Conferenza ESPAnet

ITALIA Università degli Studi di Salerno, 17 - 19 Settembre 2015

Welfare in Italia e welfare globale: esperienze e modelli di

sviluppo a confronto

Inequality in Latin America and the demand for

redistribution after Global Financial Crisis

Autori

Roberta Russo*, Altay Alves Lino de Souza**

*Università Orientale di Napoli

**Universidade de São Paulo

Inequality in Latin America and the demand for redistribution after Global

Financial Crisis

<< Suppose that I drive through a two-lane tunnel, both lanes going in the same direction, and run

into a serious traffic jam. No car moves in either lane as far as I can see (which is not very far). I am

in the left lane and feel dejected. After a while the cars in the right lane begin to move. Naturally, my

spirits lift considerably, for I know that the jam has been broken and that my lane's turn to move will

surely come any moment now.

Even though I still sit still, I feel much better off than before because of the expectation that I shall

soon be on the move. But suppose that the expectation is disappointed and only the right lane keeps

moving: in that case I, along with my left lane cosufferers, shall suspect foul play, and many of us will

at some point become quite furious and ready to correct manifest injustice by taking direct action

(such as illegally crossing the double line separating the two lanes).>>

Hirschman, A. O., & Rothschild, M. (1973)1

Following the existing literature this research aims to investigate the determinants of the demand for

redistribution of the citizens of Latin America´s Countries after the Global Financial Crisis through

different theories. This work aims to make an empirical analysis to explain what drives popular

demand of redistribution using data from Latin America Countries from four waves of the

Latinobarometro survey (2007, 2009, 2010, 2011). The aim of this research is to compare individual

demands for redistribution to understand if it prevails the tunnel effect (Hirschman, A. O., &

Rothschild, M.; 1973) or a status effect (self-interest approach).

1 Hirschman, A. O., & Rothschild, M. (1973). The changing tolerance for income inequality in the course of

economic development. The Quarterly Journal of Economics, 544-566.

1. Introduction

1.1 The decline of inequality and poverty in Latin America between 2000 and 2010

Inequality in Latin America unambiguously declined in the majority of countries in the 2000s

(Azevedo et al.,2012; Azevedo et al., 2013; Cornia, 2013; Cruces et al., 2011; Gasparini et al., 2011;

Gasparini and Lustig, 2011; Lopez-Calva and Lustig, 2010; and Lustig et al., 2013). The Gini

coefficient for household per capita income has declined over the past decade, from a weighted

average of 0.548 to 0.4882 (Lustig et al., 2013). According to Lustig et al. another key indicator to

take into account is the reduction of poverty in this decade: more in particular authors underline that

the incidence of extreme poverty and of total poverty decreased in the same years has decreased by

8.6 and 11.9 percentage points respectively. Applying the DattRavallion decomposition approach

(Datt and Ravallion, 1992) authors reveal that, on regional average, 43 percent of the reduction in

poverty is due to the decline in inequality. In accordance with several authors (Azevedo et al., 2012;

Cornia, 2013; De la Torre et al., 2012; López-Calva and Lustig, 2010; Lustig et al., 2013), the two

main explanations at the basis of the decline in inequality in Latin America are a reduction in hourly

labor income inequality3 and more robust and progressive government transfers.

1.2 The effects of global crisis on Latin America

The 2007–2009 global financial crisis has affected many countries including Latin American’s. <<In

the fall of 2008 Latin American currencies depreciated sharply versus the US dollar (Brazil and

Mexico depreciated by more than 40%, Argentina by 20%), stock markets plunged (Argentina and

Brazil by more than 50%), and spreads on yields surged (Argentina quadrupled, Mexico and Brazil

doubled)4>>.

The reduction in the growth rate of GDP between 2007 and 2009 was evident and the rapid recovery

does not seem to be stable or otherwise homogeneous across the continent.

In this unstable context is possible to hypothesize a rise of the concerns of citizens about their future

and, therefore, special attention and interest towards the allocation of public resources. According to

the governmental protection hypothesis - whereby the welfare state is a form of insurance against

2 Source: SEDLAC (CEDLAS and The World Bank) 3 According to Azevedo et al. (2012) the most important factor has been relatively strong growth in labor

income for workers at the bottom of the income distribution, and in particular, an increase in hourly earnings 4 Boonman, T. M., Jacobs, J. P., & Kuper, G. H. (2011). Why didn't the Global Financial Crisis hit Latin

America?. CIRANO-Scientific Publication, (2011s-63).

adverse macroeconomic and social conditions - agents could be more in favor of redistribution

policies in “bad times” (Blekesaune, 2007; Jæger, 2013).

