Subnational government banks profitability in Brazil between 2003 and 2019

A rentabilidade dos bancos públicos subnacionais no Brasil entre 2003 e 2019

ABSTRACT

The role of public banks in the economy has been extensively discussed in the literature. Within this debate, one of the primary criticisms of these financial institutions is their lower profitability compared to private peers. In Brazil, the subnational commercial government-owned banks remain one of the least studied bank categories, particularly following the financial crises they encountered in the 1980/90s. This article aims to empirically investigate the effect of the subnational government-owned control on banks’ performance in a more recent timeframe. Contrary to the conventional literature, the results provide evidence that this control positively impacts profitability.

KEYWORDS:
Subnational government banks profitability; government banks profitability; development emerging economies; Brazilian banking system

RESUMO

A literatura bancária tem discutido o papel dos bancos públicos na economia. Dentro desse debate, uma das críticas a essas instituições financeiras está relacionada a sua menor rentabilidade perante os pares privados. No Brasil, os bancos comerciais de controle subnacional estão entre as categorias de bancos públicos menos estudadas, principalmente após as crises financeiras por que passaram nas décadas de 1980/90. Este artigo tem como objetivo investigar empiricamente o efeito do controle subnacional sobre a rentabilidade bancária em um período mais recente. Contrariamente à literatura bancária convencional, os resultados fornecem evidências de que esse tipo de controle impacta positivamente a lucratividade.

PALAVRAS-CHAVE:
Rentabilidade bancos públicos subnacionais; rentabilidade bancos públicos; economias emergentes; sistema bancário brasileiro

1. INTRODUCTION

The role of subnational government banks (SGB) has been discussed in the literature. The empirical evidence points to the positive impact on economic development, especially in poor areas (Hakenes et al., 2015), preventing capital drain from poorer to richer areas (Hakenes & Schnabel, 2010) and playing an important role in the process of industrialization (Lehmann-Hasemeyer & Wahl, 2021).

Nevertheless, the privatization wave that transpired in the banking systems of diverse nations in the past decades left no exemption for SGB. In Italy, during the 1990s, the banking sector underwent privatization, encompassing subnational government banks, such as saving banks, to diminish public debt and enhance sector efficiency ahead of the adoption of the Euro (Gieseler, 2022a). In Spain, savings banks have undergone substantial changes in recent years, including the loss of regional focus and government control, and, following the 2008 financial crisis, these banks underwent mergers, consolidating into significant banking conglomerates (Gieseler, 2022b). In France, savings banks evolved into credit cooperatives, and in other countries like Belgium and the Netherlands, savings banks were virtually eradicated, primarily through privatization measures (Bülbül et al., 2013).

In Brazil, the political influence the SGB suffered caused severe losses in the 1980s and 1990s (World Bank, 1990; Novaes & Werlang, 1995; Paes, 1996), which turned to the predominant perception that SGB were intrinsically unviable, which resulted in the privatization or extinction of most SGB (Gutiérrez, 2006). Nevertheless, during the last decades, there were changes in the institutional framework, such as the Basel Accords, the convergence to the international financial account standards, and the implementation of new anticorruption laws that changed the way SGB was managed, which positively affected their financial performance (Gutiérrez, 2006; Mettenheim, 2010).

However, in the studies of bank profitability determinants, national and subnational government banks are analyzed under a single category, although SGB are generally smaller and have a regional focus, while the federal government banks (FGB) are larger and have a broader geographic presence. Furthermore, these studies usually use databases that cover only the largest banks in each country, which excludes a large part of SGBs, which are generally smaller institutions. In this sense, this study aims to fill these gaps in the literature on SGB by analyzing the effect of subnationally government control on banks’ performance in Brazil between 2003 and 2019.

This study challenges the conventional banking literature that views government banks as less profitable than their private peers. It shows that subnational government control positively affects profitability, meaning that financial unsustainability is no longer an obstacle for SGB to fulfill its mission of promoting local economic development.

This article is structured as follows: the next section presents a literature review; the context of SGB in the Brazilian financial system is briefly summarized in section 3; section 4 presents the data and the empirical model; section 5 presents the empirical results followed by the final remarks and conclusion.

2. LITERATURE REVIEW

The role of government banks has been the subject of several studies with different approaches. The social view highlights the government banks’ role in financing high social return projects underfinanced due to market imperfections (Stiglitz, 1994; Panizza, 2023). The development view is associated with Gerschenkron (1962) in which government banks can promote nations’ economic development, especially the economically backward ones (Panizza, 2023; Barboza et al., 2019). More recently, some studies have highlighted government banks’ financial stability role during economic downturns through increased lending (Chen et al., 2016; Brei & Schclarek, 2013).

