Has the Road Towards Strict CBA Regulations in the NBA Caused Market Size

Wage Premium to Diminish?

By: Chris Precourt

Abstract:

The National Basketball Association has a much different economic landscape today than

it did 30 years ago when David Stern’s tenure as NBA commissioner began. The league of the

past years had a Collective Bargaining Agreement signed by team owners and players that posed

minimal structure or regulations to avoid monopolistic market power. The league also lacked a

globalized atmosphere, and saw only a handful of teams thriving on the court and financially.

He has since created a global atmosphere in the league, recruiting an array of talented players

from countries globally and the worldwide fan base has increased exponentially. These factors

have proven to be a crucial in achieving the profitability and impressive net worth the association

now enjoys. More importantly, the regulation and enforcement of stricter CBA rules has created

a much more competitive marketplace within the NBA and small market teams have been able to

build successful teams around a talented core of players. Using player data from the 1993-1994

season, before strict CBA regulations were implemented, and the 2011-2012 season, after the

2010 lockout, two OLS estimated models comprised of the same independent variables are

compared to one another. The estimates suggest that market size and other economics factors of

a country no longer cause a salary premium for players among different NBA teams or foreign

players from different regions of the world, which has been a historical trend in the NBA.

Various other individual specific characteristics are regressed on a player’s salary, in addition to

market size, to analyze how the age of stricter CBA agreements has changed the structure of

salary disbursement in the NBA.

A. Introduction

The National Basketball Association has not always been the thriving powerhouse that

now dominates the attention of millions of intensely adoring fans all over the world. In today’s

society, professional basketball players are highly admired and it is no secret that they earn a

bigger salary than most people are able to dream of. When David Stern became the NBA

commissioner in 1984, the league had a much less promising financial outlook and global

presence. With a net worth of 15 million dollars and a handful of teams on the brink of

bankruptcy, Stern proved to be a visionary businessman, realizing the NBA had fundamental

flaws in various aspects of its structural and financial foundation. He acknowledged that

basketball was loved and appreciated in countries all over the world, bringing to light the

potential profit that the league could generate if the proper initiatives to globalize the league

were taken. He also noted that a small handful of large markets in the NBA, including New

York, Los Angeles, and Chicago were sharing the majority of market share and financial success

at the time. He knew what he had to do to turn the NBA into a dominant business.

Stern utilized resources such as internet social media to attract international fans, strong

recruitment to find the top foreign talent, and the signing of various contracts with international

television networks to expand its boundaries across the world. Between the years of 1999 and

2008, the league increased its number of internationally born players from 35 to over 80 and the

league has reaped huge financial benefits. International fans have contributed to the modern-day

success of the NBA through a heavy increase in merchandise sales, global media attention and

broadcasts, and an overall larger fan base.

Turning the league into a global wonder was only the beginning stage of a long journey

to success for Stern. He and his committee of team owners have instituted various financial

regulations through the Collective Bargaining Agreement, which must be agreed upon and

signed by a chosen committee of NBA players. Such negotiations have benefitted teams of all

market sizes and the overall profitability in the league. Before Stern’s reign, teams in largely

populated cities with rich owners dominated league market share by employing all the top tier

players. When the league existed in an economic environment that implemented no salary cap,

the teams in small markets were simply outbidded on any player with premier talent. The first

significant CBA agreement was signed after the 1995 season, which regimented a salary

structure highly resembling that of non-sports unions. Such unions are generally adopted to

institutionalize a wage structure and reduce productivity-based wages.

Stern’s rule over the last 3 decades has transformed the league into a multi-billion dollar

association. Nearly all 30 teams are making a yearly profit from basketball related revenues and

NBA stars are much more evenly dispersed throughout teams of varying market sizes. A team

existing in a market outside of the largest and most attractive cities no longer seems to be the

impediment to profitability that it once was.

The presence of international players in the NBA has greatly increased team revenues,

and indicates why internationally born players have portrayed a tendency to earn a wage

premium over American-born players throughout the years since Stern’s arrival. However, the

newly globalized NBA market is seen now as a “Basketball Sanctuary”, with players from all

over the globe eager for the chance to play in the NBA, creating a high level of potential supply.

With only 60 new players granted to play in the NBA through the yearly draft and the leagues

economic structure having already gone through its peak of globalization, demand for foreign

players has balanced off to equilibrium. As a result, team managers have a lot more bargaining

power to negotiate lower wages with international players, which might result in a diminished

salary premium for those born in and coming to the NBA from a large market.

Is market size even a significant variable in the current economic structure of salary

disbursement in the NBA? In the past, large market teams have enjoyed the most success and

their fans have always been spoiled with the best talent. If stricter CBA agreement enforcements

have taken the ability of large market teams to pay a premium for NBA stars and top prospected

foreign players, this could have major beneficial implications for the fans of teams that have

suffered through years of losing and missing the playoffs. Teams of all market sizes may

potentially have equal opportunity to lock in top talent in the NBA and build winning teams,

which has been an up and coming trend in the NBA. More and more teams that have missed the

playoffs for years are now building a strong core around All-Star players, which portrays a

development that could be seen more and more as time passes if market size no longer pays a

premium of the economics of NBA salary disbursement.