1.3 The demand for redistribution: Theoretical background

Several theories in political sociology and political economy seek to explain why individuals have a

different attitude regarding to the demand of redistribution.

Literature besides can be divided into two groups: from one side we found who focuses on individual

explanation about demand for redistribution; in the other side who focuses on contextual one. The

first group includes the ideology (Franklin, 1984; Fog, 2001; Oorschot, 2002; Kaltenthaler et al.,

2008; Jaeger 2006, 2008) and the self-interest (Jæger, 2006a; Kangas, 1997; Rehm, 2009) explanation

as the driving forces behind observable differences in the demand for redistribution.

Different studies that focus on self-interest or homo oeconomicus, find the existence of an inverse

relation between individual income and the support for government redistributive policies (e.g.,

Iversen 2005, 100; Meier Jæger 2005, 2006a, 2006b; Finseraas 2006; Blekesaune and Quadagno

2003; Blekasaune 2006). The reason is quite obvious: those who are likely to gain (poor people) from

redistribution are more likely to support it, instead rich people become losers of this policy because

of the tax they should pay.

Nevertheless Dion and Birchfield (2010) have shown that homo economicus assumption would be

rejected in case of less economically developed countries (such as some of Latin American’s) and

where inequality is very high.

The second group of theories is also heterogeneous but all contributions focus on effect of contextual

factors. In comparative analysis researchers have confronted the different attitudes towards

redistribution by people living in contexts with different levels of social expenditure (Bleckesaune

and Guadagno, 2003) or levels of macroeconomic variables related to the economic cycle

(Bleckesaune, 2007; Dallinger, 2010).

A fundamental contribution comes from Hirschmann and Rothschild (1973) and his theory of the

tunnel effect: according to it, people subject to inequality have a sort of initial gratification over

advances of others and it could implicate that poor individuals don’t ask for policies (like

redistribution of income). This is what can happen in emerging country where <<society’s tolerance

for such disparities will be substantial>> like in Latin America. This concept was then taken up by

Benabou and Ok (2001) to elaborate the POUM Hypothesis (prospect of upward mobility) whereby

poor people could not support redistribution because of the hope that they, or their offspring, may

make it up the income ladder.

According to this scheme the theory of tunnel effect of Hirschman contains both the individual

explanation and the contextual. However this hypothesis is in opposition to the theories that focus on

self-interest hypothesis.

We approach this issue by exploring both micro and macro factors that shape the formation of

preferences for redistributive and welfare policies.

In this paper the research questions are:

1) What drives the demand for redistribution in a continent where in ten years there has

been an increase in the relative income?

2) Did the global crisis affect the demand for redistribution? Does the demand for

redistribution depend on macroeconomic condition?

3) What effect prevails? Status effect or tunnel effect? What explanation is stronger? A

contextual explanation or personal perception of their own condition?

2. Data and Methodology

This paper proposes to analyze different hypothesis to explain the demand for redistribution in Latin

America with the aim to compare different approaches described in previous sections.

Our econometric analysis is based on four waves of the Latinobarometro Survey (2007, 2009, 2010,

2011) for 18 countries of Latin America. Based on interviews to representative samples of the

population, this database collects data about socio-demographic characteristics of respondents and

their opinions about their life well-being, politics and economics.

Assuming that people are sincere believers of their preferences, we study the determinants of

individual preferences for redistribution through the answers given to the question about the

evaluation of income distribution; in order to answer to this question, people have to choose between

four options: Very Fair, Fair, Unfair, Very Unfair. For the regression analysis we transform it in a

dummy variable – labeled REDISTRIBUTION that takes the value 0 if the respondent doesn’t ask

for redistribution and it takes the value 1 if the respondent asks for redistribution. Transforming this

variable we assume that who judges the distribution of income as “Very Unfair” and “Unfair” should

support redistributive policies and vice versa.

According to the literature reviewed in section 1.2 we have selected many explanatory variables:

some that describe individual socio-demographic and economic situation, some other contextual and

related to opinions and beliefs about political and economic issues.

