government deficits are augmented by the interest on the banks’ loans:

đ?œ‹đ?œ‹đ?œ‹đ?œ‹đ?œ‹đ?œ‹đ?œ‹đ?œ‹(đ?‘–đ?‘–đ?‘–đ?‘– ∗ + đ?œ‹đ?œ‹đ?œ‹đ?œ‹) = đ?‘‘đ?‘‘đ?‘‘đ?‘‘ − đ?‘?đ?‘?đ?‘?đ?‘?đ?‘–đ?‘–đ?‘–đ?‘– ∗

(4) Similarly, the loss of reserves at time T is: ∆đ?‘…đ?‘…đ?‘…đ?‘… = đ?œ‹đ?œ‹đ?œ‹đ?œ‹(đ?‘–đ?‘–đ?‘–đ?‘– ∗ ) − đ?œ‹đ?œ‹đ?œ‹đ?œ‹(đ?‘–đ?‘–đ?‘–đ?‘– ∗ + đ?œ‹đ?œ‹đ?œ‹đ?œ‹) + đ?‘?đ?‘?đ?‘?đ?‘? > 0 (5) It follows that Z magnifies the fall in reserves 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 paper, but Calvo and Mendoza demonstrate that if investors are optimizing a mean-variance portfolio strategy, then highly diversified portfolios can lead to calamitous results. Investors 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 diversified an investor is, the less incentive he has to learn about the actual quality of investments in a given country. Together, these results create an investment landscape in which news or rumors about a country cause sudden

and severe capital flight, even if the underlying 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 devaluation.

The Buildup The 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 creating a stable fixed exchange rate system, there was significant confidence, both within Mexico and in the global community, that this time was different. Much of this belief came from confidence in the Pacto. The Pacto was a comprehensive attempt at structural reform that created a social “pact� between the government, workers, and firms. These groups met regularly to make macroeconomic decisions concerning prices, wages, and the exchange rate. There was a general optimism that President Carlos Salinas had freed Mexico from its sordid history of economic calamity. In addition to internal reforms, Mexico was realigning itself with the global economic community and embracing globalization. The Salinas administration 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 Columbia Economics Review

position as a stable contributor to the world economy. However, between 1988 and 1994 GDP growth was somewhat uneven, 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 problems. 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 predetermined amount. However, these adjustments 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 depreciation), 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 ballooned 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. Dornbusch and Werner (1994) suggested a devaluation of 20 percent in March 1994.

The Krugman model is particularly useful in examining currency crises, because 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.

They argued that the stabilization strategy pursued by Salinas had left Mexico vulnerable, with firms facing high real interest rates, increasing nonperforming loans, and real appreciation fueling a mounting current account deficit. However, other economists argued that Mexico was simply adjusting to a new equilibrium induced by the structural reforms of the Pacto and other policies. Regardless, no one expected the collapse of the peso

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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 continue its positive economic trajectory after a period of adjustment.

The Crash: Political Shocks and a Collapsing Economy The most probable explanation for the devaluation of the peso (and the one cited by the Bank of Mexico) is a series of political 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 began an armed uprising. This raised considerable doubts about the political stability 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 future 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 reserves, almost $11 billion in a four-week period.

Reserve depletion was not the only adverse effect of the assassination, as interest 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 government 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 continually wavered. Late June saw an attempted resignation from the Minister of Interior, Jorge Capizo, who was supposed to oversee the election, as well as the kidnapping of a prominent businessman. Elections proceeded without major incident in August, and the ruling party candidate, Ernesto Zedillo, won by a convincing margin. 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-

Source: The International Monetary Fund (IMF), International Financial Statistics eral Mario Ruiz Massieu, brother of the deceased, accused the ruling party of orchestrating the assassination.

Foreign currency flooded out of Mexico as international reserves shrunk to $12 billion, from $26 billion in January, putting even more pressure on the currency peg. On December 20, 1994 the government decided to devalue the peso by 15 percent to roughly four pesos per U.S. dollar. However, this was only a temporary measure, and international reserves continued to rush out of Mexico. $4.5 billion in reserves were withdrawn the following day, on December 21, the largest single-day movement of the year. On December 22, Mexico was forced to let the peso float, and it jumped to 5.3 pesos per dollar by the end of December. The result was nothing less than financial crisis.

