Daniel Ferreira Pereira Gonçalves da Mata; Guilherme Mendes Resende (Coautor)

VII PRÊMIO INFI - FEBRABAN DE ECONOMIA BANCÁRIA

Daniel Ferreira Pereira Gonçalves da Mata Guilherme Mendes Resende (Coautor)

CATEGORIA A - Dissertações, Teses e Artigos Acadêmicos

Trabalho: Changing the Climate for Banking: The Economic Effects of Credit in a Climate-Vulnerable Area

São Paulo 2015

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Changing the Climate for Banking: The Economic Eff ects of Credit in a Climate-Vulnerable Area

Version: October 29, 2015.

Abstract We exploit plausibly exogenous variation in credit policy to study the real eff ects of credit. Producers in the semiarid, Brazil’s poorest region, are eligible to subsidized, abundant credit provided by a state-owned bank with the goal of promoting regional development. Based on objective climate criteria, the federal government added new localities to the Brazilian semiarid, while some places which were close to satisfying the criteria were not included.

We find that subsidized credit had no aggregate

impact on per capita Gross Domestic Product of the added localities. To shed light on the baseline results, we exploit detailed institutional information to understand the role of local banking. We document an increase in risky loans, but we find neither a crowding-out of credit to other banks nor a rise in delinquency rates.

Keywords: Banking, Credit, State-Owned Banks, Growth, Delinquency Rate JEL Classification: G21, H81, O16

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1

Introduction

Reforms in credit markets may have long-lasting impacts on the economy. Among many goals, credit reforms may be aimed at promoting the development of poor and vulnerable regions. Disentangling the eff ects of credit has been challenging as the literature provides competing theories and evidence. Take the case of policies which focus on expanding government-sponsored credit to cash-trapped producers who have limited access to credit markets (Banerjee and Newman (1993), Galor and Zeira (1993)). Those reforms favoring government backed credit may have an important impact on credit takers, but may also crowd out the financing of other banks which were not part of the reform. Moreover, stateowned banks may correct market failure, but they may as well create government failure by financing politically desirable projects (La Porta, Lopez-De-Silanes, and Shleifer (2002)).

In the empirical literature, several papers have been devising identification strategies to understand the channels through which credit may aff ect development, and to pin down the many unobservable factors that might be correlated with both credit and economic development.

This paper provides evidence of the impacts of credit on development by exploiting a policy reform in Brazil that “quasi-randomly” created two zones of diff erent credit characteristics. In Brazil, regional development policies have focused on the “semiarid” region, Brazil’s poorest region and an area subject to reoccurring severe droughts. The Brazilian semiarid is the more populous dry area in the tropical zone in the world (Ab’Saber (1999)), so policies may have huge social impacts. Historically, all municipalities located in a contiguous dry area with annual precipitation below a certain threshold were classified as semiarid municipalities.1 Producers located in the semiarid operate under a diff erent credit regime as they are eligible to (i) apply for a larger pool of credit by legal mandate and to (ii) obtain subsidized interest rates on loans in the form of a “solvency bonus”2 .

The funding from these loans stems from the FNE fund (in Portuguese, “Fundo Constitucional de Financiamento do Nordeste”), Brazil’s main instrument for promoting regional development. The FNE fund is funded by 1.8% of two federal taxes: Income Tax and Industrial-Product Tax. The state-owned BNB bank (“Banco do Nordeste do Brasil”) is the legally-mandated financial institution responsible for providing subsidized loans with FNE funding. In 2012, BNB bank lent US$ 5.3 billion to firms and farmers using FNE funds. It is a sizeable amount compared to policies carried out by other developmentoriented institutions. For instance, the World Bank Group committed US$ 52.6 billion in 1 There

are three administrative levels in Brazil: federal government, states, and municipalities. Municipalities are autonomous entities that are able, for instance, to set taxes. They are roughly equivalent to counties in the US. We use the words municipalities and local economies interchangeably.

2 Precisely, producers in municipalities in the semiarid are eligible to get up to 25% discount in their interest rate in case they avoid a late payment, while producers in other areas can get up to 15% discount by paying by the due date.

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loans, grants, equity investments, and guarantees during the same year for a myriad of project throughout the globe (World Bank (2013)).3 Moreover, in 2012 lending with FNE funds in the semiarid area matched the amount of cash transfers from “Bolsa Fam´ılia” – the most important nationwide cash transfer program – allocated to families in the Brazilian semiarid.

In 2005, the Brazilian federal government changed the criteria to classify municipalities as belonging to the semiarid area based on three (objective) geographical criteria. The three criteria used specific thresholds based on indexes of annual precipitation, aridness, and water deficit. These changing criteria added 102 municipalities to the new semiarid area.4 Therefore, the new definition of semiarid created an external shock of cheaper, abundant credit for 102 municipalities, as borrowers within these 102 units are eligible to obtain subsided loans and to apply for a larger pool of credit. We exploit the new semiarid definition in Brazil so as to disentangle the eff ects of credit provided by a state-owned bank on the local economy. We argue in this paper that municipalities had no control on whether they are included in the semiarid, so the semiarid reform induced a “quasi-randomization” of municipalities. The “treatment assignment” is thus related to new semiarid definition: Places which entered the semiarid were assigned to treatment, while neighboring places – which were close to satisfying the criteria and are located close to treated municipalities – are part of the control group. Bordering municipalities are our baseline control group as neighboring localities may have more similar observable and unobservable characteristics (such as natural resource endowments, geographical conditions, and institutions), but they were not included by the institutional reform “by chance”. We show that municipalities across the new semiarid border are statistically identical in terms of observable characteristics.

Since we view the new semiarid definition as the assignment to treatment, our focus is on an Intent-to-Treat (ITT) analysis, where we regress our outcome variables of interest directly on the semiarid reform. Using a diff erence-in-diff erence framework, we start by asking whether the semiarid reform has aff ected the behavior of the state-owned bank. We document an increase in the number of credit operations in the treatment group compared to those in the control group. The increase in credit operations is especially stronger to producers in the livestock sector – the most important economic sector in the semiarid.

We also show that the credit expansion was targeted at smaller-sized low-income livestock producers. The expansion in credit operations is economically and statistically significant, and is consistent with the cheaper, abundant credit environment for producers in semiarid commitment from the World Bank to Brazil was U$3.2 billion in the 2012 fiscal year. detail the climate criteria in Section 2. The 102 municipalities totaled 1.6 million people, and 10,400 formal plants in 2010. Their main economic sector is livestock, where one verifies the presence of a sizeable informal sector. All other municipalities previously inside the semiarid stayed in the new semiarid area.

3 The 4 We

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municipalities.

To shed light on the economic consequences of the credit expansion, we study how the reform has aff ected (i) the behavior of the state-owned bank, (ii) the functioning of the banking sector, and (iii) local economic growth. We first use institutional information on banking in the Brazilian semiarid to study how the reform has aff ected delinquency rates of loans provided by the BNB bank using FNE resources. Institutions governing the FNE fund indicate that being a part of the semiarid has an ambiguous eff ect on delinquency rates. On the one hand, due-date payments generate a discount in the form of a solvency bonus, so one would expect lower delinquency rates. On the other hand, Federal Law n. 11,011 in 2004 changed BNB bank’s credit-risk management. This piece of legislation authorized BNB bank to lend to producers in the semiarid with zero credit risk, as explained in detail in the institutional background (see Section 2). Once lending in the semiarid is risk-free from the viewpoint of BNB bank, one might observe a riskier behavior from the state-owned bank when it comes to lending inside the semiarid vis-`a-vis outside the semiarid, which in turn could generate higher default rates. Besides, both development-oriented and political-oriented goals of a state-owned bank change incentives to tolerate defaults.5 We document an increase in risky loans by BNB bank (which agrees with Federal Law n. 11,011 in 2004), but we do not find evidence of later payments in semiarid municipalities compared to the control group. We interpret this findings as an evidence that the solvency bonus to producers is attractive enough to guarantee no rise in delinquency rates in semiarid municipalities.

We then analyze whether the semiarid reform has distorted credit allocation and the functioning of the local banking sector. Specifically, we ask whether the reform has generated a crowding-out eff ect of loans provided by other banks. Recall that the baseline results indicate that the supply of credit operations by BNB bank has increased in the semiarid, especially to (low-income) livestock producers. The semiarid reform may have an ambiguous eff ect on total credit, because BNB bank’s credit expansion may crowd-out other banks. By looking at either the number of bank branches or the total amount of credit by other (public and private) banks, we find no evidence of crowding-out. We explain the no crowding-out eff ect by showing that there is a lack of competition when it comes to lending to (low-income) livestock producers in the area: BNB’s average loan value for agriculture and livestock is several times lower than the mean loan value of its closest competitor. Before the reform, most of the credit operations to low-income producers were already carried out by BNB bank.

5 One

might observe a moral hazard behavior from BNB bank as FNE bias or the bank may be under pressure to lend to politically-favored and other members of its board of directors are politically appointed. management fee from managing the assets of the FNE fund, so this behavior.

4

resources have a clear development sectors. Indeed, BNB’s president By contrast, BNB bank receives a might prevent such moral hazard

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Finally, we evaluate whether loosening capital constraints on producers generates measurable positive results. We look at the impacts on the livestock sector, and we provide evidence that credit has aff ected livestock composition, e.g. towards a rising population of caprine stock (goats). We carry out an additional analysis to check whether the new credit environment has generated local economic growth. The baseline results show that locations in which credit is subsidized had not experienced growth (as measured by per capita gross domestic product – GDP) over a span of five years compared to the control group.

Furthermore, we did not document an increase in sectorial per capita GDP. Measurement issues related to the GDP and the emphasis on loans to the primary sector can explain these results. Livestock production may not be entirely computed in the local GDP, as the latter includes mostly activities related to the formal sector, while livestock and related activities are characterized in Brazil by high rates of labor informality. By contrast, the results indicate that smaller-sized low-income livestock producers – that were previously cash-trapped – have invested the cheaper credit provided by the semiarid reform to expand their caprine stock, but that this was not enough to generate an eff ect on livestock-related production (e.g., as measured by the amount of milk produced). Even though we were not able to find a measurable positive result on production, the investment in caprine stock may have generated an impact on household consumption, as caprine is known for supplying higher milk yield than other small ruminants. Due to data restriction, we are only able to provide suggestive evidence of the high self-consumption of milk and meat of households in the semiarid using data from a national-level Household Food Acquisition and Purchase Survey.

This paper is related to several lines of research. This article is related to a large body of work on the eff ects of imperfect capital markets on economic development (Levine (1997)).