On the other hand, the political view emphasizes the political interference in government banks to achieve political rather than social goals, causing economic inefficiencies (Shleifer & Vishny, 1997). Under this approach, La Porta et al. (2002) and Barth et al. (2001) associate government banks with countries’ financial underdevelopment. Moreover, Dinc (2005) and Carvalho (2014) shows evidence that government banks are used for political purposes in electoral years in emergent markets; similar evidence was found in developed economies (Markgraf & Rosas, 2019; Sapienza, 2004).

These views (social, development, and political) support the lower profitability of government banks when compared to private ones (Levy-Yeyati et al., 2007). The lower incentives and less monitoring to which government companies’ executives are subject concerning private companies is another factor that, theoretically, might mitigate the government banks’ performance (La Porta & Lopez-de-Silanes, 1999).

When specifically looking at SGB performance in Brazil, severe losses in the 1980s and 1990s were related to local government’s political influence (World Bank, 1990; Novaes & Werlang, 1995; Paes, 1996). However, the problems SGB faced were not intrinsic to these banks but related to the subnational government financial crisis and the lack of a proper institutional framework before the implementation of the Basel Accords, anticorruption laws, and new banking supervision tools (Gutiérrez, 2006; Mettenheim, 2010).

The literature also highlights some government banks’ competitive advantage as the lower funding cost than private ones (Altunbas et al., 2001; Yeyati et al., 2004; Matthey, 2010). Mettenheim (2010, pg. 105) states that “government banks retain competitive advantages due to several factors, such as greater trust between depositors and brands already consecrated by time and recognition popular”. According to Butzbach & Mettenheim (2014), government banks exhibit agency cost advantages related to owners and managers, depositors and managers, and investors and borrowers.

However, empirical studies generally point to lower profitability of government banks concerning their private peers (Dietrich & Wanzenried, 2014; Cornett et al., 2010; Micco et al., 2007; Short, 1979). The exceptions are Athanasoglou et al. (2008) and Dietrich & Wanzenried (2011), who found no significant banking ownership influence on profitability, and Molyneux & Thornton (1992), who pointed to a positive relationship between profitability and government banks.

In the Brazilian case, Vinhado & Divino (2013), Dantas et al. (2011), and Tabak et al. (2013) found a negative relationship between Brazilian government banks and profitability, while Primo et al. (2013) found no significant relationship between them. However, no specific studies on profitability and SGB relationship were found, as they are, in general, included within the broader category of government banks, together with FGB. The few SGB empirical studies (Paula & Faria Júnior, 2010) focused on the efficiency evaluation and indicated a worse performance with other bank categories.

3. REGIONAL GOVERNMENT BANKS IN THE BRAZILIAN FINANCIAL SYSTEM

Brazilian SGB were opened during the last century, a period in which the direct participation of states in banking systems was a usual instrument of local governments to promote the economic development of their regions in the face of a strong shortage of long-term private credit (Salviano Junior, 2004; Paes, 1996). The problems started in the 1980s (Garman et al., 2001; Paes, 1996), when SGB began to be used by local governments as alternative sources of financing that were often not honored (Gutiérrez, 2006; Sola et al., 2001). Political patronage (Andrezo & Lima, 2007; Garman et al., 2001) and intense political use during election campaigns (Paes, 1996) are among other problems related to the political influence of SGB in Brazil.

In 1996, the Brazilian federal government instituted a Program (Proes) that created incentives to reduce the local government sector in banking activity through privatization, extinction, or transformation into a non-financial institution. As part of Proes, the federal government financed 100% of the bank’s bail-out costs, except if the subnational government remained in control of the bank, in which case the financing percentage dropped to 50% (Presidência da República, 1996). At Proes’ launch, 22 out of 24 SGB joined the Program¹ Most of them were extinguished or privatized during the program. Among the factors influencing governments’ decisions to divest from financial institutions were the ideological stance of the governor, particularly nationalist left-wing governors who opposed such sales; the financial condition of the state, with poorer financial conditions making the state more inclined not to retain the institution; and the financial situation of the institution itself (Salviano Junior, 2003, pg. 84). Currently, only five banks remain under local government control.

The SGB market share reduction was offset by Foreign Banks (FRB) and National Private Banks (NPB) increased, as shown in Table 1.

