WPS5880


Policy Research Working Paper                    5880




                    Job Growth and Finance
           Are Some Financial Institutions Better Suited
           to Early Stages of Development Than Others?

                                   Robert Cull
                                   L. Colin Xu




The World Bank
Development Research Group
Finance and Private Sector Development Team
November 2011
Policy Research Working Paper 5880


  Abstract
  This paper combines firm-level data from 89 countries                             capitalization, which is also consistent with predictions
  with updated country-level data on financial structure,                           from new structural economics, although the analysis
  and uses two estimation approaches. It finds that in low-                         is unable to provide evidence that the association is
  income countries, labor growth is swifter in countries                            causal. It finds no evidence that small-scale firms in
  with a higher level of private credit/gross domestic                              low-income countries benefit most from private credit
  product; the positive effect of bank credit is especially                         market development. Rather, the labor growth rates of
  pronounced in industries that depend heavily on external                          larger, capital-intensive firms increase more with the
  finance; and banking development is positively associated                         level of private credit market development, a finding
  with more physical and human capital investment.                                  consistent with the history-based political economy view
  These findings are consistent with predictions from new                           that banking systems in low-income countries serve the
  structural economics. In high-income countries, labor                             interests of the elite, rather than providing broad-based
  growth rates are increasing in the level of stock market                          access to financial services.




  This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of
  a larger effort by the World Bank to provide open access to its research and make a contribution to development policy
  discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
  The author may be contacted at rcull@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


                                                       Produced by the Research Support Team
    Job Growth and Finance: Are Some Financial Institutions Better Suited to
                 Early Stages of Development Than Others?1



                                                    Robert Cull

                                                    L. Colin Xu




Keywords: Financial development, economic development, banks, capital markets, financial
structure

JEL Classification: G1, G2, O16




1
 The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not
necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its
affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
We thank Stijn Claessens and participants at the World Bank Conference on Financial Structure and Economic
Development (June 16, 2011) for many helpful comments and suggestions.
Both R. Cull and L.C. Xu are affiliated with the Finance and Private Sector Unit of DECRG of the World Bank.
I.         Introduction

The relative advantages of different types of financial systems have been long debated, most
notably in comparisons between market-based and bank-based financial structures (Gershenkron,
1962; Goldsmith, 1969; Allen and Gale, 1991; Demirguc-Kunt and Levine, 2001). Banks, for
example, are able to exploit economies in processing information about the creditworthiness of
prospective borrowers, and often form long-run relationships with firms that reduce information
asymmetries and permit effective monitoring of the firms‘ activities. Securities markets provide
an incentive to gather information about firms, provide a liquid platform for investors to buy and
sell shares in those firms, and can improve corporate governance of firms by facilitating
takeovers. More generally, markets are likely to do a better job of aggregating and transmitting
information signals to investors than banks, which could improve the allocation of financial
resources and thus promote growth.2

           While the relative advantages of bank- and market-based systems have been elucidated
well in the literature, the available empirical evidence does not indicate that either type holds an
advantage in promoting growth. Indeed, cross-country evidence shows that it is the overall level
of financial development rather than its institutional composition that is robustly linked with
economic growth (Demirguc-Kunt and Levine, 2001; Levine, 2002). While cross-country
indicators of financial development have proliferated and have been refined in the past decade,
they necessarily carry limitations when describing the nuances of financial structure in a given
country. It is possible, even likely, therefore that in a given country or at a specific time,
productive activities would be better supported by banks or markets.

           This theme has been developed recently in the ―New Structural Economics‖ approach to
studying economic development (Lin, 2010). Under that approach, factors endowments at each
stage of an economy‘s development determine the optimal industrial structure in the real sector.
That optimal mix of firms – their size distribution, sectors, and risk characteristics – is, in turn,
better served by some types of financial institutions than others. As a result, proponents of this
approach hold that there is an endogenously determined optimal financial structure at each stage
of an economy‘s development. Measurement problems are a key challenge to testing this


2
    See Levine 2002 for an extensive review of the literature on the relative merits of banks versus markets.

                                                            2
approach to financial development empirically. Case studies of individual countries lack
sufficient variation in financial structure over time to permit formal hypothesis testing and,
though cross-country financial indicators have come a long way in a short time, they remain
relatively crude. Determining a country‘s optimal industrial structure and assessing the suitability
of its financial structure using only cross-country regressions would be a daunting task.3

         Moreover, economic and financial historians have devoted considerable effort to
documenting the idiosyncratic, path-dependent processes that generated the diverse set of local
financial intermediaries that came to exist across developed countries (Davis and Gallman, 2001;
Cull et al., 2006; Allen et al, 2011). The consensus view is that the specific financial institutions
that evolved were certainly influenced by economic and political considerations. In that sense,
financial development was not necessarily endogenous to an optimal matching with industrial
structure, but to a much broader range of influences. Moreover, in the best studied developed
economies, governments were not particularly active in promoting specific financial structures.
They generally did little to inhibit the formation of financial intermediaries, but they also played
little role in their creation—beyond providing a secure property-rights environment and
establishing national financial institutions, such as central banks, that helped to mitigate local
shocks. Nor were governments generally able to jumpstart economic growth by promoting local
financial institutions in regions where there was insufficient demand for their services (Cull et
al., 2006). The financial institutions that emerged were therefore generally endogenous to the
demand for specific financial services.




3
  However, Demirguc-Kunt, Feyen, and Levine (2011) define optimal financial structures at different stages of
development based on cross-country regressions, and then calculate countries‘ distances from them. To construct a
measure of optimal financial structure at each level of development, the authors regress a measure of financial
structure (such as the ratio of bank development to securities market development) on GDP per capita for the sample
of OECD countries, while also controlling for key institutional, geographic, and structural traits of those countries.
The maintained hypothesis is that conditional on these traits, the OECD countries provide information on how the
optimal financial structure varies with economic development. Next, the authors use the coefficients from the OECD
regression to compute the estimated optimal financial structure for each country in each year. They then compute a
―financial structure gap‖ which is equal to the natural logarithm of the absolute value of the difference between the
actual and the estimated optimal financial structure. They find that deviations in an economy‘s actual financial
structure from its estimated optimal one (i.e., the size of the financial development gap) are associated with reduced
economic output even when controlling for the level of bank development, securities market development, a
standard set of controls, and country fixed effects.


                                                          3
       Proponents of new structuralism might view the financial histories of developed
economies as promising for their approach – since the evolution of financial intermediaries was
endogenous to demand for financial services from the real sector, an optimal financial structure
might be a by-product of an optimal industrial structure. And yet, the industrial structures that
emerged in many countries were far from optimal, and thus the financial structures that evolved
did not function as well as they might have. As described in Calomiris and Haber (2011), in less
developed economies financial intermediaries are often created by coalitions that include the
government and business interests. Those business interests must be assured that the government
will not expropriate the intermediaries that they create, nor will it promote the entry of
competing intermediaries, because those intermediaries could finance firms that would compete
with those of the members of the coalition. The result is a financial system comprised of a small
number of relatively large banks and other intermediaries that service affiliated firms, but that do
not promote widespread access to financial services, especially credit. This is at odds with the
optimal financial structure during early stages of development from the new structural
perspective, where a large number of relatively small banks would service small-scale
entrepreneurs.

       The foregoing discussion leads to (at least) two questions. First, can it be shown that the
countries that come closer to achieving the optimal financial structure for their stage of
development also achieve higher rates of growth? More specifically, do countries in early stages
of development fare better under a bank-based system that harnesses local information rather
than a market-based system that does not? Second, if there is evidence supportive of the notion
that optimal financial structure varies with the stage of development, why is it that some
countries come closer to achieving those structures than others? This paper provides empirical
evidence from firm surveys to address the first question. Addressing the second question is
beyond the scope of the paper, though we note that if countries were equally successful in
achieving an optimal financial structure, we would see little variation in financial structure
indicators for a given level of development and find no significant relationships between those
indicators and firm growth. A quick glance at Table 2 reveals relatively wide variation in
indicators of financial development within countries at similar stages of economic development
as reflected in per capita income, and we do find significant relationships between financial


                                                  4
development indicators and firm growth in what follows, relationships that vary with per capita
income.

        Our approach relies on a measure of firm growth from the World Bank Enterprise
Surveys (WBES) database – the percentage increase in the number of workers over the two years
prior to the survey. While labor growth is a topic of intense interest, there are also practical
reasons for focusing on that measure in this analysis.4 Other measures of firm growth (such as
sales growth) are also available, but our measure of labor growth is available for a much larger
set of firms and countries. Testing the hypothesis that financial structure affects firm growth
differentially, depending on a country‘s level of economic development, requires as wide a
sample of countries as possible, since the financial structure indicators are measured at the
country level. Our most expansive regression models employ information from over 49,000
firms in 89 countries.5 Relying on labor (rather than sales) growth has another side advantage:
labor is likely to be measured with less error than sales for both accounting and tax reasons, and
such measurement errors may differ systematically by the level of development.

        To foreshadow our main result, we find that firms grow faster in countries with low
levels of income per capita when the banking system is relatively well-developed. We find no
such results for other measures of financial structure, including measures of stock market
development. Moreover, we find no strong relationships between financial structure and firm
labor growth for countries with high levels of per capita income, in line with previous findings in
the literature (Levine, 2002; Demirguc-Kunt and Levine, 2001). Our results are robust to using
the instrumental variables method to deal with the potential endogeneity of our financial
structure variables.

