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Research articles · Jun 30, 2010

A non-parametric microsimulation approach to assess changes in inequality and poverty

Rob Vos, Marco V. Sánchez

United Nations Department of Economic and Social Affairs, United States

Volume 3(1) Spring 2010

Cite as: R. Vos, M. Sánchez; 2010; A non-parametric microsimulation approach to assess changes in inequality and poverty; International Journal of Microsimulation; 3(1); 8–23. doi: 10.34196/ijm.00021

Abstract

This paper presents a non-parametric microsimulation methodology for assessing the impact of labour market changes and government transfers on income inequality and poverty at the household level. The approach assumes that labour markets are segmented and determines (as part of a randomized process) which individuals are expected to move in or out of employment and which move from one employment segment to another based on either known or counterfactual information of aggregate labour market changes. The methodology assumes that the distribution of earnings of those who become employed in a particular segment resembles that of the individuals observed to be employed in that segment. The approach can be effectively combined in top-down fashion with static or dynamic computable general equilibrium (CGE) models, which typically provide insufficient information about household income distribution. The paper discusses the virtues and limitations of applying this methodology and further explains to practitioners how to implement it as a stand-alone methodology or in combination with a CGE model. It also shows how the methodology can be generalized to also capture the poverty and inequality effects of changes in non-labour incomes, such as government transfers. One great advantage of this method is that it is not very demanding in terms of modelling labour supply and household behaviour as compared with alternative parametric approaches, while at the same time providing a plausible link between changes in overall labour market conditions and the full household income distribution.

This article contains 6 tables.

Tables

Table 1. Real GDP growth and labour-market, poverty and inequality results in the baseline scenario for Costa Rica, 2008–2012.
  2008 2010 2012
Real GDP (at factor cost) 2.6 2.2 4.4
Total unemployment rate (%) 6.0 5.9 5.9
Employment (in thousands of workers) 1,958 2,035 2,115
Labour income per worker a 239,984 242,083 254,820
Total poverty incidence (% of population) b 20.7 19.5 16.5
Extreme poverty incidence (% of population) b 4.3 4.1 3.6
Gini coefficient for labour income 0.461 0.456 0.447
Gini coefficient for per-capita household income 0.497 0.490 0.478

Source: CGE model and microsimulation results for Costa Rica.

Real monthly labour income in colones, excluding social security contributions

Calculation based on a national poverty line.

Table 2. Real labour income of workers by skill, sex and occupational category, 2008–2012.
Real labour income per worker (colones) Relative remuneration (W1) a
2008 2010 2012 2008 2010 2012
Unskilled female workers
Wage earners 167,077 165,631 167,821 0.696 0.684 0.659
Non-wage earners 44,021 44,820 47,644 0.183 0.185 0.187
Unskilled male workers
Wage earners 243,219 258,632 293,963 1.013 1.068 1.154
Non-wage earners 160,052 166,719 190,108 0.667 0.689 0.746
Skilled female workers
Wage earners 396,095 388,726 389,430 1.651 1.606 1.528
Non-wage earners 90,935 84,048 81,142 0.379 0.347 0.318
Skilled male workers
Wage earners 390,806 383,148 384,967 1.628 1.583 1.511
Non-wage earners 165,769 155,686 158,703 0.691 0.643 0.623
Average labour income economy 239,984 242,083 254,820 1.000 1.000 1.000

Source: Baseline estimates of CGE model for Costa Rica.

Changes in relative remuneration are similar across sector of activity.

Table 3. Sequential and cumulative effects for changes in the labour-market parameters for the baseline scenario for Costa Ricaa, 2008–2012.
Total poverty incidence (% of population)b Extreme poverty incidence (% of population) b Gini coefficient for labour income Gini coefficient for per-capita household income
2008
U 20.7 4.3 0.461 0.497
U+S 20.7 4.3 0.461 0.497
U+S+O 20.7 4.3 0.461 0.497
U+S+O+W1 20.7 4.3 0.461 0.497
U+S+O+W1+W2 20.7 4.3 0.461 0.497
U+S+O+W1+W2+M 20.7 4.3 0.461 0.497
2010
U 20.6 4.3 0.461 0.497
U+S 20.6 4.3 0.461 0.497
U+S+O 20.6 4.3 0.461 0.497
U+S+O+W1 19.8 4.1 0.456 0.491
U+S+O+W1+W2 19.6 4.1 0.456 0.491
U+S+O+W1+W2+M 19.5 4.1 0.456 0.490
2012
U 20.5 4.2 0.461 0.497
U+S 20.5 4.2 0.461 0.497
U+S+O 20.4 4.2 0.461 0.496
U+S+O+W1 18.1 3.8 0.447 0.479
U+S+O+W1+W2 16.6 3.6 0.447 0.479
U+S+O+W1+W2+M 16.5 3.6 0.447 0.478

Source: CGE model and microsimulation results for Costa Rica.

Key: Simulations present cumulative effects of changes in: U = unemployment; S = employment by sector; O = employment by occupational category; W1 = relative remuneration per worker; W2 = mean real remuneration per worker; M = skill level.

