Finance and Labour Reallocation: The consequences of a liquidation reform

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Finance and Labour Reallocation: The

consequences of a liquidation reform

Rafael Carlquist R. de Araujo

Advisor: Felipe Iachan

Rio de Janeiro - 2018

EPGE-FGV

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List of Tables

1 First sector classification . . . 18

2 Second sector classification . . . 19

3 First stage regression . . . 23

4 Second stage regression . . . 23

5 Estimation with the first tangibility classification . . . 24

6 Estimation with the second tangibility classification . . . 25

7 Second stage regression with the second classification for tangibility . . . 31

8 Second stage by year with first tangibility classification . . . 31

9 Second stage by year with second tangibility classification . . . 32

10 Estimation with tangibility and year dummies . . . 33

11 Estimation with second tangibility measure and year dummies . . . 34

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List of Figures

1 Private-Credit to GDP ratio before and after the reform . . . 08

2 Sectoral and total production correlation by region . . . 20

3 Distribution of tangibility measure . . . 21

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1 Abstract

In 2005, the Brazilian bankruptcy law was changed to improve secured creditor’s protection. I explore the bankruptcy reform together with heterogeneity of the judicial efficiency, at the municipal level, and heterogeneity of the firm’s asset tangibility, at the sectoral level, to study possible effects on labour reallocation. By applying an instrumental variable approach, I find that firms operating on municipalities with a more efficient judicial system observed a higher increase in their labour force after the reform, and that this effect was stronger on firms operating in sectors which use more tangible assets.

keywords: bankruptcy; bankruptcy reform; labour market; court congestion; labour reallocation; struc-tural change.

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2 Introduction and Literature Review

The purpose of this research is to analyze empirical evidence that the financial market can affect labour market in a structural way. The Brazilian bankruptcy reform of 2005, which will be more explored in the coming sections, changed the institutional environment for lenders and corporate borrowers. By exploring a panel data set, which documents all formally employed workers, and data on the efficiency of judicial court at the municipal level, I am able to explore the heterogeneity of the credit market institutions across time (before and after the reform) and across municipalities (municipalities with different levels of judicial efficiency). A third layer of heterogeneity is added to the problem by exploring sectoral differences on the type of assets employed in each industry.

I apply an instrumental variable approach to study the effect of the reform and judicial efficiency on labour reallocation and wages. In Brazil, there are minimum requirements for a municipality to become eligible to posses a judicial court. When the requirements are not met, the cases are judged by a neighbouring municipality with a seat in a judicial district. For those municipalities with a seat, a higher number of neighbours that do not meet the requirements generates a higher court congestion, that is, more cases to be judged. Thus, this measure of non-eligible neighbours is taken as an instrument for court efficiency.

I find that after the bankruptcy reform, the labour force increases in municipalities with a more efficient court and that this effect is stronger on sectors that can be classified as sectors which employ more tangible assets. Considering the effects of the reform on wages, I find no statistically significant results of judicial efficiency on wages.

This research is related to the literature on financial frictions and firm decision, although, as presented in the next section, the bulk of the literature has been more concerned with firm investment than labour demand when it comes to study financial frictions. The research is also related with the structural change literature, since the main effect explored is the reallocation of labour between sectors. The next two subsections discuss more the related literature. The next section presents a simple conceptual framework, illustrating the mechanisms which I want to study. I then present an empirical specification followed by a description of the data sets and the identification strategy. The final sections present the results and conclusion.

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2.1 The Intersection of Labour and Credit Markets

The literature linking financial markets and corporate investment has presented several possible mech-anisms to take into account heterogeneity in firms’ investment decisions. Credit market frictions can arise in an environment with asymmetric information such as adverse selection as in Majluf and Myers (1984) and moral hazard as in Holmstrom and Tirole (1997), generating different costs of inside and outside liquidity and differences in capital cost for firms, effects which would not arise in a canonical perfect information model.

Mechanisms underlying the link between financial markets and employment, however, have not devel-oped into a consensus. For example, Petrosky-Nadeau (2014), Garin (2015) and Boeri et al. (2015) start from the idea that hiring costs of a labour market with search frictions must be financed in an also frictional credit market with asymmetric information, thus generating a link between financial markets shocks and the labour market.

Other articles as Wasmer and Weil (2004) and a dynamic extension of the same model by Petrosky-Nadeau and Wasmer (2013) develop an economy where hiring costs must be financed by a credit market which presents a search and matching problem between firms and banks.

In the empirical literature, Sharpe (1994) shows the existence of a higher employment elasticity for firms that are smaller, highly leveraged and face more procyclical demand. The underlying idea is the presence of adjustment costs of the labour force, implying that firms try to dampen fluctuations of labour input, generating a “labour-hoarding”. With imperfect capital markets, this dampening is constrained to a non-optimal path. Most of the following articles apply this same idea to justify movements in employment.

