Master in Economic Development and Growth

Ethnic and Gender Discrimination in labor markets:

The Bolivian case

Diana Balderrama Durn

diana.balderrama_duran.065@student.lu.se

Abstract:The aim of this paper is to identify wage gaps attributable to discrimination in labor markets as consequence of discrimination in endowments such as education. For this purpose, the paper analyzes supply and demand-side of labor market, in order to establish a base line for the econometric analysis.

Some indexes and descriptive data were provided in order to give more support to the econometric findings. The results show that there is evidence of discrimination by gender, ethnicity and region; as well discrimination seems to be explained by education opportunities that may determine further productivity.

One striking result is given by the way to measure the variable ethnicity, depending on the methodology used to define ethnicity, results have large differentials.

Key words: Discrimination, labor market, human capital, gender, ethnicity

EKHR92

Master thesis (15 credits ECTS)

June 2012

Supervisor: Prof. Anders Nilsson

Examiner:

Website www.ehl.lu.se

Table of Contents

Acknowledgements

Firstly thank the Erasmus Mundus Programme for such amazing opportunity that not only provided me the possibility of being part of this prestigious Master, but the possibility of knowing great friends. I also would like to express my gratitude to Lund University, its faculty and the administrative staff. However, my special gratitude to my Thesis Supervisor, Prof. Anders Nilsson, who has been supportive and gave me nice feedbacks and comments to improve this works.

Thanks to GiovannAlarcn, who gave me some hints to do this paper, thanks to my friends and finally, my greatest gratitude to my beloved family, who is always there for me to make me happy.

1. INTRODUCTION

Numerous studies analyzed the gender and race pay differential and the phenomena of wage gaps by separate, however few of them and none for the Bolivian case, analyzes both situation in parallel by splitting samples; thus investigations that studied the relevance of ethnic discrimination do not take into account gender selectiveness in either in educational decisions and, furthermore, in labor markets.

According to human capital theory, higher levels of education may conduce to higher productivity and thus, higher wages. However, from the evidence found form Bolivia, is possible to wonder if labor markets pay equally to all participants, one first approach given by descriptive statistics shows pay differentials in labor markets, nevertheless, disparities can also be rooted in educational markets, as will be shown in the document.

Thus, to set the problem properly is necessary to make a brief analysis of discrimination in education, not only as the first screening filter to labor market opportunities but as a result of wage gaps and their incidence on wealth accumulation.

Is well known that Bolivia is a multi-cultural country with differences in social classes, which are a consequence of the colonial process which took place into a newborn State based in elites that controlled the power. Statistical approach shows that ethnicity is more or less identified with poverty and social exclusion, evident in participative processes like education. For the purpose of this document, from now on the label ethnic will refer to those groups that can be considered descendants of the original inhabitants of Bolivia before the European invasion, which currently show cultural characteristics that, distinguish them from the rest of national society (Stavenhagen, 1995); then, non-ethnic are those who are descendants of European colonizers without miscegenation with original inhabitants.

Despite three Structural Reforms and one change of Political Constitution[footnoteRef:2], Bolivia is still a middle-income country with economy based on extractive industries, with large patterns of discrimination by ethnicity and gender that help prevailing poverty and exclusion. [2: Main Act in Bolivia that defines the guidelines as State and represents the base for legal and legit power. ]

Considering that individuals income comes from labor activities, to explain wage gaps and its determinants, seems to be relevant in order to improve labor markets. The study focuses primarily in ethnicity disparities and found some results for gender issues; in spite the fact that both problematic ethnicity and gender are interesting, limitations of the number of observation coming from the data set, do not permit to elevate robust conclusions about gender, however, is quite strong to analyze ethnicity.

The aim of this paper is to analyze the differentials between the average hourly wages between male and women workers, with ethnic and non-ethnic features, in Bolivian labor market. For this purpose,using the methodology proposed by Oaxaca & Blinder[footnoteRef:3] (1973) will show a comparative analysis of wages controlling by age, sex, and years of schooling; region and type of occupation, among other variables. Thus, wage gaps are decomposed in observable characteristics and non-explained disparities, which are called discrimination. [3: This methodology has been widely used to explain wage gaps between two target groups, i.e. male and female, ethnic and non-ethnic; for further explanation, see methodological section. ]

As secondary objective, in order to findwage discrimination between ethnic and non-ethnic, will be necessary to identify what is ethnicity in Bolivia and the different paradigms that face the measurement of the feature.

The paper is organized as follows: one first section reviews briefly Bolivian economic background. The following section sets the theoretical framework compound by literature referencing to wage discrimination and some empirical evidence found for Bolivian case.

A third section shows a detailed analysis of educational system and labor market, supported by descriptive statistics that smoothly lead to the empirical section where some indexes were constructed to introduce the econometric model.

Methodological section followed by the data, let us reach the econometric model based on Oaxaca & Blinder model to explain wage gaps. Finally, some conclusions are presented to provide a kind of summarizing of the paper.

2. ECONOMIC BACKGROUND

During the 80s Bolivia started a Structural Program Reform which objective was to stabilize the economy after the historical hyperinflation, and thus promote economic growth. Structural Reforms allowed achieving sustainable but still insufficient economic growth during the 90s. (See Chart 1)

Chart 1. Bolivia: GDP, GDP pc and Unemployment growth rates, 1989 2010p

* PPP Adjusted

Note: GDP and GDP pc growth rate are measured in the left axis

Unemployment is measured in the right axis.

Source: Own building with data retrieved from Bolivian National Statistical Institute

Despite many efforts, Second Generation Reforms[footnoteRef:4] and socio-economic policies with different political views, living standards did not improve as expected, especially for the poorest segments of the population. However, recent important attainments in terms of economic indicators due external conditions, gives some hope to recover social indicators that still remain weak in comparison with Latin Americas average. [4: During the 90s Bolivia implemented a sort of second phase reforms under the Washington Consensus guidelines. The aim of these Second Generation Reforms was to deepening macroeconomic stabilization began in 1985. For this purpose eight reforms were established: Institutional Reform, Popular Participation Policy, Administrative Decentralization, Foreign Investment called capitalization, Pensions Reform, Educational Reform, Land Tenure Regime and a System of Sectorial Regulation. (Gray-Molina et.al., 1994)]

Nevertheless, economic recovery promoted the diminishing of unemployment, as showninChart1. During periods ofslowdownin economic activity early 80s and around 2000 rates of unemployment tend to increase while GDP growth rates were declining. In contrast, in periods where the GDP exhibited growth of the product, unemployment decreased see the 90s in the Chart. Although theory suggests that we can expect unemployment to fall when the economy is growing; however it can be seen in the Chart an increasing tendency in economic growth and unemployment in the last period, in opposition with theory. This unusual behavior could be associated with social conflicts and lower levels of investment (domestic and foreign) that may cause damages in job creation processes. (Jemio& Muriel, 2008)

Regarding that, according to the World Bank (2000), urban familys income comes from labor activities 85% in average, this situation makes us wonder if poverty is actually linked with labor access conditions. In fact, labor market features have a determinant role in terms of wage differentials (e.g. rural vs. urban, formal vs. informal, etc.); nevertheless, other features are defined by labor supply, such as willingness, qualification or educational attainment.

