1. law, govt and politics
2. government

Politicization, Political Appointments, and
Corruption in Brazilian Federal Ministries
Bruno Hoepers
University of Pittsburgh
[email protected]
Paper prepared for Thirty Years of Democracy in Brazil: A Research
Workshop
Notre Dame – IN, April 20, 2015
This is a preliminary version. Please do not cite.
1 Abstract
This paper evaluates whether and how political appointee positions are related
with corruption in the Brazilian ministries. It develops a structural theory on political
appointees (“cargos de Direção e Assessoramento Superior” – DAS) politicization which
states that higher levels of party membership among political appointees and higher
heterogeneity of DAS partisanship inside an agency affect the patterns of wrongdoings
across ministries ceteris paribus. The simple number of DAS positions would not
necessarily mean more bureaucratic corruption and possibly performance (Lewis 2008).
Instead, levels of ministerial wrongdoings would be mainly explained by the level of
politicization among appointees in conjunction with the size of the bureaucratic agency.
Ministries with more personnel and larger budgetary resources are found to be more
corrupt-prone as well. By using a novel panel dataset with information on DASs
politicization in 26 ministries from 2010 to 2014 and other covariates, it was possible to
partially confirm the main theoretical claims. Some limitations and possible avenues for
future research are also discussed.
2 1. Introduction
Presidents’ ability to manage and use personnel in the bureaucracy is crucial in
presidential attempts to govern and control the governmental apparatus (Moe 1985 and
1994, Lewis 2011). Political appointees are very instrumental in presidential efforts to
control the bureaucracy, to make the bureaucracy less insulated, and therefore more
responsive, to the popular will, as well as to bring new ideas from outside the government
agencies. Nevertheless, presidential appointments can also attract the interest of political
actors interested in influencing policy-making in specific agencies or for patronage
purposes (Horton and Lewis 2009, Lewis 2011). In Brazil, many believe that political
appointee positions in Brazilian federal agencies (“cargos de Direção e Assessoramento
Superior”, or DAS)1 are related with corruption scandals, wrongdoings, and patronage
exchanges. More specifically, it is argued that individuals with political clout among
members of the incumbent government but without professional credentials to perform
certain function in government are nominated as political appointees, who in turn would
act on the behalf of their political patrons and extract rents from their agencies for their
own benefit as well as to their patrons’. Some DAS appointee positions, especially DAS
levels 5 and 6 (from DAS 1 to 6), are responsible for making important decisions
regarding the allocation of budgetary resources that could be used for rent-seeking
purposes. Thus, agency performance would suffer as a product of excessive politicization
in the appointment process2. Recent corruption scandals would lend credence to such
1
In this paper, DASs, appointees, or political appointees are used interchangeably.
By politicization we understand “…substitution of political criteria for merit-based criteria in the
selection, retention, promotion, rewards, and disciplining of members of the public service.” (Christensen
2004). The concept also applies to nominations that try to influence policy-making (Christensen, p. 2,
2
3 allegations. Agencies considered to have politicized appointees, i.e., appointees with
partisan ties in their ranks (e.g., Ministry of Transportation, Cities, Agriculture, Labor,
Tourism, and Sports) were involved in wrongdoings that resulted in the resignation of
ministers at the start of president Dilma’s administration in 2011. Therefore, some
believe that reducing the number of appointee positions – or reforming the appointment
system in Brazil – is necessary to fight corruption and improve agency performance3.
However, there is a paucity of studies showing whether, and how, political appointee
positions are related to wrongdoings in the Brazilian agencies4 at the federal level and in
other countries as well.
In this study, my goal is to put some of the aforementioned claims about DAS
appointments and corruption under more scrutiny. Are DAS appointee positions related
to public misdeeds in Brazilian federal agencies? My argument is that we should look
more carefully at the features of appointee positions and their distribution across federal
agencies. That is, not only do agencies differ in their overall number of DAS positions in
the aggregate, but they also present important structural differences across agencies that
can explain whether and how public misuse of office for private gains take place.
Agencies differ markedly in terms of DAS’s party membership patterns, DAS’s level of
composition by civil service members, and the dispersion or concentration of “politicized
DASs” (i.e., DAS with party membership) across agencies, and those differences help
account for the patterns of corruption observed across the agencies. In order to
Lopez et al 2013, p.2). In this study, the level of politicization in an agency is assessed by the proportion of
appointees in each agency who have party membership and DAS fractionalization.
3 Aécio Neves propõe reduzir pela metade número de ministérios. Source:
<http://eleicoes.uol.com.br/2014/noticias/2014/07/30/aecio-neves-propoe-reduzir-pela-metade-numero-deministerios.htm> 4 In this paper, agencies and ministries are used interchangeably. 4 empirically test those claims, I analyze an original panel dataset containing information
about DAS positions on 26 ministries in addition to other variables (total number of
employees in an agency, agency’s budget, agency employees’ per capita wages, literacy,
and proportion of appointees that are career civil servants), from 2010 to 2014. As a
measure for corruption, I employ a proxy measure based on the number of Special
Taking of Accounts – STAs (i.e. “Tomada de Contas Especial” – TCE). Overall, it was
found that the patterns of party membership among DAS in the agencies (which serves as
a proxy for parties’ influence inside agencies) are positively related to the number of
audit reports. Besides, the fractionalization of “politicized” DAS appointees presented a
positive albeit non-statistically significant association with the number of audits. In other
words, the higher the share of DAS with party membership, the higher is the number of
Special Auditing Reports. However, the impact of other structural factors such as the
agency’s budget and total number of employees are very important predictors of
wrongdoings in the agencies. More specifically, agencies with higher budgets and more
personnel present more problems of corruption than agencies with smaller budgets and
fewer employees.
