1. 1. Are Women Paid Less than Men? Intro and revision of basic statistics

2. Learning Objectives 1.Review of basic statistics 2.Some basic stata comands 3.Nature of randomness 4.Distributions 5.The use and abuse of the Normal Distribution 6.Basic Hypothesis testing 7.The role of prediction & causation

3. Wages and Gender Some Stata commands We can use data in wages.dta to examine this issue –.dta is a stata format data file Start stata via NAL (see stata manual on blog) Review the dataset using basic stata commands – Editor – summ – Describe – sort

4. Wages and gender Basic answer to the question is to compare average wages. Stata: – summ wage if gender==1 – Summ wage if gender==2 So we can say wages different in this sample Can we say this difference would be true generally? – Note the implicit “out of sample” prediction in this question

5. Statistical Inference Key point is that would be no problem if we observed wages of all men and women. Sampling is the source of the problem – Result could it be dumb luck – Choice of sample could be dumb luck – Isolated example? What is chance that different sample lead to radically different result?

6. Statistical Inference Key Point: Statistical inference is process of deciding when we can use a sample result to make statements about the world Also referred to as Hypothesis testing – Using this data can we reject the hypothesis that both groups are paid the same? Rough ans: we can if difference between the average wage is large (How large?) To answer precisely we need to review the nature of randomness – the role of “dumb luck”

7. Random Variables Outcome of an “experiment” value unknown until it is observed – Dice, coin, roulette wheel – Getting a job, the wage – In our example the observed differences between wages may be random and have nothing to do with gender i.e. dumb luck Continuous vs Discrete Random Variables – Discrete: Finite no of possible outcomes Dice, coin, lottery Useful for intuition – Continuous: Outcome can be any value over a range Most real world data is continuous

8. Distribution of Random Variables Discrete random variable – Establish the frequency of outcomes in repeated experiments Continuous Random Variable – Outcome can be any value over a range – Can establish probability of r.v. being with a particular interval, – not of taking a particular value (zero by definition) – e.g. Household Income, interest rates, price of bread

9. Probability Density Function f(x) Mathematical representation of the distribution of a random variable f(x) = Pr(X=x) – prob of dice to get 3 i.e. f(3) = Pr(X=3) = 1/6.

10.  f(x) = 1; Sum of probabilities = 1,  0  f(x)  1 For a continuous rv – can take on any value within an interval: infinite no. of values. – NB: the probability of one exact value occurring = 0: Pr(a  X  b) = ? Probability is not measured by height now – measured by area

11. An example of a continuous RV An example of the bell curve 1200

12. Empirical vs Theoretical Distribution The examples so far are theoretical distributions Real world data wont necessarily match these exactly or even approximately We can show the empirical distribution of data on a histogram File dice.dta contains 10 dice each rolled 3449 times – hist dice1

13. Empirical Dist of Dice

14. Example using wage data Histogram will show the distribution – Stata: hist wages, bin(50) Can also do it separately for the two genders – Hist wages if gender==1, bin(50) norm – Hist wages if gender==2, bin(50) norm This shows that the distribution of wages looks a little different for both groups – Not just the average Note that bell curve is bad approximation

16. Characteristics of a Distribution We can characterise the difference between distributions in many ways – The two main are the Expected value and the Variance Expected Value is the average – Weighted by probability

17. Sample and Theoretical Mean Sample and theoretical mean will be different – dice: E(X)=1.(1/6)+2.(1/6)+3.(1/6)+4.(1/6)+5.(1/6)+6.(1/ 6)=3.5 – For dice1: summ dice1 Continuous: – We already did for gender using summ

18. Rules of Expectations 1.E(X+Y) = E(X)+E(Y) 2.E(X-Y) = E(X)-E(Y) 3.E(aX) =a E(X) 4.E(X+a) = E(X)+a 5.E(aX+bY+cZ) = aE(X)+bE(Y)+cE(X) See dice.dta for examples of this

19. Variance of a random variable Distribution has an average but it also varies around that average Need a concept to measure that dispersion In stata part of output of “summ”

20. Dice Example X 11(1-3.5) 2 = 6.25 24(2-3.5) 2 =2.25 39(3-3.5) 2 =0.25 415….0.25 525….2.25 636….6.25  =21  =91  =17.5

21. Dice Example cont. Note That the theoretical variance may differ from the variance in the sample Stata: summ dice1

22. Rules of Variance 1.Var (aX) = a 2 Var(X): 2.Var(X+a) = Var(X): 3.Var(a+bX) = b 2 Var(X): 4.Var (aX+bY) = a 2 Var(X) + b 2 Var(Y) if X and Y are independent. (deal with dependence later). Standard Deviation = Square root of Variance:  =  2

23. Normal Distribution A special continuous distribution that can be very useful AKA “Gaussian Distribution”, “Bell Curve”, “Law of Errors” Mean and variance completely define it – X~N( 

24. Bell Curve 1200 

25. Calculating Probabilities from Bell Curve Integral solved for you by computer in stata the “normal” function gives area under the curve of standard normal rv – Mean=0, variance=1 – display normal(0.5) For other normal make use of trick – If y~N(  then z=(y-  is N(0,1) – Mean 1000, stn dev 100 – Prob(x<1200)=Prob(z<2) – di normal(2)

26. Properties of the Normal Symmetric around the mean – Positive and negative deviations are equally likely The probability of a deviation declines with the size of a deviation approx. 68% of the area under the curve lies between [  and 95% is in the interval [ 

27. Using the Bell Curve We often assume that data has normal distribution – Easy to use – Often intuitive But remember nothing is actually normal We choose to model things as normal – it is only a convenient approximation – Could be very wrong Always have to ask yourself if it is reasonable to treat the data as normal Check histogram

28. Using the Bell Curve Nassim Taleb made career out of complaining that we assume data is normal when it is not – See Fooled by RandomnessFooled by Randomness Already seen that bad approx to wage data Bad approximation to stock market data as grossly underside tails – Low prob of large changes – See sandp.dta

29. Empirical dist of % Stock Returns

30. Answer the question! We now have some tools which we can use to answer the question of gender bias in wages Recall that women are paid less on average in the sample Recall that the issue is whether we can use this fact about the sample to make statements about the world (“population”)