metadata: authors: 0: author: Morais,Bernardo
1: author: Perez-Estrada,Javier
2: author: Peydró,José-Luis
3: author: Ruiz Ortega,Claudia
count: Mexico
docna: 0: docna: Expansionary Austerity : Reallocating Credit
            Amid Fiscal Consolidation
docty: Policy Research Working Paper
subtopic: Inequality,Financial Sector Policy,Public Financial Management,Economic Adjustment and Lending,Macro-Fiscal Policy,Public Finance Decentralization and
            Poverty Reduction,Public Sector Economics,Health Care Services Industry
teratopic: Poverty Reduction,Macroeconomics and Economic Growth,Public Sector Development,Industry
abstracts: cdata!: This paper shows expansionary fiscal
            austerity via reallocation of credit supply, but with a
            raise in poverty. For identification, the paper exploits the
            introduction of a Mexican law limiting the debt of
            subnational governments along with matched credit register,
            firm, bank, and state datasets. After the law, states with
            higher ex ante public debt grow substantially faster,
            despite larger fiscal consolidation (higher taxes and lower
            public expenditure). Banks operating in more indebted states
            reallocate credit supply away from local governments into
            private firms, with stronger effects for banks with higher
            exposure to local public debt, consistent with lowering
            crowding out. Effects only happen after the law, not before,
            and there are strong firm-level real effects associated. The
            reduction of crowding out is stronger for financially
            constrained firms and for firms operating in states with
            higher ex ante public spending on social services over
            infrastructure projects. In states more affected by the law,
            despite better economic effects, extreme poverty
            increases––especially in states with higher ex ante public
            spending on social services over infrastructure––consistent
            with a strong reduction for social services during the
            fiscal consolidation.
geo_reg_key: 119228,517191
repnme: repnme: Expansionary Austerity : Reallocating
            Credit Amid Fiscal Consolidation
display_title: Expansionary Austerity :
            Reallocating Credit Amid Fiscal Consolidation
listing_relative_url: /research/2021/05/33064783/expansionary-austerity-reallocating-credit-amid-fiscal-consolidation
topicv3: Public Finance Management m1326316 716,Fiscal Consolidation m1326651 1116,Poverty m1326687 695,Financial Sector Development m1327681 1920,Health m1504661 3480
publishtoextweb_dt: 2021-05-10T00:00:00Z
dois: 10.1596/1813-9450-9655
historic_topic: Poverty Reduction,Macroeconomics and Economic Growth,Public Sector Development,Industry
origu: DECRG: Finance & Priv Sec Devt (DECFP)
no_of_pages: 81
author_keys: 0: author_key: 1174517
model_extractions: Policy Research Working Paper 9655 Expansionary Austerity Reallocating Credit Amid Fiscal Consolidation _Bernardo Morais Javier Perez-Estrada José-Luis Peydró Claudia Ruiz-Ortega_ Development Economics Development Research Group May 2021
Policy Research Working Paper 9655

# **Abstract**

This paper shows expansionary fiscal austerity via reallocation of credit supply, but with a raise in poverty. For identification, the paper exploits the introduction of a Mexican law limiting the debt of subnational governments along with matched credit register, firm, bank, and state datasets. After the law, states with higher ex ante public debt grow substantially faster, despite larger fiscal consolidation (higher taxes and lower public expenditure). Banks operating in more indebted states reallocate credit supply away from local governments into private firms, with stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. Effects only happen after the law, not before, and there are strong firm-level real effects associated. The reduction of crowding out is stronger for financially constrained firms and for firms operating in states with higher ex ante public spending on social services over infrastructure projects. In states more affected by the law, despite better economic effects, extreme poverty increases––especially in states with higher ex ante public spending on social services over infrastructure––consistent with a strong reduction for social services during the fiscal consolidation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cruizortega@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Expansionary Austerity:**

# **Reallocating Credit Amid Fiscal Consolidation**

**Bernardo Morais    Javier Perez-Estrada    José-Luis Peydró    Claudia Ruiz-Ortega**<sup>∗</sup>

Originally published in the <u>Policy Research Working Paper Series on</u> _May 2021._ This version is updated on _August2021._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL codes_ : D72, G21, L33, P16.

_Keywords_ : Subnational Government Debt, Banks, Credit, Crowding Out, Poverty.

> ∗ This draft is from August 2021.  Bernardo Morais: Federal Reserve Board, bernardo.c.morais@frb.gov (contact author); Javier Perez-Estrada: Banco de México, javierpe@banxico.org.mx; Jose Luis Peydró: Imperial College London, ICREA-Universitat Pompeu Fabra-CREI-BarcelonaGSE, CEPR, jose.peydro@gmail.com; Claudia RuizOrtega: DECFP, World Bank, cruizortega@worldbank.org.  We are grateful to Banco de México for their support of this project and the data provided. We thank the seminar participants at World Bank and the Federal Reserve Board for helpful comments. We thank Carlos Zarazúa and Ben Smith for outstanding research assistance. The views in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System, the World Bank, or Banco de México. Banco de México requested to review the results of the study prior to dissemination to ensure confidentiality of the data. Peydró also acknowledges financial support from the Spanish Ministry of Science and Innovation, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S).
# **1. Introduction**

Following both the 2008-2009 global financial crisis and the current COVID-19 outbreak, many subnational governments—states, regions, and municipalities—increased their indebtedness to compensate for the decline in central transfers and tax revenues.<sup>1</sup> This rise has been partly driven by central government guarantees which, even if implicit, created incentives for local governments to potentially reach for higher-than-optimal levels of debt. This increase in indebtedness may eventually lead to periods of fiscal consolidation to fulfill debt obligations.<sup>2</sup> Overall, the economic impact of these austerity episodes is ambiguous. On the one hand, Alesina, Favaro, and Giavazzi (2019a, 2019b) argue that fiscal consolidation can have a positive impact on output through higher business confidence and private investment. On the other hand, Fatás and Summers (2018) and House, Proebsting and Tesar (2020) argue that austerity can have a permanent negative impact on output through reduced potential growth.

In this paper, we analyze the economic effects of limits to public debt. For identification, we exploit the introduction of the Mexican “Law of Financial Discipline to States and Municipalities”, enacted in April 2016. Using the ratio of public debt to freely disposable income of subnational governments as its main indicator, the Financial Discipline (FD) Law established debt ceilings to rein in the rise of local governments’ indebtedness.<sup>3</sup> Prior to the implementation of the Law, the level of indebtedness of local governments varied significantly across states. For example, in the quarter preceding the implementation of the reform, the ratio of public debt to disposable income of Mexican states in the bottom and top quartiles was around 30 percent and 100 percent, respectively.

Exploiting the introduction of the Law along with the ex-ante variation in public debt ratios across states, we estimate the impact of public debt ceilings using a difference-in-difference

> 1 From 2011 to 2018 the share of public debt of local governments worldwide increased from 14 to 22 percent (IMF, 2018). Similarly, the Fiscal Monitor of the IMF (2020) details a large increase in local government debt following the COVID-19 outbreak.

> 2 Regarding implicit guarantees, for example, Danish municipalities receive specific financial help from the central government if they get into financial difficulties, and are put under administrative control (Mau, 2015). In Germany, the constitutional court ruled that the federal government had to bail out two Länder (states) in financial distress. The Spanish financial crisis in 2012 was in large part driven by the excessive indebtedness of local regions with the _Cajas_ (Santos, 2017).

> 3 When promulgating the FD Law, President Peña-Nieto noted that “[T]he priority of the law is to ensure the stability of the country's public finances by establishing the requirements and conditions for the government to grant federal approval of the debt contracted by states and municipalities.”

2
strategy where treatment is continuous and corresponds to the ratio of a state’s total public debt to its free disposable income in 2016Q1.<sup>4</sup> That is, the treatment status of states is fixed over time and determined by their indebtedness one quarter prior to the implementation of the Law.<sup>5</sup> To examine the impact of the Law on fiscal consolidation, we match data on the indebtedness of local governments with information on their public expenditures and revenues. We also match to this data information on poverty rates as well as GDP and employment growth to analyze the impact of the reform on economic activity of states. Finally, we use exhaustive loan-level credit register data matched with information on bank and firm balance-sheets. This data helps us explore if the debt restrictions imposed by the FD Law trigger a reallocation of bank lending away from local governments and into private firms. Importantly, as local governments in Mexico fund themselves almost exclusively through banks, our loan-level data allows us to examine the effect of the Law on the supply of credit.<sup>6</sup> We find that following the FD Law, ex ante more indebted states undertake larger fiscal consolidation, in the form of higher tax rates and larger public expenditure cuts—including in areas such as infrastructure and social protection. Regarding fiscal adjustments, we find that a onestandard-deviation increase in state ex ante indebtedness is associated with a 10 percent increase in tax rates, while on spending adjustments, there is a 4.4 percent contraction in the ratio of public spending to GDP, driven in part by a reduction of spending on both social protection and on infrastructure projects (though there are stronger effects on non-infrastructure projects).

> 4 One key identifying assumption to estimate the causal effects of the introduction of the FD Law is that all outcomes of interest would have followed parallel trends across states in its absence. While we cannot explicitly test for this assumption, we check if all our outcomes of interest (at the state, bank, firm, and loan levels) follow parallel trends across states prior to the introduction of the Law. We find no differences in pre-Law trends, lending credibility to our identification strategy. Using impulse response functions, we corroborate that all our results only happen after the introduction of the Law, not before, suggesting that local governments do not change their behavior in anticipation of the implementation of the Law.

> 5 Due to the long maturity of states’ debt (of roughly 15 years), the indebtedness of states varies little throughout our sample period. For example, within a state, the average correlation of indebtedness one quarter and one year prior to the Law is 0.98. Thus, our results remain quantitatively similar if we use the indebtedness of states one year prior to the Law. For the same reason, the indebtedness of states varies little immediately following the implementation of the Law. As such, states with higher indebtedness at the time of the Law continue carrying higher levels of debt in the subsequent years.

> 6 In the quarter preceding the implementation of the Law, 90 percent of the funding of state governments was obtained from private banks. This dependence on bank financing is a feature of many emerging economies, given the scarcity of alternative sources of financing (Beck, Demirgüç-Kunt and Maksimovic, 2008).

3
Despite larger fiscal consolidation, we document that states with ex-ante higher indebtedness experience faster economic growth after the FD Law. In particular, a one-standarddeviation increase in the ex-ante public debt of a state leads to an increase of 0.2 percentage points in quarterly GDP growth and 0.1 p.p. in quarterly employment growth rates of the state. These results are significantly stronger in the secondary sector, which tends to be more capital intensive (Buera, Kaboski and Shin, 2011).

The impact of the FD Law on states’ poverty rates is mixed. Following the Law, states with higher ex-ante public indebtedness experience a reduction in their moderate poverty rate— consistent with the overall positive output effects of expansive fiscal austerity. However, more exante indebted states also experience an increase in their extreme poverty rate—consistent with the public spending cuts in social protection.<sup>7</sup> A one-standard-deviation increase in the level of state ex ante indebtedness leads to an increase in extreme poverty of around 1.4 p.p. (15.6 percent). These results suggest that, while improvements in economic activity can help individuals with a certain amount of income to raise themselves out of poverty (Banerjee and Newman, 1993; Banerjee et al., 2019), a contraction in public spending can push individuals below such level into extreme poverty.

To uncover the mechanism behind these results, we examine the dynamics of bank lending across states before and after the FD Law. We find that in states with higher ex ante public debt, local governments experience a decline in their bank liabilities after the introduction of the FD Law, while bank lending to private firms headquartered in these states increases. A one-standarddeviation increase in ex ante indebtedness leads to a reduction of around 6.3 percent in the outstanding bank credit of a local government, and to a 1.2 percent increase in average lending to local private firms after the reform. We interpret these results as our first evidence that prior to the FD Law, local governments were crowding out private firms from the bank credit market, and that fiscal austerity alleviates this crowding out problem for firms.

We also document large heterogeneity across banks after the FD Law. Banks operating in more indebted states reallocate credit supply away from local governments into private firms, with

> 7 According to the Mexican government, a household is in extreme (moderate) poverty if it cannot fulfill three or more (at most two) basic needs: Basic income, access to education, access to health, access to social security, basic housing services, and access to food.

4
stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. More concretely, a one-standard-deviation increase in a bank’s pre-Law exposure to local public debt is associated with a 28 percent reduction in the total volume lent to local governments and a 31 percent increase in the total lending volume to firms. Along with the reduction in loan volume, there is a relative increase in interest rates of loans to highly indebted local governments.

We next study the crowding-out mechanism in more detail using loan-level data. This data allows us to study changes in the loan terms of the average firm and identify adjustments in the supply of credit, by saturating our specifications with _firm*month_ fixed effects. We find that in a state with average ex ante public indebtedness, a one-standard-deviation increase in the ex-ante exposure of a bank to local government debt leads to an 8.8 percent increase in the loan volume extended to firms. This effect is substantially larger in more indebted states.

We then evaluate whether the type of ex ante spending of a local government has any impact on the state’s subsequent economic activity. We find stronger positive effects on economic activity and bank lending to the private sector in states with higher ex ante public spending on social services over infrastructure. For a state with average ex ante public indebtedness, a onestandard-deviation increase in the ex-ante share of spending in non-infrastructure projects is associated with a 0.15 p.p. increase in quarterly growth rate of GDP and a 0.1 p.p. increase in the employment ratio after the passage of the FD Law.<sup>8</sup> Credit effects vary depending on how financially constrained firms are (proxied by shorter credit histories). For firms with short credit histories, the relation between state ex ante indebtedness and firm credit access after the FD Law is significantly stronger—especially for firms headquartered in states with ex ante higher share of fiscal spending on non-infrastructure. Overall, we interpret this finding as evidence that private firms operating in states with lower public infrastructure spending benefit more from the unwinding of the crowding out. In other words, our results suggest that the marginal return of the reallocation of capital from public towards private firms is higher in states that were channeling more funding to non-infrastructure spending (i.e., more public spending on social services).

> 8 These states have not only higher share of spending in non-infrastructure, but also over social spending.

5
As a further validation of the mechanism at work, we conduct a series of checks. First, given that the FD Law also imposed limits to the indebtedness of municipal governments, we corroborate that the unwinding of crowding out of private firms is also present at the municipal level. That is, conditional on the public indebtedness of a state, we find that bank lending to the private sector increases more in municipalities with ex ante higher per capita public debt following the FD Law. Second, we use firm balance-sheet data to examine the evolution of real outcomes of firms after the Law and find that—consistent with the state-level GDP and employment results—firms headquartered in states with ex ante higher indebtedness grow more their liabilities, assets, and sales after the Law. For instance, a one-standard-deviation increase in a state’s public indebtedness is associated with an increase of 6 percent in firm liabilities, 7 percent in firm assets, and 1.2 in firm sales. These effects are stronger for firms headquartered in municipalities with branches of banks more exposed to local public debt. Third, consistent with the employment growth observed in the secondary sector, we document that in ex ante more indebted states, banks channel substantially more lending to firms in the secondary sector after the Law. Fourth, we corroborate that private-sector borrowers operating in sectors less dependent on public spending (i.e., less affected by the contraction in government spending) benefit relatively more from the unwinding of the crowding out.<sup>9</sup> Our paper contributes to the literature on local fiscal multipliers (Nakamura and Steinsson (2018); Adelino, Cunha and Ferreira (2017)) and, more generally, the literature on the impact of fiscal austerity.<sup>10</sup> Alesina, Favaro and Giavazzi (2019b) use cross-country panel data to analyze 9 To rule out that our results are driven by the most indebted states, we adopt a difference-in-difference approach where the treatment variable is discrete and equal to one for states above the median indebtedness in the quarter prior to the FD Law and zero otherwise. We find that our results remain very similar under this specification. Another threat to our identification is that the FD Law also established procurement rules intended to guarantee that subnational debt was obtained at the lowest financial cost. Such rules could have induced local governments with ex-ante lower debt to increase their indebtedness, in which case our estimates would be positively biased. To rule this out, we examine changes in bank lending by local governments and confirm that ex-ante less-indebted local governments did not expand their bank liabilities after the Law. Furthermore, we also find that our results hold when restricting the sample to states with ex-ante similar debt-to-net-income ratios but subject to different debt ceilings. Additionally, given that Mexico is an oil producer country, we also rule out that our results are explained by the global collapse in energy prices of mid-2014 (all our results hold if we drop from the sample the oil producing states). Finally, we rule out that our results are driven by variations in external conditions. Given that northern states are more exposed to external shocks due to stronger economic relations with the United States, we split the sample into northern and non-northern states and confirm that our results are not geographically concentrated.

10 Chodorow-Reich (2019) studies the effect of an increase in spending in one region of a monetary union, concluding that an average point estimate for a cross-sectional multiplier is 1.8. Using a panel of OECD countries, Guajardo, Leigh and Pescatori (2014) find that fiscal consolidation has contractionary effects on private demand an on GDP.

6
whether the consequences of austerity episodes depend on the type of fiscal consolidation and show that “expansionary austerity” can arise on increased business confidence and private investment. Corbi, Papaioannou and Surico (2019), studying Brazilian federal transfers show that fiscal spending has a positive multiplier that depends negatively on the income of recipient localities (i.e. poorer municipalities have a significantly larger multiplier).<sup>11</sup> We contribute to this literature by providing granular evidence that the reduction in the supply of bank credit towards local governments can increase credit supply to local private firms, with positive firm-level real effects. Such positive effects are also present at the state-level GDP and employment. However, and consistent with a significant reduction in social public spending, we also find increases in extreme poverty, which are stronger in states where public spending in social programs is relatively higher than on infrastructure.<sup>12</sup> Empirically, while there is evidence of a negative correlation between public debt and growth (see Reinhart and Rogoff (2010) among others), establishing causality is harder. Huang, Pagano and Panizza (2020) use firm-level data to show that local public debt in China crowded out the investment of private firms by tightening their funding, especially of more financially constrained borrowers.<sup>13</sup> Similarly, Hoffmann, Stewen and Stiefel (2021), using a German panel of firms, find that when spreads on local government debt are low, local public banks use their market power to charge higher rates to their customers, reducing investment; further, fiscal consolidation worsened this effect by putting pressure on the budgets of municipal governments which increasingly borrowed from local public banks. Different from these papers, we use an exogenous negative shock to local public debt, along with detailed loan-level data, to show a decrease in public spending and increase in taxes (fiscal consolidation) but with an overall positive effect on local bank lending to private firms, particularly to the more capital-intensive secondary sector, and especially when pre-law public spending was directed more towards social services

> 11 Likewise, Braga, Guillen, and Thompson (2017) find that negative shocks to Brazilian federal transfers have a particularly negative impact on low-skill employment.

