Unemployment Insurance with Informal
Labor Markets: Evidence from Brazil∗
Bernardus Ferdinandus Nazar Van Doornik†
David Schoenherr‡ Janis Skrastins§
January 15, 2017
This Working Paper should not be reported as representing the views
ot the Banco Central do Brasil. The views expressed in the paper are
those of the authors and not necessarily reflect those of the Banco
Central do Brasil.
Abstract
Using the universe of formal labor contracts in Brazil and an unexpected unemployment
insurance (UI) reform, this paper examines how informal labor markets affect workers’
incentives arising from UI benefits. Exploiting a sharp discontinuity in the reform’s
effect, we find that eligibility for UI benefits increases the flow from formal employment
into formal unemployment by twelve percent. This effect is mainly driven by workers
in labor markets with a high degree of informality. Collusion between workers and their
employers accounts for at least 20 percent of strategic formal unemployment. Firms
appear to hire workers informally while they are eligible for UI benefits and rehire
them formally when benefits are exhausted. Additionally, relaxing eligibility criteria
for UI benefits leads to a higher formal labor supply in the presence of informal labor
markets. Consequently, wages for formal jobs are lower for newly hired workers in more
informal labor markets and relative to informal wages within the same local industry.
These results suggest that incentive effects of UI benefits are stronger in the presence
of informal labor markets and that UI benefits lead to higher formalization of labor.
JEL Codes: J21, J22, J46, K31.
Keywords: informality, labor supply, law and economics, unemployment insurance,
collusion.
∗
We thank Henry Faber, Dimas Fazio, Camille Landais and seminar participants at the Banco Central
do Brasil, Bank of Latvia, SSE Riga, and Washington University in St. Louis for many helpful comments
and suggestions.
†
Central Bank of Brazil, Email: [email protected]
‡
Princeton University, 26 Prospect Ave, Princeton, NJ 08544, Email: [email protected]
§
Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130-4899, Email:
[email protected]
1
Introduction
Providing social insurance against adverse shocks is one of the major functions of governments in developed countries, accounting for the largest fraction of public spending.1 One of
the most important government insurance programs is unemployment insurance (UI), which
provides transfers to individuals who experience negative income shocks from loss of employment. While UI has been present in developed countries for decades, it is rapidly expanding
to middle-income and developing countries only in recent years.2 The existing academic
literature has largely focused on the effects of UI on workers’ incentives in developed countries.3 In contrast, empirical evidence on the effects of UI in middle-income and developing
countries is scarce.4 An important feature of these countries is the prevalence of informal
labor markets. Since existing studies on UI in developed countries lack this dimension, it is
unclear whether their findings can be extrapolated to mid-income and developing countries.
The novel contribution of this paper is to bridge this gap and examine the incentive effects
of UI in the context of informal labor markets.
Informal labor markets provide an additional outside option for workers who consider
formal employment. Extending UI benefits makes formal employment more attractive ex
ante, as formal employment is a prerequisite to establish eligibility for UI benefits. On
the other hand, conditional on being eligible for UI benefits, the availability of informal
labor markets may incentivize workers to exit formal employment and complement their UI
benefits with informal income. The same effect may also reduce workers’ incentive to seek
reemployment when they can work informally instead. Ultimately, informal labor markets
generate an additional set of workers whose decision, to work formally or informally, may be
affected by the design of UI programs. Thus, workers who would otherwise not be sensitive
respond to UI when informal labor markets exists. As a result, informal labor markets may
1
Expenditures on social insurance account for about 60 percent of federal government spending in the
U.S. (Gruber 2011).
2
See Holzmann et al. (2011) for data on unemployment insurance around the world.
3
See for example, Solon (1979), Moffitt (1985), Katz and Meyer (1990), Meyer (1990, 1995), Meyer and
Mok (2007), Card and Levine (2000), Johnston and Mas (2015), and Landais (2015) for the US, and Card,
Chetty, and Weber (2007), Lalive (2008), Schmieder, von Wachter, and Bender (2012, 2016), and Card et al.
(2015) for Western Europe.
4
Gasparini, Haimovich, and Olivieri (2009) and Gonzalez-Rozada, Ronconi, and Ruffo (2011) document
that transfers to unemployed individuals in Argentina made people less likely to take on formal work;
Amarante, Arim, and Dean (2013) show that an extension of unemployment insurance benefits in Uruguay
increased unemployment spells with no effects on future wages; Gerard and Gonzaga (2014) report that
extending unemployment insurance benefits leads to longer unemployment spells, although they argue that
reemployment rates are low in general even in the absence of unemployment benefits.
1
exacerbate the incentive effects of UI benefits.
To shed light on this question, we exploit an unanticipated UI reform in Brazil that significantly tightened eligibility criteria for UI benefits. Brazil constitutes an ideal laboratory
for several reasons. First, Brazil is a middle-income country where informal labor markets
are prevalent.5 Second, the country is very heterogeneous, providing ample variation in
labor market informality across municipalities and industries. This allows us to examine
differences in the effects of UI for different degrees of labor market informality. Finally, data
availability and quality are excellent even by developed country standards.6
The nature of the reform provides a sharp discontinuity in the loss of eligibility for UI
benefits.7 Prior to the reform, which came into effect in March 2015, workers with an
employment history of at least six consecutive months were eligible for three months of
UI benefits. To obtain the same benefits after the reform, workers must demonstrate a
longer employment history. Applying for benefits for the first (second) time requires formal
employment for 18 (12) months during the last 24 (16) months.8 Thus, a large fraction of
workers with a tenure of six months does not qualify for UI benefits after the reform. In
contrast, workers with a tenure of five months are not eligible for benefits both before and
after the reform. This discontinuity motivates our main identification strategy, a differencein-differences methodology, in which we compare changes in the incentives for workers with
tenure of five and six months.9 Importantly, the reform only affects workers’ eligibility for
UI benefits, but does not affect firms’ contributions to the UI program. This allows us to
identify the effects of UI benefits on workers’ incentives free from changes in firms’ demand
for formal labor.
We start our analysis by examining how UI benefits affect workers’ incentive to exit formal
employment. Our findings indicate that the UI eligibility has strong effects on unemployment
inflow. Particularly, unemployment inflow relatively drops by 0.43 percentage points for
5
According to the International Labor Organization, 36.8 percent of all workers were employed informally
in 2013. For comparison, in Europe, informal labor markets account for 17.4 percent of the total labor market
during 2008-2009 (Hazans 2011)
6
The Ministry of Labor and Employment collects comprehensive and high-quality micro-level employment
and wage data for the entire population of formal workers in Brazil.
7
Importantly, the policy change was sudden and unanticipated. The results are not affected by the two
months transition period between the announcement and the implementation of the reform (Section 5).
8
To qualify for benefits, workers must not have successfully applied for UI benefits during the previous
16 months both before and after the reform. See Section 2.2 for more details about the reform.
9
Based on a wide range of observable characteristics, workers with a tenure of five and six months are
indistinguishable.
2
workers who lose eligibility for UI benefits after the reform, which is equivalent to a 12.26
percent decline in unemployment inflow. It is important to note that this strategic inflow
into formal unemployment has not been documented for developed countries.10
Exploiting cross-sectional variation in labor market informality across industries and
municipalities, we assess whether informal labor markets exacerbate the effect of UI benefits
on unemployment inflow. We find that the drop in unemployment inflow after the reform
is almost exclusively driven by workers in industries and municipalities with large informal
labor markets. Specifically, we find that a ten percentage point increase in the share of
informal employment in a given industry or municipality corresponds to an about three
percent higher inflow into formal unemployment when workers are eligible for benefits. This
suggests that informal labor markets induce workers to exit formal employment when they
are eligible for UI benefits.
Next, we examine whether UI benefits affect reemployment probabilities after layoff.
Consistent with the existing literature, we find that dismissed workers return to formal
employment more quickly when they lose eligibility for UI benefits. Specifically, for dismissed
workers who become ineligible for UI benefits after the reform, the probability to return
to formal employment within three months of layoff relatively increases by about three
percentage points compared to workers that are ineligible for benefits before and after the
reform.
Higher inflow into and delayed outflow from formal unemployment could be driven by
workers’ incentives without involvement of their employer. For example, workers may shirk
to induce layoff when they become eligible for UI benefits. Alternatively, it is possible
that workers and employers collude using UI benefits as a government subsidy to be shared
amongst them. Firms can layoff workers when they are eligible for UI benefits, employ
them informally while they receive benefits, and hire them back when UI benefits run out.
Collusion would imply that workers are more likely to return to their previous employer
before the reform when they exploit UI benefits strategically. This is what we observe in
the data. Before the reform workers who are laid off just when they become eligible for UI
benefits are significantly more likely to be rehired by their previous employer when benefits
run out. This firing-rehiring pattern by the same firm is mostly driven by industries and
10
One implication of strategic unemployment inflow is that examining unemployment duration for people
around an eligibility threshold is subject to selection concerns that may lead to an overestimation of UI
incentive effects.
3
municipalities where informal labor markets are prevalent. We find no such pattern after
the reform for workers who lose eligibility for benefits. This suggests that collusion between
employers and employees is an important channel through which informal labor markets lead
to higher inflow into formal unemployment.
The previous analysis examines how informal labor markets affect workers’ incentives
conditional on qualifying for UI benefits. UI programs may also affect workers’ decision to
enter formal employment ex ante. An UI regime that facilitates qualification for benefits
makes formal employment more attractive ex ante by increasing expected future cashflows
from formal employment. This is particularly relevant in the presence of informal labor
markets when workers decide between working formally or informally. On examining workers’
decision to enter formal employment ex ante, we find that formal hiring relatively decreases
in more informal labor markets when the likelihood of future UI benefit eligibility is lower.
Specifically, a ten percentage points increase in labor market informality corresponds to a
drop in new hiring by 0.15 percent of the formally employed workforce in a given industry
after the reform.
Finally, we examine changes in formal wages following the reform. Our findings suggest that the reduction in formal hiring is driven by a decrease in the supply of formal
labor. Formal wages relative increase when more informal employment options are avilable.