B. Literature

Different trends and regulations surrounding salary disbursement have occurred over the

decades in the NBA. There are numerous complicated factors in the economy that have led to the

changing structure of the Collective Bargaining Agreement, but what has ultimately resulted is a

thriving NBA marketplace that is seemingly set to flourish in the future. Much of the literature

reviewed surrounding the topic of NBA disbursement was conducted during the 1990’s and early

2000’s, portraying insight on the ever-changing foundation of a players earnings:

a) Market Size Salary Discrimination

The internationalization and globalization the NBA incorporated into its culture gave rise to a

heavy influx of foreign players in the league and increase in profits from international revenues.

Escker (2004) provided an analysis with the goal of determining how foreign-born players fared

in salary compared to American-born players. This idea posed an interesting debate because

these international players would theoretically be generating millions of dollars for the NBA by

attracting a larger fan base, more international telecasting of games, and increased merchandise

sales. The author concluded that during the 1996-1997 season foreign players earned a wage

premium of nearly 20%.

Using panel data of 618 NBA players between the 1999-2000 and 2007-2008 seasons,

yielding an unbalanced panel of 3,051 observations, and employing the technique of two-stage

double fixed-effect model, Yang (2012), gave empirical results which portrayed a premium for

market-size from two different vanity points. Foreign-market size was a significant variable in a

regression composed of foreign-born players only and domestic market size was significant in a

regression composed off American-born players. The article also indicated foreign-born players

earns 17.4% less than an American- born players, ceteris paribus. Although international players

were earning a wage premium due to their home-country size (per capita GDP, population), they

still earned less overall in relation to American-born players.

The unique approach to this article presented seemed effective and valid. For the first stage

of the model, the research adopted the standard earning equation, a linear OLS model with player

individual-specific characteristics as independent variables and the logarithm of salary as the

dependent variable. The individual wage premium (ui) attributed to unobserved heterogeneity

among players represented the error term. This error term was regressed on a set of new

variables, which comprises of the second stage analysis. Results from this stage portrayed that

market size was a significant factor for American-born players and foreign- market size was

significant in regards to a foreign-born player’s salary. These results were parallel with the

economics of the NBA during the 1990’s. Large market teams were still paying star-players a

huge premium allowing smaller markets from thriving due to a lack of regulations and teams

were also able to spend top dollar to secure highly prospected foreign players.

b) CBA Agreements

There is much evidence that this premium for market size could be due to a lack of proper

regulation within the NBA’s financial structure prior to 1995. Salary structure was very loosely

constrained compared to the current NBA, which proved to be a financial benefit to the players.

Hill and Jolly (2012) review how some of the past revisions to the CBA have changed the

outlook for salary disbursement over the years.

First, in 1995, the union and league negotiated a rookie scale agreement into the 1995

CBA, in which each first-round draft pick had a designated salary that decreased in scale from

the first to the last pick and covered the players’ first 3 years in the NBA. Afterward, the players

became free agents and their salaries were determined by competitive market forces, loosely

constrained by a salary cap. Regression results indicate that pay for experience and efficiency

level is higher under a rookie scale CBA. Second round picks and undrafted players were simply

paid a league minimum wage.  Using quintile regression results, the conclusion is drawn that the

most productive players suffered the greatest rent transfer. The elite players were suddenly

incapable of earning a sky-high salary premium over other players due to the salary cap and the

rookie salary scale meant the most talented rookies were earning much less than their potential.

In the 1999 NBA CBA agreement, the players agreed to a salary cap on individual

players’ salaries, set as a percentage of the team salary cap and escalated by years of experience

and the rookie scale was changed to 4 years instead of 3. A player’s team can retain their first-

round pick for a fourth season at a percentage salary increase set by the CBA. The salary

percentages start at 26.1 percent for the first pick in the draft and increase to 80.5 percent for the

twenty-ninth pick in the first round. These changes are meant to decrease the gap in the salary

distribution of first round picks for the teams that want to retain these players for a fourth season.

In 2005 the CBA implemented a rule forcing any team that has a payroll over a certain

threshold limit to pay a 100 percent tax to the league for the overage. Basketball related income

(BRI) salary dispersion is a common topic of discussion at CBA meetings and presented a point

of disagreement between the negotiating sides in 2010 that nearly costed the NBA the entire

season. The season was reduced to 66 games because it took months for players to accept the

owners request to lower BRI paid out as salary from 57% to 49%. The United States recession

that started in 2008 caused many teams to lose money, thus owners demanded this be accounted

for in the form of paying players a smaller salary. The resulting agreement that revived the

2011-2012 season calls for a revenue split of 49-to-51.2% and a flexible salary cap structure with

harsher luxury tax. A harsher luxury tax was implemented to prevent teams from exceeding their

cap space and thus squandering the monopolistic type power they had enjoyed more freely just

years earlier.

c) Racial Salary Premium

Even with CBA agreements that have applied such restriction on pay in recent years,

questions still remain as to whether racial discrimination still exists among salary disbursement

in the NBA. Back in the early 1970’s, blacks comprised of about 50% of the players in the NBA,

which is a much lower percentage than is seen in the league today. Black players, however, have

not always been fairly compensated. This was portrayed not only in the 1970’s (Mogull, 1981),

but also persisted into the 1980’s.

