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Peer effects on locus of control


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Dissertação apresentada à Escola de Economia de São Paulo da Fundação Getulio Vargas como requisito para obtenção do título de Mestre em Economia

Campo de Conhecimento: Microeconomia

Orientador: Prof. Dr. Vladimir Pi-nheiro Ponczek

São Paulo


Chikitani, Maurício.

Peer Effects on Locus of Control / Maurício Chikitani. - 2015. 44 f.

Orientador: Vladimir Pinheiro Ponczek

Dissertação (mestrado) - Escola de Economia de São Paulo. 1. Locus de controle. 2. Ensino fundamental - Brasil. 3. Análise de interação em educação. 4. Interação social. I. Ponczek, Vladimir Pinheiro. II. Dissertação (mestrado) - Escola de Economia de São Paulo. III. Título.




Dissertação apresentada à Escola de Economia de São Paulo da Fundação Getulio Vargas como requisito para obtenção do título de Mestre em Economia

Campo de Conhecimento: Microeconomia

Orientador: Prof. Dr. Vladimir Pi-nheiro Ponczek

Data de Aprovação:

/ /

Banca examinadora:

Prof. Dr. Vladimir Ponczek (Orientador) FGV-EESP

Prof. Dr. Cristine Campos de Xavier Pinto FGV-EESP



À minha família.

Ao Vladimir Ponczek, pelo apoio e orientação. À Cristine Pinto, pela orientação e dados. Aos amigos.



I analyze the role of peer interaction in the determination of the Locus of Control, a measure of how personally responsible people feel about their life affairs. Identification is established using group size variation and instrumental variables based on the panel struc-ture of the data. I study this question on an educational setup, using Middle School data in a Brazilian municipality that includes the Tel Aviv Locus of Control questionnaire. My estimates show no sign of endogenous nor contextual peer effects.



Eu analiso o papel que a interação entre pares tem na determinação do Locus de Controle, uma medida de quão pessoalmente responsável as pessoas se sentem com relação a diferentes aspectos de suas vidas. Eu estabeleço identificação através de variação no tamanho do grupo e de variáveis instrumentais baseadas na estrutura de painel dos dados. Eu estudo a questão no contexto escolar, utilizando os dados de alunos no Ensino Fundamental de um município brasileiro, que inclui o questionário de Tel Aviv de Locus de Controle. Minhas estimativas não apresentam sinais depeer effects endógenos ou contextuais.



List of Figures . . . 9

List of Tables . . . 10

1 Introduction . . . 11

2 Identification in the linear-in-means model . . . 14

2.1 Group size variation . . . 14

2.2 Beyond group interaction . . . 15

3 Data . . . 18

3.1 Locus of Control . . . 19

3.2 BFI and SSRS . . . 22

4 Results . . . 24

5 Conclusion . . . 30

Bibliography . . . 31

A Appendix . . . 35

A.1 Factor Analysis . . . 35

A.2 Psychometric analysis . . . 36

A.2.1 Big Five Inventory . . . 36

A.2.2 Tel Aviv Locus of Control . . . 38

A.3 Questionnaires . . . 40

A.3.1 Tel Aviv Locus of Control . . . 40

A.3.2 SRSS . . . 42


List of Figures


List of Tables

Table 1 – Descriptive Statistics - 2012 Sample . . . 25

Table 2 – Mean Locus of Control Factor by group . . . 25

Table 3 – First Stage - Class Size Approach . . . 26

Table 4 – Second Stage - Class Size Approach . . . 27

Table 5 – Descriptive Statistics - Panel Sample . . . 28

Table 6 – First Stage - Peer of peer Approach . . . 29

Table 7 – BFI items loadings . . . 37


1 Introduction

There is a large body of literature documenting the importance of skills beyond cognitive ability1

in determining success in a variety of outcomes2

. This should come as no surprise: ideas matter, but they may go to waste if not conveyed properly; leaders are often expected to inspire and coordinate, rather than carrying out the work themselves; diligence and hard work are expected to precede success and achievement. As a matter of fact, some findings suggest that these soft skills might be just as important - if not more - as cognitive skills in predicting successful outcomes in life (Duckworth and Seligman (2005), Heckman et al. (2006)).

The psychological trait I study here is the Locus of Control (Rotter, 1966), a measure of how personally responsible a person feels about what happens in its life. People who believe their actions play an important role in determining the course of their lives are said to have ainternal locus; in the opposite side of the spectrum, people withexternal locus will

typically see fate and luck as the main driving power of life. Therefore internal individuals are expected to exert more effort in their endeavors - empirical evidence links a more internal locus to some positive outcomes such as higher academic achievement (Findley and Cooper, 1983), higher work productivity (Judge and Bono, 2001) and smaller likelihood of being incarcerated (Urzua, 2008).

Unlike many of the non-cognitive traits studied in the literature, the Locus of Control is not an essentially interpersonal construct3

, and yet, it is strongly influenced by the social environment (see Strauser et al. (2002) and Schneewind (1995)). Therefore, peer effects may play a role the determination of non-cognitive skills. This is, to the best of my knowledge, the first effort in estimating such relationship.

In the educational environment, it is important to understand peer effects because they may play a big role in designing optimal policies, in particular tracking and class size. Besides, technology has made high quality classes available at a cost and scale that are bound to change


Cognitive ability here is understood as the one associated with problem-solving, reasoning and memory.


Moffitt et al. (2011) provides evidence for self-control predicting crime, health and wealth; personality, as measured by the Big Five Traits, are related to health (Hampson and Friedman, 2008), scholastic achievement (Noftle and Robins, 2007) and job performance (Barrick et al., 1998); for the Locus of Control, see below.




the way education is provided4, but peer interactions is likely to be a feature of traditional

schools that the virtual ones will not be able to properly emulate - understanding it better may provide alternatives for filling this gap.

I use a longitudinal database of Middle School students in a Brazilian municipality that provides me with both a Locus of Control questionnaire and classroom composition informa-tion that will allow me to define peers. The dataset has self-reported informainforma-tion on every sixth grader in the public schooling system of Sertãozinho, a Brazilian municipality near São Paulo, including the Tel Aviv Locus of Control questionnaire (Milgram and Milgram, 1975) from which I draw the estimate of the Locus. Another important feature of my database is the fact that I know where many of these students were in the second grade - this allows me to use different estimation strategies.

I study the typical linear-in-means model of social interactions: individuals belong to a group, whose members influence and get influenced by each other, but have no links to anyone outside the group. I also assume that all individuals within a group are equally influent, so that the mean characteristic of the group - excluding oneself - is the relevant statistic in the skill production function.

