Misallocation, Access to Finance, and Public - Am MIT spezialisierte KI-Forschung

Misallocation, Access to Finance, and Public
Credit: Firm-Level Evidence
∗
MIGUEL A. LE ´ON-LEDESMA AND DIMITRIS CHRISTOPOULOS

Using a database of 23,000 ﬁrms in 45 economies, we test the quantitative
importance of access to ﬁnance and access to public and private credit for the
determination of misallocation. We ﬁrst derive measures of factor market and
size distortions, and then use these measures within a regression framework to
test the signiﬁcance of self-declared access-to-ﬁnance obstacles as well as the
effect of access to a credit line issued by either a government-owned or private
bank. We ﬁnd that access-to-ﬁnance obstacles and private credit increase the
dispersion of distortions. Public credit has a very small effect. For ﬁrms that do
not face ﬁnancial obstacles, public credit increases the dispersion of distortions;
for ﬁrms that face ﬁnancial obstacles, it slightly decreases dispersion. Public
credit does not appear to compensate for the distortions that exist in private
credit markets. Quantitatively, Jedoch, ﬁnancial variables explain a very small
part of the dispersion of factor market and size distortions.

Schlüsselwörter: ﬁnancial access, ﬁrm level, misallocation, productivity, public credit
JEL-Codes: O40, O43, O47

ICH. Einführung

Recent literature has emphasized the role of misallocation in determining total
factor productivity (TFP) differences between economies. Misallocation implies that
aggregate TFP could be higher given the same amount of capital, Arbeit, and ﬁrm-level
TFP. Because of distortions that prevent factors of production from being allocated
for their best use, ﬁrms with high productivity may be too small and ﬁrms with low
productivity may be too large, leading to a fall in aggregate (weighted) TFP. One of
the key distortions that may cause misallocation is the existence of ﬁnancial access
problems that generate quantity constraints and price dispersion in credit markets. In
order to bypass these ﬁnancial access distortions, governments often resort to public
policies for the allocation of credit through government-owned credit institutions.

∗Miguel Le´on–Ledesma (Korrespondierender Autor): School of Economics and Macroeconomics, Growth, Und
History Centre, University of Kent, Großbritannien. Email: M.A.Leon-Ledesma@kent.ac.uk; Dimitris
Christopoulos: Department of Economic and Regional Development, Panteion University, Athen, Greece. Email:
christod@panteion.gr. The authors would like to thank the participants at the Asian Development Outlook–Asian
Development Review Conference held in Seoul in November 2015, der geschäftsführende Redakteur, and an anonymous referee
for helpful comments. They would also like to thank Alessandro Cusimano and Daniel Roland for excellent research
assistance. Es gilt der übliche Haftungsausschluss. ADB recognizes “China” as the People’s Republic of China.

Asiatischer Entwicklungsbericht, Bd. 33, NEIN. 2, S. 119–143

C(cid:3) 2016 Asiatische Entwicklungsbank
und Institut der Asiatischen Entwicklungsbank

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120 ASIAN DEVELOPMENT REVIEW

In diesem Papier, we test empirically the quantitative importance of
access-to-ﬁnance obstacles, access to public and private credit, and their interaction,
for the determination of the dispersion of distortions. Using a database of around
23,000 ﬁrms in 45 economies, we ﬁrst derive measures of factor market and
size distortions from the theoretical framework proposed by Hsieh and Klenow
(2009).1 In diesem Rahmen, the dispersion of distortions determines the degree
of misallocation, and thus TFP losses, at the aggregate level. We then use these
distortion measures and, within a regression framework, test the signiﬁcance of
self-declared access-to-ﬁnance obstacles as well as the effect of access to a credit
line issued by either a government-owned or private bank. Since selection to receive
a public and/or private credit line may be endogenous, we instrumentalize these
Variablen. Dann, using a factor representation of the regression results, we obtain
a decomposition of the contribution of these variables to the dispersion of factor
market and size distortions in order to assess whether these variables increased or
decreased the dispersion of observed distortions.

Our key results are as follows. Access-to-ﬁnance obstacles increase the
dispersion of both factor market distortions and size distortions. Private credit
also signiﬁcantly increases the dispersion of both distortions, especially the
size distortion. This is an expected result since the existence of informational
asymmetries in underdeveloped ﬁnancial markets can lead to an inefﬁcient allocation
of private credit. Public credit, andererseits, has a very small effect. For ﬁrms that
do not face ﬁnancial obstacles, it increases slightly the dispersion of both distortions.
For ﬁrms that face ﬁnancial obstacles, it decreases slightly the dispersion, aber es ist nicht
signiﬁcant in the case of size distortions. Public credit does not appear to compensate
for the distortions that exist in private credit markets. Gesamt, Jedoch, a large part
of the dispersion of distortions remains unexplained. Financial variables appear
important only in driving the explained part of these distortions and are signiﬁcant.
Quantitatively, they explain too small part of them to be considered as the key drivers
of misallocation.

Our study only looks at the effects of these variables through the misallocation
channel and not through other direct channels that affect productivity and capital
accumulation. Auch, we are only looking at the misallocation effects and ignoring the
cost of setting up and running government-owned credit institutions and the cost of
subsidizing credit through taxes. In this respect, our study uncovers another channel
through which credit policies can affect aggregate outcomes. Trotzdem, it is an
important one given the potential TFP gains from reallocation. Government-owned
Kredit
institutions are common in many emerging markets and imply costly
Operationen. Banks such as the China Development Bank in the People’s Republic
of China (VR China), the Brazilian Development Bank and Caixa Economica in Brazil,

1The total number of economies in the sample is 45. In many of the empirical measures, Jedoch, economies

are dropped due to the unavailability of certain variables.

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 121

Bancoldex in Colombia, and a large number of state-owned banks in India are
examples of the proliferation of credit institutions with an important element of
government ownership and/or explicit development goals.

Related Literature

Our paper is related mainly to two strands of existing literature. Auf der einen Seite,
there is a growing body of theoretical and empirical literature on misallocation. An
die andere Hand, there is a strand of literature analyzing the effects of public credit and
development banks. The role of misallocation has been emphasized in the seminal
works of Hopenhayn (1992) and Hopenhayn and Rogerson (1993), with further
contributions from Banerjee and Duﬂo (2005); Restuccia and Rogerson (2008);
Guner, Ventura, and Xu (2008); and Bartelsman, Haltiwanger, and Scarpetta (2013).
An important work for our research is Hsieh and Klenow (2009), which develops a
method to measure how distortions at the micro level imply aggregate TFP losses
and uses this method to quantify TFP losses in the PRC and India relative to the
Vereinigte Staaten (US). Their ﬁndings show that, had the PRC and India had similar
levels of misallocation to the US, their TFP would be between 30% Und 60% higher,
jeweils. Kalemli–Ozcan and Sorensen (2012) use a similar approach to ours by
investigating the role of access to ﬁnance for misallocation in African economies.
Jedoch, they do not analyze the effect on the dispersion of distortions or focus on
the role of private and public credit.

