DEC Course on Poverty and Inequality Analysis Module 7 by c4ai9wy

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									DEC Course on Poverty and Inequality
            Analysis
Module 7: Evaluating the Distributional and
Poverty Impacts of Economy-wide Policies

  Session IV: Inequality of Opportunity
    as a Social Evaluation Criterion

            Francisco H. G. Ferreira
                        Plan of the Session

1.       Motivation: Why measure inequality of opportunity?
2.       Existing empirical literature
3.       A conceptual framework
4.       Measurement in practice
5.       Data
6.       Scalar measures of inequality of opportunity
     •       For labor earnings
     •       For household income per capita
     •       For household consumption per capita
7.       Opportunity-deprivation profiles
8.       The Equitable Development Policy
9.       Conclusions
                      1.      Motivation (i)
•   Amartya Sen‟s Tanner Lectures (1980) question:
    “Equality of what?”
    –   Modern theories of social justice want to move beyond the
        distribution-neutral, sum-based approach of utilitarianism.
    –   Desire to place some value on “equality”.
    –   But are outcomes, such as incomes, the appropriate space?
    –   What role for individual effort and responsibility?
    –   Are all inequalities unjust?


”We know that equality of individual ability has never existed
       and never will, but we do insist that equality of
               opportunity still must be sought”
             (Franklin D. Roosevelt, second inaugural address.)
                     1.       Motivation (ii)
• Equality of opportunity is a normatively appealing
  concept. Many philosophers (and politicians) increasingly
  see it as the appropriate “currency of egalitarian justice”.

   – Dworkin (1981): What is Equality? Part 1: Equality of Welfare; Part 2:
     Equality of Resources”, Philos. Public Affairs, 10, pp.185-246; 283-345.
   – Arneson (1989): “Equality of Opportunity for Welfare”, Philosophical
     Studies, 56, pp.77-93.
   – Cohen (1989): “On the Currency of Egalitarian Justice”, Ethics, 99,
     pp.906-944.
   – Roemer (1998): Equality of Opportunity, (Cambridge, MA: Harvard
     University Press)
   – Sen (1985): Commodities and Capabilities, (Amsterdam: North Holland)
                      1.       Motivation (iii)

• Economists have also become interested. John Roemer
  (1998) suggested an influential definition, based on the
  distinction between “circumstances” and “efforts” among
  the determinants of individual advantage.
   – Circumstances are morally-irrelevant, pre-determined factors
     over which individuals have no control.
   – Equality of opportunity is attained when advantage is distributed
     independently of circumstances.
                               F y C   F  y 
 “According to the opportunity egalitarian ethics, economic inequalities due to
    factors beyond the individual responsibility are inequitable and [should] be
   compensated by society, whereas inequalities due to personal responsibility
        are equitable, and not to be compensated” (Peragine, 2004, p.11)
   A non-income example of unequal opportunities: the
 distribution of survival probabilities by parental education




Source: WDR 2006.
                     1. Motivation (iv)
                                                                                Income: more equal or more unequal?

The concept may also:                                        20




                                                             15




                                             frequency (%)
1. Help policymakers identify groups
                                                             10
   that are excluded from the
   opportunity to achieve their full                         5

   potential in society.
                                                             0
                                                                   1        2      3      4      5      6       7      8      9       10

2. Help us understand differences in                                   1:Incomes should be made more equal" - 10: " We need larger
   intrinsic attitudes towards inequality.                                 income differences as incentives for individual effort."

                                                                  Source: World Value Survey (1999-2000) conducted by the
                                                                  Inter-university Consortium for Political and Social
3. Help shed new light on the                                     Research, based at the University of Michigan. The question
   inconclusive debate on the                                     asked representative samples of people in 69 countries to
                                                                  place their views on a scale from 1 to 10, where 1 implied
   relationship between inequality and                            agreement with the statement that “Incomes should be made
                                                                  more equal,” and 10 implied agreement with the statement
   growth.                                                        that “We need larger income differences as incentives for
                                                                  individual effort” (Inglehart and others 2004).
        2.        Existing Empirical Literature (i)

• Problem: How is one to measure inequality of
  opportunities?
• The literature is very much in its infancy.