Although this model may be restrictive, the literature has typically used it due to data restrictions: as there is usually no explicit information on links, they have to be inferred from some observables such as dorm room (Sacerdote, 2001), classroom (Burke and Sass, 2013), city or precinct (Glaeser et al., 1996), or work station (Hamilton et al., 2003). In such situations, the linear-in-means model is a natural candidate.

In a seminal paper, Manski (1993) points out the difficulties of properly identifying the parameters in this model. Even with prior knowledge on the group composition, disentangling between the endogenous ("the propensity of an individual to behave in some way varies with the behaviour of the group") and contextual ("the propensity of an individual to behave in

some way varies with the exogenous characteristics of the group") peer effects would be hard

because the linear-in-means model implies that one of the regressors is a linear combinations of the others by construction.

Two approaches to this issue are used here: Lee (2007) proposes a method that relies on the structural form of the model and the role it implies for group size to establish identifica-tion; Bramoullé et al. (2009) proposes a framework that can also make use of directed links, i.e., A may influence B without B influencing A. Although I do not have explicit information on links, I argue that directed links may be established by using the information of past classroom composition.

The results show no evidence of peer effects for the Locus of Control, both endogenous





2 Identification in the linear-in-means model


Group size variation

The linear-in-means model describes the outcome variableyig of individual i of groupg as a

linear function of some of its characteristicsxig and the mean outcome and characteristics of

its peers. Let Pi be the group of peers of agent i, with i /∈Pi, and ni = P

j∈N✶{j ∈Pi} be

the number of people in it, where N is the whole sample. A group g is simply {i} ∪Pi. Let

αg be a group fixed effect term. The model may be written, for everyi:

yig =αg +β P



+γxig+δ P



+ǫig (2.1)

Where❊[ǫig|X, αg] = 0. The parameterβ gives us the endogenous peer effect, while δ is the

contextual peer effect, as in Manski (1993)’s denomination.

The individuals are said to interact in groups if, within a group, any member affects and is affected by all other members, but have no relationship with anyone else outside its group. This hypothesis typically arises because, in the lack of explicit information on links between individuals, defining peers using some observable they share might be the only plausible way to infer the links. In such setup, the endogenous peer effect term is problematic to us because j ∈ Pi will then imply that i ∈ Pj, meaning that we cannot estimate (2.1) with a simple


Lee (2007) shows that group interactions also impose a lot of structure to the model, and such restrictions can be used to establish identification. In particular, we have that within a group, any member affects and is affected by all other members equally - this environment will then imply a specific role for the group size in the determination of yi.

In order to see that, we may use a within transformation of (2.1)5

and get:

yig −y¯g =

(niγ −δ)


(xig −x¯g) +



(ǫig −¯ǫg) (2.2)

Where y¯g = (ni + 1)−1(yig +Pj∈Piyjg), x¯g = (ni + 1)


(xig +Pj∈Pixjg) and ¯ǫg = (ni +


(ǫig + P

j∈Piǫjg) are the means over the whole group. Notice as well that the group

structure implies thatni =ng for any i in group g.




It can be seen that only a single composite parameter may be recovered from (2.2) for any given group, but if we have at least three differing ng’s, it is possible to identify the

parameters of (2.1), except for the group fixed effectαg. The intuition is that the impact of

a given deviation of the mean characteristic (xig −x¯g) will vary with the size of the group

i belongs to and, as the group interactions hypothesis imposes a strict pattern on how that will happen, we are able to use these variations to trace back the parameters6

. The actual estimation procedure is explained in the next section.


Beyond group interaction

Although group interaction is an interesting hypothesis due to data availability, it is also a restrictive way to model social interaction. In light of Lee’s results, one might think that departures from this framework would make identification of peer effects harder, but Bramoullé et al. (2009) shows that is not the case.

Let G be a known, row-normalized and exogenous N ×N network matrix that defines who affects whom, i.e., Gij =n


i (✶{j ∈ Pi}). It is easy to see that group interactions is a

particular case in which we have a block-diagonalG, with each block given byn−1

g (ιngι


where ιng is a column vector of ones of size ng and Ing is the identity matrix of size ng. It

also allows for more general networks, in particular, directed links (i.e., Gij 6= 0 does not

imply thatGji 6= 0). Yet, it is still restrictive in the sense that, for any given individual, all

of its peers are equally influential. In this notation, the linear-in-means model (2.1) may be written as:

Y =α+βGY +γX+δGX+ε

In whichα is a N×1vector of group fixed effects. Pre-multiplying it by G and subtracting it from itself, we have:

(I−G)Y =β(I−G)GY +γ(I−G)X+δ(I−G)GX + (I−G)ε (2.3)

Assuming that (I −βG) is invertible7

and noting that (I −βG)−1

= P∞


kGk, we have,

after some manipulations:

(I−G)Y =γ(I−G)X+ (γβ+δ)(




)(I −G)X+ (I−βG)−1



A potential problem here is inference. If the group sizes are too large, they may dwarf the influence ofδ

andβ in the composite parameter and the convergence rate of the estimator will be slow. In particular, I will violate Lee’s Assumption 6.1 by choosing the same vector both for the contextual peer effects and the own characteristics that matter, leading to poorer convergence rates (see Proposition 7 in Lee (2007)).




Which implies that:

❊[(I−G)GY|X] =γ(I−G)GX + (γβ+δ)(




)(I−G)X (2.4)

This means that, if β 6= 0 and γβ+δ6= 0,{(I−G)G2


X, ...} are correlated

with❊[(I−G)GY|X]and, asGis assumed to be exogenous, they may be used as an instru-ments for (I −G)GY in (2.3), making identification possible. Intuitively, we are using the exogenous characteristics of peers of peers8

to proxy for the peers’ behavior. The parameter restrictions are intuitive: if there is no endogenous peer effect (β 6= 0) or if it is nullified by the contextual peer effect (γβ+δ6= 0), the relationship between an individual and the peers of its peers no longer exists.

The Reflection Problem arises here because group interactions place restrictions on G such that (I −G)Gk+2

X are all linear combinations of ((I −G)X,(I −G)GX), rendering

this identification method useless. Bramoullé et al. (2009) shows that, if γβ +δ 6= 0, that

won’t be a problem if and only if I, G, G2 and

G3 are linearly independent. Under group interactions, a sufficient condition for that is to have groups of 3 different sizes - i.e., Lee’s main result. An important thing to note here is that the choice of the vector X is critical here, as it is essentially the choice of the instrument being used. In particular, being lax with it means picking weak instruments.

