Theory, General Equilibrium, and Political Economy in Development Economics by Daron Acemoglu

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Journal of Economic Perspectives—Volume 24, Number 3—Summer 2010—Pages 17–32
Theory, General Equilibrium, and
Political Economy in Development
Economics
Daron Acemoglu
D
evelopment economics investigates the causes of poverty and low incomes
around the world and seeks to make progress in designing policies that
could help individuals, regions, and countries to achieve greater economic
prosperity. Economic theory plays a crucial role in this endeavor, not only because
it helps us focus on the most important economic mechanisms, but also because it
provides guidance on the external validity of econometric estimates, meaning that
it clarifies how we can learn from specific empirical exercises about the effects of
similar shocks and policies in different circumstances and when implemented on
different scales.
General equilibrium and political economy issues often create challenges for this
type of external validity. General equilibrium refers to factors that become important when we consider counterfactuals in which large changes are contemplated.
The difficulty lies in the fact that such counterfactuals will induce changes in factor
prices and technology, which we hold fixed in partial equilibrium analysis, and create
different composition effects than in partial equilibrium. Political economy refers to
the fact that the feasible set of interventions is often determined by political factors
and that large counterfactuals will induce political responses from various actors
and interest groups. General equilibrium and political economy considerations are
important because partial equilibrium estimates that ignore responses from both
sources will not give the appropriate answer to counterfactual exercises.
In this essay, I first explain why it is important to think of external validity
in policy analysis, particularly in development economics, and I describe the role
of economic theory in this exercise. I then illustrate the importance of general
¦ Daron Acemoglu is Charles P. Kindleberger Professor of Applied Economics, Massachusetts
Institute of Technology, Cambridge, Massachusetts. His e-mail address is >.
doi=10.1257/jep.24.3.17
18
Journal of Economic Perspectives
equilibrium reasoning in several major problems in development economics.
Finally, I argue that political economy considerations have to be central to any
investigation of development problems and that inferences that ignore political
economy can go wrong.
Why Development Economics Needs Theory
There is no general agreement on how much we should rely on economic
theory in motivating empirical work and whether we should try to formulate and
estimate “structural parameters.” I argue that the answer is largely “yes” because
otherwise econometric estimates would lack external validity, in which case they
can neither inform us about whether a particular model or theory is a useful
approximation to reality, nor would they be useful in providing us guidance on
what the effects of similar shocks and policies would be in different circumstances
or if implemented in different scales. I therefore define “structural parameters” as
those that provide external validity and would thus be useful in testing theories or
in policy analysis beyond the specific environment and sample from which they are
derived.1 External validity becomes a particularly challenging task in the presence
of general equilibrium and political economy considerations, and a major role
of economic theory is in helping us overcome these problems or at the very least
alerting us to their importance.
To illustrate these points, consider the relationship between the cost of
schooling and schooling decisions. We can describe this relationship purely as a
descriptive one, focusing on a sample and looking at the correlation or the ordinary least squares relationship between these two variables. For example, we could
specify the following reduced-form relationship:
log (si ) = X ‘i ß – a log (ci ) + ei ,
where i denotes an individual in the sample, si is years of schooling, ci denotes
the cost of schooling to the individual resulting, for example, from foregone earnings and actual costs of attending schools, Xi is a vector of characteristics of this
individual for which we may wish to control, and ß is a vector of parameters. The
parameter of interest is a. We can then use ordinary least squares to estimate ß
and a.
1
See Shadish, Cook, and Campbell (2002) on internal and external validity. The notion of external
validity, in particular the emphasis on counterfactual exercises, as the defi ning characteristic of a
structural parameter is closely related to Marschak’s (1953) defi nition, which distinguishes between
structural parameters that provide “useful knowledge” for understanding the effects of policy within
a given sample and/or in new environments. It also clearly presupposes that the empirical strategy
has been successful in estimating “causal” effects (for example, as defi ned in Angrist, Imbens, and
Rubin, 1996).
Daron Acemoglu
19
Alternatively, we could start with an economic model. In fact, some simple
theories will lead to exactly this equation. Suppose, for example, that the human
capital of an individual is a function of the level of schooling for that individual. In
particular, suppose that the human capital of individual i is given by hi = s 1i – s for
some parameter s between 0 and 1 and si denotes the person’s level of schooling.
The individual can then earn income equal to yi = whi , where w is the market wage
per unit of human capital. In addition, individual i has a cost of schooling given by
?i ci si , where ?i is an unobserved nonmonetary cost component (for example due
to differential discounting or borrowing constraints), and ci is the monetary cost
of schooling for this individual. Suppose that individuals maximize net income,
so that individual i will choose schooling to maximize income net of the cost of
schooling—that is ws 1i – s – ?i ci si . After working through the maximization problem,
this model implies a relationship identical to the reduced-form equation with
which we started, but now the parameter a corresponds to 1/s
1/s. 2 Once this equation is derived, estimation is also straightforward and can be performed again by
ordinary least squares.