        We also apply the regression methodology pioneered by Rajan and Zingales (1998)
(henceforth RZ) to test whether firms in industries that rely heavily on external financing have
higher labor growth rates in countries with relatively well developed financial sectors. We find
evidence consistent with that proposition, but only for firms in low-income countries with

4
  For example, job growth is the topic of the upcoming World Development Report for 2013, and a key concern of
the current U.S. administration.
5
  Not only the firm-level survey information, but also a steady expansion in the number of countries that have
financial structure data available enables us to undertake this analysis. For comparison, the original financial
structure indicators presented in Levine (2002) were available for only 48 countries.

                                                        5
relatively well developed banking sectors. While the instrumental variables approaches focus on
between-country differences, the RZ approach captures within-country, between-industry
differences in labor growth rates. That both approaches yield similar results provides support for
the plausibility of our findings. Our confidence regarding the beneficial role of banks in poor
countries is further boosted by our finding that a more developed banking system is associated
with higher investment rates, more employee training, and larger firm sizes only in poor
countries, which suggest that banks spur both physical and human capital investment besides
boosting job growth in such countries.

        The WBES also contains a substantial amount of information on firm characteristics
(size, industry, ownership structure, legal status) that enable us to further pinpoint the types of
firms that benefit from a relatively well developed banking system in countries at early stages of
development. Although banks appear to be the financial institutions best suited to serve firms in
low income countries based on our results, it does not appear that they disproportionately serve
the small-scale firms that are said to characterize the early stages of economic development. In
low-income countries, labor growth increases with the size of the banking sector primarily for
relatively large, capital-intensive firms.

        The rest of the paper is organized as follows. Section II describes the enterprise survey
and financial structure data in greater detail and presents summary statistics. Section III
describes the variables that we use as instruments for our indicators of financial development in
firm growth regressions. It also shows how those variables are related to each other and to our
indicators of financial development. Section IV explains both our estimation approach based on
instrumental variables and that based on the RZ methodology, and presents our main regression
results. Section V examines the types of firms that are most affected by financial structure in
high- and low-income countries. Section VI examines whether other firm characteristics and
performance measures are related to financial development, so as to better understand potential
mechanisms by which banking sector development fosters firm growth in low-income countries.
Section VII concludes.




                                                  6
II.     Data

Sampling from the universe of registered businesses and following a uniform stratified random
sampling methodology, the core WBES uses a standardized survey instrument to benchmark the
investment climate of individual economies across the world.6 The survey contains sufficient
information to allow for firm performance analyses. The surveys also report detailed information
on firm employment, age, industry, ownership, legal status, and the number of establishments.

        As noted above, our analysis is designed to explain variation in firm labor growth. We
use the percentage growth in the number of full-time employees over the two years prior to the
survey as the dependent variable in our regressions because that information was asked of firms
in a wider sample of countries than, for example, information about sales growth. In addition, the
information provided by firm owners regarding their number of employees is likely to be more
accurate than information about their sales, especially for smaller firms that either do not keep
good accounting records or are reluctant to fully report their sales (e.g., because of potential tax
consequences). For the 89 countries whose firms enter our regressions, the WBES surveys were
conducted at different points in time between 2000 and 2009.

        We use three firm characteristics as controls in our firm growth regressions: age, size as
measured by the current number of employees, and the percentage of shares held by foreign
owners. Both firm age and number of employees enter the regressions in logged form. We do
not have strong priors about how these characteristics affect firms‘ labor growth, though if older
firms are more likely to have reached their equilibrium size, they might have slower growth than
younger firms. Percentage growth in employees might be greater for smaller firms, as they are
starting from a low base of employees, but larger firms may have advantages, especially in terms
of access to finance, that make hiring additional workers easier. It is also unclear whether firms
with high shares of foreign ownership would expand employment more or less quickly than
firms with higher shares of domestic ownership. Better access to finance by foreign firms
would allow faster expansion, but a shorter-term focus on the part of those firms may limit firm
expansion.



6
 A detailed description of the sample design and sample frame can be found at:
http://www.enterprisesurveys.org/documents/Sampling_Note.pdf.

                                                        7
         The key explanatory variables in our analysis are indicators of financial structure that are
drawn from the World Bank Database on Financial Development and Structure, which was
updated in November 2010.7 We rely on two primary variables that are intended to capture
different aspects of each country‘s financial structure – the ratio of private credit to GDP and the
ratio of stock market capitalization to GDP. From previous research on the finance-growth
nexus, we would expect growth in employment to be positively linked to both of these measures.
However, the new structural economics predicts that the association between firm growth and
private credit relative to GDP, which is a bank-based measure of financial development, should
be stronger for countries in early stages of development. By contrast, the association between
employment growth and stock market capitalization (relative to GDP) is likely to be stronger for
countries in more advanced stages of economic development.

         Another aspect of financial structure that could impact firm growth is the nature of
ownership—the extent to which the banking sector is state-owned or foreign. As we shall later
show, however, neither state nor foreign ownership of banks explains variation in labor growth
in our sample. The ratios of private credit to GDP and stock market capitalization to GDP are
therefore the focus of the empirical analysis that follows.8

         Perhaps the first thing to notice from Table 2, which provides summary statistics for our
four indicators of financial development, is that country coverage is broader for the private credit

7
  More detailed descriptions of the dataset can be found in Beck, Demirguc-Kunt, and Levine (2000) and Beck and
Demirguc-Kunt (2009). The permanent URL for accessing the dataset is: http://go.worldbank.org/X23UD9QUX0.
8
  In banking systems characterized by a high degree of ownership by the government, banks‘ credit growth, portfolio
quality, profitability, and productivity tend to suffer (Barth, Caprio, and Levine 2001; 2004; La Porta, Lopez-de-
Silanes, and Shleifer 2002). We would therefore expect the employment growth of firms to be slower in countries
with state-dominated banking systems. Our data on the share of banking sector assets held by state-owned banks is
taken from Micco, Panizza, and Yañez (2007). We do not have a strong prior about whether any pernicious effects
of state ownership of banks would be stronger or weaker in countries in early development stages.
The share of foreign bank ownership could also affect firms‘ employment growth. On the one hand, foreign banks
tend to be more efficient than domestic banks in developing countries (see, e.g., Claessens, Demirguc-Kunt, and
Huizinga, 2001). Their presence could therefore impose competitive pressure, resulting in lower interest rates on
loans and, perhaps, a search for new borrowers, thus expanding the outreach of the banking system. All of these
aspects of foreign bank participation could boost firms‘ labor growth. At the same time, there are longstanding
concerns about the tendency of foreign banks to focus their lending on large, well-established firms. In banking
sectors dominated by foreign banks, medium and small-sized borrowers might suffer in terms of access to credit,
resulting in slower labor growth. Proponents of New Structural Economics would be especially concerned about the
mismatch between foreign-owned banks and the large share of small businesses in developing countries. We test
these ideas in our labor growth regressions using data on the share of banking sector assets held by majority-foreign-
owned banks from Claessens and Van Horen (2009).


                                                          8
measure, with 46 countries at or below the sample median for income per capita, and 45
countries above the median. This is another reason that private credit is the financial indicator
that plays the primary role in our analysis. For stock market capitalization/GDP and the shares of
banking sector assets held by state and foreign-owned banks, the sample tilts in favor of high
income countries (37-40 country observations) as opposed to lower income countries (21-22
observations). In part, this could reflect that stock markets and large shares of foreign bank
participation are more likely to be features of the financial systems of advanced countries than of
developing countries. For the share of sector assets held by state-owned banks, however, this is
not true. The prevalence of observations from richer countries on that indicator likely indicates
the relative difficulty of collecting data summarizing financial development in low-income
countries. In any event, for the indicators other than private credit/GDP, the relatively small set
of lower income countries makes it more difficult to test hypotheses about optimal financial
structure based on predictions from new structural economics.

       As expected, higher income countries have more developed financial sectors in terms of
their average private credit/GDP, stock market capitalization/GDP, and the share of assets held
by foreign-owned banks (Table 2). The average share of sector assets held by state-owned banks
tips slightly in favor of lower income countries (0.20 vs. 0.16 for higher income countries),
though the median state-owned bank asset share is almost identical for the two groups.

       At least two other features of the summary statistics for the financial indicators stand out.
First, although higher income countries tend to have more developed financial sectors, there is
overlap in the distributions of the financial indicators between higher and lower income
countries. For example, among lower income countries, those in the top ten percentile have
private credit/GDP ratios of 37.8% or higher, which would rank them at or above the median for
higher income countries. A second feature is that within the group of low (or high) income
countries, there is substantial variation on all financial development indicators despite relatively
small sample sizes. For example, the ratio of stock market capitalization to GDP runs from 1% at
the tenth percentile to 34% at the 90th percentile for countries in the lower income sample. On
the one hand, this suggests that if there is an optimal financial structure that varies with stage of
development as reflected per capita income levels, a sizable fraction of the countries in our
sample are not achieving it. On the other hand, and perhaps more practically, the fact that there is

                                                  9
substantial variation in financial structure within both the high and low income samples enables
us to test whether certain structures yield better outcomes in terms of employment growth, and
whether those structures differ depending on the income level of a country.


III.   Instruments

Endogeneity poses the major challenge for identifying a causal relation between financial
structure and firm growth at different stages of economic development. In particular, we are
worried about two possibilities. First, there might be omitted variables that are correlated with
both labor growth and our financial structure variables. For instance, omitted business
environment indicators might be correlated with both financial development and firm growth
(Xu 2011). Second, the causality may run both ways, both from finance to growth and from
growth to finance.