    –   Bourguignon, Ferreira and Menéndez (2007)
    –   Bourguignon, Ferreira and Walton (2007)
    –   Checchi and Peragine (2005)
    –   Ferreira and Gignoux (2008)
    –   Lefranc, Pistolesi and Trannoy (2008)
    –   Paes de Barros et al. (2008)
    –   Roemer et. al. (2003)
    –   van de Gaer, Schokkaert and Martinez (2001)

    – Foster and Shneyerov (2000)
    – Elbers, Lanjouw, Mistiaen and Özler (2008)
    – Vast literature on intergenerational mobility is also related.
    2.      Existing Empirical Literature (ii)

• We will focus here on the approach followed by BFM
  and FG, which is based on a re-interpretation of
  standard inequality decompositions.
   – Defined so as to be consistent with Roemer‟s (1998)
     definition of equality of opportunity.
   – Using parametric and non-parametric methods, and a variety
     of indices and welfare concepts, in search of robustness.
   – Should be interpreted as yielding a lower-bound on
     inequality of opportunity for each particular “advantage”.
              3.       Conceptual framework (i)

Let a particular advantage be a function of a vector of circumstance variables, a
vector of effort variables, and a random term:

                           y = f(C, E, u)                                        (1)

Circumstances are (economically) exogenous by definition, but efforts are not:
                           yi = f(C, E (C, v), u)                                (2)


Equality of opportunity:      F y C   F  y 

which will generally require: (i)   f C , E , u 
                                                     0, Ck   (ii) GEl C   GEl , El
                                        Ck
                    3.               Conceptual framework (ii)
An illustration: distributions of per capita consumption conditional on
 mother‟s educational attainment in five Latin American countries.

                           Colombia                                       Ecuador                                     Guatemala
               1




                                                              1




                                                                                                            1
      .2 .4 .6 .8




                                                     .2 .4 .6 .8




                                                                                                   .2 .4 .6 .8
               0




                                                              0




                                                                                                            0
                     -2    -1    0      1    2                     -2     -1    0     1     2                    -2     -1   0      1   2
                          consumption                                   consumption                                   consumption
                    none                incomplete                 none               incomplete                 none               incomplete
                    primary complete                               primary complete                              primary complete



                           Panama                                          Peru
               1




                                                              1
      .2 .4 .6 .8




                                                     .2 .4 .6 .8
               0




                                                              0




                     -2     -1   0      1     2                    -2     -1   0      1    2
                          consumption                                   consumption
                    none                incomplete                 none               incomplete
                    primary complete                               primary complete
               3.                   Conceptual framework (iii)
A second illustration: distributions of per capita consumption conditional on
                 ethnicity in five Latin American countries.

                          Colombia                                    Ecuador                             Guatemala
           1




                                                          1




                                                                                                1
          .8




                                                         .8




                                                                                               .8
          .6




                                                         .6




                                                                                               .6
          .4




                                                         .4




                                                                                               .4
          .2




                                                         .2




                                                                                               .2
           0




                                                          0




                                                                                                0
                     -2        -1       0   1        2        -2     -1       0   1        2        -2   -1         0   1        2
                          consumption                               consumption                           consumption
                      minority                  others             minority           others             minority           others



                           Panama                                         Peru
           1




                                                          1
          .8




                                                         .8
          .6




                                                         .6
          .4




                                                         .4
          .2




                                                         .2
           0




                                                          0




                -2        -1        0       1        2        -2     -1       0   1        2
                          consumption                               consumption
                      minority                  others             minority           others
               3.           Conceptual Framework (iv)

•      Advantage being distributed independently of all circumstances
       implies that, in expectation, between-group inequality in a partition
       by all circumstance variables should be zero.

    Define y ik  as the partition of the population such that C ik  C k  i  k .

                               
                       Then F y C  F  y   IB yik          0
•      This suggests, two plausible measures of inequality of opportunity:
         : y ik    

                                  y ik   IB y ik 


                                   y   k
                                                 
                                                          
                                                     IB y ik
                                                     I F ( y ) 
                                          i
                   4.             Measurement in Practice (i)

• But IB y ik          is not a uniquely defined concept. It varies with:
       – Specific inequality index

       – Path of the decomposition
       - a smoothed distribution  ik , corresponding to a particular partition y ik , as the distribution that arises from replacing y ik

with the group-specific mean  k .

