Full text of Working Papers (Federal Reserve Bank of Chicago) : Distinguishing Limited Liability From Moral Hazard in a Model of Entrepreneurship, Working Paper 2003-06

View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

Federal Reserve Bank of Chicago

Distinguishing Limited Liability
from Moral Hazard in a Model
of Entrepreneurship∗
Anna L. Paulson, Robert Townsend and
Alexander Karaivanov

REVISED April, 2005
WP 2003-06

Distinguishing Limited Liability from Moral Hazard in a Model
of Entrepreneurship∗
Anna L. Paulson
Federal Reserve Bank of Chicago

Robert Townsend
University of Chicago
Federal Reserve Bank of Chicago

Alexander Karaivanov
Simon Fraser University
April, 2005

Abstract
We present and estimate a model where the choice between entrepreneurship and wage
work may be influenced by financial market imperfections. The model allows for limited
liability, as in Evans and Jovanovic (1989), moral hazard, as in Aghion and Bolton (1996),
and a combination of both constraints. The paper uses structural techniques to estimate
the model and identify the source of financial market imperfections using data from
rural and semi-urban households in Thailand. Structural, non-parametric and reduced
form estimates provide independent evidence that the dominant source of credit market
imperfections is moral hazard. We reject the hypothesis that limited liability alone can
explain the data.

∗

A previous version of this paper has been circulated under the title ”Distinguishing Limited
Commitment from Moral Hazard in Models of Growth with Inequality.” That version is available
at: http://www.chicagofed.org/publications/workingpapers/papers/wp2003-06.pdf. Comments from Daron
Açemoglu, Patrick Bolton, Lars Hansen, Boyan Jovanovic, Andreas Lehnert, Bernard Salanié, Chris Udry,
two anonymous referees, the editor of this journal and from conference and seminar participants at the Federal Reserve Bank of Chicago, DELTA, IUPUI, MIT, UCLA, the University of Toulouse, Stanford and Yale
are gratefully acknowledged. We also thank Francisco Buera, Xavier Giné and Yukio Koriyama for excellent
research assistance as well as the National Institute of Health and the National Science Foundation for funding. We are much indebted to Sombat Sakuntasathien for collaboration and for making the data collection
possible. The views expressed in this paper are those of the authors and do not necessarily represent those of
the Federal Reserve Bank of Chicago or the Federal Reserve System.

Introduction

Financial market imperfections shape economic outcomes in many areas. In studying these
outcomes, many papers posit a particular financial market imperfection and exclude the
possibility of alternative sources of imperfections. The goal of this paper is to identify the
source of financial constraints that limit entry into entrepreneurship. We use structural,
non-parametric and reduced form techniques to distinguish the source of financial market
imperfections using microeconomic data from Thailand.
Earlier work demonstrates that financial constraints have an important eﬀect on who
starts businesses and on how existing businesses are run in Thailand (see Paulson and
Townsend, 2004). A symptom of financial constraints is that wealth will be positively correlated with the probability of starting a business, holding constant the characteristics of
potential entrepreneurs. A strong positive correlation between becoming an entrepreneur
and beginning-of-period wealth can be seen in the non-parametric regression displayed in
Figure 1.1 However, a positive correlation between wealth and entrepreneurship only demonstrates that financial constraints are likely to be important but does not illuminate the source
of the constraint.
The literature identifies two main sources of financial constraints that influence the decision to become an entrepreneur. In Evans and Jovanovic (1989), the financial constraint is
due to limited liability. Agents can supplement their personal stake in entrepreneurial activities by borrowing. Wealth plays the role of collateral and limits default. In this environment
low-wealth households may be prevented from borrowing enough to become entrepreneurs
and others, who are able to start businesses, may be constrained in investment. Limited
liability is also featured in a variety of empirical studies of occupational choice. Evans and
Jovanovic (1989) and Magnac and Robin (1996) provide structural estimates of this model
for the U.S. and for France, respectively. In a limited liability environment, constrained entrepreneurs borrow more when wealth increases. With limited liability, borrowing does not
automatically imply being constrained. Some entrepreneurs may be able to borrow enough
to invest the optimal amount of capital, as though there were no constraints
Financial constraints that arise from moral hazard are the focus of the model of occupational choice featured in Aghion and Bolton (1996). Since entrepreneurial eﬀort is unobserved
and repayment is only feasible if a project is successful, poor borrowers have little incentive
to be diligent, increasing the likelihood of project failure and default. In order to break-even,
lenders charge higher interest rates to low-wealth borrowers. Some low-wealth potential entrepreneurs will be unable, or unwilling at such high interest rates, to start businesses at any
scale. Low-wealth entrepreneurs who do succeed in getting loans will be subject to a binding
incentive compatibility constraint that ensures that they exert the appropriate level of eﬀort.
In contrast to the limited liability case, when there is moral hazard and wealth increases,
constrained entrepreneurs will increasingly self-finance and borrowing diminishes. In a moral
hazard environment, all entrepreneurs who borrow will be constrained2 .
The model that we estimate takes into account entrepreneurial talent, allows investment
1
For each observation in Figure 1, a weighted regression is performed using 80% (bandwidth = 0.8) of the
data around that point. The data are weighted using a tri-cube weighting procedure that puts more weight
on the points closest to the observation in question. The weighted regression results are used to produce a
predicted value for each observation. Because the graphs can be fairly sensitive to outliers, we have dropped
the wealthiest 1% of the sample.
2
This is true if the moral hazard enviroment does not produce the same solution as the first best which is
generally the case.

to be divisible and agents to be risk averse. Because the scale of the business can vary, the
relationship between wealth and borrowing is not driven by indivisibilities. In addition, the
model allows entrepreneurial talent to depend on wealth and formal education. Regardless
of the assumptions regarding financial constraints, the model implies that entrepreneurship
will be positively related to pre-existing wealth. Of course the specific functional relationship
between entrepreneurship and wealth will depend on the financial constraint. In addition,
as discussed above, the relationship between being a borrower and being constrained and
the response of constrained entrepreneurs to an increase in wealth depends on the financial
market imperfection. In particular, if limited liability constraints financial markets, increases
in wealth will allow constrained entrepreneurs to borrow more. However, not all borrowers
need be constrained when there is limited liability. If moral hazard is the source of constraints, increases in wealth will be associated with less borrowing, and all borrowers will be
constrained.
A central goal of this paper is to see if limited liability can be distinguished from moral
hazard in structural estimates using cross-sectional data from a sample of households from
Thailand. We also consider the possibility that both are important.3 The estimated models share a common technology, as well as common preferences and assumptions about the
distribution of talent. They diﬀer only in the assumed financial constraint. The appropriate Vuong (1989) test is used to compare the structural estimates and to determine which
single financial constraint is most consistent with the data on entrepreneurial status, initial
wealth and education; or if both are important. The Vuong test is also featured in Wolak
(1994) and Fafchamps (1993). The structural model comparison tests are augmented with
non-parametric and reduced form estimates that capitalize on the richness of the data, which
include information on household characteristics, borrowing, and collateral.
This paper is related to other work that tries to identify the underlying source of market
imperfections. For example, Abbring, Chiappori, Heckman and Pinquet (2002) use dynamic
data to distinguish moral hazard from adverse selection in the insurance context. Their work
takes the insurance contract as given, based on the regulatory environment. Our treatment of
the limited liability constraint is conceptually similar. We assume a standard debt contract
and estimate the parameter that determines how much a potential entrepreneur can borrow
as a function of wealth and entrepreneurial talent. The estimation is more innovative when
the financial environment is aﬀected by moral hazard. The estimated financial contract
is the endogenous solution to the mechanism design problem that satisfies the incentive
compatibility constraint. To our knowledge, this is the first paper to provide structural
estimates of a moral hazard model of occupational choice based on a mechanism design
approach.
The Thai data come from a socioeconomic survey that was fielded in March - May of 1997
to 2,880 households, approximately 21% of whom run their own businesses.4 The sample
focuses on households living in two distinct regions of the country: rural and semi-urban
households living in the central region, close to Bangkok, and more obviously rural households
living in the semi arid and much poorer northeastern region.5 The data include current
3

We have also considered the possibility that occupation choices are first best and that there is neither
limited liability nor moral hazard. Structural, reduced form and non-parametric findings reject this possibility.
4
For esimation purposes, the data are restricted to households who have non-zero wealth and who either
currently own a business that was founded in the five years prior to the survey (14%) or who have no business
at the time of the survey (86%).
5
See Binford, Lee and Townsend (2001) for more details on the sampling methodology.

and retrospective information on wealth (household, agricultural, business and financial),
occupational history (transitions to and from farm work, wage work and entrepreneurship),
as well as access and use of a wide variety of formal and informal financial institutions
(commercial banks, agricultural banks, village lending institutions, money lenders, as well
as friends, family and business associates). The data also provide detailed information on
household demographics, education and entrepreneurial activities.
The results indicate that progress can be made in identifying the nature of financial
constraints. The dominant source of constraints is moral hazard. We reject the hypothesis
that limited liability alone can explain the data. The evidence in favor of moral hazard is
particularly strong for the wealthier Central region. For the poorer Northeastern region, we
cannot rule out that limited liability may have a role to play, but only in combination with
moral hazard.
These conclusions are based both on the formal financial regime comparison tests from the
structural estimation, which use data on wealth, education and entrepreneurial status, as well
as on reduced form and non-parametric estimates, which use data on wealth, entrepreneurial
status, net savings, as well as other important household characteristics. The formal financial
regime comparison tests are necessarily only informative about the relative success of a given
financial regime for the particular set of assumptions regarding preferences, technology etc.
that are imposed by the model. In contrast, the reduced form and non-parametric estimates
examine implications that are likely to distinguish moral hazard from limited liability for a
large class of potential assumptions.
The rest of the paper is organized as follows. In the next section, the model and the
financial constraints are presented. Section three describes the computational algorithm for
the structural maximum likelihood estimation. Section four describes the data. Section
five reports on the structural maximum likelihood parameter estimates. In section six, we
determine which financial regime best fits the data using structural, reduced form and nonparametric techniques. The final section concludes and suggests directions for future research.

Model and Implications

In this section, we describe the model of occupational choice and provide intuition for the
solutions and the relationships among key variables. Since structural estimation lends itself
to characterizing the model in a diﬀerent, but equivalent way, this section also describes the
general linear programming problem that forms the basis of the structural estimation. The
basic structure of the model — preferences, endowments and technology — is the same regardless of the financial environment. The financial environment depends on which constraints
are assumed to bind: limited liability, moral hazard or both.

2.1

Economic Environment

Households are assumed to derive utility, U , from their own consumption, c, and disutility
from eﬀort, z:
c1−γ 1
zγ2
U (c, z) =
−κ
(1)
1 − γ1
γ2

We assume that utility displays constant relative risk aversion in consumption. The parameter
γ 1 ≥ 0 determines the degree of risk aversion. The parameters κ > 0 and γ 2 ≥ 1 determine
the loss in utility from expending eﬀort. Consumption, c, and eﬀort, z, must be non-negative.
4

In discussing the implications of the model, we begin by assuming that agents are risk neutral, in other words that γ 1 is equal to zero. We reintroduce risk aversion in the presentation
of the linear programming problem that forms the basis for the structural estimation.
There are three sources of household heterogeneity in the model: initial wealth, A, entrepreneurial talent, θ, and years of education, S. All of these variables are determined ex
ante and can be observed by all of the agents in the model.6 Wealth is assumed to lie in the
interval (0, 1]. We assume talent is log normally distributed. Specifically:
ln θ = δ 0 + δ 1 ln (A) + δ 2 ln(1 + S) + η,

(2)

where η is normally distributed with mean zero and variance σ η . In order to avoid the
spurious inference that wealth rather than talent is the source of constraints, an individual’s
expected talent can be correlated with wealth through δ 1 . Talent may also be correlated with
formal education via δ 2 .
Entrepreneurs produce output q from their own eﬀort z and from capital k. Output q
can take on two values, namely q = θ, which corresponds to success and occurs with positive
probability, and q = 0, which is equivalent to bankruptcy and occurs with the remaining
probability. Note that output is increasing in entrepreneurial talent, θ. The technology is
stochastic and is written P (q = θ|z, k > 0), the probability of achieving output q given eﬀort
z, and capital k. Specifically:7
P (q = θ|z, k > 0) =

kα z 1−α
1 + k α z 1−α

(3)

Output can be costlessly observed by everyone.
When k = 0, the firm is not capitalized. This means that the household works in the
wage sector. Earnings, w, in the wage sector are also stochastic and depend on eﬀort. They
z
and equal to zero with the residual probability.8
are equal to one with probability 1+z
All households are price-takers and take the gross cost of borrowing, r(A, θ), which may
vary with wealth and entrepreneurial talent, as given. Entrepreneurs who do not borrow
(who have k < A) and wage workers earn the given, riskless gross interest rate, r, on their
net savings.
Occupational assignments are determined by a social planner who maximizes agents’
utility subject to constraints that describe the financial intermediary and any financial market
imperfections. This approach is equivalent to a situation in which a large number of financial
institutions compete to attract clients so that in the end it is as if the agents in the economy
maximize their utility subject to the financial institution earning zero profits, and subject,
of course, to constraints having to do with financial market imperfections.
In sum, when agents are risk neutral, the planner makes an eﬀort recommendation, z,
and a capital recommendation, k to solve:


γ
z
w 1+z
− κ zγ 2 + rA, if k = 0




2
kα z 1−α
zγ 2
−
κ
+
r(A
−
k),
if
k
>
0,
k
≤
A
θ 1+k
(4)
M ax
α z 1−α
γ2

z,k 


kα z 1−α
zγ 2
kα z 1−α
θ 1+kα z1−α − κ γ − r(A, θ)(k − A) 1+kα z1−α , if k > 0, k > A
2

The complications in estimation that arise from the fact that the econometrician cannot observe θ are
addressed in Section 3.
7
The probability of entrepreneurial success is scaled by 1 + kα z 1−α to guarantee that it will lie between
zero and one.
8
Again, this formulation guarantees that the probability of success in the wage sector will lie between zero
and one.