This paper is especially associated with the strand of the literature which focuses on the role of banking ownership and state-owned banks (La Porta, Lopez-De-Silanes, and Shleifer (2002), Sapienza (2004), Micco and Panizza (2006), Morck, Yavuz, and Yeung (2011), Coelho, De Mello, and Rezende (2013), Carvalho (2014), Coleman and Feler (2015)). Some papers have studied the phenomena of subsidized credit and banking reforms, but less attention has been given to the role of financial reforms which aim at providing governmentsponsored credit for agents in climate-vulnerable areas. Arid and semiarid regions entail more than half of the developing world’s agricultural area, so credit reforms in those areas may have relevant impacts. In particular, reforms may generate a moral hazard behavior from public banks when there is a need for credit expansion in vulnerable areas. Our paper also relates to the strand which focus on the role of credit in agriculture and livestock sectors (Karlan, Osei, Osei-Akoto, and Udry (2014)). Finally, the quasi-experiment of the Brazilian semiarid reform also connects with papers which exploits spatial variation to understand the local-level outputs (Kline and Moretti (2014), McIntosh (2008)). The

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contribution of this paper to the literature is twofold: (i) It provides quasi-experimental evidence of the impact of state-owned banks on the local economy, and (ii) it studies the impact of government ownership on the banking sector of a large developing country by using detailed institutional data from a novel type of credit shock that have generated incentives to provide risky loans and tolerate higher default rates. When one compares diff erent places, loans may not work because of diff erences in unobservables such as local infrastructure and institutions. Therefore, the advantage of the semiarid quasi-experiment is that we automatically control for unobservable characteristics by comparing places which have received the intervention and those which barely missed to receive it.

This article proceeds as follows. Section 2 provides the background on microfinance in the Brazilian semiarid. We describe the data in Section 3, where we discuss unique data from diff erent sources regarding credit characteristics. Section 4 details the identification strategy. Section 5 presents the results. Section 6 concludes.

2

Background

2.1

Rules governing the distribution of Northeast Constitutional Fund (FNE)

The Northeast Constitutional Fund (FNE) was created by the 1988 Brazilian Constitution to provide subsidized loans to promote economic and social development of the Northeast region in Brazil. The FNE fund is funded by 1.8% of two important federal taxes: Income Tax and Industrial-Product Tax. The financial agent responsible for both managing the FNE fund and lending it to producers with below-market interest rates is the state-owned BNB bank.6 BNB bank’s remuneration is twofold: the bank receives a management fee from managing the assets of the FNE fund, and a del credere fee from lending using FNE funds. According to the 1988 Constitution, BNB bank is legally-mandated to allocate 50% of FNE funding to producers in the semiarid region of Brazil.7 Figure 1 shows the location of the entire area eligible to obtain FNE credit and the location of the semiarid area.

Each year several thousands of credit operations are carried out using FNE funding.

BNB bank lends to producers in all sectors of economic activity. In 2012, BNB bank lent R$ 12 billion (an amount equivalent to US$ 5.3 billion) to half a million producers.

Cumulative lending was approximately US$ 60 billion during 1989-2012 (BNB (2012)).

The design of credit contracts using FNE funding varies depending on the goals, including schedule of repayments, collateral, and late fees for delayed payments. However, producers in the semiarid area get a 25% discounted interest rate – on top of the below-market interest 6 “Banco

do Nordeste do Brasil” (BNB bank) provides short-term microcredit. BNB bank co-exists with other state-owned banks as well as with private banks.

7 FNE fund rules were set by 1988 Federal Constitution, Art. 159, I, c and by Federal Law n. 7,827/89.

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Fig. 1: FNE Area and Semiarid Area

(a)

(b)

Notes. The pictures show the location of areas eligible to receive FNE funds in Brazil. Half of FNE funds must be distributed to areas in darker gray color (called “Sudene Area”), while the other half must be distributed to the semiarid area (in beige/light gray color).

rate – if they comply with the payment schedule. Producers outside the semiarid can only get up to 15% discount by paying by the due date. This solvency bonus diff erential was in eff ect between 2001 and 2013.8 Even though there is 50% constitutional quota to the semiarid area, historically the demand for credit by producers in that area is not enough to fulfill the quota. The 50% quota was only reached during the first 3 years (from 1989 to 1991) of the fund existence.

Since then, it has never been reached again. As a result, producers in the semiarid historically apply for a larger pool of credit. Official documents by BNB bank indicate that the bank has a “special focus” and has “put eff ort into complying with” the 50% allocation to the semiarid (BNB (2012), pp. 64-68).

Lending in the semiarid has implications regarding BNB bank’s credit risk (or default risk) management. Since Federal Law n. 11,011 in 2004, lending to selected producers within the semiarid area is risk-free in the viewpoint of BNB bank. Federal Law n. 11,011 in 2004 stated that the FNE fund should bear the risk of default when the state-owned bank lends using a specific credit line to semiarid producers. Therefore, the FNE fund bears the risk of default when the state-owned bank lends in the semiarid. If a default happens in the semiarid area, this default will be written off solely from FNE’s balance sheet, as BNB bank and FNE have separate balance sheets. Outside the semiarid, the 8 The

solvency bonus of 25% was implemented by Federal Law n. 10,177 in 2001. In 2013, the solvency bonus diff erential was repealed by Federal Law n. 12,793 in 2013, and producers inside and outside the semiarid were eligible to a solvency bonus of 15%.

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risk of default is shared between BNB bank and the FNE fund, while inside the semiarid area the default risk of each credit operation is solely absorbed by the FNE fund.9 As a result, one might expect a (potentially) riskier behavior from BNB bank when it comes to lending inside the semiarid vis-`a-vis outside the semiarid. In turn, BNB bank charges a smaller del credere fee from the FNE fund for loan contracts in the semiarid. A more detailed discussion on FNE fund and BNB bank is given in Appendix C. In short, in the semiarid credit takers are eligible for a solvency bonus, may apply for a larger pool of credit because of the 50% constitutional allocation, and receive risk-free loans in the viewpoint of BNB bank.

2.2

The Brazilian Semiarid and its New Classification

The semiarid, commonly known as the “drought polygon”, is an area with low rain volume, irregular rain, and reoccurring severe droughts. Its soil usually does not retain water. It represents 12% of the Brazilian territory, roughly equivalent to twice the size of countries such as France. The recurrent water scarcity in the semiarid is associated with worse health and socioeconomic outcomes (Rocha and Soares (2015)). In 2010, semiarid’s population was greater than 21.5 million inhabitants (circa 12% of the national population), illiteracy rate was roughly three times the national level, and per capita GDP was one-third of the national level. Agriculture and livestock are the main economic activities, and there are more than 1.7 million farmers and livestock producers in the semiarid. Given its geographical characteristics, the Brazilian semiarid is an area susceptible to land degradation and climate variability. The Brazilian semiarid is the most populous dry area in a tropical zone in the world (Ab’Saber (1999)), so climate variability and droughts have huge social impacts. Traditional public policies toward the semiarid area focused on tackling environmental problems of the region such as the quality of the soil.

FNE fund is the single most important source of credit to producers in the semiarid.

When FNE started to be distributed in 1989, the classification of municipalities as of belonging to the semiarid followed a definition of places with average annual precipitation below 800 millimeters. According to that classification, 1,033 municipalities were in the semiarid area.

The New Semiarid. In 2005, a piece of legislation changed the delimitation of the Brazilian semiarid.10 According to the new legislation, the inclusion of a municipality in the semiarid should be based on three objective criteria. A municipality would be included in case its geographical characteristics complied with at least one of the three criteria.

Since the first criterion was the one used to delimit the old semiarid – an average annual 9 Federal

Law n. 11,011 in 2004 also transferred risk of default to the FNE fund when the state-owned bank lends to agricultural and livestock producers.

10 Decree n. 89 from the Ministry of National Integration, March 2005.

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precipitation below 800 millimeters – all 1,033 municipalities previously in the semiarid stayed in the area. Two geographical criteria were used to add new municipalities to the semiarid: A Thorntwaite’s Aridness Index raging between 0.21 and 0.50 and a water deficit over more than 60% of the time. The idea was to consider several geographical indexes as arid environments can even have a high average precipitation, but – because of a high rate of evaporation and recurring droughts – they do not have enough water to be safely used for consumption. Because of factors such as changing altitudes and the existence of microclimates, each geographical criterion created “waves” or “curves” along the territory such that a group of municipalities was included, while others barely missed to be included in the semiarid. We detail these three climate indexes in Appendix B (see Figure B.1 in Appendix B for the official map of the three criteria showing the aforementioned “waves”).

The new legislation added 102 municipalities to the semiarid, so the “new semiarid” area spans through 1,135 municipalities. Notice that Figure 2 shows the location of the 102 added municipalities. The Ministry of National Integration had the legal authority to change the definition of the semiarid, and received help from several national-level institutions to define the criteria, which alleviates concerns over political influence of local governments. Municipalities do not choose whether to be considered in the semiarid area, i.e., once in the semiarid, municipalities are bound to stay in the area. Since the new semiarid definition is strictly based on climate, exogeneity is more plausible as discussed in the Subsection 4.2.

Fig. 2: New Semiarid Area

Notes. The picture shows the location of the 102 municipalities (in purple/darker gray) included in the new semiarid area by the Ministry of National Integration. The municipalities located previously in the semiarid are in beige/lighter gray.

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Banking in the New Semiarid. In 2012, there were 11 diff erent commercial or universal banks in the semiarid area, totaling 1,270 bank branches. The semiarid banking industry is highly concentrated: The four biggest banks in terms of number of branches have 1,142 branches (90% of the total). Even though BNB bank is one of the biggest banks in the region, only 98 out of the 1,135 municipalities in the semiarid had BNB branches. The concentration in the banking sector is more noticeable in the 102 municipalities added by the semiarid reform, where there were only 5 banks with 116 branches, only 6 of which are from the BNB bank. Roughly half of the 102 added-municipalities has at least one bank branch within its boundaries. Even if there is no BNB branch in the municipality, a producer can obtain a FNE loan from a BNB branch from outside. The only requirement is to produce in a semiarid municipality. Moreover, since 2010 BNB bank has been promoting a program of “temporary” bank branches in the semiarid to improve access to credit in locations without a BNB branch (BNB (2013)). The temporary branches are implemented during selected events such as agricultural fairs.

Several factors explain the evolution of FNE loans over time. One important factor is the type of economic activity in the locality since loans are demand-driven. However, one would expect an increase in loans after a municipality is included by the semiarid reform because of the solvency bonus diff erential, the 50% constitutional allocation to the semiarid producers, and BNB bank’s risk-free loans to agricultural and livestock producers in the semiarid. Indeed, municipalities in the new semiarid had a shock in terms of number of credit operations after the reform. Figure 3 shows an increase in the number of loans for the 102 newly added municipalities compared to the rest of municipalities where BNB bank lends. Credit growth started right after the reform, and the gap between groups stayed relatively constant throughout the years. It suggests that, once demand for cheaper credit from existing producers was met, loans from the newly added municipalities were subjected to the same aggregate fluctuations and trends of the total FNE loans. One observes a surge of 30-40 times in the number of credit operations over time because the funds get accumulated every year, as BNB receives more money each year to lend and can also use the resources from previous years. Regarding the supply side, important factors explaining the aggregate trend change in the 2000’s are the authorization to lend to firms in services and retail sectors and the possibility to add fixed interest rates for the terms of the loan (instead of floating rates).

