Inequality presentation

March 4th 2013 Economics Research Lounge

Justus Timmers

Glossary

①Overview

i. What is the situation

ii. What do we measure?

②Causation

i. Differences in characteristics of people

ii. Skill Biased Technological Change (sbtc)

③Other Theories

④Discussion

Overview 0

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De

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0 10 20 30 40 50Real Hourly Earnings (in 2002 £)

2010 CPI Adjusted Hourly Earnings

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0 10 20 30 40 50Hourly Earnings

2002 UK Hourly Earnings

2002

£8.00 per hour £4.40 per hour £17.50 per hour

minimum wage: £4.20

minimum wage: £4.80

£8.85 per hour £4.90 per hour £18.95 per hour, all CPI adjusted 2002 £

£6.25

2010

£13.50

£5.70 £12.60

3.993 2.186 0.548 2.130 All obs p90/p10 p90/p50 p10/p50 p75/p25

Percentile ratios

3.874 2.141 0.553 2.166 All obs p90/p10 p90/p50 p10/p50 p75/p25

Percentile ratios

• 9 out of 10 people we have become relatively more equal

Overview

• Income inequality has increased by just about every metric 0.09219 0.16440 0.28180 All obs A(0.5) A(1) A(2)

Atkinson indices, A(e), where e > 0 is the inequality aversion parameter 0.19619 0.17961 0.22027 0.67604 0.32914 All obs GE(-1) GE(0) GE(1) GE(2) Gini

sensitivity parameter, and Gini coefficientGeneralized Entropy indices GE(a), where a = income difference

0.09219 0.16440 0.28180 All obs A(0.5) A(1) A(2)



0.08406 0.15548 0.27456 All obs A(0.5) A(1) A(2)



0.08406 0.15548 0.27456 All obs A(0.5) A(1) A(2)



2002

2010

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0 .2 .4 .6 .8 1Cumulative proportion of population

Lorenze curve 2010 Line of Perfect Equality

UK Lorenz Curve 2010

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0 .2 .4 .6 .8 1Cumulative proportion of population

Lorenze curve 2002 Line of Perfect Equality

UK Lorenz Curve 2002

Gini = A / A + B

if pick two people at random, their income will on average differ twice the Gini coefficient of the mean. i.e. 64.2% and 65.8%, or £6.39 and £7.15 in 2002 and 2010 respectively.

General Entropy (α = 1, Teil T is equal weight to all, higher(/lower) α is more attention to the top(/bottom) of the distribution)

Atkinson (ε = inquality aversion, welfare loss)

A

B

A

B

Overview

• Commonly (slightly misleading) displayed as the above

Overview

• Not really the top 1%, or the top 0.1%: the very, very highest earners

100

111.9

100100

108.5

100100100

110.8

113.1

119.6

95

100

105

110

115

120

125

10th percentile

50th percentile

90th percentile

99th percentile

99.9th percentile

highest10th percentile 100 111.9

50th percentile 100 110.8



99.9th percentile 100 119.6

highest 100 527.1

2002 2010

Approach to Equality

Conservative ‘life is unfair and that is how it is’

equ

al o

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un

itie

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equal outcomes

Libertarian e.g. Milton Friedman

Socialism e.g. Karl Marx

• Equal outcomes (economic left)

• Strife for equal opportunities (economic right)

Liberalism

Approach to Equality

• Equal outcomes (economic left)

• Strife for equal opportunities (economic right)

Usual Suspects Ascribed Social Categories

0

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0 10 20 30 40 50

2010 CPI Adjusted Hourly Earnings for Men

2010 CPI Adjusted Hourly Earnings for WomenDe

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Real Hourly Earnings (in 2002 £)Graphs by gender

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0 10 20 30 40 50

2002 UK Hourly Earnings for Men

2002 UK Hourly Earnings for WomenDe

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Hourly EarningsGraphs by sex