Koch and Vander Hill (1988) employed a classic wage model using several player

characteristics and also controlled for race, in which they concluded a wage premium for white

players of 13%. The most common explanation for this phenomena was racial customer

discrimination in which owners or fans preferred having white players on their team and thus

paid them a premium. Rehnstrom (2010), concluded that there is a significant white premium of

24.5%, but the statistics used were overall career statistics rather than just representing a single

season. This econometric model detected racial discrimination among salary and was statistically

significant at the 1% level of confidence.

Kahn and Shah (2005) also investigated racial salary discrimination in the NBA using

data from the 2001-2002 NBA season. This article used a different approach, looking for racial

salary effects among different groups of players. The first group, consisting of rookies not

included in the rookie salary scale and players who were not free agents, showed nonwhite

shortfalls in salary. The rookie salary scale defines set salaries for players drafted in the first

round of the NBA draft regardless of position played, productivity stats, or race. Players who

were not under the rookie salary scale and veteran free agents, however, did not suffer from

racial salary differences. The reasoning behind their conclusion was that union pay scales and

competition in the free agent market had caused racial pay differences to disappear, which is

another key change the strict CBA agreements have caused in salary disbursement. Although the

early days of the NBA clearly portrayed racial salary discrimination against black players, the

economics of the league has seemingly sorted this issue out and there is increasing evidence that

racial discrimination has disappeared in the NBA.

d) College as Human Capital

A crucial decision that many college basketball players must make regarding their future

salary is how long they should play college basketball before advancing into the NBA. Human

capital represents the natural ability, skill set, experiences, and know how a worker brings to the

job. In the NBA, human capital is developed throughout a player’s high school and college years

as well as years spent in the NBA. For the athlete, the college years are a period of time when

players develop their athletic talent and the NBA draft is modeled as the League's assessment of

the stock of human capital each player has acquired at the start of his career in the NBA.

Langelett (2012) analyzed how many years of college lead to players being the best

compensated. The phrase “better compensated” was measured by the total salary earned in the

first 10 years of a player’s career. The opportunity cost of playing in college is the salary that the

player could be earning in the NBA, which is the reason that some of the most talented players

skip college to start making a top salary earlier in their career. Hypothetically, the most talented

players could be making tens of millions of dollars yearly in the NBA rather than spend their

time developing their skills and human capital in college.

Using both OLS and a Heckit model, the results show great variation 3 different groups

salary earnings in the NBA: Players drafted out of high school earned on average $43.5 million

in the first 10 years of their career, while the mean for college seniors is $15.9 million, and for

foreign players is $17 .4 million. The main reason that those drafted directly out of high school

showed such a drastic difference in compensation over the first ten years of their career is

because a majority of the players that have the raw to skip college basketball end up becoming

superstars later in their career. This also suggests that whether a player becomes an All-Star

caliber player or not is much more indicative of their future salary than the amount of years they

spend in college.

e) All-Star and Contract Year Premium

The NBA has always created a high level of buzz across the media for its annual All-star

weekend, as the most talented and popular basketball players from each conference take the big

stage to play against each other. Hayles (2006) uses general OLS estimates to regress All-Star, a

dummy variable representing whether a player has been an All-Star in the previous 5 NBA

seasons on player salary and reports a 36% wage premium in 2006 for such players. This is a

variable that has never been proven insignificant throughout any era of NBA history. This makes

sense because regardless of the restrictions set in the CBA, All-Star level players are always in

high demand and require higher salaries to be obtained, whether they play in large markets or

small markets.

The effects of a multi-year NBA contract on a player’s performance is a factor that has

been analyzed by Arcidiacono (2010). Players have to work hard to earn a multi-year contract,

but in theory may stop working as efficiently once they have signed a contract, a phenomena

known as ex-post opportunistic behavior, or shirking. The coefficient on the ANTE variable

measuring performance in the year prior to a contract signing year, had a positive coefficient and

it statistically significant at the 0.1% level, while the coefficient on the ex-post variable

measuring performance the year after signing a contract was not statistically significant.

f) Player Position Premium

All-Stars, along with centers, have classically been portrayed as the divisions of players

that have seemed to earn the largest wage premium above other players. There has been an

unwritten rule that has resonated through the history of the NBA called “air supremacy” in which

taller players have inherently been paid more because there is an advantage to having a player on

a team that has reached the 7 foot mark. Another common subject for debate when it comes to

player compensation in the NBA is whether or not the returns of salary on attributes may vary by

player position for low-salary, low-skill, bench-warmers on a team relative to high-salary, high-

skill superstars.

Quantile regression procedures are used by Agessa (2008) to measure the return to player

attributes for the two groups at different salary levels along the distribution of NBA salaries.

Agessa reported that the coefficient on big men was significant and portrays that big men earn

18% more than guards, with all other attributes held constant during the 2001-2002 season.

