Overall, Figure 20 exposes the limitations and the consequent partial picture that the Mincer estimates might offer in distributive analysis being a somewhat simplistic way of apprehending the interaction. The return of each of the variables is constant along the quantiles. However, since the influence of education on the hourly wage varies vis-à-vis the different quantiles, the expected conditional mean fails to be a proper representation of the heterogeneity of this relationship. Furthermore, we need to remark that UQRs curves are more pronounced – both upwards (unequalizing) and downwards (equalizing) – compared to those of CQRs for the three years and in the three groups since the former captures another potential source of inequality: the distribution of educational endowments.

To start, considering years of formal schooling, the mean of education falls from tail to tail. The order of magnitude of the impact associated to an increase of an additional year of education on individuals diminished consistently. Yet, the expected mean remains limited when it comes to fully understand the process.

That is why the use of UQRs could facilitate a more in-depth analysis exhibiting compositional changes on the variable within the distribution. Hence, it is remarkable the flattening process of these curves throughout the period. The one of 2007 started with a pronounced heterogeneous effect across the quantiles – probably incorporating a pre-existent unequal distribution of education among society – and finished in 2016 with a lower steep. This implies that the unequalizing effect of schooling softened throughout the period. Even though the upper quantiles still enjoy of a greater return to their hourly wage, the gap with the lower ones narrowed considerably. Results suggest that policy intervention targeting more vulnerable and excluded groups could model the distributional effect of education turning it pro-poor or at least diminishing its unequalizing impact.

In what concerns Primary education, we can reaffirm the equalizing effect on the wage distribution for the three years. In other words, the lower quantiles benefit proportionally more than the top. Notwithstanding, this progressive impact gained in strength during the period intensifying the equalizing role of this educational level. In graphic terms, we see a steeper and decreasing slope in 2016 (the grey line) compared to the one in place for 2007.
Furthermore, whereas in 2007 the impact of Higher education was markedly heterogeneous and increasing along the quantiles (both conditional and unconditional), for 2016 we can appreciate a more compensated curve between the marginal return at the top and the bottom of the distribution. In other words, the range narrowed exposing a somewhat convergence in terms of returns of the lower and upper quantiles. This means that, throughout this period, the return of Higher education improved relatively more for the bottom part.

Finally, estimates for educational levels in a way confirm the thesis of Gasparini et al. (2013), which states that, in Latin American region, Primary education is pro-poor while Higher education is pro-rich. Nevertheless, these two levels' effects were far from being unaltered during the period. On the contrary, we witnessed an evolving dynamic in their heterogeneous impacts. In the final part of the decade, Primary became even more pro-poor while Higher education underwent a softening in its regressive influence. This could open an interesting debate that cast doubt on the sometimes-unquestionable idea that Higher education – regardless of time, place, and type of implementation – has an unequalizing effect on wage distribution.

<caption>Figure 20 - CQRs, UQRs, Conditional Mean (Mincer)</caption>

Years of Schooling 2007-2012-2016

<figure><img src="image_24.png" /></figure>
Primary Education 2007-2012-2016

Primary - 2007

<figure><img src="image_25.png" /></figure>

Source: own elaboration based on ENEMDU.

Primary Education - 2012

<figure><img src="image_26.png" /></figure>

Source: own elaboration based on ENEMDU.

Primary Education - 2016

<figure><img src="image_27.png" /></figure>

Source: own elaboration based on ENEMDU.

Higher Education 2007-2012-2016

Higher Education - 2007

<figure><img src="image_28.png" /></figure>

Source: own elaboration based on ENEMDU.

Higher Education - 2012

<figure><img src="image_29.png" /></figure>

Source: own elaboration based on ENEMDU.

Higher Education - 2016

<figure><img src="image_30.png" /></figure>

Source: own elaboration based on ENEMDU.
# 6. LIMITATIONS

Although the work leads to insightful results regarding the distributive performance of Ecuador during 2007-2016, there are several restrictions and limitations that must be pointed out. This exercise implies not only an acknowledgement of the scope of the tools used in the research, but also a potential opportunity for future works that seek to incorporate novel and better methods.

## 6.2. Empirical Shortcomings

To start, relying on survey data could be most of the time enriching since the study can draw on multiple questions that ultimately shape the socioeconomic conditions of the sample in a given moment. However, as these data are nourished by private respondents through the intermediation of an interviewer, some drawbacks might arise. The latter includes non-respondents<fnref n="117" />, wrong responses<fnref n="118" /> and the impossibility to observe the most extreme parts of the distribution, such as top income earners (Fournier & Koske, 2013). In our case, two inconvenients might arise. On the one hand, the probability of an over-estimation of the category Mestizo – produced by a deliberate decision of not being self-recognized as Indigenous or Afro-American<fnref n="119" />. On the other, the under-estimation of the individual wage – usually, witnessed in the top part of the distribution –, which could entail a bias in the estimations.

Moreover, the selection of the type of data always brings to the table a tradeoff situation. Often this is not a decision since the researcher needs to adapt himself to the available information. Even though one of the benefits of RIF regressions is a simpler assessment of the model by using cross-sectional data – a snapshot of the sample in a given moment –, this feature can lead to methodological challenges<fnref n="120" />. Ideally, it would of utmost importance to make a follow up of the same person year to year in order to isolate fixed effects from changes in labor market variables through the use of longitudinal or panel data.

<footnote n="117">¹¹⁷ Usually, the research assumes that the personal decision not to respond to the survey is exogenous from both the dependent and explanatory variables.</footnote>

<footnote n="118">¹¹⁸ As mentioned before, this might not entail a big issue since the methodology is robust to outliers.</footnote>

<footnote n="119">¹¹⁹ See Section 2.2.2.</footnote>

<footnote n="120">¹²⁰ “Measuring inequality and poverty in a society may thus be misleading if one uses only a snapshot of income disparities at a point of time instead of individual income sequences” (Bourguignon & Moreno, 2020).</footnote>
This in turn would help compute consistent estimates (Fortin et al., 2010) as well as permit to deal with problems such as endogeneity when no instrumental variables are available<fnref n="121" />.

In this sense, ENEMDU is a cross-sectional survey whose results are released quarterly. However, given its rotating array design (See Section 4.2.1.), there are follow-up arrays (households' groups) that can offer longitudinal data estimations<fnref n="122" /> (INEC, 2019) – selected households are interviewed in successive moments of windows.

Drawing on similar type of data, for instance, Moreno (2020) constructs a series of short-term synthetic panels<fnref n="123" /> to study long-term distributive performances for the Mexican case in the past 30 years. The author circumscribes his analysis to the association between macroeconomic dynamics and income mobility<fnref n="124" /> through different indicators. Results lead to the conclusion that during the period under analysis, the country witnessed low level of intra-generational mobility. In this sense, a similar approach could be used in further research to assess the effect of educational attainment on income distribution within a larger period of time.

Second, in this work we attempted to isolate the effect of years of schooling (CQRs and UQRs) and dispersion of Human Capital endowments (UCRs) on the interaction with labor income distribution. In order to shed light over a complex phenomenon, the empirical strategy sought to restrict the evaluation to single-variable performance: education. Further analysis nonetheless must widen the scope introducing and studying the influence of other variables, and the existing relationships among them when it comes to gauge distributive dynamics. For instance, it would strengthen the research assessing the impact of minimum wage or job formalization on earnings distribution, and the way they interact, for instance, with an educational expansion.

Third, the selection of income exclusively from labor source, our dependent variable, was mostly based on two criteria. On the one hand, data sources in a country with a poor

<footnote n="121">¹²¹ “Panel data can be used to impute wages for years where an individual is not participating in the labor market” (Fortin et al., 2010).</footnote>

<footnote n="122">¹²² This scheme ensures a 40.7% overlap of the arrays in two consecutive periods.</footnote>

<footnote n="123">¹²³ These panels are based on matching individuals with the same time-invariant attributes in consecutive cross-sections (Bourguignon & Moreno, 2020).</footnote>

<footnote n="124">¹²⁴ The particular definition used in this work understands income mobility as “a transformation from an initial, or first, income distribution to a second, or terminal, income distribution” (Moreno, 2020).</footnote>
information system – concerning microeconomic performance – restricted the research to circumscribe the analysis to this particular type of income. Unfortunately, this inconvenient is a common thread among the majority of the countries in the region. On the other, one of the main channels that affect income distribution are the labor market returns through education (Galiani et al., 2017). This particularly applies to the short-term assessment because earnings are the key source of income of most households. Nevertheless, the monetary approach leaves aside multiple levels of analysis. For instance, in a country that experienced an enormous increase of public education provision such as Ecuador (See Chapter 2), labor income might be a biased measure since it is under-estimated<fnref n="125" />.

Therefore, a complementary approach that assess educational improvement' effect on equality of opportunity, rather than equality of income, might be insightful (Bourguignon et al., 2007). To a certain extent, this could be solved as well with the incorporation of more comprehensive indices that go beyond the mere monetary measure adding up living standards, health, or well-being.

Fourth, the link between education and income distribution is complex not only from a theoretical point of view (Fournier & Koske, 2013) but also in the empirical field (See Chapter 3). Besides causation discussions – already addressed in previous sections – the distributive effect of education is far from being fully understood. For instance, the traditional income-expense analysis overlooks the potential profits of a schooling expansion for those who are not directly beneficiaries of this change and the factors employed to provide those services. Not taking into account externalities hampers the possibility of measuring the effects and repercussions in a wider approach such as the general equilibrium framework (Gasparini et al., 2013).

Furthermore, given the complexity of the interaction a short-term method fails to consistently estimate the effects of education upon income distribution. Intrinsically, schooling enhancement is an investment for future generations (Bourguignon & Rogers, 2007). Increasing public expenditure in education do not necessarily means immediate income gaps reduction. On the contrary, they might be worsened at first (Paradox of

<footnote n="125">¹²⁵ Overall, this is the case of countries where the provision of public goods is non-negligeable. A considerable part of household expenses is assumed by the Government, therefore a household has much more income left.</footnote>
Progress). Probably, not even a change in consumption from the poorest households is expected before the presence of imperfect capital markets<fnref n="126" />. Even though the selected period depicts a clear picture of the income-education performance, it would be desirable to enlarge the time window to better capture the trade-off above-mentioned.

Finally, it will be of high interest to link these decomposition microeconomic models to a bigger framework searching for a detailed analysis of external shocks produced either by policies or exogenous movements. Putting forward this connection will permit to understand the underlying causes of, for instance, a schooling enhancement. In this sense, a suitable proposal could be the macro-micro approach. This method is carried out usually by a particular methodology: the Computable General Equilibrium (CGE) models (Gasparini et al., 2013). The usefulness resides on the potential information that we can extract in order to evaluate different policy alternatives with a considerable degree of accuracy.

**6.1. Methodological Drawbacks**

To conclude, in this subsection we are going to discuss several methodological inconveniences concerning the educational estimates in the particular decomposition models developed in previous chapters. First, the work will briefly summarize the theoretical, and thus general, problems that cause deficient estimates of education. Second, given the socio-economic features of the case study, we will examine concrete sources of bias that might arise when carrying on the estimation process as well as possible solutions for further research.

Typically, within our models’ framework<fnref n="127" />, there are two main sources of bias regarding all the estimators: self-selection, endogeneity.

First, self-selection specially concerns Mincerian equations. In the wage allocation model, the logarithms of the hourly wage of individuals are explained by observable socioeconomic and demographic characteristics. These regressions have a bias because the observed wage turns out to be a banned variable, since the salary corresponding to the people

<footnote n="126">¹²⁶ In economic jargon this is called “moral hazard”. Imperfect capital markets make impossible to borrow against the human capital of one’s descendant (Bourguignon & Rogers, 2007).</footnote>

<footnote n="127">¹²⁷ We talk about the three models we will rely on: the Mincer equation, centered on the conditional mean, and both, CQR and UQR (through RIF methodology), centered on the quantiles of the conditional and unconditional distribution, respectively.</footnote>
who decide not to enter the labor market cannot be observed and thus, remains in the error term. The decision to work or not (which is not observable) operates as a self-selection mechanism<fnref n="128" />.

One way to overcome this issue is to consider this bias caused by missing data as a problem of omitted variables<fnref n="129" />. In this respect, literature suggests running an auxiliary regression that estimate those unobservables. The two-steps procedure relies initially on a Probit model used to assess the probability of participation in the labor market including the variables that are expected to influence income levels (Winters & Chiodi, 2011). The intensity of participation is calculated through the inverse Mills ratio (IMR), which constructs the Heckman variable $\lambda^{130131}$. Afterwards, we include this ratio as another regressor of the Mincerian equation. In this way, according to Maurizio (2019), it is possible to avoid sample selection bias.