> 12 Becker and Ivashina (2018) study the impact of financial repression and find that the lending of banks to their respective governments increased during the European sovereign debt crisis, leading to a reduction in corporate credit. 13 Similarly, Broner, Erce, Martin, and Ventura (2014) along with Gennaioli, Martin and Rossi (2014) show that under credit rationing and financial frictions, government debt is especially damaging for credit constrained firms. Greenwood, Hanson, and Stein (2010), Graham, Leary, and Roberts (2015), and Demirci, Huang, and Sialm (2019) describe the relationship between the structure and level of government debt and firms’ leverage, focusing on their capital structure. Finally, Chakraborty, Goldstein, and MacKinley (2018) show that banks operating in strong housing markets increase mortgage lending at the expense of commercial lending, suggesting that monetary policy accommodation has some negative spillovers to the real economy.

7
instead of public investment. Moreover, we show associated positive economic growth effects, in an austerity scenario and in a setting with a privately-owned banking system. Importantly, we show negative effects on extreme poverty despite better economic effects, including overall poverty.

This paper also contributes to the theoretical literature analyzing the impact of government borrowing on bank lending to private firms. This issue is particularly relevant in developing countries, where government borrowing has increased dramatically since the late 1990s. Moreover, the effects of government bank-borrowing on private investment are likely higher in developing countries, where credit markets are less developed and both credit constraints and credit rationing are more prevalent (Banerjee and Duflo (2004), Ghosh et. al. (2000)). Some argue that access to safe government assets allows banks to take more risk and thus increase their lending to the private sector (Kumhof and Tanner, 2005).<sup>14</sup> An alternative hypothesis is that government lending may create moral hazard discouraging (lazy) banks from lending to the risky private sector (Manove, Padilla, and Pagano, 2001). We provide empirical evidence that when restrictions to local government debt are imposed, banks reallocate their lending away from local governments and into local private firms, with important effects on private investment and other real effects, though with negative consequences on extreme poverty.

The rest of this paper is organized as follows. Section 2 provides background information on the Law of Financial Discipline in Mexico. Section 3 describes the data. Section 4 discusses the identification strategy. Section 5 presents the results and section 6 concludes.

# **2. Law of Financial Discipline of States and Municipalities**

The increase in unemployment and decline in fiscal revenue that followed the 2008-09 global financial crisis induced local governments to finance fiscal deficits with debt. For example, in Mexico public debt of subnational governments increased almost threefold from 203 billion pesos (USD 15.6 billion) in 2008 to 591 billion pesos (USD 46.1 billion) in 2015, with the ratio of states’ debt-to-income increasing from 13 percent to 30 percent during that period. The explosion of local public indebtedness in Mexico was driven mainly by states, rather than municipalities, and was facilitated by a lax federal supervision (Smith, 2015).

> 14 However, this mechanism may not be in play in emerging markets given their poor access to safe assets such as U.S. or German sovereign debt.

8
To reduce the ramp-up in local debt, the Federal Government enacted the Law of Financial Discipline of States and Municipalities in April 2016.<sup>15,16</sup> The Law introduced two key reforms altering how local governments report and manage their finances. The first was the creation of a registry— _Single Public Registry_ —where all local public bodies were required to report their contracted obligations—regardless of the type of loan, type of creditor, maturity, or financing purpose. The second reform introduced three indicators to monitor the financial health of all local governments and set debt ceilings to limit their indebtedness.<sup>17</sup> To provide enough time for the relevant entities to comply with the Law, these reforms were scheduled to come into effect several months later. As such, the alert system guidelines were published by the Ministry of Finance on March 2017. The first indicator defined in the guidelines was the ratio of _total public debt to freely disposable income_ , where freely disposable income includes federal transfers received by local governments in addition to any revenues obtained either locally or from the Budgetary Income Stabilization Fund. This indicator measures the total leverage of local governments and their overall financial sustainability. The second indicator reflects the capacity of local governments to meet the principal and interest of their obligations and was defined as the ratio of _debt service and obligations to freely disposable income_ . The third indicator, the ratio of _short-term obligations to total income_ , measures the ability of local governments to pay obligations with a maturity shorter than 12 months. Starting on April 2017, the FD Law mandated all state and municipal governments to publish these debt indicators on the website of the Ministry of Finance on a quarterly and semiannually level, respectively.<sup>18</sup>

> 15 Decree of Law of Financial Discipline of States and Municipalities, published in the Mexican Official Journal of the Federation on the 27<sup>th</sup> of April 2016.

> 16 While the FD Law was enacted in April 2016, it followed a long and uncertain process that characterizes the life cycle of most legislative bills in Mexico. More broadly, laws are initiated in one of the two chambers of congress, where they are discussed, modified, approved, and sent to the other chamber for their analysis and review. At this stage, draft laws can be approved, dismissed (and even postponed until the next period of sessions of Congress), or further modified (and sent back to the first chamber for more amendments). Using impulse response functions, we find no anticipation effects of the Law – the data suggests that changes in behavior by local governments and banks only happen after April 2016 and not before.

> 17 The Law also established procurement rules with the objective of guaranteeing that subnational debt was obtained at the lowest financial cost. One potential threat to our identification is that the establishment of procurement rules could have induced local governments with ex-ante lower debt to increase their indebtedness. However, we rule this out when examining changes in bank lending by local governments. As we discuss in in Section 4, our evidence indicates that ex-ante less-indebted local governments did not expand their bank liabilities after the Law.

> 18 Alert System Regulation, Official Journal of the Federation, March 31, 2017.

9
To ease the comparison of indebtedness across local governments, the guidelines classify each indicator into _low_ , _medium_ , or _high_ .<sup>19</sup> The Ministry of Finance then summarizes these indicators into an “alert system”, where more weight is placed on the first indicator. This alert system (described in Table IA1) ranks local public indebtedness over time as _sustainable_ , _underwatch_ , or _high_ .<sup>20</sup> As stated in the FD Law, governments with a _sustainable_ indebtedness were allowed to borrow annual debt of up to 15 percent of their freely disposable income; governments with an indebtedness _under-watch_ were allowed to borrow at most 5 percent of their freely disposable income; governments with _high_ indebtedness were banned from obtaining financing unless a strict payment plan is negotiated with the federal government.

Given the long maturity of government liabilities (averaging 15 years) and the greater weight of the first debt indicator in the methodology of the alert system, the debt classifications of local governments remained largely stable over our sample period. As Figure 1 shows, the first alert system classification of state governments in 2017 is highly correlated with their _total public debt to freely disposable income_ ratio prior to the FD Law in April of 2016. As such, states with a higher public indebtedness ratio prior to the Law were more likely to face tighter debt ceilings in the following years as their indebtedness ratio remained closer to the debt ceilings established by the Law (Panel A of Figure IA1).<sup>21</sup> To improve compliance, the FD Law granted greater faculties to the national congress to monitor and deny new debt as well as to sanction non-complying authorities by, for example,

> 19 The first indicator is _low_ if its values lie below 100 percent, _medium_ if its values are between 100 and 200 percent, and _high_ for values above 200 percent. The second indicator is classified as _low_ for values below 7.5 percent, _medium_ for values between 7.5 and 15 percent, and high for values _greater_ than 15 percent. Finally, the third indicator is classified as _low_ for values below 7.5 percent, _medium_ for values between 7.5 and 12.5 percent, and _high_ for values above 12.5 percent.

> 20 Indebtedness is _sustainable_ when the first indicator is _low_ , and the other indicators are at most _medium_ . Indebtedness is _under-watch_ when the first indicator is _low_ and one of the other indicators is _high_ , or if the first indicator is _medium_ and the other indicators are at most _medium_ . Finally, indebtedness of a local government is _high_ when the first indicator is _high_ or when the other indicators are both _high_ .

> 21 The dynamics of public debt around the time of the implementation of the FD Law grant further evidence that states with ex-ante higher indebtedness were more affected by the Law. In Figure 2, we split the sample into states with a level of public debt prior to the FD Law above and below the median. On Panel A, we plot the total deflated bank lending to local governments relative to the start of our sample. The figure shows that the two groups of states followed similar trends in their bank lending in the quarters leading up to the Law. However, in the post-reform years, the two series diverged: bank loans to low indebted states rose on average 10 percent, whereas those to high indebted states fell 20 percent on average. On Panel B, we display the average interest rate on bank loans to local governments. Again, we see parallel lines in the quarters leading up to the reform, with high indebted states paying higher interest rates of around 80 basis points. However, following the reform, the spread widened to 110 basis points on average.

10
suspending federal transfers, not guaranteeing new loans, or explicitly determining situations in which local governments can default on their liabilities. Overall, the FD Law implicitly reduced the incentives of banks to lend to local governments. Evidence displayed in Figure 3 is consistent with this hypothesis—after the FD Law is introduced, banks reduce their share of lending to the government (Panel A) and the risk premium of loans to private firms vis-a-vis loans to the government drops (Panel B).

# **3. Data**

The datasets used in the analysis come from seven sources. The first data set is the _Single Public Registry_ , provided by the Ministry of Finance. This data set reports the debt of subnational governments (at the state and municipal levels) decomposed by credit sources on a quarterly frequency. In addition to total public liabilities, the data set also includes the lending terms contracted by local governments such as interest rates and maturity. Based on this data, we construct the ratio of public debt to freely disposable income of local governments in March 2016—one month prior to the enactment of the Law—to measure the ex-ante indebtedness of subnational governments. This ratio is highly persistent over time and strongly associated with the public-debt-to-net-income financial health indicator that local governments were required to report in the alert system from 2017 onwards (correlation of 0.88).

The second data set consists of a state-year panel containing the annual public expenditures and public revenues of each state in Mexico from 2014 to 2018. This data set is publicly available from the National Institute of Statistics (INEGI). On the expenditure side, we use information on the total expenditure of state governments as well as on certain public expenditure categories. The first category— _transfers, subsidies, and other aid_ —is the largest expenditure group of state governments and represents almost 60 percent of expenditure of states. 76 percent of expenditures within this category comprise funding for health, education and social security programs and institutions. The second category corresponds to _public infrastructure_ spending which represents 5 percent of state expenditures.<sup>22</sup> Public investments on construction, housing, and infrastructure projects (e.g., roads, school buildings, hospitals, sewer systems) are grouped under this category.

> 22 The remaining 35 percent of expenditures are related with salaries and other remunerations of state employees, which tend to be very stable across time.

11
On the revenue side, we analyze the two main components of state government income—taxes and federal transfers—representing 80 percent of their total revenue.

The third data set is the quarterly GDP of Mexico’s 32 states, which started being collected in 2014by INEGI. In addition to the total GDP, we also use information on the GDP contributed by each state’s primary sector—mining and agriculture—secondary sector—manufacturing and construction—and tertiary sector, mainly services.

The fourth data set consists of state-level panel data at the biennial frequency on a series of poverty indicators on even years from 2010 through 2018. Data are collected and compiled by the National Evaluation Council (CONEVAL). CONEVAL created a methodology to evaluate poverty in a state according to the following indicators: income per capita, average educational attainment, access to health services, access to social security, quality and housing spaces, access to nutritious and quality food, and access to paved roads.

The fifth data set, which we refer to as the loan-level data, consists of credit registry data containing bank commercial loans in Mexico to private firms and government-backed entities from July 2014 to June 2018. Its coverage includes the universe of bank loans issued to governmentbacked entities and all bank loans issued to a nationally representative 10 percent random sample of private firms. Loans to government-backed entities include federal, state, and municipal governments, as well as firms owned by the government (federal, state, or municipal). The data are obtained from regulatory reports monthly submitted by every commercial bank to the bank regulator (CNBV). Reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing loans made to all firms. All business loans, regardless of their size, are reported. For each loan, we use information on the issuing bank, the outstanding amount, the interest rate, loan guarantees, and the type of financing (i.e., whether the loan is intended to finance working capital or investment). There is also descriptive information about each borrower, such as its location, industry, and number of employees when the loan started. In the case of privately-owned (henceforth private) firms, we adopt a similar approach to La Porta et al. (2003) and aggregate individual loans at the _firm-bank-month_ level, as some borrowers have more than one loan issued by the same bank at a given point in time. For the same reason, we aggregate all state-government loans at the _state-bank-month_ level. Loan characteristics such as 12
interest rates are then reported using an average weighted by loan volume. Doing so puts greater weight on larger loans and ensures that our results are economically meaningful.

The sixth data set consists of the monthly balance sheet information of 22 commercial banks representing more than 98 percent of commercial bank-lending in the country.<sup>23</sup> Data are provided by the Bank of Mexico (Banxico) and variables in this data set include the total credit volume of banks, both to private firms and public entities, as well as their respective interest rates.

Finally, the seventh main data set is Orbis, a _firm-year_ level data set compiled by Bureau van Dijk, containing information on the balance sheets and income statements of a large set of Mexican firms. The data set reports information on assets and revenues of firms as well as their total and bank-specific liabilities by type of financing. As shown in Morais et al. (2019) this sample of firms is representative of the universe of sectors and locations in Mexico, albeit skewed towards larger firms.

The summary statistics of our data set are shown in Table 1, with the definitions of all variables listed in Table A1 in the Appendix. In Panel A, we display summary statistics of macrolevel data from states and municipalities. We start by presenting the ex-ante indebtedness of state governments, measured by the main indicator _DebtStates,16Q1_ , which is the ratio of states’ public debt to freely disposable income one month prior to the introduction of the FD Law.<sup>24</sup> This ratio is on average 86 percent. However, there is large variation across states (Figure 1). While states in the bottom decile have a ratio of less than 20 percent, the indebtedness ratio of states in the top decile is 225 percent.<sup>25</sup> Furthermore, the average maturity of debt to states is of 14.6 years, with bottom and top deciles having a maturity of 8.3 and 19.3 years, respectively. Still in Panel A, we also present statistics on state-level GDP and employment growth rates, as well as poverty and inequality ratios. The average quarterly growth rate is 0.8 percent, while the employment quarterly growth rate is on average 0.6 percent, with growth rates at the bottom and top deciles of -2.1 and 3.3 percent, respectively. Regarding poverty rates, 43 percent of the population of the average state is considered poor. More concretely, 34 percent of the population is considered to be in moderate

> 23 To guarantee the comparability of our results across banks, and given our focus on commercial lending, we exclude from our analysis banks that specialize in consumer lending as well as niche banking.

> 24 Of the 32 Mexican states, we only have local public debt data on 30. Both Distrito Federal (i.e. Mexico City) and Tlaxcala do not report this information.

> 25 We also present data on the debt of municipalities, which corresponds to less than 10 percent of local debt.

13
poverty, while 9 percent of population is classified as living in extreme poverty. Inequality rates are measured via the Gini coefficient, which ranks the income distribution of the population within a state on a scale between 0 (full equality) and 1 (full inequality). There is little variation in the inequality rates across states in Mexico, with the Gini coefficient averaging 0.47.

In Panel B, we display the main statistics of the fiscal revenue and spending indicators at the state-year level, measured as ratios over the state GDP. On the fiscal spending side, public expenditure represents on average 15.8 percent of the GDP of states. Public expenditures on infrastructure and social protection (including subsidies and other social aid) are on average 0.7 and 6.6 percent of states’ GDP, with large variation across states. Regarding fiscal revenue, the vast bulk of fiscal income of states comes from federal transfers: while federal transfers represent on average 13.4 percent of states’ GDP, tax revenues (not shown) only account for 0.5 percent. The last variable in Panel B, Non-Infrastructures,2015, is the share of public expenditure of a state in 2015 that was channeled to other items apart from infrastructure. We calculate this variable as the ratio of public spending of a state in 2015 in non-infrastructure projects relative to the state’s total public spending. This variable allows us to proxy for the fiscal spending composition of states prior to the FD Law, as lower values reflect state governments that favored spending on public infrastructure. On average, the share of spending on public infrastructure is around 4.5 percent, with large differences across states in the bottom and top deciles (i.e., 1.7 percent vs. 8.2 percent).

In Panel C, we display the main statistics of bank loans to state governments, with observations at the state-month level. We show the levels of total loans of banks to governments, as well as their decomposition on credit lines and term loans (all measured in logs of Mexican pesos). The main takeaways from this panel are that there is an important variation across states, and that the average volume of term loans is substantially larger than that of credit lines.<sup>26</sup> The last three rows of each panel present the statistics on loan margins, namely the interest rate (measured in percent), collateral (measured as a share of loan volume), and maturity (measured in logs of months).

> 26 Conversely, the variation in interest rates at the time of implementation of the FD Law was low. The average interest rate for the period _prior_ to the FD Law is 5.6 percent while the difference between the top and bottom decile is only 1.4 pp. This suggests that prior to the implementation of the Law, banks considered local government debt to be as risky as sovereign debt—whose interest rate at the time was 5.4 percent—as a large share of it was implicitly secured by the federal government (Revilla, 2013). The sovereign interest rate is calculated as the implied annual yield of a 5- year government bond in local currency in March 2016.

14
In Panel D, we display the statistics of the firm-bank-month level data, summarizing the characteristics of bank loans given to private firms. In addition to the volume of all outstanding loans, we show the volume of loans that are destined to working capital and investment (all measured in logs). The average bank loan has an interest rate of about 14.4 percent, collateral covering 32 percent of its volume and maturity of around 18 months. Panel E summarizes the yearly real economic outcomes of firms. Namely, we display the volume of firm liabilities, total assets, fixed assets, and sales, all measured in logs of thousands of dollars.