       While the next section describes both our instrumental variables regressions and those
based on the RZ methodology as means of confronting this endogeneity, we begin first with a
discussion of potential instruments in this section. The earliest approaches to address the
endogeneity of financial development in cross-country growth regressions focused on legal
origin variables as instruments (LaPorta, Lopes-de-Silanes, Shleifer and Vishny, 1998; Levine,
Loayza, and Beck, 2000). French legal origin, in particular, was associated with lower levels of
financial development relative to English and, especially, German legal origin. A parallel line
of research designed to explain broader institutional development and its impact on growth
emphasizes the role of endowments. In particular, commodity endowments (Engerman and
Sokoloff, 1997, 2000) and settler mortality rates (Acemoglu, Johnson, and Robinson, 2001,
2002) explain substantial variation in the quality of institutions, which in turn explain variation
in long-run growth trajectories as reflected in current per capita income levels. Another offshoot
of the literature uses aspects of a country‘s population, namely ethnolinguistic fractionalization
(Alesina, Baqir, and Easterly, 1999; Easterly and Levine, 1997) and trust levels among citizens
(Zak and Knack, 2001), to explain the quality of institutions and their relationship with economic
growth.

       We prefer to explain financial development and structure using endowments as
instruments because they are more deeply rooted in a theory that connects initial exogenous

                                                 10
conditions with the creation, quality, and persistence of institutions. To some extent,
ethnolinguistic fractionalization could also be a product of those initial conditions, while trust
levels could be a manifestation of homogeneous societies and the relatively egalitarian
distributions of income that derived from some endowment structures. While our preference is to
explain financial development using variables that summarize initial endowments, we are
mindful of the warning from Easterly and Levine (2000) that exogeneity is not the sole criteria
for selection of economically meaningful instrumental variables in this context: there must also
be compelling reasons to expect that a variable is closely linked to the behavior of financial
intermediaries. They therefore opted for the legal origin variables in their work. We
acknowledge the advantages and disadvantages of the endowments and legal origins variables as
instruments, and thus perform an exploratory analysis to see which variables best explain
variation in the broader set of countries for which financial development indicators are now
available. A brief description of the specific variables that we try and their motivation for
inclusion as instruments follows.


Commodities, Natural Resources

Engerman and Sokoloff (1997) argue that the land endowments of Latin America were amenable
to commodities that featured economies of scale in production and thus the use of a large share
of slave and indigenous labor (sugar cane, rice, silver). Power was thus historically concentrated
in the hands of the plantation and mining elite, and the institutional structure that arose and
persisted offered economic opportunity to only a small group. In contrast, the endowments of
North America lent themselves to commodities grown on family farms (particularly wheat and
maize) which fostered the growth of a relatively large middle class in which power was widely
distributed. The institutions that arose and persisted came to reflect this relative equality of
economic opportunity. These factors help account for the disparity in income per capita between
North America and Latin America. We therefore include dummy variables indicating whether
any of a given commodity is grown in each country as potential instruments. Corn, maize, and
wheat growth are our proxies for the egalitarian institutions emphasized by Engerman and
Sokoloff, which we expect to be positively linked to financial development.




                                                  11
       A related literature examines whether endowments of natural resources, particularly oil,
are associated with lower levels of economic development. The existence of a so-called resource
curse has been analyzed and debated extensively (Sachs and Warner 1995, 2001; Lederman and
Maloney, 2008; van der Ploeg, 2011). The ease with which profits can be generated from natural
resources could be associated with less investment in other forms of production, such as
manufacturing. In turn, there might be less incentive to invest in the research and development,
human capital, and institutions (property rights protection, contractual frameworks, and courts)
needed to support the market-based exchange that characterizes non-resource based production
(Besley and Persson, 2010). In terms of financial development and structure, broad-based access
to financial services is less likely to be provided in resource-based economies. In fact, recent
evidence shows that the outreach of the financial systems of the resource-based economies is
narrow, as firms in those economies are less likely to use any form of external finance and they
receive substantially less credit from banks (Beck 2011). We therefore include a measure of an
economy‘s dependence on oil, the net exports of petroleum per worker, in the set of potential
instruments. We expect that variable to be negatively associated with financial development
indicators. A key advantage of using this measure is that it is available for most countries.
Moreover, Maloney and Lederman (2008) show that measures based on net exports are more
closely linked to actual natural resource reserves than other trade-based endowment measures.


Settler Mortality

Another influential strand of the literature on the effects of endowments on institutional
development and growth trajectories focuses on rates of settler mortality (Acemoglu, Johnson,
and Robinson, 2001, 2002). In climates and environments that were inhospitable for
development as reflected in high rates of settler mortality, Europeans created states and
institutions that enabled elites to extract wealth from colonies, usually in the form of minerals
and cash crops. By contrast, in more hospitable environments, settler colonies emerged in which
institutions were created to protect property rights and thus foster more broad-based economic
opportunity. Indeed, Acemoglu, Johnson and Robinson (2001) find robust evidence that settler
mortality has a pervasive influence on a whole series of key property rights institutions such as
protection against expropriation by the government, constraints on the executive, and the
establishment of democracy. Since relatively widespread access to financial services is likely to

                                                 12
be reflective of the more egalitarian institutions that emerged in settler colonies, we include the
logarithm of annualized deaths per thousand European soldiers in the set of potential
instruments, which we expect to be negatively associated with financial development indicators.


Fractionalization and Trust

Zak and Knack (2001) develop a theoretical model of the effect of trust on growth. In it,
consumers are randomly matched with a broker who makes an investment on their behalf. Prior
to the payout from that investment, consumers decide how much time and effort to spend
investigating the quality of the broker‘s investment. Aside from the consumer‘s investigation,
two sets of institutions are incorporated into the model to provide incentives for untrustworthy
brokers to reduce the amount of the payout that they cheat consumers out of. The first, which is
operationalized in the model as a partial loss of the broker‘s fee, represents formal institutions
such as investigative agencies and the judicial system that detect and punish brokers for abuses.
The second, which is operationalized as the social proximity between consumer and broker,
represents informal institutions that can impose sanctions on cheating brokers through social ties.
These sanctions could include the guilt associated with violating social norms, religious dictates
concerning, for example, the afterlife, social ostracism, or loss of profits due to reputational
effects.

           The key insight from the model is that the amount of investment is declining in the level
of distrust between consumers and brokers, because consumers spend more resources
investigating their brokers. Lower investment has an adverse impact on growth. In turn, trust is
lower (and cheating by brokers more likely) when social distances are greater, formal institutions
are weaker, and informal social sanctions are less effective. Cross-country regressions validate
the predictions of the theoretical model in that trust among citizens is strongly positively linked
to both investment levels and growth rates.

           We hypothesize that trust, which arises as a manifestation of the social and institutional
environment, could help facilitate financial intermediation. A higher level of trust, for instance,
may lower information asymmetry between lenders and borrowers; it may also facilitate the
social norm of not defaulting on debts. We therefore include the average level of trust in a
country from 1981 to 2006 among the potential instruments and we expect it to be positively

                                                    13
linked to indicators of financial development.9 We use the average from 1981 to 2006 (rather
than from the actual year of the WBES survey) because data on trust are available for distinct
years for different countries, and relying on the survey year would lead to too many missing
values for trust for our countries. Averaging also has the benefit of smoothing the measurement
errors of trust.

        Zak and Knack also note that their framework can help account for other findings from
the cross-country growth literature. For example, the negative link between ethnic
fractionalization and growth found in Easterly and Levine (1997) and between income inequality
and growth in Easterly (1999) are both substantially weakened when levels of interpersonal trust
are included in regressions. Although the channels through which social ties and the institutional
environment affect trust are more clearly laid out than for the polarization measures, we include
measures of ethnic and linguistic fractionalization among the set of potential instruments. We
expect them to be negatively linked to financial development indicators.


Legal Origin

LaPorta, Lopez-de-Silanes, Shleifer, and Vishny (LLSV) (1997, 1998) first documented that the
legal protection of outside investors was stronger in common law countries (English legal origin)
than in civil law countries rooted in Roman legal traditions, particularly those of French legal
origin. Their interpretation was that English common law traditions evolved to protect private
property and support private market outcomes, whereas French civil law tended to unify the legal
system and cement state control of the judicial system often to the detriment of private market
development. Because legal traditions were introduced in most countries by outside colonizers,
LLSV argued that legal origin could be treated as exogenous in regressions explaining financial
development and growth. Using legal origin as an instrument for investor protection, they then
demonstrated a strong link between those protections and measures of financial development.
We therefore include dummy variables for English and French legal origins in the set of potential
instruments. We expect English (French) legal origin to be positively (negatively) linked to
financial development.


9
 The measure is based on information from the World Values Survey. See Knack and Zak (2001) for details of its
construction.

                                                      14
       To summarize the discussion to this point, we are able to exploit three advantages over
previous studies: (1) a large firm-level database drawn from extensive surveys which provide a
precise measure of firm growth as well as valuable information on firm characteristics, (2)
updated data on financial structure, which enables us to include a broader set of countries in our
regressions than what was used in previous studies of bank-based versus market-based financial
systems, and (3) a broader set of potential instruments, many of them tied directly to theoretical
arguments about how endowments shape institutions, including financial ones.

       Table 3 presents correlations between financial development indicators, potential
instruments for financial development, and the firm characteristics that we include in the labor
growth regressions. Table 4 presents regressions of financial development on firm characteristics
and potential instruments, each added to the regression separately. The strongest correlations are
negative ones between settler mortality and financial development (private credit/GDP; stock
market capitalization/GDP). Next are the positive correlations between financial development
and interpersonal trust. Note that the ethnic fractionalization measure was not strongly linked to
financial development (results not in the table) and so we present only the trust measure in
Tables 3 and 4.

       Surprisingly, neither the legal origin variables nor our resource-based measures of
endowments (commodities dummies, net oil exports) are significantly correlated with financial
development. Though we tried a variety of variables, we found no strong positive relationships
between grains and financial development, as we had expected from our reading of Engerman
and Sokoloff, and thus we do not present the commodities dummies in the tables. We do present
the net oil exports variable. Though it is not strongly correlated with our measures of financial
development, it has some explanatory power for both variables (with t-statistics between 1.2 and
1.3). Similarly, though only English legal origin appears in the tables, we experimented with
French legal origin and combinations of legal origins (e.g., non-English, non-French) and found
no strong relationships with our measures of financial development.

