Public Credit Use and Manufacturing Productivity in Brazil.*
Eduardo Pontual Ribeiro
Conselho Administrativo de Defesa Econômica CADE/MJ
and Instituto de Economia – UFRJ
João Alberto DeNegri
Instituto de Pesquisa Econômica Aplicada
Summary
We propose to measure the impact of investment credit from public banks on Brazilian manufacturing
firm productivity. In general, manufacturing surveys do not have information on whether a firm actually
benefited from government programs. An exception is the Technological Survey of Manufacturing
(PINTEC) by IBGE/Brazil that asks if a firm used state credit for equipment investment associated with
innovations. BNDES, the Brazilian federal investment bank, is the largest provider of (subsidized) credit
for equipment purchases and accounts for 20% of all credit in the economy. Our theoretical framework
suggests that lower investment costs may increase or decrease firm productivity. Estimation considers
identification on unobservable and observable characteristics. The unobservable characteristics will be
controlled for exploiting the longitudinal nature of the data and using instrumental variables. In addition,
we exploit lending policy changes by BNDES and the neutrality of disembodied TFP on capital-labor
ratios. Our estimates suggest that there is limited effect of access to BNDES credit on TFP of innovative
firms, once firm heterogeneity is controlled for, although firms that use BNDES credit are larger and
invest more, as expected.
Key words: public credit, productivity, investment, innovation.
JEL: O16, H81, D24
*
The statements presented do not represent the official position of any of the institutions cited in the paper (UFRJ, IPEA,
CADE, IBGE or BNDES). We are solely responsible for the data manipulation and estimates and opinions. The figures in the
paper have been screened by IBGE to ensure confidentiality. This paper is also part of a larger IPEA/IBGE project to
understand firm growth in Brazil. No firm data from BNDES was used. We acknowledge the partial support from the
IADB/RES. We benefited from comments by Carmen Pagés, Chad Syverson, and IADB workshop participants and BNDES
staff. Corresponding author: Eduardo Ribeiro ([email protected]). This version, July, 2009.
1
Introduction
Total factor productivity in Brazil has been largely stagnant in the past 10 years (Pessoa, Barbosa-Filho
and Veloso, 2008). Much like the rest of Latin America, this has been argued as a reason for the country
lagging behind in per capita GPD growth over the period. Our estimates, presented here, suggest that
most TFP growth has been generated by interaction effects, at least in manufacturing, and more
productive firms have not been able to expand their market shares. Should a more efficient allocation of
resources be obtained, with more productive firms gaining market share, aggregate TFP could be
bolstered. Hsieh and Klenow (2008) present evidence on the extent of TFP losses due to resource
misallocation, focusing on India and China manufacturing. Under certain assumptions, their estimates
suggest that if relative factor and output price distortions were equalized with US levels, TFP growth
would increase by at least 30%.
An important distortion factor is the cost of capital faced by firms. The cost of capital may differ across
firms due to access to limited credit with below market (subsidized) interest rates. In the Brazilian case,
the Banco Nacional de Desenvolvimento Econômico e Social (BNDES) accounts for 20% of all credit
demand in the economy, with outlays totaling 5% of GDP (BCB, 2005). BNDES funding comes mainly
from workers forced savings (severance fund, i.e., FGTS) and provide credit at below market interest
rates. For example, in 2007, while the base yearly nominal interest paid by Government bonds (Selic)
was about 12%, BNDES loans interest rate ranged from 11.5% to 8.5%. Access to BNDES credit for
firm investment can potentially significantly distort the capital-labor ratio in the economy and reduce
aggregate productivity.
On the other hand, one important aspect of Latin America financial markets is the likelihood that firms
are credit constrained, relying too heavily on own sources to finance investment (Galindo and
Schiantarelli, 2003, IADB, 2005). This has negative implications for aggregate investment levels. Bond
Soderbon and Wu (2007) estimate that about 40% of firms are credit constrained in Brazil, using data
from 2000-2003. Under this scenario, BNDES may be easing credit constraints, improving investment
levels and generating a more efficient allocation. Aggregate effects on total TFP depend on whether
firms that have access to BNDES are in fact more productive (generating a between effect in
2
productivity growth) or can also use these funds to implement better technology (i.e., a within effect on
productivity growth).
In principle, subsidized credit may increase capital investment, as the marginal cost of machinery is
reduced. Given a properly specified production function under profit maximization, there should be no
effect on total factor productivity (TFP), and an increase in average labor productivity (output per
worker).
On the other hand, when such machinery investment is associated with innovations, there is scope for
higher TFP, conditional on the larger capital stock. With access to credit, the new high quality
machinery and the associated productive process (or product) innovations may generate changes in TFP
(e.g., Parisi, Schiantarelli and Sembenelli (2006)). The subsidized credit is used to actually implement
the innovation, that depends itself on the new machinery.
It is not theoretically clear that a firm facing lower capital costs will innovate, i.e., acquiring better, more
productive technology. Using Bustos (2005, 2007) theoretical structure, Ottaviano and Souza (2008)
indicate that if the subsidized credit lowers fixed costs of technology adoption, some firms may adopt a
more productive technology, but some may implement previously used, less productive, technology.
Which technology is adopted depends on the current productivity level of the firm.
If marginal costs of both technologies fall, then the effect is undetermined: the lower marginal cost of
low quality technology eases the selection effect, allowing less productive firms to survive; while the
lower marginal cost of high quality technology induces more firms to switch to it. The total effect will
depend on the ex-ante productivity distribution. We extend their analysis and conclude that should the
subsidized credit lowers the relative marginal cost of more productive technology only, more firms will
adopt it, and fewer firms will adopt the less productive technology, increasing aggregate TFP.
The goal of the paper will be to evaluate the effect of public credit on productivity, looking at
manufacturing firms that innovate in Brazil. Recently, there have been strong efforts by government
banks in Brazil, notably BNDES, to foster innovation as means of improving competitiveness of
Brazilian manufacturing. Such increase in the role of BNDES may have positive or negative
consequences on TFP, depending on whether capital cost differentials induce investment and expansion
by more productive firms.
3
The only published independent evaluation of BNDES subsidized credit effects is Ottaviano and Souza
(2008). They measure the impact of BNDES funds use on firm average labor productivity (value added
per worker). Their results suggest that firms that contracted loans with BNDES experienced higher labor
productivity after two or three years, compared to firms that never contracted loans with the bank over a
10 year period. But note that the positive result appears only for firms that contracted larger loans.
While Ottaviano and Souza focused on value added per worker as a productivity measure, we consider
TFP. This is arguably a better measure of productivity, as it is not influenced by capital expansions. In
addition, we consider two channels for BNDES contribution to aggregate TFP growth: within firm TFP
growth, and output expansion (increased investment rates, revenues and employment).
Estimating TFP is not a trivial task. In our data set, there is a data quality problem. Only sector deflated
revenue (expenditure) is available instead of output quantity (input quantity). If firms have market
power, this so-called deflated revenue function (TFPR) may not recover the production function
coefficients nor returns to scale measures, as the mark-up will influence results (Foster, Haltiwanger and
Syverson, 2008, inter allia). Following Hsieh and Klenow (2008) we use a monopolistic competition
model and calculate an estimated output productivity measure (TFPQ). Last, but not least, CobbDouglas production function parameters are recovered using sector factor cost shares as Foster,
Haltiwanger and Syverson (2008), given a lack of convincing alternatives1.
On the other hand, our data set is unique in declaring firms that actually received subsidized credit,
instead of working with aggregate (sector) data. An additional advantage of the data set is the
longitudinal structure of the data. Time variation can be used for identification of critical parameters.
The basic estimation problem is that firms that reported government support are not a random sample of
the population. A mere comparison of mean productivity levels between supported and not supported
firms would yield biased estimates of the true effect of government support for a random firm. There is a
reverse causality possibility, as credit demand depends on investment decisions and these are a function
of TFP shocks.
1
An additional issue is input endogeneity with respect to TFP. There are a number of techniques that deal with issue, for
example, Levinsohn and Petrin (2002). Yet their model assumes properly measured quantity output, not available in our data
set. A radical perspective of the problem comes from Zellner, Kmenta and Dreze (1969), arguing that for Cobb-Douglas
technologies such problem of input endogeneity does not exist, for risk neutral expected output profit maximizing firms. See
also the appendix.
4
The key issue for the identification is the availability of variables that influence the decision to apply for
and receive BNDES credit, but are not directly associated with TFP. We propose three sets of
instruments or controls. First, control variables. Government support (BNDES credit) depends on firm
observable characteristics, such as size, age, financial ratios and cash flow, as these characteristics are
used by BNDES to evaluate loan applications (see BNDES application forms at the bank site). The
longitudinal data allows us to use fixed effects to control unobserved time invariant variables that
simultaneously influence public credit use and TFP. Continuous variables act as proxies for these
unobserved confounding effects, partialling out the endogenous variation of public credit use.
For example, Age is an important variable to explain support, as it is an important prediction of growth
and productivity (Roberts and Tybout, 1996, Cabral and Mata, 2003). Lagged financial ratios and firm
performance variables are important as the credit application process ask for at least three years of such
data. Also, firm size is an important variable, as the interest rates used by BNDES are scaled by firm
size to foster small and medium enterprises. Other variables that influence access (and application to)
BNDES funds are foreign control (see below) and being part of a corporation.
It is important to look for changes in BNDES credit availability and aggregate interest rate changes, or
sector specific policies. Such changes in BNDES lending policies took place in 1997, 2002 and 2004 (as
well as late 2006, but this is beyond our sample time frame). Particularly in 2002, the revenue threshold
for a firm to be classified as small or medium almost doubled. Small and medium firms paid at least 1%
less interest rates (more details below). Depending on the sector and location, the interest change
differential could reach 3%a.a., i.e., the interest rate would be 1/3 less for medium and small firms with
respect to larger firms. Another policy change took place in 2004, when firms in selected sectors,
regardless of size, would qualify for small firm interest rates. Thus, our second empirical strategy uses
as instrument for BNDES credit lending policy changes, namely, medium firm size classification change
in 2002 and sector targeting in 2004.
Third, the standard TFP measure we use, namely, the residual from a Cobb-Douglas technology,
generate a natural instrument, the capital-labor ratio. The Hicks’ neutral technology index does not
influence expansion paths, i.e., relative input use. In fact for any productivity separable technology, the
capital labor ratio is independent of productivity shocks. At the same time, from the financing
constraints literature (e.g. Fazzari, Hubbard and Petersen, 1988), access to credit depends on future
5
profitability. That is generally proxied by cash flow, i.e., a measure of revenue net of costs. For Latin
America, the relevance of such variable is available from Jaramillo and Schiantarelli (2003) and other
papers in Galindo and Schiantarelli (2003). Instead of using cash-flow, we argue that the capital-labor
ratio is a good measure of future profitability as this signals the value added surplus from wages to cover
investments and debt expenses. Also, firms with relatively more capital also have more collateral to
provide for loans, increasing their likelihood of obtaining public credit from BNDES. This generates a
correlation between our instrument and the endogenous explanatory variable, required for instrument
validity.