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Fig. 3: Number of Credit Operations

Notes. The picture shows the evolution of an index of credit operations in the 102 municipalities added to the semiarid (black dotted line) vis-`a-vis the index of credit operations from the rest of the municipalities eligible to receive loans using FNE resources. The index is set equal to one in 2003.

3

Data and Descriptive Statistics

The spatial unit of analysis is the municipality. Our data span from 2000 to 2013, so we are able to use pre and post-reform information. We use six datasets in this paper: FNE data, RAIS, agriculture administrative records, PPM, GDP, and ESTBAN. BNB bank’s FNE data have the list of all producers which took loans during the period from 2000 to 2013. For each credit taker, we know several characteristics of the loan contract such as the amount borrowed, the length of the contract, the contract date, and interest rates. We aggregate this information to construct municipal-level data, such as the number of credit operations and total value of loans. Table 1 shows the proportion of credit operations by sector in 2000, 2005, and 2013. The descriptive statistics from Table 1 are displayed for three areas: Entire semiarid area, the group of 102 municipalities added to the semiarid, and the bordering neighbors of these 102 municipalities. One can verify the large number of credit operations to livestock producers, since livestock is an important sector of economic activity. The large proportion of credit operations to livestock producers is consistent with the fact that FNE loans are demand-driven.

Additionally, BNB bank provides municipal-level data on delinquency rates as well as who bears the credit risk (BNB bank, FNE fund, or shared risk). The default rate ranged between 3.3% and 5.3% in the period from 2006 to 2013. Descriptive statistics suggest how the rules governing FNE may explain the patterns of default rates. The default rate for operations with risk sharing between the BNB bank and FNE fund (1.9% to 2.2%) was lower than the delinquency rate for contracts with FNE bearing the credit risk (7.8% 11

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Table 1: Credit Operations: Semiarid, 102 Localities added to Semiarid, and their Neighbors Semiarid

% Credit Operations in

102 New Semiarid

Bordering Neighbors

Sector /Year

2000

2005

2013

2000

2005

2013

2000

2005

2013

Agroindustry Agriculture Manufacturing Livestock Services

0.32 28.98 3.32 67.31 0.06

0.02 18.69 0.23 80.18 0.14

0.03 17.61 0.47 79.17 0.41

0.00 42.17 1.43 56.39 0.00

0.03 27.07 0.07 72.53 0.03

0.08 18.28 0.33 78.70 0.32

0.26 39.52 4.38 55.26 0.57

0.09 24.81 0.43 72.98 0.26

0.05 22.06 0.88 72.60 0.88

Notes. Tabulations from FNE microdata.

to 9.1%). Inside the semiarid the default rate (4% to 6.4%) was higher than outside the semiarid (2.5% to 4.3%).

Brazilian Ministry of Labor’s RAIS (Rela¸c˜ao Anual de Informa¸c˜oes Sociais) is a registry with plant-level data on formal employment. RAIS is a matched worker-firm data providing information on number of workers, wage distribution, characteristics of workers, sector of the firm, and the location of each plant. Specifically, RAIS tracks the location of plants across diff erent municipalities. We use RAIS to calculate the number of formal firms and workers by municipality. As RAIS comprises only formal firms, it essentially does not give much information for the agriculture sector, especially for small-sized farmers. To overcome this, we use additional information on individual farmers from the Ministry of Agriculture’s administrative records.

PPM (“Pesquisa Pecu´aria Municipal”) data, collected by the Brazilian Bureau of Statistics (IBGE), provide detailed counts of livestock by municipalities, so we are able study specialization and diversification of livestock activity in each municipality. PPM collects information from several local-level sources such as livestock vaccination campaigns, local cooperatives, and offices of sanitary control. To be precise, PPM data allow us to disaggregate the livestock into bovine, equine, caprine, ovine, galliformes, and porcine stocks.

PMM also allow us to obtain quantitative measures of production such as the liters of milk, number of chicken and quail eggs, and wool from sheep. We restrict the number of variables to exclude the categories which are not relevant to our sample of municipalities, such as production of wool.

Additionally, we construct yearly dependent variables by using IBGE’s Gross Domestic Product (GDP) data on municipal GDP, per capita GDP, and sectorial GDP as well as by using Brazilian Central Bank’s ESTBAN data on local banking, which include loans from all banks and number of bank branches.

Notice that all nominal variables are set to 2010 constant values. One advantage of working with a more recent period is that high inflation in Brazil (that existed before 1994) is not an issue. Descriptive statistics of the variables used are shown in Table A.1 in Appendix A. The next section discusses the empirical strategy used to recover the main 12

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estimand of interest.

4 4.1

Identification Strategy Research Design and Estimation

The empirical strategy exploits spatial variation in credit generated by the semiarid reform. The rules of the new semiarid are the “treatment” or “intervention” used to study the impact of credit. Municipalities included in the new semiarid delimitation are the group “assigned to treatment”, while municipalities that missed to be included by a small margin belong to the “control” group. The estimand of interest is the Intention-to-Treat (ITT), which is the average impact of being assigned to treatment, i.e., being assigned to the semiarid region. Let yj be the potential outcome for local economy j and let the indicator of treatment assignment be Zj = {0, 1}. The ITT estimand is represented by I T T = E[yj |Zj = 1] − E[yj |Zj = 0]. We argue that municipalities had no control over the assignment mechanism and thus could not influence their treatment regime (see Subsection 4.2 below). We follow the treatment eff ect literature that has largely focused on average treatment eff ects (Imbens and Wooldridge (2009)).

Our main question is: Does eligibility to receive subsided, abundant credit impact local-level indicators? We focus on a reduced-form relationship between the assignment mechanism and local-level variables (regarding banking and development) to study the impact of credit. The definition of the new semiarid area in Brazil generates a treatment assignment mechanism that leads to an application of a diff erence-in-diff erence design.

Producers in the units included in the semiarid faced a transition from a biding credit situation (as their municipalities were sharing the credit pool with state capitals) to a non-biding situation once their municipality was included in the semiarid (as the 50% semiarid quota is never reached). In the discussion below, the assignment to treatment status (the semiarid reform dummy) is represented by Zjt , which varies within units over time. Specifically, Zjt equals 1 if municipality j is classified as belonging to the semiarid in period t ≤ t, where t is the time of the semiarid reform. A regression using Zjt is thus an intent-to-treat (ITT) analysis. We assume an additive and linear empirical specification to estimate an ITT eff ect as follows: Yjt = α + τI T T Zjt + β ′tXj + γj + ρt + ϵjt ,

(1)

where Yjt is the outcome variable of municipality j in period t, βt is the time-varying coefficient of initial municipal characteristics Xj (pre-treatment level of the dependent variables), ϵjt is an error term, ρt are year fixed eff ects, and γj denotes municipal fixed eff ects. Our set of dependent variables Yjt includes BNB bank’s total lending, selected characteristics 13

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

of loans provided by BNB bank, municipal GDP, sectoral GDP, and measures of sectoral production. The time span t goes from 2000 to 2013. We add γj to capture time-invariant characteristics and ρt to capture aggregate shocks that are common to all groups. Lastly, note that policy variation takes place at the municipal level and errors may be correlated within the spatial units. Therefore, standard errors are clustered at the municipal level in all regressions (Bertrand, Duflo, and Mullainathan (2004)).

Equation 1 is similar in spirit to an ITT analysis, and we aim get the size and direction of selection bias, since ITT is considered a lower bound for the average treatment eff ect.

As a result, the parameter τI T T in Equation 1 should capture an intent-to-treat eff ect.

4.2

Assessing the Research Design

Exogeneity of the Intervention and Threats to Identification. The semiarid reform used climate criteria to add 102 municipalities to the Brazilian semiarid. A central point for the identification strategy is whether these climate criteria were exogenous. Since objective rules were employed, this alleviates concerns on whether the 102 municipalities entered the semiarid for reasons unrelated to their underlying economic and social characteristics, i.e., neither “better” nor “worse” municipalities self-selected into the new delimitation. The use of climate criteria also alleviates concern over expanding public credit to politically-favored regions (Carvalho (2014)).

Anecdotal evidence points out that several politicians have proposed new rules to include municipalities in the semiarid delimitation. Their argument is that the semiarid classification is unfair since two producers of neighboring municipalities have diff erent access to benefits, but they live in a similar environment. For instance, in 2013, Congressman S´ergio Aguiar has scheduled a meeting with the Ministry of National Integration to ask for a political analysis (instead of a technical analysis) to include municipalities in the semiarid region.11 The same congressman lamented that the decree has included places such as Tiangu, but at the same time has ignored neighboring places such as Granja. The population center of Granja is located just 20km from the center of Tiangu.12 Some institutions representing group of municipalities have argued that the delimitation missed to include “similar” neighboring municipalities.13 As for contracts using FNE funding, anecdotal evidence suggest no special treatment for producers in the semiarid in terms of repayment schedule compared to their peers located in non-semiarid areas, except from the eligibility for the solvency bonus.

One could argue that BNB bank may have incentives to propose a change in the defini11 http://blogs.diariodonordeste.com.br/politica/blog-politica/deputado-defende-inclusao-de-20-novos-

municipios-do-ceara-no-semiarido 12 http://diariodonordeste.verdesmares.com.br/cadernos/politica/deputado-quer-inclusao-de-33municipios-do-ce-1.1013089 13 http://www1.folha.uol.com.br/fsp/brasil/fc1103200520.htm

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tion of semiarid to add more areas in order to comply with the 50% quota to the semiarid.

Indeed, a study by BNB suggested that the semiarid should be modified to include more municipalities (BNB (2005)). Although BNB bank suggested to change the definition of semiarid, the Ministry of National Integration has the legal authority to defined its own rules, and the Ministry ended up applying objective geographical criteria to define the new semiarid as discussed before. Another argument supporting the exogeneity is that the number of municipalities wished by BNB and the one stipulated by the Ministry of Integration diff er. BNB bank suggested the inclusion of 276 municipalities in the semiarid, while the new definition by the Ministry of Integration added only 102 municipalities.

Out of the 102 added municipalities, 83 were in the BNB bank’s delimitation of the semiarid. Map C.1 in Appendix C shows the BNB’s proposal, where one can observe several municipalities along the coast being classified as semiarid municipality.

