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SIMPLES NACIONAL AND MICRO AND SMALL BRAZILIAN ENTERPRISES: A SURVIVAL ANALYSIS FROM RAIS’ 2007-2016 MICRODATA.

Abstract

The Survival Technic Analysis combined with the Propensity Score Matching allowed us to identify that establishments which opted for the Simples and emerged in 2007 had a 16.5% lower chance of mortality than companies not opting for it. Separating establishments by size and sector, the article suggests that the program has a relatively greater impact on establishments that have between two and five employees and on the service sector.

Keywords: Simples. Tax Policy. Survival Analysis. Propensity Score Matching.

JEL Classification: L20, C23, K34

1. INTRODUCTIONMost emerging companies in Brazil are either micro or small and account for nearly

54% of formal jobs, according to Sebrae (2017). OECD (2015) points out that those

companies also contribute, comparatively, to a significant share of added value and total new

jobs.

Its importance in creating jobs and encouraging new entrepreneurs has inspired

several countries to adopt innovative tax programs that are created specially for this type of

organization (ORGANIZAÇÃO PARA A COOPERAÇÃO E DESENVOLVIMENTO

ECONÔMICO, 2015). World Bank (2005) and Piza (2018) point out that this type of

intervention contributes to the improvement of the business climate, since it stimulates the

creation and formalization of micro and small companies. De Soto (1989) points out that the

presence of a complex and bureaucratic structure increases the barriers and hinders the

formalization of companies. Djankov et al. (2002) support this point of view incorporating

issues of labor costs and taxes. Consequently, according to La Porta and Shleifer (2014),

this type of intervention provides greater access to the credit market and to government

contracts and reduces the risk of fines.

McKenzie and Woodruff (2006) contradict the aforementioned points in stating that

the number of days it takes to open a company would not be one of the main obstacles to

formalization. La Porta and Shleifer (2014) corroborate this position, pointing out that the lack

of formalization has other causes, such as the low level of human capital of informal

entrepreneurs. In addition, only a few firms have the technical capacity to leave informality.

The assertion is endorsed by Bruhn and Mckenzie (2013), who do not verify increases in the

degrees of formalization of firms that benefit from this type of policy.

Within this context, we discuss the effectiveness of support policies for micro and

small businesses. Ramos (1998) states that many of these firms are labor intensive and are

flexible in incorporating technological advances, although many operate informally. Despite

the efforts to present evidence on the subject, there is still no consensus about its effects,

specially in the least developed countries (INTERNATIONAL LABOUR ORGANIZATION,

2012).

The effectiveness of tax simplification measures has been assessed by experimental

and quasi-experimental methods. Fajnzylber et al. (2011) show that Simples Nacional

(Integrated System of Tax and Contribution Payment for Micro and Small-sized Enterprise)1

generates an increase in the number of employees, of physical capital and in productivity.

Kalume et al. (2013) also find positive effects on the number of employees and on the firms’

activities. However, Piza (2018) points out that the success of this program in those aspects

is an exception. Also, regarding Simples, Delgado et al. (2007), using microdata from the

firms, show that the results for the ones that did not opt for Simples were superior in four of

the five measures analyzed.

Simple is a special tax regime for small businesses whose main mechanism of action

is the reduction and simplification of the tax burden of micro and small enterprises (MSEs).

With the new regime, companies could collect up to six taxes through a single document2,

and the calculation and simplified payment are operationalized by applying a rate on the

company’s gross revenue. The amount each company owes depends on the annual gross

revenue and economic activity.

The greatest change in the design of Simples occurred in 2006 with Ancillary

Legislation No. 123/06, which now includes state (Imposto sobre Operações Relativas à

Circulação de Mercadorias e sobre Prestações de Serviços de Transporte Interestadual e

Intermunicipal e de Comunicação - Tax on the Circulation of Goods and Interstate

Transportation Services - ICMS) and municipal (Imposto Sobre Serviço de Qualquer

Natureza - Tax on Services - ISSQN) taxes, respectively. As of July 2007, Simples Nacional

was established, and all the companies that opted for Simples Federal were automatically

1 Simples was created in 1996 and underwent a major transformation in 2006, becoming Simples Nacional.2 Imposto de Renda Pessoa Jurídica (Corporate Income Tax) (IRPJ), Programas de Integração Social e de Formação do Patrimônio do Servidor Público (Social Contributions paid by companies) (PIS/PASEP), Contribuição para Financiamento da Seguridade Social (Contribution for Social Security Financing) (COFINS), Contribuição Social sobre o Lucro Líquido (Social Contribution on Net Profits) (CSLL), Imposto sobre Produtos Industrializados (Tax on Industrialized Products) (IPI), and Contribuição Patronal Previdenciária (Employer’s Social Security Contribution) (CPP).

included in the new regime. The program was jointly managed by the municipalities, states

and the Federal Government, through the Simples Nacional Management Committee

(CGSN). This committee has the authority to determine the eligible sectors, the limitations,

the maximum gross revenues and other issues associated with the program.