Another interesting finding was that the coefficient on experience was significant for guards of

every skill level, but the coefficients were much larger for players at intermediate levels relative

to low-skill players and superstars. This represents greater returns to human capital accumulation

for the intermediate level guards in the league. Very similar findings were shown for big men as

well, with the addition that intermediate level and high-skill big men were compensated much

better for blocks per game relative to low skill big men. With respect to the restrictive CBA

agreements it makes sense that the intermediate skill level players would see the highest

fluctuations in salary, as rookies are placed on a rookie scale and top tier players are limited by

the salary cap.

II. Theory

In sports, there are many economic factors at work behind the decisions that are made by

team managers and there is much money at stake. This can make it difficult to theorize exactly

how we expect certain variables to behave among salary dispersion in a sports environment.

Based on the new economic structure of the NBA, and a little common sense, most of these ideas

and variables can be predicted in a way that makes theoretical sense.

The history of the league would indicate that variables such as player position, draft pick,

and on court performance statistics would all be significant in relation to salary. We might

expect to see that a center would earn more than any other position because a quality center in

the NBA is a rare treasure to come across, so a team that has the opportunity to pay a premium to

obtain a quality center may have feel they have enough incentive to do so. Another hypothetical

reason that centers might earn a salary premium is because their bodies are bigger and taller and

are considered more prone to injury. This would indicate that they should be compensated more

to account for their higher likelihood of having their career prematurely ending, so such a

premium might be included as an insurance policy for such unforeseen factors.

Alternatively, the CBA agreements may very well have created a competitive enough

market among all players in the NBA that this premium for centers may have diminished. There

is also the factor that teams are always chasing after players of different positions based upon

their needs, and thus may have the same incentive to pay premiums for quality guards or

forwards. Given the complexity of the matter we don’t know for sure what to expect with regards

to a salary premium for player positions, but given that centers are fairly difficult to come across

and are taller than players at every other position, it seems fair that centers might still be paid a

premium over other positions.

Being selected lower in the draft, such as the first or second pick, is another factor that

would seemingly lead to a salary premium in a player’s career because draft selections are

essentially an evaluation of talent by teams hoping to select the most talented player before

another team can. However, back in the 1990’s and earlier, the Collective Bargaining Agreement

in the NBA had many fewer restrictions and the most productive and valuable players were

likely to earn a huge salary premium over all other players. Each new CBA Agreement that has

been passed has seen the reigns of control tighten within the NBA so draft pick may have a much

less significant effect on future salary than before. With the newly implemented rookie scale

causing wages to remain relatively close for players in their first four years in the league, we

know that draft pick will be insignificant until at least a player’s fifth year.

It could be fairly assumed that experience in the league would have a positive correlation

with salary. It is logical to assume that as a player gains more experience in the NBA they

increase their value to the team and improve their overall skill level. As a player gains more

experience beyond their fourth year in the league they also break free of the rookie scale and

earn a salary based on competitive market forces. However, experience might not lead to a

premium in every scenario, such as a player that carries a bad attitude or reputation. Adding an

experience-squared variable is necessary when we are analyzing an experience variable because

we would expect that after a given amount of years, this player will begin to age and might see

his productivity and youthful talent diminish, and thus this variable should have a negative

relationship with salary in theory.

As far as productivity statistics are concerned, we would expect an increase in almost any

category (points, rebounds, assists, steals, and blocks) to translate into a wage premium because

the most productive athletes should theoretically be paid the most. Every stat in this case is a

vital component to the NBA because players at different positions excel in different categories.

Centers are more likely to block more shots and pull down rebounds off of missed shots, while

guards are more likely to dish out assists and score points. Forwards are a versatile bunch and

can perform well in multiple categories. The players with the greatest productivity statistics are

typically the most desired by prospective teams who are willing to pay them a premium for the

added value they will contribute to the team. Overall, we would theoretically expect such

variables to have some degree of a positive relationship with salary, however it isn’t obvious

how these variables will rank in order from most to least significant. It could be predicted that

points, assists, and rebounds would be the most significantly related since they are the three stats

that come up most prominently throughout a basketball game. Alternatively, players may not

earn a premium based on any productivity statistics in the current NBA economy with strict

CBA ruling, so these variables are difficult to predict with certainty.

The variable All-Star, which designates a player has been selected to play in the All-Star

within the past 5 seasons, should have a very significantly positive relationship with salary

regardless of whether we are looking at a time period before or after the CBA implemented its

strict ruling fist. The variable portrays an array of attributes about a player ranging from a

player’s popularity, value, and reputation of supremacy. Players that are going to start in the All-

Star Game rather than come off the bench are voted in by the fans and the rest of the All-Star

team is voted in by the head coaches of the league, portraying popularity among the general

population and with personnel with close ties to the league. When All-Stars are on the market as

free agents, they should in theory be the most sought after players because they have the aura of

being elite and of a caliber that could take a team to the next level. All of these factors provide

significant enough evidence that a player who has been an All-Star will earn a wage premium.

