Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)

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RETHINKING FOGAPE

An Evaluation of Chile’s Partial Credit Guarantee Scheme

Margarita Armenteros ⋅ Niccolò Artellini ⋅ Andreas Hoppe ⋅

Marco Urizar ⋅ Bernard Yaros

June 15, 2014

Abstract We analyze the effect of a partial credit guarantee scheme in Chile, called Fogape, which seeks to guarantee loans granted to small-and medium-sized enterprises. The motivation behind such guarantee funds is to ease financial constraints for targeted firms as well as aid their growth. We assess the effect of eligibility to Fogape on sales, profitability, and debt growth of eligible firms at the margins of the eligibility threshold between the years 2007 and 2009. We do not find any significant results that suggest eligibility to Fogape has an effect. In fact, we find that eligible firms, on average, had less sales and debt growth vis-à-vis non-eligible ones at the margins of the threshold. We propose that Fogape review the portfolio of loans that are being guaranteed to make certain that the right firms are indeed receiving its guarantees. We also recommend that Fogape connect its beneficiaries to other public programs that provide technical training, market research, feasibility studies, and other services on behalf of small-and medium-sized enterprises.

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Table of Contents

1 INTRODUCTION .............................................................................................. 2 2 THEORETICAL FRAMEWORK ..................................................................... 3

2.1 CREDIT RATIONING .............................................................................................. 3 2.2 Partial Credit Guarantee Schemes (PCGS) ......................................... 4

3 LITERATURE REVIEW ................................................................................... 6 3.1 EVALUATION OF PCGS AROUND THE WORLD ............................................ 6 3.2 Prior evaluations of Fogape ..................................................................... 7

4 ROLE OF SMES IN CHILE ............................................................................... 8 5 FOGAPE ............................................................................................................ 10

5.1 BACKGROUND ........................................................................................... 10 5.2 Allocation of guarantees .......................................................................... 11 5.3 Eligibility ..................................................................................................... 13

6 DATA ................................................................................................................ 13 7 METHODOLOGY ............................................................................................ 14

7.1 SHARP RDD ANALYSIS ............................................................................ 14 7.2 Caveats and limitations to approach .................................................. 16

8 RESULTS .......................................................................................................... 18 9 POTENTIAL PROBLEMS & POLICY RECOMMENDATIONS ............... 19 10 CONCLUSION ............................................................................................... 21 BIBLIOGRAPHY ................................................................................................. 23 APPENDIX ........................................................................................................... 26

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1 Introduction Financial markets face frictions whenever the market cannot rely on price

adjustments to establish an equilibrium of the supply and demand of credit. Small-‐

and-‐medium enterprises (SMEs) often find themselves credit constrained due to a

lack of collateral, limited credit history, and informational asymmetries that entail

high monitoring costs for lenders. In spite of these constraints, SMEs are significant

contributors to the health of any economy. As a result, governments around the

world have introduced partial credit guarantee schemes (PCGS) to overcome these

constraints and ease financial access for SMEs. These schemes aim to relieve credit-‐

constrained firms by providing public collateral that reduces the risk borne by

private lenders in the event of a default. In recent years, PCGS have been utilized as

a way to protect SME lending in the backdrop of the global credit crunch.1

In this policy memo, we use firm-‐level data to evaluate Fogape,2 which is a

public PCGS in Chile. We econometrically assess the impact of Fogape over a three-‐

year period, employing regression discontinuity design to compare sales growth,

profitability, and the leverage ratio of eligible and non-‐eligible firms at the margins

around the program’s threshold of eligibility. Based on the results we obtain, we

speculate on the problems that might be facing Fogape and provide our own policy

recommendations.

Section 2 of this paper delves into two theoretical models that motivate the

existence of PCGS and lists the number of characteristics that distinguish all types of

PCGS. Section 3 reviews the existing literature that evaluates PCGS around the world

and also Chile’s Fogape. Section 4 gives background to the realities facing Chilean

SMEs. Section 5 describes Fogape and those mechanisms that make it unique.

Section 6 summarizes the data we use to evaluate Fogape, and section 7 explains the

econometric methodology we undertake to do such an assessment. Section 8

presents our results. Section 9 showcases our policy recommendations that are

grounded in our findings and other illustrative data on Fogape. Section 10 concludes. 1 Honohan (2010) 2 Fogape is an acronym for Fondo de garantías para pequeños empresarios (Guarantee fund for small entrepreneurs).

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2 Theoretical Framework 2.1 Credit Rationing

There is said to be credit rationing when out of an apparently similar pool of

applicants some receive loans and others do not, even if those rejected would have

offered to pay higher interest rates. Credit rationing is also blamed when a group of

individuals cannot get loans at any interest rate at a given level of credit supply, but

would obtain them if supply were higher.

Why is there credit rationing? Stiglitz and Weiss (1981) posit that if financial

markets work, demand of credit should equal supply and that when demand

exceeds supply, prices should increase to reduce demand until the two meet in

equilibrium. Yet, in the real world, credit rationing is present. It can exist in the

short-‐run if prices fail to adjust quickly; however, in the long run, it is likely that

credit rationing is better explained by other factors. For example, it can occur

because banks are concerned with the interest rate they charge on loans and the

riskiness of these loans. The rates they charge can indeed affect the risk composition

of their loan portfolio.

Interest rates can affect risk through both adverse selection and incentive

effects, which are related to imperfect information available in loan markets. Banks

would like to screen potential borrowers in order to identify good from bad; those

willing to pay higher interest rates are usually those who have higher expected

profits, but more risk. Increasing interest rates lowers profitability of borrowers’

projects, so they take up projects with greater payoffs but also a higher probability

of failure. In a scenario where there may be excess demand, if unmet borrowers

offer to pay higher interest rates, banks, whose return is actually the risk-‐adjusted

interest rate, would not effectively be receiving such an increase in their return and

would therefore abstain from giving out loans. In such a way, credit is rationed

because market forces do not lead to the equality of supply and demand.

Banks maximize their profits by setting the interest rates and requiring

collateral for their loans. Banks cannot satisfy excess demand and eliminate credit

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rationing by requiring more collateral, because collateral also has its own effect on

riskiness. Firms that can pledge more assets are wealthier ones that tend to be less

risk-‐averse. In conclusion, when there is credit rationing, banks cannot increase

interest rates or collateral requirements beyond the levels they have set to optimize

their profits. Nevertheless, credit rationing can be addressed by increasing supply.

SMEs are typically credit constrained because of under-‐collateralization,

limited credit history, and typically a lack of expertise needed to produce

sophisticated financial statements. These weaknesses engender asymmetric

information that lead commercial banks to consider SMEs as unreliable clients and

hence reject their loan requests. This is a main reason why PCGS are widely used as

an important tool to facilitate financial access for SMEs and start-‐ups.3

The expected effects of PCGS can be analyzed using models of

entrepreneurial financing with credit rationing. According to the model developed

by Holmström and Tirole (1997), there is credit rationing due to moral hazard.

