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Growth and inequality : what are we talking about?. A within-between distinction among inequality of opportunity and inequality of effort Geoffrey TEYSSIER ( Supervisor : Charlotte Guénard ; Co-supervisor : Sandra Poncet. PLAN. I. Quick overview of the thesis defended

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Growth and inequality what are we talking about
Growthand inequality: what are wetalkingabout?

A within-between distinction amonginequality of opportunity and inequality of effort

Geoffrey TEYSSIER

(Supervisor: Charlotte Guénard; Co-supervisor: Sandra Poncet


PLAN

  • I. Quick overview of the thesisdefended

    (empirical objectives and the benchmark specification)

  • II. Micro part

    (micro dataset construction and micro results)

  • III. Macro part

    (summarystatistics and preliminary macro results)

  • IV. To bedone…


I inequality is like cholesterol
I. INEQUALITY IS LIKE CHOLESTEROL !!!

  • Growth-inequality: a puzzling relationship

  • no agreement in the litterature

  • are we in a dead-end? NO

  • Good (IE) vs bad inequality (IO)

  • incomeisfunction of 2 kinds of factorsonly:

    • thosefactorsunderyour control: study, work, …

    • thosefactorsoutsideyour control: skin color, social background (eg: parental education), …

  • incomeinequality due to whatyoucan control is the inequality of effort (IE) and ismorallyfair

  • incomeinequality due to whatyouCANNOT control is the inequality of opportunity (IO) and ismorallyunfair


I thesis defended what is morally fair is also economically efficient and vice versa
I. Thesisdefended: whatismorallyfairisalsoeconomically efficient(and vice-versa)

  • By distinguishingbetween IO and IE, wecanexplain the « growth – inequalityparadox ». The benchmark specificationis:

    Growthi(t,t+9)= Ineqit + Control_variablesit +  Ri + Tt +it+9

  • Theempirical objectiveis to show that:

    • If Ineq= IO

      • is negative and significant

  • If Ineq= IE:

    • is positive and significant

  • If Ineq=Itot

    • is not robust (negative if IO dominates, positive if IE dominates)


  • Ii micro part measures of inequality
    II. MICRO PART : measures of inequality

    • How to measure IO?

      • Total inequality= IO + IE

        • IO: inequalitybetweengroups defined by common « circumstances » (iefactorsoutside the individual’s control)

        • IE: inequalitywithingroups

          Needindividuallevel data on income and circumstances(father’seducation, father’s occupation, gender, skin color)


    Ii micro dataset
    II.MICRO DATASET

    • Sample restriction:

      • Positive incomereported

      • Agedbetween 20 and 49 yearsold

      • With all circumstancesobserved

    • 1980, 1991, 2000 BrazilianCensuses

      • takenfrom the IPUMS

      • Huge: million of observations for eachyear

      • Weights and survey design takenintoaccount


    Ii 2 adjustements for income
    II. 2 adjustements for income

    • Adjustment for the time profile of the individual (« composition effect »)

      • Because I do not want to takeintoaccountinequality due to age

    • Adjustment for samplebias:

      • becausefather’seducation and occupation are onlyobserved for those people living in the samehousehold as theirfather


    Ii 2 2 4 4 64 groups defined
    II. 2*2*4*4=64 groups defined

     So as to capture the mostcircumstances possible (otherwise, IO isunderestimated), whilehaving a reasonablenumber of observations withineach group (otherwise, IO is not accurate)

    • Race:

      • White (or asian)

      • Non white

    • Sex:

      • Male

      • Female

    • Father’seducation

      • No education

      • Primary (1-4)

      • Primary (5-8)

      • Secondary or +

    • Father’s occupation

      • 3 groups for active

      • 1 group for not economicaly active father

        (Direct question to the father)

      • could not beentirelyoutside the individual’s control

      • One specificationwithoutfather’s occupation as a robustness check


    Ii micro results the inequality measures
    II. Micro results: the inequalitymeasures

    • 80 observations: (26 regional states + 1 fedeferal state) -1 observation for the state of Toscantinwhichdid not exist in 1980

    • « not adj »: inequalitycalculated on income distributions not adjusted for age and samplebias

    • prior to the 2 adjustments:

      • IO wasoverestimated

      • total inequalitywaseven more overestimated


    Iii macro part growth inequality regression
    III. MACRO PART: growth inequality regression

    • Growthi(t,t+9)= Ineqit + Control_variablesit +  Ri + Tt +it+9

      • unit of observration: Brazilian state i at time t+9 (or alternatively t, depending on how weseethings)

      • Growthi(t,t+9): growth of GDP per capita

      • data on GDP (at constant 2000 prices) and population, Growthi(t,t+9) iscomputed as the difference of theirgrowth rates multiplied by 100

      • Ri: regionaldummy (central western regionomitted)

      • Tt: yeardummiesat time t (1980 and 1991, while 2000 omitted)

      • Control variablesit:

        • State’s GDP at time t

        • State’s public welfareexpenditures (education and culture; health and sanitation; social security and redistributive programs) at time t

          many more still to beincluded


    Iii evolution over time
    III. Evolution over time

    • IO and Itot have decreasedsince 1991

    • but IO has decreased over the wholeperiod,whileItot hase increased

    • Growth: economic stagnation in the 1990s


    Iii simple correlation io on growth negative but not signigificant
    III. Simple correlation: IO on growth (negative but not signigificant)


    Iii simple correlation itot on growth negative but not signigificant
    III. Simple correlation: Itot on growth (negative but not signigificant)


    Iv to be done
    IV. To bedone…

    • ENDOGENEITY PB:

      • IV: Easterly but time invariant

      • GMM

      • Otherleads to explore: intergenerationalmobilityliterature?

    • OTHER MEASURES OF IO

      • Recentpapersuggestsan upper-bound

      • interestingbecause IO isotherwisenecessarily a lowe-bound

      • Othermeasuresbased on anotherdefinition of types and on a parametricmethod as a robustness

    • CONTROLS TO BE INCLUDED

      • OtherfromMarrerro&Rodriguez (benchmark paper)

      • Proportion of people belonging to eachcategory of the circumstances variable (to be sure that IO does not capture the proportion of disadvantaged people)

      • Determinants of growthspecific to Brazilian states

    • STRUCTURAL FORM EQUATIONS

      In order to investigate the mechanismbetween IO and growth


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