Estimating the Firm-Level Estimating the Firm-Level Growth Effects of Small Growth Effects of Small Loan Programs Using Loan Programs Using Universal Panel DataUniversal Panel Data

from Romaniafrom Romania

J. David Brown (US Census J. David Brown (US Census Bureau)Bureau)

John S. Earle (Upjohn Institute and John S. Earle (Upjohn Institute and CEU)CEU)

June 2010

Motivation: small firms and Motivation: small firms and the crisisthe crisis What are the prospects for a “small What are the prospects for a “small

business-fueled employment recovery”? business-fueled employment recovery”? Recent credit boom was smaller for Recent credit boom was smaller for

small/young firms, and current credit small/young firms, and current credit crunch is worsecrunch is worse

New policy proposals around the world. In New policy proposals around the world. In US:US: SBA stimulusSBA stimulus Sen. Warner: Fed and TARP funds to small Sen. Warner: Fed and TARP funds to small

firms– including loss-sharing firms– including loss-sharing FDIC: matching loans to small businessFDIC: matching loans to small business

Do small business loan Do small business loan programs promote growth?programs promote growth?

Conceptually ambiguous:Conceptually ambiguous: Easier access to finance may enable expansionEasier access to finance may enable expansion But funds may be used for other purposesBut funds may be used for other purposes Displacement and substitution effectsDisplacement and substitution effects

Empirically difficult (absent an experiment): Empirically difficult (absent an experiment): Many factors influence firm growth (industry, region, Many factors influence firm growth (industry, region,

size, age…)size, age…) Need long time series on factors and outcomes – pre Need long time series on factors and outcomes – pre

and post and post Selection bias – loan could reflect growth potentialSelection bias – loan could reflect growth potential

Many studies of firm growth, but no rigorous Many studies of firm growth, but no rigorous evaluationsevaluations

N.B.: few such policy evaluations at firm-level N.B.: few such policy evaluations at firm-level more generally (except Jarmin, 1998)more generally (except Jarmin, 1998)

Broader question: growth and Broader question: growth and financefinance Do well-functioning financial markets enhance Do well-functioning financial markets enhance

growth, or does economic growth improve growth, or does economic growth improve financial markets? (Rajan & Zingales 1998; financial markets? (Rajan & Zingales 1998; Beck et al. 2000; Fisman & Love 2007)Beck et al. 2000; Fisman & Love 2007)

Macro debate: relationship of real and Macro debate: relationship of real and monetary economymonetary economy

Aggregate studies of loans and growth find Aggregate studies of loans and growth find different results for US states and Euro-zone different results for US states and Euro-zone countries (countries (Driscoll 2004; Cappiello et al. 2010)Driscoll 2004; Cappiello et al. 2010)

Relatively little micro evidence, especially Relatively little micro evidence, especially rigorous estimates of causal effectsrigorous estimates of causal effects

Small business and finance Small business and finance in transition and developmentin transition and development TransitionTransition

IFIs: size of new private business indicates IFIs: size of new private business indicates progressprogress

Policy debate: finance versus property rights, Policy debate: finance versus property rights, contracts, regulationcontracts, regulation

DevelopmentDevelopment Microcredit is fashionable but few estimates of Microcredit is fashionable but few estimates of

firm-level growth effects (many of repayment & firm-level growth effects (many of repayment & poverty, Morduch 1999; Karlan&Morduch 2009)poverty, Morduch 1999; Karlan&Morduch 2009)

Alternatives: technical assistance, business Alternatives: technical assistance, business environmentenvironment

Our case: small business Our case: small business loans in Romanialoans in Romania USAID-supported programs through March 2001USAID-supported programs through March 2001

Small size: 372 firms (=> rationing, few spillovers)Small size: 372 firms (=> rationing, few spillovers) Partial coverage: 18/41 counties (=> ineligibles)Partial coverage: 18/41 counties (=> ineligibles)

Loan conditions:Loan conditions: ““Commercial terms”; lenders “profit-oriented”Commercial terms”; lenders “profit-oriented” Decisions based on past years’ accounting cash-flowDecisions based on past years’ accounting cash-flow State-owned and some sectors ineligible, startups not State-owned and some sectors ineligible, startups not

immediately eligibleimmediately eligible Romanian context: credit markets poorly Romanian context: credit markets poorly

developeddeveloped For most recipients, first access to formal creditFor most recipients, first access to formal credit

Estimating effect of first Estimating effect of first international loan on growth international loan on growth (ATT): Our method(ATT): Our method Construct two control groups from universal panel Construct two control groups from universal panel

datadata Eligible non-recipients (same county)Eligible non-recipients (same county) Ineligibles (non-USAID counties)Ineligibles (non-USAID counties)

Match on several years of pre-loan characteristicsMatch on several years of pre-loan characteristics Outcomes: growth (employment & sales); survivalOutcomes: growth (employment & sales); survival Panel DiD regressions using matched samples, Panel DiD regressions using matched samples,