As one can see above, agents have three possibilities: 1) wage work which corresponds to
k = 0; 2) becoming an entrepreneur but not borrowing, which happens when capital is
positive and less than or equal to wealth, k > 0 and k ≤ A; or 3) becoming an entrepreneur
and borrowing, which happens when capital is positive and exceeds wealth, k > 0, k > A.
The first term in the maximand is the expected utility of a risk neutral wage worker:
expected wages minus the cost of eﬀort, plus a riskless return on wealth. The second term is
the expected utility of a risk neutral entrepreneur who does not need to borrow to carry out
the recommended k: expected output minus the cost of eﬀort, plus a riskless return on any
wealth not invested in the project. The final term is the expected utility of an entrepreneur
who must borrow to carry out the assigned k: expected output minus the cost of eﬀort, minus
the expected cost of repaying the loan. Note that the loan is only repaid when the project
is successful. The planner’s problem is subject to a constraint which guarantees that the
expected rate of repayment on such loans covers the cost of outside funds, so that lenders
break even:
kα z 1−α
= r, for k > A, ∀ θ, ∀ A
(5)
r(A, θ)
1 + k α z 1−α

2.2

Financial Environment

We introduce variations in the financial environment through additional constraints on the
planner’s problem. When financial markets are ”first best” and are subject to neither limited
liability nor moral hazard no further constraints are imposed.
Limited Liability To model limited liability, we assume, as in Evans and Jovanovic (1989),
that households can borrow up to some fixed multiple of their total wealth, but no more. The
maximum amount that can be invested in a firm is equal to λA and the maximum amount
that a household can borrow is given by (λ − 1)A. When limited liability is a concern, the
planner’s maximization problem will be subject to:
k ≤ λA

(6)

in addition to equation (5).
Moral Hazard When there is moral hazard, entrepreneurial eﬀort is unobservable and the
financial contract cannot specify an agent’s eﬀort. In terms of the planner’s problem, this
translates into a requirement that the capital assignment and the interest rate schedule are
compatible with the eﬀort choice that a borrowing entrepreneur would have made on his
or her own. In other words, the capital assignment and the interest rate schedule must not
violate the first order condition with respect to eﬀort of the entrepreneur’s own maximization
problem. In this case, in addition to equation (5), the planner’s maximization problem will
be subject to:
¸
·
(1 − α)k α z −α
(7)
− κz γ 2 −1 = 0,
[θ − r(A, θ)(k − A)]
(1 + k α z 1−α )2
which is an entrepreneurial household’s first order condition for eﬀort, z, for a given interest
rate schedule and capital, k.9 Equation (7) requires that the planner’s eﬀort recommendation equate the marginal benefit of eﬀort with the marginal cost of eﬀort plus a term that
9

See Karaivanov (2005) for a proof that this approach is valid here.

represents the marginal impact of eﬀort on loan repayment, through the eﬀect of eﬀort on
kα z 1−α
the probability that an entrepreneurial project will be successful: 1+k
α z 1−α .
Note that when agents are risk neutral, moral hazard is only an issue for entrepreneurs who
borrow. The lack of observability of eﬀort is not an issue for wage workers and entrepreneurs
who self-finance. The planner can assign eﬀort to them without having to satisfy the incentive
compatibility constraint, equation (7), because there is no moral hazard problem when the
optimal capital investment does not require borrowing.
Moral Hazard and Limited Liability We also consider the possibility that credit markets are characterized by both moral hazard and limited liability. This is modeled by assuming
that the entrepreneurial choice problem is subject to both equation (6) and equation (7), in
addition to equation (5).

2.3

Characterization of Solutions

Risk Neutral Case Figure 2 describes the optimal assignment of eﬀort and capital for a
risk neutral entrepreneurial household for each of the three potential financial regimes and
compares them to the first best solution in which there are no financial constraints. We
assume that the household has wealth, A, equal to 0.1 and talent θ, equal to 2.56.10 The
first best capital, eﬀort and welfare levels are, as one might imagine, highest. The ellipses
that radiate out from the first best solution are the agent’s indiﬀerence curves in eﬀort and
capital. Utility is decreasing as one moves away from the first best solution.
The vertical dotted line to the left of the first best solution represents the set of potential allocations of capital and eﬀort when there is a binding limited liability constraint and
investment can be at most λA, or 0.25 in this example. As seen in the graph, imposing the
limited liability constraint results in lower capital and eﬀort and, naturally, lower welfare.
The set of possible allocations of capital and labor in the moral hazard case are described
by the ear-shaped curve that begins in the lower left-hand corner of the graph. When there is
moral hazard, utility is maximized at the point where the allocation possibilities are tangent
to the entrepreneur’s indiﬀerence curve. In this example, this occurs where investment is
equal to 0.38 (of which 0.1 comes from the agents own wealth and the remaining 0.28 must
be borrowed) and eﬀort is equal to 0.99. When there is moral hazard and binding limited
liability, both constraints must be satisfied and the solution is found where the moral hazard
allocation curve intersects the vertical line that describes the limited liability constraint,
where investment is equal to 0.25 and eﬀort is equal to 1.04. Note that for these parameter
values, welfare is lowest when both limited liability and moral hazard are an issue and that
moral hazard alone delivers higher welfare than limited liability alone.
Regardless of the financial constraint, when wealth increases, capital and eﬀort both
increase toward the first best solution, although the path will of course depend on the financial environment. If there are no constraints and the solution is first best, the solution is
unchanged when wealth increases.
10
A wealth level of 0.1 corresponds to the 89th percentile of wealth in the data. Figure 2 shows the optimal
assignment of eﬀort and capital for an entrepreneurial household assuming that α is equal to 0.78, κ is 0.08,
γ 2 is equal to one and λ is equal to 2.5. These parameter values are within the range of the values produced
by the structural estimation.

Special Cases The risk neutral model described above includes special cases which have
been studied in the literature. For example, Evans and Jovanovic (1989) can be derived by
first eliminating a role for entrepreneurial eﬀort by setting z to one and setting the disutility
of eﬀort, κ, to zero. Next, assume that output is a deterministic function of capital, k, so that
q = θkα and that loans must be fully repaid in the amount rk, no matter what. The maximum
loan size is determined by the limited liability constraint, equation (6), which requires k ≤ λA.
Apart from the normalized probabilities, these assumptions deliver the limited liability model
of Evans and Jovanovic. The likelihood of becoming an entrepreneur is increasing in wealth
and entrepreneurial talent. Holding wealth fixed, more talented entrepreneurs are more likely
to be constrained. Entrepreneurial households who face a binding limited liability constraint
will borrow and invest more when wealth increases.
We can also use our framework to generate the model of Aghion and Bolton (1996).
Assume that capital can be either 0 or 1. In other words, firms must be capitalized at k = 1.
Eliminate any role for entrepreneurial talent by setting θ equal to one, and assume that the
income of wage workers is unaﬀected by eﬀort, or equivalently assume that z = 1 for wage
workers. Finally, assume that γ 2 is equal to two, so that the disutility of eﬀort is quadratic.
Apart from the normalized probabilities, these assumptions deliver the model of Aghion and
Bolton. As they stress, eﬀort, z, which must be incentive compatible, will be a monotonically
increasing function of wealth. As wealth increases, the probability of entrepreneurial success
increases, which means that wealthier households will face lower interest rates. Low wealth
households face such high interest rates that they may choose not to borrow and become wage
workers rather than entrepreneurs. Entrepreneurial households with wealth less than one
must borrow an amount equal to 1−A to finance their firm, which, again, must be capitalized
at one. These households are subject to a binding incentive compatibility constraint. In
contrast to the limited liability model of Evans and Jovanovic (1989), when wealth increases
for these constrained households they will borrow less and continue to invest the same amount
in their firms.

2.4

The Linear Programming Problem

In this section, we restate the occupational choice problem faced by an agent with wealth A,
schooling S and entrepreneurial talent θ as a principal-agent problem between the agent and
a competitive financial intermediary. The optimal contract between the two parties consists
of prescribed investment, k, recommended eﬀort, z, and consumption, c. Consumption can
be contingent on the output realization, q. Agents assigned zero investment are referred to
as ”workers”, while agents assigned a positive level of investment are called ”entrepreneurs”.
Agents may now be risk averse, with risk neutrality embedded as a special case.
Non-convexities arising from the incentive constraints, from the indivisibility of the choice
between wage-work and entrepreneurship, and from potential indivisibilities in k mean that,
in general, standard numerical solution techniques that rely on first order conditions will
fail. By writing the principal-agent problem as a linear programming problem with respect
to lotteries over consumption, output, eﬀort and investment, we can restore convexity and
compute solutions.
Let the probability that a particular allocation (c, q, z, k) occurs in the optimal contract for
agent (θ, A, S) be denoted by π(c, q, z, k|θ, A, S). The choice object, π(c, q, z, k|θ, A, S), enters
linearly into the objective function as well as in every constraint. See Prescott and Townsend
(1984) and Phelan and Townsend (1991) for a detailed description of this methodology. The

linear program approach allows us to use a set of well-known maximization routines in the
structural estimation.
In particular we solve the following linear programming problem:
X
π(c, q, z, k|θ, A, S)U (c, z)
(LP)
max
π(c,q,z,k|θ,A,S)≥0

s.t.

X
c

π(c, q, z, k|θ, A, S) = p̃(q|z, k, θ)

c,q,z,k

π(c, q, z, k|θ, A, S) for all q, z, k

(8)

c,q

π(c, q, z, k|θ, A, S)(c − q) = r
>

π(c, q, z, k|θ, A, S)U (c, z) =

c,q

c,q,z,k

π(c, q, z, k|θ, A, S)(A − k)

π(c, q, z, k|θ, A, S)

c,q

for all k > 0, z, z 0 6= z
X
π(c, q, z, k|θ, A, S) = 1

p̃(q|z 0 , k, θ)
U (c, z 0 )
p̃(q|z, k, θ)

(9)

(10)

(11)

c,q,z,k

The function pe(q|z, k, θ) defines the probability of output q, given eﬀort, capital and entrepreneurial talent. It is analogous to the original P (q = θ|z, k > 0), see equation (3), but
here it is conditioned on θ as well as z and k, and it is also relevant for wage workers, who
have k = 0.
The first constraint, equation (8) is a Bayesian consistency constraint, ensuring that
the conditional probabilities, pe(q|z, k, θ), are consistent with the production function. The
second constraint, equation (9), is a break-even condition, which ensures that the financial
intermediary earns zero profits. Intuitively, financial intermediary payments, c−q, must equal
interest earnings, r(A − k). The third constraint, equation (10), is the incentive compatibility
constraint, which ensures that the recommended eﬀort, z, will be undertaken rather than any
alternative eﬀort, z 0 . Because agents may be risk averse and value insurance that is provided
by the financial intermediary, the incentive compatibility constraint may bind for all firms,
not just firms which require outside capital. The final constraint, equation (11), ensures that
the probabilities sum to one.
We consider three alternative specifications of the above linear programming problem,
which correspond to diﬀerent assumptions about the informational and financial constraints
faced by agents in the model. In the first specification, moral hazard, we assume that eﬀort is
unobservable and that the incentive compatibility constraint, equation (10) must be satisfied.
In this specification, the feasible investment levels are independent of A, i.e. each agent can
invest any feasible amount no matter what her wealth is.
In the second specification, limited liability, we assume that eﬀort is observable and that
the incentive compatibility constraint does not have to be satisfied. In the case of limited
liability, the investment levels that an agent with wealth A can undertake are constrained
to lie in the interval [0, λA], with λ > 0 as in Evans and Jovanovic (1989). In the final
specification, both limited liability and moral hazard, we assume that eﬀort is unobservable
and that investment must be less than λA.
The contract elements c, q, z, k are assumed to belong to the finite discrete sets C, Q, Z, K
respectively. These sets, which are represented for computational purposes by grids of real
numbers, are defined in more detail below.
9

Computational Algorithm for Structural Estimation

The algorithm for computing and estimating the occupational choice problem uses a structural maximum likelihood approach and consists of the following main stages.
• Stage 1: Solve for the optimal contract between the financial intermediary and an
agent with given ability, θ, education, S and initial wealth, A. As discussed above,
three alternative specifications of the constraints on the optimal contract are considered:
moral hazard, limited liability, and both moral hazard and limited liability.
• Stage 2: Construct the likelihood function from the solutions of the stage 1 problems
for the occupational choices, wealth and education observed in the data.
• Stage 3: Maximize the likelihood function to obtain estimates for the structural parameters of the model and standard errors.
The general idea of the algorithm is to obtain the probability of being an entrepreneur
for given model parameters and input data, θ, S and A in stage 1 and then integrate over
entrepreneurial ability θ, which is not observed by the econometrician, to obtain the expected
probability that an agent with wealth A and education S would be in business for all wealth
and education levels in the data. The expected probabilities generated from the model are
then used to construct and maximize the appropriate likelihood function. The rest of this
section details the procedures followed in each of the above stages.

