Gender Differences in Poverty in Italy

UNIVERSITY OF PISA & SAN’T ANNA SCHOOL OF ADVANCED STUDIES

HWTK – UNIVERSITY OF APPLIED SCIENCES

MASTER OF SCIENCE IN ECONOMICS

DEPARTMENT OF ECONOMICS AND MANAGEMENT

Gender Differences in Poverty in Italy

Instructor

Professor Monica Pratesi

Supervisor

Professor Dr. Dr. Hermann Knoedler

Student

Linh Mai Vu

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Abstract

This study describes the predominance of women living in poverty in Italy across its 20 regions and investigates some key explanations of gender differences in 20 regions on the risk of becoming poor.

The matter is approached with two perspectives: individual characteristics (composition effects) and sub-national context. The former can inspect closely and thoroughly the causes leading to the prevalence of poor women but it ignores information on the characteristics of the region (contextual effects) that can influence the percentage of Italian men and women with low income differently. Thus by estimating the parameters and comparing the fit of the models, we evaluate the explanatory power of both types of variables.

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Table of contents

2. Poverty and the possible influential factors of gender differences in poverty ... 3

2.1. Poverty ... 3

2.2. Possible influential factors of gender differences in poverty ... 4

2.2.1. Micro-level factors ... 4 2.2.2. Macro-level factors ... 6 3. Empirical studies ... 6 4. Description of data ... 9 4.1. Explanatory variables ... 11 4.2. The model ... 12 5. Empirical results ... 17 6. Conclusion ... 26 References ... 28

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List of figures

Figure 1 At-risk-of-poverty rate of single households by gender in 2015, Italy...11

Figure 2 GDP per capita by regions in 2015, Italy (thousands of euros)...17

Figure 3 Regional poverty rate in 2015, Italy ...18

Figure 4 Percentage of poor single women and men by regions in 2015, Italy ...18

List of tables

Table 2 Odds ratios of model A and B ... 19

Table 3 Odds ratios of model C and D ... 21

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Table of abbreviation

EU : European Union

EU-SILC : European Union Statistics on Income and Living Conditions

GDP : Gross Domestic Product

ILO : International Labor Organization ISTAT : Italian National Institute of Statistics

ISCED : International Standard Classification of Education

LIS : Luxembourg Income Study

SCIP : Social Citizenship Indicator Program

UN : United Nations

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1. Introduction

As economies around the world undergo major and challenging changes, poverty has increasingly become a problem in almost every country. Economic research related to this topic lays forward that poverty is not gender neutral. Feminization of poverty is rising as a worrisome aspect of poverty. Persisting difference of income between men and women after the Second World War was first recognized in the US (Pearce, 1978) and recently became a prevailing issue of a lot of societies. In some countries, men and women share almost equal possibility to be poor; and in other countries such as Great Britain and Ireland, it is men who has to face with a higher poverty risk than women (Pressmen, 1998). Nevertheless, more women are likely to be poor than men in many countries (Wright, 1995).

Larger multinational incidence of poverty for women than for men is owing to the discrimination against women in education, employment and wages. According to Carlos Gradin, Coral del Rio, and Olga Canto (2010), among European countries, women are paid less in spite of having the same qualifications and same paid working hours as men; women are divided into low-income occupations; women spend more time in unpaid caregiving than men; and pregnancy influences career as well as educational opportunities of women more than men’s. It is also pointed out that this phenomenon is occurred due to an inadequate social welfare system regarding family needs (Albelda, 1999). Therefore, we can say that the gendered impact of poverty is a result of not only the individual characteristics but also of the lack of capabilities and gender biases present in societies and governments.

From 2009 to 2016, the percentage of people whose disposable income falls below their national at-risk-of-poverty threshold in Italy has been gradually increasing, varying between 18.4 and 20.6 percent (Eurostat, 2018). It is 2 to 3 percent higher than the average of 27 countries in the EU (Eurostat, 2018). An Italian female employee in 2015 earns 5.5 percent less than an Italian male employee, this is considered markedly lower than the gender wage gap of EU 27’s average of 16.4 percent (Eurostat, 2016). However, the fact that Italy has a low rate of women in employment, 50.6 percent in the same year (Eurostat, 2016), is one of the reasons the overall picture of gender pay gap is more positive (Olivetti and Petrongolo, 2008).

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Furthermore, among 27 European countries, Italy reports a high rate of poverty risk for women at active age with dependent children of 24 percent which is relatively higher than the average 19 percent of EU-27 (Lancker, 2015). In general, the rate of poor women remains higher than that of men and gradually increases in Italy from 2012 to 2015 (Eurostat, 2018).

This thesis describes the predominance of women living in poverty in Italy across its 20 regions and investigates some key explanations of gender differences in 20 regions on the risk of becoming poor.

Why do women tend to be poor more than men in Italy? This question is approached with two potential answers: individual characteristics and macro-level context. The personal characteristics (compositional effects) can inspect closely and thoroughly the causes leading to the prevalence of poor women but it ignores information on the characteristics of the region (contextual effects) that can influence the percentage of Italian men and women with low income differently. Thus examining feminization of poverty in Italy with both perspectives might provide a more profound explanation for the differences in the effect of gender on poverty in Italy. Besides the aim of checking the effect of factors such as education level, age, employment status and household structure on the rate of poverty by gender, this thesis is also carried out with the purpose of reviewing the possible effect of macro level factors as well. Along with social benefits and empowerment, urbanization is chosen for analyzation of its impact on the proportion of Italian poor women. As addressed in the report Poverty and Social Exclusion in Rural Areas (2008) studying fifteen European countries including Italy, women belong to the groups that are most vulnerable to rural poverty. Italy also has the lowest women employment rates in predominantly rural areas. Despite acting as an important aspect of European poverty in particular and of the world in general, rural areas have been neglected in existing poverty studies of developed countries. Urbanization thereby acts in this paper as one of the key factors to help investigate the question whether or not the types of area in which Italian women are living have an impact on their exposure to poverty.

The study is making use of data withdrawn from the European Union Statistics on Income and Living Conditions (EU-SILC) of Italy, carried out by Italian National Institute of Statistics (ISTAT). It focuses on households with only one adult who do not need to be literally single but can also be widows or widowers or divorcees and

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regardless of living with children or not. The main reasons for the study to target only single-adult households are firstly, researches on poverty commonly examine incomes on household level but it is not fully clear to know how economic resources are distributed to each household member. Majority of poverty studies accordingly assume that all household members are equally poor due to the difficulties in specifying poor and non-poor individuals within one household. Secondly, gender is a personal trait, thus studying households with both a man and a woman does not contribute to poverty-gender-gap since there exists a difference of levels (individual and household) between gender and poverty.

