RETHINKING	
  FOGAPE	
  
An	
  Evaluation	
  of	
  Chile’s	
  Partial	
  Credit	
  
Guaran...
  1	
  
Table	
  of	
  Contents	
  
1	
  INTRODUCTION	
  ....................................................................
  2	
  
1	
   Introduction	
  
	
  
Financial	
   markets	
   face	
   frictions	
   whenever	
   the	
   market	
   canno...
  3	
  
	
  
2	
  	
   Theoretical	
  Framework	
  
	
  
2.1	
  	
   Credit	
  Rationing	
  
	
  
There	
  is	
  said	
  t...
  4	
  
rationing	
  by	
  requiring	
  more	
  collateral,	
  because	
  collateral	
  also	
  has	
  its	
  own	
  effec...
  5	
  
targeting	
   of	
   specific	
   sectors	
   and	
   regions,	
   timing	
   of	
   guarantee	
   payouts,	
   co...
  6	
  
ratio	
  substantially	
  lower	
  than	
  50%	
  might	
  fail	
  to	
  attract	
  lenders.	
  In	
  practice,	
 ...
  7	
  
access	
  to	
  credit	
  and	
  improvements	
  in	
  loan	
  conditions	
  through	
  longer	
  maturities	
  an...
  8	
  
indicators.	
   If	
   anything,	
   treated	
   firms	
   had	
   marginally	
   lower	
   sales	
   growth	
   t...
  9	
  
contributed	
  to	
  20%	
  of	
  GDP,	
  and	
  the	
  percentage	
  of	
  workers	
  employed	
  in	
  SMEs	
  s...
  10	
  
5	
   Fogape	
  
	
  
5.1	
   Background	
  
	
  
Fogape	
  was	
  first	
  created	
  in	
  September	
  1982.	
...
  11	
  
5.2	
   Allocation	
  of	
  guarantees	
  
	
  
Fogape	
   disseminates	
   its	
   guarantees	
   into	
   the	
...
  12	
  
excessively	
  high	
  or	
  if	
  they	
  used	
  less	
  than	
  80%	
  of	
  the	
  guarantee	
  rights	
  won...
  13	
  
the	
   period	
   before	
   the	
   financial	
   crisis.	
   Beck,	
   Klapper,	
   and	
   Mendoza	
   (2010)...
  14	
  
The	
  “Second	
  Longitudinal	
  Survey	
  of	
  Enterprises	
  2011”	
  is	
  a	
  follow	
  up	
  to	
  the	
 ...
  15	
  
between	
  Fogape	
  and	
  our	
  outcome	
  variables.	
  RDD,	
  first	
  introduced	
  by	
  Thistlewaite	
  ...
  16	
  
We	
  seek	
  to	
  estimate	
  the	
  intention-­‐to-­‐treat	
  effect	
  of	
  Fogape	
  with	
  the	
  followi...
  17	
  
be	
  evidence	
  that	
  firms	
  are	
  misreporting	
  their	
  annual	
  sales	
  in	
  order	
  to	
  be	
  ...
  18	
  
might	
   be	
   influenced	
   by	
   such	
   an	
   event.	
   We	
   would	
   expect	
   that	
   during	
  ...
  19	
  
to	
   have	
   higher	
   sales	
   growth	
   because	
   of	
   the	
   investment	
   in	
   working	
   capi...
  20	
  
capital	
  or	
  fixed	
  assets	
  to	
  have	
  an	
  impact	
  on	
  sales	
  growth	
  and	
  profit	
  margi...
  21	
  
propose	
   more	
   complementarity	
   between	
   Fogape	
   and	
   other	
   pro-­‐SME	
   institutions	
  
...
  22	
  
discontinuity	
   design.	
   Our	
   analysis	
   is	
   limited	
   to	
   indicators	
   of	
   sales	
   grow...
  23	
  
Bibliography	
  
	
  
Arping,	
   S.,	
   Lóránth,	
   G.,	
   and	
   A.D.	
   Morrison	
   (2010),	
   “Public	...
  24	
  
Green,	
   A.	
   (2003),	
   “Credit	
   Guarantee	
   Schemes	
   for	
   Small	
   Enterprises:	
   An	
   Eff...
  25	
  
Zia,	
  B.	
  (2008),	
  “Export	
  incentives,	
  financial	
  constraints,	
  and	
  the	
  (Mis)allocation	
  ...
  26	
  
Appendix:	
  
	
  
Table	
  1	
  
	
  
Size	
  of	
  Enterprise	
   Frequency	
   Percentage	
  
Micro	
   344	
 ...
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)
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 (Paper)

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Barcelona GSE Master Project by Margarita Armenteros, Niccolò Artellini, Andreas Hoppe, Marco Urizar, and Bernard Yaros

Master Program: International Trade, Finance and Development

About Barcelona GSE master programs: http://j.mp/MastersBarcelonaGSE

Published in: Economy & Finance
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Rethinking Fogape: An Evaluation of Chile's Partial Credit Guarantee Scheme (Paper)

  1. 1.               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.  
  2. 2.   1   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                          
  3. 3.   2   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).    
  4. 4.   3     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  
  5. 5.   4   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)  
  6. 6.   5   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)    
  7. 7.   6   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)      
  8. 8.   7   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  
  9. 9.   8   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  
  10. 10.   9   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  (Millions)     SME  Outstanding  Loans  from  Commercial  Banks   Chile   Source:  World  Bank  
  11. 11.   10   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  
  12. 12.   11   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.        
  13. 13.   12   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)  
  14. 14.   13   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.    
  15. 15.   14   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.  
  16. 16.   15   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.  
  17. 17.   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  
  18. 18.   17   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  
  19. 19.   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.  
  20. 20.   19   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  
  21. 21.   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.  
  22. 22.   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.  
  23. 23.   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.                                        
  24. 24.   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.    
  25. 25.   24   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.    
  26. 26.   25   Zia,  B.  (2008),  “Export  incentives,  financial  constraints,  and  the  (Mis)allocation  of   credit:   micro-­‐level   evidence   from   subsidized   export   loans,”   Journal   of   Financial   Economics,  87.                                                                                        
  27. 27.   26   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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