3.1

Solve the Linear Programming Problem

The numerical procedure for solving the linear programming problem LP takes the following
steps:
• Create grids for c, q, z, k : we use 10 linearly spaced grid points for c on [0, 10] and 10
linearly spaced grid points for z on [0.0001, 5]. For capital we use 16 log-spaced grid
points for k on [0, 5], when limited liability is not a concern. This range for capital was
chosen to ensure that it did not place restrictions on capital choices in a ”first best”
environment. When limited liability constrains financial contracts, the investment grid,
K consists of 16 points on [0, λA] for each given A at which the linear program is
computed. As explained in the model description, output, q, can take three possible
values, 0 (entrepreneurial failure), θ (entrepreneurial success) and 1 (success in wage
work).11
• Use Matlab to construct the matrices of coeﬃcients corresponding to the constraints
and the objective of the linear program (LP). We use the single crossing property to
eliminate some of the incentive constraints as they do not bind at the solution.
11

The dimension of the grids was influenced by computational time considerations. Notice that even with
these grid dimensions, we still have to solve a constrained optimization problem with 2,400 variables (the π0 s)
and, potentially, 802 constraints for each (θ, A, S) we consider. When limited liability is the only constraint,
the 320 incentive compatibility constraints are eliminated. We can handle a much larger number of variables,
but then computational time increases exponentially in the estimation stage of the algorithm.

• Solve for the optimal contract, π ∗ (c, q, z, k|θ, A, S) using a call to the linear programming commercialPlibrary CPLEX12 and obtain the probability of being entrepreneur,
π ∗ (c, q, z, k|θ, A, S, k > 0). The probability of being a worker is
π E (θ, A, S) ≡
simply 1

c,q,z,k
E
− π (θ, A, S).

Stage 1 is the building block of each of the following stages. Since it is moderately
time consuming, it is crucial to minimize the number of linear programs computed in the
estimation procedure.

3.2

Construct the Likelihood Function

In Stage 2, we construct the log-likelihood function that is used to estimate the structural
models. For estimation purposes, observed wealth in Thai baht is rescaled on (0,1], where ‘1’
corresponds to the wealth of the wealthiest household in the data. Recall that entrepreneurial
ability is given by:
(12)
ln θ = δ 0 + δ 1 ln A + δ 2 ln(S + 1) + η
where η is distributed N (0, 1). For a given wealth level, A and education level, S we compute
the expected probability that an agent (A, S) will be an entrepreneur by numerically integrating over the ability distribution. In other words, we numerically approximate the following
expression:13
Z∞
E
π̄ (A, S) =
π E (θ, A, S)dφ(η)
(13)
−∞

Since the linear programming stage 1 is costly in terms of computation time14 , we cannot
aﬀord to compute π̄ E (A, S) at all possible combinations of A and S (more than 2000) because
it would take at least 1.5 hours for each likelihood function evaluation. We overcome this
problem by constructing a 20-point log-spaced grid for wealth, A.15 The function π̄ E (A, S)
is computed only at these 20 grid points.
In order to be able to compute the probability for all data points, which is necessary to
evaluate the likelihood, we use a cubic spline interpolation of π̄ E (A, S) over the wealth points
in the data, which generates the expected probability of being entrepreneur, predicted by
the model, for an agent with wealth Ai in the data, which we denote by H(Ai |ψ)16 , where
12

Using CPLEX instead of Matlab’s internal linear programming routine (linprog) improves computational
time by a factor of 10 to 15.
13
The numerical integration method used is Gauss-Legendre quadrature with 5 nodes for η on [−3, 3] (see
Judd, 1998). This method was chosen because it minimizes the number of linear program computations (we
solve only five linear programs for a given A, S pair) and because of it has desirable asymptotic properties.
14
Three seconds for each A, S pair. All calculations were performed on a 3 Ghz Pentium 4 machine with 1
GB RAM running Windows XP with hyperthreading.
15
The log-spaced grid takes into account that the actual wealth data is heavily skewed toward the low end of
the wealth distribution. In order to compute π̄E (A, S), we also need values for education, S, that correspond
to the grid points for wealth, A. We obtain these by running a nonparametric lowess regression of education
on wealth using all of the data. The resulting nonlinear function that relates education to wealth is then
evaluated at the 20 wealth grid points to obtain the corresponding 20 values for S. This method is preferable
to simply picking an education value corresponding to the data point closest to a particular wealth grid point
as more information is used in the non-parametric regression to compute the education values corresponding
to the wealth grid points.
16
Notice that H is implicitly a function of agents’ education levels.

ψ ≡ (γ 1 , γ 2 , κ, α, δ 0 , δ 1, δ 2 , λ) is the vector of model parameters. This procedure reduces the
computational time to 30-50 seconds per likelihood evaluation depending on the regime. The
log-likelihood function is given by:
n

1X
Ei ln H(Ai |ψ) + (1 − Ei ) ln(1 − H(Ai |ψ))
L(ψ) =
n

(14)

i=1

In the above equation n is the number of observations, Ei is a binary variable, which takes
the value of 1 if agent i is entrepreneur in the data and 0 otherwise, and Ai is the wealth
level of agent i (again from the data).

3.3

Solve for Optimal Parameter Values

In Stage 3, we solve for the parameter values that maximize the likelihood of model occupational assignments that correspond to the occupational assignments in the data. In other
words, we maximize the likelihood function, equation (14), over the choice of parameter values
- the vector ψ ≡ (γ 1 , γ 2 , κ, α, δ 0 , δ 1, δ 2 , λ), given the data.17
The riskless gross interest rate is assumed to be 1.1. In comparison, the net annual interest
rate on collateralized loans to individuals from the Bank for Agriculture and Agricultural
Cooperatives (BAAC) is roughly 13% in the data and interest rates on loans from commercial
banks, the vast majority of which are collateralized, average 22%. In addition, there are
many informal loans, often between relatives, where the reported interest rate is zero.18 The
relevant interest rate for the model is a riskless one, where default is not an option. Clearly
default is a possibility for the loans and interest rates observed in the data, so we assume
that the riskless gross interest rate is lower than those observed in the data.
The actual maximization of the log-likelihood function L(ψ) is performed in the following way. First, in order to ensure that a global maximum is reached we do an extensive
deterministic grid search over the parameters and pick the parameter configuration which
maximizes L.19 The best parameter configuration from the grid search is then taken as the
initial parameter guess for a second-stage likelihood optimization procedure.20
Finally, we compute the standard errors for the estimated parameters using standard
bootstrapping methods drawing with replacement from the original sample.21

Data and Background Information

This section briefly describes some of the salient features of the data and reviews the evidence
that financial constraints seem to play an important role in determining who becomes an
17

In some specifications only a subset of these parameters is estimated. Section 5 reports on the parameter
estimates for each specification.
18
For further details see Giné (2005).
19
The grid search is computationally time intensive and can take up to 2-3 days depending on the number
of estimated parameters.
20
This latter procedure solves the non-linear optimization problem of maximizing L by using the Matlab
routine fminsearch which is a generalization of the polytope method using the Nelder-Mead simplex algorithm. We chose this method because of its high reliability, relative insensitivity to initial values, and good
performance with low-curvature objective functions. Typically the optimization takes 300-400 iterations which
amounts to 2.5-7 hrs of computer time depending on the regime.
21
Even with a fairly small number of bootstrap draws (10) this is the most time intensive part of the
algorithm and can take up to 3-4 days for each estimated parameter configuration.

entrepreneur and how existing businesses are run. The reader who is interested in more
details is referred to Paulson and Townsend (2004).
The data we analyze cover four provinces in Thailand. Two of the provinces are in the
Central region and are relatively close to Bangkok. The other two provinces are much further from Bangkok and are located in the relatively poor northeastern region. The contrast
between the survey areas is deliberate and has obvious advantages. Within each province a
stratified random sample of twelve geographic areas (tambons) was selected.22 The stratification ensured that the sample was ecologically diverse. In each tambon, four villages were
selected at random. In each village, a random sample of fifteen households was interviewed.
The businesses we study are quite varied and include shops and restaurants, trading
activities, raising shrimp or livestock and the provision of construction or transportation
services.23 While there are many diﬀerent types of businesses, shrimp and/or fish raising,
shops and trade account for 70% of the businesses in the whole sample and make up a similar
percentage of the businesses in each region. Median initial investment in the households
business varies substantially with business type.
Despite this variation, the median initial investment appears to be relatively similar across
regions for the same type of business, particularly for the most common business types. For
example, the median investment in a shop is 16,000 baht in both the Northeast and the central
region. In the Northeast, the median initial investment in trade is 21,000 baht compared to
23,000 baht in the central region.24 For future reference, note that average annual household
income in Thailand at the time of the survey is 105,125 baht, or roughly $4,200.
Most business households run a single business and rely heavily on family workers. Only
10% of the businesses paid anyone for work during the year prior to the survey.25 More
than 60% of the businesses were established in the past five years. In the empirical work we
restrict our attention to these businesses.26 Savings (either in the form of cash or through
asset sales) is the most important source of initial business investment. Approximately 60%
of initial investment in household businesses comes from savings. Loans from commercial
banks account for about 9% of initial business investment and the Bank for Agriculture and
Agricultural Cooperatives (BAAC) accounts for another 7%. In the Northeast, the BAAC
plays a larger role than commercial banks, and in the central region the opposite is true.
Entrepreneurial households are a bit younger and more educated than non-business households. The current median income of business households is about twice that of non-business
22

A tambon typically includes 10 - 12 villages.
We are aware that some farms are run like businesses and that the dividing line between businesses and
farms is not always clear. However, farming, particularly of rice and other crops, can be thought of as a
“default” career choice. An active decision to do something else has been taken by the households that we
define to be business households. We experimented with alternative categorizations and found that the one we
use has content in the sense that the performance of the structural estimation deteriorates when entrepreneurial
status is randomly assigned compared to when entrepreneurial status is determined by the data.
24
Median investment in shrimp and/or fish does diﬀer depending on the region: in the Northeast it is 9,000
baht compared to 51,000 baht in the central region. This is because shrimp farming, which requires substantial
initial investment, is concentrated in the central region, while fish farms are more important in the Northeast.
25
This means that the set of entrepreneurial firms is unlikely to be very aﬀected by the case where wealthy,
but untalented, households hire poor, but talented, managers to run their firms.
26
Although these results are not presented in the paper, we have also looked at businesses that were established in the past 10 years. This group includes 83% of the businesses in the sample. None of the results
are sensitive to which group of businesses we examine. The decision to focus on businesses that were started
in the past 5 years was the result of weighing the benefit of having more accurate measures of beginning of
period wealth against the cost of eliminating the 224 households that start businesses more than five years
ago.
23

households. This diﬀerence is used to calibrate the talent parameter, δ 0 , in the baseline
structural estimates. Business households are wealthier both at the time of the survey as well
as prior to starting a business, compared to their non-business counterparts. In addition,
business households are more likely to be customers of commercial banks and the BAAC,
and to participate in village financial institutions.
Table 1 summarizes the data for business and non-business households that are used in
the structural maximum likelihood estimates and the business household information that
is used in the reduced form and non-parametric analysis. The wealth variable measures the
value of real, non-financial wealth that the household owned six years prior to the survey. It
is equal to the total value of the household, agricultural and land assets that the household
owned then. This corresponds to beginning of period wealth, that is, wealth prior to choosing
an occupation. The value of any business assets that the household may have owned six years
ago is excluded.27
In addition to using data on past wealth, entrepreneurial status, and years of education, the reduced form and non-parametric analyses make use of additional data on the
demographic characteristics of the head of the business household (age, age-squared) and on
characteristics of the household (the number of adult males, adult females and children in
the household). All of these variables are measured at the time of the survey. We also use
data on net financial savings at the time of the survey, which is equal to the financial savings
of the household plus the value of loans that are owed to them minus current debt. In some
estimates, we control for the impact of credit market availability by including measures of
whether or not the household was a member or a customer of various financial institutions
in the past.
Household business reports of whether or not they are ”constrained” are a key variable
in the reduced form and non-parametric analysis. Household businesses are considered constrained if they answer yes to the question ”Would your business be more profitable if it were
expanded?”. Fifty-six percent of business households answer yes to this question. Further information from the survey suggests that household responses to this question may reasonably
approximate the theoretical notion of being constrained, being subject to a binding limited
liability or incentive compatibility constraint. For example, of the businesses who reported
that they were constrained, 37% said that they had not expanded their business because they
lacked suﬃcient funds to do so. Another 30% said that they did not have enough land to
expand. An additional 13% reported that they lacked time or labor for expansion.
27

The past value of real assets is found by depreciating the purchase price of the asset (in 1997 baht) from
the time of purchase to what it would have been worth six years prior to the survey. We assume that the
depreciation rate for all household and agricultural assets is 10% per year. If the household purchased a tractor
10 years before the survey for 100,000 baht, we would first convert the purchase price to 1997 baht (using the
Thai consumer price index) and then multiply this figure by (0.90)4 to account for four years of depreciation
between the purchase data and six years prior to the survey. This procedure would give us the value of the
tractor six years prior to the survey. Past values of land are treated diﬀerently. Households were asked to
report the current value of each plot that they own. In calculating past land values, we assume that there
have been no real changes in land prices. So if the household has had one plot for ten years and the current
value of that plot is 100,000 baht, then six years ago the value of that plot will also be 100,000 baht (in 1997
baht). In addition land purchase and sale information is used to measure the value of land that a household
owned in the past.

