WPS5032


Policy Research Working Paper                       5032




     Big Constraints to Small Firms' Growth?
        Business Environment and Employment Growth
                         across Firms

                               Reyes Aterido
                          Mary Hallward-Driemeier
                               Carmen Pagés




The World Bank
Development Research Group
Macroeconomics and Growth Team
August 2009
Policy Research Working Paper 5032


  Abstract
  Using data on more than 56,000 enterprises in 90                                  finance, particularly for sources of finance associated
  countries, this paper finds that objective conditions in                          with investment and growth. The findings are also
  the business environment vary substantially across firms                          sensitive to how "small" is defined. Differentiating micro
  of different sizes and that there are important non-                              (less than 10 employees) from other small firms shows
  linearities in their impact on employment growth. The                             that, while small firms can be disadvantaged in such an
  paper focuses on four areas: access to finance, business                          environment, micro firms tend to be proportionally less
  regulations, corruption, and infrastructure. The results,                         affected by a weak business climate--and, on occasion,
  particularly on the impacts of finance and corruption                             it can help them to grow. Overall, allowing different size
  on growth, depend on whether and how the analysis                                 classifications provides insights into the impact of the
  accounts for the possible endogeneity of the business                             business environment that are lost in more aggregate
  environment. Controlling for endogeneity revises the                              analyses.
  finding that small firms benefit most from access to




  This paper--a product of the Growth and the Macroeconomics Team, Development Research Group--is part of a larger
  effort in the department to understand the microeconomics of growth. Policy Research Working Papers are also posted on
  the Web at http://econ.worldbank.org. The author may be contacted at mhallward@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
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         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


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                  Big Constraints to Small Firms' Growth?
        Business Environment and Employment Growth across Firms




                 Reyes Aterido*, Mary Hallward-Driemeier*, Carmen Pagés**




* World Bank ** Inter-American Development Bank. The views expressed here do not necessarily represent
the views of the World Bank, the Inter-American Development Bank or their Boards of Executive Directors.
The authors would like to thank Francis Teal and participants at IADB seminars and at the LACEA conference
in Bogota and anonymous referees for valuable comments and suggestions. Corresponding author: Mary
Hallward-Driemeier, World Bank MC3-301, 1818 H St. NW, Washington DC 20433. Tel: 202-473-9120;
Fax: 202-522-1152; email: mhallward@worldbank.org.
1. Introduction

An issue of particular relevance for economic growth is how the development of markets
and institutions influences the growth of firms. This issue is even more important in
developing countries, where markets and institutional infrastructure are less developed. A
related concern is whether an adverse business environment affects the efficiency and the
dynamism of the business sector.
        This paper uses firm- level data on more than 56,000 enterprises in 85 developing
countries and 5 high-income economies to assess the effects of the broader business
environment on employment growth by firms, focusing on differences across firm size. This
paper is related to two strands of literature. First it relates to recent studies (Restuccia and
Rogerson (2008), Hsieh and Klenow (2008)) that show that there is considerable resource
misallocation across firms, which is driven by firm specific distortions. While these studies
are silent about the nature of the distortions, they show such distortions lead to substantial
aggregate productivity losses, and that differences in the degree of distortions would explain
a significant portion of why India and China exhibit lower manufacturing productivity than
the United States. This paper examines differences in business environment across firm size,
and therefore it helps to provide a better understanding of which dimensions of the business
environment matter and the heterogeneity of distortions across firms.
        This paper is also related to a number of studies that assess the effect of different
dimensions of the business environment on firms' performance. Several studies in the
literature examine the effect of employment regulations on firms' adjustment and on labor
market outcomes more generally.1 Others look at the effect of regulations of entry of firms
on firm creation and growth.2 A number of them investigate the importance of access to
finance for firm development and growth.3 Overall, these studies indicate that regulations on
labor, entry barriers for firms, and financing conditions can have a direct impact on firms'


1
  A few recent examples are: Botero et al. (2004), Besley and Burgess (2004), Almeida and Carneiro (2009),
Haltiwanger, Scarpetta and Schweiger (2006), Micco and Pagés (2006), Petrin and Sivadasan (2006) and
Autor, Kerr and Kugler (2007).
2
  For example: Djankov et al (2002) and Klapper, Laeven and Rajan (2004).
3
  For example: Demirgüç-Kunt and Maksimovic (1998), Rajan and Zingales (1998), Beck, Demirgüç-Kunt
and Maksimovic (2005), Galindo and Micco (2007).


                                                    2
employment decisions. However, other aspects of the business environment can affect these
decisions as well, raising costs and risks associated with doing business or by creating
barriers to competition. By affecting the overall growth prospects, issues from the quality of
infrastructure, the strength of property rights, the nature and enforcement of business
regulations and the overall openness in the management of public resources can all affect
firms' growth (World Bank, 2004). This paper looks at the joint and relative importance of a
number of dimensions of the business environment on firms' growth.
       Unlike most existing studies which rely on variation in business environment
conditions across countries, sectors or sub-national territories (states), this paper exploits
firm level data on the business environment conditions faced by firms. Yet, there are a
number of methodological issues that need to be addressed, including potential measurement
error, omitted variable bias and endogeneity. To minimize the risk of the first two problems,
the analysis is conducted with full set of sector-survey interaction dummies. In addition to
controlling for some potentially important omitted variables, and measurement errors, they
also control for survey differences and differences in demand conditions. In addition,
incorporating multiple dimensions of the broader business environment simultaneously deals
seriously with concerns of omitted variable bias of papers that include only a single
dimension, such as labor regulations or finance.
       To address potential endogeneity, this paper uses location-sector-size averages
(minus individual firms' own responses) of the business environment measures rather than
individual firms' own responses. The paper shows that there is a large potential degree of
endogeneity in individual responses and that the results vary considerably if such
endogeneity is not accounted for. The paper also makes the point that how these averages
enter into the regressions matters greatly. Using the location-sector-size average does not
necessarily address the endogeneity problem ­ if the averages used are based on current size.
As objective conditions can be more challenging for smaller firms (e.g. smaller firms are
less likely to access finance and more likely to pay bribes), assigning firms that grow
substantially their current more favorable IC conditions, rather than those faced by firms of
their initial size, can introduce a mechanical relationship between employment growth and
many of the IC measures. Matching the averaged indicators to initial size is needed to avoid




                                                3
this problem. This methodological contribution is most visible in estimating the effects of
access to finance and corruption, which are the two areas where endogeneity is most likely.
       Using firm level data allows us to focus on the question of whether the impact of the
business environment is neutral across firm size. Market failures or policy created
distortions can create fixed costs in the operation of businesses, creating cost disadvantages
for smaller firms (see Tybout 2000 for review of issues). The costs of dealing with credit
market information imperfections or with a complex and non-transparent regulatory
environment are some examples of such non-linearities. Large firms may also have more
political influence and shape regulations and policies in their favor. On the other hand,
smaller firms may benefit from lax enforcement of regulations or lower harassment from
corrupt public officials. Across the world, a sizeable amount of public resources are devoted
to the development of micro, small and medium firms, yet unlocking potential constraints to
their performance is another, perhaps equally effective, development strategy. The main
objective of this paper is to assess whether there are heterogeneous effects of unlocking
business environment constraints.
       Differential effects across firms of different size can stem from two sources. First,
there can be differences in the underlying objective conditions faced by firms. The paper
focuses on the effect of four areas of the business environment: access to finance, the
regulatory environment, corruption and access to infrastructure. Second, the same
conditions can potentially have differential or non-linear effects on employment growth by
size. Thus, it could be that the same extent of external finance is more beneficial to smaller
firms or that the same extent of power outages is less damaging to larger firms because they
have access to generators.
       The paper finds evidence of both effects. The paper does find that micro and small
firms have less access to formal finance, face significantly greater interruptions in
infrastructure services, and pay more in bribes--as a percentage of sales--than do larger
firms. In turn, larger firms spend significantly more time dealing with officials and red tape.
The results indicate significant non linearities across firm size in the impact of these
conditions too. A lack of finance and poor infrastructure reduce the growth of medium and
large firms. Business regulations affect mostly the growth of small firms, which seemingly
prefer to remain small to keep below the radar of regulators.



                                                4
       The results also show the importance of distinguishing between micro (less than 10
employees) and small firms (10-49 employees). By refining the definition of small firms, the
message is refined to show that while small firms can face more challenging conditions, the
very small or micro firms can actually benefit--in relative, and in some cases even in
absolute, terms--from an adverse business environment. This can potentially explain why
very small firms account for a larger share of employment in countries with an adverse
business climate.
       The paper is structured as follows. Section 2 reviews the related literature. Section 3
describes the data. Section 4 examines how objective business conditions vary by types of
firms, emphasizing the different patterns across different sizes of firms. Including lagged
performance variables in these regressions reinforces the need to address endogeneity--and
indicate the likely directions that employment growth might have on different dimensions of
the business environment. Section 5 describes the impact of objective conditions on
employment growth using location-sector-size averages instead of individual firm responses.
Section 6 conducts a number of robustness checks. Section 7 concludes.


2. Literature Review


There is a growing literature that assesses the effects of the set of factors, policies and
institutions, commonly known as business environment or investment climate, on the
performance of firms and economic growth. The methodologies used by these studies are
very diverse. A number of studies have focused on cross-country variation to identify the
effect of labor regulations (Botero et al, 2004; Heckman and Pagés, 2004), regulation of
entry (Djankov et al., 2002) or a wide set of regulations (Loayza, Oviedo and Servén, 2006).
These studies relate objective (de jure) measures of regulation at the country level to
aggregate country outcomes. Although the results are suggestive of the importance of
appropriate regulations for business development, they suffer from important
methodological constraints ranging from omitted variable bias to endogeneity concerns.
       Another group of studies employ a difference-in-differences method first developed
by Rajan and Zingales (1998). These studies analyze the effect of different aspects of the
business environment at the country-industry level. They show that financially dependent



                                                5
industries grow faster in financially developed markets (Rajan and Zingales (1998)), and
that firms in industries dominated by small firms grow faster in more financially developed
markets (Beck et al. (2008)). Studies have also found that industries in which entry is more
important exhibit less growth in countries with restrictions to firm entry ( Klapper, Laeven
and Rajan (2004)). Finally, Micco and Pagés (2006) show that industries that are inherently
more volatile create fewer jobs and are less developed in countries with very restrictive
hiring and firing regulations.
        While improving on cross-country studies, the former studies suffer from three
potential shortcomings that can be addressed in our study. First, in most cases variation in
business environment conditions is captured with country level variables that reflect de jure
regulations or conditions, that is, the procedures and costs that would be incurred if firms
fully complied with what is on the books. However, there can be large gaps between what is
on the books and what is experienced on the ground. This is particularly true in lower
income countries and those with higher levels of corruption. (Hallward-Driemeier and
Aterido (2007); Kaufmann, Kraay and Mastruzzi (2007)). In that regard, it is desirable to
have measures of de facto regulation.
        Second, it is important to explore variation in the business environment not only
across countries but also within country boundaries--across sub-national areas and
especially, across firm size and ownership.4 Having disaggregated variables allows for
hypotheses to be tested without having to assume that one country is the benchmark or
"best" case against which to compare other countries.
        Third, while most studies look at the effects of individual dimensions of the
investment climate, our focus is on regressions that combine multiple dimensions in a single
regression. This not only addresses potential bias in the estimates, it allows us to test
directly which dimensions have the biggest impact on firm performance.
        A number of recent studies make use of the World Business Environment Survey
(WBES) 5--a firm-level dataset with 4,000 firms across 54 countries--to study the effect of