       - a standardized distribution  ik  corresponding to a particular partition y ik  as the distribution that arises from replacing y ik
                                     

           
with yik      (where μ is the grand mean).
           k


                 • Which gives rise to two alternative indices

                          d  I       i
                                           k
                                                I y 
                                                         k
                                                         i
                                                                                   r  1  I  ik  I y ik 
                                                                                               
       – Estimation procedure
     4.        Measurement in Practice (ii)

– It may be axiomatically desirable to choose I() such that

                I y   
       d  I ik             k
                              i          r             I y 
                                               1  I  ik         k
                                                                   i

– The only inequality index anchored by the arithmetic mean which
  satisfies this property (as well as the transfer principle) is E(0).
  (Foster and Shneyerov, 2000)
– To see why other members of the Generalized Entropy class do
  not satisfy it, note that:
                                          
                                n  k    k
                                                              
                          K       k
                I  y               I  yik   I ik
                                         
                           k 1 n      
For α≠0, changes in relative means affect not only IB, but also weights in IW.
        4.            Measurement in Practice (iii)

• Imposing path-independence:
   – Eliminates multiplicity of scalar measures and decomposition paths with
     one stroke, focusing attention on a unique scalar I.Op. measure.
   – But estimation procedure still matters, since most samples are too small
     for reliable fully non-parametric estimation.


                                  BRAZIL   COLOMBIA   ECUADOR   GUATEMALA   PANAMA   PERU

         Maximum number of
                                   108       54         108        108       108      54
         groups

         Number of groups
                                   108       54         102        96         84      53
         observed

         Mean number of
                                  663.8     394.8       124       71.5       67.7    257.5
         observations per group

         Proportion of groups
         with fewer than 5         0.06      0.06      0.17       0.23       0.30    0.08
         observations
           4.      Measurement in Practice (iv)
• The obvious alternative to use data more efficiently is a parametric
  approach. This requires estimating a specific model for
•
                          yi  f C , E C , vi , ui 
• Following Bourguignon et al. (2007), we use a log-linear approximation:
                             ln y  C  E  u
                             E  BC  v
• If one is only interested in the overall effect of circumstances, estimating
  the reduced form is sufficient:
                              ln y  C  
                    where         B
                                u  v

• The PStD is then simulated as:                  
                                      ~  exp C   
                                      yi         ˆ ˆi   
                                  ~  expC  
                                  ˆ
                                  zi         ˆ
• The PSmD is simulated as:                i
           4.       Measurement in Practice (v)
• This gives rise to two parametric indices which are analogous to the
  non-parametric measures previously defined:

                      rP  1  I ~i  I y ik 
                                    y

                       dP  I ~i  I y ik 
                                 z

          Non-parametric estimation of  is very data-intensive. With six
                                              N
                                              d ,r

          circumstances, three of which with three categories, there are 216
          possible cells in the partition. As cell size declines, sampling
          variance on  ik becomes problematically large.
                        ˆ

          The parametric approach allows for partial measures of inequality of
          opportunity due to individual circumstances:
                       ˆ
                       yi        
                       ~ J  exp C J J  C j  J j  J  
                                  i
                                     ˆ     i
                                                           ˆi   
                                         I { y }
                             r  1  I ~iJ
                              J
                                        y            k
                                                     i
          4.        Measurement in Practice (vi)

• This effectively leaves us with two estimates of the unique path-
  independent, Roemer-consistent inequality of opportunity index.
• Interpretation
    – Omitted circumstances can only lead to a finer partitioning of {yik},
      which can not reduce, but may increase measure.
    – Similarly for the parametric method,  dP  I ~i  I y ik  is proportional to
                                                      z
      the R2 of the regression. Recall that


                                     ~  expC  
                                     ˆ
                                     zi         ˆ
                                              i


    – Implication (i): these indices are lower bound estimates of inequality
      of opportunity
    – Implication (ii): if omitted variables are thought to be a significant
      problem, causal attribution to specific variables is unwarranted.
                                       5.          Data (i)
                             Table 1: Survey names, dates and sample sizes

                          BRAZIL       COLOMBIA    ECUADOR     GUATEMALA     PANAMA        PERU

      Survey           PNAD 1996       ECV 2003   ECV 2006    ENCOVI 2000    ENV 2003   ENAHO 2001

 Sample selection      30-49 head or    30-49       30-49         30-49       30-49     30-49 head
    criteria              spouse                                                         or spouse