Note that if our identification rules are invalid, we shall be overestimating the effect of BNDES credit on
TFP, as positive shocks to TFP also are expected to increase the likelihood of credit from BNDES (since
the firm collateral increases with current and future TFP levels; alternatively, high TFP signals cost
advantages and higher future profitability). Hence not finding any effect from BNDES credit recipient
status (or proxies for the proportion of investment funding received from BNDES) is strong evidence
that such effect indeed does not exist.
The paper is divided in five sections. First, we sketch the theoretical arguments on the impacts of
subsidized credit on productivity. The following section describes the data and BNDES credit policies,
with particular emphasis on interest rate differential levels. The fourth section presents the empirical
results and the last section bring final comments.
2. Subsidized credit on productivity: theoretical framework
As mentioned above, it is not clear a priori that access to subsidized credit would influence TFP.
If the lower marginal cost of capital induces more investment, value added per worker increases from
operation expansion, but TFP should not change, as TFP is calculated conditional on the (larger) capital
stock. TFP changes must come from technology enhancing investment. Bustos (2005, 2007) presents a
theoretical framework that may explain the effect of access to subsidized credit on productivity, as
Ottaviano and Souza (2008) point out. We summarize parts of the Bustos model relevant to our
arguments and the later authors’ conclusions, as well as expanding the results.
6
Consider a density of firms, indexed by their heterogeneous productivity ϕ, operating under
monopolistic competition in a given industry. The demand function in a given sector is q=Ap-σ. There
are two available technologies, represented by their associated cost functions:
TCj= fj + cjq/ϕ,
for j=h,l
Where fj is the fixed cost, cj the marginal cost and j indexes the low (l) and high (h) productivity
technologies, where fh>fl, and ch<cl.2 Profit maximization yields the well known price (p) condition
p(ϕ)=ρ cj/ ϕ,
where σ /( σ –1)=ρ. Replacing it in the demand function yields the profit function, that depends on the
idiosyncratic technology index ϕ and the chosen technology j
π j (ϕ)=Aρ σ –1 (cj/ ϕ)1-σ/σ – fj
Firm technology choice is made by comparing profits for each technology (h, l), given its idiosyncratic
productivity. A firm purchases the more productive technology h if πh(ϕ)>πl(ϕ). This condition depends
on the firm idiosyncratic productivity and both fixed and marginal costs of the technologies, i.e., the
more productive technology is chosen if
ϕ > ρ (σ/A)1/(1-σ) [(fh –fl) / (ch1-σ− cl1-σ)] 1/(1-σ)
There is also a firm selection criteria that must be met3, namely, firms must have positive profits or exit:
ϕ∗ > ρ (σ/A)1/(1-σ) [fl /cl1-σ]1/(1-σ) . Graphically it easier to see the distribution of adopted technologies
given firm types (Figure 1). Bustos (2007) states that lowering the high technology fixed cost leads to
more firms adopting it. Keeping the fixed cost of the l technology constant, the proportion of firms
whose idiosyncratic technology does not allow them to survive (π(ϕ)<0) does not change and there is an
2
It is not hard to see that under a Cobb-Douglas technology with constant returns to scale, total factor productivity may be
written as TFP=ϕγ, ch=c/γ and cl=c. A higher TFP leads to a lower marginal cost, under constant input prices (indexed by c).
In this model, TFP is decomposed in a constant idiosyncratic level (ϕ) and a variable, choice based factor (γ).
3
Actually, Bustos’ model has an entry condition as well, but this is not key to our argument. Please refer to the original
works for more detail.
7
unambiguous increase in average (or aggregate) productivity. On the other hand, as Ottaviano and Souza
(2008) point out, if access to BNDES credit lowers the fixed cost of both technologies, some firms with
low productivity will be able to survive, using the low technology and some firms will switch from the l
to the h technology. The total effect on aggregate productivity is not clear a priori.
Access to subsidized credit arguably lowers marginal costs, as the unit cost of capital is smaller. Should
BNDES not discriminate against any technology type, there is a reduction in both technology types
marginal costs. Again, the total effect on productivity is ambiguous, as the firm selection process has a
lower minimum productivity level ϕ* for survival, but the threshold level for high technology decreases.
Figure 1 – Profits, productivity and technology adoption
20
οδ .
h
l
ο
(
0
0
2
exit
4
6
8
low prod.
technology
10
12
14
16
18
high prod.
technology
-20
Source: authors’ calculations. The scale is arbitrary. Adapted from Bustos (2007) and Ottaviano and Souza (2008).
Actually, BNDES interest rates depend on whether purchased machinery is domestic or imported (in
addition, (domestic) suppliers are shortlisted by BNDES; see more below) with imported machinery
purchases exposed to exchange rate risk. If one is willing to assume that imported technology is more
8
productive, BNDES has a policy of actually lowering the relative marginal cost of the less productive
technology. This weakens the selection effect on aggregate productivity and increases the threshold level
for a firm to switch to higher quality technology. This unambiguously reduces total productivity.
Yet, the analysis so far does not consider the effect of BNDES on easing credit restrictions. The credit
constraints literature indicates that credit rationing is not based on firm characteristics, as equally credit
worth firms may and may not receive credit. Should BNDES eases credit constraints in the economy, it
does not follow that less productive firms or firms with smaller productivity growth projects only will be
attended by the bank. In fact, by allowing credit constrained firms tap credit markets, BNDES credit
may be bolstering TFP levels by a composition effect.
3. Data set and BNDES lending policy
The details on the manufacturing survey used and TFP calculation details appear in the Appendix. We
have firm level information from 1996 to 2006. In general, long firm panels, based on industrial surveys,
do not have information on whether a firm tapped subsidized or public credit. If one does not have
access to actual lender records, as it is our case, it is not possible to evaluate firm level impacts of
subsidized credit. The Manufacturing Technology Survey (PINTEC) conducted by IBGE is an
exception. This survey is based on the CIS-4 surveys of the European Community. Questions 40 and 43
from the PINTEC survey (2000, 2003 and 2005) ask about the share of funds (own funds, private or
public) used to cover R&D and innovation activities expenses in each of those years. Hence, our data
cover firms that innovate with positive innovation expenditures.4 About 20-15% of the sample uses
public funds and the mode use of public funds (for firms that used public funds) is 80%.
While BNDES is not the only source of subsidized public credit, it is by and large the one with the
biggest outlays for machinery and equipment acquisition in Brazil. Many public banks, such as regional
development banks act only as financial intermediaries to BNDES, basically. The other two large public
banks, Banco do Brasil and Caixa, provide mainly agriculture credit and housing credit, respectively, as
4
Those expenses are reported on questions 31-32 (R&D) and 33-37 (innovation activities) of the PINTEC survey.
9
well as acting as financial intermediaries to BNDES. We deal specifically with machinery acquisition in
manufacturing. It is telling that the public credit share of investment outlays mode of 80% is the most
common BNDES limit of coverage operations, suggesting that those firms actually used BNDES credit.
BNDES has different credit lines5, including export credit (BNDES-exim), equity acquisition (Finem and
BNDES-Par) and machinery acquisition (Finame, Finame leasing and BNDES automático). Each one
has its own lending policies that influence the interest rates charged. We focus on Finame as it is the
largest outlay and the one specifically targeted for machinery acquisition.6
BNDES loans have two important characteristics. They are long term (up to 60 months in general),
much longer than private sector credit duration. And the bank charges below market interest rates.
BNDES funds have basically a three part cost: (i) base interest rate; (ii) BNDES funding and credit risk
spread; and (iii) financial intermediary spread.
The base interest rate (i) is either the long term interest rate (Taxa de Juros de Longo Prazo, TJLP), or
international basic rates (such as Libor). The TJLP is a government set rate, created in 1994. It changes
by a Central Government decision approximately periodically. Up to 1999, it was based on the nominal
returns on government bonds. Since 1999, it is based on the Banco Central do Brasil (Brazilian Central
Bank) inflation target (based on consumer prices) and a “risk premium”, following a country risk
premium and international interest rates. To illustrate its values, in 2003 and 2005 the TJLP was 11%a.a.
and 9.75%a.a., respectively, in nominal terms, while the Selic rate (basic public debt rate) was 17%a.a.
and 18.24%a.a., respectively. The TJLP applies for investment on machines that have at least 60%
certified domestic content. For imported machinery and multinationals in non-strategic sectors7, the
BNDES basic interest rates are based on BNDES’ international funding costs (pegging international
5
All information presented here is available at www.bndes.gov.br. Historical information on lending policies is based on
public access documents obtained at the “fale conosco” from BNDES. We thank BNDES for prompt access to the
documents.
6
In 2003 BNDES started a specific credit line for innovative investment, but its outlays were very small. From interviews
with experts and staff, we are led to believe that the credit line was not successful as it was not clear to bank staff how to
separate process innovation from machine acquisition. Many of the projects presented as innovation ended up eligible for
Finame.
7
A list of such sectors is available (Decree 2233, on 23.05.1997). In practice, only textiles and garments multinationals are
excluded from BNDES credit access.
10
base rates) and the US dollar, the so called UMBNDES (Unidade Monetária do BNDES). This difference
may be quite significant over time, but depends on the strength of the Brazilian Real. From 2006 on,
given the weakening US dollar, UMBNDES rates were actually negative in nominal terms, leading to a
halt of imported equipment financing in late 2006. The figure below presents an overview of annual
Selic, TJLP, CD’s and non-subsidized loan average interest rates (including working credit) in recent
years.
Figure 2 – Brazil interest rates (annual rates)
50
45
F lo a tin g exch a n g e r a te
P eg g ed exch . r a te
40
35
30
25
20
15
10
5
0
5
-9
n
aJ
5
9
lu
J
6
-9
n
aJ
6
9
lu
J
7
-9
n
aJ
7
9
lu
J
8
-9
n
aJ
8
9
lu
J
9
-9
n
aJ
9
9
lu
J
G o v t.B o n d s (S elic )
0
-0
n
aJ
0
0
lu
J
1
-0
n
aJ
1
0
lu
J
2
-0
n
aJ
2
0
lu
J
3
-0
n
aJ
3
0
lu
J
4
-0
n
aJ
4
0
lu
J
Ba se BN D ES (TJL P )
5
-0
n
aJ
5
0
lu
J
6
-0
n
aJ
6
0
lu
J
7
-0
n
aJ
7
0
lu
J
8
-0
n
aJ
8
0
lu
J
C o rp o ra te L o a n s
Source: Banco Central do Brasil, Ipeadata.