4
  A few studies assess differences in business environment conditions within countries. Besley and Burgess
(2004) and Ahsan and Pagés (2009) exploit differences in labor market regulations across Indian states to
assess how different labor market regulations affect economic performance across Indian states. Almeida and
Carneiro (2009) assess the effect of variation of enforcement in labor regulations within Brazil.
5
  Despite their similar names, the World Business Environment Survey (WBES), a one off survey in 1999, is
not part of the larger and ongoing World Bank firm survey effort, the Enterprise Surveys (ES). The earlier


                                                    6
the business environment on firm growth. Using subjective firm-level data measures of the
business environment these studies show the importance of finance, corruption and property
rights (Batra, Kaufman and Stone, 2003; Ayyagari, Demirgüç-Kunt and Maksimovic, 2006).
Some other studies examine the relationship between business environment and firm growth
using the newer Enterprise Survey, but for individual or small groups of countries (Dollar,
Hallward-Driemeier and Megistae, 2005, for India, Pakistan, Bangladesh and China; Fisman
and Svensson, 2007, and Rienikka and Svensson, 2002, for Uganda; and Bigsten and
Soderbom, 2006, which reviews the literature for Africa).
        A central area of focus for this paper is whether there are significant differences in
the impact across firm types, particularly by firm size. The literature has largely examined
this issue with regard to access to finance, generally finding smaller firms are more
constrained (Galindo and Micco, 2007; Clark, Dollar, and Micco, 2004; Love and Mylenko
2005). Beck et al. (2005) is of particular interest here, as these authors include additional
measures of corruption and property rights using the WBES firm-level dataset. Using firms'
perceptions on potential constraints, they also find patterns across countries, with small
firms benefiting most from greater financial and institutional development.
        Our work expands on this literature in several dimensions. This paper nearly
doubles the number of countries covered, with country samples approximately five to 10
times larger. Whereas Beck et al. (2005) use subjective firm responses as measures of the
business environment at the firm level, we use objective measures (i.e., time spent with
officials dealing with permits and licenses rather than a ranking of how burdensome red tape
is on a scale of 1-5). Beck et al (2005) include a final table to address potential endogeneity
of business environment conditions on firm growth using location-size averages of
responses as instruments. However, as the averages are based on current size, we show that
this strategy does not actually address endogeneity. Once we control for endogeneity,
matching average conditions based on initial size, we no longer find that smaller firms
benefit most from improved access to finance. We also use narrower bands on firm size
categories, which lead to some interesting extensions on their results, and look at how
results vary across country groupings, focusing on measures of institutional development.

round primarily collected perception data regarding constraints and some information on firm performance. In
some regions firms only provided information on their employment range, i.e., small, medium, large, making it
impossible to use that data to study differences between small firms below and above 10 employees.


                                                     7
3. The Data


Our study is primarily based on the World Bank Enterprise Surveys (ES), a newly available
collection of firm-level datasets for developing countries as well as a limited number of
high-income countries.6 Questionnaires are administered within a framework of common
guidelines in the design and implementation.7 The dataset is assembled using the core
survey, which is a module of identical questions included in all questionnaires, thereby
enabling cross-country analyses.
         While in principle, the dataset contains information for approximately 80,000 firms
in more than 110 countries, complete data for the variables used in our estimations are
available for a subsample of approximately 56,000 firms in 90 countries in seven different
regions surveyed during the period 2000-2006. For some countries more than one survey is
available.8 (See Table A1 in the Appendix for a complete list of countries and surveys
available, as well as the number of observations in each country.) The median sample size is
350 firms, with several large countries having substantially larger samples (Brazil, China,
India, Turkey and Vietnam have samples over 1,500). The sample of firms in each country
is stratified by size, sector and location.9 Because of this stratification, large enterprises are
in general over-sampled in the ESs compared to their share in the number of firms, but not
in terms of their contribution to GDP. The unit of analysis is the "Establishment" in the
manufacturing and service sectors.10 Most firms are registered with local authorities,
although they may be only in partial compliance with labor and tax authorities. Unlike most


6
   The terms Enterprise Surveys includes surveys that have been implemented regionally under other names,
such as includes BEEPS, Investment Climate Surveys and RPED surveys (World Bank 2004b and World Bank
2007).
7
  From http://rru.worldbank.org/InvestmentClimate/Methodology.aspx
8
  While efforts are shifting to building a panel dataset, most repeat surveys have been additional cross-sections.
There are approximately 2,000 firms that enter twice in the dataset.
9
    From http://www.enterprisesurveys.org/Methodology/default.aspx#weights: ES have been conducted
following simple random sampling or random stratified sampling. In a simple random sample, all members of
the population have the same probability of being selected and no weighting of the observations is necessary.
In a stratified random sample, all population units are grouped within homogeneous groups and simple random
samples are selected within each group
10
   In Europe and Central Asia, the unit is the "firm." In all other regions it is the plant or establishment. As
over 90 percent are single-plant firms, the distinction is not likely to affect the results.


                                                        8
firm-level datasets, this data spans the manufacturing and service sectors. Approximately 61
percent of the observations reflect establishments in manufacturing, while the rest are in the
service sector.
         The World Bank enterprise survey data was developed to provide information on
firms' performance as well as on aspects of the investment climate faced by firms.
Regarding firms' performance, the dataset contains information on a number of variables
such as current employment, investment, or sales. It also contains retrospective information
for these three variables, in most cases for up to three periods before the year of reference of
the survey, but in some few cases only for two or one period before. We focus on the
employment level or growth of permanent workers as our outcome variables of interest. We
do not include other forms of employment, such as contract labor or temporary workers
because there are differences in the consistency of reporting of total employment (i.e.
permanent plus temporary) across some countries. Other important, additional, reasons to
focus on the level and growth of permanent employment are that these variables are more
likely to reflect the long-run performance of the firm, and their evolution is of higher
concern for policymakers, as non permanent forms of employment tend to be considered as
substandard or less preferred by workers.
         Following Davis and Haltiwanger (1992, 1999), our measure of employment growth
refers to the change of permanent employment during the period t and three years before,
divided by the firm's simple average of permanent employment during the same period.11
The measure is symmetric around zero and bounded by values -2, and +2 and while
monotonically related to the conventional growth rate and a second order approximation of
the logarithmic first difference, it allows computing meaningful growth rates for firms
suffering sharp expansions or contractions, avoiding any arbitrary treatment of outliers. This
is quite useful, because even when by construction our sample does not register firm exit,
some firms experience sharp changes in employment due to its early/late stage of




11
   For firms that have been in business less than four years, we compute employment growth based on a two
year difference, or if in business for only two years, one year difference. We then define controls EG(s) as one
if we have computed employment growth s years apart, and zero otherwise and include these variables in our
growth estimates to account for such differences in our estimations.


                                                       9
development.12 Young, small firms may experience very large growth in their labor force;
Bankrupt firms or firms on the verge of closure may suffer very large contractions prior to
their final exit from the market. Due to differences in data availability, employment growth
refers to different periods for different firms. We account for this difference in the period of
reference in our empirical specifications.
         The data also contains subjective and objective information on the business climate
faced by individual firms, reflecting conditions at the time of the survey (period t).
Regarding objective measures, the ones we focus on in this study, the dataset contains a set
of measurements relative to various aspects of the investment climate as they are being
experienced by the firm. We focus on three measures of access to finance and two measures
of each of remaining three areas of the business climate. (See Table 2 for a description and
summary statistics of these variables). We measure access to finance, by means of the share
of investment financed with funds external to the firm (sh-invest-fin) or alternatively, with
the share of working capital financed externally (sh-work-cap) or the share of sales on credit
(sh_sales_cr) The sample means of those two variables are 21, 20 and 42 percent,
respectively.
         We measure business regulatory conditions with the percentage of time managers
(mng-time) devote to dealing with authorities, or alternatively with the days firms devote to
inspections. On average, firms spent 8.9 percent of their time dealing with government
authorities, and almost 16 days dealing with inspections during the year previous to the
survey. We measure the extent of corruption by means of either a dummy variable (bribe y-
n) which captures whether a firm had to pay a bribe or the percentage of sales paid on
bribes during the year previous to the sample (bribe %). To minimize the amount of sub-
reporting the survey inquires about the amount paid in bribes by asking: "How much do
firms similar to yours pay in bribes in order to get things done?" The survey means show a
considerable incidence of corruption: 42 percent of firms declare having paid bribes, with an
average amount of 1.5 percent of sales paid. Finally, we capture the extent of infrastructure
bottlenecks by measuring the number of days firms remained without power service (days-
no power) as well as the share of the average cargo's value lost while in transit (losstransit

12
  In principle, our sample allows identifying new entrants, i.e. firms that have been in business one year or
less. However, as the sampling frame can be a year or two old, there is an under-representation of new
entrants.


                                                       10
%) during the last year. Again, the survey means show non-negligible bottlenecks, with an
average of 38 days during the year previous to the survey without power service, and 1.5 %
of sales lost during transit.
           Finally, the dataset also includes information on a number of firm characteristics
such as size, age, location, export activity, ownership, and sector. This is an important
feature of the data, which allows expanding previous aggregate cross-country analyses
attempting to assess the economic impact of different aspects of the business climate to an
analysis of whether business environment conditions differ across, and affect differently,
different types of firms. The firm characteristics used in this study include: four size classes
(micro 1-10 permanent employees, small 11-50, medium 51-200 and large more than 200,);
three age categories (young 1-5 years, mature 6-15, and older­more than 15); two location
types (smallcity or cities with less than 1 million or more and largecity, i.e capital cities or
with one million or more) ownership (whether foreign or government represents 10 percent
or more of ownership); whether the firm is an exporter (10 percent or more of sales); and a
two-digit industry classification.
           The sample contains a large proportion of micro and small establishments (39
percent micro and 34 percent small firms), with size measured with retrospective data
generally referring to three years before the current period.13 Only 11 percent are large
firms. The sample also consists largely of young firms, with age also referred to the initial
period for which information for a firm is available. Table A2 shows that 31 percent of firms
in our sample were five or fewer years old, and 38 percent were between six and 15 years).
According to the 10-percent thresholds noted above, 11 percent of the firms are foreign-
owned, and 23 percent of firms are exporters.
           The sample is well diversified across regions; 27.2 percent of the sample corresponds
to East Europe and Central Asian countries; 20.9 percent of firms in sample are from Latin
American and the Caribbean; 16 percent are from sub-Saharan Asia, 14.5 percent from East
Asia and 10.2 percent from South Asia (see Table A.2 in the Annex). There is a smaller, but
still significant representation of North Africa and the Middle East region (6.7 percent) and
high income European countries (4.6 percent).