Original sample size      85,692        22,517      12,650        6,956       6,339       13,947

 Observations with        50,560        16,575      9,671         4,661       4,127       9,830
   earnings and
  circumstances
 (share of original       0.590         0.736       0.765         0.670       0.644       0.704
     sample)

  Observations with       71,688        22,436      12,643        6,865       5,653       13,649
income/consumption
 and circumstances

 (share of original       0.837         0.996       0.999         0.984       0.889       0.979
     sample)
                                                            5.                Data (ii)
                                                    The circumstance variables
                                 BRAZIL              COLOMBIA                    ECUADOR                GUATEMALA                 PANAMA                  PERU

Ethnicity
            category 1   self reported white    Other                    self-reported ethnicity:   European maternal                           European maternal
                         ethnicity                                       white, mixed blood         language                                    language
                                                                         (“mestizo”) or other


            category 2   self reported black    self-reported minority   self-reported ethnicity:   indigenous maternal   speaks indigenous     indigenous maternal
                         (“negro”) and mixed    ethnicity: “indígena,    indigenous, black          language              language              language
                         blood (“pardo”)        gitano, archipiélago o   (“negro” or “mulato”).
                         ethnicity              palenquero”

Father's occupation
            category 1   agricultural worker    Missing                  agricultural worker or     agricultural worker   agricultural worker   missing
                                                                         domestic worker
            category 2   Other                                           Other                      other                 other

Mother‟s and father‟s
education
            category 1   None or unknown        none or unknown          none or unknown            none or unknown       none or unknown       none or unknown
            category 2   completed grade 1      primary incomplete       Primary                    primary incomplete    primary               primary incomplete
                         to 4
            category 3   completed grade 5      primary complete or      secondary or more          primary complete or   secondary or more     primary complete or
                         or more                more                                                more                                        more

Birth region
            category 1   Sao Paulo &            departments at the       Sierra & Amazonia          Guatemala city,       cities and            Inland non-southern
                         Federal district       periphery                provinces                  North-East            intermediate urban    departments
                                                                                                    departments and El    centers
                                                                                                    Petén
            category 2   South East, Center-    Central                  Costa & Insular            North & North-West    other urban centers   Southern and other
                         West & South           departments(a)           provinces                  departments                                 costal departments
            category 3   North-East, North or   Bogota, San Andres       Pichincha province         South-East, South-    rural areas           Arequipa, Callao &
                         missing                and Providencia          (with Quito) & Azuay       West & Center                               Lima
                                                islands and foreign      province                   departments
                                                country
                      5.       Data (iii)

• Comparability caveats
  – Consumption data not available for Brazil.

  – Father‟s occupation data not available for Colombia and Peru.

  – Household income and consumption data adjusted for spatial price
    differences in Ecuador, Guatemala, Panama and Peru (but not in
    Brazil or Colombia).

  – Imputed rents for owner-occupied housing included in income and
    consumption aggregates everywhere, except in Brazil.

  – Reference period for earnings of the self-employed vary somewhat
    across surveys.
   6.                  Scalar Measures of Inequality of Opportunity (iii)
              Table 8: Inequality of Opportunity Indices for Household Consumption Expenditures (per capita)
                                   COLOMBIA                   ECUADOR               GUATEMALA                   PANAMA                   PERU
                           E(0)      E(1)     E(2)    E(0)      E(1)    E(2)    E(0)    E(1)    E(2)    E(0)     E(1)    E(2)    E(0)    E(1)    E(2)

TOTAL INEQUALITY           0.449     0.503    1.013   0.354    0.375    0.574   0.409   0.436   0.676   0.381    0.374   0.539   0.351   0.384   0.660
                           0.018     0.024    0.079   0.015    0.018    0.047   0.024   0.023   0.039   0.016    0.019   0.042   0.015   0.022   0.076

NON PARAMETRIC
ESTIMATES

                    dN    0.265     0.275    0.177   0.344    0.347    0.270   0.524   0.536   0.440   0.417    0.385   0.285   0.348   0.339   0.229
                           0.017     0.017    0.013   0.021    0.025    0.028   0.023   0.026   0.031   0.016    0.018   0.020   0.017   0.017   0.016
                    rN    0.265     0.304    0.456   0.344    0.353    0.427   0.524   0.542   0.630   0.417    0.405   0.475   0.348   0.389   0.533
                           0.017     0.023    0.035   0.021    0.024    0.033   0.023   0.023   0.022   0.016    0.024   0.044   0.017   0.024   0.040

