Abstract
Purpose
This study examines whether Brazilian health insurance companies (HICs) engage in earnings management through discretionary accruals or operational decisions by refraining from reporting a low indicator of sustainability in the market (IDSM).
Design/methodology/approach
The study used the Jones and Modified Jones models to identify earnings management through discretionary accruals and used the model described by Roychowdhury to estimate the abnormal behaviors of operational decisions. Data covering 2012 to 2018 were collected from the ANS website.
Findings
The results show that HICs engaged in earnings management to avoid reporting a low IDSM. The findings should help health insurance clients make decisions regarding the purchase or change of health insurance. The findings should also encourage regulators to improve their evaluation of the economic and financial risks around HICs.
Originality/value
The National Agency of Supplementary Health (ANS) established a qualification program for HICs, monitoring them based on a set of indicators. Managers may have an incentive to use earnings management to obtain indices that meet the requirements of the ANS qualification program in order to avoid showing signs of abnormality.
研究目的
本研究旨在探討巴西的醫療保險公司，透過避免報導低的市場可持續性指標，是以裁決性應計，抑或是以操作決策來進行盈餘管理。
研究的原創性
國立輔助醫療辦事處 (註：此為直譯)(The National Agency of Supplementary Health)為醫療保險公司建立了一個資格檢定機制，基於一套指標來監管這些公司。公司主管或會有鼓勵性的誘因去使用盈餘管理來取得符合國立輔助醫療辦事處資格檢定機制所要求的指標，從而避免顯示異常的跡象。
研究設計/方法/理念
本研究使用Jones 模型及Modified Jones模型，透過裁決性應計來辨認盈餘管理，研究亦使用羅伊喬杜里 (Roychowdhury) 描述的模型， 來估計操作決策的異常行為。數據取自國立輔助醫療辦事處的網址，並涵蓋2012年至2018年期間。
研究結果
研究結果顯示，醫療保險公司進行盈餘管理，以求避免報導低的市場可持續性指標。研究結果應可幫助醫療保險客戶在購買或更改醫療保險時作適當的決定。研究結果亦可鼓勵監管者改善他們對醫療保險公司的經濟及財務風險所作的評估。
Keywords
Citation
Martinez, A.L. and Carvalho, F.A.d. (2022), "Earnings management by health insurance companies in Brazil", European Journal of Management and Business Economics, Vol. 31 No. 4, pp. 453-468. https://doi.org/10.1108/EJMBE-01-2021-0023
Publisher
:Emerald Publishing Limited
Copyright © 2021, Antonio Lopo Martinez and Flávio Alves de Carvalho
License
Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Health insurance companies (HICs) in Brazil are regulated by the Agência Nacional de Saúde Suplementar (National Agency of Supplementary Health; ANS), which operates under the umbrella of the Ministry of Health. The agency was established by Law 9961/2000 and serves to regulate, standardize, control and supervise activities related to supplementary health (Cardoso, 2005; Guimarães and Alves, 2009). The ANS runs a qualification program for service providers. The program includes an evaluation system based on the Índice de Desempenho de Saúde Suplementar (Supplementary Health Performance Index; IDSS). The index’s indicators are aggregated into four dimensions: quality in health care, guarantee of access, sustainability in the market, and process and regulatory management. The last indicator is based on an evaluation of how quality in the sector is being enhanced according to the ANS.
The ANS has also measured performance using the Indicador da Dimensão Econômic-Financieira (Indicator of the Economic and Financial Dimension; IDEF) and the Indicador da Dimensão de Sustentabilidade no Mercado (Indicator of Sustainability in the Market; IDSM); the latter replaced the former after the reformulation of the qualification program in 2015. It monitors HIC sustainability, verifying the firm’s economic and financial stability, as well as its ability to fulfill its obligations. The results are calculated based on a set of indicators: net assets and current liquidity, the provision for events incurred but not reported, sufficient guarantee assets, own resources, financial availability, inspection rate, and rate of resolution of preliminary intermediation notifications.
The ANS uses the qualification program to promote quality, comparability and competition in the sector. The program reports the overall performance of HICs and thus increases transparency and allows the firms’ beneficiaries to make informed choices of service provider (ANS, 2017a). According to Pinheiro et al. (2015), the issuance of the ANS provisions related to economic and financial factors has forced HICs to improve their management.
However, Mensah et al. (1994) suggest that, despite the regulatory supervision, managers in this industry conduct earnings management to achieve specific goals. Greenwood et al. (2017) provide econometric evidence that earnings management is used to avoid regulatory intervention when firm performance is just below or well above the intervention threshold.
