Career Duration in the NBA

Abstract

Using a panel of National Basketball Association players from 1990 through 2013, we analyze the determinants of career length in the league. We find that foreign-born players who did not play college basketball in the United States have shorter careers than do American-born players holding performance constant. Foreign-born players who played college basketball in the United States do not have shorter careers. We suggest that both push and pull immigration factors might cause this early exit.

Keywords

National Basketball Association discrimination professional sports

As employers pursue talent, labor markets have increasingly become more international. For instance, in the United States in the health-care profession, 27% of surgeons are foreign-born, while in the education profession, 40% of engineering professors are foreign-born. Aslanbeigui and Montecinos (1998) estimated that in the 1990s, approximately 30% of U.S. economics professors were foreign-born. The internationalization of labor markets has led researchers to question whether foreign workers are more productive because of self-selection on the part of immigrants (Borjas & Bratsberg, 1996), less productive due to language and cultural differences than native-born workers (Dustmann & Soest, 2002), or discriminated against (Åslund, Hensvik, & Skans, 2014)? All sports leagues in pursuit of the most talented players have international labor markets. For instance, in 2014, 25% of Major League Baseball (MLB) players were foreign-born, 51% of National Hockey League (NHL) players were born in Canada, 24% were born in the United States, and 25% were born in Europe.

Sports leagues provide a fertile ground to further the research on immigration due to the increasing degree of internationalization. For instance, Kahane, Longley, and Simmons (2013) find that NHL teams who employ higher proportion of Europeans perform better if the Europeans are from the same country compared to teams with less Europeans or Europeans from many different countries. Alvarez, Forrest, Sanz, and Tena (2011) analyze soccer data and find that increases in the number of international players in a domestic league tend to generate improvements of a national team that is only comprised of domestic players. Frick (2009) finds that the globalization of factor mobility leads to decreased playing time of local players in European soccer leagues but has no influence on competitive balance. Last, Frick, Pietzner, and Prinz (2007) find that soccer players from Eastern Europe have shorter careers in soccer than their performance would indicate. They suggest that it may be due to exit discrimination.

Focusing of the National Basketball Association (NBA), Eschker, Perez, and Siegler (2004) find that there was a premium paid to international players for the 1996-1997 and 1997-1998 seasons due to a “winner’s curse” in the market from the inability of scouts and general managers to properly evaluate the worth of foreign-born players who did not play college basketball in the United States. Motomura (2016) found that international players with no U.S. college experience were undervalued in the NBA draft prior to 2001 but overvalued after 2001.

Using an unbalanced panel data set (1999-2008) and a two-stage double fixed-effect model, Yang and Lin (2012) find evidence of salary discrimination against all international players. Hoffer and Freidel (2014) using data from the 2010-2011 season, however, find the opposite with all foreign-born players paid a premium in the NBA. Two studies, Groothuis and Hill (2004) and Groothuis and Hill (2013), find that foreign players do not have a higher likelihood of exit than do native-born players when looked at as a whole. Neither study analyzed the foreign group with two subsets of foreign-born players with no U.S. college experience and those with U.S. college experience. Given the mixed results of the previous research on international players in the NBA, we analyze career duration of both foreign-born players who were trained in U.S colleges and those who were trained in international leagues.

The foreign dummy variable for those who were trained in international leagues would include Yao Ming who was drafted directly from China but exclude Hakeem Olajuwon who was born in Nigeria and played basketball at the University of Houston. The second foreign dummy variable for all foreign-born players who played college basketball in the United States would include Hakeem Olajuwon and exclude Yao Ming. These measures are consistent with the measures used by Eschker, Perez, and Siegler (2004) and more recently Motomura (2016) who analyzes drafting international players into the NBA. We posit that both push and pull factors may cause shorter careers for athletes trained in foreign leagues to a greater extent than foreign-born individuals who played college basketball in the United States. Push factors include exit discrimination, whereas pull factors include players returning to finish their careers in their home countries. A pull factor that might reduce career length occurs when there is an increase in lucrative opportunities in the native country of a foreign worker or a sufficient amount of savings on the part of a foreign worker, so that the worker finds it more attractive to return to their native country rather than remain in the host country. In the case of professional sports athletes, this would lead to their native country pulling them back home.

A push factor might occur if there is exit discrimination. Exit discrimination may arise if fans, managers, or other players prefer native-born players over immigrants providing a push for players to exit the league. For instance, many European soccer leagues limit the number of foreign-born players on a team by requiring that at least eight players on the team are locally trained. In the Kontinental Hockey League, Russian teams are not allowed more than five foreign players. Although no U.S. league explicitly limits the number of foreign-born players, teams may still discriminate against foreign players due to fan, player, or management preferences.