2. Poverty and the possible influential factors of gender

differences in poverty

2.1. Poverty

According to World Bank (2000), “poverty is pronounced deprivation in well-being”. The main measure of poverty included in the EU list of indicators is known as the “at-risk-of-poverty” rate. The at-risk-of-poverty rate (or poverty rate) is the share of people with an equivalized disposable income below the at-risk-of-poverty threshold. This indicator is conceived of as relative to a country (Bradshaw and Mayhew, 2011, page 6). In the scope of this thesis which deals with Italy, the poverty threshold defined by European Commission has been fixed as 60 percent of national median net income. Gender is used as a fundamental criterion to divide the labor force in most societies: the “productive” and “reproductive” activities (Cagatay, 1998). Productive activities refer to activities that generate earnings while reproductive activities refer to activities concerning the care and development of people. The latter can vary from looking after children, the sick and elders, cooking, cleaning to grocery shopping and clothes amending. In most societies, they are unpaid and performed predominantly by women, contrary to compensated productive activities carried out mostly by men. Feminist economists have used gender as a category of analysis especially at micro level to criticize conventional economic approach and to construct

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a new method of economics study with the ultimate purpose of creating policies that reduce gender discrimination. Gender approach thus can be integrated in poverty study to combat poverty.

2.2. Possible influential factors of gender differences in

poverty

Poverty continues to be one of the most problematic arena of statistics in general, and for gender statistics in particular. Disparities in poverty stems from inequality in access to economic resources. In the report of UN in 2015 – The World’s Women, women are less likely to get employed than men, and when women are hired, their jobs are usually associated with irregular low earnings or no income at all. Due to the unequal division of paid and unpaid jobs, women share a lower fraction than men in having their own cash income. The average gender wage gap in a global scale is 23 percent (ILO, 2016) and 700 million more men than women are in paid work (UN, 2015). These facts and numbers clearly show an urging situation that requires the trace to the causes of feminization of poverty as well as incorporating new insights into the relationship between gender and poverty. In the scope of this thesis, several factors of individual (micro) and national or sub-national (macro) levels have been considered to explain why household headed by women facing a larger poverty risk than household headed by men in Italy.

2.2.1. Micro-level factors

▪ Human capital

From the theory of human capital of Gary Becker (1964), it can be withdrawn that women generally expose to greater risk of poverty than men. This theory focuses on investment in human capital, particularly in education and training, from the data analyzed, people with higher education and skilled earn more than others. Often due to maternity, women’s career or education are interrupted, they thus build up human capital less than men. Meanwhile, employers are less willing to invest in training for women for the same reason. As obtaining human capital leads to better job and financial security, we can assume that women are at higher risk of poverty than men.

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Demographic aspect (mostly age) can be a factor of poverty if it is an important factor determining the likelihood of being unemployed. Those age groups which are more vulnerable to unemployment are more vulnerable to enter poverty. Older singles are mainly women, due to the difference in life expectancy and the inclination of women to marry older men. Gender differences in poverty tend to be larger among older singles for various reasons, from depreciation of human capital rate to shorter employment period, which results in less stimulus for employers to invest in their skills (Kyzyma, 2013).

▪ Household structure

Household structure is another micro factor that may affect the rate of poverty risk of one individual. Some features of parenthood lead to lower income for women; which are firstly, mothers will care of children, an activity that takes away opportunities to paid or highly paid jobs that are time-demanding ones (Wiepking and Mass, 2005); lastly families headed by a single mother are likely to have only one adult earner and therefore face not only reduced income, but also increased income risk (Pressman, 2003).

▪ Employment status

Employment status is connected to children/ household structure hypothesis, mainly because of looking after the children, women are more likely than men not to have paid work or to be excluded from higher-paying occupations.

Gender differences in poverty tend to be larger among singles with unpaid jobs, who have to take care of children, do not attain a bachelor or higher degree and older. Household structure correlates with employment status and human capital through parenthood or maternity. Single women more often take care of children than single men. Having to take care of children, women are more likely than men to not have paid work or earn less income since upbringing children requires a large amount of time, single women are more likely to not work or to have part-time jobs which clearly compensate less. Being pregnant or raising a child also in some cases prevents women from building up more human capital due to its interruption during their education or career. Age also partly determines human capital with its effect, older people have higher depreciation of human capital rate, thus more vulnerable to unemployment and poverty.

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2.2.2. Macro-level factors

▪ Social protection

Combating poverty also depends on social protection. A critical factor in the poverty of women with low-income is the complexion of the state, including the welfare regime, and the type of social policies available for women, children and families. West and North European countries, with their more encompassing welfare regimes, have fended off the extent of the feminization of poverty that exists in the US (Casper, McLanahan & Garfinkel, 1994). In this paper’s extent, the importance of minimum income support within social protection is stressed. Minimum income schemes are comprehended as important in fighting poverty, for it provides a decent minimum standard of living for individuals and their dependents who do not have access to other or adequate financial resources.

▪ Urbanization

The different features of the areas where people live such as land, water, climate and infrastructure associate with standards of living, which is usually better in urban areas. Besides higher life expectancy, higher level of literacy, better supply of basic services, urban centres can also provide better capacity to meet with large population and economic activities through technology innovation and more efficient use of natural resources (Tacoli, 2012). More women engage in paid work in the cities and also are offered a wider range of employment opportunities than in rural areas. Urbanization thus can act as a sub-national factor influencing gender differences in poverty.

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Empirical studies

One of the first studies published with a micro-level perspective was by Timothy, Rainwater, Reid, Hauser and Schaber (1990). Their study did not concentrate exactly on gender differences in poverty, it instead focused on differences in income

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poverty among seven countries, however some things can still be learned from their research. They analyzed single-parent households which are largely female-headed and found that social welfare system fail to alleviate these single-parent families from poverty. In view of poverty and its gender impact, there are some studies analyzing and comparing the mentioned issue among Italy and different western countries. Lynne, Sara and Garfinkel (1994), using LIS data for eight industrialized countries including Italy, concluded that demographic characteristics (such as employment and parenthood) play an important role in determining the differences between two genders in poverty rates within as well as across the countries studied. Regarding to the specific case of Italy, it is shown that marital status is a factor accounting for gender differences in poverty. A low gender-poverty ratio is achieved by increasing the prevalence of marriage. However, the assumption that income is shared equally within one household must be strongly hold, if not, women in reality may be poorer than men. Wright (1993), who after studying eleven industrialized countries, found that women are over-represented amongst the poor in some countries and under-represented in other countries. Among these countries, Italy continues to be chosen for his further study in 1996 since it represents one of the two ends of extreme positions where women are under-represented amongst the poor. The statement that female-headed households suffer comparatively greatly from poverty than other households is strengthened in a research on developed and transitional economies by Pressman (1998, 2002, 2003). He also suggested that country specific tax and social security measures influence gender differences in poverty.