If the agents no longer interact in groups, it is possible for two directly unrelated in-dividuals to have a friend in common. Therefore, the behavior of this common peer can be instrumented by the indirect peer’s behavior. More generally, Bramoullé et al. shows that identification is established once the network has intransitive triads, that is, a set of 3 individuals i, j, k such that i is affected by j, and k affects j but noti (directly).

A natural way to overcome the lack of explicit data on network information is to use panel data and treat the same individual in different points in time as distinct nodes. In such setup, time allow us to establish directed links as long as we have serial correlation - your past self affects your current self, but the opposite is not true.

Say we observe the same sample of N individuals in two different periods {1,2}. In each period the network structure is given by Gt. Even if G1 and G2 have a group structure, we

may find intransitive triads as long as peers in period2have different backgrounds in terms of which group they belonged to in period 1. This is the same approach as the one used

in Hanushek et al. (2003): instrument the current peer outcome by his past outcome. The caveat is that we cannot use this strategy if this pair of individuals were already peers of


That is the case if we use(I−G)G2




3 Data

The data here is due to Felicio et al. (2013). It is a panel with information on every stu-dent enrolled in the second grade of elementary school in 2008 in the public educational system of Sertãozinho, a city in São Paulo State. Four years later, the researchers went back to Sertãozinho and gathered a second round of information that included self-reported questionnaires for psychological traits measurement.

Sertãozinho is a 110,000 inhabitants city in the northern part of São Paulo State. It is relatively rich (R$ 39,000 per capta income in 2011, while Brazil’s was R$ 21,000) and, more importantly, it has a public educational system far better than the national standard: 74% of its public students have appropriate knowledge in math in the 5th grade, according toProva Brasil, a nationwide standardized test, while the same figures for the rest of the country and

for the rest of São Paulo State are 33% and 42%, respectively.

In 2008, information was gathered in 22 schools that had 2nd graders in Sertãozinho: all of the 14 public ones and 8 private that volunteered to participate. The 2219 students took a survey home to be answered by their parents; it contained basic information like race, age and parental education and questions about the child’s home environment, but no psychological questionnaire. The fundamental piece of information here is the classroom identification - I use it to define the network in 2008.

Four years later, the initial idea was to study the same children, so information was to be gathered on every public school that had 6th graders and any private ones that were willing to participate. Yet, many students did not progress properly, so, in order to diminish the attrition problem, the study was conducted on any school that had 2008 students on either the 4th or 5th grade. Conditional on the school-grade, the data was collected irrespective of whether the student had participated on the study before. Therefore, the 2012 sample has information on 17 municipal, 8 state and 6 private schools of Sertãozinho - every public school with 6th graders is in the sample -, amounting to 5233 students. I can find 1094 (49%) students of the 2008 sample, but only 656 are on 6th grade - 307 and 131 are on 5th and 4th grades, respectively.



of their non-cognitive traits9. I will focus on the Tel Aviv Locus of Control questionnaire

(Milgram and Milgram, 1975), which was designed to be applied to children aged 10 to 14 years old10. The Big Five Inventory (BFI, John et al. (1991), Benet-Martínez and John

(1998), John et al. (2008)) and the Social Skills Rating System (SSRS, Gresham and Elliott (1990)) were also applied, but, for reasons discussed below, I do not use them in my analysis.


Locus of Control

The Locus of Control is a personality trait developed by Rotter (1966) that measures how personally responsible people feel about their life affairs. Those who believe their actions play an important role in determining the course of their lives are said to have an internal

locus; in the opposite side of the spectrum, people with an external locus will typically see

fate and luck as the main driving power of life. Therefore, internal individuals are expected to exert more effort in their endeavors - empirical evidence links a more internal locus to some positive outcomes such as higher academic achievement (Findley and Cooper, 1983), higher work produtivity (Judge and Bono, 2001), smaller likelihood of being incarcerated (Urzua, 2008) and greater care with their health (see Wallston and Wallston (1978) for an early review). Despite these results typically associating an internal locus to positive outcomes, the general understanding is that this relationship is not monotonic. Indeed, there are aspects of life that are beyond control, and people who cannot perceive that are more likely to be stressed and depressed (Benassi et al., 1988).

The Tel Aviv questionnaire contains 24 questions that present a hypothetical situation and ask the student to pinpoint the probable cause11

. By using common factor analysis, I can summarize the information of these responses in a tractable low dimensional scale. In fact, as the screeplot in Figure 1 shows, the information contained in the whole questionnaire may be reasonably described by a scalar - I use it as one measure of the Locus of Control.


Appendix A.3 presents the full questionnaires in Portuguese.


The idea is to use vocabulary and present hypothetical situations that are familiar to people at this age.




Figure 1 – Scree plot of Locus of Control Questionnaire

The loadings of the extracted factor typically have the expected sign, but that is not the case for 3 of the 24 questions12

. Although it may be simply that these questions were particularly hard for the students to respond, it could be that the underlying factor I am capturing is not actually the Locus of Control, so there would be no reason to expect a particular sign for the loadings. I may avoid the latter problem by using the raw score in the questionnaire, which is the usual for Rotter’s original questionnaire. Each internal answer is worth a point, meaning that the score ranges from 0 to 24. The empirical distribution of scores is presented in Figure 2. In the following analysis, I standardize both measures.




Figure 2 – Histogram for the Locus of Control Score

One important data limitation I face is that the Locus of Control questionnaire was applied only in the second period, so I cannot measure it for the 2008 period. This means the natural intransitive triad estimator is off the table - I cannot use the past locus of a peer as an instrument for its current one. Yet, if the Locus of Control is serially correlated within individuals and endogenous peer effects are present, I may use a third individual’s current locus as an instrument given that he was a peer of a peer in the past, but is not a peer nowadays.

A simple example may be enlightening. Letyit denote the Locus of Control of individual

iin time t. Ignoring most explanatory variables for simplicity, we may write:

yit =ρyit−1 +β P




Take three individuals it, jt and kt, in which it and jt are classmates, i.e., i and j are

classmates in period t. I would like to useyjt−1 as an instrument for yjt init’s equation, but

I do not observe that. Even so, if I’m able to find a third individual - k - who was a peer of jt−1, but no longer is - and therefore, is not a peer of it as well -, I may use his observed

Locus -ykt - to instrument for jt’s in it’s equation. As the equation above holds true for any





In addition to the Tel Aviv Locus of Control questionnaire, the BFI and the SSRS were also applied in 2012. The SSRS is typically administered in order to diagnose disruptive behavior in students, and it has a set of three questionnaires that are meant to be answered by the parents, the teachers, and the children themselves. Yet, only the latter was administered here, so there is little information I can draw from it.