The role of distortions to capital and credit markets in determining
misallocation has attracted increasing interest in recent years. Midrigan and Xu
(2014) report that the dispersion of the marginal product of capital is of an order
of magnitude several times larger than that for the marginal products of labor
and intermediate inputs. Außerdem, this dispersion is very persistent, welche
implies that capital adjustment costs cannot be the sole source of misallocation.
Since ﬁnancial systems channel funds from less to more productive projects,
a lack of ﬁnancial development can hinder TFP. Banerjee and Duﬂo (2005),
Zum Beispiel, provide evidence on the role of credit constraints and other credit
market imperfections in misallocation and, somit, productivity differences across
economies. Jedoch,
the literature on the relationship between ﬁnance and
misallocation is far from settled. Moll (2014) shows that in a simple setting where
ﬁrms face collateral constraints `a la Kiyotaki and Moore (1997), if productivity
shocks are persistent, then misallocation losses can be large and disappear only
slowly. Andererseits, they are unimportant in steady state. This is because
ﬁrms facing persistent shocks can use self-ﬁnancing as a form of insurance against
incomplete access to credit markets. Banerjee and Moll (2010) argue, Jedoch, Das
misallocation can still exist in steady state at the extensive rather than intensive
margin (through the ﬁrm entry and exit channel). Buera, Kaboski, and Shin (2011),
using a quantitative model with ﬁnancial frictions, ﬁnd that they account for around

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122 ASIAN DEVELOPMENT REVIEW

50% of TFP gaps between economies. The mechanism is that ﬁrms with larger
scales of operations are more productive and have more ﬁnancing needs, daher
ﬁnancial frictions affect them disproportionately. Jedoch, Midrigan and Xu (2014),
using ﬁrm-level data for the Republic of Korea, ﬁnd that ﬁnancial frictions have
a quantitatively small effect on misallocation. This is consistent with the micro
evidence reviewed by Udry (2011), who ﬁnds that ﬁnancial constraints do not play
a dominant role in determining misallocation.

Our paper also relates to the literature on the role of public credit and
government-owned banks for development. Early empirical literature such as La
Porta, Lopez de Silanes, and Shleifer (2002) ﬁnd a negative effect of public
ownership of banks on subsequent productivity growth. Carvalho (2014), verwenden
ﬁrm-level data for Brazil, ﬁnds that public credit is directed to shifting employment
toward politically attractive areas before elections. Ribeiro and de Negri (2009),
using ﬁrm-level data for ﬁrms accessing credit from the Brazilian Development
Bank, ﬁnd very limited effects of public credit on TFP levels and growth. Banerjee
and Duﬂo (2014) use a policy change in India that modiﬁed eligibility for directed
credit and ﬁnd that public credit was used to expand economic activity rather than
substitute for other forms of credit. This is interpreted as evidence that ﬁrms were
credit constrained before accessing public credit. Eslava, Mafﬁoli, and Mel´endez
(2014) also ﬁnd that access to ﬁnancing from a nontargeted, unsubsidized program
of Bancoldex in Colombia had positive effects on employment and investment,
especially for long-term lending. Using a heterogeneous-agents model calibrated
to the US and Brazil, Antunes, Cavalcanti, and Villamil (2015) ﬁnd that credit
subsidy policies have no effect on output and almost no aggregate effects. Our paper
complements this literature by providing a direct empirical analysis of the effect
of credit policies on productivity through the misallocation channel for a large
number of economies. Our ﬁndings are consistent with previous results. Public
credit reduces misallocation only for ﬁnancially constrained ﬁrms that face ﬁnancial
obstacles. Jedoch, it increases misallocation for the rest and, on aggregate, Die
total effect is very small.

Der Rest der Arbeit ist wie folgt gegliedert. Section II presents the theoretical
framework used to derive distortions from the data. Section III discusses the
econometric methodology. Section IV presents and describes the data. Section V
presents the results. Abschnitt VI schließt ab.

II. Measuring Distortions

In Hsieh and Klenow (2009), misallocation arises as a consequence of
distortions or wedges that affect heterogeneous ﬁrms in an idiosyncratic manner.
These wedges, which are akin to taxes, prevent heterogeneous ﬁrms from achieving
their optimal size, thereby leading to aggregate TFP losses. Below, we brieﬂy explain

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 123

the quantitative measures of distortions proposed by Hsieh and Klenow (2009),
which we will use later in the empirical analysis.

There are s = 1, . . . , S sectors and Ms ﬁrms within each of the S industries.
The total ﬁnal output in sector s (Ys), is a Dixit–Stiglitz aggregator of the output
produced by each ﬁrm (Ysi):

Ys =

(cid:2)

MS(cid:3)

i=1

(cid:4) σ
σ −1

σ −1
σ

(1)

where σ is the elasticity of substitution between varieties. Each ﬁrm’s production
function is given by a Cobb–Douglas aggregator of capital (K) and labor (L), mit
individual ﬁrm’s TFP given by Asi :2

Ysi = Asi K

si L 1−αs
αs

(2)

There are two distortions or wedges affecting ﬁrms. One that affects output or ﬁrm
Größe (τy,si ), and another that affects relative factor inputs (τk,si ). Since it is not
possible to separately identify wedges that affect capital and labor, we choose to
impose the wedge on capital, but this is to be interpreted as a distortion that affects
the relative price of capital and labor. As these wedges are ﬁrm speciﬁc, they will
not affect all ﬁrms the same way, thus generating differences in capital–labor ratios
between ﬁrms. With these wedges, the problem of the ﬁrm is to choose between K
and L to maximize proﬁts (πsi ):

πsi = max
K ,L

[(1 − τy,si )Psi Ysi − wL si − (1 + τk,si )R Ksi ]

(3)

where P is the price of the ﬁnal good, w is the wage rate, and R is the rental price of
capital. Since factor markets are competitive, all ﬁrms face the same factor prices.
Using the ﬁrst-order conditions for capital and labor, substituting them in the
production function and ﬁnding the optimal price for each variety yields the standard
result that price is a markup over marginal costs: Psi = σ
Asi (1−τy,si ) .
1−σ
With this pricing rule, the quantities of labor demanded and the quantity of output
produced by each ﬁrm are proportional to their individual TFP and the idiosyncratic
distortions or wedges they face. In the absence of distortions, ﬁrms’ relative shares

1−α (1+τk,si )α

w
1−αs

(cid:6)α(cid:5)

R
αs

(cid:5)

(cid:6)

2The Cobb–Douglas assumption is not innocuous. If the elasticity of substitution between capital and labor
differs from 1, then the dispersion of the marginal product of capital, and hence the gains from reallocation, can
change substantially. The more that capital and labor are substitutes for one another, the more technologically similar
they are and the less important relative factor market distortions will be. Recent evidence suggests that this elasticity
signiﬁcantly differs from unity (sehen, Zum Beispiel, Le´on–Ledesma, McAdam, and Willman 2010, 2015).

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124 ASIAN DEVELOPMENT REVIEW

of output and labor would just be a function of Ai. The capital–labor ratio is given
von

Ksi
L si

αs
1 − αs

1
1 + τk,si

(4)

which implies that the idiosyncratic factor market distortion prevents ﬁrms from
equalizing their capital–labor ratios.

The marginal revenue product of capital is given by MRPKsi = Psi MPKsi .

Given the deﬁnition of MPK, we obtain

MRPKsi = αs

σ − 1
σ

Psi Ysi
Ksi

= R

1 + τk,si
1 − τy,si

(5)

Likewise, TFP revenue (TFPR) is deﬁned as TFPRsi = Psi Asi , welche, verwenden

the deﬁnition of prices, yields

TFPRsi =

σ
σ − 1

(cid:7)

R
αs

(cid:8)αs

(cid:7)

(cid:8)

w
1 − αs

1−αs (1 + τk,si )α
(cid:6)
(cid:5)
1 − τy,si

(6)

From equations (5) Und (6) über, it is clear that, in the absence of distortions,
marginal revenue products of capital and TFPRsi would equalize across ﬁrms. Wenn
a ﬁrm has a relatively high Ai, it will attract more capital and labor, until its
price falls such that its TFPRsi equalizes with that of lower productivity ﬁrms.
Daher, as discussed below, the dispersion of MRPKsi and/or TFPRsi is a measure of
idiosyncratic distortions affecting ﬁrm sizes.