Several studies analyze determinant characteristics that determine the probability of being poor by work conditions, explaining differences between individuals such as the jobs where wages are obtained, labor categories and selection of workplaces are closely related to human capital accumulation, indeed, evidence suggest that human capital is one of the most important determinants of income achievements and as a consequence, the reason to differentials in basic needs access.

Table 1. Bolivia: Poverty indicators by geographic situation, 2005-2010e

(percentage)

(e) Estimation, (p) preliminary, n.d. no-data.

Source: Own constructions with data retrieved from Bolivian Economic and Social Policy Analysis Unit (UDAPE).

By 2005, 60% of countrys population was under the poverty line and the incidence of moderate poverty barely reduced 11percentage points until 2010. In Table 1, poverty indicators are shown; is important to notice that rural area has a deeper incidence of moderate and extreme poverty in comparison with the urban area. This is consistent with ethnic distribution, which has more density in rural areas and in the West-side of the country. (See Table 2)

Table 2. Bolivia: Distribution of the population by region and ethnicity

(percentage)

Source: Own constructions with data retrieved from Bolivian National Statistical Institute.

Rural poverty is largely explained by the low levels of productivity in agriculture, in addition, the low prices of their products in marketplaces. Small-scale production, unskilled or semi-skilled labor, natural resources and infrastructure scarcity, as well as high costs of capital, lack of well-defined property rights for land use among others, constraint the enhancement of productivity which finally determines the opportunities to overcome poverty. (Jemio&Choque, 2003)

In urban areas, poverty is closely related to entrepreneurial and self-employment conditions, which are more vulnerable to shocks in demand for unskilled and semi-unskilled labor, the incidence of wage disparities is relatively more important for poorest ones, due the composition of consumption and save; poor householdsspendhigher sharesof their incomein consumption andsave less, in relative terms, whilethe richest familiesspend lower shares to consumption and save bigger shares proportionately. (Kliksberg, 1993)

From the brief analysis above is important to highlight the nature of poverty and productivity, as well as the relevance of labor markets, which in sum, will determine the access to education and thus, much of the future individuals constraints in labor market achievements; topics that will be presented in the paper later.

3. THEORETICAL FRAMEWORK

3.1. Wage Differentials and Discrimination

One way to analyze wage differentials theories by gender and/or ethnicity is to divide discrimination in two mainstream theories: supply-side and demand-side[footnoteRef:5], both complementary. In fact, the close relationship between both roots makes important feed-backs that help to maintain differentials even when open markets tend to minimize the gaps. (Blauet.al., 1998) [5: In the same way that Labor Economics, the supply-side observes the features and abilities of the work force. In the other hand, the demand-side looks for characteristics of workplaces and the performance of the work force. ]

On the supply-side explanations comes from the individual features and decisions that each worker takes. Thus differentials in wages are a result of different qualifications, expectations and behaviors due its gender or ethnicity. Mincer (1974) and Becker (1985) analyze gender discrimination highlighting the relevance of individuals choice whether to invest in human capital or not.

Returns to education are to workers as output to enterprises; individuals choose to invest in human capital to increase their future productivity and thus their returns wages. The mechanism appears to be clear, higher levels of education increase potential productivity of workers, which compensates direct and indirect costs of education.

The assumption behind this theory is that workers always maximize their returns. Human Capital theory explains individuals behaviors in terms of efficiency, thus distribution of workers according to their endowments and expected returns, are observed as a strategy to maximize wellbeing. (Mincer &Polachek, 1974)

Goldin(2006) reveal that men and women have different expectations, showing that women are less ambitious and, in consequence, their real productivity is non-observable. Giving the asymmetric information in labor market, men are averse to hire women when womens productivity is not observable and verifiable by all.It is also attributable to women the higher rates of absenteeism, being an imperfect substitute to male work (Blauet. al. op. cit.). Wage gaps are a result of different endowments, initial rates of investment and continuous productivity that can include learning by doing/apprenticeship and experience.

The individuals decisionsregarding theirlevels of educationor other trainingdeterminesto a largeextent,theirjob characteristics when facing the labor market. These features, however, are often a result of environmental and/or exogenous conditions. Socio-economic environment where individuals develop their abilities can encourage or limit the act of choosing a given level of investment in education. Galor&Zeira (1993) shows, in a two period model, the implications of the accumulation of human capital in an economy with bequests, where the extent endowments will determine the whether choose to accumulate human capital or consume and get into labor market.

Therefore, disparities in human capital accumulation can be determined by family conditions and socio-economic characteristics of the community such as expectations of labor force participation, access to educations, institutions, cultural constraints, or self-selection[footnoteRef:6]. (Altonji& Blank, 1999) [6: Corresponds to the casein whichthe observabilityof the dependent variableis a function ofvalueto take anothervariable.It will be analyzed with more detail in the following sections.]

Nevertheless, dissimilarities in educational quality in schools and universities also creates disincentives to demand education, high quality of education may expect higher returns and advantages in labor markets. In one hand, if employers are aware of the disparities of educational quality, it will be considered while hiring workers; thus discrimination at the demand-side, which was a consequence of discrimination on the supply-side, is not only a result of accumulation of human capital but also the quality of investment in education and training.

On the other hand the absence of complete information about individual characteristics or chosen actions leads to not efficient decisions, typical effects of self-selection models. Models of self-selection illustrate the effects of asymmetric information in a wide range of economic environments; self-selection often maximizes the benefits of the firm. Guasch& Weiss (1980) consider tests that directly discover an individuals true characteristics or actions. This kind of testingcan reduce the cost of adverse selection and moral hazard, besides could lead to the full-information competitive equilibrium.

Explanations on the demand-side are based basically on the labor markets; Aigner& Cain (1977) call them post-market discrimination. Characteristics of labor markets and workplaces preserve discrimination allowing dissimilarities in returns according to appreciate features in the market, such as gender, ethnicity, productivity, etc.

Since discrimination between two target groups, can be produced by several mechanisms, is important to understand the dynamics of each one. Firstly, one group can be allocated in occupations with lower wages or less productivity. Then, discrimination is reflected in the process of hiring, promoting and dismissals; this kind of discrimination is called allocative discrimination. Secondly, some group can be discriminated by receiving lower wages due the type of occupation in the same business; this is called within the wage discrimination. Thirdly, valuative discrimination that is caused by labor market bias, consists in some occupations that are traditionally performed by certain type of individuals.(Petersen & Morgan, 1995)

Considering that the typology presented by Petersen & Morgan could be biased by factors such as education, health or self-selection, as Heckman (1979) pointed out; this typology will be useful to analyze results of discrimination after labor market entering (e.g. valuative discrimination is closely related with segregation, term that is introduced further).