Table 1
Share of assets of commercial banks by category

Date	FGB (%)	SGB (%)	NPB (%)	FRB (%)	Total (%)
Dec/1995	37	21	35	7	100
Dec/2019	36	2	45	17	100
Δ	-1	-19	10	10

• Source: Elaborated from Brazilian Central Bank (Bacen) data.

The NPB and FRB growth share was also driven by the acquisition of SGB. The Bank of the State of São Paulo (Banespa), purchased by Santander, and the Banks of the States of Rio de Janeiro (Banerj) and Minas Gerais (Bemge) by Itaú, currently the largest private bank in the country, are among the most significant of these transactions.

Despite losing national relevance, the SGB still has regional importance and is among the largest commercial institutions in their respective states, as shown in Table 2.

Table 2
SGB credit operations and saving deposits share in Dec/19

Bank	Home State	Credit Operation		Saving deposits
		% total	Ranking	% total	Ranking
Banese	Sergipe	19	3º	22	3º
Banestes	Espírito Santo	15	3º	20	2º
Banrisul	Rio Grande do Sul	20	3º	13	4º
Banpará	Pará	20	3º	10	4º
BRB	Distrito Federal	6	4º	9	4º
Average		16		15

• Source: Elaborated from Bacen data.

Moreover, as demonstrated in Table 3, the SGB boasts the most extensive network of branches within their respective states. A considerable number of these branches are exclusive to a city, potentially streamlining the community’s access to credit and financial services according to various studies that have established a correlation between credit availability and the presence of bank branches (Martin-Oliver, 2019; Crocco et al., 2014; Chakravarty, 2006).

Table 3
SGB branches distribution in Dec/19

Bank	Home State	Total branches	Home State branches	Ranking	Home State unique branches
Banese	Sergipe	63	63	1º	14
Banestes	Espírito Santo	117	116	1º	19
Banpará	Pará	124	124	1º	34
Banrisul	Rio Grande do Sul	518	497	1º	117
BRB	Distrito Federal	123	107	1º	2

• Source: Elaborated from Bacen data.

4. MATERIALS AND METHODS

4.1 Data

The data used in this article is available on the Brazilian Central Banking website (IFdata) and represents the population of commercial banks, multiple banks with commercial portfolios, and conglomerates whose composition includes at least one commercial bank or multiple banks with commercial portfolios. The data is semi-annual, but they were processed annually for comparability with most studies, to eliminate seasonality between semesters, and to be on the same basis as important annual financial statements such as International Financial Reporting Standards Report, Reference Form, and Risk Management Report.

The sample is an unbalanced panel dataset of 108 banks in Brazil from 2002 to 2019. As shown in Table 4, the sample is made up of 6 regional government banks, 7 federal government banks, 56 national private banks, and 43 foreign banks, respectively, with 91, 81, 715, and 502 observations.

Table 4
Sample by bank control category

	Total	FGB	SGB	NPB	FRB
Nº banks	108	7	6	56	43
Observations	1.389	81	91	715	502

• Source: Elaborated from Bacen data.

Institutions whose average percentage of credit operations or deposits on assets was less than 5% were excluded from the study, assuming that they are banks with a business model other than financial intermediation, such as brokers in which the bank within the conglomerate has only one accessory function. Institutions operating during less than three consecutive data years, involved in accounting fraud practices, the only two cooperative banks, and outliers (data with deviations from the mean greater than 3 standard deviations) were excluded from the sample.

Figure 1 illustrates the return on assets (Roa) evolution by bank category in the period. It can be seen that SGB had the highest average in most of the period.

Figure 1
Roa evolution by bank category

Figure 2 illustrates the return on equity (Roe) evolution by banking category in the period. The SGB, once again, achieved the highest average in most years.

Figure 2
Roe evolution by bank category

Table 5 presents the descriptive statistics.

Table 5
Descriptive statistics

Variable	Mean	Std. Dev.	Min	Max
Dependent variables
Roa	0.01	0.03	-0.14	0.16
Roe	0.09	0.18	-0.70	0.79
Independent variables
Size	21.71	2.32	16.95	28.06
Efic	0.74	0.72	0.00	4.38
Risk	0.37	0.22	0.00	1.00
Npl	0.05	0.04	0.00	0.26
Equity	0.17	0.10	0.02	0.64
Selic	0.12	0.04	0.06	0.23
Inf	0.06	0.02	0.03	0.11
GDP	0.02	0.03	-0.04	0.07

4.2 Variables

Table 6 presents the definitions of the variables.