More in detail, the socio-demographic variables we consider for the regression are the gender (we use

the dummy variable FEMALE that takes the value 1 if the respondent is female), the age (we use

dummies for five age categories: YOUNG, AGE30_40, AGE40_50, AGE50_60 and OLD) and the

marital status (we use the dummy variable MARRIED that takes the value 1 if the respondent declared

to be married/to live with partner and it takes value 0 if the respondent declare to be

single/separated/divorced/widow. With regard to the gender, according to part of the literature,

women should be more inclined to solidarity and therefore to support policies of redistribution more

than men (Svallfors, 1997; Edlund et al., 2005), however this argument is rejected by other studies

(Garcia-Valinas et al., 2008).

According to several studies, the age and the preferences for redistribution could be directly

proportional: the higher is the age and the greater should be the support towards the redistribution

because the older people perceive that the prospect of moving up the income ladder is likely to

decrease, otherwise the younger believe they have a longer time to achieve greater well-being (Gaeta,

2011; Ravaillon and Lokshin, 1999; Ohtake and Tomioka, 2004). A controversial result in several

empirical studies on welfare policies is about the marital status: in accordance with Singhal (2008),

Alesina et al. (2001) and Fong (2001) unmarried people are more disposed to redistribution and it

could be because they cannot lean on the support of partner; however Corneo and Gruner (2002) don’t

confirm this result.

For the empirical analysis is useful to consider one contextual variable: like in other studies (Alesina,

2001; Gaeta, 2011), we choose to test the impact of the size of the city of residence; we use dummies

for five size categories: CAPITAL, CITYSIZE1 (for cities with more than 100.000 inhabitants,

CITYSIZE2 (between 50.000 and 100.000 inhabitants), CITYSIZE3 (between 20.000 and 40.000

inhabitants) and CITYSIZE4 (for cities with less than 20.000 inhabitants). According with Gaeta

(2011), living in big cities should be negatively correlated with higher preferences for redistribution

policies, because it seems plausible to look at larger contexts like more competitive and

individualistic, unlike small city more socially cohesive (with the consequence that people would be

more in favor of social justice).

The educational level could play an important role in the preferences for redistributive policies: in

fact Da Fonseca and De Figueiredo (2013) in their research about the support for welfare policies in

Latin America, underline that it tends to increase with educational level; opposite results were

obtained instead by others (Kaltenthaler et al.,2008; Gaeta, 2011) whereby a higher level of education

was related to higher expectations of social mobility. Also for this variable we use dummies:

NOEDUC for illiterate people, PRIMARY for incomplete/complete primary level of education,

SECONDARY for incomplete/complete secondary level and TERTIARY for incomplete or complete

high.

This research use three explanatory variables linked with the employment status of the respondents:

1) It is reasonable to think that self employed have more risk propensity than salaried and we imagine

that they are less supportive of redistribution of income; 2) in a perspective of status effect (relative

position of the individual in society) or Self-interest Hypothesis (Reveillon and Lokshin, 2000;

Corneo and Gruner, 2002) people out of the labor market and not included in training programs should

have a greater propensity toward redistribution policies; to test this theories we use a dummy variable

(NEET5) that assume value 1 if the respondent declares to be out of work; 3) Latinobarometro surveys

have one question about how the respondent is concerned about the possibility to lose his job during

the following 12 months and it is quite obvious to imagine that this concern makes individuals more

favorable to welfare policies (we use dummy RISK that assume value 1 if individuals are “concerned”

or “very concerned”).

In order to verify the different theories described in section 1.2 we also consider some variables

related to personal financial situation. The first is FINANCIAL that comes from the question “In

general, how would you describe your present economic situation and that of your family?. Would

you say that it is very good, good, about average, bad or very bad?.” We use a reverse scale for this

variable. The second variable we chose is the dummy POVERTY that assumes value 1 if the

respondent declares that her/his salary and the total of her/his family´s salary is not sufficient, we have

problem or it is not sufficient, we have big problems to satisfactorily cover their needs. As it has been

pointed out by Gaeta (2011) the satisfaction about the financial condition is a subjective perception

and it depends on various factors, including expectation.

According to Hirshmann (1973) and Benabou and Ok (2001), who believes to improve in her/his

future personal economic situation shouldn’t ask for redistribution policies. For this reason we

collocate in the analysis the dummy variable HOPE that assumes value 1 if respondent describes his

future condition like “a little better” and “much better”.