Entrepreneurs have projects, but insufficient cash to finance them, and also lack

personal collateral to obtain loans. The success of the project depends in part on the

entrepreneur’s effort. When he uses his own funds, he will work hard to succeed,

but when partly financed by loans, the optimal level of effort goes down. To address

moral hazard, banks have to monitor firms and incur a cost in doing so. When banks

receive guarantees on their loans, the incentive to monitor falls since the losses from

defaults are lower. Therefore, it is imperative that under any PCGS the benefits of

monitoring outweigh the costs so that banks still properly monitor firms.

2.2 Partial Credit Guarantee Schemes

PCGS are a risk-‐transfer mechanism in which the state or private guarantee

provider assumes a share of the lender’s risk by ensuring the partial repayment of a

loan in the event of a default.4 There are five general standards by which one can

assess the efficaciousness of PCGS around the world, namely: loan recovery,

3 OECD (2013) 4 Beck, Klapper, and Mendoza (2008)

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targeting of specific sectors and regions, timing of guarantee payouts, coverage

ratios, and eligibility rules.

Lenders will usually have more information concerning their borrowers than

the public entity issuing the guarantees. Therefore, in the event of a default, the

literature on PCGS favors loan recoveries that are conducted by commercial banks

rather than the guarantee fund.5

Regarding the timing of guarantee payouts, the guarantee fund can pay the

lender either after default and the loan is written-‐off or after legal action has been

undertaken against the defaulter.6 In many developing countries, the latter is not

possible. Nevertheless, if guarantee payouts are granted too soon before the lender

exerts effort to recover the non-‐performing loan, this might induce moral hazard on

the part of lenders.7

Policymakers might also be keen on targeting specific economic sectors or

geographic regions where enterprises face constraints in terms of their access to

credit. On the one hand, this could be a positive measure in order to facilitate such

access for them. On the other hand, if too small a group is targeted, it might generate

relatively large bureaucratic costs that would make the PCGS unsustainable.8

Moreover, as stated by Zia (2008), such restrictions might even distort the lending

market and lead to deadweight losses.

The coverage ratio refers to the fraction of the loan that is guaranteed in the

case of a default. The coverage ratios adopted by PCGS are different across programs

and countries, but there is widespread agreement in the literature that such ratios

ought to fall within a range of 60 to 80%, so that commercial banks still retain a

sizable portion of the loss from a non-‐performing loan and hence screen and

monitor SMEs requesting loans in the same way they would in the absence of a

guarantee.9 On the other hand, according to Honohan (2010), offering a coverage

5 Ibid 6 Ibid 7 Ibid 8 Ibid 9 Levitsky (1997) and Green (2003)

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ratio substantially lower than 50% might fail to attract lenders. In practice, most

schemes offer slightly higher rates of guarantee with 70 to 80% being the norm.

There are two real-‐world examples of PCGS that actively ensure that the right

coverage ratios are fixed. Honohan (2010) refers to the due diligence done by an

Italian public PCGS, which conducts ex ante a risk assessment of the loan in question

so as to determine the appropriate coverage ratio. Benavente, Galetovic, and

Sanhueza (2006) as well as Bennett, Doran, and Billington (2005) mention Fogape

for its unique feature of the auction system in which guarantees are allocated to

lenders bidding for different coverage ratios. Those banks that bid the lowest

coverage ratios are given priority by Fogape. This auction system has allowed for

coverage rates to fall within the recommended range, hence leading to 20 to 30% of

the loss on a defaulted loan to be retained with the primary lender.

The final feature of PCGS into which we delve in this section relates to how

broad or narrow the eligibility criteria should be for firms to access guaranteed

loans. In this respect, a trade-‐off can be detected between the complexity of the

eligibility requirements and their enforcement. Honohan (2010) emphasizes that a

byzantine set of criteria may lead to political interference in the assignment of

guarantees and not so transparent a process for their enforcement. On the other

hand, a broad set of eligibility criteria may result in the misallocation of guarantees

to borrowers, who in fact have no need of them.

3 Literature Review 3.1 Evaluation of PCGS around the world

Despite the existence of many PCGS in the developed and developing world,

there is no consensus on their effects. The need for public guarantees comes from a

gap in private credit markets and firms being credit rationed.10 The aim of these

policies is for the public guarantee fund to absorb part of a borrower’s insolvency

risk and hence encourage private lenders to ease access to credit for such

constrained firms. Public guarantees can have positive effects through increases in

10 Arping, Lóránth, and Morrison (2010)

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access to credit and improvements in loan conditions through longer maturities and

lower interest rates. The shortcomings of public guarantees are increases in moral

hazard on the side of the firm and bank as well as potential substitutability of

private for public credit. When banks can rely on public guarantees to cover losses

from bad loans, this modifies their monitoring incentives. High coverage ratios can

lead to a decrease in monitoring and a worsening of banks’ loan portfolios. Firms

benefiting from limited liability tend to make riskier choices when they put up lower

amounts of equity and fund projects through loans.

Previous studies have found mixed results when evaluating credit guarantee

programs. Banerjee and Duflo (2004) find that a PCGS program in India led to an

increase in production, acceleration of growth, and profits of targeted firms. They

also reveal that there was no problem of substitutability of private for public

guarantees and hence conclude that firms must have previously been credit

constrained. D’Ignazio and Menon (2013) study a PCGS implemented in Italy and

come across mixed results. Using firm-‐level data, they find that the policy improved

treated firms’ financial conditions by increasing their long-‐term debt, even though

the total amount of debt was not affected. Likewise, firms also benefited from

substantial decreases in interest rates, but there was an apparent increase in moral

hazard and the probability of default. Arráiz, Meléndez, and Stucchi (2012) find that

Colombia’s PCGS – the National Guarantee Fund – was effective in relaxing credit

constraints and fostering enterprises’ growth both in terms of employment and

output. They conclude that the program has had no impact on investment,

suggesting that firms are using the new funds for working capital rather than for

investment in durable goods, which would increase their capital stock.

3.2 Prior evaluations of Fogape

There is limited literature analyzing the impact of Chile’s Fogape program on

the economic performance of local SMEs. Tan (2009) assesses the effect of a wide

array of pro-‐SME programs, including Fogape. The study, using propensity score

matching and difference-‐in-‐differences methodologies, finds that the use of such

programs was not associated with any improvement in critical performance

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indicators. If anything, treated firms had marginally lower sales growth than the

control group. It appears that improved access to finance by itself is insufficient to

spur firms to make the necessary organizational and technological changes to

improve future performance.

Larraín and Quiroz (2006), on the other hand, undertake an impact

evaluation of Fogape, considering “access to credit” and “economic performance”

outcome variables. They compare “treated” firms that entered the program in 2000

when Fogape was relaunched and “control” firms that entered in the ensuing years.

They find that participating in Fogape increased the firm’s debt by $18,000 on

average and that enterprises that received a Fogape loan in 2000 (that is, the

“treated”) were 14% more likely to get a normal loan from the banking system

afterwards. In terms of economic performances, Larraín and Quiroz conclude that

“treated” firms increased their sales and profits after five years. On balance, sales

increased by 32% and profits by 24%, thereby demonstrating a positive impact of

the program.