A second source of bias, this time specifically for education variable, is the presence endogeneity in the regression<fnref n="132" />. By and large, this means that there is no conditional independence between schooling ($S_i$) and the error term, $E(\varepsilon|S_i) \neq 0$. Hence, years of schooling could potentially be related to the error term through an unobservable determinant – for instance, unobserved ability –, of the hourly labor income. In fact, one can think that more skillful people would tend to attain higher educational levels and thus, earn greater wages.

<footnote n="128">¹²⁸ This may not only arise from female labor participation (Maurizio, 2019) – vastly documented by the gender wage gap literature –, but also from ethnic discrimination in the labor market (Fortin et al., 2010).</footnote>

<footnote n="129">¹²⁹ Heckman (1979).</footnote>

<footnote n="130">¹³⁰ It is explained by variables that can be the same as those of the Mincer regression, with the addition of some extra variables that do not explain the salary but does explain the participation, and thus can define the convenience or not of working given the market wage.</footnote>

<footnote n="131">¹³¹ As Winters & Chiodi (2011) point out there are some inconvenients when using this approach. Particularly, “it requires instruments in the instruments in the first stage that predict participation in the income-generating activities but do not influence the level of income earned” In order to overcome these issues, in this investigation we will use the number of children in the household as such variable for female labor participation, understanding that the greater the number of young children, the lesser the propensity to work.</footnote>

<footnote n="132">¹³² Typically, there are three sources of endogeneity: a) specification errors (Angrist et al., 2006), b) $S_i$ is not accurately measured (Montes-Rojas, 2011), and c) simultaneity: $y_i$ and $S_i$ are determined within a system.</footnote>
The conventional method used in micro-econometric theory is Instrumental Variables (IV). In this specific case, this means to find a valid instrument<fnref n="133" /> for years of schooling. Usually, literature has attempted to use IV such as the IQ test in order to capture the potential unobservable heterogeneity. However, this test might not only encompass ability, but also other factors that ultimately will produce inconsistent estimates (Wooldridge, 2010).

Moreover, beyond the conditional average analysis, when regarding CQRs<fnref n="134" />, exogeneity means that the position of an individual in the ranking is randomly assigned (Alejo et al., 2021). Though still in an early phase, Alejo et al. (2021) have attempted to focus on this unresolved methodological issue. They propose a first-stage model to evaluate the association between the IV and the endogenous regressors. Ultimately, what is at stake is the IV validity for specific quantiles.

Likewise, some authors suggest that the IV method could also apply for UQRs estimated through RIF methodology<fnref n="135" />. However, Firpo et al. (2009) disregard the issue reducing their set of parameters solely to those that capture changes on unconditional quantiles in the presence of exogenous regressors. Bearing this in mind, Alejo et al. (2014) refrain from diving into the effect of treating education as an endogenous variable. Unlike conditional mean results, in distributive effects, methods handling with this inconvenient are still premature. It is still not clear ex-ante nonetheless that methodological concerns affecting the mean estimates could translate into other type of functionals, as the quantiles<fnref n="136" />. As a result, although a relevant route for further exploration in order to enact more comprehensive studies, this methodological drawback remains beyond the scope of this research.

<footnote n="133">¹³³ Being $z_i$ a proposed instrument for $S_i$, it has to satisfy two conditions: a) range condition: $E(z_iS'_i) \neq 0$, and b) orthogonality: $E(z_iu_i) = 0$. In other words, we search for an instrument that has to be strongly related to the endogenous variable and, at the same time, it has to be unrelated to the error term (Wooldridge, 2010).</footnote>

<footnote n="134">¹³⁴ The same sources of endogeneity for the conditional average apply for the quantiles.</footnote>

<footnote n="135">¹³⁵ Frolich & Melly (2014), for instance, within an impact policy evaluation framework, advice to use IV estimators for unconditional Quantile Treatment Effect for compliers as a powerful tool to identify causal effects.</footnote>

<footnote n="136">¹³⁶ “When the interest lies in inequality, a biased counterfactual distribution that arises by ignoring endogeneities does not necessarily biased the functional of interest for distributive purposes. For example, if neglected endogeneities bias the whole conditional distribution up (or down), this negatively affects the estimation of the mean effect, but not necessarily that of distributive effects, which depend on distances between quantiles and not on their levels.” (Alejo et al., 2014).</footnote>
Scaling down to the national boundaries we will now discuss some possible sources of bias in the estimation of the education's coefficient for Ecuador during the period 2007-2016. Given the socioeconomic features of the Andean country, it is possible to encounter several underlying mechanisms through which a down (up) ward estimation might be in process. Of course, the oncoming reasoning could be extended to other nations in the Latin American region since they have numberless traits in common.

First of all, the huge rate of investment concerning the educational expansion deeply fleshed out in this work would have held a limited impact if a somewhat similar allocation of resources in teaching workforce had never been implemented. When building up solid foundations in an educational system teachers and professors play a meaningful role. In this respect, during the period 2007-2016 the number of professors with a post-graduate level incremented 23% (SENPLADES, 2017). Simultaneously, 19.586 scholarships for studies abroad were granted<fnref n="137" />. The number of teaching professionals within the Primary and Secondary level (children from 5 to 17 years old) in the public system moved only in the period 2009-2014 from 135.593 to 146.500¹³⁸. Particularly, higher education underwent structural changes throughout the decade. Professors with post-graduate studies by 2008 represented 29% of the total while for 2015 the percentage soared to 80.9%. Moreover, the ratio of full-time jobs in universities passed from 39.5% to 58.2% (SENPLADES, 2017). Likewise, the plan Prometeo<fnref n="139" /> brought 507 international academics all over the world to give classes and advocate research.

This other side of the coin – a quality enhancement of the educational system from the provision part of the service – is probably capture neither by the average years of schooling nor by the educational level attained. The students’ academic performance is undoubtedly related to the professor’s educational level. Such a determinant variable nonetheless remains in the error term and hence biases upwardly our estimate $\beta_1$. In order to overcome this artificial overestimation we could think of an IV that incorporates the teaching

<footnote n="137">137 In the previous period (1997-2006) only 237 were granted (SENPLADES, 2017).</footnote>

<footnote n="138">138 Source: Ministerio de Educación (MINEDUC). https://educarecuador.gob.ec/visualizador-estadistico/</footnote>

<footnote n="139">139 “The plan consisted of hiring academics and professionals, through a rigorous selection process, to strengthen the competencies of the Ecuadorian university and other areas of the public sector” (Benito Gil, 2017).</footnote>
improvement occurred during the period. This can be for instance the results of a national
evaluation through the construction of an indicator for each of the selected years.

Another relevant factor that potentially can bias the estimation is the impossibility to split up the education variable into public and private source. As we have seen all along the work, during the period under analysis, Ecuador has witnessed an unprecedented educational expansion compared to previous decades. However, the actor that mostly fueled this dynamic was the government through the provision of this public good. Whereas from the total final consumption in education in 2007 a 54% was provided by the government, in 2016 the number increased to 58.9% (INEC, 2019). At the same time, the educational demand underwent non-negligible changes as well. The expansion of the public apparatus was due not only by an increasing educational supply but also by the rise of the demand. Given the quality improvements in the public provision children, adolescents and young people who previously were part of the private system moved to the former. Within the aggregate Primary and Secondary levels the public enrolment passed from 50.2% in 2009 to 71.5% (Ministerio de Educación, 2018). Similarly, public University enrolment represented for 2016 the 57.85% of the total enrolment. Moreover, this percentage for public non-tertiary level was in 2015 72% (SENESCYT, 2018).

Consequently, the inability to disaggregate the provision of education is overestimating the unequalizing and heterogenous effects of schooling in both CQRs and UQRs. We believe that, if possible, this separation could lead to even more impressive results regarding the compositional change of education along the decade. This might be the case as well for higher education that could perhaps show a sharper decline of its regressive effects on the income distribution.

Finally, the last factor proposed here that future research should take into account is the malnutrition of great part of children, adolescents, and young people in age of attending classes specially for the public educational system. Chronic malnutrition leaves serious consequences. Among all, one of the most serious is the impact on mental and intellectual development, which will affect school performance and the capacities of children, as well as their possibilities of accessing to higher educational levels in the future (OSE, 2019). Although the country experienced some progress – between 2009 and 2016 the
multidimensional poverty<fnref n="140" /> for children and adolescents plummeted from 60% to 42% –
there are still structural problems. For instance, in 2012, the national average of chronic
malnutrition for children younger than 5 years old was of 25%. This panorama worsens
drastically for children pertaining to Indigenous households where the percentage accounted
for 42% in the same year. Surprisingly, Afro-American households showed a smaller
number, barely a 18% (OSE, 2019).

Inasmuch as this underlying dynamic, whose scope clearly surpasses the educational
realm, is not capture by the models – hence, it remains in the error term – our β₁ is
underestimated. This downward bias hampers a correct estimation of the impact of education
on the hourly wage. Unfortunately, the Multidimensional Poverty Index, a feasible way to
overcome this issue, was implemented in 2016, hence it does not cover the period under
analysis.

To conclude, we believe that rather than empirical limitations, these points ought to
be seen as further lines of research. Their improvement will unquestionably help shed light
over the complex dynamic addressed in this paper leading to a better apprehension of the
phenomenon.

<footnote n="140">¹⁴⁰ It is an index constructed from 4 dimensions each of one weight 25%: education, work and social security, health, water and food, and habitat, housing, and healthy environment. Two subdimensions concern exclusively children ageing 5 to 17 years old: “basic education and high school non-attendance”, and “child and adolescent employment” (Castillo & Jácome, 2016).</footnote>
# 7. CONCLUSIONS

This work addressed a particular interaction between two variables, that of education and income distribution. Through the use of modern statistical techniques we attempted to shed light over the distributive impact of a schooling enhancement.

Nevertheless, it is necessary to bear in mind two meaningful aspects. First, the estimations correspond to a particular case in a given moment of time. As any other nation, Ecuador entails a variety of socioeconomic, demographic, and historical characteristics that model a specific context. Second, the link of education and inequality is far from being straightforward (Battistón et al., 2014). On the contrary, both the length and the sign of the relationship can differ substantially according to the theoretical approach and the empirical study case.

In this sense, the research contributes to a developing literature that has recently gained in strength. Although representative of the regional average (Cetrángolo et al., 2018), the Ecuadorian pattern of educational expansion underwent during the decade 2007-2016 stands for its own particularities.

The findings in this analysis suggest that education plays a vital role in determining the hourly labor income with a high statistical significance along all the quantiles. As supported by a vast literature, Human Capital accumulation rises monetary returns consistently during time. However, as already discussed in previous sections, the impact is heterogeneous and increasing once we move to the upper part of the distribution. Yet, we demonstrated that the unequal effect of education decreased considerable shortening the return gap between the bottom and the top.

The expected average return of schooling towards the hourly income sharply dropped from 2007 to 2016. Not only this variable lost its relative importance compared to other socioeconomic attributes of individuals but also the way market rewards it underwent a compositional change. This is particularly interesting when focusing on the two ethnicities. Although we can appreciate a similar declining movement from tail to tail, the level and the extent of the schooling return is considerably smaller. Put it differently, Human Capital accumulation does not entail the same reward through market mechanism for Indigenous and
Afro-American. This kind of inequality is complex to address since it is based on biases that are beyond the control of individuals (Aranda & Ratzlaff, 2018).

Moreover, the flattening process of the unconditional quantiles is likely to be explained by the direct intervention of the State by a twofold mechanism. On the one hand, a schooling expansion whose target lied in the lowest quintiles of the income distribution – who are mostly the least educated –, which in turn meant an increase of the average educational level. The fact coincides with the empirical study carried out by Galiani et al. (2017). The authors point out a remarkable drop in the wage premium to education witnessed in the region with special emphasis during the first decade of the 2000s (See also Birdsall et al., 2011). On the other, a soaring public expenditure whose main sectors were construction and services. This shift towards less skilled-labor industries reconfigured the market returns at the expenses of high-skilled workers. Hence, the two dynamics implied not only a rise in the mean hourly wage but also an enlargement of observations around this value (See Figure 16). This dynamic could have compensated the second mechanism of the Paradox of Progress mentioned by Bourguignon et al. (2005). Namely, the loss of household income due to the insertion of children and young in the education system, especially in those families placed in the lower quantiles of the distribution, might have been offset by the increment in terms of salaries witnessed in the low-skilled sectors of economy.