It is also possible to obtain independent measures of size and factor market
distortions, which are the ones we will work with as they allow us to separate the
effects of access to ﬁnance and public credit by type of distortion. From equation
(4), we get

1 + τk,si =

αs
1 − αs

wL si
R Ksi

and combining this with (5), we ﬁnd

1 − τy,si =

σ
σ − 1

wL si
(1 − αs) Psi Ysi

(7)

(8)

Daher, the factor market distortion measures the ﬁrm’s relative cost share of
labor and capital relative to that for the sector represented by αs/(1 − αs). The size
distortion measures the cost share of labor for the ﬁrm relative to that for the sector
given by (1 − αs).

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 125

What we observe in the data are the MRPKsi and TFPRsi (and not the MPKsi
and TFPsi) for every ﬁrm as we do not observe individual ﬁrm prices. This is why
Hsieh and Klenow (2009) make an assumption about market structure to infer prices
as a function of ﬁrm productivity and distortions.

Aggregate TFP in any sector s is deﬁned as TFPR over aggregate prices.

Using the ﬁnal product aggregator, we obtain

TFPs = TFPRs
Ps

(cid:2)

MS(cid:3)

⎡

⎣

i=1

Asi

TFPRs
TFPRsi

(cid:4)σ −1

1
σ −1

⎤

⎦

(9)

where TFPR is the weighted average of TFPR for all ﬁrms in the sector. If all ﬁrms
were the same (no heterogeneity), the ratio in brackets would disappear. At this point,
sectoral (and aggregate) TFP is maximized.3 That is, aggregate TFP is maximized
when there is no dispersion in TFPRsi. Since by equation (6) the dispersion of TFPR
is driven by the dispersion of distortions, then zero dispersion in both factor market
and size distortions would imply maximum sectoral TFP.

III. Methodik

Our aim is to uncover the effect of ﬁnancial access and access to public
and private credit on the two measures of ﬁrm-level distortions derived from the
model used by Hsieh and Klenow (2009): log(1 + τk,si ), and log(1 − τy,si ).4 Seit
misallocation depends on the distribution of these measures, we are also interested
in uncovering the effect of these variables on their dispersion. To do so, we proceed
as follows. We ﬁrst regress the two measures of distortions on variables measuring
ﬁnancial access, variables measuring access to public credit and private credit, Und
a set of controls. We will also interact the credit variables with the access-to-ﬁnance
variables to show whether public and private credit have a different effect for ﬁrms
that face ﬁnancial access difﬁculties. If these variables are not important in affecting
the distribution of distortions, they will not be signiﬁcant. In order to measure
whether the different variables increase or decrease the dispersion of distortions,
we then use a Fields (2003) decomposition, which we explain below in more detail.
This decomposition also allows us to understand better the role of ﬁnancial variables
as some coefﬁcients are not directly interpretable in the initial regressions when we
use an instrumental variables approach.

(cid:13)

Y =

3In Hsieh and Klenow (2009), the ﬁnal economy’s output is a Cobb–Douglas aggregator of sectoral outputs
S Y θS
4In equation (12), we ignore the ﬁrst term driven by the demand elasticity for the different varieties as it drops

, where θS represents sector shares.

as a constant.

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126 ASIAN DEVELOPMENT REVIEW

Konkret, for the more general case with interaction terms, we run the

following cross-sectional regression:

log Di = β0 + β1FAi + β2PUBi + β3PRIVi + β4FAi × PUBi +

β5FAi × PRIVi + Xi B + εi

(10)

where Di represents the distortion of interest; FAi is a self-reported ﬁnancial access
difﬁculty binary variable taking the value of 1 if the ﬁrm faces ﬁnancial obstacles and
zero otherwise; PUBi is a binary variable taking the value of 1 if the ﬁrm has access
to public credit and zero otherwise; PRIVi is, similarly, a binary variable for private
Kredit; and Xi is a vector of control variables that includes economy and sector
dummies, and other variables.5 The coefﬁcients β1 to β3 give us the direct effect of
ﬁnancial variables, while β4 and β5 show how the effect of public and private credit
changes when the ﬁrm faces ﬁnancial obstacles. A positive coefﬁcient for the factor
market distortion implies that the wedge increases for capital relative to labor. Für
the size distortion, a positive coefﬁcient implies that the variable reduces the wedge
and acts as a size subsidy in the sense that the ﬁrm’s labor share is higher than
the average for its sector. Financial constraints can have either positive or negative
effects on size distortions depending on how they affect the activities of the ﬁrm.
Financial constraints may lead to lower labor costs if ﬁrms need working capital to
pay wages, or to higher costs if they distort the relative use of capital and labor in
the ﬁrm given an elasticity of substitution between them.

Wie oben erklärt, if these variables appear to be signiﬁcant, then they are
drivers of misallocation. Jedoch, looking at the coefﬁcients by themselves does not
inform us whether these variables lead to an increase or a decrease in the dispersion
of distortions. To do so, we look at the Fields (2003) decomposition, which is based
on previous contributions by Shorrocks (1982). This decomposition has been used
widely in the labor literature to analyze the dispersion of outcome variables such as
wages and earnings that can be explained by regressor variables such as education,
Alter, and gender. In unserem Fall, we analyze the effect of the different ﬁnancial variables
on the dispersion of the explained part of distortions. We can estimate equation (10)
by using ordinary least squares (OLS) or instrumental variable (IV). The resulting
predicted distortion can be written as a factor model:

log (cid:14)Di = β0 + ˆz1 + ˆz2 + · · · + ˆzk

(11)

where a hat over a variable denotes its predicted value and ˆz j = ˆβ j X j for j = 1, . . . ,
k regressors. The Fields (2003) decomposition exploits this factor structure to study
the effect of the composite variables ˆz j on the dispersion of the explained part of the

5We also experimented using year dummies to control for the year the survey was implemented. The results,

Jedoch, were not affected by their inclusion.

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 127

distortion. Das ist, it will tell us the percentage increase or decrease in the dispersion
of the predicted distortion that is explained by each of the regressors. This allows
us to assess the effect of ﬁnancial variables on misallocation directly.

The main problem with estimating equation (10) by OLS is that access to
public or private credit may not be an exogenous treatment. Receiving credit from
both types of institutions may depend on unobserved factors that also affect observed
distortions, leading to correlation between the credit variables and the error term.
Aus diesem Grund, we also run regressions instrumentalizing both the PUBi and PRIVi
binary variables.6 In order to do so, we ﬁrst run a probit model where we project
the variables on a set of instruments plus the controls. We cannot, Jedoch, use the
ﬁtted values of this regression on a second stage due to the problem of “forbidden
regression” as explained by Angrist and Pischke (2009). This happens because the
conditional expectation function of the ﬁrst stage is nonlinear. To get around this
Problem, we follow Wooldridge (2010) and proceed using the following steps:

(ich) Estimate a probit of the determinants of the credit variables using a set of
instruments hi and the control variables Xi . Obtain the ﬁtted values (cid:15)Pi .

(ii) Regress Pi on (cid:15)Pi and the control variables Xi , not on the instruments. Obtain

the second-stage ﬁtted values

(cid:15)(cid:15)P.

(iii) Regress Di on Xi and the second-stage ﬁtted values

(cid:15)(cid:15)Pi .