The Big Five Personality traits is a framework that was shown to consistently describe human personality in a variety of cultural settings (McCrae and Costa, 1987) using five broad domains: Openess to experience, Conscientiousness, Extraversion, Ageeableness and Neuroticism. The BFI was designed to measure these domains using 44 questions in a 5-point likert scale - by using factor analysis and mapping the extracted factors to the questions, I could attribute a score for each individual in each domain and use them as a variable of interest.

Unfortunately, as shown in Figure 3, the factor analysis did not yield the expected 5-factors structure, at least not clearly. Given the age of the students in our sample, this is not surprising because children are not likely to fully comprehend the items or differentiate between domains (Soto et al., 2008).

Figure 3 – Scree plot of BFI



is associated with a domain, so it is expected that the extracted factors load heavily on questions of the same domain, but lightly on the others. If I use the strongest loading in absolute value as the criteria for deciding which factor each question is most related to, I find that the first two factors are related to at least one question of each domain. Even worse, both of the first two factors have strong positive loadings on questions of the same domain with reversed scales13


In order to understand better the BFI responses, I checked the within-domain consistency of the answers through the Cronbach’s alpha, a measure of how the answers covariate on average. The results show that the BFI responses are not likely to have been driven by an underlying latent factor: the highest alpha is 0.21, for the Openness to experience domain, which is very low - a usual rule-of-thumb is that the alpha must be higher than 0.70 in order to be satisfactory (Kline, 2013).

An interesting feature of the BFI is that some items are paired, with one being the opposite of the other14

. Therefore, we can expect the paired answers to mirror each other, i.e., if the respondent strongly agrees with one, he must also strongly disagree with its pair. Yet, in an average through the 16 opposite pairs, 20.74% of my sample Strongly Agrees or Strongly Diasgrees with both statements. Taken together, this evidence makes me skeptical

about the information I can draw from the BFI, so I decided not to use it in my analysis. A natural concern is whether the Locus of Control measures are inadequate as well - it could have been that the students did not answer any questionnaire properly, perhaps because stakes were low. The Cronbach’s alpha for the Locus of Control is 0.48, which still is far from great, but a significant improvement from the BFI. This might be related to the fact that the questionnaires were all applied sequentially, with the Tel Aviv questionnaire being the first one, folowed by the SSRS and the BFI - although they were all designed to be answered by children this age, perhaps three of them in a row may require too much concentration. That would explain why the consistency of the answers decayed so rapidly, but does not warrant that the Locus of Control questionnaire was able to properly capture the underlying factor, so caution is required when interpreting the empirical evidence.


The detailed psychometric analysis can be found in the Appendix A.2.



4 Results

It is important to note that the choice of the covariates X requires great care here, be-cause they are essentially the instruments as well. Variables that are unimportant in the determination of an individual’s Locus of Control are likely to be weak instruments.

Although the Locus of Control does not seem to differ by gender among adults (De Bra-bander and Boone, 1990), teenage girls tend to present more internal control than their male counterparts (Manger and Eikeland, 2000). Age is also important by itself, as people be-come more internally oriented as they grow older, peaking in middle age (Lefcourt (1982), Heckhausen and Schulz (1995)). On the family background, Strauser et al. (2002) argue that working parents are important role models for valuing effort, resulting in more internal chil-dren, while Schneewind (1995) notes, among other things, that having more siblings makes children more external. Lower socioeconomic status is also associated with more external loci (Freed and Tompson, 2011), but the channels through which this occurs is still in debate. Ethnicity and culture matter too: Berry (2002) argue that black people are more external than white in the U.S. even after controlling for their socioeconomic status; Uba (2003) concludes that more individualistic societies also promote more internal loci by comparing chinese in America and Hong Kong - the latter are typically more external.

The choice of a gender and a white ethnicity dummies is, therefore, straightforward. So would be using the students’ age, but I only have year of birth in the data, which means that I can only measure age in a discrete number of years. In any given classroom, the variation of my age measure would be very small, so I decided not to include it in my regression. For family background, I use a dummy for whether the child currently lives with any siblings and a dummy for whether the father is unemployed. Although the survey had a question about household income, the number of missing observations in it is very high, and using it would significantly reduce the number of usable observations15, so I decided to proxy

socioeconomic status using a dummy for the father having completed High School. Table 1 provides descriptive statistics of the sample I am left with once I discard students that had missing covariates.

Table 2 presents the mean Locus of Control factor for each group in my covariates. The




Table 1 – Descriptive Statistics - 2012 Sample

Variable Mean SD

Locus of control Factor 0.09 0.77

Locus of control raw Score 16.78 2.80

Male 0.50 0.50

White 0.40 0.49

Lives with siblings 0.76 0.43

Father is unemployed 0.05 0.22

Father has completed High School 0.25 0.44

Observations 1382

only covariate that does not go in line with the Psychology literature is thelives with siblings

dummy: in my data, children that live with siblings are on average more internal. Girls and white people are more internal on average, just like children whose fathers are employed or have completed High School.

Table 2 – Mean Locus of Control Factor by group

Variable No Yes

Male 0.089 -0.087

White -0.098 0.065

Lives with siblings -0.044 0.139

Father is unemployed 0.013 -0.239

Father has completed High School -0.102 0.035

Observations 1382

The class size approach proposed by Lee (2007) consists in using deviations of the mean characteristics of the group and its size to identify the parameters, thanks to the strict pattern they play once I assume group interactions. Bramoullé et al. (2009) shows that this means I can estimate (2.3) using (I −G)G2

X as an instrument for (I −G)GY. In my analysis, the dependent variableY is my measure of the Locus of Control - I estimate the model with the standardized extracted factor and questionnaire score -, with the choice of my vectorX described above.



are more external, and white people are more internal; the coefficients of having a father that completed High School and living with siblings do not have the sign I anticipated, but the former is not significant at the 5% level. Roughly, the same pattern is found for the corresponding instruments, and the F-statistic of 203.83 indicates that they do not lack predictive power.

Table 3 – First Stage - Class Size Approach

(I −G)GY (Factor) (I −G)GY (Score)

Own characteristics: (I−G)GX

Male -1.288∗ (-3.99) -1.654∗ (-3.04)

White 0.897∗ (3.17) 1.853(3.47)

Lives with siblings 0.427 (1.44) 0.864∗ (2.45)

Father is unemployed -1.882∗ (-3.37) -1.352∗ (-3.78)

Father has completed High School -0.721+ (-1.80) -1.052+ (-1.90)

Instruments: (I−G)G2 X

Male -4.585∗ (-4.74) -5.947∗ (-3.75)

White 1.855∗ (2.03) 4.324(3.03)

Lives with siblings 0.624 (0.67) 1.274 (1.01)

Father is unemployed -1.489∗ (-2.82) -0.690+ (-1.89)

Father has completed High School 0.289 (0.65) -0.314 (-0.68)

Observations 1382 1382

Joint significance of instruments F-stat 82.83 203.83

t statistics in parentheses

Standard errors calculated with classroom clusters.