Note: interbank loans are indistinguishable from Govt. Bonds in the above scale.
The BNDES funding and credit risk spread has changed over time. In general terms, the base funding
rate and the credit risk spread vary according to firm location, firm size and sector. We provide a
sinthesis based on FINAME lending policy documents8.
8
Namely carta circular 157/1997, 187/2004, 195/2006 and other documents on firm size definitions. We thank “fale
conosco” for the documents.
11
1997-2002
BNDES
Base
Spread
(a.a.)
2.5%
2002-2004
2.5%
2004-2006
2.0%
Period
Table 1 – BNDES FINAME Guidelines
Size categories
Firm size and
(Small / Medium / Large
region
thresholds based on
interest rate
revenues)
Differences
-1.5%
Sm: <R$6Mi,
(Sm, IR, MIR)
Med: [R$6Mi; R$35Mi]
Lg: >R$35Mi
-1.5%
Sm:<R$10.5Mi
(Sm, IR , MIR)
Med: [R$10.5Mi; R$60Mi]
Lg: >R$60Mi
-1.5% (Sm, Med)
Sm<R$10.5Mi
-1.0%(LIR)
Med: [R$10.5Mi; R$60Mi]
+1.5%(Lg)
Lg: >R$60Mi
BNDES risk
exposure
(Maximum
participation)
80-100%
80-100%
Up to 100% (Sm,
Med)
Up to 80% (Lg)
Notes: Sm- Small firms; Med – Medium firms; Lg – large firms; IR- Special Interest Regions (North, Northeast, CenterWest and Southern border). MIR – Medium firms located in Special Interest Regions. LIR – Large firms located in Special
Interest Regions. All values nominal. Size categories indicate, e.g., that a firm is considered small if its gross operating
revenues are less than R$6 million.
As seen on Table 1 above, basic interest spreads can vary significantly across firms. Given TJLP 2003
rates of about 11%a.a., small firms enjoyed 12% base interest rate and larger firms 13.5% base rates.
Two significant changes were observed in 2002 and 2004. First, in 2004, while the base spread for
smaller firms fell -0.5p.p., compared to 2003, for larger firms it rose 1p.p.. More important, in 2002, the
limits for classifying a small and medium firm changed dramatically, almost doubling. This led to the
share of BNDES loans for medium, small and micro firms jump from 22% in 2002 to 30% in 2003
according to BNDES documents. For example, a firm with gross revenues of R$10Million in Brazil’s
Southeast or South (richer regions, where most economic activity is located) in 2001 would be classified
as medium in 2001 and small in 2002. This led to a -1.5p.p. reduction in annual interest rates. In 2004
there would be another -0.5p.p. cut in its base interest rates. This change is very important for our
identification of causal effects.
The last part of interest rate charges depend on whether it is a large project or not (more than
R$7Mi/R$10Mi in 1997-2004/2004 on, respectively). Large projects are evaluated by BNDES itself
(direct operations - operações diretas) while others are handled by financial intermediaries. As BNDES
is a bank with no branches, it channels credit through regular and regional development banks. Banks
12
can access BNDES funding under lower rates and offer it to consumers. These funding sources are
earmarked to machine acquisition or the specific credit line from BNDES and are marketed to the public
as such. Credit risk is borne by the banks, as BNDES payments are not linked with firm loan defaults.
Banks are also free to use their own credit scoring methods. In direct operations, the credit risk spread is
0.5% (medium and small firms are exempt, from 2006 on). For loans through financial intermediaries,
the spread is unlimited, but most operations use a 4% cap9.
Guarantees are required by the bank. In general, guarantees presented cover 130% of the loan value.
Frequently, BNDES has temporary ownership of the equipment (garantia fiduciária) during loan
duration. There are important fixed costs to apply for BNDES credit. The fixed costs can range from
1.0% to 2.0% of loan value in administrative costs and there is a 0,2% non-refundable application fee.
The interest rate structure for BNDES presented above indicates that it is very hard to anticipate the
actual cost of credit for a specific firm, as the size of the loan has an impact on the final rate. Yet, there
are important discrete changes in interest rate spreads, with differing levels across observable
characteristics (date, location and firm size). These changes will be used to identify credit demand shifts.
4. Empirical Results
We present our manufacturing TFP estimates in Figure 2. There are two TFP measures, given our data
that have firm revenue but no quantity, similarly to most firm level data: revenue TFP (TFPR), or
revenue per input use, and quantity TFP (TFPQ). The latter is estimated under a monopolistic
competition hypothesis to recover quantities from revenues (see appendix for calculation details). TFPR
does not measure true productivity , depending actually on input costs, markups and price differentials
(Katayama, Lu and Tybout, 2008). We present estimates for both productivity measures for
completeness.
9
This 4% cap allows the loan to qualify for a federal credit guarantee fund (FGPC) criteria. This limit is generally binding,
according to market participants and cursory evidence from the largest financial intermediaries internet information.
13
From Figure 2, our estimates suggest that TFPQ was largely stagnant from 1996 to 2000 and
experienced a 10% increase from 2000 to 2004, remaining stable until 2006. The revenue TFP trend is
similar, but with a larger dip in 2000 and a sharper increase from that year on. The TFPR and TFPQ
difference in 2006 suggest that price heteregeneity increased in that year. The manufacturing trend is
correlated with aggregate TFP estimated independently by Pessoa, Barbosa-Filho and Veloso (2008),
particularly the 1999-2000 dip.
Figure 2 – TFP – Brazil Manufacturing 1996-2006
150.00
140.00
130.00
120.00
110.00
100.00
90.00
80.00
70.00
60.00
50.00
1995
1996
1997
1998
1999
2000
2001
tfpr
2002
2003
2004
2005
2006
2007
tfp q
Note: authors´calculations based on PIA data. TFPR is revenue TFP and TFPQ is output TFP where output is inferred from
revenue using a monopolistic competition model. See appendix for details. The productivity indices are based on sector
normalized TFP (4 digit) indices, aggregated using sector revenues.
The manufacturing trend may obscure significant firm heterogeneity. This is explored using the Foster,
Haltiwanger and Krizan (2001) decomposition described in the Appendix. The results are on Table 2,
for the 1996-2006 period and the (five year) intervals with differing trends, 1996-2001 (stagnation) and
2001-2006 (growth). Overall, the within and between effects are negative for both productivity measures
14
(TFPQ and TFPR)10. The negative within effect indicates that, on average, larger firms in 1996 that
survived until 2001 and until 2006 had lower productivity growth. The negative between share informs
us that firms with higher TFP levels in 1996 did not gain market share, on average.
Productivity growth over the period depended on two effects: market selection (net entry) and the
interaction effects. Entering firms were more productive than exiting firms over the period, particularly
in the second (growth) period. The positive interaction effects suggest that productivity growth was
directly associated with market share gains. That is, firms with positive TFP growth were more likely to
expand their relative size, compared to other surviving firms over the time interval.
Table 2 – Productivity decompositions of TFP changes over 1996-2006
Year
Total
1996-2006 .067
TFPR
Within Between Interaction
-0.78
-0.72
1.87
1996-2001 -.001
2001-2006 .068
112.29
-0.60
Year
Total
1996-2006 .330
TFPQ
Within Between Interaction
-1.44
-0.90
3.01
1996-2001
2001-2006
.058
.272
-8.76
-1.70
101.85
-0.52
-6.67
-1.31
-212.12
1.75
16.46
3.85
Net Entry
0.62
-1.02
0.36
Net Entry
0.33
-0.03
0.16
Note: FHK decomposition, see appendix for details. Columns Within, Between, Interaction
and Net Entry are shares of column Total and add up to 1. Total is based on Figure 1.
The negative between effect is one of the motivators of our study, as more productive firms in 1996 or
200511 were not the ones that increased their market share on average. Market share gain was correlated
with TFP growth, on average. It remains to be seen whether firm public credit use is associated with
firm growth and productivity growth.
10
The 1996-2001 (stagnation) figures for TFPR are quite large as the decomposition shares try to explain a very small
growth. For example, the TFPR within growth of 5.8% is 100 times larger than the total 0,05% growth in productivity.
11
Or any year from 1996 to 2001, in the Appendix.
15
From a static perspective, we present the TFP distribution for firms that use BNDES credit (TFPQ_FP)
and firms that did report use of such funds on Figure 3. Figure 3a pictures raw TFPQ measures and
Figure 3b standardized TFPQ measures (deviation from sector mean), to glance over sector differences
on public credit use and productivity.12
0
.2
.4
.6
Figure 3a – TFPQ densities for firms that used public credit (TFPQ_FP solid line) and did not use public
credit(TFPQ dashed line).
6
7
8
9
10
11
x
kdensity TFPQ_FP
kdensity TFPQ
Note: TFPQ levels for firms that appear on PINTEC and PIA, on 2000, 2003 and 2005.
See appendix for details on TFPQ calculation.
Productivity distributions are similar across firms that used and did not use public credit (Figure 3a).
The former tend to have higher productivity, although there does not seem to be first order stochastic
dominance. When sector differences are controlled for (Figure 3b), the TFP distributions become
asymmetric to the right and differences across firm types are much smaller, suggesting that credit use is
quite associated to industry characteristics, and less so to firm productivity. Disregarding sample
variability, the average TFPQ for firms that use public credit seems slightly smaller now. In principle,
one could argue that firm productivity does not differ whether a firm uses public credit or not. The same
12
As mentioned data description section in the appendix, while we calculate TFP for every year from 1996 to 2006 using the
manufacturing survey, the public credit use information is available only from the innovation survey, for the years 2000,
2003 and 2005. We use matched firm data from the two surveys.
16
pattern of strong sector differences of TFP and public credit use appears on TFPR densities (see
Appendix).
0
.05
.1
.15
.2
Figure 3b – Sector deviation TFPQ densities for firms that used public credit (tfpq_FP – solid line) and
did not use public credit(tfpq – dashed line).
-5
0
5
10
x
kdensity tfpq_FP
kdensity tfpq
Note: TFPQ levels for firms that appear on PINTEC and PIA, on 2000, 2003 and 2005.
See appendix for details on TFPQ calculation.
Public credit use differs remarkably across firms (Table 4). Most firms self finance their investments.
