Why are migrants paid more?

Alex Bryson NIESR and CEP

Rob Simmons Lancaster University

Institute of Education 5th October 2011 National Institute of Economic and Social Research Research Issue

• In efficient labour markets for very highly paid workers one expects wage differentials between migrant and domestic workers to reflect differences in labour productivity. • Yet, using panel linked employer-employee data for a single industry, we find a persistent wage penalty for domestic workers having conditioned on individual time-varying labour productivity and firm fixed effects. • Why? Contribution

• Examines role of migration in generating within-occupation wage differentials • Is migrant wage premium a superstar effect? – Will explore two definitions of superstar: • Higher output • More popular or charisma • If so does migration affect team fortunes? • Or do natives face employer monopsony power in wage setting resulting in wages below marginal product? • Able to account for time-varying individual labour productivity and team FE Empirical strategy

• Individual wage equations containing nationality and locality indicators • OLS and quantile regression – superstars in the upper tail – returns to talent should be magnified at the top • Wage decomposition – with and without individual labour productivity – Oaxaca/Blinder and Machado/Mata for quintiles • Match attendance and team points models – change in nationality shares on change in team attendance controlling for team FE – If migrants > productive should show up in wins – if migrants > popular should show up in attendance

Theory: migration effects and wages

• Migrant wage penalty: - unable to pursue previous career in new host country - assimilation (qualification accreditation, language) - employer discrimination (tastes) • Offsetting effects – Working harder/longer – „Better‟: drawn from different part of ability distribution -> more productive – Bargaining power of highly skilled with good outside options • Employer competition for their scarce skills • Broader labour market impacts: – Increase in labour supply lowering ambient wages – Ability to substitute for natives -> unemployment? • Lump of labour fallacy Migration empirical evidence

US literature • Migrant wage penalty but convergence with assimilation (Lazear 1999; Hu, 2000) • Effects on native workers hotly disputed – Borjas (2006): significant negative impact on native wages – Card (1990): little/no impact of Mariel boatlift on Miami unemployment rates UK literature: • Manacorda et al. (2011) GHS and LFS, 1973-2007 – NS effect on native wages due to imperfect substitution – Sizeable negative effect on existing immigrants‟ wages • US v UK – Higher degree of substitution of migrants for natives in US than UK? (Ottaviano and Peri, 2011) Evidence on Skilled Migration Effects

Some evidence that highly skilled immigrants: - Have lower probability than natives of similar education levels to obtain highly skilled jobs - Get lower wages than natives (see Clark & Drinkwater, 2008, UK using LFS; Friedberg, 1996, Israel) - Penalty could be due to misallocation/skills mismatch or discrimination - Possible assimilation but mixed evidence on wage convergence - Can reduce native and migrant returns to higher qualifications but size of migrant cohorts too small to have big effects - But problems with survey data - education and occupation are just proxies for ability, don‟t observe individual productivity, unobserved heterogeneity at individual and industry levels

Superstars • Superstardom: “relatively small numbers of people earn enormous amounts of money and dominate the activities in which they engage” (Rosen) • Rosen (1981) versus Adler (1985) on superstar formation • Rosen‟s (1981) superstars are individuals who are slightly better than their peers – lower talent=imperfect substitute for higher talent – convex relationship between talent distribution and distribution of rewards “small differences in talent become magnified in large earnings differences” – scale economies of joint consumption allow relatively few sellers to service a large market (“one‟s personal market scale”) • Adler (1985): A reputation arising through popularity – Underpinning = Stigler/Becker consumption capital – “Stardom is a market device to economise on learning costs in activities where the more you know the more you enjoy. Thus stardom may be independent of the existence of a hierarchy of talent”. Implications of Rosen V Adler