Exit discrimination has a long history in sports economics. Johnson and Marple (1973) pioneered this branch of discrimination research when they found evidence from 1970 to 1971 NBA data that marginal White players had longer careers than marginal Black players. Hoang and Rascher (1999) more formally developed a model to explore the concept of racially based retention barriers in the NBA. They, too, found evidence that, performance being equal, there was exit discrimination in the NBA. Groothuis and Hill (2004) failed to confirm Hoang and Rascher’s results using more recent data, adding height as an added explanatory variable, and using a duration model that allows for both stock and flow samples. In addition, Ducking, Groothuis, and Hill (2015) find no racially based exit discrimination in the National Football League.

Jiobu (1988) found evidence that race decreased career length, ceteris paribus, for Black players but not for Hispanics in MLB from 1971-1985. Groothuis and Hill (2008) failed to find exit discrimination in MLB using more recent data from 1990 to 2004 for either Black or internationally born Hispanic players. Depken, Ducking, and Groothuis (2015), however, do find that European-born hockey players have shorter careers than North American-born players, suggesting that foreign-born exit discrimination may exist.

Nonparametric and Semiparametric Analysis of Career Duration

To help understand career duration in the NBA, we use a panel of all players, 1989 to 2013. We calculate yearly hazard functions as:

h_{t} = \frac{d_{t}}{n_{t}},

where d_t is the number of players who end their career in year t and n_t is the number of players at risk of ending their career in year t. The hazard rate can be interpreted as the percentage of players who exited the NBA given they have survived up to some level of tenure. Although this measure does not control for performance, it provides insight on the raw measure of exit. In Table 1, we report the hazard rates, and in Figure 1, we plot the hazard functions for each group of players. We find that foreign-born players who did not play college in the United States have a higher exit rate than both native-born and foreign-born with college for the first 7 years of experience. All three series follow a bathtub plot where initially the hazard rates decrease and then increase at higher levels of tenure. The increase at higher levels suggests that the wear and tear from playing basketball as players age increases the likelihood of exit. Interpreting the differences in hazard rates as either caused by discrimination, a push factor, or outside opportunities in foreign league, a pull factor, is potentially misleading because there is no control for productivity differences across players. Given these concerns, we analyze career duration using semiparametric techniques to control for differences in productivity.

Table 1.

Hazard Rates.

Tenure	Native Born	Foreign-Born/No College	Foreign-Born/College
1	.0708	.0865	.0807
2	.0449	.0598	.048
3	.045	.0566	.0515
4	.0356	.0693	.055
5	.0401	.056	.0454
6	.0409	.072	.0322
7	.0408	.102	.0444
8	.0479	0	.0588
9	.0525	.133	.0535
10	.0542	0	.069
11	.0628	.15	.0714
12	.0672	.136	.0535
Max

Figure 1.

Hazard plot.

We estimate semiparametric hazard functions following Berger and Black (1998) and Groothuis and Hill (2004). Because the data are reported at the season level, we calculate the hazard rate as a discrete random variable. As with Groothuis and Hill (2004), we model the durations of a single spell and assume a homogeneous environment, so that the length of a particular spell is uncorrelated with the calendar time at which the spell begins. This assumption lets us treat all the players’ tenure as the same regardless of when it occurred in the panel study. For instance, all fourth-year players are considered to have the same baseline hazard regardless of calendar time, so a fourth-year player in 2000 has the same baseline hazard as a fourth-year player in 2009. For details of the model, see Berger and Black (1998) and Groothuis and Hill (2004).

Because the players in the panel have varying degrees of job tenure prior to the beginning of the panel, we identify the hazard function for both long and short careers. The disadvantage of the Berger–Black (1998) modified Cox model approach is that the vector of tenure dummy variables can be very large. In our study, it would require 25 dummy variables for all years of experience, and we have too few players who have long careers. To simplify the computation of the likelihood function and to be able to keep the long careers, we approximate the γ _t vector with a fifth-order polynomial of player’s tenure, which reduces the number of parameters to be estimated from 25 to 5. Thus, the hazard function becomes:

\frac{h (t, x, β)}{1 - h (t, x, β)} = Φ (t) e^{x β} = \exp (φ (t) + x β),

where φ(t) is a fifth-order polynomial in the worker’s tenure. We choose the Taylor series approximation technique over using tenure dummies due to the small number of observations for high tenures. This method provides a very flexible specification of the baseline hazard but does impose more restrictions than Cox’s model.¹

Data Set, Variables, and Results

The data set for our analysis includes all players in the NBA from the 1989-1990 season through the 2012-2013 season. In our analysis, we use two measures of being foreign-born; the first dummy variable, foreign-born, is equal to one if the player was born outside the United States and its territories to non-U.S. citizens and played college basketball in the United States; the second dummy is equal to one if the player was born outside the United States and its territories to non-U.S. citizens and did not play college basketball in the United States.