Table 6
Variable definitions

Variable	Definition	Formula	Expected effect
Roa	Return on assets	net income over average total assets (%)
Roe	Return on equity	net income over average total equity (%)
SGB	Dummy variable: subnational government-controlled bank (reference category)		N/A
FGB	Dummy variable: federal government-controlled bank		=
NPB	Dummy variable: national private capital-controlled bank		+
FRB	Dummy variable: Foreign capital-controlled bank		+/-
Size	Bank size	Ln on average total assets	+/-
Equity	Capital adequacy	Average total equity over average total assets (%)	+
Npl	Credit risk	Average total loan loss provision over average total loans (%)	-
Efic	Operational efficiency	Services and tariffs revenue over personal expenses (%)	+
Risk	Risk	Average total credit operations over Average total assets (%)	+/-
Selic	Base interest rate		+
Inf	Extended National Consumer Price Index
GDP	Annual GDP variation (%)		+
T	Time dummies

4.2.1 Dependent variables

The indicators chosen to measure the institutions’ profitability are the dependent variables, Roa and Roe, after paying taxes. According to Berger (1995, pg. 414), Roa and Roe are the standard profitability measures for banking research.

4.2.2 Independent Variables

The main variable is the subnational government control (SGB) to assess its impact on bank profitability. The other types of bank control categories used in the model are federal government (FGB), national private (NPB), and foreign (FRB).

• - SGB: Subnational government bank (dummy variable). The results of the remaining variables related to the type of control are standardized against this chosen reference variable.

• - FGB: Federal government bank (dummy variable). Similar to the reference category, this category is also a government bank category; hence, no significant impact is anticipated from this variable in the model, either positive or negative.

• - NPB: National private bank (dummy variable). The conventional banking literature points to a higher profitability of private banks concerning government banks (Dietrich & Wanzenried, 2014; Cornett et al., 2010; Micco et al., 2007; Short, 1976). Hence, a positive and statistically significant coefficient is anticipated for this variable in the model.

• - FRB: Foreign bank (dummy variable). International studies such as those by Demirguc-Kunt & Huizinga (1999) and Claessens et al. (2001) indicate that foreign banks tend to perform better in developing countries. However, Brazilian bank studies such as those by Primo et al. (2013), Vinhado & Divino (2013), and Tabak et al. (2013) demonstrate that foreign banks exhibit lower profitability compared to domestic banks, encompassing both private and public institutions. Thus, the anticipated relationship between this category and the reference category is ambiguous.

The model also includes control variables related to banking activity (internal) and macroeconomic environment (external). These variables were all selected based on the literature review of the main national and international studies available on the database used.

Internal control variable

• - Size: variable used as a proxy for the size of the bank. In theory, this variable can positively affect profitability through economies of scale and scope and negatively through the need for higher administrative costs to manage its structure. The empirical studies show negative (Tabak et al., 2013), positive (Flamini et al., 2009) and non-significant impacts (Athanasoglou et al., 2008; Goddard et al., 2004). Then, the overall impact of this variable is indeterminate.

• - Equity: this is a measure of capital adequacy. In theory, the effect of this measure on profitability is ambiguous. If, on the one hand, higher capitalization is related to lower risk, which theoretically would incur lower returns, on the other hand, a higher capitalization can favor the institution’s image with depositors and the market, incurring a lower cost of funding that can positively affect profitability. Regarding the empirical studies, this variable is more common in other countries and positively influences bank profitability (Dietrich & Wanzenried, 2011; Flamini et al., 2009; Athanasoglou et al., 2008). Then, a positive impact for this variable is expected.

• - Risk: risk proxy measure. It does not have a clear effect on profitability, as high ratios tend to boost profit through the profitability of credit operations to the detriment of other investments; but high levels of the indicator can also affect the institution’s image in the market, negatively impacting its funding cost and, consequently, profitability. Empirical studies that apply this variable also do not show a clear direction. While Tabak et al. (2013) identify a positive influence, Maffili et al. (2007) and Demirguc-Kunt & Huizinga (1999) show opposite results. Then, the overall impact of this variable is indeterminate.

• - Npl: credit risk measure. High provision rates negatively impact profitability by eroding the institution’s capital and thus its ability to originate new operations and by the drop in return on the current credit portfolio. Most empirical studies (Pintor, 2017; Vinhado & Divino, 2013; Dietrich & Wanzenried, 2014; Athanasoglou et al., 2008) also identify its negative impact on profitability.

• - Efic: efficiency proxy measure. Its effect on profitability tends to be positive, as a high percentage of this indicator opens the way for the bank’s other activities to be more aggressive, increasing the returns. Most empirical studies (Primo et al., 2013; Vinhado & Divino, 2013; Maffili et al., 2007; Dietrich & Wanzenried, 2011, 2014; Athanasoglou et al., 2008; Tregenna, 2009) also identify its positive impact on profitability.