Seit

(cid:15)(cid:15)P now comes from an OLS regression, the problem of the nonlinear
conditional expectation function has been eliminated. In unserem Fall, Jedoch, we also
have interaction terms between FAi and the two instrumentalized credit variables.
Interaction variables also suffer from the same forbidden regression problem
mentioned above. In order to address this, we follow a similar procedure. Wir
run a ﬁrst-stage probit and obtain (cid:15)Pi . We then calculate (cid:15)Pi × FAi . We regress
(cid:2)(cid:15)Pi × FAi . We then
Pi × FAi on (cid:15)Pi × FAi and the exogenous controls to obtain
(cid:2)(cid:15)Pi × FAi , and the other controls. This gives
regress the distortion variables on
us consistent standard errors and unbiased estimates and allows us to carry out
the Fields (2003) decomposition. The coefﬁcients on the instrumentalized variables
cannot be interpreted the same way as the coefﬁcients of the original binary variables
since they are now continuous. Jedoch, the decomposition of the dispersion of the
distortions still has the same interpretation. This is the added advantage of this
decomposition.

(cid:15)(cid:15)Pi ,

6The ﬁnancial access variable is a self-declared variable in the survey. Since there is no a priori reason for

ﬁrms to declare ﬁnancial access difﬁculties to surveyors, we believe it is safe to treat it as exogenous.

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128 ASIAN DEVELOPMENT REVIEW

IV. Data and Descriptive Statistics

We use ﬁrm-level survey data from the World Bank’s Enterprise Surveys
for the period 2006–2014. This is a stratiﬁed survey of ﬁrms containing ﬁnancial
and business environment information. The data are purely cross-sectional. Some
economies have been surveyed more than once during the review period, so we keep
the data for the survey year with more available observations in order not to bias the
results by weighting some economies twice. The original data contains results from
134 surveys and a total of 61,669 ﬁrms. Jedoch, this number was considerably
reduced in the data-cleaning process (nachstehend beschrieben) and because of the lack of
availability of some of the credit variables required in the analysis. Since we do
not have price data for each ﬁrm, we are working with revenue-based measures as
discussed in the model used by Hsieh and Klenow (2009). Data are in local currency
units for the survey year. We do not transform them into a common currency since
the measures we use are ratios and shares rather than absolute values. We calculate
the variables of interest as follows:

(ich) Output is measured as value added (VA). This is calculated as total annual

sales minus the cost of raw materials and intermediate goods.

(ii) The number of workers (L) is the total number of full-time employees adjusted

for temporary workers.

(iii) Capital (K) is deﬁned as the net book value of machinery, Ausrüstung, Land,

and buildings.

(iv) Total wage bill (WTOT) corresponds to total wages, salaries, and bonuses

paid.

(v) Labor productivity is VA/L.

We drop ﬁrms for which either VA, K, or WTOT are negative. We are then
left with 23,023 ﬁrms and 45 economies. We also dropped any economy for which
there are less than 150 ﬁrms in the sample. In many of the speciﬁcations used below,
Jedoch, the lack of availability of some of the credit variables and variables used as
instruments in the ﬁrst-stage probit regressions leaves us with approximately 14,800
ﬁrms.

Tisch 1 presents the list of economies, the number of ﬁrms, und ihre
distribution by size when we use the sample of 23,023 ﬁrms for 45 economies.
Size is deﬁned as “small” for ﬁrms with fewer than 20 employees, “medium” for
ﬁrms with between 20 Und 99 employees, and “large” for ﬁrms with more than 100

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 129

Tisch 1. Number of Firms and Size Distribution

Distribution by Number
of Employees (Verhältnis)