We also include two variables related to social mobility. People seem to support policies for reducing

social inequalities “when they perceive their living standards to be lower than their parents’ ” (Da

Fonseca C.R. and De Figueiredo E.A., 2013). This argument is widely held in the literature and the

impact of past mobility (Piketty, 1995), together with the future mobility is required to test the POUM

Hypothesis (Benabou and Ok, 2001) and the Tunnel Effect (Hirshmann and Rotschild, 1973).

Following this reasoning, the prospect of upward mobility should decrease the preference for

redistributive and welfare policies. To test these hypothesis we use the dummy variable

PAST_MOBILITY (related to the Latinobarometro’s questions Imagine a staircase with 10 steps, in

which on the first step are located the poorest and on the 10th step, the richest. Where would you put

your parents on this staircase6? and Imagine a staircase with 10 steps, in which on the first step are

5 Not (engaged) in Education, Employment or Training 6 Scale poor – rich in the past

located the poorest and on the 10th step, the richest. Where would you put yourself on this staircase7?)

that assume value 1 the value of “Scale poor – rich in the past” is less than “Scale poor – rich in the

present”.

Like Alesina and Ferrara (2005) we examine not only the individual perception about respondents

own past and expected mobility, but also the role of general improvement of economy in individuals’

attitude toward redistributive policies. In order to do it we use the dummy variable IMPROVEMENT

that assumes value 1 if the respondent believes that the past economic situation of her/his country was

much better or a little better in confront of the present.

To deepen the effect of the crisis we also include time dummies _2007, _2009, _2010, _2011 to

examine the impact of changes due to economic instability.

3. Results

Our dependent variable is REDISTRIBUTION, a dummy that assumes value 1 if the person

interviewed responds “Unfair” or “Very Unfair” and value 0 if she/he responds “Fair” or “Vary Fair”

to the question How fair is income distribution in your country?”. Because of the nature of this

variables, regression analysis have been carried out using a logistic model. The reference group is

REDISTRIBUTION=1 (support for redistributive policies).

To test the hypothesis described in previous sections we have realized five models: we have created

for each year separated analysis considering the same variables, while one model brings together all

the answers and uses four dummy variables referring to the years. In the models we have included

explanatory variables related to socio-demographic characteristics (AGE, FEMALE, MARITAL

STATUS, EDUCATION), the context (CITYSIZE), the employment condition (SELF, NEET and

RISK), the household income (POVERTY and HOPE), the perception of social mobility (PAST

MOBILITY and FUTURE MOBILITY) and the opinion about the state of economy

(IMPROVEMENT). As we have underlined, in the model (1) we aggregate all the answers of

Latinobarometro and we add the dummies _2007, _2009, _2010, _2011.

Table 1 displays the results of the logit regression in which we have aggregated all the answers of

four waves (2007, 2009, 2010, 2011) of Latinobarometro survey and we have added year dummies

to test the implications of global financial crisis.

As we expected the coefficient of dummies related to the age is negative if the respondent is young

and the size of this impact rises with the increment of the age: being young reduces by 5.74% the

probability to ask for welfare policies compared to the reference group (more than 60 years). Although

this result has not significance. Also the variables FEMALE and MARRIED have not significance.

7 Scale poor – rich in the present

Unlike large part of literature (Kaltenthaler et al., 2008; Gaeta, 2011), which affirms that higher is

the educational qualification less should be the demand for redistribution (because the greater chance

to have a good job and an high income) our data show, at the contrary that having secondary or tertiary

educational level improve the probability to be in favor of redistribution than who is illiterate.

Therefore secondary level of education increases by 19.27% the chance to be in the reference group

(pro-redistribution) and Tertiary level increases this probability by 45.67%.

An interesting result we have obtained, is about the contextual variable CITY SIZE:

living in a capital increases the probability to ask for redistribution, probably because in Latin

American countries the contradictions of rapid development are more evident and inequality is more

prominent. Variable CAPITAL is positive and significant: living in a capital increases by 19.74% the

chance to be part of a reference group of the dependent variable. This finding is different from what

is supported by (Alesina, 2001; Gaeta, 2011), instead it confirms the idea that redistribution in many

countries has been a response to the physical power of the poor and the threat of riot and revolution

(Acemoglu and Robinson, 2000)

According to previous literature, the coefficient of variable SELF is negative because it is possible to

suppose that people who are self employed have a great risk propensity. However the coefficient has

a low significance.