An implicit assumption in the analysis is the stable unit treatment value assumption, i.e., that there is no interference of the treatment on the control group. One might fear spillovers from the intervention: In the presence of spillover eff ects, neighboring locations may also receive part of the treatment. In our application, concerns about spillovers are alleviated since producers in neighboring municipalities cannot apply for subsidized FNE. However, we need to look at migration patterns between the groups. While it is unlikely that municipalities were able to influence whether they enter or not in the semiarid (selfselection into treatment is less likely as sorting from control to treatment is impossible by definition), producers can sort into treatment as they can migrate from a non-semiarid municipality to a semiarid municipality. In other words, “shopping for credit” may happen when one looks at producer-level data. Microdata from the Ministry of Labor and from the Ministry of Agriculture Development show that few firms and farmers have migrated to new semiarid municipalities, so this alleviates concerns over self-selection. Moreover, the legislation forbids a firm to set a small office or plant inside the semiarid region to benefit from the subsidy and to use the loan to expand its activities outside the semiarid.

In addition, we analyze the role of policies in Brazil which aff ect the semiarid. Take the case of the PNDR (“Pol´ıtica National de Desenvolvimento Regional”) implemented in 2007. The PNDR classifies municipalities into four categories: Stagnated, dynamic, low-income, and high-income. According to the PNDR, producers located in high-income municipalities do not have priority to receive FNE loans, while agents in the other categories have priority in receiving these loans. There is only one high-income municipality in our treated group and five in the neighboring municipalities. We do not believe that those few high-income municipalities can have any eff ect on the results, but we perform a robustness check excluding high-income units. One might fear that the 102 municipalities added to the semiarid are exactly the the ones with banks branches to facilitate lending.

Since there are only six BNB branches in the group assigned to treatment, the aforemen-

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DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

tioned relationship seems very unlikely. Moreover, BNB bank did not open new branches in the 102 units, so the demand shock in the treatment group happened only through credit expansion.

Control Group. Our baseline strategy to control for unobservables is to use neighboring municipalities that barely missed to be included in the semiarid as our control group. However, even if being included in the semiarid is sort of a “lottery winner”, which would guarantee unconfoundedness, a lack of overlap (or common support) would still be a threat to internal validity. Using the neighboring municipalities, we automatically control for some unobservables as municipalities close to each other share (i) common geographical features and natural resource endowments (such as water access), (ii) they are equally far from market potential and from the center of decision making (state capitals), and (iii) subject to common aggregate and regional shocks.

Are our treatment and control municipalities really comparable? We test for overlap between treatment and control groups to verify whether these groups diff er in terms of observables. We perform a sort of “randomization check” to check whether the groups are balanced across a wide set of pre-treatment observable characteristics, such as per capita income, average education, and provision of basic services. Rubin (2001) proposes a set of criteria to check for overlap. In this paper, we use the normalized (or standardized) diff erence to assess the diff erence in location in the covariate distributions (Imbens and Wooldridge (2009)). The normalized diff erence (ND) for continuous variables is given by µ −µ N D = √ t2 c , σ t + σc2 where µt and σ 2t is the mean and variance of the treated group, and µc and σ 2c are the corresponding values for the control group.

Imbens and Wooldridge (2009) suggest that for a standardized diff erence of more than 0.25 “linear regression methods tend to be sensitive to the specification” (p.24). Table 2 shows the results of this assessment. As can be seen, neighboring municipalities constitute a good control group based on observables (but also potentially based on unobservables as argued previously in the research design).14 Notice that the boundary of the new semiarid comprises diff erent segments in several States (recall Figure 2 in Section 2), but the results from Table 2 show that on average both groups are balanced.

We construct two additional subsets from the baseline control group to check the robustness of the results: (i) baseline control group without two state capitals and (ii) baseline control group without five high-income municipalities. Because we are excluding 14 We

include both GDP level and GDP growth to provide some guidance on levels and trends of observables.

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Table 2: Overlap between Treated and Various Control Groups (I) Treatment

(II) Neighbors

Group

(III) Neighbors excluding

(IV) Neighbors excluding

(V) Municipalities previously

High-Income Municipalities (n=94)

in the Semiarid (n=1033)

(n=102)

(n=99)

State Capitals (n=97)

Avr S.D

76.49 121.35 -

70.83 104.14 0.035

70.40 105.00 0.038

71.72 106.37 0.030

101.90 127.39 -0.144

# Loans sole FNE risk

Avr S.D Std Diff erences

3.64 6.25 -

2.94 11.88 0.052

3.00 12.00 0.047

3.08 12.18 0.041

7.42 15.17 -0.230

# Loans by BNB bank / # Firms

Avr S.D Std Diff erences

70.57 122.00 -

52.81 90.94 0.117

53.90 91.56 0.109

54.87 92.80 0.102

77.04 105.88 -0.040

Years of schooling

Avr S.D Std Diff erences

7.38 1.06 -

7.24 1.23 0.089

7.19 1.20 0.120

7.15 1.19 0.145

7.03 1.18 0.223

Gini index

Avr S.D Std Diff erences

0.56 0.06 -

0.57 0.06 -0.054

0.57 0.05 -0.037

0.57 0.05 -0.058

0.57 0.06 -0.035

Poverty rate

Avr S.D Std Diff erences

36.57 10.36 -

35.16 12.82 0.085

35.72 12.34 0.052

36.22 12.08 0.022

38.28 11.36 -0.112

% workers in public sector

Avr S.D Std Diff erences

7.98 4.59 -

6.59 4.13 0.226

6.57 4.16 0.227

6.61 4.18 0.221

6.39 4.20 0.256

% houses with water

Avr S.D Std Diff erences

44.42 15.05 -

46.68 17.31 -0.099

45.77 16.25 -0.061

45.05 15.61 -0.029

40.16 18.97 0.176

% houses with no electricity

Avr S.D Std Diff erences

23.52 15.02 -

22.24 17.33 0.056

22.69 17.21 0.037

23.26 17.16 0.012

23.47 19.20 0.002

% Rural population

Avr S.D Std Diff erences

0.50 0.18 -

0.47 0.20 0.100

0.48 0.19 0.066

0.49 0.19 0.026

0.53 0.19 -0.121

Per capita income

Avr S.D Std Diff erences

171.20 46.43 -

192.38 95.75 -0.199

183.11 71.01 -0.140

179.75 62.79 -0.110

167.18 55.67 0.055

Per capita GDP

Avr S.D Std Diff erences

2326.0 355.7 -

2551.3 3432.9 -0.065

2586.3 3460.1 -0.075

2597.2 3511.6 -0.077

3507.21 3813.88 -0.308

Illiteracy rate

Avr S.D Std Diff erences

44.91 6.05 -

42.67 10.70 0.182

43.25 9.99 0.142

43.76 9.52 0.102

45.13 8.18 -0.022

GDP Growth 2000-2004

Avr S.D Std Diff erences

0.20 0.61 -

0.27 0.75 -0.073

0.27 0.76 -0.070

0.27 0.77 -0.070

0.31 0.60 -0.133

Pre-Treatment Characteristics # Loans by BNB bank

Notes. Municipalities in the semiarid are the treated group of 102 units. Four control groups are shown: (i) Municipalities which are neighbors from the treatment group (column II: “Neighbors”), Neighboring municipalities excluding two state capitals, Natal and Fortaleza (column III: “Neighbors excluding State Capitals”), (iii) Neighboring municipalities excluding five high-income municipalities, Natal, Fortaleza, Parnamirim, Pacatuba, and Guaiu ´ ba (column IV: “BNB semiarid”), and (iv) the municipalities previously classified as belonging to the semiarid area (column V: “Municipalities previously in the Semiarid”). Control group (i) in column II is our baseline control group. We use control groups (ii) and (iii) in the robustness exercises.

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richer, more populous place, Table 2 indicates an improvement in terms of standardized diff erences when we consider the two new groups. We will use our original baseline control group (all neighbors) in the analysis below, and apply the two additional control groups in the robustness exercises. Lastly, Table 2 compares treatment group with the rest of semiarid municipalities. The analysis of the pre-existing semiarid municipalities delivers quite diff erent results with much higher standardized diff erence. This indicates that the 102 added-municipalities are more comparable to their immediate neighbors in terms of observables.

5

Results

We divide the results into three parts. We start by discussing the ITT eff ects of the semiarid reform on the behavior of the BNB bank. In the second part, we discuss the role of the banking sector. We finish with the relationship between the credit reform and local development.

5.1

Eff ects on the Behavior of the State-Owned Bank

The institutional reform of the semiarid may interfere with the behavior of BNB bank in a myriad of ways. We aim at providing evidence of three questions: Has BNB bank increased loans to the 102 added municipalities? Has BNB bank increased (potentially) riskier loans after the reform? What is the impact on delinquency rates? We perform regressions in a similar spirit to Equation 1 with dependent variables directly related with the BNB bank.

We first analyze the evolution of FNE loans over time. Table 3 indicates a negative (but statistically not significant) impact for FNE loan amount per firm (see columns (i) and (ii)), and a positive and significant impact for the number of credit operations per firm (see columns (iii) and (iv)). It suggests that there was an increase in contracts to smaller-sized producers with a consequent reduction in the value of the average credit contract. Our data allow us to analyze the impact of the semiarid-reform dummy on the types of loans made by BNB. When we disaggregate loans by sector, we verify that the increase on the number of credit operations is led by the livestock sector. The results are in columns (v) to (viii) of Table 3. We find no significant impact on credit operations for other sectors of economic activity. This result is consistent with the high presence of livestock producers in the treatment group before the intervention. Columns (ii) and (iv) of Table 3 show that the regression results are qualitatively unchanged after using geographical controls and initial conditions with time varying coefficients. As discussed in the estimation section (see Section 4.1), we include municipal and year fixed eff ects and cluster standard errors at the municipal level in all regressions. Additionally we control for geographic characteristics and initial conditions with time varying coeffi cients.

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Table 3: Semiarid Reform and FNE Loans Baseline Control Group: Bordering Municipalities All Sectors

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality & Year FE Observations Number of Municipalities Estimation

Sectorial Results

FNE Loan Value

# of Credit Oper-

per Firm

ations per Firm

# of Credit Operations per Firm in Livestock

Agricult.

Manufact.

Services

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

-0.225 (0.144) No No Yes 1,693 201 FE

-0.143 (0.139) Yes Yes Yes 1,393 155 FE

1.776* (0.929) No No Yes 2,005 201 FE

1.865** (0.748) Yes Yes Yes 1,980 198 FE

1.600** (0.704) Yes Yes Yes 1,980 198 FE

0.109 (0.192) Yes Yes Yes 1,980 198 FE

0.000156 (0.00151) Yes Yes Yes 1,980 198 FE

-0.00227 (0.00168) Yes Yes Yes 1,980 198 FE

Notes. This table presents results from the estimation of Equation 1. The unit of observation is a municipality-year. Robust standard errors (in parentheses) are clustered at the municipal level. The overall sample includes 200 municipalities: The treated group is the set of 102 municipalities included in the new semiarid, while the control group includes their 99 bordering neighbors.