Small Medium Large
(>100)
(20−100)
(<20) No. No. 247 965 272 172 197 875 472 401 438 Angola Argentina Bangladesh Bolivia Brazil Bulgaria Chile PRC Colombia Costa Rica Croatia Ecuador Egypt El Salvador Ghana Guatemala Honduras India Indonesia Iraq Jordan Kenya Lao PDR 173 532 1,039 198 925 362 572 1,327 545 172 209 222 1,299 282 261 244 184 4,586 536 452 234 208 311 0.85 0.25 0.28 0.47 0.34 0.31 0.30 0.13 0.33 0.34 0.34 0.41 0.38 0.35 0.64 0.40 0.52 0.29 0.46 0.76 0.39 0.27 0.47 209 644 231 228 236 265 424 253 245 159 595 279 343 23,023 Lao PDR = Lao People’s Democratic Republic, PRC = People’s Republic of China. Source: Authors’ compilation. Zambia Zimbabwe Total Federation Peru Philippines 0.02 Mali 0.35 Mexico 0.35 Mozambique 0.15 Nepal 0.21 Nicaragua 0.22 Nigeria 0.30 0.44 0.31 Russian 0.22 Senegal 0.33 South Africa 0.22 Sri Lanka 0.22 Sweden 0.28 Tanzania 0.11 Tunisia 0.23 0.18 Turkey 0.25 Uganda 0.25 Ukraine 0.01 Uruguay 0.28 Viet Nam 0.36 0.17 0.13 0.40 0.38 0.37 0.45 0.47 0.41 0.43 0.36 0.44 0.33 0.37 0.40 0.37 0.26 0.37 0.30 0.46 0.29 0.23 0.33 0.38 0.36 Distribution by Number of Employees (ratio) Small Medium Large (>100)
(20−100)
(<20) 0.79 0.33 0.63 0.32 0.59 0.68 0.24 0.22 0.36 0.76 0.34 0.51 0.28 0.49 0.18 0.23 0.58 0.47 0.45 0.14 0.46 0.35 0.36 0.19 0.34 0.31 0.47 0.34 0.28 0.40 0.47 0.41 0.17 0.40 0.29 0.52 0.35 0.45 0.41 0.35 0.35 0.45 0.41 0.37 0.38 0.39 0.02 0.33 0.06 0.21 0.07 0.04 0.36 0.31 0.23 0.07 0.26 0.20 0.21 0.17 0.37 0.36 0.07 0.18 0.10 0.45 0.17 0.27 0.25 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 3 2 1 1 9 1 6 4 3 2 7 0 a d e v _ a _ 0 0 0 7 5 p d . / employees. There are nine Asian economies in the sample. The sample is dominated by Bangladesh, the PRC, Egypt, and India. While the sample mainly comprises small and medium ﬁrms, large ﬁrms represent a sizable 25% of the total. Given the prevalence of small ﬁrms in these economies, large ﬁrms may be overrepresented. The World Bank argues that this is the case since larger ﬁrms tend to have a larger impact on employment creation. The credit and access-to-ﬁnance variables also come from the surveys. Firms were asked to answer the following question: “How much of an obstacle is ﬁnancial access for the operation of the ﬁrm?” Firms then choose between “no obstacle,” “minor obstacle,” “moderate obstacle,” “major obstacle,” and “very severe obstacle.” We translate these into a numeric, binomial variable taking the value of zero for no obstacle, minor, and moderate obstacle; and 1 for major and very severe obstacle. We also do a robustness check by classifying moderate obstacle as 1 rather than zero. This variable is used as a measure of ﬁnancial access obstacles (FAi ), which will then be interacted with indicators of the type of credit available. f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 130 ASIAN DEVELOPMENT REVIEW Firms were also asked whether they currently have a line of credit. If so, they were asked the following question: “Is this credit provided by a state-owned bank or a private credit bank?” We use these variables as a proxy for access to public and private credit, which are our PUBi and PRIVi variables, respectively. Ideally, such variable should account for the amount of the ﬁrm’s capital ﬁnanced by both types of institutions. However, this measure is only available for a very small number of ﬁrms. Thus, this variable is taken as a proxy for being able to access either type of credit. The variables public credit (PUBi ) and private credit (PRIVi ) are binary variables that take the value of 1 if ﬁrms have access to a public or a private line of credit and zero otherwise. infrastructure (transportation, electricity); goods markets We also use a set of control variables related to other types of obstacles to the operation of ﬁrms. These obstacles are measured as binary variables just like the access-to-credit variable. These obstacles can be classiﬁed into the following categories: (trade regulations, informal sector); taxation and licensing; insecurity (political instability, corruption, theft, corruption, and courts); and labor markets (regulations and skill inadequacy). These obstacles can affect the optimal size of ﬁrms, and hence the dispersion of their marginal products, and can affect different ﬁrms heterogeneously and act as wedges that prevent ﬁrms from growing to their optimal size.7 Finally, we make use of a set of instruments for the ﬁrst stage of the IV regressions. These include (i) a set of ﬁve dummies for the size of the city where the ﬁrm operates (each dummy takes the value of 1 for a particular population size range), (ii) the percentage of foreign ownership of the ﬁrm, and (iii) the percentage of sales of the ﬁrm going to foreign markets. We also experimented with other potential instruments including ﬁrm age, legal status of the ﬁrm, and percentage of capital held by the main owner of the ﬁrm, among others. However, none of these appeared to be signiﬁcant in the ﬁrst stage. We will explain below the instruments used for each of the public and private credit binary variables. Figure 1 shows the percentage of ﬁrms in each economy that declare that ﬁnance is an obstacle. The economies where ﬁrms most commonly declare ﬁnancial access problems are mainly African economies with a lower level of ﬁnancial development, followed by mainly Latin American economies. In economies like the PRC and India, the share declaring ﬁnancial access problems is much lower, which is generally also true for all other Asian economies in the sample with the exception of Bangladesh and Nepal. Figure 2 shows the shares of ﬁrms in each economy that receive a credit line from either public or private institutions. The sum of PUBi and PRIVi does not cover all ﬁrms in the sample since a sizable proportion of them do not have a credit line. The PRC, India, the Lao People’s Democratic Republic, and Viet Nam are the economies with the largest shares of ﬁrms with access to public credit lines. In general, Asian economies tend to have 7See Le´on–Ledesma (2016) for a detailed analysis of the role of these obstacles in driving misallocation. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 3 2 1 1 9 1 6 4 3 2 7 0 a d e v _ a _ 0 0 0 7 5 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 131 Figure 1. Percentage of Firms Declaring Finance is an Obstacle to Operations l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . PRC = People’s Republic of China. Source: Authors’ calculations based on data from World Bank. Enterprise Surveys and Indicator Surveys Sampling Methodology. www.enterprisesurveys.org Figure 2. Percentage of Firms Receiving Loan from Public Institutions / e d u a d e v / a r t i c e - p d l f / / / / 3 3 2 1 1 9 1 6 4 3 2 7 0 a d e v _ a _ 0 0 0 7 5 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 PRC = People’s Republic of China. Source: Authors’ calculations based on data from World Bank. Enterprise Surveys and Indicator Surveys Sampling Methodology. www.enterprisesurveys.org 132 ASIAN DEVELOPMENT REVIEW a larger proportion of ﬁrms with access to public credit, which is consistent with the presence of directed credit institutions. Outside this group, economies such as Brazil, Egypt, and Russian Federation have a large proportion of ﬁrms with access to public credit as well, which is (again) consistent with the existence of state-owned lenders in the market. Table 2 shows the distribution of public credit by ﬁrm size in each economy.8 On average, it appears that public credit is slightly biased toward medium and large ﬁrms, and against small ﬁrms, when compared to the distribution of all ﬁrms in Table 1. This varies substantially by economy, but it is evident for large economies with an important element of public lending such as the PRC, India, and Russian Federation. For economies with a low prevalence of public credit lines, all public credit appears concentrated in a single size category. Table 3 shows how the three possible credit outcomes (public, private, or no credit) are distributed in each economy for ﬁrms that face ﬁnancial obstacles and those that do not. It is quite striking that, on average, the proportions are almost the same for both categories of ﬁrms. Neither public nor private credit appears to be more prevalent for ﬁrms that do not face ﬁnancial obstacles. Of course, this result compounds demand and supply effects, so we cannot extract meaningful structural interpretations. Interestingly, this appears to be the case for most economies in the sample. V. Results We now proceed to analyze the regression results. The dependent variables measuring distortions were calculated in equations (7) and (8) and then logged. To calculate αs, we averaged the capital share for ﬁrms by economy and sector, and trimmed the upper and lower 5% to smooth out the effect of outliers. Unfortunately, we only know a ﬁrm’s sector at a high level of aggregation; there are a total of 15 sectors for both industry and services, with agricultural ﬁrms being excluded. Although it would be desirable to obtain capital shares for a larger number of sectors to obtain measures of misallocation, this will not affect the regression results as we include sector dummies that capture sector ﬁxed effects. We then obtain the standard deviation of the two distortions for each sector and average them by economy. The average results for all economies are displayed in Table 4. The results show a high level of dispersion for both measures. Consistent with other studies—such as Hsieh and Klenow (2009) and Ha, Kiyota, and Yamanouchi (2016)—the factor market distortion is larger than the size distortion. There is also wide variability between economies. Angola displays the lowest size distortion and the Philippines displays the largest. For factor market distortions, South Africa displays the lowest and Sri Lanka displays the largest dispersions. 8There are only 39 economies out of the 45-economy sample for which public credit information is available. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 3 2 1 1 9 1 6 4 3 2 7 0 a d e v _ a _ 0 0 0 7 5 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 133 Table 2. Size Distribution of Firms Receiving Public Credit Size Distribution by Number of Employees Small (<20) Medium (20–100) Large (>100)

Argentina
Bangladesch
Bolivia
Brasilien
Bulgaria
Chile
VR China
Kolumbien
Costa Rica
Croatia
Ecuador
Ägypten
El Salvador
Ghana
Guatemala
Honduras
Indien
Indonesien
Irak
Kenya
Lao PDR
Mali
Mexiko
Mozambique
Nepal
Peru
Philippinen
Russian Federation
Südafrika
Sri Lanka
Schweden
Tanzania
Tunesien
Truthahn
Ukraine
Uruguay
Vietnam
Zambia
Zimbabwe
Total
Lao PDR = Lao People’s Democratic Republic, VR China = Volksrepublik China.
Quelle: Authors’ compilation.

44%
40%
0%
44%
60%
32%
35%
63%
58%
26%
100%
39%
20%
37%
50%
0%
49%
29%
20%
38%
38%
100%
29%
100%
45%
0%
67%
42%
0%
21%
33%
27%
43%
60%
33%
46%
38%
43%
33%
44%

16%
23%
100%
37%
20%
42%
4%
25%
35%
26%
0%
29%
60%
32%
50%
100%
23%
35%
70%
13%
38%
0%
64%
0%
45%
100%
33%
18%
0%
46%
50%
45%
20%
25%
33%
42%
12%
29%
33%
23%

41%
36%
0%
19%
20%
26%
61%
13%
6%
48%
0%
32%
20%
32%
0%
0%
28%
36%
10%
50%
24%
0%
7%
0%
9%
0%
0%
40%
100%
33%
17%
27%
37%
15%
33%
13%
50%
29%
33%
33%

The results of the regression analysis are presented in Tables 5 Und 6 for τk
and τy, jeweils. We use as controls the economy and sector dummies, sowie
a set of other binary obstacle variables that were explained in the previous section.
The tables present the results with and without interaction terms for the OLS and
IV regressions.

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134 ASIAN DEVELOPMENT REVIEW

Tisch 3. Allocation of Public and Private Loans by Financial Obstacles

If credit is an obstacle, do
you have. . .?

If credit is not an obstacle,
do you have. . .?