Credit markets dried up, the peso plummeted, and interest rates increased sharply. Mexico had lost its reputation as a safe

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

The Role of Debt Leading 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 investing 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: The International Monetary Fund (IMF), International Financial Statistics

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26 an interesting policy maneuver designed to quell investors’ worry and simultaneously 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 signal to the global community that Mexico

The real appreciation of the peso and the mounting current account deficit were Mexico’s central economic problems. did not plan to devalue the currency. The large spread between cetes and tesobonos implied that the market expected a devaluation, and yet still the Mexican government 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 policy tool; devaluation would now signal to investors that the situation was beyond repair.

Issuing tesobonos absent any other policy action was a critical mistake. The economic 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 inconsistency 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 Facts One of the puzzling aspects of the Tequila Crisis was that while the pattern of reserve losses at first glance looks consistent with a Krugman model of speculative attack, there was no fiscal deficit that produced 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

Spring 2013 imbalance that contributed to the draining 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 expansion (Whitt 1996). This line of thinking justifies the current account deficit by implying that once the investment projects are completed, exports will rise and balance the current account. Thus, it is possible that had there not been any adverse shocks to Mexican credibility, the current account situation would have been rectified. However, Dornbusch and Werner (1994) argue vehemently that this structural 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-

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 interpreted as a risk premium that investors demanded to justify the perceived riskiness 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 selffulfilling 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 contributing 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 aggressively and the entire process would have played out very differently. While a single cause for the crisis is not readily apparent, it is easy to see that multiple equilibria may arise from the feedback

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool 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 foretold 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 afColumbia Economics Review

loop between investors and policy-makers.

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

Spring 2013 occurred the following day. The global investor community rejected the devaluation as a tool that was being used to stabilize the economy and rather interpreted it as a signal of weakness.

In this framework, it is easy to understand why investors with so many opportunities 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 capital 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 trajectory of a country’s financial condition.

The expectations mechanism certainly has a self-fulfilling aspect, but it is reinforced by the speed with which investors move capital around the world and the lack of incentives they have in determining whether to react to shifts in financial markets or underlying macroeconomic fundamentals.

Source: Inter-American Development Bank, Latin American and Caribbean Macro Watch Data Tool 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 macroeconomic fundamentals of Mexico leading up to the crisis do not seem to align with the end result. This gap between fundamentals and outcomes appears to

o

Source: Inter-American Development Bank, Latin American and Caribbean Watch Data Tool Conclusion Studying the peso crisis of 1994 reveals a highly complex interaction of political shocks, policy decisions, and investor reactions. 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

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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 policy ever changed, and as such investors were mainly shaken by general concern rather than anything endogenous to the economic situation.

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This crisis demonstrated that expectations about the future of a country could have dire consequences for economic stability, even if the expectations do not reflect 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 international investing, which hinges more on news and expectations than economic fundamentals, is a by-product of high degrees of capital mobility and a defining factor of our world economy. By studying events like the Tequila Crisis we can gain insight into how to prevent such disastrous episodes from repeating themselves. This crisis, in particular, makes it clear that appreciating currencies can present a very real problem 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 Macro financial markets. Ultimately, whether the financial crisis could have been avoided by any policy package is uncertain, 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’ expectations play in a time of crisis.

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Lost and Found Is a Lost Generation of Young Workers an Evitable Outcome of Recession?

Inna Grinis Cambridge University

Youth unemployment has been one of the principal concerns of the Great Recession. 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 population (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 better in countries using the “flexicurity model”, a combination of labor market flexibility with generous welfare provisions. 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 workers might arise not only from the recession but also from country specific labor markets.

The subpar labor market experiences that these young workers receive in their early years will continue to influence their careers years after recessions end, putting them at a disadvantage relative to those graduating in more prosperous times and thereby producing a “lost generation.”