• Both agree on need for economies of scale – Football industry is good example • Rosen: labour productivity should capture superstar effects – Search for convex relationship between productivity and rewards – If fully capture productivity should be no role for residuals nor for talent-based migrant effects on team fortunes • Adler: superstardom also requires popularity – Players can increase wages by investing in on-field talent or popularity – Popularity may be unobservable even when have all on-field performance • Own thoughts: an unobserved dimension of “effort” – Distinction between „what you do‟ and „the way that you do it‟ – Not all goals scored are the same - role for charisma? • Is this equivalent to „popularity‟ or more like effort being multi- tasked? • What you do v how you do it or on-field versus off-field activities? • Might expect important role for residuals in team outcomes even having controlled for time-varying individual labour productivity

Literature on superstars

• Andersson et al. (2009) superstar wages in software design – programmers are rewarded highly for their potential to deliver high (but unknown) returns i.e. a successful computer game • Football (soccer) papers – Lucifora and Simmons (2003) mid-1990s, one superstar () – Brandes et al. (2008) on superstars in German football - apparently Bayern Munich had 6 superstars in 2002/03 (too many?) – Franck & Nüesch (2008), German football, evidence in favour of Adler but Franck & Nüesch, Econ Inquiry forthcoming find support for Adler and Rosen (better data) • These authors don‟t look at migration • Only one paper looks at migration and superstars – Kleven et al (2010): top rate taxation affects location of superstar professional footballers in Europe Institutional setting: (1) The footballers‟ labour market

• In principle, football labour market is highly competitive - players are globally mobile. • No barriers to entry across EU post-Bosman, no unions, observable performance, so pay = MRP, should not observe any discrimination. • OK but performance is multi-dimensional, questions of productivity spillovers, peer effects. • And restrictions remain: immigration controls (visas, work permits) and quotas for non-EU players. • And players have preferences e.g. few English players outside England – Preference to remain gives employer some monopsony power in wage setting where few credible outside options – Compensating differential for amenity of remaining local Institutional setting for our paper (2) Italian football • Our sample period is 2000-2008, just after boom period • High dependence on sale of broadcast rights • In our sample period rights were sold individually by clubs. • And gate attendances and receipts fell during this period. – Role of hooliganism: teams who were forced to play behind closed doors – Calciopoli corruption effects. • So negative shocks to revenues, in contrast to England, France, Germany and Spain. • Quotas imposed on club hiring of non EU players but incremental and not very apparent on the ground – Teams able to trade quotas – % non-EU migrants in sample constant over time (21-23%) • Most famous clubs are most reliant on migrants – : 30% Italian; 56% non-EU Italians in Italy

• % Italian (domestic) players is between 69-74% over sample period, somewhat higher than native shares in other European leagues (Frick, 2007) • Other evidence that Italians have strong preference for remaining in Italy and for remaining „local‟ • Boeri and van Ours (2008): intra-regional mobility dominated by net migration of foreign workers • Manacorda and Moretti (2006): lack of mobility among native Italians partly reflects parental preferences to have children co-resident • 19% of Italian players in our sample are „local‟ (playing for a team <=200km from birth place) Why football data can contribute

• Able to pin down within-occupation wage differentials • Good data on migration – Where born – Assimilation: years playing professionally in Italy • Very complete time-varying measures of on- field worker performance • Good data on team revenues • Worker and employer panels – Take out FE‟s Wage equation Log real salary = f(age, age squared, experience, team characteristics, player productivity, playing for national team, team fixed effects, nationality dummies). • Nationality = Italian (base), non-Italian EU, non-EU. • This is our most complete model. • But we‟ll estimate in stages to see how nationality coefficients vary. • Raw, then demographics, then productivity, then team time-varying covariates and finally with team FE • Also check on productivity squared (Rosen) Data