Exploring a maturing debt approach (as was done to analyze investment decision in Almeida et al. (2011)) Benmelech et al. (2015) study the impact of financial restrictions on firms during the Great Recession. Firms with more maturing debts during the crisis had the biggest fall in the number of employees, even controlling for leverage and size. A similar approach is presented in Benmelech et al. (2017) for the Great Depression, where the interaction between maturing debt and local banks failures affects the layoff of workers. Studying this bank-firm relationship, Chodorow-Reich (2014) shows that firms which had loan relationships with more fragile institutions during the Great Recession reduced employment more than firms which had relationships with healthier institutions, that is, a cost to switch lenders would explain the difficulty in getting new loans to maintain the labour force.

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Those previous papers are interested in how employment can vary within firms along the business cycles in the presence of shocks in the credit market. Nonetheless, an even smaller literature asks if financial frictions can affect employment in a structural way. For example, a model describing how finance can affect structural unemployment is given by Acemoglu (2001). The author studies how differences between the credit markets in Europe and USA can generate persistent unemployment, beyond the most common explanation of Europe’s rigidity in labour law. In his model, technological changes arrives faster in the market in economies with less credit frictions, boosting job creation. 1 I intend to focus my research not on short term effects of financial frictions on labour, but on the more structural aspects of it. The reason I do not follow the bulk of the literature is that I want to explore the episode of the Brazilian bankruptcy reform, which can be seen as a structural change in Brazil’s credit market rather than a transitory credit shock. To understand the connection of the bankruptcy reform and the credit markets, the next subsection presents a brief overview of the law reform. Following that, the next section present a simple framework to access the ideas underlying the effects of credits market changes on the labour market.

2.2 The Brazilian New Bankruptcy Law

Following the exposition in Araujo et al. (2012), the previous legislation regulating the Brazilian bankruptcy procedure ranked the obligations of insolvent firms as: (1) labour claims then (2) tax claims, (3) secured creditors’ claims and finally (4) unsecured creditors’ claims, resulting in an environment very punitive to creditors. Besides this succession problem, the whole process was slow and costly, damaging the firm’s value along the way. Theoretically, the firm’s value in case of default would be very low and the credit market would ask for a higher return for any loan and/or for higher value of collateral.

The new legislation from 2005 introduced important changes as: (1) faster bankruptcy process; (2) ranked the obligations as labour then secured creditors’ claims, tax claims and unsecured creditors’

1_{As the models above were thought to explain employment in developed countries, no mechanisms considered other}

institutions of the labour market such as, for example, informality. For developing countries, however, the sizable informal sector could change the way finance affects labour. Although, the informal sector is usually seen as a result of labour market rigidity and taxes structure, as in Meghir et al. (2015), there has been research about a “credit channel” affecting the size of the underground economy. Antunes and Cavalcanti (2007), D’Erasmo and Boedo (2012) and Quintin (2008) study a calibrated dynamic model where entrepreneurs operating in the informal sector cannot enforce financial contracts, in a way that their decision on capital input is greatly restricted to its own wealth. Applying these models to Brazil’s data, D’Erasmo (2016) found a positive impact of the Brazilian bankruptcy reform on the size of the formal sector. Finally, Straub (2005) considers a model with formal and informal credit markets. Those models share the idea of competitive labour markets and the underlying mechanism that after paying for the cost of being formal, firms benefit from better access to credit markets through the legal system.

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claims; (3) introduced a cap of 150 minimum wages for each worker, once this cap was reached se-cured creditors start to posses the priority of obligations; (4) practically introduced the reorganization process, which was possible in the old legislation but was used only as a way to postpone bankruptcy. Theoretically, the return for loans, under the new legislation and the requirement for collateral, should be lower, implying a higher level of debt at the firm’s level. Araujo et al. (2012), applying a diff-in-diff approach show that it indeed happened. The authors found a significant increase in firms’ long-term debt - which is more correlated with secured creditors. The improvement in the credit market envi-ronment was strong, as illustrated in figure 1.

Source: adapted from Araujo et al. (2012)

For reasons which will be clearer soon, it is interesting to note that the authors did not find hetero-geneity of results for firms with different levels of asset tangibility, as measured by the ratio between PP& E (property,plant and equipment) and total assets.

The law is important but so is enforcement. That is the message from Ponticelli and Alencar (2016). In Brazil, a bankruptcy process is ruled at the municipal level. Applying an instrumental variable approach, the authors show that, after the reform, firms operating in municipalities with less congested courts - seem as more efficient courts - experienced: (1) higher increase in the use of secured loans; (2) higher increase in investment; (3) higher increase in the value of output.

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Contrary to the results presented by Araujo et al. (2012), the authors found that the effects were stronger for firms with more tangible assets. The reason to expect this result is the following: firms which hold more tangible assets are more likely to access credit through secured loans; as secured creditors were the most benefited by the new law, those firms is more likely to be the most benefited. Thus, the bankruptcy reform has generated three important variations: (1) across time: the legislation before and after the reform; (2) across municipalities - municipalities with different levels of justice efficiency experienced differently the effects of the reform; (3) across firms - firms operating in sectors which operate with higher levels of asset tangibility, were more benefited than firms operating in other sectors. I want to study how those three components can explain movements in the labour market, as described in the next section.

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3 Conceptual Framework

In this section I build the simplest possible model to explain the mechanism which I want to test. The generalization is left only to the reduced form approach described in the next section.