Managers may use earnings management due to various incentives (Dechow et al., 2010), such as to hide administrative abnormalities, satisfy the indicators used by the ANS qualification program (e.g. net assets, liquidity, provision for events incurred but not reported, sufficient guarantee assets, own resources and financial availability), and to improve their IDSS ranking, which consumers can use when choosing their health insurance company. Thus, this study seeks to determine whether Brazilian HICs use earnings management to avoid reporting a low IDSM, particularly through discretionary accruals and operational decisions.
Several important Brazil-focused studies have examined this issue. For example, Cardoso (2005) verified the impact of health insurance regulation on incentives to choose accounting practices using data from 2001 to 2003. Guimarães and Alves (2009) tested a prediction model for estimating the probability of insolvency, and Ferreira et al. (2011) examined whether external audit minimizes the propensity to engage in earnings management. Sancovschi et al. (2014) examined the relationship between HICs’ IDSS ranking and their likelihood of being subject to special regimes. Pinheiro et al. (2015) analyzed the dynamics of financing strategies, pointing to the need for more comprehensive analyses of HICs. Bragança et al. (2019) argued that more studies are necessary on HICs in Brazil and other countries, especially regarding their solvency and sustainability.
None of the abovementioned studies discussed performance indicators (e.g. IDEF, IDSM) or their relationship to earnings management. To address this lacuna, this study offers original empirical evidence drawn from Brazil of the association between earnings management and the ANS sustainability indicators, providing clear evidence that companies manage earnings in order to increase them, which impedes the agency’s ability to perceive risks.
This study contributes to the literature on the supplementary health market in Brazil, which, according to the ANS, was worth approximately R$179 billion and served approximately 24% of the Brazilian population. We seek to demonstrate the existence of earnings management in this sector by discussing the incentives for manipulating accounting information. The study highlights the risks to sector regulators and calls for improvements in the tools used to monitor HICs. It also seeks to help HIC users choose health insurance firms and to encourage regulators to improve their assessment of the economic and financial risks of HICs.
The study used data available on the ANS website based on yearly reports of registered HICs active from 2012 to 2018. The sample comprised data for all HICs that had sent complete information to the ANS during the study period. A total of 1,362 companies were included, providing 7,549 observations.
The next section presents theoretical background for the study. Then, we discuss the study’s methodology. Next, we present our results. Finally, the study concludes with a critical assessment of the results.
2. Theoretical framework
The ANS regulations establish the conditions for ensuring the performance of private health agents such as HICs and meeting global criteria of economic and social sustainability in order to protect the public interest (Silva et al., 2000; Pinheiro et al., 2015). Cardoso (2005) claims that some markets feature only a small number of companies, and some services are monopolized. The author emphasizes that consumers lack access to complete information, creating significant information asymmetry regarding the firms’ economic and financial situations. Cardoso (2005) also highlights the role of the state in minimizing market failure.
Campos and Camacho (2014) conducted market structure analyses and identified the existence of inefficiencies that generated a social cost and that called for public policies aimed at promoting efficiency and regulations aimed at preventing failures and universalizing products and services. Therefore, the Brazilian government created executive agencies in areas of social interest, such as sanitary control and supplementary health. According to Pinheiro et al. (2015), the supplementary health sector faces higher risk, since it is not possible to predict future expenses in the health services, even though the HIC is in a positive financial cycle due to the anticipated level of monthly fees.
The Instituto de Estudos de Saúde Suplementar (Institute for Supplementary Health Studies; IESS) monitors the functioning of supplementary health within a production chain (IESS, 2016). The process starts in the consumer industry and progresses through distributors, health care providers, and, finally, health insurance beneficiaries, who pay for their services through monthly fees. Three agencies regulate the supplementary health system in Brazil: the Agência Nacional de Vigilância Sanitária (National Agency of Sanitary Control; ANVISA), which is responsible for the sanitary and economic regulation of the hospital supplies market; the ANS, which regulates the financial and service dynamics among health insurance companies, beneficiaries, and service providers; and the Sistema Brasileiro de Defesa da Concorrência (Brazilian System for Competition Defense; SBDC), which aims to ensure competitiveness in the sector.
The ANS is a regulatory agency linked to the Ministry of Health. It has the authority to issue norms and is independent in its decision-making, administrative and financial processes. The ANS is responsible for regulating HICs (Cardoso, 2005). According to the IESS (2016), HICs are legal entities registered with the ANS that operate and commercialize private health care plans. They are classified into the medical/hospital and dental segments. Cardoso (2005) points out that some HICs may be subject to more than one regulatory body and scheme. Closed complementary pension entities, for example, have their main activity regulated by the national superintendence of complementary pensions. These entities have to meet the accounting standards established by the superintendence, along with those of the ANS.