In Panel F, we display the summary statistics for the monthly bank-balance-sheet outcomes. The first variable corresponds to the overall volume lent to the government (including subnational governments) and private-sector firms. The next two outcomes disaggregate this measure into the lending volumes channeled by banks to the government and private-sector firms. All lending volumes are measured in logs of millions of pesos. The next two outcomes correspond to the interest rates charged on loans to the government and private sector firms, measured in percent. The next outcome, _BankExposureGovb,Mar16,_ measures the share of lending that bank _b_ channeled to the government in March 2016. We use this variable to proxy for the ex-ante exposure that banks had to public debt. The last variable, _BankExposureGovs,b,Mar16_ , reports the share of lending of bank _b_ to state government _s_ in March 2016. This measure proxies for the ex-ante exposure of a given bank to a given state government prior to the Law.

Finally, Panel G displays the summary statistics of two firm-level time-invariant variables. The first one reports the share of revenue obtained from selling inputs to government entities and is measured at the 4-digit economic NAICS sector using U.S. Input-Output tables. Higher values indicate firms operating in sectors with greater dependence to government spending. The second one is an indicator that equals one for firms headquartered in states of the North of Mexico, which include the states of Baja California, Baja California Sur, Chihuahua, Coahuila, Durango, Nuevo León, Sinaloa, Sonora, and Tamaulipas.

# **4. Empirical Strategy**

We now describe the empirical strategy we follow to identify the impact of the FD Law on states’ fiscal balance, macroeconomic activity, and bank lending to local governments. We then discuss how we map this methodology to more granular data at the bank, loan, and firm level, 15
which allows us to investigate the impact of the debt ceilings on the unwinding of crowding out, including changes in loan conditions to private-sector borrowers and subsequent real effects.

# **4.1. Impact of the FD Law on Fiscal Balances, Economic Activity and Bank Lending**

Our identification strategy exploits the introduction of the Financial Discipline Law in April 2016, which imposed lending restrictions to state governments based on their indebtedness. While there was large variation in the indebtedness of state governments at the time of the FD Law (Figure 1), the indebtedness within a state varied little afterwards—in large part due to the long maturity of states’ debt (of roughly 15 years). Therefore, states with higher (lower) indebtedness at the time of the Law tended to carry higher (lower) debt levels in the immediately following years (see Panel A of Figure IA1). We thus adopt a difference-in-difference approach where the continuous treatment variable _DebtStates,16Q1_ corresponds to the ratio of total public debt of a state to its free disposable income in 2016Q1.<sup>27</sup> That is, the treatment status of states is fixed over time and determined by their indebtedness one quarter prior to the implementation of the Law.<sup>28</sup> Our baseline specification is as follows: In equation 1, the dependent variable _ys,t_ consists of a series of outcomes at the state and time _t_ level.<sup>29</sup> The impact estimate is given by _β_ , the coefficient of the interaction of our treatment variable, _DebtStates,16Q1_ , and _Postt_ , a dummy that equals one from April 2016 onwards and zero otherwise. All specifications of equation 1 include state fixed effects _γs_ , and in most of the specifications we also include time fixed effects _γt_ . Standard errors are double clustered at the state and time level.

Based on equation 1, we first identify the impact of public debt limits on fiscal balance dynamics of state governments. We then examine the impact of the FD Law on state-level GDP

> 27 Our main analysis focuses on the impact of the FD Law across states, rather than municipalities, as state-level debt represents 90 percent of the total debt of local governments. Nevertheless, to validate our findings, we conduct a robustness check where we analyze the impact of the FD Law across municipalities of varying ex-ante indebtedness. Our findings (discussed in Section 5) remain similar to those at the state-level.

> 28 Not surprisingly, results are very similar if we use the debt state just prior to our sample period (end of 2013) as debt state is very similar within the same state across different years.

> 29 Depending on the frequency of the data, time _t_ can be at a monthly, quarterly, yearly, or biennial level. In some specifications, we further use data at the firm-bank-month level to analyze lending outcomes of firms headquartered in states of varying ex-ante indebtedness levels.

16
and employment growth rates, as well as on states’ poverty and income inequality rates. Finally, we identify the impact of public debt ceilings on bank lending to local governments as well as to private-sector firms. That is, we study whether the restrictions to local public debt introduced in the FD Law altered the credit allocation of banks away from the public sector and into privatesector firms. Bank lending dynamics to private-sector firms in states below and above the median ex ante public indebtedness suggest this is the case (Figure 4). While credit volumes to private borrowers in states below and above the median public indebtedness followed a similar trend before the FD Law, bank lending volumes to private firms in states with higher public debt begin increasing relatively more afterwards (Panel A). The increased lending volumes experienced by firms in states with ex ante higher public debt were further met by relatively lower interest rates (Panel B), hinting that these changes in lending were the result of an expansion of credit supply to private borrowers in ex ante more publicly indebted states.

One key identifying assumption to estimate the causal effects of the introduction of the FD Law using a difference-in-difference approach is that in the absence of the Law, the trends in outcomes between treatment and control states would have been the same.<sup>30</sup> While this assumption cannot be tested, we test for differences in the trends of our outcomes of interest across states of varying indebtedness before the introduction of the Law. To do this, we use the regression outlined in equation 2, where we restrict the sample to the pre-reform period.

The variable _Trendt_ in equation 2 is a linear time trend, and as before, _DebtStates,16Q1_ captures the indebtedness measure of states in the first quarter of 2016. The coefficient _β1_ captures the average trend over time of the outcome of interest _ys,t_ prior to the Law. The coefficient _β2_ , the main coefficient of interest, tests for differences in the trends of _ys,t_ across states of varying indebtedness levels in the pre-reform period. Included in the regression are fixed effects at the state level γ _s_ and in some specifications, we further include time-level fixed effects. The results of this test for the state-quarterly, state-yearly, state-biyearly, and state-monthly variables are displayed

> 30 This assumption is more plausible when the outcomes in the pre-reform period are similar in levels across states of varying ex-ante public indebtedness. In Table A3A, we test for differences in mean outcomes in the pre-reform period between states below or above the median public indebtedness. We find that across a series of economic and financial measures, states with varying ex-ante indebtedness were statistically similar prior to the implementation of the Law.

17
in Tables A2A through A2D of the Appendix. The results for the firm-bank-month level are summarized in Table A2E of the Appendix. Overall, we find that the outcomes of states of varying indebtedness levels indeed followed parallel trends before the FD Law was introduced.<sup>31</sup> We additionally check the validity of our estimates by relying on alternative identification strategies. First, we estimate the impact of the Law by exploiting differences in the alert system classification (i.e., sustainable vs under-watch) of states with ex ante similar debt-to-net-income ratios. We summarize these results in Internet Appendix Tables IA2 and IA3. Second, we adopt a difference-in-difference approach where the treatment variable is discrete and equal to one if a state's measure of public indebtedness in the quarter prior to the FD Law is above the median indebtedness and zero otherwise. This specification allows us to rule out that our results are driven by states with extreme values in their 2016Q1 ratios of total public debt over free disposable income. The results of this alternative specification are displayed in Internet Appendix Tables IA4 and IA5.

# **4.2. Bank Heterogeneity in the Impact of the FD Law**

Banks provide us with yet another layer of variation to study the impact that the FD Law, and debt ceilings in particular, had on credit markets. In particular, we expect that banks that lent a larger share of their funds to ex ante more indebted local governments, would likely experience more drastic changes to their lending once the debt ceilings were introduced. Descriptive evidence displayed in Figure 5 shows that there was a large divergence in lending across banks with varying ex ante exposures to local government debt after the Law.<sup>32</sup> In the figure, we split banks into two

> 31 We further check for differences in the non-linear trends of bank lending volumes to local governments and private firms across states before and after the reform. To examine bank lending volumes to local governments, we run a series of regressions using the specification summarized in Equation 3:

> where _HighDebtStates,16Q1_ is indicator variable that equals one for states that in the quarter prior to the FD Law had public indebtedness above the median and zero otherwise. _Monthm_ is an indicator variable that equals one in month _m_ and zero otherwise, while the other variables are defined as before _._ To analyze bank lending volumes to private-sector firms, we run a series of regressions akin to Equation 3, where observations are at the firm-bank-month level _._ The coefficients of interest _βm_ are plotted in Figure A1. We corroborate that prior to the Law, the dynamics of bank lending to local governments (Panel A) and private-sector firms (Panel B) were similar across states, and only begun diverging after the implementation of the Law.

> 32 In Table A3B, we compare a series of statistics prior to the FD Law between banks with an exposure to local governments in April 2016 above vs below the median. The results show that banks with different exposures to local governments were statistically similar along several characteristics and help us rule out that banks of varying exposures to local government debt are different in other dimensions, such as their capital and liquidity ratios, or their

18
groups depending on whether their exposure to local government debt prior to the Law is above or below the median. In Panel A, we display the dynamics of total bank lending to local governments (normalized to April 2016) for banks with high and low exposure to local public debt. Prior to the implementation of the Law, the two groups of banks followed similar trends in their lending to local governments. However, six months after the passage of the FD Law, a gap in local government lending begins opening between the two groups of banks, with the growth of subnational government lending being driven by banks ex ante less exposed to public debt. Panel B shows that the patterns of bank lending to firms across banks are reversed, with ex ante more exposed banks increasing relatively more their lending to private-sector firms after the Law.

To investigate heterogeneity in the lending dynamics of banks after the FD Law, we relate banks’ ex ante exposures to local government debt to adjustments in their lending to local governments and the corporate private sector after the Law. Our specification, summarized in equation 4, analyzes a series of outcomes at the bank-month level to the bank’s ex ante exposure to debt from local governments.

The dependent variable _yb,m_ consists of a lending outcome of bank _b_ in month _m_ . The five outcomes that we examine correspond to: total volume lent (in logs), volume lent to private-sector firms (in logs), volume lent to government entities (in logs), average interest rate on loans to private-sector firms (weighted by loan size), and average interest rate on loans to government entities (weighted by loan size). The variable _BankExposureGovb,Mar16_ consists of a bank-level measure of exposure to local governments’ debt and is defined as the share of lending to local governments by bank _b_ in the month prior to the reform. To examine changes in lending outcomes across banks after the FD Law, we interact this variable by the indicator variable _Postm_ . Included in the specification are a series of bank and month fixed effects.

> appetite for risk. We further run a regression at the bank-month level to test whether the share of loans channeled to state governments varied across banks with varying ex-ante exposures in quarters around the implementation of the reform. Figure A2 plots the coefficients of this regression. Results indicate that the share of bank lending destined to local governments by banks with level of indebtedness above/below median were unchanged in the quarters leading up to the reform.  However, after the reform, the share of bank lending destined to local governments declined substantially more for the banks that were more exposed to local governments at the time of the implementation of the reform.

19
To examine more granularly whether the debt ceilings introduced in the FD Law induced an unwinding of the crowding-out in credit markets, we next apply our identification strategy to loan-level data. More precisely, we first study changes in bank lending to local governments by using data at the state-bank-month level. We then examine variation in bank credit supply to firms using data at the firm-bank-month level. Using loan-level data allows us to test for differences in the loan conditions of firms and local governments across states of varying ex ante public debt. Furthermore, this data helps us examine if within a state, the loan conditions obtained by firms and local governments differ across banks of varying ex ante exposures to local public debt. The specification we use is outlined in equation 5.

The dependent variable _yf,b,m_ corresponds to the total loan volume as well as the loan volume destined to working capital and to investment projects extended to firm _f_ by bank _b_ in month _m_ (all in logs). Other outcomes examined include the interest rate, collateral rate, and maturity of loans obtained by firm _f_ from bank _b_ in month _m._ The regressors _DebtStates,16Q1_ and _Postm_ are defined as above. The variable _BankExposureGovs,b,Mar16_ is a state-bank level measure of the exposure of banks to debt from local governments and is defined as the share of lending to local governments of state _s_ by bank _b_ in the month prior to the reform. We include in the specification bank-month fixed effects _γb,m_ to isolate variation in the data within the same bank in the same period. Doing so help us identify whether changes in the relative exposure of a bank to a given state government affects the lending conditions of borrowers in that state after the FD Law. To attribute changes in loan conditions to adjustments in the supply of credit, we need to exhaustively control for time-varying credit demand movements. We do this in two ways. First, we include in some specifications industry-month fixed effects, which help us remove from the analysis sectordriven shocks. Second, in some specifications we control comprehensively for time-varying changes in the demand for credit by including firm-month fixed effects, which allow us to compare the loan conditions that the same firm obtains from banks of varying ex ante exposures to public debt. Standard errors are double clustered at the state and time levels.

We again test for the validity of our identification strategy by examining the trends of our loan-level outcomes across banks in the pre-reform period. The non-linear trends of the share of 20
bank lending to local governments are displayed graphically in Figure A2.<sup>33</sup> Prior to the Law, relative lending to local governments and firms followed a relatively similar trend across banks. However, in the quarters following the implementation of the FD Law, banks ex ante more exposed to local public debt begin contracting relatively more their lending to local governments.

# **4.3. Impact of the FD Law on Firm-Level Outcomes**

Finally, we examine the impact of public debt limits on real outcomes of firms. To do this, we apply our identification strategy on firm-year level data obtained from Orbis. With this data, we compare changes in the real activity of firms across states of varying ex ante indebtedness before and after the FD Law. Since Orbis identifies the municipality where the firm is headquartered and given that bank lending tends to be local, we additionally examine heterogeneity in firms’ real activity across municipalities of varying exposure to the FD Law. We define the exposure of a municipality to the FD Law by averaging the exposure to local government debt of the banks that operate in the municipality. Our intuition is as follows. If changes in credit supply are concentrated in banks that were ex ante more exposed to local public debt, then firms headquartered in municipalities where these banks operate should be relatively more impacted by the FD Law. Our specification is outlined in equation 6.

The outcomes of interest _yf,y_ in equation 5 correspond to the total liabilities, assets, fixed assets, and sales (all in logs) of firm _f_ in year _y_ . The variable _DebtBankMunim,Mar16_ is our measure of exposure of a municipality to the FD Law and is calculated as the weighted average of _BankExposureGovs,b,Mar16_ of the banks operating in municipality _m_ in March of 2016. All other variables are as defined above. We include year fixed effects to control for yearly shocks, as well

> 33 The test of non-linear trends is based on regressions akin to equation 3, where we relate the loan-level outcome to a set of interactions of the ex-ante exposure to local government debt of banks with monthly dummy variables, and further include bank and time fixed effects. Figure A1 plots the coefficients and respective confidence intervals of these interactions. In results not shown, we also confirm that our loan-level outcomes follow parallel linear trends across states of varying ex-ante indebtedness and banks of varying exposure during the pre-reform period.

21
as firm-level fixed effects to control for all time-unvarying characteristics of a firm. Finally, we cluster the standard errors at the firm level.<sup>34</sup>

# **5. Results**

We now discuss our findings. We first summarize the effect that the FD Law had on aggregate state outcomes, including public fiscal balance, economic activity, and poverty rates. We then discuss our loan-level evidence on the unwinding of crowding out in the credit markets after the introduction of the FD Law. Next, we describe our firm-level results of the impact of the crowding-out unwinding on firms’ real outcomes. Finally, we discuss the extent to which the impact of the FD Law depends on the ex-ante composition of states’ fiscal spending.

# **5.1. Impact of the FD Law on Fiscal Balance, Economic Activity and Bank Lending of States**

In this section, we first discuss the impact that the debt ceilings introduced in the FD Law had on states’ fiscal balances. We then summarize how aggregate economic activity of states as well as their poverty rates and income inequality measures were affected by the Law.

# _5.1.1. Fiscal Balance of States_

Table 2A reports our estimates of equation 1 on the fiscal components of states. In Panel A, we show the results for the yearly fiscal expenditure indicators of states. The first indicator, _Total Expendituress,y_ , corresponds to the ratio of total public spending over a state’s GDP. Consistent with the fiscal austerity that the new debt ceilings impose, we find evidence that more ex-ante indebted state governments contract their fiscal expenditures after the introduction of the FD Law (columns 1 and 2). A one-standard-deviation increase in the ex-ante public indebtedness of a state resulted in a 4.4 percent contraction in public spending after the Law (with the state’s ratio of public expenditure to GDP dropping by -0.012*0.7=0.7 p.p.). The second indicator, _Infrastructures,y_ , is the ratio of public spending on infrastructure to the state’s GDP. This

> 34 To validate our difference-in-difference strategy in the firm-year-level data, we again conduct several checks. First, we compare firm outcomes in the pre-reform period for firms located in municipalities with local public debt exposure above and below the median (Table A3C). The data confirms that both groups of firms are statistically indistinguishable on our outcomes of interest prior to the Law. Second, we check if the firm-level outcomes followed parallel trends in the pre-reform period. To conduct this test, we run regressions outlined on equation 2 on our firmyear data, restricting the sample to the period prior to the introduction of the FD Law. Results summarized in Table A2F, confirm that in the pre-reform period, the outcomes of interest follow parallel trends across states of varying indebtedness.

22
expenditure category mostly comprises investment in public infrastructure and construction projects. Our estimates in columns 3 and 4 indicate that as a result of the FD Law, ex ante more indebted state governments also contracted their expenditures in infrastructure projects relative to their GDP. A one-standard-deviation increase in a state’s ex ante indebtedness leads to a contraction in public infrastructure spending over GDP of around 0.3 p.p. (0.004*0.7). The next indicator, _Social Servicess,y_ , consists of the ratio of the public spending category “transfers, subsidies and other aid” to the state’s GDP. This category is the largest expenditure of local governments and is mainly directed to more vulnerable populations via funding of health, education, and social assistance programs/institutions. Results in columns 5 and 6 show that ex ante more indebted states also cut their spending on social aid after the Law. Our estimates indicate that a one-standard-deviation increase in a state’s ex ante indebtedness is associated with a reduction in social-aid public spending of around 0.4 p.p.. Finally, the last two columns of Panel A present the results of the impact of the FD Law on the ratio of public debt servicing to states’ GDP. Consistent with the increased borrowing risk from the introduction of debt ceilings, we find that more ex-ante indebted state governments had to channel more of their resources to service their outstanding debt after the FD Law.