Only 6% does not use own funds, and 71% use retained earnings only to finance investments. 84% of
the firms report no use of public funds, slightly less than the 86% of the firms with no use of private
credit. The mode use of Public funds, excluding zero, is 80%. For private sources, the non-zero mode is
100%. Certainly not a coincidence, the most common participation percentage for BNDES financing
programs is 80% of the investment outlays, as mentioned before.
While unconditional external funding shares distributions seem similar, their conditional distributions
differ. Comparing substitution among funding sources, Table 5a shows that public funds tend to replace
17
self financing, as the largest off-margin probabilities are in the main diagonal. 72% of the firms use only
own funds to cover investment costs on innovation. Table 5b confirms this pecking order of investment
financing, as the mode private financing, conditional on public credit is zero. Simple correlations on the
pooled data indicate that Corr(Self, Public)= –0.64, Corr(Self, Private)= –0.67, and Corr(Private,
Public)= –0.12. Firms either use combinations of self and public financing or self and private financing,
and are less likely to use public and private simultaneously to reduce self financing.
Table 4 – Total firm distribution of share of funds used for investment, among innovative firms,
Brazil, Manufacturing, 2000, 2003, 2005
Share of Funds
0
10
20
30
40
50
60
70
80
90
100
Own
6.4% 2.1% 6.5% 3.8% 2.2% 3.5% 1.1% 1.2% 1.2% 0.6% 71.4%
Public
84.3% 0.5% 0.9% 1.0% 0.8% 1.9% 1.2% 2.0% 4.2% 0.9% 2.1%
Private
85.5% 0.4% 0.9% 0.8% 0.7% 1.9% 1.0% 1.6% 2.1% 1.2% 4.0%
Source: authors´ estimates, based on PINTEC data; Pooled data for 2000, 2003 and 2005. Actual shares are
rounded to the nearest tens.
Own Funds
Table 5a – Cross tabulation of innovation investment funding shares
– Brazil Manufacturing, 2000, 2003, 2005.
0
10
20
30
40
50
60
70
80
90
100
0
3.95
1.05
2.04
1.56
0.93
1.68
0.49
0.46
0.57
0.25
71.36
10
0.03
0.02
0.03
0.02
0.01
0.02
0.01
0.01
0.03
0.33
20
0.01
0.01
0.02
0.07
0.01
0.06
0.03
0.05
0.59
30
0.11
0.01
0.04
0.08
0.07
0.03
0.02
0.63
Total
84.34
0.51
0.85
0.99
Public Funds
40
50
60
0.01 0.09 0.03
0.02 0.02 0.01
0.10 0.03 0.05
0.05 0.04 0.02
0.02 0.05 1.14
0.02 1.71
0.59
0.81
1.94
1.25
70
0.05
0.02
0.01
1.94
80
0.04
0.01
4.19
90
0.01
0.94
100 Total
2.11
6.44
2.09
6.52
3.78
2.23
3.52
1.14
1.16
1.19
0.58
71.36
2.02
4.24
0.95
2.11
84.34
18
Private Funds
Table 5b – Cross tabulation of innovation investment funding shares
– Brazil Manufacturing, 2000, 2003, 2005.
0
10
20
30
40
50
60
70
80
90
100
Total
0
71.34
0.18
0.61
0.47
0.50
1.68
0.91
1.47
2.02
1.16
3.99
10
0.32
0.02
0.02
0.01
0.02
0.01
0.01
0.03
0.04
0.03
20
0.58
0.03
0.05
0.06
0.01
0.07
0.03
0.00
0.02
30
0.63
0.02
0.01
0.13
0.03
0.05
0.00
0.12
Public Funds
40
50
60
0.58 1.71 1.14
0.02 0.04 0.02
0.02 0.05 0.05
0.05 0.03 0.01
0.11 0.02 0.03
0.01 0.09
0.01
70
80
90 100 Total
1.93 4.19 0.94 2.11 85.47
0.01 0.01 0.01
0.36
0.01 0.04
0.88
0.06
0.83
0.73
1.91
0.97
1.62
2.07
1.19
3.99
84.33 0.52 0.85 0.99 0.80 1.95 1.25 2.01 4.24 0.95 2.11 100.00
Source: authors´ estimates, based on PINTEC data; Pooled data for 2000, 2003 and 2005. Actual shares are
rounded to the nearest tens.
As seen in Table 4 above, about 16% of the firms that invest and innovate use public credit sources.
Over time (Table 6) this figure is slightly increasing. In 2000, medium and large firms13 use public funds
more than smaller firms, but this is reversed in 2003 and 2005, likely due to changes in size
classification criteria. As seen in the previous section, in 2002 BNDES changed the small firm
classification criteria, expanding it significantly. The 2005 expansion does seem genuine, i.e., smaller
firms did increase BNDES credit use relative to larger firms.
Table 6 – Firm size and public credit use among firms with positive innovation
and investment spending, Brazil, Manufacturing, 2000, 2003, 2005 (%)
Year
2000 2003 2005
Small
10.35 15.24 18.84
Medium and Large 15.14 15.66 15.39
Total
14.35 15.37 17.40
Source: authors´ estimates, based on PINTEC data; Totals
may differ from table 5 due to rounding
13
Size classification according to BNDES criteria listed on table 1.
19
In addition to size, other variables may influence public credit use. We entertain a public credit use
prediction empirical model, under two different measures and different estimation methods, Public
credit use based on the share of public funds used to finance investment and a dummy variable for non
zero public funds share. Differences across columns reflect intensive (more public credit use) and
extensive margin effects (use of public credit) differences. Note that the implicit counterfactual of no
public credit use in the analysis is investment using other credit sources such as own funds or private
sources, since the sample consists of firms that actually invested only (those are ones that answered the
public credit use question in the PINTEC survey). We estimate the models by pooled least squares and
by fixed effects. Sector-year interactions and region controls are used to minimize endogeneity from
sector specific aggregate shocks.
A striking feature of Table 7 is the role of firm fixed effects. While age, size and multinational firm
status, and the capital-labor ratio (to a lesser extent) are associated with public credit use in least squares
(pooled) estimates, no varying observable firm characteristic is significant with fixed effects. Firm
effects seem to explain most of the variation in public credit use. There are sign changes between pooled
and fixed effects estimates for multinational status, size and capital intensity, suggesting that variable
levels are negatively associated with changes.
Nevertheless, focusing on the pooled estimates, we see that older firms tend to use less public credit
while larger firms are more likely to use it and use it to a greater extent to finance their investment. The
relationship of public credit use with capital intensity is non-linear. It is positive for public credit use in
the short run, but negative in the long run, and negative for public credit intensity (share). That is, capital
intensive firms use less public credit as a share of their investment funds, but there seem to be a positive
effect to induce a firm to use such credit, conditional on size. An alternative interpretation is that firms
that use public credit, conditional on size, are less capital intensive on average (w.r.t. their sector
counterparts) and tap on such funds to expand their machinery up to the industry average. The effect is
positive (albeit insignificant) for fixed effects estimates. Multinational firms use less public credit (as
expected, since they either are pay higher interest rates under BNDES guidelines or could use own funds
cheaper from international credit markets), but, again, only if fixed effects are not used.
20
Table 7 – Public credit use and firm observed and unobserved characteristics,
Brazil, Manufacturing, 2000, 2003, 2005
Aget
Sizet-1
Treatt
K/Lt
(K/Lt)2
K/Lt-1
(K/Lt-1)2
Skilledt
MULTt
EXPt
IMPt
R2
Share
var FE
Least Squares
Pub.Cred.Use
Pub.Cred.Share
Coef.
Std.Err.
Coef.
Std.Err.
-.001 (.0003)**
-.074 (.0203)**
.032 (.0041)**
2.177 (.2753)**
.005 (.0073)
1.165 (.5052)
.029 (.0528)
5.466 (4.190)
-.002 (.0024)
-.283 (.1879)
-.049 (.0515)
-6.806 (4.149)*
.003 (.0023)
.352 (.1866)*
-.063 (.0379)*
-2.204 (2.405)
-.127 (.0110)**
-7.029 (.6952)**
-.008 (.0088)
-1.286 (.6014)*
.011 (.0082)
.539 (.5596)
.0422
.0385
Fixed Effects
Pub.Cred.Use
Pub.Cred.Share
Coef.
Std.Err.
Coef.
Std.Err.
-.009 (.0144)
-.599 (0.787)
.021 (.1217)
-2.699 (8.110)
.045 (.0685)
2.831 (4.681)
.284 (.4289)
12.800 (27.54)
-.015 (.0182)
-.778 (1.150)
.170 (.5992)
16.896 (38.21)
-.007 (.0248)
-.699 (1.555)
-.003 (.2697)
-4.627 (12.88)
.062 (.1504)
2.019 (9.886)
.025 (.0577)
.235 (3.949)
.052 (.0570)
2.204 (3.836)
.0652
.0591
.683
.708
Source: authors’ estimates based on PIA and PINTEC data, for 2000, 2003 and 2005. Variable codes: age – firm age (in
years), Size – employment level, treat – dummy indicator for medium firm re-classified in 2003 as small, K/L – log capital
labor ratio, (K/L)2 – log capital labor ratio squared, Skilled – share of skilled workers (at least competed high school), MULT
– multinational firm indicator, EXP – export activity in the year indicator, IMP – import activity in the year indicator.
Dependent variables: Pub.Cred.Use - Public credit use dummy for whether the firm reported use of public credit to finance
innovation investment; Pub.Cred.Share - Public credit share of investment expenditures financed by public funds. Controls
used: region, and two digit sector and year dummies interactions. R2 for panel data is within-R2. Sample sizes: 6550 for panel
and 13226 for pooled least squares. * - significant at 10%; ** - signif. at 5%;
An important issue is whether the above estimates identify causal effects. Fixed effects do control for
firm specific time invariant characteristics that jointly determine public credit use and firm
characteristics, such as size. Nevertheless, there may be time varying unobservables that simultaneously
influence BNDES credit use and firm characteristics. We explore two situations when there were
exogenous changes in interest rates for some firms to identify exogenous public credit use variation. As
mentioned in section 3, on 2002 there were BNDES lending policy changes such that firms between R$6
million and R$10.5 million on gross revenues were now considered small firms and entitled a 1.5p.p.
decrease in interest rates (this is a 10% reduction in interest rates, at least). The firm size classification
change led to an increase in the share of manufacturing firms classified as small in our data set, from
21
74% in 2001 to 95% in 2002. The share of employment accounted for these now small firms actually
doubled from 22% in 2001 to 44% in 2002.