Considering a different set of outcome variables, Cowan, Drexler, and Yañez

(2009) analyze Fogape’s impact on liquidity constraints and default rates, using the

number of loans issued to SMEs, the SMEs’ average loan size, and the default rate in

SME lending by financial institutions. In such a way, they focus on the dimension of

access to credit rather than SMEs’ economic performance. Additionally, they study

how credit insurance affects the repayment behavior of clients that have insured

and uninsured loans. Their findings suggest that credit insurance is an effective

mechanism to increase the total amount lent to SMEs. Furthermore, they show that

credit insurance does not significantly affect the repayment incentives of the

entrepreneurs, but that it does seem to strongly reduce the banks’ incentive to

monitor.

4 The Role of SMEs in Chile

Any economy is dependent on the innovation, technological change, and job

creation that new enterprises bring. In most cases, such new enterprises are small

in size. The role of SMEs in Chile is no exception. By 2009, the SME sector in Chile

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contributed to 20% of GDP, and the percentage of workers employed in SMEs stood

at 56.4% in 2011.11

Even though there has been a steady increase in the volume of commercial

lending to SMEs, Chilean SMEs face challenges to survive in their initial stage, and

later on to achieve sustainable growth and compete in the market. Some of these

difficulties are related to competition with foreign or larger firms, an inability to

absorb productive technologies, and a general dearth of expertise among others.

Chilean SMEs have pointed to the fact that their difficulties in obtaining a formal

loan rest with the lack of guarantees and high financial costs12. This is not surprising

in a continental context where less than 40% of households in Latin America have a

deposit account with a formal financial institution.13

With the goal of supporting SMEs, a handful of public institutions have been

utilized on their behalf, for example: the Technical Assistance Fund (FAT), Technical

Cooperation Services (SERCOTEC), and the Export Promotion Program (PROCHILE)

among others. Of all of these state programs, Fogape is noteworthy not only for its

scope, but also its specific aim to improve lending conditions for SMEs.

11 Timm (2012) 12 Ferraro (2001) 13 CGAP/World Bank Group (2010)

-‐

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

2005 2006 2007 2008 2009 2010 2011 2012

Chilean Pesos (M

illions)

SME Outstanding Loans from Commercial Banks

Chile

Source: World Bank

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5 Fogape 5.1 Background

Fogape was first created in September 1982. Since its inception, the goal was

to provide public guarantees for loans taken out by SMEs with private financial

institutions. It was initially inactive for two decades and then relaunched in 2000

with an initial capitalization of $13 million.14 In 2007 and 2009, the Chilean

government approved the re-‐capitalization of the fund by $10 million and $130

million respectively. The massive injection that was carried out in 2009 was a direct

response to the international financial crisis.

It is important to note that Fogape can legally leverage its capital to

guarantee a total loan volume that is 10 times greater. As depicted in the graph

below, both the total number of loans and aggregate amount of credit guaranteed by

Fogape peaked in 2010 at the height of the government’s countercyclical efforts to

safeguard SMEs’ access to financing. Such figures have since declined yet are still

higher than pre-‐crisis levels.

14 This amount is equivalent to the 2013 price index.

0

20000

40000

60000

80000

0 20 40 60 80 100

Thousands (UF)

Thousands

Total Number of Operations(left axis)

Total Annual Amount of Guaranteed Financing (right axis)

Source: Fogape

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5.2 Allocation of guarantees

Fogape disseminates its guarantees into the credit market by way of an

auction that takes place 4 to 6 times a year. In each auction, participating financial

institutions bid for both the total amount of loans they want guaranteed and, more

importantly, the fraction they wish their loans to be insured. Fogape gives priority

to those competing financial institutions that bid the lowest coverage ratios at which

they want their loans to be insured. Participating banks are then ordered from the

lowest to highest bidders in terms of these requested coverage ratios. Guarantees

for the total loan amount, requested by the participants, are then allocated to the

banks in such an order until all the funds for a particular auction are used up.

Three types of credit are available for Fogape guarantees in these auctions:

long-‐term credit with a minimum maturity of 37 months and a maximum of 120

months; short-‐term credit with a maximum maturity of 36 months; and contingent

credits. On average, 60% of auctioned guarantees are allocated to credit for working

capital and the rest for short-‐and long-‐run investment projects.

By law, the maximum coverage ratio that a bidder can request in an auction

is 80% for loans below $120,000 and 50% for loans above that value.15 The average

coverage ratios provided by Fogape have been subject to change over time. The

mean coverage ratio prior to the financial crisis was 65%; however, after the

downturn, when more firms were prone to default, the ratio rose, peaking at 77% in

2010 and subsequently dropping to 68% in 2011.

The auctioning system, carried by Fogape, is widely lauded in the literature

as a sui generis method to countering the potential moral hazard on the part of

participating banks. By only granting guarantees at coverage ratios within the range

of 65 to 80%, Fogape ensures that banks still have an incentive to screen and

monitor their clients that receive guaranteed loans. Moreover, Fogape can exclude

lenders from future auctions if their previous default rates are considered

15 Loans, guaranteed by Fogape, cannot surpass $270,000 in value.

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excessively high or if they used less than 80% of the guarantee rights won in past

auctions.

If a Fogape-‐guaranteed loan is defaulted upon, banks have to follow all of the

required compliance mechanisms and start the debt collection process. Only if the

defaulter is still unable to repay their debt after 425 days, can the bank request that

Fogape pay out the guarantee to it. Fogape authorities then have 15 days to deny or

accept the reimbursement to the bank in question. Historically, the default rate on

Fogape loans have hovered just above 1%, which is almost on par with that on

uninsured, commercial loans. However, since the financial crisis, the default rate on

Fogape-‐backed credit has risen to slightly over 2%.16

It is the responsibility of the bank itself to allocate the guarantees it receives

to credit-‐seeking clients within a period of seven months. Fogape is not active in the

origination of loans to SMEs that end up guaranteed by its fund. On the contrary, it is

up to both the SME client to request a Fogape-‐backed loan from the bank as well as

the lender itself to match Fogape guarantees with pending, unissued loans or newly

requested ones. If the bank is unable to use all of its guarantee rights within the

allotted time, a new auction will take place for its unused guarantees.

At the beginning of Fogape’s relaunch, only 14 financial institutions

participated in the auctions; however, that number climbed to 32 in 2011 and has

since dropped slightly to 28 in 2013. In spite of the increase in the number of

participating financial intermediaries, 90% of the guarantee rights usually end up

going to only five banks.17 In 2005, a financial institution exploited a previous

loophole in the auctioning process and received an absolute majority of all the funds

supplied by Fogape in one particular auction that year. To prevent such a re-‐

occurrence, Fogape introduced a new policy, only allowing a lender to receive a

maximum of two thirds of all guarantees per auction.

To finance its operations, Fogape charges a commission of 1 to 2% of the

total loan amount that participating financial institutions ask to be guaranteed.