Private
Credit

Public
Credit

NEIN
Credit

Private
Credit

Public
Credit

NEIN
Credit

Angola
Argentina
Bangladesch
Bolivia
Brasilien
Bulgaria
Chile
VR China
Kolumbien
Costa Rica
Croatia
Ecuador
Ägypten
El Salvador
Ghana
Guatemala
Honduras
Indien
Indonesien
Irak
Jordanien
Kenya
Lao PDR
Mali
Mexiko
Mozambique
Nepal
Nicaragua
Peru
Philippinen
Russian Federation
Senegal
Südafrika
Sri Lanka
Schweden
Tanzania
Tunesien
Truthahn
Uganda
Ukraine
Uruguay
Vietnam
Zambia
Zimbabwe
Total
Lao PDR = Lao People’s Democratic Republic, VR China = Volksrepublik China.
Quelle: Authors’ compilation.

96%
43%
52%
47%
36%
54%
23%
36%
26%
37%
14%
36%
86%
36%
83%
50%
52%
61%
66%
95%
81%
52%
60%
97%
54%
92%
46%
69%
26%
47%
65%
87%
78%
58%
67%
76%
40%
18%
87%
85%
47%
40%
83%
81%
62%

0%
56%
37%
55%
44%
41%
77%
5%
78%
27%
57%
56%
6%
68%
9%
46%
51%
4%
19%
2%
30%
43%
3%
7%
51%
8%
31%
37%
87%
37%
20%
10%
34%
32%
27%
19%
44%
55%
20%
20%
35%
31%
13%
9%
27%

0%
11%
14%
0%
23%
0%
3%
58%
2%
39%
17%
0%
8%
5%
6%
2%
0%
35%
17%
2%
0%
4%
25%
1%
2%
1%
15%
0%
1%
2%
12%
0%
0%
11%
0%
5%
15%
12%
0%
0%
21%
30%
2%
1%
12%

4%
46%
35%
53%
42%
46%
74%
7%
72%
24%
69%
64%
6%
59%
11%
48%
48%
4%
17%
3%
19%
43%
15%
2%
44%
7%
39%
31%
72%
51%
23%
13%
22%
31%
33%
18%
45%
69%
13%
15%
33%
30%
15%
18%
26%

0%
12%
8%
1%
26%
2%
3%
25%
1%
33%
12%
1%
3%
1%
11%
2%
1%
28%
13%
2%
0%
4%
6%
2%
1%
0%
3%
0%
0%
1%
11%
0%
1%
10%
3%
4%
11%
11%
0%
2%
13%
38%
3%
1%
13%

100%
32%
55%
44%
30%
57%
19%
71%
21%
40%
31%
44%
91%
32%
80%
52%
47%
68%
68%
95%
70%
54%
91%
91%
48%
92%
67%
63%
13%
61%
69%
90%
65%
58%
70%
77%
45%
34%
80%
78%
52%
32%
85%
90%
60%

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 135

Tisch 4. Misallocation Measures: Dispersion of Distortions

Variable
StDev(τ _y)
StDev(τ _k)

Mean

Standard Deviation

Min

0.902
1.252

0.184
0.190

0.478
0.843

Max

1.255
1.604

Quelle: Berechnungen der Autoren.

The instruments used for the ﬁrst-stage probit for the public credit variable
are as follows. Erste, a set of dummies is created for the city in which the ﬁrm
is located, taking the value of 1 for a particular city size and zero otherwise. Der
sizes are “capital city,” “more than 1 Million,” “between 250,000 Und 1 Million,”
“between 50,000 and 250,000,” and “less than 50,000.” The second instrument used
is a variable measuring the percentage of foreign ownership in the ﬁrm. Endlich,
we use the rest of the control variables included in the regression. The set of city
dummies are undoubtedly exogenous and unlikely to be correlated with determinants
of distortions other than access to infrastructure, which we control for. We would
also expect foreign ownership to reduce access to public credit as local ﬁrms are
normally given preferential treatment.

For the ﬁrst-stage probit for the private credit variable, we use the same
city-size dummies plus a variable measuring the percentage of sales going to
international markets (exports). It is likely that ﬁrms located closer to ﬁnancial
centers and with more diversiﬁed and/or larger markets (exporters) will have better
access to private credit. Our ﬁrst-stage probits, which are available upon request,
show that all the instruments are signiﬁcant, the sign of their coefﬁcients are as
expected, and the regression is well behaved in general. We also experimented with
a wider set of instruments, but their correlation with the endogenous variables proved
to be too weak to identify a causal effect.

The results in Table 5 show that several variables are signiﬁcant drivers of
the factor market distortion τk. This is especially the case for our ﬁnance-related
Variablen. The ﬁrst result is that ﬁnancial obstacles appear to increase the relative
cost of capital by close to 18% in all speciﬁcations as expected. This is also a very
signiﬁcant effect. In the OLS regressions, access to both private and public loans
appears to signiﬁcantly reduce the distortion. The interaction term appears to be
insigniﬁcant for private credit but signiﬁcant for public credit. The negative effect
of public credit on the distortion appears to be stronger for ﬁrms that face ﬁnancial
obstacles than for those that do not. Jedoch, as seen in Table 3, the distribution of
public credit does not appear to change signiﬁcantly between these two types of ﬁrms.
daher, the total effect on the distribution of the distortion can only be inferred
from the Fields (2003) decomposition. The IV regressions, Jedoch, tell a slightly
different story. The interpretation of the public and private loan variables cannot be
done the same way since the variables are now continuous. In the speciﬁcation with
no interactions, having access to a private credit line reduces the distortion, while
the effect of the public credit variable is not signiﬁcant. The interaction variables

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136 ASIAN DEVELOPMENT REVIEW

Tisch 5. Regression Results for Factor Market Distortions (τ _k)

OLS Regressions

IV Regressions

Access to ﬁnance

Private loan (yes = 1)

Public loan (yes = 1)

Access to ﬁnance × Private loan

Access to ﬁnance × Public loan

Electricity

Transportation

Customs and trade regulations

Informal sector

Access to land

Crime, Diebstahl

Tax rates

Tax administration

Business licensing

Political instability

Corruption

Courts

Labor regulations

Inadequate education workers

Country dummies
Sector dummies
Constant

(1)
0.184∗
(7.36)
−0.120∗
(−4.30)
−0.113∗
(−3.66)

−0.0335
(−1.40)
−0.0231
(−0.82)
−0.0982∗∗
(−2.97)

0.0679∗∗
(2.69)
0.00510
(0.17)
−0.0367
(−1.18)
−0.0465
(−1.85)

0.0525
(1.77)
−0.0326
(−1.06)

0.0176
(0.59)
−0.0371
(−1.44)

0.0808∗∗∗
(2.45)
0.0178
(0.54)
0.0328
(1.12)
YES
YES
−0.539∗∗∗
(−2.03)

(2)
0.206∗
(6.68)
−0.125∗
(−3.97)
−0.0693∗∗∗
(−2.11)

0.00938
(0.18)
−0.200∗∗
(−2.70)
−0.0334
(−1.40)
−0.0231
(−0.82)
−0.0972∗∗
(−2.94)

0.0675∗∗
(2.68)
0.00663
(0.22)
−0.0372
(−1.19)
−0.0468
(−1.86)

0.0530
(1.78)
−0.0327
(−1.06)

0.0183
(0.61)
−0.0380
(−1.47)

0.0810∗∗∗
(2.45)
0.0181
(0.55)
0.0321
(1.10)
YES
YES
−0.547∗∗∗
(−2.05)

(3)
0.175∗
(6.27)
−2.418∗
(−4.51)
0.497
(1.82)

−0.0186
(−0.71)

0.00365
(0.11)
0.0131
(0.29)
0.0364
(1.32)
−0.0754∗∗∗
(−2.17)
−0.0622
(−1.77)
−0.0659∗∗∗
(−2.25)