External control variables

• - Inf: The banking theory does not point to a clear bias for the effect of inflation on profitability due to its corrosive effect on nominal interest rates that affect bank assets and liabilities. The empirical studies do not indicate a clear inflation influence. Brazilian banks’ studies (Primo et al., 2013; Pintor, 2017) tend to show a negative impact; while in other countries’ studies the result is the opposite (Demirguc-Kunt & Huizinga, 1999; Claessens et al., 2001; Molyneux & Thornton, 1992; Dietrich & Wanzenried, 2014). Then, the overall impact of this variable is indeterminate.

• - Selic: The risk-free rate has the potential to influence profitability positively on the asset side, attributable to the interest accrued from government-issued public bonds. Conversely, it may exert a negative impact on the liability side due to the remuneration associated with financial instruments linked to it. The majority of empirical investigations (Short, 1979; Demirguc-Kunt & Huizinga,1999; Claessens et al., 2001; Primo et al., 2013; Pintor, 2017; Vinhado & Divino, 2013) have shown a positive relationship between the Selic rate and profitability, providing evidence that the positive impact outweighs the negative.

• - GDP: This variable is expected to be positively correlated with profitability as it causes an increase in credit operations in the economy. The empirical studies, mainly those related to Brazilian banks (Primo et al., 2013; Vinhado & Divino, 2013; Tabak et al., 2013; Dantas et al., 2011), also show a positive GDP influence.

4.3 Econometric model

The model aims to investigate the effect of bank ownership on commercial banks’ profitability. Panel data analysis offers several advantages to improve parameter estimations by allowing the use of a wider sample of information, thereby increasing the variability and degrees of freedom of the model (Baltagi, 1995). Moreover, econometric methodologies for panel data are also very suitable for dealing with models that suffer from endogeneity, as in the case of the investigation we are performing, especially the accounting variables. In addition, profitability may be quite persistent, meaning that the bank’s profitability today is influenced by its past values. Therefore, including an autoregressive term may improve estimations considerably.

Hence, the dynamic panel data methodology is then very suitable for investigating the relationships behind our model. We used a Generalized Method of Moments (GMM) dynamic panel data model, developed by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) to deal with the persistence of profitability over time and the endogeneity between the dependent variable and explanatory variables. This approach is analogous to the recent literature on bank profitability, such as García-Herrero et al. (2009), Dietrich & Wanzenried (2014), Vinhado & Divino (2013).

System-GMM reduces the risk of generating biased parameters in the expected results in linear regressions, through the use of lagged values of the dependent variable, as instruments, in levels and in differences, and the lagged values of the other regressors that may eventually have an endogenous character.

To empirically investigate the effects of factors on bank profitability, we used the linear dynamic model given by equation (1):

$Y_{it}=c+{\alpha }_1{Y}_{i,t-1}+{\alpha }_{2}Siz{e}_{it}+{\alpha }_{3}Equit{y}_{it}+{\alpha }_{4}Np{l}_{it}+{\alpha }_{5}Ris{k}_{it}+{\alpha }_{6}Efi{c}_{it}+{\alpha }_{7}FG{B}_{it}+{\alpha }_{8}FR{B}_{it}+{\alpha }_{9}NP{B}_{it}+{\alpha }_{10}In{f}_t+{\alpha }_{11}Seli{c}_t+{\alpha }_{12}GD{P}_t+\beta t{T}_t+{\mu }_{it}$ (1)

where i (i=1, ..., N) is the bank, t (t=1, ..., N) is the year; Y _it are the dependent variables (Roa _it e Roe _it ); c is the constant term; Size, Equity, Npl, Liq, Risk, Efic, FGB, FRB, NPB, Inf, Selic and GDP are the independent variables defined above; T _t are the time dummies; and µ_it is the idiosyncratic error.