Fogape has exhibited sustainability over its lifetime, making a small yearly profit in 16 “Estudio sobre los Programas de Crédito con Garantía Estatal” (2014) 17 Bozzo (2009)

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the period before the financial crisis. Beck, Klapper, and Mendoza (2010), who

review PCGS around the world, note that guarantee funds initially suffer only minor

losses but usually face setbacks later on as loan losses begin to accumulate. Fogape

has been no exception, and its most recent financial statement for 2013 has indeed

shown losses.

5.3 Eligibility

To be an eligible beneficiary of Fogape, the SME has to meet certain criteria

regarding its size, risk classification, and the conditions on the loan it wishes to

obtain. The main determinant of participation in Fogape depends on the annual

sales of an SME. Only enterprises that report a total amount of sales falling below

$750,000 in the previous accounting year are eligible to receive a Fogape guarantee.

There are no criteria, though, that exclude SMEs based on their sector of economic

activity or the number of years they have been in business. In addition, Fogape does

not retroactively guarantee loans that have already been issued. Enterprises that

have existing arrears in the financial system or whose expected loss is greater than

3% at the time of loan issuance are ineligible.

6 Data Our empirical evaluation of Fogape is based on two longitudinal surveys

obtained from the Ministry of Economy. The two surveys were conducted by the

National Institute of Statistics, which randomly sent out questionnaires to firms

across Chile. All surveyed businesses are registered with the Internal Revenue

Service and boast annual sales greater than $35,000.

The “First Longitudinal Survey of Enterprises 2009” contains 10,157

observations and 520 variables that encompass a wide range of firm-‐specific

characteristics regarding the enterprises’ balance sheet, finances, clientele,

management, workforce, and use of technology. Although this survey was released

in 2009, it is important to note that its firm-‐level data are from the accounting year

of 2007.

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The “Second Longitudinal Survey of Enterprises 2011” is a follow up to the

2009 survey and has 7,060 observations and 579 variables that are largely identical

to those in the previous survey. Likewise, the survey was undertaken in 2011, yet

the firm-‐level data that it contains is from the accounting year of 2009.

37.8% of the observations in the 2011 survey – 2,650 in total – were also

part of the 2009 survey, allowing us to merge the two datasets and construct a panel

of firms. The firms in our panel are distributed across a wide range of economic

sectors and the different regions of Chile. Moreover, firms of different sizes – micro,

small, medium, and large – are all represented as can be seen in Table 1 of the

Appendix.18

We are interested in those outcome variables in which we expect to observe a

change due to Fogape’s presence. The main performance indicators of Fogape

selected as outcome variables in our regression analysis are:

• Log Salesi,2009 – Log Salesi,2007

• Debt-‐to-‐equity ratio in 2009

• Profit margin in 2009

• Long-‐term debt over total debt in 2009 We are interested in the proportional change in sales from 2007 to 2009,19 profit

margins of firms in 2009, as well as the debt-‐to-‐equity20 and long-‐term over total

debt, which provides insight into debt sustainability.

7 Methodology 7.1 Sharp RDD Analysis

We employed an empirical strategy that comes from the econometric

framework of regression discontinuity design (RDD) in order to identify a link

18 See Table 2 in Appendix as well for the legal definitions of SMEs in Chile. 19 Following the approach taken by Bannerjee and Duflo (2004), we take the difference between the logarithms of total sales of firm i in 2009 and 2007 to capture this evolution. 20 From our dataset, we construct the debt-‐to-‐equity ratio as the sum of short and long term leasing and obligations with banks and other financial institutions over total equity in 2009.

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between Fogape and our outcome variables. RDD, first introduced by Thistlewaite

and Campbell (1960), is a quasi-‐experimental research approach in which receipt of

the treatment depends discontinuously on the value of one or more observable

covariates that at most can be manipulated only to a minimal extent by the subjects.

If the treatment is assigned wholesale to subjects whenever a specific

covariate value falls either above or below a cutoff, then RDD estimations take the

form of what is known as sharp RDD. On the other hand, if the probability of

treatment assignment merely jumps discontinuously without going to either 0 or 1

as the covariate value crosses the given cutoff, then fuzzy RDD is used instead.

Such a design normally appears in administrative settings with resource

constraints where a set of criteria determines who is eligible to receive such

resources rather than the discretion of administrators. Numerous studies have

exploited such settings in order to evaluate real-‐world policies, and our case is no

different. We take advantage of the eligibility requirements to receive a Fogape-‐

backed loan. Throughout Fogape’s operational lifetime since 2000, only firms whose

annual sales fall under $750,000 have been eligible to receive its credit guarantees.

The only exception was between January 2, 2009 to January 2, 2011 when the

eligibility was expanded to include all firms with annual sales under $21 million.21

In the case of Fogape, the normal course of analysis would be to perform

fuzzy RDD since Fogape guarantees are not assigned wholesale to eligible firms.

However, due to limitations in our data, fuzzy RDD is not possible. In the “First

Longitudinal Survey of Enterprises 2009,” there was no variable identifying firms

that received a Fogape-‐guaranteed loan.

We employ the next best methodology, which is to estimate the intention-‐to-‐

treat, or the effect of eligibility to Fogape, by performing sharp RDD. To do so, we

use the merged dataset. It fortunately includes total sales for firms in 2006, allowing

us to compare firms that were eligible for Fogape in 2007 and those that were not,

since eligibility is decided by the previous year’s total sales.

21 This expansion of Fogape-‐backed credit was intended as a countercyclical measure to restore financing to credit-‐constrained SMEs and even large enterprises that were also hurt by the financial crisis.

16

We seek to estimate the intention-‐to-‐treat effect of Fogape with the following

regression model:

yi = α + βTi + ρ⋅f( Si,2006 – S*) + ϕ⋅f{(Si,2006 – S*)⋅Ti} + Xiγ + δr + µc + εi

where y is the outcome variable, Ti is the treatment dummy equaling 1 if firm i was

eligible in 2007 for a Fogape guarantee and 0 otherwise, S* is the threshold level of

annual sales up to which a firm is eligible, Si,2006 is firm i’s sales in 2006, X is a vector

of firm-‐specific controls, and δ and µ are fixed effects for r regions and c sectors

respectively. The remaining error component, ε, is specific to firms. S* – Si,2006 is a

support variable in which we normalize firms’ total sales in 2006 to the eligibility

threshold, and f(⋅) is a polynomial function of our normalized support variable. For

more robustness, we include the interaction between normalized sales in 2006 and

our eligibility in 2007 dummy variable in order to allow the fitting line to have

different slopes on either side of the threshold.

In keeping with Imbens and Lemieux (2007), we estimate the effect of

Fogape on our dependent variables by employing a local linear regression and

focusing on all enterprises whose sales in 2006 fall within a distance h on either side

of the discontinuity point, which is S*, or $750,000. We ran two regressions – one

excluding our control variables and another including them – using two different

bandwidths. We selected one bandwidth to be a distance equal to one half the value

of S* and another to be three-‐quarters of this threshold sales value. Using two such

bandwidths of different distances h is a routine robustness check in RDD analysis

and provides insight as to how our estimators change in value and significance as

we restrict our sample with a smaller h.

