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Measuring Inequality of Opportunity in more periods than ever before: the capital income approach Hugo del Valle-Incl an Cruces RGEA, ECOBAS LIS research visit, January 2019 H. del Valle-Incl an Cruces IOPK: the capital income approach

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Measuring Inequality of Opportunity in more periods than ever before: the capital income approach

Hugo del Valle-Incl´ an Cruces

RGEA, ECOBAS

LIS research visit, January 2019

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 1 / 48

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We are going to talk about...

1

What is Inequality of Opportunity? The Ferreira and Gignoux’s method The outcome of choice The limitation of relying on parental education

2

IOPK: the capital income approach Capital income as a proxy of family background Validation of IOPK Taking advantage of IOPK Robustness and future analyses

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 2 / 48

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  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 3 / 48

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What is inequality of Opportunity?

The Inequality of Opportunity approach considers that any outcome (such as income, wealth, scientific achievement...) is a function of circumstances and efforts: I = Φ(C, E)

  • Circumstances are factors that might influence a given outcome but cannot be

chosen by individuals. These include gender, race, geographical origin, family background...

  • Effort is the intensity with which individuals devote themselves to work, and can, on

the contrary, be decided by them Thus, the field of Inequality of Opportunity distinguishes between inequalities that are due to personal responsibility —for which individuals can be held responsible— and may therefore be considered ethically acceptable; and those that are not —for which individuals cannot be blamed—, which are therefore deemed unjust.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 4 / 48

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The Ferreira and Gignoux (2011) method

We employ the parametric procedure proposed by Ferreira and Gignoux (2011). It consists on regressing an outcome yi on a set of circumstances Ci lnyi = ψCi + ui Actually, that is a reduced form of lnyi = αCi + βEi + ei where Ei is the influence of circumstances on the level of effort. Then, to “remove” the effect of effort Ei on the outcome, we predict its values ˆ yi (based

  • nly in the circumstances set Ci) and apply an inequality measure to these fitted values,

usually the Gini index or the GE(0) (Mean Log Deviation). Finally, we get an absolute measure of IOP IOPabs = I({µk

i })

and a relative one IOPrel = I({µk

i })

I(y)

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 5 / 48

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The outcome of choice

The outcome considered, yi, can be wealth, health status, educational attainment... but income is the one most frequently studied. Yet, once we have decided to study income, we must choose between different concepts. For example, should we consider personal income or household income? In the study of income inequality the standard practice is to consider household income, but should it also be the case in the study of opportunity inequality?

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 6 / 48

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The outcome of choice

Shapley value decomposition of the role of circumstances. Source: LIS and author’s calculations.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 7 / 48

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The outcome of choice

Shapley value decomposition of the role of circumstances. Source: EU-SILC and author’s calculations.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 8 / 48

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The limitation of relying on parental education

In the EU-SILC database we have information on parental background in 2 of the 13 waves, those corresponding to 2004 and 2010, which is around a 15%. In the LIS database we have this information in 63 of the 344 datasets, approximately an 18%. Implications of this limitation are, for example, that we do not know how the level of IOP is nowadays or how has evolved in most countries, or if its evolution is linked to income

  • r wealth inequality, or any other economic or social phenomena, at a cross-country level.
  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 9 / 48

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How can we overcome the limitation of relying on parental education?

We propose a new empirical strategy that is not limited by the scarcity of data on parental education. It consists of using another variable, widely available, as a proxy of socioeconomic origin. Capital income of households may be a good proxy of socioeconomic origin because positive returns on investment are linked to financial literacy, cognitive abilities and education (Gaudecker2013). Also, stock market participation increases with income and education (Mankiw and Zeldes, 1991) and it is influenced by behavioral biases and cognitive abilities (Andersen and Nielsen, 2010). Furthermore, individuals with high financial literacy and high cognitive abilities face lower cost of acquiring information and thus lower participation cost than individuals who know little about financial markets and have low cognitive abilities (Grinblatt et al., 2010). We use data on the capital income of households to construct a discrete variable that classifies individuals into levels according to the importance of capital income in respect to their household’s total income. This variable is built such that its Cumulative Distribution Function tries to resemble that of the variable parental education.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 10 / 48