The time dummies (“T _t ”), one for each of the sample’s 17 years, were added to control for non-observed factors that vary with time but not between individuals, like macroeconomic variables. The reliability of the estimates relies on the validity of the instruments and the absence of second-order serial correlation in the error term. To assess these conditions, we employ two specification tests recommended by Arellano & Bond (1991), Arellano & Bover (1995), and Blundell & Bond (1998). Notably, the null hypothesis is not rejected in both tests. The first test is the Hansen test for overidentification restrictions, where the null hypothesis posits that the model is correctly specified, and the instruments collectively are valid. The second is the Arellano-Bond AR (2) test, which assumes first-order correlation in AR (1) but tests the null hypothesis of the absence of second-order serial correlation in the error term. The results of these tests for all presented models affirm their consistency. The quantity of instruments consistently falls below the number of groups, a condition that, as noted by Roodman (2009), would otherwise suggest an overabundance of instruments, potentially jeopardizing the results. In addition, Roodman (2009) highlights that one can only be confident with the results if the Hansen test falls between 0.1 and 0.25, which is the case of all our models. The F-test validates the collective significance of the explanatory variables across all estimations. Lastly, in a dynamic panel data, all the variables in the model must be stationary to guarantee robustness and avoid biases in the results. Therefore, the Fisher test, a unit root test applied for unbalanced panels (Maddala & Wu, 1999), was implemented and the null of non-stationarity was rejected at the 1% level for all variables (see Appendix 1).

5. RESULTS AND DISCUSSION

Table 7 summarizes the empirical results for the profitability measures. An initial analysis confirms the dynamic nature of the model, with the lagged dependent variables significant at 1% in the four simulations. There are also similar qualitative results for all simulated conditions.

Table 7
Regression results

VARIABLES	Roa		Roe
	1	2	3	4
Roa it-1	0.360*** (0.064)	0.364*** (0.067)
Roe it-1			0.404*** (0.072)	0.383*** (0.072)
FRB	-0.022*** (0.006)	-0.022*** (0.006)	-0.166*** (0.036)	-0.167*** (0.033)
NPB	-0.009** (0.004)	-0.008** (0.004)	-0.086*** (0.025)	-0.085*** (0.022)
FGB	-0.010** (0.005)	-0.010** (0.005)	-0.079** (0.035)	-0.076** (0.030)
Equity	0.044* (0.025)	0.042* (0.025)	-0.055 (0.155)	-0.103 (0.153)
Efic	0.005 (0.004)	0.005 (0.004)	0.032 (0.027)	0.029 (0.025)
Npl	-0.204*** (0.072)	-0.217*** (0.070)	-1.491*** (0.467)	-1.574*** (0.439)
Risk	-0.012 (0.017)	-0.012 (0.018)	-0.148* (0.084)	-0.149* (0.086)
Size	0.002* (0.001)	0.002* (0.001)	0.009 (0.007)	0.008 (0.006)
T	-0.001** (0.000)		-0.003* (0.001)
Selic		0.070** (0.029)		0.454** (0.187)
Inf		-0.087** (0.041)		-0.581** (0.250)
GDP		0.046* (0.024)		0.353** (0.145)
Constant	1.099** (0.484)	-0.016 (0.026)	5.600* (2.980)	0.084 (0.161)
Time dummies	Yes	No	Yes	No
Observations	1,172	1,172	1,176	1,176
Group numbers	107	107	107	107
F test (p-value)	0.000	0.000	0.000	0.000
P-AR (1)	0.000	0.000	0.000	0.000
P-AR (2)	0.243	0.251	0.440	0.425
P-Hansen	0.214	0.194	0.155	0.201
Number of instruments	36	38	36	38

• Source: Elaborated from Bacen data.

The bank control variables (FRB, NPB, and FGB) exhibited negative and significant effects compared to the reference variable, SGB, across all simulations. This suggests evidence that subnational government control positively and significantly influences banking profitability, even in comparison to NPB, which is traditionally regarded as more lucrative according to existing literature, which challenges most of the conventional literature that shows a negative or neutral relationship between profitability and government banks.

Consequently, based on the estimated results and holding other factors constant, subnational government control is associated with an average increase of one percentage point in Roa relative to federal government control and national private control. Furthermore, in comparison to foreign control, it corresponds to an average increase of two percentage points in Roa.

In the analyzed dataset, it is observed that government banks run with a lower equity-to-assets ratio compared to national and foreign private banks, as illustrated in Figure 3. The implicit guarantee against failure (Pinheiro, 2007, pg. 196) may be one of the factors enabling government banks to run in this manner which presents a distinct advantage for these institutions, particularly when measuring Roe, where capital constitutes the denominator in the formula.

Figure 3
Capital level evolution by bank category²

Furthermore, SGBs demonstrate a tendency to increase risk-taking, represented by the variable “Risk”, by substituting less risky assets with credit operations. These banks exhibit the highest indices from the midpoint of the series onward, as illustrated in Figure 4, which may have also driven their profitability.

Figure 4
Risk evolution by bank category³

Concurrently with the increase in assumed risk, SGBs reduced the credit risk of their portfolios, as indicated by the “Npl” variable, more significantly than other categories, as shown in Figure 5.