Another stream of study analyzed poverty with contextual perspective. Walter and Joakim (1998)’s study which exploits two sets of data LIS and SCIP advocates the superior capacity of encompassing welfare state to reduce inequality and poverty. That countries with provisions of social welfare generosity encounter lower risk of poverty was concluded in a study of 22 countries by Tai and Treas (2008). Main finding of the research from Callens, Croux and Avramov (2009) reaffirms mentioned argument when stating that welfare regime is highly significant for poverty entry. Although there is much literature on poverty and its gender differences, most focus on cross-national comparison, those that zoom in specific economy mainly disperse around developing countries. As having seen an increase of the gender pay gap in Italy during 2008 – 2012 which is opposite to EU-wide tendency of decreasing gender pay gap, Piazzalunga and Laura di Tommaso (2015) published a paper

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examining this negative trend. They found out that since more women than men working in public sector, the wage freeze in public sector as an austerity measure contributed greatly to the growth of gender pay gap in Italy during economic crisis period. These studies demonstrate that macro factors relatively affect probability of poverty.

With respect to research combining both micro and macro aspects, which are rather scarce, Wiepking and Maas (2005) find that country-level characteristics somewhat explain the differences in the gender poverty gap more than composition effects. Callens, Croux and Avramov (2009) used discrete-time recurrent hazard model to obtain the result that personal traits such as employment are more important for men but demographic events are only important for women in poverty transitions. The welfare regime and GDP growth affect both genders’ poverty entry but only women’s poverty exit. Recently, Bárcena-Martín and Moro-Egido (2013) carried out a research integrating personal and structure context dimensions to explain differences in poverty outcomes by gender, they concluded that macro-level effects such as welfare state policies, labor market characteristics, level of inequality and the level of women’s empowerment in the country seem to account for more than individual effects in interpreting the differences of gender in poverty.

Compared to existing research, this thesis focuses on the situation of one specific country – Italy, the personal characteristics thus could be better analyzed to provide a more exact result whether it greatly or insignificantly affect the gender outcomes of poverty. Furthermore, according to Dietz (2014), urbanization is one cause of poverty, the macro factor urbanization therefore is added besides the other contextual effects such as labor market flexibility and social protection which have been studied by various authors before. New insights are analyzed with a hope of providing a better picture of the gender differences in poverty, specifically in Italy.

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4.

Description of data

The analysis is carried out using data set for 2015 from EU-SILC of Italy, of which the survey had been conducted by ISTAT (Italian National Institute of Statistics). EU-SILC is a cross-sectional and longitudinal sample survey, coordinated by Eurostat (statistical office of the EU). This instrument provides comparable cross-sectional data with variables on income, poverty, social exclusion and other living conditions in the EU. The data set’s focus is on income, of which components are detailed and are collected at personal level. The total number of individuals in the data set is 36602, total number of households in the data set is 17985. All households in which the head of the household did not have a partner is 6849 across 20 regions. Table 1 shows that in all 20 regions of Italy, single women account for more than half of all single-headed households.

Table 1 Number of single-adult households

Regions No. of obs. % Women

EMI 482 63.07% FRI 463 62.63% LIG 440 64.09% LOM 651 62.21% PIE 541 60.99% TRE 251 55.37% VAL 123 55.28% VEN 472 62.07% MAR 365 64.93% LAZ 614 65.47% TOS 446 64.79% UMB 220 65.00% ABR 156 65.38% BAS 125 63.20% CAL 228 70.61% CAM 395 69.11% MOL 90 62.22%

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PUG 315 68.88%

SAR 157 64.96%

SIC 315 65.39%

Total 6849 63.92%

Notes: ABR = Abruzzo; BAS = Basilicata; CAL = Calabria; CAM = Campania; EMI = Emilia-Romagna; FRI = Friuli-Venezia Giulia; LAZ = Lazio; LIG = Liguria; LOM = Lombardia; MAR = Marche; MOL = Molise; PIE = Piemonte; PUG = Puglia; SAR = Sardegna; SIC = Sicilia; TOS = Toscana; TRE = Trentino-Alto Adige; UMB = Umbria; VAL – Valle D’Aosta; VEN = Veneto.

In each country, poverty is defined based on the poverty line which is drawn differently among countries and over time. Therefore, in this study, a relative definition of poverty is adopted and applied suitably with the purpose of the research. One individual is considered to be poor when his or her adjusted household income is below 60 percent of the median equivalized disposable income (at-risk-of-poverty threshold) of all households in their corresponding region in Italy. The OECD-modified scale is used to calculate the equivalized income for the composition of the household as below:

𝐼 = 𝐻

1 × 𝐻𝑒𝑎𝑑 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 + 0.5 × 𝑁𝑜. 𝑜𝑓 𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑑𝑢𝑙𝑡 + 0.3 × 𝑁𝑜. 𝑜𝑓 𝑐ℎ𝑖𝑙𝑑𝑟𝑒𝑛

where I = equivalised disposable income, H = total household net income, the modified OECD scale gives a weight of 1.0 to the first adult, 0.5 to other household members who aged 14 or over and 0.3 to each child aged under 14.

Figure 1 shows the poverty rate (at-risk-of-poverty rate) of single households by gender in Italy. The at-risk-of-poverty rate is calculated as the percentage of persons with an equivalized net total income below the at-risk-of-poverty threshold of each regions, exploiting the above equivalized scale.

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Figure 1 At-risk-of-poverty rate of single households by gender in 2015, Italy

It is found that 15.6 percent of all single men are poor, against 20.9 percent of all single women.

4.1. Explanatory variables

The key variable in this study is gender (dummy variable), which is coded as 1 if the adult in the household is a woman, and 0 if the adult is a man, men is thus the default group. Women outnumbered men in the data set because of the over-representation of older single women, which possibly due to the longer life expectancy of women.

In term of micro-level analysis, these following variables are chosen. To indicate the Human Capital factor, Education variable is coded into classes based on different attained ISCED level of education: 0 – Less than primary education; 1 – Primary education; 2 – Lower secondary education; 3 – Upper secondary education; 4 – Post-secondary non-tertiary education; 5 – Tertiary education (which includes short cycle tertiary, bachelor or equivalent, master or equivalent and doctorate or equivalent).

For Age variable, three age groups are calculated by 2015 – the year the survey was taken and categorized as following: under 30 years old, between 30 and 65 years old

20.9 15.6

19.5

0 5 10 15 20 25

At-risk-of-poverty rate of single households by gender,

Italy

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and over 65 years old. In this data set, there are only 39 people who are under 25 years old, we therefore set 𝑦𝑜𝑢𝑛𝑔 variable for people under 30 and 𝑜𝑙𝑑 variable for people over 65 which is the retirement age in Italy. The age reference group includes individuals between 30 and 65 years of age. It was found out that 51.7 percent of young singles who are under 30 years old are women, 55.7 percent of middle-aged singles are women while 74.8 percent women account for older singles. It is clearly that due to life expectancy, majority of people over 65 are women.

For Household Structure hypothesis, variable Children is coded as 1 for individuals taking care of children under 18 and coded 0 otherwise. Having children is more common among single women (10.2 percent) than among single men (2.6 percent).

Work is the variable to test Employ Status hypothesis. Work is coded as 1 for those who have a paid job and 0 for those who do not. In 2015, 55 percent of Italian single men managed to have compensation for work, compared with only 34.1 percent of single women.