We compare BNDES credit use in this group of firms over time, compared to firms that did not change
size category according to BNDES classification, i.e., firms with less than R$6 million or more than
R$10.5 million revenues. The estimated equation on Table 8 is a standard Differences-in-Differences
model, namely
Publicit= α + β Smallit + β2 Treati + β3 D2003t + γ1 (Treati*D2003) + εit
where Public is a dummy indicating whether a firm tapped public credit to finance innovation
machinery investments; Small is a dummy that indicates whether a firm is classified as small or not on
year t; Treat equals 1 if the firm changed size classification categorie due to the threshold change
indicated on Table 1; D2003 is a dummy for 2003. Alternatively, Public is also defined as the share of
the investment costs covered by public credit as in Table 7. This allows a complementary interpretation
of the coefficients, as mentioned previously.
The model is estimated using two available data points (2000 and 2003), before and after the
classification change, and also with three data points (2000, 2003 and 2005), including an addition year
after the policy change. We use alternative specifications for the error term εit: as a purely random term
(pooled least squares), and as a composite error term with a firm specific, time invariant term (fixed
effects).
22
Table 8 – Differences-in-Differences analysis for firm small size classification change,
Innovative firms, Manufacturing, Brazil.
Variable
Treat. *2003
Public
Credit
Dummy
2003
-.001
(.033)
Treat. *2005
Pooled Least Squares
Public
Public
Credit
Credit
Share
Dummy
2003
2005
.310
-.001
(2.511)
(.031)
.052*
(.028)
Public
Credit
Share
2005
.310
(2.511)
2.418
(2.206)
Treatment
R2
F
Public
Credit
Dummy
2003
.066
(.050)
0.001
0.355
0.001
0.885
0.003
1.543
0.003
1.708
0.006
2.143
Fixed Effects
Public
Public
Credit
Credit
Share
Dummy
2003
2005
Public
Credit
Share
2005
5.200
(3.279)
2.057
(2.354)
.016
(.036)
0.007
2.704
0.001
0.913
0.002
1.253
Source: authors’ estimates based on PIA and PINTEC data, for 2000 and 2003 (columns labeled 2003) and also 2005
(columns labeled 2005). Variable codes: Treatment indicates whether a firm is classified as small after 2002 by BNDES but
not considered small in 2002 by the previous criteria. See table 1 above for classification thresholds. Dependent variables:
Public credit use dummy for whether the firm reported use of public credit to finance innovation investment; and Public
credit share of investment expenditures financed by public funds. Sample size: 8131 for 2005 results, and 5353 for 2003
results. 4 digit sector cluster standard errors used. F-statistics are insignificant except when indicated. *** - significant at 1%;
** - signif. at 5%; * - signif. at 10%.
The results are disappointing. In general, the classification change (and the associated interest rate drop)
did not significantly induce more firms to tap on public credit on average. The coefficients signs are
positive, but small and non-significant. The exception is a marginally significant positive effect with a
two year lag, i.e., in 2005, for pooled data. Yet, this is not robust, as the significance disappears when
firm unobserved characteristics are controlled for.
Later, in 2004, there was another important policy change. Firms in selected sectors, of any size, were
entitled treatment akin to small and medium firms. That represented an interest rate spread of -2.5p.p.
compared to large firms in non-preferential sectors. We evaluate whether the preferential sectors
increased their use of public credit in 2005, compared with 2003 and compared with firms in other
sectors, using a similar diff-in-diff model,
Publicit= α + β3 D2005t + γ1 (Piorii*D2005) + γ2 (Smalli*D2005) + δX+ εit
23
where Public is defined as above; Priori is a dummy indicating whether a firm is in a preferential sector;
Small is a dummy that indicates whether a firm is currently classified as medium or small; D2005 is a
dummy for 2005 and X a vector of controls namely, sector time interactions and region dummies. The
model is estimated using two available data points (2003 and 2005), before and after the policy change.
As in the previous model we consider pooled least squares and fixed effects (within) estimates to
evaluate the role of unobservables.
Table 9 – Differences-in-Differences analysis for firm sector interest rate
preferential treatment change,
‘Innovative firms, Manufacturing, Brazil.
Variable
Preferential
Sector*2005
Small*2005
R2
F
Pooled LS
Public
Public
Credit
Credit
Dummy
Share
.835**
5.644*
(.038)
(2.955)
Fixed Effects
Public
Public
Credit
Credit
Dummy
Share
.031
1.872
(.039)
(2.535)
-.029
(.018)
-.188
(1.864)
.084**
(.035)
2.847
(2.260)
0.020
0.355
0.017
0.885
0.009
2.240
0.004
1.040
Source: authors’ estimates based on PIA and PINTEC data, for 2003 and 2005. Variable
codes: Preferential Sector indicates whether a firm was in a preferential treatment sector
in 2005. Small indicates whether a firm was classified as small according to BNDES
thresholds. See table 1 above. Dependent variables: Public credit use dummy for whether
the firm reported use of public credit to finance innovation investment; and Public credit
share of investment expenditures financed by public funds. Sample size: 4967. 4 digit
sector cluster standard errors on least squares estimates; Additional controls: region and
sector-year dummies. F-statistics are insignificant. *** - significant at 1%; ** - signif. at
5%; * - signif. at 10%.
Again, the results are weak. When fixed effects are controlled for, there is no systematic time difference
in average public sector use or average public sector intensity for those firms in priority sectors.
Significance levels aside, the estimated effects are positive, as expected, suggesting that the share of
firms that used public credit rose, echoing the unconditional results of Table 6. It is interesting to note
that the 2002 small firm limit threshold change does identify firms more likely to use public credit. This
positive effect appeared on Table 8 with the fixed effect estimates, but not statistically significant.
24
The lack of clear significance and robustness in the estimates of exogenous changes in public credit use
leads us not to rely on instrumental variable estimates only to study the effect of public credit use on
productivity. We consider a variety of estimation methods and specifications, from pooled least squares
to (likely weak instrument) instrumental variables, with and without observed and unobserved (fixed)
firm characteristics. The different estimation methods will inform on the possible role of endogenous
omitted variables in biasing results. Intuitively, one argument for the bias is that organizational or
structural changes, as well as innovations, may improve public credit access – as bank or financial
intermediary officials may have the information of project profitability with the new technology– and
productivity, generating a spurious positive causal effect between public credit use and productivity.
Nevertheless, note that theoretically, as seen in the previous section, the causal effect may be positive or
negative, as access to subsidized (public) credit may lead to the adoption of low productivity
technologies due to BNDES bias to domestic technology.
In addition to productivity, we consider the association between public credit use and firm
characteristics, such as firm size and the investment rate. Our piori is that public credit use should be
positively associated with firm size (either revenues or employment) and the investment rate for two
reasons: first, the lower interest rate from public funds may lead firms to overinvest and expand; second,
unobserved variables in a firm size model such as productivity should be larger for larger firms and
larger firms are more likely to have access public credit. Such positive association between BNDES
credit and revenues was found by Ottaviano and Souza (2008) using least squares, within estimates, and
propensity score matching (PSM) and between BNDES credit use and employment by Pereira (2007)
using least squares.
As mentioned before, there are two counterfactuals underlying the public credit use variable. Our sample
includes only firms that invested, so that no credit use imply use of private credit, or, more likely, own
funds to finance investment, as seen above. The public credit dummy reflects an extensive margin effect,
where no public credit use firms are compared with firms that use such credit. The public credit
investment funding share coefficient measures the effect of an increase in the share of public credit on
funding. This is an intensive margin effect. In both cases, there is investment, as only firms with positive
investment outlays are included (i.e., answer the credit financing share question in PINTEC).
25
Table 10 presents the public credit use dummy estimates only (extensive margin), to save space14. The
use of public credit to finance innovation investment in machinery and associated costs is positively
related to productivity (TFPQ) with a two year lag, when sector differences are not standardized. The
immediate impact is negative. The rationally is that when credit is used, the retooling of the production
process associated with investments has a negative impact on productivity due to learning costs. After a
couple years, the production increases. On the other hand, public credit use is negatively correlated with
revenue productivity (TFPR) at all lags. This is expected, as this measure reflects input costs, and
BNDES credit is cheaper than private credit or even self-financing (government bonds opportunity
costs, e.g.).
When fixed effects are used (i.e., when firm variation and not firm differences identify the coefficients),
the impact of BNDES credit use on all productivity measures statistically disappear. In other words,
within firm TFPQ, or TFPR changes are not influenced by whether a firm used public credit. The pooled
data results were likely driven by public credit use proxying for between firm differences in productivity
levels. Once these differences are netted out, there is no significant association between credit use and
productivity. This is in line with the unconditional analysis from Figure 3.
Table 10b presents estimates of public credit use on the frequently use labor productivity (value added
per worker), as well as firm size (employment, revenue) and the investment ratio. Somewhat
surprisingly, given that public credit use should increase the capital stock, current labor productivity is
lower for public credit use, but future labor productivity increases. This result was also obtained by
Ottaviano and Souza (2008), using different BNDES credit use information.
As expected, given the results in the literature, firms that use more public credit are larger on average
when credit is used (employment and revenues). The employment-credit semi-elasticity is larger than
the revenue-employment semi elasticity, at least numerically, which is consistent with the negative
14
Other results are available with the authors upon request. The results do not differ significantly as far as significance and
sign are concerned.
26
effect on value added per worker15. There is a positive effect of BNDES credit use on investment rates.
The positive effect is significant for current credit use and a two year lagged credit use. The effect
disappears with a five year lag. Pending further estimates, beyond the scope of this paper, this is a
suggestion that BNDES credit is important to ease financial constraints (i.e., other funding sources
replacement by public credit boosts total investment levels). Again, our sample includes firms that had
positive innovation investment, so we cannot evaluate whether public credit use led firms to invest to
begin with.
Fixed effects estimates weaken some of the results, as seen above for TFP estimates. There is now no
association between public credit use and value added per worker. The effects of public credit on
employment and revenues are smaller in magnitude and become less significant with lags. It is
comforting that public credit still has a positive impact on the investment ratio conditional on fixed
effects, but this effect also fades over time.
A limitation of the above models is that no control is made for bias due to time varying firm
characteristics. We present regression models for the effect of public credit on productivity and other
firm characteristics using firm time varying controls, under our first strategy mentioned in the
introduction. These may proxy for the unobserved innovation and productivity shocks and should
control for ex-ante likelihood of a firm applying for public credit and being granted such credit
(selection on observables). Again, we consider pooled estimates and fixed effects to evaluate the role of
time invariant firm unobserved characteristics. Proxies are a valid strategy to control for endogeneity,
but proxy coefficients do not carry any causal interpretation, as seen in Wooldridge (2002). The control
variables used were age, firm size (measured by lagged log total employment), the share of skilled
workers, whether a firm is a multinational, whether a firm exported in the current year, and whether the
firm imported in the current year, as well as sector-year interaction dummies to account for sector
15
If willing to assume that materials and energy use move proportional to revenues, the estimated employment and revenue
elasticities are indeed consistent with the value added per worker result. Revenue impacts have to be larger than employment
effects so to increase revenue per worker.