As Salvatori and Ventura (2012) make clear, regulatory agencies perform executive, legislative and judiciary roles, since they oversee economic activities and rights, publish legal regulations (such as sectorial norms) and procedures, and impose sanctions on the players under their control. Among the legal powers granted to ANS in Law 9961/2000 are the following: proposing general policies and guidelines for regulating the sector; establishing standards, routines and procedures for recording, authorizing, maintaining and canceling the registration of the plans commercialized by HICs; issuing norms and standards for the presentation of the HICs’ economic and financial information; and establishing parameters and indicators of quality for the health care coverage provided by the services rendered directly by HICs as well as services provided by third parties based on the commercial plans of HICs (ANS, 2017c).
Cardoso (2005) points out that the standardization of accounting practices allows comparability across HICs and highlights their economic and financial situations. Accounting and financial information are necessary in order to form an understanding of a firm’s economic and financial position, development, and trends (Assaf, 2010, p. 35), and other types of regulatory standardization helps consumers and other entities choose between HICs.
Brazil’s qualification program for service providers began in 2004 and was redesigned in 2015 to improve services and bring them in line with the new rules and practices in the sector (ANS, 2017a). The evaluation carried out in the program is based on the IDSS, calculated using aggregated indicators in four dimensions: quality in health care, guarantee of access, sustainability in the market, and process and regulatory management. The IDSS bands range from 0 to 1, as shown in Figure 1.
The ANS claims that the qualification program evaluates improvements in HICs, stimulates competition and benefits the consumer. The ANS seeks to ensure that its dimension analysis is as close to the real-world situation as possible in order to improve the qualification program. The methodology used to evaluate the IDSS criteria thus changes as necessary. The dimension used to evaluate HICs’ economic and financial stability and their capacity to fulfill their obligations has evolved, as shown in Box 1.
One issue regarding the sustainability indicator is that HICs may make discretionary accounting choices (about recognition and measurement), take operational decisions and/or select criteria for the presentation of financial statements (disclosure) within the limits of accounting standards designed to modify their reported earnings in order to influence the ANS’ perceptions of the underlying economic facts.
Earnings management occurs when managers use discretionary criteria to manipulate financial statements, which can mislead users about the firm’s economic performance (Cardoso, 2005). Martinez and Cardoso (2009) described earnings management as accounting practices and decision-making designed to prepare and disclose accounting numbers different from those that would be prepared and disclosed if such practices were not adopted. For Healy and Wahlen (1999) “earnings management occurs when managers use judgment in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting numbers” (p. 368).
Dong (2016) collected financial data from all US hospitals that had requested reimbursement from the federal government for treating Medicare patients and regressed discretionary accruals on hospital size, profitability, asset liquidity, operating efficiency, labor cost and ownership. The findings provided direct evidence of the use of discretionary accruals to manage financial earnings among US hospitals in this special public (or quasi-public) service sector.
Research on earnings management in Brazil has used methods based on statistical models, including discretionary accruals detection models such as the Jones and Modified Jones model, which Dechow et al. (1995) argue explain variations in accruals most accurately. Brazilian research has also adopted management decision-making models for operational decisions, income distribution, specific accruals, income smoothing and accruals of abnormal working capital.
Although regulation is generally perceived as a mechanism that limits opportunistic behavior, it can have the opposite effect. Regulations generally aim at enhancing management incentives to improve performance, but such incentives may create pressure to manage earnings (El-Diri, 2018). Firms may manage earnings in different ways under the pressure of regulation. Regulatory investigations or new regulatory projects can drive firms to manage discretionary accruals downwards in order to demotivate regulators from issuing strict norms.
Therefore, the managers of HICs may have an incentive to avoid presenting indications of abnormality to avoid monitoring by the ANS. As it is the qualification program that evaluates the overall performance of HICs, users can refer to it when buying or changing plans; firms thus seek a high IDSS ranking. Accordingly, this study proposes the following:
The managers of HICs use earnings management to avoid reporting a low sustainability performance indicator.
3. Methodology
This study performed hypothetical-deductive and quantitative analyses using secondary and longitudinal data. The main method used was an empirical and descriptive analysis of a sample composed of all HICs actively enrolled in the ANS. A probabilistic sample was selected covering 2012 to 2018.
The study examined HICs that, according to ANS data, conducted financial transactions totaling approximately 179 billion reals and served about 24% of the Brazilian population via private health care and dental (exclusive) plans in 2017. Since HICs send accounting and financial information to the ANS quarterly, the database was constructed using information from the fourth quarter of each year.
The study used several measures to detect earnings management: the Jones (1991) and Modified Jones model (Dechow et al., 1995) was used to estimate discretionary accruals, and the model described by Roychowdhury (2006) was used to detect abnormal behavior in operational decisions. According to Dechow et al. (1995), standard errors tend to be lower for the Jones and Modified Jones models, in which they are more efficient in the time series. According to Magro et al. (2019), the Modified Jones Model has been the most widely used in both the international and national literature, though they recognized that other, more recent models may offer different perspectives.