Semiarid Reform Dummy is the diff erence-in-diff erence coefficient which corresponds to τI T T from Equation 1. Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. *** p<0.01, ** p<0.05, * p<0.1

Table 4 indicates that the credit expansion happens mostly for low-income producers in the livestock sector. Regressions (i) and (ii) show that the increase in credit operations happened for producers in rural areas who have accessed a credit line called “Pronaf B”.

Only lower income producers with annual income up to R$ 20,000 (approximately US$ 8,000) can borrow from the “Pronaf B” credit line. Regression (iii) points out the lowincome livestock producers were the main credit takers. Taken together, the results from Table 3 and from regressions (i), (ii), and (iii) of Table 4 indicate that smaller-sized lowincome livestock producers – that were previously cash-trapped – have accessed the cheaper credit provided by the semiarid reform.

While the increase in credit operations can be explained by both the incentives from the semiarid reform and by the development-oriented goals from the state-owned bank, the surge in credit may be associated with a changing behavior from BNB in terms of risk taking and tolerance to defaults. We document a changing behavior from the BNB bank by verifying an increase of loans with risk solely taken by FNE fund (see regression (i) of Table 4). This relates with the rules governing the use of FNE resources, given that Federal Law n. 11,011/2004 altered the incentives regarding BNB bank’s credit-risk management in the semiarid area. Given the potentially higher risk tolerance of the BNB bank, is FNE in the semiarid associated with a higher delinquency rate? We showed in the descriptive statistics part that the default rate inside the semiarid is higher than outside the semiarid. Therefore, one would expect an increase in default rates in the newly-added 102 municipalities. However, the institutions governing FNE have ambiguous eff ects on delinquency rates. There are some factors inducing more defaults. For instance, as a

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state-owned bank, BNB bank may seek development-oriented and/or short term politicaloriented goals.15 Both development-oriented and political-oriented goals change incentives behavior toward tolerating defaults. Besides, as a development bank, BNB bank may serve a diff erent clientele such that the new semiarid rule may increase its costs and risks. It may be the case that the risk composition of borrowers is influenced by the cheaper credit environment. By contrast, other factors prevent default from happening. One example is the incentive eff ect created by the solvency bonus, as discussed in Section 2. Moreover, recall that BNB bank receives a management fee from managing the assets of the FNE fund. The lower the delinquency rate, the higher the management fee. This provides another incentive eff ect to avoid defaults so as to preserve the assets of the FNE fund in order to maintain a relevant source of remuneration.

Table 4: Semiarid Reform: Impact on Type of Loan, Risk, and Delinquency Rates Control Group: Bordering Municipalities # of Credit Operations by Firm Producers in Rural Areas

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality & Year FE Observations Number of Municipalities Estimation

with sole

with Default

All Producers

Low Income

Low-Income In Livestock

FNE risk

All Sectors

in Livestock

(i)

(ii)

(iii)

(iv)

(v)

(vi)

1.774** (0.751) Yes Yes Yes 1,980 198 FE

1.880*** (0.672) Yes Yes Yes 1,980 198 FE

1.638*** (0.620) Yes Yes Yes 1,980 198 FE

1.107** (0.516) Yes Yes Yes 1,980 198 FE

0.174 (0.348) Yes Yes Yes 1,400 200 FE

-0.00323 (0.278) Yes Yes Yes 1,400 200 FE

Notes. Robust standard errors (in parentheses) are clustered at the municipal level. Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. *** p<0.01, ** p<0.05, * p<0.1

We use information on non-payments and the type of loan to investigate if the moral hazard behavior from BNB bank lending is associated with higher default rates. We measure delinquency rates by the variable number of credit operations with late payments, which follows an official definition by the Ministry of National Integration.16 According to the official definition, a credit operation is late if the full amount of one (or more than one) that BNB’s president and other members of its board of directors are politically appointed.

2005, the definition of a default was changed by “Portaria Interministerial n.11/2005 do Minist´erio da Integra¸c˜ao e do Minist´erio da Fazenda”. This should not influence our results because the changes were the same regardless of the place of the default, i.e., were orthogonal to the characteristics of the municipalities. Moreover, there were changes over time regarding repayment schedule, but those changes were applied simultaneously to both treated and control groups. Field and Pande (2008) show that diff erent repayment schedules have no impact on delinquency rates.

15 Recall 16 In

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installment is not payed back by the due date. One initial concern would be whether FNE is more like a “cash-transfer” program rather than a proper loan because of its developmentoriented goals. In this case, producers would not pay back regardless of their place of residence, and we would see no eff ect on delinquency rates. This concern is alleviated because delinquency rates fluctuates between 3.3% and 5.3% which is relatively low figure to consider FNE as a cash-transfer program.17 The results from regressions (ii) and (iii) in Table 4 indicate no diff erence in default rates between the treatment and control groups.

One potential explanation is that the solvency bonus for semiarid producers is attractive enough to prevent higher delinquency rates.

5.2

Eff ects on Banking

We investigate whether the credit reform has distorted credit allocation and the functioning of the local banking sector. Specifically, we ask now whether FNE generated crowding-out of credit to other banks. An extreme case scenario would be a complete crowding-out of the banking system due to the presence of BNB bank.

Who is competing with BNB in the semiarid? Due to data availability, only in 2012 we know exactly the participation of each bank in terms of lending. In 2012, there were 11 banks with bank branches in semiarid municipalities, including the BNB bank. In 2012, 73.5% of total credit went to livestock producers. Recall that BNB lends mostly to livestock producers. When it comes to the participation of each bank out of credit for livestock sector, BNB is the second most important lender in the semiarid area. BNB had 28% of the loans (with 97 branches), while Banco do Brasil had 64% (with 522 branches).

The additional nine banks had a share of 8%. BNB bank lends directly FNE resources and only uses other development-oriented banks as “indirect lenders”, and these indirect lenders correspond to only 0.0001% of the total loans.

We analyze how the semiarid reform has impacted other banks which operate in the area. Regression (i) in Table 5 shows that the reform has not impacted the amount of credit provided by banks other than BNB. However, since the credit expansion was mainly to the livestock sector, do we observe a crowding-out of loans in livestock sector? A priori, one could expect a crowding-out of other banks in livestock credit operations. However, institutions of the financial markets in Brazil play a role explaining the lack of crowdingout. As BNB bank increased lending mostly to livestock, and given that basically one bank (the state-controlled Banco do Brasil) also lends to agriculture and livestock, the crowing-out scenario is less likely. Moreover, even though both BNB and Banco do Brasil lend to agriculture and livestock producers, they occupy diff erent market segments. Table possibility is that “lemon” firms might migrate to the semiarid in order to get cheap loans and this increase delinquency rates. We can follow whether firms have migrated in order to obtain subsidized credit. This can be one way how credit generate more defaults.

17 Another

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A.2 in Appendix A points out that (i) the average value of a BNB’s loan contract is much lower than the average value of a Banco do Brasil’s contract and (ii) BNB implements more credit operations to producers. Take the case of investment loans for livestock producers in 2012: the average value of BNB’s loans were 4 times lower for than the value Banco do Brasil’s loans; and BNB’s number of credit operations was 45 times higher than the one from Banco do Brasil. These tabulations are consistent with the results (from the previous Subsection 5.1) that the expansion of credit operation in the semiarid was targeted at low-income producers – the ones which take smaller amounts per credit contract.

The results from regression (ii) in Table 5 provides evidence of no crowding-out from other banks when it comes to lending to the livestock sector. A caveat is that we do not have data on the number of credit operations from other banks, but only on the total amount of loans from other banks. Since this variable can suff er from variations in local prices, we also investigate the evolution of the number of bank branches. The idea is to improve on the crowding-out analysis by using a “real” variable.18 Regression (iii) in Table 5 supports the no-crowding out eff ect story.

Table 5: Semiarid Reform: Impact on Local Banking Control Group: Bordering Municipalities Ln Value of Loans

by Banks other than BNB

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality & Year FE Observations Number of Municipalities Estimation

# of Bank Branches

All Sectors

Livestock

(i)

(ii)

(iii)

0.133 (0.126) Yes Yes Yes 596 62 FE

-0.248 (0.180) Yes Yes Yes 579 59 FE

-0.0323 (0.0779) Yes Yes Yes 2,814 201 FE

Notes. Robust standard errors (in parentheses) are clustered at the municipal level. Geographical controls and initial conditions have time-varying coefficients.

The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. *** p<0.01, ** p<0.05, * p<0.1

Finally, one can observe the number of municipalities in regressions (i) and (ii) of Table 5 to see that there were roughly 60 municipalities with loans from other banks apart from BNB. This indicates the lower penetration of finance in the municipalities of the semiarid region.

18 From

2000 to 2011, the number of branches in the treated group evolved from 68 to 83.

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5.3

Eff ects on Local Development

We now evaluate whether loosening capital constraints on producers generate measurable positive results. We look at the livestock sector as well the the entire local economy.

We initially ask whether the cheaper credit environment was capable of creating locallevel growth as measured by municipal per capita GDP. The baseline results show that locations in which credit is subsidized has not obtained a (statistically significantly) higher per capita GDP over a span of up to five years compared to those in the control group.

Columns (i) and (ii) of Table 6 show that the semiarid rule had no eff ect on per capita GDP. Taken together, the results from Table 6 indicate that the smaller-sized livestock producers – that were previously cash-trapped – have accessed the cheaper credit provided by the semiarid reform, but that this was not enough to generate an eff ect on per capita GDP. We inspect then if the rise in credit operations is associated with an impact on GDP of the livestock sector. Regional accounts in Brazil do not allow us to disaggregate local GDP into agriculture GDP and livestock GDP, so we work with the combined agriculture and livestock GDP. The results are shown in regressions (iii) and (iv) of Table 6. Although there is a grow in the number of credit operations for livestock producers, we find no evidence of growth in agriculture and livestock GDP. Measurement issues related to the GDP and the emphasis on loans to the primary sector can explain the lack of impacts.

Livestock production may not be entirely computed in the local GDP, as the latter includes mostly activities related to the formal sector, while livestock and related activities are characterized in Brazil by high rates of labor informality. In other words, although FNE did not have impact on per capita GDP, this do not rule out the impact on other forms of local production, as primary industries are very dominated by informal production.

Besides, municipal GDP is deflated using the national implicit price deflator, so local price eff ects may prevent us from capturing a variation in real local GDP. As a result, local economic growth as measured by GDP may not capture the impacts of subsidized credit because of GDP’s measurement.

We use GDP information at the sectoral level to check whether other sectors have benefited from the improved credit access of livestock producers. Even though we did not find an increase of loans for services and manufacturing, the existence of a spillover eff ect is still possible. Regressions (iv) and (v) of Table 6 show no evidence of spillover eff ects to other sectors of economic activity.