Structural Maximum Likelihood Estimates

In this section the structure of the model is taken literally to determine how well it fits
the observed pattern of who becomes an entrepreneur as a function of wealth, the imputed
distribution of entrepreneurial talent in the Thai data and various assumptions about the
financial regime. We consider three financial regimes: moral hazard, limited liability and
both moral hazard and limited liability.
Each structural maximum likelihood estimate produces a measure of the likelihood that a
given set of assumptions about the financial environment could have generated the patterns
of wealth, education and entrepreneurial status observed in the Thai data. In addition, the
estimation delivers the maximized values of the model parameters, the probability that each
agent will become an entrepreneur as well as assignments of capital, eﬀort, and consumption
for each agent.
Most of the structural estimates are produced assuming that the talent parameters, δ 0 ,
δ 1, and δ 2 are fixed. This is done to ensure that a given agent has the same expected talent
regardless of the financial environment. The talent parameter δ 1 is set equal to 0.06, which
means that a 10% increase in wealth raises entrepreneurial talent by 0.6%. The parameter
δ 2 is set equal to 0.125, which means that a 10% increase in years of schooling increases
entrepreneurial talent by 1.25%. Throughout the estimation, we also assume that the standard deviation of shocks to entrepreneurial talent, σ η , is one. The values of δ 1 , δ 2 and
σ η were chosen to be consistent with structural estimates of a version of the model of Evans
and Jovanovic (1989) using the Thai data.28 Because these estimates also use income data,
they bring additional information to bear on the relationship between entrepreneurial talent,
wealth and education. Current computational methods prevent us from using income data
in the structural estimates discussed below.
We consider two methods of fixing the talent parameter, δ 0 . In the first method, which
is referred to as ”income” in the tables, δ 0 is assigned based on the observed income of entrepreneurs relative to non-entrepreneurs. Ignoring the scaling required to ensure that probabilities lie between 0 and 1, the model implies that the output of a successful entrepreneur
is equal to θ and the output of a successful wage-worker is equal to one. The data reveal
that the median entrepreneur has income that is 2.56 times higher than that of the median
wage-worker. Mapping from the data back into the model, this implies that the median
entrepreneur has a θ of 2.56. Using equation (2), which maps wealth and schooling into log
talent, as well as the assumptions about δ 1 and δ 2 discussed above, this implies that δ 0 must
be equal to 0.922.
In the second method, which we refer to as the ”% entrepreneur” case, δ 0 is chosen so
that the predicted percentage of entrepreneurs from the structural estimation of the model
matches the percentage of entrepreneurs observed in the data, namely 14%. In this case, δ 0
is set equal to 1.295.29
We also estimate δ 0 , δ 1 , and δ 2 for each of the financial regimes. These estimates are
labeled ”estimated delta” in the tables. Both the model and common sense suggest that
entrepreneurial talent plays an important role in occupational choice and, potentially, in
determining the availability and cost of credit. However, success in this area is necessarily
28
These estimates were produced using the methods described in Evans and Jovanovic (1989). Their
methodology cannot be used to estimate the model discussed in this paper.
29
We assumed financial markets were characterized by moral hazard and used the whole sample to callibrate
δ 0 so as to deliver the percentage of entrepreneurs observed in the data.

incomplete since direct data on the distribution, let alone the level, of entrepreneurial talent
is not available.30 Therefore, we allow estimated talent parameters to vary freely with the
financial regimes and compare these estimates with estimates where the talent parameters
are fixed a priori, as described above.
Table 2 reports on the structural estimates for the whole sample for the three financial
market possibilities: moral hazard, limited liability and both. Each column of information
in the table corresponds to a financial market regime. There are four sets of estimates for
each financial market regime. The first set assumes that average entrepreneurial talent is
set according to the ”income” method described above and that agents may be risk averse.
We treat these estimates as the ”benchmark” case and use the others to make sure that our
conclusions are robust. The second set makes the same assumptions about entrepreneurial
talent but assumes that agents are risk neutral. The third set of estimates returns to the
assumption that agents may be risk averse and uses the ”% entrepreneur” method to set
the average talent parameter. In the final set of estimates, talent parameters are estimated
as discussed above and agents are assumed to be risk averse. The predicted relationships
between capital, eﬀort, consumption and wealth for entrepreneurs in the benchmark case are
described in Figure 3.

5.1

Parameter Estimates

Across the financial regimes, in the benchmark case (Panel 1 of Table 2), the production
parameter, α, is estimated to range from 0.69 to 0.78. This means that, all else equal, a 10%
increase in business investment would lead to a 4.2% to 5.1% increase in the probability of
entrepreneurial success. The parameter estimates for α can be used together with predicted
values for eﬀort and investment to calculate the implied probability that the average business
will be successful. In the baseline case, an entrepreneur who invests the average amount of
capital and exerts the average amount of eﬀort has a 32% chance of success in the moral
hazard case, 41% in the limited liability case and 33% when both moral hazard and limited
liability are a concern. These figures are relatively low partly due to the normalization that
ensures the probability of success will always lie between 0 and 1 (see equation (3)). When
we ignore the normalization, the probability of success is 47% in the moral hazard case,
71% in the case of limited liability and 49% when both limited liability and moral hazard
are important. By comparison, survey data from Thailand suggest that 67% of businesses
started in 1998 were still in operation in 2001.
Estimates of α are very similar when the income method is used to determine talent
and risk neutrality is assumed (Panel 2 of Table 2). Comparing the benchmark income
method (Panel 1) with the estimates where talent parameters are estimated (Panel 4), α
stays roughly the same for the moral hazard and both cases and falls from 0.69 to 0.23 in
the case of limited liability. When the ”% entrepreneur” method is used to pin down talent
(Panel 3), the estimates produce values of α that are close to one for the moral hazard and
limited liability case. With these assumptions, the predicted probability of entrepreneurial
success is 46% for moral hazard, 42% for limited liability and 36% when financial markets
are characterized by both moral hazard and limited liability.
The degree of risk aversion is estimated to be fairly consistent both across financial regimes
30

Other researchers have used information from the distribution of test scores to pin down the talent distribution (see Cunha, Heckman and Navarro (2004), for example). Equivalent information for the individuals
in the Thai data is not available.

and across assumptions about the talent parameters. The estimates for γ 1 are generally
close to 0.1, which implies that households are not particularly risk averse. There are three
exceptions to this general finding. Estimated risk aversion is considerably higher when the
”% entrepreneur” method is used to calibrate talent and there is moral hazard (see Panel
3). In the case of moral hazard alone, γ 1 is equal to 1.07, and when there is moral hazard
together with limited liability, γ 1 is estimated to be 0.78. Moral hazard alone generates a γ 1
of 0.58 when talent parameters are estimated (Panel 4).
There are two parameters that determine the disutility of eﬀort, κ and γ 2 (see equation
(1)). Estimates of κ, a scale parameter measuring the distastefulness of eﬀort, are very
consistent across the three financial regimes, ranging from 0.11 to 0.13 in the benchmark case,
0.05 to 0.08 when we assume risk neutrality and from 0.09 to 0.12 when the ”% entrepreneur”
method is used to calibrate talent. However, when talent parameters are estimated, κ is much
higher, ranging from 0.99 to 1.23.
There is some variation in the parameter γ 2 across financial regimes. This parameter,
which is similar to a risk aversion parameter, measures the extent to which agents dislike
variability in eﬀort. For example, in the benchmark case, this parameter is lowest in the
limited liability case at 1.2, goes up to 2.1 in the case of moral hazard and reaches 2.5
when both moral hazard and limited liability are a concern. This reveals some interesting
interaction between the financial regime and the parameters. In the limited liability case, the
estimates want to assign relatively low disutility of eﬀort compared to the moral hazard and
”both” cases when eﬀort assignments must satisfy an incentive compatibility constraint. This
is also consistent with information on how eﬀort assignments are made across the financial
regimes (see Figure 3). Entrepreneurs are assigned higher levels of eﬀort in the limited liability
financial regime compared to the regime where moral hazard is also a concern. There is some
tendency for the structural estimation to produce parameters which make higher eﬀort less
costly to agents when there is limited liability and no moral hazard.
Estimates of the parameter, λ, which determines how much agents can borrow in the
limited liability and both cases, seem too high. In the benchmark estimates, λ is estimated
to be between 21 and 23. This means that agents can borrow between 20 to 22 times their
wealth.
The limited liability parameter, λ, is very sensitive to assumptions about average talent,
δ 0 . When average talent is calibrated to fit the observed percentage of entrepreneurs in the
data (see panel 3 of Table 2), estimates of λ decline markedly, ranging from 1.9 when both
moral hazard and limited liability are a concern to 10.7 when the financial environment is
characterized by limited liability alone.
To further explore this issue, we have estimated the limited liability model fixing the
value of λ at 2 (i.e. households can borrow an amount equal to their own wealth). In these
estimates, the other parameter values are similar to the values that are obtained when λ
is also estimated, although the overall fit of the model, as measured by the log likelihood,
declines compared to the case where λ is estimated.31
An examination of the data reveals that, in practice, loan to collateral values are typically
quite low and very often the value of the loan is significantly less than the value of the collateral
used to secure it, consistent with a λ of less than one.32 On the other hand, there are also
31

These estimates are available from the authors.
Land is the most common source of collateral and indivisibilities in land may account for some of the very
low loan to collateral ratios that we see. For example, if a household wishes to borrow 10,000 baht and has
a plot of land worth 100,000 baht that they use as collateral, the loan to collateral ratio will be 0.1.
32

many unsecured loans in the data. That is, there are many loans where λ would appear to
be infinite.
As discussed above, in the first three sets of estimates, the parameters which describe
the relationship between entrepreneurial talent and wealth and schooling are held fixed at
δ 1 = 0.06, δ 2 = 0.125. These two parameters remain the same and δ 0 is set equal to 0.922
for the benchmark ”income” case and is higher, at δ 0 = 1.295 in the ”% entrepreneur case”.
In the final set of results (panel 4 of Table 2), these parameters are estimated for each of
the financial regimes. Estimates of δ 0 range from a low of 0.1 in the case of both limited
liability and moral hazard to a high of 1.0175, when moral hazard alone is assumed to govern
financial constraints. Estimates of δ 1 , which measures the relationship between wealth and
entrepreneurial talent, are all positive and range from 0.03 in the limited liability case to 0.06
in the moral hazard case. This range includes the assigned value for δ 1 , 0.06, that is assumed
in the other sets of estimates.
Estimates of the parameter δ 2 , which captures the relationship between entrepreneurial
talent and formal schooling, display the most variation across the financial regimes. In the
case of limited liability and no moral hazard, estimates of δ 2 suggest that entrepreneurial
talent decreases with formal schooling, with each additional year of schooling decreasing
entrepreneurial talent by 4%. When moral hazard is a concern, either on its own or together
with limited liability, additional schooling is associated with higher entrepreneurial talent,
with an additional year of schooling increasing entrepreneurial talent by 0.9% in the case of
moral hazard alone and by 8% in the case of moral hazard and limited liability.
Despite the variation in talent parameters across the financial regimes, especially in δ 2 ,
average entrepreneurial talent is estimated to be relatively similar across the regimes: 2.8 in
the case of moral hazard, 2.1 in the case of limited liability and 2.0 when both moral hazard
and limited liability are an issue. By comparison, average entrepreneurial talent is estimated
to be about 3.0 for all of the financial regimes in the benchmark “income” case and about
3.9 in the “% entrepreneur” case.

5.2

Benchmark Assignments of Capital, Eﬀort and Consumption

Figure 3 uses simulated data from each of the three model regimes evaluated at their respective structural maximum likelihood parameter estimates to describe how expected assigned
entrepreneurial capital, eﬀort and consumption vary with wealth for the whole sample, benchmark case with risk aversion. To illustrate more clearly the distinctions between the regimes
and the intuition behind the solutions to the corresponding linear programs from section 2,
the simulations were performed at all actual wealth and schooling levels from the data, i.e. no
splines were used, unlike in the actual estimation. Each graph shows the expected assignment
of consumption, capital and eﬀort as a function of wealth for agents that the structural estimates assign to have k > 0, in other words, entrepreneurs. The discreteness of the grids we
use for computational reasons as well as the heterogeneity in average entrepreneurial talent,
which fluctuates with schooling through δ 2 and thus plays an important role in determining
capital, eﬀort and consumption, account for the variability and ”clustering” displayed in the
figures.
Turning first to consumption, the figure shows that consumption increases more or less
linearly with wealth, regardless of what is assumed about financial market imperfections.
This is what we would expect for unconstrained entrepreneurs, regardless of what is assumed
about financial market imperfections. In the limited liability case, most entrepreneurs turn

out to be unconstrained. However, in the moral hazard case, all risk averse entrepreneurs
are subject to a binding incentive compatibility constraint. For these households the roughly
linear relationship between consumption and wealth is a result of the large fraction of capital
assignments that are the same regardless of wealth. With recommended investment often
invariant to wealth, additional wealth is invested at the gross interest rate, r, and augments
consumption by the gross interest rate multiplied by any additional net savings.
Looking at the relationship between capital and wealth reveals diﬀerences in what is
expected across the models. The straight line in the capital figures is the 45◦ line. Capital
assignments above the 45◦ line correspond to borrowing and capital assignments below the
line involve no borrowing. When financial markets are characterized by moral hazard alone,
there appear to be two groups of entrepreneurs. The largest group has investment that is
largely unchanged with wealth. For this group, borrowing decreases unambiguously with
wealth, as we would expect as constrained entrepreneurs relax the incentive compatibility
constraint by relying less on outside funding when wealth goes up. This group has higher
average talent and wealth. The second group, who have lower talent and lower wealth, has
investment that first declines with wealth and then increases with wealth. The range where
investment decreases when wealth increases is also a range where borrowing is decreasing,
which has the eﬀect of relaxing the incentive compatibility constraint. The range where
investment increases with wealth is a range where the entrepreneurs are net savers and do
not rely on outside funding for their businesses.
Entrepreneurial investment, and hence borrowing, increase sharply with wealth along
several distinct lines when limited liability is a concern. This eﬀect is driven by λ. Constrained
entrepreneurs increase investment and borrowing as increasing wealth relaxes the limited
liability constraint. Note that the rate of increase in investment is higher for low wealth
entrepreneurs that borrow (their capital assignments are above the 45◦ line) than it is for
higher wealth households that are net savers. When both moral hazard and limited liability
are a concern, the relationship between investment and wealth is a combination of what was
observed for the cases where there was only moral hazard or only limited liability, with the
exception that there is no group of entrepreneurs for whom investment appears to be the
same regardless of wealth.
Eﬀort tends to be higher when there is limited liability and no moral hazard, as one might
expect. In this case, the structural estimates predict essentially two levels of eﬀort, high and
low, that do not vary with wealth. There is some tendency for the low wealth entrepreneurs to
have higher eﬀort and wealthier entrepreneurs to have lower eﬀort33 . In addition, although
this cannot be seen in the figure, the low wealth, high eﬀort group tends to have greater
entrepreneurial talent on average compared to the high wealth, low eﬀort group.
When moral hazard constrains financial contracts, there is also a large group of entrepreneurs who have the same, relatively low, eﬀort regardless of wealth. This group accounts for 78% of the businesses produced by the moral hazard estimation. However, there
is another, much smaller group of entrepreneurs with low to medium wealth who exert more
eﬀort as wealth increases. This group has lower average entrepreneurial talent compared to
the group whose eﬀort does not vary with wealth. When both moral hazard and limited
liability are a concern the data produced by the structural estimation more closely mimics
the situation when there is only moral hazard.
33
Notice that there are relatively more points on the upper eﬀort level ”line” in the ”eﬀort” panel of the
limited liability part of fig. 3 for low wealth levels and relatively more points on the low level ”line” for higher
wealth levels.