The paper proceeds as follows. First, I put the theoretical discussion and
contribution of this study in context by comparing it to what has been done in previous
studies on appointee positions in Brazil and overseas (the US case). Next, I develop more
clearly the theoretical argument, implications, and main hypotheses. Third, I present and
explain the data used in this study and the variables. Forth, I proceed to the empirical
analysis of the data and describe the models’ specifications and main results. Last, I
5 conclude with some general remarks about the main findings in the paper, empirical
limitations, and possible avenues for future research on the subject.
2. Studying political appointee positions: the role of politicization
Studies on political appointees have attracted the attention of scholars for decades.
Attention has been paid to the study of White House personnel operations and the use of
appointments. The literature has detected a growth in the number of such appointees,
which has been more pronounced in agencies located at the Executive Office of the
Presidency (Heclo 1975, Lewis 2008). Presidents have been found to politicize the
appointment process. When doing so, ideology matters. Presidents target agencies whose
ideologies are more similar to their own (Lewis 2008, 2011).
Moreover, studies have also examined the change in the number of appointees
across agencies. In the US, presidentes have been particularly focused on the goals of
controlling the bureaucracy and satistying demands for patronage (Huber and Shipan
2000 and Lewis 2009, p.61), even though they are constrained in their efforts by concerns
about administrative performance and the preferences of members of Congress. Scholars
have also studied the background and qualifications of appointees (e.g., Krause and
O’Connell 2013, Cohen 1998, Heclo 1975 and 1977, Mackenzie 1981). Few studies have
investigated modern patronage practices in the US federal government, though (Horton
and Lewis 2009, p.2, Gallo and Lewis 2012). Regarding the appointment of more than
1,000 individuals by the Obama administration, Horton and Lewis (2009) found that
presidents tend to place patronage appointees in agencies with the same political ideology
6 as the president, that are less neutral to the president’s agenda, and where appointees are
less able to compromise agency performance. Regarding the literature on the effects of
workforce diversity on bureaucratic performance, scholarship is more limited in the
public management literature and also in political science, which focuses more on social
diversity impact (e.g., gender and race) on descriptive representation, with some
important exceptions. For instance, Krause et al (2006) assess how “organizational
balancing” between politically appointed agency executives and merit-selected
subordinates lead to more accurate revenue forecasts in the U.S. states. Still, the partisan
aspect (i.e., employees from different parties and their interactions inside agencies) is not
explored.
With respect to the Brazilian case, studies on appointments and personnel in the
federal bureaucracy still have much to cover. Most studies are influenced by the public
administration’s literature and concepts, are mainly descriptive or focus on specific
agencies (e.g., Loureiro and Abrucio 1999, Santos 2009, de Almeida Corrêa 2011). Some
studies have also analyzed the profile of medium and top-level bureaucrats in the
Brazilian federal government with emphasis on their socio-demographic characteristics
(D'Araujo 2007). Departing from previous studies and using newly available data, Praça
et al (2011) found that the distribution of partisan and non-partisan appointees between
and within ministries differ considerably and are not proportional to the number of seats
parties have in Congress and the number of political appointment offices they control
(2011, p.150). Another area of recent inquiry has been the analysis of turnover in federal
agencies (Praça et al 2012, Lopez et al 2013).
7 Overall, the scholarship on appointees is varied, in development, and still very
concerned with the trade-off between loyalty and competence. The role of patronage and
the relationship between political appointees, politicization, and corruption are still
underdeveloped. There are two important exceptions in the Brazilian case, though. For
instance, Lopez et al (2013) assessed whether political factors such as minister’s party
membership, minister’s party faction, and minister’s party ideology (among others)
impact DASs’ turnover rates. Meneguin and Bugarin (2012) studied the role that two
different incentive structures facing public employees (public civil servants or political
appointees – DAS) exert at following or not corrupt conducts. Also using data on STAs,
they found that ministries which have higher percentages of appointees as members of
the public civil service (i.e., that joined the civil service via public examinations)
presented fewer STAs after controlling for other factors such as year and agency’s
budget. The current study also uses data on STAs and covers ministries, but uses a panel
dataset with fewer time period (T). Differently from Meneguin and Bugarin, I consider
more complex and direct differences among appointees on politicization across agencies
by considering DASs’ party membership in each agency and its impact in agency
corruption level.