Estimates of the effect of the FD Law on states’ yearly fiscal revenue indicators are displayed in Panel B. More concretely, we focus on the two main sources of operational income of subnational governments— federal transfers and taxes. As expected, results from columns 9 and 10 show that the FD Law did not affect the ratio of federal transfers to a state’s GDP. However, our estimates in columns 11 and 12 suggest that tax rates in more ex-ante indebted states increased after the FD Law (by 10 percent on average), likely in an attempt to raise fiscal revenue. Overall, these results suggest that more indebted states reduce public spending and raise taxes to increase payments of their outstanding debt.

# _5.1.2. Economic Activity of States_

Table 2B presents our estimates from equation 1 using the state-quarterly data. Our first outcome of interest corresponds to the GDP of states. The next outcomes are states’ total employment, as well as employment in the primary, secondary, and tertiary sectors. All these variables are measured in quarterly growth rates. Overall, the estimates suggest that after the introduction of the FD Law, states with ex ante higher indebtedness experienced an increase in 23
their aggregate production and employment. The impact of the FD Law on state GDP is positive and statistically significant: a one-standard-deviation increase in the ex-ante public indebtedness measure of a state led to an average increase in the quarterly growth rate of state GDP of around 0.2 p.p. (0.003*0.7). The impact on employment varied across sectors of production, with no aggregate change on the primary sector, and a mildly positive (albeit noisy) impact on the tertiary sector. The increase in employment appears to be stronger in the secondary sector, which tends to be more capital intensive (Buera, Kaboski and Shin, 2011) and therefore is likely to benefit relatively more from looser financing. As Internet Appendix Table A4 shows, the economic magnitudes of these results remain similar when we adopt a difference-in-difference approach with discrete treatment.

Table 2C displays the impact estimates of the FD Law on poverty and inequality measures, reported at the state-level at a biennial frequency (i.e. even years). Our outcomes of interest include the share of the state’s population living in total, moderate and extreme poverty, as well as the Gini coefficient. Estimates in columns 1 through 4 indicate that states with higher ex ante indebtedness—which increased substantially their GDP and employment following the FD Law— also saw declines in their total and moderate poverty rates after the FD Law. More concretely, a one-standard-deviation increase in ex ante state indebtedness led to a reduction in the share of poverty of around 1.4 p.p. (3.3 percent) and a reduction in share of moderate poverty of around 2.8 p.p. (8.3 percent). However, compared to states with ex ante lower public debt, the share of population living under extreme poverty increased in states that had higher indebtedness prior to the FD Law (columns 5 and 6). A one-standard-deviation increase in the level of state ex ante public indebtedness leads to an increase in extreme poverty of around 1.4 p.p. (15.6 percent). This result is consistent with our previous finding that the FD Law induced states with ex ante higher public debt to carry out spending cuts in areas such as social protection.<sup>35</sup> Nevertheless, despite the increase in extreme poverty, we do not find evidence of rising income inequality after the FD Law in ex ante more indebted states (columns 7 and 8).

# _5.1.3. Bank Lending to State Governments and Private-Sector Firms_

> 35 In other results not shown, we find that extreme poverty of a state is highly sensitive to changes in the state’s spending on social programs, suggesting that spending cuts in this area presumably hurt the most vulnerable population.

24
Our findings so far suggest that after the FD Law, ex ante more indebted states grow faster—despite adopting fiscal austerity measures. One potential mechanism explaining these results has to do with credit reallocation (away from the public sector and possibly into privatesector firms seeking financing). Our intuition being that the restrictions to local government debt introduced by the FD Law likely pushed banks previously lending to the public sector to channel their credit elsewhere possibly within the same state. To establish if the FD Law indeed induced banks to reallocate their credit, we examine the evolution of banking lending to local governments and private-sector firms after the Law. We further investigate bank heterogeneity in credit reallocation, exploiting variation across banks given their exposure to debt from local governments prior at the time of the implementation of the Law.

Table 3A displays the estimates of equation 1 on the bank loans to local governments at the state-month level. The three outcomes of interest are the evolution of total bank lending, total bank lending in term loans—which tends to be used to finance investment projects—and total bank lending in credit lines—which is credit primarily used to finance working capital. All variables are in logs. Overall, the results suggest that ex ante more indebted state governments experienced a decline in their bank borrowing once the FD Law was introduced. Columns 1 and 2 show that, after the implementation of the Law, state governments with higher ex ante debt significantly contracted their total loan volume. More concretely, a one-standard-deviation increase in the exante indebtedness of a state government led to a reduction of around 0.09*0.7 = 6.3 percent in its bank credit volume. As columns 3 to 6 show, the reduction in lending to state governments was driven by a reduction in term loans, with the impact on credit lines being statistically indistinguishable from zero.

Table 3B further displays the estimates of equation 1 on the terms obtained by local governments on their bank loans. We find that after the FD Law, more ex-ante indebted states experience an increase in the cost of credit relative to other state governments, in the form of higher interest rates, and higher collateral requirements. Albeit with no adjustment in the maturity of their loans. For example, a one-standard-deviation increase in the ex-ante indebtedness of a state government, leads to a 60 basis points increase in their interest rate. We interpret these findings as evidence that the FD Law had a larger impact on the relative tightening of bank lending to state governments with higher ex ante levels of debt.

25
While more indebted state governments contracted their bank borrowing after the FD Law, the _a priori_ direction of the impact of government borrowing restrictions on lending to private firms is unclear. On the one hand, governments might have crowded-out private firms and banks may redirect lending towards the private sector. On the other hand, banks may contract their lending in highly indebted states due to the predicted lower government spending and consequential lower economic activity.<sup>36</sup> We now discuss our loan-level evidence of the impact of government borrowing restrictions on bank credit to private firms. To do so, we use a specification akin to equation 3, where observations are at the firm-bank-month level. We include in this specification fixed effects at the bank-month level, which help us isolate variation in the credit supply of a given bank in a given month across states of different ex ante public indebtedness. We also introduce firm-bank fixed effects to control for time-invariant demand factors within a firm-bank pair. Thus, we compare changes in lending outcomes of the same firm-bank pair before and after the introduction of the FD Law. Finally, we saturate some specifications with sector-month fixed effects to control for monthly changes in the credit demand of firms from different sectors.

Table 4A displays our estimates for three outcomes of interest, all in logs: _Volumef,b,m_ , which corresponds to the total credit volume extended to firm _f_ by bank _b_ in month _m_ , as well as credit volume issued to finance investment projects ( _Investmentf,b,m_ ) and working capital ( _Working Capitalf,b,m_ ). Results in columns 1 and 2 show that an increase in ex ante state public indebtedness is associated with increases in bank lending to private firms after the implementation of the FD Law. More concretely, a one-standard-deviation increase in the indebtedness of a state leads to a 0.015*0.7=1 percent increase in lending to private firms. Results in columns 3 through 6 show that the increased lending is mainly in the form of credit towards investment projects. More concretely, a one-standard-deviation increase in the indebtedness of a state leads to a 3.6 percent rise in credit to finance investment of firms, and a 1.2 percent increase in credit destined to working capital. In Table 4B, we further examine the evolution of the credit terms of bank loans to firms following the FD Law. We find that a one-standard-deviation increase in the public indebtedness of a state

> 36 These predictions assume that capital markets in Mexico are not perfectly integrated. If they were, any potential crowding-out would happen at the national level, not the state level, since a local increase in demand for credit would not result in higher interest rates at the local level,  instead capital would flow across locations, equalizing interest rates across states. Huang, Pagano and Panizza (2020) also show that Chinese capital markets are not perfectly integrated.

26
leads to an increase of 1 percent in the collateral rate on loans to private-sector firms in the state, with no change in other margins.

# _Unwinding of Crowding Out: Robustness Checks_

We conduct a series of robustness checks to confirm the validity of our crowding-out mechanism. First, given our results suggesting that state-level employment in the secondary sector is positively impacted by the FD Law, we explore whether bank lending is disproportionally reaching firms operating in the secondary sector which tend to be more capital intensive (Buera, Kaboski, and Shin, 2011). Table A4 summarizes our findings, which overall confirm that firms operating in the secondary level experience a relatively larger increase in their bank credit volumes, especially on loans destined to investment. For these firms, a one-standard deviation increase in ex ante indebtedness of states leads to a 6.7 percent increase in loan volume.

Second, to ensure that our results are not driven by variations in external conditions— mainly from the United States—we split the sample into northern and non-northern states and check whether the effects we obtain are concentrated geographically. Northern states in Mexico are more exposed to external shocks as they have substantially more economic relations with the United States (INEGI, 2014) as well as a larger share of exports to GDP (39 percent compared to 12 percent). We rule out that our results are concentrated in the Northern states and thus driven by external conditions as opposed to the FD Law (Table A5).<sup>37</sup> Third, as we discussed earlier, the Law of Financial Discipline affects state as well as municipal governments. While the overall bank debt of states is 12 times larger than that of municipalities, we also check if after the Law, bank lending to private firms expands more in more indebted municipalities. Our results, summarized in Table A6, confirm that conditional on the public indebtedness of a state, bank lending to the private sector increases more in municipalities with ex ante higher per capita public debt following the FD Law. In a state of average public indebtedness, a one-standard-deviation increase in the ex-ante per capita public debt of a municipality where a firm is headquartered, leads to the firm experiencing an increase in its loan volume of 0.4 percent, and an increase in its loan volume to finance investment and working capital

> 37 We also find that these results go through if we split the sample into tradable and non-tradable sectors (results available upon request).

27
of 3.2 and 0.4 percent, respectively. These results provide further evidence that lending to local public governments in the pre-reform period was crowding out lending to private firms.

Fourth, we further test whether private-sector borrowers operating in sectors less dependent on public spending (i.e., less affected by the contraction in government spending) benefit relatively more from the reform. Following Belo, Gala, and Li (2013), we use the Mexican Input-Output table at the 4-digit NAICS level to calculate the share of revenues that comes from sales to the government for each sector. The results of this exercise are displayed in Table A7. They suggest that private borrowers operating in sectors less dependent on government spending benefit relatively more from the reform curtailing local public debt. More concretely, a one-standarddeviation decrease in the level of government exposure is associated with a 0.1 percent increase in total loan volume, and a 0.7 percent increase in loans destined to investment.

Fifth, along with the debt ceilings, the Law also introduced procurement rules requiring state-owned entities to carry out a competitive procurement process with multiple lenders and select the offer with the lowest cost. One potential threat to our identification is that by potentially reducing the cost of credit of local governments, the new procurement rules might have induced local governments with ex ante lower debt (i.e., the states that belong to our control group) to increase their indebtedness. In such case, the gap in public debt that widened across states of varying indebtedness after the Law would be driven not exclusively by the debt contraction of ex ante more indebted governments (due to the new debt ceilings), but also by the rise in debt of ex ante less indebted governments (due to the procurement rules). We rule out this concern by examining changes in bank lending of ex ante less indebted local governments before and after the Law. As Figure 2 shows, while local governments with ex ante higher public debt contracted considerably their bank liabilities ex-post, bank lending of ex ante less-indebted local governments did not increase. If anything, the liabilities of local governments with ex ante lower debt decreased after the Law, albeit at a slower pace than their more indebted peers. Similarly, as Panel B of Figure IA1 shows, even state governments of ex ante lower indebtedness saw an increase in the cost of credit in the years following the FD Law. In addition, we rely on an alternative identification strategy to estimate the impact of the debt ceilings. Under this strategy, we exploit differences in the alert system classification (i.e., _sustainable_ vs _under-watch_ ) of 10 states with ex ante similar debt-to-net-income ratios around the threshold splitting five _sustainable_ and five 28
_under-watch_ states. We confirm that our results under this alternative methodology remain similar in magnitude (see Internet Appendix Tables IA2 and IA3).

# **5.2. Bank Heterogeneity in the Impact of the FD Law and the Crowding Out Channel**

So far, our results indicate that in states with ex ante more public indebtedness, local governments contracted their bank borrowing after the FD Law, and in turn, bank lending to private firms headquartered in the states increased. However, we would expect the adjustment in credit to vary across banks. Banks with no lending relationship with local governments prior to the Law would likely see little change in their lending patterns. In contrast, the FD Law likely induced more adjustments among ex ante more aggressive lenders of local governments. Following the lending contraction to local governments due to the debt ceilings, lenders ex ante more exposed to local governments would have to reallocate their funds.

We now discuss our evidence on bank heterogeneity in the lending adjustments to local governments observed after the FD Law. To capture the exposure of a bank to local governments prior to the FD Law, we calculate the share of lending the bank channeled to subnational governments relative to its total lending in March 2016. We then relate the ex-ante exposure of banks to local governments to lending outcomes before and after the introduction of the Law.

Table 5 presents the estimates of equation 4 on a series of monthly balance-sheet outcomes of banks. As column 1 shows, differences in ex ante exposures of banks to local government lending are not associated with variations on their ex-post aggregate lending. However, estimates in columns 2 and 3 show that there is large variation in the composition of bank lending after the FD Law. More concretely, a one-standard-deviation increase in ex ante bank exposure to public local debt is associated with a 28 percent reduction in volume lent to local governments, and a 31 percent increase in volume lent to private-sector firms. Results in columns 4 and 5 also indicate that a one-standard-deviation increase in ex ante bank exposure to local public debt is associated with a 0.30 p.p. increase in interest rates on loans to local governments, with no effect on the interest rate charged to private-sector borrowers. Overall, these results suggest that, while the FD Law did not impact aggregate lending of banks, it did change the composition of lending of moreexposed banks, with more lending being channeled to private-sector firms as opposed to local governments.

29
Further evidence of the heterogeneity across banks in the crowding-out channel comes from analyzing changes in bank lending more granularly. Doing so allows us to relate lending adjustments within a state by banks with varying ex ante exposures to the state’s public indebtedness. Our exercise is based on regressions summarized in equation 5, with observations at the bank-state-month and firm-bank-month levels. Table 6 summarizes our results for the statebank-month (Panel A) and firm-bank-month (Panel B) data. In Panel A, the outcome of interest corresponds to the total volume lent to state-government _s_ by bank _b_ at month _m_ . Our estimates from columns 1 to 3 indicate that the contraction in lending to ex ante more indebted local governments after the Law is mainly driven by banks with higher ex ante exposures to local public debt. That is, in a state with an average ex ante public indebtedness ratio of around 0.86, a onestandard-deviation increase in bank exposure leads to a 23 percent decline in lending to the government. This effect remains significant and larger in magnitude when bank-month and statemonth fixed effects are further introduced in the regression (columns 2 and 3).

In Panel B, the outcome of interest corresponds to the volume of loans extended by bank _b_ to firm _f_ in month _m_ . Our results show that after the Law, banks ex ante more exposed to the public debt of a state increase their lending to private firms headquartered in the state (Columns 4 to 6). More concretely, a one-standard-deviation increase in bank exposure is associated with a 2.3 percent increase in lending to firms (Column 4).<sup>38</sup> Estimates from Column 5 further indicate that this adjustment in credit supply remains (and becomes economically larger) once we saturate our specification with bank-month and state-month fixed effects. In Column 6, we further include firmmonth fixed effects to exhaustively control for unobserved time-varying firm fundamentals (e.g., firm risk, investment opportunities, and balance sheet movements). One drawback of the inclusion of firm-month fixed effects is that we restrict the sample to firms that at a given month have loans with more than one bank. Thus, this exercise could bias our results downwards since firms with multiple lenders tend to have lengthier credit histories and greater access to financing. While we drop more than half of the observations, the results overall corroborate that after the FD Law, firms in more indebted states experience an expansion in the supply of credit by banks previously more exposed to local public debt. In a state with an average public indebtedness ratio, a one-standard-

> 38 While the magnitude of the impact on bank lending to states (around 30 percent) is higher than the impact to private firms (2 percent) this regression is only analyzing the impact at the intensive margin, not the bank lending to new firm-bank relations. As noted in Table 5, where we analyze results at the aggregate bank-month level, the magnitudes of the impact of the FD Law on bank lending to states and firms are relatively similar (albeit with opposing signs).

30
deviation increase in the ex-ante exposure of a bank to local debt leads to an increase in loan volume to private firms of the state of 8.8 percent.

# **5.3. Impact of the FD Law on Firm-Level Outcomes**

We now analyze the impact of the unwinding of crowding-out in the credit markets on the real outcomes of firms. For that, we run equation 6 to relate yearly outcomes of firms to the public indebtedness of their states as well as to the public debt exposure of banks operating in their municipalities. All specifications include firm fixed effects, while some include year fixed effects or state-year fixed effects. Our results are displayed on Table 7. As noted from columns 1, 3 and 7, after the FD Law, firms in more ex-ante indebted states experience an increase in their liabilities, assets, and sales. More concretely, a one-standard-deviation increase in the ex-ante indebtedness of a state results in an increase in liabilities, assets, and sales of around 6 percent, 7 percent, and 1.2 percent, respectively. In columns 2, 4, 6 and 8, we further examine if changes in the real effects of firms vary with the average ex ante exposure to local public debt of banks in their municipalities.<sup>39</sup> Our findings suggest that within a state, real outcomes of firms tend to grow relatively more after the Law the higher the exposure of banks in their municipalities to local public debt. More concretely, a one-standard-deviation increase in the measure of municipality exposure, in a state with average public indebtedness, is associated with an increase in liabilities of 2.5 percent, in assets of 2.2 percent, in fixed assets of 0.5 percent, and in sales of 3.8 percent. We again interpret these results as evidence that the implementation of debt ceilings in the FD Law helped reduce the crowding out in lending of private firms, especially of those operating in locations where banks used to finance more heavily local governments, with strong positive firm-level real effects.

# **5.4. Heterogeneity in the Impact of the FD Law given Composition of Fiscal Spending**

Given that public spending on infrastructure projects tends to be more productive (Cohen and Paul, 2004), we now investigate whether the impact of the FD Law differed depending on the type of spending carried out by state governments. To do so, we define the variable _Non-_

> 39 The intuition for this exercise is that given that bank lending is local (Degryse and Ongena, 2005), we expect firms in the same state, but located in municipalities with a large fraction of banks exposed to lending to local governments, to be relatively more impacted by the FD law relative to other firms in the same state but in municipalities with banks less exposed to bank lending to local governments.