Specifically, wages for newly hired formal workers relatively increase by 0.15 percent for a
ten percent increase in labor market informality. Furthermore, we find that this increase in
wages is not driven by changes in economic conditions or productivity in certain industries or
municipalities after the reform. Survey data on both formal and informal wages shows that
within the same industry and municipality, wages increase for formally employed relative
to informally employed workers by more than three percent when qualifying for UI benefits
becomes more likely after the reform.
Together, these results suggest that informal labor markets have three distinct effects on
how UI benefits affect workers’ incentives. First, conditional on eligibility for UI benefits,
informal labor markets lead workers to exit formal employment. Workers collude with their
employers and collect government transfers before returning to formal employment. Second,
the option to remain employed informally further reduces search intensities to reenter formal
employment compared to markets without informal labor markets. Finally, UI benefits induce workers to enter formal rather than informal employment in order to establish eligibility
for UI benefits, leading to lower formal wages in equilibrium.
4
The combination of effects we observe in the data imposes high hurdles for alternative
explanations. Due to the sharp discontinuity of the UI reform’s effect around a six months
tenure threshold, any alternative story would need to explain a change in behavior for workers with a tenure of six months relative to workers with a tenure of five months that exactly
coincides with the month that the reform takes effect. With respect to the cross-sectional
differences in the reform’s effect depending on the level of labor market informality, any
factor that might be correlated with labor market informality and could affect how workers respond to UI benefit eligibility needs to i) be correlated with labor market informality
both geographically and across industries, ii) both lead to higher ex-post inflow into formal
unemployment and higher ex-ante inflow into formal employment, iii) explain higher collusion between employers and employees to strategically exploit UI benefits, and iv) explain
differential effects on employment and wages for formal and informal jobs within the same
municipality and within the same industry.
We perform several additional robustness tests to further ensure that the results are
driven by differences in labor market formality. First, we control for cyclical patterns by
examining the year before the reform. None of the effects are present in the previous year,
confirming that the results are not driven by cyclical patterns. Second, we confirm that
workers do not substitute to other forms of job separation, such as voluntary departures or
job changes, after the reform. Third, we show that neither informal industries nor informal
municipalities are differentially affected by the recession in Brazil during our sample period.
The paper contributes to several strands of literature. Most importantly, the paper documents how informal labor markets interact with the incentive effects of UI benefits. Some
recent studies analyze UI programs in middle-income and developing countries (Gasparini,
Haimovich, and Olivieri 2009; Gonzalez-Rozada, Ronconi, and Ruffo 2011; Amarante, Arim,
and Dean 2013; Gerard and Gonzaga 2014). However, these papers do not directly examine
how differences in labor market formality influence the effect of UI programs with the exception of Gerard and Gonzaga (2014), which is the closest to our paper. They examine whether
the size of informal labor markets affect the incentive effect of UI benefits on unemployment
outflow using fifteen years of unemployment data in Brazil. In contrast, we examine how the
presence of informal labor markets changes the effects of UI benefits on ex ante labor supply, wages, and unemployment inflow and outflow. Additionally, we exploit an unexpected
reform that affects the eligibility for UI benefits. This reform occurs after the sample period
in Gerard and Gonzaga (2014) and allows us to provide a sharper identification of the effects
5
of labor market informality on how workers’ incentives are affected by UI benefits.
The paper also provides new insights into the impact of UI benefits on hiring and wages.
Most studies in developed countries find no or a slightly negative effects on reemployment
wages (Card, Chetty, and Weber 2007; Lalive 2007; van Ours and Vodopivec 2008; Centeno
and Novo 2009; Degen and Lalive 2013; Johnston and Mas 2015; Schmieder, von Wachter,
and Bender 2016).11 Since workers incorporate expected future benefit payments in their
optimization, they are more likely to enter formal employment when future eligibility for
UI benefits is more likely. This positive effect on formal labor supply leads to lower formal
wages relative to informal wages ex ante.
It is important to consider the relevance of our results beyond the specific context in
Brazil. First, our data spans the entire population of formal employees in the private sector
in Brazil. Thus, the results are not subject to any selection bias. Second, our findings are
obtained during a severe recession in Brazil. Schmieder, von Wachter, and Bender (2012)
show that incentive effects from UI are significantly weaker during recessions in Germany.
This suggests that the results we find are rather conservative estimates of the effects of
UI insurance during non-recessionary period. Additionally, evidence on the effects of UI
benefits in recessionary periods are of particular interest, as they provide a fiscal stimulus
during recessions and are often extended during downturns (Rothstein 2011; Valletta 2014;
Farber and Valletta 2015; Kroft and Notowidigdo 2016).12 Finally, while informal labor
markets are less prevalent in developed countries, non-negligible informal labor markets exit
in those countries as well. Hence, we think that the results in this paper are relevant and
informative beyond the specific context of this paper.
2
Institutional Background and Data
This section provides information about Brazil’s UI system and the UI benefits reform implemented in March 2015.
11
Nekoei and Weber (2015) find a slightly positive effect on reemployment wages.
The magnitudes from our main tests for the full sample are comparable to the magnitudes found in the
literature, for example Card, Chetty, and Weber (2007).
12
6
2.1
Unemployment Insurance in Brazil
In Brazil, every formal worker is required to hold a working card. It is mandatory for
employers to sign workers’ cards whenever a worker is hired, promoted, or dismissed. This
information is reported to the Ministry of Labor. Hiring an employee formally rather than
having an informal relationship with the employee involves costs. Formal employees are
entitled to a the minimum wage. Payroll taxes amount to twenty percent to finance the
public pension system plus eight percent for workers seniority account (FGTS).13 Other
mandatory contributions such as the social integration program (PIS) and contributions to
social security funding (COFINS) depend on the industry that the firm operates in. These
contributions are paid from net profit and sales. Funding for the UI system stems from these
contributions.
Laying off a worker is costly. If a firm dismisses a worker without a justified reason,
it must pay an additional fifty percent of the total contribution that has accumulated in
an employee’s FGTS. Laying off workers with a valid legal justification does not involve
penalties. However, this is rare (only 3.5 percent of all layoffs) since the hurdle to provide
sufficient evidence is high, for instance proving low productivity is hard to prove, and judges
tend to rule in favor of employees. UI applies to formally employed workers in the private
sector.14
Benefits are paid for three to five months, depending on workers’ time in formal
employment.15 In 2015, the monthly payment ranges from 1 to 1.76 minimum salaries,
depending on the average pre-layoff wage.
2.2
UI Benefits Reform
Eligibility criteria for UI benefits did not undergo a major reform from 1994 to 2015. To be
eligible for UI benefits prior to March 1 2015, a worker had to be employed over a consecutive
period of at least 6 months prior to layoff, had to be fired without a justified reason, may
not earn any other formal labor income, and may not have sucessfully applied for UI benefits
during the previous sixteen months.
13
This account can be withdrawn when a worker retires, is fired, or suffers from a serious illness.
These are all employees that are subject to consolidation of labor laws (Consolidao das Leis do Trabalho).
15
The unemployment insurance benefit is paid for a minimum of three months and a maximum of five
months: three payments if a worker was employed between six and eleven months in the last 36 months, four
payments if a worker was employed between 12 and 23 months in the last 36 months, and five payments if
a worker was employed for at least 24 months in the last 36 months.
14
7
In December 30, 2014, the parliament passed a provisional measure that tightened eligibility criteria for UI benefits. The new criteria were set to be enforced from March 1,
2015. While it was anticipated that UI would be reformed at some point, both the sudden
implementation and the content of the new law were fully unexpected.16 The main driver for
the quick implementation and the tightening in eligibility criteria was the desperate attempt
on part of the government, to find sources of consolidation for reducing the growing budget
deficit. The size and duration of UI benefits was not altered.
The eligibility criteria were tightened for all dismissed workers who applied for UI benefits
for the first or second time.17 Both groups had to have longer pre-layoff employment history
to be eligible for UI benefits. In particular, workers who applied for the first time required
documented employment of at least 18 months in the 24 months prior to layoff. Workers
who applied for the second time required 12 months in formal employment during the last
16 months.18
This measure was turned into law in July 2015 with only minor adjustments. While
stricter than the old requirements, these requirements were somewhat laxer as compared to
the ones in the provisional measure. Specifically, a first time applicant had to have at least
12 months of employment in the last 18 months. A second time applicant had to have at
least 9 months of employment in the last 12 months.
2.3
Data
In this paper, we use two large restricted-access matched employee-employer administrative
datasets from Brazil, RAIS (Relao Anual de Informaes Sociais) and CAGED (Cadastro
Geral de Empregados e Desempregados). While the RAIS database records information
on all formally employed workers in a given year, CAGED records the flow of employees,
that is information on hirings and layoffs. Both datasets are maintained by the Labor
Ministry of Brazil. All formally-registered firms in Brazil are legally required to report
annual information on each worker that the firm employs.
RAIS includes detailed information on the employer (tax number, sector of activity,
16
Estadao Politica, December 29, 2014, “Forca Sindical nega ter sido consultada sobre ajuste em beneficios”.
17
Our data does not allow us to identify how many times a worker has applied for UI benefits in the past
because the data is truncated.
18
See Figure 1 for a summary of eligibility criteria under the different regimes.
8
establishment size, geographical location), the employee (social security number, age, gender,
education), and the employment relationship (wage, tenure, type of employment, hiring date,
layoff date, reason for layoff, etc.). We use data from RAIS for the period from 2013–2014.
By the end of 2014, the database covers about 50 million formal employees.
The information contained in CAGED is similar to the information reported in RAIS.
For example, it includes the hiring and layoff date, the reason for layoff, wage, etc. We use
CAGED for 2015 to complement the data from RAIS as RAIS is currently only available
up to 2014. Both datasets contain a common identifier for each worker, which remains with
the worker throughout his or her work history as well as the tax identifier of the worker’s
employer. Combining both datasets allows us to trace the duration of formal employment
for each individual. We exclude all public sector employees, since they do not participate in
the UI program.
For our main identification strategy, we focus on employees with a consecutive formal
work history of five or six months in a given month. Additionally, we exploit information on
the location of the firm (municipality), its two digit industry classification (National Classification of Economic Activities), and information on employees’ occupations (Classificao
Brasileira de Ocupaes) for our empirical analysis. Our main empirical specification compares the period after the announcement but before the implementation of the UI reform
(January–February 2015) and the period after the implementation of the reform (March–
April 2015).