Descriptions for disparities in wages based on gender and/or ethnicity from the demand-side are mainly focused on discrimination in labor markets and the structure of it. It is possible to find some markets that provide opportunities only for one type of worker, or gender. Thus, the main theories of discrimination from the demand-side can be resumed as follows.

Taste for discrimination:Discrimination occurs not by a personal bias, but because is assumed that there are significant differences in average e.g. productivity between two groups of people. This means that since hiring is an imperfect information process, the criteria used to assess the suitability of the candidates is based on stereotypes skills of both groups, giving advantage to one observed group. (Becker, 1971)

Monopolistic model of discrimination: Is defined by non-ethic practices that determine the employability of certain group of people. Thus, employers can collude themselves to hire only women or ethnics. (Madden, 1975)

Overcrowding:This model analyzes the pay gap between men and woman (also applicable to ethnicity or other feature), from segregation information. Segregation is the social process that leads to separations by features between particular activities barely interacted. (Bergmann, 1974) However, the use of this segregation for gender purposes was introduced by Gross (1968). Another extension of the concept was introduced by Hakim (1996), he postulates that segregation canbe horizontalwhen men andwomen work in differenttypesof occupations or verticalwhenmen dominatesectors most important andbestpaid.

Statistical Discrimination: Occurs when disparities between two groups are made on the basis of real or imagined statistical distinctions (Oaxaca & Dickinson, 2005). Arrow (1973) and Phelps (1972) emphasize that the unequal treatment of equally productive workers can occur when employers have imperfect information about the characteristics of the applicants.

Institutional Models: Explain patterns of discrimination of population groups linked to segregation in employment caused by labor market rigidities, which arise from institutional arrangements in some companies or barriers to competition introduced by market powers such as monopoly.

3.2. Empirical Evidence

A significant number of studies assessed the issue of ethnic and gender discrimination combined and separately. However none of them uses the methodology proposed in this paper based on Oaxaca & Blinder (1973) with the particularity of splitting the sample to avoid female selection bias.

Using the Integrated Household Survey (HIS) for 1989, Psacharopoulos(1992)analyzed the effect of education and ethnicity on income for Bolivia and Guatemala, departing from Mincerian equations, he found that ethnic workers receive 23% less than non-ethnic, in the same way, returns to education have 2,4% less return for ethnic than non-ethnic. The main critique to his paper is that total income may contain non-labor revenues; thus finding can be biased by individuals wealth.

Fields(1998)foundthatnon-ethnic workers earnbetween 13% and 28% more thannatives and income differences between ethnics and non-ethnics, explains around 3%-10% of income inequality in urban labor markets; for this purpose, he uses the HIS for 1992 to 1995. Using the same survey, Jimnez &Rivero, analyzes ethnic discrimination. Authors find thatthe absolute difference inhourly earningsby ethnic groupincreased duringthe periods of study, they explain the gap by ethnic discrimination.

Estimating Mincerian equations with Fields Decomposition, Contreras &Galvn (2002) analyzes wage discrimination by adding dummy variables for gender and ethnicity. The principal finding is that discrimination between 1994 and 1999 increased. Authors find that the worst situation is being woman and ethnic. In the same field, Andersen, Mercado & Muriel (2003) examine ethnic discrimination in the educational system and labor market; the analysis of descriptive statistics done by the authors found that labor market discriminations are rooted in educational quality.

opo, Atal& Winder (2006) studied 18 Latin American countries in a panel data using Oaxaca-Blinder Decomposition; they found that men earns 9%-27% more than women in the whole region Latin America), with high cross-country heterogeneity. Differences are larger while talking about formal and informal sector and ethnic differences may appear more important than gender differences.

The study took as"minorities" topersons in the household surveywho speak native languages or self-identified in cases where current language wasnt available.An important methodological concept is, despite beingthe majority in somecountries consideredthe study,for purposes of analysisthese groupswere classified as "minorities, same concept used in the present document.

The paper also conclude thatmen earnmore than womeninany age group,at every level of education inany employment(whetherself-employed,employer oremployee), both large and small companies.Authors moreover foundthat onlyrural womenearn on average the same asmen. Finally they conclude that the ethnic wage gap for Bolivia is 17% and establish this percentage as a non-significant result, because the average for the region is 28% less for ethnic workers.

4. EDUCATION IN BOLIVIA

Bolivia is a multi-ethnic, multi-cultural and multi-lingual country where coexists at least 40 different cultures in nine geographical unites called Departments (see Annex I). Bolivian ethnic diversity is the most important in Latin America, representing 60% of Bolivian total population a unique situation followed by Mexico.

In spite that 94% of total population speaks Spanish only or an ethnic language and Spanish combined, ethnic bindings determine early life conditions and educational exposure regarding geographic situation and family income. Recent studies show a 22% urban citizens learnt ethnic languages as mother tongue, in comparison with 69% for rural areas; in average 38% of total population over five years old learnt to speak with a native language (INE, 2009).

The importance of these data relies in the potential of linguistic integration of ethnic population, which is result of two significant policies, in one hand the universalization of Spanish and in the other hand the Educational Reform started on 1994. Even if Educational Reforms (ER) were no recent, the Reform started on 1994 change the guidelines followed in the past, which, according to Contreras (2000), tried to spread the Spanish language as the key to achieve development.

However in 1994, the new Reform established childrens right to learn in their mother tongue. The purpose was to improve early knowledge basis and teach Spanish as second language gradually, to avoid linguistic barriers and, at the same time, promote better performance at school -attainment and returns- (Cajas, 2000). Other economic reforms accompanied the ER, the Administrative Decentralization and the policy called Popular Participation, oriented to improve the quality of the institutions allowing the different geopolitical structures (Departments and Municipalities) participate in the development of sectors such as education, defining budgets and academic contents according to the region under the supervision of the main structure, the Ministry of Education.

Since the Reform in Education started late regarding the creation of the State and the dimension of the cultural barriers, ethnic population and issues related were undervalued, economic resources are still insufficient to make compulsory both school cycles (primary and high school); by the moment primary schooling is compulsory, high school and college are strongly encouraged.

Disparities in education outcomes have origin in basic constraints, firstly in the access to school and secondly in the permanence and attainment. These differences are observable if we analyze by type: gender, geographic situation and ethnicity. (See Table 3)

Table 3. Bolivia: Compulsory education by rate of enrolment and level of education

(percentage)

Source: Own building with data retrieved from Bolivian National Statistical Institute

In theory,rates must notexceed 100%, however,since the datawereobtained directly fromthe schools, there public registers do not take into account changes in educational unitsdue changes of neighborhoodor city inthe shortterm. Additionally,public schools, for administrative reasonsrelated toeconomic incentives,tend not toreport suchabsences. On the otherhand, the criteria for considering a dropout of school states that onlyafter90 days ofabsence it can be considereda dropout.