Figure 5
Npl evolution by bank category⁴

To this end, in addition to the competitive advantages of government banks identified in the literature review, such as lower funding costs (Altunbas et al., 2001; Yeyati et al., 2007; Matthey, 2010), well-established brands (Mettenheim, 2010, pg. 105), and agency cost advantages (Butzbach & Mettenheim, 2014), one hypothesis that may contribute to explaining the strong performance of the SGB is their capacity to capture “soft” information more effectively than larger interregional banks, due to their proximity to customers and greater regional knowledge (Slotty, 2009).

In Germany, local saving banks have a regional focus, in addition to exercising a social mandate and acting independently, despite forming a cooperation network. Although profit is not the only objective of these institutions, Altunbas, Evans & Molyneux (2001) and Chakravarty & Williams (2006) found evidence that local saving banks are more efficient than private competitors. Williams & Gardener (2003) investigated the efficiency of regional banks in providing financial services by employing stochastic frontier analysis. Their findings revealed that regional banks exhibit a high level of efficiency, with those established by municipal authorities demonstrating greater efficiency compared to other analyzed control structures, such as banks founded by individuals or groups, as well as a combination of the two. Gärtner (2009), to explain the good financial performance of the German savings banks, states that,

“Any proponent of classic banking theory, which disregards factors such as geographical proximity, trust, and knowledge of the local markets, would find it hard to explain how banks, forced by regional circumstances to limit themselves to a weaker customer group, still manage to generate similar or even higher profits than those in prosperous areas”.

Burgstaller (2013, pg. 1133) states that “The common opinion has been that small banks with regional roots and a strong commitment to local affairs have a comparative advantage in the gathering and processing of soft information...”. Zhao, Luintell & Matthews (2021) substantiate this comprehension by presenting empirical evidence indicating that major banks, characterized by intricate structures, exhibit greater proficiency in managing hard information as opposed to soft information.

This small banks’ competitive advantage in gathering and processing soft information is also important to provide loans to small businesses that do not have enough “hard” information available (Burgstaller, 2013). Hakenes et al. (2015) reinforce the importance of SGB to small to medium-sized enterprises (SME) when demonstrating that German saving banks are important SME supporters in the poorest regions.

Another characteristic of the SGBs that may also have contributed to their robust performance is the management of government employees’ accounts. The SGB managerial reports underscore the significance of managing accounts for the government workforce within the credit portfolio and overarching strategy of these banks. For instance, at Banco do Estado de Sergipe S.A. (Banese), government servants constitute approximately 77% of the personal credit balance as of December 2019 (Relatório de Desempenho - 2019 - Banco do Estado de Sergipe S.A. - Banese, 2020). At Banco do Estado do Pará (Banpará), credits to government employees account for 80% of the bank’s credit portfolio (Moody’s affirms ratings of Banpará, stable outlook - Moody’s, 2020). Similarly, in Banco de Brasília (BRB), the majority of the customer base comprises government sector employees (Moody’s rates BRB-Banco de Brasília, outlook stable - Moody’s, 2021). Banestes underscores the importance of operations targeting the state and municipal employees within its strategic framework (Demonstrações Financeiras Padronizadas - 2019 - Banestes S.A. - Banco do Espírito Santo, 2020, pg. 43).

Banestes has been solidifying its leadership in payroll loans within the State of Espírito Santo, employing a focused approach supported by a close relationship and partnership with public sector entities at both the state and municipal levels. Its strategic initiatives aim to foster growth in operations targeting the government employees and beneficiaries of the National Social Security Institute (INSS). As of December 2019, the bank recorded 95,533 active contracts with a financial volume of R$ 1.3 billion, representing a notable 9.9% increase compared to 2018. The revenues generated from these contracts until December reached R$ 274 million, reflecting a growth of 6.3%.

Despite not disclosing specific figures, Banrisul underscores the importance of government sector-focused credit within its business strategy (Demonstrações Financeiras Padronizadas - 2019 - Banco do Estado do Rio Grande Do Sul S.A., 2020, pg. 32).

Concerning the commercial strategy targeting the retail segment, Banrisul adheres to a direction aimed at expanding the credit portfolio through operations characterized by lower risk and higher liquidity. This focus is notably evident in the provision of payroll loans for government servants and retirees.