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Grouping individuals according to the relative importance of their households’ capital income

First, we look at the distribution of parental education in the periods and countries for which we have this information. We have previously constructed a variable of parental education with three levels (primary or less, secondary, and tertiary or more), and hence we can see how the education of parents is distributed across

  • individuals. We look at the values of its CDF, and store them

Second, we generate a variable on the relative importance of households’ capital income in respect to their total income (a simple ratio) for every individual in the

  • sample. This is:

Kinci = Capital income of individuali’s household Total income of individuali’s household Third, we find the exact values of this variable at which its CDF takes the same values of the parental education’s CDF Finally, we create a discrete variable with three groups of a size determined by the parental education’s CFD

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 11 / 48

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Grouping individuals according to the relative importance of their households’ capital income: an example

A fictitious distribution of parental education

Parental education Frequency Percent Cumulative Primary or less 6,000 60 60 Secondary 3,000 30 90 Tertiary or more 1,000 10 100 Total 10,000 100

The highest education level attained by any of the parents is considered.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 12 / 48

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Grouping individuals according to the relative importance of their households’ capital income: an example

A fictitious distribution of levels of capital income relative importance

K income to total income Frequency Percent Cumulative Kinci ≤ 0.01 6,000 60 60 0.01 < Kinci ≤ 0.03 3,000 30 90 Kinci > 0.03 1,000 10 100 Total 10,000 100

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 13 / 48

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Grouping individuals according to the relative importance of their households’ capital income: an example

Capital income levels =

1

if Kinci ≤ 0.01 2 if 0.01 < Kinci ≤ 0.03 (1) 3 if Kinci > 0.03 where: Kinci is the ratio of capital income to total income of the household of individual i

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 14 / 48

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Validation of IOPK: methodology

To assess the validity of the capital income approach we make a strong assumption: that the estimates returned by the standard approach (including parental education in the set

  • f circumstances) are accurate. Assuming that, we will compare them with the results

returned by the capital income approach, and conclude that the latter are valid only to the extent that they are similar to the former. We will use the LIS and the EU-SILC database, and employ both the Gini index and the Mean Log Deviation as inequality measures. We will mostly show results using Personal Income as outcome, but we will refer to Equivalized Household Disposable Income at some points as well. We restrict our sample to individuals... aged 25 to 64 who are either active in the labor market (employed full or part time or unemployed)

  • r fulfilling domestic tasks and care responsibilities

As the set of circumstances, we consider binary gender, 4 age groups, immigrant status/ethnicity, population density and parental education/capital income. This means that the number of types can be up to 96.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 15 / 48

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Validation of IOPK: evolution of IOP and IOPK in Germany using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 16 / 48

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Validation of IOPK: evolution of IOP and IOPK in Italy using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 17 / 48

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Validation of IOPK: evolution of IOP and IOPK in Brazil using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 18 / 48

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Validation of IOPK: evolution of IOP and IOPK in Chile using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 19 / 48

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Validation of IOPK: evolution of IOP and IOPK in Guatemala using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 20 / 48

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Validation of IOPK: evolution of IOP and IOPK in Peru using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 21 / 48

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Validation of IOPK: evolution of IOP and IOPK in South Africa using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 22 / 48

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Validation of IOPK: overall correlation of IOP and IOPK using the LIS database

IOP estimates’ pairwise correlations between ’Baseline’ and ’Capital’ circumstances, by income concept and inequality measure, 7 countries – LIS database

Capital \ Baseline PI Gini EDHI Gini PI MLD EDHI MLD PI Gini .9701 EDHI Gini .9470 PI MLD .9873 EDHI MLD .9767

PI refers to personal income, EDHI to equivalized disposable household income. Includes 7 countries, N = 50. All coefficients have a p-value < 0.0001.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 23 / 48