       For the most part, the regressions that control for firm characteristics in Table 4 confirm
the correlations in Table 3. For example, private credit is significantly negatively linked to settler
mortality and significantly positively linked to interpersonal trust. We also obtain the expected


                                                 15
signs for net petroleum exports (negative) and British legal origin (positive), though neither
achieves significance. The instruments do a poorer job of explaining variation in stock market
capitalization, with the exception of settler mortality which is negative and significant. Trust and
British legal origin both have the expected signs, and the trust coefficient comes reasonably close
to significance. Weaker relationships between the potential instruments and stock market
capitalization could be due to the smaller sample size for that variable relative to private credit.

        Based on Tables 3 and 4, settler mortality and trust appear to be the most relevant
instruments for explaining financial development. Note, however, that the net petroleum exports
variable becomes significantly negative in the private credit regressions when it appears with
other instruments. For example, in the first stage of the GMM regressions that we describe in the
next section, both trust and net petroleum exports are highly significant predictors of private
credit (see Appendix A. for an example).

        In the end, settler mortality does not appear in the first stage results despite its strong
association with financial development in Tables 3 and 4. This is because the instrument set does
not pass Hansen‘s J test of over-identifying restrictions when settler mortality is included. For
the exclusion restrictions to be satisfied, the instruments should affect labor growth only through
their effects on financial development. Because settler mortality has been shown to be so
strongly linked to broad measures of institutional and economic development (Acemoglu,
Johnson, and Robinson, 2001, 2002)—for instance, we can conceivably see how settler mortality
may directly affect local infrastructure and labor regulation, both of which may directly affect
firm growth (Xu 2011)—it does not surprise us that its inclusion results in failure of the over-
identification restriction test (i.e., Hansen‘s J test).

        Because trust, especially as formulated in the Knack and Zak (2002) framework, could
have a strong influence on contracting arrangements, we find it more plausible that its effects on
labor growth could work largely through its positive impact on private credit levels. Similarly,
the strong negative links between measures of countries‘ reliance on oil exports and various
measures of financial development recently documented in Beck (2011) indicate that widespread
access to financial services, particularly credit, is rare in resource-based economies. Therefore,
the notion that net oil exports affects labor growth largely through its effects on financial


                                                    16
development and structure also does not seem farfetched. In the GMM regressions that follow,
we will show that the test of over-indentifying restrictions is passed when trust and net oil
exports are used as instruments in most cases, and we will offer additional estimation approaches
in the few cases when the test is not passed.

         Our approach in this section has been to experiment with a wide set of potential
instruments suggested by the literature, and to let the data inform us about which ones best
explain variation in financial development. At the same time, we bear in mind that legitimate
instruments must affect firm labor growth only through their effect on financial development.
Our assessment is that trust and net oil exports best meet those criteria, and thus those two
variables form the instrument set in the instrumental variables regressions that follow.


IV.      Regressions

A.       OLS

We estimate the following base regression:

                                                                                                                (1)

Where        is the percentage change in the number of workers employed by firm i in country j
over the two years prior to the enterprise survey, as described above. FIRM represents the three
firm characteristics that we use as controls: age, size measured in (logarithm of) total workers,
and the share of foreign ownership.10 As described in Section 2, we expect that labor growth
would be slower for older, better established firms. We have no strong priors about how firm size
or foreign ownership share affect labor growth. FIN represents the indicators of financial
development and structure that are the focus of the analysis. These include private credit/GDP,
stock market capitalization/GDP, and the shares of sector assets held by foreign and state banks.

         We begin with ordinary least square (OLS) regressions describing the associations
between indicators of financial development and structure and labor growth. We present results
for the full sample of countries, and for high- and low-income sub-samples. We divide countries
into high and low-income based on the median per capita income level for the 91 countries for

10
  In unreported specifications, we include industry dummy variables. Qualitative results are similar to those
presented here.

                                                         17
which we have private credit/GDP figures, as we did for the summary statistics in Table 2.
Standard errors are clustered at the country level in all models to avoid exaggeration of precision
in firm-level regressions with country-level variables (Moulton 1990).

         The OLS regressions do suggest relationships between financial indicators and firm labor
growth that are consistent with predictions from new structural economics (Table 5). For
example, private credit/GDP is significantly positively associated with labor growth only for the
sample of low-income countries, while stock market development/GDP is positively associated
with labor growth among the high-income group. The magnitude of those coefficients is also
large. For example, a one-standard deviation increase in private credit/GDP in the low-income
group yields roughly a twenty percentage point increase in labor growth, compared to a mean
labor growth rate of 58.8 percent (37.8 percent median) for that sample.11                 A one-standard
deviation increase in stock market capitalization/GDP in the high income group is associated
with a 17-27 percentage point increase in labor growth, compared to the 31 percent mean (24
percent median) growth rate for that sample.

         In the low-income sample, the coefficient for stock market capitalization is positive in
one specification, but does not achieve statistical significance. In the high-income sample, the
coefficient for private credit/GDP is negative and significant in one specification, but that is
likely due to the high correlation between the private credit and stock market capitalization
indicators. Among the 64 countries that have values for both of those indicators, which is biased
toward high-income countries since many low-income countries do not have information on
stock market capitalization, the correlation is .66. When the stock market capitalization variable
is dropped, the private credit variable is no longer significant in the high-income sample
(regressions unreported).12 In all, the associations between private credit and labor growth are
strong in the low- income sample, while stock market capitalization is not linked to labor growth.
The reverse is true among the high-income countries.

         Among the control variables, there is a strong negative relationship between firm age and
labor growth across both high- and low-income countries. There is also a significant positive
11
  Keep in mind that all labor growth rates are based on a two-year period.
12
  Note also that when the private credit variable is dropped from Table 5, model 9, the stock market capitalization
variable remains positive and significant in the regressions for the high-income sample. Results are unreported but
available from the authors.

                                                         18
association for some models between firm size and labor growth. The share of foreign ownership
in a firm is not strongly associated with labor growth for either sample. Because we split the
sample by the level of per capita income, there is less need to include that variable in the labor
growth regressions. However, in the regressions that pool all countries, that variable is negative
and significant indicating that labor growth is slower in countries with high incomes (Table 5,
model 3). For the sub-sample regressions, the coefficient for per capita income is also negative,
and it is significant in the low-income sample. Perhaps more importantly, its inclusion does not
qualitatively change results for other variables (Table 5, models 6 and 9).

        In Appendix B, we present OLS regressions that control for additional country-level
variables to assess the robustness of our findings regarding financial development and firm labor
growth. Because job growth likely depends on the quality of the institutional environment and
the general level of stability, we include two variables: an index of adherence to rule of law and
another measuring political stability. Certain industries may have experienced more rapid job
growth during the period in which our surveys were conducted, and thus we also include
variables that measure the share of GDP attributable to agriculture, manufacturing, and services
(the omitted category) in our analysis. While these control variables are often significant, they do
not change the coefficients for our indicators of financial development much. Private credit/GDP
remains significantly positively linked to labor growth within the low-income sample; stock
market capitalization/GDP is positively linked to labor growth in the high-income sample.


B.      Instrumental Variables Regressions

Because labor growth is as likely to affect financial structure as the reverse, or some omitted
factors could be driving both high labor growth and financial development, it is important to
investigate whether the associations between financial structure and firm labor growth in Table 5
are causal. To address these concerns regarding the endogeneity of the financial structure
variables, we present Generalized Method of Moments (GMM) estimates in Table 6.13 As
described above, we use the average level of interpersonal trust from 1981 to 2006 and net
petroleum exports per worker as instruments. Both are measured at the country level. The first-

13
  We choose GMM over two-stage least squares because GMM can account for heteroskedasticity of unknown
form (Woolridge, 2001). In practice, the two-stage least squares results were qualitatively similar to the GMM
results presented here.

                                                        19
stage F statistics for the excluded instruments and Shea‘s adjusted partial R-squared statistics
indicate that the instruments are strong predictors of financial development in the poor sub-
sample. They also perform reasonably well for the full sample and for the rich sub-sample when
private credit/GDP is the endogenous variable. They do not perform as well when stock market
capitalization is treated as endogenous. This is not surprising because, as shown above, trust
levels and net petroleum exports are not as strongly correlated with stock market capitalization.

       The main finding from the GMM regressions in Table 6 is that private credit/GDP
remains positively associated with labor growth in the sample of low-income countries, though
the coefficient is smaller than in the OLS regressions. We view this as evidence consistent with
predictions from new structural economics and Gerschenkron (1962): in the early stages of
economic development, banks are better able to foster firm growth than are stock markets. The
p-values for Hansen‘s J test of over-identifying restrictions are far larger than critical values,
providing additional support for the validity of our instruments.

       Stock market capitalization/GDP is not significantly linked to labor growth in the sample
of high-income countries, in contrast to the OLS regressions. The first-stage F statistics for the
excluded instruments are small (around 2) whenever stock market capitalization is the
endogenous financial structure variable in the GMM regressions. This reflects the low degree of
correlation between stock market capitalization and our instruments. The instruments also do not
pass Hansen‘s J test of over-identifying restrictions when stock market capitalization appears in
the GMM regressions, indicating that trust and net petroleum exports likely do not affect labor
growth only through their effects on market capitalization. In all, the GMM regressions do not
provide support for a causal link between stock market capitalization and labor growth among
firms in high-income countries.