Coefficients of(I-G)X and constant were omitted from the table.


p <0.10,∗ p <0.05

The second stage is reported in Table 4. All standard errors were calculated with class-room clusters and bootstrapped with 1000 repetitions. Almost no coefficient is significant at the 5% level16

: there is no evidence of endogenous or contextual peer effects, and even the own characteristics coefficients are imprecise. It could be the case that the slow convergence rates of Lee’s method was the problem, as it would make inference on both peer effects harder. Yet, in my sample of 98 classes, the mean class size is 14.10 with a standard deviation of 8.86, which is closer to what Lee defined as a small size in his Monte Carlo simulations - the lack of peer effects is a more plausible explanation.

Taking the fact that it is not significant aside, my point estimate of the endogenous peer effect is worrisome. It does not satisfy the sufficient condition for(I−βG) to be invertible,

and being negative is fairly unusual. When statistically significant, such estimates tend to




be positive (see Hamilton et al. (2003), Mas and Moretti (2009), Sacerdote (2001)), which is certainly what is expected when you think of endogenous peer effects as some influence by example17. Although the role peer effects play on the determination of the Locus of Control

- or non-cognitive abilities, in general - is largely unexplored, Bursztyn and Jensen (2014) shows that High School students put great value in conforming to their classroom social norm - or at least in signaling that they do -, which would also imply positive endogenous peer effects.

Table 4 – Second Stage - Class Size Approach

LC Factor LC Score

Endogenous peer effect

(I-G)GY -1.590 (-1.14) -1.196 (-1.13)

Own characteristics: (I−G)X

Male -0.219∗ (-2.46) -0.090 (-1.25)

White 0.063 (0.85) 0.046 (0.59)

Lives with siblings 0.184+ (1.75) 0.195+ (1.75)

Father is unemployed -0.046 (-0.15) -0.080 (-0.27)

Father has completed High School 0.170 (1.61) 0.106 (1.02)

Contextual peer effects: (I−G)GX

Male -0.927 (-1.23) -1.192 (-1.56)

White -0.283 (-0.40) -0.371 (-0.50)

Lives with siblings -0.158 (-0.14) -0.043 (-0.03)

Father is unemployed 0.479 (0.13) 0.566 (0.16)

Father has completed High School 1.770 (1.39) 1.357 (1.10)

Observations 1382 1382

R2 0.252 0.209

t statistics in parentheses

Standard errors calculated with classroom clusters and bootstrapped with 1000 repetitions. Coefficient of constant omitted from the table.

+ p <0.10, p <0.05

I also used an alternative approach, similar to the one found in Hanushek et al. (2003). They used past peer achievement as an instrument for current one, but that is not possible here because the Locus of Control questionnaire was not applied in 2008. Instead, I use the current Locus of a third individual - somebody that was in the same class as one of my peers in the past, but now is not. Note that there is an important underlying hypothesis here: if there were no endogenous peer effects in the past, there is no link between my peer and this third individual.




In order to do this, I need the classroom identification of 2008. Of the 1094 individuals I can find in both periods, only 383 (35.0%, or 17.3% of the original 2008 study) do not lack any information required for the analysis18. Table 5 presents the descriptive statistics for

this subsample, and they are very similar to those presented in Table 1. This is reassuring because, if the samples used for each strategy were very distinct, any comparison between the results could easily be attributed to this observable difference in characteristics.

Table 5 – Descriptive Statistics - Panel Sample

Variable Mean SD

Locus of control Factor 0.00 0.84

Locus of control raw Score 16.63 2.99

Male 0.50 0.50

White 0.42 0.49

Lives with siblings 0.73 0.44

Father is unemployed 0.05 0.23

Father has completed High School 0.24 0.43

Observations 383

Any individual i may have several current peers j, who, in turn, may also have several former peersk that are eligible as instruments ini’s equation, i.e.,k is not a currently a peer

of i. To construct the instrument for (I−G)GY, which I denote byZ, I average the Locus

of Control through k, given the pair (i, j), and then average again, but this time through j. Once more, I do not find any compelling evidence of an endogenous peer effect for the Locus of Control. Table 6 shows some indirect evidence of it, as my proposed instrument has no predictive power over the mean Locus of one’s peers. (the unreportedF statistics for

the instrument significance for the Locus of Control factor and score models are, respectively 0.63 and 0.37). Under the hypothesis that endogenous peer effects exist, that should not be the case.

This is not particularly surprising in light of the earlier results, specially because the 4 year gap between the data collection weakens the link between your past and current self. The group interactions hypothesis on each period also implies that every peer is equally influential. This might be a strong assumption on any given period, but it is particularly troublesome in this latter strategy because we rely on it twice - on the link betweenitandjt,

and on the one betweenjt−1 andkt−1. Despite all these limitations, this later exercise provides

further support to the claim that there are no endogenous peer effects in the determination of the Locus of Control on average.




Table 6 – First Stage - Peer of peer Approach

(I −G)GY (Factor) (I −G)GY (Score)

Instrument: Z

Average LC of former peers -0.041 (-1.22) 0.034 (0.69)

Own characteristics: (I−G)GX

Male -0.821∗ (-3.12) -0.906∗ (-3.36)

White 1.151∗ (5.49) 1.175(4.05)

Lives with siblings 0.828∗ (2.64) 1.099∗ (4.35)

Father is unemployed 0.441 (1.05) -0.613 (-0.83)

Father has completed High School -0.212 (-0.49) -0.199 (-0.40)

Observations 373 373

t statistics in parentheses

Standard errors calculated with classroom clusters.

Coefficients of(I-G)X and constant were omitted from the table.



5 Conclusion

Non-cognitive abilities have recently surged in Economics as an important determinant of successful life outcomes. Given the inherently social nature of many of these skills, it is natural to believe that social interactions play a role in their development. In this paper, I study this question focusing on the Locus of Control, a psychological trait of how personally responsible people feel about what happens around them.

My empirical model is the standard linear-in-means model, that posits that the outcome is a linear function of the mean characteristics and mean outcome of a reference group. The econometric issues it poses (Manski, 1993) are addressed using the methodology proposed in Lee (2007) and Bramoullé et al. (2009).