27
specific aggregate shocks. Models without fixed effects include also region dummies. Lagged variables
are used under a weakly exogenous argument. The results are on Table 11 below16
Using proxies to control for unobservables does not change the results significantly. While the proxies
are significantly associated with TFP (results available upon request), recall from table 7 that these
proxies are weakly correlated with public credit use, thus not changing the public credit variable
coefficient. Coefficients are somewhat smaller and tend to be less significant. Nevertheless, The main
message that public credit use is associated negatively with revenue TFP and positively with quantity
TFP, with lags, remains.
Fixed effects render public credit coefficient estimates smaller, to the point that there is not a significant
effect on any TFP measure. This is an important result, namely, that firm unobserved, fixed,
characteristics explain much of the alleged association between public credit use and productivity.
Compared with Table 10b above, the use of controls tend to lower public credit effect coefficients on
firm size and investment but signs nor significance are altered. Once observables are controlled for,
firms reporting public credit use have higher labor productivity with a lag, employ more workers and
invest more. But with controls, there is a contemporary revenue fall associated with public credit use.
This fall is reverted in following years. As before, fixed effects weaken correlations and intensity, but
here they do not alter the conclusions remarkably for these size-related variables.
One may be concerned that the error terms still contain elements that shift TFP and public credit use,
even after we control for observables and constant unobservable effects. As mentioned in the
introduction we use instrumental variables to identify causal effects, isolating exogenous public credit
use variation based on variables that significantly influence public credit use but are not correlated with
productivity.
16
As in table 10, the results are qualitatively similar whether we use a dummy variable for credit use or the actual share of
investment funded by public credit, so we report results for the former to save space.
28
Our first attempts to recover such variables, based on policy changes by BNDES, did not prove fruitful,
as changes in interest rates from firm size classification changes and sector priority changes explained
very little, if anything of firm public credit use (see Tables 8 and 9). The implied first stage F statistics
from tables 8 and 9 are, at most, equal to 5, below the recommended threshold of 10 (Wooldridge,
2002). We consider additional instrumental variables, namely the capital-labor ratio, interest coverage
(interest payments relative to revenues), both squared and lagged, and average four digit sector-year
public credit use (proportion). While the latter is calculated using the explanatory variable, suggesting
that it is endogenous, it could be argued that such aggregated variable is not correlated with individual
firm public credit use, once firm controls are used17. Interest coverage is a relevant variable for credit
rating and is used by BNDES for its loan evaluation. Last, and certainly not least, the capital labor ratio
is unrelated to our productivity measure, namely, a Hicks Neutral TFP (disembodied technical change)
by construction (see the Appendix for details). Firms with higher capital-labor ratio have more collateral
to offer BNDES and should have relatively more guarantees, in case of default,18 theoretically justifying
its use as an instrument. We generate additional instruments from this variable, exploiting nonlinearities, namely, using the capital labor ratio, its lag and these variables squares.
The results are on Table 12. We focus on the TFP productivity measures, as some of the instruments are
invalid for firm size (e.g. the capital labor ratio clearly is not exogenous to size). Overall, the results do
not allow for sharp conclusions of the causal effect of public credit. When strong instruments are used
(IV set 1), the Sargan overidentification restrictions test is clearly rejected. This is also the case for IV
set 2, with the important exception of TFPQ estimates. The Hausman test suggests significant
differences between LS and IV estimates for pooled data. For fixed effect estimates, the very large
coefficient standard errors may explain the acceptance of the Hausman test null. In the fixed effects
estimation, it should be noted that instruments are particularly week, with an F test less than unity,
withering IV estimates credibility. The coefficients estimated with IV set 2 are too big, likely as a
consequence of the weak instruments.
17
We thank project participants for this suggestion.
18
Brazilian bankruptcy codes indicate that labor obligations (and any due taxes) have priority over private claimants on
revenues obtained from sale of assets in case of firm liquidation. The larger the capital-labor ratio, the more residual income
should be left from assets sale, after labor costs payments, increasing the default value of collateral.
29
The estimates themselves, caveat emptor specification tests, suggest a negative effect of public credit
use on TFPR, similarly to least square and fixed effects estimates. Under the popular Hsieh and Klenow
(2008) distortions analytical framework, TFPR is proportional to capital cost distortions. The
coefficients should be negative, as estimated, since public credit use reduces the capital cost. The
estimates also indicate a positive effect of public credit use on output TFP, as in the non-IV estimates
above (with lags). While the specification tests indicate that the IV results may be mispecified, the
coefficient signs agree with the ones from Tables 10 and 11.
30
Table 10a –Public credit use effect on productivity. Innovation investing firms, Manufacturing, Brazil
Least Squares – no controls
Variable
Public credit
TFPQ
-.037♦
(.015)
Public crdt-2
TFPR
-.075*
(.039)
.074♦
(.020)
Public crdt-5
R2
F
Tfpq
-.079♦
(.009)
.064
(.052)
.011
(.030)
0.101
26.50
0.102
21.43
0.086
15.07
-.089♦
(.009)
-.058♦
(.011)
.011
(.079)
0.306
103.52
tfpr
0.307
83.23
0.293
66.10
-.061♦
(.012)
-.087♦
(.016)
0.123
33.21
0.089
18.36
0.123
22.16
-.077♦
(.018)
0.096
25.24
0.083
16.87
0.114
20.42
Fixed Effects – no controls
Variable
Public credit
TFPQ
.033
(.021)
Public crdt-2
.036
(.038)
0.009
2.83
.040
(.114)
0.035
11.35
.005
(.015)
-.017
(.023)
-.018
(.089)
0.011
7.37
tfpr
.013
(.014)
.025
(.034)
0.019
12.39
TFPR
-.024
(.062)
Public crdt-5
R2
F
Tfpq
0.001
0.45
0.014
4.47
-.028
(.025)
-.006
(.021)
0.004
2.70
0.003
0.94
0.034
10.86
-.008
(.023)
0.006
3.98
0.003
0.87
Source: authors’ estimates based on PIA (2000-2006) and PINTEC data (2000, 2003 and 2005). Variable codes: TFPQ – log output TFP; TFPR – log revenue
TFP; tfpq – sector deflated standardized log TFPQ; tfpr – sector deflated standardized log TFPR. See appendix for calculation details. Public credit is a dummy
for whether the firm reported use of public credit to finance innovation investment. Sample size LS (FE): 18629 (8131) with current Public credit, 10220 (4598)
with two year lagged Public Credit and 4664 (4334) with 5 year lagged public credit. Controls used: four digit sector-year interaction dummies on least squares.
F-statistics are significant except when indicated in italics. * - significant at 10%; ** - signif. at 5%; ♦ - signif. at 1%.
Table 10b –Public credit use effect on labor productivity, firm size and investment. Innovation investing firms, Manufacturing, Brazil
31
0.033
10.29
Least Squares – no controls
Variable
Public credit
lLP
-.075♦
(.025)
Public crdt-2
lRL
.055*
(.028)
.184♦
(.026)
.163♦
(.036)
0.163
45.58
0.175
42.83
0.207
47.66
.011♦
(.001)
.286♦
(.040)
.159♦
(.040)
0.068
17.12
I/K
.077**
(.030)
.138♦
(.020)
Public crdt-5
R2
F
lPO
0.065
14.03
0.064
12.44
.010♦
(.002)
.259♦
(.059)
0.151
41.61
0.151
35.82
0.148
31.70
.000
(.003)
0.049
11.49
0.063
12.59
0.044
7.45
Fixed Effects – no controls
Variable
Public credit
lLP
-.008
(.039)
Public crdt-2
-.022
(.057)
0.057
21.11
.048
(.036)
0.002
0.52
.014♦
(.003)
.080*
(.043)
.083**
(.035)
0.086
60.33
I/K
.040*
(.023)
.007
(.054)
0.009
5.80
lRL
.055♦
(.020)
Public crdt-3
R2
F
lPO
0.024
8.58
0.037
12.75
-.001
(.005)
.095**
(.042)
0.030
20.03
0.010
3.61
0.023
7.87
-.007
(.005)
0.012
7.10
0.015
4.41
0.017
5.02
Source: authors’ estimates based on PIA (2000 – 2006) and PINTEC data (2000, 2003 and 2005). Variable codes: lLP – log labor productivity (value added per
worker), lPO – log employment, lRL – log net revenues, I/K – investment capital ratio. See appendix for calculation details. Public credit is a dummy for whether
the firm reported use of public credit to finance innovation investment. Sample size LS (FE): 18629 (8131) with current Public credit, 10220 (4598) with two
year lagged Public Credit and 4664 (4334) with 5 year lagged public credit. Controls used: four digit sector-year interaction dummies on least squares. Fstatistics are significant at 5% except where indicated in italics. * - significant at 10%; ** - signif. at 5%; ♦ - signif. at 1%.
32
Table 11a –Public credit use effect on productivity – with controls. Innovation investing firms, Manufacturing, Brazil
Variable
Public credit
TFPQ
.041
(.043)
-.064♦
(.013)
Public crdt-2
.003
(.017)
Public crdt-5
R2
F
.058**
(.027)
0.550
208.13
0.467
121.68
0.414
79.29
0.384
106.27
Least Squares – with controls
tfpq
TFPR
-.050♦
(.010)
.006
-.054♦
(.055)
(.012)
.165*
(.087)
0.379
84.60
0.350
60.26
0.159
32.46
0.152
24.73
tfpr
-.046♦
(.010)
-.057♦
(.013)
-.027
(.017)
0.187
25.53
-.004
(.019)
0.119
23.21
0.105
16.18
0.162
21.51
Fixed Effects – with controls
Variable
Public credit
TFPQ
.020
(.020)
Public crdt-2
.048
(.041)
0.018
1.68
.115
(.132)
0.040
3.79
.009
(.015)
-.008
(.025)
.056
(.096)
0.030
6.37
Tfpr
.008
(.014)
.040
(.037)
0.154
37.93
TFPR
.017
(.067)
Public crdt-3
R2
F
tfpq
0.004
0.41
0.036
3.35
-.010
(.027)
.002
(.024)
0.031
6.58
0.030
2.85
0.034
3.16
-.001
(.025)
0.033
6.98
0.022
2.06
0.036
3.34
Source: authors’ estimates based on PIA (2000-2006) and PINTEC data (2000, 2003 and 2005). Variable codes: TFPQ – log output TFP; TFPR – log revenue
TFP; tfpq – sector deflated standardized log TFPQ; tfpr – sector deflated standardized log TFPR. See appendix for calculation details. Public credit is a dummy
for whether the firm reported use of public credit to finance innovation investment. Sample size LS (FE): 18629 (8131) with current Public credit, 10220 (4598)
with two year lagged Public Credit and 4664 (4334) with 5 year lagged public credit. Controls used: age, lagged log employment, lagged share of skilled
workers, multinational, exporter, input importer, and year dummies, in addition to sector (4 digit)-year interaction and region dummies. Sample size LS (FE):
14222 (6886) with current Public credit, 8673 (3928) with two year lagged Public Credit and 4251 (3771) with 5 year lagged public credit. F-statistics are
significant except when indicated in italics. * - significant at 10%; ** - signif. at 5%; ♦ - signif. at 1%.