3. The role of DAS’s politicization on agency corruption
How does politicization among political appointees affect political corruption
levels in bureaucratic agencies? The conventional wisdom in the literature and among
followers of the Brazilian poilitics would suggest that ministries with more appointees
8 would be more corrupt and scandal prone than ministries with fewer appointee positions.
As previously mentioned, I argue that the raw number of appointees per agency is not
enough to understand the impact of politicization. First, agencies differ in terms of how
many appointee positions are filled by career civil servants. Second, agencies with many
appointee positions do not necessarily have more audits showing problems with
corruption.
[Figure 1 about here]
For instance, as Figure 1 shows, the Ministry of Planning (MP) presents
approximately 1000 appointees for the period 2010-2014, but accounts for only 2% of the
STAs during the period. In contrast, the Ministry of Tourism (MTUR) presents the lowest
number of appointees on average (total of 150 appointees in 2013), but ranks 4th in the
proportion of STAs audited for the same period, as Figure 2 shows. On the other hand,
several ministries with a higher proportion of DAS with party membership rank higher in
number of STAs, such as MTUR and Labor (MTE).
[Figure 2 about here]
The figures aforementioned, although not conclusive, suggest the relationship
between political appointments and corruption needs more theorizing. Drawing from
insights in social identity theory and societal fractionalization (Cerqueti et al 2012 and
Schikora 2014) and political economy regarding groups differences and their impacts on
patterns of coordination and competition over resources (e.g., Fiorino & Petrarca 2012,
9 Roubini& Sachs 1989a and 1989b), I develop a structural theory which posits that the
larger share of partisan appointees changes the relationship between regular civil
servants and intergroup relationships between parties in an agency by increasing the
degree of non-cooperative (or competitive) interactions between parties. As a result, a
prisoner’s dilemma with respect to funding allocations ensues in which parties try to
secure a larger share of agency resources to themselves and their constituents and
interests in detriment of a more efficient allocation based on universalistic and efficiency
parameters established by the career civil servants. The end result would be a net increase
in budgetary spending being allocated inappropriately.
The distribution of party membership among DASs is important in the broader
context of coalition building in Brazil. The division of positions in the federal
bureaucracy is a valuable commodity that the Executive can use in order to organize a
broad governing coalition, along with the distribution of other resources such as
individual amendments, public works contracts, and others. DAS positions allow parties
to exert influence over the agencies’ operations, policy making, and resource allocations.
Hierarchical institutions such as government agencies have as one of their main purposes
to foster cooperative behavior among their employees in order to achieve their goals,
which is important at different tasks such as spending decisions. However, many factors
affect the capacity of employees to work cooperatively, such as power relations, career
ambitions, and miscommunication, to name just a few. Heterogeneity of preferences
inside an agency figures among those factors. Individual government coalition parties
(represented in agencies by partisans possessing appointee positions) have different
interests and constituencies to promote and appease. As a result, the struggle to promote
10 different party interests occurs inside the agencies. Partisan appointees (i.e., DAS with
party membership) from each party constitute groups that engage in intergroup relations.
Intragroup relations are usually assumed as more cooperative, while intergroup relations
are considered to be more distrustful and resulting in more competitive interactions.
Thus, as the proportion of partisan appointees inside an agency increase, the more
resource allocation is seen as a zero-sum game, i.e. the higher the perception among
employees of zero-sum goal relations. In other words, if one party does not secure funds
to its interests then other parties, or the non-partisan career civil servants, will secure
funds to promote their interests. Hence, relations among groups of partisans and of
partisans among career civil servants become less cooperative and more competitive. The
result of heightened competition for resources is a prisoner’s dilemma situation with
respect to funding allocation. All employees inside an agency would prefer resource
allocations to be more “rational” (less predatory) and more guided by universalistic and
efficiency principles in the context of lower intergroup conflict (i.e., more internal
cooperation inside an agency). In a more competitive setting and absent strong
coordination in budget allocations inside an agency, the non-cooperative solution is for
each party to try to divert resources for its interests and constituents in detriment of a
more rational allocation. Thus, a larger proportion of DAS appointees with party
membership inside an agency fosters non-cooperative behavior and distorts the allocation
of government expenditures.
Agencies also differ with respect to the degree to which one party is more
“dominant” than others in an agency (i.e., whether a party has a higher number of DAS
than other parties inside the agency). Higher levels of party fragmentation inside the
11 agencies (i.e., more DASs as members of different parties) lead to more internal
competition for power inside the agencies. The higher the number of party interests
struggling for space inside each agency, the higher will be the diversity of the political
interests willing to use appointee positions for patronage and rent extraction.
Heterogeneity in party membership inside an agency can be expected to aggravate
resource misallocation in the bureaucracy.
In agencies that are more heterogeneous in party membership composition among
DASs, there will be more competition for resources because of the presence of more
rivals (i.e., DASs members of different parties). In agencies with more heterogeneous
party membership patterns, the transaction costs incurred in securing patronage goods
and rent seeking goods (obviously scarce ones) can be higher because a) it is more
difficult to successfully coordinate efforts to obtain the desired goods (i.e., need for more
reliance on opposing interest in order to obtain goods that one side wants to appropriate
for itself and deny to others), and b) there are more incentives for internal acts of
sabotage and whistleblowing in heterogeneous agencies. Nonetheless, in agencies that are
more homogeneous in party composition among DASs, there will be potentially less
predatory competition for resources. The presence of rival partners is lower, and therefore
the incentives to engage in possibly self-inflicted acts such as whistleblowing decrease.