PARAMETRIC
ESTIMATES

                     rP   0.244     0.271    0.408   0.321    0.326    0.389   0.503   0.519   0.606   0.386    0.362   0.417   0.340   0.375   0.512
                           0.017     0.023    0.041   0.022    0.028    0.042   0.020   0.020   0.021   0.016    0.023   0.046   0.017   0.022   0.036
                     rJ
                  Race     0.001     0.001    0.002   0.032    0.027    0.036   0.141   0.123   0.136   0.121    0.065   0.047   0.054   0.051   0.065
                           0.002     0.002    0.003   0.006    0.005    0.006   0.013   0.011   0.013   0.014    0.012   0.016   0.008   0.007   0.008

   Father's occupation                                0.106    0.103    0.120   0.073   0.071   0.088   0.071    0.069   0.090
                                                      0.010    0.011    0.018   0.011   0.011   0.015   0.011    0.010   0.013

    Father's education     0.154     0.179    0.288   0.141    0.148    0.192   0.202   0.219   0.285   0.108    0.109   0.146   0.142   0.147   0.199
                           0.016     0.019    0.028   0.015    0.019    0.032   0.021   0.021   0.027   0.014    0.016   0.023   0.013   0.014   0.019

   Mother's education      0.166     0.194    0.314   0.186    0.193    0.244   0.256   0.279   0.353   0.167    0.177   0.241   0.204   0.222   0.306
                           0.014     0.018    0.028   0.015    0.019    0.032   0.019   0.021   0.028   0.017    0.020   0.029   0.014   0.017   0.025

            Birth region   0.040     0.032    0.035   0.040    0.044    0.058   0.102   0.103   0.137   0.096    0.096   0.127   0.109   0.124   0.195
                           0.011     0.016    0.033   0.010    0.014    0.025   0.014   0.015   0.020   0.015    0.016   0.023   0.013   0.019   0.038
Sampl: individuals 30-49 with positive household consumption and information on a set of circumstances; standard errors in italics; father’s occupation is
missing for Colombia and Peru.
6.                     Scalar Measures of Inequality of Opportunity (iv)

                                    Per capita household consumption
                          Total inequality and levels of inequality of opportunity

               0,500
               0,450
               0,400
                                                                    Total inequality
               0,350
E(0) indices




               0,300
               0,250                                                Inequality of opportunity
                                                                    (difference between non-
               0,200                                                parametric and parametric
               0,150                                                estimates)
                                                                    Inequality of opportunity
               0,100                                                (parametric estimate)
               0,050
               0,000
                        COL    ECU      GUA      PAN     PER
6.                     Scalar Measures of Inequality of Opportunity (v)


                         Earnings, per capita household income and consumption
                                       Parametric estimates for E(0)

               0.800

               0.700

               0.600
E(0) indices




               0.500                                                       Individual earnings

               0.400
                                                                           Per capita household
               0.300                                                       income

               0.200                                                       Per capita household
                                                                           consumption
               0.100

               0.000
                       BRA    COL     ECU     GUA      PAN     PER
 6.                 Scalar Measures of Inequality of Opportunity (v)


             Shares of  r in per capital household consumption Parametric
                                   P
                       IOp
                                    estimates for E(0)

 0.600

 0.500                                                                                                          Colombia
 0.400                                                                                                          Ecuador
                                                                                                                Guatemala
 0.300                                                                                                          Panama
 0.200                                                                                                          Peru

 0.100

 0.000




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  7.    Opportunity-deprivation profiles (i)

 “The rate of economic development should be
       taken to be the rate at which the mean
   advantage level of the worst-off types grows
  over time. […] I look forward to a future number
   of the WDR that carries out the computation,
     across countries, of this new definition of
          economic development” (p.243).

Roemer, John E. (2006): “Review Essay, „The 2006 world
 development report: Equity and development”, Journal of
           Economic Inequality (4): 233-244
            7.             Opportunity-deprivation profiles (ii)

        Roemer‟s types                 k : i i  k            are groups such that:


                                  C ik  C k  i  k , k  1,..., K

At least conceptually, it is not unreasonable to see Fk  y  as an individual i’s ( i  k ) opportunity set for outcome y. Given i’s
circumstances C ik , only i’s own choices, efforts and luck will determine his final position, p i  Fk  y i  .