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A Appendix


Factor Analysis

Factor analysis is a statistical technique designed to reduce the dimension of a data set while retaining as much of the original information as possible. Although economy of description may be a goal in itself, factor analysis is also very useful when the variables of interest are hard to directly assess, but some noisy measurements of them are available: these several measurements will covariate according to the latent variable and factor analysis can disen-tangle the noise from the systematic variation. One important hypothesis here is that we posit that these measurements are linear combinations of the latent variables and an error term; the basis forming the measurements are called factors and the coefficients associated with them are called loadings.

Its use in Psychology dates back to Charles Spearman’s research on intelligence (Spear-man, 1904), commonly known as g theory. By noting that seemingly unrelated test scores

were positively correlated, Spearman postulated that people had an underlying general men-tal ability that manifested itself on all these tests, and factor analysis was used to extract the so called g factor. Besides its use on empirical assessment of theoretical constructs, factor

analysis was also employed as the basis for the development of the Five Factor Model theory (John et al., 2008), also known as Big Five Personality traits.

My main use of it here was to extract factors that could be interpreted as an unobservable variable of interest. One particular shortcoming of factor analysis is that it does not yield unique solutions: as its goal is to maximize the explained variance given a set of factors, any unitary transformation of a solution is also a solution. A standard procedure, called varimax rotation, is to use a transformation that differentiates factors the most, i.e., given that an item has a strong loading on one factor, it will also have weak loadings on the others. The interpretation of the factors becomes an interpretation of the items they are mostly associated with.



that the data may have some problems.

Another issue is that, as long as I have more observations than questions, I can always explain the whole variation in the data using as many factors as questions. Because each subsequent added factor explains a smaller part of the variance in the data, this turns into a matter of deciding when the marginal factor is not significantly important anymore. However, the analysis itself provides no guidance for evaluating which factors indeed matter, so several methods were proposed. Here, I compare the eigenvalues associated with each factor and stop whenever the difference between two subsequent values gets too small - this is also known as the Scree plot test19

, because this evidence is usually presented through a plot of the factors and its associated eigenvalues. I also complement this analysis with the Kaiser criterion of keeping factors that have an eigenvalue of at least 1.


Psychometric analysis


Big Five Inventory

The Big Five Inventory (BFI) is a 44-items questionnaire designed to measure the Big Five personality traits and, by performing factor analysis in its items, I try to estimate this underlying structure that describes human personality. Ideally, the factor analysis would indicate that 5 factors were enough to explain well the variability in the answers, but as shown in the scree plot in Figure 3, the decay in eigenvalues looks continuous, though some may argue that there is a slight kink in the 4th factor - which would suggest a 3-factor structure, as the factor in the kink itself is discarded. The fact that the third factor is also the last one to have an eigenvalue of at least 1 (1.13) makes me settle with 3. Although far from ideal, not recovering the 5 factors in a sample of children this age is not unheard of (Soto et al., 2008), so I proceed anyway.

As stated above, the main problem is the mapping between the factors and the questions. Each item in the BFI has a factor it supposedly evaluates and an orientation on whether the item should load positively or negatively; Openness has 9 items, while the others have 8. Ideally, items of the same construct would load heavily only on the same factor, with the proper sign, making their interpretation easy. Table 7 provides the loadings of each BFI item on the 3 factors, together with its theoretical construct and orientation. They were grouped by personality trait, and the strongest loading of each item, in absolute value, is marked with a (*).




Table 7 – BFI items loadings

Item F1 F2 F3 Trait Orientation

2 0.169(*) 0.162 0.163 A

-7 0.482(*) 0.063 0.101 A +

12 0.497(*) 0.031 -0.100 A

-17 -0.095 0.504(*) -0.047 A +

22 0.202 0.324(*) -0.191 A +

27 0.483(*) -0.208 0.112 A

-32 0.302(*) 0.011 0.193 A +

37 0.326(*) 0.155 -0.027 A

-42 -0.015 0.528(*) -0.084 A +

3 -0.162 0.557(*) -0.018 C +

8 -0.060 0.169(*) 0.137 C

-13 0.480(*) -0.049 -0.035 C +

18 0.562(*) -0.103 0.022 C

-23 0.478(*) -0.213 0.104 C

-28 0.358(*) -0.103 0.338 C +

33 0.054 0.280(*) 0.145 C +

38 0.387(*) 0.188 -0.104 C +

43 0.448(*) -0.093 0.150 C

-1 0.528(*) -0.002 0.025 E +

6 0.481(*) -0.181 0.220 E

-11 0.195 -0.030 0.415(*) E +

16 0.578(*) -0.127 0.110 E +

21 -0.131 0.292 0.305(*) E

-26 0.305 -0.226 0.364(*) E +

31 0.555(*) -0.112 0.164 E

-36 -0.148 0.532(*) 0.020 E +

4 0.310(*) 0.234 -0.063 N +

9 -0.092 0.510(*) -0.079 N

-14 -0.003 0.262 0.268(*) N +

19 0.378(*) -0.046 0.188 N +

24 0.428(*) 0.056 -0.026 N

-29 0.508(*) -0.026 -0.046 N +

34 -0.008 0.430(*) 0.004 N

-39 0.441(*) 0.005 0.021 N +

5 0.417(*) 0.108 0.170 O +

10 0.535(*) -0.095 0.062 O +

15 -0.086 0.384(*) 0.110 O +

20 0.102 -0.080 0.498(*) O +

25 0.474(*) 0.021 0.106 O +

30 0.451(*) 0.136 -0.062 O +

35 0.603(*) -0.055 0.051 O

-40 0.480(*) -0.038 -0.029 O +

41 0.179 0.307(*) -0.016 O

-44 0.270 0.033 0.288(*) O +



27 items have their strongest loading on the first factor20 and they are relatively well

spread among the different traits (6 Agreeableness, 6 Conscientiousness, 4 Extraversion, 5 Neuroticism and 6 Openness items). This pattern is also found for the second factor (3 Agreeableness, 3 Conscientiousness, 1 Extraversion, 2 Neuroticism and 2 Openness items), while the third one is more concentrated, but has the strongest loadings of only 6 items (3 of them are Extraversion items). Surely I could not have expected to see a clear differentiation between factors because I have kept only 3 to describe a 5 trait structure. Yet, there seems to be no differentiation at all.

When you look at the expected orientations of each item, the picture gets more confusing. For example, items 7 and 12 are both measuring Agreeableness, but the latter has a reversed scale - low scores on item 12 point to high Agreeableness, while the opposite is true for item 7. They both load strongly on the same factor, with similar magnitude, but with the same sign! Unfortunately, a quick glance at Table 7 shows they are not an exception. In fact, when I calculate the Cronbach’s alpha for the items of each trait, they point to a very low reliability of the questions: 0.21 for Openness, 0.14 for Agreeableness, 0.10 for Neuroticism, 0.01 for Conscientiousness and a null value for Extraversion21

. As a comparison, Soto et al. (2008) report alpha’s in the 0.60 range for children aged 10.