33
Table 11b –Public credit use effect on labor productivity, firm size and investment. Innovation investing firms, Manufacturing, Brazil
Variable
Public credit
lLP
-.077♦
(.026)
Public crdt-2
.036♦
(.008)
.010
(.028)
Public crdt-5
R2
F
.142♦
(.035)
0.335
85.92
0.360
83.39
0.384
79.59
0.895
1450.69
Least Squares – with controls
lPO
lRL
-.034*
(.018)
.060♦
.112♦
(.015)
(.026)
.113♦
(.026)
0.783
537.27
0.716
321.74
0.771
572.69
0.704
353.68
I/K
.016♦
(.002)
.005♦
(.002)
.195♦
(.042)
0.660
248.54
.000
(.003)
0.058
9.89
0.053
7.69
0.066
7.80
Fixed Effects – with controls
Variable
Public credit
lLP
-.009
(.041)
Public crdt-2
-.035
(.061)
0.084
9.26
.051
(.036)
0.009
0.85
.012♦
(.003)
.091**
(.046)
.066*
(.038)
0.502
209.73
I/K
.023
(.021)
.053
(.059)
0.021
4.57
lRL
.037**
(.015)
Public crdt-5
R2
F
lPO
0.036
3.79
0.050
5.15
.000
(.005)
.112**
(.044)
0.266
75.68
0.027
2.82
0.035
3.51
-.007
(.006)
0.022
4.33
0.014
1.26
0.025
2.22
Source: authors’ estimates based on PIA (2000 – 2006) and PINTEC data (2000, 2003 and 2005). Variable codes: lLP – log labor productivity (value added per
worker), lPO – log employment, lRL – log net revenues, I/K – investment capital ratio. See appendix for calculation details. Public credit is a dummy for whether
the firm reported use of public credit to finance innovation investment. Controls used: age, lagged log employment, lagged share of skilled workers,
multinational, exporter, input importer, and year dummies, in addition to sector (4 digit)-year interaction and region dummies. Sample size LS (FE): 14222
(6886) with current Public credit, 8673 (3928) with two year lagged Public Credit and 4251 (3771) with 5 year lagged public credit. F-statistics are significant
except when indicated in italics. * - significant at 10%; ** - signif. at 5%; ♦ - signif. at 1%.
34
Table 12 –Public credit use effect on productivity, with controls. Innovation investing firms, Manufacturing, Brazil
Pooled IV
Variable
Public credit
LS
-.057♦
TFPQ
IV1
-.251
IV
10.106♦
(.013)
(.186)
(3.168)
0.006
0.000
0.000
0.132
Hausman
Sargan
Variable
Public credit
Hausman
Sargan
2
LS
.045
Tfpq
IV1
-4.019♦
2
IV
4.615
(.044)
(.851)
(2.095)
0.000
0.000
0.000
0.001
LS
-.053♦
TFPR
IV1
-.976♦
2
IV
-10.089♦
(.010)
(.187)
(3.117)
0.000
0.000
LS
-.049♦
Tfpr
IV1
-1.385♦
IV2
-9.451♦
(.010)
(.233)
(2.925)
0.000
0.000
0.000
0.001
0.000
0.001
TFPQ
FE
FE-IV2
-.003
.942*
Panel IV
tfpq
FE
FE-IV2
-.007
-.909
TFPR
FE
FE-IV2
-.004
.474
FE
-.004
FE-IV2
.303
(.014)
(.048)
(.010)
(.011)
(.271)
(.498)
0.004
0.000
(1.026)
0.341
0.000
(.299)
0.039
0.000
tfpr
0.217
0.000
Source: authors’ estimates based on PIA and PINTEC data, for 2000, 2003 and 2005. Variable codes: TFPQ – log TFPQ, output TFP; TFPR – log TFPR,
revenue TFP. See appendix for calculation details. Public credit is a dummy for whether the firm reported use of public credit to finance innovation investment.
Sample size: 13334 for LS, 12039 for IV1, 13226 for IV2 and 6550 for GMM estimates. Controls used: age, lagged log employment, lagged share of skilled
workers, multinational, exporter, input importer, region, and year-sector interaction dummies. Instrument list: IV1 – sector-year average public credit use,
dummies for BNDES policy change target (see tables 8 and 9), log capital-labor ratio, its squared and lags, and interest coverage (interest paid over revenues)
and its square; IV2 - log capital-labor ratio, its squared and lags. First stage F tests(p-value): IV1:42.21(0.00); IV2:2.22 (0.04) for Pooled IV and IV2:0.53 (0.76)
for Panel IV. Sargan is an overidentifying restrictions test p-value. Hausman is the Hausman test of equality between instrumented (LS) and non-instrumented
models (IV). Robust standard errors used everywhere. * - significant at 10%; ** - signif. at 5%; ♦ - signif. at 1%.
35
5. Concluding comments
In the past years, productivity growth in Brazil has been small, with a 10% increase in manufacturing
TFP in a decade, according to our estimates. Using a productivity decomposition, this growth can be
attributed to firm entry and exit and an interaction effect between productivity and market share growth.
While market selection seems to be contributing to productivity growth, large, dominant, firms did not
increase their productivity above the economy average nor more productive firms in the earlier part of
the sample gained market share.
An important source of productivity growth is innovation investment (Parisi, Schiantarelli and
Sembenelli, 2006). In Brazil, previous studies indicated that many firms may be financially constrained
for investment (Bond et al., 2007). At the same time, machinery investment in Brazil is mostly financed
by public credit, namely BNDES credit. BNDES funding comes from forced savings and it provide
loans with below market interest rates.
We propose estimating the impact of public credit use on productivity. We take advantage of the
Brazilian Technology Survey (PINTEC) that has information on whether a firm used public credit
(mainly BNDES) to finance its machinery acquisition for innovation. While the sample of firms with
positive innovation investment only seems restrictive at first glance, it is not so. TPF should not change
if investment only expands capacity. Capital investment should increase TFP only if it is associated with
innovation. We use three waves of PINTEC, namely, 2000, 2003 and 2005 to explore time variation for
identification of causal effects.
BNDES has a variety of products with differing interest rates. For machinery acquisition, we focus on
FINAME, the largest non-equity credit line to understand potential interest rates faced by firms and
coverage changes over time. Unfortunately, the interest rate structure of BNDES makes it difficult to
estimate the actual interest rate charged. There can be significant differences (by more than 1/3) should
the project financed be large (direct operation) or small (through a financial intermediary). Yet there
seems to be enough variation on lending policies over time that allows identification of exogenous
changes in BNDES credit use, such as a 2002 small firm classification threshold change and sector
priorities re-definition in 2004.
36
Our data yield interesting results on firm credit use. While 72% of the firms in our sample self finance
their investment completely, 82% did not use private or public credit. Very few firms use both public
and private credit to self finance their investment, suggesting an ordering of funds: self financing, public
credit and, finally, private credit. Firms that use public credit are larger, younger, use less skilled
workers than the manufacturing average and are not multinationals. Yet, once firm fixed effects are
controlled for, there is no firm observables variation that is associated with public credit use or intensity.
Fixed effects account for more than 2/3 of the variation in public credit use. Surprisingly, the strong
interest changes across firms and sectors, from changes in lending policies, could not be consistently
associated with any change in public credit use over time. The apparent inelastic credit demand (once
aggregate shocks are controlled for) deserves further study.
The actual effect of public credit on productivity is not clear a priori. The lower interest rates from
public credit provide incentives for machinery acquisition and innovation. BNDES interest rates are
smaller for domestic machinery. Should one assume that such machinery is less productive, the
subsidized public credit relative change in marginal costs for less productive technology would tend to
decrease aggregate productivity, as seen in our theoretical framework following Bustos (2005,2007).
The market selection process would allow less productive firms to survive and the minimum
idiosyncratic productivity level that supports the more productive technology adoption rises, reducing
the proportion of firms that would adopt it. This would lead to a negative effect on productivity. On the
other hand, public credit may allow credit constrained firms to invest and innovate, improving their
productivity and leading to positive effect on productivity.
The greatest challenge to identify causal effects is the fact that public credit use not only depends on
endogenous application choice, but also BNDES selection of the firm application. From a reverse
causality argument, we should expect an upward bias in the public credit use effect on productivity, as
more productive firms should be more profitable, inducing credit application and increasing their
chances of receiving it. As true instruments are difficult to come by, we use a variety of methods, with
more and more controls for endogeneity. Our ex-ante view is that simpler models are more likely to find
positive effects even if the true one is zero or negative.
37
Under strict exogeneity assumptions (pooled least squares), public credit use was found to be negatively
correlated with contemporary quantity TFP, changing the sign of the coefficient with lags. Interestingly,
public credit use firms had lower revenue TFP contemporaneously and with lags. This is expected, as
revenue TFP is proportional to capital cost distortions, under the Hsieh and Klenow (2009) framework.
Firm observables are introduced as controls, under the assumption of selection on observables. The
results are not qualitatively different.
Controlling for time invariant endogenous variables (firm fixed effects), all of the above results become
insignificant. This suggests that firm heterogeneity is much more important than time variation to
explain TFP levels and, as seen above, BNDES credit use. Please note that our data cover very different
periods of the business cycle with a slow recovery (2000), contraction (2003) and the start of a solid
growth period (2005).
Attempts to further control for possibly endogenous, time varying, unobserved variables using
instrumental variables proved unfruitful, as specification and weak instruments tests suggested
misspecified models. As mentioned above, recall that most of public credit variation were found
unrelated to firm variables and strongly influenced by firm fixed effects.