Concerning educational levels we appreciate different performances according to the category we select. Primary education remained strongly equalizing, albeit with a more accentuated slope in the last part of the period. Overall, its impact is greater in the lower quantiles of the distribution. Moreover, higher education started the decade with a heterogeneous and increasing effect upon labor income. The situation bolsters the widespread consensus about the regressivity of this educational level (Gasparini et al., 2013) and the attention policy makers are advised to put when allocating resources here. Notwithstanding, surprisingly, the effect of higher education reverted by the last year, which means that before such an expansion the lower quantiles of the distribution would benefit more economically<fnref n="141" />.

<footnote n="141">141 This could be clearly the result of an educational investment that was mostly concentrated in Primary and Higher Education at the expense of, for instance, Secondary level (See Section 2.2.1.).</footnote>
An underlying cause of this overall equalizing pattern could probably be placed in the educational distribution. As mentioned before, the reason why UQRs estimates are markedly more pronounced than those of CQRs is because the former incorporates to the standard channel whereby education impacts the income distribution – the increasing wage dispersion as education rises<fnref n="142" /> – a second mechanism: ex ante educational inequality. This dynamic might be playing a meaningful role when the direction of the impact goes the other way around. In other words, the reduction in the monetary return gaps among the quintiles could have been caused by a more equal distribution of educational endowments within all members in society.

Contrary to Bourguignon et al. (2005), we believe that the distribution of years of schooling is tightly associated with the mechanism of transmission from education to household incomes. In fact, rather than be separate these two phenomena are mutually interacting and two a large extent shaping the way Human Capital accumulation impacts inequalities.

To conclude, by and large, complexity arisen from the topic comes not only from features within national boundaries, but also from their constant change throughout time. The main thrust of future research ought to be the inclusion of these characteristics in both empirical and methodological analysis. That is the reason why the development of more study cases is urgently needed since it would shed light to underlying dynamics that are far from being understood.

<footnote n="142">¹⁴² For the underlying reasons see specifically Section 4.1.2.</footnote>
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# 9. ANNEX

## 9.1. Annex I

<figure><img src="image_31.png" /></figure>

Source: Muñoz Bravo (2017)

## 9.2. Annex II

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>
<figure><img src="image_34.png" /><figcaption>Log Hourly Wage Histogram - 2016</figcaption></figure>

Source: own elaboration based on ENEMDU.

Indigenous vs Mestizo

<figure><img src="image_35.png" /><figcaption>2007</figcaption></figure>

Source: own elaboration based on ENEMDU.

<figure><img src="image_36.png" /><figcaption>2012</figcaption></figure>

Source: own elaboration based on ENEMDU.

<figure><img src="image_37.png" /><figcaption>2016</figcaption></figure>

Source: own elaboration based on ENEMDU.
## Afro-American vs Mestizo

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

## 9.3. Annex III

### Auxiliar Equation - Heckman's Correction

<table><thead><tr><th rowspan="2">Dependent variable: Occupation</th><th colspan="5">2007</th></tr><tr><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P > |z|</th><th colspan="2">[95% Conf. Interval]</th></tr></thead><tbody><tr><td>sex</td><td>0.987</td><td>-0.010</td><td>77.377</td><td>0.000</td><td>0.962</td><td>1.012</td></tr><tr><td>year2</td><td>0.187</td><td>0.000</td><td>132.916</td><td>0.000</td><td>0.185</td><td>0.190</td></tr><tr><td>year3</td><td>-0.002</td><td>0.000</td><td>-117.148</td><td>0.000</td><td>-0.002</td><td>-0.002</td></tr><tr><td>education</td><td>0.028</td><td>0.000</td><td>19.986</td><td>0.000</td><td>0.025</td><td>0.031</td></tr><tr><td>zunderage</td><td>0.035</td><td>-0.010</td><td>6.093</td><td>0.000</td><td>0.024</td><td>0.046</td></tr><tr><td>zunderagesex</td><td>-0.079</td><td>-0.010</td><td>-9.382</td><td>0.000</td><td>-0.095</td><td>-0.062</td></tr><tr><td>_cons</td><td>-3.986</td><td>-0.030</td><td>-129.138</td><td>0.000</td><td>-4.047</td><td>-3.926</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.
Auxiliar Equation - Heckman's Correction

<table><thead><tr><th rowspan="2">Dependent variable: Occupation</th><th colspan="5">2012</th></tr><tr><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P > |z|</th><th colspan="2">[95% Conf. Interval]</th></tr></thead><tbody><tr><td>sex</td><td>0.987</td><td>-0.010</td><td>77.377</td><td>0.000</td><td>0.962</td><td>1.012</td></tr><tr><td>year2</td><td>0.187</td><td>0.000</td><td>132.916</td><td>0.000</td><td>0.185</td><td>0.190</td></tr><tr><td>year3</td><td>-0.002</td><td>0.000</td><td>-117.148</td><td>0.000</td><td>-0.002</td><td>-0.002</td></tr><tr><td>education</td><td>0.028</td><td>0.000</td><td>19.986</td><td>0.000</td><td>0.025</td><td>0.031</td></tr><tr><td>zunderage</td><td>0.035</td><td>-0.010</td><td>6.093</td><td>0.000</td><td>0.024</td><td>0.046</td></tr><tr><td>zunderagesex</td><td>-0.079</td><td>-0.010</td><td>-9.382</td><td>0.000</td><td>-0.095</td><td>-0.062</td></tr><tr><td>_cons</td><td>-3.986</td><td>-0.030</td><td>-129.138</td><td>0.000</td><td>-4.047</td><td>-3.926</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Auxiliar Equation - Heckman's Correction

<table><thead><tr><th rowspan="2">Dependent variable: Occupation</th><th colspan="5">2016</th></tr><tr><th>Coef.</th><th>Std. Err.</th><th>z</th><th>P > |z|</th><th colspan="2">[95% Conf. Interval]</th></tr></thead><tbody><tr><td>sex</td><td>0.987</td><td>-0.010</td><td>77.377</td><td>0.000</td><td>0.962</td><td>1.012</td></tr><tr><td>year2</td><td>0.187</td><td>0.000</td><td>132.916</td><td>0.000</td><td>0.185</td><td>0.190</td></tr><tr><td>year3</td><td>-0.002</td><td>0.000</td><td>-117.148</td><td>0.000</td><td>-0.002</td><td>-0.002</td></tr><tr><td>education</td><td>0.028</td><td>0.000</td><td>19.986</td><td>0.000</td><td>0.025</td><td>0.031</td></tr><tr><td>zunderage</td><td>0.035</td><td>-0.010</td><td>6.093</td><td>0.000</td><td>0.024</td><td>0.046</td></tr><tr><td>zunderagesex</td><td>-0.079</td><td>-0.010</td><td>-9.382</td><td>0.000</td><td>-0.095</td><td>-0.062</td></tr><tr><td>_cons</td><td>-3.986</td><td>-0.030</td><td>-129.138</td><td>0.000</td><td>-4.047</td><td>-3.926</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.
## 9.4. Annex IV