The institutional advances that occurred in the last decades also may have enhanced the control over SGB and mitigated the strong political influence from controllers. Andrezo & Lima (2007) state that since 1994, several measures have been taken to strengthen the national financial system, such as the adoption of the Basel Accord and tougher requirements for opening and operating financial institutions, the creation of the credit risk center to allow better analysis and control over credit operations granted by banks; the modernization of accounting standards, and the establishment of rules on internal controls and risk control. Additionally, since the 1980s, new laws and resolutions have been created to deal with transactions between financial institutions and their controllers to mitigate fraud and mismanagement practices. Table 8 summarizes the other explanatory variable results, where it is possible to verify that there was no major discrepancy between their found and expected results. The Size, Equity, Selic and GDP show positive and significant impacts; Npl, Risk and Inf revealed negative and significant impacts, while Efic was not significant.

Table 8
Explanatory variables expected versus final results

Variable	Expected effect	Results
NPB	+	-
FGB	=	-
FRB	+/-	-
Size	+/-	+
Equity	+	+
Npl	-	-
Efic	+	+
Risk	+/-	-
Selic	+	+
Inf	+/-	-
GDP	+	+

6. FINAL REMARKS AND CONCLUSION

This article endeavors to fill a gap in the literature concerning subnational government banks in Brazil. These financial institutions have received limited scholarly attention since the financial crises they encountered in the 1980s and 1990s, persisting with a reputation for poor management, serving as sources of deleterious political influences, and being generators of recurrent losses. In this context, the present study seeks to ascertain the impact of subnational government banks on banking profitability in a more recent timeframe. Notably, this research introduces a novel categorization by segregating subnational government banks and federal government banks into two distinct control categories, marking a significant departure in the methodology employed in such investigations.

The empirical investigation found evidence that regional government control impacts positively and significantly on the profitability of commercial banks. This finding challenges the conventional banking literature that considers government banks less profitable than private banks and demystifies the concept that SGB is unprofitable and a permanent source of financial problems.

The intrinsic competitive advantages associated with government banks may have played a significant role in the favorable outcomes observed in empirical tests. These advantages include effective management of government sector accounts, governmental support that reduces funding costs and allows for operations with higher leverage ratios compared to competitors, access to cost-effective funding sources such as fiscal and parafiscal funds, and the robustness of their well-established brands within their respective regions.

Institutional advancements observed in recent decades also may have contributed to the results, such as the adoption of the Basel Accord, convergence with international accounting standards, and the creation of laws and resolutions to deal with transactions between financial institutions and their controllers. These measures may have mitigated political influences that affected SGB’s performance in the past, enhancing the possibility of reconciling the promotion of local development and financial sustainability.

In essence, it is evident that subnational government banks have evolved from being deficit-prone institutions, characteristic of the 1980s and 1990s, to becoming financially profitable entities. Furthermore, this category has been recognized for its pivotal role as a significant financial intermediary in the economies of their respective states. Nonetheless, opportunities have been identified for these institutions to make additional contributions to the economic development of their states, specifically, adopting a business model that is less reliant on the management of public sector accounts and more oriented towards catering to segments that are typically underserved by the banking sector, such as small and medium-sized enterprises.

Finally, it is essential to note that the results presented pertain to a specific period and the BPSs account for only 91 observations from 6 banks out of a total of 1,389 observations, representing a sample of 108 financial institutions.

As an understudied segment in the existing literature, there exists a myriad of potential research avenues regarding subnational government banks. These encompass the identification of key determinants influencing the profitability of this category, examining their impact on the banking spread within their respective states, assessing their countercyclical role across diverse economic cycles, investigating their involvement in the financing of micro and small enterprises, exploring the political influence exerted by controllers in more recent periods, and delving into various other pertinent themes.

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• 1
  BRB and Paraiban were the only SGBs that did not join the Proes program, with the latter being privatized in 2001 outside the program’s scope.
• 2
  Measured by the median of the banks.
• 3
  Measured by the median of the banks.
• 4
  Measured by the median of the banks.
• JEL Classification:
  G21; G32; L30.

APPENDIX

Table A1
Fisher-Type unit root testing inverse chi-squared

Variable	Statistic	p-value
Roa	497.60	0.0000
Roe	524.31	0.0000
Size	356.27	0.0000
Efic	456.38	0.0000
Risk	313.53	0.0000
Npl	396.14	0.0000
Liq	409.12	0.0000
Equity	322.97	0.0000
Selic	500.74	0.0000
Inf	270.82	0.0067
GDP	710.65	0.0000

• Source: The null hypothesis is that the variable is non-stationarity. For all variables, the null hypothesis is rejected at the 1% level. The variables in italics were tested in difference.

Publication Dates

• Publication in this collection
  11 July 2025
• Date of issue
  2025

History

• Received
  21 Mar 2024
• Accepted
  05 Aug 2024