Gender

• Women on average earned 26.6% less than men in 2002 and 22.5% than men in 2010 • The largest discrepancy is in the top earners

• Closing the gap across the board • Fastest catching up at the bottom (75th percentile)

• ‘Within group’ inequality is far greater than ‘between-group inequality’ • i.e. <7% (in 2002) and <4% (in 2010) of the inequality would be removed if

the group means were equalised and distribution scaled proportionally

Usual Suspects Ascribed Social Categories

_cons 2.143396 .040543 52.87 0.000 2.063923 2.22287 ni .0597708 .0539356 1.11 0.268 -.0459551 .1654967 ros .1244974 .0464454 2.68 0.007 .033454 .2155409 s .0915385 .0492179 1.86 0.063 -.0049397 .1880166 w .0016495 .0479755 0.03 0.973 -.0923933 .0956924 sw .0828506 .0441603 1.88 0.061 -.0037136 .1694148 se .2178462 .0426048 5.11 0.000 .1343313 .3013611 ol .3468207 .0462804 7.49 0.000 .2561007 .4375407 il .4461553 .0518309 8.61 0.000 .344555 .5477556 eoe .1948355 .0438399 4.44 0.000 .1088996 .2807715 rowm .1464171 .0467938 3.13 0.002 .0546908 .2381435 wmmc .1492206 .0486802 3.07 0.002 .0537965 .2446446 em .0637065 .0447424 1.42 0.155 -.0239987 .1514117 royah .013041 .052325 0.25 0.803 -.0895277 .1156097 wy -.0070748 .049681 -0.14 0.887 -.1044608 .0903111 sy (dropped) ronw .0971534 .0478947 2.03 0.043 .0032691 .1910377 m -.040631 .0626806 -0.65 0.517 -.1634991 .082237 gm .0906087 .0505545 1.79 0.073 -.0084895 .1897069 rone -.063895 .0531041 -1.20 0.229 -.167991 .040201 taw -.0039876 .0563258 -0.07 0.944 -.1143987 .1064236 fb .0993994 .0255687 3.89 0.000 .0492791 .1495197 other -.0771674 .0546348 -1.41 0.158 -.1842639 .0299291 Chinese .2022185 .1609669 1.26 0.209 -.1133128 .5177498 Bdeshi -.4497993 .1621786 -2.77 0.006 -.7677059 -.1318928 Pstani -.2244202 .0891448 -2.52 0.012 -.399164 -.0496764 indian -.1241947 .0557507 -2.23 0.026 -.2334787 -.0149107 blackoth -.2383267 .1538694 -1.55 0.121 -.5399452 .0632918 African -.4293042 .0862351 -4.98 0.000 -.5983445 -.260264 caribbean -.1540092 .0735647 -2.09 0.036 -.2982125 -.0098059 female -.2834079 .0111393 -25.44 0.000 -.3052435 -.2615724 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 2873.27978 9125 .314879976 Root MSE = .5312 Adj R-squared = 0.1039 Residual 2566.64371 9096 .282172792 R-squared = 0.1067 Model 306.636072 29 10.5736577 Prob > F = 0.0000 F( 29, 9096) = 37.47 Source SS df MS Number of obs = 9126