In Table 1, we confirm that workers with a tenure of six months, who are directly affected
by the reform, and workers with a tenure of five months, who are not directly affected by the
reform, are indistinguishable in terms of observable characteristics before the implementation
of the reform in March 2015. For a person to be employed for five or six consecutive months
during January to April 2015, this person must have been employed before the announcement
of the reform. Thus, initial employment decisions are not subject to strategic selection in
anticipation of the reform. We find that both groups of workers are virtually identical
in terms of age, average salary, gender, university education, the size of the firm they are
employed at, and the industries that they are employed in. They do, however, differ in terms
of the probability of becoming unemployed in a given month. In particular, a worker with six
month tenure is by ten percent more likely to become unemployed and 27 percent more likely
to be laid off by the firm without a justified reason. Thus, while workers with tenures of
five and six months look identical based on observable characteristics, unemployment inflow
9
differs significantly, which will be a focus of our empirical analysis.
To exploit cross-sectional variation in labor market informality, we combine the linked
employer-employee data from RAIS and CAGED with information on labor market informality from the Brazilian census in 2010. The census survey asks whether or not an individual
has a job, and whether or not this job is formal. We classify workers as informal if they do
not work on a formal contract (i.e., a signed worker’s card).19 The census groups workers
in twenty different industry classifications (see Table 2). 66 percent of domestic services
employees are working informally. The most formal industry, electricity and gas, has only
5.5 percent of informal workers. In terms of geographic variation in informality, most municipalities fall within the range of 20 to 70 percent of labor market informality (Figure 2).
Importantly, informality is not clustered in some parts of Brazil but is prevalent throughout
the country (Figure 3).
Finally, we also take advantage of the National Household Sample Survey (Pesquisa
Nacional por Amostra de Domiclios). This quarterly survey collects information on formal
and informal employment and salaries for the working age population in 20 municipalities
that are the respective state’s “capital municipalities”.
3
Empirical Strategy
This section outlines the empirical strategy to assess how UI benefits affect workers’ incentives the presence of informal labor markets.
3.1
Labor Market Informality
We exploit two sources of variation in labor market informality. First, we exploit crosssectional variation in informality across industries. As documented in Table 2, there is a
wide heterogeneity in labor market informality across industries in Brazil, ranging from 5.56
percent in electricity and gas to 66.17 percent in domestic services. Second, we exploit variation in labor market informality across municipalities in Brazil. Brazil is a very heterogenous
country with large geographical variation in labor market formality (see Figures 2 and 3).
19
We verify that the results are robust to alternative definitions of labor market informality provided in
the census.
10
Naively comparing differences in workers’ incentives across industries and municipalities
is problematic, as workers’ incentives may differ across industries and municipalities for
reasons other than labor market informality. To control for such spurious effects, we exploit
a UI benefits reform that generates time-series variation in UI benefits eligibility. The design
of the reform induces a sharp discontinuity in changes in UI benefit eligibility. Workers with
a tenure of six months lose eligibility for UI benefits after the reform, whereas workers with
a tenure of five months are ineligible both before and after the reform (see Section 2.2 for
more details). Thus, while the reform equally applies to all industries and municipalities in
Brazil, whether a given worker is affected depends on her employment history.
The sharp discontinuity in the reform’s effect allows us to examine changes in unemployment inflow and outflow for workers just above the eligibility threshold (six months
tenure) and workers just below the threshold (five months tenure). Any general differences
in workers’ incentives in industries and municipalities with different levels of labor market
formality should apply to workers with tenure of five and six months before and after the
reform. However, for workers with a tenure of six months, UI benefits eligibility affects their
incentives only before the reform. This allows us to identify how UI benefits affect workers’
incentives in the presence of informal labor markets, controlling for general differences in
workers’ incentives across industries and municipalities.
3.2
Unemployment Inflow and Outflow
We start by examining changes in unemployment inflow after the implementation of the
reform for all workers with a tenure of five or six months by estimating:
P [uunjust ]it = α + β1 · Eligibleit + β2 · Ref ormt
(1)
+β3 · Eligibleit ∗ Ref ormt + it
where P [uunjust ]it is a dummy variable that takes the value of one if worker i is laid off in
month t, and zero otherwise.20 The dummy variable Eligibleit takes the value of one for
workers with a tenure of six months, and zero for workers with a tenure of five months. The
dummy variable Ref ormt takes the value of one for the two months after the reform, and
zero for the two months before the reform.21
20
21
We only consider layoffs that are legally unjustified, as workers only obtain UI benefits in this case.
We ensure that the results are not driven by cyclical effects in Section 5.1.
11
The parameter of interest is β3 , which is informative about the effect of UI benefits
on unemployment inflow. The difference in unemployment inflow for workers with tenure
of five and six months after the reform, when both groups of workers are not eligible for
benefits, provides us with the undistorted difference in unemployment inflow between both
groups of workers. The coefficent β3 compares this undistorted difference to the difference in
unemployment inflow between both groups of workers before the reform when workers with
a tenure of six months are eligible for UI benefits. Since the reform eliminated eligibility for
workers with tenure of six months, a negative value of β3 implies that UI benefits lead to
higher unemployment inflow when workers are eligible for UI benefits, and vice versa.
We apply the same identification strategy to estimate the effect of informal labor markets
on unemployment outflow by replacing the dependent variable with P [e ≤ 3]it , a dummy
variable that takes the value of one if worker i is reemployed within three months after being
laid off, and zero otherwise. The three months time-period is motivated by the fact that
UI benefits are available for at least three months after layoff for eligible workers. Here, a
positve value of β3 implies lower unemployment outflow in the presence of UI benefits, and
vice versa.
To formally assess how unemployment insurance benefits affect workers’ incentives in the
presence of informal labor markets, we estimate:
P [uunjust ]it = α + β1 · Eligibleit + β2 · Ref ormt
(2)
+β3 · Eligibleit ∗ Ref ormt + β4 · Inf ormal
+β5 · Eligibleit ∗ Inf ormal + β6 · Ref ormt ∗ Inf ormal
+β7 · Eligibleit ∗ Ref ormt ∗ Inf ormal + it
where Inf ormal is the share of informal employment in a given industry or municipality, all
other variables are defined as before.
We can further saturate equations (1) and (2) with month, municipality-month, municipalityindustry-month, and municipality-industry-occupation-month fixed effects to control for
location-specific, industry-specific, and even occupation-specific shocks that may affect workers with with tenure of five and six months differentially. Parameter β7 is informative about
the effect of UI benefits in labor markets with high levels of informality relative to labor markets with lower informality. A negative value of β7 implies that UI benefits have a stronger
effect on unemployment inflow when labor market informality is higher, and vice versa.
12
3.3
Formal Employment and Wages
In addition to unemployment inflow and outflow, conditional on eligibility for UI benefit,
the prospects of future eligibility for UI benefits may also affects workers’ incentives to
enter formal employment in the first place. After the reform, qualifying for UI benefits
is substantially more challenging. To assess the effect of the reform on workers’ ex ante
incentive to enter formal employment, we compare changes in hiring rates in industries and
municipalities with different levels of labor market informality:
W orkers Hiredt = α + β1 · Ref ormt + β2 · Inf ormal
(3)
+β3 · Ref ormt ∗ Inf ormal + t
where W orkers Hiredt is defined as the number of workers hired in a given industry (municipality) in month t scaled by the number of workers employed in the respective industry
(municipality) in December 2014, the month before the reform was announced. Since the
reform was announced in January 2015, and workers’ incentives to enter formal employment should be affected from the time they are aware of the reform’s effects, we define the
Ref ormt dummy as one from January 2015. Inf ormal is the share of informal employment
in a given industry or municipality. Coefficient β3 measures the relative change in the number of workers hired in more informal industries (municipalities) after the reform compared
to the period before the reform.
Finally, to assess whether changes in hiring are driven by changes in the demand for
or the supply of formal labor, we examine changes in hiring wages in industries (municipalities) with higher levels of labor market informality relative to changes in hiring wages
in industries (municipalities) with lower levels of labor market informality by replacing the
dependent variable in equation (3) with the log of the average hiring wage in a given industry
(municipality) in month t.
Additionally, we use data on wages in informal jobs from the quarterly PNAD survey, to
directly compare the effect of UI benefits on wages in formal and informal labor markets:
log(wage)it = α + β1 · Ref ormt + β2 · F ormal Jobit
(4)
+β3 · Ref ormt ∗ F ormal Jobit + it
where F ormal Jobit takes the value of one if worker i is formally employed in quarter t, and
13
zero if worker i is informally employed in quarter t. We can saturate equation (4) to compare
changes in formal and informal wages within the same industry (industry-time fixed effects),
the same municipality (municipality-time fixed effects), or the same industry in the same
municipality (municipality-industry-time fixed effects) at a given point in time.
4
Results
This section presents the empirical results. We document how UI benefits affect workers’
incentives in the presence of informal labor markets. Specifically, we assess how UI benefits
affect workers’ incentives to enter formal employment ex ante and to exit formal employment
ex post in labor markets with different levels of informality.
4.1
UI Benefits Reform and Unemployment Inflow
Figure 4 depicts the probability of being laid off for workers with different tenure for each
month from January to April 2015. While there are no significant changes in unemployment
probabilities for workers with a tenure between one to five months, for workers with tenure
of six to sixteen months the probability of being laid off significantly decreases after the
reform. In particular, there is a sharp drop in the probability of being laid off for workers
with tenure of six months who lose eligibility for UI benefits after the reform, whereas there
is no change in unemployment inflow for workers with a tenure of five months who were
ineligible for UI benefits even before the reform.
We confirm the insights from the graphical analysis statistically in Table 3. Controlling for
time-series variation in unemployment inflow (month fixed effects) in column I, we find that
unemployment inflow relatively decreases by 0.44 percentage points for workers with tenure
of six months compared to workers with a tenure of five months. Further saturating the
specification with municipality-month fixed effects to account for local shocks in column II,
the effect remains almost identical with 0.47 percentage points. The results are not affected
by controlling for industry-specific local shocks (municipality-industry-month fixed effects)
in column III. Even within the same occupation in the same local industry (municipalityindustry-occupation-month fixed effects), we find that unemployment inflow drops by 0.41
percentage points for workers with six months tenure compared to workers with five months
tenure (column IV).