### 9.4.1. Conditional Quantile Regressions

<table><thead><tr><th rowspan="2">Variable</th><th colspan="10">2007</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><th></th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>year2</td><td>0.231***<br>-0.03</td><td>0.208***<br>-0.02</td><td>0.182***<br>-0.02</td><td>0.179***<br>-0.02</td><td>0.194***<br>-0.02</td><td>0.193***<br>-0.02</td><td>0.201***<br>-0.02</td><td>0.201***<br>-0.02</td><td>0.196***<br>-0.02</td><td>0.109***<br>-0.02</td></tr><tr><td>year3</td><td>0.274***<br>-0.03</td><td>0.295***<br>-0.02</td><td>0.292***<br>-0.02</td><td>0.299***<br>-0.02</td><td>0.326***<br>-0.02</td><td>0.344***<br>-0.02</td><td>0.375***<br>-0.02</td><td>0.405***<br>-0.02</td><td>0.444***<br>-0.03</td><td>0.237***<br>-0.02</td></tr><tr><td>sex</td><td>0.23***<br>-0.02</td><td>0.199***<br>-0.01</td><td>0.182***<br>-0.01</td><td>0.177***<br>-0.01</td><td>0.153***<br>-0.01</td><td>0.153***<br>-0.01</td><td>0.126***<br>-0.01</td><td>0.118***<br>-0.01</td><td>0.09***<br>-0.02</td><td>0.085***<br>-0.01</td></tr><tr><td>marital</td><td>0.121***<br>-0.02</td><td>0.108***<br>-0.01</td><td>0.101***<br>-0.01</td><td>0.09***<br>-0.01</td><td>0.097***<br>-0.01</td><td>0.099***<br>-0.01</td><td>0.097***<br>-0.01</td><td>0.107***<br>-0.01</td><td>0.141***<br>-0.02</td><td>0.107***<br>-0.01</td></tr><tr><td>education</td><td>0.053***<br>0</td><td>0.053***<br>0</td><td>0.052***<br>0</td><td>0.052***<br>0</td><td>0.053***<br>0</td><td>0.056***<br>0</td><td>0.057***<br>0</td><td>0.06***<br>0</td><td>0.062***<br>0</td><td>0.054***<br>0</td></tr><tr><td>boss</td><td>0.194**<br>-0.07</td><td>0.379***<br>-0.05</td><td>0.474***<br>-0.04</td><td>0.576***<br>-0.04</td><td>0.685***<br>-0.04</td><td>0.802***<br>-0.04</td><td>0.935***<br>-0.04</td><td>1.191***<br>-0.04</td><td>1.536***<br>-0.06</td><td>0.765***<br>-0.04</td></tr><tr><td>self-employed</td><td>-0.394***<br>-0.06</td><td>-0.217***<br>-0.04</td><td>-0.105**<br>-0.04</td><td>-0.019<br>-0.03</td><td>0.077*<br>-0.03</td><td>0.155***<br>-0.03</td><td>0.268***<br>-0.03</td><td>0.384***<br>-0.04</td><td>0.608***<br>-0.05</td><td>0.082**<br>-0.03</td></tr><tr><td>salaried</td><td>0.251***<br>-0.06</td><td>0.296***<br>-0.04</td><td>0.308***<br>-0.04</td><td>0.297***<br>-0.03</td><td>0.31***<br>-0.03</td><td>0.314***<br>-0.03</td><td>0.341***<br>-0.03</td><td>0.369***<br>-0.03</td><td>0.456***<br>-0.05</td><td>0.329***<br>-0.03</td></tr><tr><td>manufacture</td><td>0.16***<br>-0.04</td><td>0.11***<br>-0.02</td><td>0.098***<br>-0.02</td><td>0.077***<br>-0.02</td><td>0.099***<br>-0.02</td><td>0.111***<br>-0.02</td><td>0.083***<br>-0.02</td><td>0.088***<br>-0.02</td><td>0.087**<br>-0.03</td><td>0.1***<br>-0.02</td></tr><tr><td>construction</td><td>0.352***<br>-0.04</td><td>0.299***<br>-0.03</td><td>0.272***<br>-0.02</td><td>0.263***<br>-0.02</td><td>0.256***<br>-0.02</td><td>0.252***<br>-0.02</td><td>0.232***<br>-0.02</td><td>0.208***<br>-0.02</td><td>0.153***<br>-0.03</td><td>0.259***<br>-0.02</td></tr><tr><td>commerce</td><td>0.108**<br>-0.03</td><td>0.073**<br>-0.02</td><td>0.08***<br>-0.02</td><td>0.082***<br>-0.02</td><td>0.088***<br>-0.02</td><td>0.11***<br>-0.02</td><td>0.107***<br>-0.02</td><td>0.14***<br>-0.02</td><td>0.166***<br>-0.03</td><td>0.137***<br>-0.02</td></tr><tr><td>transport</td><td>0.28***<br>-0.05</td><td>0.255***<br>-0.03</td><td>0.245***<br>-0.03</td><td>0.209***<br>-0.03</td><td>0.216***<br>-0.03</td><td>0.232***<br>-0.03</td><td>0.238***<br>-0.03</td><td>0.231***<br>-0.03</td><td>0.235***<br>-0.04</td><td>0.261***<br>-0.02</td></tr><tr><td>tourism</td><td>0.196***<br>-0.05</td><td>0.217***<br>-0.04</td><td>0.168***<br>-0.03</td><td>0.141***<br>-0.03</td><td>0.155***<br>-0.03</td><td>0.201***<br>-0.03</td><td>0.183***<br>-0.03</td><td>0.178***<br>-0.03</td><td>0.175***<br>-0.05</td><td>0.216***<br>-0.03</td></tr><tr><td>teaching</td><td>0.496***<br>-0.05</td><td>0.52***<br>-0.03</td><td>0.55***<br>-0.03</td><td>0.547***<br>-0.03</td><td>0.525***<br>-0.03</td><td>0.495***<br>-0.03</td><td>0.452***<br>-0.03</td><td>0.408***<br>-0.03</td><td>0.384***<br>-0.04</td><td>0.485***<br>-0.03</td></tr><tr><td>public</td><td>0.769***<br>-0.06</td><td>0.759***<br>-0.04</td><td>0.732***<br>-0.03</td><td>0.723***<br>-0.03</td><td>0.711***<br>-0.03</td><td>0.688***<br>-0.03</td><td>0.657***<br>-0.03</td><td>0.646***<br>-0.03</td><td>0.623***<br>-0.05</td><td>0.705***<br>-0.03</td></tr><tr><td>others</td><td>0.264***<br>-0.04</td><td>0.241***<br>-0.02</td><td>0.255***<br>-0.02</td><td>0.253***<br>-0.02</td><td>0.265***<br>-0.02</td><td>0.297***<br>-0.02</td><td>0.305***<br>-0.02</td><td>0.328***<br>-0.02</td><td>0.414***<br>-0.03</td><td>0.301***<br>-0.02</td></tr><tr><td>urban</td><td>0.218***<br>-0.02</td><td>0.146***<br>-0.02</td><td>0.132***<br>-0.01</td><td>0.123***<br>-0.01</td><td>0.1***<br>-0.01</td><td>0.082***<br>-0.01</td><td>0.087***<br>-0.01</td><td>0.078***<br>-0.01</td><td>0.09***<br>-0.02</td><td>0.123***<br>-0.01</td></tr><tr><td>coast</td><td>0.052**<br>-0.02</td><td>0.038**<br>-0.01</td><td>0.021<br>-0.01</td><td>0.005<br>-0.01</td><td>0.013<br>-0.01</td><td>0.011<br>-0.01</td><td>0.022<br>-0.01</td><td>0.026*<br>-0.01</td><td>0.044**<br>-0.02</td><td>0.056***<br>-0.01</td></tr><tr><td>amazon</td><td>-0.001<br>-0.05</td><td>-0.026<br>-0.03</td><td>-0.03<br>-0.03</td><td>-0.023<br>-0.03</td><td>-0.024<br>-0.03</td><td>-0.005<br>-0.03</td><td>0.001<br>-0.03</td><td>-0.017<br>-0.03</td><td>-0.02<br>-0.04</td><td>-0.018<br>-0.02</td></tr><tr><td>lambda</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.19***<br>-0.02</td></tr><tr><td>_cons</td><td>-1.744***<br>-0.06</td><td>-1.394***<br>-0.04</td><td>-1.16***<br>-0.04</td><td>-0.977***<br>-0.04</td><td>-0.865***<br>-0.03</td><td>-0.762***<br>-0.03</td><td>-0.654***<br>-0.04</td><td>-0.552***<br>-0.04</td><td>-0.439***<br>-0.05</td><td>-0.758***<br>-0.04</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2012</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><th></th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>year2</td><td>0.265***<br>-0.03</td><td>0.191***<br>-0.02</td><td>0.18***<br>-0.02</td><td>0.176***<br>-0.02</td><td>0.186***<br>-0.01</td><td>0.185***<br>-0.01</td><td>0.19***<br>-0.02</td><td>0.214***<br>-0.02</td><td>0.198***<br>-0.02</td><td>0.152**<br>-0.06</td></tr><tr><td>year3</td><td>0.251***<br>-0.03</td><td>0.237***<br>-0.02</td><td>0.238***<br>-0.02</td><td>0.248***<br>-0.02</td><td>0.265***<br>-0.01</td><td>0.279***<br>-0.02</td><td>0.304***<br>-0.02</td><td>0.337***<br>-0.02</td><td>0.339***<br>-0.02</td><td>0.233***<br>-0.04</td></tr><tr><td>sex</td><td>0.216***<br>-0.02</td><td>0.194***<br>-0.02</td><td>0.184***<br>-0.01</td><td>0.171***<br>-0.01</td><td>0.155***<br>-0.01</td><td>0.136***<br>-0.01</td><td>0.124***<br>-0.01</td><td>0.114***<br>-0.01</td><td>0.087***<br>-0.02</td><td>0.132**<br>-0.04</td></tr><tr><td>marital</td><td>0.14***<br>-0.02</td><td>0.107***<br>-0.01</td><td>0.104***<br>-0.01</td><td>0.102***<br>-0.01</td><td>0.105***<br>-0.01</td><td>0.099***<br>-0.01</td><td>0.1***<br>-0.01</td><td>0.103***<br>-0.01</td><td>0.134***<br>-0.01</td><td>0.122***<br>-0.01</td></tr><tr><td>education</td><td>0.049***<br>0</td><td>0.048***<br>0</td><td>0.047***<br>0</td><td>0.046***<br>0</td><td>0.047***<br>0</td><td>0.047***<br>0</td><td>0.048***<br>0</td><td>0.049***<br>0</td><td>0.054***<br>0</td><td>0.048***<br>0</td></tr><tr><td>boss</td><td>0.094<br>-0.07</td><td>0.215***<br>-0.05</td><td>0.319***<br>-0.04</td><td>0.405***<br>-0.04</td><td>0.468***<br>-0.04</td><td>0.589***<br>-0.04</td><td>0.761***<br>-0.04</td><td>0.961***<br>-0.04</td><td>1.144***<br>-0.06</td><td>0.542***<br>-0.04</td></tr><tr><td>self-employed</td><td>-0.563***<br>-0.06</td><td>-0.461***<br>-0.04</td><td>-0.361***<br>-0.03</td><td>-0.248***<br>-0.03</td><td>-0.18***<br>-0.03</td><td>-0.098**<br>-0.03</td><td>0.023<br>-0.03</td><td>0.134***<br>-0.04</td><td>0.266***<br>-0.05</td><td>-0.184***<br>-0.03</td></tr><tr><td>salaried</td><td>0.227***<br>-0.06</td><td>0.176***<br>-0.04</td><td>0.164***<br>-0.03</td><td>0.179***<br>-0.03</td><td>0.169***<br>-0.03</td><td>0.181***<br>-0.03</td><td>0.227***<br>-0.03</td><td>0.252***<br>-0.04</td><td>0.26***<br>-0.05</td><td>0.188***<br>-0.03</td></tr><tr><td>manufacture</td><td>0.221***<br>-0.03</td><td>0.205***<br>-0.03</td><td>0.195***<br>-0.02</td><td>0.164***<br>-0.02</td><td>0.132***<br>-0.02</td><td>0.117***<br>-0.02</td><td>0.086***<br>-0.02</td><td>0.063**<br>-0.02</td><td>0.014<br>-0.03</td><td>0.123***<br>-0.02</td></tr><tr><td>construction</td><td>0.354***<br>-0.04</td><td>0.307***<br>-0.03</td><td>0.267***<br>-0.02</td><td>0.239***<br>-0.02</td><td>0.21***<br>-0.02</td><td>0.201***<br>-0.02</td><td>0.158***<br>-0.02</td><td>0.125***<br>-0.02</td><td>0.039<br>-0.03</td><td>0.224***<br>-0.02</td></tr><tr><td>commerce</td><td>0.213***<br>-0.03</td><td>0.179***<br>-0.02</td><td>0.152***<br>-0.02</td><td>0.125***<br>-0.02</td><td>0.095***<br>-0.02</td><td>0.097***<br>-0.02</td><td>0.082***<br>-0.02</td><td>0.067***<br>-0.02</td><td>0.051*<br>-0.02</td><td>0.137***<br>-0.02</td></tr><tr><td>transport</td><td>0.351***<br>-0.04</td><td>0.307***<br>-0.03</td><td>0.253***<br>-0.02</td><td>0.217***<br>-0.02</td><td>0.19***<br>-0.02</td><td>0.18***<br>-0.02</td><td>0.161***<br>-0.02</td><td>0.139***<br>-0.02</td><td>0.089**<br>-0.03</td><td>0.24***<br>-0.02</td></tr><tr><td>tourism</td><td>0.266***<br>-0.05</td><td>0.248***<br>-0.03</td><td>0.223***<br>-0.03</td><td>0.206***<br>-0.03</td><td>0.179***<br>-0.02</td><td>0.155***<br>-0.02</td><td>0.162***<br>-0.03</td><td>0.121***<br>-0.03</td><td>0.045<br>-0.04</td><td>0.218***<br>-0.02</td></tr><tr><td>teaching</td><td>0.63***<br>-0.05</td><td>0.577***<br>-0.03</td><td>0.578***<br>-0.03</td><td>0.596***<br>-0.03</td><td>0.577***<br>-0.02</td><td>0.59***<br>-0.02</td><td>0.552***<br>-0.02</td><td>0.505***<br>-0.03</td><td>0.424***<br>-0.04</td><td>0.574***<br>-0.02</td></tr><tr><td>public</td><td>0.822***<br>-0.05</td><td>0.768***<br>-0.04</td><td>0.747***<br>-0.03</td><td>0.741***<br>-0.03</td><td>0.739***<br>-0.02</td><td>0.735***<br>-0.03</td><td>0.74***<br>-0.03</td><td>0.75***<br>-0.03</td><td>0.701***<br>-0.04</td><td>0.757***<br>-0.03</td></tr><tr><td>others</td><td>0.404***<br>-0.03</td><td>0.353***<br>-0.03</td><td>0.314***<br>-0.02</td><td>0.297***<br>-0.02</td><td>0.28***<br>-0.02</td><td>0.294***<br>-0.02</td><td>0.304***<br>-0.02</td><td>0.31***<br>-0.02</td><td>0.281***<br>-0.03</td><td>0.329***<br>-0.02</td></tr><tr><td>urban</td><td>0.157***<br>-0.02</td><td>0.115***<br>-0.02</td><td>0.099***<br>-0.01</td><td>0.098***<br>-0.01</td><td>0.088***<br>-0.01</td><td>0.089***<br>-0.01</td><td>0.086***<br>-0.01</td><td>0.088***<br>-0.01</td><td>0.088***<br>-0.02</td><td>0.118***<br>-0.01</td></tr><tr><td>coast</td><td>0.114***<br>-0.02</td><td>0.078***<br>-0.01</td><td>0.049***<br>-0.01</td><td>0.036***<br>-0.01</td><td>0.027**<br>-0.01</td><td>0.025**<br>-0.01</td><td>0.017<br>-0.01</td><td>0.018<br>-0.01</td><td>0.01<br>-0.01</td><td>0.088***<br>-0.01</td></tr><tr><td>amazon</td><td>-0.035<br>-0.04</td><td>0.01<br>-0.03</td><td>0.015<br>-0.03</td><td>0.01<br>-0.03</td><td>0.035<br>-0.02</td><td>0.044*<br>-0.02</td><td>0.047*<br>-0.02</td><td>0.061*<br>-0.03</td><td>0.094**<br>-0.03</td><td>0.036<br>-0.02</td></tr><tr><td>lambda</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.102<br>-0.09</td></tr><tr><td>_cons</td><td>-1.424***<br>-0.07</td><td>-0.95***<br>-0.05</td><td>-0.717***<br>-0.04</td><td>-0.574***<br>-0.04</td><td>-0.433***<br>-0.03</td><td>-0.319***<br>-0.03</td><td>-0.247***<br>-0.04</td><td>-0.151***<br>-0.04</td><td>0.046<br>-0.05</td><td>-0.459***<br>-0.13</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2016</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><th></th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>year2</td><td>0.275***<br>-0.02</td><td>0.221***<br>-0.02</td><td>0.207***<br>-0.01</td><td>0.198***<br>-0.01</td><td>0.201***<br>-0.01</td><td>0.21***<br>-0.01</td><td>0.219***<br>-0.01</td><td>0.241***<br>-0.01</td><td>0.262***<br>-0.02</td><td>0.169***<br>-0.04</td></tr><tr><td>year3</td><td>0.28***<br>-0.02</td><td>0.245***<br>-0.02</td><td>0.241***<br>-0.02</td><td>0.242***<br>-0.01</td><td>0.246***<br>-0.01</td><td>0.26***<br>-0.01</td><td>0.285***<br>-0.01</td><td>0.323***<br>-0.01</td><td>0.375***<br>-0.02</td><td>0.229***<br>-0.03</td></tr><tr><td>sex</td><td>0.196***<br>-0.02</td><td>0.162***<br>-0.01</td><td>0.153***<br>-0.01</td><td>0.138***<br>-0.01</td><td>0.128***<br>-0.01</td><td>0.123***<br>-0.01</td><td>0.124***<br>-0.01</td><td>0.122***<br>-0.01</td><td>0.127***<br>-0.01</td><td>0.123***<br>-0.03</td></tr><tr><td>marital</td><td>0.122***<br>-0.02</td><td>0.113***<br>-0.01</td><td>0.113***<br>-0.01</td><td>0.116***<br>-0.01</td><td>0.115***<br>-0.01</td><td>0.112***<br>-0.01</td><td>0.108***<br>-0.01</td><td>0.107***<br>-0.01</td><td>0.111***<br>-0.01</td><td>0.115***<br>-0.01</td></tr><tr><td>education</td><td>0.048***<br>0</td><td>0.046***<br>0</td><td>0.046***<br>0</td><td>0.045***<br>0</td><td>0.044***<br>0</td><td>0.044***<br>0</td><td>0.047***<br>0</td><td>0.051***<br>0</td><td>0.056***<br>0</td><td>0.047***<br>0</td></tr><tr><td>boss</td><td>0.347***<br>-0.06</td><td>0.303***<br>-0.04</td><td>0.358***<br>-0.04</td><td>0.449***<br>-0.03</td><td>0.513***<br>-0.03</td><td>0.662***<br>-0.03</td><td>0.776***<br>-0.03</td><td>0.945***<br>-0.04</td><td>1.221***<br>-0.05</td><td>0.655***<br>-0.03</td></tr><tr><td>self-employed</td><td>-0.439***<br>-0.04</td><td>-0.441***<br>-0.03</td><td>-0.365***<br>-0.03</td><td>-0.278***<br>-0.03</td><td>-0.202***<br>-0.02</td><td>-0.129***<br>-0.02</td><td>-0.018<br>-0.02</td><td>0.101***<br>-0.03</td><td>0.278***<br>-0.04</td><td>-0.152***<br>-0.02</td></tr><tr><td>salaried</td><td>0.503***<br>-0.04</td><td>0.333***<br>-0.03</td><td>0.285***<br>-0.03</td><td>0.274***<br>-0.03</td><td>0.261***<br>-0.02</td><td>0.255***<br>-0.02</td><td>0.272***<br>-0.02</td><td>0.295***<br>-0.03</td><td>0.337***<br>-0.04</td><td>0.33***<br>-0.02</td></tr><tr><td>manufacture</td><td>0.359***<br>-0.03</td><td>0.267***<br>-0.02</td><td>0.229***<br>-0.02</td><td>0.19***<br>-0.02</td><td>0.155***<br>-0.01</td><td>0.115***<br>-0.01</td><td>0.102***<br>-0.02</td><td>0.078***<br>-0.02</td><td>0.069**<br>-0.02</td><td>0.186***<br>-0.01</td></tr><tr><td>construction</td><td>0.324***<br>-0.03</td><td>0.298***<br>-0.02</td><td>0.275***<br>-0.02</td><td>0.239***<br>-0.02</td><td>0.21***<br>-0.02</td><td>0.175***<br>-0.02</td><td>0.152***<br>-0.02</td><td>0.124***<br>-0.02</td><td>0.078**<br>-0.03</td><td>0.238***<br>-0.02</td></tr><tr><td>commerce</td><td>0.312***<br>-0.02</td><td>0.247***<br>-0.02</td><td>0.216***<br>-0.01</td><td>0.185***<br>-0.01</td><td>0.168***<br>-0.01</td><td>0.141***<br>-0.01</td><td>0.124***<br>-0.01</td><td>0.111***<br>-0.01</td><td>0.128***<br>-0.02</td><td>0.214***<br>-0.01</td></tr><tr><td>transport</td><td>0.437***<br>-0.03</td><td>0.373***<br>-0.02</td><td>0.328***<br>-0.02</td><td>0.284***<br>-0.02</td><td>0.249***<br>-0.02</td><td>0.211***<br>-0.02</td><td>0.176***<br>-0.02</td><td>0.139***<br>-0.02</td><td>0.103***<br>-0.03</td><td>0.297***<br>-0.02</td></tr><tr><td>tourism</td><td>0.426***<br>-0.03</td><td>0.343***<br>-0.02</td><td>0.281***<br>-0.02</td><td>0.243***<br>-0.02</td><td>0.23***<br>-0.02</td><td>0.196***<br>-0.02</td><td>0.172***<br>-0.02</td><td>0.163***<br>-0.02</td><td>0.133***<br>-0.03</td><td>0.294***<br>-0.02</td></tr><tr><td>teaching</td><td>0.81***<br>-0.04</td><td>0.689***<br>-0.03</td><td>0.644***<br>-0.02</td><td>0.605***<br>-0.02</td><td>0.567***<br>-0.02</td><td>0.512***<br>-0.02</td><td>0.421***<br>-0.02</td><td>0.343***<br>-0.02</td><td>0.263***<br>-0.03</td><td>0.578***<br>-0.02</td></tr><tr><td>public</td><td>0.959***<br>-0.04</td><td>0.84***<br>-0.03</td><td>0.774***<br>-0.02</td><td>0.726***<br>-0.02</td><td>0.705***<br>-0.02</td><td>0.686***<br>-0.02</td><td>0.65***<br>-0.02</td><td>0.63***<br>-0.02</td><td>0.57***<br>-0.03</td><td>0.76***<br>-0.02</td></tr><tr><td>others</td><td>0.515***<br>-0.03</td><td>0.425***<br>-0.02</td><td>0.406***<br>-0.02</td><td>0.387***<br>-0.01</td><td>0.376***<br>-0.01</td><td>0.377***<br>-0.01</td><td>0.379***<br>-0.01</td><td>0.373***<br>-0.02</td><td>0.372***<br>-0.02</td><td>0.419***<br>-0.01</td></tr><tr><td>urban</td><td>0.098***<br>-0.02</td><td>0.082***<br>-0.01</td><td>0.081***<br>-0.01</td><td>0.07***<br>-0.01</td><td>0.065***<br>-0.01</td><td>0.076***<br>-0.01</td><td>0.077***<br>-0.01</td><td>0.09***<br>-0.01</td><td>0.094***<br>-0.02</td><td>0.095***<br>-0.01</td></tr><tr><td>coast</td><td>0.025<br>-0.02</td><td>0.014<br>-0.01</td><td>0.003<br>-0.01</td><td>-0.006<br>-0.01</td><td>-0.014<br>-0.01</td><td>-0.027**<br>-0.01</td><td>-0.037***<br>-0.01</td><td>-0.051***<br>-0.01</td><td>-0.055***<br>-0.01</td><td>0.016<br>-0.01</td></tr><tr><td>amazon</td><td>-0.134***<br>-0.02</td><td>-0.102***<br>-0.01</td><td>-0.094***<br>-0.01</td><td>-0.096***<br>-0.01</td><td>-0.085***<br>-0.01</td><td>-0.08***<br>-0.01</td><td>-0.066***<br>-0.01</td><td>-0.069***<br>-0.01</td><td>-0.053**<br>-0.02</td><td>-0.085***<br>-0.01</td></tr><tr><td>lambda</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.125<br>-0.07</td></tr><tr><td>_cons</td><td>-1.566***<br>-0.05</td><td>-0.968***<br>-0.04</td><td>-0.717***<br>-0.03</td><td>-0.516***<br>-0.03</td><td>-0.36***<br>-0.03</td><td>-0.234***<br>-0.03</td><td>-0.156***<br>-0.03</td><td>-0.083**<br>-0.03</td><td>0.023<br>-0.04</td><td>-0.455***<br>-0.09</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2007</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><td></td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td></tr></thead><tbody><tr><td colspan="11"><em>Aggregate sample</em></td></tr><tr><td>Primary</td><td>0.309***<br>-0.04</td><td>0.299***<br>-0.03</td><td>0.243***<br>-0.02</td><td>0.236***<br>-0.02</td><td>0.235***<br>-0.02</td><td>0.244***<br>-0.02</td><td>0.231***<br>-0.02</td><td>0.213***<br>-0.02</td><td>0.208***<br>-0.03</td><td>0.247***<br>-0.02</td></tr><tr><td>Basic Education</td><td>0.301***<br>-0.06</td><td>0.292***<br>-0.05</td><td>0.252***<br>-0.04</td><td>0.246***<br>-0.03</td><td>0.241***<br>-0.03</td><td>0.258***<br>-0.04</td><td>0.233***<br>-0.04</td><td>0.225***<br>-0.04</td><td>0.248***<br>-0.05</td><td>0.265***<br>-0.03</td></tr><tr><td>Secondary</td><td>0.491***<br>-0.04</td><td>0.469***<br>-0.03</td><td>0.429***<br>-0.03</td><td>0.426***<br>-0.02</td><td>0.43***<br>-0.02</td><td>0.444***<br>-0.02</td><td>0.454***<br>-0.03</td><td>0.451***<br>-0.03</td><td>0.479***<br>-0.04</td><td>0.474***<br>-0.02</td></tr><tr><td>Middle Education</td><td>0.366***<br>-0.07</td><td>0.351***<br>-0.05</td><td>0.277***<br>-0.04</td><td>0.323***<br>-0.04</td><td>0.325***<br>-0.04</td><td>0.327***<br>-0.04</td><td>0.34***<br>-0.04</td><td>0.355***<br>-0.05</td><td>0.421***<br>-0.06</td><td>0.373***<br>-0.04</td></tr><tr><td>Higher</td><td>0.877***<br>0.772***</td><td>0.884***<br>0.772***</td><td>0.864***<br>0.763***</td><td>0.88***<br>0.76***</td><td>0.899***<br>0.766***</td><td>0.917***<br>0.787***</td><td>0.944***<br>0.809***</td><td>0.971***<br>0.806***</td><td>1.053***<br>0.884***</td><td>0.943***<br>0.794***</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)