2002

_cons 2.279213 .0317056 71.89 0.000 2.217067 2.341358 ni (dropped) ros .0517595 .0353718 1.46 0.143 -.0175722 .1210913 s .0342436 .0372987 0.92 0.359 -.0388649 .1073521 w -.0339234 .0365517 -0.93 0.353 -.1055678 .037721 sw .0343687 .0339125 1.01 0.311 -.0321026 .10084 se .1720574 .0330258 5.21 0.000 .1073241 .2367906 ol .2713739 .0352879 7.69 0.000 .2022067 .3405411 il .4053498 .0388162 10.44 0.000 .3292668 .4814327 eoe .1201216 .0336419 3.57 0.000 .0541806 .1860626 rowm .0033402 .0356753 0.09 0.925 -.0665864 .0732668 wmmc -.0026973 .0377421 -0.07 0.943 -.0766749 .0712803 em -.0060544 .0341957 -0.18 0.859 -.0730809 .0609721 royah -.0017247 .038367 -0.04 0.964 -.0769271 .0734778 wy .0180399 .0368997 0.49 0.625 -.0542866 .0903665 sy -.0182286 .0405242 -0.45 0.653 -.0976594 .0612021 ronw .0301396 .0355451 0.85 0.396 -.0395318 .0998109 m -.0068597 .0414699 -0.17 0.869 -.0881441 .0744247 gm .0365281 .0366583 1.00 0.319 -.0353252 .1083814 rone -.0081565 .0399454 -0.20 0.838 -.0864529 .0701399 taw -.0789492 .0418327 -1.89 0.059 -.1609447 .0030464 fb -.0757003 .0146869 -5.15 0.000 -.1044878 -.0469128 other -.0779976 .0302154 -2.58 0.010 -.1372223 -.0187729 Chinese .0423675 .0646826 0.66 0.512 -.0844159 .1691509 Bdeshi -.4089386 .0837097 -4.89 0.000 -.5730166 -.2448605 Pstani -.1883778 .0496057 -3.80 0.000 -.285609 -.0911465 indian .0246024 .0301945 0.81 0.415 -.0345814 .0837861 blackoth -.0101515 .0653209 -0.16 0.877 -.138186 .117883 African -.1711463 .0453957 -3.77 0.000 -.2601256 -.0821669 caribbean -.1462988 .0459935 -3.18 0.001 -.2364498 -.0561478 female -.2081133 .0077271 -26.93 0.000 -.2232591 -.1929675 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval]


2010

• Large significant effects, e.g. • Bangladeshi’s earn >40% less • Women earn over >20% less

• Between-group differences in 2010 are roughly 4% for gender, 0.3% for ethnicity, 4% for region and 0% for non-UK born

• Overall accounts for 10.7% of the variance in 2002 and 6.9% of the variance in 2010

Usual Suspects Skill-Biased Technological Change

University education

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2002 UK Hourly Earnings for without University Degree

2002 UK Hourly Earnings for with University DegreeDe

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Hourly EarningsGraphs by university degree

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2010 CPI Adjusted Hourly Earnings for without University Degree

2010 CPI Adjusted Hourly Earnings for with University DegreeDe

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Real Hourly Earnings (in 2002 £)Graphs by degree

• Between-group differences account for about 17% • university degree holders, they have barely improved between 2002 and 2010 • large increase in degree holders, from 19.2% in 2002 to 28.3% in 2010


Age (correlated with education)

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2002 UK Hourly Earnings for ages 16 - 24



Den

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Hourly EarningsGraphs by age cohorts

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0 10 20 30 40 50

0 10 20 30 40 50

2010 CPI Adj. Hourly Earnings for ages 16 - 24

2010 CPI Adj.Hourly Earnings for ages 25 - 49

2010 CPI Adj. Hourly Earnings for ages 50 - 65

Den

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Real Hourly Earnings (in 2002 £)Graphs by age cohorts

• The average salary of a young person has barely improved

• Removing ‘between group’ could remove inequality by 8.5% in 2002 and by 6.5% in 2010


• In 2002 an additional year of education is estimated to add 10.0% to earnings • In 2010 this is only 8.4%

• This coefficient overestimates the upper educational levels.