0.0805∗∗∗
(2.35)
−0.0927∗∗∗
(−2.50)
−0.0190
(−0.59)
−0.0184
(−0.60)

0.0875∗∗∗
(2.31)
0.105∗∗
(2.79)
0.110∗∗
(3.10)
YES
YES
0.0451
(0.24)
14,828
0.013

(4)
0.175∗∗
(3.13)
−2.540∗
(−4.71)

0.608∗∗∗
(2.20)
0.234∗∗∗
(2.00)
−0.415∗∗∗
(−2.16)
−0.0186
(−0.72)

0.00375
(0.11)
0.0158
(0.35)
0.0359
(1.30)
−0.0665
(−1.91)
−0.0641
(−1.82)
−0.0670∗∗∗
(−2.29)

0.0830∗∗∗
(2.42)
−0.0917∗∗∗
(−2.47)
−0.0175
(−0.54)
−0.0186
(−0.61)

0.0826∗∗∗
(2.18)
0.104∗∗
(2.75)
0.108∗∗
(3.04)
YES
YES
0.0577
(0.31)
14,828
0.014

18,025
0.014

N
R∧2
IV = instrumental variable, OLS = ordinary least squares.
Notes: ∗∗∗ = 10% level of signiﬁcance, ∗∗ = 5% level of signiﬁcance, ∗ = 1% level of signiﬁcance. t statistics
in parentheses. Heteroscedasticity-consistent standard errors. See text for the IV procedure implemented using a
ﬁrst-stage probit for public and private credit.
Quelle: Berechnungen der Autoren.

18,025
0.015

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 137

Tisch 6. Regression Results for Factor Market Distortions (τ _y)

OLS Regressions

IV Regressions

Access to ﬁnance

Private loan (yes = 1)

Public loan (yes = 1)

Access to ﬁnance × Private loan

Access to ﬁnance × Public loan

Electricity

Transportation

Customs and trade regulations

Informal sector

Access to land

Crime, Diebstahl

Tax rates

Tax administration

Business licensing

Political instability

Corruption

Courts

Labor regulations

Inadequate education workers

Country dummies
Sector dummies
Constant

(1)
0.133∗
(7.27)
−0.083∗
(−3.97)
−0.0728∗∗
(−3.11)

−0.0220
(−1.25)
−0.00757
(−0.37)
−0.0716∗∗
(−2.87)

0.0453∗∗∗
(2.50)
0.00463
(0.21)
−0.0325
(−1.40)
−0.0221
(−1.17)

0.0346
(1.55)
−0.0293
(−1.27)

0.00970
(0.42)
−0.0362
(−1.89)

0.0694∗∗
(2.86)
0.0125
(0.51)
0.0264
(1.21)
YES
YES
−0.379∗∗∗
(−2.20)

(2)
0.146∗
(6.58)
−0.086∗
(−3.70)
−0.0455
(−1.85)

0.00946
(0.25)
−0.126∗∗∗
(−2.18)
−0.0220
(−1.25)
−0.00755
(−0.37)
−0.0710∗∗
(−2.85)

0.0451∗∗∗
(2.49)
0.00567
(0.26)
−0.0328
(−1.42)
−0.0223
(−1.17)

0.0349
(1.57)
−0.0293
(−1.27)

0.0101
(0.44)
−0.0367
(−1.92)

0.0695∗∗
(2.86)
0.0126
(0.52)
0.0259
(1.19)
YES
YES
−0.383∗∗∗
(−2.22)

(3)
0.125∗
(6.08)
−1.720∗
(−4.35)
0.326
(1.63)

−0.0128
(−0.67)

0.0186
(0.75)
0.00625
(0.19)
0.0224
(1.11)
−0.0537∗∗∗
(−2.10)
−0.0530∗∗∗
(−2.04)
−0.0376
(−1.75)

0.0520∗∗∗
(2.06)
−0.0737∗∗
(−2.70)
−0.0188
(−0.79)
−0.0192
(−0.86)

0.0739∗∗
(2.65)
0.0728∗∗
(2.63)
0.0784∗∗
(3.01)
YES
YES
−0.05
(−0.39)

(4)
0.104∗∗∗
(2.51)
−1.802∗
(−4.54)
0.367
(1.80)
0.166
(1.93)
−0.152
(−1.07)
−0.0129
(−0.67)

0.0188
(0.76)
0.00707
(0.21)
0.0224
(1.11)
−0.0480
(−1.87)
−0.0538∗∗∗
(−2.08)
−0.0384
(−1.78)

0.0531∗∗∗
(2.10)
−0.0728∗∗
(−2.67)
−0.0185
(−0.78)
−0.0187
(−0.83)

0.0707∗∗∗
(2.54)
0.0718∗∗
(2.59)
0.0773∗∗
(2.96)
YES
YES
−0.04
(−0.30)

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18,027
0.02

14,828
0.02

18,027
0.02

14,828
0.02

138 ASIAN DEVELOPMENT REVIEW

in column (4) show that public credit now reduces the distortion for ﬁrms that face
ﬁnancial obstacles. The total effect for these ﬁrms is, Jedoch, still negative. Für
private loans, the effect is the opposite; it reduces the distortion more for ﬁrms
that do not report ﬁnancial obstacles. Gesamt, the regression is jointly signiﬁcant.
Jedoch, the variables explain only a small part of the variation of the endogenous
variable as shown by the R∧2 coefﬁcient.

For the size distortion, the results are presented in Table 6. Lack of access to
ﬁnance acts as a size subsidy in the sense that it increases the cost share of labor
in value added. Das, Jedoch, may well be the consequence of a lack of access to
ﬁnance leading to higher labor intensity due to a lack of access to capital. Public and
private credit have the opposite effects as were expected from the OLS regressions.
The signiﬁcant result for the public credit variable disappears when we introduce
the interaction with ﬁnancial access in the IV regressions. Jedoch, in the OLS
regression, access to a public loan for ﬁrms that face ﬁnancial difﬁculties has a
signiﬁcantly negative effect; it acts as a size tax. Turning to the IV regressions, Die
only variable that appears to be signiﬁcant is access to private credit, which reduces
the cost share of labor. Public credit has a positive effect, but it is not signiﬁcantly
different from zero. The interaction terms are not signiﬁcant either. Somit, Sein
the recipient of a public or a private loan when the ﬁrm faces ﬁnancial obstacles
does not appear to be signiﬁcantly different from when they do not face ﬁnancial
obstacles. Gesamt, the results are not very conclusive beyond the fact that obstacles
to ﬁnance and accessing a private loan are signiﬁcant drivers of size distortions.

Endlich, we turn to the Fields (2003) decompositions for both distortions in
Tables 7 Und 8. The results show the percentage of the explained part of the dispersion
of distortions to which each variable contributes. This contribution can be positive
if the variable increases the dispersion of the distortion, or negative if it reduces it.
These results should be seen in conjunction with the regression results to analyze
whether these effects are signiﬁcant. The sum of the contributions of the variables,
including economy and sector dummies, Ist 100 as we are looking at the explained
Teil. We focus here on the IV regression results in columns (3) Und (4), although we
also present the results from the OLS regressions.