       Since weak instruments could be the cause of the non-results for stock market
capitalization in the GMM regressions, we also present Limited Information Maximum
Likelihood (LIML) estimates (Appendix C). Weak instruments cause less serious problems
when estimation is conducted by LIML than by GMM. However, qualitative results are similar
for stock market capitalization in the LIML and GMM regressions, again providing no support
for a causal link between that variable and labor growth in our sample. Indeed, the coefficients


                                                  20
for market capitalization are negative in the high-income sample. At the same time, we recognize
that this does not necessarily rule out a causal link. Our process for indentifying potential
instruments simply failed to come up with candidates that worked well for stock market
capitalization. In short, the positive coefficient for that variable in the OLS regressions might or
might not reflect a causal link with firms‘ labor growth but our analysis did not uncover an
instrument that could reveal the ‗true‘ relationship.

        We also present LIML results for private credit/GDP in Appendix C. Although the
statistical tests from the GMM models supported the relevance and validity of our instruments
(implying that recourse to LIML is not necessary), the LIML results for low-income countries
are similar to the GMM results, though significance levels are a bit lower. Still, the one result
that is consistent across all estimation techniques in this section is the positive, significant
relationship between private credit and firm labor growth in low-income countries.


C.      Rajan-Zingales Methodology

Another widely used method for identifying a causal link between financial sector development
and growth comes from Rajan and Zingales (1998), focusing on how industry-level growth rates
vary with both dependence on external sources of finance and the level of financial sector
development in a country.14 The key identifying assumption is that capital markets in the United
States are relatively frictionless, and thus an industry‘s reliance on external finance in that
country is a reliable indication of its true technological demand for external financing. Industry-
level dependence on external finance computed from U.S. data is therefore the key exogenous
source of variation in their analysis.

        The equation that we estimate is:

                                                                                                              (2)

where       is the percentage change in the number of workers employed by firm i in industry j in
country k over the two years prior to the enterprise survey, as described above.                 represents


14
  See, for example, Fisman and Love (2007) and Kroszner, Laeven, and Klingebiel (2007) for applications of this
method, and Xu (2011) for summary of the use of this method in identifying the effects of business environment in
general.

                                                        21
dummy variables for each industry;        represents dummies for each country. EXTDEP is the
industry-level dependence on external finance based on U.S. data, which is equal to external
finance (capital expenditures minus cash flow from operations) divided by capital expenditures,
as defined in Rajan and Zingales (1998). We then interact that variable with FIN, which
represents our indicators of financial development (private credit/GDP and stock market
capitalization/GDP) in each country. A positive, significant coefficient for that interaction term is
supportive of the notion that the labor growth of firms in externally dependent industries is faster
relative to other industries in countries with relatively well developed financial sectors. Since our
aggregate variable now varies at the country-industry level, we cluster our standard errors at the
country-industry level in our analysis.

       Because it focuses on within-industry, between-country differences in labor growth, this
approach is complementary to our instrumental variables regressions (which focus on cross-
country differences). Moreover, the inclusion of country and industry dummy variables makes
this approach less susceptible to criticism about omitted variable bias and model mis-
specification than more traditional cross-country regressions. RZ relied on industry-level data
because it provided the most disaggregated comprehensive data on growth at that time, whereas
our surveys enable us to examine firm-level growth in a consistent manner across countries.
Finer disaggregation should improve the precision of our estimates relative to industry-level
approaches.

       Our main finding is that the interaction between industry-level dependence on external
finance and private credit/GDP is significantly positively linked to labor growth in the sample of
low-income countries (Table 7). By contrast, there is no significant relationship between labor
growth and the interaction between stock market development and dependence on external
finance in the sample of low-income countries. This is the same pattern as in our instrumental
variables regressions. Our finding indicates that, when evaluated at the median industry level of
external finance (0.24), the country at the 90th percentile of banking development would have a
labor growth rate higher by 8.4 percentage points relative to the country at the 10th percentile of




                                                 22
banking development.15 This higher labor growth rate amounts to 16% of the standard deviation
of labor growth for the poor country sample.

         Perhaps surprisingly, we find a negative relationship between labor growth and
interactions between industry-level dependence on external finance and indicators of financial
development within the sample of high-income countries (Table 7, models 7 and 8). This could
indicate that firms in externally financially dependent industries in countries with well-developed
financial sectors were more likely to be down-sizing at the time of our surveys than firms in
other industries. However, our sample is weighted heavily towards manufacturing firms and
away from services. This result could also therefore reflect the shift of employment from
manufacturing to services in many industrialized countries during this period.


V.       Interactions between Firm Characteristics and Financial Structure

In the previous section, we presented evidence consistent with a positive causal link between
private credit/GDP and firm growth in poorer countries. To get a better idea of what types of
firms are benefiting from bank finance in these poorer countries,16 we interact the financial
structure variables with firm characteristics using the following equation:

                                                                                                (3)

Notation is the same as in equation (1). We focus first on the relationship between firm size,
financial structure and firm growth. Small firms are defined as those with ten or fewer workers;
medium-sized firms have from eleven to fifty workers; and large firms have more than fifty
workers. Note that our definitions of size cutoffs are somewhat smaller than many studies of
mature economies, reflecting the fact that our sample has many more developing countries,
which feature smaller firms in general than mature economies.

         In models where private credit/GDP is the financial structure variable, the coefficients for
medium- and large-size are negative, and the one for large-size is significant indicating that the
growth rates of small firms are higher than for others.17 The negative coefficients for medium-
and large-size are offset, however, by positive coefficients on the interactions between firm size
15
   That is, 1.108 * 0.24 * (0.378 – 0.060).
16
   We do not find strong and consistent patterns for the rich subsample.
17
   Those coefficients are not shown in Table 8, but are discussed in the table notes.

                                                          23
and private credit/GDP. The one for large firms is significant in the OLS regression and the ones
for both medium and large-sized firms are significant in the GMM regressions (Table 8).18 Those
coefficients imply that labor growth rates for large firms in the low-income sample begin to
exceed those for small firms only when private credit reaches 30-35 percent of GDP. Within the
low income sample, only about a fifth of the countries meets or exceeds that standard.


         This suggests that the positive relationships between private credit/GDP and labor growth
for the poorer countries in the previous section were driven by large firms in a small set of
countries with well-developed credit markets. This would seem to be at odds with predictions
from new structural economics in which banks are hypothesized to be well-positioned to help
small-scale entrepreneurs in the earliest stages of development.19


         We next divide firms by age, with old, mid-age, and young corresponding to the top,
middle, and bottom thirds of the age distribution in our sample (Table 8). In the low-income
sample, labor growth is declining in firm age, indicating that better established firms are less
likely to be in an expansionary phase requiring the hiring of new workers.20 Also, because we
compute labor growth on a percentage basis as (L(t) – L(t-2))/L(t-2), incremental gains in
employment affect small firms much more than they do large ones. Within the low-income
sample, the beneficial effects of private credit development on the labor growth of firms appear
to be largely independent of firm age. The coefficients on all of the interactions between firm age
and private credit in the OLS regressions are positive, significant, and of almost identical
magnitude. In the GMM regressions, coefficients on the interactions are also positive and of
similar size across age categories, though they are only significant for middle-age and old firms.


         While firm age does not appear to have strong effects on the relationship between
financial structure and firm labor growth, those relationships are sensitive to firms‘ capital
intensity (Table 8). We divide firms into high, mid, and low capital intensity, corresponding to
the top, middle, and bottom thirds of the sample as measured by the ratio of capital to labor at the
18
   In GMM estimation, the interaction terms of financial structure variables with firm characteristics are considered
endogenous. Instruments consist of the interaction of our old instrumental variables with firm characteristics.
19
   Of course, well-developed banks might have enabled these firms to grow from small to large over time.
20
   This is also true for the high-income sample, though results for that sample are not shown in Table 8.

                                                         24
industry level. Within the sample of poorer countries, the positive effects of private credit/GDP
on labor growth rates are higher among more capital-intensive firms. Similar to the results for
firm size, this casts doubt on whether relatively well-developed credit markets in low income
countries improve labor growth rates for less capital-intensive firms. This, too, would seem to be
at odds with predictions from new structural economics.21


        To summarize, contrary to predictions from new structural economics that banks should
disproportionately benefit small-scale entrepreneurs and manufacturers in the early stages of
economic development, our results indicate that the labor growth rates of larger, capital-intensive
firms are increasing in the level of private credit to GDP. While banks appear to be more
important than stock markets for generating firm growth in low income economies, they do not
seem to target the clients that new structural economics predicts would be most natural. We
cannot, however, rule out the possibility that banks enabled a subset of small firms to grow and
become more capital-intensive over time. Of course, why only a subset of firms would have
benefited from bank finance remains an open question.


VI.     Mechanisms through Which Bank Finance Spurs Firm Development

As a final empirical exercise and to further assess the plausibility of our main finding, we
examine the relationships between our indicators of financial development and other firm-level
performance variables (Table 9). Within the sample of low-income countries, we find that
private credit/GDP is positively and significantly associated with firms‘ investment rates, which
indicates that the labor growth associated with banking sector development is accompanied by
capital investment. Similarly, private credit/GDP is positively associated with a dummy variable
indicating that the firm provides training to its employees. This suggests that bank finance is
associated not only with increasing the number of workers at firms in low-income countries, but
also with developing their human capital, presumably to achieve productivity gains. Finally, in
low-income countries private credit is also positively associated with firm size measured by
number of employees. One concern was that our measure of labor growth focused on a short
time horizon (2 years). The positive significant link between private credit and firm size is

21
  Again, a relatively well developed banking sector might have might have enabled a subset of firms to become
more capital-intensive over time.

                                                       25
consistent with the idea that bank finance sustains firms over longer periods enabling them to
expand over time.