The most difficult variable to predict in regards to its relationship with salary is market

size. In my analysis, a market size premium will be measured two different ways: whether it

exists among domestic markets of NBA teams and whether there is a premium for foreign-born

players that are generating a high level of international buzz around the NBA. Historically, the

larger the market size that an NBA player is associated with, the larger the wage premium he

earns, which has also been the case for international players born in a larger market generating

more revenue for the NBA. Prior to certain structural changes of the NBA, it could have been

said with a fair amount of certainty that market size directly affects the salary that athletes are

paid. There are reasons to believe that the impact of market size on salary could greatly be

reduced in today’s economy. The current Collective Bargaining Agreement has changed the

structural backbone of salary dispersion and thus market size might have lost is strong

relationship with salary. The newly institutionalized salary cap and various other restrictions

within the current CBA cause it to be unclear whether we can expect market size to shape the

salary of an NBA player.

III. Data

For my analysis, I plan to compare salary disbursement determinants between the 1993-

1994 season and the 2011-2012 season. The mid 1990’s was the last year in the NBA before the

Collective Bargaining Agreement began to impose restrictions that broke up the monopolistic

power of rich teams, while 2012 is the season directly after the most constraining CBA

agreement was signed. Many of the variables selected to be tested as potential determinants of

salary disparity for NBA players during the 2011-2012 NBA season were retrieved from

Dougstats.com, a website that contains yearly statistics for both the MLB and NBA for each of

the previous 25 seasons, dating back to the 1988-1989 season. The statistics include team

statistics, such as overall record and a plethora of individual player statistics. The individual

statistics that were relevant for my research were recorded as yearly totals on Dougstats.com and

included: games played, minutes played, points, rebounds, assists, steals, and blocks. Other, non-

varying statistics included the position of the player and the player’s respective team.

Nearly every other individual player statistic was collected from the official website of

the NBA. This site is loaded with advanced statistics about every team and player, and includes a

biography for every player with qualitative information about every year of that player’s career.

My salary dispersion research required me to find the country of birth of all NBA players, in

which NBA.com conveniently presented a list of foreign born players that were active on an

NBA roster during every NBA season, the home country of each player, their current contract

length, whether or not they played for a playoff caliber team that year, their overall tenure in the

league, their draft positioning, and the salary they earned during 2011-2012 season. Lastly I used

Google.com to find the per capita income of the population and overall population in each NBA

city to determine if a strong economy and large fan base was a variable causing a salary

premium.

My analysis utilizes a general OLS estimated regression model. The natural logarithm of

a player’s salary will be used as the dependent variable regressed on various individual

characteristics that are representative of potential factors of salary premium among NBA players.

The two regressions will include all of the same variables so results can be compared between

the 1993-1994 and 2011-2012 seasons:

(1)

LnSalary = C0 + C1(Center)i + C2(PG)i + C3(SG)i + C4(All-Star)i + C5(Draft)i + C6(Exp)i

+ C7(ExpSq)i + C8(LnMarket) + C9(BPG)i + C10(SPG)i + C11(APG)i + C12(RPG)i +

C13(PPG)i + C14(PPG)i + C15(Africa)i +C16(Asia-Pacific)i + C17(Caribbean)i +

C18(Europe)i + Ui

Many of these variables were created as dummy variables for which a player would

receive a “1” if they possessed the trait described by such variable, or a “0” otherwise. The

dummy variables included in this regression include: “Center”, “PG”, “SG”, “All-star”, “Africa”,

“Asia-Pacific”, “Caribbean”, and “Europe”. Each player was designated a “1” for the variable

“All-Star” if they had been an All-Star at some point in their career. I designated the variable to

players who had been an All-Star at some point in their career rather than only the players that

were All-Stars the year prior for specific purposes. Measuring this variable using players who

have been in the All-Star game within the last five years of his career portrays a more accurate

evaluation of All-Star value rather than just selecting players that were players in the previous

year, as this method could potentially leave out many players that have All-Star value. The

variables “Center”, “PG”, and “SG” portray player positions, including center, point guard, and

shooting guard. I excluded small forwards and power forwards from the regression so that the

parameter estimates on guards and centers could be compared to those of forwards. The final

four dummy variables designate different regions of the world that players were potentially born

in. These regions are self-explanatory based on the variable names but nonetheless include:

Africa, the Asia-Pacific region, the Caribbean (including South America), and Europe. American

born players received a “1” for the variable “America”, designating that a player was born in

America, but this variable was excluded from the regressions Variables of different regions of

the world will be used to measure the significance in foreign-market size on a foreign-born

player’s salary.

All of the data I collected from dougstats.com for each player was in terms of yearly totals. I

took stats such as total points, assists, rebounds, steals, and blocks and divided each by a players

total games played. This created the variables of points per game; “PPG”, assists per game;

“APG”, “rebounds per game; “RPG”, blocks per game; “BPG”, and steals per game; “SPG”. The

dependent variable “LnSalary” is the natural logarithm of a player’s salary during the 2011-2012

season, which is the first year after the 2010 NBA lockout. This is a point of interest because the

aftermath of the lockout could potentially have a major impact on salary dispersion in the NBA

compared to years past. I used the natural logarithm to make the salary variable more normally

distributed, which helps to account for any outliers that would skew the parameter estimates.

There is potential for outliers because the top paid players in the league earn more than 20

million while the player’s with the smallest salaries earn less than 300,000. Also, with the

logarithmic function, regression coefficients are semi-elasticities, showing the approximate

percentage change in income for a one-unit increase in an explanatory variable, which makes

more sense when conducting a salary analysis such as this.