The control variables were calculated and the performance indicators, IDSM (2015–2018) and IDEF (2012–2014), were compiled from all HICs with valid ANS registration. IDSM data for 2019 were not used because the ANS had not made them available at the time of the study. We excluded HICs that sent no lagged accounting information (t−1) to the ANS and those that presented negative net assets (NA). The full set of data adjustments are shown in Table 1.
Data on 1,362 health insurance companies were analyzed covering 2012 to 2018, for a total of 7,549 observations and an average of 5.54 observations per company, as seen in Panel A, Table 2. The distribution of observations per year was relatively homogeneous. Therefore, the inhomogeneous sample bias was controlled for (Ferreira et al., 2012), as shown in Panel B, Table 2.
3.1 Model for detecting earnings management
According to Martinez (2013), most Brazilian empirical studies use accrual-based detection models. As noted above, the study used the Jones and Modified Jones models to detect earnings management through discretionary accruals (DA), and the model described by Roychowdhury was used to detect abnormal behaviors in operational decisions due to its popularity and wide use in academic research (Almeida, 2006; Martinez, 2013; El-Diri, 2018). The Fama and MacBeth (1973) method was used in the regressions of the models to detect earnings management.
The ANS does not make HIC cash flow statements (CFSs) available, so total accruals (TAit) were estimated based on their balance sheets (Dechow et al., 1995; Martinez, 2013; Heese, 2018) using Equation (1):
The coefficients αi, β1i, and β2i, of Equation (3) are estimated via the regression of Equation (2), and the discretionary accruals of the Jones model are predicted by εit:
To calculate non-discretionary accruals using the Modified Jones model, we use the coefficients and the variables of Equation (3):
As Dechow et al. (1995) point out, discretionary accruals (DA) are estimated by subtracting nondiscretionary accruals (NDA) from total accruals (TA), as in Equation (4). Paulo (2007) points out that, with the inclusion of the variable ΔRECit in the Modified Jones model, discretionary accruals are no longer found by the residuals of Equation (2):
Real earnings management (REM) was calculated using the model proposed by Roychowdhury (2006), as shown in Equations (5) and (6). According to Paulo and Mota (2019), this model measures the normal activity levels of a company in order to predict abnormal behaviors via the residue; the result is obtained by summing the behaviors, as in Equation (7). No estimation of FCS behavior was attempted because the net effect is ambiguous, as it is affected in different directions (Zang, 2012; Paulo and Mota, 2019):
3.2 Proposed model
The measurements expressed in Equations (8–9) verify whether the performance (IDSM and IDEF) of HICs is related to the DA:
The control variables are firm size, degree of total leverage, asset return and indebtedness calculated as shown in Box 2.
The study used firm size (SIZE), expressed as the natural log of total assets, because HIC performance may be partly explained by its size. Dechow and Dichev (2002) also used the logarithm of total assets as a measure of firm size. Barnett and Salomon (2012) used financial leverage (LEV) as a control proxy in their performance analysis, as debt increases the volatility of future earnings. Return on assets (ROA) was used as a measure of performance because they represent future economic benefits (Joia and Nakao, 2014). Kothari et al. (2005) found that tests that used return on assets as a measure of performance were well-specified and had substantial power. Indebtedness (INDEBT) was adopted as a variable following Watts and Zimmerman’s (1990) finding that managers of companies with higher indebtedness tended to use methods that allowed them to show increasing results and Barnett and Salomon’s (2012) finding that debt affected the behavior of managers.
4. Analysis of results
This section presents a data description and analysis of the Jones and Modified Jones models, the model described by Roychowdhury, and the proposed regression model.
4.1 Jones, Modified Jones and Roychowdhury models
Table 3 presents the descriptive statistics of the variables used in our Jones, Modified Jones and Roychowdhury models. Panel A describes the variables used for the accruals earnings management calculation, and Panel B describes the variables used for the real earnings management calculation. Total accruals (TAit) had an average of −0.0291, with a SD of −0.0276. Prodit and Despit had averages of 1.4796 and 1.1255, respectively, indicating that, on average, both act in favor of a positive REM signal (Paulo and Mota, 2019).
The variables shown in Table 3 did not follow a normal distribution, at a significance level of 0.01, in Jarque–Bera and Kolmogorov–Smirnov tests. Hoffmann (2016, p. 47) states that, based on the central limit theorem, a large number of independent random variables have approximately normal distributions when none of them is dominant; thus, the estimates of the ordinary least squares (OLS) method satisfy the asymptotic normality.
Paulo (2007) states that estimates of the coefficients used to calculate discretionary accruals through time series require an expanded data-gathering period. Therefore, the regression performed to estimate the coefficients of the Modified Jones model used the cross-sectional approach via the Fama and MacBeth (1973) method.