Suppose an economy with two firms (i = 1, 2), operating at different sectors s = s1, s2 and locations

j = j1, j2. For simplicity, I assume each firm operates a Cobb-Douglas production function with

decreasing returns to scale on labour (l) and capital (k).

fi= (kαil1−αi)ρ (1)

Where αi> 0 and 0 < ρ < 1

The labour market is competitive with an exogenous and constant supply L. Denote the wage by w. The capital market is imperfect. The price (r) of capital is exogenous, firms can borrow as much capital as they want, up to a limit, denoted by λj,s. Later I will assume a functional form for this

limit. Notice that I index the limit by the sector and location of the firm. I interpret this limit as a reduced form approach to incentive constraints - from the firm’s owners, or the manager, for example - that must be satisfied.

Denote by l∗_i and k∗_i the optimal unconstrained levels of capital and labour, which solve problem 2

max

ki,li

fi(ki, li) − wli− rki (2)

Under the imperfect capital market condition, each firm solves problem 3

max

ki,li

fi(ki, li) − wli− rki

subject to ki≤ λji,si

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It is critical to the results I want to study to assume that firms are capital constrained, that is λsi,ji < k

∗

i. With this hypothesis, the optimal capital demand in problem 3 is given by k∗∗i = λj,sand

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l_i∗∗= _w ρ(1 − αi) λ−αiρ j,s _{ρ(1−αi)−1}1 (4)

Therefore, the labour market equilibrium condition can be written as equation 5

" wλ−α1ρ j1,s1 ρ(1 − α1) #_{ρ(1−α1)−1}1 + " wλ−α2ρ j2,s2 ρ(1 − α2) #_{ρ(1−α2)−1}1 = L (5) Finally, I specify λs,j as λs,j = eδλjλs.

The parameter λsis sector specific. It can be seen as related with the scrap value of a firm if it decides

to go bankrupt. A firm which operates in a sector where its assets are easily resold or more tangible will present a greater λs.

λj is a location effect, more specifically, at the municipal level. I interpret this parameter as the

efficiency of the judicial system in the municipality in which the firm operates. A less efficient justice (a smaller λj) makes the bankruptcy process more costly and time consuming, deteriorating the firm’s

scrap value.

The last parameter δ is the same across all firms and all municipalities. It can be seen as capturing the effect of the bankruptcy legislation, common to all firms at the national level.

Those three parameters deliver three potential heterogeneity: (1) λj, spatial heterogeneity across

municipalities; (2) λsdifferences across sectors; (3) δ different legislation across time.

My interest is to study the effect of a change in the legislation (δ) on the allocation of labour. Consider a change in δ, small enough that firms continue being capital constrained. Differentiating expression 5 with respect to δ, I obtain that the effect of a better bankruptcy legislation on wage is positive, the result is stated in lemma 0.

Lemma 0: The effect of an increase in δ is a increase in w, that is ∂w

∂δ > 0 (6)

Demonstration: By inspection of expression 5, an increase of δ generates a decrease of the numerator of each labour demand. The only variable that can possibly adjust is the wage variable w, which must increase for the equality to hold.

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An increase in w will change the allocation of labour. The labour market equilibrium guarantees that it cannot be that both firms will increase or decrease the labour force. It must be so that one will increase its labour force, while the other one will decrease the quantity o labour.

Differentiating expression 4 with respect to δ I obtain the first comparative statistics, described in lemma 1.

Lemma 1: If both firms have the same values for αi, λj, the general equilibrium effect of an increase

in δ is an increase in the labour force of the firm with more tangible assets (higher λs) and a decrease

in the labour force for the firm with less tangible assets.

Demonstration: Assume, without loss of generality, that λs=1 > λs=2. Differentiating expression 4

with respect to δ results in expression 7

∂l∗∗_i ∂δ = c ∂w ∂δ − αiρλjλs (7) where c = 1 ρ(1 − αi) − 1 _w ρ(1 − αi) λ−αiρ j,s _{ρ(1−αi)−1}1 < 0

As the derivative on wages, given by expression (6), is the same for both firms and the values for αi, λj

are also the same for both firms, then:

∂w ∂δ − αiρλj=1λs=1 < ∂w ∂δ − αiρλj=2λs=2 (8)

That is the expression in brackets of 7 is different for each firm. It cannot be the case that both firms increase or decrease the labour force, since the labour supply is exogenous, neither can be the case that the labour force will remain unchanged, since it would contradict expression 8. Thus, the labour force increases in one firm - the firm with more tangible assets - and decreases in the other one. Lemma 1 shows that controlling for αi, λj the general equilibrium effect of an increase in δ is an

increase in the labour force of the firm with more tangible assets and a decrease in the labour force for the firm with less tangible assets, which I will call a labour reallocation.

The comparative statistics with respect to the efficiency of justice follows the same steps and is pre-sented in Lemma 2, for later reference.

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Lemma 2: If both firms have the same values for αi, λs, the general equilibrium effect of an increase

in δ is an increase in the labour force of the firm which operates in a municipality with a more efficient justice system (higher λj) and a decrease in the labour force for the firm which operates in a

municipality with a less efficient justice.