Differences between gross[footnoteRef:7] and net[footnoteRef:8] percentages shows large differences, this situation can be explained by the survey system. Since the lack of economic and human resources do not permit to make filters of enrolment tuition and current students each semester of the year, and it is done each two years with results on the third year, net rates attributed to each year are largely different that gross ones. The main reasons are dropouts, re-enrolment, changes of school (to or from private schools), etc. [7: Gross rates are obtained from the Registers of the schools, at the first month of the year.] [8: Net rates correspond to data obtained by the Ministry of Education as part of the Program of School Monitoring, which has as the main objective to ensure the quality of education by resource allocation according to enrolment rates.]

The educational system in Bolivia consists in two years of pre-school, eight years of Primary School compound for five years of basic cycle and three years of intermediate. Four years of Secondary School or high school. In Secondary School is possible to have two kinds of education, the regular one and a technical one, oriented to some non-academic careers.

Non-academic (or technical) careers last three years, college usually takes between five to six years. Post graduate lasts one or two years, master degree lasts two years and PhD. Degrees often takes around four to five years. (For more detail see Annex II)

Given that the rate of illiteracy for the decade is 10% in average, and by 2008 decline to cero;

Bolivian average of years of schooling increased form 7 years to 8.6 years, nevertheless, if is non-ethnic male and live in the city, the probability of belong to the richer 20% is almost 80%, and then years of schooling rises to 14 years. In the other hand, if the individual is ethnic women living in the rural area, average years of schooling decline to 2 years. (UNDP, 2010)

Table 4. Bolivia: Population over 19 years old

by highest level of education achieved and geographic area (percentage), 2001

Note: High school is used as synonymous of Secondary school. Third cycle education can be college or non-universitary education, usually, technic.

Source: Own building with data retrieved from Census 2001 performed by Bolivian National Statistical Institute, other data retrieved form Ministry of Education.

From the Table 4, there are cleardifferencesbetween geographic areasand genderwhen consideringthe maximum educational attainment.Rural areas shows educational levels below than the urban levels, indeed, only 4,1% of rural men and 2,4% of rural women reach the third cycle. In comparison, with 25% and 19,6% in urban areas for men and women, respectively; furthermore, in rural areas 39% of women have no any educational level accomplished while 84,7% of male have at least Primary schooling.

Attendance rates in school primary and secondary cycles- are shown in the following charts, which show clearly that women generally have lower educational levels and higher rates of un-attendance or dropout of school; in the same way, urban and rural gaps in the same aspects.

Chart 2. Bolivia: Attendance rate

by age and mother tongue, 2008

Chart 3. Bolivia: Attendance rate

by age and geographic condition, 2008

Chart 4. Bolivia: Urban attendance rate

by age and gender, 2008

Chart 5. Bolivia: Rural attendance rate

by age and gender, 2008

Source: Own building with data retrieved from Bolivian National Statistical Institute and Ministry of Education

Low rates of attendance reflect the number of children who have to dropout the school, temporary or permanent. According to the Authority in Education, the most frequent reason for non-attendance is the economic scarcity at home. This situation determines the early participation into labor markets in both genders, regardless geographic situation. About 60% of ethnic absences are explained by lack of economic resources, while 30% are explained by the remoteness of the school, and among females, non-attendance around 15 years old is related to maternity. Higher gaps of school access are observable in disperse communities, e.g. Amazon region, where absenteeism rate arises 85%.

Noteworthy thatthe major cause ofabsenteeism areeconomic reasons,followed byfamily reasons, suggestingthat educational policiesdo not affectnon-attendancebutcan have a big impact oneducational quality. This is particularly important because as stated above, the most vulnerable share of the population is ethnic one, which is, at the same time, the poorest share of the population.

Meanwhile, it can be said that economic constraints are identified as the most frequent reason to dropout school, also represents the principal reason to disparities or gaps in terms of educational quality. Certainly, the possibility to access to education and to choose a particular kind of education has its origin in the economic profile of households.

As an illustration, let us suppose that there is a unique demand of education, which represents the disposition of enrolment. Since supply is divided in two bidders, public education implemented by the State and private education, equilibrium will come according the willingness to pay higher prices for education. Even if public education is free of charge, there are some direct and indirect costs that must to afford to have education schooling, represented by P1. P2 represents the cost of the same direct and indirect costs of public education, plus tuition fees. (Figure 1)

Supply curves have different elasticities, representing the costs that each school has to afford. In the case of public education, public expenditure for teachers, infrastructure, etc. Private educational centers have to invest in the same items and also in permits and taxes among others.

As we can see in the figure, according economic profiles of households, richer population will enroll into private supply of education and the poorest will enroll into public education. This is an example of self-selection process that might affect further decisions and accesses to labor markets.

Figure 1. Public education vs. Private education markets

(PEnrolment P2P1E1E2Private supplyPublic supplyDemand)

Source: Own construction.

Likewise countries with low-middle income as Bolivia, Public education is known as low quality education in contrast with private education which has better outcomes, due high ratios of teacher per student, investment in infrastructure and fewer interruptions during the year. Public education is more vulnerable to social and political conflicts that force public schools to stop activities, thus sometimes schooling calendar is not always accomplished.

In the same way, third cycle compound by College and Technical studies, is divided in high quality, low quality institutions and Colleges of special regime[footnoteRef:9]. A difference of Schooling systems, to identify high or low quality is not enough to separate between private and public. High quality is branded as CEUB[footnoteRef:10] affiliated and low quality, usually called as Private Colleges[footnoteRef:11], are part of the universe of 100 Universities and 10 Technic Universities. [9: In accordance with the administration and precedence of budgetary resources, Bolivian third cycle is divided by four categories: States Autonomous Public Universities, States Public Universities, Private Universities and Special Regime Universities -exclusively for ARMY, Police and Diplomacy body-. In addition, the category of Technic Universities, which are under a mix complex regime.] [10: CEUB: Executive Committee of Bolivian University, compound by ten Public Autonomous Universities, nine Departmental and one regional Catholic Universities, seven Departmental Military Engineering Universities.] [11: 40 Private Universities/colleges, those are full-recognized by the Ministry of Education. Special Regime Universities are not considered because they are not really part of the college supply, since the enrolment is constrained.]

Selection bias comes from the comparative advantage of Private School graduates in terms of knowledge and, as well as the economic profile of households as we saw before, there is a correlation between those who can afford private schools and complete the high school, postposing labor market entry; these segment of students have higher probabilities to enroll into CEUB environment.