31
_Infrastructures,2015_ as the share of fiscal spending of a state in non-infrastructure projects (out of total fiscal spending) in 2015.<sup>40</sup> We then introduce this variable in equation 1, both in levels and in interactions with the _Postt_ and _DebtStates,16Q1_ variables, to examine differences in state-level outcomes of economic activity.

Results displayed in the first two columns of Table 8 indicate that the ex-ante composition of fiscal spending indeed affects the subsequent behavior of macro outcomes of states. More concretely, in a state with average ex ante public indebtedness after the passage of the FD Law (~0.86), a one-standard-deviation increase in the share of public spending in non-infrastructure projects is associated with an increase of around 0.062*0.028*0.86 = 0.15 p.p. of GDP growth and an increase in employment growth of 0.09 p.p.. These findings suggest that the crowding-out effect was greater in states where governments were spending a larger fraction of their budget on projects unrelated with public investment. Thus, the marginal return of capital reallocation from public towards private firms is higher in states channeling relatively more resources on expenditures different from public infrastructure projects.

Similarly, we analyze the impact of the FD Law on poverty rates and income inequality of states of varying ex ante indebtedness and composition of public spending. Our results are summarized in Columns 3 to 6 of Table 8. The estimated coefficients of the triple interaction in Columns 4 and 5 indicate that at similar ex ante indebtedness levels, states that ex ante channeled more public spending in non-infrastructure projects experience higher reductions (increases) in their moderate (extreme) poverty rates. For a state of average ex ante public indebtedness, a onestandard-deviation increase in the ex-ante share of public spending in non-infrastructure is associated with a reduction in moderate poverty of 2.8 percent and an increase in extreme poverty of 5.8 percent.

We further investigate the differential impact of the FD Law on lending outcomes to private firms by states’ type of public spending. The results, displayed in Column 7 of Table 8, suggest that the unwinding of crowding out experienced after the Law was stronger in states spending more on non-infrastructure projects. More concretely, in a state with average ex ante public

> 40 Results are qualitatively similar if we average a state’s investment rate over the previous three or five years.

32
indebtedness, a one-standard-deviation increase in the share of spending on non-infrastructure is associated with an additional 1.5 percent increase in bank loans to firms.

Finally, we investigate whether the crowding out in credit markets was more severe among firms with shorter credit histories with their banks, which tend to be more credit constrained.<sup>41</sup> To do so, we proxy for the credit history of a borrower using the length of time that has passed since the creation of the firm-bank relationship. With this variable, we then rerun the previous exercise splitting the sample of firms across borrowers with a credit history above/below the median (three years). Our results are shown in Table A8 in the Appendix. The outcomes we examine are the bank loan volumes (total, to investment and to working capital) to firms with short and long credit histories. Our results show that while both types of firms benefit from larger bank lending volumes after the FD Law, the unwinding of the crowding out was concentrated on borrowers with short credit history. That is, a one-standard-deviation increase in a state’s indebtedness is associated with a 1 (Column 1) and 2 (Column 7) percent increase in loan volume for borrowers with long and short credit histories respectively.<sup>42</sup> Moreover, while there is no relation between the rate of fiscal public infrastructure spending of more ex-ante indebted states and lending to borrowers with long credit history, this relation is strong and economically large for borrowers with short credit history. More concretely, for an average indebted state, a one-standard-deviation increase in the share of non-infrastructure spending is associated with an additional 4.7 percent increase in the volume of loans. Overall, these results suggest that the crowding out of government borrowing prior to the FD Law was having an outsized negative impact on bank lending by more credit constrained firms.

# **6. Conclusions**

In this paper, we exploit the potential crowding out in credit markets of private firms by the government. To do this, we study a Mexican reform imposing restrictions on subnational bank debt—the Financial Discipline Law to States and Municipalities—establishing ceilings on public debt for local governments.

> 41 In the presence of credit rationing and financial frictions, government debt is especially damaging for firms with restricted credit access (Broner, Erce, Martin, and Ventura, 2014).

> 42 This effect is even stronger when analyzing the impact of loans destined to investment projects. In this case a onestandard-deviation increase in a state’s indebtedness is associated with a 3 percent and 9 percent increase in loan volume for borrowers with long and short credit histories respectively.

33
We show expansionary fiscal austerity via reallocation of credit supply, but with a raise in poverty. In particular, after the law, states with higher ex ante public debt grow substantially faster, despite larger fiscal consolidation (higher taxes and lower public expenditure). Banks operating in more indebted states reallocate credit supply away from local governments into private firms, with stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. Effects only happen after the law, not before, and there are strong firm-level real effects associated. The reduction of crowding out is stronger for financially constrained firms and for firms operating in states with higher ex ante public spending on social services over infrastructure projects. In states more affected by the law, despite better economic effects, extreme poverty increases––especially in states with higher ex ante public spending on social services over infrastructure––consistent with a strong reduction for social services during the fiscal consolidation.

34
# **References**

Adelino, Manuel, Igor Cunha, and Miguel Ferreira, 2017, “The Economic Effects of Public Financing: Evidence from Municipal Bond Ratings Recalibration.” _The Review of Financial Studies_ , 30, 3223-3268.

Alesina, Alberto, Carlo Favero, and Francesco Giavazzi, 2019a, “Effects of Austerity: Expenditure- and Tax-based Approaches.” _Journal of Economic Perspectives_ , 33, 141-162.

Alesina, Alberto, Carlo Favero, and Francesco Giavazzi, 2019b, “Austerity: When It Works and When It Does Not.” _Princeton University Press_ .

Banerjee, Abhijit and Esther Duflo, 2014, “Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program.” _Review of Economic Studies_ , 81, 572-607.

Banerjee, A., Breza, E., Duflo, E. and Kinnan, C., 2019. Can microfinance unlock a poverty trap for some entrepreneurs? (No. w26346). National Bureau of Economic Research.

Banerjee, A.V. and Newman, A.F., 1993. Occupational choice and the process of development. Journal of political economy, 101(2), pp.274-298.

Beck, Thorsten, Asli Demirgüç-Kunt, and Vojislav Maksimovic, 2008, "Financing Patterns Around the World: Are Small Firms Different?” _Journal of Financial Economics_ , 89, 467-487.

Becker, Bo, and Victoria Ivashina, 2018, “Financial Repression in The European Sovereign Debt Crisis.” _Review of Finance, Forthcoming_ .

Belo, Frederico, Vito Gala, and Jun Li, 2013, “Government Spending, Political Cycles, and the Cross Section of Stock Returns.” _Journal of Financial Economics_ , 107, 305-324.

Blanchard, Olivier, 2019, "Public Debt and Low Interest Rates." _American Economic Review_ , 109, 1197-1229.

Braga, Breno, Diogo Guillen, and Benjamin Thompson, 2017, “Local Government Spending and Employment: Regression Discontinuity Evidence from Brazil” Available at SSRN: https://ssrn.com/abstract=2966187.

Broner, Fernando, Aitor Erce, Alberto Martin, and Jaume Ventura, 2014, “Sovereign Debt Markets in Turbulent Times: Creditor Discrimination and Crowding-out Effects” _Journal of Monetary Economics_ , 61, 114-142.

Buera, Francisco, Joseph Kaboski, Yongseok Shin, 2011, “Finance and Development: A Tale of Two Sectors” _American Economic Review_ , 101, 1964-2002.

Chakraborty, Indraneel, Itay Goldstein, and Andrew MacKinlay, 2018, “Housing Price Booms and Crowding-Out Effects in Bank Lending” _Review of Financial Studies_ , 7, 2806-2853.

Chodorow-Reich, Gabriel, 2019, “Geographic Cross-Sectional Fiscal Multipliers: What Have We Learned?” _American Economic Journal: Economic Policy_ , 11, 1-34.

35
Cohen, Jeffrey, and Catherine Paul, 2004, "Public infrastructure investment, interstate spatial spillovers, and manufacturing costs." _Review of Economics and Statistics_ 86, 551-560.

Corbi, Raphael, Elias Papaioannou, and Pablo Surico, 2019, “Regional Transfer Multipliers” _Review of Economic Studies_ , 86, 1901-1934.

Demirci, Iran, Jennifer Huang, and Clemens Sialm, 2019, “Government Debt and Corporate Leverage: International Evidence” _Journal of Financial Economics_ , 133, 337-356.

Fatás, Antonio, and Lawrence Summers, 2018, “The Permanent Effects of Fiscal Consolidations” _Journal of International Economics,_ 112, 238-250.

Gennaioli, Nicola, Alberto Martin, and Stefano Rossi, 2014, “Sovereign Default, Domestic Banks and Financial Institutions” _Journal of Finance_ , 69, 819-866.

Ghosh, Parikshit, Dilip Mookherjee, and Drebraj Ray, 2000, “ <mark>Credit Rationing in Developing Countries: An Overview of the Theory”</mark> _Readings in the Theory of Economic Development_ , 383-401.

Graham, John, Mark Leary, and Michael Roberts, 2015, “A Century of Capital Structure: The Leveraging of Corporate America”, _Journal of Financial Economics_ , 118, 658-683.

Greenwood, Robin, Samuel Hanson, and Jeremy Stein, 2010. “A Gap-Filling Theory of Corporate Debt Maturity Choice” _Journal of Finance,_ 65, 993-1028.

Guajardo, Jaime, Daniel Leigh, and Andrea Pescatori, 2014, “Expansionary Austerity? International Evidence” _Journal of the European Economic Association_ , 12, 949-68.

Hoffmann, Matias, Iryna Stewen, and Michael Stiefel, 2021. "Growing like Germany: Local Public Debt, Local Banks, Low Private Investment." _Working Paper 380, Department of Economics - University of Zurich_ .

House, Christopher, Christian Proebsting, and Linda Tesar, 2020, “Austerity in the Aftermath of the Great Recession” _Journal of Monetary Economics_ , 115, 37-63.

Huang, Yi, Marco Pagano, and Ugo Panizza, 2020, “Local Crowding Out in China.” _Journal of Finance,_ 75, 2855-2898 _._ IMF, 2018, _Historical Public Debt Database._ IMF, 2020, _Fiscal Monitor._ INEGI, 2014, _Exportaciones por Entidad Federativa_ .

La Porta, Rafael, Florencio López-de-Silanes, and Guillermo Zamarripa, 2003, “Related Lending” _Quarterly Journal of Economics,_ 118, 231-268.

Kumhof, Michael and Ivan Tanner, 2005, “Government Debt: A Key Role in Financial Intermediation” _IMF Working Paper_ , _05/57._ 36
Manove, Michael, Jorge Padilla, and Marco Pagano, 2001, “Collateral versus Project Screening: A Model of Lazy Banks” _RAND Journal of Economics_ , 32, 726-744.

Mau, Niels Jorgen, 2015, “Municipal Bailouts in Denmark – How to Avoid Them?” _Institutions of Intergovernmental Fiscal Relations: Challenges Ahead. OECD Publishing, Paris_ 227-238.

Mian, Atif, and Amir Sufi, 2014, “What Explains the 2007-2009 Drop in Employment” _Econometrica_ , 82, 2197-2223.

Morais, Bernardo, Jose Luis Peydró, Jessica Roldán-Peña, and Claudia Ruiz, 2019, “The International Bank Lending Channel of Monetary Policy Rates and QE: Credit Supply, Reach‐for‐ Yield, and Real Effects” _Journal of Finance_ , 74, 55-90.

<mark>Nakamura, Emi, and Jón Steinsson, 2018, “Identification in Macroeconomics”</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>, 32, 59-86.</mark> Degryse, Hans, and Steven Ongena, 2005, "Distance, lending relationships, and competition." _The_ _<mark>Journal of Finance</mark>_ <mark>60, 231-266.</mark> Reinhart, Carmen, and Kenneth Rogoff, 2010, “Growth in a Time of Debt” _American Economic Review P&P_ , 100, 573-78.

Revilla, Ernesto, 2013, “Subnational Debt Management in Mexico: A Tale of Two Crises” _Until Debt Do Us Part: Subnational Debt, Insolvency, and Markets. The World Bank_ .

Santos, Tano, 2017, “El Diluvio: The Spanish Banking Crisis”, _NBER Working Paper._ Smith, Heidi., 2015, “Now for Public Debt in Mexico: Policy Lessons for the Effective Oversight of State and Municipal Government Finances”, _Wilson Center_ .

37
**Figure 1 – Public Debt of States in 2016Q1**

This figure plots the ratio of public debt to freely disposable income of Mexican states in 2016Q1 – one month prior to the implementation of the FD Law – along with the first public <u>debt classification of states under the alert system in April 2017.</u> <!-- Start of picture text --> Alert system - public debt classification (April 2017)<br>High<br>Under watch<br>Sustainable<br>300<br>200<br>Percent<br>100<br>0<br>Qui Coa Chi Nue Ver Son BCN Nay Zac Dur Mic Oax Col Chp Tam Jal Mor BCS Mex Hid Sin Agu San Pue Gua Yuc Tab Gue Cam Que<br><!-- End of picture text --> 38
# **Figure 2 – Bank Lending and Interest Rates to Local Governments with Low vs High Public Debt**

Panel A plots bank quarterly lending to state governments, relative to 2016Q2, for states with high and low public debt. A state is defined with high (low) public debt if its debt as a share of income in the quarter prior to the reform is above or below the median. Panel B plots the monthly interest rate on bank loans paid by state governments with high and low <u>public debt. The vertical lines in both panels mark the introduction of the FD Law.</u> <!-- Start of picture text --> Liabilities Interest Rate<br>2015m1 2016m1 2017m1 2018m1<br>2014q1 2015q1 2016q1 2017q1 2018q1 Time<br>Time<br> States Low Public Debt States High Public Debt<br> States Low Public Debt States High Public Debt Monthly Data<br>Lending Relative to 2016Q2 - Quarterly Data<br>5 7<br>0 6<br>-5 5<br>Index Percent<br>-10 4<br>-15 3<br><!-- End of picture text --> 39
# **Figure 3 – Bank Lending and Interest Rates to Local Governments and Firms**

Panel A plots the share of bank lending channeled to the government (out of total bank lending to all government entities and private firms) relative to March 2016, the month prior to the implementation of the Law. Panel B plots the average interest rates on bank loans—net of cost of funds—charged to state governments and private firms. The vertical lines in both panels mark the <u>introduction of the FD Law.</u> <!-- Start of picture text --> Share of Bank Lending to Government Interest Rate Spread<br>2014m1 2015m1 2016m1 2017m1 2018m1<br>Time<br>2014m1 2015m1 2016m1 2017m1 2018m1 Government Firms<br>time<br>.28 5<br>.26<br>4<br>.24 Percent<br>.22 3<br>.2<br>Share of Lending to Government<br>2<br>.18<br><!-- End of picture text --> 40
# **Figure 4– Bank Lending and Interest Rates to Firms Given State Public Debt**

The figures plot the evolution of bank lending (Panel A) and interest rates on bank loans (Panel B) to private firms relative to January 2014. In both panels, states are divided into two groups depending on whether their indebtedness in 2016Q1 was above <u>or below the median indebtedness. The vertical lines in both panels mark the introduction of the FD Law.</u> <!-- Start of picture text --> Loan Volume Interest Rate of Loans to Private Firms<br>2015m1 2016m1 2017m1 2018m1 2015m1 2016m1 2017m1 2018m1<br>Time Time<br> States Low Public Debt States High Public Debt States Low Public Debt States High Public Debt<br>1.4 .5<br>0<br>1.2<br>-.5<br>1<br>Index<br>-1<br>.8 -1.5<br>Percentage Points Relative to April 2016<br>.6 -2<br><!-- End of picture text --> 41
**Figure 5 –Bank Lending to Local Governments and Firms given Bank Exposure to Local Public Debt**

The figures plot the evolution of lending to state governments (Panel A) and private firms (Panel B) relative to March 2016 by banks with low and high ex ante public debt exposure. Banks are considered to have high (low) ex ante exposure to public debt if their share of lending to public entities in 2016Q1 was above (below) the median. The vertical lines in both panels mark the <u>introduction of the FD Law.</u> <!-- Start of picture text --> Loans to Local Governments Loans to Firms<br>2014m7 2015m7 2016m7 2017m7 2018m7<br>Time 2015m1 2016m1 2017m1 2018m1<br>Time<br> Low Exposure High Exposure<br> Low Exposure High Exposure<br>April 2016 = 100<br>April 2016 = 100<br>140 110<br>130<br>105<br>120<br>Index Index 100<br>110<br>100 95<br>90<br>90<br><!-- End of picture text --> 42
# **<mark>Table 1 - Summary Statistics</mark>**

Panel A exhibits macro level data of states and municipalities. Panel B presents statistics on the fiscal balance components of local governments. Panel C presents a series of statistics of bank lending to local governments. See Table A1 in the Appendix for variable definitions.