Lemma 2 shows that controlling for αi, λs the general equilibrium effect of an increase in δ is an

increase in the labour force of the firm which operates in a more judicially efficient municipality and a decrease in the labour force of the firm which operates in a less judicially efficient municipality. Finally, the comparative statistics of wage with respect to the efficiency of justice is stated in lemma 3.

Lemma 3: The increase in wages w, due to an increase in δ, as stated in lemma 0, is stronger for higher values of judicial efficiency, that is, higher λj.

Demonstration: By inspection of expression 5, an increase in δ generates a decrease of the numerator of each labour demand, and this decrease is higher for higher values of λs. Thus, as wage is the only

variable of adjustment, the increase in w is higher for higher values of λs.

Lemma 3 shows that, after the reform, municipalities with a more efficient judicial system observed a higher increase on wages.

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4 Empirical Application

An empirical version of the mechanism described in Lemma 2 of the previous section, could be stated as in equation 9

ls,j,t= αt+ αs+ β1(inef fj) + β2(ref )t.(inef fj) + s,j,t (9)

where: (ref )t is a dummy variable, equals to zero on the pre-reform period and equals to one after;

(inef f )j is the inefficiency of the judicial system on location j; αt is a time fixed effect and αs is a

sector fixed effect.

To get rid of the fixed effect and potential auto-correlation of the error term, I propose to estimate equation 9 in first difference as in equation 10

∆ls,j= α + β1(ref )t.(inef fj) + s,j+ s,j (10)

Where α is a constant. Testing the mechanism of the previous section is to test whether β1< 0, that

is, firms operating in municipalities with a more efficient judicial system observed an increase in their labour force.

An empirical version of the mechanism described in Lemma 1 of the previous section, could be stated as in equation 11

ls,j,t= αt+ αs+ β1(Highs)(inef fj)+

β2(Lows)(inef fj) + β3(Highs)(ref )t.(inef fj)+

β4(Lows)(ref )t.(inef fj) + s,j,t

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where: (Highs) is a dummy variable, equals to one if sector s belongs to a group of sectors which

employs more tangible assets; (Lows) is a dummy variable, equals to one if sector s belongs to a

group of sectors which employs less tangible assets; (ref )t is a dummy variable, equals to zero on

the pre-reform period and equals to one after; (inef f )j is the inefficiency of the judicial system on

location j; αt is a time fixed effect and αs is a sector fixed effect. The construction of the groups of

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Again, to get rid of the fixed effect and potential auto correlation of the error term, I propose to estimate equation 11 in first difference as in equation 12

∆ls,j= α + β1(Highs)(ref )t.(inef fj) + β2(Lows)(ref )t.(inef fj) + s,j (12)

Testing the mechanism of the previous section is to test whether β1< 0 for sectors with more tangible

assets and β2> 0 for sectors with less tangible assets.

The data studied, which will be explained on the next section, presents multiple periods. I then define ∆ls,j to be the difference between the averages of labour force before and after the reform, summed

by sector (s) within each municipality (j). Note that the variable (inef f )j can be interpreted as a

continuous treatment variable, where the treatment is the bankruptcy reform. Municipalities with an efficient judicial system will experience the full impact of the reform, while a municipality with a very inefficient system will observe no impact.

Testing the result of lemma 3 can be done by equation 13, where I want to test whether β1< 0.

∆ws,j= α + β1(ref )t.(inef fj) + s,j (13)

In the conceptual model presented in the previous section the labour market is frictionless, which generates the homogeneity of wages across sectors. Nonetheless, it is reasonable to suppose high transition costs for workers to change from one firm to another. These costs can be seen as search costs or due to job-specific human capital accumulation.

Thus, the effect on wages could be heterogeneous among sectors. From one side, it could be that firms more benefited by the new law would offer higher wages to attract more workers, while less benefited firms would offer lower wages to retain less workers. From another side, the effects could be the opposite if it were the case that workers transitioning - from the less benefited sector to the other sector - lose job-specific human capital, thus lowering their productivity and wage. Meanwhile, in the less benefited sector, wages go up, because now in the data I observe a selection of workers who firms decided to keep because of their higher productivity in the sector. Which side makes sense is an empirical question.

The heterogeneity of wages is not presented in the conceptual model, thus I wish to test if the assump-tion of homogeneous effect on wages is a reasonable one.

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Equation 14 is the empirical version of that mechanism, which has the same specifications as in 10, except that now I want to test if municipalities with a more efficient judicial system observed a higher increase on wages and if that effect is homogeneous across sectors, even if some sectors were more benefited from the law reform other sectors. That is, I want to test whether β1= β2.

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5 Data

For the judicial system variables, the CNJ (National Council of Justice - Conselho Nacional de Justiça) through its program "Justiça em Números" provides data on the number of judges, pending cases, sentences and new cases for every justice court in the country. Unfortunately, the data set ranges from 2015 to 2017, that is, after the reform. Ponticelli and Alencar (2016) used a data set from "Justiça Aberta", also a program of CNJ, where the data ranges from 2009-2017 (also after the reform), although the authors used data only for the year of 2009. I will be using the data from "Justiça em Números" and interpreting the average data of 2015-2016-2017 as a proxy to the variables I am interested in for the period of 2005-2017.