Similarly, to incorporate the sub-national characteristics as afore described in the macro-level effects, the following variables are considered as explanatory factors of gender differences in poverty by regions. To cover the Social Protection hypothesis, the analysis uses an indicator to demonstrate the presence of the minimum income scheme in certain regions of Italy. Since in Italy, a nation-wide minimum income (MI) does not exist, but there are 8 MI schemes managed by the concerned regional government in 8 out of 20 regions (Frazer and Marlier, 2016).

Regarding Urbanization hypothesis, a variable provided by Eurostat and illustrating the degree of urbanization (DEGURBA) is included. It classifies the degrees of urbanization of the area where the respondent’s household belongs into 3 groups and coded as following: 1 – Densely-populated area (cities), 2 – Intermediate area (towns and suburbs) and 3 – Thinly-populated area (rural areas). DEGURBA creates a classification of areas using a criterion of geographical borders with a minimum population threshold based on population grid square cells of 1 km (EUROSTAT, 2015).

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The personal risk of being poor is our dependent variable, which is considered as binary (poor or not poor). To represent this categorical outcome, we use dummy variable. The logistic regression model thus is used to estimate this type of variables.

Let 𝑦𝑖𝑟 denote the poverty outcome of an individual i (from hereafter individual i is referred as the head of a single household) living in region r, and 𝑥𝑖𝑟 be an explanatory variable. The simple linear regression equation with dependent variable 𝑦𝑖𝑟 and explanatory variable 𝑥𝑖𝑟 is as followed:

𝑦𝑖𝑟 = 𝛽0+ 𝛽1× 𝑥𝑖𝑟

Since in logistic regression, the probability of the outcome dependent variable is concerned, probability of individual i being poor is 𝑃 (𝑃𝑝𝑜𝑜𝑟) and probability of individual i being not poor is 1 − 𝑃𝑝𝑜𝑜𝑟 (0 ≤ 𝑃 ≤ 1). Then

𝑃𝑝𝑜𝑜𝑟 = Pr(𝑦𝑖𝑟 = 1), 𝑦𝑖𝑐 being 1 if person i in region r is poor. In order to satisfy the condition that probability 𝑃 is positive and less than or equals to 1, the linear equation is put under the exponential form and divided by a number greater than 𝑃, the equation becomes:

𝑃 = exp(𝛽0+ 𝛽1× 𝑥𝑖𝑟) 1 + exp(𝛽0+ 𝛽1× 𝑥𝑖𝑟) = ℯ (𝛽0+𝛽1×𝑥𝑖𝑟) 1 + ℯ(𝛽0+𝛽1×𝑥𝑖𝑟)= ℯ𝑦𝑖𝑟 1 + ℯ𝑦𝑖𝑟 (1)

Probability of individual i being not poor is:

1 − 𝑃 = 1 − ( ℯ

𝑦𝑖𝑟

1 + ℯ𝑦𝑖𝑟) (2)

Dividing (1) by (2) and take log (natural logarithm) on both sides, we get:

log(𝑃 1 − 𝑃⁄ = 𝑦𝑖𝑟) = 𝛽0+ 𝛽1× 𝑥𝑖𝑟

The ratio 𝑃𝑝𝑜𝑜𝑟⁄1 − 𝑃𝑝𝑜𝑜𝑟 is the odds ratio of the probability of being poor over the probability of being not poor. The equation is the binomial logistic regression to be used through this study. However, our research question focuses on the differences between individual level effect under regional factors which is at a higher level, this question clearly imposes a multilevel analyzation. It requires the intercept and/or the coefficients (slopes) to vary randomly. Six logistical regression models are therefore estimated to answer the question.

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The first two models are used to examine whether there are actually differences among regions with respect to the effect of gender on the risk of poverty. Model A includes the explanatory dummy variable – Women to indicate the general effect of gender and the region dummies to examine the region differences in the level of poverty:

log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟

⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑒𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 (A)

The effect of gender is expressed through parameter 𝛽1 and general regional differences in the level of poverty are expressed through 𝛽2. 𝛽0 is the log odds of poverty for male living in Sardegna since men living in Sardegna is the reference group. Besides being an island with low population density, Sardegna is chosen as the reference group since according to ISTAT Regional Accounts report in 2015, it is one of the six regions with the lowest GDP per capita rate at market prices. With model A, we can investigate whether the poverty risk level varies over the regions by keeping variable Women constant and whether the difference in term of gender affect the risk of being poor by keeping variable Region at a fixed value. In model B, we add an interaction term of two predictor variables Women and Region with coefficient 𝛽3 with an attempt to see their effect on each other, in other words whether the effect of gender varies over the regions.

log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟

⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑒𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽3× 𝑊𝑜𝑚𝑒𝑛 × 𝑅𝑒𝑔𝑖𝑜𝑛 (B)

By investigating these two models, we will know whether model B with interaction variable fits the data better or model A without does, if model B fits the data better, we can learn which regions correspond to larger gender differences.

In the following step, model C and D are proposed to analyze whether the region differences with respect to the gender effect can be explained by differences among regions in the composition of its habitants. In model C, individual level variables from the hypotheses are added to model A and the same variables are added to model B to create model D. Model D shows the differences between regions in term of gender effect on poverty, taking into account the composition effect (micro-level effect). By simply comparing 𝛽3 effects in model B and model D, we can have a grasp of the extent to which the region differences in gender effect on poverty are explained by composition factors.

15 log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟 ⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑒𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽4× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽5× 𝑦𝑜𝑢𝑛𝑔 + 𝛽6× 𝑜𝑙𝑑 + 𝛽7× 𝑊𝑜𝑟𝑘 + 𝛽8× 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛 (C) log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟 ⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑒𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽3× 𝑊𝑜𝑚𝑒𝑛 × 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽4× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽5× 𝑦𝑜𝑢𝑛𝑔 + 𝛽6× 𝑜𝑙𝑑 + 𝛽7× 𝑊𝑜𝑟𝑘 + 𝛽8× 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛 (D)

However, the main purpose of this thesis is to get knowledge of the key explanations (composition and context variables) for the gender poverty gap in 20 Italian regions. Thus by comparing the models’ likelihood ratio chi-squared indicator which demonstrates the fit of models, we can learn about the explanatory power of the micro-level variables. If the region differences in poverty rate can be totally explained by the compositional variables (being a woman or a man is indifferent), model D should not fit the data better than model C (the variable added 𝑊𝑜𝑚𝑒𝑛 × 𝑅𝑒𝑔𝑖𝑜𝑛 is not statistically significant), their likelihood ratio chi-squared (from hereafter being denoted as 𝐶ℎ𝑖𝑠𝑞) should be approximately equal. If 𝐶ℎ𝑖𝑠𝑞𝐷 is larger than 𝐶ℎ𝑖𝑠𝑞𝐶, the micro-level variables affect the region differences in gender poverty gap. Nevertheless, if 𝐶ℎ𝑖𝑠𝑞𝐷 is larger than 𝐶ℎ𝑖𝑠𝑞𝐶 but the gain in fit between model D and C are as large as the gain in fit between model B and A, then model D and B fit the data better thanks only to the interaction variable. Therefore, the explanatory power of the composition factors can be calculated as the difference between the gain in fit of model B and A and model D and C. More specifically, 𝐶ℎ𝑖𝑠𝑞 of each model is calculated as the difference between the null deviance (deviance of the model without predictors) and the residual deviance. Since 𝐶ℎ𝑖𝑠𝑞 is the “distance” between two models, the further apart the two models are, the more likely the variable added last is to be statistically significant. This is the reason why the 𝐶ℎ𝑖𝑠𝑞 of a certain model in this study will be higher should that model have more independent variables. The explanatory power of the compositional variables as a result can be expressed as a percentage as the followed formula:

16

(𝐶ℎ𝑖𝑠𝑞_𝐵− 𝐶ℎ𝑖𝑠𝑞𝑠𝐴) − (𝐶ℎ𝑖𝑠𝑞𝐷− 𝐶ℎ𝑖𝑠𝑞𝐶) 𝐶ℎ𝑖𝑠𝑞𝑠𝐵− 𝐶ℎ𝑖𝑠𝑞𝐴

With an aim to also examine which individual-level variable is the most important, we use model C and D, each time estimating with only one of the compositional predictor. The explanatory power of each variable is calculated in the same way with the explanatory power of all individual-level variables together.

Regarding the contextual variables, we use model E and model F to estimate their effects. We test their effects with and without taking compositional effects into account. Model F corresponds to the former and model E corresponds to the latter. The sub-national level variables are built as interactions between the region characteristics and being a woman. These interaction variables demonstrate whether women profit more (or less) than men from the regional characteristics. The characteristics are not modeled separately because they are fully covered by the region dummies. Below are model E and F:

log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟 ⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑎𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽9× 𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛 × 𝑊𝑜𝑚𝑎𝑛 + 𝛽10× 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛 × 𝑊𝑜𝑚𝑎𝑛 (𝐸) log (𝑃𝑝𝑜𝑜𝑟 _{1 − 𝑃} 𝑝𝑜𝑜𝑟 ⁄ ) = 𝛽0+ 𝛽1× 𝑊𝑜𝑚𝑎𝑛 + 𝛽2× 𝑅𝑒𝑔𝑖𝑜𝑛 + 𝛽4× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + 𝛽5× 𝑦𝑜𝑢𝑛𝑔 + 𝛽6× 𝑜𝑙𝑑 + 𝛽7× 𝑊𝑜𝑟𝑘 + 𝛽8× 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛 + 𝛽9× 𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛 × 𝑊𝑜𝑚𝑎𝑛 + 𝛽10× 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛 × 𝑊𝑜𝑚𝑎𝑛 (𝐹)

Similar to compositional effects, we use 𝐶ℎ𝑖𝑠𝑞 indicator to estimate the explanation extent of contextual variables. Investigating explanatory power of the regional effects is simpler than that of the compositional effects. The explanatory power of region characteristics expressed through interaction variables can be linearly estimated by the difference of two models’ 𝐶ℎ𝑖𝑠𝑞. As mentioned before, since the main effects of sub-country variables are covered by the region dummies, the explanatory power of regional interaction variables are obtained by dividing the difference in fit between model E and A by gain in fit between model B and A. The below equations are used

17

respectively to calculate the extent of the contextual effects before and after taking into account the composition variables:

𝐶ℎ𝑖𝑠𝑞𝐸− 𝐶ℎ𝑖𝑠𝑞𝐴 𝐶ℎ𝑖𝑠𝑞𝐵− 𝐶ℎ𝑖𝑠𝑞𝐴

𝐶ℎ𝑖𝑠𝑞𝐹− 𝐶ℎ𝑖𝑠𝑞𝐶 𝐶ℎ𝑖𝑠𝑞𝐷− 𝐶ℎ𝑖𝑠𝑞𝐶

5.

Empirical results

Before analyzing the results of six proposed models, an overall picture of poverty in Italy is provided by the below figures. Figure 2 illustrates the GDP by regions. Figure 3 presents poverty rate of each regions, sorted from the lowest to the highest percentage. Figure 4 shows the percentages of poor single women and men in 20 regions in Italy. They are ordered based on their gender differences in poverty, from smallest to largest.

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Figure 3 Regional poverty rate in 2015, Italy

Figure 4 Percentage of poor single women and men by regions in 2015, Italy

It is easily recognized that northern and middle Italy regions have higher GDP per capita and lower poverty rates than regions in the south. The differences in poverty rate among regions obviously exist and the rate reflects a geographic pattern of increasing from north to south. However, Figure 4 shows that the regional gender

0 5 10 15 20 25 % p o ve rty Regions

Regional poverty rate

Poverty rate 0 5 10 15 20 25 % p o o r Regions

Percentage of poor single women and men by region

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differences in poverty do not correspond to the found tendency of poverty rate throughout the country. It even displays a slightly opposite trend when majority of southern regions appear to have smaller gender poverty gap and northern regions are among the ones with the bigger gap. We can also notice that in all 20 regions, the rate of poor single women is higher than that of single men and there is no exception. Valle D’Aosta (a northern region) has the lowest poverty rate and the smallest gender difference in poverty. In this region, 7.28 per cent of the households having income under the at-risk-of-poverty threshold and the risk of single women being poor is just 0.8 per cent higher than for men. Lazio (a southern region) has the fourth highest poverty rate of 19.75 per cent and a gender difference of 14.5 per cent. Meanwhile, a region above the middle of Italy, which is Emilia-Romagna, has a lower than average poverty rate of 12.5 per cent, yet has the second biggest gender poverty gap of 11.8 per cent.

After having an overall look at the descriptive statistics, we will interpret the results from the six models estimated before to evaluate the extent of explanation of the compositional and contextual variables. Below are the estimation results of the first two models, converted into odds ratios for an easier interpretation.

Table 2 Odds ratios of model A and B

Model A Model B

Odds ratio Odds ratio

(𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡) 0.29 ∗∗∗ 0.34 ∗∗∗ 𝑊𝑜𝑚𝑒𝑛 1.42 ∗∗∗ 1.11 𝑃𝑖𝑒𝑚𝑜𝑛𝑡𝑒 0.56 ∗∗ 0.52 ∙ 𝑉𝑎𝑙𝑙𝑒 𝐷’𝐴𝑜𝑠𝑡𝑎 0.42 ∗∗ 0.50 𝐿𝑜𝑚𝑏𝑎𝑟𝑑𝑖𝑎 0.57 ∗∗ 0.41 ∗ 𝑇𝑟𝑒𝑛𝑡𝑖𝑛𝑜 𝐴𝑙𝑡𝑜 𝐴𝑑𝑖𝑔𝑒 0.58 ∗ 0.29 ∗∗ 𝑉𝑒𝑛𝑒𝑡𝑜 0.68 ∙ 0.54 𝐹𝑟𝑖𝑢𝑙𝑉𝑒𝑛𝑒𝑧𝑖𝑎𝐺𝑖𝑢𝑙𝑖𝑎 0.58 ∗∗ 0.32 ∗∗ 𝐿𝑖𝑔𝑢𝑟𝑖𝑎 0.71 0.71 𝐸𝑚𝑖𝑙𝑖𝑎 − 𝑅𝑜𝑚𝑎𝑔𝑛𝑎 0.62 ∗ 0.29 ∗∗ 𝑇𝑜𝑠𝑐𝑎𝑛𝑎 0.55 ∗∗ 0.48