|governments. See Table A1 in t|he Appendi<br>Obs.|x for varia<br>Mean|ble definition<br>p10|s.<br>Median|p90|Std. Dev.|
|---|---|---|---|---|---|---|
|**Panel A. Economic Indicators**<br>|**of States/M**|**unicipali**|**ties**||||
|DebtStates,16Q1|30|0.86|0.20|0.64|2.25|0.70|
|Maturitys,16Q1|30|14.6|8.3|14.2|19.3|3.9|
|DebtBankMunim,Mar16|984|0.24|0.02|0.34|0.37|0.15|
|DebtMunicipalitym,16Q1|1,083|0.03|0.00|0.00|0.08|0.05|
|Employment|434|0.006|-0.021|0.006|0.033|0.022|
|- Primary|434|0.004|-0.13|0.003|0.141|0.109|
|- Secondary|434|0.011|-0.049|0.009|0.074|0.05|
|- Tertiary|434|0.006|-0.028|0.006|0.039|0.028|
|GDPs,q|496|0.008|-0.05|0.009|0.062|0.039|
|Povertys,y|155|0.43|0.28|0.42|0.64|0.14|
|Poverty – Moderates,y|155|0.34|0.25|0.34|0.44|0.08|
|Poverty – Extremes,y|155|0.09|0.02|0.06|0.19|0.08|
|Ginis,y|155|0.47|0.42|0.47|0.51|0.04|
|**Panel B. Fiscal Balance Comp**<br>|**onents of S**|**tate Gove**|**rnments**||||
|Total Expenditures,y|174|0.158|0.090|0.149|0.258|0.060|
|Infrastructures,y|174|0.007|0.002|0.006|0.016|0.005|
|Social Servicess,y|180|0.066|0.029|0.057|0.125|0.038|
|Debt Servicings,y|174|0.007|0.001|0.004|0.018|0.008|
|Transferss,y|174|0.135|0.074|0.127|0.214|0.058|
|Tax Rates,y|150|0.490|0.289|0.465|0.670|0.168|
|Non-Infrastructures,2015|180|0.045|0.017|0.044|0.082|0.028|
|**Panel C. Bank Lending to Sta**|**te Governm**|**ents**|||||
|Loans – Govts,m|1,080|22.66|21.28|22.51|24.24|1.07|
|Credit Line – Govts,m|891|19.40|16.61|19.88|21.65|2.40|
|Term Loan – Govts,m|1,080|22.57|21.17|22.48|24.09|1.10|
|Interest Rates,m|1,080|6.81|5.51|6.38|8.74|1.31|
|Collaterals,m|1,080|0.01|0.00|0.01|0.01|0.00|
|Maturitys,m|1,080|5.00|4.59|5.07|5.32|0.34|

43
# **<mark>Table 1 - Summary Statistics (Cont’d)</mark>**

Panel D presents the summary statistics of outcomes at the loan (firm-bank-month) level. Panel E shows the summary statistics of outcomes at the firm-year level. Panel F displays the summary statistics of bank-month-level outcomes. Panel G presents statistics of firm and state characteristics. See Table A1 in the Appendix for variable <u>definitions.</u>

|definitions.|Obs.|Mean|p10|Median|p90|Std. Dev.|
|---|---|---|---|---|---|---|
|**Panel D. Bank Lending to Private**|**Firms**||||||
|Volumef,b,m|1,216,258|13.27|10.64|13.46|15.77|2.41|
|Investmentf,b,m|200,318|12.85|9.55|12.93|16.14|2.64|
|Working Capitalf,b,m|1,100,190|13.27|10.77|13.46|15.66|2.35|
|Interest Ratef,b,m|1,216,258|14.42|8.65|13.43|20.72|5.32|
|Collateralf,b,m|1,216,258|0.32|0.00|0.00|1.00|0.41|
|Maturityf,b,m|845,642|2.89|1.36|3.09|4.22|1.13|
|**Panel E. Firm Real Outcomes**|||||||
|Liabilitiesf,y|1,755|10.06|8.09|9.68|12.96|1.93|
|Assetsf,y|1,818|11.12|9.09|10.54|14.52|2.09|
|Fixed Assetsf,y|1,747|9.44|6.40|8.94|13.89|2.73|
|Salesf,y|1,911|10.68|9.62|10.46|11.99|1.16|
|**Panel F. Bank-Level Indicators**|||||||
|Loansb,m|468|11.92|9.91|12.34|13.33|1.35|
|Loans – Govtb,m|468|8.34|3.85|9.44|10.93|2.94|
|Loans – Non-Govtb,m|468|11.23|9.40|11.57|12.65|1.28|
|IntRate – Govtb,m|468|6.51|4.20|6.23|9.08|2.13|
|IntRate – Non-Govtb,m|468|8.32|5.55|7.78|11.78|2.73|
|BankExposureGovb,Mar16|468|0.32|0.06|0.3|0.75|0.25|
|BankExposureGovs,b,Mar16|468|0.10|0.00|0.05|0.29|0.14|
|**Panel G. Other Variables**|||||||
|Tradable Sectori|83|0.26|0.00|0.00|1.00|0.44|
|GovernmentExposurei|83|0.09|0.01|0.08|0.19|0.08|
|Norths|30|0.46|0.00|0.00|1.00|0.51|

44
# **<u><mark>Table 2A – Impact of FD Law on States’ Fiscal Balance Components Given Their Public Debt</mark></u>**

This table reports OLS estimates of the impact of the FD Law on fiscal balance components of states of varying ex ante public indebtedness. On the public expenditure side, the variables _TotalExpendituress,y_ , _Infrastructures,y, SocialServicess,y_ and _DebtServicings,y_ correspond to the ratios of total public expenditures and expenditures on infrastructure projects, social aid, and debt servicing over the GDP of state _s_ in year _y_ . On the public revenue side, the variables _Transferss,y_ and _TaxRates,y_ correspond to the amount of federal government transfers and tax income obtained by state _s_ in year _y_ and are calculated as ratios over the state yearly GDP. _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Posty_ is an indicator variable that equals one from 2016 onwards. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, ** *** <u>, denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Total Expe<br>|ndituress,y<br>|Panel<br>Infrastr<br>|A. Public<br>uctures,y<br>|Expenditu<br>Social S<br>|res<br>ervicess,y<br>|Debt Ser<br>|vicings,y<br>|Pan<br>Transf<br>|el B. Pub<br>erss,y<br>|licReven<br>Tax<br>|ues<br>Rates,y<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Posty|-0.002||-0.001||0.001||0.002||-0.003**||-0.024||
||(0.002)||(0.001)||(0.001)||(0.001)||(0.001)||(0.016)||
|Posty*DebtStates,16Q1|-0.011**|-0.012**|-0.003*|-0.004*|-0.003*|-0.005*|0.003*|0.002|-0.002|-0.002|0.070*|0.068*|
||(0.005)|(0.005)|(0.002)|(0.002)|(0.002)|(0.003)|(0.002)|(0.003)|(0.003)|(0.003)|<br>(0.039)|<br>(0.038)|
|Observations|174|174|174|174|180|180|174|174|174|174|150|150|
|R-squared|0.990|0.990|0.682|0.703|0.981|0.982|0.471|0.506|0.991|0.992|0.886|0.890|
|Macro Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

45
**<mark>Table 2B – Impact of FD Law on States’ GDP and Employment Given Their Public Debt</mark>** This table reports OLS estimates of the impact of the FD Law on GDP and employment of states of varying ex ante public indebtedness. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postq_ is an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Empl<br>To|oyment<br>tals,q|Emplo<br>Prim|yment<br>arys,q|Emplo<br>Second|yment<br>arys,q|Empl<br>Terti|oyment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|0.002||0.002||0.012||-0.010***||-0.002||
||(0.013)||(0.004)||(0.009)||(0.000)||(0.005)||
|Postq*DebtStates,16Q1|0.003***|0.003***|0.001|0.001***|0.002|0.002|0.007***|0.007***|0.003*|0.003|
||(0.001)|(0.001)|(0.001)|(0.000)|(0.007)|(0.009)|(0.002)|(0.001)|(0.002)|<br>(0.002)|
|Observations|480|480|420|420|420|420|420|420|420|420|
|R-squared|0.044|0.473|0.015|0.180|0.017|0.092|0.027|0.042|0.019|0.146|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

46
# **<mark>Table 2C - Impact of FD Law on States’ Poverty and Inequality Given Their Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on poverty and inequality of states of varying ex ante public</mark> indebtedness. The variable _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Posty_ is an indicator variable that equals one from 2016 onwards. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||Povert<br>|ys,y<br>|Moderate<br>|Povertys,y<br>|Extreme P<br>|overtys,y<br>|Gini<br>|s,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Posty|-0.04***||-0.00||-0.04***||-0.04***||
||(0.01)||(0.01)||(0.00)||(0.01)||
|Posty*DebtStates,16Q1|-0.02*|-0.02*|-0.04***|-0.04***|0.02**|0.02**|0.00|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|
|Observations|150|150|150|150|150|150|150|150|
|R-squared|0.96|0.96|0.91|0.91|0.96|0.97|0.54|0.58|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

47
# **<mark>Table 3A – Impact of FD Law on Bank Lending to Local Governments Given State Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on bank lending to local governments of varying ex ante public indebtedness.</mark> The variable _Loans-Govts,m_ is the total bank lending (in logs) of state government _s_ in month _m_ . _Term Loan-Govts,m_ is the bank lending (in logs) in term loans of state government _s_ in month _m. Credit Line-Govts,m_ is the bank lending (in logs) from credit lines of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. * ** *** <u>, , denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Loans-<br>|Govts,m<br>|Term Loa<br>|n-Govts,m<br>|Credit Line<br>|-Govts,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm|0.04*||0.10***||-3.19***||
||(0.02)||(0.02)||(0.55)||
|Postm*DebtStates,16Q1|-0.09**|-0.09**|-0.14***|-0.14***|-1.19|-1.19|
||(0.04)|(0.04)|(0.04)|(0.04)|(1.20)|(1.19)|
|Observations|1,080|1,080|1,080|1,080|1,080|1,080|
|R-squared|0.96|0.96|0.96|0.96|0.52|0.54|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|No|Yes|No|Yes|No|Yes|

48
# **<mark>Table 3B –Impact of FD Law on Bank Loan Margins to Local Governments Given State Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on credit margins of bank loans to local governments of varying ex ante</mark> public indebtedness. The variable _Interest Rates,m_ is the annualized average interest rate (in percent) of the outstanding loans of state government _s_ in month _m_ . The variable _Collaterals,m_ is the average share of bank loans of state government _s_ in month _m_ that are guaranteed. _Maturitys,m_ is the average maturity (in logs) of the outstanding loans of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent <u>levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Interest<br>|Rates,m<br>|Collat<br>|erals,m<br>|Matu<br>|ritys,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm|1.49***||0.00***||-0.00||
||(0.07)||(0.00)||(0.02)||
|Postm*DebtStates,16Q1|0.86***|0.86***|0.00**|0.00***|0.01|0.01|
||(0.18)|(0.09)|(0.00)|(0.00)|(0.04)|(0.04)|
|Observations|1,080|1,080|1,080|1,080|1,080|1,080|
|R-squared|0.59|0.91|0.46|0.70|0.60|0.60|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|No|Yes|No|Yes|No|Yes|

49
# **Table 4A – Impact of FD Law on Bank Lending to Firms Given State Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms in states of varying ex ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volu<br>(1)|mef,b,m<br>(2)|Investm<br>(3)|entf,b,m<br>(4)|Working<br>(5)|Capitalf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.013**|0.015**|0.046***|0.051***|0.016**|0.017**|
||(0.006)|(0.006)|(0.013)|(0.013)|(0.007)|(0.007)|
|Observations|1,252,105|1,252,105|206,666|206,655|1,131,483|1,131,483|
|R-squared|0.803|0.803|0.870|0.870|0.799|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

50
# **Table 4B - Impact of FD Law on Bank Loan Margins to Firms Given State Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank credit margins to private firms in states of varying ex ante public indebtedness. The variable _InterestRatef,b,m_ is the annualized average interest rate of the outstanding loan given to firm _f_ , by bank _b_ in month _m_ . _Collateralf,b,m_ is the average share of loans received by firm _f_ from bank _b_ in month _m_ that are guaranteed. _Maturityf,b,m_ is the average monthly maturity (in logs) of the outstanding loans received by firm _f_ from bank _b_ in month _m_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Interest<br>(1)|Ratef,b,m<br>(2)|Collate<br>(3)|ralf,b,m<br>(4)|Matur<br>(5)|ityf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.004|0.005|0.016***|0.015***|-0.001|0.003|
||(0.014)|(0.014)|(0.002)|(0.002)|(0.007)|(0.007)|
|Observations|1,252,105|1,252,105|1,252,105|1,252,105|870,454|870,454|
|R-squared|0.859|0.859|0.747|0.749|0.678|0.679|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

51
# **Table 5 – Impact of FD Law on Balance Sheets of Banks Given Their Exposure to Local Public Debt**

This table reports OLS estimates of the impact of the FD Law on the balance sheet of banks of varying ex ante exposure to local public indebtedness. The variable _Loansb,m_ is the total volume of outstanding loans (in logs) extended by bank b in month m. _Loans-NonGovtb,m_ is the total volume of outstanding loans (in logs) extended by bank _b_ in month _m_ to all borrowers excepting the government. _Loans-Govtb,m_ is the total volume of outstanding loans (in logs) extended to the government by bank _b_ in month _m_ . _IntRate-NonGovtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to all borrowers except government. _IntRate-Govtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to the government. _BankExposureGovb,Mar16_ is a measure of the exposure of a bank to local public entities in the month prior to the FD Law and is calculated as the ratio of bank lending to local public entities over total lending. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the bank-month level. Standard errors are reported in parentheses and are doubled clustered at the bank and month levels. *, **, *** denote significance at the 10, 5 <u>and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Loansb,m<br>(1)|Loans-Non-Govtb,m<br>(2)|Loans-Govtb,m<br>(3)|IntRate-Non-Govtb,m<br>(4)|IntRate-Govtb,m<br>(5)|
|---|---|---|---|---|---|
|Postm*BankExposureGovb,Mar16|0.01|1.26*|-1.18***|-0.02|1.23**|
||(0.03)|(0.71)|(0.37)|(0.31)|(0.60)|
|Observations|468|417|456|468|468|
|R-squared|1.00|0.96|0.94|0.95|0.76|
|Bank FE|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|Yes|

52
**<mark>Table 6 – Impact of FD Law on Bank Lending to Local Governments and Firms Given Bank Exposure and State Public Debt</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on the lending volumes issued to local governments (Panel A) and private</mark> firms (Panel B) in states of varying ex ante public indebtedness by banks of varying ex ante exposure to local public debt. The variable _Loans-Govts,b,m_ is the total bank lending (in logs) of state government _s_ with bank _b_ in month _m_ . _Loans-Firmsf,b,m_ is the bank lending (in logs) of firm _f_ extended by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public  indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _BankExposureGovs,b,Mar16_ is a measure of the exposure of a bank to local public entities of a state in the month prior to the reform. It is calculated as the ratio of loans extended by bank _b_ to state government _s_ over bank _b_ ’s total lending. _Postm_ is an indicator variable that equals one from April 2016 onwards. In Panel A, observations are at the state-bank-month level. Borrower fixed effects correspond to fixed effects at the state-level. In Panel B, observations are at the firm-bankmonth level.  Borrower fixed effects correspond to fixed effects at the firm-level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable definitions are <u>provided in Table A1 in the Appendix.</u>

|provided in Table A1 in the Appendix.|Pane<br>|l A. Loans-Go<br>|vts,b,m<br>|Panel<br>|B. Loans-Firm<br>|sf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*DebtStates,16Q1*BankExposureGovs,b,Mar16|-1.87***|-2.89***|-4.61***|0.189*|0.279*|0.734*|
||(0.56)|(0.64)|(1.09)|(0.113)|(0.142)|(0.391)|
|Postm*DebtStates,16Q1|-0.08|0.15||0.016***|0.022***||
||(0.12)|(0.11)||(0.006)|(0.006)||
|Postm*BankExposureGovs,b,Mar16|1.33***|1.65***|2.81***|-0.224|-0.363*|-1.263**|
||(0.29)|(0.35)|(0.44)|(0.200)|(0.199)|(0.539)|
|Observations|7,568|7,512|7,499|1,136,616|1,136,616|469,977|
|R-squared|0.78|0.80|0.85|0.798|0.798|0.897|
|Bank-Borrower FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|-|-|-|-|-|
|Bank-Month FE|No|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|-|-|-|No|Yes|-|
|Borrower-Month FE|No|No|Yes|No|No|Yes|

53
**<mark>Table 7 – Impact of FD Law on Firm Outcomes Given State Public Debt and Municipality Bank Exposure</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on real outcomes of private firms in states of varying ex ante public</mark> indebtedness and across municipalities of varying ex ante bank exposure to local public debt. The variable _Liabilitiesf,y_ is the volume (in logs) of liabilities of firm _f_ in year _y_ . _Assetsf,y_ and _Fixed Assetsf,y_ are the volume (in logs) of total and fixed assets of firm _f_ in year _y_ . _Salesf,y_ is the volume (in logs) of sales of firm _f_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _DebtBankMunim,Mar16_ measures the average exposure to local public debt of banks operating in municipality _m_ in the month prior to the reform. It is calculated as the municipality’s weighted average of _BankExposureGovs,b,Mar16_ which is a measure of the exposure of a bank to local public entities of a state in March 2016. The standard deviation of this variable for our sample of firms is equal to 0.02. _Posty_ is an indicator variable that equals one from 2016 onwards.  Observations at the firm-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable definitions are provided in Table A1 in the <u>Appendix.</u>

||Liabil<br>|itiesf,y<br>|Ass<br>|etsf,y<br>|Fixed A<br>|ssetsf,y<br>|Sal<br>|esf,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Posty*DebtStates,16Q1|0.024**||0.019*||0.037||0.023*||
||(0.009)||(0.012)||(0.029)||(0.014)||
|Posty*DebtBankMunim,Mar16||0.743||0.339||-0.176||0.022|
|||(0.762)||(0.463)||(0.989)||(0.914)|
|Posty*DebtStates,16Q1||1.452***||1.280***||0.329||2.224*|
|*DebtBankMunim,Mar16||(0.358)||(0.285)||(0.842)||(0.938)|
|Observations|4,452|4,292|4,460|4,318|4,387|4,139|4,443|4,206|
|R-squared|0.967|0.979|0.983|0.991|0.976|0.988|0.941|0.963|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|-|Yes|-|Yes|-|Yes|-|
|State-Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