With this data set, the variable (inef f )j can be defined as (inef f )j = log

h_{#pendingcases}

#judges

i

, that is, the log of the ratio between the number of pending cases and the number of judges on location j. I cannot distinguish on the data which cases corresponds to bankruptcy cases. Thus, the ’pending cases’ and ’number of judges’ variables are taken to be the those corresponding to the civil courts of the municipality. There are municipalities which do not have a separate court for civil cases, those are usually small municipalities with an unique court. For those, the variables are taken to be the ones corresponding to this general court. Finally, some municipalities have courts specialized in bankruptcy process, for this case the variables assigned where those from this specific court. All the results consider only municipalities which are a seat of a judicial district. Municipalities which do not posses a court of their own is dropped out of the sample.

Data from employer-employee variables comes from RAIS (Relação Anual de Informações Sociais), which documents all formally employed workers. For each job observation there is information on the municipality location, worker’s ID (CPF - cadastro de pessoa física), firm’s ID (CNPJ - cadastro nacional de pessoa jurídica), date of admission, month of separation, wage, sector information based on the CNAE classification , working hours among other variables. I build two different data sets using two different aggregations of the CNAE classification: (1) in the first one the data is grouped in sectors by the less complex measure, where each sector is classified by a letter as described in table 1. In this classification there is more variety of sectors, although each sector corresponds to a larger number of firms when compared to the other aggregation; (2) in the second data set, I select only firms in industrial sectors, and apply a more refined classification, as stated in table 2. The data ranges from 2002 to 2009.

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Table 1: First sector classification Code

Farming A

Mining B

Manufacturing C Electricity,gas, water D/E and waste services

Construction F

Trade G

Transportation and H warehousing

Information J Finance and Insurance K

Real state L

Management and N/S other services

Education services, health P/Q care and social assistance

Accommodation and food I services

Source: IBGE

Finally, I use statistics from IBGE (The Brazilian Institute of Geography and Statistics) about popu-lation at municipal level (from the popupopu-lation census) and sectoral production.

To map the relation between the CNAE sector classification and a measure of asset tangibility I follow two approaches.

The first follows Almeida and Campello (2007) and Sharpe (1994). In this methodology I group sectors in two discrete measures - high tangibility and low tangibility. The group is determined by the correlations between sectoral production and total production, that is, sectoral GDP and total GDP. The idea underlying this classification is that for a firm operating in sectors which are more cycle-sensitive, a negative shock on the economy is more likely to affect all best alternatives uses of the firm’s assets, decreasing its tangibility/saleability.

The classification will depend on the macro geographical region, because as illustrated in figure 2, those correlations vary substantially among different regions. For each macro region, the sector which falls below the mean is classified as high tangibility, otherwise is classified as low tangibility.

The second approach follows Braun (2003). The measure of tangibility is calculated by the PB&E ratio, that is, the ratio between the value of property,building and equipment of a firm over its total

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Table 2: Second sector classification

Industry Tangibility Capital intensity

Beverages 0.2794 0.0620

Fabricated metal product 0.2812 0.0531 Food product 0.3777 0.0616 Footwear, except rubber or plastic 0.1167 0.0181 Furniture, except metal 0.2630 0.0390 Glass and product 0.3313 0.0899 Industrial chemicals 0.4116 0.1237 Iron and steel 0.4581 0.1017 Leather product 0.0906 0.0324 Machinery, electric 0.2133 0.0765 Machinery, except electrical 0.1825 0.0582 Misc. petroleum and coal products 0.3038 0.0741 Non-ferrous metal 0.3832 0.1012 Other chemicals 0.1973 0.0597 Other manufactured products 0.1882 0.0393 Other non-metallic mineral products 0.4200 0.0684 Paper and products 0.5579 0.1315 Petroleum refineries 0.6708 0.1955 Plastic products 0.3448 0.0883 Pottery, china earthenware 0.0745 0.0546 Printing and publishing 0.3007 0.0515 Professional and scientific equipment 0.1511 0.0525 Rubber products 0.3790 0.0656

Textiles 0.3730 0.0726

Tobacco 0.2208 0.0181

Transport equipment 0.2548 0.0714 Wearing apparel, except footwear 0.1317 0.0189 Wood products except furniture 0.3796 0.0653

Source: Adapted from Braun (2003)

assets value. This measured was computed in Braun (2003), for U.S firms contained in the Compustat data base for the period 1986-1995. The average value of this measure for the firms is presented by sector in table 2. This second approach is a different view of the concept of asset tangibility; instead of interpreting tangibility as saleability, this approach interpret tangibility as an intrinsic characteristic of the asset hardness. The distribution of this measure of tangibility is described in figure 3. Again, I classify a sector as being high tangibility if its measure lies above the mean, and low tangibility otherwise.

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Source: (Own elaboration). The correlations were calculated using data from IBGE on regional real GDP from 1985 to 2009.