<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2012</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><td></td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td></tr></thead><tbody><tr><td colspan="11"><em>Aggregate sample</em></td></tr><tr><td>Primary</td><td>0.306***<br>-0.04</td><td>0.306***<br>-0.04</td><td>0.29***<br>-0.02</td><td>0.266***<br>-0.02</td><td>0.246***<br>-0.02</td><td>0.226***<br>-0.02</td><td>0.211***<br>-0.02</td><td>0.171***<br>-0.02</td><td>0.16***<br>-0.03</td><td>0.233***<br>-0.02</td></tr><tr><td>Basic Education</td><td>0.311***<br>-0.06</td><td>0.311***<br>-0.06</td><td>0.3***<br>-0.04</td><td>0.266***<br>-0.03</td><td>0.258***<br>-0.03</td><td>0.253***<br>-0.03</td><td>0.242***<br>-0.03</td><td>0.226***<br>-0.04</td><td>0.21***<br>-0.05</td><td>0.276***<br>-0.03</td></tr><tr><td>Secondary</td><td>0.52***<br>-0.04</td><td>0.52***<br>-0.04</td><td>0.457***<br>-0.03</td><td>0.434***<br>-0.02</td><td>0.411***<br>-0.02</td><td>0.393***<br>-0.02</td><td>0.377***<br>-0.02</td><td>0.344***<br>-0.03</td><td>0.353***<br>-0.03</td><td>0.418***<br>-0.02</td></tr><tr><td>Middle Education</td><td>0.365***<br>-0.06</td><td>0.365***<br>-0.06</td><td>0.397***<br>-0.04</td><td>0.411***<br>-0.04</td><td>0.38***<br>-0.03</td><td>0.364***<br>-0.03</td><td>0.357***<br>-0.03</td><td>0.328***<br>-0.04</td><td>0.331***<br>-0.05</td><td>0.381***<br>-0.03</td></tr><tr><td>Higher</td><td>0.772***<br>0.831***</td><td>0.772***<br>0.771***</td><td>0.765***<br>0.749***</td><td>0.76***<br>0.737***</td><td>0.766***<br>0.756***</td><td>0.787***<br>0.796***</td><td>0.809***<br>0.86***</td><td>0.806***<br>0.903***</td><td>0.884***<br>0.903***</td><td>0.794***<br>0.8***</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)