Among the main changes brought about by Legislation No. 123/06, the shared

management of the program and the inclusion of new eligible activities stand out. The tax

relief rates and the maximum gross revenues have also been expanded. The eligible micro

and small businesses could bill up to R$ 120 thousand and R$ 720 thousand, respectively.

The new maximum gross revenue was increased to R$ 360 thousand and R$ 3.6 million,

respectively, for micro and small companies.

Several studies show that among the taxation programs of different nations of the

world, Simples Nacional (SN) is the most generous regarding the revenue limit (PAES and

ALMEIDA, 2009; APPY, 2015). In the USA, for example, the maximum gross revenue to

remain eligible for a simplified tax regime is U$ 48,000 annually, in Canada, U$ 121,000 in

the United Kingdom, U$ 114,000, and in Brazil, U$ 1 million a year. It is also worth noting

that, even among emerging countries, the revenue limit of Simples is high: in Argentina this

figure is U$ 48,000, in Colombia it is U$ 60,000, and in Mexico it is U$ 148,000 a year

(APPY, 2015).

Given the relevance of the program to Brazil, the objective of the article is to assess

the effect of Simples Nacional on the longevity of Brazilian firms for the industry, trade and

service sectors, focusing on the microenterprises of sectors eligible to the program and that

started their activity in 2007. The methodology used in the study is divided into two stages.

First, the Propensity Score Matching, before the longitudinal follow-up of the firms, aims to

avoid, by means of observable characteristics, the comparison of very different enterprises

among themselves in the possibility of being eligible for Simples. Subsequently, the Survival

Analysis (Cox) technique is used in approximately 114 thousand observations, organized

according to the activity sector (industry, trade and service).

The program is expected to increase the longevity of the beneficiaries, since its

adoption leads, according to Monteiro and Asunción (2006), to a reduction of 8% in the taxes

of the beneficiary firms. Viol and Rodrigues (2000) and Gonzalez (2006) point out that the

explanation for this behavior seems to be connected to the tax incentive coming from

Simples, by separating the salary and the number of employees from the cost with the

simplified regime. This incentive, represented by the change in the calculation basis of

Contribuição Previdenciária Patronal (Employer’s Social Security Contribution) (CPP) for

enterprises that opt for Simples, allows the company to have the same CPP cost regardless

of the number of employees. This does not apply to companies not opting for Simples, for

which the salary and the number of employees impact the amount due for CPP. In addition,

Gonzalez (2006) points out that Simples cuts the cost of social security, a contribution paid

by the employer, from the labor cost. Gonzales (2006) points out that the program impacts

the contracting and formalization of employees in the firm.

The article is divided into five sections, in addition to this introduction. Section two

presents the literature review on Survival Analysis and the effects on firms. It also addresses

the main features of Simples, its history over time, as well as its structuring. Section three

describes the methodology and the database. Section four presents and discusses the

results. The last section presents the final considerations.

2 LITERATURE REVIEW3

2.1 Studies that deal with SimplesSeveral studies employ Survival Analysis to assess the effects on the longevity of

companies. With regard to Simples, Cechin and Fernandes (2000) observed a major and

relative increase through GFIP (Collection Form for the Severance Premium Reserve Fund

and Information to Social Security - Guia de Recolhimento do Fundo de Garantia por Tempo

de Serviço e Informações à Previdência Social) in the number of companies opting for the

3 Table A1, in the annex, presents a summary of the literature we used.

simplified regime. In addition, the average wages of companies that adopt Simples is

equivalent to half of their counterparts’.

Monteiro and Assunção (2006) used the 1997 ECINF (Pesquisa da Economia

Informal Urbana - Urban Informal Economy survey), which is a cross section database. The

effect of Simples was assessed based on the formalization rate of companies emerging

between 1996 and 1997. According to the authors, the sample has 75% of informal

enterprises, which are in a business environment subject to expropriation (through corrupt

tax inspectors), and with less access to credit and state protection. The authors used the

differences in differences methodology, comparing eligible and ineligible sectors before and

after the change in legislation. The effect was significant in the retail sector, and the other

beneficiary sectors (industry, services and construction) were not affected. By using Simple

as an instrumental variable, in order to assess the effect of formalization and investment, the

results showed that the beneficiaries invest more when compared to the others.