54
# **<mark>Table 8 - Impact of FD Law on States’ Outcomes Given Their Public Debt and Fiscal Spending Composition</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on outcomes at the state-quarter, state-year and firm-bank-month levels for states</mark> of varying ex ante public indebtedness and spending composition. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y. Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _NonInfrastructures,2015_ is the ratio of public spending in all categories except for infrastructure over the total public spending of state _s_ in 2015. _Postt_ is an indicator variable that equals one from in periods following the FD Law. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels (Columns 1 and 2), state and year levels (Columns 3 to 6), and state and month levels (Column 7). *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

||GDPs,q|Employment<br>Totals,q|Povertys,y|Moderate<br>Povertys,y|Extreme<br>Povertys,y|Ginis,y|Volumef,b,m|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Postt*DebtStates,16Q1*Non-<br>Infrastructures,2015|0.062*|0.039*|-0.176|-0.396***|0.219***|0.386|0.637***|
||(0.036)|(0.021)|(0.150)|(0.122)|(0.080)|(0.246)|(0.198)|
|Postt*DebtStates,16Q1|0.004*|0.001***|-0.009*|-0.017***|0.007**|0.002|0.019***|
||(0.003)|(0.000)|(0.005)|(0.004)|(0.003)|(0.008)|(0.006)|
|Postt*Non-Infrastructures,2015|-0.061|0.084***|0.434***|0.465***|-0.025|0.275*|-0.503***|
||(0.062)|(0.001)|(0.151)|(0.130)|(0.075)|(0.162)|(0.178)|
|Observations|480|420|150|150|150|150|1,252,105|
|R-squared|0.473|0.184|0.964|0.920|0.969|0.608|0.803|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|-|
|Quarter FE|Yes|Yes|No|No|No|No|-|
|Year FE|-|-|Yes|Yes|Yes|Yes|-|
|Firm-Bank FE|No|No|No|No|No|No|Yes|
|Bank-Month FE|No|No|No|No|No|No|Yes|
|Sector-Month FE|No|No|No|No|No|No|Yes|

55
# **Figure A1 – Dynamic Impact of FD Law on Bank Lending to Local Governments and Firms given State Public Debt**

These figures display quarterly coefficients of state-month level regressions where the dependent variables are: the volume (in logs) of bank loans to local governments (Panel A) and the volume (in logs) of bank loans to private firms (Panel B). The coefficients in the figures correspond to interactions of quarterly dummy variables with an indicator variable that equals one for states that in the quarter prior to the FD Law had public indebtedness above the median and zero otherwise. The regressions further include bank and month fixed effects. Standard errors are doubled clustered at the state and month level. The blue vertical bars represent confidence intervals of the coefficients at the 90 percent significance level. The red vertical lines in both <u>panels mark the introduction of the FD Law.</u> <!-- Start of picture text --> Lending to Local Governments Lending to Private Firms<br>States with High Government Debt States with High Government Debt<br>Jan 15 Jul 15 Jan 16 Oct 16 Apr 17 Oct 17 Jan 15 Jul 15 Jan 16 Oct 16 Apr 17 Oct 17<br>Apr 15 Oct 15 Jul 16 Jan 17 Jul 17 Apr 15 Oct 15 Jul 16 Jan 17 Jul 17<br>Months Months<br>.15<br>.2<br>0 .1<br>-.2<br>.05<br>-.4<br>Percentage Points Percentage Points<br>0<br>-.6<br>-.05<br>-.8<br><!-- End of picture text --> 56
# **Figure A2 – Dynamic Impact of FD Law on Share of Bank Lending to Local Governments Given Bank Exposure to Local Public Debt**

This figure displays quarterly coefficients of bank-month level regressions where the dependent variable is the share of bank loans channeled to state governments. The coefficients in the figure correspond to interactions of quarterly dummy variables with an indicator variable that equals one for banks that had an exposure to local public debt above the median in March 2016 and zero otherwise. The regressions further include month fixed effects. Standard errors are doubled clustered at the bank and month level. The blue vertical bars represent confidence intervals of the coefficients at the 90 percent significance level. The red vertical line marks the introduction of the <u>FD Law.</u> <!-- Start of picture text --> Jan 15 Jul 15 Jan 16 Oct 16 Apr 17 Oct 17 Apr 18<br>Apr 15 Oct 15 Jul 16 Jan 17 Jul 17 Jan 18<br>Months<br>.1<br>0<br>-.1<br>-.2<br>-.3<br><!-- End of picture text --> 57
|**Table A1- Variable Def**<br>_State/Municipalities_<br>DebtStates,16Q1|**initions**<br>Ratio of total liabilities of local governments of state_s_over net income of state_s_in<br>the first quarter of 2016.|
|---|---|
|Maturitys,16Q1|Outstanding maturity of state government_s_’ bank debt in the first quarter of 2016<br>(in logs)_._|
|DebtBankMunim,Mar16|<br>Average_BankExposureGovs,b,Mar16_ of banks operating in municipality_m_in March<br>2016, weighted by the share of total lending in the municipality of each bank.|
|DebtMunicipalitym,16Q1|Total public debt per capita of the municipality_m_in 2016Q1.|
|Employments,q|Quarterly growth of employment in state_s_in quarter_q._|
|Employment Primarys,q<br>|Quarterly growth of employment of primary sectors in state_s_in quarter_q_.|
|Employment<br>Secondarys,q|Quarterly growth of employment of secondary sectors in state_s_in quarter_q_.|
|Employment Tertiarys,q<br>GDPs,q|Quarterly growth of employment of tertiary sectors in state_s_in quarter_q_.<br>Real GDP index (relative to 2014) of state_s_in quarter_q_(in logs).<br>Share of population in state_s_that is poor in year_y_. The government defines<br>|
||poverty when a person cannot fulfill one of these six basic needs: Education|
|Povertys,y|,<br>access to health, access to social security, basic housing services, access to food,<br>and basic income.|
|Poverty – Moderates,y|Share of population in state_s_that in year_y_that cannot fulfill at most two basic<br>needs.|
|Poverty – Extremes,y|Share of population in state_s_that in year_y_that cannot fulfill three or more basic<br>needs.|
|Ginis,y|Gini coefficient of state_s_in year_y_that takes values between zero (lowest<br>concentration) and one (highest concentration).|
|_Fiscal Balance Compone_<br>|_nts_<br>|
|Total Expenditures,y|Ratio of total public spending over GDP in state_s_in year_y_.<br>|
|Infrastructures,y|Ratio of spending on public projects towards infrastructure over GDP in state_s_in<br>year_y_.|
|Social Servicess,y|Ratio of spending on social services (_ayudas sociales_) over GDP in state_s_in year<br>_y_.|
|Debt Servicings,y|Ratio of spending on debt costs over GDP in state_s_in year_y_.|
|Transferss,y|Ratio of federal transfers over GDP in state_s_in year_y_.|
|Tax Rate|Ratio of tax reene in statein earoer GDP|
|s,y|vu_s_y_y_v .|
|Non-Infrastructures,2015|Share of public spending in all categories except for infrastructure over the total<br>public spending of state_s_in 2015.|
|_Lending to Local Govern_|_ments_|
|Loans – Govts,m|Volume of total loans extended to state_s_in month_m_(in logs).|
|Credit Line – Govts,m|Volume of loans from credit lines extended to state_s_in month_m_(in logs).|
|Term Loan – Govts,m|Volume of term loans extended to state_s_in month_m_(in logs)_._|
|Interest Rates,m|<br>Average interest rate of loans extended to state_s_in month_m_(percent).|
|Collaterals,m|Share of loan that state_s_has in a month_m_that is guaranteed_._|
|Maturitys,m|Outstandingmaturity of theloan thatstate shasin a month_m _(in logs)_._|

58
|_Lending to Private Firms_|**Table A1- Variable Definitions (Cont’d)**|
|---|---|
|Volumef,b,m|Volume of loans firm_f_has from bank_b_in a month_m_(in logs).<br>|
|Investmentf,b,m|Volume of loans destined to investment projects that a firm_f_has from bank_b_in a<br>month_m_(in logs).|
|Working Capitalf,b,m|Volume of loans destined to working capital that a firm_f_has from bank_b_in a<br>month_m_(in logs).|
|Interest Ratef,b,m|Annualized interest rate of the loan that firm_f_has from bank_b_in a month_m_<br>(percent)_._|
|Collateralf,b,m|Fraction of loan that firm_f_has from bank_b_in a month_m_that is guaranteed by<br>firm’s assets_._|
|Maturityf,b,m|Outstanding maturity of the loan that firm_f_has from bank_b_in month_m_(in logs)_._|
|_Firm Outcomes_||
|Liabilitiesf,y|Total liabilities of firm_f_in year_y_(in logs).|
|Assetsf,y|Total assets of firm_f_in year_y_(in logs).|
|Fixed Assetsf,y|Total fixed assets of firm_f_in year_y_(in logs).|
|Salesf,y|Operational revenue of firm_f_in year_y_(in logs).|
|_Bank Variables_||
|Loansb,m|Volume of commercial loans extended by bank_b_in month_m_(in logs)_._|
|Loans – Govtb,m|Volume of loans to local governments extended by bank_b_in month_m_(in logs)_._|
|Loans – Non-Govtb,m|Volume of loans to private firms extended by bank_b_in month_m_(in logs)_._|
|IntRate – Govtb,m|Average interest rate of loans extended to local governments by bank_b_in month<br>_m_(percent)_._|
|IntRate – Non-Govtb,m|Average interest rate of loans extended to private firms by bank_b_in month_m_<br>(percent)_._|
|BankExposureGovb,Mar16|Ratio of lending volume by bank_b_to all local governments over its total lending<br>volume in March 2016.|
|BankExposureGovs,b,Mar16|Ratio of lending volume by bank_b_to local governments in state_s_over its total<br>lending volume in March 2016.|
|_Other Variables_||
|Postm|Indicator that takes value 1 if month_m_if after April 2016.|
|Tradable Sectori|Indicator that sector_i_produces tradables following Mian and Sufi (2014).|
|Government Exposurei|Measure of the dependence of firms in industry_i_on government spending<br>following Belo, Gala, and Li (2013).|
|Norths|<br>Indicator of northern states according to the National Statistics Agency (INEGI).<br>Companies classified in the_primary_sector mainly extract and harvest resources,|
|Primary, Secondary and|like agriculture, mining, or forestry. The_secondary_sector comprises businesses|
|Tertiary Sectors|that are involved in processing, manufacturing, and construction. Businesses in|
||the_tertiary_sector provide services, such as retailers or financial companies.|

59
# **<mark>Table A2A – Parallel Trend Test of Fiscal Balance Components Across States of Varying Public Debt Before FD Law</mark>**

<mark>This table reports OLS estimates of the trends of fiscal balance components across states of varying public indebtedness prior to the introduction</mark> of the FD Law. On the public expenditure side, the variables _TotalExpendituress,y_ , _Infrastructures,y_ , _SocialServicess,y_ and _DebtServicings,y_ correspond to the ratios of total public expenditures and expenditures on infrastructure projects, social aid, and debt servicing over the GDP of state _s_ in year _y_ . On the public revenue side, the variables _Transferss,y_ and _TaxRates,y_ correspond to the amount of federal government transfers and tax income obtained by state _s_ in year _y_ and are calculated as ratios over the state yearly GDP. _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend over time from the start of our sample in 2014 up to 2015. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

||To<br>Expend<br>|tal<br>ituress,y<br>|Panel<br>Infrastruc<br>|A. Public<br>tures,y<br>|Expendit<br>So<br>Servi<br>|ures<br>cial<br>cess,y<br>|D<br>Servi<br>|ebt<br>cings,y<br>|Pan<br>Trans<br>|el B. Pu<br>ferss,y<br>|blic Reven<br>Tax<br>|ues<br>Rates,y<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Trendy|0.000||-0.001***||0.002*||0.000||0.001||0.002||
||(0.001)||(0.001)||(0.001)||(0.001)||(0.001)||(0.017)||
|Trendy*DebtStates,16Q1|-0.003|-0.003|-0.000|-0.000|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|0.018|0.018|
||(0.002)|(0.003)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|<br>(0.014)|(0.014)|
|Observations|90|90|90|90|90|90|90|90|90|90|90|90|
|R-squared|0.989|0.989|0.776|0.776|0.981|0.981|0.640|0.641|0.994|0.994|0.899|0.900|
|Macro Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

60
# **Table A2B – Parallel Trend Test of GDP and Employment Across States of Varying Public Debt Before FD Law**

This table reports OLS estimates of the trends of GDP and employment across states of varying public indebtedness prior to the introduction of the FD Law. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendq_ is a linear trend over time from the start of our sample in 2014Q1 up to 2016Q1, the quarter prior to the implementation of the FD Law. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the <u>Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prim|yment<br>arys,q|Emplo<br>Secon|yment<br>darys,q|Emplo<br>Terti|yment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Trendq|-0.00*||-0.00||-0.00||0.00||-0.00||
||(0.00)||(0.00)||(0.01)||(0.01)||(0.00)||
|Trendq*DebtStates,16Q1|0.00|0.00|-0.00|-0.00|-0.00|-0.00|-0.02|-0.02|0.00|0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.03)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|210|210|150|150|150|150|150|150|150|150|
|R-squared|0.04|0.52|0.06|0.28|0.10|0.20|0.08|0.09|0.10|0.30|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

61
# **<mark>Table A2C – Parallel Trend Test of States’ Poverty and Inequality Given Their Public Debt Before FD Law</mark>**

<mark>This table reports OLS estimates of the trends of bank lending to local governments across states of varying public indebtedness prior to the</mark> introduction of the FD Law. The variable _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend for the years prior to the implementation of the FD Law (2010, 2012 and 2014). Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

||Pove<br>|rtys,y<br>|Moderate<br>|Povertys,y<br>|Extreme Po<br>|vertys,y<br>|Gi<br>|nis,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Trendy|-0.01*||0.00||-0.01***||0.00||
||(0.01)||(0.00)||(0.00)||(0.00)||
|Trendy*DebtStates,16Q1|0.01|0.01|-0.01|-0.01|0.02|0.02|-0.01|-0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|
|Observations|90|90|90|90|90|90|90|90|
|R-squared|0.97|0.97|0.94|0.94|0.98|0.98|0.63|0.64|
|Bank-State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|No|Yes|No|Yes|No|Yes|No|Yes|
# **<mark>Table A2D – Parallel Trend Test of Bank Lending to Local Governments Across States of Varying Public Debt Before</mark> FD Law**

<mark>This table reports OLS estimates of the trends of bank lending to local governments across states of varying public indebtedness</mark> prior to the introduction of the FD Law. The variable _Loans-Govts,m_ is the total bank lending (in logs) of state government _s_ in month _m_ . _Term Loan-Govts,m_ is the bank lending (in logs) in term loans of state government _s_ in month _m. Credit Line-Govts,m_ is the bank lending (in logs) from credit lines of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendm_ is a linear trend over time from the start of our sample in January 2014 up to March 2016, the month prior to the implementation of the FD Law. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

||Loans -<br>(1)|Govts,m<br>(2)|Term Loan<br>(3)|- Govts,m<br>(4)|Credit Li<br>(5)|ne- Govts,m<br>(6)|
|---|---|---|---|---|---|---|
|Trendm|0.00||0.00||0.00||
||(0.01)||(0.01)||(0.01)||
|Trendm*DebtStates,16Q1|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|450|450|450|450|450|450|
|R-squared|0.98|0.98|0.98|0.98|0.98|0.98|
|Bank-State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|No|Yes|No|Yes|No|Yes|
|State-Month FE|No|No|No|No|No|No|

63
# **<mark>Table A2E – Parallel Trend Test of Bank Lending to Firms Across States of Varying Public Debt Before FD Law</mark>**

This table reports OLS estimates of the trends of bank lending to private firms across states of varying public indebtedness prior to the introduction of the FD Law. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendm_ is a linear trend over time from the start of our sample in January 2014 up to March 2016, the month prior to the implementation of the FD Law. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volu<br>|mef,b,m<br>|Investm<br>|entf,b,m<br>|Working<br>|Capitalf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Trendm*DebtStates,16Q1|-0.000|-0.000|-0.002|-0.001|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Observations|524,710|524,710|29,383|29,383|502,403|502,403|
|R-squared|0.840|0.840|0.895|0.897|0.834|0.834|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month|No|Yes|No|Yes|No|Yes|

64
# **<mark>Table A2F – Parallel Trend Test of Firm Real Outcomes Across States of Varying Public Debt Before FD Law</mark>**

This table reports OLS estimates of the trends of real outcomes of private firms across states of varying public indebtedness prior to the introduction of the FD Law. The variable _Liabilitiesf,y_ is the volume (in logs) of liabilities of firm _f_ in year _y_ . _Assetsf,y_ and _Fixed Assetsf,y_ are the volume (in logs) of total and fixed assets of firm _f_ in year _y_ . _Salesf,y_ is the volume (in logs) of sales of firm _f_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend over time from the start of our sample in 2013 up to 2015, the year prior to the implementation of the FD Law. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Liabil|itiesf,y|Asse|tsf,y|Fixed As|setsf,y|Sal|esf,y|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Trendy|-0.07||-0.03||-0.19**||-0.05||
||(0.06)||(0.05)||(0.09)||(0.06)||
|Trendy*DebtStates,16Q1|0.01|0.01|0.06|0.06|0.06|0.06|-0.01|-0.01|
||(0.10)|(0.10)|(0.09)|(0.09)|(0.15)|(0.15)|(0.11)|(0.11)|
|Observations|654|654|654|654|652|652|654|654|
|R-squared|0.93|0.93|0.93|0.93|0.95|0.95|0.90|0.90|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

65
# **<mark>Table A3A – Comparison of Means Between States with Low and High Public Debt Before FD Law</mark>**