In addition, it is easier to coordinate and obtain cooperation for the acquisition and
distribution of patronage goods and rent seeking goods than in agencies with more
heterogeneous party membership outlook among DASs. In sum, the transaction costs
involved in the acquisition of both types of goods will be lower in more homogeneous
party membership composition among DASs.
12 Based on the aforementioned theoretical argument we can expect the following
empirical implications from the theory. We can hypothesize that H1) corruption levels
(or the number of STAs) will be higher in agencies with higher numbers of DASs party
members (i.e., DASs that officially belong to a political party) than othersise. Likewise,
H2) the more heterogeneous the party membership composition among DASs in an
agency, the higher the corruption level (or higher number of STAs) in that agency,
holding other factors constant.
4. Data and variables
The Brazilian bureaucracy comprises more than 530,000 active employees. As of
April 2013, 22,074 of these employees were political appointees, or DAS (an acronym of
“Direção e Assessoramento Superior”, or “High Level Execution and Advisory”). They
are responsible for the most important decisions inside federal agencies along with the
ministers. They can be divided into two basic groups: DAS 1 to 3 and DAS 4 to 6. The
first group comprises lower-level positions while the second occupy higher-level
positions inside the Brazilian agencies’ organizational structures. Approximately 80% of
DASs are of levels 1 to 3 and 20% of levels 4 to 6 (see Praça et al 2011 and 2012).
Few scholars have collected data on DAS appointees and career civil servants in
Brazil and carried out empirical analyses with it. Regarding the study of the relationship
between bureaucracy and corruption in Brazilian federal agencies, the analysis of the
share of career civil servants among appointees in each agency is an improvement
(Meneguin & Bugarin 2012), but still not enough. A measure of partisan alignments
13 among appointees would be better. However, individual-level data on the subject is not
currently available. Nonetheless, based on data made available by the Brazilian Electoral
Supreme Court (TSE) in recent years, it became possible to cross the information on
political appointees with party membership rolls and therefore to obtain an overall
measure of party membership per agency. It is not a perfect measure of politicization, for
many individuals in Brazil have party attachments without necessarily being officially
members of a political party. That said, the measure on party membership among
appointees is still valid for it is plausible to suppose that those in the federal government
that are in party rolls are on average more devoted, interested in politics, and willing to be
influenced by parties than other individuals.
In a collective research effort with Sergio Praça and Andrea Freitas, we crossed
the information from the Electoral Supreme Court on party membership rolls (name and
party membership) for the entire country with information on each political appointee
available at the federal government’s website “Portal da Transparencia”5. Based on that, I
was able to compile the percentage of political appointees in each agency/year who
are members of a political party. Furthermore, I also calculated a measure capable of
assessing the dispersion or concentration of DAS party members inside the “population”
of DAS in each agency. Based on Rae (1967), I calculated a DAS fractionalization
index that uses the same formula as in the party fractionalization index: F = 1 - ∑vi2 .
However, in this study “vi” is the share of DAS employees who belong to each political
party i inside each agency. We can say that this index assesses the probability that two
political appointees chosen at random would belong to different parties in a given agency.
5 Source: < http://www.portaltransparencia.gov.br >. 14 In addition to these variables, the dataset also includes the following covariates:
the agency’s total number of employees (including non-DAS servants), the agency’s
budget (in R$ millions), the agency’s wage per capita (i.e., the annual spending with
personnel salary in the agency divided by the total number of agency employees), the
agency’s literacy level (i.e., the percentage of public servants who have college-level
education), the total number of DAS per agency, and the agency’s percentage of civil
servants who are career civil servants. The data for total number of employees,
agency’s wage per capita, and agency’s literacy level were obtained from the Ministry of
Planning (Boletim Estatistico de Pessoal, or Personnel Statistical Report6). Data on the
share of appointees who are part of the career civil service were obtained from the
Ministry of Planning’s Secretaria de Gestão Pública (as in Meneguin and Bugarin 2012).
Finally, the data on the number of STAs was obtained from CGU’s website7. The unit of
analysis is ministry/year. There are data for 26 ministries and agencies with ministry
status from 2010 to 2014. A list of the name of all ministries can be found in the
Appendix. The summary statistics for the variables used in the multivariate analysis can
be found on Table 1.