7.2 Caveats and limitations to approach

Before we proceed, a couple caveats are in order. First, eligibility to Fogape is

not assigned randomly to firms in our dataset. The best that we can hope for is that

eligibility is as if randomly assigned. There are two validity checks that we ran to

determine if this is the case. If firms bunch right before the cutoff level, then it would

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be evidence that firms are misreporting their annual sales in order to be eligible for

Fogape. When we look at a distribution of firms by their total sales in 2006 in Graph

1 in the Appendix, we do not observe any suspicious bunching around the eligibility

threshold of $750,000. The second validity check is to ascertain whether the mean

baseline characteristics of eligible and non-‐eligible firms differ substantially. In

Table 3 of the Appendix, we regress separately three of our main controls – number

of workers in 2007, whether the firm was an exporter or not in 2007, and the

number of years in operation – on eligibility in 2007 and the rest of our specification.

If the coefficient on eligibility in 2007 came out significant in any of these

regressions, it would be evidence that there are some systematic differences

between eligible and non-‐eligible firms in our sample, thereby making the two

groups possibly incomparable for analysis. We ran this second validity check using

the smallest of our two bandwidths and did not find any statistically significant

evidence that eligibility determines such characteristics.

The second caveat is that we are not estimating the treatment effect of

Fogape, but rather the effect of eligibility to Fogape on eligible firms vis-‐à-‐vis non-‐

eligible ones. Since we do not find evidence that firms are manipulating their

reported sales nor that the mean baseline characteristics of firms at the margins on

either side of the threshold are different, there should, in theory, be no difference in

the outcome variables of firms that lay close to the eligibility cutoff within a distance

h. If any statistically significant difference between eligible and non-‐eligible firms

appears in our outcome variables, then it ought to be attributable to the effect of

eligibility to Fogape. There are no other public credit schemes or SME-‐related

programs that have a similar eligibility threshold, which would contaminate our

results.

Besides these two caveats, we must point out a few realities that limit the

extrapolations that we can make from these results on the impact of Fogape. First,

we are using survey data, which is inherently vulnerable to measurement error.

Hence, we dropped outliers from our observations before we ran our regressions.

Second, the period of time from 2007 to 2009 for which we were analyzing our data

coincides almost perfectly with the financial crisis. It is possible that our results

18

might be influenced by such an event. We would expect that during the crisis

Fogape-‐backed firms would outperform non-‐guaranteed ones because the effects of

a credit crunch that an economy faces during a crisis are muted for those benefitting

from state guarantees. Third, since we are restricting our sample to enterprises that

lie within a bandwidth h around the eligibility threshold, we are only estimating the

effect of eligibility on firms, which fall to the left of the eligibility threshold within

our bandwidth. Due to the possibility of heterogenous effects, we do not estimate

the effect of eligibility to Fogape on eligible firms that fall outside our chosen

bandwidths. Finally, it would be valuable to evaluate the program during a longer

horizon; Larraín and Quiroz (2006) surveyed firms that accessed Fogape in different

years from 2000 to 2005 and hence were able to track the evolution of profits, sales,

and debt holdings for Fogape-‐backed firms over a five-‐year period. Since there may

be firms that received Fogape guarantees for productive investments in fixed assets,

such as technology or machinery, that may show results in a longer horizon, we

could be underestimating the effect of eligibility on firms’ profit margin and sales

growth.

8 Results22 We do not obtain any statistically significant results, suggesting that the

effect of eligibility is neither positive nor harmful to the various performance

indicators of enterprises at the margins around the sales threshold of eligibility.

Furthermore, in all cases but one, the sign of our coefficient of interest is the

opposite of what we expected a priori. This came as a surprise for us because credit

guarantees, such as Fogape, ought to improve loan conditions for SMEs in terms of

the maturity and interest rate.

We found that eligible firms within our bandwidth h, ceteris paribus,

experienced less proportional change in their sales from 2007 to 2009 than

ineligible ones. We had expected to see firms that are eligible for credit guarantees

22 Refer to the last four regression tables in the Appendix to see the details of our econometric results for our four outcome variables: sales growth from 2007 to 2009 as well as profit margin, debt-‐to-‐equity ratio, and long-‐term debt over total debt in 2009.

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to have higher sales growth because of the investment in working capital and

productive assets that such access to credit would allow for. The finding from our

sharp RDD analysis that eligible firms had less debt-‐to-‐equity in 2009 than non-‐

eligible ones was equally puzzling. We expected eligibility to have increased their

debt-‐to-‐equity ratio vis-‐à-‐vis similar ineligible firms because of the loans they are

getting through Fogape. Finally, the result that eligible, surveyed firms had less long-‐

term to total debt in 2009 than ineligible ones within our bandwidth h was also

contrary to our expectations. Fogape has put emphasis on its allocation of

guarantees to long-‐term credit, which led us to believe that there would be a

corresponding increase in the long-‐term over total debt ratio of eligible firms. The

only exception in our results was for profit margins in 2009. Eligible firms within

our sample of interest did have a higher profit margin than non-‐eligible ones.

To complement the results of our regressions we perform graphical analysis,

which has the advantage of being a non-‐parametric approach where we are not

imposing any particular functional form. We obtain consistent results finding that

eligible firms have lower sales growth, debt-‐to-‐equity ratio, and long-‐term debt to

total debt than ineligible firms. This can be seen in Graphs 2 to 5 in the Appendix.

The discontinuous breaks in these four outcome variables can be observed at the

eligibility cutoff, although they are not statistically significant.

9 Potential Problems & Policy Recommendations We start with the premise that SMEs are credit constrained, which validates

Fogape’s raison d’être in the economy as a provider of credit. We also assume that

this guaranteed credit would be used for productive investments, which would then

be reflected in firm profitability and sales growth. Why do we find no evidence of

Fogape’s impact during the period of 2007 to 2009? According to the literature,

Fogape operates within the recommended range of coverage rates, employs the

right loan recovery mechanism to safeguard against moral hazard on the part of

lenders, and even has broad eligibility criteria, which ought not to overbear the

system with complex rules that are prone to administrative manipulation.

Furthermore, a three-‐year time frame is enough for the effects of a loan for working

20

capital or fixed assets to have an impact on sales growth and profit margins. Are

firms receiving Fogape-‐guaranteed loans not truly credit constrained? Or are

lenders substituting Fogape guarantees for private ones? Do these firms have the

expertise or productivity to undertake successful investments?

It is difficult to know the answers to these questions with certainty, but there

is evidence at the macro-‐level that such substitution is happening. From 2000 to

2009, Fogape guaranteed just over 250,000 loans, yet only 124,000 enterprises

received such Fogape-‐backed credit.23 This means that approximately one in two

Fogape beneficiaries within the first decade of Fogape’s relaunch had been backed

more than once by the fund. Larraín and Quiroz (2006) also report that 82% of

firms from the Metropolitan Region of Santiago that first received Fogape in 2000

had already accessed credit before. Even among the “control” group of firms that

received a Fogape-‐guaranteed loan in the years subsequent to the program’s re-‐

initiation, 87% had loans prior to using Fogape.