20 𝑈𝑚𝑏𝑟𝑖𝑎 0.64 ∙ 0.77 𝑀𝑎𝑟𝑐ℎ𝑒 0.44 ∗∗∗ 0.28 ∗∗ 𝐿𝑎𝑧𝑖𝑜 0.99 0.62 𝐴𝑏𝑟𝑢𝑧𝑧𝑜 0.76 0.93 𝑀𝑜𝑙𝑖𝑠𝑒 0.69 0.76 𝐶𝑎𝑚𝑝𝑎𝑛𝑖𝑎 0.63 ∗ 0.72 𝑃𝑢𝑔𝑙𝑖𝑎 0.45 ∗∗∗ 0.33 ∗ 𝐵𝑎𝑠𝑖𝑙𝑖𝑐𝑎𝑡𝑎 0.83 1.15 𝐶𝑎𝑙𝑎𝑏𝑟𝑖𝑎 0.70 1.16 𝑆𝑖𝑐𝑖𝑙𝑖𝑎 0.74 1.06 𝑃𝑖𝑒𝑚𝑜𝑛𝑡𝑒 × 𝑊𝑜𝑚𝑒𝑛 1.10 𝑉𝑎𝑙𝑙𝑒 𝐷’𝐴𝑜𝑠𝑡𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.71 𝐿𝑜𝑚𝑏𝑎𝑟𝑑𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.65 𝑇𝑟𝑒𝑛𝑡𝑖𝑛𝑜𝐴𝑙𝑡𝑜𝐴𝑑𝑖𝑔𝑒 × 𝑊𝑜𝑚𝑒𝑛 2.87 ∙ 𝑉𝑒𝑛𝑒𝑡𝑜 × 𝑊𝑜𝑚𝑒𝑛 1.39 𝐹𝑟𝑖𝑢𝑙𝑖𝑉𝑒𝑛𝑒𝑧𝑖𝑎𝐺𝑖𝑢𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 2.35 ∙ 𝐿𝑖𝑔𝑢𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.98 𝐸𝑚𝑖𝑙𝑖𝑎𝑅𝑜𝑚𝑎𝑔𝑛𝑎 × 𝑊𝑜𝑚𝑒𝑛 2.89 ∗ 𝑇𝑜𝑠𝑐𝑎𝑛𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.24 𝑈𝑚𝑏𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.76 𝑀𝑎𝑟𝑐ℎ𝑒 × 𝑊𝑜𝑚𝑒𝑛 1.95 𝐿𝑎𝑧𝑖𝑜 × 𝑊𝑜𝑚𝑒𝑛 1.95 𝐴𝑏𝑟𝑢𝑧𝑧𝑜 × 𝑊𝑜𝑚𝑒𝑛 0.74 𝑀𝑜𝑙𝑖𝑠𝑒 × 𝑊𝑜𝑚𝑒𝑛 0.85 𝐶𝑎𝑚𝑝𝑎𝑛𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.85 𝑃𝑢𝑔𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.53 𝐵𝑎𝑠𝑖𝑙𝑖𝑐𝑎𝑡𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.58 𝐶𝑎𝑙𝑎𝑏𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.48 𝑆𝑖𝑐𝑖𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.56 𝐶ℎ𝑖𝑠𝑞 79.28 133.72 𝐷𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝑓𝑟𝑒𝑒𝑑𝑜𝑚 20 39 Signif. codes: 0 ‘∗∗∗’ 0.001 ‘∗∗’ 0.01 ‘∗’ 0.05 ‘∙’ 0.1 ‘ ’ 1

21

As shown in the table, the likelihood ratio 𝐶ℎ𝑖𝑠𝑞 of model B is higher than that of model A. Clearly model B, with 19 more interaction variables compared to model A, has decreased the deviance more than model A does by 54.44. This means that the variables added increase the fit of the model and regions differ with respect to the gender poverty gap. The odds ratios result estimates the gender gap for people in each region. It reveals that women are much more likely to be poor than men in Emilia Romagna (3.2 times or 220% higher, i.e. 1.11 × 2.89), Trentino Alto Adige (3.1 times), Friuli Venezia Giulia (2.6 times), Marche and Lazio (2.2 times), Lombardia (1.8 times) and Puglia (1.7 times). However, men have larger poverty chances in Molise (0.94), Campania (0.94), Umbria (0.84), Abruzzo (0.82), Valle D’Aosta (0.79), Basilicata (0.64), Sicilia (0.62) and Calabria (0.53). By comparing with Figure 3, we see that the regions having the higher odds of being poor for females than for males are also the regions having the biggest gender poverty gap (Lazio, Emilia Romagna, Trentino Alto Adige, Friulia Venezia Giulia). Meanwhile, the regions having smaller difference between the percentage of poor women and men have shown that men are more likely to be poor than women (Valle D’Aosta, Basilicata, Sicilia, Calabria, Molise). The results have shown in some regions men having higher odds of being poor than women since in small samples, odds ratios are more likely to be insignificant, this might be the reason leading to the opposite outcome of these regions.

Table 3 Odds ratios of model C and D

Model C Model D

Odds ratio Odds ratio

(𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡) 0.97 1.03 𝑊𝑜𝑚𝑒𝑛 1.27 ∗∗∗ 1.20 𝑃𝑖𝑒𝑚𝑜𝑛𝑡𝑒 0.69 0.75 𝑉𝑎𝑙𝑙𝑒 𝐷’𝐴𝑜𝑠𝑡𝑎 0.50 ∗ 0.80 𝐿𝑜𝑚𝑏𝑎𝑟𝑑𝑖𝑎 0.73 0.58 𝑇𝑟𝑒𝑛𝑡𝑖𝑛𝑜 𝐴𝑙𝑡𝑜 𝐴𝑑𝑖𝑔𝑒 0.74 0.37 ∗ 𝑉𝑒𝑛𝑒𝑡𝑜 0.82 0.75 𝐹𝑟𝑖𝑢𝑙𝑉𝑒𝑛𝑒𝑧𝑖𝑎𝐺𝑖𝑢𝑙𝑖𝑎 0.73 0.48 ∙ 𝐿𝑖𝑔𝑢𝑟𝑖𝑎 0.89 1.08