RISK has a positive and significant coefficient: the concern over the unemployement in the next

future increases by 52.98% the probability to ask for redistribution in comparison to who isn´t

concerned. This finding is consistent with the theory of status effect because it is quite obvious that

those who believe to lose their job should gain from welfare policies.

This result is in contrast with what is obtained for the variable POVERTY, whose coefficient has an

unusually negative sign, suggesting an interpretation as the tunnel effect or POUM hypothesis. As it

will be seen later, the coefficient has, instead, positive sign in others four model.

An unexpected result concerns the variable PAST MOBILITY, whose coefficient is positive and

significant. Although people believe they are wealthier than their parents, the probability to ask for

welfare policies increases by 12.37% compared with who declare to be less rich than their parents.

The interpretation of this output is not univocal: people who have experienced social mobility may

think it is not enough; they are simply concerned with their security because of the phenomenon of

criminality linked with poverty and inequality (very common in developing countries, as analyzed by

Yusuf et al. , 2001); they have an altruistic approach. Another point of view regards who doesn’t have

experience of social mobility in her/his family: the probability that they ask for redistributive policies

is less than others and we can read it through the tunnel effect theory.

IMPROVEMENT is another variable that it is possible to read like a tunnel effect, in fact the positive

opinion about the economic prospective of the country reduces by 55.42% the probability to ask for

redistribution in comparison with who doesn’t think that economy of her/his country will improve:

the improvement of the economy should be compared with the cars in the right lane that begin to

move like in the Hirschman’s analogy. People can’t know if the growth of the economy will be equally

distributed but they don’t ask for redistributive policy because the expectation that they should soon

be on the move – citing Hirschman. In this scheme we can read also the coefficient of the variable

HOPE that is negative although not very significant.

Finally, reading the results of the variable YEAR, we can deduce the existence of a crisis

effect: be part of those who were interviewed in 2011 increases by 14.89% the chance to support

welfare policy than respondents in 2007 (reference year). The instability and the uncertainty resulting

from the global financial crisis increase the concern towards the future and this affects the attitude

toward redistributive policies.

Now we consider four separate analysis one for each year to test theories about tunnel

effect, status effect and those related to social mobility.

The first clear result relates to three variables in the models referred to single year are highly

significant: POVERTY, HOPE and IMPROVEMENT. The sign of dummy POVERTY is positive in

all the years under analysis and this outcome could be interpreted with theories about status or homo

oeconomicus effects: people who should gain from redistributive policies ask for them; this result,

however, should be analyzed together with that relating to the variable HOPE that is negative – people

who believe to improve their own economic situation in the future don’t ask for welfare policies –

and could be read in different ways. This variable, from one side, could be used when testing the

tunnel effect or the prospect of upward mobility hypothesis because the hope – according with these

theories – is negatively correlated with the demand for redistribution. From another point of view,

this output may be in conformity with the homo oeconomicus theories because if people believe that

their income will increase, policies of redistribution would mean higher taxes for the wealthiest. The

last variable that is high significant in these models is IMPROVEMENT, that refers to the opinion on

the future performance of the economy of interviewee’s country. This variable is always negative

probably because in the last ten year of rapid GDP growth there was – in the same time – an important

reduction of extreme poverty and GINI index, as we underlined in the section 1.1.

It is possible to observe another relevant detail: the size of odds ratio of explanatory variables we

have just described, follows a trend which suggests the existence of an impact of the global financial

crisis on the preferences of Latin Americans: in fact, declaring to be poor increases by 41.73% the

probability to ask for redistribution in 2007 and this percentage raises of about 16 points in 2009,

where the effect of the crisis is more evident on GDP growth. It is possible to observe a similar effect

for the variable HOPE that have a zigzag trend: the confidence in improving the personal economic

condition in the next future reduces by 38.62%, 31.97%, 34.59% and 30.87% the preference for

redistribution, respectively in the years 2007, 2009, 2010 and 2011: it is possible to interpret these

outputs taking into account the instable trend of Latin American’ economies after 2007. In regard to

the variable IMPROVEMENT it could underline how the fall of GDP growth had an important impact

on the evaluation on social fairness: the confidence in the improvement of a country’ s economic

condition reduces by approximately 60% the probability to ask for policies in 2007, instead it reduces

by only 44.27% this probability in the 2009.