       In contrast, neither private credit nor stock market capitalization is significantly
associated with investment rates, firm size, or employee training within the sample of high-
income countries. Further, there is no strong relationship between stock market capitalization and
the other firm outcome variables within the sample of low-income countries. These patterns
mimic the ones that we found for labor growth and thus support our interpretation of our main
finding regarding the beneficial effects of banking sector development in lower-income
countries.


VII.   Conclusions

We have combined data from detailed firm surveys with indicators of financial development and
structure that cover a broader set of countries than in earlier studies in order to study the
relationships between firm growth, financial structure, and stage of economic development. In
low-income countries, we find evidence that labor growth is swifter in countries with a higher
level of private credit/GDP, consistent with predictions from new structural economics. This
conclusion holds using two well-established estimation approaches for identifying the causal
impact of financial development on growth: instrumental variables regressions and the Rajan-
Zingales methodology. The plausibility of the finding of the beneficial role of banks in poor
countries is further boosted by our finding that a more developed banking system is associated
with higher investment rates, more employee training, and larger firm sizes only in poor
countries, which suggests that banks spur both physical and human capital investments besides
boosting job growth in such countries. In high-income countries, we find that labor growth rates
are increasing in the level of stock market capitalization, which could also be viewed as being
consistent with predictions from new structural economics, though we are unable to provide
evidence that the association is causal.

       At the same time, when we examine the types of firms that appear to benefit from well-
developed private credit markets in low-income countries, we find no evidence that it is the
small-scale firms that benefit most. Rather, labor growth rates are increasing in the level of
private credit market development for larger, capital-intensive firms. This pattern is more

                                                  26
consistent with political economy explanations of how banking systems in low-income countries
serve the interests of the elite, rather than providing broad-based access to financial services.

       We acknowledge that our approach comes with caveats and highlights gaps in our
knowledge about the effects of financial structure. For example, our inability to find a suitable
instrument for stock market development makes it difficult to study whether market
capitalization enables firms to grow more quickly in high-income countries. Though again, our
results using the RZ methodology also show no evidence of a positive link between stock market
development and labor growth of firms in industries that depend heavily on external finance.

       Perhaps more importantly, though the enterprise surveys that we use offer rich
information across a wide range of countries, our labor growth rates can only be computed over a
two-year window. We are therefore relying on a snapshot of firm growth to draw inferences.
Case studies of firm financing patterns and financial development in low income countries over
longer periods are likely to offer a more complete picture of how firms grow. To the extent that
some low-income countries have been better able to achieve financial inclusion, those case
studies could shed light on how banks can foster growth across a broader spectrum of firms
during the early stages of development. However, to the extent that the political economic
incentives that limit the inclusiveness of financial systems in low-income countries cannot be
overcome, it would be wise to explore how alternative policies and intermediaries could better
achieve that aim.




                                                 27
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                                            31
Table 1. Variable definitions and sources
Variable name                   Definitions
Lgrow (labor growth)            Firm-level employment growth rate, from ICA data (i.e., the investment climate data).
Private credit/GDP              Private Credit/GDP, from World Bank Financial Development and Structure Database. (Also available from WDI).
Stock market/GDP                Stock Market Capitalization/GDP, World Bank Financial Development and Structure Database.
Share of state banks            Share of banking sector assets held by government-owned banks, from Micco, Panizza, and Yanez (2007).
Share of foreign banks          Share of banking sector assets held by foreign-owned banks, from Claessens and Van Horen (2009).
Ln(L)                           Log(firm size in terms of the number of employees), from ICA.
Ln(firm age)                    Log(firm age), from ICA.
Ln(GDP per capita)              Log(GDP per capita), country level, WDI data.




                                                                     32
                                                   Table 2. Variations in poor and rich countries
                                                                    For the poor region
                                 N         mean        sd         p10          p25       p50        p75      p90              CV    p75/p25    p90/p10
 Labor growth                    46.000    0.588       0.495      0.126        0.233     0.378      0.826    1.179          0.842     3.538         9.366
 Private credit/GDP              46.000    0.203       0.196      0.060        0.076     0.153      0.234    0.378          0.965     3.064         6.342
 Stock market cap./GDP           21.000    0.145       0.127      0.011        0.037     0.101      0.226    0.342          0.874     6.117        31.290
 Share of state banks            22.000    0.204       0.252      0.000        0.000     0.083      0.315    0.688          1.238       n.a.         n.a.
 Share of foreign banks          22.000    0.363       0.275      0.057        0.134     0.330      0.487    0.757          0.758     3.634        13.279
 Investment Rate                 41.000    0.045       0.031      0.015        0.024     0.043      0.059    0.072          0.699     2.430         4.723
 Training dummy                  46.000    0.333       0.169      0.126        0.204     0.318      0.438    0.512          0.509     2.144         4.063
 Ln(workers)                     46.000    3.296       0.769      2.213        2.887     3.240      3.662    4.311          0.233     1.268         1.948

                                                                   For the rich region
                                 N         mean        sd         p10          p25       p50        p75      p90              CV    p75/p25    p90/p10
 Labor growth                    45.000    0.308       0.237      0.120        0.171     0.239      0.327    0.743          0.770     1.914         6.191
 Private credit/GDP              45.000    0.567       0.432      0.185        0.284     0.379      0.767    1.458          0.762     2.698         7.878
 Stock market cap./GDP           40.000    0.440       0.473      0.078        0.169     0.318      0.476    0.961          1.075     2.810        12.267
 Share of state banks            37.000    0.162       0.199      0.000        0.010     0.096      0.203    0.452          1.230    20.300          n.a.
 Share of foreign banks          37.000    0.460       0.326      0.065        0.181     0.435      0.730    0.917          0.708     4.033        14.043
 Investment Rate                 24.000    0.038       0.026      0.012        0.018     0.035      0.048    0.077          0.688     2.621         6.363
 Training dummy                  45.000    0.507       0.182      0.297        0.398     0.466      0.660    0.745          0.358     1.660         2.508
 Ln(workers)                     45.000    3.282       0.530      2.680        2.929     3.226      3.504    4.030          0.161     1.196         1.504
Note. The poor and the rich samples each account for 50 percent of the (collapsed) country sample that deletes firms that have missing
observations for labor growth and our base control variables. “N� is the number of countries. “sd� is standard deviation. “CV� is coefficient of
variation. “p10, p25, p50, p75, p90� represent the 10th,25th, 50th, 75th, and 90th percentile, respectively.




                                                                          33
                                      Table 3. Correlation matrix of key variables
                                               Stock                                                                         British
                      Labor       Priv         Mkt                       Ln(firm      % foreign               Net Petrol.    Legal
                      growth      Credit/GDP   Cap/GDP     Ln(workers)   age)         ownership   Trust       Exp/worker     Origin
 Private Credit/        -0.2053
 GDP                     0.0536
                             89

 Stock Market             0.049       0.6625
 Capitalization/GDP      0.7102            0
                             60           64

 Ln(workers)            -0.1868       0.0824      0.0941
                         0.0779       0.4222      0.4596
                             90           97          64

 Ln(firm age)           -0.4425       0.3731      0.1781        0.4932
                              0       0.0002      0.1591             0
                             90           97          64            98

 % foreign               0.1682      -0.1197      -0.018        0.0742      -0.2563
 Ownership               0.1129       0.2429      0.8878        0.4675       0.0109
                             90           97          64            98           98

 Trust                   0.0835       0.3569      0.2564        0.3525       0.2382     -0.2288
                         0.5564       0.0069      0.0891        0.0077        0.077      0.0899
                             52           56          45            56           56          56

 Net Petroleum          -0.0421       -0.084      0.1981        0.1167       0.0443     -0.0975      0.2875
 Exports per worker      0.6935       0.4136      0.1167        0.2525       0.6651      0.3398      0.0317
                             90           97          64            98           98          98          56

 British Legal           0.1538       0.1228     -0.0147        0.1748      -0.2012      0.3061      0.1101        -0.0222
 Origin                   0.249       0.3376      0.9271        0.1672       0.1109      0.0139      0.5419         0.8618
                             58           63          41            64           64          64          33             64

 Settler Mortality       0.3123      -0.5626     -0.5048        -0.305      -0.4241      0.4961     -0.1148         0.132        -0.0893
 Rate                     0.044            0      0.0072        0.0371        0.003      0.0004      0.5846        0.3764         0.5508
                             42           46          27            47           47          47          25            47             47
Note. The three numbers for each variable pair are the correlation coefficient, the p-value of statistical significance, and the number of observations.


                                                                               34
                              Table 4. Regressions of Financial Development on Potential Instruments and Firm Controls

                               Private     StockMarket      Private     StockMarket       Private     StockMarket     Private     StockMarket
                             Credit/GDP     Cap/GDP       Credit/GDP     Cap/GDP        Credit/GDP     Cap/GDP      Credit/GDP     Cap/GDP
 intercept                     1.315***        1.882**      0.258***       0.262***         -0.044          0.121     0.219***       0.227***
                                 (3.486)        (2.159)      (4.718)          (2.860)      (-0.400)       (0.950)      (3.381)        (4.275)
 ln(firm age)                     0.024          0.014       0.040**          0.026*      0.046***        0.028**      0.045**       0.029***
                                 (1.203)        (0.891)      (1.999)          (1.782)      (2.998)        (2.066)      (2.161)        (2.678)
 foreign ownership                0.002          0.031        -0.044           0.009         0.009          0.026       -0.045         -0.006
                                 (0.033)        (0.761)      (-0.720)         (0.226)      (0.236)        (0.683)      (-0.758)       (-0.153)
 ln(L)                            0.015          0.014         0.025           0.010         0.005          0.004        0.023          0.013
                                 (0.658)        (1.087)      (1.182)          (0.862)      (0.363)        (0.310)      (1.052)        (1.024)
 Settler Mortality            -0.221***       -0.367**
                                (-2.929)       (-1.991)
 Net Petroleum                                                -0.055           0.093
 Exports per Worker                                          (-1.162)         (1.276)
 Trust                                                                                    0.015***          0.007
                                                                                           (2.928)        (1.442)
 British Legal Origin                                                                                                    0.094          0.059
                                                                                                                       (0.697)        (0.344)
 Number of observations        62,485        50,648         62,532        50,648          62,303        50,466        62,485        50,648
 Adjusted R2                   0.161          0.232         0.034          0.042          0.171          0.038        0.032          0.010
Note. The table reports OLS regressions of indicators of financial development on potential instrumental variables and a set of controls. *, ** and
*** represent statistical significance at the 10, 5, and 1 percent levels; t-statistics are in parentheses.
We have also tried dummies for commodity production as explanatory variables. They tend to have insignificant coefficients for private
credit/GDP and stock market capitalization/GDP; when they are significant, they are often of different sign than in Easterly and Levine (2003).