13
     In some cases the reference period is less than 3 years due to firm age or data availability.


                                                          11
       Table 1 reports the mean and standard deviation of the employment growth measure
across firm size with size measured as the initial size (three, two, or one year ago). There is
positive employment growth across all firm sizes except for larger firms, which on average
experience negative growth. The average firm level employment growth, according to our
measure, is 12 %. Micro firms grew over the average, while small and medium growth was
below average.


4. Variations in Objective Investment Climate Conditions by Firms


We start our analysis by examining differences in the reported business environment
conditions across firms for the different investment climate (IC) variables defined in Section
3 and summarized in Table 2. To do so, we estimate the following specification:
IC variable  1  2Small  3Medium  4L argeict  5Foreign  6Exporter 
          ict        ict      ict                   ict        ict

  7Mature  8Older  9Government  10Smallcity  11Expand 1  12Contract 1 
        ijct    ijct        ijc           ijc       ict           ict
                                                                                      (1)
   c * j ict

       To capture differences in the objective conditions facing firms of different sizes we
include size dummies (micro-enterprises is the omitted category), with the size measured by
number of employees at the time of the survey (period t). We also control for whether the
firm is foreign or government owned, whether it is an exporter, whether it is located in a
large city (or capital) or not (large city is the omitted), as well as age categories (young is the
excluded category), all measured at period t.
       We also include variables Expandict-1 and Contractict-1 , the rate of employment
expansion or contraction in firm i, country c, between period t-1 and period t-3. The omitted
category is that employment remained unchanged in the last period. The measures of
contraction and expansion are thus lagged one period, to have them pre-determined, but the
claims here are not causal. We are simply interested in describing the patterns across firms,
examining whether there are differences in the reported objective characteristics across
expanding and contracting firms. These coefficients will also provide information on
whether endogeneity is likely to be an issue in the employment regressions.
       Our focus is on within-country variations rather than across countries, therefore we
include a full set of sector-survey interaction dummies (  j *  S ). This allows us to control



                                                12
not only for potential omitted variables at the country level but also for some possible
measurement errors or differences in survey implementation across countries and sectors.
       Table 3 reports the results of estimating this specification for the nine IC variables.
The results show that there are significant differences across firms in most of the variables in
each of the four areas of the investment climate (access to capital, business regulations,
corruption and infrastructure.) Again, the focus is on differences across firms of different
size. Regarding access to finance, our estimates indicate that controlling for firm
characteristics and sector-survey interaction dummies, access to finance, measured as share
of investment or working capital financed externally, grows monotonically with firm size,
while the share of sales on credit picks for medium-size firms. Overall, large firms have
more access to finance than medium, small and micro enterprises.
       Regarding business regulations, micro firms report a lower share of management
time devoted to dealing with government regulations, than small, medium and large firms.
The pattern again is increasing in firm size, although in this case medium firms spend more
time dealing with regulations than large firms. Days of inspections also increases
monotonically with size with large firms spending more than triple the time dealing with
inspections than small firms.
       Regarding corruption, the incidence of bribes is the highest among small firms. In
addition, medium and large firms pay less in bribes ­as percentage of sales-- than small or
micro firms. This can reflect fixed payments being relatively larger for small firms, or that
they have less recourse to avoid making payments. The payments may also be correlated
with the degree of compliance with regulations; micro and small firms may not meet all of
the requirements and have to pay officials to maintain this position.
       For infrastructure, the frequency of power outages hits small and medium firms
hardest and large firms the least. Larger firms are more likely to have alternative sources of
electricity (given the substantial fixed costs of owning and operating a generator). It is also
possible that larger firms have a greater choice among possible locations and thus chose to
operate in areas with more reliable electricity, a hypothesis that is tested for below.
Similarly, losses in transit are largest among small and medium size firms.
       The specifications reported on Table 3 provide some other important results.
Perhaps the most important one is that there are indications that reported business climate



                                               13
conditions are related to firm performance. For example, firms that expanded or contracted
in the period previous to the survey report a higher dependency on external funds and more
management time dealing with officials relative to firms that were stable in size. On their
part, contracting firms spend more time in inspections than expanding or stable firms.
Similarly, the incidence of corruption (whether a firm pays bribes) appears to be more
severe for firms that are expanding or contracting relative to stable firms. It appears
corruption and dealing with authorities are the price to pay to either get ahead or preserve
the firm in bad times. Finally, firms that are growing or contracting report more blackouts
and more losses in transit than firms that are stable in size. While it may be that objective
conditions vary across stable firms and the rest, it is also likely that power outages and
transit losses are more distortive for growing or contracting firms, and therefore, they recall
more such events and report higher values in those questions than firms that are stable.
       The former regressions suggest that objective business conditions vary
systematically across firms of different size and economic performance. Yet, none of these
claims should be interpreted as causal, as one could also expect causality to run in the
opposite direction, that is, the constraints firms face determine their size and their
performance. However they do caution of a possible endogeneity of reported investment
climate conditions relative to firm performance. We address this topic in the next section of
the paper.


5. Methodology to Estimate the Impact of Investment Climate Conditions on Firm
Employment Growth


We estimate the effect of investment climate (IC) conditions on employment growth by
means of the following specification:
Empgict,t s   0 Smallict3  1 Medium 3   2 L argeict3    3k ICikct
                                   ict
                                                         k

   4k Smallict3 * ICikct    5k Medium 3 * ICikct    6k L argeict3 * ICikct
                                     ict
  k                        k                         k
                                                                                             (2)
  7 Foreign  8Government   9 Exporter  10 Mature 3  11Older 3  12 Smallcity 
          ict          ict          ict        ict        ict             ict

 13 EG(s) ic  14 Empg_ cellic   j * s   ict




                                               14
where Empgict,t-s refers to the growth of permanent employment of firm i in country c
between period t and t-s (generally three years before, but in a few cases, where information
for period t-3 is not available, at periods t-1 or t-2). Differences in the period of definition of
employment growth are identified and controlled for by the dummies EG(s)i which take the
value of 1 if employment growth is computed between periods t and t-s and zero otherwise.
Investment climate conditions faced by firm i, in country c are measured along four
dimensions (k): finance, regulations, corruption and infrastructure and are denoted by ICikc .

While most previous studies focus on the effect of one aspect of the investment climate, our
concern is that omitted variables may bias results. After all, environments characterized by
ineffective business regulations, or high incidence of corruption are also often characterized
by poor infrastructure or low access to finance. To overcome some of these concerns, we
control for different indicators of business climate conditions and their interactions with firm
size simultaneously. Size dummies are defined in four groups: micro, small, medium and
large measured at the initial period of observation. The omitted category is micro enterprises
employing between 1 and 10 employees. Age of the firm is specified in three categories:
young, from 1 to 5 years old, mature, between 5 and 15 years old and older, above 15 years
old and also refers to the initial period of observation. The other firm characteristics are
dated at period t, the only period for which such information is available. We also control
for ownership (foreign, government), exporting status (exporter), and location, with
variables defined as reported in section 3.
           To account for differences in demand conditions and productive structure, the
regressions also include a full set of sector-country interaction dummies.14 Thus, our focus is
on within-country rather than across country variation. This decision reflects several
factors. First, there is substantial variation of investment climate indicators within countries
and we want to test if its impact is significant. Second, it controls for potential omitted
variables at the country level. In addition, it could control for some possible measurement
errors or differences in implementation across countries. As Haltiwanger and Schweiger
(2005) note, in the ES there are some countries with particularly high net employment
growth rates. However, there is a strong country-specific component, such that country
effects yield relative magnitudes of job creation and employment growth. They also noted

14
     See Table A2 for the list of sectors included.


                                                      15
that the relationships of employment variations between size and age classes appear right.
Thus, small and younger firms have higher job creation and destruction than older and larger
firms within the country, when considering total employment. Therefore, statistical methods
that remove country level effects are appropriate to draw conclusions within countries on the
difference of the impact of investment climate on different types of firms.
        The construction of the investment climate variables ICikc requires some explanation.
A number of previous studies assess the investment climate experienced by firm i, using
firm level subjective measures. Such studies rely on answers to questions of the form "how
much (the dimension k of the business climate) is an obstacle for the growth of your firm?"
with possible answers ranking between 1 and 5 (Ayyagari et al. 2008; Beck et al. 2005;
Pierre and Scarpetta 2006; Johnson, McMillan and Woodruff 2002). Subjective assessments,
while insightful, have a number of possible drawbacks. There is a concern that firms'
perceptions of the business environment reflect idiosyncratic differences in the degree of
optimism or pessimism of the individuals responding to the survey. Answers are also likely
to be strongly influenced by the experience and performance of the firm (see Hallward-
Driemeier and Aterido 2007).
        Our analysis uses objective measures to gauge the relative importance of the
investment climate to firms. While, in principle, objective measures may be less likely to be
influenced by firms' performance than subjective measures, the results presented in Table 3
suggest that individual firms´ reported objective investment climate conditions are likely to
be endogenous to firm performance, a feature that we need to take into account when
estimating the effect of investment climate variables on firm performance.
        To account for endogeneity, we construct a measure of the IC conditions faced by
firm i on dimension k in country c, by averaging the responses of firms in the same country-
location-sector-size cell, excluding the observation from firm i in computing that average.15
To ensure adequate numbers of firms in each location-sector-size cell average, we drop one


15
  In some specifications we also assess whether results vary if we instead compute IC conditions by averaging
the responses of firms in the same country-location-sector bins (i.e. excluding size as a dimension). Here we
use a finer disaggregation of location. Above, locations are aggregated by city-size (e.g. capital cities and
those with a population above one million, and cities below 1 million people). In the country-location-sector
averages, we construct the cells based on the actual cities or towns the firms are located in. If the
representation is low, locations are aggregated based on 5 size groups (capital cities or more than 1 million;
500,000-1 million; 250,000-500,000; 50,000-250,000; less than 50,000).


