1. Promotions, pay & gender discrimination  1. Background  2. Theory –Tournaments –Glass ceilings & sticky floors  3. Empirical evidence –Promotion –Pay

2. 1. Background  International evidence on gender pay gaps (table 1) –Males = largest share of the workforce (Finland) –Variations between public & private sectors –Wide variation in the mean gender gaps  Pooled: Britain = 25% versus Italy = 6%  Public sector lower than private sector –Variation in gender pay gap across the pay distribution  E.g. 10 th percentile (low pay) versus 90 th percentile (high pay)  Glass ceilings?

3. 1.Company level (Wal Mart) Source: ‘Wal Mart and the Glass Ceiling, Feb 2004)

4. Women in Management at Wal-Mart and Competitors (Source: See above)

5. Gender pay gaps, 2001 (Average annual earnings; Source: See above) Note: Full time staff, includes bonuses, Data from Richard Drogin (2003) ‘Statistical analysis of gender patterns in Wal Mart workforce’. Job % of Women in Job Male Salaries ($) Female Difference in Salaries ($) Regional Vice Pres. 10419,400279,800139,600 District Manager 10 239,500 239,500 177,100 177,10062,400 Store Manager 14 105,700 105,700 89,300 89,30016,400 Assistant Manager 36 39,800 39,800 37,300 37,3002,500 Management Trainee 41 23,200 23,200 22,400 22,400800 Department Head 78 23,500 23,500 21,700 21,7001,800 Sales Associate 68 16,500 16,500 15,100 15,1001,400 Cashier93 14,500 14,500 13,800 13,800700

6. 2. Theory  A) Tournaments –Who gets promoted?  ILM – promotion ladders  Slots – predetermined pay (prize)  Relative performance  The larger the spread between current position & promoted position – greater the effort  Luck (or noise) –Offsets effort –Larger spread

7. 2. Theory  Tournament: winner (promoted) =W 1 and loser W 2  Two workers j and k  Output  q j = m j + e j (1a)  q k = m k + e k (1b)  Each workers behaviour  Max W 1 P + W 2 (1 - P) – C(m)(2) (for given effort)  (W 1 – W 2 ) dP/dm = C’(m)(3)

8. 2. Theory  Probability that j wins –P = Prob(m j + e j > m k + e k ) – = Prob(m j – m k > e k - e j ) – = G(m j – m k ) –G is the distribution function, we want g = the density function  Equilibrium –(W 1 – W 2 )g(0) = C’(m)  See Lazear for firms problem – max expected profit

9. 3. Evidence  Glass ceilings & sticky floors –(i) Glass ceilings  Why are gender pay gaps larger at the upper end of the distribution? –(ii) Sticky floors  Why are gender pay gaps smaller at the lower end of the distribution?

10. 3. Empirical evidence  Causes of differences in gender gaps –Gender-specific policies – cross sectional  a) Parental leave & state provision of child care  b) Pay-bargaining institutions  c) Other factors –Appointments & promotions

11. 3. Empirical evidence  Gender & promotion – single company  ‘glass ceiling’ v. other causes  Financial services –4,379 employees - closed ILM –port = low level clerical –pay = f(grade)  Determinants of allocation to each grade? –Education, tenure, age on entry –simulate distribution of females if treated as males

12. (i) cont. –Little change in distribution  Officer grades: rise 14% to 19%  Managers: rise 1% to 3% –Women have different LM characteristics  e.g. lower tenure –No ‘glass ceiling’

13. Conclusions  Promotion is an incentive mechanism in ILMs –Understood via tournament theory  Promotion & pay are linked  Gender differences in pay –Differences in promotion rates - discrimination –Or other factors? –Evidence is mixed