The variable “Exp” represents experience in the league, in other words, number of years

that a player has been in the league prior to the season of interest. “ExpSq” is the square of the

experience variable and is used as a means of capturing the diminishing marginal returns from

experience, due to aging and declining from their “peak” period. The variable “Draft” represents

the number pick each player was in their respective drafts, 1-60. If a player graduated college

and made it into the NBA undrafted, they received a 61 for this variable. More perceived

potential talent is represented by a lower number draft pick, such as first or second.

The last independent variables are “LnMarket” and “LnCapita”. These represent the

natural logarithm of the population of the city of each player’s team and the natural logarithm of

the per capita income from each respective city. The natural logarithm aids in tightening the

range of population measurement between relatively small markets and cities with considerably

larger markets, such as Los Angeles and New York City.

I determined that all of these independent variables were acceptable to put in the same

regression because none of them were correlated strongly enough to skew the parameter

estimates. The variables that were the closest to portraying multicollinearity were on-court

statistics. Points per game has a correlation coefficient of 0.59 in 1993-1994 and a coefficient of

0.58 in 2011-2012 with assists per game, 0.58 in 93-94’ and 0.58 in 11-12’ with rebounds per

game, 0.64 in 93-94’ and 0.65 in 11-12’ with steals per game which were all moderately high,

but not significant enough to determine that these variables would skew the estimates. The

variables “LnMarket”, “LnSalary”, and “Draft” also had no correlation with any other variables

that exceeded 0.60 and thus these were acceptable to include with all other variables. The

number I used as a benchmark to dismiss a variable from the regression was a correlation

coefficient of 0.70 which never proved to be an issue.

The fact that the natural logarithm was used on market size and player salary created a

very small range in the maximum and minimum values of these two variables. The logarithm

function decreased the range in the salary variable “LnSalary” to a minimum value of 10.65 and

a maximum value of 17.04 in 2011-2012 and a minimum of 12.51 and a maximum value of

16.87 in 1993-1994, which is a much smaller range when considering that salary for some

players is higher than 20 million and less than 1 million for some. The range in the market

variable, “LnMarket” was also very small with a minimum value of 5.27 and a maximum value

of 6.99 in 2011-2012 and a minimum value of 4.60 and a maximum value of 7.31 in 1993-1994.

Cities such as Los Angeles have a population over 9 million and a smaller market such as

Portland, Oregon has a population of less than a million, so the logarithm served as a vital tool in

decreasing such a wide range and bringing values within a range that excludes the potential for

heteroskedasticity in the results.

The on-court statistics were not tampered with because they didn’t portray any potential

outliers. These statistics interestingly showed very similar trends in both time periods with the

mean, maximum, and minimum values in all categories within a small range of each other. The

descriptive statistics and correlation coefficients are all accurate measures because my variables

didn’t include any missing data.

IV. Results

The financial and economic landscape in the NBA was structured much differently

during the 1993-1994 season than it is today. During this time span, the NBA and its owners

passed various restrictive regulations through several CBA agreements that have become crucial

to the league’s success and competitive environment. The most surprising aspect was that a

majority of the variables had parameter estimates that came close to what should have been

expected in economic theory.

The significance and magnitude of the estimates on the productivity statistics were

difficult to predict, but can all be logically explained by the economic outlook during each time

period. The 1993-1994 season showed a wage premium for increasing statistics in nearly every

productivity category, except assists per game. Meanwhile the 2011-2012 season portrayed a

wage premium for just points, assists, and rebounds, excluding blocks and steals. Every point per

game that a player averages in 2011-2012 increases his salary by 2.1%, while each assist per

game increases salary by 4.8% and each rebound increases salary by 7.4%. In 1993-1994, each

point per game increased salary by 2.5%, each rebound by 9.4%, each block by 6.0%, and each

steal by 4.7%.

Blocks are significant in the 1990’s and are no longer appear to be so today as centers

alike showed not to earn a wage premium in 2011-2012. If centers are no longer being paid extra

for their height and unique talents, it logically follows that players will see the premium diminish

for blocks per game, as centers block more shots on average than any other position. It also

makes sense that players only see a small increase in their salary for every extra point and a

premium higher than 9% or every rebound because players are more likely to average more

points than rebounds. A solid scorer in the league can average anywhere from 15 to 30 points

whereas a dominant rebounder generally won’t average more than 15 rebounds.

The variables “All-Star” and “Exp” were both expected to be positive and highly

significant and both variables were at the 1% significance level. During the 1993-1994 season

there was a 75.4% wage premium on the “All-Star” variable and a 12.2% wage premium for

each year of experience. During the 2011-2012 season, these variables caused premiums of

62.4% and 21.2% respectively. The premium is likely higher for All-Star level players in the

1990’s because there was lack of a strict salary cap, meaning teams in rich markets were able to

pay All-Star players such a premium, whereas the salary cap is strictly enforced nowadays. The

reason experienced players are paid a larger premium today than in the 90’s could be a result of

disbursed wages as a result of economic rent accumulated from paying rookies and young

players under a “rookie scale” system. There could be various other reasons why these variables

have shifted slightly over time, but it is no surprise that these variables have remained

statistically significant when regressed on salary. The variable “ExpSq” was also statistically

significant at the 1% level and had a negative parameter estimate for both time periods. Players

do not keep their young, agile, lightning-quick abilities forever, so this simply portrays the

diminishing effect that experience will have on salary over time as a player will eventually age

after their peak years.