                                                     16
dimension of the cell until an adequate number is reached (i.e. first we substitute country-
location-size bins, then country-sector-size)16.
        This captures the broader environment in which the firm operates and allows the
individual firm's own contribution to the average to be excluded. The approach also has the
benefit of not losing those observations where a firm did not answer all the individual
investment climate questions.17
        In constructing these measures, we actually take two steps before entering the
averages into the regressions, with the second one being particularly important if
endogeneity concerns are to be addressed. An issue is how the investment climate indicators
are calculated and matched to firms in the regression. This requires differentiating between
a firm's current size ­ that can be important in the conditions they report facing now ­ and
their initial size ­ and the average conditions faced by firms of the size they were initially.
Simply giving firms the average value of firms in their current size category is giving them
the ex post conditions not the initial conditions. To justify this, one has to assume that the
conditions faced by firms have remained constant over time, regardless of how they may
have grown. The alternative we present assumes instead that conditions faced by firms are
likely to vary as they grow. Rather, we make the less restrictive assumption that, within a
location and sector, conditions facing firms of a certain size now are the same as the
conditions facing the same sized firm in the earlier period. For example, we can use the
information on conditions facing micro firms now to measure what micro firms faced three
years before.
        Thus, in the first step, we use the information that firms report about investment
climate conditions they face in the current period. While it would be desirable to have the
conditions they faced in the initial period three years ago, this information was not collected.
However, we also recognize that firms that have recently changed size may report very

16
   If we were to keep always the original 4 dimensions same country-location-sector-size cell there are
approximate 5,000 observations that are lost and results do not change substantially. We also used different
minimum cell-size requirements (4-10 firms) and found results were robust. Results presented use the cutoff
of at least 4 firms in a cell.
17
   This approach is very similar to using location-sector-size dummies as instruments (except that the firm's
own value is not excluded in this calculation, the number of observations averaged in a cell may be very small,
and the additional observations cannot be recovered if a single investment climate variable is not available.).
The test of over-identifying restrictions could not be estimated using the full specification due to the large
number of dummies and instruments. However, in specifications not using a full set of sector-survey
interaction dummies, the restrictions could not be rejected at the 0.3 level.


                                                      17
different IC conditions relative to what is typical for firms that have been in a given size
category for a while. To account for this we use a firm's average size in constructing the
location-sector-size_averages of investment climate conditions. We do so by constructing
averages of investment climate indicators within cells, which are defined according to firms'
average size.18 In the second step, we match these averaged indicators to firms ­ based on
their initial, not current, size.19
         This procedure has a number of advantages. The first is that as advanced in the
former paragraph, IC averages based on firms' average size allows obtaining a more
accurate measure of the IC conditions facing a firm in a given size category. Thus, if for
example, firms that have recently expanded report lower access to capital than firms that
have been large for a while, then accounting for the observations of growing firms in the
construction of the IC may underestimate the average access to capital of large firms. A
similar effect may happen for firms that have recently contracted, and report access to
capital or other IC, which are more representative of firms that are large than of firms that
are small. Considering firms based on their average size ameliorates this problem.
         Second, constructing a measure of IC conditions based on the initial instead of the
current size is a key feature to minimize endogeneity problems. Thus, if we consider again
the example of a firm that expands from being small in period t-3 to medium in t, it matters
whether the firm is assigned the average IC conditions of small or medium firms. The main
point is that matching based on current size reintroduces endogeneity as firms that grow are
assigned higher access to credit, just because they have grown, which creates a positive
correlation between the error term and the IC conditions. Our procedure, assumes that the
conditions that matter for employment growth are those prevalent in the past, and that
objective conditions remain constant over time for size classes of firms, not for individual
firms themselves. These hypotheses allow us constructing measures which are exogenous to

18
   We repeated the exercise using current size in defining the cells for calculating the IC variables and did not
find significant differences. For firms that remain in the same size category there is no difference between the
two approaches.
19
   An example may be useful to clarify our procedure. Let us consider, for example, a textile producing firm in
the capital city of Chile, which was small in period t-3, has grown to be of medium size in period t and the
average size in the two periods is medium. We use its responses on the investment climate indicators to
construct the average of Santiago-textile-medium size firms. However, this average is not what is then used for
that firm in the regressions. Since we want to capture the average IC conditions of that firm in period t-3,
when it was small, we match it to the average reported IC investment climate conditions of Santiago-textile-
small firms.


                                                       18
the firm and control for the fact that firms may choose to grow/or not grow to face a
determined set of IC. We show in our estimates the importance of how the IC variables are
constructed for the results.
         While we believe our constructed IC variables to go a long way towards addressing
endogeneity, a lingering concern is that our results may be capturing the effects of shocks
that are correlated across firms, which drive our constructed average IC and individual firm
performance. We reduce any possible biases brought by these correlations, by introducing
as an additional regressor the average employment growth of the cell used to compute the IC
conditions of firm i (excluding as usual, the employment growth of firm i in that
calculation). This variable is denoted as Empg_celli.


6. Results


We now describe the results attained with our estimation procedure, and presented in Table
4, column (1). The results indicate important non-linearities in the effects of business
environment constraints across firm size. They also suggest that a weak business
environment tends to hurt the growth of small, medium and large firms, and benefit in
relative terms ­and for some variables, even in absolute terms--the growth of micro firms.
As addressing endogeneity is one of our chief concerns, columns (2) ­ (5) demonstrate the
significance of alternative means of addressing it ­ or of failing to do so.
         For all specifications, coefficients on firm characteristics (other than size), the set of
sector-survey interaction dummies and the constant are not reported due to space constraints.
As expected, the coefficients on firm size's categories indicate that employment growth
declines monotonically with firm size.20 We focus next on the coefficients on the
investment climate and their interactions with firm size.


         6.1. Access to Finance
20
   There is a debate in the literature whether it is appropriate to use initial size or average size in firm growth
regressions (see Teal 1999; Bigsten and Soderbom 2006). Proponents of Gibrat's Law favor the latter,
although even using average size can still lead to the result that smaller firms grow faster than larger firms. We
repeated our regressions defining the size controls and the investment climate interactions based on average
size and found very robust results. (Note, to keep from reintroducing endogeneity, what one must avoid is
using a measure of size from a time period prior to the one used in matching the investment climate averages to
firms.)


                                                       19
         The impact of better access or lower cost of finances on firms' growth has been
studied by a number of authors.21 Beck et al. (2005) argue that financial access should favor
small firms. In their work, they find that firms' self reported (subjective) constraints on
access and cost to finance are associated with lower growth of small firms relative to large
firms. Table 3 showed that there are differences in the amounts of finance available, which
could already explain why smaller firms are more likely to complain about finance. Here,
we test whether, even for the same amount of financing (measured as percentage of
investment financed externally), the impact would be different across different types of
firms. Notice that finance is defined such that a larger number implies better access to
finance. The results show strong non-linearities across firm size, and unlike earlier results,
we find a positive effect of increased access to finance on the employment growth of
medium and large firms and no significant effect among micro and small enterprises.22
         We investigate the sensitivity of these results to different ways of estimating the IC
measures and find the results to be quite sensitive to whether and how we account for
endogeneity.
         It is useful to compare our results in column (1) with those obtained using firms'
individual own responses (column 2). While the number of observations declines ­since not
all firms report information for such variables--we find that using reported individual
measures, access to finance has a positive effect across all firms which does not differ
substantially across firm size, suggesting, that not controlling for endogeneity may
overestimate the effect of finance on micro and small firms. Notice that differences in
coefficients across columns (1) and (2) are not due to the smaller number of observations in
column (2). Column (3) reports the results of reproducing the estimates presented in column
(1) for the same sample as in column (2) and the results do not vary much: If anything the
finding that finance helps medium and larger firms if endogeneity is properly controlled for
becomes even stronger.
         In column (4), we report the results when using IC measures which are constructed
as reported in Section 3, but with the only difference that the averages within country-city-
sector-size cells are matched to current rather than initial size. As mentioned, this is likely to

21
  See for example, Demirgüç-Kunt and Maksimov (1998), Rajan and Zingales (1998), and Beck et al. (2005).
22
  The test that the overall effect of access to finance on medium and large firms is equal to zero is rejected at
the 10 percent significance level for large firms and at the 12 percent level for medium firms.


                                                       20
re-introduce some level of endogeneity as growing firms are matched with higher levels of
access to finance. In this case we find that all firms benefit from access to finance, and
while the coefficients point to smaller benefits for larger firms, differences across firm size
are not statistically significant.
        The fact that results are fairly similar when using individual firm responses or cell
averages matched to current size, and at the same time quite different from the results when
measuring IC based on cell averages matched to initial size, suggests that (i) endogeneity is
likely to bias the estimates when not accounting for it, and that (ii) using averages matched
on current size does not solve the endogeneity problem. Quite the opposite, it reintroduces
endogeneity in the IC measures due to the match.
        Our findings also suggest that the results found by previous studies that access to
finance benefits mostly the smaller firms may be driven by endogeneity. In our sample,
there are as many as 16% of the firms that start up as micro and transit to larger firms.
Depending on the procedure, such firms either report higher access to capital for
endogeneous reasons (i.e. because they have grown), or are matched with higher access to
capital than the average of initially small firms (i.e. are given the average access to capital of
larger firms). This creates a positive bias in the coefficient for micro and small firms.
Conversely about 7 % of large firms shrink and become smaller firms in our sample. As they
become smaller, they are likely to report lower access to finance or, depending on the
method, be matched to the access to capital of smaller firms. This creates a downward bias
in the coefficient of larger firms.
        Column (5) presents the results of further assessing the robustness of our main
results when constructing the IC averages with country-sector-location averages. Results
confirm the findings presented in our basic specification (column 1): increased access to
external finance to fund investments seems to benefits mostly medium-size and larger firms.
        Why would micro or small firms not be positively affected by increased access to
finance? Jeong and Townsend (2007) show that with a wide range of talent among micro-
entrepreneurs, access to finance may not be the binding constraint they face in growing their
firms. If access to finance is restricted, individuals with high entrepreneurial talent but low
wealth are size constrained, and the demand for wage employment is low, which drives
many individuals to become entrepreneurs even when they do not have much



                                               21
entrepreneurial talent. Increased access to finance sorts the good from the less talented
entrepreneurs. Those who are good can grow their firms; those who have lower
entrepreneurial talent suffer as wages increase due to an increase demand for labor. Our
estimates, particularly the negative, albeit not statistically significant coefficient on micro
firms, may then reflect this selection effect.


       6.2. Regulatory Environment
       Measures of the regulatory environment show that business regulations measured as
the share of time that management devotes to dealing with government regulations have
differential effects across firm size. Our results suggest that regulations create growth
bottlenecks for small firms, which may fear/avoid expansion to limit their exposure to red
tape and increased enforcement. Testing the overall significance by size confirms that the
overall effect of regulations on employment growth for small firms (the sum of the
coefficient on micro and small firms) is significantly different from zero while those for
medium and large firm are not. Thus, business regulations do not appear to affect the growth
of larger firms. On the other hand, the positive effect for micro firms suggests that micro
firms benefit from a generally lower enforcement stand for micro firms, which may help
divert some economic activity to micro firms.
       Results are quite similar across all specifications regardless of how the IC variables
are computed, suggesting that endogeneity may be a less important concern in this case.


       6.3. Corruption
       Moving on to results for corruption, we find that as in the case of access to finance,
results vary substantially whether and how we account for endogeneity. Results based on
individual responses (column 2) or cell averages matched to current size (column 4) would
suggest that corruption helps micro firms, while having a much smaller, and in some cases
negative effect on the growth of larger firms. Measuring the incidence of corruption based
on country-industry-location size averages matched to initial size or on country-industry
locations, in our opinion, two better ways of accounting for endogeneity, yields results that
go in the same direction but are not statistically significant. This suggests that while
corruption may have differential effects across firm size, and possibly adverse effects on



                                                 22
medium size firms, the presence of endogeneity creates biases that increase the probability
of finding this result.