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Validation of IOPK: regression of IOP against IOPK using the LIS database

Regression of IOP, estimated using the set of ’Baseline’ circumstances, against IOPK, estimated with the ’Capital’ set, 7 countries – LIS database

Dependent variable: IOP of PI and EDHI

PI Gini EDHI Gini PI MLD EDHI MLD IOPK 1.007 1.054 1.077 1.049 (.0787) (.0837) (.0469) (.0437) N 50 50 50 50 t-statistic 12.80 12.60 22.96 24.02 R2 .9411 .8968 .9748 .9539

PI refers to personal income, EDHI to equivalized disposable household income. Gini refers to the Gini index, MLD to the Mean Log Deviation. Clustered robust standard errors in parenthesis. Intercepts included but not reported.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 24 / 48

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Validation of IOPK: levels of IOP and IOPK using the EU-SILC database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 25 / 48

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Validation of IOPK: levels of IOP and IOPK using the EU-SILC database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 26 / 48

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Validation of IOPK: levels and CIs of IOP and IOPK using the EU-SILC database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 27 / 48

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Validation of IOPK: overall correlation of IOP and IOPK using the EU-SILC database

IOP estimates’ correlations between ’Baseline’ and ’Capital’ circumstances, by income concept and inequality measure, 26 countries, 2004 & 2010 – EU-SILC database

Capital \ Baseline PI Gini EDHI Gini PI MLD EDHI MLD PI Gini .9470 EDHI Gini .5803 PI MLD .9509 EDHI MLD .6836

PI refers to personal income, EDHI to equivalized disposable household income. Includes 26 countries, N = 45 (46 for EDHI). All coefficients have a p-value < 0.0001.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 28 / 48

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Validation of IOPK: regression of IOP against IOPK using the EU-SILC database

Regression of IOP, estimated using the set of ’Baseline’ circumstances, against IOPK, estimated with the ’Capital’ set, 26 countries, 2004 & 2010 – EU-SILC database

Dependent variable: IOP of PI and EDHI

PI Gini EDHI Gini PI MLD EDHI MLD IOPK .9375 .7063 .9849 .8128 (.0649) (.1658) (.0741) (.1735) N 45 46 45 46 t-statistic 14.43 4.26 13.29 4.68 R2 .8969 .3367 .9041 .4672

PI refers to personal income, EDHI to equivalized disposable household income. Gini refers to the Gini index, MLD to the Mean Log Deviation. Clustered robust standard errors in parenthesis. Intercepts included but not reported.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 29 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances using the EU-SILC database, 2004

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 30 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances using the EU-SILC database, 2010

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 31 / 48

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Validation of IOPK: regression of ˆ yIOP

i

against ˆ yIOPK

i

using the EU-SILC database

Regression of the fitted values ˆ yIOP

i

, obtained with the ’Baseline’ set of circumstances, and ˆ yIOP K

i

, obtained with the ’Capital’ set, 26 countries, 2004 & 2010 – EU-SILC database

Dependent variable: ˆ yIOP

i

  • f PI and EDHI

PI EDHI ˆ yIOP K

i

.9850 .9507 (.0081) (.0146) N 279,839 284,884 t-statistic 122.24 65.31 R2 .9479 .9256

PI refers to personal income, EDHI to equivalized disposable household income. Clustered robust standard errors in parenthesis. Intercepts included but not reported.

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 32 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Germany using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 33 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Italy using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 34 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Brazil using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 35 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Chile using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 36 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Guatemala using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 37 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in Peru using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 38 / 48

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Validation of IOPK: kernel density estimations of ˆ yi with ’Baseline’ and ’Capital’ circumstances in South Africa using the LIS database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 39 / 48

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Validation of IOPK: comparison of moments

Percentual differences between moments of the ˆ yIOP

i

and ˆ yIOP K

i

distributions using the LIS database, Personal Income, average differece across different years per country

Mean Median Standard deviation Brazil

  • 2.84

0.19

  • 20.00

Chile 0.13 6.35

  • 7.44

Germany

  • 0.32
  • 0.43
  • 1.73

Guatemala

  • 0.86
  • 0.25
  • 18.24

Italy

  • 0.47

5.77

  • 4.22

Peru

  • 0.79

2.54

  • 9.44

South Africa

  • 0.24
  • 2.14
  • 11.26

Average diff.