• Unbalanced panel N=2,601 player-year observations – 914 footballers, 38 clubs in and B. Estimation sample 906 players at 34 clubs: 2488 player-year observations • Wages: basic contractual net salary excluding bonuses and endorsements, image rights etc. • These are actual salaries reported in Italian newspapers and annuals: Corriere dello Sport Stadio (2001), Il Messaggero (2002), La Pagelle di Paolo Ziliani (2003-06), Gazzetta dello Sport (2007) • Most studies use expert valuations (Frick, 2007) • These valuations include estimates of transfer fees and signing bonuses- our measure excludes these • Productivity: www.paninidigital.com, detailed performance statistics of players – Much more detailed indicators of performance than previous literature – Appearances, minutes played, assists, passes, total shots, shots on target, goals, balls lost, balls won, goalkeeper saves, tackles, N Euro championship appearances, N footballer of year, N world cup appearances. • So we can isolate relationship between labour productivity and wages, controlling for time-varying individual performance and firm (team) fixed effects.

Kernel Densities for log real annual net wages net annual real log for Densities Kernel

Density

.1 .2 .3 .4 0 kernel = epanechnikov, bandwidth =bandwidth 0.1949 epanechnikov, = kernel -4 Kernel Kernel density estimate -2 Normaldensity Kerneldensity estimate lrsalary 0 2

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.1 .2 .3 .4 0 -4 -2 Migrants lrsalary 0 2 Italians

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Kernel Densities for log real annual net wages

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-4 -2 0 2 4 lrsalary

EU non-Italian Non-EU Italians Possible superstars in Italian football? Salary > €10m • (Arg, 2) • (Italy, 2) • Kaka (Brazil, 2) • Alvaro Recoba (Uruguay, 2) • (Italy, 7) • (Italy although originally Australian, 2) • But we do not identify individual superstars Controls

• Player: age, age squared, two-footed, left- footed, position dummies (defender = 0), total seasons experience in Italy • Performance: career goals A, career goals B, appearances A, appearances B, minutes played, minutes squared, lost balls, recovered balls, season goals, goalkeeper saves, goal assists, total shots, shots on target , successful passes, tackles, fast breaks, footballer of the year, World Cup selection, Euro championship selection • Team: division; points ratio; attendance Performance controls

Variable Coefficient (t statistic) Saves 0.0069 (6.31) Recovered balls 0.00042 (2.89) Successful passes 0.00021 (2.98) Lost passes -0.00044 (3.18) Assists 0.029 (3.95) Shots 0.0020 (1.87) Shots on target 0.0060 (3.42) Career goals 0.0026 (2.98) Season goals 0.0097 (1.73) Salary Regression Results: n = 2488, 906 players

Model Non EU EU non Italian R2 (1) Raw nationality 0.361 (3.58) 0.821 (5.28) 0.087

(2) As (1) + player Xs 0.656 (7.10) 0.973 (6.71) 0.357

(3) As (2) + player performance 0.318 (4.69) 0.344 (2.89) 0.614

(4) As (3) + club time-varying Xs 0.130 (2.63) 0.230 (2.51) 0.738

(5) As (3) + FE but w/o club Xs 0.135 (2.82) 0.246 (3.12) 0.766

(6) As (3) + performance squared 0.266 (4.13) 0.235 (2.00) 0.635

(7) As (4) + performance squared 0.109 (2.26) 0.170 (1.87) 0.746

(8) As (5) + performance squared 0.110 (2.37) 0.186 (2.36) 0.773 Assimilation

• Interact nationality dummies with number of seasons experience in Italy

• These interaction terms have insignificant coefficients

• As anticipated given ease with which footballer skills are general and transferable across countries Some robustness checks

Specification Non EU Non Italian EU

Without Juventus 0.168 (3.17) 0.229 (2.70) 2005/06 Outfield only 0.135 (2.35) 0.177 (2.19)

Appearances >= 10 0.153 (2.82) 0.245 (2.90) Potential explanations for the migrant wage premium

• Differences in observable attributes - Oaxaca-Blinder - Machado/Mata quantile distribution • Monopsony employer power or compensating wage differential: – Italian players prefer to stay in Italy and accept lower pay to express this preference – Migrants bargain for higher wages than they can obtain at home, seeking compensation for move • Migrant superstar status - more talented or more popular? Oaxaca decompositions