4.2 Results of proposed model in terms of performance
Table 4 presents the descriptive statistics of the variables in the regression model used to capture the effect of earnings management in HIC performance. The variables did not follow a normal distribution at the 0.01 significance level in Jarque–Bera and Kolmogorov–Smirnov tests.
The graphical representation in Figure 2 shows that, on average, the smaller the band of the HIC performance (i.e. the worse the indicator), the greater the discretionary accruals (DA), indicating that discretionary accruals are managed to avoid reporting poor indicators (Greenwood et al., 2017). The data used to calculate the mean of the DA values were in module form.
We conducted Student’s t-test, a parametric test, and a non-parametric Kruskal–Wallis test to verify if the means of the discretionary accruals by band were significantly different. The parametric test was performed with the 1st and 5th bands, and the non-parametric test was performed with all the ranges. Both tests showed evidence of significant mean differences in the discretionary accruals.
The greatest level of earnings management appeared in the 1st band (worst indicator); the level reduced gradually in the 2nd, 3rd, 4th and 5th bands (with the least earnings management seen in the 5th, the best indicator). These results are consistent with the hypothesis that HICs with the worst performance indicators tend to use earnings management more aggressively and frequently.
Table 5 presents the results of the proposed regression model used to capture the effect of earnings management in HIC performance.
The parameters generated by the regression of Equations (8–9) are shown in Table 5. In both models, statistical significance at the 0.01 level was found in DAit, REMit, IDSMit, LEVit, SIZEit and INDEBTit; statistical significance at the 0.05 level was found in ROAit; and statistical significance at the 0.10 level was found in DACCit.
As the Chow, Breusch–Pagan and Hausman tests were used to define the model, the fixed effects for the regression model of Equations (8–9) were thus most appropriate. The Wooldridge test detected the existence of serial autocorrelation, and the Wald test detected the existence of heteroscedasticity. The variables of the proposed regression model did not follow a normal distribution, at a significance level of 0.01, in the Jarque–Bera and Kolmogorov–Smirnov tests.
The study corrected for serial autocorrelation and heteroscedasticity using the robust estimation method in the adjusted fixed effects model, as shown in Table 6. Normality assumptions were relaxed in the inferences of the model parameters, since the coefficients were asymptotically consistent and not biased, even in the presence of serial autocorrelation and heteroscedasticity (Formigoni et al., 2009; Ferreira et al., 2012).
The results indicate, with strong statistical support, that an increase in discretionary accruals leads to an increase in HIC performance. These findings corroborate the hypothesis that earnings management is used to increase these indicators and thus improve perceptions of the economic and financial vitality of HICs among regulators and users. The positive and significant coefficient provides evidence that increased earnings management leads to increased HIC performance. Thus, earnings management is used to increase the key indicators, reducing the possibility of more rigorous ANS supervision.
We verified the robustness of our conclusions using the proposed regression model, which included DAit in the module (see Table 6). This test examined two earnings management scenarios: income-increasing and income-decreasing. A higher DAit in the module represents more intense earnings management, while a lower (i.e. near zero) DAit reflects less intense earnings management.
The coefficients of DAit, DACCit and REMit estimated in the regression were negative and significant. This result corroborates the finding that more intense earning management is associated with lower HIC performance. Thus, earnings management is more pronounced in HICs with lower HIC performance scores. These statistical findings offer sufficient evidence that HIC managers engage in earnings management through discretionary accruals and operational decisions to avoid reporting low sustainability indicators, with the practice more pronounced in companies showing lower HIC performance.
Table 7 presents the results for the performance indicators in the IDEF (2012–2014) and IDSM (2015–2018) periods. Evidence of earnings management is seen in both periods, via both accruals and operational decisions in order to improve HICs’ scores.
5. Final considerations
This study aimed to empirically determine whether there is statistical evidence that HICs use earnings management to avoid reporting a low IDSM. The proposed regression model sought to capture whether HIC performance was significantly impacted by the use of earnings management through discretionary accruals and operational decisions. To ensure robustness, we used control variables employed in previous studies and static models.
Earnings management has negative consequences for financial reporting because it masks the consequences of management decisions. Earnings management manipulates accounting to meet a certain target. This may be motivated by a need to maintain certain economic and financial indicator levels due to the pressure to show an increasing trend or financial vitality, or to achieve goals set by regulators.
This study’s data suggest that Brazilian HICs use earnings management through discretionary accruals and operational decisions to avoid reporting a low performance. The performance measured by IDSM is part of the calculation of the IDSS, an evaluation method adopted in the HIC qualification program of the ANS.
The ANS has established a recovery plan for HICs with very low IDSM and IDEF values. Such values may create serious administrative abnormalities, such as practices that lead to service denial or interruption on collective, recurrent and non–point-in-time levels, thus causing serious assistive, actuarial, structural or operational failures that carry a risk to the quality and continuity of health care, as pointed out by RN 256/2011 of the ANS (ANS, 2017d). According to the ANS, the risco assistencial (service risk) consists of administrative and service abnormalities (ANS, 2017b).