Delgado et al. (2007) use three different databases: RAIS, ENCIF and GFIP to

measure the effect of Simples on: i) the number of formal employments and establishments;

(ii) the compensation of employees of the participating companies; iii) social security

collection; and iv) the average salary. The authors followed a group of 4 thousand

establishments of the service sector that adopted Simples between the years of 2000 and

2005 based on the microdata of GFIP. The objective is to determine whether Simples

contributes to the strengthening of the labor market. Evidence shows that, in all the

measures observed, the establishments not opting for Simples managed to grow more the

opting ones. The exception is the variable average salary of self-employed workers. The

result may suggest that even in the absence of the program, there would be creation of new

jobs. When considering the longitudinal nature and relevance of this research, these results

cast doubt on the ability of Simples to promote job creation in the Brazilian economy.

The results of Fajnzylber, Maloney and Montes-Rojas (2009) corroborate the position

of Monteiro and Assunção (2006) on the positive effect of Simples on the formalization of

micro enterprises emerging after the program. By using data from the same survey employed

by Monteiro and Assunção (2006), the authors observe a considerable increase in the

percentage of formalized companies (possession of a license as proxy of formality) after the

creation of Simples: from 30% in 1997 in companies with up to one employee to 49% in 2003

(ECINF 1997 and 2003).

The results of the treatment effect models used in the analysis (Regression

Discontinuity and differences in differences) also suggest that formal companies created in

this period do better than their informal counterparts, separated between eligible and

ineligible to Simples according to the activity sector. However, the authors did not address

sectoral differences that could indicate some effect in the industry and thus compare the

results with the literature.

Galvão (2010) points out that tax reduction and tax simplification lead to the

redistribution of wealth. The author seeks to answer two questions: which firms benefit from

tax reduction and tax simplification and whether there are heterogeneous effects associated

with Simples. The author analyzes micro and small enterprises (one to five employees) and

uses quantile regression procedures and instrumental variables. The database used was the

1997 ECINF. Estimates show the existence of heterogeneous effects and that the drop in the

cost of formalization affects more the micro enterprises that are in the lower tail.

Formalization positively affects revenues from 40% to 50% of micro enterprises.

Piza (2016) critically reviews the works of Fajnzylber, Maloney and Montes-Rojas

(2009) and Monteiro and Assunção (2006) in order to test the validity of the identification

strategies used by the authors to determine the impact of Simples on formalization. Piza

(2016) realizes that with the administration of robustness tests and control groups with

placebo treatment, the results of the previous studies do not prove persistent to the

sensitivity tests. Thus, the most recent evidence seems to question the previous positioning

regarding the effectiveness of Simples in stimulating the formalization of informal companies.

Several studies have assessed the effect of Simples on micro and small enterprises.

Caetano (2010) observes the effects of the program on the manufacturing industry of Ceará

in the period of 1996 and 2008 using the methodology of differences in differences. The

period of analysis comprises the ten years of Simples Federal (1996 is the year before the

program was created) and 2007 and 2008 correspond to the first two years of Simples

Nacional. The effects of the program on the number of establishments and employees are

observed for the mesoregions. The identification strategy considers micro and small

industrial companies as a treatment group and, as a control group, medium-sized and large

firms. The main results point to positive but not significant effects on the number of

establishments and employees.

Corseuil and Moura (2011) analyze the effects of Simples on job creation in two

different moments: in 1997, the year following the creation of Simples, and in 1999, when the

eligibility criteria changed. The database used was IBGE’s PIA (Annual Survey of Industry -

Pesquisa Industrial Anual), which is a census base for companies with more than 30

employees. The identification strategy considers the companies that had revenue levels

close to the limits of the program’s eligibility criteria. Thus, the comparison group is formed

by the business units that were above the revenue threshold. Those who were below this

threshold form the treatment group. The methodology used was a regression discontinuity.

The effect of the program is positive and statistically significant in order to keep smaller firms

from closing in 1997. The intervention for the result derives from job creation caused by

bureaucratic simplification. However, the change in the eligibility criteria observed in 1999

had no effect on the level of employment. The results provide evidence that bureaucratic

simplification is more beneficial vis à vis with tariff reduction.

Franco et al (2017) show that with the use of PIA data from 2000 to 2012 and an

identification strategy and methodology like that of Corseuil and Moura (2011), the program

generates a 21.5% increase in the number of jobs. In addition, a drop of 23% in the industrial

operating cost and a 25.18% increase in the payroll were noted. The estimate corresponds to

the increase of R$ 130.00 in the payroll of each company. For the wages of workers directly

involved in production, there was an increase of 26.8% in the participating companies vis à

vis with the others. Franco et al (2017) point out that the results are robust to different

falsification and sensitivity tests. The authors attribute the increase in employment and

wages to the reduction of the industry’s operating cost.