1992-20061992-2006 Pre- and post-dynamics of the effectPre- and post-dynamics of the effect

Pre-loan: diagnose selection bias (“pseudo-outcomes”)Pre-loan: diagnose selection bias (“pseudo-outcomes”) Post-loan: long- versus short-term effectsPost-loan: long- versus short-term effects

DataData

List of 372 firms receiving a USAID loan List of 372 firms receiving a USAID loan by March 2001 – most in 1999-2000by March 2001 – most in 1999-2000

Annual balance sheet information for Annual balance sheet information for universe of registered firms from 1992-universe of registered firms from 1992-2006: about 200,000 firms per year2006: about 200,000 firms per year

ExclusionsExclusions All “old” firms (ever have any state All “old” firms (ever have any state

ownership)ownership) Ineligible industries (tobacco, weapons)Ineligible industries (tobacco, weapons) >49 employees (only small and micro start->49 employees (only small and micro start-

ups left)ups left)

Matching Heterogeneity: recipients versus

nonrecipients Industry (manufacturing) Age (older) Size (smaller in early years, larger later)

Exact matching Always: 2-digit industry, age, year Sometimes: county, +/- 10% t-1 outcome, 3-

digit ind Propensity score matching

Lagged outcomes(to t-4), other characteristics Nearest neighbor, radius, kernel methods

Control Groups Same county (eligible non-recipients)

Exact match on county Selection problem (applicants and loan

officers) Non-USAID county (ineligibles)

No matching on county No self-selection problem Possible program selection & heterogeneity P-scores estimated from relationship in

USAID counties

Romanian counties with USAID loans

Specification Checks

Identifying assumption: unconfoundedness Balancing tests for covariates

Rosenbaum-Rubin standardized differences (bias)

t-tests Hotelling’s T2 test by P-score quintiles Smith-Todd regression test

“Pseudo-outcome” (Imbens-Wooldridge) tests Pre-treatment outcomes (Heckman-Hotz 1989) Estimation using two control groups

(Rosenbaum 1987; Heckman et al. 1997)

Pseudo-outcome test: 2 control groups Definitions

Yi(1), Yi(0) = outcomes for treatment, non-treatment

Gi {-1, 0, 1} (∊ 2 control groups, 1 treatment group)

Wi = 0 if Gi = -1, 0; Wi = 1 if Gi = 1

Unconfoundedness requires Yi(0), Yi(1) ╨ Wi|Xi

Stronger condition: Yi(0), Yi(1) ╨ Gi|Xi

=> Yi(0) ╨ Gi | Xi , Gi {-1, 0}∊

Test: E[E(Yi|Gi=-1, Xi ]- E[E(Yi|Gi=0, Xi ]=0

Characteristics: Employment Distribution (1999)Number of Employees

USAID Non-USAID

0-9 61.4% 83.5%

10-49 31.9% 12.1%

50-249 6.8% 3.3%

250+ 0% 1.0%

Total Firms 339 218,759

Start-up Year (%)

Full Samples Truncated Samples

USAID

NonUSAID

USAID

NonUSAID

1992 25.75 22.57 24.68 23.30

1993 14.63 16.76 14.94 16.21

1994 22.22 17.99 23.05 18.38

1995 14.91 14.12 15.26 13.44

1996 7.32 6.73 7.14 6.94

1997 8.40 7.10 8.77 7.26

1998 4.34 8.05 4.22 8.53

1999 2.44 6.67 1.95 5.94

Exit Year (%)

Full Samples Truncated Samples

USAID

NonUSAID

USAID

NonUSAID

2000 0.81 5.09 0.00 0.00

2001 2.44 6.42 2.27 3.63

2002 2.98 6.18 2.92 3.28

2003 1.63 1.30 1.62 1.24

2004 4.88 10.45 5.52 6.64

2005 5.15 5.41 5.19 4.58

2006 5.15 9.36 5.84 8.21

2007+ 76.96 55.80 76.62 72.41

Distribution of Year of First International Loan

Number of Firms Percent of Firms

1993 1 0.3

1994 1 0.3

1995 4 1.1

1996 7 1.9

1997 8 2.2

1998 62 16.9

1999 200 54.4

2000 82 22.3

2001 3 0.8

N 368 100.0

Results: Estimates of the Loan Impact on Employment (NN matching)NN matching)

Same Same county county controlscontrols

OLSOLS OLS OLS with with

covariatcovariateses

FEFE

Post-Loan Post-Loan DummyDummy

0.2320.232 0.2020.202 0.1680.168

(0.056)(0.056) (0.048)(0.048) (0.045)(0.045)

Number of Number of Treated FirmsTreated Firms

203203

Number of Number of ObservationsObservations

3,9563,956

Estimates of the Loan Impact on Employment (NN matching)NN matching)