The ANS performance indicators (IDEF and IDSM) are not effective at limiting opportunistic behavior. To the contrary, HICs with low indicators have an incentive to manipulate earnings. Regulations, in their quest to monitor performance, create management incentives to manage earnings (El-Diri, 2018). Firms may manage earnings in different ways under the pressure of regulation, such as by misrepresenting income increases or decreases.
The effective monitoring of the economic and financial dimensions of HICs is crucial for the maintenance of contracts signed in accordance with legislation. The fact that HICs may use earnings management introduces additional risk to the system, which must be diagnosed and treated to avoid negative repercussions for users.
As Macadar et al. (2015) point out, Brazil, as an evolving democracy, has followed global trends and allowed the expansion of mechanisms of social control and the exercise of citizenship through transparency. Platt et al. (2007) argue that three elements are required to ensure this transparency: (1) publicity and the wide public dissemination of information; (2) comprehensibility, via the visual presentation of information; and (3) usefulness for decision-making (i.e. relevance). Thus, transparent and reliable financial and economic information about HICs is crucial to ensure the efficient operation of the system.
Future research should conduct a more detailed analysis of the service risk faced by the ANS by investigating other evaluation dimensions of HICs and examining how these correlate to the companies’ economic and financial characteristics. Such studies should determine whether HICs are using earnings management to avoid the revelation of serious administrative abnormalities and mask serious problems with the operation and sustainability of the plans they are offering.
This study may be limited due to its use of the Modified Jones Model. Though it is very common in the literature, it can create serious measurement errors in the computation of discretionary accruals, and it is not ideal for companies with profiles similar to those of HICs.
Healthcare is a serious issue, and it has no room for pretenses, falsehoods, or commercial interests. It is thus crucial that regulators improve the mechanisms that monitor the financial health of HICs in order to combat the use of earnings management to artificially increase indicators and hide risks from regulators and users. Proactive regulatory and monitoring instruments are required to preserve the interests of the millions of people who depend on the support of HIC plans when they are at their most vulnerable.
Figures
Data adjustments
| Year | Observations | Exclusions | Adjusted | Exclusions | Adjusted | |
|---|---|---|---|---|---|---|
| Adjustment (t−1) | NA < 0 | Observations for earning management | Without IDSM/IDEF | Final sample for IDSM/IDEF analysis | ||
| 2012 | 1,283 | 31 | 113 | 1,139 | 162 | 977 |
| 2013 | 1,263 | 41 | 111 | 1,111 | 158 | 953 |
| 2014 | 1,240 | 42 | 96 | 1,102 | 177 | 925 |
| 2015 | 1,203 | 31 | 83 | 1,089 | 209 | 880 |
| 2016 | 1,173 | 32 | 90 | 1,051 | 194 | 857 |
| 2017 | 1,129 | 30 | 64 | 1,035 | 201 | 834 |
| 2018 | 1,112 | 44 | 46 | 1,022 | 215 | 807 |
| Total | 8,403 | 251 | 603 | 7,549 | 1,316 | 6,233 |
Descriptive statistics of sample for earning management
| Panel A: Sample | |
|---|---|
| Number of HPCs studied | 1,362 |
| Total number of observations | 7,549 |
| Number of years studied | 6 |
| Average of observations per HPCs | 5.54 |
| Panel B: Sample per year | ||
|---|---|---|
| Year | Observations | % |
| 2012 | 1,139 | 15 |
| 2013 | 1,111 | 15 |
| 2014 | 1,102 | 15 |
| 2015 | 1,089 | 14 |
| 2016 | 1,051 | 14 |
| 2017 | 1,035 | 14 |
| 2018 | 1,022 | 14 |