In the survey used in our study, it is possible to identify 369 firms that

received Fogape guarantees for a secondary loan in 2009. Out of these firms, 40%

obtained their primary loan using physical collateral and 18% using private

guarantees, thereby hinting at a substitutability problem.24 However, it is still not

possible to say that Fogape users, which already had access to the fund or other

sources of credit, were not credit constrained to begin with. We suggest more

research be carried out and that the portfolio of participating lenders be reviewed to

determine whether lenders have been substituting private for public guarantees and

if Fogape beneficiaries were truly credit constrained.

We find evidence that firms also face difficulties besides credit constraints. In

the 2010 World Bank Enterprise Survey, Chilean firms identify an inadequately

educated workforce as their second largest constraint. Furthermore, 25% of small

and 22% of medium-‐sized firms identify this very constraint as their main obstacle.

To address productivity concerns as well as competitiveness issues facing SME’s, we

23 Bozzo (2009) 24 See Table 3 in the Appendix.

21

propose more complementarity between Fogape and other pro-‐SME institutions

and public programs.

We are agnostic as to whether those firms that receive Fogape-‐backed loans

indeed have the technical skills and expertise to make productive investments.

Lenders ought to refer their client recipients of Fogape guarantees to other public

programs that seek to strengthen the business acumen and networks of SMEs. These

programs can assess the specific needs of firms and provide assistance when

necessary. The programs that incorporate these elements are the Technical

Assistance Fund (FAT), Technical Cooperation Services (SERCOTEC), and Export

Promotion Program (PROCHILE). FAT provides technical assistance to address

specific problems of SMEs, including marketing, product design, production

processes, information systems, and pollution control. SERCOTEC, founded in 1952,

is Chile’s business development agency that aims to improve the competitiveness of

micro and small businesses. It mobilizes training and technical services to develop

managerial skills as well as promote networking and technology use among SMEs.25

PROCHILE, which was established in 1975, manages the country’s export promotion

program and aims to promote Chilean exports and facilitate entry of exporting firms

into international markets. In this program, PROCHILE works jointly with export

committees, comprised of four or more enterprises, in the financing, design, and

implementation of international promotional campaigns, market research, and

feasibility studies, and supports the participation of Chilean firms in international

fairs.26 These programs can provide firms that have received Fogape-‐backed finance

the complementary tools to maximize the return to their investments.

10 Conclusion

We assess the impact of Fogape, a PCGS in Chile that aims to relieve credit

constraints faced by SMEs by providing public collateral. We have evaluated the

effect of firm eligibility to Fogape over a three-‐year period employing regression

25 See www.sercotec.cl for more description on SERCOTEC. 26 See www.prochile.gob.cl for more information on PROCHILE.

22

discontinuity design. Our analysis is limited to indicators of sales growth,

profitability, and debt growth for eligible and ineligible firms around a selected

bandwidth of the eligibility threshold. In none of our regressions do we find any

significant effect of eligibility on our sample of interest. Some possible explanations

for these results are substitutability of private for public guarantees as well as

challenges regarding productivity and inadequate workforce faced by SMEs. We

suggest that further research on Fogape look at a similar panel of enterprises that

precisely isolates Fogape users and tracks their performance vis-‐à-‐vis non-‐users. In

addition, other outcome variables ought to be adopted such as monthly and annual

interest rates as well as default rates, which would give much insight into the

average level of risk Fogape firms pose. Furthermore we suggest more research be

carried out to determine whether Fogape is truly easing financial constraints on the

right firms as well as a review of the loan portfolio of participating lenders to

determine the extent of substitutability problems.

23

Bibliography Arping, S., Lóránth, G., and A.D. Morrison (2010), “Public initiatives to support entrepreneurs: Credit guarantees versus co-‐funding,” Journal of Financial Stability, Elsevier, vol. 6(1), pp. 26-‐35, April. Arráiz, I., Meléndez, M., and R. Stucchi (2012), “Evidence from the Colombian National Guarantee Fund,” Working Paper, Inter-‐American Development Bank, Office of Evaluation and Oversight, September. Bannerjee, A. and E. Duflo (2004), “Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program,” BREAD Working Paper No. 005, August. Beck, T., Klapper, L.F. and J.C. Mendoza (2010), “The Typology of Partial Credit Guarantee Funds around the World,” Journal of Financial Stability, Elsevier, vol. 6(1), pp. 10-‐25, April. Benavente, J.M., Galetovic, A., and R. Sanhueza (2006), “Fogape: An economic analysis,” Working Paper 222, University of Chile, Santiago, Chile. Bennett, F., Doran, A., and H. Billington (2005), “Do credit guarantees lead to improved access to financial services? Recent evidence from Chile, Egypt, India, and Poland,” Department for International Development, London, Financial Sector Team. Policy Division Working Paper. Bozzo, A. (2009), “Generando Confianza con Garantías Públicas en el Financiamiento a PYMEs,” Presentation (August (26)). Consultative Group to Assist the Poor/The World Bank Group (2010), “Financial Access 2010: The State of Financial Inclusion Through the Crisis,” Washington, DC. Cowan, K., Drexler, A., and A. Yañez (2009), “The Effect of Credit Insurance on Liquidity Constraints and Default Rates: Evidence from a Governmental Intervention,” Working Paper 524, Central Bank of Chile, Santiago, Chile. D’Ignazio, A. and C. Menon (2013), “The causal effect of credit guarantees for SMEs: evidence from Italy,” Working Paper Number 900, Banca D’Italia, February. Ferraro, C. (2001), “Eliminando Barreras: El financiamiento a las pymes en América Latina (LC/R.2179),” Santiago de Chile, Comisión Económica para América Latina y el Caribe (CEPAL). “Estudio sobre los Programas de Crédito con Garantía Estatal,” Gobierno de Chile, Gerencia de Inversión y Financiamiento, March 2014.

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Green, A. (2003), “Credit Guarantee Schemes for Small Enterprises: An Effective Instrument to Promote Private Sector-‐Led Growth?” SME Technical Working Paper No. 10, UNIDO, Vienna. Holmström, B. and J. Tirole (1997), “Financial intermediation, loanable funds and the real sector,” Quarterly Journal of Economics 112 (August (3)), 663-‐691. Honohan, P. (2010), “Partial Credit Guarantees: Principles and Practice,” Journal of Financial Stability, Elsevier, vol. 6(1), pp. 1-‐9, April. Imbens, G. and T. Lemieux (2007), “Regression Discontinuity Designs: A Guide to Practice,” NBER Working Paper 13039. Larraín, C., and J. Quiroz (2006), “Estudio para el fondo de garantía de pequeños empresarios,” Mimeographed. Levitsky, J. (1997), “Credit Guarantee Schemes for SMEs – an international review” Small Enterprise Development, vol. 8(2) pages 4-‐17, June. Ministerio de Economía, Fomento y Turismo (2012) “Caracterización del Emprendedor Chileno y sus Emprendimientos: Análisis a partir de los resultados de la 2° Encuesta Longitudinal de Empresas 2011,” División de Estudios. Ministerio de Economía, Fomento y Turismo (2012), “Segunda Encuesta Longitudinal de Empresas: Presentación de resultados generales.” OECD (2013), “Financing SMEs and Entrepreneurs 2013: An OECD Scoreboard Final Report,” Centre for Entrepreneurship, SMEs and Local Development, February. Stiglitz, J., and A. Weiss (1981), “Credit rationing in markets with imperfect information,” American Economic Review 71, 393-‐410. Tan, H. (2009), “Evaluating SME Support Programs in Chile Using Panel Firm Data,” Policy Research Working Paper 5082, Impact Evaluation Series No. 39, World Bank, October. Thistlewaite, D., and D. Campbell (1960), “Regression-‐Discontinuity Analysis: An Alternative to the Ex-‐Post Facto Experiment,” Journal of Educational Psychology 51, 309-‐317. Timm, S. (2012), “How the state and private sector can partner to boost support to SMEs: Lesson from Chile and Malaysia,” A Report for the Department of Trade and Industry and TIPS, June.