The variable “draft” turned out to be significant at the 1% significance level for both time

periods, however the magnitude of wage premium for being picked earlier in the draft was much

larger during the 1993-1994 season at 10.1% than in 2011-2012 at 1.4%. This may be directly

due to the rookie salary scale that has been implemented since 1995. This premium is likely

diminished because players earn similar salaries for the first 4 years of their career under the

recent CBA agreement. The negative parameter estimates on the draft variables actually model a

positive relationship because a low draft number portrays more talent potential, hence why it is

measured backwards.

The previously analyzed variables share a portion of the story, but market size variables

are the missing pieces to the puzzle. Without taking “LnMarket”, “LnCapita”, and the four

foreign region variables into consideration, we don’t see the true effect that the CBA has had on

the NBA’s success and financial capabilities of teams in smaller markets. All of these variables

were significant in 1993-1994 to at least the 5% significance level, whereas during the 2011-

2012 season, each and every one of these variables was statistically insignificant.

In 1993-1994, players in densely populated markets, designated by “LnMarket”, earned a

13.7% wage premium and a 22.3% premium was earned in markets with higher per capita

incomes, represented by “LnCapita”. The 2011-2012 season data results portray that market

forces are no longer playing a prominent role in player’s salary. Significant factors pertaining to

market size during the 93-94’ showed that before regulations were set, markets had freer reign to

pay top talent whatever they required to lock them into a contract. However, now that the CBA

has gone through various structural changes, more talent is falling into the hands of smaller

market teams and market related variables are no longer significant in the wage equation.

The variables that represented foreign player country of birth: “Africa”, “Asia-Pacific”,

“Caribbean”, and “Europe”, proved to provide another point of interest in showing how CBA

agreements over the years have ultimately changed the structure of salary disbursement. The

league was experiencing huge revenue gains from the introduction of more international players

over the last few decades and foreign players have been paid a premium based upon the country

they originate from. Yang (2012), attributed this premium to a foreign markets population, GDP,

and presence of a basketball league, which can all theoretically lead to a wage premium when

these players enter the NBA.

During the 1993-1994 season, players from Africa earned a 10.6% premium, from Asia

earned a 5.6% premium, from the Caribbean earned a 7.3% premium, and from Europe earned a

15.8% premium. Fast forward to 2011-2012 and all four of these variables are insignificant.

Using a Wald-Test to check for joint significance, these variables were not even significant at the

45% level, with a probability of 45.18. Using the same test for these variables from the 1993-

1994 regression, these variables are significant with 100% confidence, with a probability of 0.00.

The vast changes in the significance of these variables over time is no coincidence. The nearly

20 years that separates these measurements were the years that the CBA experienced the most

drastic changes towards a tight and strict salary system. Decades ago, international players were

a hot commodity and teams had the ability to pay these players a premium. As the CBA has

changed and market share has become more evenly distributed across various teams, the NBA

has adopted a much more competitive market; thus the price for acquiring foreign players no

longer comes with a foreign-market size premium

V. Conclusion

The overlying message is that teams are becoming more and more successful on a

financial basis regardless of their market’s location. All-star players seem to be increasingly

signing long-term deals with franchises in smaller markets, whereas before this was much more

unheard of. The rules governing the business of today's NBA have done well to mitigate the

importance of factors far beyond the control of any given organization—namely, the size and

appeal of the city in which it's based.

Fans who have been tortured their entire lives by not having a winning basketball team

to root for may soon be pleasantly surprised as more and more small market teams are reaping

the benefits of the new regulations and successful generation of the NBA. Small-market teams

have historically been at risk of seeing their resident stars force their way out of town as free-

agency and big offers from big markets loomed. Rather than just a few successful teams enjoying

a majority of the success every year, such as the Lakers Celtics have for decades, there are many

up and coming teams in the current competitive NBA market. This day and age is an exciting

time for basketball fans everywhere as market size is losing its significance in determining

basketball player salary and competitive market forces are creating a much more balanced

economic structure in the league.

Table 1: List and description of all independent variables included in both regressions (1993-1994 season and 2011-2012 season)

Variable Name Variable Description PPG Player’s average points per game for respective

yearAPG Player’s average assists per game for respective

yearRPG Player’s average rebounds per game for

respective yearAPG Player’s average assists per game for respective

yearBPG Player’s average blocks per game for respective

yearExp Years a player has been active on a roster in the

NBA prior to year of measurementExpSq Years a player has been active on an NBA roster,

squared.Draft The number that a player was selected in their

respective draft class, 1-60.LnCapita The natural logarithm of the average per capita

income of the city of a player’s team during the respective year

LnSalary The natural logarithm of a player’s salary during the respective year

LnMarket The natural logarithm of the population of the city of player’s respective NBA team

Caribbean Player receives a “1” if born in the Caribbean/South America region “0” otherwise