        6.4. Infrastructure
        With regards to infrastructure bottlenecks, measured as the incidence of power
outages, results are again quite sensitive to whether we control or not for endogeneity. Using
our baseline specification (column 1), we find evidence that infrastructure bottlenecks tend
to stunt the growth of medium and large firms. Instead, the coefficient for micro firms is
positive, indicating that again, micro firms may benefit from the troubles of larger, possibly
more productive firms as some production may be diverted to micro firms. The coefficient
on micro firms however is not statistically significant. But the difference between micro and
small/medium firms is larger taking into account that small and medium firms report more
frequent outages than micro firms. This is consistent with micro firms using less capital and
energy intensive modes of production and benefiting that these processes can be sustained
due to hardships hitting the SMEs.
        Results appear stronger when individual firms' responses are used to compute IC but
the coefficients are of similar magnitudes than those reported in column (1). This suggests
that endogeneity does not appear as a large concern and that results in column (3) are
estimated with more precision as IC vary at the firm level. Computing IC out of the average
within country- location-sector cells also yields similar but stronger results than in column
(1). All in all, the results suggest that poor infrastructure has a detrimental effect on the
growth of all firms but the micro ones.


7. Robustness


We now examine whether our results are robust to changes in the definition of variables, set
of controls, and sample of countries.


        7.1. Addressing Other Possible Sources of Endogeneity
        Another source of potential endogeneity is whether better performing firms choose
locations based on the quality of the business environment. To see if this could be



                                                23
significant, column (1) reports the results if we restrict the sample to domestic-owned, micro
and SME's who are least likely to be footloose and where the location tends to be associated
with the places of birth or residence of the business owner. Foreign firms and large
domestic firms are the most likely to be selective in their choice of location. The results are
very robust.


       7.2. Alternative Definitions of Investment Climate Indicators
       The second set of robustness checks involves using alternative measures of the
different investment climate areas. The surveys generally provide various measures to
choose from. Of particular interest are measures on access to finance. There are two sets of
results, that the nature of the finance being captured matters, and, that the results are
somewhat sensitive to whether variation across sectors within a country is included or not.
       To examine the different types of finance, two additional measures are included ­ the
share of sales made on credit and access to working capital from external institutions. The
measures used so far captures a type of finance that is relatively more difficult to access,
namely financing for longer term investment projects from formal financial institutions. The
other two alternatives presented in Table 5 measure access to a more informal form of
financing and to shorter term financing from institutions. In the case of the share of sales on
credit, the results show that micro firms do not share in the benefits of access to this
financing, consistent with such credit being offered less by micro firms. Small and medium
sized firms do benefit. The impact on large firms is not significant, which again is not so
surprising as this type of finance is relatively less important for them. For access to working
capital, column (3) the benefits are more widespread. If anything, this type of financing is
most beneficial for the micro firms, although the differences across sizes are not significant.
       The second point is that these results are sensitive to whether one allows for sectoral
variation within countries to be included. Our results so far have included the full set of
interacted sector-survey dummies. However, here (Table 5, columns 2-4) the results are
more significant when sector and country dummies enter separately. There are two
interpretations that can be given to this finding. First, the original rationale to include the
broader set of dummies was in part to control for demand shocks that could vary by country
and sector. Failing to control for sector-country shocks, endogeneity could be re-entering



                                                24
the regressions. Such shocks could affect firm performance and increase their demand for
financing. This would imply caution in interpreting the results. However, there is a second
interpretation. Work by Rajan and Zingales points to potentially important differences
across sectors in the impact of improved access to finance for growth. Beck et al. 2008
show that sectors with a larger proportion of small firms stand to benefit disproportionately
from financial development. Including interactions sector-survey dummies then shuts down
this channel, explaining why these two variables, share of sales sold on credit and the share
of working capital financed externally lose their significance when all the interactions are
included in the regression.
       Column (4) also includes alternative measures for the other 3 investment climate
measures. They are days of inspections, the size of bribes paid and losses incurred during
transportation delays. The measure of inspections has the same effect as that of
management time spent dealing with officials, deterring small and medium sized firms from
growing. However, the new measures of corruption do not offer new insights, and the use of
the transportation related variable provides weaker results. One conclusion is that power,
not transportation (at least when measured as percentage of sales lost in transportation), is
the more important dimension of infrastructure affecting firm growth.


       7.3. Variations in Definition of Size Categories
       Table 6 investigates whether results are robust to variations in size classifications.
For comparison purposes, column (1) reproduces the results of Table 4, column (1).
Column (2), Table 6 reports the results of defining micro firms as firms with 5 to 10
employees, and excluding from the estimation firms with less than 5 employees. The other
size categories remain unchanged. The results on access to finance weaken for medium
sized firms, suggesting that the differential effects of finance between micro and medium
size firms are in part explained by the more negative effect of finance on the smallest firms.
This lends additional support to the selection story proposed by Jeong and Townsend, 2007,
as the very small firms are the ones that are likely to be less productive, and therefore, more
likely to be hurt by increasing costs of inputs driven by a higher supply of credit.
       In column (3) we follow the size category cutoffs used by Beck et al (2005), which
implies combining most of our micro with all our small into one category and combining our



                                               25
medium with many of our large into the medium category and leaving only the very largest
in the large category. With these cutoffs, and controlling for endogeneity, we do not
replicate their finding that the smallest firms benefit the most (although their result is
reproduced if we match conditions based on current rather than initial size). The results also
show that it is not the very largest firms that benefit from finance relative to the small, but
rather those in the mid size (50-500 employees) range.
        The results on regulations show that the positive relationship is driven by the
smallest firms, excluding those with less than 5 employees leaves the effect insignificant for
the remaining micro firms. Still, the effect remains negative for the small firms. In column
(3), when all the micro are excluded, the omitted category is now small and the effect is
negative but not significant. Medium and large firms still have a more positive response to
regulations than the small. However, combining the firms into the larger size categories
misses the entire story, with no results appearing significant.
        For corruption, however, the story does become stronger once the micro firms are
dropped (column 3) or absorbed with small firms (column 2). Now there is a positive and
significant effect for the smaller firms that is offset for the larger firms, significantly so for
the very large firms. Combined with the regulation results, small firms above the 10
employee threshold, i.e. those that are large enough to find evasion from regulations more
difficult, find measures of regulation more detrimental to their growth--but that corruption
offers a way of mitigating these effects. For medium firms, regulations are not so
detrimental as for small firms (in fact they have a small net positive effect) ­ and corruption
is correspondingly less beneficial for medium firms.


        7.4. Age versus Size
        It is well known that size is strongly correlated with age, as firms tend to start
operations small and grow overtime. We assess whether differences in the impact of
business environment variables could be related to age rather than size by adding a full set
of interactions of age dummies with IC variables to our baseline results (Table 4, column 1).
Results for size*IC interactions remain very similar however, some interesting results
emerge from the size interactions (see Table 7). We find that regulations tend to affect the
growth of older, more established firms, relative to younger ones. Combined with the size



                                                26
interactions, these results provide some interesting qualifications to our baseline results.
They suggest that the adverse effect of regulations on the growth of small firms is more
visible among firms that are small, but have been in business for a while, rather than for
firms that are small and young. Similarly, these results allow qualifying the statement that
micro firms benefit from a poor regulatory environment. In fact, the firms that have been
around for a while are the ones that benefit from regulations, while micro-young ones do
not. It may well be that a poor regulatory environment increases the survival of low
productivity firms (i.e. firms that do not have the entrepreneurial ability to grow) because it
penalizes more severely, higher productivity firms.
         Another interesting result of the size interactions is that corruption seems to
disproportionally benefit older, more established firms. Perhaps, age is correlated with the
access to networks and influence channels. We may also be capturing that a corrupt
environment makes the survival of certain types of firms more likely, which then are
observed as older at the time of the survey. Addressing this form of sample selection is
beyond the scope of this paper.


         7.5. Different Samples
         The results so far have pooled countries together (with sector-survey fixed effects),
but it could be that the effects vary by country groupings, along dimensions that can
reinforce or weaken the broader business environment. To test whether the institutional
development of countries matter, we divide the sample according to the depth of credit
markets (Table 8, columns 1-2), the degree by which countries control corruption (columns
3-4) and the strength of their rule of law (columns 5-6).23 Quite interestingly, results for
increased access to finance turn out to be much stronger in countries with a more developed
financial system, suggesting again that a marginal development in a country's financial
system tends to favor small, medium and to a lower extent large size firms, rather than
micro firms. This also suggests that finance operates better in economies with more
developed financial markets. This result is further confirmed by the results in columns (3) ­


23
   We also looked whether there were significant patterns by income or GDP per capital. As the differences
were less pronounced than those that capture the institutional environment, we do not report them. We do
note, however, that this implies that it is indeed the broader institutional environment that does matter rather
than income itself.


                                                        27
(4), which show that the effect of increased access to finance is stronger in economies with
higher control of corruption and better rule of law all hallmarks of better functioning
economies.
       Business regulations, measured as the percentage of management time spent dealing
with regulations, have a negative effect on the growth of small firms throughout most
samples of countries, suggesting that the conclusion that stringent regulations may lead
many small firms to grow less in order to hide from the authorities is quite robust to the
inclusion or exclusion of countries. What is striking is that results are stronger in countries
with either lower control of corruption or a weaker rule of law. It may be that the quality of
the regulations in lower institutional environments are more burdensome, resulting in the
greater constraints on small firms. It is also possible that more onerous regulations in a
weak institutional setting opens the way to corruption or harassment on the part of officials
encouraging firms to remain small and below the radar screen where possible.
       The direct effects of corruption on the growth of firms tend to be more significant in
countries with higher control of corruption. Corruption, in a setting where corruption is not
controlled, does not have much of an effect. However, if, in a stronger institutional
environment, there is still corruption, it provides incentives to keep operations small. The
effects of corruption on larger firms are significantly negative in countries with better
control of corruption. It thus appears that the distortions generated by corruption are more
important and distort firm growth more in countries where markets and institutions are more
developed and a higher share of resources is allocated via markets.
       Finally, infrastructure bottlenecks appear as a constraint to the growth of small,
medium and /or large firm in most samples of countries although the levels of significance
vary across samples, with the stronger effects for medium-sized firm.


       All in all, the results above suggest that improving on some domains of the
investment climate constraints may be more important to the extent that other aspects of the
business climate and institutional development improve. Thus, financial market
development brings higher development to medium and large firms in regions where there is
higher control of corruption and a better rule of law. Similarly, improving business
regulations may be more important where institutional development otherwise lags.


                                               28
Cracking down on corruption is still an issue even in more developed institutional countries,
with particularly deleterious effects on medium and large firms.