To capture the influence of performance on the probability of exit, we use variables to measure on-court performance, including points scored per game, rebounds per game, assists per game, steals per game, turnovers per game, blocks per game, and games played in the first specification. In the second specification, we use the NBA efficiency formula. The efficiency rating for each player is calculated using the following: ((Points + Rebounds + Assists + Steals + Blocks) − ((Field Goals Att. − Field Goals Made) + Turnovers))/Games Played.

In addition, we use a dummy variable for White players in both specifications to control for potential exit discrimination caused by racial characteristics. In both specifications, we also include age and age squared at the start of the season to capture the wear and tear factor on a player’s body that may cause retirement. We include height because tall players may last longer in the league than shorter players. We also include a player’s draft number to capture any effects on duration over and above on-court performance. In addition, we use experience, experience squared, experience cubed, and experience to the fourth and fifth power as described above for Equation 2.

In Table 2, we report the means of the variables. In column 1, we report the means for all players. This column shows that from 1989-2013, 11% of the observations in data set were foreign-born. A closer look at the growth of foreign-born players in the league, not shown in Table 2, reveals that for the 1989-1990 season, there were 19 foreign-born players, 4 with no college experience and 15 with college experience. By the 2012-2013 season, these figures had escalated substantially, particularly for those without college experience; there were 77 foreign-born players, 59 without college experience and 18 with college experience.

Table 2.

Descriptive Statistics for Variables.

	Mean/(Standard Deviation)
Variable	All NBA Players	Foreign-Born/No College	Foreign-Born/College	Native Born
Foreign-born/no college	0.07 (0.26)	1 (0)	0 (0)	0 (0)
Foreign-born/college	0.04 (0.21)	0 (0)	1 (0)	0 (0)
White dummy	0.22 (0.41)	0.80 (0.40)	0.38 (0.49)	0.16 (0.37)
Age at start of season	27.21 (4.31)	25.78 (3.79)	27.94 (4.43)	27.29 (4.32)
Draft number	28.90 (23.40)	31.67 (21.88)	24.82 (21.32)	28.88 (23.59)
Year	2001.43 (7.18)	2005.85 (5.41)	2001.78 (7.06)	2005.85 (5.41)
Height (in.)	79.09 (3.74)	81.45 (3.56)	81.69 (3.55)	78.77 (3.64)
Rebounds per game	3.60 (2.56)	3.94 (2.56)	4.45 (3.15)	3.53 (2.52)
Assists per game	1.86 (1.87)	1.59 (1.57)	1.37 (1.83)	1.90 (1.89)
Blocks per game	0.43 (0.52)	0.54 (0.56)	0.74 (0.86)	0.40 (0.48)
Steals per game	0.67 (0.48)	0.58 (0.41)	0.51 (0.36)	0.69 (0.48)
Turnovers per game	1.24 (0.81)	1.21 (0.70)	1.28 (0.87)	1.24 (0.82)
Points per game	7.98 (6.02)	7.70 (5.72)	7.48 (5.98)	8.03 (6.04)
Games	54.31 (24.94)	54.47 (23.92)	53.34 (25.41)	54.34 (25.00)
Experience (seasons)	4.60 (3.98)	3.26 (3.24)	4.69 (4.15)	4.71 (4.01)
No. of observations	10,787	764	475	9,548

In the next columns, we compare the means of both foreign-born categories to native-born players. The comparison suggests that foreign-born players are on average over 2 in. taller than native-born players. Another interesting comparison reveals that while foreign-born players without college are overwhelming White, the opposite is true for those with college experience. Comparing the performance statistic shows that foreign-born players have more rebounds and blocks, functions of height, and fewer points and assists. Foreign-born players with college experience are chosen earlier in the draft than foreign-born players who do not. Overall, the means suggest that the NBA uses the international market to obtain the tallest players. It also suggests that college basketball in the United States prepares players for the NBA or provides a signal for future productivity in the NBA.