     An opportunity profile is a ranking of types, in ascending order of some criterion
     to measure the opportunity sets.
     An appealing, but partial, ranking is provided by (first or second-order)
     stochastic dominance.
     For a complete ordering, need to rank Fk(y) by some other indicator, moment or
     quantile. We use the type‟s mean advantage.
     An opportunity-deprivation profile is a subset of the opportunity profile below
     some arbitrary threshold in the ranking criterion. We use p = 10%
          7.              Opportunity-deprivation profiles (iii)
                                The Brazilian profile, by income per capita
Least advantaged types by per capita income in Brazil (PNAD, 1996): adults aged 30-49.


                                Ethnicity                  Father's occupation       Father's education          Mother's education

Individual groups               black and mix-raced        agricultural worker       none or unknown             none or unknown
                                black and mix-raced        agricultural worker       upper primary (5) or more   none or unknown
                                black and mix-raced        agricultural worker       none or unknown             lower primary
                                black and mix-raced        agricultural worker       lower primary               none or unknown
                                black and mix-raced        agricultural worker       upper primary (5) or more   none or unknown
                                black and mix-raced        other                     none or unknown             none or unknown

10% most disadvantaged          100% black and mix-raced   88% agricultural worker   89% none or unknown         91% none or unknown
                                                                                     11% lower primary           9% lower primary
                                                           Share of national
Place of birth                  Estimated population       population                Mean outcome                Ratio of overall mean

Nordeste or North               2,276,662                  6.78%                     105.9                       26.1%
Sao Paulo or Federal District   1,417                      0.00%                     116.5                       28.7%
Nordeste or North               313,664                    0.93%                     136.6                       33.7%
Nordeste or North               352,729                    1.05%                     136.9                       33.8%
Nordeste or North               7,564                      0.02%                     144.2                       35.5%
Nordeste or North               2,063,415                  6.14%                     144.5                       35.6%

100% Nordeste or North          5,015,451                  14.9%                     116.8                       28.8%
7.              Opportunity-deprivation profiles (iv)
                                                Table 11: Opportunity-deprivation profiles

                                BRAZIL     COLOMBIA    ECUADOR      GUATEMALA    PANAMA      PERU

Member of ethnic minority       100.0        32.8        61.0         100.0       75.9       100.0

Father's agricultural            87.9                    93.4         99.9        83.5
occupation
Other father's occupation        12.1                     6.6          0.1        16.5

Father without education         89.2        76.6        86.9         99.4        58.0       99.8

Father's primary education       10.5        23.4        11.2          0.3        37.0        0.2

Father's secondary education     0.3         0.0          1.9          0.3         5.0        0.0
(or complete primary)

Mother without education         90.7        96.0        98.3         99.1        92.6       99.4

Mother's primary education       9.3         3.8          1.1          0.3         5.7        0.0

Mother's secondary education     0.0         0.2          0.6          0.6         1.7        0.6
(or complete primary)

Birth regions                  Northeast   Periphery   Coast and    North and     Rural      South
                               and North    (99%)        insular    Northwest     areas        and
                                (100%)                   (51%),      (99%)        (96%)      Coast
                                                       Sierra and                            (58%),
                                                       Amazonia                              inland
                                                          (48%)                              (42%)

Share of total outcome           2.9         5.0          4.4          3.5         2.7        4.8
        7.           Opportunity-deprivation profiles (v)
                                                          Table 12: Poverty profiles

                                 BRAZIL      COLOMBIA    ECUADOR      GUATEMALA      PANAMA    PERU

Member of ethnic minority         68.5         14.9        31.8          70.2         53.7      56.4

Father's agricultural             56.3                     80.0          75.7         80.3
occupation
Other father's occupation         43.7                     20.0          24.3         19.7

Father without education          77.2         57.8        55.0          90.1         66.8      59.9

Father's primary education        21.8         40.3        42.2           9.0         29.0      31.8
Father's secondary education
(or complete primary)             1.0          1.9          2.8           0.9         4.2       8.3

Mother without education          79.4         53.5        59.7          96.3         75.2      82.5

Mother's primary education        19.4         44.6        38.5           3.0         23.7      15.4

Mother's secondary education      1.1          1.9          1.8           0.7         1.1       2.2
(or complete primary)