8. Conclusions


This paper has provided new evidence of the role of the investment climate in employment
growth. The results indicate significant differences across size categories of firms--both in
terms of differences in objective conditions faced by firms and in terms of non-linearities in
the impact of these conditions. The paper also devotes substantial effort to surmount
endogeneity concerns between investment climate measures and business performance and
finds that accounting for endogeneity reduces the differential effects of business climate on
the performance of different types of firms. Yet, the results indicate that a poor business
climate tends to hurt the growth of small, medium and large firms, and benefit in relative--
but in cases also in absolute cases, micro firms.
       It also finds that increased access to finance, particularly external finance to fund
investment, has a larger positive effect in the growth of small, medium and large firms,
particularly in economies with more developed financial systems or better rule of law.
Thus, unlike previous literature, this paper does not find that the impact on employment
growth of an extra unit of external finance is highest for micro firms. We interpret these
results as evidence that micro firms are not necessarily more credit constrained. Instead,
many micro entrepreneurs may have little willingness or capabilities to grow their firms. As
access to finance increases, micro firms may endure input price increases and production
displacement caused by the growth of large, more productive firms that attain higher
benefits of increased access to finance. The results also reinforce the importance of
differentiating the impact across size classes of firms and allowing micro firms (less than 10
employees) to be different from "small" firms.
       Another important finding of this paper is that business regulations measured as a
higher share of management time spent dealing with regulations or inspections, may stunt
the growth of small firms. Thus, as management time dealing with authorities as well as
inspections increase substantially with firm size, the marginal increase in enforcement can
act as a strong disincentive to grow a firm.


                                               29
       What are the aggregate implications of these findings for the size, efficiency and
dynamism of the business sector in developing countries? The results found in this paper
suggest that a weak business environment displaces activity from large, medium and small
firms in benefit of micro ones. To the extent that larger firms are more productive--as
suggested by many theoretical models of firm dynamics--this would imply substantial
resource reallocation from more productive to less productive ones, potentially leading to
important losses in aggregate productivity. In addition, given that enforcement of taxes and
regulations tends to be smaller in micro firms, a shift of economic activity towards these
firms is likely to imply that a larger share of firms remain informal or semi-informal,
reducing the capacity of the state of collecting taxes and paying for fundamental inputs for
development such as education. Finally, if there are substantial fixed costs in activities like
innovation or worker training, a higher share of activity in micro firms may imply more
firms and workers locked in activities with limited innovation and growth opportunities.




                                               30
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                                                   33
                                  Table 1: Employment Growth


                   Initial size,t-3     Mean            p25    Median    p75
                   size 1-10             0.220         0.000    0.000   0.444
                   size 11-50           0.081          0.000    0.000   0.261
                   size 51-200          0.033         -0.087    0.000   0.200
                   size +200            -0.022        -0.115    0.000   0.122
                   Total                0.117          0.000   0.0000   0.293




                                  Table 2: Variable Description


Variable                                   Description                             mean     sd
Emp-gr             Employment growth                                               0.117 0.411
Labor,t            Number of employees in period t (log)                           3.112 1.618
Sh-invest-fin      Share of investments financed externally                        0.209 0.347
Sh-work-cap-fin    Share of working capital financed externally                    0.201 0.137
Sh-sales-cr        Percentage of sales sold on credit                              0.424 0.396
Mng-time           % of management's time dealing with regulations                 8.872 15.104
Days-inspections   Total days spent on inspections during last year                15.990 31.721
Bribe y-n          Bribes given to get things done (yes-no)                        0.425 0.494
Bribe (%)          Amount of bribe paid to get things done                         1.507 4.242
Days-no power      Number of power outages experienced during the last year        38.143 83.993
Loss_transit (%)   Percentage of the average cargo's value lost while in transit   1.547 5.736




                                                 34
                                          Table 3: Investment Climate by Firm's Characteristics
                                                    Dependent variable: Investment climate variable
                          (1)             (2)          (3)          (4)           (5)           (6)            (7)           (8)        (9)
                       Sh-invest-    Sh-work-      Sh-sales-cr     Mng-         Days-         Bribe         Bribe (%     Days-no      Lost in
                        fin (%)      capital-fin      (%)        time (%)    inspections     (yes/no)        sales)        power    transit (%)
                                         (%)                                     (log)                                      (log)
Small                  0.049***      0.045***      0.072***      1.239***     0.238***      0.029***          0.054     0.061***     0.143*
                         (0.004)       (0.003)       (0.004)      (0.152)      (0.014)        (0.006)        (0.051)      (0.015)    (0.074)
Medium                 0.093***      0.087***       0.096***     1.767***     0.508***        0.014*        -0.250***   0.050***    0.202**
                         (0.005)       (0.004)       (0.005)      (0.199)      (0.017)        (0.008)        (0.060)      (0.018)    (0.086)
Large                   0.119***     0.117***       0.090***     1.468***     0.715***         0.001        -0.361***   -0.088***     0.039
                         (0.007)       (0.005)       (0.006)      (0.234)      (0.022)        (0.010)        (0.067)      (0.022)    (0.099)
Mature                  0.013***       0.006*       0.009**       0.278*       0.038**         -0.004        -0.114*    0.063***    -0.192**
                         (0.004)       (0.003)       (0.004)      (0.150)      (0.015)        (0.007)        (0.058)      (0.016)    (0.083)
Older                   0.010**      0.011***         0.006      0.494***     0.059***       -0.020**       -0.236***   0.072***    -0.204**
                         (0.005)       (0.004)       (0.004)      (0.180)      (0.017)        (0.008)        (0.065)      (0.018)    (0.089)
Exporter               0.030***      0.037***       0.048***     0.504***     0.085***       0.040***         -0.057    0.058***    0.217***
                         (0.005)       (0.003)       (0.004)      (0.159)      (0.014)        (0.007)        (0.050)      (0.015)    (0.067)
Foreign                -0.041***     -0.025***     0.043***        0.094      0.061***       -0.016**         -0.080     -0.038**   -0.213**
                         (0.006)       (0.004)       (0.005)      (0.183)      (0.018)        (0.008)        (0.064)      (0.018)    (0.088)
Smallcity              0.010***      0.009***      -0.014***       -0.190      0.025*       -0.053***       -0.228***   0.118***      -0.072
                         (0.004)       (0.003)       (0.003)      (0.144)      (0.013)        (0.006)        (0.043)      (0.013)    (0.063)
Government             -0.035***      -0.013**     -0.041***     0.599**        0.000       -0.132***       -0.496***   -0.112***     -0.078
                         (0.007)       (0.005)       (0.006)      (0.242)      (0.026)        (0.010)        (0.064)      (0.025)    (0.125)
Expand                  0.041***     0.029***       0.029***     0.491**        0.018       0.053***         0.142**    0.098***     0.243**
                         (0.006)       (0.004)       (0.005)      (0.209)      (0.019)        (0.008)        (0.069)      (0.020)    (0.098)
Contract               0.027***      0.040***      0.044***      1.771***     0.140***      0.088***        0.523***      0.059**    0.435**
                         (0.009)       (0.006)       (0.007)      (0.308)      (0.027)        (0.012)        (0.105)      (0.028)    (0.169)
Survey*Sector f.e.         yes           yes           yes           yes          yes            yes            yes          yes        yes
Constant                0.133***     0.124***       0.359***     7.477***     1.516***                      1.812***    1.570***    1.465***
                         (0.005)       (0.003)       (0.005)      (0.184)      (0.018)                       (0.066)      (0.018)    (0.097)
Observations              42519         63020         65531        63295        56794         51707           50610        61309      45272
R-squared                  0.19          0.21          0.31         0.18         0.31          0.17            0.10         0.53       0.07
* significant at 10%; ** significant at 5%; *** significant at 1%; Robust standard errors in parentheses.
Firm characteristics are at time t.
col(6) is a dprobit, so coefficients are marginal effects and R-squared is Pseudo R-squared.
                 Table 4: Impact of Investment Climate on Employment Growth
          Dependent Variable: Employment Growth (Emp(t)-Emp(t-3))/((Emp(t)+Emp(t-3))/2)
                               (1)           (2)           (3)            (4)            (5)
Investment Climate         ICvar avg     Individual    ICvar as in    ICvar avg      ICvar cell
variable (ICvar)          matched to        firm     col. (1), with   matched to    avg: survey-
                         *initial* size   response       col. (2)     *current*        sector-
                                                         sample          size         location
Sh_invest_fin                        -0.055          0.083***           -0.095           0.583***             -0.008
                                    (0.059)           (0.018)          (0.074)            (0.075)            (0.033)
(Small)*Sh_invest_fin                0.092             -0.022           0.090              0.039              0.047
                                    (0.065)           (0.023)          (0.077)            (0.070)            (0.040)
(Medium)*Sh_invest_fin              0.130*             -0.028          0.159*              -0.024            0.115**
                                    (0.075)           (0.024)          (0.086)            (0.083)            (0.045)
(Large)*Sh_invest_fin               0.136*             -0.019         0.221***             -0.134            0.110**
                                    (0.071)           (0.028)          (0.085)            (0.087)            (0.056)
Sh_mng_time                        0.003**           0.001***         0.005***           0.011***          0.002***
                                    (0.001)           (0.000)          (0.002)            (0.002)            (0.001)
(Small)*Sh_mng_time               -0.005***         -0.002***        -0.008***          -0.004***         -0.004***
                                    (0.001)           (0.000)          (0.002)            (0.001)            (0.001)
(Medium)*Sh_mng_time                 -0.003          -0.001**         -0.005**            -0.003*           -0.002**
                                    (0.002)           (0.001)          (0.002)            (0.002)            (0.001)
(Large)*Sh_mng_time                -0.003*          -0.002***        -0.007***          -0.005***           -0.002**
                                    (0.002)           (0.001)          (0.002)            (0.002)            (0.001)
Bribe_y_n                            0.051           0.027***           0.043              0.015               0.025
                                    (0.041)           (0.010)          (0.046)            (0.045)            (0.021)
(Small)*Bribe_y_n                    -0.004         -0.035***           -0.023             -0.031             -0.012
                                    (0.045)           (0.014)          (0.047)            (0.048)            (0.024)
(Medium)*Bribe_y_n                   -0.073         -0.058***           -0.032            -0.104*             -0.041
                                    (0.054)           (0.016)          (0.053)            (0.058)            (0.030)
(Large)*Bribe_y_n                   -0.056             -0.026          -0.025             -0.099*             -0.029
                                    (0.051)           (0.017)          (0.055)            (0.058)            (0.033)
Days-no power                         0.008          0.009***            0.016              0.002              0.006
                                    (0.008)           (0.003)          (0.010)            (0.009)            (0.004)
(Small)*Days-no power                -0.006           -0.008*           -0.011             -0.007           -0.008**
                                    (0.005)           (0.004)          (0.007)            (0.006)            (0.004)
(Medium)*Days-no power             -0.014*           -0.010**         -0.021**          -0.024***         -0.015***
                                    (0.008)           (0.005)          (0.009)            (0.008)            (0.005)
(Large)*Days-no power              -0.015*           -0.010**         -0.020**           -0.019**           -0.014**
                                    (0.008)           (0.005)          (0.009)            (0.009)            (0.006)
(Small)                           -0.110***         -0.108***         -0.071**          -0.136***         -0.103***
                                    (0.028)           (0.013)          (0.028)            (0.031)            (0.017)
(Medium)                          -0.149***         -0.162***        -0.151***          -0.143***         -0.162***
                                    (0.032)           (0.017)          (0.032)            (0.035)            (0.021)
(Large)                           -0.210***         -0.239***        -0.217***          -0.181***         -0.224***
                                    (0.031)           (0.019)          (0.034)            (0.037)            (0.023)
Firm Controls                          Yes              Yes               Yes                Yes                Yes
Survey*Sector f.e.                     Yes              Yes               Yes                Yes                Yes
Observations                         56880             28619            28616              56880              55612
R-squared                             0.15              0.18             0.18               0.17               0.15
Significant at * 10%; ** at 5%; *** at 1%; s.e. are clustered on survey-location-sector-size.
Firm Controls: dummies for firm age (t-3), foreign or government owned, exporter status, if in small city,
   empg base yrs, avg. employment growth in cell, and a constant term. Size is t-3. (Definitions in Section 3).
cols. (1) & (3) cells based on survey-sector-location-avg_size (excluding own); col. (4) uses current size.
                                                 Table 5: Robustness
                 Dependent Variable: Employment Growth (Emp(t)-Emp(t-3))/((Emp(t)+Emp(t-3))/2)
                                        (1)                        (2)                   (3)                    (4)
                               Control for possible
                                location selection                          Alternative measures of IC
                                       bias
Investment Climate              Sample restricted            Finance =               Finance =           4 alternative IC
variable (ICvar)                 SMEs domestic             Sh_sales_credit           sh_wkcap               variables
Finance                               -0.046                   -0.039                 0.194**               0.272***
                                     (0.061)                  (0.033)                 (0.083)                 (0.089)
(Small)*Finance                       0.093                  0.092***                  -0.108               -0.245***
                                     (0.065)                  (0.033)                 (0.072)                 (0.075)
(Medium)*Finance                     0.168**                   0.066*                  -0.126               -0.237***
                                     (0.079)                  (0.035)                 (0.085)                 (0.091)
(Large)*Finance                                                0.034                   -0.111                -0.178*
                                                              (0.037)                 (0.084)                 (0.093)
Regulations                           0.003**                0.004***                0.004***                  0.012
                                      (0.001)                 (0.001)                 (0.002)                 (0.012)
(Small)*Regulations                  -0.005***               -0.006***               -0.005***              -0.029***
                                      (0.001)                 (0.001)                 (0.001)                 (0.009)
(Medium)*Regulations                   -0.001                -0.004***                -0.003*                -0.023*
                                      (0.002)                 (0.001)                 (0.002)                 (0.013)
(Large)*Regulations                                          -0.005***                -0.003*                  -0.017
                                                              (0.001)                 (0.002)                 (0.014)
Corruption                             0.047                   0.007                   0.033                   0.004
                                      (0.042)                 (0.024)                 (0.034)                 (0.006)
(Small)*Corruption                     0.013                   0.043*                  -0.007                  0.002
                                      (0.046)                 (0.025)                 (0.033)                 (0.005)
(Medium)*Corruption                    -0.055                  -0.001                  -0.070                  -0.003
                                      (0.057)                 (0.029)                 (0.044)                 (0.007)
(Large)*Corruption                                              0.021                  -0.043                  -0.009
                                                              (0.033)                 (0.043)                 (0.008)
Infrastructure                          0.006                    0.008                  0.008                  0.000
                                       (0.009)                  (0.005)                (0.008)                (0.004)
(Small)*Infrastructure                  -0.003                   -0.000                  0.000                 -0.000
                                       (0.005)                  (0.004)                (0.005)                (0.004)
(Medium)*Infrastructure                 -0.012                   -0.008                 -0.006                 0.002
                                       (0.008)                  (0.005)                (0.007)                (0.004)
(Large)*Infrastructure                                           -0.007                 -0.009                  0.003
                                                                (0.006)                (0.007)                (0.005)
Firm Controls                           Yes                      Yes                    Yes                    Yes
Survey*Sector f.e.                      Yes                      No                     No                     No
Survey and Sector f.e.                   No                      Yes                    Yes                    Yes
Observations                           46122                    54442                  54013                  48411
R-squared                               0.15                    0.115                  0.122                  0.122
Significant at * 10%; ** at 5%; *** at 1%; s.e. are clustered on survey-location-sector-size.
Firm Controls: dummies for firm size and age (t-3), foreign or government owned, exporter status, if in small city,
  empg base yrs, avg. employment growth in cell, and a constant term.
All ICvar use cell avg, matched to initial size; cells based on survey-sector-location-avg_size (excluding own).
Col. (1) Finance=Sh_invest_fin; Regulations=Sh_mng_time; Corruption=Bribe y-n; Infrastr.=Days_no power.
Results in columns (2) and (3) use the same variables as column (1) with exception of Finance variable.
Col(4) IC alternative: Finance=sh_wkcap; Regul.=days-inspections;Corrup.= bribe-%; Infrastr.=losstransit.