Conceição et al (2018) used RAIS from the year 2007 to 2013. The use of Propensity

Score and Survival Analysis reveals that the companies of Rio Grande do Sul that adopted

Simples survived 30% more in comparison to the others. These evidences were presented

for the companies belonging to sectors less intensive in technology (low and medium-low).

For firms belonging to other levels of technological intensity, there is no effect of the

simplified regime.

The results discussed in this section show the importance Simples Nacional,

especially considering that the effectiveness of the program on employment was

demonstrated in the two phases of the simplified regime in Brazil. The available evidence,

however, does not account for the effect of the program on the longevity of industrial micro

enterprises, the object of this work.

3 METHODOLOGY

The methodology used in the exercise is divided into two stages. Firs, a Propensity

Score Matching was employed so that the companies included in RAIS and that were

created in 2007 were equally likely to participate in Simples. This procedure prevents

companies created before this period, and therefore prior to the start of the program, to skew

the sample. In the second stage, a Survival Analysis was performed.

3.1 Propensity Score Matching

The Propensity Score methodology, created by Rosenbaum and Rubin (1983), aims

to reduce the problem of multidimensionality. This negative aspect is present in the pairing

as the number of observable characteristics in vector X increases. Thus, the substitution of

this vector by a function of X allows us to incorporate all the observable characteristics. This

function corresponds to the probability of receiving the Simples program and the Propensity

Score, p(X), is defined as follows:

p (X )=Pr [T=1∨X ] (1)

The hypotheses present in the pairing are maintained: i) selection in observables; ii)

independence between the potential outcome in the absence of the treatment and the

decision to adopt Simples or not. Thus,

(Y i (0 ) , Y i (1 ) )⊥T i∨X i=(Y i (0 ) ,Y i (1 ))⊥T i∨ p ( X i ) (2)

As the Propensity Score is an unknown function of X, we used the Logit model,

whose dependent variable is the chance to participate or not in the program,

P (X )= exp (x β )1+exp (x β )

(3)

where β is the vector of parameters that will be estimated in a first stage and β is the

estimator of the parameter β. The pairing, based on the propensity score between the

participating and non-participating companies, will be performed through the nearest-

neighbor estimator, with replacement. The estimator consists of using the propensity score of

the non-participating M companies, however, with values close to the ones that are

participants. After being paired by means of the propensity score of the nearest neighbor, the

next step was the Survival Analysis.

3.2 Survival Analysis.The methodology was employed with the use of nonparametric (Kaplan-Meier) and

semiparametric (Cox) models. The methodology seeks to analyze the survival time of a

variable T that is random and not negative, representing the time up to the event loss. The

survival function, S(t ) is defined as the probability that the event does not occur (that the loss

does not happen), within the time of analysis t. It should be noted that t is a censored

variable on the right and concerns the duration of the company. The event study is the

closing time of the company.

The survival function, S (t )=Pr (T ≥ t ) is defined as the probability that the company

closing does not occur until some time t. The cumulative distribution F (t )=1−S (t)

corresponds to the probability that the firm closes before time t (HOSMER and LAMESHOW,

1999)9. The function is equal to 1 at t=0 and decreases to zero, when it tends to infinity.

The non-parametric Kaplan-Meier estimator, proposed by Kaplan and Meier (1958)10

to estimate the survival function is defined as:

q j=the number of firms that remain∈activity until the time t j

total number of firms present∈the sample∧at risk of loss∈t j−1

(4)

q j is the probability of the company closing within the interval ¿, since the closing did not

occur until t j−1 and considering 0 as the initial period. The procedure avoids censoring on the

left, since all firms were created in 2007.

The Kaplan-Meier estimator, S (t ) is defined as a staircase function whose steps have

length 1n j

where n j is the total number of companies in the study that were at risk of closing

in t j. The number of steps corresponds to the number of companies that went out of

business within the time, t j. Therefore, the estimator is:

S (t )=∏j : t j<t

(1−d j

n j) (5)

The next step to the Kaplan-Meier model will be the estimation of the Cox

proportional hazards model. The model is flexible since it allows the incorporation of

covariables and has two components: a parametric one, g (x ' β ), which is used in

multiplicative form, and a nonparametric one, λ0(t), which is unspecified, and is a non-

negative function of time.

λ (t )=λ0 (t )g (x ' β ) (6)

where β is the parameter vector associated with the covariates. Proportionality is assumed

between the groups, so the failure rate function of the group composed by the firms that are

beneficiaries of Simples, λ1( t), and the failure rate function of the group formed by those that

are not beneficiaries, λ0(t), remains constant and is equal to λ1(t )λ0(t )

=K . Therefore, the ratio

of the closure rate functions of firms i and j does not depend on the time t j, and is given by:

λi ( t )λ j (t )

=λ0 ( t ) exp {x i

' β }λ0 ( t ) exp {x j

' β }=exp {x i

' β−x j' β }

(7)

Consequently, the model assumes that firms' closure rates (accumulated or not) are

proportional. The interpretation of the coefficients coming from the model is the acceleration

or deceleration of the risk function of company closure.