We need then to look at better measures of local production related to the livestock sector. Our alternative measures of local production employ data from PPM dataset, which give detail information on livestock counts and production. Table 7 extends the analysis using counts of the total livestock, and the proportion of caprines, cattle, and porcine of the total livestock as well as milk production. When we analyze the impact of the reform 23

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table 6: Semiarid Reform and Local Economic Activity Results: GDP Growth Baseline Control Group: Bordering Municipalities Ln Gross Domestic Product per capita

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality & Year FE Observations Number of Municipalities Estimation

Ln GDP per capita Agriculture

Services

Manufacturing

(i)

(ii)

(iii)

(iv)

(v)

-0.0471 (0.0414) No No Yes 2,005 201 FE

-0.0245 (0.0319) Yes Yes Yes 1,980 198 FE

0.00477 (0.0432) Yes Yes Yes 1,980 198 FE

-0.0125 (0.0287) Yes Yes Yes 1,980 198 FE

-0.0473 (0.0512) Yes Yes Yes 1,980 198 FE

Notes. This table presents results from the estimation of Equation 1. The unit of observation is a municipality-year. Robust standard errors (in parentheses) are clustered at the municipal level. The overall sample includes 200 municipalities: The treated group is the set of 102 municipalities included in the new semiarid, while the control group includes their 99 bordering neighbors. Semiarid Reform Dummy is the diff erence-in-diff erence coefficient which corresponds to τI T T from Equation 1.

Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. *** p<0.01, ** p<0.05, * p<0.1

on the total counts of livestock, we see a positive impact. However, that result is sensitive to changing definitions of counts of livestock. For instance, when we consider a restricted definition of livestock counts – looking at the sum of bovine, equine, caprine, ovine, and porcine stocks excluding galliformes – the statistical significance vanishes. However, we observe a positive and statistically significant coeffi cient for the proportion of caprine stock (see Column (iii) of Table 7. This result for caprine stock indicates a convergence of the caprine share of the 102 municipalities of the new semiarid (around 10% of the total livestock count) to that of the entire semiarid area (circa 15%). Additionally, we verify an impact on the total number of caprine stock – a 14% increase compared to the control group. The expansion of caprine stock reveals that the reform has aff ected livestock composition towards a rising population of caprine stock. This finding can be explained by the fact that FNE loans mean a lower cost of capital to livestock producers.

The literature in agriculture points out the benefits of investing in caprine in desert environments. Energy requirements and digestive efficiency plays a central role in selecting appropriate animal breeds (Silanikove (2000), Shankarnarayan, Bohra, and Ghosh (1985)). Goats have several advantages compared to other types of livestock, namely: higher mobility, flexible feeding habit, more effi cient digestive system, higher efficiency of water utilization, and greater milk yield. In arid and semiarid areas, larger animals are at a disadvantage because of their lower mobility and greater maintenance requirements.

Smaller animals, like goats, may search for food on a larger area due to goats’ higher mobility. Additionally, goats have a flexible feeding habit. For example, while sheep usually

24

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

prefer to grazing, goats prefer browsing on small bushes. Caprine breeds have also an effi cient digestive system which allows them to attain maximum food intake and maximal food utilization. Finally, goats are more effi cient to cattle and sheep in terms of water utilization. The ability to reduce metabolism allows goats to survive When deprived from water, goats lose less body weight per day than cattle. Only camel is more effi cient in terms of water use efficiency. After a period of drought, when water is available, goats recover more quickly their body weight. Caprines also supply higher milk yield than other small ruminants, such that milk in quantities can be used for household consumption.

Table 7 shows that there was no significant impact of the credit reform on milk production. As there was neither an increase in milk production nor a rise in caprine stock, we observe a negative impact of the reform in terms of milk production per caprine. One interpretation is that the rise of caprine was not used for production (both using sectorial measures or more aggregated measure such as GDP), but for consumption since the new loan contracts were targeted at low-income producers.

The results from this subsection suggest that small-sized cash-trapped producers used credit to invest in caprine stock, which requires lower capital investment, but that such investment did not reflect in production, but may reflect in household consumption. Data restrictions do not allow us to investigate the impacts on consumption using our quasiexperimental design. There is only one Household Food Acquisition and Purchase Survey in Brazil (“Pesquisa de Or¸camentos Familiares”) for selected years (the latest years are 2002 and 2008), and the survey’s sample does not allow us to obtain data at municipallevel. We can only disaggregate the data for urban and rural parts for selected areas in Brazil. Therefore, we can obtain an approximate information for the whole semiarid area when we analyze the rural part of the Northeast region in Brazil (recall Map 1 in Section 2). Table A.3 in Appendix A provides suggestive evidence that the credit reform might have impacted consumption, as households in the semiarid have higher self-consumption of milk and meat than households in urban area. Additional research need to be done to investigate the role of credit in the expansion of consumption.

Robustness. One concern with the baseline control group is that it contains bigger and richer municipalities as discussed in Subsection 4.2. In the first robustness exercise, we exclude 2 state capitals (“Fortaleza” and “Natal”) from the control group. State capitals have a diff erent scale in terms of population size and this may be driving some of the results. When we run the regressions with the new control group, we find qualitatively similar results, but quantitatively diff erent. Table A.4 in Appendix A indicates that the results are mostly unchanged when we use the reduced control group. The only diff erence is that the results using total livestock are sensible for changing units in control group.

Notice that the results using total livestock were already sensitive to changing definitions of counts of livestock. The other results from the livestock analysis remain unchanged, 25

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table 7: Semiarid Reform and Local Production: Impact on Livestock Baseline Control Group: Bordering Municipalities Ln Livestock

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality FE Year FE Observations Number of Municipalities Estimation

Share of Livestock in

Ln

Production in Ln

Total

Restricted

Caprine

Bovine

Porcine

Caprine Stock

Milk

Milk per Caprine

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

0.0743* (0.0434) Yes Yes Yes Yes 2,814 201 FE

0.0339 (0.0312) Yes Yes Yes Yes 2,814 201 FE

-0.00016 (0.0021) Yes Yes Yes Yes 2,814 201 FE

0.142* (0.084) Yes Yes Yes Yes 2,814 201 FE

-0.0665 (0.054) Yes Yes Yes Yes 2,814 201 FE

-0.186** (0.092) Yes Yes Yes Yes 2,814 201 FE

0.0042** -0.0026 (0.002) (0.0097) Yes Yes Yes Yes Yes Yes Yes Yes 2,814 2,814 201 201 FE FE

Notes. Robust standard errors (in parentheses) are clustered at the municipal level. Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates.

Total Livestock is the sum of galliformes, bovine, equine, caprine, ovine, and porcine stocks. Restricted livestock is the sum of bovine, equine, caprine, ovine, and porcine stocks. The variable “Ln Milk Production per Cow” measures litters of milk (in thousands) produced per one unit of dairy cattle. *** p<0.01, ** p<0.05, * p<0.1

such as the rise in the proportion of caprine stock. We perform another robustness check excluding five high-income municipalities from the control group because producers in these municipalities do not have priority to receive FNE loans. The results are similar to those obtained excluding the two state capitals.

We re-run the regressions with a diff erent time span to exploit symmetry. In our baseline regressions, the dependent variables are from 2000 to 2013. Now we keep the dependent variables ending in 2009 to have a 5-year period in the diff erent-in-diff erence design. Once more the results are mostly unchanged.

6

Conclusion

This paper uses a quasi-experimental design to study the eff ects of credit on economic development. The credit reform in the Brazilian semiarid – Brazil’s poorest region – provides an interesting institutional reform where we are able to analyze the behavior of a state-owned bank. By using unique data, we were able to exploit an exogenous variation in credit policy which created two neighboring areas with diff erent credit rules to study the eff ects of credit on both local growth and local banking. Our paper aims to go further than previous works in finding out the channels through which credit reforms aff ects development and the local banking sector. The semiarid credit reform made it easier for producers in selected municipalities to access abundant credit with below-market interest rates. We ask whether there were benefits associated with being classified as belonging to the semiarid area as opposed to a non-semiarid one, that is, we study what happened with the local

26

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

economy and the banking sector in those places which have received the external shock of cheaper, abundant credit.

Our baseline results show that there was an increase in loans in the treatment group compared to the control group, especially to low-income producers in the livestock sector.

We do not find an eff ect in per capita GDP and other measures of local development.

Moreover, we did not document an increase in sectorial per capita GDP. Since we observed that the livestock sector stood to benefit from the credit expansion, we check alternative measures of local production apart from the GDP. We provide evidence that credit has aff ected production composition, e.g. towards a rising population of caprine stock, which is consistent with the fact that the institutional reform has reduced the capital cost of producers. We interpret these results as evidence that smaller-sized livestock producers – that were previously cash-trapped – have accessed cheaper credit after the reform, but that access was not enough to generate an eff ect on per capita GDP as primary industries are very dominated by informal production. Additionally, we provide suggestive evidence that the rising caprine stock may have been utilized for self-consumption by poor producers as the credit expansion was targeted at low-income livestock producers.

A second group of findings concentrates on analyzing the impact on banking. Firstly, we document an increase in risky loans which is consistent with the rules governing FNE loans. Secondly, as default rate inside the semiarid is higher than outside the semiarid, we would expect an increase in delinquency rates in the newly-added 102 municipalities.

However, our results suggest no diff erence in default rates between the treatment and control groups. This indicates that the solvency bonus for semiarid producers is attractive enough to prevent higher delinquency rates. Finally, we investigated whether the semiarid reform has generated a crowding-out eff ect on loans provided by other banks, and we found no evidence of a crowding-out of credit to other banks. Furthermore, since the credit expansion was mainly to the livestock sector, we examine a crowding-out of loans in livestock credit operations, but we found no crowding out eff ect.

Designing reforms which use funding from national-level taxes is challenging as one must consider all the benefits and costs associated with those reforms. For instance, public funds to the construction of thousands of cisterns in the semiarid in 2009 totaled US$ 26 million (Kuester and Marti (2009)), while FNE lending was circa US$ 5 billion in the same period. Public lending to approximately 500,000 producers per year is a complex task since it entails a myriad of features ranging from project selection to repayment schedules. Producers may invest substantially in acquiring information to gain access to credit, only to end-up with either no new product or none that are profitable.

It is important to highlight that our results apply to a specific institutional framework, given that we are studying the eff ects of a credit reform on the development of a climatevulnerable area of only one country. While the credit reform was specific to Brazil, it

27

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

sheds light on how public banks react to incentives and may engage in riskier behavior.

This lesson may extend to other countries, but it is useful to highlight that diff erent credit reforms may generate distinct incentives regarding the tolerance of riskier loans.

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Field, E., and R. Pande (2008): “Repayment Frequency and Default in Microfinance: Evidence from India,” Journal of the European Economic Association, 6(2-3), 501–509.