                                                              37
                                      Table 6: Other Size Definitions
                Dependent Variable: Employment Growth (Emp(t)-Emp(t-3))/((Emp(t)+Emp(t-3))/2)
                                          Baseline                  (1)              (2)                (3)
                                        (Table 4 (1))          Exclude<5,
                                        Micro (<10),          Micro (5-10),      Small(5-50)        Excluding
                                        Small(10-50)         rest of sizes as   Med. (51-500)       micro(<10)
                                       Medium(51-200)           in col. (1)     Large(+500)
                                        Large(+200)
Sh_invest_fin                               -0.055               -0.078             -0.047             -0.032
                                           (0.059)              (0.069)            (0.048)            (0.046)
(Small, t-3)*Sh_invest_fin                   0.092                0.084
                                           (0.065)              (0.070)
(Medium)*Sh_invest_fin                      0.130*               0.132            0.114**              0.057
                                           (0.075)              (0.085)           (0.053)             (0.052)
(Large)*Sh_invest_fin                       0.136*               0.149*            0.062               0.070
                                           (0.071)              (0.081)           (0.073)             (0.059)
Sh_mng_time                                0.003**                0.002            -0.001              -0.002
                                           (0.001)              (0.002)           (0.001)             (0.001)
(Small)*Sh_mng_time                       -0.005***            -0.004***
                                           (0.001)              (0.002)
(Medium.t-3)*Sh_mng_time                    -0.003               -0.001            0.002             0.003**
                                           (0.002)              (0.002)           (0.001)            (0.001)
(Large)*Sh_mng_time                        -0.003*               -0.002            -0.000             0.002
                                           (0.002)              (0.002)           (0.002)            (0.002)
Bribe_y_n                                    0.051                0.014           0.007**            0.008**
                                           (0.041)              (0.044)           (0.004)            (0.004)
(Small)*Bribe_y_n                           -0.004                0.037
                                           (0.045)              (0.043)
(Medium)*Bribe_y_n                          -0.073               -0.026             -0.004             -0.005
                                           (0.054)              (0.055)            (0.005)            (0.005)
(Large)*Bribe_y_n                           -0.056               -0.005            -0.019*             -0.012
                                           (0.051)              (0.053)            (0.011)            (0.008)
Days-no power                                0.008                0.002             -0.004              0.001
                                           (0.008)              (0.009)            (0.009)            (0.008)
(Small)*Days-no power                       -0.006               -0.002
                                           (0.005)              (0.006)
(Medium)*Days-no power                     -0.014*               -0.010             -0.007            -0.007
                                           (0.008)              (0.008)            (0.006)            (0.006)
(Large)*Days-no power                      -0.015*               -0.010             0.009              -0.002
                                           (0.008)              (0.009)            (0.010)            (0.008)
Firm Controls                                 Yes                  Yes               Yes                Yes
Survey*Sector f.e.                            Yes                  Yes               Yes                Yes
Observations                                56880                47491              47222              34474
R-squared                                     0.15                 0.13              0.12               0.12
Significant at * 10%; ** at 5%; *** at 1%; s.e. are clustered on survey-location-sector-size.
Firm Controls: dummies for firm size and age (t-3), foreign or government owned, exporter status, if in small city,
  empg base yrs, avg. employment growth in cell, and a constant term.
All ICvar use cell avg, matched to initial size; cells based on survey-sector-location-avg_size (excluding own).



                                                        38
                                Table 7: Is it Size or Age that Matters?
            Dependent Variable: Employment Growth (Emp(t)-Emp(t-3))/((Emp(t)+Emp(t-3))/2)

Investment Climate variable (ICvar)                                    Continuation
Sh_invest_fin                                -0.053
                                            (0.064)
(Small, t-3)*Sh_invest_fin                    0.093
                                            (0.065)
(Medium)*Sh_invest_fin                       0.127*          (Mature, t-3)*Sh_invest_fin              -0.004
                                            (0.076)                                                  (0.037)
(Large)*Sh_invest_fin                        0.132*          (Older)*Sh_invest_fin                    0.003
                                            (0.072)                                                  (0.041)
Sh_mng_time                                0.005***
                                            (0.001)
(Small)*Sh_mng_time                        -0.005***
                                            (0.001)
(Medium.t-3)*Sh_mng_time                     -0.002          (Mature)*Sh_mng_time                  -0.002***
                                            (0.002)                                                  (0.001)
(Large)*Sh_mng_time                           -0.002         (Older)*Sh_mng_time                   -0.004***
                                            (0.002)                                                  (0.001)
Bribe_y_n                                     0.029
                                            (0.042)
(Small)*Bribe_y_n                             -0.011
                                            (0.046)
(Medium)*Bribe_y_n                           -0.087          (Mature)*Bribe y_n                      0.037
                                             (0.056)                                                (0.024)
(Large)*Bribe_y_n                             -0.071         (Older)*Bribe y_n                      0.058**
                                             (0.053)                                                (0.029)
Days-no power                                 0.006
                                             (0.009)
(Small)*Days-no power                        -0.006          (Mature)*Days_no_power                  0.003
                                             (0.005)                                                (0.003)
(Medium)*Days-no power                      -0.015*          (Older)*Days_no_power                   0.003
                                             (0.008)                                                (0.004)
(Large)*Days-no power                       -0.015*          Mature                                -0.086***
                                             (0.008)                                                (0.013)
(Small)                                    -0.110***         Older                                 -0.137***
                                             (0.029)                                                (0.016)
(Medium)                                   -0.148***         Firm Controls                            Yes
                                             (0.032)         Survey*Sector f.e.                       Yes
(Large)                                    -0.209***         Observations                            56880
                                             (0.032)         R-squared                                0.15
Significant at * 10%; ** at 5%; *** at 1%; s.e. are clustered on survey-location-sector-size.
Firm Controls: dummies for whether firm is foreign or government owned, exporter status, if in small city,
  empg base yrs, avg. employment growth in cell, and a constant term. Size and age are t-3.
All ICvar are cell avg, matched to initial size; cells based on survey-sector-loc.-avg_size (excluding own).
This is a single regression, reported in two columns due to the many variables included.