Non-Non-eligible eligible controlscontrols

OLSOLS OLS, OLS, covariatcovariat

eses

FEFE

Post-Loan Post-Loan DummyDummy

0.2950.295 0.2420.242 0.2340.234

(0.044)(0.044) (0.041)(0.041) 0.0460.046

Number of Number of Treated FirmsTreated Firms

267267

Number of Number of ObservationsObservations

5,2035,203

Estimates of the Loan Impact on Employment (Kernel matching)

Same County Matches

Non-eligible Matches

OLS FE OLS FE

Post Loan

0.242***

0.221***

0.323***

0.176***

(0.034) (0.034) (0.034) (0.041)

Age 0.157***

0.139***

(0.017) (0.023)

Age2 -0.011***

-0.010***

(0.001) (0.002)

Firms 6,026 6,026 52,373 52,373

Obs 57,407 57,592 503,658 504,794

Estimates of the Loan Impact on Sales (Kernel matching)

Same County Matches

Non-eligible Matches

OLS FE OLS FE

Post Loan

0.326***

0.295***

0.601***

0.376***

(0.044) (0.051) (0.052) (0.058)

Age 0.180***

0.165***

(0.031) (0.034)

Age2 -0.013***

-0.015***

(0.002) (0.002)

Firms 4,209 4,209 48,182 48,182

Obs 46,980 46,980 521,993 521,993

Dynamics of Employment Effect(Same county controls)

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

-3 -2 -1 0 1 2 3 4 5+

Years Before/After Loan

Lo

g E

mp

loym

ent

Dif

fere

nce

Dynamics of Employment Effect(Non-eligible controls)

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

-3 -2 -1 0 1 2 3 4 5+

Years Before/After Loan

Lo

g E

mp

loym

ent

Dif

fere

nce

Dynamics of Sales Effect(Same county controls)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

-3 -2 -1 0 1 2 3 4 5+

Years Before/After Loan

Lo

g S

ales

Dif

fere

nce

Dynamics of Sales Effect(Non-eligible controls)

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-3 -2 -1 0 1 2 3 4 5+

Years Before/After Loan

Lo

g S

ales

Dif

fere

nce

Estimates of Loan Effects on Estimates of Loan Effects on Exit (Cox proportional Exit (Cox proportional hazards)hazards)

Log-odds ratios

Same county matches

Non-eligible matches

no Et-1 restrict

ion

Et-1

within 10%

no Et-1 restrict

ion

Et-1

within 10%

Without covariates

0.911 0.954 0.945 1.120

(0.150) (0.166) (0.190) (0.242)

With covariates

0.919 0.936 0.977 1.137

(0.154) (0.165) (0.204) (0.252)

ConclusionConclusion Results suggest loans have long-lasting Results suggest loans have long-lasting

effects on job and sales growth, but little on effects on job and sales growth, but little on survivalsurvival

Evidence of causal link: finance -> growthEvidence of causal link: finance -> growth Mechanism unclearMechanism unclear

lower cost of capitallower cost of capital alleviate credit rationingalleviate credit rationing open access to formal credit marketsopen access to formal credit markets

Approach (data, methods) widely applicableApproach (data, methods) widely applicable Loan programs in other countries (SBA)Loan programs in other countries (SBA) Other policies with differential effects on firmsOther policies with differential effects on firms

Implications for the US? SBA Implications for the US? SBA ProjectProject

Previous Research:Previous Research: Studies of local employment growth Studies of local employment growth

and per capita income as a function of and per capita income as a function of the amount of SBA loans, but not the amount of SBA loans, but not effect on loan recipientseffect on loan recipients

Urban Institute (2008) study for SBAUrban Institute (2008) study for SBA Dun & Bradstreet data Dun & Bradstreet data Incomplete coverage of small firmsIncomplete coverage of small firms Biased toward larger, more successful Biased toward larger, more successful

recipientsrecipients No control group of non-recipientsNo control group of non-recipients

SBA Project (with Census SBA Project (with Census Bureau)Bureau) SBA has detailed data on loan SBA has detailed data on loan

recipients:recipients: Name and addressName and address Loan dateLoan date Loan amountLoan amount Interest rateInterest rate Credit score at time of applicationCredit score at time of application Demographic information about borrowerDemographic information about borrower Loan performanceLoan performance

Also some data on rejected applicantsAlso some data on rejected applicants

SBA Project (with Census SBA Project (with Census Bureau)Bureau)

Link SBA to Census data for all Link SBA to Census data for all establishments in 1976-2008 on age, establishments in 1976-2008 on age, employment, payroll, industry code, etc.employment, payroll, industry code, etc.

Subset of the Census Bureau Subset of the Census Bureau establishments have sales, capital stock, establishments have sales, capital stock, and profitand profit

Match to select controls most similar to Match to select controls most similar to loan recipients prior to loanloan recipients prior to loan

Compare performance of recipients and Compare performance of recipients and non-recipients before and after the loannon-recipients before and after the loan

22ndnd control group: rejected applicants control group: rejected applicants