For the factor market distortion, all obstacles explain almost half of the
dispersion of the distortion, the other half is explained mainly by economy dummies.
Of the obstacles, the main driver is access to ﬁnance. Financial obstacles increase
the dispersion of τk, leading to misallocation. Being the recipient of a public loan has
a small positive effect on dispersion as well. For ﬁrms facing ﬁnancial obstacles, Die
effect is negative. Jedoch, the magnitude of the effect is very small in comparison
with not having a loan. For private loans, the effect is positive. The effect of having a
private loan for ﬁrms that face ﬁnancial obstacles is positive and sizable. Private loans
appear to increase the dispersion of the distortion, thus their allocation across ﬁrms
increases allocative inefﬁciency. This is indicative of possible distortions in private
credit markets. Public credit, which is designed to allow ﬁrms with viable investment

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 139

Tisch 7. Contribution of Variables to the Explained Dispersion of τ _k (%)

OLS Regressions

IV Regressions

(1)
24.36
7.45
3.55

Access to ﬁnance
Private loan (yes = 1)
Public loan (yes = 1)
Access to ﬁnance × Private loan
Access to ﬁnance × Public loan
Electricity
Transportation
Customs and trade regulations
Informal sector
Access to land
Crime, Diebstahl
Tax rates
Tax administration
Business licensing
Political instability
Corruption
Courts
Labor regulations
Inadequate education workers
All obstacles
Country dummies
Sector dummies
Total
IV = instrumental variable, OLS = ordinary least squares.
Quelle: Berechnungen der Autoren.

0.48
0.13
3.40
4.00
0.10
0.15
0.98
0.89
0.19
−0.35
0.40
2.55
0.29
0.73
49.29
48.17
2.54

100

(2)
26.35
7.47
2.11
0.20
1.59
0.46
0.12
3.26
3.85
0.12
0.15
0.95
0.87
0.18
−0.35
0.39
2.47
0.28
0.70
51.19
46.41
2.40

100

(3)
21.68
7.97
0.20

0.23
−0.04
−0.51
1.73
−0.12
0.62
1.80
0.81
1.51
0.47
0.36
3.70
2.07
2.25
44.74
55.50
−0.24
100

(4)
20.37
7.86
0.23
8.89
−3.70
0.22
−0.04
−0.57
1.60
−0.10
0.60
1.72
0.79
1.40
0.40
0.34
3.28
1.92
2.07
47.26
53.14
−0.41
100

projects to bypass ﬁnancial constraints, only reduces the allocative inefﬁciency of
factors of production by a small amount for all ﬁrms. It even increases it marginally
for ﬁrms that do not report facing ﬁnancial obstacles. Gesamt, Jedoch, the role of
public credit in improving aggregate TFP through improved allocation of capital
and labor is almost negligible.

A very similar picture arises from the decomposition of τy. In this case, Zugang
to ﬁnance has a smaller effect, and it is being a recipient of a private loan that seems
to dominate by increasing substantially the dispersion of the size distortion. Public
credit also increases this dispersion, except for ﬁrms that face ﬁnancial obstacles.
Jedoch, the effect of these interaction variables and the direct effect of public credit
is not signiﬁcant when we look at the regression results. Tatsächlich, not having a credit
line at all while facing ﬁnancial obstacles appears to lead to a smaller dispersion of
distortions than having a credit line.

Distortions that affect ﬁrms in an idiosyncratic way lead to an inefﬁcient
allocation of capital and labor, and ﬁrm sizes. These distortions, whether generated
by market or government failures, may be caused by an inefﬁcient allocation of
Kredit. Our evidence shows that the allocation of both private and public credit
leads to an increase in the dispersion of distortions. Public credit only reduces

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140 ASIAN DEVELOPMENT REVIEW

Tisch 8. Contribution of Variables to the Explained Dispersion of τ _y (%)

OLS Regressions

IV Regressions

(1)
17.71
5.79
1.71

0.46
0.02
2.65
2.40
0.08
0.34
0.44
0.51
0.33
−0.41
0.84
2.28
0.20
0.48
35.82
61.27
2.91

100

(2)
19.03
5.93
1.05
0.19
0.79
0.45
0.02
2.58
2.34
0.10
0.33
0.43
0.51
0.32
−0.42
0.84
2.24
0.20
0.47
37.38
59.83
2.80

(3)
15.17
24.96
1.27

0.21
−0.15
−0.29
0.82
0.29
1.09
1.15
0.09
2.17
0.96
0.61
3.13
1.13
0.75
53.36
45.22
1.42

(4)
12.25
25.44
1.39
5.81
−1.64
0.21
−0.15
−0.32
0.80
0.25
1.07
1.14
0.09
2.08
0.92
0.58
2.92
1.08
0.72
54.63
44.09
1.27

100

this dispersion for ﬁrms that face (self-declared) ﬁnancial obstacles. It is clear that
public credit does not appear to compensate for the distortions that exist in private
credit markets. Jedoch, the bulk of the dispersion of these distortions remains
unexplained. Finance appears to be important only when we look at the part of the
distortions that we are able to explain with observations. In that sense, our results are
consistent with several results in the literature that attribute a minor role to ﬁnancial
access in explaining misallocation.

VI. Conclusions

Misallocation implies that, with the same amount of capital, Arbeit, Und
ﬁrm-level TFP, aggregate TFP can be higher if factors of production were reallocated
between ﬁrms. Distortions that affect ﬁrms in a heterogeneous way lead to
suboptimal capital–labor ratios at the ﬁrm level and a distribution of ﬁrm sizes
that is not consistent with the distribution of their TFP. One of the factors that may
drive these distortions are ﬁnancial frictions that prevent viable projects from being
ﬁnanced and allow unviable projects to be ﬁnanced.

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MISALLOCATION, ACCESS TO FINANCE, AND PUBLIC CREDIT 141

We have studied quantitatively the effect of access to ﬁnance to explain the
dispersion of factor market and size distortions that drive misallocation. Our focus
is not only on the effect of ﬁnancial obstacles, but also on whether public credit
has a signiﬁcant effect in improving allocation. Directed credit policies through
government-owned institutions are very common in many emerging markets and
have gained importance in recent decades. Daher, it is important to understand
whether government credit has any positive effect on aggregate TFP, and hence per
capita income, through an improved allocation of resources.

We use a database of close to 23,000 ﬁrms in 45 economies and derive
two measures of distortions from the Hsieh and Klenow (2009) Modell. Der Erste
measures factor market distortions that prevent ﬁrms from achieving their optimal
capital–labor ratio. The second measures size distortions that prevent ﬁrms from
achieving their optimal size as dictated by their TFP. We then use a regression
approach to measure the effect of self-declared access-to-ﬁnance obstacles, Zugang
to a government-owned bank credit line, and access to a private-owned bank credit
line on these two measures of distortions. We instrumentalize the public and private
credit line variables to isolate their treatment effect. We then use a regression-based
decomposition that allows us to see whether these variables increase or decrease the
dispersion of distortions across ﬁrms.

Our results show that access-to-ﬁnance obstacles increase the dispersion
of both factor market distortions and size distortions. Private credit increases
the dispersion of both distortions, especially the size distortion. This is not
surprising given that it is the existence of informational asymmetries together with
underdeveloped ﬁnancial markets that can lead to an inefﬁcient allocation of private
Kredit. Public credit, andererseits, has a very small effect. For ﬁrms that do
not face ﬁnancial obstacles, it increases slightly the dispersion of both distortions.
For ﬁrms that face ﬁnancial obstacles, it decreases slightly the dispersion, but it
is not signiﬁcant in the case of size distortions. Jedoch, public credit does not
appear to compensate for the distortions that exist in the private credit markets.
We thus conclude that public credit does not appear to improve signiﬁcantly the
informational and regulatory frictions that exist in credit markets even among our
sample that is dominated by developing economies with lower levels of ﬁnancial
depth. The majority of the dispersion of these distortions remains unexplained.
Financial variables appear only to be important in driving the explained part of these
distortions and are signiﬁcant. Jedoch, they cannot explain a sizable enough part
of them to be considered as the key drivers of misallocation.

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