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Kuester, A., and J. F. Marti (2009): Pol´ıticas Pu ´blicas para o Semi´arido. Funda¸c˜ao Konrad Adenauery, Fortaleza.

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Rubin, D. (2001): “Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation,” Health Services and Outcomes Research Methodology, 2(3-4), 169–188.

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A

Additional Tables and Figures Table A.1: Summary statistics Variable Latitude Longitude Ln GDP per firm Ln Agriculture GDP per firm Ln FNE loan per firm \ # of Credit Operations per Firm \ # of Credit Operations in Livestock per Firm \ # of Credit Operations in Agriculture per Firm \ # of Credit Operations in Manufacturing per Firm \ # of Credit Operations in Services per Firm \ # of Credit Operations with solely FNE risk per Firm \ # of Credit Operations with Late Payments per Firm Ln Loans from other Banks per Firm Ln Loans from other Banks for Livestock Producers per Firm Ln Total Livestock Ln Livestock Restricted Definition Proportion of Goats of the Total Livestock Proportion of Cattle of the Total Livestock Proportion of Pigs of the Total Livestock Ln Production of Milk divided by Dairy Cattle

Mean -11.189 -40.445 7.129 5.346 4.349 3.588 2.935 0.624 0.005 0.002 2.319 2.683 10.757 11.075 10.904 9.866 0.026 0.322 0.049 -2.434

Std. Dev. 5.008 2.652 0.736 1.166 1.675 8.356 7.706 1.713 0.046 0.018 6.878 7.395 1.326 1.686 1.01 0.923 0.048 0.235 0.039 0.897

N 2814 2814 2005 2005 1693 2005 2005 2005 2005 2005 2005 2005 795 719 2814 2814 2814 2814 2814 2814

Notes. Total number of observations is 2814, which represents 14 years (2000-2013) and 201 spatial units (102 municipalities in the group assigned to treatment and 99 bordering municipalities in the baseline control group). Total Livestock is the sum of galliformes, bovine, equine, caprine, ovine, and porcine stocks. Restricted livestock is the sum of bovine, equine, caprine, ovine, and porcine stocks.

30

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table A.2: Loans of PRONAF program in the Northeast Region: 2004 and 2012 2004 Agriculture Operating Loans

Funding source:

Average Loan Amount (R$):

FNE Saving accounts Other Total FNE Saving accounts

Livestock Investment Loans

Operating Loans

Investment Loans

# of Operations (i)

Amount (R$) (ii)

# of Operations (iii)

Amount (R$) (iv)

# of Operations (v)

Amount (R$) (vi)

# of Operations (vii)

Amount (R$) (viii)

18,580 565 143,804 162,949

30,451,684.62 1,854,788.05 241,355,194.26 273,661,666.93 1639 3283

38,604 0 1,491 40,095

59,168,380.30 0.00 4,726,649.51 63,895,029.81 1533 -

2,177 5 53,734 55,916

1,118,165.53 22,524.50 71,573,519.02 72,714,209.05 514 4505

264,802 0 14,302 279,104

244,802,954.72 0.00 70,478,654.16 315,281,608.88 924 -

(ii)/(i)

31

Other

(iv)/(iii)

1678

(vi)/(v)

3170

(viii)/(vii)

1332

4928

2012 Agriculture Operating Loans

Funding source:

Average Loan Amount (R$):

FNE Saving accounts Other Total FNE Saving accounts Other

Livestock Investment Loans

Operating Loans

Investment Loans

# of Operations (ix)

Amount (R$) (x)

# of Operations (xi)

Amount (R$) (xii)

# of Operations (xiii)

Amount (R$) (xiv)

# of Operations (xv)

Amount (R$) (xvi)

5,433 31,329 172 36,934

38,164,925.84 165,973,469.38 2,561,298.67 206,699,693.89 7025 5298

235,080 1,389 4,750 241,219

736,831,868.22 18,338,516.80 33,426,663.48 788,597,048.50 3134 13203

29,272 12,294 8 41,574

106,667,807.29 78,559,146.59 2,041,899.10 187,268,852.98 3644 6390

478,127 10,167 18,595 506,889

988,955,925.37 87,121,509.62 128,796,669.13 1,204,874,104.12 2068 8569

(x)/(ix)

14891

(xii)/(xi)

7037

(xiv)/(xiii)

255237

(xvi)/(xv)

6926

Notes. Data from Central Bank of Brazil (http://www.bcb.gov.br/?RELRURAL). Rural credit in Brazil has diff erent fund sources. The Constitutional Fund FNE is one funding source, and only BNB bank can lends using FNE funding. A percentage of “saving accounts” of selected banks must be used also as a funding to rural credit. The most important lender using “saving accounts” funding is Banco do Brasil.

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table A.3: Annual Per Capita Household Food Acquisition in Brazil - 2008-2009 Location

Total

Urban Areas Total

Purchase Monetary

32

Cereals Green vegetables Fruits Coconuts, chestnuts and walnuts Flour, starch and pasta Bakery products Meats Viscera Fish products Poultry and eggs Dairy products Sugar, sweets and confectionery products Salt and condiments Oils and fats Beverages and infusions Ready-to-eat food and processed food mixtures Other products

15.15% 7.67% 9.30% 0.17% 8.45% 7.50% 7.66% 0.36% 1.73% 6.20% 9.55% 7.22% 1.57% 2.54% 14.28% 0.66% 0.02%

84.84% 93.21% 89.16% 67.49% 93.22% 97.65% 96.55% 96.41% 86.26% 92.54% 80.90% 97.12% 98.05% 97.58% 96.63% 91.10% 7.41%

Total

Non Monetary 15.16% 6.79% 10.84% 32.51% 6.78% 2.35% 3.45% 3.59% 13.74% 7.46% 19.10% 2.89% 1.95% 2.43% 3.37% 8.90% 92.59%

Purchase Monetary

12.39% 8.18% 10.17% 0.17% 6.91% 8.40% 7.38% 0.37% 1.58% 6.40% 9.75% 6.51% 1.43% 2.37% 17.17% 0.82% 0.01%

Rural Areas

93.12% 96.51% 94.69% 79.06% 96.35% 97.77% 98.00% 96.53% 93.08% 97.30% 90.97% 97.77% 98.00% 97.46% 96.85% 92.02% 20.83%

Non Monetary 6.88% 3.49% 5.31% 21.15% 3.65% 2.23% 2.00% 3.47% 6.90% 2.71% 9.03% 2.23% 2.00% 2.54% 3.15% 7.98% 79.17%

Notes. Tabulations from Brazil’s National Household Food Acquisition and Purchase Survey (“Pesquisa de Or¸camentos Familiares 2008-2009”).

Purchase

Total Monetary 22.85% 6.26% 6.86% 0.18% 12.75% 4.97% 8.42% 0.32% 2.13% 5.62% 8.99% 9.20% 1.96% 3.04% 6.20% 0.20% 0.05%

72.32% 81.19% 66.23% 37.63% 88.47% 97.08% 93.01% 96.06% 72.09% 77.41% 50.42% 95.81% 98.14% 97.83% 94.89% 81.09% 0.00%

Non Monetary 27.68% 18.81% 33.76% 62.37% 11.53% 2.92% 7.00% 3.94% 27.91% 22.59% 49.58% 4.19% 1.86% 2.19% 5.11% 18.91% 100.00%

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table A.4: Robustness of GDP and Credit Results: Testing for Diff erent Control Groups Control Group: Bordering Municipalities excluding 2 States Capitals

33

Semiarid Reform Dummy Geographical Controls Initial Conditions Municipality FE Year FE Observations Number of Municipalities Estimation

Control Group: Bordering Municipalities excluding 5 High-Income Municipalities

Ln GDP per capita

Ln Agriculture GDP per capita

FNE Loan Value per Firm

# Credit Operations per Firm

# Credit Operations per Firm Livestock

Ln GDP per capita

Ln Agriculture GDP per capita

FNE Loan Value per Firm

# Credit Operations per Firm

# Credit Operations per Firm Livestock

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

(x)

-0.0295 (0.0349) Yes Yes Yes Yes 1,960 196 FE

-0.00403 (0.0432) Yes Yes Yes Yes 1,960 196 FE

-0.144 (0.140) Yes Yes Yes Yes 1,373 153 FE

1.752** (0.748) Yes Yes Yes Yes 1,960 196 FE

1.507** (0.705) Yes Yes Yes Yes 1,960 196 FE

-0.0270 (0.0357) Yes Yes Yes Yes 1,930 193 FE

-0.0133 (0.0434) Yes Yes Yes Yes 1,930 193 FE

-0.138 (0.141) Yes Yes Yes Yes 1,353 151 FE

1.607** (0.751) Yes Yes Yes Yes 1,930 193 FE

1.373* (0.707) Yes Yes Yes Yes 1,930 193 FE

Notes. Robust standard errors (in parentheses) are clustered at the municipal level. Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. In regressions (i) to (v), the two states capital were excluded (Natal and Fortaleza), so the control group has 97 units. In regressions (vi) to (x), five high-income municipalities were excluded (Natal, Fortaleza, Parnamirim, Pacatuba, and Guaiu ´ ba), so the control group has 94 units. *** p<0.01, ** p<0.05, * p<0.1

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Table A.5: Robustness of Livestock Results: Testing for Diff erent Control Groups Control Group: Bordering Municipalities excluding 2 States Capitals Ln Livestock

Semiarid Reform Dummy

34

Geographical Controls Initial Conditions Municipality FE Year FE Observations Number of Municipalities Estimation

Share of Livestock in

Control Group: Bordering Municipalities excluding 5 High-Income Municipalities Ln Milk

Ln Livestock

Share of Livestock in

Ln Milk

Total

Restricted

Caprine

Bovine

Porcine

Production

Total

Restricted

Caprine

Bovine

Porcine

Production

(i)

(ii)

(iii)

(iv)

(v)

(vi)

(vii)

(viii)

(ix)

(x)

(xi)

(xii)

0.0453 (0.0379) Yes Yes Yes Yes 2,786 199 FE

0.0254 (0.0306) Yes Yes Yes Yes 2,786 199 FE

0.00125 (0.00173) Yes Yes Yes Yes 2,786 199 FE

-0.0807* (0.0435) Yes Yes Yes Yes 2,786 199 FE

0.0381 (0.0380) Yes Yes Yes Yes 2,744 196 FE

0.0303 (0.0309) Yes Yes Yes Yes 2,744 196 FE

0.00432** -0.00114 (0.00206) (0.00973) Yes Yes Yes Yes Yes Yes Yes Yes 2,786 2,786 199 199 FE FE

0.00449** -0.000992 0.00135 (0.00213) (0.00986) (0.00175) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 2,744 2,744 2,744 196 196 196 FE FE FE