6 Estimation Strategy

Explaining the instrumental variable approach is the goal of this subsection.

The main variable of interest is (inef f )j which can be interpreted as a continuous treatment variable,

where the treatment is the bankruptcy reform. As pointed in Ponticelli and Alencar (2016), the problems with estimating equations 10,14 without an instrumental variable are two:

• An omitted variable problem: as I only have data for court congestion after the reform, then it could be that municipalities with better institutions anticipated the effects of the reform, which made them invest more in the judicial system to take advantage of the new law. In those municipalities I could observe higher levels of investment in capital, private debt and labour demand because of those better institutions and not only because of the new law.

• A self selection problem: since the firms do not randomly choose their local of operation, it could be that the sorting of firms among different municipalities already imposed correlations between the variables of interest and court congestion.

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Source: (Own elaboration). The data is from Braun (2003)

Brazil, small municipalities are not eligible to become a seat of a judicial district, in a way that the judicial disputes at these locations are solved in a neighbouring municipality, seat of a judicial district. The instrument for the efficiency of justice in location j is the number of neighbouring municipalities which are not eligible to be a judicial seat; the conditions on which it occurs are defined in table A12_.

After controlling for variables determining the municipality size - which are all observed - this measure should be exogenous.

Thus, the first stage regression is given by expression 15

(inef f )j = α0+ α1(neighbour)j+ ρ ¯Xj+ ηj (15)

Where ¯Xjis a vector of controls, mainly the number of neighbours, as a way to control for geographical

effects such as coastal location. Finally, the observations are weighted by the average number of firms of each municipality and sector on the years being considered.

2_{For Amazonas and Distrito Federal all municipalities are seats of judicial districts, thus I dropped them out of the}

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Results

The first stage regression, which gives the predicted value of the judicial inefficiency as a function of the number of potential extra-jurisdiction (number of neighbours) is illustrated in figure 4, where both variables are weighted by the average number of firms in the municipality from 2006 to 2009.

Source: (Own elaboration)

The results of the regression is given in table 3; the variables are weighted by the average number of firms. The measure of potential extra-jurisdiction is positively correlated with the measure of judicial inefficiency; the effect is statistically significant even after controlling for the number of geographical neighbours. The sign was as expected, more potential neighbouring municipalities without a court of its own will generate more cases to be judged in the municipality which is a seat of the judicial district. It is interesting to note that the sign of the ’Geographical Neighbours’ coefficient is positive, contrary to the sign found by Ponticelli and Alencar (2016).

For the second stage of the regression I chose two features as the dependent variable: (1) the number of workers3_{to study the transition of labour force between sectors; (2) average wage by hour to study the}

effects on wages. Firms are aggregated by the CNAE classification, within their municipalities. Wages were deflated by the consumer price index IPCA, from IBGE. The basis year is 2002. All regressions were weighted by the average number of firms in each pair ’CNAE,municipality’, where the average is

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Table 3: First stage regression Model 1 Model 2 Potential extra-jurisdiction 0.78∗∗∗ _0.33∗∗∗ [0.07] [0.12] Geographical Neighbours 0.38∗∗∗ [0.08] constant 0.05∗∗∗ 0.04∗∗∗ [0.003] [0.003] R-squared 0.19 0.70 No. observations 2354 2354 F-statistic: 108.74 86.41

Robust standard errors in brackets * p<.1, ** p<.05, ***p<.01

taken across the years 2006-2009. All results considers the period 2002-2005 as the pre-reform period, that is, the steady state from which the differences are taken.

The results for the second stage are presented in table 4, using the data set constructed with the first approach of asset tangibility, which results in more firms and sectors being observed. In this table I do not distinguish between high tangibility and low tangibility sectors. The signs are as expected. Firms in municipalities with a more congested court experienced a decrease in their amount of labour. The effect on wages is not statistically significant, although the sign was as expected.

Table 4: Second stage regression

∆ hourly wage ∆ labour force1

Inefficiency -0.1678 −78.3∗∗∗

[0.1446] [23.71] log(avg neigh pop) −0.1107∗∗∗ _-0.98

[0.0178] [3.10] constant 3.5969∗∗∗ 467.6∗∗∗

[1.0211] [158.6] No. observations 30261 30261 Fixed effect: State, Sector State, Sector

1 _{in thousands}

The next step is to include the tangibility classification. Applying the first approach (the saleability approach), table 5 presents the results when I include sector dummies interactions with the endogenous variable ’inefficiency’ (here I denote by ’High.inefficiency’ the interaction term of the high tangibility

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dummy and the inefficiency measure, the same applies to ’Low.inefficiency’, but for the low tangibility dummy). 4 _{The point estimate presents a higher impact on the labour force for firms operating in the}

high tangibility sector, although the effects are not statistically different. For the wage variable, the result continues to be insignificant.