[Table 1 about here]8
6 Source: < http://www.planejamento.gov.br/ministerio.asp?index=6&ler=t10204 >. 7 Source: <
http://www.cgu.gov.br/ControleInterno/AvaliacaoGestaoAdministradores/TomadasContasEspecial/index.a
sp >. 8 propntce = number of STAs, lactive = agency’s total number of employees, budget = agency’s budget,
wagecapita = agency’s wage per capita, literacy = agency’s literacy level, ldastotal = total number of DAS
per agency, dasfilratio = percent of agency’s DAS that belong to a political party, dasserv = DAS part of
career civil service, fracdastotal = DAS fractionalization. 15 The dependent variable is the proportion of number of Special Taking of
Accounts (i.e. Tomada de Contas Especial, TCE)9. The STAs are one of the tools used by
the General Conptroller Office (CGU) in order to detect wrongdoings in the Brazilian
bureaucracy. It is an administrative procedure that is carried out in agencies to recover
misspent public funds from public officials or non-governmental organizations. CGU
supervises the process and issues an Audit Report and Certificate giving its opinion on
whether the investigation concerning the facts was appropriate or not and indicating
which rules or regulations might have been disregarded10. The STAs are latter judged by
the Court of Audit of the Union (Tribunal de Contas da União – TCU), which in the end
attributes responsibilities and penalties to those deemed responsible for misdeeds in
misallocation of budgetary spending allocations. Between 2001 and 2010, these special
investigations of accounts identified loses to the state estimated in U$ 2.8 billion dollars
(Vieira dos Reis 2012). A general overview of the distribution of STAs per ministry can
be viewed at Figure 2.
[Figure 2 about here]
According to the graph, the number of STAs varies considerably across
ministries. The Ministry of Health has had by far the highest number of STAs, followed
by the Ministry of Education. There is considerable variation in the number of STAs
across ministries. Some ministries such as the Ministry of Energy and Mining (MME)
9 I also considered the use of the number of firings of employees per ministry as an alternative dependent
variable. However, the results obtained in the statistical analyses were dubious, of questionable reliability,
and therefore were not included in the paper. 10 Source: < http://www.cgu.gov.br/english/default.asp >. 16 and Ministry of Development, Industry and Commerce (MDIC) present very few STAs.
Most ministries have an average number of STAs between 10 and 50. Few ministries are
responsible for most part of STAs, especially the Ministry of Health (MS) with more than
30% of STAs and the Ministry of Education (MEC). These numbers are not surprising.
Both ministries are among the largest in personnel, budget, and national scope. However,
some smaller ministries also present a high number of STAs, such as the Ministry of
Tourism (MTUR) and Labor (MTE). A general preliminary overview of patterns among
the independent variables can be visualized at Table 2.
[Table 2 about here]
The table shows a correlation matrix of the variables included in the multivariate
analysis. The number of STAs correlate more significantly with “structural” factors such
as the agency’s total number of appointees and the agency’s budget. Almost all the
independent variables present significant correlations among each other. The matrix also
suggests that agencies’ percentages of DAS party members is inversely correlated to total
agency’s employee size, total agency’s DAS size, and also to DAS fractionalization or
dominance - of lack thereof – of one party in an agency vis-a-vis other parties. In other
words, political appointees who belong to political parties tend to be concentrated in
agencies of small to middle size in personnel, of lower wages per capita, with larger
budgets, and less where there is a larger proportion of career civil servants and a larger
proportion of career civil servants with higher levels of formal education (i.e., college
degree and higher).
17 5. Estimation procedures and results
In order to empirically analyze the relationship between DASs’ politicization
patterns and corruption in ministries, I proceed to a multivariate analysis of the panel
dataset on STAs in the Brazilian ministries. There are different challenges to model
estimation that must be addressed given the nature of the data at hand (short panel with a
small number of observations, N = 130). Assumptions of normality are more difficult to
meet. Moreover, there are important concerns with autocorrelation and hereroskedasticity
in the residuals which, if present and unaccounted for, can severely bias the standard
errors.
First it is necessary to decide which type of panel estimation procedure is more
appropriate: fixed effects, random effects, or simply ordinary least squares. I tested the
null hypothesis that the coefficients estimated by the random effects estimator are the
same as the ones estimated by the consistent fixed effects estimator. According to the
Hausman test (Prob > chi2 = .55), it is not possible to reject the null, which means that
the random effects model is more appropriate than the fixed efffect model. Additionally, I
also tested whether a random effects estimator is more efficient and appropriate than an
ordinary least squares estimator. By running a Breusch-Pagan Lagrangian multiplier
(LM) test11, I verified whether the null hypothesis that variances across entities is zero
(i.e., no significant difference across units, or ministries). The test rejects the null (Prob >
chibar2 = .00), which means that there is evidence of significant differences accross
ministries and therefore the random effects estimator is preferred over the OLS estimator.
11 xttest0 command in Stata. 18 Hence, the Breusch-Pagan test buttresses the study’s substantive claim that there are
reasons to believe that differences across ministries have some influence on the
proportion of STAs (e.g., agency size).
Another concern is the presence of serial correlation. Usually not a significant
problem in short panels (with larger Ns and small Ts), serial correlation causes the
standard errors to be smaller than they actually are and also to have higher R-squares. I
ran a Wooldridge test12 for autocorrelation in the data and tested for the null hypothesis
of no first-order – AR (1) – autocorrelation. The null was rejected (Prob > F = 0.00),
which suggests that the data has first-order autocorrelation.
