FACTORS ASSOCIATED WITH POVERT Y:A COMPARATIVE STUDY OF

DETERMINANTS OF POVERTY IN BRAZIL AND KOREA

Ana Paula Gomes Matias – M 13089Professor Advisor: Choi Youseok

STATE OF THE PROBLEM• Brazil and South Korea have experienced rapid and

remarkable improvement in economic growth; however, social problems and social inequality also increased. Both countries present a high rate of relative poverty (World Bank, 1990, 2013; OECD, 2013)

• Hardly ever can we find a comparative study of poverty in countries with different historical background, as, for example, a country in East Asia and one South America (Coes, 2007).

PURPOSE OF THE STUDY AND RESEARCH QUESTIONOBJECTIVES:(1)Attempts to look at aspects of poverty to identify the profile of

poor households in Brazil and South Korea;(2)Examines the differences and similarities between Brazil and

South Korea regarding factors that determine poverty.

RESEARCH QUESTIONS:(1) What are the determinants of poverty in individual household

level in Brazil and Korea? (2) What are the differences and similarities of determinants of

poverty status in Brazil and Korea?

RESEARCH METHOD- An empirical quantitative analysis were made through two

data sets: the Brazilian household survey (PNAD 2012) and the Korean Social Welfare Panel (KoWePS 2012).

- Samples: Heads of households - Brazil sample, the total head of households sample are 110, 509 individuals and Korea sums 7,532 individuals.

RESEARCH METHOD• Dependent variable: “Poverty status” whether the head of

household is poor or non- poor• Independent variable: Demographic and socio economic

characteristics of households such as the householder’s gender, age, marriage status, number of family members, education level, work status, work sector (formal, informal or self employment), region (rural or urban and housing access

• Research analysis: Descriptive Analysis & Logistic Regression Analysis

RE

SU

LT

SCharacteristics/ HHKorea Regression coefficient Standard Error (S.E.)

Male (female) -.187* .095

Age (70 +)

15 – 29 -.378 .236

30 – 39 -.559*** .106

40 – 49 -.648*** .090

50 – 59 -.505*** .090

60 - 69 -.155 .088

Spouse (non-spouse) -.374*** .098

Education (high School)

Under high school .702*** .078

Over College .414*** .088

Working (not working) -3.577*** .155

Work sector (formal)

Informal 2.110*** .162

Self Employment 2.318*** .160

Family size -.411*** .034

Housing access (rented)

Owned dummy -.527*** .082

Ceded/others dummy .268*** .122

Urban (rural ) -.584*** .076

-2 Log likelihood 6625.748

*p<.05, **P<.01, ***p<.001The categories in parenthesis are reference.

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RE

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SCharacteristics/ HH Brazil Regression coefficient Standard Error

(S.E.)

Male ( female ) -.272*** .017

Age (70 +)

15 – 29 .111*** .032

30 – 39 .027 .029

40 – 49 .043 .029

50 – 59 .029 .029

60 - 69 .033 .032

Spouse (non-spouse ) .324*** .029

Education (high School)

Under high school .663*** .020

Over College -1.295*** .044

Working (not working) -.483*** .018

Work sector (formal)

Informal -.268*** .023

Self Employment -.099*** .019

Family size .544*** .006

Housing access (rented)

Owned -.261*** .022

Ceded/others .340*** .033

Urban (rural ) -.994*** .021

-2 Log likelihood 103237.344

*p<.05, **P<.01, ***p<.001The categories in parenthesis are reference.

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Brazil and South Korea signif icant results by Logistic regression analysis

BRAZIL

KOREA

• Gender – Male heads of households are less likely to be poor than female;

• Age – The category 15 – 29 years was the only significant and they are more likely to be poor comparing to people over 70 years old;

• Marital Status – People who are married are more likely to be poor than single householder´s

• Education - People who are high educated (over college) are less likely to be poor. On the other hand, people who have low level of education classified as under high school are more likely to be poor.

• Work - People who are working are less likely to be poor. Regarding the work sector, people who have informal and self-employed work comparing to formal are less likely to be poor (contradict result)

• Family size– Larger household’s size was found to have a higher probability of being poor

• Housing Access - People who have house that is ceded or in other condition are more likely to be poor than people who have their house rented. But, people who own a house are less poor than people who rent the house.

• Area - urban area comparing to rural area are less poor

• Gender - Male compared to female is less likely to be poor;

• Age – People who are in age between 30 to 39; 40 to 49 as well as 50 to 59 years old are less likely to be poor than people who are 70 years old or over;

• Marital Status – People who are marriage comparing to single people are less likely to be poor

• Education – The model shows that people who have a higher level of education are more likely to be poor (contradict result)

• Work – Heads of households who working are less likely to be poor than people who are not working. And people who are self-employed or have a informal work have a probability to be poor comparing to people who have formal work

• Family size – Large household’s size are less likely to poor comparing the small one’s.

• Housing Access – High probability of being poor people who live in a house ceded by someone or other unknown circumstances than people who owned a house. Householder´s who owned a house are less likely to be poor.

• Area – people who live in urban are comparing to rural area less poor.

CONCLUSIONDeterminants of Poverty similarit ies & differences

BRAZIL KOREA

Poor people live in big family size

Adolescent and young (20s) age are the poorest

High level of Low education among poor heads of households

High level of poor elderlypeople (over 70 age)

Poor people live in small family size

Female head of households

High level of working poor (informal workers)

High rate of unemploymentRural areas

Ana Paula G. Matias