Table 5: Estimation with the first tangibility classification ∆ hourly wage ∆ labour force1

High.inefficiency -0.1876 −91.34∗∗∗

[0.1556] [25.5] Low.inefficiency -0.148 −64.14∗∗∗

[0.1384] [23.37] log(avg neigh pop) −0.1097∗∗∗ _0.218

[0.0177] [3.22] constant 3.654∗∗∗ _502.9∗∗∗

[1.057] [168.1] No. observations 30261 30261 Fixed effect: State,Sector State, Sector

1_{in thousands}

The second approach (the asset hardness approach) results are described in table 6. Firms operating in a high tangibility sector observed a higher increase in their labour force; the low tangibility result for this measure of tangibility is not significant. The wage variable presents a different behaviour from the previous ones; the impact is significant for the two sectors and positive.

4_{When I include the dummies interaction with the (inef f ) variable, the instruments are taken to be (High).(Potential}

extra-jurisdiction) and (Low).(Potential extra-jurisdiction), that is, the instruments now are the interaction of the tan-gibility dummies with the previous instrument, the number of potential extra-jurisdiction

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Table 6: Estimation with the second tangibility classification ∆ hourly wage ∆ labour force High.inefficiency 0.2651∗ _−470.92∗

[0.1374] [282.25] Low.inefficiency 0.4058∗∗∗ -481.89 [0.1344] [354.81] log(avg neigh pop) −0.1321∗∗∗ _67.99

[0.0259] [75.1] capital intensity 25.028∗∗∗ -2959.1 [2.4148] [19700.1] constant -0.0479 2321.4 [1.0132] [2072.8] No. observations 57749 57749 Fixed effect: State State

7 Conclusion

This research derived an empirical model to better understand the impact of the bankruptcy reform on labour market. The identification of the effect followed the strategy first described in Ponticelli and Alencar (2016), exploring the fact that, in Brazil, municipalities must reach minimum requirements to be eligible to posses a court. For a municipality which is a seat of a judicial district, the measure of neighbouring municipalities which do not meet the minimum requirements correlates positively with its judicial inefficiency. This measure of potential extra-jurisdiction is exogenous, once I accounted for observable variables which determine the eligibility of a municipality to posses a court. I applied an instrumental variable approach to study the structural effect of the new law and judicial efficiency on the labour market, where the instrument for the efficiency measure is the number of neighbouring municipalities without a court of their own.

Two measures of asset tangibility were introduced, allowing the empirical specifications to study het-erogeneous effects between different sectors, classified by those tangibility measures.

The results point in the expected direction: there seems to be a labour transition from the sectors less benefited by the new law to the more benefited ones, that is, after the reform, municipalities with a more efficient judicial system observed an increase in their labour force and the effect was stronger on sectors which employ more tangible assets.

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a higher increase in wages, although the result is not statistically significant. All those results are consistent with the simple conceptual model described in the text. The final mechanism that I tested is the homogeneity of the effect on wages, across sectors. The conceptual model did not allow for heterogeneity of wages between sectors, thus I wanted to test if this assumption is unrealistic. For the first tangibility measure, the results were as expected: the sign of the effect of judicial inef-ficiency on wages was negative on both sectors and there was no statistically significant difference of wage change between sectors. For the second tangibility measure, there was no statistically significant difference of wage change between sectors, although the signs were positive, that is, contrary to the expected. This unexpected result suggests that the effects on wages should be further studied, possibly applying empirical specifications at the worker’s level.

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8 Appendix

8.1 A possible extension

The two empirical specifications that were presented in the text are looking at firm’s decision on labour force. Here, I present an alternative empirical model that could focus on the worker side. As described in the model section, I assume that the total labour supply is fixed, but not the composition between sectors. From a worker’s perspective, after the reform it should be easier to end up working for a firm with more tangible assets.

Denote by pi,t,sp,sq the probability that a worker i from sector sp on time t starts working on sector

sq next period. Then a system of equations for every pair sq, sp could be stated as 16

pi,t,sp,sq = αi+ αt+ βsp,sq(ref )t.(inef f )j+ i,t (16)

where: (ref )tis a dummy variable, equal zero on the pre-reform period and equal to one after; (inef f )j

is the inefficiency of the judicial system on the location of the worker’s job j; αiis a fixed effect at the

worker’s level. The system in 16 can be seen as describing the flow of workers between sectors. The model described in section (1) implies that βsp,sq< 0 if sector sq has more tangible assets than sector

sp, and βsp,sq > 0, otherwise.

Taking the first difference, I obtain equation 17

∆pi,sp,sq = α + βsp,sq.(inef f )j+ i (17)

In this specification I could follow a worker and see if the results are more significant for those workers who have changed sector.

8.2 Additional tables

Table 8 presents the results when included a year dummy interacted with the endogenous measure of inefficiency. The labour force effect is significant and negative for all years considered. For the dependent variable ’wages’, the results for 2006-2007 are negative and statistically significant, while

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Table 7: Second stage regression with the second classification for tangibility ∆ hourly wage ∆ labour force1

Inefficiency 0.4050∗∗∗ _-482.71

[0.1381] [354.97] log(avg neigh pop) −0.1234∗∗∗ _66.83

[0.0262] [69.06] Tangibility −1.7153∗∗∗ _-494.52 [0.5403] [662.68] Capital intensity −19.23∗∗∗ _-372.95 [2.4893] [11020] constant 0.1450 2397.7 [1.0346] [2170.4] No. observations 57749 57749 Fixed effect: State State

the sign changes in 2008 and becomes insignificant in 2009. Thus, the effects are as expected for wages until 2008. Firms operating in municipalities with a more efficient judicial system observed an increase in their labour force and wages.