  • 0.77

1.72

  • 10.33
  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 40 / 48

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Validation of IOPK: comparison of moments

Percentual differences between moments of the ˆ yIOP

i

and ˆ yIOP K

i

distributions using the EU-SILC database, Personal Income, 2011

Mean Median Standard deviation Austria

  • 0.30
  • 1.35
  • 2.59

Belgium

  • 0.36
  • 1.92
  • 13.49

Cyprus

  • 0.53

0.92

  • 7.63

Czech Republic

  • 0.13
  • 8.79
  • 10.68

Germany 0.88 7.13 7.23 Denmark

  • 0.07

0.99

  • 6.44

Estonia

  • 0.10

2.72 2.65 Greece 0.68

  • 0.43
  • 0.90

Spain

  • 0.56

3.79

  • 10.60

Finland 1.14 0.03 4.14

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 41 / 48

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Validation of IOPK: comparison of moments

Continuation of table XX: Percentual differences between moments of the ˆ yIOP

i

and ˆ yIOP K

i

distributions using the EU-SILC database, Personal Income, 2011

Mean Median Standard deviation France

  • 0.73
  • 0.99
  • 13.38

Hungary

  • 1.12

4.70

  • 31.16

Ireland 1.36

  • 9.68

26.24 Iceland

  • 0.15

1.21

  • 6.92

Italy

  • 0.80
  • 2.89
  • 12.77

Lithuania

  • 0.64
  • 3.53
  • 26.33

Luxembourg

  • 2.86

3.09

  • 22.67

Latvia

  • 0.32
  • 0.52
  • 17.17

Netherlands 1.51 3.82 8.41

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 42 / 48

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Validation of IOPK: comparison of moments

Continuation of table XX: Percentual differences between moments of the ˆ yIOP

i

and ˆ yIOP K

i

distributions using the EU-SILC database, Personal Income, 2011

Mean Median Standard deviation Norway

  • 0.04
  • 1.38
  • 1.40

Poland 0.20

  • 0.71
  • 10.18

Portugal

  • 0.27

1.17

  • 24.73

Sweden 0.46 0.91 2.85 Slovenia 1.04

  • 0.66

6.83 Slovakia

  • 0.22
  • 1.58
  • 14.74

United Kingdom 0.09

  • 0.18

0.53 Average diff.

  • 0.18

0.12

  • 8.98
  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 43 / 48

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Taking advantage of IOPK: indexed Opportunity Inequality using the EU-SILC database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 44 / 48

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Taking advantage of IOPK: indexed Opportunity Inequality using the EU-SILC database

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 45 / 48

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Robustness

The results hold when we vary parameters of the analysis such as: The inequality measure The database employed Consider Net Personal Income instead of Gross (in the EU-SILC database) The sample selection: excluding people fulfilling domestic tasks or care responsibilities and keeping only individuals who are active in the labor market The way in which we construct the ’Capital income levels’ variable: apart from the described relative measure of capital income importance we have tested an absolute measure and a combination of both The set of circumstances: we have conducted the same analysis but excluding ’Population density’ from the set of circumstances Using the LIS database, both PI and EDHI returns similar results, although that is not the case with the EU-SILC; this may be due to the different composition of the sample (mainly middle income countries in LIS)

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 46 / 48

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Future analyses

We can use the information on the evolution of IOPK to: Employ other methods to estimate Opportunity Inequality Obtain estimates at a regional level Estimate its relationship with income and wealth inequality Study the role of institutions and public policies Analyze the relationship with economic growth

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 47 / 48

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Thank you

  • H. del Valle-Incl´

an Cruces IOPK: the capital income approach LIS, January 2019 48 / 48