As a result, I estimated four multivariate panel models. Three models employ
random effects estimators. The first model is a simple random effects model. The second
model presents a random effects with Huber/White estimators (robust standard errors)
due to concerns over eventual heteroskedasticity that might be present (if so, the standard
errors would be biased). The third model is a random effects where the disturbances
follow an AR (1) process (with autocorrelated disturbances). The fourth model fits a
panel data linear model by using feasible generalized least squares (GLS), which allows
estimation in the presence of first-order autocorrelation within panels and cross-sectional
correlation and heteroskedasticity across panels. In this model each group is assumed to
have errors that follow the same AR (1) process (i.e., the autocorrelation parameter is the
same for all groups).
In order to account for possibly skewed distributions that can be expected in a
dataset with a small number of observations, I proceeded to the transformation of some
12 xtserial command in Stata. 19 variables in order to make the distribution of some variables closer to normal 13. I
obtained the square root of DAS party membership and Total number of DAS, the cubic
root of DAS fractionalization, and the log of the total number of employees. The
following table presents the results.
[Table 3 about here]
In general, the four models display the same pattern of results. All models reject
the null that the independent variables are jointly equal to zero. The first three models
also explain the same amount of variance on STAs (R2 overall of approximately 5657%). Moreover, the coefficients in the three models present basically the same sign for
each variable. The model accounting for AR (1) disturbances present slightly different
coefficients than the other two models, but the overall results are mostly the same. The
model shows a test for autocorrelation, based on the Durbin-Watson statistic. The value
of the modified Durbin-Watson statistic or Baltagi-Wu LBI statistic (Baltagi and Wu
1999) indicates no autocorrelation. It presents a statistic of 1.92 (the values can be
between 0 and 4). Thus, we can be moderately confident that autocorrelation is not
biasing the results. The fourth model (GLS with AR (1) ) presents results are are mostly
the same in comparison to the random effects models, except that DAS fractionalization
and ministry literacy level present negative coefficients (in disagreement with theory for
the former and in agreement with theory for the latter). The similarity of results across the
13 Selection of the appropriate transformation were based on numeric and graphical results obtained
through the ladder and gladder commands in Stata. Based on the ladder command, I chose the
transformations with the smallest chi-square statistics reported (the lower the Chi2, the more appropriate a
transformation). 20 four models, notably for those which account for heteroskedasticity and first-order
autocorrelation, gives us more confidence that the results are consistent and not affected
by autocorrelation and hereroskedasticity in the disturbances.
The variables that account for structural factors at the agency level (i.e., not
directly related to political appointees’ politicization) strongly and positively predict
more STAs, holding the other variables constant. Ministries with larger budgets tend to
be more corrupt, which is in accord with our expectations. Ministries with larger budgets
attract the attention of rent-seekers and tend to spend more money that can be diverted for
specific constituencies in exchange for electoral support, kickbacks, or other advantages.
The coefficient for total personnel size is positive and statistically significant. Such result
conforms to this study’s theoretical expectations. In larger groups, incentives to free-ride
are larger. It is more difficult to organize and coordinate collective actions in large groups
(Olson 1965), which makes more difficult for authorities to sanction illegal behavior. In
sum, larger agency size increases the potential for corruption by providing more
resources that can be diverted and also by making more difficult to detect and sanction
inappropriate or illegal actions.
It is also worth noting the results for two other covariates, wage per capita and
literacy level. Ministries with larger wages per capita present lower proportion of STAs,
although the coefficients across models do not reach conventional levels of statistical
significance. Such results are in line with expectations that higher wages decrease
bureaucrats’ incentives to engage in corrupt acts. Furthermore, the coefficient for literacy
level in the three random effects models were all positive and not statistically significant.
Nevertheless, the GLS model present a significant and negative coefficient for that
21 variable, which is in conformity to the notion that agencies with more qualified personnel
should be better managed, more concerned with efficiency (Lewis 2008, 2009, 2011), and
possibly more capable of fostering cooperative behavior and detecting suspicious or
inappropriate spending allocations.
Tellingly, the coefficient for the total number of DAS in ministries is negatively
and significantly related to the number of accounts in all models, which suggests that the
total number of DAS per se is not necessarily related to more corruption and patronage as
is usually assumed in the literature. Indications of this result are also displayed in Figure
1 and Table 2. The data suggest that in fact ministries with more DAS positions are more
efficient and less corrupt-prone on average holding other factors constant. Such
apparently counterintuitive result can at least in part explained by the fact that most DAS
positions are available in ministries where concern for efficiency are more pronounced,
such as in the Ministry of Finance (MF), Planning (MP), and the Attorney General of the
Union (AGU). These type of ministries also allocate more DAS positions to career civil
servants as a way of retaining and rewarding civil servants in those agencies, for DAS
positions (especially DAS level 5 and 6) pay better salaries and give their occupants
higher decision making power.