Furthermore, recessions are not only problematic to unemployed young people. By constraining their choice sets and forcing them to engage on suboptimal Columbia Economics Review

paths, such as job/skill mismatches, recessions can also have significant, long-term negative effects on those “lucky” youths that do find jobs. The subpar labor market experiences that these young workers receive in their early years will continue to influence their careers years after recessions end, putting them at a disadvantage 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 economics 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 efficiency 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 unemployment rates. Mismatch turns out to be significant in most fields of study, suggesting that the “lost generation” effects can be lessened by addressing the mis-

Spring 2013

29 ployment rate at the time of graduation1 translates into an initial wage loss of 6 to 7 percent per year. The effect is diminishing, but significant and persistent: 15 years after graduation the wage loss is still 2.5 percent per year. Another perverse 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 empirically establish that whole worker cohorts can be disadvantaged if, at the time they enter the labor market, the average starting wage is lower and the national unemployment rate higher. Different theoreti-

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 measures: long-term effects of graduating in a recession Recently, there has been a renewed interest among labor economists in investigating 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 foretell 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 Consequences 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 unemColumbia Economics Review

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

Spring 2013

30 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 rewards more workers with greater human capital accumulation. Such human capital encompasses both observed (education, experience) and unobserved (ability, motivation) characteristics. Labor economists use the Mincerian wage equation (1974) to summarize and test the relationship 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 experienced workers. This may force young graduates to accept positions that are less-skilled than the ones they are qualified 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 reengage onto the optimal path.

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

in which mismatchit measures the degree 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 education (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 losing their jobs and thus more willing to accept wages below their marginal productivities. 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 Mincerian equation above. Unfortunately, this would also require an important amount of micro-level data to which access is restricted.

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

Figure 1: Education/occupation mismatch (ISCED 5-6) of persons aged 25-34 by study field (20032007) 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

Education Science, mathematics and computing Services

Humanities and Arts Engineering, manufacturing and construction

Source: EUROSTAT Columbia Economics Review

Social sciences, business and law Health and Welfare

Spring 2013

31

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

The dependent variable is the fieldspecific youth unemployment rate. I am also controlling for educational attainment and cross-equation countryspecific 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 significant 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 instance, 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. Furthermore, Services is probably the sector which requires the least amount of specific education, making job/skill mismatch less problematic.

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

Section 3: Making It Evitable: Centralized Coordinated Matching Mechanism “The labor market suffers from asymmetric information, coordination, and collective action 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 because 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 workers and jobs. For example the European Youth Guarantee provides young people

an apprenticeship, a job, or further training 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 positions 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 financial rewards, they do not coordinate both sides of the labor market and are usually in small-scale.

Another perverse consequence is lower occupational attainment, implying that young workers have difficulties switching into better jobs once the recession is over.

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 information and addressing adverse selection

Columbia Economics Review

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 unemployed workers with different characteristics (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 negotiable and that they are part of the vacancy description, along with other characteristics such as contract type, work hours, location, promotion perspectives, bonuses, and pension plans.

Within this framework, a central authority 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 centralized 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 positions corresponding to their educational backgrounds. Moreover, by concentrating all the information in one central body, the costs of job search and advertisement could be significantly lowered.

32

Spring 2013

Many commercial Internet job boards, like Monster.com for instance, suffer from an adverse selection problem, because firms believe that those who decide to post their CVs on such websites are below average quality and do not disclose full information about themselves.

In the case of young workers, a centralized mechanism could easily solve this “lemons” problem by providing certified information on all students, such as their scores, peer-rankings, courses taken, etc.

The economic and statistical significance of education and occupation mismatch suggests that countries with high mismatch rates could greatly benefit from achieving more efficient matching of their college graduates.

Furthermore, the mechanism allows for open days, interviews, and advising services before the deadline.

At the deadline, the central authority would collect both preference lists and use an algorithm, for instance the deferred 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 likely 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 often called clearinghouses and have been used, for instance, in the U.S., to allocate new doctors to hospitals (National Resident 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 understand from the existing literature the

main causes of the “lost generation” effect. 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 capital, job/skill mismatch engages them onto suboptimal career paths from which they have difficulties escaping later on. Recessions increase both unemployment and mismatches, putting the unlucky generation graduating at that time at a persistent disadvantage.

Most of the current policies aimed at helping young people have two main objectives: increasing youth employability through policies such as redirecting them towards education or training and promoting youth employment. However, very few initiatives realize that their efficiency crucially depends on successful job/skill matches. As I investigate in section 2, job/skill mismatches and youth unColumbia Economics Review

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 population as a whole by significantly lowering youth unemployment rates.

In the last section I outline a centralized coordinated matching mechanism that could be a useful starting point for thinking about how both sides of the labor market could be more efficiently coordinated on a large scale.

Further research shall investigate how such a centralized coordinated matching mechanism could work in practice, and how it should be designed in order to avoid problems like congestion or unraveling 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.

▪

COLUMBIA ECONOMICS REVIEW

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