This table displays the summary statistics of states given their public indebtedness in March of 2016, one month prior to the implementation of the FD Law. A state is defined as with low (high) debt if its public indebtedness in March 2016 is below (above) the median. _Lending to states_ is the share of liabilities to net income. _Population_ is the total population of the state in millions. _Employment_ is the quarterly growth rate of employment. _GDP_ is the quarterly GDP growth. _Investment_ is the ratio of spending on public investment projects relative to the state’s GDP. _Services_ is the ratio of spending on public services relative to the state’s GDP. _Social Services_ is the ratio of spending on social services relative to the state’s GDP. _Debt Servicing_ is the ratio of spending on debt outlays relative to the state’s GDP. _Transfers_ is the ratio of federal transfers relative to the state’s GDP. _Tax Rate_ is the ratio of tax revenues of a state relative to the state’s GDP _. Poverty_ is the share of households in a state living in poverty _. Gini_ is the Gini coefficient in a state _. Northern State_ is an indicator of whether the state is classified as a northern state by the national statistics agency. The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between states with high and low debt (based <u>on Imbens and Wooldridge, 2009). All differences are insignificant, except for</u> _<u>Liabilities/Net Income</u>_ <u>, which is higher in states with high debt (by definition).</u>

|on Imbens and Wooldridge, 2009). All differen|ces are insignificant, except<br>**States**|for_Liabilities/Net Income_<br>**with Low Public Debt**|, which is higher<br>**States with Hi**|in states with high<br> **gh Public Debt**<br>Stdd|debt (by definition).<br>Stddid|
|---|---|---|---|---|---|
||Mean|Standard Deviation|Mean|anar<br>Deviation|anarze<br>Difference|
|Lending to states (share)|0.391|0.12|1.128|0.423|-1.68|
|Population (millions)|3.64|3.848|3.227|1.77|0.1|
|GDP|-0.031|0.022|-0.035|0.043|0.09|
|Employment|-0.013|0.022|-0.011|0.021|-0.05|
|- Primary|-0.044|0.114|-0.061|0.116|0.11|
|- Secondary|0.009|0.064|0.025|0.067|-0.17|
|- Tertiary|-0.007|0.023|-0.005|0.03|-0.05|
|Investment|0.006|0.004|0.006|0.005|-0.04|
|Services|0.005|0.003|0.007|0.004|-0.46|
|Social Services|0|0|0|0|-0.02|
|Debt Servicing|0.002|0.002|0.008|0.007|-0.84|
|Transfers|0.129|0.053|0.146|0.075|-0.18|
|Tax Rate|0.485|0.197|0.483|0.169|0.01|
|Poverty Total|0.416|0.12|0.415|0.179|0|
|- Moderate|0.351|0.076|0.337|0.106|0.1|
|- Extreme|0.065|0.055|0.078|0.089|-0.13|
|Gini|0.451|0.039|0.469|0.043|-0.3|
|Northern State|0.4|0.507|0.533|0.516|-0.18|

66
# **<mark>Table A3B – Comparison of Means Between Banks with Low and High Exposure to Local Public Debt Before FD Law</mark>**

This table displays the summary statistics of banks given their lending to state governments in March of 2016, one month prior to the implementation of the FD Law. A bank is defined as with low (high) local public lending if its share of lending to local governments in March of 2016 is below (above) the median. _Lending to States_ is the share of bank loans channeled to local governments. _Assets_ is the volume (in logs) of bank assets. _Capital_ is the tier1 capital ratio (percent). _Liquidity_ is the ratio of liquid assets to total assets (percent). _Delinquency_ is the share of loans that are more than 90 days in arrears (percent). The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between banks with high and low local <u>public debt (based on Imbens and Wooldridge, 2009).</u>

||**Banks with L**<br>Mean|**ow Local Public Debt**<br>Standard Deviation|**Banks with Hi**<br>Mean|**gh Local Public Debt**<br>Standard Deviation|Standardized<br>Difference|
|---|---|---|---|---|---|
|Lendingto states(share)|0.0|0.0|0.2|0.2|-1.22|
|Assets (logs)|12|1.8|12.4|1.5|-0.22|
|Capital (%)|11|3.8|9.6|6.8|0.18|
|Liquidity (%)|6.9|2.3|6.4|3|0.04|
|Delinquency (%)|2.3|1.8|2.2|1.4|0|

67
# **<mark>Table A3C – Comparison of Means Between Firms in Municipalities with Low and High Bank Exposure to Public Local Debt</mark> Before FD Law**

This table displays the summary statistics of firms given the exposure to local public lending of the banks they borrow from. All statistics recorded in March of 2016, one month prior to the implementation of the FD law. A bank is defined as with low (high) public debt if its share of lending to local governments in March of 2016 is below (above) the median. _Liabilities, Assets, Fixed Assets_ and _Sales_ are the volume (in logs) of firms’ total liabilities, total assets, fixed assets, and total sales. The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between banks with high and low public debt (based on Imbens and Wooldridge, 2009).

||**Banks wi**<br>Mean|**th Low Local Public Debt**<br>Standard Deviation|**Banks with Hi**<br>Mean|**gh Local Public Debt**<br>Standard Deviation|Standardized<br>Difference|
|---|---|---|---|---|---|
|Liabilities (logs)|19.13|1.8|19.19|1.87|-0.02|
|Assets (logs)|19.85|1.79|19.93|1.8|-0.03|
|Fixed Assets (logs)|17.93|2.48|18.34|2.38|-0.12|
|Sales(logs)|20.17|1.39|20.2|1.47|-0.01|

68
# **<mark>Table A4 – Impact of FD Law on Bank Lending to Firms (Across Sectors) Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms operating in the primary, secondary, and tertiary sectors, in states of varying ex ante public indebtedness. Primary sector includes the sectors of agriculture and mining, secondary sector includes sectors such as construction and manufacturing, and tertiary sector includes sectors such as retail, finance, education, and health. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

|||Volumef,b,m<br>|||Investmentf,b,<br>|m<br>|W<br>|orking Capita<br>|lf,b,m<br>|
|---|---|---|---|---|---|---|---|---|---|
||Primary<br>(1)|Secondary<br>(2)|Tertiary<br>(3)|Primary<br>(4)|Secondary<br>(5)|Tertiary<br>(6)|Primary<br>(7)|Secondary<br>(8)|Tertiary<br>(9)|
|Postm*DebtStates,16Q1|-0.003|0.042***|0.006|-0.063*|0.095***|0.040**|-0.002|0.042***|0.010|
||(0.015)|(0.008)|(0.007)|(0.036)|(0.020)|(0.017)|(0.016)|(0.008)|(0.007)|
|Observations|54,027|348,390|849,588|10,355|65,068|131,104|47,983|311,905|771,476|
|R-squared|0.869|0.809|0.790|0.905|0.877|0.858|0.871|0.805|0.788|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

69
# **<mark>Table A5 – Impact of FD Law on Bank Lending to Firms (North vs non-North States) Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms headquartered in Northern vs nonNorthern states of varying ex ante public indebtedness. North states are defined following the definition of the Mexican National Statistics Agency. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are <u>provided in Table A1 in the Appendix.</u>

||Vol<br>North<br>(1)|umef,b,m<br>Non-North<br>(2)|Invest<br>North<br>(3)|mentf,b,m<br>Non-North<br>(4)|Workin<br>North<br>(5)|g Capitalf,b,m<br>Non-North<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.015**|0.017*|0.042***|0.092**|0.017**|0.009|
||(0.008)|(0.010)|(0.015)|(0.036)|(0.008)|(0.011)|
|Observations|631,424|620,646|113,370|93,220|566,800|564,604|
|R-squared|0.811|0.795|0.876|0.865|0.806|0.793|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|

70
# **<mark>Table A6 – Impact of FD Law on Bank Lending to Firms Given Public Debt of Their Municipalities</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on bank lending to private firms in states and municipalities of varying ex ante public indebtedness.</mark> The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _DebtMunicipalitym,16Q1_ measures the public indebtedness of municipality _m_ and is calculated as the municipal public debt per capita in 2016Q1. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in <u>Table A1 in the Appendix.</u>

|||Volumef,b,m<br>|||Investmentf,b,<br>|m<br>|W<br>|orking Capitalf<br>|,b,m<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Postm*DebtStates,16Q1|0.018***|0.019***||0.049***|0.054***||0.019***|0.019***||
||(0.006)|(0.006)||(0.012)|(0.013)||(0.007)|(0.007)||
|Postm*DebtMunicipalitym,16Q1|-0.126***|-0.118***|-0.103***|-0.340***|-0.339***|-0.510***|-0.094***|-0.083***|-0.047**|
||(0.024)|(0.023)|(0.023)|(0.065)|(0.067)|(0.100)|(0.024)|(0.023)|(0.023)|
|Postm*DebtStates,16Q1*|0.073***|0.079***|0.103***|0.450***|0.446***|0.733***|0.081***|0.090***|0.103***|
|DebtMunicipalitym,16Q1|(0.022)|(0.022)|(0.029)|(0.063)|(0.064)|(0.099)|(0.023)|(0.023)|(0.030)|
|Observations|1,252,105|1,252,105|1,252,105|206,666|206,655|206,666|1,131,483|1,131,483|1,131,483|
|R-squared|0.803|0.803|0.803|0.870|0.870|0.871|0.799|0.800|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|State-Month FE|No|No|Yes|No|No|Yes|No|No|Yes|

71
# **<mark>Table A7 – Impact of FD Law on Bank Lending to Firms (by Their Dependence to Government Spending) Given State</mark>**

# **Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms from sectors with different dependence on government spending and across states of varying ex ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _GovernmentExposurei_ measures the dependence of firms from industry _i_ to government spending (following Belo, Gala, and Li, 2013), and is calculated as the share of revenues in industry _i_ derived from sales to the government (or to its providers). _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Volum|ef,b,m|Investm|entf,b,m|Working|Capitalf,b,m|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*DebtStates,16Q1|0.028***||0.092***||0.028***||
||(0.006)||(0.016)||(0.007)||
|Postm*GovernmentExposurei|0.049***|-0.025***|0.081*|0.106***|0.018|-0.047***|
||(0.013)|(0.006)|(0.042)|(0.023)|(0.014)|(0.007)|
|Postm*DebtStates,16Q1*GovernmentExposurei|-0.020***|-0.018**|-0.145***|-0.135***|-0.013*|-0.009|
||(0.007)|(0.008)|(0.024)|(0.024)|(0.007)|(0.007)|
|Observations|824,687|824,687|130,532|130,574|747,875|747,875|
|R-squared|0.80|0.80|0.870|0.871|0.800|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|State-Month FE|No|Yes|No|Yes|No|Yes|

72
# **Table A8 - Impact of FD Law on Bank Lending to Firms (with Long vs Short Credit History) Given State Public Debt and Fiscal Spending Composition**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms of different credit history length in states of varying ex ante public indebtedness and spending composition. A firm is defined to have a long (short) credit history if the relationship duration with its bank is above (below) the median at the time of the FD Law. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _NonInfrastructures,2015_ is the ratio of public spending on all items except for infrastructure over the total public spending of state _s_ in 2015. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed <u>variable definitions are provided in Table A1 in the Appendix.</u>

|variable definitions are provide|d in Table|A1 in the A<br>**Firms**|ppendix.<br>**with Lon**|<br>**g Credit H**|**istory**<br>Wo|kin||**Firms**|**with Shor**|**t Credit**|**History**<br>Wo|rkin|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Volu<br>|mef,b,m<br>|Invest<br>|mentf,b,m<br>|<br>Capi<br>|g<br>talf,b,m<br>|Volu<br>|mef,b,m<br>|Investm<br>|entf,b,m<br>|<br>Capi<br>|g<br>talf,b,m<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Postm*DebtStates,16Q1|0.01*|0.02***|0.03**|0.05***|0.02***|0.02***|0.02*|0.01|0.09***|0.09***|0.02*|0.02|
||(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|
|Postm*NonInfrastructures,2015||-0.84***||-0.80**||-0.65***||0.09||-0.40||0.08|
|||(0.17)||(0.40)||(0.18)||(0.30)||(0.77)||(0.32)|
|Postm*DebtStates,16Q1||-0.12||0.19||0.08||1.94***||1.52**||1.65***|
|*NonInfrastructures,2015||(0.20)||(0.51)||(0.21)||(0.30)||(0.73)||(0.33)|
|Observations|721,946|721,946|112,551|112,551|661,194|661,194|530,145|530,145|94,024|94,024|470,261|<br>470,261|
|R-squared|0.80|0.80|0.86|0.86|0.79|0.79|0.81|0.81|0.87|0.87|0.81|0.81|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

73
# **<mark>Table IA1. Classification of Local Public Indebtedness in the Alert System</mark>**

<mark>This table displays the classification of indebtedness of states given their financial health indicators as defined by the Mexican</mark> <u>Ministry of Finance.</u>

|Ministry of Finance.|**Indicator 1**|**Indicator 2**|**Indicator 3**|
|---|---|---|---|
||_Total public debt to freely_|_Debt service and obligations to_|_Short-term obligations to total_|
||_disposable income_|_freely disposable income_|_income_|
|**Classification of Ind**|**ebtedness**|||
|Sustainable|Low|Low|Low|
||Low|Low|Medium|
||Low|Medium|Low|
|Under-Watch|Low|Medium|Medium|
||Low|High|Low|
||Low|High|Medium|
||Low|Low|High|
||Low|Medium|High|
||Medium|Medium|Medium|
||Medium|High|Low|
||Medium|High|Medium|
||Medium|Low|High|
||Medium|Medium|High|
|High|High|-|-|
||Low|High|High|
||Medium|High|High|

74
# **Table IA2 – Impact of Debt Ceilings on States’ GDP and Employment (Sample of States with** **_Sustainable_ and** **_Under-Watch_ Debt)**

<mark>This table reports OLS estimates of the impact of the FD Law on GDP and employment for the sample of ten states classified in 2016Q1 as with</mark> “Sustainable” and “Under-Watch” public indebtedness. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary and tertiary sectors of state _s_ in quarter _qUnderWatchs,16Q1_ is an indicator variable that equals one for the five states that had an indebtedness classification of “Under-Watch” in 2016Q1 and equals zero for the remaining five states with an indebtedness classification of “Sustainable”. _Postq_ is an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent <u>levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prima|yment<br>rys,q|Employ<br>Second|ment<br>arys,q|Empl<br>Ter|oyment<br>tiarys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|-0.006||-0.011*||-0.047***||-0.018*||-0.007||
||(0.008)||(0.006)||(0.014)||(0.008)||(0.012)||
|Postq*UnderWatchs,16Q1|0.007*|0.007*|0.016***|0.016***|0.078**|0.072***|0.020*|0.02|0.014*|0.015*|
||(0.004)|(0.004)|(0.003)|(0.004)|(0.029)|(0.023)|(0.02)|(0.021)|(0.007)|(0.009)|
|Observations|192|192|144|144|144|144|144|144|144|144|
|R-squared|0.316|0.608|0.281|0.411|0.153|0.259|0.128|0.216|0.251|0.366|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

75
# **Table IA3 – Impact of Debt Ceilings on Bank Lending to Firms (Sample of States with** **_Sustainable_ and** **_Under-Watch_ Debt)**

This table reports OLS estimates of the impact of the FD Law on bank lending to private firms for the sample of ten states classified in 2016Q1 as with “Sustainable” and “Under-Watch” public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. UnderWatchs,16Q1_ is an indicator variable that equals one for the five states that had an indebtedness classification of “Under-Watch” in 2016Q1, and equals zero for the remaining five states with an indebtedness classification of “Sustainable”. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions <u>are provided in Table A1 in the Appendix.</u>

||Volu<br>(1)|mef,b,m<br>(2)|Investm<br>(3)|entf,b,m<br>(4)|Working<br>(5)|Capitalf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|Postm*UnderWatchs,16Q1|0.05***|0.04***|0.05|0.05|0.05***|0.04***|
||(0.01)|(0.01)|(0.04)|(0.04)|(0.01)|(0.01)|
|Observations|275,534|275,534|43,980|43,980|248,858|248,858|
|R-squared|0.83|0.83|0.89|0.89|0.82|0.82|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

76
**<mark>Table IA4 – Impact of FD Law on States’ GDP and Employment for States Below and Above Ex ante Public Debt</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on GDP and employment of states above the ex-ante public indebtedness.</mark> _<mark>GDP</mark> s,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _I(DebtState)s,16Q1_ is an indicator variable that equals one if a state's measure of public indebtedness in the quarter prior to the FD Law (calculated as the ratio of public debt of a state over its net income) is above the median state and zero otherwise. _Postq_ an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prim|yment<br>arys,q|Employ<br>Second|ment<br>arys,q|Empl<br>Terti|oyment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|0.003||0.001||0.017||-0.009***||-0.002||
||(0.012)||(0.004)||(0.013)||(0.001)||(0.006)||
|Postq*I(DebtState)s,16Q1|0.005**|0.006*|0.004*|0.004*|-0.009|-0.009|0.008**|0.008**|0.007*|0.007|
||(0.002)|(0.004)|(0.002)|(0.002)|(0.015)|(0.017)|(0.003)|(0.003)|(0.003)|(0.004)|
|Observations|480|480|420|420|420|420|420|420|420|420|
|R-squared|0.044|0.473|0.016|0.176|0.014|0.092|0.027|0.037|0.017|0.140|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

77
# **<mark>Table IA5 – Impact of FD Law on Bank Lending to Firms Given State Public Debt</mark>**

This table reports OLS estimates of the impact of the FD Law on employment and GDP of states above the ex-ante public indebtedness. The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. I(DebtStates,16Q1)_ is an indicator variable that equals one if a state's measure of public indebtedness in the quarter prior to the FD Law (calculated as the ratio of public debt of a state over its net income) is above the median state and zero otherwise. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance <u>at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Volu|mef,b,m|Investm|entf,b,m|Working|Capitalf,b,m|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm*I(DebtStates,16Q1)|0.011*|0.013*|0.054***|0.058***|0.008|0.012|
||(0.006)|(0.007)|(0.021)|(0.021)|(0.011)|(0.011)|
|Observations|1,252,105|1,252,105|206,666|206,655|1,131,483|1,131,483|
|R-squared|0.802|0.803|0.870|0.871|0.799|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

78
<!-- Start of picture text --> —<br>oo<br>oo<br>Re1.<br>oa=:<br>ee<br>Fa<br>ao " loP..<br>a . *<br>* *<br>* **<br>oa * *<br>0 wed Debtincome4 - 2016:& ci] 1<br><!-- End of picture text --> <!-- Start of picture text --> o<br>7” *<br>.<br>oo * . * Fea “% *<br>* *<br>oa<br>Bio1<br>2<br>oe<br>z<br>Eia +<br>o<br>0 2 Interest4 Rate - 20766 8 10<br><!-- End of picture text -->