Comparison of the Financial Regimes

In this section the financial regimes are compared using two complementary techniques.
First we distinguish between the financial regimes using formal tests based on the structural
estimates discussed above. Next, non-parametric and reduced form techniques are used to
provide additional, independent evidence about the source of financial market imperfections
in the Thai data.
While the structural estimates impose a number of restrictions on the data, they rely on
a very limited subset of the available data: past wealth, the entrepreneurial status of the
household, and the years of schooling of the household head. In contrast, the non-parametric
estimates impose almost no structure on relationships between the key variables of interest
and explore relationships between variables that are not used in the structural estimation.
The reduced form estimates draw on the richness of the available survey data, while imposing
a particular functional form on the relationship between the dependent and independent variables. Both the non-parametric and the reduced form findings oﬀer completely independent
evidence of the nature of financial constraints and enhance the overall interpretation of what
we see in the data.

6.1

Structural Evidence

In this sub-section, we provide formal tests of which of the candidate financial regimes best
fit the whole sample and the various sub-samples of the data that were described earlier. The
financial regimes are compared using the Vuong likelihood ratio test (see Vuong (1989)). One
attractive feature of the Vuong test is that it does not require either model to be correctly
specified. This feature is appealing given the necessity of studying models that are much
simpler than reality. The null hypothesis is that the two models are equally near the actual
data generating process. The Vuong test delivers an asymptotic test statistic that measures
the weight of the evidence in favor of one model or the other.34
We use the Vuong test for strictly non-nested models. For the purposes of this test,
model A nests model B, if, for any possible allocation that can arise in model B, there exist
parameter values such that this is the allocation in Model A. In the current context, the
case with both limited liability and moral hazard nests the case where financial markets are
characterized by only moral hazard. This is because for a suﬃciently large λ, the ”both”
case will reproduce the exact same assignment of households to occupations as the moral
hazard alone case. On the other hand, the ”both” case does not nest the limited liability
case, because there is no parameter that can make eﬀort observable and ”turn-oﬀ” the moral
hazard constraint and deliver the same assignment of entrepreneurial status as in the limited
liability alone case. In any case, the likelihood ratio test statistic that Vuong proposes
is appropriate regardless of whether the three financial regimes are completely non-nested,
overlapping or nested. However, the asymptotic distribution of the test statistic depends
on the relationship between the models.35 Using the distribution that is appropriate for
34
One could use the same procedure where the null hypothesis was that one model was closer to the actual
data generating process. The test statistic would remain the same, however, the critical values for rejecting
the null would of course change.
35
The comparisons of financial regimes that we report are based on the more conservative critical values
for the case of strictly nested models, where the test statistic has a χ-squared distribution. In the case of
non-nested models the test statistic is normally distributed.

non-nested models is the conservative choice, in the sense that is makes it more diﬃcult to
statistically distinguish the financial regimes.
6.1.1

Whole Sample Findings

Tables 3A - C report the log likelihoods for each of the three possible financial regimes (moral
hazard, limited liability, and both) and the four sets of assumptions we make in estimation
(”income” with risk aversion and with risk neutrality, ”% entrepreneur” with risk aversion
and the case where the talent parameters are estimated). The likelihoods are reported for
the whole sample (3A), the Northeast (3B) and the central region (3C). The results of the
comparison tests for the three possible financial regimes, moral hazard, limited liability and
both, are provided in Tables 4A B and C, for the whole sample, Northeast and central region,
respectively.
For the whole sample, the case where moral hazard alone describes financial markets
significantly outperforms the limited liability case and the case where financial markets are
characterized by both moral hazard and limited liability. This finding is robust to alternative assumptions about risk aversion, and to alternative methods of calibrating average
entrepreneurial talent. Because the moral hazard case performs best even when talent is
calibrated to match the observed percentage of entrepreneurs in the data, we gain confidence
that the results are not in some way driven by the relatively low number of entrepreneurs produced by the estimates which use the relative income of entrepreneurs and non-entrepreneurs
to fix the mean of the talent distribution.36
When the estimation also produces estimates of the talent parameters (the fourth row),
the distinction between the moral hazard and the both case decreases somewhat. While these
estimates strongly reject the possibility that financial markets are characterized by limited
liability alone, they do allow for the possibility that limited liability in concert with moral
hazard might be as good a candidate for explaining the data as moral hazard alone.
6.1.2

Regional Findings

We next consider the possibility that the financial regime varies by region. There are a
number of reasons to consider this possibility, the first being the large diﬀerences in wealth
between the more developed Central region and the less developed Northeastern region. In
addition to this diﬀerence, the dominant financial institution is diﬀerent in the two regions
and one prominent lender, the BAAC, appears to operate diﬀerently in the two regions.
In the Northeast the percentage of total funds lent is very concentrated compared to the
Central region. The BAAC accounts for 39% of all funds lent. Other formal lenders account
for only 11% of lending. In the Central region lending is much more dispersed. The BAAC
accounts for 24% of lending. Commercial banks and relatives account for another 21% and
17% of lending, respectively.
Despite these regional diﬀerences, the comparisons of the financial regimes for the Northeast and the Central region in Tables 4B and C reinforce the findings for the whole sample.
36

The benchmark ”income” results imply that 3% of the sample will become entrepreneurs when there is
moral hazard, 6% if there is limited liability and 5% when there is limited liability and moral hazard. In
the data, 14% of households have a business. By design, the ”% entrepreneur” estimates imply that 14% of
households will have a business when there is moral hazard. When there is limited liability or limited liability
and moral hazard, 26% of households are predicted to have a business in the ”% entrepreneur” case.

Hidden information, specifically hidden action, drives the key financial constraint in Thailand. For the Central region, the findings are even stronger than for the Whole Sample.
Regardless of assumptions about risk aversion and talent, these estimates favor moral hazard alone as an explanation for the patterns of entrepreneurship in the Central region. In
the Northeast, the same pattern emerges, with one exception. When the estimation allows
talent parameters to vary with the financial regime, the three financial regimes cannot be
statistically distinguished from one another.
6.1.3

Robustness Checks

Grid Sizes and bounds In producing the structural estimates, we have experimented
with diﬀerent grid sizes for investment and eﬀort, as well as with diﬀerent upper bounds on
the potential range for investment and eﬀort.37 The superior fit of the moral hazard financial
regime is not aﬀected by alternative assumptions about the number of grids or the range of
potential investment and eﬀort levels.
Sensitivity of Results to Outliers In order to ensure that the findings are not driven by
a outliers in the data, we have estimated the model, under the benchmark assumptions, for
each of the financial regimes dropping observations that fall into the top 5% or the bottom
5% of the wealth distribution. When the influence of potential outliers is eliminated, the
moral hazard regime continues to significantly outperform the limited liability regime as well
as the regime where both moral hazard and limited liability are a concern.
Identification of Business Households We return now to the issue of whether the assignment of entrepreneurial and non-entrepreneurial status to the sample households has
content. This is evaluated using simulations of the Evans and Jovanovic (1989) limited liability model, because this model is relatively speedy to estimate numerically. We construct
100 samples of the Thai data where entrepreneurial status is randomly assigned, ignoring the
actual occupation of the household. The overall fraction of randomly assigned entrepreneurs
is fixed at the proportion of business households actually observed in the original data. The
overall fit of the limited liability model deteriorates substantially when it is estimated using
the simulated data.
6.1.4

Summary of Structural Evidence

Taking all of the evidence from the formal comparison of the three financial regimes together,
we conclude that moral hazard is the key financial market imperfection that impacts who
becomes an entrepreneur in Thailand. We reject the possibility that limited liability alone
could explain the data.
Figures 4A and B compare the predicted likelihood of starting a business as a function
of wealth at the maximized parameter values produced by each financial regime for the
benchmark whole sample results. These figures also include non-parametric estimates of
the probability of starting a business as a function of wealth from the survey data. In the
case of the structural estimates, the graphs represent the non-parametric relationship between
37

Specifically, we computed versions of the model with 5 grid points for eﬀfort, versions with 10 grid points
for investment, as well as versions with higher upper bounds on the grids for eﬀort and investment (10 instead
of 5).

entrepreneurship and wealth implied by the assignments of capital and eﬀort produced by the
structural estimates. For each wealth and talent value, the structural estimates generate the
probability that a household with that wealth and talent will become an entrepreneur. The
curve labeled ”data” in Figures 4A and 4B is the non-parametric estimate of the relationship
between the survey reports of entrepreneurial status and wealth. For each structural estimate
and the data, non-parametric estimates of the relationship between entrepreneurship and
wealth were produced using the same techniques as Figure 1 (see footnote 1 for details).
Figure 4A shows what happens to the likelihood of starting a business over the entire
domain of wealth and Figure 4B restricts the wealth domain to the 5th through the 95th
percentile. It is important to keep in mind that the probability of starting a business as a
function of wealth produced by the structural estimates also includes the impact of integrating
out over the talent distribution. Similarly, the estimates produced from the survey data make
no attempt to control for entrepreneurial talent or schooling.
Looking first at Figure 4A, it appears that the predicted probability of being an entrepreneur generated by the moral hazard regime is closest to the Thai data. Further from
Figure 4B, one can see that while the moral hazard estimate underpredicts the percentage of
entrepreneurs relative to most of the data, this estimate does a good job of matching the slope
observed in the data. In other words, the moral hazard regime closely mimics the relatively
constant observed rate of increase of entrepreneurship with wealth in the data.
In contrast the limited liability and the ”both” estimates over-estimate the rate of increase
in entrepreneurship with wealth for the majority of households. Specifically, both of these
regimes suggest that the rate of increase in entrepreneurship with wealth is highest among
low wealth households and this slows down only when wealth reaches approximately 0.55,
or nearly the 99th percentile of the wealth distribution (see Figure 4A). In comparison, the
moral hazard estimate implies that entrepreneurship increases more modestly with wealth for
almost all of the wealth distribution and then increases sharply with wealth at the highest
wealth levels. Some intuition is provided by an examination of Figure 2, the risk neutral
case, and Figure 3. Under limited liability, increases in wealth for constrained entrepreneurs
sharply increase the level of capital with only small variation in eﬀort. In contrast, under
moral hazard, capital is on average not moving much with wealth while eﬀort increases,
starting from a lower value. Evidently the moral hazard constraint is more damaging at low
levels of wealth than is limited liability.