3.3 Data sourcesRAIS (Relação Anual de Informações Sociais - Annual Social Information Report) is

mandatory for all companies that are active. The information is kept by the Brazilian Ministry

of Labor and provides a complete set of information on the formal non-agricultural labor

market. The companies fill out information on how the employee is hired (subject to the

Consolidation of Labor Laws, statutory, temporary), employees' salaries, skin color, age,

education and whether some type of leave was granted (work accident, maternity leave,

retirement). Regarding establishments, the base includes aspects of economic activity,

geographical area, number of employees, whether it opted for Simples, whether it

participates in the Worker's Food Program (Programa de Alimentação do Trabalhador -

PAT), among others. However, the database has as a limitation the noncompliance with the

company’s revenue.

The variables employed in the pairing were, at company level: the number of

employees, the average salary, the average education, the average age and the average

employment time. In addition, we take into consideration the CNAE (National Classification of

Economic Activities - Classificação Nacional de Atividades Econômicas) and the

municipality. The years analyzed were between 2007 and 2016. The estimates were made

according to the activity sectors: industry, trade and services. The companies eligible for

Simples belonging to the industry have between 2 and 19 employees. For the other sectors,

the eligibility of firms for the program is given by the number of employees (between two and

nine employees).

Table 1 shows the descriptive statistics of the variables used in the study.

Table 1 - Descriptive statistics of the variables used in the study: 2007-2016.

Variable Variable description Mean:all firms

Mean: Industry sector firms

Mean: Trade sector firms

Mean: Services

sector firms

opting_simples Dummy that takes value 1 when the company opts for Simples and

zero if otherwise.

0.688(0.46)

0.588(0.49)

0.794(0.40)

0.544(0.49)

years_study Average number of employees’ years of

study

9.743(2.43)

8.685(2.63)

9.897(2.25)

9.920(2.54)

number_employees

Number of active employees

4.237(35.10)

8.821(44.14)

2.894(12.46)

4.675(53.23)

average_salary Average salary paid by the firm

619.941(546.14)

671.369(660.93)

584.233(394.33)

661.620(698.84)

age_work Employees’ average age

30.873(9.33)

33.131(9.15)

29.780(9.18)

31.857(9.41)

time_job Average time on the job 0.692(1.455)

0.792(1.89)

0.58(1.01)

0.847(1.84)

Number of observations 114298 15191 63475 35632Source: Prepared by the authors, using microdata from RAIS. 2007-2016.Standard deviation in parentheses.

According to the table above, most of the companies that adopted Simples belong to

the Trade sector. In addition, it is the sector that pays the lowest salaries, on average, and

whose average age of employees is also slightly lower. It should be noted that Trade is the

sector whose number of employees is also smaller. Industry is the sector whose salary and

average age are slightly higher than the others. There is no difference in the average

schooling of employees between sectors. In general, they finished elementary school.

It is important to note that a company is considered inactive (dead) when it ceases to

appear in the RAIS declaration for two consecutive years. This avoids possible measurement

errors due to incomplete information.

4. RESULTS ANALYSIS

4.1 Non-parametric Kaplan-Meier resultsThe strategy was to make estimates for the whole period (2007 to 2016), in addition

to estimates for five-year periods: 2007-11; 2008-12; 2009-13; 2010-14; 2011-15; and 2012-

16. For each of these periods, models were estimated for all sectors and specifically for

industry, trade and services. In addition, in all the estimated models, different company sizes

were considered.

According to Kaplan-Meier's non-parametric estimates, different behaviors can be

observed between companies opting and not opting for Simples. The log rank test (the risk

functions of the groups to be compared is approximately constant) is the ratio between the

number of observed events (closure of firms) in each group (opting and not opting for

Simples) with the number of firms under risk of closure (survival of firms). The test has as

null hypothesis the nonexistence of difference in the survival distributions between the

groups of firms. According to the log rank test of Table 2, it can be stated that the survival

curves are statistically different, except for the period between 2008 and 2012. According to

the log rank test, it can not be said that there are differences between the curves of the

companies opting and not opting for Simples for those that emerged in 2008.