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Appendix: Table 1 Size of Enterprise Frequency Percentage Micro 344 12.98 Small 883 33.32 Medium 519 19.58 Large 904 34.11 Total 2,650 100 Table 2 Legal Definition of Enterprise Size in Chile Annual Sales Micro Less than $100,000 Small From $100,000 to $1 million Medium From $1 million to $4 million Large More than $4 million Table 3 Of 369 firms that reported using Fogape, what percentage obtained their biggest loan in 2009 using: Physical collateral 40% Private guarantee 18% Public guarantee 27%

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Table 4 _________________________________________________________________________________________________________ (1) (2) (3) No. of Workers Exporter in 2007 No. of Years in 2007 in Operation _________________________________________________________________________________________________________ Eligibility in 2007 -‐42.60 -‐0.0577 -‐9.580 (53.59) (0.0779) (7.502) Normalized Sales in 2006 -‐0.00000173 1.47e-‐10 -‐0.000000215 (0.00000297) (2.58e-‐09) (0.000000236) Normalized Sales in 2006 0.000000624 7.81e-‐11 0.000000158 x Eligibility in 2007 (0.00000249) (2.98e-‐09) (0.000000272) (Normalized Sales in 2006)2 2.58e-‐14 3.95e-‐18 1.84e-‐15 (3.64e-‐14) (2.64e-‐17) (2.12e-‐15) (Normalized Sales in 2006)2 -‐3.86e-‐14 4.57e-‐18 -‐2.02e-‐15 x Eligibility in 2007 (4.38e-‐14) (3.23e-‐17) (2.43e-‐15) (Normalized Sales in 2006)3 -‐8.32e-‐23 -‐2.21e-‐26 -‐4.65e-‐24 (1.09e-‐22) (7.30e-‐26) (5.52e-‐24) (Normalized Sales in 2006)3 4.82e-‐23 5.42e-‐26 4.92e-‐24 x Eligibility in 2007 (9.01e-‐23) (8.29e-‐26) (6.50e-‐24) Exporter in 2007 12.68 -‐2.505 (26.52) (2.442) No. of Years in Operation -‐0.514 -‐0.000885 (0.826) (0.000854) No. of Workers in 2007 0.0000163 -‐0.00187 (0.0000459) (0.00165) Regions Yes Yes Yes Categories Yes Yes Yes Constant 189.1 0.445* 26.40*** (164.1) (0.199) (7.867) _________________________________________________________________________________________________________ N 389 389 389 R-‐sq 0.105 0.137 0.101 _________________________________________________________________________________________________________ Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

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Graph 1

Note that the red line represents the sales eligibility threshold of $750,000, or 471,832,500 Chilean pesos. Graph 2

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Graph 3

Graph 4

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Graph 5

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LogSalesi,2009 -‐ LogSales i,2007 _________________________________________________________________________________________________________________________________________________ (1) (2) (3) (4) No Controls Controls No Controls Controls (+/-‐ 50% of Threshold) (+/-‐ 50% of Threshold) (+/-‐ 75% of Threshold) (+/-‐ 75% of Threshold) _________________________________________________________________________________________________________________________________________________ Eligibility in 2007 -‐0.339 -‐0.343 -‐0.00837 -‐0.113 (0.326) (0.347) (0.257) (0.268) Normalized Sales in 2006 -‐2.21e-‐08* -‐1.75e-‐08 -‐3.53e-‐09 -‐5.86e-‐09 (1.12e-‐08) (1.17e-‐08) (5.80e-‐09) (5.87e-‐09) Normalized Sales in 2006 1.99e-‐08 1.92e-‐08 2.07e-‐09 5.51e-‐09 x Eligibility in 2007 (1.39e-‐08) (1.45e-‐08) (7.51e-‐09) (7.55e-‐09) (Normalized Sales in 2006)2 2.37e-‐16* 1.69e-‐16 3.19e-‐17 5.02e-‐17 (1.13e-‐16) (1.15e-‐16) (4.05e-‐17) (4.03e-‐17) (Normalized Sales in 2006)2 -‐2.67e-‐16 -‐1.59e-‐16 -‐5.01e-‐17 -‐5.96e-‐17 x Eligibility in 2007 (1.37e-‐16) (1.36e-‐16) (5.01e-‐17) (4.95e-‐17) (Normalized Sales in 2006)3 -‐6.55e-‐25* -‐4.25e-‐25 -‐7.14e-‐26 -‐1.08e-‐25 (3.17e-‐25) (3.08e-‐25) (7.59e-‐26) (7.45e-‐26) (Normalized Sales in 2006)3 5.68e-‐25 4.44e-‐25 2.85e-‐26 8.19e-‐26 x Eligibility in 2007 (3.82e-‐25) (3.92e-‐25) (9.15e-‐26) (9.16e-‐26) No. of Workers in 2007 -‐0.000152 -‐0.0000843 (0.000122) (0.000101) Exporter in 2007 -‐0.292 -‐0.0822 (0.347) (0.224) No. of Years in Operation -‐0.00629 -‐0.00522 (0.00467) (0.00347) Regions No Yes No Yes Categories No Yes No Yes Constant 0.458 0.392 0.130 0.143 (0.234) (0.557) (0.178) (0.388) _________________________________________________________________________________________________________________________________________________ N 380 375 663 655 R-‐sq 0.017 0.125 0.007 0.093 _________________________________________________________________________________________________________________________________________________ Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