22 𝐸𝑚𝑖𝑙𝑖𝑎 − 𝑅𝑜𝑚𝑎𝑔𝑛𝑎 0.82 0.44 ∙ 𝑇𝑜𝑠𝑐𝑎𝑛𝑎 0.66 ∙ 0.62 𝑈𝑚𝑏𝑟𝑖𝑎 0.76 1.14 𝑀𝑎𝑟𝑐ℎ𝑒 0.51 ∗∗ 0.39 ∗ 𝐿𝑎𝑧𝑖𝑜 1.27 0.92 𝐴𝑏𝑟𝑢𝑧𝑧𝑜 0.81 1.02 𝑀𝑜𝑙𝑖𝑠𝑒 0.89 1.14 𝐶𝑎𝑚𝑝𝑎𝑛𝑖𝑎 0.66 ∙ 0.93 𝑃𝑢𝑔𝑙𝑖𝑎 0.47 ∗∗ 0.41 ∙ 𝐵𝑎𝑠𝑖𝑙𝑖𝑐𝑎𝑡𝑎 0.96 1.29 𝐶𝑎𝑙𝑎𝑏𝑟𝑖𝑎 0.67 1.14 𝑆𝑖𝑐𝑖𝑙𝑖𝑎 0.69 1.09 𝑃𝑖𝑒𝑚𝑜𝑛𝑡𝑒 × 𝑊𝑜𝑚𝑒𝑛 0.88 𝑉𝑎𝑙𝑙𝑒 𝐷’𝐴𝑜𝑠𝑡𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.43 𝐿𝑜𝑚𝑏𝑎𝑟𝑑𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.41 𝑇𝑟𝑒𝑛𝑡𝑖𝑛𝑜𝐴𝑙𝑡𝑜𝐴𝑑𝑖𝑔𝑒 × 𝑊𝑜𝑚𝑒𝑛 2.91 ∙ 𝑉𝑒𝑛𝑒𝑡𝑜 × 𝑊𝑜𝑚𝑒𝑛 1.15 𝐹𝑟𝑖𝑢𝑙𝑖𝑉𝑒𝑛𝑒𝑧𝑖𝑎𝐺𝑖𝑢𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.79 𝐿𝑖𝑔𝑢𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.75 𝐸𝑚𝑖𝑙𝑖𝑎𝑅𝑜𝑚𝑎𝑔𝑛𝑎 × 𝑊𝑜𝑚𝑒𝑛 2.35 ∙ 𝑇𝑜𝑠𝑐𝑎𝑛𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.09 𝑈𝑚𝑏𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.54 𝑀𝑎𝑟𝑐ℎ𝑒 × 𝑊𝑜𝑚𝑒𝑛 1.46 𝐿𝑎𝑧𝑖𝑜 × 𝑊𝑜𝑚𝑒𝑛 1.58 𝐴𝑏𝑟𝑢𝑧𝑧𝑜 × 𝑊𝑜𝑚𝑒𝑛 0.70 𝑀𝑜𝑙𝑖𝑠𝑒 × 𝑊𝑜𝑚𝑒𝑛 0.68 𝐶𝑎𝑚𝑝𝑎𝑛𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.62 𝑃𝑢𝑔𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 1.20 𝐵𝑎𝑠𝑖𝑙𝑖𝑐𝑎𝑡𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.62 𝐶𝑎𝑙𝑎𝑏𝑟𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.46 𝑆𝑖𝑐𝑖𝑙𝑖𝑎 × 𝑊𝑜𝑚𝑒𝑛 0.49 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛1 1.14 1.11 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛2 0.87 0.86

23 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛3 0.57 ∗∗ 0.56 ∗∗∗ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛4 0.45 ∗∗ 0.46 ∗∗ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛5 0.37 ∗∗∗ 0.37 ∗∗∗ 𝑦𝑜𝑢𝑛𝑔 2.76 ∗∗∗ 2.65 ∗∗∗ 𝑜𝑙𝑑 0.24 ∗∗∗ 0.24 ∗∗∗ 𝑊𝑜𝑟𝑘 0.27 ∗∗∗ 0.26 ∗∗∗ 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛 2.65 ∗∗∗ 2.62 ∗∗∗ 𝐶ℎ𝑖𝑠𝑞 607.29 656.90 𝐷𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝑓𝑟𝑒𝑒𝑑𝑜𝑚 29 48 Signif. codes: 0 ‘∗∗∗’ 0.001 ‘∗∗’ 0.01 ‘∗’ 0.05 ‘∙’ 0.1 ‘ ’ 1

After adding compositional variables into the model, the 𝐶ℎ𝑖𝑠𝑞 has greatly increased. This means that the data fits better with the models including compositional variables. By comparing the 𝐶ℎ𝑖𝑠𝑞 of model C and D, we can learn whether the individual characteristics such as education, age, having a job and having children of people living in 20 regions in Italy can explain the gender differences in poverty. Taking calculation of the 𝐶ℎ𝑖𝑠𝑞 indicators as illustrated before: ((133.72 − 79.28 = 54.44) − (656.90 − 607.29 = 49.61)) ⁄ 54.44 = 0.09, we can see that compositional variables can explain only 9 per cent of the region differences in the gender poverty gap.

By comparing the odds ratios before and after taking into account the personal traits, most regions show a decrease in the relative probability for women to be poor (the odds ratios approach 0). Only in Trentino Alto Adige and Basilicata, women are more vulnerable to poverty after taking the compositional effects into account. However, these effects do not affect the poverty chances much in Abruzzo and Calabria. In Liguria, the chance of poverty is in favor of men but after taking compositional variables into consideration, men have a higher chance of being poor than women.

It is quite noticeably that a person with better education is less likely to be poor. Having the highest level of education (tertiary education – from bachelor to doctorate or equivalent), versus having the lowest level of education (less than primary education), decreases the odds of being poor by 0.37 times or 63 per cent. However the margin of the decreasing rate of poverty risk reduces as the level of education increases. Due to short employment period and reduced human capital in old ages, it

24

is believed that poverty risk of older people to be high, yet the opposite is true. Middle aged singles (reference group) are three times more likely to be poor than older singles. Holding all else constant, the odds of being poor for an young single is about 2.76 times the odds for middle aged singles (a 176 per cent increase). In Italy, having a paid job indeed greatly reduces the chance to be poor by 73 per cent. Meanwhile having children raises the poverty risk by 2.65 times.

