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Poverty, income distribution and CGE micro-simulation modeling: Does the functional form of distribution matter?

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An Erratum to this article was published on 29 March 2008

Abstract

This paper explores income distribution modeling approaches for poverty analysis in a CGE micro-simulation context. Income distribution functional forms such as the lognormal, Pareto, beta distribution and empirical methods are currently used in CGE models in parallel with the estimation of FGT poverty indices. The particular methods or functional forms used in this context are not always clearly defined and justified. In this paper, we investigate and provide better criteria for selecting a functional distribution for poverty analysis. To achieve this, we apply parametric estimation to seven functional forms and compare the results to a purely “empirical” method. The results showed that no single form is more appropriate in all instances or for all household subgroups. The choice of a modeling approach should be motivated by a search for best fit and should be based on appropriate statistical tests. Selecting inappropriate distributional forms can lead to biased results in terms of poverty analysis. Introducing functional forms in the empirical approach can also provide greater confidence in the results obtained.

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Abbreviations

AUE:

other urban educated

AUNE:

other-urban non-educated

BN:

basic needs

CDF:

cumulative density function

CES:

constant elasticity of substitution

CET:

constant elasticity of transformation

CGE:

computable general equilibrium

Champ:

Champernowne

DAD:

distributive analysis – analyse distributive

DAG:

Dagum

DKRE:

Dakar educated

DKRNE:

Dakar non-educated

E:

nominal exchange rate

EDF:

empirical distribution function

ESAM:

enquête sénégalaise auprès des ménages

FGT:

Foster, Greer and Thorbecke

Logn:

lognormal

Logn3:

displaced lognormal

OECD:

Organization for Economic Co-operation and Development

Pdf:

probability density function

r:

rental rate of capital

RE:

rural educated

RNE:

rural non-educated

w:

skilled wages;

SAE:

sum of absolute errors

SAM:

social accounting matrix

Sg:

government savings;

SM:

Singh–Maddala

wn:

unskilled wages;

SSE:

sum of squared errors

Yg:

Government income

Ytm:

total household income;

Va:

value-added

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Authors and Affiliations

  1. GRÉDI, Faculté d’administration, Université de Sherbrooke, 2500, boul. de l’Université, Sherbrooke (Québec), Canada, J1K 2R1

    Dorothée Boccanfuso & Luc Savard

  2. CIRPEE, Université Laval, J-A De Sève, Québec, Canada

    Bernard Decaluwé

Authors
  1. Dorothée Boccanfuso
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  2. Bernard Decaluwé
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  3. Luc Savard
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Correspondence to Luc Savard.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s10888-008-9084-1

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Boccanfuso, D., Decaluwé, B. & Savard, L. Poverty, income distribution and CGE micro-simulation modeling: Does the functional form of distribution matter?. J Econ Inequal 6, 149–184 (2008). https://doi.org/10.1007/s10888-007-9055-y

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