6.2

Non-parametric and Reduced Form Evidence

In addition to comparing the financial regimes based on the structural evidence about who
will start a business as a function of wealth and talent, we can also use non-parametric
and reduced form techniques and additional variables to try to distinguish financial regimes.
While none of the findings presented here is definitive on its own, taken together they reinforce
the findings from the structural model comparisons: the dominant financial market constraint
is due to moral hazard.
Limited liability and moral hazard have diﬀerent implications for how borrowing will
change with wealth, particularly for constrained business owners. Recall that constrained
business households are those that report that their business would be more profitable if it
were expanded and that 56% of the business households are ”constrained” according to this
definition. In the limited liability case, constrained business owners have borrowed up to the
maximum multiple of wealth allowed, so increases in wealth will necessarily lead to increased

borrowing for these businesses. In the moral hazard case, the opposite is true: borrowing
will decrease with wealth for constrained business owners. Business owners can relax the
incentive compatibility constraint by borrowing less. We investigate these implications by
examining the relationship between the likelihood of being a borrower and wealth and the
level of net savings and wealth for constrained business households.
6.2.1

Non-parametric Evidence

Figure 5A summarizes the non-parametric relationship between the probability of being a
borrower and wealth for constrained business households. Figure 5B reports on the predicted
the relationship between net savings and wealth for constrained business households. Both
figures were produced using the same non-parametric techniques that were used to create
Figure 1. The domain of wealth is restricted to the 5th to the 95th percentile. The dashed
lines in the figures represent the 25th percentile and the 75th percentile bootstrap estimates
of the relationship between borrowing and wealth and between net savings and wealth.
Turning first to Figure 5A, we see that the probability of being a borrower decreases with
as wealth goes from 0 to about 0.02. Approximately 60% of the survey households have
wealth in this range. This relationship is consistent with moral hazard. As wealth goes from
0.02 to about 0.05, the likelihood of borrowing increases with wealth, as we would expect
if limited liability constrained financial markets. This range corresponds to about 17% of
the survey households. When wealth is greater than 0.08, the probability of borrowing again
decreases with wealth as would be expected if moral hazard was responsible for restrictions on
financial contracts. This range accounts for about 9% of households. Thus for the majority
of households in the Thai data, the relationship between borrowing and wealth is consistent
with moral hazard, although we cannot rule out the possibility that limited liability also plays
a role in shaping financial markets.
The relationship between the level of borrowing, or, equivalently, net savings, is examined in Figure 5B. Here we see a similar pattern. As wealth goes from 0 to 0.005, net
savings increases, as we would expect if moral hazard were important. This range accounts
for approximately one-third of households. As wealth goes from 0.005 to 0.09, net savings
decrease, or equivalently borrowing increases. This range is consistent with limited liability
and corresponds to about 55% of households in the sample. When wealth is greater than
0.09, net savings again increases with wealth and this range accounts for the remaining 12%
of households. These estimates suggest that both moral hazard and limited liability may be
important for explaining the data, with about half of the observations being consistent with
each financial constraint. However, limited liability alone cannot account for the relationship
between the likelihood of borrowing and borrowing levels and wealth described in Figures 5A
and B.38
6.2.2

Reduced Form Evidence

Whole Sample Findings We now turn to reduced form parametric estimates to examine the relationship between the borrowing and wealth and between net savings and wealth
for constrained business households. Table 5A reports on probit estimates of whether entrepreneurial households borrow as a function of demographic controls, past use of various
financial institutions, past wealth and whether or not the household reports that its business
38

Small sample sizes preclude us from creating regional versions of these estimates.

is constrained. For the whole sample, these results suggest that constrained business households are 8.5 percentage points more likely to borrow than their unconstrained counterparts.
This finding is more consistent with moral hazard than with limited liability. When
financial markets are characterized by moral hazard and incentive constraints bind, everyone
who borrows will be constrained. In the limited liability case, the relationship between
borrowing and being constrained is much weaker. Some households who borrow will be able
to invest the optimal amount of capital and will not be constrained and others will not be
able to borrow enough to invest the optimal amount and will be constrained.
Table 5B reports on the relationship between the extent of borrowing, or, equivalently,
net savings, and wealth for constrained and unconstrained business households. This table
includes regression estimates of net savings for business households as a function of various
demographic controls and wealth for business households. The eﬀect of wealth is allowed
to diﬀer depending on whether the business is constrained or not. For the whole sample,
net savings is positively correlated (or equivalently borrowing is negatively correlated) with
wealth for constrained businesses. A 1,000,000 baht increase in wealth for a constrained
business would increase net savings (decrease borrowing) by 48,000 baht.
The same increase in wealth for an unconstrained business is predicted to increase net
savings by 12,000 baht, and the coeﬃcient on wealth for unconstrained businesses is not
statistically diﬀerent from zero. This is the relationship we would expect to see between net
savings and wealth among constrained businesses if financial markets are characterized by
moral hazard and households are risk neutral. By decreasing borrowing when wealth goes
up, constrained businesses can relax the incentive compatibility constraint associated with
moral hazard. If financial markets were characterized by limited liability, we would expect
net savings to go down (borrowing to increase) with wealth for constrained businesses.
Regional Findings The results for the Central region favor moral hazard and are very
similar to the results for the whole sample. The likelihood of being a borrower is predicted to
be 13 percentage points higher among constrained business households in the central region
(see Table 5A). Table 5B shows that a 1,000,000 baht increase in wealth is predicted to
increase net savings by 48,000 baht in the Central region, as we would expect if moral hazard
were a concern.
According to the estimates reported in Tables 5A, being constrained has no statistically
significant eﬀect on the likelihood of borrowing for businesses in the Northeast. When financial markets are characterized by limited liability, the probability of borrowing should not
be related to wealth, which is consistent with the findings in Table 5A for the Northeast. A
much stronger case would exist if the point estimate for the eﬀect of being constrained on the
probability of borrowing were close to zero and precisely estimated. As it is, the precision of
the estimate is consistent with the impact of being constrained having either a negative or a
positive impact on the likelihood of borrowing in the Northeast.
We also find that the level of net savings is imprecisely related to wealth among constrained businesses in the Northeast (see Table 5B). We cannot rule out the possibility that
an increase in wealth would be associated with a decrease in net savings (increase in borrowing) as we would expect if limited liability constrains financial markets. On the other hand,
the results do not allow us to rule out the opposite either.

6.2.3

Summary of Non-parametric and Reduced Form Evidence

Taken together the non-parametric and reduced form evidence indicate that limited liability
alone cannot explain the observed relationship between borrowing and wealth and net savings
and wealth. Figures 5A and B suggest that both moral hazard and limited liability have
a role to play in explaining patterns of entrepreneurship in Thailand. The strength of the
evidence in favor of moral hazard for the Central region and the lack of evidence to distinguish
moral hazard from limited liability in the Northeast provide independent confirmation of the
patterns observed in the formal model comparison tests for the two regions.

Conclusions and Discussion

Identifying the source of financial constraints that limit entry into entrepreneurship was a key
objective of the paper. Non-parametric, reduced form and structural evidence all indicate
that moral hazard is the key financial constraint that restricts entrepreneurship in Thailand.
To the extent that limited liability plays a role in constraining entrepreneurs and potential
entrepreneurs, it is in conjunction with moral hazard.
The paper emphasizes diﬀerent potential assumptions regarding the constraints on financial contracting. The model has common assumptions about utility, production, the
distribution of talent and error terms, regardless of financial constraints. Therefore, these aspects of the model do not account for the success of the moral hazard model in the structural
estimates. In addition, non-parametric and reduced form evidence, which is independent of
assumptions regarding utility functions, production, talent and errors, also points to moral
hazard being the dominant financial market imperfection.
The issues raised in the paper contribute to the discussion of the desirability of policy
interventions that are intended to alleviate financial constraints. In particular, the paper
highlights the fact that the presence of financial constraints does not establish grounds for
a policy intervention. Given the financial market imperfections, the existing set of contracts
may be the optimal ones. Nonetheless, the findings suggest useful directions for policy discussions.
Currently the BAAC emphasizes joint liability lending groups for poor farmers. Our
findings suggest that these groups, which may use superior information that villagers have
about one another to mitigate moral hazard problems, could be usefully extended to more
households. Indeed, we find some evidence that wealthier households who participate in
BAAC borrowing groups may be less constrained in the Central region (see Paulson and
Townsend 2004), as though the BAAC were using these groups as a screening mechanism
and channeling larger loans to individuals who are deemed acceptable group members by
their peers. In contrast, a program to establish secure property rights in land (so that it
could serve as collateral and overcome limited liability constraints) might be a lower priority
for much of Thailand. The main point is that a successful policy intervention must address
the underlying financial market imperfection, rather than its symptoms.
Our work suggests a number of fruitful avenues for future research. Clearly more work
on the role of entrepreneurial talent is a priority. Success in this area is likely to require
additional data to help pin down both the distribution of talent and its role in production.
In addition, it would be valuable, from both a theoretical and an empirical perspective, to
extend the cross-sectional framework and findings reported on here to a dynamic setting.
Finally, it would be interesting to explore the extent to which the findings for Thailand
26

generalize to other developing and developed countries.

References
[1] Abbring, Jaap, Pierre Andre Chiappori, James Heckman and Jean Pinquet. “Adverse
Selection and Moral Hazard in Insurance: Can Dynamic Data Help to Distinguish?”
Journal of the European Economic Association, 2003, Volume 1(Issue 2/3): 512-521.
[2] Aghion, Phillippe and Patrick Bolton. “A Trickle-down Theory of Growth and Development with Debt Overhang.” Review of Economic Studies (1996).
[3] Binford, Michael, Tae Jeong Lee and Robert Townsend. “Sampling Design for an Integrated Socioeconomic and Ecologic Survey Using Satellite Remote Sensing and Ordination.” Manuscript. Chicago: University of Chicago, 2001.
[4] Blanchflower, D. and A. Oswald. “What Makes an Entrepreneur?” Journal of Labor
Economics, 16(1998): 26-60.
[5] Buera, Francisco. “Identification of and Testable Implications of Occupational Choice
Models.” Manuscript. Chicago: University of Chicago, 2002.
[6] Buera, Francisco. “A Dynamic Model of Entrepreneurial Choice with Borrowing Constraints.” Manuscript. Chicago: University of Chicago, 2002.
[7] Cunha, Flavio, James Heckman and Salvador Navarro. “Separating Uncertainty from
Heterogeneity in Life Cycle Earnings.” IZA Discussion Paper #1437, December 2004.
[8] Evans, David S. and Boyan Jovanovic. “An Estimated Model of Entrepreneurial Choice
under Liquidity Constraints.” Journal of Political Economy 97 (1989): 808 - 827.
[9] Fafchamps, Marcel. “Sequential Labor Decisions Under Uncertainty: an Estimable
Household Model of West African Farmers.” Econometrica 61(5) (1993): 1173-1197.
[10] Feder, Gershon et. al. Land Policies and Farm Productivity in Thailand. Baltimore:
Johns Hopkins University Press, 1988.
[11] Giné, Xavier and Robert Townsend, “Evaluation of Financial Liberalization: A general equilibrium model with constrained occupation choice.” Journal of Development
Economics, 2004.
[12] Giné, Xavier. “Access to Capital in Rural Thailand: An Estimated Model of Formal vs.
Informal Credit.” World Bank Working Paper #3502, 2005.
[13] Greenwood, Jeremy and Boyan Jovanovic. “Financial Development, Growth, and the
Distribution of Income.” Journal of Political Economy 98 (1990): 1076 - 1107.
[14] Holtz-Eakin, Douglas, Joulfian, David and Harvey S. Rosen. “Sticking It Out: Entrepreneurial Survival and Liquidity Constraints.” Journal of Political Economy 102
(1994): 53 - 75.

[15] Jeong, Hyeok and Robert Townsend. “Household Businesses and their Financing in
Semi-Urban and Rural Thailand.” Manuscript. Chicago: University of Chicago, 1998.
[16] Judd, Kenneth, “Numerical Methods In Economics”, MIT Press, Cambridge, MA, 1998.
[17] Kaboski, Joseph and Robert Townsend. “Borrowing and Lending in Semi-Urban and
Rural Thailand.” Manuscript. Chicago: University of Chicago, 1998.
[18] Karaivanov, Alexander. “Incomplete Financial Markets and Occupational Choice: Evidence from Thai Villages.” Manuscript. Simon Fraser University, 2005.
[19] Lloyd-Ellis, Huw and Dan Bernhardt, “Enterprise, Inequality and Economic Development.” Review of Economic Studies 67 (2000): 147 - 168.
[20] Magnac, Thierry and Jean-Marc Robin, “Occupational Choice and Liquidity Constraints.” Richerche Economiche (1996) 50: 105 - 133.
[21] Paulson, Anna L. and Robert Townsend “Entrepreneurship and Financial Constraints
in Thailand.” Journal of Corporate Finance (2004) Vol 10 No 2 229-262..
[22] Petersen, Mitchell A. and Raghuram G. Rajan. “The Benefits of Lender Relationships:
Evidence from Small Business Data.” Journal of Finance 49 (March 1994): 3 -37.
[23] Petersen, Mitchell A. and Raghuram G. Rajan. “The Eﬀect of Credit Market Competition on Lending Relationships.” Quarterly Journal of Economics 110 (May 1995):
407-444.
[24] Phelan, Christopher and Robert Townsend. “Computing Multi-Period, InformationConstrained Equilibria.” Review of Economic Studies 58 (1991): 853-881.
[25] Prescott, Edward and Robert Townsend. “Pareto Optima and Competitive Equilibria
with Adverse Selection and Moral Hazard.” Econometrica 152 (January 1984): 21-45.
[26] Seiler, Edward and Robert Townsend. “Assets in Semi-Urban and Rural Thailand.”
Manuscript. Chicago: University of Chicago, 1998.
[27] Townsend, Robert principal investigator with Anna Paulson, Sombat Sakuntasathien,
Tae Jeong Lee and Michael Binford. “Questionnaire design and data collection for
NICHD grant ‘Risk, Insurance and the Family’ and NSF grants”, 1997.
[28] Townsend, Robert and Jacob Yaron. “The Credit Risk-Contingency System of An Asian
Development Bank”, Economic Perspectives, Federal Reserve Bank of Chicago, 2001.
[29] Vuong, Quang H. “Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses.” Econometrica, Volume 57, Issue 2, (March 1989): 307-333.
[30] Wolak, Frank. “Estimating Regulated Firm Production Functions with Private Information: An Application to California Water Utilities.” Annales d’Economie et de Statistique
(1994) 34: 13-69.

.8
Probability of Entrepreneurship
0
.2
.6
.4
0

.4
2.5th percentile
median
95th percentile

Wealth

5th percentile
97.5th percentile

Figure 1: Lowess Estimates of the Probability of Being an Entrepreneur and Wealth
500 bootstrap estimates of the relationship between being an entrepreneur and wealth were created using a
bandwidth of 0.8. The 2.5th percentile (dashed line), 5th percentile (dashed line) median (solid line), 95th
percentile (dashed line) and 97.5th percentile (dashed line) estimates are shown in the figure.