14
The relative decrease in unemployment inflow for workers with six months tenure after
the reform is not compensated for by an increase in alternative forms of layoffs. Table 4
shows that there is no significant increase in voluntary layoffs (columns I–IV). Similarly,
there is only a mild and marginally significant increase in job changes after the reform for
workers with a tenure of six months (columns V–VIII). The increase in job changes accounts
for less than five percent of the decrease in layoffs. Thus, net layoffs adjusted for job changes
are virtually identical qualitatively and quantitatively compared to the raw results reported
in Table 3.
Brazil provides large heterogeneity in labor market informality across municipalities and
industries. In Figure 5, we split the sample into workers employed in industries with above
(top panel) and below (bottom panel) median levels of labor market informality. The graphical evidence reveals that higher unemployment inflow for workers with six months tenure
before the reform is driven by workers in industries with above median levels of informality.
For these workers, we observe a substantial drop in unemployment inflow in March and April
when they lose eligibility for UI benefits. In contrast, for workers in industries with below
median levels of informality we observe no significant change in unemployment inflow. In
Figure 6, we split workers into those employed in municipalities with above (top panel) and
below (bottom panel) median levels of informality. We find that in municipalities with above
median levels of informality unemployment inflow decreases by more than one percentage
point for workers that lose eligibility for UI benefits after the reform. In municipalities with
below median levels of informality, the magnitude of the effect is less than a third as large
as for municipalities with above median levels of labor market informality.
Figure 7 plots the drop in unemployment inflow for workers with six months tenure
relative to workers with five months tenure (x-axis) and the share of informal labor (y-axis)
for each industry. The plot shows that the trends in figure 5 are not driven by individual
industries but there is a clear relationship between the drop in unemployment inflow after
the reform and labor market informality across industries.22 While it is infeasible to plot
the same graph for all municipalities due to the large number of municipalities in Brazil,
the dashed line in Figure 7 shows the regression line from regressing the relative change in
unemployment inflow for workers with six and five months tenure on the share of informal
employment across all municipalities. The results in Figures 5 to 7 suggest that informal
labor markets induce workers to terminate formal employment when they qualify for UI
22
We confirm that none of our results is driven by inidividual industries by performing all tests dropping
each industry individually.
15
benefits. Informal employment opportunities allow them to simultaneously receive income
from informal work and UI benefits.
In Table 5, we formally assess how informal labor markets affect workers’ response to
UI benefits. The top panel shows the results for variation in labor market formality at the
industry level. We find that a ten percentage points increase in labor market informality
leads to a 0.10 percentage points stronger decrease in unemployment inflow after the reform
(column I). Controlling for local shocks that are specific to workers affected by the reform,
the effect is almost identical with 0.12 percentage points (column II). Additionally controlling
for local industry shocks leaves the effect virtually unchanged with 0.09 percentage points
(column III). When we further add controls for shocks to specific occupations within a local
industry, the magnitude of the effect is similar with 0.10 percentage points (column IV).
Finally, accounting for unemployment inflow patterns of workers with six months tenure at
the industry (column V) or local industry (column VI) level leaves the results qualitatively
unchanged with a magnitude of 0.11 and 0.08 percentage points, respectively.
The bottom panel lists results of the same analysis exploiting variation in labor market
formality at the municipality level. A ten percentage points increase in labor market informality leads to a 0.13 percentage points stronger decrease in unemployment inflow after the
reform compared to the pre-reform period accounting for industry and municipality-specific
shocks (column I). To account for the fact that industry-specific patterns that differ for workers with tenure of six months compared to workers with tenure of five months, we control
for industry-level shocks specific to workers with different tenure in column II. We find that
the results are virtually unchanged with 0.11 percentage points. Even after controlling for
local industry shocks, we find that unemployment inflow decreases significantly more by 0.10
percentage points per ten percentage points increase in labor market informality (column
III). When we additionally control for occupation-specific local industry shocks in column
IV, the effect is 0.08 percentage points. Controlling for unemployment inflow patterns of
workers with six months tenure at the municipality (column V) or municipality-industry
(column VI) level leaves the results unaffected with 0.11 percentage points each. Together,
the results in Table 5 show that UI benefits have a stronger effect on unemployment inflow
in the presence of informal labor markets.
16
4.2
UI Benefits Reform and Unemployment Outflow
Figure 8 depicts reemployment probabilities conditional on unemployment duration for workers laid off during the months from January to April 2015, separately for workers laid off
with a tenure of six months (top panel) and workers laid off with a tenure ot five months
(bottom panel). For workers with six months tenure at the time of layoff, reemployment is
significantly less likely to occur during January and February when they are eligible for UI
benefits, compared to March and April when they are no longer eligible for UI benefits. In
contrast, for workers with five months tenure at the time of layoff, unemployment outflow
does not change significantly from January and February to March and April.
The results in Table 6 show that reemployment probabilities within four months after
layoff increase by 3.44 percentage points more for workers with a tenure of six months,
compared to workers with five months tenure (column I). The effect is slightly weaker with
2.97 percentage points when we compare workers in the same geographical area (columns
II). Further restricting the comparison to workers within the same local industry does not
affec the results with 2.94 percentage points (column III), as does comparing workers within
the same occupation with 3.17 percentage points (column IV).
4.3
Collusion
The previous results suggest that, in the presence of more informal labor markets, the availability of UI benefits induces workers more strongly to exit and stay out of formal employment. This could be driven through different mechanisms. Workers may switch from formal
to informal employment without involvement of their employer, for example by eliciting layoff through shirking. On the other hand, firms may collude with workers to extract rents
from the government, for example by hiring them informally while they are eligible for UI
benefits.
To identify whether collusion between workers and their employers occurs, we explore
whether firms that lay off workers just when they become eligible for UI benefits rehire these
workers after benefits run out. For this test, we examine the probability of being rehired
four to seven months after a layoff, since this is when benefits run out and we would expect
strategic rehiring to occur. We follow our main identification strategy comparing dismissed
workers with six months of tenure at the layoff who lose eligibility after the reform to those
17
with five months tenure who are always ineligible.23
The results are gathered in Table 7. Column I shows that before the reform the probability to be rehired by the same employer four to seven months after layoff is about one
percentage point higher for workers with a tenure of six months compared to those with five
months of tenure. After the reform, when both types of workers are ineligible for UI benefits,
the difference in rehiring by the same firm four to seven months after layoff vanishes. The
results hold even within the same local industry (municipality-industry-month fixed effects)
in column II, and within the same occupation within a local industry (municipality-industryoccupation-month fixed effects) in column III. These results suggest that there is collusion
between workers and their employers. Firms formally fire workers when they qualify for UI
benefits and formally rehire them when benefits are exhausted.
In columns IV to VII, we examine whether collusion between workers and their firms is
concentrated in industries and municipalities with large informal labor markets. The results
in columns IV–V show that firms in more informal industries are significantly more likely
to lay off workers when they are eligible for benefits to rehire them after benefits run out.
Specifically, a ten percentage increase in labor market informality leads to an about 0.8
percentage points increase in strategic layoff and rehiring. The results are similar at the
municipality level with slightly higher magnitudes (columns VI–VII). This cross-sectional
evidence suggests that informal labor markets provide firms and workers with an opportunity
to collude. They can extract rents through UI benefit payments, while maintaining an
informal relationship.
Finally, we are interested in what fraction of the lower unemployment outflow of eligible
workers is driven by the type of collusion described above. This allows us to understand
how prevalent collusion is. The additional number of workers that does not return to formal
employment while qualifying for UI benefits amounts to 3.5 percent of all laid off workers.
Subsequently, an additional 0.7 percent of all laid off workers are rehired by the same firm
when their benefits run out. Put differently, about 20 percent of workers who strategically
remain unemployed when on benefits are rehired by the same employer. This suggests that 20
percent of strategic unemployment involves direct collusion between employees and firms.24
23
For this test, we cannot compare workers laid off in March to April against January to February 2015,
as their rehiring probabilities are influenced by the reform, which is announced in December 2014. Instead,
we compare workers laid off in March and April 2015 to workers laid off in March and April 2014 as these
workers are laid off and rehired within the same regime, respectively. Additionally, examining workers laid
off during the same calender months directly controls for cyclical effects.
24
Note that these are difference-in-differences estimates where we compare the mean probabilities for
18
Clearly, the collusion that we pick up here captures only part of all possible types of
collusions. For example, several firms and employees as a group could engage in collusion
in a way that our test would not be able to identify as collusion. However, our analysis
identifies the easiest to implement and the most direct form of collusion. Thus, we believe
it is plausible that we pick up a large fraction of the possible collusion in the data.
4.4
Formal Employment and Wages
In this section, we examine whether the reduction in expected future UI benefits has a
differential effect on workers’ decision to enter formal employment ex ante in the presence
of informal labor markets. In the absence of informal labor markets, higher expected future
income from formal employment may lead the marginal worker to prefer formal employment
over unemployment leading to an increase in formal employment. When informal labor
markets exist, in addition to unemployed workers, workers that would otherwise have chosen
informal employment may shift to formal employment when qualifying for UI benefits is
easier.
Figure 9 depicts the time-series evolution in average formal hiring scaled by total employment in a given industry (top panel) and municipality (bottom panel), separately for labor
markets with above (solid lines) and below (dashed lines) median levels of informality.25 We
adjust all plots for industry-calender month fixed effects to take out cyclical fluctuations. In
January 2015, the month after the reform was announced, we start to see a drop in formal
hiring in industries with above median levels of labor market informality relative to industries with below median levels of labor market informality. The evolution in hiring continues
to diverge over the next 12 months with formal hiring in highly informal industries continuing to relatively decrease. We observe the same time-series pattern for municipalities sorted
based on labor market informality.26
workers with six months tenure against workers with five month tenure before and after the reform. The
difference between workers with five and six months tenure after the reform provide us an estimate of what
fraction of workers would have returned to employment within three months in the absence of benefits as
both types of workers do not qualify for benefits after the reform. Then we compare this difference to the
pre-reform period to gauge the fraction of worker with six months tenure staying unemployed strategically
before the reform.
25
To control for industry-driven hiring trends in aggregated municipality-level hiring, we adjust
municipality-level data for industry composition to capture labor market informality not explained by industry composition.
26
The patterns are unchanged when we consider net hiring rates, that is the difference between newly
hired and fired workers.