In Table 3, we report the results of estimating Equation 2 using two different specifications to show the robustness of our results. In the first specification, we find that performance increases career length (or decreases the likelihood of exit), since the coefficients of points per game, rebounds per game, assists per game, blocks per game, and games played per season are all negative and significant. In the second specification, we find that a player’s efficiency rating is negative and significant again showing that performance on the court increases career length. In addition, we find that in both specifications, the coefficient of draft number does not have any significant impact on career length. We find that height only appears to decrease the probability of exit in the model that uses the efficiency rating. The results in the first specification suggest that performance variables capture the influence of height and draft number on career length. We do find, however, that the coefficient on age is positive and significant and age² is negative and significant suggesting that older players are more likely to exit than younger players in a concave fashion, ceteris paribus. This result suggests that age captures the wear and tear on the body from playing in the NBA. We also find that the race of the player does not influence career length showing that racial-based exit discrimination is not present in today’s NBA. We also find that foreign-born players with U.S. college experience do not have shorter career than do native-born players.

Table 3.

Exit Logit Duration.

Independent Variables	Model 1	Model 2
Foreign-born/no college	0.677** (4.92)	0.644** (4.68)
Foreign-born/college	0.089 (0.53)	0.033 (0.20)
White dummy	−0.050 (0.61)	0.018 (0.22)
Age	0.594** (4.35)	0.596** (4.67)
Age squared	−0.007** (2.91)	−0.008** (3.61)
Draft number	−0.004 (1.86)	−0.003 (1.72)
Year	0.012** (2.12)	0.012** (2.14)
Height (in.)	−0.020 (1.53)	−0.033** (3.29)
Efficiency rating		−0.181** (10.17)
Rebounds per game	−0.154** (3.85)
Assists per game	−0.136** (2.57)
Steals per game	−0.072 (0.47)
Blocks per game	−0.606** (3.54)
Turnovers per game	−0.076 (0.58)
Points per game	−0.034** (2.60)
Games	−0.033** (17.18)	−0.033** (15.49)
Constant	−31.033** (2.99)	−30.381** (2.83)
Pseudo R ²	.308	.306
Observations	10,787	10,787

Note. z-statistics in parentheses. Fifth-order tenure polynomial included. Robust standard error by player.

*Significant at 5%. **Significant at 1%.

We do find, however, that being foreign-born with no college experience increases the likelihood of exit. Our results suggest that either exit discrimination exists for foreign-born players who did not play college basketball in the United States; this pushes them out of the NBA. Or these players are pulled back to their home countries to finish their careers and exit earlier on their own accord. Our results are consistent with exit discrimination where foreign-born basketball players have shorter careers because of either fan preference for American-born players or employee discrimination that might arise with possible language or cultural differences (see Kahane, Longley, & Simmons, 2013). It also suggests that college experience in the United States minimizes the influence of exit discrimination for foreign-born athletes. It could be that fan loyalty to a player is established for collegiate experience or it could be that collegiate experience trains foreign-born players on how basketball is played in the United States.² The result is also consistent with foreign players obtaining skills in the NBA and returning home to be stars in their home country leagues where the best players make millions of Euros each year (Borden, 2014).

To get a feel for the magnitude of exit likelihood, we convert the coefficients into a percentage change by using 100(exp(β) − 1) for the dummy variable in both specifications. We find that for foreign-born players who did not play collegiately in the United States have a 90% or 95% higher likelihood of exiting than a native-born player, holding performance constant. Our results suggest that although there has been a large influx of foreign players from foreign leagues without U.S. college experience, exit discrimination or other reasons limit the proportion of foreign players in the NBA to a smaller percentage than performance would suggest.

Conclusion

We find that foreign-born basketball players who enter the league without U.S. college experience have shorter careers than their performance statistics would suggest ceteris paribus. We do not find this true for foreign-born players with U.S. college experience, who have the same expected career length as native-born players. Our statistical evidence suggests that either there is something pushing and/or pulling on foreign-born players without U.S. college experience in the NBA. While customer-based, coworker-based, or employer-based exit discrimination might be pushing on these players, lucrative opportunities to play basketball in their native countries and the desire to return to their homeland might be pulling players to leave the NBA earlier than otherwise predicted. There is anecdotal evidence that many players choose to retire from the NBA early so to wind down their careers in leagues with shorter seasons, fewer injuries, although with lower salaries. Future research would do well to focus on these options to foreign-born NBA players.³

Footnotes

Acknowledgment

The authors would like to acknowledge helpful comments and suggestions made by participants in a sports session at the 2015 Southern Economics Association conference in New Orleans.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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