Table 8: Second stage by year with first tangibility classification ∆ hourly wage ∆ labour force1

2006.ineff −0.2009∗∗ _−19.23∗∗∗ [0.096] [5.146] 2007.ineff −0.1749∗ _−19.08∗∗∗ [0.0958] [5.149] 2008.ineff 0.3326∗∗∗ −19.37∗∗∗ [0.0934] [5.147] 2009.ineff -0.0259 −19.32∗∗∗ [0.0958] [5.1487] log(avg neigh pop) −0.0831∗∗ _10.43∗∗∗

[0.0366] [3.772] constant 2.4389∗∗∗ 7.074

1_{in thousands}

The same results folow when I apply the second tangibility classification, as presented in table 9 I can repeat the exercise of estimating the second stage for the first approach, while interacting the

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Table 9: Second stage by year with second tangibility classification ∆ hourly wage ∆ labour force 2006.ineff 0.2168∗∗ _−810.0∗ [0.1017] [452.97] 2007.ineff 0.2445∗∗∗ −801.89∗ [0.1016] [452.83] 2008.ineff 0.7865∗∗∗ −861.0∗ [0.1007] [452.33] 2009.ineff 0.4060∗∗∗ −865.23∗ [0.1012] [452.17] log(avg neigh pop) −0.1337∗∗∗ _102.56

[0.0182] [88.114]

constant 0.0748 4339.7

[0.7637] [2683.4] No. observations 230996 230996 Fixed effect: State State

variables with year dummies. Table 10 presents the results. The labour force results are consistent across all years. The high tangibility sector observed an increase in its labour force when located in a more judicially efficient municipality. The low tangibility sector observed a lower impact. The behaviour of the wage variable is similar to the one observed in table 8. The effect is negative for 2006 and 2007, on both sectors; then it changes sign and becomes insignificant in 2009.

Finally, I repeat the estimation with year dummies for the second approach, as presented in table 11. Breaking the results year by year enlarges the standard errors on the labour force regression, such that the statistical significance is lost. For the wage variable, the results continue being positive and significant.

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Table 10: Estimation with tangibility and year dummies ∆ hourly wage ∆ labour force1 2006.High.ineff −0.2623∗∗ _−33.03∗∗∗ [0.1108] [7.909] 2006.Low.ineff −0.1684∗ _−10.35∗ [0.0905] [5.681] 2007.High.ineff −0.2447∗∗ _−32.92∗∗∗ [0.1108] [7.91] 2007.Low.ineff -0.1383 −10.18∗ [0.0904] [5.68] 2008.High.ineff 0.4429∗∗∗ −33.3∗∗∗ [0.1099] [7.912] 2008.Low.ineff 0.2827∗∗∗ −10.43∗ [0.0882] [5.678] 2009.High.ineff -0.0802 −33.3∗∗∗ [0.1108] [7.912] 2009.Low.ineff 0.0033 −10.38∗ [0.0904] [5.678] log(avg neigh pop) −0.0830∗∗ _10.52∗∗∗

[0.0375] [3.831] constant 2.5081∗∗∗ 58.06

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Table 11: Estimation with second tangibility measure and year dummies ∆ hourly wage ∆ labour force 2006.High.ineff 0.0690 -489.89 [0.1060] [365.49] 2006.Low.ineff 0.1916∗ -459.56 [0.1042] [470.29] 2007.High.ineff 0.0912 -487.05 [0.1060] [365.5] 2007.Low.ineff 0.2147∗∗ -449.79 [0.1042] [470.26] 2008.High.ineff 0.6164∗∗∗ -491.27 [0.1061] [365.51] 2008.Low.ineff 0.7865∗∗∗ -544.89 [0.1030] [470.07] 2009.High.ineff 0.2423∗∗ -490.73 [0.1060] [365.51] 2009.Low.ineff 0.3806∗∗∗ -556.56 [0.1033] [470.01] log(avg neigh pop) −0.1316∗∗∗ _69.98

[0.0188] [98.825] capital intensity 24.717∗∗∗ -3498.3 [1.9609] [26300] constant 0.0142 2442.9 [0.7697] [2753.1] No. observations 230996 230996 Fixed effect: State State

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Table 12: Minimum requirements - potential extra-jurisdiction threshold State Minimum population

AC 4,000 AL 10,000 AP 5,000 BA 20,000 CE 10,000 ES 20,000 GO 20,000 MA 20,000 MG 18,000 MS 10,000 MT 10,000 PA 5,000 PB 20,000 PE 20,000 PI 10,000 PR 30,000 RJ 15,000 RN 10,000 RO 10,000 RR 8,000 RS 20,000 SC 20,000 SE 30,000 SP 10,000 TO 21,000

Source: adapted from Ponticelli and Alencar (2016)