Regarding social mobility we can note that in these models only the dummy FUTURE MOBILITY

is high significant but only in 2010 and 2011 waves of Latinobarometro survey. This variable have a

positive sign and this finding contradicts, in some way, the tunnel effect. On the other hand, we can

imagine that people who are in agreement with the policies of income redistribution because of the

uncertain economic situation after the economic crisis in the USA (despite they may believe that their

own children will go up the social scale), makes this result less certain.

4. Conclusion

This paper investigates the determinants of preference for redistributive policies, with a focus on the

effect of USA subprime crisis of 2007 on perspective of Latin America’s citizens. Several authors

have explored this issue because its relation with the size of government expenditure. Using data from

Latinobarometro surveys collected in 2007, 2009, 2010 and 2011 we apply a logistic regression

analysis to study the citizens’ attitudes towards welfare policies in rapidly development countries

where are evident large social contradictions.

More in particular we have tested many groups of theories: I) contextual explanation like

governmental protection hypothesis according to which the demand for redistribution depends on the

overall level of social risk in the country; II) status effect like homo oeconomicus effect and self –

interest hypothesis according to which the demand for redistribution reflects individuals’

socioeconomic position and their exposure to social risk; what we named expectation factor that

include the tunnel effect and the prospect for upward mobility, in addition to the impact of social

mobility.

The popular demand in Latin America during the years of global financial crisis is certainly affected

by contextual factors and we can see it analyzing different variables. Living in a big city or in a capital,

places of great social contradictions and conflicts, increase the probability to be supporters of

redistribution of income; the year dummy _2011 underline a crisis effect on respondents of that wave

of survey; the negative coefficient – in all models - of the explanatory variable related with the

positive perception about future trend of economy – that could be interpreted also with the point of

view of expectation factor – underlines the importance of macroeconomic context.

At last the positive coefficient of FUTURE MOBILITY tell us about the sentiment of uncertainly that

could characterize a period of economic instability.

Afterwards the status effect is evident when we focus on POVERTY (in models 2, 3, 4, 5) and RISK:

to declare to have personal economic problems and to be concerned to lose her/his job in the next

year increase the probability to ask for redistribution because, in a self interest scheme they will gain

from it.

Finally, we have tested the expectation factors related with the attitude toward welfare policies: the

hope of improve self economic condition and the belief on economic growth reduce this preference.

It could be possible to read a similar effect in who haven’t experience of social mobility in her/his

family but don’t ask for redistribution: as we already affirmed, it could be for the tunnel effect.

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Appendix

REDISTRIBUTION Coeff.

Odds

Ratio Std. Err. %

YOUNG -0.0591 0.94 0.03 -5.74

AGE30_40 0.0119 1.01 0.03 1.20

AGE40_50 0.0347 1.04 0.04 3.53

AGE50_60 0.0502 1.05 0.04 5.15

FEMALE 0.0398 1.04 0.02 4.06

MARRIED 0.0521* 1.05 0.02 5.35

PRIMARY 0.0675 1.07 0.04 6.98

SECONDARY 0.176*** 1.19 0.05 19.27

TERTIARY 0.376*** 1.46 0.06 45.67

CAPITAL 0.180*** 1.20 0.04 19.74

CITYSIZE1 0.0472 1.05 0.03 4.84

CITYSIZE2 0.0368 1.04 0.04 3.75

CITYSIZE3 -0.0147 0.99 0.03 -1.46

SELF -0.0583* 0.94 0.02 -5.66

NEET -0.0132 0.99 0.03 -1.31

RISK 0.425*** 1.53 0.03 52.98

POVERTY -

0.396***

0.67 0.01 -

32.71

HOPE -0.0489* 0.95 0.02 -4.77

PAST_MOB 0.117*** 1.12 0.02 12.37

FUTURE_MOB 0.0178 1.02 0.02 1.80

IMPROVEMENT -

0.808***

0.45 0.01 -

55.42

_2009 0.0238 1.02 0.03 2.41

_2010 0.0153 1.02 0.03 1.54

_2011 0.139*** 1.15 0.03 14.89

Tab 1. Results model 1 – four waves of Latinobarometro survey using year

dummies

2007

2009 2010

2011

REDISTRIBUTION (2) (3) (4) (5)

