inequality in latin america and the demand for redistribution after … · 2017-02-24 ·...
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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