                                                                         35
                                        Table 5. OLS Regressions of labor growth on financial structure:
                                                          Pooled, poor and rich samples
                                          Pooled                              Poor                          Rich
                               (1)          (2)        (3)         (4)         (5)         (6)       (7)     (8)       (9)
ln(workers)                    0.035*        0.043      0.026      -0.000       0.043       0.011 0.029***    0.022     0.013
                              (1.684)      (1.611)    (1.462)     (0.011)     (1.225)     (0.547)   (2.664) (1.591)   (1.012)
ln(firm age)               -0.197***    -0.199*** -0.173*** -0.188*** -0.220*** -0.161*** -0.168*** -0.170*** -0.158***
                              (7.786)      (7.535)   (-9.657)     (9.461)     (7.698)    (-9.481)   (9.111) (8.421) (-9.365)
foreign ownership               0.006       -0.000      0.014       0.079       0.009      0.140*     0.022   0.026     0.047
                              (0.100)      (0.002)    (0.218)     (1.436)     (0.068)     (1.689)   (0.431) (0.446)   (0.919)
private credit                 -0.040                   0.075      0.345*               0.746***     -0.028          -0.258*
                              (0.253)                 (0.352)     (1.937)                 (2.673)   (0.178)         (-1.722)
Stock mkt cap/GDP                            0.045      0.134                   0.205      -0.604             0.178 0.339**
                                           (0.252)    (0.761)                 (0.249)    (-0.548)           (1.199)   (2.096)
ln(GDP per capita)                                    -0.156***                               -0.271**                              -0.029
                                                        (-2.872)                               (-2.070)                            (-0.462)
Intercept                   0.835***     0.766***      1.904***    0.963***     1.012***     2.582***     0.665***    0.597***      0.911*
                             (10.799)       (8.513)      (4.763)      (8.585)      (5.534)     (3.101)      (8.404)      (8.156)    (1.853)
Number of observations       49,366       40,522       40,522       19,736       12,573       12,573       29,630      27,949      27,949
Adjusted R2                   0.040        0.041        0.087        0.055        0.048        0.114       0.033        0.046      0.066

Standard errors clustered at the country level.
*, **, and ***: significance at the 10, 5, and 1 percent levels; t-statistics are in parentheses.




                                                                           36
                      Table 6. GMM estimates of determinants of labor growth: the pooled, poor, and rich samples
                                              Pooled sample                               Poor sub-sample                             Rich sub-sample
                                   (1)        (2)         (3)        (4)        (5)        (6)        (6)        (8)        (9)        (10)       (11)       (12)
ln(GDP per capita)                                      -0.117*** -0.122***                          -0.169**     -0.184                           -0.024     -0.064
                                                          (2.749)    (2.711)                          (2.304)    (1.210)                          (0.361)    (1.391)
ln(workers)                       0.030**    0.039***    0.024**       0.021     -0.006      0.006      0.010      0.018   0.027***    0.036**   0.026**      0.028*
                                   (2.129)    (2.859)     (2.153)    (1.478)    (0.237)    (0.137)    (0.474)    (0.469)    (3.119)    (2.462)    (2.522)    (1.929)
ln(firm age)                     -0.204*** -0.188*** -0.169*** -0.164*** -0.188*** -0.211*** -0.178*** -0.194*** -0.160*** -0.157*** -0.161*** -0.153***
                                   (8.342)    (8.348)    (11.627)   (11.513)    (9.403)   (10.388)    (8.795)    (9.765)    (9.044)    (8.252)   (10.466)    (8.480)
foreign ownership                   0.013       0.019      0.025       0.040     0.080*     0.115*      0.062   0.137**       0.043      0.030      0.043      0.033
                                   (0.297)    (0.393)     (0.593)    (0.856)    (1.772)    (1.824)    (1.167)    (1.964)    (1.066)    (0.653)    (1.000)    (0.709)
private credit                      0.147                  0.079                 0.404*               0.494**                -0.191                -0.111
                                   (0.452)                (0.387)               (1.690)               (2.166)               (0.927)               (0.508)
Stock mkt cap/GDP                              -0.095                  0.053                 1.228                 1.486                -0.262                -0.140
                                              (0.379)                (0.304)               (1.002)               (1.253)               (0.844)               (0.507)
Intercept                        0.780***    0.797***   1.625***    1.685***   0.954***   0.948***   1.922***   2.015**    0.731***   0.739***     0.899*   1.235***
                                   (6.940)    (7.915)     (5.706)    (4.817)    (8.536)    (4.246)    (4.211)    (1.988)    (6.908)    (6.058)    (1.826)    (3.236)
Number of observations           49,162      40,364     49,162      40,364     19,690     12,573     19,690     12,573     29,472     27,791     29,472     27,791
Adjusted R2                       0.028      0.034       0.077      0.080      0.055      0.017      0.076      0.029      0.022        .        0.031      0.002
First-stage F for excluded IVs    8.33        1.69       8.80        1.38      14.24      11.22      17.00      12.07       3.32       2.24       8.57       1.91
p-value, Hansen‘s J               0.227      0.053       0.227      0.035      0.671      0.281      0.666      0.205      0.119      0.062      0.081      0.053
Shea‘s adj. partial R-sq          0.861      0.702       0.531      0.521      0.550      0.704      0.793      0.472      0.390      0.484      0.426      0.568
 standard errors clustered at the country level. *, **, and ***: significance at the 10, 5, and 1 percent levels; t-statistics are in parentheses.
The two financial variables (private credit/GDP and stock market capitalization/GDP) are considered endogenous. Instruments are: petroleum net exports per
capita (lagged), average trust 1981-2006.




                                                                                 37
        Table 7. Rajan-Zingales Approach: The Effect of Financial Development on Labor Growth by Dependence on External Finance
                                                        Poor                                                         Rich


                                  (1)            (2)             (3)            (4)             (5)           (6)            (7)            (8)
ln(L)                              0.044**        0.078**       0.065***       0.079***         0.018*           0.012      0.031***       0.031***
                                   (2.378)        (2.508)         (8.593)        (6.634)        (1.888)        (1.050)        (6.718)        (6.337)
ln(firm age)                     -0.180***      -0.207***      -0.187***      -0.204***      -0.161***      -0.154***       -0.191***      -0.188***
                                   (8.978)        (7.831)        (17.349)       (13.996)       (10.131)        (8.385)       (20.216)       (19.113)
foreign ownership                   -0.017         -0.090          -0.039         -0.083         0.005          -0.002         -0.013         -0.017
                                   (0.219)        (0.618)         (1.145)        (1.187)        (0.140)        (0.037)        (0.671)        (0.829)
ln(GDPpc)                           -0.118         -0.116          -0.166         0.122          -0.014         -0.035          0.003         -0.007
                                   (1.463)        (0.809)         (1.439)        (0.818)        (0.286)        (0.718)        (0.149)        (0.273)
external dependence              -0.305***         -0.085                                        0.051          -0.127
                                   (2.789)        (0.352)                                       (0.421)        (0.748)
Private credit*Ext. Dep.         0.890***                       1.108***                         -0.123                     -0.149***
                                   (6.754)                        (5.116)                       (0.703)                       (3.439)
Stock market cap*Ext.                               0.786                         -0.791                         0.138                     -0.130***
Dep.                                              (0.649)                        (1.155)                       (0.529)                       (3.073)
Country Dummies                   NO             NO             YES            YES              NO            NO            YES            YES
Industry Dummies                  NO             NO             YES            YES              NO            NO            YES            YES
Number of observations          14,132          9,384          14,132          9,384          19,660        18,547          19,660        18,547
Adjusted R2                      0.080          0.063          0.244           0.232           0.034         0.034          0.155          0.158
Notes. The Table shows OLS regressions; standard errors clustered at the country-industry level. *,**, and *** represent significance at the 10,
5, and 1 per cent levels; t-statistics are in parentheses. External dependence is the industry-level dependence on external finance based on U.S.
data, which is equal to external finance (capital expenditures minus cash flow from operations) divided by capital expenditures, as defined in
Rajan and Zingales (1998). The correspondence between the industry classifications in the Rajan and Zingales paper and in our dataset is as
follows:
1 "Textiles": same
2 "Leather": same
3 "Garments": same
4 "Agroindustry": average of “beverage� and “food products� from RZ.
5 "Food": same.