Table 2 – Log rank test – Observed events 2007-2016

Years 2007 to 2016

2007 to 2011

2008 to

2012

2009 to

2013

2010 to 2014

2011 to 2015

2012 to 2016

Not opting for Simples

35614 19001 8828 16264 17972 18419 29866

Opting for Simples

78688 34472 20878 38625 46351 54413 94958

Total 114302 53473 29706 54889 64323 72832 124824Chi square 246.98 88.91 2.39 243.4 452.45 447.68 586.65P value 0.00 0.000 0.12 0.00 0.00 0.000 0.000Source: RAIS. 2007-2016. Prepared by the authors.

During the analyzed period, there is an increase in the number of companies that

opted for Simples. The lowest number of new companies is in 2008, an international crisis

period (CANO, 2012). For the remaining years, there is a growth in the number of opting

firms. It is important to state that, in terms of unconditional median, there is no difference in

survival times between firms opting and not opting for Simples.

Figure 1 below shows the Kaplan-Meier non-parametric model, after the pairing, for

all sectors between 2007 and 2016. Companies not opting for Simples have higher mortality

rates. Approximately 50% of the companies emerging in 2007 and that adopted Simples

remain in operation at the end of 2016. However, this context is different for non-adopters,

since only 30% remain in operation at the end of this period.

Figure 1: Kaplan-Meier non-parametric model for micro and small enterprises and that are adopters and non-adopters of Simples Nacional between 2007 and 2016.

Source: prepared by the authors.

Figure 2 below shows that companies that emerged in 2007 and that adopted

Simples show longer survival time compared to their non-adopter peers, regardless of the

industry. It should be noted that, from the fourth year onwards, there are no differences in the

closing of companies belonging to the trade and services sectors. The differences in the

closing of firms regarding Simples are more pronounced in the industrial sector, since

approximately 40% of the adopter firms remain in operation after three years.

Figure 2: Kaplan-Meier non-parametric model for micro and small enterprises and that are adopters and non-adopters of Simples Nacional for firms that emerged in 2007 with a cycle time of up to 2011, according to the activity sector: Industry, Trade and Services.

Source: RAIS 2007-2016. Prepared by the authors.

According to Figure 3, there are no differences in survival rates between the adopters

and non-adopters that emerged in 2008. It should be noted that this result is also observed

when considering the different sectors.



Estimates of the Kaplan-Meier model shown in Figure 4 show that firms opting for

Simples survive longer, regardless of the activity sector. It is important to emphasize the low

percentage of companies in operation, since less than 25% are still active at the end of the

fourth year.



Estimates for companies that emerged in 2012 and belong to the trade sector show

that the survival rate of the adopters of Simples is slightly higher. At the end of the fourth

year, there are small differences in survival between adopters and non-adopters. In addition,

approximately 50% of them remain in operation by the end of the fifth year.



For the industry sector, there are larger differences in survival rates between adopters

and non-adopters. It is verified that those belonging to Simples have a survival rate of 40%.

The others have rates below 30%. Service companies have survival rates of more than 50%

by the end of the fifth year for those who adopt Simples. For the other companies belonging

to this sector, slightly lower rates are perceived.

4.2 Results of the Cox model

In Table 3 are the results of the estimated effects of the adoption of Simples on the

risk of closing, separated by size, sectors and different periods of analysis. The size refers to

the number of employees of each company and is divided into five strata: 2-19 employees, 2-

5 employees, 6-10 employees, 11-15 employees, and 16-19 employees, displayed in the

columns of the table. The sectors include industry, trade, services and all sectors grouped,

which can be seen in the table rows. Finally, the periods are 2007-11, 2008-12, 2009-13,

2010-14, 2011-15, 2012-16, and 2007-16. These also appear highlighted in the rows. In

summary, the rows in the table show the different models separated by sectors and periods

of analysis and the columns show the results for the different size strata of the companies.

Due to the large number of estimated models, we chose to present only the results of

the model that represents the main estimates of the effects of adopting Simples on the risk of

companies closing. These results, however, are controlled by the number of employees, by

the average number of years of study, by the average salary and the age of the company’s

employees, respectively, by the average time of employment, by municipalities and by

sectors of the CNAE 2.0 to three digits (divisions).

The first general aspect to be highlighted in Table 3 is the Schoenfeld test for the

proportional hazard hypothesis, assumed by the Cox regression model. The null hypothesis

of the test is the proportionality of risks between the companies. This hypothesis was not

rejected in almost all models, except for some of the models belonging to the size strata of

11-15 employees and 16-19 employees. Thus, all the results of the Cox model regressions

for companies of the first three strata of the table, 2-19 employees, 2-5 employees and 6-10

employees are valid.