<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2016</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Mean</th></tr><tr><td></td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td><td>Est./S.E.</td></tr></thead><tbody><tr><td colspan="11"><em>Aggregate sample</em></td></tr><tr><td>Primary</td><td>0.332***<br>-0.04</td><td>0.295***<br>-0.03</td><td>0.278***<br>-0.02</td><td>0.253***<br>-0.02</td><td>0.227***<br>-0.02</td><td>0.199***<br>-0.02</td><td>0.2***<br>-0.02</td><td>0.199***<br>-0.02</td><td>0.168***<br>-0.03</td><td>0.238***<br>-0.02</td></tr><tr><td>Basic Education</td><td>0.328***<br>-0.06</td><td>0.251***<br>-0.04</td><td>0.244***<br>-0.04</td><td>0.246***<br>-0.03</td><td>0.241***<br>-0.03</td><td>0.246***<br>-0.03</td><td>0.256***<br>-0.03</td><td>0.271***<br>-0.03</td><td>0.289***<br>-0.05</td><td>0.265***<br>-0.03</td></tr><tr><td>Secondary</td><td>0.517***<br>-0.04</td><td>0.452***<br>-0.03</td><td>0.426***<br>-0.02</td><td>0.4***<br>-0.02</td><td>0.368***<br>-0.02</td><td>0.341***<br>-0.02</td><td>0.345***<br>-0.02</td><td>0.356***<br>-0.02</td><td>0.355***<br>-0.04</td><td>0.402***<br>-0.02</td></tr><tr><td>Middle Education</td><td>0.453***<br>-0.05</td><td>0.386***<br>-0.04</td><td>0.393***<br>-0.03</td><td>0.371***<br>-0.03</td><td>0.348***<br>-0.03</td><td>0.338***<br>-0.03</td><td>0.339***<br>-0.03</td><td>0.341***<br>-0.03</td><td>0.336***<br>-0.05</td><td>0.374***<br>-0.03</td></tr><tr><td>Higher</td><td>0.831***<br>0.771***</td><td>0.771***<br>0.771***</td><td>0.765***<br>0.749***</td><td>0.749***<br>0.737***</td><td>0.737***<br>0.756***</td><td>0.756***<br>0.796***</td><td>0.796***<br>0.86***</td><td>0.86***<br>0.903***</td><td>0.903***<br>0.903***</td><td>0.8***<br>0.8***</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
9.4.2. Unconditional Quantile Regressions