2
First Best
k =0.42, z=1.69, welfare=0.448

Limited Liability
k =0.25, z=1.44
welfare=0.413

effort, z

Both LL & MH
k =0.25, z=1.04
welfare=0.409

Moral Hazard
k =0.38, z=0.99, welfare=0.434

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.1

1.2

1.3

capital, k

Figure 2: Assignments of Capital (k) and Effort (z) for the Entrepreneurs in the Risk Neutral Model
Moral Hazard, Limited Liability and Both Moral Hazard and Limited Liability
assumptions: θ=2.56, A = 0.10, α=0.78, κ=0.08, γ2=1.00, r=1.10, λ = 2.50

Moral Hazard

Limited Liability

a ~·
---~

~LO

:;:;I()

E.,...

a. .

;:,

cn.,....

01()

.4
.6
wealth

_,.,..

..,.
N
N
t::<!)

0
:;
.,.....
WN

.,....

.4
.6
wealth

E .,...
;:,

cn.,....

.4
.6
wealth

(X)

1§~

~~.
·a.

rov
.
N

.4
.6
wealth

-·-····

Limited Liability

..,.

--·
-·
.
·--·---·
5<!)

:=.,...:
WN

•

.4
.6
wealth

Both

..,.
N
N
t::<!)

~ .,...:
.,....

(X)

.4
.6
wealth

~·

•

N
N

Both

·a. .
ro'<t
.
N

Moral Hazard

Limited Liability

(X)

gco

a. .
0

Moral Hazard
·a. .
ro'<t
oN·

0
:;:;I()

01()

•..

Both
eN

··-·-·

.4
.6
wealth

Figure 3: Expected Assigned Consumption, Capital and Effort Relative to Wealth for
Entrepreneurs
From Benchmark Structural Estimates of Moral Hazard, Limited Liability and both Moral Hazard
and Limited Liability Financial Regimes
Note: 45° Line is included in Capital Figures

Predicted Probability of Entrepreneurship and Wealth
a.

·..c

Benchmark Case: Delta determined by Income
1.()

~T""
:::J
Q)

c:
~

~
cT""

COL()

..0

ea.

"0
Q)

-go
'a.

wealth
data
limited liability

•

moral hazard
both

Figure 4A: Predicted Probability of Entrepreneurship and Wealth,
Entire Wealth Domain
Lowess estimates of the relationship between entrepreneurial status and wealth from survey data and
entrepreneurial status assigned in benchmark structural estimates (moral hazard , limited liability and both
moral hazard and limited liability). Bandwidth 0.8

Predicted Probability of Entrepreneurship and Wealth
Delta determined by Income, 5th to 95th %-ile of Wealth

a.
..C'o:t
(/)

:::J
Q)

c:
~

Q.C"")

Q)
'.......

•

~·

..0

eT""".
a.

"0
Q)

t5
~0
'a.

.05

.1
wealth
data
limited liability

.15

•

moral hazard
both

Figure 48: Predicted Probability of Entrepreneurship and Wealth,
5th to 95th Percentile of Wealth
Lowess estimates of the relationship between entrepreneurial status and wealth from survey data and
entrepreneurial status assigned in benchmark structural estimates (moral hazard , limited liability and both
moral hazard and limited liability). Bandwidth = 0.8

Predicted Probability of Being a Borrower

-......" ,_
_,,-- -- ~"'
'
''"". ., .,r , r---- --- --\,...,
.....
I'---"\

· - <.0
~
.

I...
I...

-;z:.·
0

O LO

'~-~-

----------------

,--'

~-~

\......_

-''
......

..c
ro
..c

ev
a..·

-- ' '

' ' ............ _
............

.02

.04

.06

.08

.1
Wealth

- - - - - 25th percentile
- - - - - 75th percentile

.12

.14

.16

. 18

............

......

median

Figure 5A: Lowess Estimate of the Probability of Being a Borrower for Constrained Business Households
500 bootstrap estimates of the relationship between being a borrower and wealth were created using a bandwidth of 0.8.
The 25th percentile (dashed line), median (solid line) and 75th percentile estimates (dashed line) are shown in the figure.
Note that the figure shows the relationship for the 5th to the 95th percentile of wealth.

Predicted Savings and Wealth

0
0
0
0

L{)

cno
0>

c
">
ro

C/)

--a
a> a

Zo
0

L{)

0
0
0
0
0

'----.r-' """

T"""
I

.02

.04

.06

.08

---

..,., /

---

.1
Wealth

- - - - - 25th percentile
- - - - - 75th percentile

.12

.14

.16

.18

median

Figure 58: Lowess Estimate of Net Savings and Wealth for Constrained Business Households
500 bootstrap estimates of the relationship between net savings and wealth were created using a bandwidth of 0.8. The 25th
percentile (dashed line), median (solid line) and 75th percentile estimates (dashed line) are shown in the figure. Note that the
figure shows the relationship for the 5th to the 95th percentile of wealth.

Table 1: Summary Statistics
Whole Sample
Northeast
Central
Variables used in Structural and Reduced Form/Non-parametric Estimation (All Households)
Number of Households
2,313
1,209
1,104
% Business Households
14%
9%
19%
Years of Schooling
All Households
4.03
3.97
4.09
(2.56)
(2.45)
(2.67)
Business Households
4.70
5.00
4.50
(2.90)
(3.00)
(2.80)
Wealth Six Years Prior to survey
All Households
1,007,166
355,996
1,712,046
(3,929,520)
(648,590)
(5,545,901)
Business Households
2,532,464
428,490
3,614,755
(7,603,877)
(558,630)
(9,168,505)
Constrained Business Households*
1,199,500
313,093
1,655,471
(5,770,877)
(546,497)
(7,051,744)
Unconstrained Business Households
1,562,854
137,406
2,296,109
(5,550,756)
(343,281)
(6,713,852)
Variables used in Reduced Form/Non-parametric Estimation (Business Households Only)
Number of Households
361
122
239
Initial Business Investment
148,734
81,311
179,349
(339,562)
(176,918)
(388,312)
Net Savings
4,562
-13,680
13,946
(714,701)
(410,166)
(829,564)
% who are net borrowers
55%
61%
51%
% who report they are constrained*
56%
68%
50%
Age of Head
49.5
48.4
50.1
(13.9)
(13.6)
(14.1)
# of Adult Females in the
1.6
1.6
1.7
Household
(0.9)
(0.8)
(0.9)
# of Adult Males in the Household
1.6
1.5
1.7
(0.9)
(0.9)
(0.9)
# of Children (< 18 years) in the
1.5
1.5
1.6
Household
(1.2)
(1.1)
(1.3)
% of Business Households who were Member/Customer of Organization/Institution Six Years Ago
Formal Financial Inst.
23%
16%
27%
Village Inst./Org
11%
10%
12%
Agricultural Lender
33%
33%
33%
BAAC Group
22%
29%
18%
Money Lender
4%
5%
4%
Notes: Standard errors in parentheses. Wealth is in Thai Baht. The exchange rate at the time of the survey
is 25 Baht to $1.
*Households who reported that their businesses would be more profitable if it were expanded are labeled
“constrained”. Households who report that their business would not be more profitable if it were expanded
are labeled “unconstrained”.

1.
γ1
γ2
κ
α
λ
2.
γ2
κ
α
λ
3.
γ1
γ2
κ
α
λ
4.
γ1
γ2
κ
α
δ0
δ1
δ2
λ

Table 2: Parameter Values from Structural Estimation, Whole Sample
Bootstrap Standard Errors in Parentheses
Moral Hazard
Limited Liability
Both
Risk Aversion, Talent (Income)
0.0985
0.0982
(0.0125)
(0.0003)
2.1007
1.1713
(0.3216)
(0.0037)
0.1257
0.1079
(0.0227)
(0.0003)
0.7775
0.6937
(0.0325)
(0.0165)
22.9885
-(0.0727)
Risk Neutral, Talent (Income)
1.5801
1.3475
(0.0243)
(0.0167)
0.0530
0.0675
(0.0009)
(0.0009)
0.7700
0.6800
(0.0099)
(0.0273)
24.5000
-(0.3307)
Risk Aversion, Talent (% Entrepreneur)
1.0737
0.0668
(0.0123)
(0.0004)
1.0000
1.0000
(0.0192)
(0.0141)
0.0904
0.0722
(0.0001)
(0.0001)
0.9780
0.9702
(0.0032)
(0.0003)
10.7281
(0.0305)
Risk Aversion, Estimated Talent
0.5753
0.0957
(0.0175)
(0.0002)
1.0494
1.2314
(0.0171)
(0.0120)
1.2312
0.9889
(0.0649)
(0.0049)
0.7931
0.2283
(0.0148)
(0.0030)
1.0175
0.8853
(0.0464)
(0.0108)
0.0604
0.0285
(0.0218)
(0.0002)
0.0516
-0.2226
(0.0053)
(0.0046)
-21.0118
(0.2223)

0.1025
(0.0046)
2.4753
(0.1797)
0.1190
(0.0062)
0.7208
(0.0108)
20.8082
(1.4882)
1.5511
(0.0171)
0.0789
(0.0008)
0.6902
(0.0043)
28.3848
(0.3095)
0.7781
(0.0035)
1.0000
(0.0105)
0.1219
(0.0016)
0.5062
(0.0066)
1.9014
(0.0042)
0.1002
(0.0005)
1.0939
(0.0061)
1.0022
(0.0065)
0.7985
(0.0188)
0.1002
(0.0007)
0.0503
(0.0004)
0.3005
(0.0018)
5.0088
(0.0970)

Table 3: Log Likelihoods from Structural Estimation
A. Whole Sample
Moral Hazard
Risk Aversion,
Talent (Income)
Risk Neutral,
Talent (Income)
Risk Aversion,
Talent (% Entrepreneur)
Risk Aversion, Estimated Talent

Limited Liability

Both

-0.4038

-0.4706

-0.4683

-0.4104

-0.4608

-0.4372

-0.4590

-0.7514

-0.6064

-0.3996

-0.4134

-0.4035

B. Northeast
Moral Hazard
Risk Aversion,
Talent (Income)
Risk Neutral,
Talent (Income)
Risk Aversion,
Talent (% Entrepreneur)
Risk Aversion, Estimated Talent

Limited Liability

Both

-0.3044

-0.3474

-0.3258

-0.3046

-0.3474

-0.3408

-0.4588

-0.4250

-0.3040

-0.3045

-0.3029

C. Central
Moral Hazard
Risk Aversion,
Talent (Income)
Risk Neutral,
Talent (Income)
Risk Aversion,
Talent (% Entrepreneur)
Risk Aversion, Estimated Talent

Limited Liability

Both

-0.5014

-0.5966

-0.5668

-0.5190

-0.5966

-0.5553

-0.6104

-0.8658

-0.7902

-0.4991

-0.5355

-0.5185

Table 4: Comparison of Financial Regimes, Vuong Test Results
A. Whole Sample
MH v. LL
Risk Aversion,
Talent (Income)
Risk Neutral,
Talent (Income)
Risk Aversion,
Talent (% Entrepreneur)
Risk Aversion, Estimated Talent

MH***
(0.0000)
MH***
(0.0010)
MH***
(0.0000)
MH***
(0.0046)

MH v. Both
MH***
(0.0001)
MH**
(0.0252)
MH***
(0.0000)
MH
(0.3402)

LL v. Both
Both
(0.8866)
Both***
(0.0033)
Both***
(0.0000)
Both***
(0.0046)

Best Overall
Fit
MH
MH
MH
MH or Both

B. Northeast
MH v. LL
Risk Aversion,
Talent (Income)
Risk Neutral,
Talent (Income)
Risk Aversion,
Talent (% Entrepreneur)
Risk Aversion, Estimated Talent

MH***
(0.0071)
MH***
(0.0073)
MH***
(0.0000)
MH
(0.4213)

MH v. Both
MH*
(0.0519)
MH***
(0.0073)
MH***
(0.0012)
Both
(0.3718)

LL v. Both
Both***
(0.0081)
Tie
(0.1018)
Both***
(0.0000)
Both
(0.1846)

Best Overall
Fit
MH
MH
MH
MH, LL or
Both

C. Central
MH v. LL

MH v. Both

LL v. Both

Best Overall
Fit
MH

MH***
MH***
Both
(0.0003)
(0.0008)
(0.1897)
MH***
MH**
Both**
MH
Risk Neutral,
(0.0007)
(0.0263)
(0.0133)
Talent (Income)
MH***
MH***
Both***
MH
Risk Aversion,
(0.0000)
(0.0000)
(0.0027)
Talent (High)
MH***
MH**
Both
MH
Risk Aversion, Estimated Talent
(0.0004)
(0.0426)
(0.1342)
Note: MH = Moral Hazard, LL = Limited Liability, Both = Moral Hazard and Limited Liability. The
abbreviation for model which best fits the data in the pairwise comparison is reported. The p-value for the
Vuong tests are in parentheses. *** indicates significance at at least the one percent level, ** at at least the
5% level and * at at least the 10% level.
Risk Aversion, Talent (Income)

Northeast
dF/dx*
Z-statistic
-0.0491
-0.48
0.1880
1.75
-0.0149
-0.58
0.0001
0.47
-0.0027
-0.16
0.1320
1.81
-0.1838
-2.64
0.1338
2.63

Central Region
dF/dx*
Z-statistic
0.1321
1.97
0.0007
0.12
-0.0116
-0.67
0.0001
0.49
0.0010
0.07
0.0268
0.62
-0.0334
-0.82
0.0059
0.21

Observed Frequency
0.5457
0.6066
0.5146
Predicted Frequency at mean of X
0.5483
0.6367
0.5153
Log Likelihood
-237.02
-70.50
-158.47
Pseudo R-squared
4.70%
13.79%
4.28%
Number of Observations
361
122
239
Net savings is defined to be financial assets plus loans owned to household minus debt. Dummy variables are marked by an asterisk. †Wealth six years ago is
made up of the value of household assets, agricultural assets and land. Number in table is estimated coefficient multiplied by 1,000,000. The sample excludes
the top 1% of households by wealth. The estimates also include controls for past membership/patronage of various financial institutions and organizations.