Table 4 Odds ratios of model E and F

Model E Model F

Odds ratio Odds ratio

(𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡) 0.25 ∗∗∗ 0.90 𝑊𝑜𝑚𝑒𝑛 1.44 ∗∗ 1.21 𝑃𝑖𝑒𝑚𝑜𝑛𝑡𝑒 0.54 ∗∗ 0.68 𝑉𝑎𝑙𝑙𝑒 𝐷’𝐴𝑜𝑠𝑡𝑎 0.40 ∗∗ 0.48 ∗ 𝐿𝑜𝑚𝑏𝑎𝑟𝑑𝑖𝑎 0.58 ∗ 0.75 𝑇𝑟𝑒𝑛𝑡𝑖𝑛𝑜 𝐴𝑙𝑡𝑜 𝐴𝑑𝑖𝑔𝑒 0.56 ∗ 0.72 𝑉𝑒𝑛𝑒𝑡𝑜 0.66 ∙ 0.82 𝐹𝑟𝑖𝑢𝑙𝑉𝑒𝑛𝑒𝑧𝑖𝑎𝐺𝑖𝑢𝑙𝑖𝑎 0.58 ∗ 0.72 𝐿𝑖𝑔𝑢𝑟𝑖𝑎 0.71 0.91 𝐸𝑚𝑖𝑙𝑖𝑎 − 𝑅𝑜𝑚𝑎𝑔𝑛𝑎 0.62 ∗ 0.83 𝑇𝑜𝑠𝑐𝑎𝑛𝑎 0.55 ∗ 0.66 𝑈𝑚𝑏𝑟𝑖𝑎 0.63 ∙ 0.76 𝑀𝑎𝑟𝑐ℎ𝑒 0.42 ∗∗∗ 0.50 ∗∗ 𝐿𝑎𝑧𝑖𝑜 1.00 1.29 𝐴𝑏𝑟𝑢𝑧𝑧𝑜 0.70 0.78 𝑀𝑜𝑙𝑖𝑠𝑒 0.65 0.84 𝐶𝑎𝑚𝑝𝑎𝑛𝑖𝑎 0.66 ∙ 0.69 𝑃𝑢𝑔𝑙𝑖𝑎 0.46 ∗∗ 0.47 ∗∗ 𝐵𝑎𝑠𝑖𝑙𝑖𝑐𝑎𝑡𝑎 0.80 0.93 𝐶𝑎𝑙𝑎𝑏𝑟𝑖𝑎 0.66 0.66 𝑆𝑖𝑐𝑖𝑙𝑖𝑎 0.74 0.69 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛1 1.15

25 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛2 0.88 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛3 0.59 ∗∗ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛4 0.47 ∗∗ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛5 0.39 ∗∗∗ 𝑦𝑜𝑢𝑛𝑔 2.75 ∗∗∗ 𝑜𝑙𝑑 0.24 ∗∗∗ 𝑊𝑜𝑟𝑘 0.27 ∗∗∗ 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛 2.67 ∗∗∗ 𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖𝑜𝑛 × 𝑊𝑜𝑚𝑒𝑛 0.96 ∗ 0.99 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛2 × 𝑊𝑜𝑚𝑒𝑛 1.19 ∗∗ 1.28 ∗ 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛3 × 𝑊𝑜𝑚𝑒𝑛 0.79 ∗∗ 0.87 ∗ 𝐶ℎ𝑖𝑠𝑞 96.26 616.18 𝐷𝑒𝑔𝑟𝑒𝑒𝑠 𝑜𝑓 𝑓𝑟𝑒𝑒𝑑𝑜𝑚 25 34 Signif. codes: 0 ‘∗∗∗’ 0.001 ‘∗∗’ 0.01 ‘∗’ 0.05 ‘∙’ 0.1 ‘ ’ 1

We continue with model E and F where the sub-national variables are added into. The purpose of these two models is to investigate the explanation extent of contextual effects on gender differences in poverty from one region to another. Region characteristics can be correlated to the individual characteristics; thus the two models E and F are built with and without compositional variables respectively to evaluate their correlations. Before taking composition effects into account, the sub-national characteristics can explain (96.26 − 79.28 = 16.98) ⁄ (133.72 − 79.28 = 54.44) = 0.31 or 31 per cent of the region differences in poverty rates between men and women. After taking composition effects into account, they can explain (616.18 − 607.29 = 8.89) (656.90 − 607.29 = 49.6) =⁄ 0.18 or 18 per cent of the remaining differences among 20 regions.

Women living in regions which have the minimum income scheme are better off with less exposure to poverty risk than women living in regions without. However even with the existence of minimum standard of living, women are still more likely to be poor than men. As expected, women living in intermediate areas are 1.71 (i.e.

26

1.19 × 1.44) times more likely to be poor than men and the chance of being poor for women living in thinly-populated areas is 13% higher than men living in the same areas. Surprisingly, if compared to densely-populated areas, people living in thinly-populated areas have smaller odds to be poor. This might be explained by the fact that the resident population density of Italy is generally lower in the north (where the statistical description before showed a much lower poverty rates) than in the centre and the south (ISTAT, 2016) except Lombardia. When taking composition of population into account, the minimum income scheme does not significantly affect the gender poverty gap between regions, this can explain why the explanatory power of contextual variables decreases compared to before taking compositional variables into account.

6.

Conclusion

In this study, we portray an overall image of Italy feminism of poverty throughout its 20 regions and try to explain the gender differences in poverty amongst these regions. In particular, we incorporated compositional and contextual predictors into our models to evaluate the risk of being poor of the population. Compositional variables include individual characteristics such as education level, age, employment status and having children, which hypothetically increases the probability of becoming poor for women and men. Contextual variables consist of minimum income schemes that applied only in 8 out of 20 regions in Italy and urbanization degrees which describe the population density of three types of areas. These sub-national variables are expected to directly influence the risk of being poor of males and females. The data used comprises of variables on income, poverty, social exclusion and other living conditions and is from the data set for 2015 from EU-SILC, more specifically, from ISTAT. The results obtained by the applications have extended the following conclusions.

We can conclude that there exists the gender difference in poverty amongst 20 regions in Italy and both compositional and contextual effects explain to a certain extent the regional differences in the gender poverty gap. Four personal traits which

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are human capital (education), age, employment status and household structure that contribute to compositional effects have similar power in explaining the differences, varying from 4 to 6 per cent and in total explain up to 9 per cent of the differences. This result is different from “education matters most” finding of Wiepking and Mass (2005). Yet both studies share the view of better education fights against poverty. Like education, age also has impact on the female risk of being poor. Being an older single woman decreases the risk by 70 per cent compared to middle aged women and single females under 30 have to face the risk three times higher than middle aged women. Likewise, having a job and children affect the poverty risk and can explain from 4 to 5 per cent of the differences. These low rates of explanation are contrary to Lynne, Sara and Garfinkel (1994) who concluded that employment and parenthood play an important role in determining the differences between two genders in poverty rates within a country.

From the results obtained, contextual effects somewhat strike as more important than the compositional effects. Regarding the social protection hypothesis with a stress on minimum income scheme, people living in regions with MI scheme are better off, however women in these regions still bare 18 per cent higher of poverty risk than men. The hypothesis is thus not supported. This can possibly be explained by the fact that the level of income support in these regions are assessed as very inadequate, and as regards coverage of those in need of support, coverage of these schemes is partial and very limited (Frazer and Marlier, 2016). Habitants or women specifically, benefit little from current MI schemes in Italy. Lastly, in regard to urbanization, women living in densely populated areas have higher chances to escape from poverty risk than women living in less habituated areas. In all three types of areas (densely-populated, intermediate and thinly-populated), women are more likely than men to be poor, but that odds for women living in thinly-populated areas somehow are lower than that of women living in densely-populated areas. At this point, it is unclear why the application shows an arguable result of lower risk of poverty for people living in areas with lower population density rates. In addition, since northern and central Italian regions have higher GDP per capita (ISTAT, 2015), the effect of urbanization thus should probably be interpreted that even if women living in a regions with low population density in the centre or the north, they would suffer less from poverty risk than women living in densely-populated areas in the south.

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