However, the heterogeneous supply of colleges, also allow the possibility to enroll into private Colleges and Public Universities, both have the particularity of different sort of academic schedules. Whereas many of the CEUB environment have full-time schedules, private ones have in addition partial time or schedules that permit the student work during the day and study during late-afternoon.

Chart 6. Bolivia: Population over 19 years old by highest level of education achieved and Regime (percentage), 2001

Source: Own building with data retrieved from Census 2001 performed by Bolivian National Statistical Institute, other data retrieved form Ministery of Education.

An alternative way to observe educational outcomes by gender and ethnicity are shown in Table 5, which illustrates the unconditional probability of schooling transition[footnoteRef:12]. The total probability of overcoming Primary school was estimated around 70%, which declines to 66% while talking about incomplete Secondary and only 15% for complete College. Observed probabilities are higher for men rather than women, whereas by ethnic condition declines much more. Note that according to household profiles, ethnic and rural students are exposed to low quality schooling. [12: This indicator predicts hours of schooling; although this calculus is not the objective of the paper, for more detail see Greene, 2003. Pp. 910]

Table 5. Bolivia: Probabilities of Schooling Transition

by gender and ethnicity (percentage), 2009

Source: Own building with data retrieved from EPH, 2009 performed by Bolivian National Statistical Institute, other data retrieved form Ministry of Education.

Summarizing, it can be said that ethnic population are exposed to low-quality education due households budgetary profiles, at the same time differences by region are also important, being the less qualified rural population. Analyzing the situation by gender, women are in disadvantage in both areas, rural and urban; however the share of population that shows itself as the most vulnerable are ethnic women living in the rural areas, which average of years of schooling is only two years. In the other hand, the advantaged share of the population is non-ethnic male living in urban areas, experiencing in average 14 18 years of schooling.

5. LABOR MARKET

To analyze Bolivian case is important to understand the main problematic of the country. The fact is that through history Bolivia passed from colonization to the new configuration of the State, the vocational tradition was miner extraction, which still remains nowadays with different characteristics. In 1985 Structural Reforms, as response to the deepest crisis of all the times, in its attempt to stabilize the economy, drove Bolivia into open and free markets, with capitalization processes. Changes of policies reached labor markets; re-localization of miner workers gave place to unemployment, and forward, the large informal sector.

National statistics recognize that over 380.000 individuals lay into poverty and marginality between 1998 and 2002. The same five year period, GDP growth rate decreased in 2,5 percentage points while the rate of population growth was 2%, thus income per capita decreased in 1,9 percentage points[footnoteRef:13] (UDAPE, 2003). With regard to labor market, Structural Reforms also allowed free contract and labor market flexibility, mainly with the purpose to fight against high rates of unemployment caused by the new States structure. Then economic recovery started to be observable and unemployment rates started to decrease, but productive sector did not change substantially; in fact, Bolivian economic growth is highly determined by extraction (oil and gas) and mining sector, and recently in the past decade, some agricultural commodities such as soy and wood. [13: GDP growth rate in 1998 was 5% due to deceleration, by 2002 the GDP growth rate was 2,5%. The same period GDP pc decreased from 2,1% to 0,2% while population growth rate experienced 2% in average. Source, Bolivian Economic and Social Policy Analysis Unit (UDAPE)]

Thus, Bolivian economic growth has been experiencing asymmetric growth, highly concentrated in few sectors oriented to exports; which is relevant because industries before Capitalization[footnoteRef:14] contributed at marginal rates to the GDP formation. In sum, it can be said that the economy was based in sectors that demand low-qualified work force and capital intensive. [14: The primary objective of the Bolivian capitalization of public companies was to move Bolivia to a higher growth path. The Program pursued to increase the capital of six major state-owned enterprises (YPFB, ENTEL, LAB, VINTO, ENFE and ENDE) with input from international capital, but keeping the majority of Bolivians. In addition to monetary capital, international partners would provide technology and management skills. To accomplish this objective, the administrative and technical management of each of the capitalized companies would charge to members contributors of capital and technology, through management contracts.Despitesimilarities withthetraditional privatization, capitalization isa process fundamentally differentand original.While traditionalprivatizationisa direct transferof public assets toprivate sector, benefitingonly the National Treasury, capitalizationis a partnership betweenthe State whoprovides itspublic enterprises anda strategic investorthat providesthe same value in capital, creatinga newanonymous society,with twice theoriginal value of thepublic company the existing stocks are not sold, new stock quotes are delivered to rise capital. Oncecapitalizedthe company,the investor receives49%of the shares andmanagement administrationand theremaining 51% ofthe shares isdistributed for freeamongBolivian citizens older than 21 by 1993. (Antelo, 1994) and (World Bank, 1999)]

By 90s the second group of Structural Reforms improve labor markets, new International Agreements promote industry creation, which helped to reduce unemployment by developing productive sectors labor intensive. However, the coexistence of formal and informal sectors was inescapable; this surviving strategy hastaken strongroots andhas grownin differentformssuch as micro-enterpriseand self-employment, mostly covered by ethnic population and female, who presumably show discriminatory patterns the core of the present study.

Contemporary data presented in Table 7, show remarkable information. Bolivian hourly wage is 7,4 monetary units[footnoteRef:15] in average, however splitting by region we can observe that the less paid region is the West, precisely the region with higher concentration of ethnic population (see Table 2 and Table 7). The highest paid are those who live in the East-side, the lower-ethnic concentrated. [15: Since Bolivian is the way to call national people and Bolivian is also the name of the currency, in this document we use monetary unit instead of Bolivian currency to avoid confusions. As reference, 7,4 monetary units (Bolivians) are equivalent to 0,7 constant 2000 year US dollars.]

Is striking to note that there are no significant geographical differences in average years of schooling, but the gap between professionals and non-professionals is at least eight years, and two distinguishing by ethnicity.

Also important, professional activities receive almost four times the wage that receives a non- professional individual. Ethnic population receives, in average, only the 67% of non-ethnic workers.The Central region concentrates only 23% of total workers, while 77% works in the West or East-side. Nevertheless, 85% of ethnic population is concentrated between West and Central regions. Each region concentrates a productive sector; noteworthy that manufacturing industry is important in both three regions. (More detailed information about sectorial industry in Annex III)

Table 6. Bolivia: Population Distribution by mother tongue,

gender and geographic situation, 2003

* We exclude people who do not talk yet.

Source: Own building with data retrieved from Bolivian National Statistical Institute and Ministry of Education

Table 7. Bolivia: Descriptive statistics of labor market, 2002

(percentage)

Note: (1) In monetary units. (2) Approved years of schooling. (3) Experience = age - years of schooling 6

n.a.: non-applicable

Source: EPH, 2004

With respect of gender and ethnicity, participation in labor markets is almost equal by gender, however, it can be seen the differences between gender and ethnicity (see Table 8). The Table suggests two earlier thoughts: employability of non-ethnic individuals is higher and gender of equal ethnic condition is not significant; however, the last postulation ignores features like education, type of work, sector, work conditions, etc., which could explain differences in wages.