                                                                        38
6 "Beverages": same
7 "Metals and machinery": metal products.
8 "Electronics": electric machinery (which contains electronic components)
9 "Chemicals and pharmaceutics": average of “drugs� and “other chemicals� from RZ.
10 "Construction": “transportation equipment�
23 "Transport": “transportation equipment�
11 "Wood and furniture": same.
12 "Nonmetallic and plastic materials": plastic products.
13 "Paper": average of “pulp, paper� and “paper and paper products� from RZ.
14 "Sport goods": “other industries�.
15 "IT services": missing.
16 "Other manufacturing": “other industries�.
26 "Auto and auto components": “motor vehicles�.
27 "Other transport equipment": “ships�.
The following industries (mostly services) in our data are coded as missing, because external financial dependence measures are not available for
them from RZ: Other unclassified, Telecommunications, Accounting and finance, Advertising and marketing, Other services, Retail and
wholesale trade, Hotels and restaurants, Real estate and rental services, Mining and quarrying.




                                                                       39
       Table 8. Private Credit and Labor Growth by Firm Characteristics, Poor Sub-sample
                                                    OLS                                  GMM
Private Credit by Firm Size
Priv*Small (<= 10 workers)                         -0.529                                -0.202
                                                  (-0.989)                              (-0.246)
Priv*Medium (11-50 workers)                         0.348                               0.747**
                                                  (1.531)                               (2.451)
Priv*Large (>50 workers)                         0.669***                              0.791***
                                                  (7.293)                               (9.038)
Private Credit by Firm Age
Priv*Young                                       0.498***                                0.388
                                                  (3.205)                               (1.591)
Priv*Middle-aged                                 0.497***                               0.484**
                                                  (2.838)                               (2.142)
Priv*Old                                         0.497***                               0.508**
                                                  (3.102)                               (2.565)
Private credit by Firm Capital
Intensity
Priv*Low                                           0.381                                 0.284
                                                  (1.571)                               (0.876)
Priv*Medium                                      0.615***                              0.524***
                                                  (4.787)                               (3.008)
Priv*High                                        0.472***                                0.302
                                                  (3.740)                               (1.116)
Notes. This table shows regression coefficients for the interaction between private credit/GDP and
various firm characteristics (size, age, and capital intensity). The interaction terms for each characteristic
were included in separate regressions. In addition to the firm characteristics, the regressions also
control for the share of foreign ownership in the firm and the logarithm of GDP per capita in each
country. Standard errors are clustered at the country level. *,**, and *** represent significance at the
10, 5, and 1 per cent levels. Firm characteristics enter the regressions not only as part of an interaction
with private credit, but also on their own. For reference, the coefficients for the medium and large firm
dummies for the OLS regression described in the top third of the table are -.097 and -.221**,
respectively. The coefficients for the middle-aged and old dummies for the OLS regression described in
the middle third of the table are -.126*** and -.311***, respectively. The coefficients for the medium
and high capital intensity dummies for the OLS regression described in the bottom third of the table are
-.122*** and 0.038, respectively.




                                                     40
Table 9. Potential Mechanisms through Which Financial Development Spurs Firm Development
Explanatory                               Dependent Variable
Variable
                      Investment Rate            Training             Firm Size
Private Credit

Poor Sub-sample           0.032***              0.441***              1.588***
                            (5.026)               (3.670)              (7.582)
Rich Sub-sample              -0.001                -0.063               0.190
                           (-0.093)              (-1.139)              (0.816)

Stock Market
Capitalization

Poor Sub-sample             -0.005                -0.127               2.198*
                           (-0.135)              (-0.286)              (1.733)
Rich Sub-sample             -0.010                -0.063                0.364
                           (-1.551)              (-1.455)              (1.296)
This table shows coefficients for indicators of financial development from OLS regressions of firm
outcomes (Investment rate, training, and firm size) on financial indicators and other controls. Each
coefficient in the table comes from a separate regression. Those regressions also control for firm
characteristics (number of workers, age, and share of foreign ownership in the firm) and per capita
income in each country. Standard errors are clustered at the country level. *,**, and *** represent
significance at the 10, 5, and 1 per cent levels. Training is a dummy variable indicting whether the firm
offers training to its employees. Firm size is measured by (log of) total workers.




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                            Appendix A.
                        First stage results

                         Private         Stock Mkt.
                       Credit/GDP     Capitalization/GDP

ln(firm age)             4.546***                  0.029**
                           (3.144)                    (2.075)
foreign ownership            0.363                     0.029
                           (0.098)                    (0.809)
ln(workers)                  0.620                     0.003
                           (0.483)                    (0.213)
Trust                    1.559***                      0.005
                           (3.206)                    (1.443)
Petroleum net export
                          -9.032**                     0.080
per worker
                          (-2.410)                    (1.220)
Intercept                   -6.773                     0.146
                          (-0.624)                    (1.277)
Number of
                         62,303               50,466
observations
Adjusted R2              0.200                0.063




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Appendix B. Sensitivity Checks, Industry Controls, Rule of Law, Political stability

                                                                 Poor                                                   Rich
                                            (1)           (2)             (3)           (4)         (5)         (6)              (7)          (8)
ln(L)                                        -0.002         0.022           0.015      0.050**    0.035***     0.028***          0.020*         0.011
                                            (0.101)       (1.018)         (1.282)       (2.305)     (3.735)      (2.869)         (1.931)      (0.969)
ln(firm age)                              -0.187***    -0.176***        -0.172***     -0.215***   -0.176***   -0.179***        -0.161***   -0.153***
                                            (9.640)       (8.113)         (7.741)       (6.209)     (6.804)      (7.332)         (8.136)      (8.024)
foreign ownership                            0.103*         0.053           0.064        -0.026       0.015       0.029            0.038        0.040
                                            (1.707)       (0.567)         (1.302)       (0.274)     (0.334)      (0.631)         (0.773)      (0.798)
private credit                             0.611**                      0.498***                      0.145                        0.113
                                            (2.476)                       (3.419)                   (0.684)                      (0.536)
stock/GDP                                                  -0.077                         0.448                0.299***                       0.256*
                                                          (0.079)                       (0.603)                  (2.826)                      (1.933)
Agri./GDP                                     0.872       1.591*                                     -0.059       1.820
                                            (1.622)       (1.789)                                   (0.057)      (1.638)
Maufacture/GDP                               -0.738       2.584*                                   -1.746**   -2.344***
                                            (0.638)       (1.925)                                   (2.535)      (4.039)
rule of law                                                             -0.520***     -0.630***                                   -0.150   -0.236***
                                                                          (3.643)       (3.421)                                  (1.336)      (2.653)
political stability                                                         0.105         0.253                                    0.004        0.083
                                                                          (0.727)       (1.229)                                  (0.050)      (0.906)
intercept                                 0.790***          0.229       0.565***      0.778***    0.952***     0.910***        0.610***    0.585***
                                            (2.790)       (0.538)         (3.656)       (3.865)     (6.716)      (6.750)         (5.638)      (7.381)
Number of observations                    18,868        12,573          19,736        12,573      24,062       22,580          29,630      27,949
Adjusted R2                                0.077        0.090            0.105         0.114       0.052       0.088            0.048       0.075
The table reports OLS regressions of labor growth on indicators of financial development, firm characteristics, and country-level controls.
Standard errors clustered at the country level.
*, **, and ***: significance at the 10, 5, and 1 percent levels; t-statistics are in parentheses.




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                     Appendix C. LIML estimates of determinants of labor growth: the pooled, poor, and rich samples
                                     Pooled sample                                      Poor sub-sample                                Rich sub-sample
                         (1)         (2)         (3)         (4)         (5)             (6)        (7)         (8)         (9)        (10)        (11)        (12)
ln(GDP per capita)                             -0.148**      -0.179                               -0.164**      -0.320                               0.007      -0.053
                                                (2.273)     (1.522)                                (2.154)     (1.349)                             (0.084)     (1.046)
ln(workers)             0.029**    0.042**     0.024**        0.019    -0.003          -0.000        0.011       0.005   0.028***     0.039**    0.029***      0.031*
                        (2.041)     (2.513)     (2.040)     (0.924)    (0.137)         (0.007)     (0.502)     (0.104)     (3.227)     (2.477)     (2.623)     (1.896)
ln(firm age)          -0.201***   -0.200***   -0.173***   -0.176***   -0.187***    -0.212***     -0.178***   -0.193***   -0.156***   -0.157***   -0.158***   -0.152***
                        (7.074)     (4.724)    (10.664)     (6.743)    (9.232)     (10.257)        (8.718)     (8.597)     (8.778)     (8.176)    (10.722)     (8.339)
foreign ownership         0.016       0.002       0.014       0.017    0.086*          0.099         0.067       0.097       0.036       0.018       0.035       0.023
                        (0.342)     (0.023)     (0.301)     (0.271)    (1.869)         (1.240)     (1.197)     (0.905)     (0.936)     (0.373)     (0.869)     (0.467)
private credit            0.113                   0.235                   0.374                    0.475*                   -0.254                  -0.235
                        (0.298)                 (0.736)                (1.496)                     (1.944)                 (1.114)                 (0.807)
Stock mkt cap/GDP                     0.112                   0.412                    1.461                     2.354                  -0.306                  -0.211
                                    (0.168)                 (0.542)                    (1.083)                 (1.440)                 (0.879)                 (0.612)
Intercept              0.791***   0.750***    1.794***    2.042***    0.962***     0.912***      1.898***      2.766*    0.768***    0.747***        0.702   1.161***
                        (6.067)     (4.271)     (4.525)     (2.701)    (8.620)         (3.641)     (4.059)     (1.902)     (6.468)     (5.688)     (1.216)     (2.828)
Observations           49,162      40,364      49,162      40,364      19,690          12,573     19,690      12,573      29,472      27,791      29,472      27,791
Adjusted R2             0.032      0.039       0.077       0.069       0.055           0.002      0.076          .        0.012          .        0.015          .
standard errors clustered at the country level. *, **, and ***: significance at the 10, 5, and 1 percent levels; t-statistics are in parentheses.
The two financial variables are considered endogenous. Instruments are: net petroleum export per worker (2000-2005 average), average trust 1981-2006.




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