Birth regions                   Northeast    Periphery   Sierra and     North and     Rural     Inland
                                and North     (65%),     Amazonia       Northwest     areas    (59%),
                                 (70%),       Center       (48%),     (49%), South   (91%),   South and
                               Southeast,     (34%)      Coast and     and Center     small     Coast
                                 Center-                   insular       (46%)        towns     (40%)
                               west, South                  (45%)                      (5%)
                                  (28%)

Share of total outcome            0.7          1.5          1.9           1.8         1.5       1.8
          7. Opportunity-deprivation profiles (vii):
                      Opportunity for education in Turkey

          Enrollment-age profiles for two extreme groups
 1




                                                                         The highly disadvantaged group
                                                                         encompasses girls in rural areas
.8




                                                                         of the East region whose mother
                                                                         has no education and is a non-
                                                                         Turkish native speaker living in a
.6




                                                                         household with six children or
                                                                         more; it encompasses 1.0% of the
.4




                                                                         population of 6-24 years-old. The
                                                                         highly advantaged group
                                                                         encompasses boys in urban areas
.2




                                                                         of the Center region whose
                                                                         mother has some education and is
                                                                         a Turkish native speaker living in a
 0




                                                                         household with one or two
      6     8    10      12     14         16   18    20     22     24
                                     age                                 children; it encompasses 2.5% of
                                                                         the population of 6-24 years-old
                  Disadvantaged group            Advantaged group




     Source: Ferreira and Gignoux, forthcoming.
  8. The Equitable Development Policy (i):

• At a general (and somewhat abstract) level, one could
  think of the equitable development policy problem as:

                               
                  max min  e  t  s   s ds
                                           T
                   t    T
                               t


                  subject to utiT  ut i, T , t

• The choice of policies from a feasible set so as to maximize the
  future stream of „advantage‟ for the most disadvantaged type,
  subject to a no-deprivation constraint and to a feasibility constraint.


    Source: Bourguignon, Ferreira and Walton, JEI 2007.
      8. The Equitable Development Policy (ii):

    • „Deconstructing‟ the equitable development policy
      problem:
                                                                     “Growth matters”
                            max min  e  t  s   s ds
                                                     T
                            t      T
                                          t


                        subject to utiT  ut i, T , t

Permissible Policy Set:                            Poverty eradication as a „constraint‟.
Technical feasibility and
  social acceptability
                                    “Rawlsian” criterion. All weight on the least advantaged.


        Source: Bourguignon, Ferreira and Walton, JEI 2007.
    8. The Equitable Development Policy (iii):

•     Questions for this course:
     1. Should economy-wide policies be assessed not
        only in terms of their impacts on (outcome) poverty
        and inequality, but also in terms of their impacts on
        inequality of opportunity?
     2. Might policies even be designed (and ex-ante
        evaluated) with a view to improving the welfare of
        the worst-off types?
     3. Dynamically, is there any evidence of inequality
        traps?
        –   Persistence of group-based disadvantage.
                9.        Concluding Remarks (i)

1.       If one adopts Roemer‟s definition of inequality of opportunity, it is
         possible to measure it empirically, for a given set of observed
         circumstance variables.
     •      All methods are essentially variants of a within/between-group
            decomposition, implemented either non-parametrically or
            parametrically.
     •      Parametric and non-parametric approaches are complementary: the
            former is less data intensive, and allows for partial analysis; the latter
            is more flexible (makes no functional form assumptions).
2.       In Latin America, inequality of economic opportunity:
     •      ranges from 17% to 34% for earnings.
     •      Ranges from 23% to 35% for income per capita.
     •      ranges from 24% to 50% for consumption per capita.
3.       Estimates are consistent with higher IOp for permanent income.
                9.       Concluding Remarks (ii)

4.       Family background variables (particularly mother‟s
         education) are the most important circumstance
         variables in LAC.
     •     But spatial differences and ethnicity are very important in
           Guatemala and Panama.

5.       Opportunity-deprivation profiles
     •     Rank types by mean outcome in opportunity set.
     •     Permit identifying key circumstance markers for deprivation
           and exclusion
     •     Different from poverty profiles
           •   And in an informative way (about “mobility at the bottom”, “traps”)
     •     In Latin America, ethnicity and family background are powerful
           predictors of opportunity-deprivation.

								
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