Table 8. Employed populationby sex andethno-linguistic

conditionin capital cities(percentage)

Note: * 55% of male and 45%** of total female participation -in average- in labor market.

Source: Own calculations based on statistics from Bolivian National Statistical Institute.

Some preliminary evidence of wage gaps due features are given; however, descriptive statistics are not decisively, indeed is necessary to deepen the analysis with the objective to determine if wage gaps are caused by demand discrimination or supply discrimination, which in fact will be ethnic discrimination by non-observable features, as will be illustrated with a theoretical example in the following lines.

Before analyzing Bolivian labor markets with data, let us make an illustration of wage formation. Neoclassical theory suggests that the relationship between productivity and wages is positive, where productivity is a function of years of education. Thus, higher levels of education are associated with higher productivity leading to higher returns. (See Figure 2)

Figure 2.MarginalProductivityand Wages

withHomogeneousEducation

(MgPMgP = f(N)MgPW = (MgP)N* Educationw* WagesMgP*MgP*)

Source: Own construction based on Andersen, et.al. (2003)

Illustration shows a simple linear function based on years of schooling as fundamental base for wage formation due productivity performance. However, this figure shows a perfect market with a simple distribution. If we introduce two kinds of workers, called ethnic and non-ethnic, we should have different productivities according their elasticities.

Figure 3.MarginalProductivityand Discrimination in Wages

with Homogeneous Education

(MgPMgP = f(N)MgPNon-ethnicN* EducationwewneWagesMgP*MgP*EthnicEthnic Discrimination)

Source: Own construction based on Andersen, et.al. (2003)

In the figure it can be seen post-market discrimination, typically associated to taste of discrimination or statistical discrimination, while considering that ethnic people have less productivity and then salaries will reflect a gap that is explained only by labor market conditions. The example intuitively shows that minorities cannot be observed in the same way as majorities, thus discrimination occurs due absence of complete information regardless the level of education.

Heckman & Li (2003) demonstrate that returns to schooling are substantial; heterogeneity in returns arises from individuals differentials education, experience, age, etc. while selecting into schooling is based on differences in opportunities and choices. Figure 4 illustrates a simple selection between two schooling options, where household economic profiles shown in Figure 1, will determine the opportunities to enroll into schools type A or B.

Figure 4.MarginalProductivityand Discrimination in Wages

with Heterogeneous Education

(MgPBMgPNon-ethnicN* EducationwebwbwneaMgP*MgP*EthnicEthnic Dis.Educational Dis.AGap Educ.Total Discrimination)

Note: (A) is high-quality education (e.g. private schools), and (B) is low-quality education (e.g. public schools)

Source: Own construction based on Andersen, et.al. (2003)

Under the assumption that ethnic people are exposed to low-quality education (B), after controlling by education, it can be seen that ethnic people are receiving web while non-ethnic people are receiving wnea, the wage gap is attributed to conditional selections giving before entering into labor market, education plus ethnic discrimination. Suppose that a non-ethnic individual is exposed to low-quality education, this individual will receive the same wage than an ethnic individual with high-quality education (A).

The figure shows both kind of discrimination, after market giving by ethnicity discrimination and before labor market, given by the educational system, as analyzed earlier. The same kind of analysis is possible taking into account investment rates, where approaches such as Woytinsky (1967), in his description of "elite" and "egalitarian" approaches to investment in human capital, related to demand-side or supply-side, respectively, where in elitist approach the conditions of the supply are identical, thus everyone has more or less the same real opportunities. In the other postulate, inequality comes from the supply side, since the availability of resources is a constraint to invest in human capital.

6. EMPIRICAL STUDY OF LABOR MARKET

Wage gaps are often associated to discrimination; however discrimination is not homogeneous at all different occupational levels or geographic areas. According to Becker (1971), discrimination is typical from areas with low levels of concentration of the segregated group; the reason is that discrimination is closely related to prejudices and lack of information. Indeed, asymmetric information may cause fears and even rejection of minorities, constraining their access to labor markets.

Thus following Beckers hypothesis, we should expect observable differences in areas where ethnic concentration is lower and/or in occupational stratums where women participation is less likely, being part of minorities.

The level of segregation[footnoteRef:16] is other possible element that may cause wage discrimination; in fact, segregation has its roots on ancient history, this practice became one of the most common ways of discrimination that persists until now. [16: Segregation is understood as the human separation by identifiable characteristics or features such as race, religion or social status, allowing close contact in a hierarchal situation.]

6.1. Duncan Index for Segregation

The objective of calculating the Duncan Index is to observe the distribution of employment by type (e.g. gender or ethnicity) in each occupational group. Is a measure ofdissimilarity thatmeasures the magnitude ofthe deviationsthat exist in reality; with respect toareference valuewhichwe assume isoptimal one. This indexis commonly usedto estimatethe existence of "occupational segregation" inaspecificstratumof the labor forceinalabor market.[footnoteRef:17] [17: Mathematically the Duncan Index is calculated as follows:

Where: ; Mi: number of men in occupation iM: total men in the labor marketFi: number of women in occupation iF: total women in the labor market

Note that the example identifies gender differentials; nevertheless, changing the feature it is also possible to capture differentials in ethnicity, geographic areas and other concepts of interest.]

The simplicity of the index gives as a result the intuition of existence of resistances in labor markets, in terms of job assignment.The Duncan Indexis assessedfrom the averageof the differences inthe relative values ofthe labor force participationbetween two groups (menand women or white and non-white) in acertain occupation.The index fluctuates between (0 - 100), the closer to 100% the higher evidence of segregation[footnoteRef:18]. [18: When the Index takes the value 0, implies that there is no segregation in any job category. In other words, Mi=Fi; however, when the Index takes the value 1, implies that there is a complete segregation in all job categories. This can be seen since when Mi> 0, the Fi = 0 and vice versa.]

Thus the calculus of the urban Duncan Index shows a 20% of segregation between ethnic and non-ethnic workers. The Index by itself cannot assert segregation issues in labor market, however taking a close look of Index composition we can observe that unskilled occupations are concentrating ethnic workers, while white-collar jobs are mainly performed by non-ethnic workers.

Table 9. Urban Duncan Index, 2010

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

The composition of labor market concentration by occupational segment has remained practically the same. In recent years at urban and rural levels we can observe an increasing trend even when at national level the Index shows less segregation, this is explained by the highly concentration of skilled and unskilled occupations, rural areas are more likely to hire unskilled workers as well in urban areas labor markets are more likely to hire skilled workers. This differentials between urban and rural compensates each other, thus the National Duncan Index reflects this increasing of segregation in both partial indexes.