<table><thead><tr><th rowspan="2">Variable</th><th colspan="10">2007</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Gini</th></tr><tr><th></th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>Indicator</td><td>0.51</td><td>0.85</td><td>1.07</td><td>1.34</td><td>1.62</td><td>1.97</td><td>2.54</td><td>3.54</td><td>5.48</td><td>52.14</td></tr><tr><td colspan="11"><em>Marginal Effects</em></td></tr><tr><td>year2</td><td>0.165***<br>-0.03</td><td>0.207***<br>-0.02</td><td>0.188***<br>-0.02</td><td>0.216***<br>-0.02</td><td>0.239***<br>-0.02</td><td>0.282***<br>-0.02</td><td>0.291***<br>-0.02</td><td>0.259***<br>-0.02</td><td>0.167***<br>-0.02</td><td>-4.2***<br>-1</td></tr><tr><td>year3</td><td>0.082*<br>-0.04</td><td>0.162***<br>-0.02</td><td>0.191***<br>-0.02</td><td>0.259***<br>-0.02</td><td>0.33***<br>-0.02</td><td>0.413***<br>-0.02</td><td>0.516***<br>-0.02</td><td>0.638***<br>-0.02</td><td>0.621***<br>-0.03</td><td>6.4***<br>-2</td></tr><tr><td>sex</td><td>0.322***<br>-0.03</td><td>0.224***<br>-0.02</td><td>0.156***<br>-0.01</td><td>0.142***<br>-0.01</td><td>0.116***<br>-0.01</td><td>0.104***<br>-0.01</td><td>0.096***<br>-0.01</td><td>0.086***<br>-0.02</td><td>0.093***<br>-0.02</td><td>0<br>-1</td></tr><tr><td>marital</td><td>0.106***<br>-0.03</td><td>0.086***<br>-0.02</td><td>0.089***<br>-0.01</td><td>0.104***<br>-0.01</td><td>0.108***<br>-0.01</td><td>0.101***<br>-0.01</td><td>0.109***<br>-0.01</td><td>0.147***<br>-0.02</td><td>0.162***<br>-0.02</td><td>2.9**<br>-1</td></tr><tr><td>education</td><td>0.039***<br>0</td><td>0.035***<br>0</td><td>0.037***<br>0</td><td>0.041***<br>0</td><td>0.046***<br>0</td><td>0.054***<br>0</td><td>0.069***<br>0</td><td>0.085***<br>0</td><td>0.094***<br>0</td><td>1.4***<br>0</td></tr><tr><td>boss</td><td>0.079<br>-0.09</td><td>0.276***<br>-0.06</td><td>0.422***<br>-0.04</td><td>0.525***<br>-0.04</td><td>0.658***<br>-0.04</td><td>0.771***<br>-0.04</td><td>1.046***<br>-0.05</td><td>1.275***<br>-0.06</td><td>1.283***<br>-0.08</td><td>46.3***<br>-7</td></tr><tr><td>self-employed</td><td>-0.603***<br>-0.08</td><td>-0.216***<br>-0.05</td><td>0.007<br>-0.04</td><td>0.13***<br>-0.03</td><td>0.237***<br>-0.03</td><td>0.315***<br>-0.03</td><td>0.445***<br>-0.03</td><td>0.45***<br>-0.04</td><td>0.303***<br>-0.04</td><td>5.3*<br>-2</td></tr><tr><td>salaried</td><td>0.234**<br>-0.07</td><td>0.297***<br>-0.05</td><td>0.338***<br>-0.04</td><td>0.337***<br>-0.03</td><td>0.359***<br>-0.03</td><td>0.375***<br>-0.03</td><td>0.45***<br>-0.03</td><td>0.423***<br>-0.04</td><td>0.269***<br>-0.04</td><td>-2.3<br>-2</td></tr><tr><td>manufacture</td><td>0.218***<br>-0.05</td><td>0.184***<br>-0.03</td><td>0.145***<br>-0.02</td><td>0.149***<br>-0.02</td><td>0.148***<br>-0.02</td><td>0.136***<br>-0.02</td><td>0.081***<br>-0.02</td><td>-0.049<br>-0.03</td><td>-0.129***<br>-0.03</td><td>-8.4***<br>-2</td></tr><tr><td>construction</td><td>0.414***<br>-0.04</td><td>0.381***<br>-0.03</td><td>0.384***<br>-0.02</td><td>0.41***<br>-0.02</td><td>0.402***<br>-0.02</td><td>0.315***<br>-0.02</td><td>0.196***<br>-0.03</td><td>-0.017<br>-0.03</td><td>-0.091**<br>-0.03</td><td>-7.9**<br>-3</td></tr><tr><td>commerce</td><td>0.278***<br>-0.05</td><td>0.166***<br>-0.03</td><td>0.11***<br>-0.02</td><td>0.131***<br>-0.02</td><td>0.149***<br>-0.02</td><td>0.141***<br>-0.02</td><td>0.124***<br>-0.02</td><td>0.024<br>-0.03</td><td>-0.052<br>-0.03</td><td>-5.4*<br>-3</td></tr><tr><td>transport</td><td>0.531***<br>-0.05</td><td>0.357***<br>-0.03</td><td>0.294***<br>-0.03</td><td>0.299***<br>-0.03</td><td>0.316***<br>-0.03</td><td>0.293***<br>-0.03</td><td>0.265***<br>-0.04</td><td>0.126**<br>-0.04</td><td>-0.079<br>-0.05</td><td>-12.6***<br>-3</td></tr><tr><td>tourism</td><td>0.479***<br>-0.06</td><td>0.303***<br>-0.04</td><td>0.219***<br>-0.03</td><td>0.252***<br>-0.03</td><td>0.221***<br>-0.03</td><td>0.238***<br>-0.03</td><td>0.211***<br>-0.04</td><td>0.08<br>-0.05</td><td>-0.09<br>-0.05</td><td>-14.8***<br>-3</td></tr><tr><td>teaching</td><td>0.297***<br>-0.04</td><td>0.311***<br>-0.03</td><td>0.325***<br>-0.02</td><td>0.428***<br>-0.02</td><td>0.521***<br>-0.02</td><td>0.66***<br>-0.03</td><td>0.845***<br>-0.03</td><td>0.991***<br>-0.05</td><td>0.624***<br>-0.07</td><td>-18.8***<br>-3</td></tr><tr><td>public</td><td>0.291***<br>-0.04</td><td>0.342***<br>-0.02</td><td>0.403***<br>-0.02</td><td>0.543***<br>-0.02</td><td>0.712***<br>-0.02</td><td>0.884***<br>-0.03</td><td>1.206***<br>-0.04</td><td>1.424***<br>-0.06</td><td>1.108***<br>-0.08</td><td>-9.5**<br>-3</td></tr><tr><td>others</td><td>0.358***<br>-0.04</td><td>0.236***<br>-0.03</td><td>0.201***<br>-0.02</td><td>0.244***<br>-0.02</td><td>0.293***<br>-0.02</td><td>0.325***<br>-0.02</td><td>0.386***<br>-0.03</td><td>0.344***<br>-0.03</td><td>0.19***<br>-0.04</td><td>-4.4<br>-3</td></tr><tr><td>urban</td><td>0.233***<br>-0.03</td><td>0.151***<br>-0.02</td><td>0.129***<br>-0.01</td><td>0.114***<br>-0.01</td><td>0.096***<br>-0.01</td><td>0.086***<br>-0.01</td><td>0.098***<br>-0.02</td><td>0.119***<br>-0.02</td><td>0.079***<br>-0.02</td><td>-3.1*<br>-1</td></tr><tr><td>coast</td><td>0.265***<br>-0.02</td><td>0.143***<br>-0.01</td><td>0.081***<br>-0.01</td><td>0.037***<br>-0.01</td><td>-0.003<br>-0.01</td><td>-0.018<br>-0.01</td><td>-0.05***<br>-0.01</td><td>-0.052**<br>-0.02</td><td>-0.029<br>-0.02</td><td>-1.2<br>-1</td></tr><tr><td>amazon</td><td>0.019<br>-0.06</td><td>-0.02<br>-0.03</td><td>-0.016<br>-0.03</td><td>0.013<br>-0.02</td><td>0.021<br>-0.02</td><td>0.025<br>-0.03</td><td>0.031<br>-0.03</td><td>-0.045<br>-0.04</td><td>-0.111*<br>-0.05</td><td>0.4<br>-3</td></tr><tr><td>_cons</td><td>-1.793***<br>-0.08</td><td>-1.31***<br>-0.05</td><td>-1.068***<br>-0.04</td><td>-1.006***<br>-0.04</td><td>-0.957***<br>-0.03</td><td>-0.92***<br>-0.03</td><td>-0.964***<br>-0.04</td><td>-0.825***<br>-0.04</td><td>-0.178***<br>-0.05</td><td>43.3***<br>-3</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2012</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Gini</th></tr><tr><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>Indicator</td><td>0.67</td><td>1.11</td><td>1.45</td><td>1.78</td><td>2.09</td><td>2.43</td><td>2.84</td><td>3.72</td><td>5.57</td><td>43.29</td></tr><tr><td colspan="11"><em>Marginal Effects</em></td></tr><tr><td>year2</td><td>0.19***<br>-0.03</td><td>0.219***<br>-0.02</td><td>0.217***<br>-0.02</td><td>0.23***<br>-0.02</td><td>0.202***<br>-0.01</td><td>0.21***<br>-0.01</td><td>0.249***<br>-0.02</td><td>0.282***<br>-0.02</td><td>0.191***<br>-0.02</td><td>-1.9<br>-1</td></tr><tr><td>year3</td><td>0.034<br>-0.04</td><td>0.146***<br>-0.03</td><td>0.184***<br>-0.02</td><td>0.235***<br>-0.02</td><td>0.23***<br>-0.01</td><td>0.283***<br>-0.01</td><td>0.387***<br>-0.02</td><td>0.541***<br>-0.02</td><td>0.542***<br>-0.03</td><td>6.2***<br>-2</td></tr><tr><td>sex</td><td>0.399***<br>-0.03</td><td>0.291***<br>-0.02</td><td>0.186***<br>-0.01</td><td>0.151***<br>-0.01</td><td>0.092***<br>-0.01</td><td>0.085***<br>-0.01</td><td>0.094***<br>-0.01</td><td>0.081***<br>-0.02</td><td>0.074***<br>-0.02</td><td>-0.5<br>-1</td></tr><tr><td>marital</td><td>0.165***<br>-0.02</td><td>0.146***<br>-0.02</td><td>0.1***<br>-0.01</td><td>0.106***<br>-0.01</td><td>0.09***<br>-0.01</td><td>0.092***<br>-0.01</td><td>0.105***<br>-0.01</td><td>0.119***<br>-0.01</td><td>0.144***<br>-0.02</td><td>-2.2<br>-2</td></tr><tr><td>education</td><td>0.041***<br>0</td><td>0.042***<br>0</td><td>0.035***<br>0</td><td>0.038***<br>0</td><td>0.036***<br>0</td><td>0.04***<br>0</td><td>0.051***<br>0</td><td>0.072***<br>0</td><td>0.079***<br>0</td><td>1***<br>0</td></tr><tr><td>boss</td><td>-0.173*<br>-0.08</td><td>-0.086<br>-0.06</td><td>0.16***<br>-0.05</td><td>0.364***<br>-0.04</td><td>0.45***<br>-0.04</td><td>0.54***<br>-0.04</td><td>0.939***<br>-0.04</td><td>1.179***<br>-0.06</td><td>1.25***<br>-0.08</td><td>44.8***<br>-11</td></tr><tr><td>self-employed</td><td>-0.934***<br>-0.07</td><td>-0.748***<br>-0.05</td><td>-0.339***<br>-0.04</td><td>-0.15***<br>-0.03</td><td>-0.015<br>-0.03</td><td>0.061*<br>-0.03</td><td>0.304***<br>-0.03</td><td>0.361***<br>-0.04</td><td>0.243***<br>-0.04</td><td>9.8***<br>-2</td></tr><tr><td>salaried</td><td>-0.08<br>-0.07</td><td>0.005<br>-0.05</td><td>0.161***<br>-0.04</td><td>0.247***<br>-0.04</td><td>0.253***<br>-0.03</td><td>0.225***<br>-0.03</td><td>0.385***<br>-0.03</td><td>0.43***<br>-0.04</td><td>0.262***<br>-0.04</td><td>-4.9*<br>-2</td></tr><tr><td>manufacture</td><td>0.327***<br>-0.04</td><td>0.257***<br>-0.03</td><td>0.216***<br>-0.02</td><td>0.241***<br>-0.02</td><td>0.157***<br>-0.02</td><td>0.071***<br>-0.02</td><td>0.05*<br>-0.02</td><td>-0.071**<br>-0.02</td><td>-0.179***<br>-0.03</td><td>-9.8**<br>-4</td></tr><tr><td>construction</td><td>0.435***<br>-0.04</td><td>0.422***<br>-0.03</td><td>0.36***<br>-0.02</td><td>0.348***<br>-0.02</td><td>0.244***<br>-0.02</td><td>0.154***<br>-0.02</td><td>0.162***<br>-0.02</td><td>-0.017<br>-0.03</td><td>-0.117***<br>-0.03</td><td>-13.4***<br>-2</td></tr><tr><td>commerce</td><td>0.361***<br>-0.04</td><td>0.231***<br>-0.03</td><td>0.154***<br>-0.02</td><td>0.186***<br>-0.02</td><td>0.122***<br>-0.02</td><td>0.077***<br>-0.01</td><td>0.083***<br>-0.02</td><td>-0.011<br>-0.02</td><td>-0.118***<br>-0.03</td><td>-7.8***<br>-2</td></tr><tr><td>transport</td><td>0.546***<br>-0.05</td><td>0.449***<br>-0.03</td><td>0.329***<br>-0.03</td><td>0.318***<br>-0.03</td><td>0.218***<br>-0.02</td><td>0.156***<br>-0.02</td><td>0.174***<br>-0.03</td><td>0.024<br>-0.03</td><td>-0.161***<br>-0.04</td><td>-18.6***<br>-2</td></tr><tr><td>tourism</td><td>0.557***<br>-0.06</td><td>0.395***<br>-0.04</td><td>0.252***<br>-0.03</td><td>0.256***<br>-0.03</td><td>0.156***<br>-0.02</td><td>0.132***<br>-0.02</td><td>0.112***<br>-0.03</td><td>0.032<br>-0.04</td><td>-0.136***<br>-0.04</td><td>-16.4***<br>-2</td></tr><tr><td>teaching</td><td>0.366***<br>-0.04</td><td>0.342***<br>-0.03</td><td>0.316***<br>-0.02</td><td>0.467***<br>-0.02</td><td>0.463***<br>-0.02</td><td>0.524***<br>-0.02</td><td>0.808***<br>-0.02</td><td>1.087***<br>-0.04</td><td>1.12***<br>-0.07</td><td>-8.1**<br>-2</td></tr><tr><td>public</td><td>0.326***<br>-0.04</td><td>0.36***<br>-0.03</td><td>0.362***<br>-0.02</td><td>0.554***<br>-0.02</td><td>0.563***<br>-0.02</td><td>0.668***<br>-0.02</td><td>1.034***<br>-0.03</td><td>1.546***<br>-0.05</td><td>1.541***<br>-0.08</td><td>4.8*<br>-2</td></tr><tr><td>others</td><td>0.44***<br>-0.04</td><td>0.338***<br>-0.03</td><td>0.288***<br>-0.02</td><td>0.367***<br>-0.02</td><td>0.313***<br>-0.02</td><td>0.269***<br>-0.02</td><td>0.336***<br>-0.02</td><td>0.329***<br>-0.03</td><td>0.201***<br>-0.03</td><td>-10.8***<br>-2</td></tr><tr><td>urban</td><td>0.195***<br>-0.03</td><td>0.138***<br>-0.02</td><td>0.099***<br>-0.01</td><td>0.096***<br>-0.01</td><td>0.096***<br>-0.01</td><td>0.076***<br>-0.01</td><td>0.073***<br>-0.01</td><td>0.088***<br>-0.02</td><td>0.094***<br>-0.02</td><td>-2.3<br>-1</td></tr><tr><td>coast</td><td>0.385***<br>-0.02</td><td>0.25***<br>-0.02</td><td>0.094***<br>-0.01</td><td>0.042***<br>-0.01</td><td>-0.006<br>-0.01</td><td>0.002<br>-0.01</td><td>-0.022*<br>-0.01</td><td>-0.065***<br>-0.01</td><td>-0.111***<br>-0.02</td><td>-7.1***<br>-1</td></tr><tr><td>amazon</td><td>-0.062<br>-0.06</td><td>0.001<br>-0.04</td><td>0.007<br>-0.03</td><td>-0.009<br>-0.02</td><td>-0.006<br>-0.02</td><td>0.022<br>-0.02</td><td>0.054*<br>-0.02</td><td>0.078*<br>-0.04</td><td>0.096<br>-0.05</td><td>2.6<br>-3</td></tr><tr><td>_cons</td><td>-1.403***<br>-0.07</td><td>-0.924***<br>-0.06</td><td>-0.591***<br>-0.04</td><td>-0.614***<br>-0.04</td><td>-0.328***<br>-0.03</td><td>-0.244***<br>-0.03</td><td>-0.466***<br>-0.03</td><td>-0.548***<br>-0.04</td><td>0.068<br>-0.05</td><td>43.9***<br>-2</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
<table><thead><tr><th rowspan="2">Variable</th><th colspan="9">2016</th></tr><tr><th>q10</th><th>q20</th><th>q30</th><th>q40</th><th>q50</th><th>q60</th><th>q70</th><th>q80</th><th>q90</th><th>Gini</th></tr><tr><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th><th>Est./S.E.</th></tr></thead><tbody><tr><td>Indicator</td><td>0.73</td><td>1.25</td><td>1.65</td><td>2.00</td><td>2.35</td><td>2.68</td><td>3.14</td><td>4.12</td><td>5.94</td><td>44.45</td></tr><tr><td colspan="11"><em>Marginal Effects</em></td></tr><tr><td>year2</td><td>0.207***<br>-0.03</td><td>0.244***<br>-0.02</td><td>0.274***<br>-0.02</td><td>0.248***<br>-0.01</td><td>0.216***<br>-0.01</td><td>0.229***<br>-0.01</td><td>0.282***<br>-0.01</td><td>0.333***<br>-0.02</td><td>0.242***<br>-0.01</td><td>0.3<br>-1</td></tr><tr><td>year3</td><td>0.104**<br>-0.03</td><td>0.186***<br>-0.02</td><td>0.238***<br>-0.02</td><td>0.241***<br>-0.02</td><td>0.233***<br>-0.01</td><td>0.273***<br>-0.01</td><td>0.374***<br>-0.01</td><td>0.507***<br>-0.02</td><td>0.457***<br>-0.02</td><td>2.5<br>-2</td></tr><tr><td>sex</td><td>0.381***<br>-0.02</td><td>0.27***<br>-0.02</td><td>0.19***<br>-0.01</td><td>0.124***<br>-0.01</td><td>0.073***<br>-0.01</td><td>0.074***<br>-0.01</td><td>0.073***<br>-0.01</td><td>0.067***<br>-0.01</td><td>0.103***<br>-0.01</td><td>-8.4<br>-8</td></tr><tr><td>marital</td><td>0.096***<br>-0.02</td><td>0.132***<br>-0.01</td><td>0.132***<br>-0.01</td><td>0.124***<br>-0.01</td><td>0.102***<br>-0.01</td><td>0.095***<br>-0.01</td><td>0.112***<br>-0.01</td><td>0.137***<br>-0.01</td><td>0.135***<br>-0.01</td><td>2.2<br>-2</td></tr><tr><td>education</td><td>0.04***<br>0</td><td>0.037***<br>0</td><td>0.037***<br>0</td><td>0.036***<br>0</td><td>0.034***<br>0</td><td>0.036***<br>0</td><td>0.05***<br>0</td><td>0.071***<br>0</td><td>0.069***<br>0</td><td>1.5*<br>-1</td></tr><tr><td>boss</td><td>0.162*<br>-0.08</td><td>0.163**<br>-0.06</td><td>0.312***<br>-0.04</td><td>0.335***<br>-0.04</td><td>0.381***<br>-0.03</td><td>0.52***<br>-0.03</td><td>0.867***<br>-0.04</td><td>1.224***<br>-0.05</td><td>1.203***<br>-0.06</td><td>89.1*<br>-45</td></tr><tr><td>self-employed</td><td>-0.754***<br>-0.07</td><td>-0.642***<br>-0.05</td><td>-0.401***<br>-0.04</td><td>-0.248***<br>-0.03</td><td>-0.102***<br>-0.02</td><td>0.026<br>-0.02</td><td>0.213***<br>-0.02</td><td>0.343***<br>-0.03</td><td>0.276***<br>-0.03</td><td>18.6***<br>-2</td></tr><tr><td>salaried</td><td>0.313***<br>-0.07</td><td>0.297***<br>-0.05</td><td>0.341***<br>-0.04</td><td>0.317***<br>-0.03</td><td>0.285***<br>-0.02</td><td>0.275***<br>-0.02</td><td>0.409***<br>-0.02</td><td>0.493***<br>-0.03</td><td>0.344***<br>-0.03</td><td>2.1<br>-2</td></tr><tr><td>manufacture</td><td>0.498***<br>-0.04</td><td>0.348***<br>-0.03</td><td>0.318***<br>-0.02</td><td>0.255***<br>-0.02</td><td>0.169***<br>-0.01</td><td>0.099***<br>-0.01</td><td>0.056***<br>-0.02</td><td>-0.051**<br>-0.02</td><td>-0.115***<br>-0.02</td><td>-13.5**<br>-5</td></tr><tr><td>construction</td><td>0.586***<br>-0.03</td><td>0.481***<br>-0.03</td><td>0.436***<br>-0.02</td><td>0.334***<br>-0.02</td><td>0.19***<br>-0.02</td><td>0.126***<br>-0.02</td><td>0.036*<br>-0.02</td><td>-0.066**<br>-0.02</td><td>-0.111***<br>-0.02</td><td>7<br>-17</td></tr><tr><td>commerce</td><td>0.52***<br>-0.04</td><td>0.374***<br>-0.03</td><td>0.311***<br>-0.02</td><td>0.248***<br>-0.02</td><td>0.173***<br>-0.01</td><td>0.117***<br>-0.01</td><td>0.083***<br>-0.01</td><td>-0.008<br>-0.02</td><td>-0.041*<br>-0.02</td><td>-10.5<br>-6</td></tr><tr><td>transport</td><td>0.906***<br>-0.04</td><td>0.564***<br>-0.03</td><td>0.416***<br>-0.03</td><td>0.296***<br>-0.02</td><td>0.214***<br>-0.02</td><td>0.16***<br>-0.02</td><td>0.126***<br>-0.02</td><td>-0.002<br>-0.03</td><td>-0.1***<br>-0.03</td><td>-15.7***<br>-2</td></tr><tr><td>tourism</td><td>0.841***<br>-0.05</td><td>0.547***<br>-0.03</td><td>0.427***<br>-0.03</td><td>0.303***<br>-0.02</td><td>0.19***<br>-0.02</td><td>0.146***<br>-0.02</td><td>0.111***<br>-0.02</td><td>0.003<br>-0.03</td><td>-0.06*<br>-0.03</td><td>-19.6*<br>-9</td></tr><tr><td>teaching</td><td>0.547***<br>-0.04</td><td>0.486***<br>-0.02</td><td>0.529***<br>-0.02</td><td>0.559***<br>-0.02</td><td>0.556***<br>-0.01</td><td>0.633***<br>-0.02</td><td>0.885***<br>-0.02</td><td>1.109***<br>-0.04</td><td>0.335***<br>-0.04</td><td>-20.2*<br>-8</td></tr><tr><td>public</td><td>0.507***<br>-0.03</td><td>0.489***<br>-0.02</td><td>0.567***<br>-0.02</td><td>0.626***<br>-0.01</td><td>0.641***<br>-0.01</td><td>0.75***<br>-0.01</td><td>1.104***<br>-0.02</td><td>1.305***<br>-0.03</td><td>1.053***<br>-0.05</td><td>4.2<br>-5</td></tr><tr><td>others</td><td>0.657***<br>-0.04</td><td>0.51***<br>-0.03</td><td>0.474***<br>-0.02</td><td>0.42***<br>-0.02</td><td>0.349***<br>-0.01</td><td>0.32***<br>-0.01</td><td>0.36***<br>-0.02</td><td>0.369***<br>-0.02</td><td>0.276***<br>-0.02</td><td>-9.2<br>-6</td></tr><tr><td>urban</td><td>0.118***<br>-0.02</td><td>0.12***<br>-0.02</td><td>0.096***<br>-0.01</td><td>0.08***<br>-0.01</td><td>0.067***<br>-0.01</td><td>0.056***<br>-0.01</td><td>0.079***<br>-0.01</td><td>0.098***<br>-0.01</td><td>0.087***<br>-0.01</td><td>0.6<br>-2</td></tr><tr><td>coast</td><td>0.261***<br>-0.02</td><td>0.143***<br>-0.01</td><td>0.043***<br>-0.01</td><td>0<br>-0.01</td><td>-0.051***<br>-0.01</td><td>-0.054***<br>-0.01</td><td>-0.089***<br>-0.01</td><td>-0.125***<br>-0.01</td><td>-0.14***<br>-0.01</td><td>-5.4*<br>-2</td></tr><tr><td>amazon</td><td>-0.146***<br>-0.03</td><td>-0.152***<br>-0.02</td><td>-0.126***<br>-0.01</td><td>-0.085***<br>-0.01</td><td>-0.065***<br>-0.01</td><td>-0.039***<br>-0.01</td><td>-0.04***<br>-0.01</td><td>-0.067***<br>-0.02</td><td>-0.081***<br>-0.02</td><td>0.7<br>-3</td></tr><tr><td>_cons</td><td>-1.73***<br>-0.08</td><td>-1.056***<br>-0.05</td><td>-0.807***<br>-0.04</td><td>-0.495***<br>-0.03</td><td>-0.183***<br>-0.03</td><td>-0.112***<br>-0.02</td><td>-0.337***<br>-0.03</td><td>-0.457***<br>-0.03</td><td>0.194***<br>-0.03</td><td>30.6***<br>-2</td></tr></tbody></table>