                                                        39
                    Table 8: Institutional Effects (IC on Employment Growth)
            Dependent Variable: Employment Growth (Emp(t)-Emp(t-3))/((Emp(t)+Emp(t-3))/2)
                                 (1)                 (2)            (3)           (4)          (5)        (6)
                            Domestic credit to private sector      Control corruption           Rule of law
                              <=median            >median           <=0           >0           <=0        >0
Sh_invest_fin                   0.070              -0.112          0.020        -0.110       0.035     -0.158*
                               (0.094)             (0.076)        (0.079)       (0.081)     (0.080) (0.085)
(Small)*Sh_invest_fin          -0.097             0.202**          0.015        0.185*       -0.017 0.222**
                               (0.093)             (0.084)        (0.081)       (0.099)     (0.079) (0.096)
(Medium)*Sh_invest_fin         -0.019              0.203*          0.051        0.204*       0.016     0.311**
                               (0.113)             (0.116)        (0.096)       (0.113)     (0.096) (0.127)
(Large)*Sh_invest_fin          -0.026               0.145          0.026         0.096       0.005      0.197*
                               (0.115)             (0.097)        (0.093)       (0.120)     (0.097) (0.112)
Sh_mng_time                     0.001             0.006**          0.002         0.002        0.002      0.002
                               (0.002)             (0.003)        (0.002)       (0.003)     (0.002) (0.003)
                                                                                                -
(Small)*Sh_mng_time               -0.002         -0.009***        -0.003**       -0.005     0.004** -0.004
                                 (0.002)          (0.003)          (0.002)      (0.003)     (0.002) (0.002)
(Medium)*Sh_mng_time             -0.000            -0.005           0.000        -0.002      -0.000     -0.001
                                 (0.002)          (0.004)          (0.002)      (0.003)     (0.002) (0.003)
(Large)*Sh_mng_time                0.001         -0.010***          0.001       -0.006*       0.001     -0.004
                                 (0.002)          (0.003)          (0.002)      (0.003)     (0.002) (0.003)
Bribe_y_n                         0.049            0.043            0.016       0.136**       0.008      0.004
                                 (0.048)          (0.064)          (0.051)      (0.065)     (0.006) (0.013)
(Small)*Bribe_y_n                 -0.021           0.029            0.043       -0.118*      -0.003      0.006
                                 (0.045)          (0.070)          (0.055)      (0.063)     (0.006) (0.014)
                                                                                    -
(Medium)*Bribe_y_n               -0.059             -0.072         -0.026      0.252***      -0.007     0.005
                                 (0.063)           (0.087)        (0.069)       (0.086)     (0.008)    (0.015)
(Large)*Bribe_y_n                 -0.030            -0.045         0.019       -0.178**      -0.010     -0.008
                                 (0.063)           (0.076)        (0.064)       (0.078)     (0.010)    (0.017)
Days-no power                     0.003             0.012          -0.000        0.024       -0.000     0.018
                                 (0.010)           (0.015)        (0.010)       (0.020)     (0.010)    (0.017)
(Small)*Days-no power            -0.007             0.004          -0.000        -0.011     -0.001      -0.011
                                 (0.006)           (0.012)        (0.007)       (0.018)     (0.007)    (0.012)
(Medium)*Days-no
power                            -0.015*              -0.002         -0.005        -0.043*      -0.005 -0.030*
                                 (0.009)              (0.015)        (0.009)       (0.022)     (0.009) (0.017)
(Large)*Days-no power             -0.007               -0.005         0.001         -0.025      0.009    -0.024
                                 (0.010)              (0.014)        (0.010)       (0.024)     (0.011) (0.017)
Firm Controls                      Yes                  Yes            Yes           Yes         Yes      Yes
Survey*Sector f.e.                 Yes                  Yes            Yes           Yes         Yes      Yes
Observations                      31854                23157          41575         15205       33104    23667
R-squared                          0.16                 0.11           0.16          0.12        0.17     0.12
Significant at * 10%; ** at 5%; *** at 1%; s.e. are clustered on survey-location-sector-size.
Firm Controls: dummies for firm size and age (t-3), foreign or government owned, exporter status, if small
city, empg base yrs, avg. employment growth in cell, and a constant term.
All ICvar are cell avg, matched to initial size; cells based on survey-sector-loc.-avg_size (excluding own).




                                                        40
                                          Table A1: Dataset

    Country       N. obs.    %           Country      N. obs.    %          Country      N. obs.    %
Albania2002         166     0.29   Ghana2007            249     0.44   Panama2006          550     0.97
Albania2005         202     0.35   Greece2005           539     0.95   Paraguay2006        561     0.99
Angola2006          274     0.48   Guatemala2003        451     0.79   Peru2006            603     1.06
Argentina2006       950     1.67   Guatemala2006        494     0.87   Philippines2003     614     1.08
Armenia2002         170      0.3   Guinea2006           181     0.32   Poland2002          495     0.87
Armenia2005         351     0.62   GuineaBissau2006     129     0.23   Poland2003          105     0.18
Azerbaijan2002      162     0.28   Guyana2004           152     0.27   Poland2005          969      1.7
Azerbaijan2005      348     0.61   Honduras2003         436     0.77   Portugal2005        501     0.88
Bangladesh2002      964     1.69   Honduras2006         419     0.74   Romania2002         254     0.45
Belarus2002         250     0.44   Hungary2002          245     0.43   Romania2005         586     1.03
Belarus2005         322     0.57   Hungary2005          602     1.06   Russia2002          488     0.86
Belarus2008         232     0.41   India2006           2,919    5.13   Russia2005          592     1.04
Benin2004           182     0.32   Indonesia2003        707     1.24   Rwanda2006          154     0.27
BiH2002             171      0.3   Ireland2005          499     0.88   Senegal2007         473     0.83
BiH2005             194     0.34   Jamaica2005           70     0.12   Serbia2003          399      0.7
Bolivia2006         556     0.98   Jordan2006           451     0.79   Slovakia2002        163     0.29
Botswana2006        253     0.44   Kazakhstan2002       249     0.44   Slovakia2005        208     0.37
Bulgaria2002        245     0.43   Kazakhstan2005       578     1.02   Slovenia2002        188     0.33
Bulgaria2005        291     0.51   Kenya2003            211     0.37   Slovenia2005        221     0.39
BurkinaFaso2006     131     0.23   Kyrgyzstan2002       166     0.29   SouthAfrica2003     551     0.97
Burundi2006         215     0.38   Kyrgyzstan2003       102     0.18   SouthKorea2005      594     1.04
Cambodia2003        464     0.82   Kyrgyzstan2005       199     0.35   Spain2005           601     1.06
Cameroon2006        165     0.29   Laos2005             225      0.4   Swaziland2006       208     0.37
CapeVerde2006        93     0.16   Latvia2002           170      0.3   Tajikistan2002      170      0.3
Chile2004           942     1.66   Latvia2005           200     0.35   Tajikistan2003      107     0.19
Chile2006           962     1.69   Lebanon2006          345     0.61   Tajikistan2005      199     0.35
Colombia2006        936     1.64   Lesotho2003           44     0.08   Tajikistan2008      316     0.56
CostaRica2005       333     0.59   Lithuania2002        193     0.34   Tanzania2003        245     0.43
Croatia2002         174     0.31   Lithuania2004        230      0.4   Tanzania2006        360     0.63
Croatia2005         229      0.4   Lithuania2005        196     0.34   Turkey-b2005       1,238    2.18
Czech2002           261     0.46   Madagascar2005       269     0.47   Turkey2002          513      0.9
Czech2005           327     0.57   Malawi2005           150     0.26   Turkey2005          544     0.96
DRC2006             271     0.48   Mali2003             123     0.22   Turkey2008          900     1.58
Ecuador2003         411     0.72   Mauritania2006       204     0.36   Uganda2003          291     0.51
Ecuador2006         592     1.04   Mauritius2005        177     0.31   Uganda2006          509     0.89
ElSalvador2003      459     0.81   Mexico2006          1,343    2.36   Ukraine2002         463     0.81
ElSalvador2006      620     1.09   Moldova2002          173      0.3   Ukraine2005         586     1.03
Estonia2002         160     0.28   Moldova2003          103     0.18   Ukraine2008         733     1.29
Estonia2005         214     0.38   Moldova2005          346     0.61   Uruguay2006         540     0.95
FYROM2002           165     0.29   Montenegro2003       100     0.18   Uzbekistan2002      255     0.45
FYROM2005           194     0.34   Mozambique2007       429     0.75   Uzbekistan2003      100     0.18
Gambia2006          126     0.22   Namibia2006          242     0.43   Uzbekistan2005      298     0.52
Georgia2002         173      0.3   Nicaragua2003        448     0.79   Uzbekistan2008      361     0.63
Georgia2005         199     0.35   Nicaragua2006        461     0.81   Vietnam-b2005       497     0.87
Georgia2008         325     0.57   Nigeria2007         2,005    3.52   Vietnam2005        1,109    1.95
Germany2005        1,195     2.1   Pakistan2002         937     1.65   Zambia2002          187     0.33



                                                 41
                             Table A2: Sample Composition

Region                          Percent   Sector                               Percent
Sub-Sahara Africa                16.01    Textiles                               5.3
East Asia & Pacific              14.45    Leather                                1.15
East Europe & Central Asia       27.21    Garments                               8.46
Western Europe                    4.57    Agroindustry                           6.65
Latin America                    20.87    Beverages                              7.17
Middle East & North Africa        6.72    Metals and machinery                   9.42
South Asia                       10.16    Electronics                            1.55
                                          Chemicals and pharmaceutics            4.9
Income Level                              Construction                           5.07
Low                              31.43    Wood and furniture                    5.02
Lower-middle                     44.59    Non-metallic and plastic materials     4.37
Upper-middle                     16.36    Paper                                  1.98
High                              7.62    IT services                            3.85
                                          Other manufacturing                    1.39
Initial size                              Advertising and marketing              1.73
Micro (1-10 employees)           39.36    Other services                         7.28
Small (11-50 employees)          33.64    Retail and wholesale trade            17.67
Medium (51-200 employees)        16.39    Hotels and restaurants                 3.41
Large (+200 employees)           10.61    Transport                              2.52
                                          Mining and quarrying                   0.46
Initial age                               Other Transport Equipment              0.67
Young 1-5                        30.71
Mature 6-15                      38.34    Labor intensive                      31.69
Older +16                        30.95    Capital intensive                    31.89
                                          Services                             36.42
Non-exporter                     77.35
Exporter                         22.65    Capital or city >=1million           35.27
                                          City 500k to 1 million               13.22
Domestic owned                   88.82    City 250k-500k                       15.43
Foreign owned                    11.18    City 50k-250k                        17.86
                                          City <50k                            18.22




                                                  42