19
In Table 8, columns I to VI, we examine changes in formal hiring in industries and
municipalities with high levels of labor market informality compared to industries and municipalities with lower levels of labor market informality statistically. The results in column
I show that a ten percentage points increase in labor market informality is associated with
a 0.15 percent decrease in hiring relative to the total workforce in a given industry after
the reform. Analyzing changes in hiring at the municipality-industry level allows us to control for local shocks in column III. We find that formal hiring decreases by 0.13 percent of
the local workforce per ten percentage points increase in labor market informality within a
given local industry. Exploiting cross-sectional variation in labor market informality at the
municipality-level in columns V-VIII, we observe similar effects. Magnitudes on the municipality level are somewhat lower which is mostly driven by small municipalities with several
months without new hiring.
Next, we examine changes in wages in industries and municipalities with different levels
of labor market informality. This helps us to differentiate between a drop in supply of or
demand for labor in indistries and municipalities with higher levels of labor market informality when qualifying for UI benefits becomes less likely. Figure 10 depicts the time-series
evolution of wages of newly hired workers for industries (top panel) and municipalities (bottom panel) with above (solid lines) and below (dashed lines) median levels of labor market
informality. We adjust the plots for industry-calender month fixed effects to take out cyclical fluctuations. As for formal hiring patterns, we observe a clear break after January 2015
the month after the reform was announced. Formal wages start to relatively increase in
industries and municipalities with above median levels of labor market informality almost
immediately after the announcement of the reform.
The results in Table 9 show that formal wages relatively increase by 0.15 percent for
a ten percentage points increase in labor market informality at the industry level after
the announcement of the reform (column I). Examining changes in formal wages at the
municipality-industry level and controlling for local wage shocks, the effect slightly decreases
to 0.11 percent per ten percentage points increase in labor market informality (column III).
On the municipality level, we find that wages relatively increase by 0.12 percent per ten
percentage points increase in labor market formality (column V). Controlling for industryspecific wage shocks, the effect remains statistically significant with 0.11 percent (column
VII). These results suggest that the reduction in formal hiring after the reform is driven by
a drop in formal labor supply.
20
Differences in hiring and wages in industries and municipalities after the announcement
of the reform could be driven by factors correlated with but different from labor market
informality. To examine directly whether lower formal hiring and higher formal wages are
driven by changes in the relative attractiveness of formal and informal employment, we
use data on formal and informal wages from the quarterly PNAD survey. The results are
collected in Table 10. We find that formal wages increase relative to informal wages by 3.33
percent within the same industry (column I). Within the same municipality, the relative
increase in formal wages is 2.29 percent (column III). The effect is similar with 2.45 percent
when we compare changes in formal and informal wages within the same local industry
(column III). While stronger in more informal industries, the relative effect on formal to
informal wages is not significantly different in industries with a higher share of labor market
informality (column IV), or municipalities with different degrees of labor market informality
(column V). This is not too surprising. While we find that formal wages react more strongly
when the fraction of informal labor markets is large (and thus the share of formal jobs is
small), informal wages may react less strongly to positive supply shocks when informal labor
demand is larger.
These results suggest that the reduction in the likelihood to qualify for UI benefits after taking on formal employment induces a reoptimization on part of workers leading them
to demand higher compensation for entering formal employment when informal employment
opportunities are available. This drop in the supply of formal labor leads to lower formal hiring and higher wages in formal labor markets, in particular when informal jobs are prevalent.
Additionally, the relative change in wages for formal compared to informal jobs within the
same industry or municipality mitigates concerns that the results we document in this paper are driven by industry-specific or municipality-specific characteristics unrelated to labor
market informality. The evidence on relative changes in wages controls for any unobservable
industry-specific or municipality-specific effects.
5
Alternative Explanations
This section discusses alternative explanations that might be consistent with the results
presented in Section 4 and presents additional tests that address these alternative stories.
From the outset it should be noted that any alternative story would need to explain a host of
findings. Any alternative story would need to explain a change in behavior for workers with
21
a tenure of six months relative to workers with a tenure of five months that exactly coincides
with the month that the reform takes effect. With respect to the cross-sectional differences in
the reform’s effect depending on the level of labor market informality, any factor that might
be correlated with labor market informality and could affect how workers respond to UI
benefit eligibility needs to i) be correlated with labor market informality both geographically
and across industries, ii) both lead to higher ex-post inflow into formal unemployment and
higher ex-ante inflow into formal employment, iii) explain higher collusion between employers
and employees to strategically exploit UI benefits, and iv) explain differential effects on
employment and wages for formal and informal jobs within the same municipality and within
the same industry.
5.1
Cyclical Effects
The most important remaining concern is that there could be cyclical effects that lead to
lower layoffs for workers with tenure of six months in March and April relative to January
and February. For example, if seasonal workers in some industries are commonly hired in
July and August and fired in January and February. These effects could be industry-specific
explaining not only differences in unemployment inflow and outflow for workers with tenure
of five or six months, but also cross-sectional differences in unemployment inflow and outflow
after the reform.
Figure 11 provides graphical evidence that the reform effects are not driven by cyclical
effects. When we examine unemployment inflow by tenure in 2014, the year before the
reform, we do not observe any drop in layoffs for workers with tenure of six months in March
and April compared to January and February. To confirm these results statistically, we
estimate equation 2 including the months from January to April for both the years 2014 and
2015. The results are displayed in Table 11. The coefficient on Eligibilityit ∗2015t ∗Ref ormt
describes the change in unemployment inflow for workers with tenure of six month compared
to workers with tenure of five months after the reform in 2015, controlling for cyclical effects
through comparison to the same time period in 2014. The results show that controlling for
cyclicality leaves the results virtually unaffected.
Similarly, we see no changes in unemployment outflow for workers laid off with tenure
of six months in March and April, compared to January and February in 2014 (Figure 12).
Additionally, Figure 12 affirms that UI benefits eligibility leads to higher job search intensi22
ties. For workers with six months tenure we observe a spike in reemployment probabilities
around the expiration of UI benefits in all months, whereas there is no such spike for workers
with five months tenure who are not eligible for UI benefits. Table 12 statistically confirms
that accounting for cyclical effects does not affect the results. Unemployment outflow significantly increase after the reform for workers laid off with a tenure of six months relative to
workers laid off with a tenure of five months even after controlling for cyclical effects through
comparison to the previous year.
5.2
Announcement Effects
The announcement of the reform could have a direct effect on unemployment inflow for
workers with a tenure of five and six months before the reform. Workers with a tenure of six
months have an additional incentive to flow into unemployment in January or February when
they are still eligible for UI benefits anticipating that eligibility vanishes after the reform.
Similarly, workers with a tenure of five months in January may have a strong incentive hang
on to employment for one more months to qualify for benefits in February, whereas workers
with a tenure of five months no longer have this incentive in February to April.
To ensure that these pre-reform announcement effects do not have significant effects on
the magnitues of our results, we perform two additonal robustness tests. First, we compare
workers with a tenures of five and six months for November and December 2014, the two
months before the announcement of the reform to the post-reform period in March and April
2015. Second, instead of comparing workers with five and six months, we compare workers
with a tenure of six months to workers with a tenure of four months, who are not subject to
different incentive effects before and after the reform.
In Table 13, we examine changes in unemployment inflow for workers with a tenure of six
months and workers with a tenure of five months after the reform compared to the period
before the announcement of the reform in November and December 2014. The results are
even stronger than the main results comparing the period after the reform to the two months
after the announcement in Janauary and February 2015.
When we compared workers with a tenure of six months to workers with a tenure of four
months in Table 14, the results are qualitatively identical to the results comparing workers
with a tenure of six months and five months and similar in magnitude. Thus, the main results
23
are not biased due to announcement effects that could change the incentives of workers with
a tenure of five or six months in anticipation of the reform.
6
Conclusion
In this paper, we examine how UI benefits affect workers’ incentives in the presence of
informal labor markets. We find that workers strategically exit formal employment when
they become eligible for UI benefits. This effect has not been documented for developed
countries and is mostly driven by industries and municipalities with large informal labor
markets. Additionally, we find that collusion between workers and employers is an important
channel through which strategic inflow into unemployment occurs. The option to employ
workers informally allows firms and workers to share rents from UI benefits that constitute
a quasi-subsidy from the government.
These results have direct policy implications. The design of UI programs should take into
account differences in strategic behavior of workers and firms in the presence of informal labor
markets. Eligibility for UI benefits could be set in a way that prevents firms from colluding
with their employees. For instance, benefits could be reduced if workers return to the same
firm after benefits run out. Alternatively, the expected costs of informal employment could
be increased through either higher penalties or targeted auditing of firms that frequently lay
off workers who become eligible for UI benefits and rehire them when benefits run out.
Finally, reducing access to UI benefits leads to a decrease in the supply of formal labor.
This is reflected in less formal hiring in industries with large informal labor markets and
a higher formal relative to informal wages when qualifying for UI benefits becomes harder.
Thus, UI benefits have indirect effects on firms’ labor costs. This could at least partially
offset some of firms’ costs associated with financing UI programs. This implies that the net
costs that firms incur for financing UI programs is lower than commonly argued (Levy 2008).
24
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27
Table 1: Summary Statistics for Workers Around Threshold
6 Months Tenure 5 Months Tenure Difference
Age (Years)
Salary (Real)
Male
University Education
Firm Size (Employees)
Fraction in Construction
Fraction in Manufacturing
Fraction in Agriculture
P[U ]
P[Uunjust. ]
P[e ≤3]
31.56
1,337.58
0.60
0.12
69
0.11
0.15
0.04
0.059
0.037
0.125
31.52
1,322.03
0.60
0.12
68
0.11
0.15
0.04
0.054
0.029
0.156
0.04
15.55
0.00
0.00
1
0.00
0.00
0.00
0.005
0.008
-0.031
This table reports descriptive statistics for workers with a tenure of six and five months at the time of layoff,
respectively. The last column depicts the difference between workers with six and five months tenure.