Perhaps the most compelling evidence that the BFI answers would not yield reliable information is the inconsistency within individuals between items that were designed to mirror each other. Among its 44 items, the BFI has a set of 16 paired questions with reversed statements, i.e., #24 is "I see myself as someone who is emotionally stable, not easily upset"

and #29, its opposite, states "I see myself as someone who moody". The idea is that a person

who Strongly Agrees with the first statement should Strongly Disagree with its pair. Yet,

20.74% of my sample Strongly Agrees or Strongly Disagrees with both statements - in the

case of numbers 6 and 16, 30.80% either strongly agrees or disagrees with them. Soto et al. (2008) argue that some people have a natural tendency for agreeing/disagreeing with others, which they call aquiescence, and provide a simple method to correct for it by standardizing the answers using the mean and standard error of the 16 paired items within individual. I have done that and it does not change qualitatively the previous analyses.


Tel Aviv Locus of Control

I also checked for the soundness of the Tel Aviv Locus of Control answers. The questionnaire has a much simpler structure, containing only 24 items with 2 alternatives each: an internal


Some items do not load clearly stronger on any single factor (items 2, 8, 28, 21, 26, 4, 14 and 44), despite the varimax rotation, so simply checking the strongest loading may not be enough.




and an external one. As shown in Figure 1, a single factor could account for most of the variance observed in the data, which is consistent with the theoretical framework. Still, it could be the case that, as it was with the BFI, interpreting this single factor is not possible because its loadings are inconsistent with the Locus of Control.

Table 8 presents the loadings of each item, with the expected sign if we want to interpret the factor as a measure of how internal people are. As discussed above, only 3 of the 24 items do not have the anticipated sign (numbers 11, 12 and 22), but it is worth noting that they are all among the weakest loadings - only 5 items have a loading with an absolute value lower than 0.1, and 3 of them are precisely the ones with the wrong sign. This is reassuring because even if these items are not driven by what I want to interpret as Locus of Control, at least they are not clearly contradicting this interpretation.

Table 8 – Tel Aviv Locus of Control items loadings

Item Loading Expected Sign

1 0.022 +

2 -0.332

-3 0.378 +

4 0.322 +

5 0.226 +

6 0.262 +

7 -0.187

-8 0.260 +

9 0.192 +

10 -0.421

-11 -0.014 +

12 -0.039 +

13 -0.109

-14 -0.472

-15 -0.181

-16 -0.137

-17 0.363 +

18 -0.287

-19 0.263 +

20 -0.162

-21 0.445 +

22 0.085

-23 -0.303

-24 0.030 +



checked the reliability of the items through the Cronbach’s alpha and, as stated above, its value of 0.48 is not great, but a big improvement in comparison with the BFI’s.



In this section I present the full psychological questionnaires that were applied, in Portuguese.


Tel Aviv Locus of Control

The bolded item is the internal answer.

1.Suponha que você mudou de bairro, fez amizade com algumas crianças novas e se tornou o chefe do grupo. Isso aconteceu porque: A) Você encontrou o grupo certo. B) Você tem as qualidades de chefe.

2.Quando um de seus irmãos ou amigos aceita uma de suas sugestÕes é porque: A) Você explicou a razão de sua sugestão e o convenceu de que você está certo. B) Ele fica cansado de discutir com você.

3.Suponha que você se saiu bem na escola em uma matéria que geralmente você acha difícil. Isto foi porque: A) Você teve sorte e alguém o ajudou. B) Você se esforçou mais do que de costume.

4.Se o seu professor acha bom o seu trabalho na escola é porque: A) Ele gosta de você.

B) Seu trabalho na escola é bom mesmo.

5.Imagine que voce queira assistir a um filme e seus amigos concordem em ir com você. Eles concordaram porque: A) Não havia outro filme que valia à pena ver. B) Você os convenceu de que valia à pena assistir àquele filme.

6.Suponha que seus pais deram uma bronca em você. Foi porque: A) Eles estavam nervosos naquele momento. B) Você fez alguma coisa errada.

7.Quando você tira nota baixa numa prova é porque: A) Você não estava preparado para a prova. B) A prova era muito difícil.

8.Quando seus pais brigam com você é porque: A) Eles estão de mau humor. B) Geral-mente a culpa é sua.

9.Suponha que você mude para outro bairro e não consiga fazer novos amigos de sua idade. Isto acontece porque: A) Você não teve sorte, pois eles não deixam ninguém entrar no grupo. B) Você não se esforçou bastante para fazer novos amigos.



11.Quando você briga com um de seus irmãos é porque: A) Geralmente a culpa é dele.

B) Geralmente a culpa é sua.

12.Quando seus irmãos não concordam em brincar daquilo que voce quer, é porque: A) Eles nunca querem brincar da mesma coisa que você. B) Você não aceita brincar daquilo que eles querem.

13.Suponha que você não tenha muitos amigos. Isto é porque: A) Você não tem muita facilidade em fazer amigos. B) Sempre existem crianças que não gostam de você.

14.O professor elogiou seu comportamento durante um passeio da escola. Isto foi porque:

A) Seu comportamento no passeio mereceu mesmo ser elogiado. B) Seu professor estava de bom humor.

15.Depois de discutir com você por muito tempo, seus pais lhe permitiram fazer uma viagem com seus amigos. Isto foi porque: A) Você os convenceu. B) Eles ficam cansados de discutir com você.

16.Imagine que voce encontrou um grupo de crianças na praia. Aí você sugeriu que todos brincassem de um certo jogo e todos aceitaram a sugestão. Isto aconteceu porque: A) Você conseguiu convencê-los. B) Eles já queriam brincar daquele jogo.

17.Suponha que seus pais digam que você está indo bem na escola. Isto é porque: A) Eles gostam de qualquer coisa que você faça. B) Seu trabalho na escola é bom.

18.Quando você esquece alguma coisa que foi dita em aula, isto provavelmente accontece porque: A) Você não fez bastante esforço para lembrar. B) O professor não explicou muito bem.

19.Se você é muito querido entre os amigos é por causa: A) Das pessoas com quem você anda e das coisas que você tem. B) Do esforço que você faz para que os amigos gostem de você.

20.Quando você discute com seus amigos é porque: A) Você não desiste de suas idéias. B) Seus amigos são cabeças duras.