Source: own elaboration based on ENEMDU.

Note: sample consists on salaried workers between ages 15 and 65.

Significance level: p<0.05 (*), p<0.01 (**), p<0.001 (***)
## 9.6. Annex V

***************************

**********ESTIMATIONS**********

***************************

*Run the append dataset "2016-2012-2007_home"

cd "C:\Users\nicol\Documents\LAGLOBE\4th Semester\2. Mémoire\Plan mémoire\7. Estimations\3. Brouillons excel"

use "C:\Users\nicol\Documents\LAGLOBE\4th Semester\2. Mémoire\Plan mémoire\7. Estimations\1. Bases de Datos\1. ENEMDU\2007-2012-2016_home\2016-2012-2007_home", clear

**We show the regressions for 2016:

***************************

******MINCERIAN EQUATIONS******

***************************

***TOTAL POPULATION

****Without Heckman's Correction

reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612

****Heckman's Correction

probit ocup sexo edad edad2 educa zmenor zmenorsex if periodo==201612

drop xb

predict xb, xb

g lambda16 = normalden(xb)/(normal(xb))

reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz lambda16 if periodo==201612

***INDIGENOUS (etnia==1) & AFRO-AMERICAN (etnia==2)

****Without Heckman's Correction

reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2)

est store mco

****Heckman's Correction

probit ocup sexo edad2 edadt3 educa zmenor zmenorsex if periodo==201612 & (etnia==1 | etnia==2)

drop xb

predict xb, xb

g lambda16_e = normalden(xb)/(normal(xb))

reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz lambda16 if periodo==201612 & (etnia==1 | etnia==2)
**************************************************

**CONDITIONAL QUANTILE REGRESSIONS**

**************************************************

**YEARS OF SCHOOLING**

***TOTAL POPULATION***

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile (.1)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile (.5)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile (.9)

quietly reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612

***INDIGENOUS (etnia==1) & AFRO-AMERICAN (etnia==2)***

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), quantile
(1)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), quantile
(.5)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), quantile
(9)

quietly reg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer
transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2)

**EDUCATIONAL LEVEL**

***TOTAL POPULATION***

quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal
indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile
(1)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal
indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile
(.5)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal
indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, quantile
(9)

quietly reg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal
indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612

***INDIGENOUS (etnia==1) & AFRO-AMERICAN (etnia==2)***

quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal
indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 &
.etnia==1 | etnia==2), quantile (.1)
quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), quantile (.5)

quietly qreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), quantile (.9)

quietly reg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2)

*******************************

**UNCONDITIONAL QUANTILE REGRESSIONS**

*******************************

**YEARS OF SCHOOLING**

***TOTAL POPULATION***

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(10)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(50)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(90)) robust

****Gini's RIF (levels)**

rifhdreg ingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz [aw=fexp] if periodo==201612, rif(gini) robust

noi pctile deciles16=ingtotalh [aw=fexp] if periodo==201612, nq(10)

noi list deciles16 in 1/9

***INDIGENOUS (etnia==1) & AFRO-AMERICAN (etnia==2)**

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(10)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(50)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(90)) robust

**Gini's RIF (levels)**

rifhdreg ingtotalh edadt2 edadt3 sexo emarital educa patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz [aw=fexp] if periodo==201612 & (etnia==1 | etnia==2), rif(gini) robust

noi pctile deciles16_e=ingtotalh [aw=fexp] if periodo==201612 & (etnia==1 | etnia==2), nq(10)

noi list deciles16_e in 1/9

**EDUCATIONAL LEVELS**

drop deciles16
drop deciles16_e

***TOTAL POPULATION

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(10)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(50)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612, rif(q(90)) robust

**Gini's RIF (levels)

rifhdreg ingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz [aw=fexp] if periodo==201612, rif(gini) robust

noi pctile deciles16=ingtotalh [aw=fexp] if periodo==201612, nq(10)

noi list deciles16 in 1/9

***INDIGENOUS (etnia==1) & AFRO-AMERICAN (etnia==2)

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(10)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(50)) robust

quietly rifhdreg logingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz if periodo==201612 & (etnia==1 | etnia==2), rif(q(90)) robust

**Gini's RIF (levels)

rifhdreg ingtotalh edadt2 edadt3 sexo emarital Prim Educ_bas Sec Educ_med Sup patron cuentprop asal indust_man construc comer transp hot_restore ensen secpub otros urbano costa amaz [aw=fexp] if periodo==201612 & (etnia==1 | etnia==2), rif(gini) robust

noi pctile deciles16_e=ingtotalh [aw=fexp] if periodo==201612 & (etnia==1 | etnia==2), nq(10)

noi list deciles16_e in 1/9
10. TABLE & FIGURES

<table><tr><td>Table 1 - Quintiles & Ethnic Origin</td><td>22</td></tr><tr><td>Table 2 - Variables' Summary</td><td>64</td></tr><tr><td>Table 3 - Ethnic Origin: Wage per Hour</td><td>66</td></tr><tr><td>Table 4 - Income quantiles & Educational Levels</td><td>71</td></tr><tr><td>Table 5 - Educational Levels & Ethnic Origin</td><td>72</td></tr><tr><td>Table 6 - Mincerian equation and returns of education. Heckman's correction.</td><td>74</td></tr><tr><td>Table 7 - Educational Level & Poor/Non-Poor</td><td>75</td></tr><tr><td>Table 8 - Conditional Quantile Regressions (CQRs) - Years of Schooling</td><td>77</td></tr><tr><td>Table 9 - Conditional Quantile Regressions (CQRs) - Educational Level</td><td>79</td></tr><tr><td>Table 10 - Unconditional Quantile Regressions (UQRs) - Years of schooling</td><td>82</td></tr><tr><td>Table 11 - Unconditional Quantile Regressions (UQRs) - Educational Level</td><td>84</td></tr><tr><td>Figure 1 - Accrued education public budget (%GDP)</td><td>17</td></tr><tr><td>Figure 2 - Distribution of Public-School Attendance by deciles of per capita income - Ecuador 2006</td><td>18</td></tr><tr><td>Figure 3 - Net Enrolment Rate 2007-2016</td><td>19</td></tr><tr><td>Figure 4 - Gross University Enrolment Rate</td><td>20</td></tr><tr><td>Figure 5 - Wage Gini Coefficient in Ecuador 2007-2016</td><td>21</td></tr><tr><td>Figure 6 - Hourly Wage and Schooling</td><td>28</td></tr><tr><td>Figure 7 - Unemployment and Underemployment in Ecuador 2007-2016</td><td>33</td></tr><tr><td>Figure 8 - Income growth incidence in Ecuador 2007-2016</td><td>34</td></tr><tr><td>Figure 9 - Quantiles Returns to Schooling by Country (LAC)</td><td>39</td></tr><tr><td>Figure 10 - Conditional Mean Expectation</td><td>50</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Figure 11 – Heteroskedasticity</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Figure 12 – Limitations of the Mean</td>
      <td>52</td>
    </tr>
    <tr>
      <td>Figure 13 – Family of Conditional Quantiles</td>
      <td>53</td>
    </tr>
    <tr>
      <td>Figure 14 – Conditional Quantiles: τ = 0.75</td>
      <td>53</td>
    </tr>
    <tr>
      <td>Figure 15 – Conditional vs. Unconditional Distribution</td>
      <td>55</td>
    </tr>
    <tr>
      <td>Figure 16 - Log Hourly Wage Densities by Kernel 2007-2021-2016</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Figure 17 - Lorenz Curve - 2007-2012-2016</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Figure 18 - Estimated Densities by Kernel – Indigenous vs Mestizo</td>
      <td>69</td>
    </tr>
    <tr>
      <td>Figure 19 - Estimated Densities by Kernel – Afro-American vs Mestizo</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Figure 20 - CQRs, UQRs, Conditional Mean (Mincer)</td>
      <td>87</td>
    </tr>
  </tbody>
</table>
Résumé

Parmi tous les facteurs susceptibles d'aggraver la distribution des revenus, l'éducation a suscité un intérêt particulier dans l'académie dans la mesure où son impact distributif reste encore ambigu. Dans ce contexte, le cas de l'Équateur mérite une attention particulière pour la période 2007-2016, au cours de laquelle le pays a connu une hausse de la scolarisation jamais vue dans son histoire. Ainsi, le but principal de ce mémoire est d'analyser les effets distributifs de l'éducation sur les revenus professionnels ainsi que l'impact des niveaux d'éducation (élémentaire, secondaire, universitaire, etc.) sur les différents quantiles de la distribution globale pour l'Équateur tout au long de la décennie analysée. En d'autres termes, l'objectif est d'examiner si l'expansion de l'éducation a exacerbé les inégalités à court terme – en particulier, au profit des couches supérieures de la distribution – ou si elle a été décisive pour améliorer le niveau de scolarisation – et donc, pour réduire les écarts de revenus – pour les plus pauvres.

Mots-clés : éducation, répartition des revenus, Équateur.

Abstract

Among all the possible transmission channels that could worse income distribution, education
has specifically provoked a significant interest in academy insofar as its distributive impact still
remains ambiguous. In this context, the case of Ecuador deserves special attention for the period
2007-2016, during which the country experienced a schooling upgrading never seen before in
its history. Hence, the main thrust of this master thesis is to analyze the distributive effects of
education on labor income as well as the impact of educational levels (basic, primary,
secondary, university, etc.) on the different quantiles of the aggregate distribution for Ecuador
throughout the decade under analysis. In other words, the aim is to examine whether the
educational expansion abovementioned has exacerbated short term inequalities–particularly,
benefiting the upper layers of the distribution–or has been decisive in improving the schooling
attainment–and therefore, narrowing income revenue gaps–for the poorer.

Keywords: education, income distribution, Ecuador.