| Total | 7,549 | 100 |
Descriptive statistics
| Variables | Mean | Median | SD | Minimum | Maximum |
|---|---|---|---|---|---|
| Panel A: Variables for accruals earnings management calculation | |||||
| TAit | −0.0291 | −0.0276 | 1.3473 | −26.8898 | 97.6802 |
| 1/Ait−1 | 2.32E-06 | 1.19E-07 | 7.00E-06 | 9.39E-10 | 5.13E-05 |
| ∆REVit | 0.6785 | 0.1839 | 6.9852 | −9.1695 | 408.2076 |
| ∆RECit | 0.0590 | 0.0006 | 1.3224 | −0.9531 | 98.4902 |
| PPEit | 0.1928 | 0.1148 | 0.2068 | 0.0000 | 0.9998 |
| Panel B: Variables for real earnings management calculation | |||||
|---|---|---|---|---|---|
| Ab_Prodit | 1.4796 | 1.1817 | 5.3153 | 0.0000 | 352.1589 |
| Ab_Despit | 1.1255 | 0.7083 | 2.0622 | −0.0016 | 62.4721 |
| 1/Ait−1 | 2.32E-06 | 1.19E-07 | 7.00E-06 | 9.39E-10 | 5.13E-05 |
| REVit/Ait−1 | 2.7188 | 2.1323 | 7.0366 | −0.0060 | 408.2076 |
| REVit−1/Ait−2 | 2.0403 | 1.8888 | 1.3737 | −0.0027 | 20.5024 |
| ∆REVit/Ait−1 | 0.6785 | 0.1839 | 6.9852 | −9.1695 | 408.2076 |
| ∆REVit−1/Ait−1 | 0.2293 | 0.1822 | 0.9121 | −20.3082 | 13.7827 |
Note(s): The variables do not follow the normal distribution in the Jarque-Bera and Kolmogorov–Smirnov tests
Descriptive statistics – Proposed regression model
| Variables | Mean | Median | SD | Minimum | Maximum |
|---|---|---|---|---|---|
| IDSMit | 0.8323 | 0.8907 | 0.1887 | 0.0000 | 1.0000 |
| IDEFit | 0.7172 | 0.8296 | 0.2895 | 0.0000 | 1.0000 |
| DAit | −0.0291 | −0.0574 | 0.2572 | −0.3399 | 14.6767 |
| DACCit | −0.0587 | −0.0348 | 1.3570 | −27.0438 | 95.2814 |
| REMit | 0.3412 | 0.3079 | 4.6350 | −8.5985 | 295.4753 |
| LEVit | 0.5244 | 0.5671 | 0.2971 | −5.1109 | 0.9995 |
| ROAit | 0.0273 | 0.0193 | 0.4760 | −10.8802 | 10.0939 |
| SIZEit | 15.7553 | 16.1261 | 2.3881 | 9.8784 | 20.7863 |
| INDEBit | 0.0453 | 0.0000 | 0.0998 | 0.0000 | 1.3226 |
Note(s): 1The variables do not follow the normal distribution in the Jarque-Bera and Kolmogorov–Smirnov tests. 2HIC performance is presented via IDSM (2015–2018) and IDEF (2012–2014) indices. 3IDSM data for the 2019 base year were not included because the ANS did not make them available during the study period
Coefficients – Proposed regression model for HIC performance
| Variables | Jones model | Modified Jones model | Roychowdhury model2 |
|---|---|---|---|
| DAit | −0.0615*** (0.0203) | ||
| DACCit | 0.0126* (0.0072) | ||
| REMit | −0.0037*** (0.0008) | ||
| d_IDSMit | 0.0447*** (0.0085) | 0.0452*** (0.0085) | 0.0425*** (0.0086) |
| LEVit | −0.2283*** (0.0340) | −0.2266*** (0.0344) | −0.2308*** (0.0350) |
| ROAit | 0.0570** (0.0224) | 0.0552** (0.0223) | 0.0545** (0.0226) |
| SIZEit | 0.0877*** (0.0140) | 0.0873*** (0.0140) | 0.0925*** (0.0142) |
| INDEBTit | −0.3188*** (0.0667) | −0.3200*** (0.0669) | −0.3212*** (0.0677) |
| Constant | −0.5220** (0.2249) | −0.5136** (0.2244) | −0.5933*** (0.2292) |
| Observations | 6,233 | 6,233 | 6,170 |
| R2 | 0.123 | 0.122 | 0.124 |
| AIC | −4987.7003 | −4977.0206 | −4969.7878 |
Note(s): 1*p < 0.1, **p < 0.05, ***p < 0.01. 2Proposed by Paulo and Mota (2019)
Proposed coefficients regression model for HIC performance – adjusted fixed effects per DA module
| Variables | Jones model | Modified Jones model | Roychowdhury model2 |
|---|---|---|---|
| DAit | −0.0599*** (0.0211) | ||
| DACCit | −0.0156** (0.0077) | ||
| REMit | −0.0035*** (0.0007) | ||
| d_IDSMit | 0.0448*** (0.0085) | 0.0447*** (0.0086) | 0.0429*** (0.0086) |
| LEVit | −0.2291*** (0.0341) | −0.2277*** (0.0342) | −0.2301*** (0.0352) |
| ROAit | 0.0560** (0.0223) | 0.0552** (0.0223) | 0.0532** (0.0224) |
| SIZEit | 0.0880*** (0.0140) | 0.0881*** (0.0140) | 0.0915*** (0.0142) |
| INDEBTit | −0.3191*** (0.0667) | −0.3173*** (0.0667) | −0.3209*** (0.0679) |
| Constant | −0.5201** (0.2248) | −0.5246** (0.2249) | −0.5768** (0.2286) |