-0.0811* (0.0442) Yes Yes Yes Yes 2,744 196 FE

Notes. Robust standard errors (in parentheses) are clustered at the municipal level. Geographical controls and initial conditions have time-varying coefficients. The geographical controls with time-varying coefficients are: Latitude and Longitude coordinates. Total Livestock is the sum of galliformes, bovine, equine, caprine, ovine, and porcine stocks. Restricted livestock is the sum of bovine, equine, caprine, ovine, and porcine stocks. The variable “Ln Milk Production per Cow” measures litters of milk (in thousands) produced per one unit of dairy cattle. In regressions (i) to (v), the two states capital were excluded (Natal and Fortaleza), so the control group has 97 units. In regressions (vi) to (x), five high-income municipalities were excluded (Natal, Fortaleza, Parnamirim, Pacatuba, and Guaiu ´ba), so the control group has 94 units. *** p<0.01, ** p<0.05, * p<0.1

DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

B

Detailing the new Semiarid Classification

In March 2005, Decree n. 89 from the Ministry of National Integration stated that a municipality should comply with at least one of three climate criteria to be classified as belonging to the semiarid. MI (2005) describes the geographical indexes that were used to add 102 municipalities to the Brazilian semiarid. According to MI (2005) (p.15), “subjective” criteria were ruled out as criteria must comply with certain requirements such as provide a consistent and quantitative measure of climate and to have a long time series of available data. The former classification of semiarid had 1,033 municipalities, while the “new semiarid” area spans through 1,135 municipalities in 9 Brazilian states (Piau´ı, Cear´a, Rio Grande do Norte, Para´ıba, Pernambuco, Alagoas, Sergipe, Bahia, and Minas Gerais).19 The first criterion is an average annual precipitation below 800 millimeters. The period used to calculate the historical average precipitation was from 1960 to 1990. Data from approximately 1,250 precipitation stations were employed to compute the isohyet – a line on a map connecting points of equal precipitation – of 800mm in order to delineate the semiarid area. The rainfall threshold of 800mm was also used in the old semiarid classification that existed between 1989 (the year FNE fund stated to be distributed) and 2005, so the entire group of 1,033 municipalities previously in the semiarid was classified as belonging to the new semiarid delimitation.

The second metric is based on an aridness index. The idea is to evaluate the evaporation rate apart from the average precipitation. Several arid and semiarid areas around the world have a high average precipitation, but high rates of evaporation generate water scarcity, so water cannot be safely used for consumption. Thornthwaite’s aridness index corresponds to ratio of the annual average precipitation to the potential evapotranspiration (the combination of evaporation of water and plant transpiration) of the area. Therefore, this aridness index compares the supply of water (precipitation) in an area relative to the demand for water under prevailing climatic conditions (evapotranspiration). The period used to calculate Thornthwaite’s index was from 1960 to 1994. The potential evapotranspiration was calculated by using FAO’s Penman-Monteith equation (Allen, Pereira, Raes, and Smith (1998)): 900 0.408∆(Rn − G) + γ T +273 u2 (es − ea ) , ET0 = ∆ + γ(1 + 0.34u2 )

(B.1)

where ET0 is the reference evapotranspiration, Rn is the net radiation at the surface, G is the soil heat flux density, T is the mean daily air temperature at two meters height, u2 is the wind speed at two meters height, es is the saturation vapor pressure, ea is the actual vapor pressure, ∆ is the slope vapor pressure temperature relationship, and γ is a psychrometric constant. The diff erence (es − ea ) is the saturation vapor pressure deficit 19 An

intense process of creation of new municipalities took place in Brazil during the last decades.

In 2000, the semiarid had 1,031 municipalities, but two municipalities (Barrocas/BA and Aroeiras do Itaim/PI) were created from existing localities during 2000-2005. Therefore, in 2005 the number of municipalities in the semiarid was 1,033. The Official Bureau of Statistics (IBGE) considers the splits and detachments of municipalities that took place during time to classify municipalities in the semiarid. According to the legislation, new municipalities created from existing ones from the Sudene area belong automatically to the Sudene area (Complementary Law n. 125/07). In 2009, Naz´aria split from Teresisa/PI and increased the number of municipalities in the Sudene area to 1990. There was no detachment or split in both our treatment and control groups during our period of analysis.

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DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

of the air. The parameters in Equation B.1 stem from equations of the aerodynamic and surface resistance. See Allen, Pereira, Raes, and Smith (1998) for a detailed explanation of Equation B.1.

Municipalities entered the semiarid border if their aridness index was between 0.2 and 0.5. Only 396 municipalities complied with the semiarid values. Notice that the aridness index was employed only to add municipalities to the semiarid, because the precipitation criterion had already guaranteed that all local economies in the previous definition would stay in the new semiarid delimitation.

The third criterion is related to the frequency of water deficit. Water deficit is calculated from a water balance equation, which displays the water resource availability in an area by comparing inflows and outflows of water. Selected characteristics are considered by a water balance equation, such as soil quality, daily evapotranspiration, and the quantity of water stored in the soil. The following balance equation was used: St+1 = St + P RE − ET R − DP,

(B.2)

where S corresponds to soil storage, P RE stands for precipitation, ET R is the evapotranspiration, and DP is deep drainage. FAO’s Penman-Monteith equation was used to calculate ET R. The diff erence St+1 − St shows the change in the amount of water stored in the soil. Notice that deep drainage DP , a downward movement of water, occurs usually during periods of heavy rainfall.

The water balance equation shows how much water is stored in a period, which usually have seasonal patterns. For instance, in wet periods, inflows are greater than outflows, creating water surplus and a surplus in terms of water storage in the soil. The opposite takes place in dry seasons, when water deficit is verified. The number of days with water deficit is obtained so as to calculate the frequency of water deficit, that is, the number of days with water deficit divided by the total number of days. An area would belong to the semiarid if it had water deficit frequency higher than 60% during the period from 1970 to 1990.

Figure B.1 shows the official map of the three criteria used by the Ministry of National Integration.

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DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Fig. B.1: Offi cial Map of New Semiarid Definition and its Three Criteria

Notes. The picture shows how the three criteria (precipitation, aridness index, and risk of drought) was used by the Ministry of National Integration to define the new semiarid area. A municipality is included in case it complies with at least one of the three criteria. Source: MI (2005).

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DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

C

Detailing the FNE Fund

Three Constitutional Funds were created by the 1988 Brazilian Constitution to provide subsidized loans to promote economic and social development of Northeast, North, and Midwest regions. The fund allocated to the Northeast region is called Northeast Constitutional Fund (FNE, for “Fundo Constitucional de Financiamento do Nordeste”). The others two are: North Constitutional Fund (FNO, for “Fundo Constitucional de Financiamento do Norte”), and the Midwest Constitutional Fund (FCO, for “Fundo Constitucional de Financiamento do Centro-Oeste”). The institutions responsible for the operation of the FNE, FNO, and FCO are, respectively, the BNB Bank (“Banco do Nordeste do Brasil”) the Amazon Bank (BASA, “Banco da Amazˆonia”) and Bank of Brazil (BB, “Banco do Brasil”) which are state owned banks.

Cumulative FNE lending was equivalent to R$ 133 billion (US$ 60 billion) in real terms.

In 2013, the balance sheet of FNE showed an amount of R$ 47 billion in credit operations.

Detailing the Legislation Governing FNE. The most important piece of legislation for our research design is the Decree n. 89 from the Ministry of National Integration (March 2005) which changed the delimitation of the Brazilian semiarid. As explained in Section 2 and Appendix B, this legislation included 102 municipalities in the semiarid area. BNB bank manages the FNE fund and lends to producers in the so-called SUDENE area. The semiarid area is a subset of the SUDENE area. SUDENE area comprises all municipalities (of the 9 States) in the Northeast region as well as 196 municipalities located in the North of Minas Gerais and Esp´ırito Santo totaling 1,990 municipalities.

Important issues related to the exogeneity of the definition of semiarid have been discussed in Subsection 4.2. Here, we aim to examine the BNB bank’s proposal, which suggested that the semiarid should be modified to include more municipalities (BNB (2005)).

As discussed earlier, this “political enlarged” proposal did not succeed. In the end, the Ministry of National Integration applied objective geographical criteria to define the new semiarid municipalities. Figure C.1 shows the 276 new semiarid municipalities proposed by the BNB study and the 102 added municipalities by the Ministry of National Integration. There is a clear diff erence in the spatial distribution of these two groups. We observe that in the BNB proposal there is a great number of costal municipalities (located further North in the country) labeled as semiarid. This fact suggests that BNB criteria might be guided by political motivation. It is worth noting that out of the 102 added municipalities, 83 were in the BNB bank’s delimitation of the semiarid. These evidences support our argument of the criteria’s exogeneity defined by the Ministry of National Integration.

BNB Bank - Banco do Nordeste. BNB bank is a universal bank created in 1952

by Federal Law n. 1,649/52. The federal government has 99.14% of the shares of the BNB bank. For lending using FNE resources, the bank receives two remunerations: An administrative fee (2% of the total FNE fund, even without borrowing it) and a del credere fee. BNB bank charges a del credere fee of 3% from the FNE fund for loan contracts in the semiarid, and a fee of 6% for operations outside the semiarid, when the state-owned bank bears the credit risk jointly with FNE fund. New programs were also implemented in recent years to boost the number of credit operations. BNB bank is responsible for the largest microfinance program in South America and second in Latin America, “CrediAmigo”.

Regarding the 102 added-municipalities, in 2012, there were 116 bank branches, only 6 of which are from the BNB bank.

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DANIEL FERREIRA PEREIRA GONÇALVES DA MATA GUILHERME MENDES RESENDE

Fig. C.1: New Municipalities in the BNB proposal and New Municipalities defined by the Ministry of Integration

Notes. The picture shows the location of the 276 municipalities proposed by the BNB study as well as the 102 new municipalities included by Ministry of National Integration in the official semiarid. In beige/light gray color, the municipalities only considered by the BNB bank’s proposal. In black, the 83 municipalities in both BNB proposal and the official definition. In blue, the municipalities only in the official definition of the semiarid. Delimitation of SUDENE area is represented by the black line.

During the period from 2000 to 2014, BNB had 5 diff erent chief executive officers. One president stayed much of the relevant period: 8 years (2003-2010). See Table C.1. The process of choosing the BNB’s CEO is ultimately a political one. For instance, President Luis In´acio “Lula” da Silva and the head of Ministry of Finance chose Roberto Smith to be the BNB’s CEO during the 2003-2010 period. However, this political choice did not have any relation to credit policies for the semiarid, which is relevant for our identification purposes.

Table C.1: List of BNB’s CEOs during the Period from 2000 to 2014 Year

President

2014 Nelson Antˆonio de Sousa 2012-2013 Ary Joel de Abreu Lanzarin 2011 Jurandir Vieira Santiago 2003-2010 Roberto Smith 2000-2002 Byron Costa de Queiroz Notes. This tables shows the chief executive officers from BNB bank during the period from 2000 to 2014.

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