Chart 7.Evolution of the Ethnical Duncan Index, 2005-2010

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

Finally, the selection of the field of labor specialization can also be influenced by the geographical and socio-economic environment, and thus, affect individuals wage expectations. For example, in Bolivia population that lives in rural areas highly correlated with ethnicity are principal employed in agricultural activities, sectors that show low-productivity levels giving as result, low-wages paid.

Analyzing the corresponding Duncan Index for women[footnoteRef:19] we can see that the trend has fluctuated around 40%, this is suggesting that there is more segregation between male and female workers, which remains almost constant in the past decade. [19: The Gender Duncan Index is calculated based on national employment level by categories because there was no available data for desegregated categories of employment by gender and geographic situation combined.]

Chart 8. Evolution of the Gender Duncan Index, 1999-2009

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

Ethnic women are more likely to suffer segregation; a combined Index shows an average of 48% of segregation against this particular group, being the most vulnerable among the target population, while being men and non-ethnic is the optimal condition[footnoteRef:20]. [20: Results are consistent with Aylwin (1995) while stating that there is a huge deterioration in living conditions for the middle class and the poorest in the Latina American Region, however it has become apparent particularly since the 80s. Affects proportionally more women than men, especially since Latin American region have percentages of female-headed households above 20%, which contributes to the phenomenon known as feminization of poverty.]

6.2. Disparity Index

With the purpose to find more hints of discrimination, statistical data retrieved from the household survey[footnoteRef:21] allows the calculus of the Disparity Index ()[footnoteRef:22], which is basically an index that measures the level of representation of certain groups according chosen characteristics, with mobile means. [21: More detail about the survey is explained further in the Data section.] [22: Further details from the Index, uses and calculus, see Koronkiewicz (2008).]

In general the index is calculated to measure the relative access of ethnic people into segmented labor markets. In its simplest way, the index is calculated by finding the proportion of selected population that accomplishes of characteristic one by the proportion of population that accomplishes the characteristic two given certain condition of ethnicity[footnoteRef:23]. The closer to 100% indicates that there are no hints of discrimination, if the index takes values closer to 0%, the more evidence to discrimination in the particular market.[footnoteRef:24] [23: Ethnicity is what people are since they were born in their home country. Ethnic identity is the balance between commitment to or self-identification with the culture and society of origin and commitment to or self-identification with the culture and society. Whereas ethnicity is a permanent characteristic and a static concept, ethnic identity is dynamic and may evolve in several directions. (Constant & Zimmerman, 2006)] [24: Sub index i, represents the salaried group, while sub index j, represents the independent work status. Thus the share of minority is calculated based on the sum of i plus j in row, and in the same way for majority.]

Where majority and minority concepts, are nothing else than the tabulation of the frequencies of population in labor market, that actually accomplishes with the main feature (ethnicity or not), according to two selected employment status salaried[footnoteRef:25] and independent or self-employee. Then, majority is population that is non-identified as ethnic and minority is population in labor market identified as ethnic; those values are taken as representatives to calculate the Index following the theory.[footnoteRef:26] [25: To find the definition of salaried, see Annex IV.] [26: More information about mathematic treatment, see World Bank (2006) and Handbook of Labor Economics (1999).]

To introduce more complexity to the index, the chosen feature ethnicity will be desegregated, in response to the multiple definitions of the variable. According to Constant & Zimmerman (2006), to operationalize ideally the general term of ethnic identity is useful to employ five groups of quantifiable attributes, frequently used in previous research on the measurement of this type of concepts: (i) linguistic; (ii) visible cultural elements; (iii) ethnic self-identification; (iv) ethnic network; and (v) migration history. The relevance of the variable relies in the intricacy of the concept; dimensions like self-identification or just mother tongue might over valuate or under valuate the real character of the variable.

The survey permits to identify only two characteristics, which gives three possibilities: mother tongue, self-identification and the combination of both, which are related but not perfect correlated.

Disparity Index by mother tongue

The Disparity Index () is the measure that points out the gap of individuals choice of employment status (salaried or self-employment / independent worker), selected by groups of mother tongue identification.

For the calculus of this Index, we use one of the most common available options to define ethnicity. Indeed, mother tongue is the characteristic that describes the language skills acquired in the family environment and has the closer relation with Primary Schooling outcomes. In addition, captures non-observable characteristics of cultural environment of households (Howard-Malverde&Canessa, 1995); concept that will be useful further in order to interpret the econometric model.

Hence, Ethnic 1 represents the dummy variable coded:

0 if ethnic mother tongue

Ethnic 1 =

1 If non-ethnic mother tongue

Table 10. Calculus of the Market Shares

by employment status given Ethnic 1

Source: Own calculations based on MECOVI implemented by the Bolivian National Statistical Institute.

Table 11. Calculus of the Disparity Index by employment status given Ethnic 1

Note: (1) corresponds to percentages of chosen characteristic i salaried.

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

As we can see in the Index result, there is no conclusively information about discrimination or not; however, since the index shows almost a 50%, according to the information reviewed above, it might be explained by the abilities obtained before the Educational Reform while Spanish was attempted to spread, and after the Educational Reform, by introducing the language gradually. Then, ethnic students learnt both languages with almost the same proficiency than non-ethnic.

Disparity Index by self-identification

Self-identification sometimes is a sub valuated category; the principal reason is discrimination. Since, Ethnic 2 represents the dummy variable coded:

0 if ethnic self-identification

Ethnic 2 =

1 If non-ethnic self-identification

Table 12. Calculus of the Market Shares

by employment status given Ethnic 2

Source: Own calculations based on MECOVI implemented by the Bolivian National Statistical Institute.

Table 13. Calculus of the Disparity Index by employment status given Ethnic 2

Note: (1) corresponds to percentages of chosen characteristic i salaried.

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

Given that this Index is a measure of representation, the result obtained by using self-identification shows a higher value than the previous one. The analysis might be showing two different possibilities: a) over representation of non-ethnic self-identified workers, and b) the possibility of carrying with the data, the conceptual issues of self-identification.

Bolivia is a multi-ethnic country where coexists at least 40 different cultures, the main problem of self-identification comes from the miscegenation with origins in Colonization and still remains nowadays. Thus, children from ethnic mother and non-ethnic father could identify themselves as ethnic or non-ethnic. Surveys provide data about the first and second ethnic identities, since the survey does not take into account miscegenation as an option and, considering that is not practical to have people partial ethnic/non-ethnic; then, only first identity is taken into account.

What is shown in the table below, is the self-identification by first and second ethnic identity; hence first identification will provide information about the ties and commitment with a particular group, while the second identity refers partially to the origin of one of the parents, since miscegenation can be done not only between ethnics and non-ethnics, but among two different ethnic groups[footnoteRef:27]. [27: Hintsto interpret Table 12: taking as example the city named La Paz, 68,4% of total population identifies itself as ethnic in their first ethnic-identity (commitment). From this 68,4% only 22,5% identifies itself as non-ethnic, which means that they have a father or mother non-ethnic (evidence of miscegenation).]