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Profit Margin in 2009 _________________________________________________________________________________________________________________________________________________ (1) (2) (3) (4) No Controls Controls No Controls Controls (+/-‐ 50% of Threshold) (+/-‐ 50% of Threshold) (+/-‐ 75% of Threshold) (+/-‐ 75% of Threshold) _______________________________________________________________________________________________________________________________________________ Eligibility in 2007 0.0614 0.0351 0.0310 0.0170 (0.0583) (0.0657) (0.0485) (0.0529) Normalized Sales in 2006 -‐6.69e-‐10 -‐5.32e-‐10 1.00e-‐10 -‐6.45e-‐11 (1.83e-‐09) (1.78e-‐09) (1.10e-‐09) (1.10e-‐09) Normalized Sales in 2006 3.05e-‐09 2.66e-‐09 7.27e-‐11 5.36e-‐10 x Eligibility in 2007 (2.54e-‐09) (2.73e-‐09) (1.40e-‐09) (1.40e-‐09) (Normalized Sales in 2006)2 6.62e-‐18 3.20e-‐18 -‐2.21e-‐19 7.63e-‐19 (1.89e-‐17) (1.76e-‐17) (7.86e-‐18) (7.74e-‐18) (Normalized Sales in 2006)2 1.33e-‐17 1.40e-‐17 -‐5.64e-‐19 1.05e-‐18 x Eligibility in 2007 (2.59e-‐17) (2.66e-‐17) (9.60e-‐18) (9.72e-‐18) (Normalized Sales in 2006)3 -‐1.46e-‐26 -‐1.75e-‐27 3.93e-‐29 -‐1.92e-‐27 (5.36e-‐26) (4.82e-‐26) (1.48e-‐26) (1.45e-‐26) (Normalized Sales in 2006)3 6.08e-‐26 3.89e-‐26 -‐4.09e-‐27 2.83e-‐27 x Eligibility in 2007 (7.29e-‐26) (7.32e-‐26) (1.79e-‐26) (1.75e-‐26) No. of Workers in 2007 -‐0.0000148 -‐0.00000945 (0.0000290) (0.0000252) Exporter in 2007 0.0422 0.0616 (0.0525) (0.0362) No. of Years in Operation 0.0000686 0.000328 (0.000686) (0.000572) Regions No Yes No Yes Categories No Yes No Yes Constant 0.0755* 0.0784 0.0596 0.0305 (0.0362) (0.0758) (0.0305) (0.0580) _________________________________________________________________________________________________________________________________________________ N 376 371 656 648 R-‐sq 0.008 0.138 0.005 0.113 _________________________________________________________________________________________________________________________________________________ Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

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Debt to Equity in 2009 _________________________________________________________________________________________________________________________________________________ (1) (2) (3) (4) No Controls Controls No Controls Controls (+/-‐ 50% of Threshold) (+/-‐ 50% of Threshold) (+/-‐ 75% of Threshold) (+/-‐ 75% of Threshold) _________________________________________________________________________________________________________________________________________________ Eligibility in 2007 -‐0.138 -‐0.101 -‐0.182 -‐0.192 (0.201) (0.223) (0.150) (0.163) Normalized Sales in 2006 -‐1.79e-‐09 -‐2.21e-‐09 -‐8.66e-‐10 -‐1.38e-‐09 (5.59e-‐09) (5.91e-‐09) (2.92e-‐09) (3.07e-‐09) Normalized Sales in 2006 2.72e-‐09 5.57e-‐09 -‐1.29e-‐09 -‐9.64e-‐10 x Eligibility in 2007 (8.37e-‐09) (9.04e-‐09) (4.03e-‐09) (4.24e-‐09) (Normalized Sales in 2006)2 1.31e-‐17 1.68e-‐17 7.65e-‐18 1.09e-‐17 (5.89e-‐17) (6.28e-‐17) (2.01e-‐17) (2.11e-‐17) (Normalized Sales in 2006)2 6.58e-‐18 2.98e-‐17 -‐1.56e-‐17 -‐2.10e-‐17 x Eligibility in 2007 (8.39e-‐17) (9.04e-‐17) (2.63e-‐17) (2.80e-‐17) (Normalized Sales in 2006)3 -‐2.01e-‐26 -‐3.09e-‐26 -‐1.86e-‐26 -‐2.41e-‐26 (1.71e-‐25) (1.82e-‐25) (3.80e-‐26) (3.98e-‐26) (Normalized Sales in 2006)3 7.96e-‐26 1.70e-‐25 1.46e-‐26 1.61e-‐26 x Eligibility in 2007 (2.36e-‐25) (2.47e-‐25) (4.77e-‐26) (4.97e-‐26) No. of Workers in 2007 0.0000427 0.00000829 (0.0000527) (0.0000414) Exporter in 2007 -‐0.0528 -‐0.0562 (0.103) (0.0727) No. of Years in Operation -‐0.00334 -‐0.00342* (0.00228) (0.00163) Regions No Yes No Yes Categories No Yes No Yes Constant 0.340** 0.624 0.318** 0.572* (0.125) (0.321) (0.102) (0.249) N 363 358 634 626 R-‐sq 0.008 0.084 0.020 0.070 _________________________________________________________________________________________________________________________________________________ Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

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Long Term over Total Debt in 2009 _________________________________________________________________________________________________________________________________________________ (1) (2) (3) (4) No Controls Controls No Controls Controls (+/-‐ 50% of Threshold) (+/-‐ 50% of Threshold) (+/-‐ 75% of Threshold) (+/-‐ 75% of Threshold) _________________________________________________________________________________________________________________________________________________ Eligibility in 2007 -‐0.394 -‐0.253 -‐0.0876 -‐0.0950 (0.245) (0.258) (0.193) (0.193) Normalized Sales in 2006 -‐2.72e-‐09 -‐3.27e-‐09 2.69e-‐09 2.48e-‐09 (5.99e-‐09) (5.96e-‐09) (3.57e-‐09) (3.50e-‐09) Normalized Sales in 2006 -‐8.22e-‐09 -‐3.74e-‐09 -‐3.31e-‐09 -‐3.35e-‐09 x Eligibility in 2007 (8.90e-‐09) (8.82e-‐09) (4.95e-‐09) (4.78e-‐09) (Normalized Sales in 2006)2 4.90e-‐17 5.18e-‐17 -‐2.07e-‐17 -‐2.30e-‐17 (6.03e-‐17) (6.26e-‐17) (2.43e-‐17) (2.46e-‐17) (Normalized Sales in 2006)2 -‐1.52e-‐16 -‐1.15e-‐16 1.97e-‐17 2.13e-‐17 x Eligibility in 2007 (8.55e-‐17) (8.92e-‐17) (3.27e-‐17) (3.28e-‐17) (Normalized Sales in 2006)3 -‐1.85e-‐25 -‐1.89e-‐25 3.81e-‐26 5.05e-‐26 (1.71e-‐25) (1.80e-‐25) (4.58e-‐26) (4.71e-‐26) (Normalized Sales in 2006)3 -‐8.82e-‐26 3.27e-‐26 -‐3.87e-‐26 -‐5.16e-‐26 x Eligibility in 2007 (2.34e-‐25) (2.32e-‐25) (6.06e-‐26) (6.11e-‐26) No. of Workers in 2007 0.000169*** 0.000136** (0.0000477) (0.0000426) Exporter in 2007 0.0295 -‐0.00166 (0.158) (0.107) No. of Years in Operation 0.00355* 0.00376* (0.00175) (0.00174) Regions No Yes No Yes Categories No Yes No Yes Constant 0.457** 0.745** 0.382** 0.680** (0.155) (0.282) (0.130) (0.246) _________________________________________________________________________________________________________________________________________________ N 210 210 332 330 R-‐sq 0.043 0.244 0.009 0.122 _________________________________________________________________________________________________________________________________________________ Standard errors in parentheses * p<0.05, ** p<0.01, *** p<0.001

Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)

Economy & Finance

Transcript of Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)