21.Quando você se sai muito bem em uma prova na escola, isso acontece porque: A) Você teve sorte. B) Você estudou muito para a prova.

22.Quando você não chega a um acordo com outras crianças é porque: A) Você não aceita a opinião delas. B) As outras crianças geralmente não aceitam as suas opiniões.

23.Imagine que seu professor não tenha uma boa opinião a seu respeito. Isto é porque:





The SRSS presents a situation and asks the respondent to assert how often he finds himself in that position on a 3-point scale: Never, Sometimes and Very often.

1.Eu faço amigos facilmente.

2.Eu sorrio, aceno ou cumprimento os outros com a cabeça. 3.Eu peço antes de usar as coisas das outras pessoas.

4.Eu não dou atenção aos colegas de classe que ficam fazendo palhaçada. 5.Eu fico triste pelos outros quando coisas ruins lhes acontece.

6.Eu digo para os outros quando estou chateado com eles.

7.Eu não brigo ou discuto quando eu discordo das ideias de um adulto. 8.Eu deixo minha carteira limpa e arrumada.

9.Eu participo das atividades esportivas e festas da escola. 10.Eu faço minhas tarefas de casa no tempo estabelecido. 11.Eu digo meu nome às pessoas sem esperar que perguntem. 12.Eu controlo minha raiva quando as pessoas me deixam bravo(a). 13.Quando eu acho uma regra injusta, eu questiono sem brigar. 14.Eu mostro ou digo aos meus amigos que gosto deles.

15.Eu ouço os adultos quando eles estão falando comigo. 16.Eu mostro que gosto de elogios e cumprimentos de amigos. 17.Eu ouço meus amigos quando eles falam dos problemas deles.

18.Eu evito fazer coisas com outras pessoas que possam me trazer problemas com os adultos.

19.Eu termino calmamente as brigas com meus pais.

20.Eu elogio os outros quando eles fazem alguma coisa bem feita. 21.Eu presto atenção no professor quando ele está ensinando uma lição. 22.Eu termino minha atividade em classe no tempo estabelecido.

23.Consigo puxar conversa com os colegas de classe. 24.Eu digo para os adultos que gostei do que eles fizeram. 25.Eu sigo as instruções do professor.

26.Eu tento entender como meus amigos se sentem quando estão zangados, aborrecidos ou tristes.

27.Eu peço a amigos para me ajudarem com meus problemas.

28.Eu não ligo para outras crianças quando elas me provocam ou me xingam. 29.Eu aceito que as pessoas sejam diferentes de mim.



32.Eu uso um tom de voz adequado nas discussões de classe.

33.Eu peço a adultos para me ajudarem quando outras crianças tentam me bater ou me empurram.

34.Eu tento por panos quentes nas brigas ou discussões dos meus colegas.



The respondent must answer on a 5-point scale how strongly he agrees or disagrees with the statement. The items were scrambled in this questionnaire, so I put the original BFI number in brackets.

1[1].Eu me vejo como alguém que é conversador, comunicativo.

2[27].Eu me vejo como alguém que às vezes é frio e distante, indiferente aos outros. 3[2].Eu me vejo como alguém que tende a encontrar defeitos nos outros.

4[3].Eu me vejo como alguém que é caprichoso e detalhista nas tarefas escolares.

5[26].Eu me vejo como alguém que tem opiniões fortes e não teme expressar o que sente. 6[28].Eu me vejo como alguém que não desiste até concluir a tarefa ou o trabalho, não deixa nada pela metade.

7[4].Eu me vejo como alguém que é deprimido, triste.

8[42].Eu me vejo como alguém que gosta de cooperar com os outros. 9[5].Eu me vejo como alguém que é original, tem sempre novas ideias.

10[29].Eu me vejo como alguém que é temperamental, muda de humor facilmente. 11[25].Eu me vejo como alguém que é inventivo, criativo.

12[6].Eu me vejo como alguém que é reservado, fica na sua. 13[30].Eu me vejo como alguém que valoriza a arte e a beleza.

14[24].Eu me vejo como alguém que é emocionalmente estável, não se altera facilmente. 15[7].Eu me vejo como alguém que está sempre disposto a ajudar os outros.

16[31].Eu me vejo como alguém que é, às vezes, tímido, inibido.

17[8].Eu me vejo como alguém que pode ser um tanto descuidado, despreocupado em fazer tudo certinho.

18[32].Eu me vejo como alguém que é amável, tem consideração pelos outros. 19[23].Eu me vejo como alguém que tende a ser preguiçoso.

20[33].Eu me vejo como alguém que consegue fazer as coisas bem feitas sem precisar gastar muita energia ou tempo com isso.

21[9].Eu me vejo como alguém que é calmo, controla bem o estresse. 22[43].Eu me vejo como alguém que é facilmente distraído.



24[35].Eu me vejo como alguém que prefere ter uma rotina de atividades bem planejada e organizada.

25[10].Eu me vejo como alguém que tem curiosidade em relação a várias coisas diferentes. 26[36].Eu me vejo como alguém que é desinibido, amigável, comunicativo.

27[22].Eu me vejo como alguém que geralmente é de confiança.

28[37].Eu me vejo como alguém que é, às vezes, grosseiro ou mal educado com os outros. 29[11].Eu me vejo como alguém que é cheio de energia.

30[12].Eu me vejo como alguém que começa bate-boca com os outros.

31[13].Eu me vejo como alguém que é confiável na hora de realizar uma tarefa que lhe é atribuída.

32[38].Eu me vejo como alguém que faz planos e os cumpre, vai até o fim. 33[20].Eu me vejo como alguém que tem uma imaginação fértil.

34[14].Eu me vejo como alguém que pode ficar tenso dependendo da situação.

35[15].Eu me vejo como alguém que é engenhoso, alguém que pensa em todos os detalhes do problema.

36[39].Eu me vejo como alguém que fica nervoso facilmente.

37[16].Eu me vejo como alguém que contagia os outros com seu entusiasmo. 38[18].Eu me vejo como alguém que tende a ser desorganizado.

39[40].Eu me vejo como alguém que gosta de refletir, trabalhar com as ideias. 40[17].Eu me vejo como alguém que tem capacidade de perdoar, perdoa fácil. 41[19].Eu me vejo como alguém que preocupa-se muito com tudo.

42[21].Eu me vejo como alguém que tende a ser quieto, calado.

43[41].Eu me vejo como alguém que não se interessa muito por arte (escultura, pintura, teatro, etc).


Figure 1 – Scree plot of Locus of Control Questionnaire
Figure 2 – Histogram for the Locus of Control Score
Figure 3 – Scree plot of BFI
Table 1 – Descriptive Statistics - 2012 Sample


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