| Observations | 6,233 | 6,233 | 6,170 |
| R2 | 0.123 | 0.122 | 0.124 |
| AIC | −4985.8862 | −4979.5060 | −4968.2537 |
Note(s): 1*p < 0.1, **p < 0.05, ***p < 0.01. 2Proposed by Paulo and Mota (2019)
Coefficients – proposed regression model for HIC performance – adjusted fixed effects per DA module by period
| Variables | IDEF (2012–2014) | IDSM (2015–2018) | ||||
|---|---|---|---|---|---|---|
| Jones model | Modified Jones model | Roychowdhury model2 | Jones model | Modified Jones model | Roychowdhury model2 | |
| DAit | −0.0886*** (0.0237) | −0.0718** (0.0330) | ||||
| DACCit | −0.0483*** (0.0090) | −0.0418*** (0.0081) | ||||
| REMit | −0.0066*** (0.0014) | −0.0269*** (0.0025) | ||||
| LEVit | −0.2755*** (0.0203) | −0.2737*** (0.0202) | −0.2848*** (0.0205) | −0.2693*** (0.0149) | −0.2630*** (0.0149) | −0.2603*** (0.0148) |
| ROAit | 0.1459*** (0.0144) | 0.1447*** (0.0143) | 0.1461*** (0.0149) | 0.0385*** (0.0081) | 0.0377*** (0.0081) | 0.0358*** (0.0081) |
| SIZEit | 0.0203*** (0.0024) | 0.0194*** (0.0024) | 0.0198*** (0.0024) | 0.0124*** (0.0014) | 0.0115*** (0.0014) | 0.0094*** (0.0014) |
| INDEBTit | −0.3967*** (0.0491) | −0.3926*** (0.0490) | −0.3910*** (0.0492) | −0.0799** (0.0388) | −0.0822** (0.0386) | −0.0904** (0.0384) |
| Constant | 0.5722*** (0.0361) | 0.5855*** (0.0362) | 0.5845*** (0.0363) | 0.7762*** (0.0227) | 0.7883*** (0.0226) | 0.8322*** (0.0229) |
| Observations | 2,855 | 2,855 | 2,821 | 3,378 | 3,378 | 3,349 |
| R2 | 0.135 | 0.140 | 0.140 | 0.114 | 0.120 | 0.143 |
| AIC | 614.5280 | 599.6681 | 579.0325 | −2081.1838 | −2103.2181 | −2184.6460 |
Note(s): 1*p < 0.1, **p < 0.05, ***p < 0.01. 2Proposed by Paulo and Mota (2019)
| Year | Abbreviation | Indicators | Formulas |
|---|---|---|---|
| 2012–2014 | IDEF | NAI – Net asset indicator | NAI = (Adjusted net asset)/(Solvency margin) |
| CLI – Current liquidity indicator | ILC = (Current asset)/(Current liability) | ||
| IPEINR – Indicator of provision for events incurred but not reported | IPEINR = (0 if the accounting of PEINR is insufficient in the 4th quarter; 1 if PEINR is sufficient in the 4th quarter) | ||
| ISG – Indicator of sufficient guarantee assets | ISG = (0 if there is no sufficient guarantee; 1 if there are guarantee assets) | ||
| 2015–2019 | IDSM | IOR – Indicator of own resources | IOR = (Adjusted net asset)/(Solvency margin) |
| IFA – Indicator of financial availability | IFA = (Current assets)/(Current liability) | ||
| IR – Inspection rate | IR = (1.0 × UDA* + 0.5 × UDN−A** + 0.25 × EVRA** + 0.25 × EVRN−A****)/(2 × Average_Beneneficiary) × 10,000 | ||
| RR – Rate of resolution of preliminary intermediation notification | RR = (Total demand of preliminary intermediation notification (PIN), classified as: RVE, Inactive, Preliminary notification)/(Total of social classified PIN demands) × 100 |
Note(s): *Total of the HIC’s unresolved demands of preliminary intermediation notification (PIN) related to services in the period; **Total of the HIC’s unresolved demands of PIN non-related to services in the period; ***Total of the HIC’s demands of PIN related to services classified as Effective Voluntary Reparation (EVR); ****Total of the HIC’s demands of PIN non-related to services classified as EVRSource(s): ANS website
| Variable | Abbreviation | Formulas | References |
|---|---|---|---|
| Firm size | SIZE | Natural log of total assets | Dechow and Dichev (2002) |
| Financial leverage | LEV | Total liabilities/Total assets | Barnett and Salomon (2012) |
| Return on assets | ROA | Edit/Total assets | Kothari et al. (2005), Joia and Nakao (2014) |
| Indebtedness | INDEBT | Onerous liability/Total assets | Watts and Zimmerman (1990) |
| Joia and Nakao (2014) |
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