Table 2: Informality by Industry
Industry
Informal Employment Share Total Employment
Domestic Services
Agriculture, Livestock, Forestry, Fisheries, Aquaculture
Other Services
Arts, Culture, Sports, Recreation
Construction
Accomodation, Food
Real Estate
Trade, Repair of Motor Vehicles and Motorcycles
Water, Severage, Waste Management, Decontamination
Professional, Scientific, and Technical Activities
Transport, Storage, Postal Services
Education
Manufacturing
Human Health, Social Services
Information, Communication
Public Administration, Defense, Social Security
Extractive Industries
Administrative Activities and Complementary Services
Financial Activities and Related Insurance and Services
Electricity and Gas
0.6617
0.5693
0.4788
0.4315
0.4074
0.3155
0.2850
0.2562
0.2211
0.2144
0.2012
0.1828
0.1547
0.1542
0.1441
0.1422
0.1408
0.1389
0.0903
0.0556
0.0002
0.0546
0.0350
0.0075
0.0796
0.0405
0.0099
0.1893
0.0067
0.0459
0.0393
0.0402
0.1417
0.0365
0.0387
0.1311
0.0045
0.0821
0.0145
0.0020
This table lists information on all industries, including the share of informal employment in an industry and
the share of all Brazilian employees employed in the respective industry.
28
Table 3: Unemployment Inflow
Dep. Var.: P [uunjust. ]it
I
II
III
IV
Eligibleit
0.0083*** 0.0078*** 0.0074*** 0.0073***
(0.0006)
(0.0005)
(0.0004)
(0.0004)
Eligibleit ∗ Ref ormt
-0.0043*** -0.0047*** -0.0043*** -0.0041***
(0.0010)
(0.0006)
(0.0006)
(0.0006)
Month FE
yes
Month*Municipality FE
no
yes
Month*Municipality*Industry FE
no
no
yes
Month*Municipality*Industry*Occupation FE
no
no
no
yes
Firm FE
no
no
no
no
Firm-Month FE
no
no
no
no
Clustered SE
muni
muni
muni
muni
Observations
7,027,525 7,027,525 7,027,525 7,026,747
Adjusted R2
0.001
0.019
0.036
0.043
This table reports changes in unemployment inflow around the enactment of the UI benefits reform from
January to April 2015. The sample is limited to workers with tenure of five or six months in a given month.
The dependent variable is a dummy variable that takes the value of one if worker i is laid off in month
t and zero otherwise. The dummy variable Eligibleit takes the value of one for workers with six months
tenure in month t, and zero for workers with tenure of five months. The dummy variable Ref ormt takes
the value of one for the post-reform period from March to April 2015 and zero for the pre-reform period
from January to February 2015. Standard errors are reported in parantheses. The bottom part of the table
reports information on fixed effects and the clustering of standard errors. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% level, respectively.
Table 4: Unemployment Inflow - Substitution
I
Dep. Var.:
Eligibleit
-0.0026***
(0.0007)
Eligibleit ∗ Ref ormt
0.0018
(0.0012)
Month FE
yes
Month*Municipality FE
no
Month*Municipality*Industry FE
no
Month*Municipality*Industry*Occupation FE
no
Clustered SE
muni
Observations
7,161,905
Adjusted R2
0.001
II
III
P [ujust. ]it
-0.0030***
(0.0004)
0.0008
(0.0006)
yes
no
no
muni
7,161,905
0.035
-0.0027***
(0.0003)
0.0004
(0.0005)
yes
no
muni
7,161,905
0.046
IV
V
-0.0025***
(0.0002)
0.0001
(0.0004)
yes
muni
7,161,905
0.041
-0.0006***
(0.0001)
0.0002
(0.0001)
yes
no
no
no
muni
7,161,905
0.001
VI
VII
P [job change]it
-0.0006***
(0.0001)
0.0002*
(0.0001)
yes
no
no
muni
7,161,905
0.001
-0.0006***
(0.0001)
0.0002*
(0.0001)
yes
no
muni
7,161,905
0.001
VIII
-0.0006***
(0.0001)
0.0002
(0.0001)
yes
muni
7,161,905
0.001
This table reports changes in voluntary unemployment inflow and job changes around the enactment of the
UI benefits reform from January to April 2015. The sample is limited to workers with tenure of five or six
months in a given month. The dependent variable is a dummy variable that takes the value of one if worker
i quits her job month t and zero otherwise in columns I–IV, and a dummy variable that is one of worker i
changes job in month t. The dummy variable Eligibleit takes the value of one for workers with six months
tenure in month t, and zero for workers with tenure of five months. The dummy variable Ref ormt takes
the value of one for the post-reform period from March to April 2015 and zero for the pre-reform period
from January to February 2015. Standard errors are reported in parantheses. The bottom part of the table
reports information on fixed effects and the clustering of standard errors. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% level, respectively.
29
Table 5: Unemployment Inflow by Informality
I
II
Dep. Var.: P [uunjust. ]it
III
IV
Industry-Level
V
VI
Eligibleit
-0.0011
(0.0011)
Eligibleit ∗ Ref ormt
-0.0020
(0.0018)
Eligibleit ∗ Inf ormal
0.0346*** 0.0280*** 0.0238*** 0.0238***
(0.0048) (0.0044) (0.0038) (0.0038)
Eligibleit ∗ Ref ormt ∗ Inf ormal
-0.0099* -0.0118*** -0.0092** -0.0098** -0.0111** -0.0075*
(0.0056) (0.0051) (0.0042) (0.0043) (0.0051) (0.0041)
Industry*Month FE
yes
yes
yes
yes
Industry*Eligibility FE
no
no
no
no
yes
Month*Municipality FE
yes
Month*Municipality*Eligibility FE
no
yes
yes
yes
yes
yes
Month*Municipality*Industry FE
no
no
yes
no
no
Month*Municipality*Industry*Occupation FE
no
no
no
yes
no
no
Municipality*Industry*Eligibility FE
no
no
no
no
no
yes
Clustered SE
muni
muni
muni
muni
muni
muni
Observations
7,161,905 7,161,905 7,161,905 7,161,905 7,161,905 7,161,905
Adjusted R2
0.025
0.027
0.036
0.042
0.027
0.033
Dep. Var.: P [uunjust. ]it
Municipality-Level
Eligibleit
0.0025***
(0.0009)
Eligibleit ∗ Ref ormt
-0.0015
(0.0011)
Eligibleit ∗ Inf ormal
0.0212***
(0.0030)
Eligibleit ∗ Ref ormt ∗ Inf ormal
-0.0126***
(0.0045)
Municipality*Month FE
yes
Municipality*Eligibility FE
no
Month*Industry FE
yes
Month*Industry*Eligibility FE
no
Month*Industry*Municipality FE
no
Month*Industry*Municipality*Occupation FE
no
Industry*Municipality*Eligibility FE
no
Clustered SE
muni
Observations
7,161,905
Adjusted R2
0.024
0.0120***
(0.0044)
-0.0106**
(0.0044)
yes
no
yes
no
no
no
muni
7,161,905
0.025
0.0119***
(0.0029)
-0.0103**
(0.0041)
no
yes
yes
no
no
muni
7,161,905
0.037
0.0092***
(0.0032)
-0.0084* -0.0108*** -0.0114***
(0.0048) (0.0040) (0.0037)
yes
yes
no
yes
yes
yes
yes
no
no
yes
no
no
no
no
yes
muni
muni
muni
7,161,905 7,161,905 7,161,905
0.043
0.025
0.033
This table reports changes in unemployment inflow around the enactment of the UI benefits reform from
January to April 2015. The sample is limited to workers with tenure of five or six months in a given month.
The dependent variable is a dummy variable that takes the value of one if worker i is laid off in month t and
zero otherwise. The dummy variable Eligibleit takes the value of one for workers with six months tenure in
month t, and zero for workers with five months tenure. The dummy variable Ref ormt takes the value of
one for the post-reform period from March to April 2015 and zero for the pre-reform period from January to
February 2015. The variable Inf ormal is the share of informal employment in a given industry in the top
panel or given municipality in the bottom panel. Standard errors are reported in parantheses. The bottom
part of the table reports information on fixed effects and the clustering of standard errors. ***, **, and *
denote statistical significance at the 1%, 5%, and 10% level, respectively.
30
Table 6: Unemployment Outflow
Dep. Var.: P [e ≤ 3]it
Eligibleit ∗ Ref ormt
I
II
III
IV
0.0344*** 0.0297*** 0.0294*** 0.0317***
(0.0045) (0.0039) (0.0040) (0.0046)
Month FE
yes
Month*Municipality FE
no
yes
Month*Municipality*Industry FE
no
no
yes
Month*Municipality*Industry*Occupation FE
no
no
no
yes
Month*Industry*Eligibility FE
yes
yes
yes
yes
Clustered SE
muni
muni
muni
muni
Observations
371,459 371,459 371,459 371,459
Adjusted R2
0.011
0.017
0.015
0.021
This table reports changes in unemployment outflow around the enactment of the UI benefits reform from
January to April 2015. The sample is limited to workers with tenure of five or six months at layoff. The
dependent variable is a dummy variable that takes the value of one if worker i enters formal employment
within three months after being laid off and zero otherwise. The dummy variable Eligibleit takes the value of
one for workers with six months tenure at layoff, and zero for workers with five months tenure at layoff. The
dummy variable Ref ormt takes the value of one for the post-reform period from March to April 2015 and
zero for the pre-reform period from January to February 2015. Standard errors are reported in parantheses.
