14/04/11

Relaxing Credit Constraints:

The Impact of Public Loans

on the Performance of Brazilian Firms

IDEAS International

Assembly 2011

* Corresponding Author fls@bndes.gov.br

Filipe Lage de Sousa* and Gianmarco Ottaviano

MotivationEmerging markets are enlarging their participation in world GDP and trade, especially BRICS.

Some of them have financial institutions to provide long term loans.

BRICS are not exception to this rule (all have at least one)

Brazilian Government provide those long term loans through its Brazilian Development Bank (BNDES).

BNDES mandatory goal is to burst Brazilian economy without neglecting social aspects.

Therefore, it is important to evaluate whether their financial support has been able to improve firms’ competitiveness, which will be measured by productivity.

Why productivity?

“Productivity isn’t everything, but in the long run it is

almost everything. ..... Compared with the problem of slow

productivity growth, all other long-term economic

concerns - foreign competition, the industrial base, lagging

technology, deteriorating infrastructure and so on - are

minor issues”

(Krugman, 1992)

Asumption:

In order to implement one project, firms decide on whether using:

1. Old (Low) Technology – lower fixed cost, but higher marginal cost

2. New (High) Technology – higher fixed cost, but lower marginal cost

According to our model, credit constraints might affect those two fixed costs asymmetrically.

1. If it is more towards new technology, productivity increases.

2. If it is towards old technology, productivity reduces.

3. If it affects them symmetrically, productivity remains constant

Theoretical Foundation

BNDES finances different types of firms, from micro to multinationals.

In 2009, its disbursements reached US$ 78 billion (representing 13,3% of aggregate investment).

We investigate two types of loans:

1. FINEM (direct – over US$ 5 millions);

2. Automatic BNDES (indirect – below this threshold);

Both cover: creation of new plants; enlargement of existing ones; restructuring and modernization of processes; and

innovation and technological development

Why BNDES?

• From 1995 to 2007, 9,828 firms were granted one of these loans;

• These two loans represents around 40% of BNDES resources.

Some figures

Using different data sources,

some firms are discarded:

• Too small (no information in main data source where we measured productivity);

• If two firms merge, the loan will be registered to the new company, which didn’t exist before when there is the information;

• There is a time-lag for a firm to take part in the survey.

Empirical Strategy

1. Choosing treated group (1995 to 2007)

a. Previous Period to find a reasonable counterfactual group

b. Post Period to evaluate impact

2. Finding counterfactual groups by PSM or by other method.

3. Verify whether treated firms perform differently from non-treated firms.

4. Estimating by Naive Model (OLS) and Sophisticated Model (Dif-in-Dif) governments’ impact.

Treated Firms

How many Treated Firms received first loan in 1998

How often? Indirect Loans Direct and Indirect

Just Once 69 75

More than Once 43 66

Total 112 141

Year Selected = 1998.

Treated x Non Treated

Non Treated Firms Treated Firms

Variables All Sur&Inv 1st 2007 1st 98 Ind 1st 98 Just 98 Ind Just 98

Labour Productivity 26,6 26,8 27,0 35,5 29,7 31,8 27,4

TFP (All 1996 = 100) 99,3 101,8 89,1 97,7 100,7 104,7 102,5

Capital Stock 31,6 19,6 34,9 84,4 29,0 53,9 24,1

% Qualified Workers 5,8% 6,8% 5,7% 9,2% 8,1% 9,2% 8,4%

Number of Employees 175 196 255 620 332 468 285

Market Share 0,09% 0,11% 0,12% 0,33% 0,11% 0,31% 0,09%

Profitability 5,9% 6,7% 7,9% 5,7% 5,9% 6,4% 6,1%

Fin Costs / Total Costs 3,9% 3,6% 3,2% 4,7% 4,5% 5,0% 4,9%

Fin Costs / Net Revenue 3,9% 3,0% 2,2% 2,8% 2,8% 3,1% 3,1%

Investments 1,17 0,86 1,24 5,45 1,58 4,79 1,13

Revenue Growth 22,1% 20,6% 16,9% 17,5% 13,7% 13,8% 11,9%

Labour Productivity Growth 30,3% 26,0% 14,3% 31,7% 27,6% 34,6% 33,8%

Employment Growth 0,1% 4,3% 1,8% 8,8% 10,3% 6,2% 6,3%

High and Med-High (OCDE) 22% 26% 18% 32% 32% 35% 32%

Number of Firms 21.380 6.344 128 141 112 75 69

Treated (1st 98) x Non-Treated

0,0

0,5

1,0

1,5

2,0

2,5

3,0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Years

Pro

du

citi

vity

Pre

miu

m

Labor - Group A Labor - Group B Labor - Group C TFP - Group A TFP - Group B TFP - Group C

Refined Control Group: Propensity Score Matching (PSM)

1st 98 Ind 1st 98 Just 98 Ind Just 98

Paired 118 99 65 61

Non-Paired 23 13 10 8

After pairing, we tested whether average from treated and non-treated firms are similar and even productivity (which was not in the PSM) showed similar means.

Treated (1st 98) x Control Group

Graph 2: Comparing 118 Paired Firms (Treated / Non-treated)

0,60

0,70

0,80

0,90

1,00

1,10

1,20

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

Pro

du

ctiv

ity

Pre

miu

m

Labour Productivity TFP

Main Results from Econometrics

Control Groups or Counterfactual Groups

a) Firms which have survived and invested (6 thousand firms)b) Firms first granted in 2007c) Paired Firms using PSM

Regardless which control group is considered, results are the same.

1. Naive Model (OLS)• Labour Productivity - Positive• Total Factor Productivity - Negative

• Sophisticated Model (Difference-in-Differences)a) Non Significant Results

Main Conclusions

BNDES Loans might be able to relax credit constraintsHowever, it seems that it is not impacting productivity

Reasons:

1. Projects selected might not be the most prominent

2. Loans may be reducing implementation costs for both old and new technology projects symmetrically, therefore not impacting average firms’ productivity

Increase number of manufacturing firms in Brazil from 20 thousand to more than 40 thousand during 1996 to 2006.

Filipe Lage de Sousa fls@bndes.gov.br