The second general aspect to be considered is the fact that few models for the size

strata of companies with more than ten employees presented statistical significance in

relation to the HR (Hazard Ratio) coefficient, associated to Simples. This is possibly due to

the small number of observations of these cases, since they only contemplate the industry

sector. In addition, some of these models reject the proportionality hypothesis of the risk of

death, which makes them not valid for survival analysis. Only two models presented, at the

same time, statistical significance and proportional risks (All sizes and sectors 2010-14 and

11-15 employees and All sizes and sectors 2012-16 and 11-15 employees). In both cases,

the effect of Simples was to reduce the risk of companies closing.

Given the aspects outlined above, the analyzes and policy recommendations that

followed prioritized the first three size strata: 2-19 employees, 2-5 employees, and 6-10

employees. All models of these strata have a high number of observations from more than

1,000 companies. In all the models that presented statistical significance for the coefficient

associated with Simples, there was a reduction in the risk of death for the companies that

adopted the Program. These coefficients are interpreted as risk ratios, so that if it is less than

the unity, there is a decrease in the risk of death. On the other hand, risk ratios higher than

one suggest increased risk of occurrence of the event.

When evaluating the results of the first two size strata (2-19 employees and 2-5

employees) and comparing the HR coefficient line by line, it can be seen that the values in

the 2-5 employees stratum, with only one exception, are lower than one. This means that the

effect of adopting Simples on reducing the risk of death is relatively higher in smaller

companies. This result is also evident when comparing the coefficients of the 2-5 employees

stratum with that of 6-10 employees. It can be seen again that, in almost all the cases that

presented statistical significance, the effect of Simples on reducing the risk of death of

companies is stronger in smaller companies.

From this point onwards, the idea is to analyze in more detail the results of the

different models and to make some comparisons between them. The first model considered

is the most general, involving all sectors and periods (All 2007-16). The results indicate that

companies adopting Simples decrease their risk of death in comparison with similar non-

adopter companies by 16.5% ((1 - 0.835) * 100). Observing the effect by size of the

companies, it is noticed that the reduced risk of death (20.7%) is greater in the 2-5

employees stratum. In terms of sectors, the effect is more pronounced in the services sector

and in the 6-10 employees stratum. These results corroborate a similar study carried out by

Conceição et al (2018) for Rio Grande do Sul from 2007 to 2013.

Considering the models separated by sectors and different periods of time, it is

possible to confirm that the sector most impacted by Simples in terms of reducing the risk of

death is that of services, especially in the periods after the crisis of 2008. The only exception

was the model for the 2007-11 period, in which the industry had the greatest reduction in

death risk.

Table 3: Results of Cox regressions for models with paired data

Sectors & Size2-19 employees(a) 2-5 employees 6-10 employees(b) 11-15 employees 16-19 employees

HR Simples(c)

No.Obs(d)

TPR(e)

Pr>chi2HR

SimplesNo.Obs

TPRPr>chi2

HR Simples

No.Obs

TPRPr>chi2

HRSimples

No.Obs

TPRPr>chi2

HR Simples

No.Obs

TPRPr>chi2

All sizes and sectors 2007-16

0.835*** 114,271 1.00 0.793*** 40,356 1.00 0.910*** 8,127 1.00 0.999 2,714 0.99 1.038 1,087 0.77

Industry 2007-16 0.850*** 8,419 1.00 0.815*** 5,719 1.00 0.963 1,721 0.80 1.083 666 0.00 2.779 313 0.00Trade 2007-16 0.808*** 25,166 1.00 0.775*** 21,937 1.00 0.957 3,229 1.00Services 2007-16 0.804*** 15,199 1.00 0.799*** 12,700 1.00 0.742*** 2,499 1.00All sizes and sectors 2007-11

0.913*** 53,473 1.00 0.894*** 41,280 1.00 0.959* 8,341 1.00 1.030 2,791 0.99 1.027 1,061 0.99


0.964*** 29,672 1.00 0.949*** 22,290 1.00 0.974 5,038 1.00 1.079 1,681 1.00 0.984 663 1.00


0.825*** 54,889 1.00 0.796*** 42,623 1.00 0.877*** 8,400 1.00 0.949 2,788 1.00 0.751*** 1,078 0.01


0.796*** 64,323 1.00 0.778*** 49,679 1.00 0.823*** 10,004 1.00 0.780*** 3,351 1.00 1.215* 1,289 0.07


0.816*** 72,776 1.00 0.787*** 56,151 1.00 0.838*** 11,449 1.00 0.983 3,672 1.00 1.125 1,504 0.98


0.814*** 124,461 1.00 0.771*** 93,868 1.00 0.869*** 20,919 1.00 0.916*** 6,891 1.00 1.037 2,783 1.00


0.886*** 8,777 1.00 0.841*** 5,953 1.00 1.038 1,796 1.00 1.094 701 0.18 1.777 327 0.00