Both main measures of DASs’ politicization (DAS party membership and
fractionalization) are positively related to higher number of STAs (the former being
statistically significantly while the latter is not across the first three models, and with a
negative coefficient in the GLS model). That is, when the share of DAS with party
membership is higher in a ministry, the number of STAs also tends to increase. The
values for DAS fractionalization are also of some significance. It suggests that more
22 heterogeneous ministries in party membership composition among DASs may be prone
to present more problems with wrongdoings than more homogeneous ministries. Overall,
all four models depict a similar pattern. Ministries with more corruption tend to be those
with more employees and more sizable budgets, but not those with more political
appointees. It is higher in ministries with more DASs with party membership.
In susbtantive terms, the difference in impact of DAS party membership and total
number of DAS per ministry is also detected, as Figure 3 illustrates.
[Figure 3 about here]
The graphs in Figure 3 show the marginal effects of each variable on the
proportion of STAs with a 95% confidence interval. According to the figures, as the
proportion of DAS with party membership increase in a ministry, the probability of
observing an increase in the proportion of STAs also increases sharply. As the proportion
of partisan DASs double (e.g., from .2 to .4) the proportion in STA moves from 0 to .1. In
sharp contrast, the figure on the right displays a sharp negative relationship between the
proportion of STAs and the number of DAS per ministry. For instance, as the number of
DAS moves from 0 to 20, the probability of observing an increase in the proportion of
STAs decreases from .25 to approximately .1, everything else constant. Again, the
politicization of DAS positions, rather than the sheer number of DAS positions across
ministries, seem to be a key factor driving corruption in Brazilian ministries.
23 6. Conclusion
It has been commonly assumed in Brazil that political appointee positions in the
Brazilian bureaucracy are currently one of the main explanations for the corruption
scandals that from time to time plague the federal government in Brazil. Either for
patronage or rent-seeking purposes, federal appointees would be behind many attempts to
embezzle funds to private interests and to curry favors by using the agencies’ resources.
The politicization of the nomination of the appointee positions would be one of key
reasons why DAS positions would be directly related to corruption. Nevertheless, there
has not been a careful account of whether and how politicization would explain
corruption levels in the Brazilian bureaucracy. Usually higher numbers of DASs have
been equated to more wrongdoings. In this paper, I argue that the mere number of
appointee positions per agencies belies more important distinctions that exist across
DASs and agencies that are important at understanding whether and how DAS positions
may be related to wrongdoings. I theorize that the politicization of DAS positions (as
party membership among DASs inside the agencies and the degree of dispersion or
concentration of DAS with party membership) would help explain differences in
corruption patterns across agencies. Both factors would operate at increasing intergroup
rivalry and lead to an increase in competitive rather than cooperative behavior among
employees inside agencies. As a result of such heightened competition, the struggle for
resources would assume a zero-sum aspect and a prisoner’s dilemma regarding the
allocation of resources among parties would follow (Fiorino & Petrarca 2012, Roubini &
Sachs 1989a and 1989b), thus affecting the divertion of funds in the ministries.
24 By using a dataset with information on the patterns of party membership and the
fractionalization of such membership across 26 ministries from 2010 to 2014, in
conjunction with other covariates, I was able to empirically provide some preliminary
results. So far, the findings corroborated the theory that politicization at the appointee
level, and not the sheer number of appointee positions per se, affects corruption levels as
measured by the number of Special Taking of Accounts (STAs) as the proxy for
corruption. However, agencies’ overall resource pool in terms of personnel and budget
are also significantly related to corruption.
That said, more needs to be done in order to buttress such results. For instance,
there are concerns regarding the appropriateness of using STAs as a proxy for corruption.
It is certainly a partial measure of corruption that can make some agencies to be more
prone to investigations than others. Misallocation of funds is an important corrupt
practice. However, other important practices not captured by the proxy such as regulatory
activity, concession of rights to groups, and provision of non-monetary services can also
result in corrupt exchanges between public and private actors. Maybe different
accounting and investigation tools would show a different pattern of results. The models
may not be thoroughly specified (i.e., with all the relevant independent variables), which
can raise concerns about omitted variable bias. Furthermore, ministries are just a part of
the Brazilian bureaucracy. Many important governmental activities are performed by
other agencies with different structures than ministries, such as state-owned companies –
some more impervious to public scrutiny and with ample resources that can be diverted
for private gains, as the recent scandal14 involving the Brazilian oil company Petrobras
14 Source: < http://www.economist.com/news/americas/21637437-petrobras-scandal-explained-big-oily>.
25 shows – and more decentralized bureaus (autarquias). That said, it is always important to
remember how difficult it is to measure corruption anywhere and what can be gained at
using measures such as STAs for such purposes. The investigation of corruption patterns
in other federal agencies and a movement towards individual-level analysis seem to be
promising directions in this line of inquiry. We also need more and better qualitative
accounts of the relationship between DAS employees, the importance of partisanship
among DAS employees, and how they relate to other groups inside and outside the
government. Much remains to be done and gained by better assessing the relationhsip
between political appointees and their actions inside the bureaucracy. This paper is an
attempt at providing a theory with applicability to other polities and contexts.