• In 2002 an additional year of experience is estimated to add 5.4% to earnings (peaking at 38.4 years of experience) • In 2010 this is 5.1% (peaking at 37.8 years of experience)

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5 10 15 20 25Education in Years

Individual Log Earnings

Upper Limit 95% Confidence Interval

Estimated Log Earnings given Education holding experience constant (at mean)

Lower Limit 95% Confidence Interval

2002 UK Log Earnings and Education

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5 10 15 20 25Education in Years

Individual Log Earnings

Upper Limit 95% Confidence Interval

Estimated Log Earnings given Education holding experience constant (at mean)

Lower Limit 95% Confidence Interval

2010 CPI Adjusted Log Earnings and Education


2002 2010

_cons .1682376 .0386905 4.35 0.000 .0923955 .2440798 exp2 -.0007475 .0000228 -32.83 0.000 -.0007921 -.0007028 exp .0537047 .0014191 37.85 0.000 .0509231 .0564864 educ .099628 .0020078 49.62 0.000 .0956922 .1035638 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval]


_cons .3863562 .026951 14.34 0.000 .33353 .4391825 exp2 -.0006751 .0000174 -38.83 0.000 -.0007092 -.000641 exp .0510363 .0010966 46.54 0.000 .0488868 .0531858 educ .0844415 .0013007 64.92 0.000 .0818921 .086991 logearning Coef. Std. Err. t P>|t| [95% Conf. Interval]


• The explanative power of education and experience is decreasing (R2 = 0.23 in 2002 and R2 = 0.20 in 2010)

Log(earnings) = β0 + β1*Education + β2*Experience + β3 *Experience^2

Usual Suspects

• The usual suspects (asc, sbtc) do not account for the increase in inequality

• The usual suspects account for less of the facts (asc: 10.7% in 2002, 6.9% in 2010; sbtc 23.3% in 2002, 20.3% in 2010)

• Old categories of sociologists and economists seem to have become increasingly inadequate irrelevant in explaining trends of inequality.

Other Theories

• Our inequality is caused by a spiral of political – economic connections, Stiglitz (2012)

• Our inequality is worsened by lack of global accountability by politicians and financial elite have access to global diplomats (e.g. WTO), Pogge (2002)

• i.e. income leads to (exclusive) connections, leads to more income

Other Theories

• Inspired by ‘For Richer (Not For Poorer): The Inequality Crisis of Marriage’ (The Atlantic, 14/03/12)

• E.g. in Sweden Björklund (2012) estimates are that the income correlation coefficients 0.26 for the general population, and these rise to an extreme 0.96 when only the top 0.1% is considered.

Other Theories

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0 1 2 3 4Log CPI Adjusted Earnings (in 2002 £)

Upper 95% Confidence Interval Lower 95% Confidence Interval

Estimated Probability of being Married given Earnings holding age constant (at mean)

2010 Probability Married and Log Earnings for Mothers*

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Estimated Probability of being Married given Earnings holding age constant (at mean)

2002 UK Probability Married and Log Earnings for Mothers*

_cons -1.779603 .2008241 -8.86 0.000 -2.173211 -1.385995 age .0586509 .0048839 12.01 0.000 .0490786 .0682232 logearning .1335943 .0668153 2.00 0.046 .0026386 .26455 married Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -893.43794 Pseudo R2 = 0.0910 Prob > chi2 = 0.0000 LR chi2(2) = 178.81Probit regression Number of obs = 1685

_cons -1.907614 .1414378 -13.49 0.000 -2.184827 -1.630401 age .0404978 .0031672 12.79 0.000 .0342903 .0467053 logearning .3999236 .0460117 8.69 0.000 .3097424 .4901048 married Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -1905.968 Pseudo R2 = 0.0759 Prob > chi2 = 0.0000 LR chi2(2) = 313.22Probit regression Number of obs = 3294

Summary

• Income inequality is increasing from the very top

• The cause of income inequality is far from obvious – Labour models of discrimination, human capital, or sbtc less relevant

• Hopefully interesting topic for our discussion

Discussion

• What is your experience with/opinion of income inequality?

• What in your experience/opinion causes income inequality?

• How can micro/macro economics help us understand the changes in income inequality? – Skill Biased Technological Change (contrary to evidence) – Economies of scale (why now?) – Information (herding behaviour) (why now?) – Winner-takes-all (who? just luck?)

Technology

Inequality presentation