Industry 2008-12 0.942* 4,822 1.00 0.944 3,126 1.00 0.942 1,077 1.00 0.917 427 1.00 1.456 192 0.00Industry 2009-13 0.834*** 9,719 1.00 0.787*** 6,608 1.00 0.943 1,989 1.00 1.021 795 0.08 0.286*** 327 0.00Industry 2010-14 0.850*** 13,106 1.00 0.837*** 8,748 1.00 0.847*** 2,772 1.00 0.929 1,136 0.99 1.292 450 0.00Industry 2011-15 0.864*** 15,985 1.00 0.837*** 10,662 1.00 0.877*** 3,480 1.00 1.133 1,289 0.99 0.889 554 0.07Industry 2012-16 0.863*** 24,725 1.00 0.827*** 16,815 1.00 0.926* 5,128 1.00 0.933 1,990 1.00 0.988 792 0.57

Source: Prepared by the authors.

(continue)

2-19 employees(a) 2-5 employees 6-10 employees(b)

HR Simples(c) No.Obs(d)

TPR(e)

Pr>chi2HR Simples No.

ObsTPR

Pr>chi2HR Simples No.

ObsTPR

Pr>chi2Trade 2007-11 0.903*** 25,571 1.00 0.893*** 22,272 1.00 0.953 3,299 1.00Trade 2008-12 0.953*** 14,450 1.00 0.950*** 12,457 1.00 0.984 1,993 1.00Trade 2009-13 0.820*** 25,440 1.00 0.792*** 22,256 1.00 0.955 3,184 1.00Trade 2010-14 0.771*** 28,367 1.00 0.752*** 24,832 1.00 0.862*** 3,535 1.00Trade 2011-15 0.787*** 31,154 1.00 0.769*** 27,208 1.00 0.843*** 3,946 1.00Trade 2012-16 0.813*** 52,201 1.00 0.786*** 44,537 1.00 0.912*** 7,664 1.00Services 2007-11 0.917*** 15,605 1.00 0.910*** 13,055 1.00 0.921 2,550 1.00Services 2008-12 0.922*** 8,237 1.00 0.920*** 6,707 1.00 1.040 1,530 1.00Services 2009-13 0.782*** 16,316 1.00 0.779*** 13,759 1.00 0.755*** 2,557 1.00Services 2010-14 0.753*** 19,039 1.00 0.756*** 16,099 1.00 0.678*** 2,940 1.00Services 2011-15 0.741*** 21,485 1.00 0.736*** 18,281 1.00 0.758*** 3,204 1.00Services 2012-16 0.701*** 38,941 1.00 0.691*** 32,516 1.00 0.719*** 6,425 0.97

Notes: *, **, *** indicate significance at 10%, 5% and 1%; (a, b) for the trade and services sectors the sizes are respectively 2-9 and 6-9 employees; c) HR (Hazard ratio) coefficient associated to the indicator of companies adopting Simples Nacional; (d) number of observations of the regression; (e) TPR = Schoenfeld test for the proportional hazard hypothesis in the Cox regression.

5. FINAL CONSIDERATIONS

Simples reduces the mortality of companies, regardless of sector, size and period

analyzed. The risk of death of the opting companies on average decreases by 16.5%,

ranging from 3.6% to 30.9%.

This means that the effect of adopting Simples on reducing the risk of death is

relatively higher in smaller companies (2-5 employees), regardless of the sector and period.

The only sector that presented some results that deviate from this standard is that of

services, because in three of the seven different periods analyzed, the effect of Simples was

greater in the 6 to 10 employees stratum.

In general, it can be said that the effect of Simples on reducing the risk of death of

companies is higher in the service sector, but this result may be different according to the

size of the companies and the period analyzed. The effect of the Simples on this sector was

relatively stronger in the more recent periods, after the crisis of 2008.

In terms of the analyzed periods, which consider the effects of Simples on the risk of

death of companies that emerged in different years (2007 to 2012), the results show that the

effects are slightly better in the period 2010-14 and may vary according to the sector and the

size of companies. For the industry sector, especially the size of 2 to 5 employees, the period

in which the effect was most pronounced was 2009-13. Kelly et al. (2015) point out that

companies that are born in times of macroeconomic crisis or times of credit shortages tend to

be more resistant to adverse economic situations and survive longer. A specific study would

be required to prove this hypothesis, but results, particularly for industry, which is relatively

more dependent on credit, make it possible to consider this possibility.

Finally, we conclude that Simples has been effective in terms of reducing the risk of

closing for companies that adopt the Program. However, in order to improve its

effectiveness, the Program could choose between two and five employees as its priority

target audience. Thus, the results point in the opposite direction to the recent change in the

program, which has increased corporate income as an eligibility criterion.

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