YOUNG 0.91 (0.06) 0.93 (0.06) 1.01 (0.06) 0.94 (0.06)

AGE30_40 0.97 (0.07) 0.92 (0.06) 1.16* (0.08) 1.05 (0.07)

AGE40_50 1.02 (0.07) 1.01 (0.07) 1.12 (0.08) 1.03 (0.07)

AGE50_60 1.06 (0.08) 1.05 (0.08) 1.03 (0.08) 1.09 (0.08)

FEMALE 0.98 (0.04) 1.07 (0.04) 1.07 (0.04) 1.05 (0.05)

MARRIED 0.97 (0.04) 1.09* (0.05) 1.07 (0.04) 1.09* (0.05)

PRIMARY 1.23** (0.08) 1.19* (0.08) 0.83* (0.06) 1.00 (0.08)

SECONDARY 1.40*** (0.10) 1.40*** (0.10) 0.92 (0.07) 1.03 (0.08)

TERTIARY 1.71*** (0.14) 1.73*** (0.15) 1.08 (0.10) 1.30** (0.12)

CAPITAL 1.40*** (0.09) 1.15* (0.08) 1.00 (0.07) 1.27*** (0.09)

CITYSIZE1 1.15** (0.06) 1.09 (0.06) 0.98 (0.05) 0.99 (0.06)

CITYSIZE2 0.98 (0.06) 1.09 (0.08) 0.96 (0.07) 1.10 (0.08)

CITYSIZE3 1.04 (0.06) 0.91 (0.05) 1.03 (0.07) 0.98 (0.06)

SELF 0.96 (0.05) 0.94 (0.05) 0.98 (0.05) 0.90* (0.04)

NEET 0.92 (0.05) 0.98 (0.06) 0.95 (0.05) 1.08 (0.06)

RISK 0.88** (0.04) 1.08 (0.05) 1.05 (0.05) 1.07 (0.05)

POVERTY 1.42*** (0.06) 1.57*** (0.06) 1.55*** (0.06) 1.60*** (0.07)

HOPE 0.61*** (0.02) 0.68*** (0.03) 0.65*** (0.03) 0.69*** (0.03)

PAST_MOB 0.97 (0.04) 0.92 (0.04) 0.96 (0.04) 0.97 (0.04)

FUTURE_MOB 1.06 (0.04) 1.06 (0.04) 1.22*** (0.05) 1.19*** (0.05)

IMPROVEMENT 0.39*** (0.02) 0.56*** (0.02) 0.48*** (0.02) 0.38*** (0.02)

Tab. 2: model 2, 3, 4, 5. Odds ratio and standard errors (in parentheses). *,**,*** mean

significantly different from zero at the 0.10, 0.05, 0.01

2007 Coeff.

Odds

Ratio Std. Err. %

YOUNG -0.0904 0.91 0.06 -8.64

AGE30_40 -0.0303 0.97 0.07 -2.98

AGE40_50 0.018 1.02 0.07 1.82

AGE50_60 0.0549 1.06 0.08 5.64

FEMALE -0.0178 0.98 0.04 -1.76

MARRIED -0.0298 0.97 0.04 -2.93

PRIMARY 0.205** 1.23 0.08 22.80

SECONDARY 0.340*** 1.40 0.10 40.50

TERTIARY 0.534*** 1.71 0.14 70.50

CAPITAL 0.335*** 1.40 0.09 39.81

CITYSIZE1 0.137** 1.15 0.06 14.70

CITYSIZE2 -0.019 0.98 0.06 -1.88

CITYSIZE3 0.0401 1.04 0.06 4.09

SELF -0.0448 0.96 0.05 -4.39

NEET -0.0862 0.92 0.05 -8.26

RISK -0.127** 0.88 0.04 -11.96

POVERTY 0.349*** 1.42 0.06 41.73

HOPE -

0.488***

0.61 0.02 -38.62

PAST_MOB -0.031 0.97 0.04 -3.05

FUTURE_MOB 0.0611 1.06 0.04 6.30

IMPROVEMENT -

0.934***

0.39 0.02 -60.69

Tab 3. Results model 2 – year 2007. *,**,*** mean significantly different from zero at the