The bottom part of the table reports information on fixed effects and the clustering of standard errors. ***,
**, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
31
Table 7: Collusion
I
Dep. Var.: Psame [4 − 7]it
Eligibleit
Eligibleit ∗ Ref ormt
II
Main Effect
III
VI
VII
Municipality-Level
0.0029
0.0034
0.0053*
(0.0023) (0.0025) (0.0030)
-0.0106*** -0.0092*** -0.0139***
(0.0030) (0.0033) (0.0044)
Eligibleit ∗ Inf ormal
Eligibleit ∗ Ref ormt ∗ Inf ormal
Month*Municipality FE
Month*Municipality*Industry FE
Month*Municipality*Industry*Occupation FE
Month*Municipality*Eligible FE
Month*Industry*Eligible FE
Clustered SE
Observations
Adjusted R2
IV
V
Industry-Level
yes
no
no
no
no
muni
77,654
0.373
yes
no
no
no
muni
77,654
0.306
yes
no
no
muni
77,654
0.223
0.0214
(0.0164)
-0.0597**
(0.0242)
yes
no
yes
no
muni
77,654
0.180
0.0358**
(0.0171)
-0.0758**
(0.0298)
yes
yes
no
muni
77,654
0.001
0.0062
(0.0340)
-0.0730*
(0.0455)
yes
no
no
yes
muni
77,654
0.306
0.0054
(0.0484)
-0.0929
(0.0649)
yes
no
yes
muni
77,654
0.222
This table reports changes in reemployment of workers by the same firm after UI benefits run out before and
after the UI benefits reform from in March to April in 2014 and 2015. The sample is limited to workers with
tenure of five or six months at layoff. The dependent variable is a dummy variable that takes the value of
one if worker i is formally reemployment by the same firm four to seven months after being laid off and zero
otherwise. The dummy variable Eligibleit takes the value of one for workers with six months tenure at layoff,
and zero for workers with five months tenure at layoff. The dummy variable Ref ormt takes the value of one
for the post-reform period from March to April 2015 and zero for the pre-reform period from March to April
2014. The variable Inf ormal is the share of informal employment in a given industry in columns labeled
“Industry-Level” or in a given municipality in columns labeled “Municipality-Level””. Standard errors are
reported in parantheses. The bottom part of the table reports information on fixed effects and the clustering
of standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
32
Table 8: Formal Hiring
I
II
Dep. Var.:
Industry
III
IV
V
VI
Hired/Employed W orkers
Industry-Muni
Muni
VII
VIII
Industry-Muni
Inf ormal ∗ Ref ormt
-0.0150* -0.0346** -0.0125*** -0.0129*** -0.0030*** -0.0181*** -0.0081** -0.0088***
(0.0085) (0.0138) (0.0016) (0.0016) (0.0003) (0.0009) (0.0034) (0.0034)
Month FE
yes
yes
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
no
no
no
Industry Trends
no
yes
no
no
no
no
no
no
Industry-Month FE
no
no
no
no
no
no
no
yes
Municipality FE
no
no
no
yes
yes
yes
yes
Municipality Trends
no
no
no
no
no
yes
no
no
Municipality-Month FE
no
no
no
yes
no
no
no
no
Clustered SE
ind
ind
muni
muni
muni
muni
muni
muni
Observations
720
720
2,639,916 2,639,916 200,340
200,340 2,639,916 2,639,916
Adjusted R2
0.901
0.865
0.038
0.198
0.400
0.713
0.115
0.147
This table reports changes in formal hiring around the announcement of the UI benefits reform from January
2013 to December 2015. The dependent variable is the share of workers hired in a given industry (municipality) relative to the total number of workers in the industry (municipality) in month t. The dummy
variable Ref ormt takes the value of one for the post-announcement period from January 2015 and zero for
the pre-announcement period from January 2013 to December 2014. The variable Inf ormal is the share
of informal employment in a given industry (municipality). The unit of observation is at the industry level
in columns I and II, the municipality-level in columns V and VI, and the municipality-industry-level in
columns III, IV, VII, and VIII. Standard errors are reported in parantheses. The bottom part of the table
reports information on fixed effects and the clustering of standard errors. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% level, respectively.
33
Table 9: Formal Wages
I
II
III
Dep. Var.:
Industry
IV
V
log(W ages)
Industry-Muni
VI
Muni
VII
VIII
Industry-Muni
Inf ormal ∗ Ref ormt
0.0147** 0.0191*** 0.0109*** 0.0109*** 0.0117* 0.0132* 0.0112*** 0.0084**
(0.0066) (0.0065) (0.0012) (0.0013) (0.0069) (0.0070) (0.0045) (0.0040)
Month FE
yes
yes
yes
yes
yes
yes
Industry FE
yes
yes
yes
yes
no
no
no
Industry Trends
no
yes
no
no
no
no
no
no
Industry-Month FE
no
no
no
no
no
no
no
yes
Municipality FE
no
no
no
yes
yes
yes
yes
Municipality Trends
no
no
no
no
no
yes
no
no
Municipality-Month FE
no
no
no
yes
no
no
no
no
Clustered SE
ind
ind
muni
muni
muni
muni
muni
muni
Observations
720
720
1,407,787 1,407,787 192,238 192,238 1,407,787 1,407,787
Adjusted R2
0.979
0.981
0.294
0.412
0.336
0.337
0.121
0.398
This table reports changes in formal hiring around the announcement of the UI benefits reform from January
2013 to December 2015. The dependent variable is the log of the average wage for newly hired workers in
a given industry (municipality) in month t. The dummy variable Ref ormt takes the value of one for the
post-announcement period from January 2015 and zero for the pre-announcement period from January
2013 to December 2014. The variable Inf ormal is the share of informal employment in a given industry
(municipality). The unit of observation is at the industry level in columns I and II, the municipality-level in
columns V and VI, and the municipality-industry-level in columns III, IV, VII, and VIII. Standard errors are
reported in parantheses. The bottom part of the table reports information on fixed effects and the clustering
of standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
34
Table 10: Formal and Informal Wages
I
II
Dep. Var.:
F ormal Jobit ∗ P ostt
III
log(W ages)it
IV
0.0333*** 0.0229*** 0.0245*** 0.0265**
(0.0062) (0.0051) (0.0051) (0.0115)
F ormal Jobit ∗ Inf ormal
0.5700***
(0.1028)
F ormal Jobit ∗ Ref ormt ∗ Inf ormal
0.0213
(0.0218)
Formal Job FE
yes
yes
yes
yes
Industry-Quarter FE
yes
no
Municipality-Quarter FE
no
yes
Municipality-Industy-Quarter FE
no
no
yes
yes
Clustered SE
muni
muni
muni
muni
Observations
1,593,043 1,593,043 1,593,043 1,593,043
Adjusted R2
0.254
0.273
0.387
0.258
V
0.0146
(0.0097)
0.4236***
(0.0518)
0.0293
(0.0197)
yes
yes
muni
1,593,043
0.389
This table reports changes in formal and informal wages around the announcement of the UI benefits reform
from July 2013 to June 2016. The dependent variable is the log of worker i’s wage in a given quarter t. The
dummy variable Ref ormt takes the value of one for the post-announcement period from January 2015 to
June 2016 and zero for the pre-announcement period from July 2013 to December 2014. The dummy variable
F ormal Jobit takes the value of month if worker i is employed formally in quarter t, and zero if she is employed
informally. The variable Inf ormal is the share of informal employment in a given industry (municipality).
Standard errors are reported in parantheses. The bottom part of the table reports information on fixed
effects and the clustering of standard errors. ***, **, and * denote statistical significance at the 1%, 5%,
and 10% level, respectively.
35
Table 11: Unemployment Inflow - Cyclicality
Dep. Var.: P [uunjust. ]it
Eligibleit
I
0.0051***
(0.0003)
Eligibleit ∗ 2015t
0.0032***
(0.0005)
Eligibleit ∗ Ref ormt
-0.0000
(0.0006)
Eligibleit ∗ 2015t ∗ Ref ormt
-0.0043***
(0.0013)
Month FE
yes
Month*Municipality FE
no
Month*Municipality*Industry FE
no
Month*Municipality*Industry*Occupation FE
no
Firm FE
no
Firm-Month FE
no
Clustered SE
muni
Observations
14,176,473
Adjusted R2
0.002
II
0.0049***
(0.0003)
0.0029***
(0.0004)
-0.0003
(0.0003)
-0.0044***
(0.0007)
yes
no
no
no
no
muni
14,176,473
0.019
III
0.0047***
(0.0003)
0.0027***
(0.0004)
-0.0000
(0.0003)
-0.0043***
(0.0006)
yes
no
no
no
muni
14,176,473
0.033
IV
0.0046***
(0.0002)
0.0026***
(0.0004)
0.0000
(0.0003)
-0.0041***
(0.0006)
yes
no
no
muni
14,418,317
0.035
V
0.0059***
(0.0003)
0.0037***
(0.0004)
0.0007**
(0.0003)
-0.0037***
(0.0006)
no
no
no
yes
no
muni
14,176,473
0.071
VI
0.0045***
(0.0004)
0.0028***
(0.0005)
0.0013***
(0.0004)
-0.0024***
(0.0006)
no
yes
muni
14,418,317
0.096
This table compares changes in unemployment inflow around the enactment of the UI benefits reform from
January to April 2015 to the period from January 2014 to April 2014. The sample is limited to workers
with tenure of five or six months in a given month. The dependent variable is a dummy variable that takes
the value of one if worker i is laid off in month t and zero otherwise. The dummy variable Eligibleit takes
the value of one for workers with six months tenure in month t, and zero for workers with tenure of five
months. The dummy variable Ref ormt takes the value of one for March and April and zero for January
and February. The dummy variable 2015t takes the value of one for the year 2015 and zero for the year
2014. Standard errors are reported in parantheses. The bottom part of the table reports information on
fixed effects and the clustering of standard errors. ***, **, and * denote statistical significance at the 1%,
5%, and 10% level, respectively.
36
Table 12: Unemployment Outflow - Cyclicality
Dep. Var.: P [e ≤ 3]it
Eligibleit
I
-0.0542***
(0.0035)
Eligibleit ∗ 2015t
0.0263***
(0.0039)
Eligibleit ∗ Ref ormt
0.0036
(0.0057)
Eligibleit ∗ 2015t ∗ Ref ormt
0.0172***
(0.0064)
Month FE
yes
Month*Municipality FE
no
Month*Municipality*Industry FE
no
Month*Municipality*Industry*Occupation FE
no
Clustered SE
muni
Observations
368,312
Adjusted R2
0.006
II
-0.0493***
(0.0033)
0.0229***
(0.0036)
0.0040
(0.0045)
0.0123**
(0.0053)
yes
no
no
muni
368,312
0.017
III
IV
-0.0449*** -0.0454***
(0.0036)
(0.0042)
0.0201*** 0.0203***
(0.0037)
(0.0043)
-0.0016
-0.0006
(0.0049)
(0.0065)
0.0161*** 0.0163**
(0.0056)
(0.0073)
yes
no
yes
muni
muni
368,312
368,291
0.017
0.023
This table compares changes in unemployment outflow around the enactment of the UI benefits reform from
January to April 2015 to the period from January 2014 to April 2014. The sample is limited to workers with
tenure of five or six months in a given month. The dependent variable is a dummy variable that takes the
value of one if worker i enters formal employment within four months after being laid off and zero otherwise.