Constrained (= 1 if constrained, 0 otherwise)*
Wealth Six Years ago†
Age of Head
Age of Head Squared
Years of Schooling – Head
# of Adult Females in household
# of Adult Males in household
# of Children (< 18 years) in household

Whole Sample
dF/dx*
Z-statistic
0.0849
1.55
-0.0013
-0.24
-0.0115
-0.82
0.0001
0.65
0.0049
0.47
0.0494
1.37
-0.0701
-2.05
0.0344
1.47

Table 5A: Probit Estimates of Being a Net Borrower (Net Savings < 0), Business Households

Adjusted R-squared
7.86%
9.94%
8.71%
Number of Observations
361
122
239
Net savings is defined to be financial assets plus loans owned to household minus debt. Dummy variables are marked by an asterisk. †Wealth six years ago is
made up of the value of household assets, agricultural assets and land. Number in table is estimated coefficient multiplied by 1,000,000. The sample excludes
the top 1% of households by wealth.

Wealth Six Years ago – Constrained Business†
Wealth Six Years ago – Unconstrained Business†
Age of Head
Age of Head Squared
Years of Schooling – Head
# of Adult Females in household
# of Adult Males in household
# of Children (< 18 years) in household
Constant

Whole Sample
Northeast
Central Region
Coeff.
T-statistic
Coeff.
T-statistic
Coeff.
T-statistic
0.048
4.32
-0.004
0.05
0.048
3.63
0.012
1.42
0.383
3.31
0.012
1.19
9592.724
0.52
5639.596
0.29
15814.300
0.60
-93.922
-0.56
-71.272
-0.41
-161.393
-0.68
-23179.890
-1.67 -12283.410
-0.96 -28433.790
-1.35
-105875.200
-2.18 -133223.000
-2.59 -104812.200
-1.56
108636.700
2.37
60962.520
1.22 140117.500
2.22
37710.180
1.21 -60660.900
-1.68
64761.760
1.54
-234535.400
-0.48 121595.300
0.25 -461081.300
-0.65

Table 5B: Regression Estimates of Net Savings, Business Households

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Dynamic Monetary Equilibrium in a Random-Matching Economy
Edward J. Green and Ruilin Zhou

WP-00-1

The Effects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior
Eric French

WP-00-2

Market Discipline in the Governance of U.S. Bank Holding Companies:
Monitoring vs. Influencing
Robert R. Bliss and Mark J. Flannery

WP-00-3

Using Market Valuation to Assess the Importance and Efficiency
of Public School Spending
Lisa Barrow and Cecilia Elena Rouse
Employment Flows, Capital Mobility, and Policy Analysis
Marcelo Veracierto
Does the Community Reinvestment Act Influence Lending? An Analysis
of Changes in Bank Low-Income Mortgage Activity
Drew Dahl, Douglas D. Evanoff and Michael F. Spivey

WP-00-4

WP-00-5

WP-00-6

Subordinated Debt and Bank Capital Reform
Douglas D. Evanoff and Larry D. Wall

WP-00-7

The Labor Supply Response To (Mismeasured But) Predictable Wage Changes
Eric French

WP-00-8

For How Long Are Newly Chartered Banks Financially Fragile?
Robert DeYoung

WP-00-9

Bank Capital Regulation With and Without State-Contingent Penalties
David A. Marshall and Edward S. Prescott

WP-00-10

Why Is Productivity Procyclical? Why Do We Care?
Susanto Basu and John Fernald

WP-00-11

Oligopoly Banking and Capital Accumulation
Nicola Cetorelli and Pietro F. Peretto

WP-00-12

Puzzles in the Chinese Stock Market
John Fernald and John H. Rogers

WP-00-13

The Effects of Geographic Expansion on Bank Efficiency
Allen N. Berger and Robert DeYoung

WP-00-14

Idiosyncratic Risk and Aggregate Employment Dynamics
Jeffrey R. Campbell and Jonas D.M. Fisher

WP-00-15

Working Paper Series (continued)
Post-Resolution Treatment of Depositors at Failed Banks: Implications for the Severity
of Banking Crises, Systemic Risk, and Too-Big-To-Fail
George G. Kaufman and Steven A. Seelig

WP-00-16

The Double Play: Simultaneous Speculative Attacks on Currency and Equity Markets
Sujit Chakravorti and Subir Lall

WP-00-17

Capital Requirements and Competition in the Banking Industry
Peter J.G. Vlaar

WP-00-18

Financial-Intermediation Regime and Efficiency in a Boyd-Prescott Economy
Yeong-Yuh Chiang and Edward J. Green

WP-00-19

How Do Retail Prices React to Minimum Wage Increases?
James M. MacDonald and Daniel Aaronson

WP-00-20

Financial Signal Processing: A Self Calibrating Model
Robert J. Elliott, William C. Hunter and Barbara M. Jamieson

WP-00-21

An Empirical Examination of the Price-Dividend Relation with Dividend Management
Lucy F. Ackert and William C. Hunter

WP-00-22

Savings of Young Parents
Annamaria Lusardi, Ricardo Cossa, and Erin L. Krupka

WP-00-23

The Pitfalls in Inferring Risk from Financial Market Data
Robert R. Bliss

WP-00-24

What Can Account for Fluctuations in the Terms of Trade?
Marianne Baxter and Michael A. Kouparitsas

WP-00-25

Data Revisions and the Identification of Monetary Policy Shocks
Dean Croushore and Charles L. Evans

WP-00-26

Recent Evidence on the Relationship Between Unemployment and Wage Growth
Daniel Aaronson and Daniel Sullivan

WP-00-27

Supplier Relationships and Small Business Use of Trade Credit
Daniel Aaronson, Raphael Bostic, Paul Huck and Robert Townsend

WP-00-28

What are the Short-Run Effects of Increasing Labor Market Flexibility?
Marcelo Veracierto

WP-00-29

Equilibrium Lending Mechanism and Aggregate Activity
Cheng Wang and Ruilin Zhou

WP-00-30

Impact of Independent Directors and the Regulatory Environment on Bank Merger Prices:
Evidence from Takeover Activity in the 1990s
Elijah Brewer III, William E. Jackson III, and Julapa A. Jagtiani
Does Bank Concentration Lead to Concentration in Industrial Sectors?
Nicola Cetorelli

WP-00-31

WP-01-01

Working Paper Series (continued)
On the Fiscal Implications of Twin Crises
Craig Burnside, Martin Eichenbaum and Sergio Rebelo

WP-01-02

Sub-Debt Yield Spreads as Bank Risk Measures
Douglas D. Evanoff and Larry D. Wall

WP-01-03

Productivity Growth in the 1990s: Technology, Utilization, or Adjustment?
Susanto Basu, John G. Fernald and Matthew D. Shapiro

WP-01-04

Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
The Regulated in Banking
Richard J. Rosen
Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks
Robert DeYoung
The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s
Great Depression 1928-37
Jonas D. M. Fisher and Andreas Hornstein

WP-01-05

WP-01-06

WP-01-07

Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans

WP-01-08

Outsourcing Business Service and the Scope of Local Markets
Yukako Ono

WP-01-09

The Effect of Market Size Structure on Competition: The Case of Small Business Lending
Allen N. Berger, Richard J. Rosen and Gregory F. Udell

WP-01-10

Deregulation, the Internet, and the Competitive Viability of Large Banks
and Community Banks
Robert DeYoung and William C. Hunter

WP-01-11

Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards
Christopher R. Knittel and Victor Stango

WP-01-12

Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles

WP-01-13

A Real Explanation for Heterogeneous Investment Dynamics
Jonas D.M. Fisher

WP-01-14

Recovering Risk Aversion from Options
Robert R. Bliss and Nikolaos Panigirtzoglou

WP-01-15

Economic Determinants of the Nominal Treasury Yield Curve
Charles L. Evans and David Marshall

WP-01-16

Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders
Edward J. Green and Ruilin Zhou

WP-01-17

Earnings Mobility in the US: A New Look at Intergenerational Inequality
Bhashkar Mazumder

WP-01-18

Working Paper Series (continued)
The Effects of Health Insurance and Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones

WP-01-19

The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules
Daniel Aaronson and Eric French

WP-01-20

Antidumping Policy Under Imperfect Competition
Meredith A. Crowley

WP-01-21

Is the United States an Optimum Currency Area?
An Empirical Analysis of Regional Business Cycles
Michael A. Kouparitsas

WP-01-22

A Note on the Estimation of Linear Regression Models with Heteroskedastic
Measurement Errors
Daniel G. Sullivan

WP-01-23

The Mis-Measurement of Permanent Earnings: New Evidence from Social
Security Earnings Data
Bhashkar Mazumder

WP-01-24

Pricing IPOs of Mutual Thrift Conversions: The Joint Effect of Regulation
and Market Discipline
Elijah Brewer III, Douglas D. Evanoff and Jacky So

WP-01-25

Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in
Bilateral and Multilateral Settings
Herbert L. Baer, Virginia G. France and James T. Moser

WP-01-26

Outsourcing Business Services and the Role of Central Administrative Offices
Yukako Ono

WP-02-01

Strategic Responses to Regulatory Threat in the Credit Card Market*
Victor Stango

WP-02-02

The Optimal Mix of Taxes on Money, Consumption and Income
Fiorella De Fiore and Pedro Teles

WP-02-03

Expectation Traps and Monetary Policy
Stefania Albanesi, V. V. Chari and Lawrence J. Christiano

WP-02-04

Monetary Policy in a Financial Crisis
Lawrence J. Christiano, Christopher Gust and Jorge Roldos

WP-02-05

Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers
and the Community Reinvestment Act
Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg
Technological Progress and the Geographic Expansion of the Banking Industry
Allen N. Berger and Robert DeYoung

WP-02-06

WP-02-07

Working Paper Series (continued)
Choosing the Right Parents: Changes in the Intergenerational Transmission
of Inequality  Between 1980 and the Early 1990s
David I. Levine and Bhashkar Mazumder

WP-02-08

The Immediacy Implications of Exchange Organization
James T. Moser

WP-02-09

Maternal Employment and Overweight Children
Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine

WP-02-10

The Costs and Benefits of Moral Suasion: Evidence from the Rescue of
Long-Term Capital Management
Craig Furfine

WP-02-11

On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation
Marcelo Veracierto

WP-02-12

Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps?
Meredith A. Crowley

WP-02-13

Technology Shocks Matter
Jonas D. M. Fisher

WP-02-14

Money as a Mechanism in a Bewley Economy
Edward J. Green and Ruilin Zhou

WP-02-15

Optimal Fiscal and Monetary Policy: Equivalence Results
Isabel Correia, Juan Pablo Nicolini and Pedro Teles

WP-02-16

Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries
on the U.S.-Canada Border
Jeffrey R. Campbell and Beverly Lapham

WP-02-17

Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects:
A Unifying Model
Robert R. Bliss and George G. Kaufman

WP-02-18

Location of Headquarter Growth During the 90s
Thomas H. Klier

WP-02-19

The Value of Banking Relationships During a Financial Crisis:
Evidence from Failures of Japanese Banks
Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman

WP-02-20

On the Distribution and Dynamics of Health Costs
Eric French and John Bailey Jones

WP-02-21

The Effects of Progressive Taxation on Labor Supply when Hours and Wages are
Jointly Determined
Daniel Aaronson and Eric French

WP-02-22

Working Paper Series (continued)
Inter-industry Contagion and the Competitive Effects of Financial Distress Announcements:
Evidence from Commercial Banks and Life Insurance Companies
Elijah Brewer III and William E. Jackson III

WP-02-23

State-Contingent Bank Regulation With Unobserved Action and
Unobserved Characteristics
David A. Marshall and Edward Simpson Prescott

WP-02-24

Local Market Consolidation and Bank Productive Efficiency
Douglas D. Evanoff and Evren Örs

WP-02-25

Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure
Nicola Cetorelli

WP-02-26

Private School Location and Neighborhood Characteristics
Lisa Barrow

WP-02-27

Teachers and Student Achievement in the Chicago Public High Schools
Daniel Aaronson, Lisa Barrow and William Sander

WP-02-28

The Crime of 1873: Back to the Scene
François R. Velde

WP-02-29

Trade Structure, Industrial Structure, and International Business Cycles
Marianne Baxter and Michael A. Kouparitsas

WP-02-30

Estimating the Returns to Community College Schooling for Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel G. Sullivan

WP-02-31

A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
at Large Insolvent Banks
George G. Kaufman

WP-03-01

Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman

WP-03-02

Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall

WP-03-03

When is Inter-Transaction Time Informative?
Craig Furfine

WP-03-04

Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
in Chicago
Maude Toussaint-Comeau and Sherrie L.W. Rhine

WP-03-05

Distinguishing Limited Liability from Moral Hazard in a Model of Entrepreneurship∗
Alexander Karaivanov, Anna L. Paulson and Robert Townsend

WP-03-06

Full text of Working Papers (Federal Reserve Bank of Chicago) : Distinguishing Limited Liability From Moral Hazard in a Model of Entrepreneurship, Working Paper 2003-06

FRASER