0.10, 0.05, 0.01

2009 Coeff. Odds Ratio Std. Err. %

YOUNG -0.0735 0.93 0.06 -7.09

AGE30_40 -0.0838 0.92 0.06 -8.04

AGE40_50 0.00658 1.01 0.07 0.66

AGE50_60 0.0496 1.05 0.08 5.09

FEMALE 0.0635 1.07 0.04 6.56

MARRIED 0.0893* 1.09 0.05 9.34

PRIMARY 0.170* 1.19 0.08 18.58

SECONDARY 0.334*** 1.40 0.10 39.71

TERTIARY 0.546*** 1.73 0.15 72.69

CAPITAL 0.144* 1.15 0.08 15.50

CITYSIZE1 0.0897 1.09 0.06 9.39

CITYSIZE2 0.0904 1.09 0.08 9.46

CITYSIZE3 -0.0971 0.91 0.05 -9.25

SELF -0.0633 0.94 0.05 -6.13

NEET -0.02 0.98 0.06 -1.98

RISK 0.0814 1.08 0.05 8.48

POVERTY 0.453*** 1.57 0.06 57.36

HOPE -

0.385***

0.68 0.03 -

31.97

PAST_MOB -0.0859 0.92 0.04 -8.23

FUTURE_MOB 0.0592 1.06 0.04 6.10

IMPROVEMENT -

0.585***

0.56 0.02 -

44.27

Tab 4. Results model 3 – year 2009. *,**,*** mean significantly different from zero at the

0.10, 0.05, 0.01

2010 Coeff.

Odds

Ratio Std. Err. %

YOUNG 0.00504 1.01 0.06 0.51

AGE30_40 0.144* 1.16 0.08 15.53

AGE40_50 0.11 1.12 0.08 11.63

AGE50_60 0.0298 1.03 0.08 3.03

FEMALE 0.0688 1.07 0.04 7.12

MARRIED 0.0698 1.07 0.04 7.23

PRIMARY -0.186* 0.83 0.06 -16.98

SECONDARY -0.0845 0.92 0.07 -8.10

TERTIARY 0.0814 1.08 0.10 8.48

CAPITAL -0.00112 1.00 0.07 -0.11

CITYSIZE1 -0.0252 0.98 0.05 -2.48

CITYSIZE2 -0.0427 0.96 0.07 -4.18

CITYSIZE3 0.0297 1.03 0.07 3.02

SELF -0.0201 0.98 0.05 -1.99

NEET -0.0471 0.95 0.05 -4.60

RISK 0.0464 1.05 0.05 4.75

POVERTY 0.441*** 1.55 0.06 55.43

HOPE -

0.424***

0.65 0.03 -34.59

PAST_MOB -0.0417 0.96 0.04 -4.08

FUTURE_MOB 0.199*** 1.22 0.05 22.01

IMPROVEMENT -

0.734***

0.48 0.02 -52.01

Tab 5. Results model 4 – year 2010. *,**,*** mean significantly different from zero at the

0.10, 0.05, 0.01

2011 Coeff.

Odds

Ratio Std. Err. %

YOUNG -0.0624 0.94 0.06 -6.05

AGE30_40 0.0452 1.05 0.07 4.62

AGE40_50 0.0296 1.03 0.07 3.01

AGE50_60 0.086 1.09 0.08 8.98

FEMALE 0.0459 1.05 0.05 4.70

MARRIED 0.0854* 1.09 0.05 8.92

PRIMARY 0.00285 1.00 0.08 0.29

SECONDARY 0.0264 1.03 0.08 2.68

TERTIARY 0.261** 1.30 0.12 29.84

CAPITAL 0.242*** 1.27 0.09 27.44

CITYSIZE1 -0.0142 0.99 0.06 -1.41

CITYSIZE2 0.097 1.10 0.08 10.19

CITYSIZE3 -0.0236 0.98 0.06 -2.33

SELF -0.111* 0.90 0.04 -10.49

NEET 0.0795 1.08 0.06 8.28

RISK 0.0653 1.07 0.05 6.75

POVERTY 0.467*** 1.60 0.07 59.59

HOPE -

0.369***

0.69 0.03 -30.87

PAST_MOB -0.0326 0.97 0.04 -3.21

FUTURE_MOB 0.171*** 1.19 0.05 18.64

IMPROVEMENT -

0.958***

0.38 0.02 -61.65

Tab 6. Results model 5 – year 2011. *,**,*** mean significantly different from zero at the

0.10, 0.05, 0.01