• Decompose Italian-migrant wage gap into “explained” (player X‟s, productivity) and unexplained differences • Do this with and without productivity; with and without team FE • Use the pooled option which averages effects across the two groups – pooled model contains a group membership dummy

Oaxaca results on wage differences between Italians and non-Italians

Model Explained Unexplained % difference due to unexplained Years, personal, 0.204 (2.80) 0.273 (4.82) 57.2 team Add productivity 0.326 (4.17) 0.151 (3.22) 31.7 Add prod sq 0.355 (4.46) 0.121 (2.68) 25.4 Years, personal, 0.223 (2.92) 0.253 (4.66) 53.0 team FE Add productivity 0.318 (3.97) 0.159 (3.66) 33.3 Add prod sq 0.351 (4.29) 0.126 (2.99) 26.4 Oaxaca interpretation

• Even with productivity controlled for, there is a large unexplained pay differential of between 1/4 and 1/3 • Replacing team covariates by team FE makes little difference to this result • Unexplained gap between migrants and natives may reflect unobserved differences in productivity and/or tastes • This could be either compensating differentials or unobs superstar effects Quantile regression

Nationality 0.25 0.5 0.75 0.9 Club covariates Non EU 0.016 (0.36) 0.095 (2.40) 0.182 (3.69) 0.204 (2.35) Non-Italian EU 0.058 (0.77) 0.262 (4.22) 0.288 (4.68) 0.349 (3.52) R2 0.48 0.52 0.55 0.55 Club FE Non EU 0.095 (2.19) 0.111 (2.83) 0.136 (3.20) 0.137 (1.86) Non-Italian EU 0.215 (2.98) 0.286 (4.76) 0.221 (3.72) 0.286 (3.12) R2 0.51 0.55 0.59 0.59 Quantile results

• Panel A with team co-variates – Wage premium for foreigners rises through the distribution • At 90th pc it‟s 23% for non-EU and 42% for EU migrants – No migrant premium in bottom half of distribution – Non-Italian EU premium always larger than that for non-EU • Panel B with team FE – now evidence of domestic wage penalty relative to EU migrants at 25th percentile – although the migrant-native wage differential is larger at the 90th percentile than it is at the 25th percentile, there is no monotonic increase in the size of the differential as we move up the earnings distribution • Why? Decompose quantile wage distribution • Melly (2006) routine delivers Machado/Mata decomposition • the introduction of individual performance covariates substantially reduces the "unexplained" component throughout the wage distribution • the proportion of the migrant wage gap that is not accounted for by worker and club characteristics tends to rise as one goes up the wage distribution, irrespective of whether one conditions on labour productivity. – consistent with superstar effects arising from unobserved labour productivity differentials or a popularity premium.

Log crowd attendance models

M1 M2 M3 M4 M5 M6 ITALIAN -0.463 * -0.449 -0.450 * -2.18 -1.97 -1.99 PTS ratio 0.231 0.202 0.201 0.197 0.160 0.158 1.43 1.47 1.46 1.33 1.30 1.28 Years -0.071 ** -0.066 ** -0.065 ** -0.070 ** -0.064 ** -0.064 ** -5.81 -5.44 -5.44 -5.79 -5.52 -5.54 Pred salary 0.142 * 0.130 * 0.156 ** 0.144 ** 2.39 2.28 2.79 2.70 Residual 0.111 0.117 1.31 1.40 EU non-Italian 1.233 ** 1.354 ** 1.367 ** 2.78 2.85 2.92 Non-EU 0.255 0.193 0.188 1.03 0.75 0.71 _cons 3.417 ** 3.468 ** 3.456 ** 2.976 ** 3.052 ** 3.040 ** 14.60 14.69 14.78 19.16 26.56 26.15 r2_a 0.924 0.930 0.930 0.927 0.934 0.935 N 121.000 121.000 121.000 121.000 121.000 121.000 Attendance models • Crowd attendance rises with % migrants • Predicted salaries positive and significant • Both consistent with superstar status effects drawing in crowds even after controlling for team success – Adler‟s popularity • Elasticity: eg. EU non-Italian 1.4*0.68 (mean of EU non-Italian)=0.095 so circa 10%. Saying that a rise in share EU non- Italian of 10% will increase crowd attendance by circa 1%.