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29 Appendix MPS
MEC
MJ
MF
MS
MMA
MTE
MAPA
MDA
MT
MP
MME
MDIC
AGU
PR/CGU/ABIN
MI
MINC
MD
MCT
MC
MRE
MCID
MTUR
MDS
MPA
ME
Ministries included in the study
Ministério da Previdência Social
Ministério da Educação
Ministério da Justiça
Ministério da Fazenda
Ministério da Saúde
Ministério do Meio Ambiente
Ministério do Trabalho e Emprego
Ministério da Agricultura, Pecuária e Abastecimento
Ministério do Desenvolvimento Agrário
Ministério dos Transportes
Ministério do Planejamento
Ministério de Minas e Energia
Ministério do Desenvolvimento, Indústria e Comércio
Advocacia Geral da União
Presidência da República
Ministério da integração Nacional
Ministério da Cultura
Ministério da Defesa
Ministério da Ciência e Tecnologia
Ministério das Comunicações
Ministério das Relações Exteriores
Ministério das Cidades
Ministério do Turismo
Ministério do Desenvolvimento Social e Combate 'a Fome
Ministério da Pesca e Aquicultura
Ministério do Esporte
Note: Although AGU and PR are not ministries, both their office heads (Advogado-Geral da União and Ministro-Chefe
da Casa-Civil) have ministerial status.
30 Figure 1. Average number of DAS and proportion of DAS with party membership per ministry (2010-2014)
MF
PR/CGU/ABIN
MS
MP
MAPA
AGU
MJ
MEC
MCT
MDS
MTE
MRE
MD
MINC
MT
MME
MDA
MC
MPA
MI
MDIC
MPS
MMA
ME
MCID
MTUR
MDA
ME
MTE
MCID
MPA
MAPA
MS
MTUR
MI
MPS
PR/CGU/ABIN
MINC
MEC
MDS
MP
MJ
MMA
MDIC
MT
MME
MC
MCT
MF
MD
AGU
MRE
0
500 1,000 1,500 2,000 2,500
# DAS
0
.1
.2
.3
% DAS with party membership
Figure 2. Average proportion of STAs per ministry
MS
MEC
MI
MTUR
MTE
MCT
MINC
MDA
MPS
MDS
ME
MF
MMA
MC
MP
MD
MAPA
MCID
MJ
MT
MDIC
MPA
PR/CGU/ABIN
MME
MRE
AGU
0
.1
.2
.3
% STAs
31 Table 1. Summary statistics
Mean Std. Dev.
Proportion of STAs
.149
.127
DAS party membership (%)
.376
.071
DAS fractionalization (0-1)
.577
.172
Total number of employees
8.717
1.592
Total number of DAS
21.778
8.445
Budget (R$ million)
6246.3 15531.95
Wage per capita (R$)
202612 131117.2
Literacy (%)
.355
.162
DAS part of civil service (%)
3.214
14.577
Min
0
0
0.085
5.808
0
150.6
38512.4
.08
0.171
Max
.623
.579
1
12.449
49.345
92702
584555.7
.691
86.49
32 Table 3. Determinants of the proportion of STAs in the Brazilian ministries
Variables
(1). RE
(2). RE
w/robust SE
(3). RE with
AR (1)
(4). GLS with
AR (1)
DAS party membership (% squared)
.373**
(.124)
.373***
(.096)
.368**
(.127)
.357***
(.048)
DAS fractionalization (0-1 cubic)
.074
(.060)
0.074
(.043)
0.073
(.060)
-.0170
(.021)
Total number of employees
(log)
.036**
(.011)
.036***
(.009)
.032**
(.010)
.036***
(.006)
Total number of DAS
(squared)
-.004*
(.002)
-.004**
(.001)
-.004*
(.001)
-.006***
(.000)
Budget (R$ million)
4.27e-06***
(1.01e-06)
4.27e-06***
(4.86e-07)
4.35e-06***
(9.26e-07)
5.70e-06***
(5.65e-07)
Wage per capita (R$)
-1.38e-07
(1.23e-07)
-1.38e-07
(8.68e-08)
-9.48e-08
(1.11e-07)
-7.05e-08
(3.95e-08)
Literacy (%)
.051
(081)
0.051
(.066)
0.031
(.078)
-.099***
(.023)
DAS part of civil service (%)
-.000
(.000)
-.000***
(.000)
-.000
(.000)
-.000***
(.000)
Constant
-.261*
(.120)
-.261***
(.073)
-.242*
(.113)
-.185**
(.061)
Wald chi2
44.19
515.71
51.33
438.86
Prob > chi2
.000
.000
.000
.000
R2 within
.078
.078
.068
R2 between
.638
.638
.658
R2 overall
.558
.558
.572
Rho
.679
.679
.553
Baltagi-Wu LBI
N
1.92
130
130
130
130
Note: cells present coefficients followed by standard errors in parentheses.
a+ p<.10, * p<0.05, ** p<0.01, *** p<0.001
33 .3
-.1
-.1
0
0
.1
.1
.2
.2
Figure 3. Marginal effect of DAS party membership and Total number of DAS on the Proportion of STAs
95% CI
0
.2
.4
DAS party membership (%)
Note: other variables are hold at their means
.6
0
10
20
30
40
Total number of DAS
50
34