Africa Player receives a “1” if born in Africa, “0” otherwise

Asia-Pacific Player receives a “1” if born in the Asia-Pacific region, “0” otherwise

Europe Player receives a “1” if born in Europe, “0” otherwise

SG Player receives a “1” if he is a shooting guard, “0” otherwise

PF Player receives a “1” if he is a power forward, “0” otherwise

PG Player receives a “1” he is a point guard, “0” otherwise

Center Player receives a “1” if he is a center, “0” otherwise

Table 2: Below are two bivariate correlation tables that show the strength of the relationship between the variables in each regression. Table a) presents results from the 1993-1994 season

while Table b) presents results from the 2011-2012 season

a)

APG BPG PPG RPG SPG LNMARKET LNSALARY DRAFT

APG  1.000000 -0.100350  0.593212  0.117473  0.502999  0.024183  0.391786 -0.286289

BPG -0.100350  1.000000  0.252010  0.697263  0.134608 -0.027107  0.329521 -0.263377

PPG  0.603212  0.252010  1.000000  0.581109  0.657357  0.045056  0.606664 -0.436558

RPG  0.117473  0.697263  0.581109  1.000000  0.386422  0.011425  0.540608 -0.342940

SPG  0.702999  0.134608  0.657357  0.386422  1.000000  0.021142  0.456434 -0.301953

LNMARKET  0.024183 -0.027107  0.045056  0.011425  0.021142  1.000000 -0.041599  0.020629

LNSALARY  0.391786  0.329521  0.606664  0.540608  0.456434 -0.041599  1.000000 -0.532902

DRAFT -0.286289 -0.263377 -0.436558 -0.342940 -0.301953  0.020629 -0.532902  1.000000

b)

APG BPG PPG RPG SPG LNMARKET LNSALARY DRAFT

APG  1.000000 -0.122273  0.584860  0.099265  0.658042  0.039345  0.402272 -0.238773

BPG -0.122273  1.000000  0.233522  0.687165  0.110272 -0.020780  0.311810 -0.233152

PPG  0.584860  0.233522  1.000000  0.583206  0.647472  0.049528  0.603708 -0.407231

RPG  0.099265  0.687165  0.583206  1.000000  0.369624  0.026788  0.546974 -0.314622

SPG  0.658042  0.110272  0.647472  0.369624  1.000000  0.013360  0.441344 -0.283974

LNMARKET  0.039345 -0.020780  0.049528  0.026788  0.013360  1.000000 -0.020679  0.001705

LNSALARY  0.402272  0.311810  0.603708  0.546974  0.441344 -0.020679  1.000000 -0.501261

DRAFT -0.238773 -0.233152 -0.407231 -0.314622 -0.283974  0.001705 -0.501261  1.000000

Table 3: Below are two descriptive statistic tables that portray features of variables within each regression. The first table presents results from the 1993-1994 season while the ladder presents

results from the 2011-2012 season

a)APG BPG PPG RPG SPG LNMARKET LNSALARY

 Mean  1.873875  0.592822  8.322191  4.284051  0.622062  6.602005  15.10194 Median  1.095238  0.438596  7.844828  3.507692  0.596491  5.555840  15.27413 Maximum  10.70968  3.651515  27.64516  11.48000  1.462963  7.313260  16.86784 Minimum  0.000000  0.000000  0.000000  0.000000  0.000000  4.607190  12.50776 Std. Dev.  2.114542  0.607768  5.133711  2.522272  0.319441 0.756645  1.016556

 Observations  402  402  402  402  402  402  402

b)

APG BPG PPG RPG SPG LNMARKET LNSALARY

 Mean  1.832617  0.464189  8.335048  3.724470  0.672575  6.017598  14.75342 Median  1.182576  0.310345  7.062019  3.258974  0.595121  5.846012  14.76172 Maximum  10.69811  3.651515  28.03030  14.53704  2.516667  6.997909  17.04412 Minimum  0.000000  0.000000  0.000000  0.000000  0.000000  5.277183  10.64564 Std. Dev.  1.851154  0.474636  5.575671  2.395657  0.413896  0.473107  1.118111

 Observations  478  478  478  478  478  478  478

Table 4:

Regression estimates of individual player characteristics as the independent variables and the logarithm of player salary as the dependent variable. The first column represents 2011-2012

season results and the second column represents 1993-1994 season results

Variables Coefficients for 2011-2012 Coefficients for 1993-1994

All-Star 0.624*** 0.754***

Exp 0.212*** 0.122**

ExpSq -0.010*** -0.014**

Draft -0.014*** -0.109***

LnMarket -0.117 0.137***

LnCapita -0.615 0.223***

APG 0.048*** 0.068

RPG 0.074*** 0.094***

PPG 0.021** 0.025***

BPG 0.060 0.060**

SPG 0.088 0.047*

Center -0.0077 0.139***PG -0.130 0.079

SG 0.0065 0.057

Africa 0.174 0.106***

Asia-Pacific -0.287 0.056*

Caribbean 0.261 0.073*

Europe 0.126 0.158***

R-Squared 0.682 0.722

Adjusted R-Squared 0.668 0.698

F-Statistic 46.654 48.845

P-Value for F-Statistic 0.000 0.000Bibliography

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