Yet one should not attribute our insignificant public credit effect on TFP results to our data set. As
robustness checks, least squares and fixed effect models (with and without controls) for labor, revenues
and investment rate tell coherent stories, as public credit use is associated with firm employment and
revenue growth, currently and with lag, and contemporary increase in the investment ratio, as expected.
Last, but not least, labor productivity (value added per worker) estimates provide results similar to
Ottaviano and Souza (2008), namely, positive public credit effects under strong exogeneity and
insignificant linear effects with fixed effects.
A couple comments must be made. First, our implicit counterfactual is that firms would still invest and
innovate, using self financing or – to a lesser extent given our estimates – private credit, since we
include only innovative firms with positive investment. This is partially a data limitation, as only
innovative firms are asked about the type of credit used to finance investment. But actually, this is the
right sample to look for public credit effect as only innovations should systematically alter TFP. Finding
38
TFP effects of firm investment (capital expansion) in the common Hick´s neutral TFP measurement is a
model misspecification. To understand if public credit use would foster investment itself and innovation
is the natural extension of the study. Yet our results suggest that it is very hard to identify instruments
for public credit use. Expanding an innovation system model like the popular CDM (Crepón et al., 1998)
is not simple and plugging in public credit use as an explanatory variable entails very strong exogeneity
assumptions.
Last but not least, we did not propose a complete evaluation of BNDES strategy, its applicant screening
process, nor fund allocation. No consideration was given to credit risk issues or rates of returns on loans.
According to BNDES’ website, the Bank mission is “to foster sustainable development and competitive
growth of the Brazilian economy with job creation and social and regional inequality reduction”
(authors’ translation from bank webpage). Productivity growth is not directly stated as part of its
mission, nor part of the formal screening process. Nevertheless, should one recall that sustainable
economic growth comes from productivity growth, Brazil would benefit from having its largest
equipment investment financing entity focusing on this issue.
39
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Appendix
TFP measurement
We measure TFP using a simple Cobb-Douglas production function under constant returns to scale. We
estimate
tfpt=ωit= yit – (βl lit + βk kit +βm mit +βe eit)
where βj=Cj/C, and Cj represent expenditures on input j (j=L,K,M) and C=ΣjCj where the summation is
over firms from a three digit sector (CNAE)
Output (y) is measured by deflated net sales (using a two digit sector deflator, IPA-OG).
Labor is measured by the number of permanent workers and labor cost by the total wage bill (including
social security payments). The wage bill is deflated using the national inflation index used for minimum
wage and retirement earnings adjustments (INPC).
The capital stock is calculated from a perpetual inventory model on net investment. Investment is
deflated using the price deflator for machinery and equipment (IPA-DI). The estimated capital stock for
each year is augmented with rented or leased equipment and buildings values, under a 10% rental rate.
The initial capital stock is based on average depreciation expenditures over time, and we use a 5%
depreciation rate. Capital expenditures are measured by a 5% cost of capital in addition to rental and
leasing expenditures. The capital stock adjustments are required so to keep total capital stock from
decreasing sharply over time and account for the fact that firms have increasingly used leasing or
equipment rent over time. This is incompatible with national accounts records.
Energy was not included in the TFP calculation, as its share of total cost expenditures is very small (less
than 5%) at any period.
Materials are deflated by the two-digit industry specific sales deflator. Note that mit=ln(Cm).
Aggregate TFP is obtained using a revenue weighted firm TFP average.
TFPt=Σi θit tfpit
Where θit=salesit/Σi salesit.
TFP decomposition
We decompose aggregate TFP change, ∆TFPt using the well known Foster, Haltiwanger and Krizan
(2001) decomposition.
∆TFPt=Σi∈C θit-1 ∆tfpit + Σi∈C (tfpit-1 – TFPt-1)∆θit + Σi∈C ∆θit ∆wit
42
+ Σi∈N θit-1 (tfpit – TFPt-1) + Σi∈X θit-1 (tfpit-1 – TFPt-1),
where TFPt is the revenue wheighted average (aggregate) productivity for period t, tfpit is the TFP for
firm i in period t, θit is the share of each firm for total revenue, C indicates continuing firms, N new
(entering firms) and X exiting firms.
Capital-Labor ratio and TFP
A key instrument for identifying the causal effect of public credit use on productivity is the capital-labor
ratio. Here we demonstrate that under the productivity estimation framework mostly used, the capital
labor ratio is independent of TFP. The point is that we estimate Hicks-neutral productivity. By definition
(and made explicit by the Cobb-Douglas technology above), Hicks neutral productivity changes
(changes in TFP) do not alter relative factor use. While input use does depend on productivity levels and
changes, relative input use does not.
In general terms, technologies with Hicks-neutral productivity and homotheticity can be represented by
a cost function that is separable in TFP, e.g., C(w,y,tfp) = C(w,y)g(tfp), where c() and g() are
differentiable functions, and w is a vector of input costs (see, e.g., Chambers, 1988). Using Shephard´s
lemma, factor i demand is given by xi = ci’(w,y)g(tfp), where ci’=∂C/∂wi. It is easy to see that xi/xj=
ci’(w,y)/ cj’(w,y). Profit maximizing relative input use are also independent of tfp, under perfect and
monopolistic competition. The idea that on a Cobb-Douglas technology, tfp does not generate a bias in
production function estimates is not new (e.g. Zellner, Kmenta and Dreze, 1969), but seems to be
obscured in the literature. Note that our goal is not to obtain production function coefficient estimates,
but to recognize tfp independent variables consistent with the firm maximization problem, namely the
capital labor ratio.
Additional Results
Table A1 – Five-year productivity growth decompositions.
TFPR
Year
Total
Within Between Interaction Net Entry
1996-2001
1997-2002
1998-2003
1999-2004
2000-2005
2001-2006
Year
1996-2001
1997-2002
1998-2003
1999-2004
-0.001
0.003
0.001
0.023
0.048
0.067
Total
-0.017
0.038
-0.081
-0.093
112.29
-24.93
-67.25
-2.41
-0.80
-0.60
101.85
-26.06
-52.10
-2.00
-0.63
-0.52
-212.12
50.65
116.50
5.19
2.20
1.75
TFPQ
Within Between Interaction
31.84
-15.03
9.01
8.01
23.08
-8.67
4.34
4.33
-55.60
25.43
-12.90
-11.62
-1.02
1.33
3.85
0.23
0.22
0.36
Net Entry
1.68
-0.73
0.55
0.28
43
2000-2005
2001-2006
-0.032
-0.051
20.33
13.61
8.06
7.06
-28.89
-20.68
1.50
1.02
Note: FHK decomposition, see appendix for details. Columns Within, Between, Interaction
and Net Entry are shares of column Total and add up to 1. Total is based on Figure 1 (log
difference of TFP measures).
0
.5
1
1.5
Figure A1a – TFPR densities for firms that used public credit (TFPR_FP solid line) and did not use
public credit(TFPR dashed line).
0
1
kdensity TFPR_FP
2
x
3
4
kdensity TFPR
Note: TFPR levels for firms that appear on PINTEC and PIA, on 2000, 2003 and 2005.
See appendix for details on TFPR calculation.
Figure A1b – Sector deviation TFPR densities for firms that used public credit (tfpr_FP – solid line) and
did not use public credit(tfpr – dashed line).
44
1.5
1
.5
0
-2
0
x
kdensity tfpr_FP
2
4
kdensity tfpr
Note: TFPR levels for firms that appear on PINTEC and PIA, on 2000, 2003 and 2005.
See appendix for details on TFPR calculation.
Data sources.19
1.1.a. Pesquisa Industrial Anual (PIA)
Pesquisa Industrial Anual is an annual survey sampling formally established Brazilian mining and
manufacturing firms and plants, conducted by the census bureau IBGE (InstitutoBrasileiro de Geografia
e Estatística). In 1996 and 2005, the methodology changed in ways that affects the temporal
comparability of productivity estimations. In 1996 it experienced major transformations both in the
sampling scheme and the information collected. The change in 2005 can be considered a minor one
since it is limited to include firms that employ less than five workers in the sampling scheme from the
previous ten employee threshold.
The sample of firms in PIA is drawn from two strata: a census converage of firms with 30 or more
workers (Estrato Final Certo, receiving a complete questionnaire called modelo completo), and a
random sample of small to medium-sized firms with a labor force of ten (five after 2005) to 29 workers
and employees (Estrato Final Amostrado, receiving a simplified questionnaire called modelo
simplificado).
19
We thank Carlos Henrique Corseuil for detailed comments on this part.
45
A firm is eligible to be sampled in PIA if at least half of its revenues stem from manufacturing and if it is
formally registered as a tax payer with the Brazilian tax authorities. In 2004, the PIA total sample
covered 42,371 firms from 155,656 eligible ones.
PIA contains three main groups of variables: (a) Information about longitudinal relations across firms,
(b) balance sheet and income statement information, and (c) economic information beyond the balance
sheet and income statement. The tax payer register number (CNPJ code), while coded by IBGE for
confidentiality reasons allows us to link observations longitudinally, as well as combining it with other
sources such as RAIS. Variables in group (b) include cost, revenue, and profit information, detailed in a
manner similar to a typical Brazilian income statement. In the revenue side, for example, we are able to
segregate non operational revenues, while on the cost side it is possible to identify intermediate inputs,
among other details. Variables in group (c) go beyond the income statement and include data such as
investment flows by type of asset, numbers of workers and employees. Employment is broken down in
production and non-production workers.
1.1.b. Pesquisa de Inovação Tecnológica (PINTEC)
This is a regular survey by IBGE on manufacturing firms, aiming to measure and understand the county
innovation process. Sampling weights are used to compensate the deliberate oversampling of firms that
engaged in some form of innovation or machinery acquisition. Detailed quantitative and qualitative
information on R&D expenditures and innovation is provided. The design of the survey is based on CIS4 surveys of the European Community. There is data for 2003 and 2005, as well as 2000, with a slightly
different questionnaire. The sampling scheme includes all firms with 500 workers or more or firms that
have engaged in at least one type of innovation information and a sample of firms with 5 employees or
more. The sample size of PINTEC surveys are about 10,000 per year.
1.1.c Relação Anual de Informação Sociais (RAIS)
Relação Anual de Informações Sociais is an administrative file maintained by the Brazilian Ministry of
Employment and Labour (Ministério do Trabalho e Emprego - MTE). All tax registered establishments
must file yearly information about every single worker who had been employed by the unit anytime
during the reference year.
The RAIS information provides a matched employer-employee longitudinal data set, similar to those
available in developed countries. The novelty differential of these data is to combine the matched
employer-employee structure with detailed information available on workers' occupation and schooling.
The main use of RAIS is to provide labor inputs variables.
46