Points models

• First stage individual wage equation run for Italians only then out-of-sample prediction. (Very similar if use all players model for first stage). • Model controls are: $person $lprod $lprodsq $internat i.yearid i.clubid • Then predicted salary and residuals carried over into second stage club FE panel model of team points. • Italian share –ve sig throughout. Predicted salary and residuals both NS • When split migrants into EU and non-EU both +ve sig, with larger coeff for EU migrants. Are migrants Rosen-style superstars?

Team Points models

(1) M7 (2) M8 (3) M9 (4) M10 (5) M11 (6) M12 b/t _star b/t _star b/t _star b/t _star b/t _star b/t _star Italian -0.581 ** -0.575 ** -0.575 ** -3.13 -3.01 -3.00 Year -0.004 -0.003 -0.003 -0.004 -0.002 -0.002 -0.48 -0.28 -0.28 -0.42 -0.21 -0.21 Pred SALARY 0.041 0.040 0.044 0.044 0.86 0.87 0.93 0.94 Residuals 0.004 0.005 0.05 0.07 EU non-It 0.912 * 0.937 * 0.938 * 2.26 2.28 2.26 Non-EU 0.509 * 0.491 * 0.490 * 2.33 2.15 2.15 _cons 1.299 ** 1.304 ** 1.304 ** 0.711 ** 0.724 ** 0.723 ** 9.58 9.17 9.17 10.28 11.04 10.56 r2_a 0.664 0.663 0.659 0.666 0.666 0.662 N 112.000 112.000 112.000 112.000 112.000 112.000 Local Heroes?

• Local Italians: defined as players who are with teams < 200km from birthplace • E.g. Pirlo (AC Milan), Totti (Roma) • Local preferences may generate a salary penalty • Classify nationality as Non EU, EU non Italian, Local Italian; Italian non-local as base • Repeat earlier salary regressions Salary Regression Results: n = 2488, 906 players

Model Italian Locals Non EU EU Non R2 Italian Raw -0.038 (0.25) 0.355 (3.52) 0.815 (5.24) 0.087 nationality Add player Xs 0.059 (0.55) 0.666 (7.09) 0.984 (6.75) 0.357 Add player -0.071 (1.12) 0.304 (4.41) 0.329 (2.75) 0.615 performance Add club time- -0.119 (2.77) 0.106 (2.13) 0.205 (2.22) 0.739 varying Xs Club FE but -0.104 (2.38) 0.116 (2.38) 0.226 (2.83) 0.767 w/o club Xs Local Italians

• 12% wage penalty for local Italians relative to other Italians when control for team effects. • Effect is robust to the replacement of club covariates with club dummies so the effect persists having accounted for fixed unobservable characteristics of the employer. • Part of the wage penalty domestic workers face is due to their preference for staying at home • Worker preferences give employers some bargaining power which they do not have over other workers- creates monopsony power. • Share of local Italians in team has no significant impact on team attendances nor points

Conclusions

• We have found robust evidence of sizeable pay premium for non-Italians playing in Italian football • Premium persists having controlled for individual productivity and team FE • Premium is more pronounced at top of the salary distribution • Increase in % migrant also increases crowd attendances and team points – EU migrants +ve for crowd and points – Non-EU migrants +ve for points only • Using birthplace data to proxy local preferences we find evidence of an additional wage penalty for local Italians