The dummy variable Eligibleit takes the value of one for workers with six months tenure at layoff, and zero
for workers with five months tenure at layoff. The dummy variable Ref ormt takes the value of one for March
and April and zero for January and February. The dummy variable 2015t takes the value of one for the year
2015 and zero for the year 2014. Standard errors are reported in parantheses. The bottom part of the table
reports information on fixed effects and the clustering of standard errors. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% level, respectively.
37
Table 13: Unemployment Inflow - Announcement Effects - Time-Period
Dep. Var.: P [uunjust. ]it
Eligibleit
I
0.0116***
(0.0010)
Eligibleit ∗ Ref ormt
-0.0075***
(0.0013)
Month FE
yes
Month*Municipality FE
no
Month*Municipality*Industry FE
no
Month*Municipality*Industry*Occupation FE
no
Firm FE
no
Firm-Month FE
no
Clustered SE
muni
Observations
7,054,523
Adjusted R2
0.002
II
0.0108***
(0.0007)
-0.0077***
(0.0008)
yes
no
no
no
no
muni
7,054,523
0.031
III
0.0108***
(0.0006)
-0.0077***
(0.0008)
yes
no
no
no
muni
7,054,523
0.064
IV
0.0109***
(0.0008)
-0.0077***
(0.0008)
yes
no
no
muni
7,054,523
0.076
V
0.0130***
(0.0009)
-0.0071***
(0.0009)
yes
no
yes
no
muni
7,054,523
0.121
VI
0.0131***
(0.0016)
-0.0086***
(0.0016)
no
no
no
yes
muni
7,054,523
0.130
This table reports changes in unemployment inflow around the announcement of the UI benefits reform. The
sample is limited to workers with tenure of five or six months in a given month. The dependent variable
is a dummy variable that takes the value of one if worker i is laid off in month t and zero otherwise. The
dummy variable Eligibleit takes the value of one for workers with six months tenure in month t, and zero for
workers with tenure of four months. The dummy variable Ref ormt takes the value of one for the post-reform
period from March to April 2015 and zero for the pre-announcement period from November to December
2014. Standard errors are reported in parantheses. The bottom part of the table reports information on
fixed effects and the clustering of standard errors. ***, **, and * denote statistical significance at the 1%,
5%, and 10% level, respectively.
Table 14: Unemployment Inflow - Announcement Effects - Tenure
Dep. Var.: P [uunjust. ]it
Eligibleit
I
0.0121***
(0.0008)
Eligibleit ∗ Ref ormt
-0.0041***
(0.0009)
Month FE
yes
Month*Municipality FE
no
Month*Municipality*Industry FE
no
Month*Municipality*Industry*Occupation FE
no
Firm FE
no
Firm-Month FE
no
Clustered SE
muni
Observations
6,961,738
Adjusted R2
0.001
II
0.0115***
(0.0006)
-0.0049***
(0.0006)
yes
no
no
no
no
muni
6,961,738
0.019
III
0.0112***
(0.0006)
-0.0049***
(0.0005)
yes
no
no
no
muni
6,961,738
0.036
IV
0.0113***
(0.0006)
-0.0048***
(0.0005)
yes
no
no
muni
6,961,738
0.041
V
0.0112***
(0.0005)
-0.0021***
(0.0005)
yes
no
yes
no
muni
6,961,738
0.099
VI
0.0093***
(0.0006)
-0.0012*
(0.0006)
no
no
no
yes
muni
6,961,738
0.113
This table reports changes in unemployment inflow around the enactment of the UI benefits reform from
January to April 2015. The sample is limited to workers with tenure of five or six months in a given month.
The dependent variable is a dummy variable that takes the value of one if worker i is laid off in month
t and zero otherwise. The dummy variable Eligibleit takes the value of one for workers with six months
tenure in month t, and zero for workers with tenure of four months. The dummy variable Ref ormt takes
the value of one for the post-reform period from March to April 2015 and zero for the pre-reform period
from January to February 2015. Standard errors are reported in parantheses. The bottom part of the table
reports information on fixed effects and the clustering of standard errors. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% level, respectively.
38
Figure 1: UI Eligibility Around the Reform
1st:
Jan-Feb
Mar-Apr
2nd:
Jan-Feb
Mar-Apr
More:
Jan-Feb
Mar-Apr
0
1
2
3
4
5
6
ineligible
7
8
9 10 11 12 13 14 15 16 17 18 19
Tenure
cond. eligible
uncond. eligible
This figure depicts eligibility for UI benefits before and after the reform for workers that apply for UI benefits
for the first time, the second time, and the third time or more. The red areas indicates job tenure that does
not satisfy eligibility criteria, the dark green areas indicate tenure that satisfies eligibility criteria conditional
on the worker not having received benefits during the past sixteen months, and the light green areas indicate
tenure that satisfies eligibility criteria unconditionally.
Figure 2: Distribution of Labor Market Informality across Municipalities
1.75
1.50
1.25
1.00
0.75
0.50
0.25
0.00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Informality Share
This figure depicts the distribution of the share of informal in total workers across all municipalities in Brazil.
39
Figure 3: Labor Market Informality by Municipality
Fraction informal
0
0.2
0.4
0.6
0.8
This figure depicts the share of informal in total workers by municipality.
Figure 4: Unemployment Inflow by Tenure
P [uunjust. ]
0.04
0.03
0.02
0.01
0.00
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
January
February
March
April
This figure depicts the probability of workers with different tenure to be laid off for the months from January
to April 2015, separately.
40
Figure 5: Unemployment Inflow by Informality - Industry Level
High Informality
P [uunjust. ]
0.04
0.03
0.02
0.01
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
Low Informality
January
February
March
April
P [uunjust. ]
0.04
0.03
0.02
0.01
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
January
February
March
April
This figure depicts the probability of workers with different tenure to be laid off for the months from January
to April 2015, separately. The sample is restricted to workers in industries with above median levels of labor
market informality in the top panel, and to workers in industries with below median levels of labor market
informality in the bottom panel.
41
Figure 6: Unemployment Inflow by Informality - Municipality Level
High Informality
P [uunjust. ]
0.04
0.03
0.02
0.01
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
Low Informality
January
February
March
April
P [uunjust. ]
0.04
0.03
0.02
0.01
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
January
February
March
April
This figure depicts the probability of workers with different tenure to be laid off for the months from January
to April 2015, separately. The sample is restricted to workers in municipalities with above median levels of
labor market informality in the top panel, and to workers in municipalities with below median levels of labor
market informality in the bottom panel.
42
Figure 7: Unemployment Inflow by Informality
slopes
industries: -0.0275 (0.0032)
municipalities: -0.0137 (0.0059)
∆ P [uunjust. ]
0.005
0.000
−0.005
−0.010
−0.015
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70
Informal Employment Share
This figure plots, for each industry, the difference between the probabilities of workers with six and five
months to be laid off during the two months after the reform compared to the two months before the
reform on the y-axis. The x-axis depicts the share of informal employment in a given industry. The solid
line represent the linear regression line for industries sorted by labor market formality. The dashed line
represents the same linear regression line for municipalities sorted by labor market informality.
43
Figure 8: Unemployment Outflow by Tenure
Six Months Tenure
0.07
P [e]
0.06
0.05
0.04
0.03
1
2
3
4
5
6
7
8
9
6
7
8
9
Months Unemployed
Five Months Tenure
0.07
P [e]
0.06
0.05
0.04
0.03
1
2
3
4
5
Months Unemployed
January
February
March
April
This figure depicts the probability of re-employment in the months after losing their job for workers with
a tenure of six (top panel) and five (bottom panel) months tenure at layoff separately for the months from
January to April 2015.
44
Figure 9: Hiring by Informality
Worker Hired/Total Workers
Industry
0.005
0.000
-0.005
-0.010
201301
201306
201311
201404
201409
201502
201507
201512
201502
201507
201512
Month
Worker Hired/Total Workers
Municipalities
0.03
0.02
0.01
0.00
-0.01
-0.02
201301
201306
201311
201404
201409
Month
Low Informality
High Informality
This figure depicts the time-series evolution of the number of newly hired workers relative to the total
number of worker in industries (top panel) or municipality (bottom panel) with above (solid lines) and
below (dashed lines) median levels of labor market informality. Each monthly observation represents the
average value across all industries or municipalities with above or below median levels of labor market
informality. Monthly values are adjusted for cyclical effects by controlling for calender month fixed effects
at the industry or municipality levels, respectively.
45
Figure 10: Hiring Wages by Informality
Industry
0.015
Hiring Wage
0.01
0.005
0
-0.005
-0.01
-0.015
-0.02
201301
201306
201311
201404
201409
201502
201507
201512
201502
201507
201512
Month
Municipalities
0.015
Hiring Wage
0.01
0.005
0
-0.005
-0.01
-0.015
201301
201306
201311
201404
201409
Month
Low Informality
High Informality
This figure depicts the time-series evolution of the log of wages for newly hired workers in industries (top
panel) or municipality (bottom panel) with above (solid lines) and below (dashed lines) median levels of
labor market informality. Each monthly observation represents the average value across all industries or
municipalities with above or below median levels of labor market informality. Monthly values are adjusted
for cyclical effects by controlling for calender month fixed effects at the industry or municipality levels,
respectively.
46
Figure 11: Unemployment Inflow by Tenure - Previous Year
0.05
P [uunjust. ]
0.04
0.03
0.02
0.01
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Tenure
January
February
March
April
This figure depicts the probability of workers with different tenure to be laid off for the months from January
to April 2014, separately.
47
Figure 12: Unemployment Outflow by Tenure - Previous Year
Six Months Tenure
0.12
0.11
P [e]
0.1
0.09
0.08
0.07
0.06
0.05
1
2
3
4
5
6
7
8
9
6
7
8
9
Months Unemployed
Five Months Tenure
0.13
0.12
0.11
P [e]
0.1
0.09
0.08
0.07
0.06
0.05
0.04
1
2
3
4
5
Months Unemployed
January
February
March
April
This figure depicts the probability of re-employment in the months after losing their job for workers with
a tenure of six (top panel) and five (bottom panel) months tenure at layoff separately for the months from
January to April 2014.
48