Sequential and cumulative effects are presented for changes in the type of labour market variables as explained in the text.

Calculation based on a national poverty line.

Table 4. Macro-micro effects of simulating an increase in government cash transfers to poor households for Costa Ricaa, 2011–2012 (deviation with respect to the baseline). b
2011 2012
Gross capital formation −1.0 −1.0
Real GDP at market prices −0.2 −0.3
Government income −0.5 −0.6
Government expenditure 0.2 0.1
Fiscal deficit/GDP 0.1 0.1
Unemployment rate 0.1 0.2
Employment 0.0 0.0
 Wage-earners, unskilled women 0.2 0.2
 Non-wage earners, unskilled women 0.0 0.0
 Wage-earners, unskilled men 0.0 0.0
 Non-wage earners, unskilled men 0.0 0.0
 Wage-earners, skilled women −0.1 −0.2
 Non-wage-earners, skilled women 0.0 0.0
 Wage-earners, skilled men 0.1 0.0
 Non-wage earners, skilled men 0.0 0.0
Labour income −0.5 −0.6
 Wage-earners, unskilled women −0.4 −0.5
 Non-wage earners, unskilled women −1.2 −1.3
 Wage-earners, unskilled men −0.4 −0.5
 Non-wage earners, unskilled men −0.8 −0.9
 Wage-earners, skilled women −0.5 −0.6
 Non-wage-earners, skilled women −0.8 −1.0
 Wage-earners, skilled men −0.5 −0.5
 Non-wage earners, skilled men −0.5 −0.7

Source: CGE model and microsimulation results for Costa Rica.

The performed simulation is explained in the text.

Percentage deviation for all variables except for fiscal deficit for which results are presented in percentage points of GDP, and poverty and inequality for which absolute changes of the indicator are used.

Table 5. Decomposition of the poverty and inequality effect of simulating an increase in government cash transfers to poor households for Costa Rica, 2011–2012 (Absolute deviation with respect to the baseline).
2011 2012
Direct effect CGE effect Total effect Direct effect CGE effect Total effect
Total poverty incidence a −1.6 0.0 −1.6 −1.5 0.1 −1.4
Extreme poverty incidence a −0.9 0.1 −0.8 −0.9 0.2 −0.7
Gini – labour income 0.000 0.000 0.000 0.000 0.000 0.000
Gini – per-capita household income −0.004 0.000 −0.004 −0.003 0.000 −0.004

Source: CGE model and microsimulation results for Costa Rica.

Incidence as a percentage of the population. Calculation based on nationally defined poverty lines.

Table A.1. Sequential and cumulative effects and confidence intervals for changes in the labour-market parameters for the baseline scenario for Costa Ricaa, 2012.
Variable Obs. Mean Standard Error [95% Confidence Interval]
U
Total poverty incidence b 30 20.45249 0.00685 20.43850 20.46649
Extreme poverty incidence b 30 4.21342 0.00788 4.19730 4.22954
Gini – labour income 30 0.46117 0.00003 0.46111 0.46123
Gini – per-capita household income 30 0.49661 0.00003 0.49655 0.49668
U+S
Total poverty incidence b 30 20.48056 0.00658 20.46711 20.49402
Extreme poverty incidence b 30 4.22287 0.00857 4.20533 4.24040
Gini – labour income 30 0.46119 0.00005 0.46109 0.46129
Gini – per-capita household income 30 0.49662 0.00005 0.49652 0.49672
U+S+O
Total poverty incidence b 30 20.44773 0.00622 20.43501 20.46044
Extreme poverty incidence b 30 4.20806 0.00825 4.19119 4.22492
Gini – labour income 30 0.46098 0.00005 0.46088 0.46108
Gini – per-capita household income 30 0.49647 0.00005 0.49636 0.49657
U+S+O+Wa
Total poverty incidence b 30 18.12911 0.01060 18.10742 18.15079
Extreme poverty incidence b 30 3.76363 0.00698 3.74935 3.77790
Gini – labour income 30 0.44659 0.00005 0.44648 0.44669
Gini – per-capita household income 30 0.47856 0.00005 0.47845 0.47867
U+S+O+W1+W2
Total poverty incidence a 30 16.56747 0.01072 16.54556 16.58939
Extreme poverty incidence b 30 3.58093 0.00593 3.56881 3.59305
Gini – labour income 30 0.44667 0.00005 0.44656 0.44677
Gini – per-capita household income 30 0.47851 0.00005 0.47840 0.47862
U+S+O+W1+W2+M
Total poverty incidence b 30 16.52524 0.01902 16.48635 16.56413
Extreme poverty incidence b 30 3.59980 0.01135 3.57659 3.62302
Gini – labour income 30 0.44682 0.00007 0.44668 0.44696
Gini – per-capita household income 30 0.47790 0.00009 0.47771 0.47809

Source: CGE model and microsimulation results for Costa Rica.

Sequential and cumulative effects are presented for changes in the labour market variables as explained in the text.

The poverty incidence is expressed as a percentage of the total population and was estimated using nationally defined poverty lines.