Table 14. 1st and 2ndEthnic Self-Identification by

Department and region, 2001 (percentage)

* 1ST and 2nd ethnic identities, where N: no-ethnic and E: ethnic.

Source: Own calculations based on Census 2001, retrieved from the Bolivian National Statistical Institute.

Sometimes, in countries with large ethnic diversity, is difficult to separate between ethnic features, culture or race. Self-identification provides then, important data in order to analyze the behavior of particular groups.

Disparity Index by self-identification and mother tongue, combined

Following Constant (et.al.op.cit.), Ethnic 3 represents the combination of two categories, the dummy variable is coded as:

0 if ethnic self-identification and/or native mother tongue[footnoteRef:28] [28: Considering miscegenation and educational programs, but also the criteria expressed by Constant et.al., we will consider as ethnic any individual that accomplishes at least one criteria: mother tongue or self-identification.]

Ethnic 3 =

1 If non-ethnic self-identification neither mother ethnic tongue

Table 15. Calculus of the Market Shares

by employment status given Ethnic 3

Source: Own calculations based on MECOVI implemented by the Bolivian National Statistical Institute.

Table 16. Calculus of the Disparity Index by employment status given Ethnic 3

Note: (1) corresponds to percentages of chosen characteristic i salaried.

Source: Own calculations based on statistic information retrieved from the Bolivian National Statistical Institute.

Even if the Index is closer to 100% with respect of the first index calculated, still remains an important measure of disparity, because there is a 39% preventing to achieve the perfect situation with none disparity between chosen groups. This might be explained by the characteristics of Ethnic 3, with not only non-observable features as language, but self-identification as well, which adds some individual behavior or attachment to the native cultures, aspect that is actually observable by the employer.

The evidence found form the three Disparity indexes suggests that there is a hint of discrimination in employment status, although are no conclusively; however if we combine recent results with the Duncan Index results, it can be suggesting that individuals chooses to be salaried or independent worker if relative income exceeds his alternative paid by being independent (see Table 6 and Annex III). Wage in this case will be set according observable features like years of schooling, and non-observable features like ethnicity or gender. Thus, concentration of native employment will be observable in particular segments of employment which does not require many years of schooling and independent work, e.g. commerce (See Table 7); which is in fact the pattern of individuals choice of employment status given certain features, that are also valid for gender behavior.

Overall, it appears that many workers are willing to enter and remain in self-employment despite receiving returns substantially below their alternative paid employment wage, Hamilton (2000), evidence of large negative self-employment premium. However, as Evans and Leighton (1989) stated, is more likely for poorer wage workers (unemployed, lower-paid workers, discriminated and who have changed jobs frequently) to enter or be self-employment, which supports the previous assumption.

6.3. Mean Test

In spite of the fact that the data shows the hint of discrimination by indexes o representation, the means test for the average hourly salary shows as well interesting results. With the same logic of the frequency calculus, we found again two samples, majority and minority of population that accomplishes ethnic characteristics given by already defined variables ethnicity by mother tongue, by self-identification and both combined.

The mean test is useful to determine whether there is a relationship between two variables. In this case, the relationship between a categorical variable (located in the column named Group/Over) which shows the condition majority/minority, and a scale variable (located in rows) that identifies the mean hourly wage given majority or minority.

The objective of the test is whether there is a statistically significant difference in the average of the scale variable according to the categories. For example, say we have two normally distributed populations, minority and majority, with unknown means mi and ma, respectively. The first sample has m observations and the second sample n observations; if the variances are equal it can be said that it is distributed as a t with m+n-2 degrees of freedom.

In formal terms:

If then,

Then, the population variance is calculated by:

Were,

: mean of the minority sample

: mean of the majority sample

: variance of minority sample

: variance of majority sample

: estimated standard deviation for the population

: estimatedvariance for the population

The confidence interval for 95% of confidence is given by:

Thus the null hypothesis is:

Ho: mi = mawages for minority and majority are equal

and the alternative hypothesis is given by

H1: mit[95% Conf.Interval]

-1-2,057820,498784-4,130-3,035768-1,07988

Ethnic 2GroupOverMean

Linearized

Std. Err.

[95% Conf.Interval]

wagexh

Minority_subpop_15,741760,24702875,2574216,2261

Majority_subpop_27,6587430,43862546,7987478,518738

Test

MeanCoef.Std. Err.tP>t[95% Conf.Interval]

-1-1,916980,503404-3,810-2,903987-0,92998

Ethnic 3GroupOverMean

Linearized

Std. Err.

[95% Conf.Interval]

wagexh

Minority_subpop_16,0843390,27270925,5496486,619029

Majority_subpop_27,5654110,46758266,648648,482182

Test

MeanCoef.Std. Err.tP>t[95% Conf.Interval]

-1-1,481070,541298-2,740,006-2,542375-0,41977

Sample

Characteristic

Sample 1

Ethnic 2

Sample 2

Ethnic 2

Sample 1

Ethnic 3

Sample 2

Ethnic 3

(3)(4)(5)(6)

lnwagexhlnwagexhlnwagexhlnwagexh

Mother tongue (Ethnic 1)

Sex0.09680.0933

(0.0680)(0.0681)

Age0.0146***0.003720.0147***0.00318

(0.00225)(0.00534)(0.00225)(0.00535)

Years of schooling0.0852***0.0728***0.0857***0.0724***

(0.00519)(0.00854)(0.00520)(0.00858)

Experience0.004450.0204**0.004320.0220***

(0.00337)(0.00822)(0.00338)(0.00821)

Sector of employment (0 public, 1 private)-0.0295-0.451***-0.0170-0.454***

(0.0645)(0.0977)(0.0645)(0.0978)

Manufacture (categorical)-0.173**-0.280*-0.172**-0.263*

(0.0699)(0.146)(0.0701)(0.146)

Automotive (categorical)-0.301***-0.283*-0.289***-0.278

(0.0896)(0.171)(0.0899)(0.171)

Hotels and restaurants (categorical)-0.677***-0.380**-0.680***-0.371**

(0.162)(0.152)(0.162)(0.152)

Transports (categorical)-0.230***0.162-0.233***0.159

(0.0820)(0.234)(0.0822)(0.234)

Financial Sector (categorical)0.532***0.1590.535***0.203

(0.163)(0.255)(0.163)(0.254)

Self-identification (Ethnic 2)0.139***0.215***

(0.0409)(0.0790)

Ethnic identity (Ethnic 3)0.0901**0.206***

(0.0410)(0.0790)

Constant0.005790.945***0.02270.969***

(0.143)(0.270)(0.144)(0.270)

Observations1,7204821,720482

R-squared0.2630.3900.2600.389

Standard errors in parentheses

*** p