Next comes the harder part. We have seen that the same equation can be
posited as a reduced-form relationship, or it can be derived from an economic
model. But at the end, it is the same equation, and it can be estimated in the same
manner. So in what sense can we think of it as a “structural relationship”? The
answer is related to the notion of external validity introduced above. Suppose we
now ask the question: what would be the effects of subsidies to reduce the cost of
schooling, ci , for a set of individuals? This counterfactual experiment could be
motivated by a potential policy that is being contemplated or it may be used for
understanding and testing the implications of our theory. The question might be
for the same sample on which the initial estimation was performed or it could be for
an entirely different sample or population. In either case, one answer to the above
question readily follows from using the estimates of a to compute the increase in
the years of schooling for individuals whose cost of schooling has declined. But can
we trust this answer?
If a is indeed a structural parameter, then we should trust this answer (obviously, subject to standard errors), but not otherwise.3 To illustrate what might
go wrong when a does not correspond to a structural parameter, imagine, for
example, that years of schooling are constrained by school enrollments, which are
in turn constrained by the sizes of schools. In this setting, let us further assume
that individuals with low cost of schooling get proportionately more of the available
2
Specifically, the optimal choice of individual i is si = K(?i ci )–1/s, where in this case K = ((1 – s)w)1/s.
After taking logs and defining ei = – log ?i /s and a = 1/s, this gives the reduced-form equation above.
3
Many empirical equations that do not correspond to structural relationships may nonetheless contain
useful information; they just cannot be used for counterfactual policy analysis. We might simply be
interested in uncovering correlations, which may help us distinguish between theories, since many
relevant theories will have implications about what these correlations should look like. This suggests
that it is often useful to estimate reduced-form relationships that do not have structural interpretations, but when doing so, we should be explicit about how they should be (and not be) interpreted.
20
Journal of Economic Perspectives
school resources (for instance, due to some type of efficient rationing).4 In this
example, we can still estimate the relationship between s and c,, and we will obtain a
meaningful-looking estimate of a. However, the estimate will lack external validity.
Consider a policy of expanding the subsidy for schooling to individuals that does
not change the constraint that total years of schooling are determined by the sizes
of schools. Then the estimate of a from the pre-subsidy regime will not necessarily
inform us about the post-subsidy relationship between cost of schooling and years
of schooling and will not give us accurate predictions about the effect of the policy.
The problem described here is of course a version of the Lucas (1976) critique
that reduced-form relationships will not be stable in the face of policy interventions.
However, the discussion also highlights that this problem is not simply circumvented by deriving the relationship of interest from an economic model, unless this
model incorporates the relevant constraints and margins of choice. In the above
example, no model that fails to incorporate the constraint on total enrollments
will be informative about counterfactuals involving large-scale interventions. Thus,
our confidence in the implied answers to policy experiments crucially depends on
our confidence in having captured the appropriate structural relationship with the
model we are estimating.
How do we convince others and ourselves that our estimates have external
validity and can be used for policy analysis or for testing theories? This is where
economic theory becomes particularly useful. As a first step, we have to defend—
using economic theory, common sense, and evidence—that key factors potentially
affecting the response to the relevant counterfactual are accounted for and that the
model and the functional form we chose indeed capture the salient aspects of the
reality and are a good approximation to that reality. This in turn involves arguing
that the functional form is stable over time and across relevant samples, that variation across individuals not captured by the covariates and the cost of schooling can
be incorporated into the error term ei , and that this error term can be modeled as
additive and orthogonal to (that is, uncorrelated with) the other variables included
in the equation. Using economic theory is often the best way of clarifying whether
key factors have been omitted and whether the underlying assumptions can be
defended and whether they provide a good approximation to reality.
However, the previous discussion also highlights that specifying a model that
justifies a specific estimating equation is typically not difficult, and may not solve
the underlying problem. For example, we saw how we could derive exactly the same
estimating equation from a model of individual schooling choice; but if in reality
4
More specifically,_the constraint _on school enrollments might imply that total years of schooling
should be equal to S that is, ?i si = S . Suppose that the economic relationship si = K(?i ci ) –1/s still holds
at the individual level (i.e., individuals with low cost of schooling get proportionately more of the available school resources). But it must do so with
_ a different value of K than in footnote 2. In particular, the
constraint on total schooling implies K = S (?i ?? (??i ci )–1/s)–1. When the cost of schooling is subsidized,
the underlying economic relationship with the new definition of K given here remains unchanged, but
the reduced-form relationship captured by our estimating equation above changes (exactly as shown
by the above formula for K).
Theory, General Equilibrium, and Political Economy in Development Economics
21
years of schooling are constrained by the sizes of schools, the estimates of a will
still not be useful for understanding the implications of a large-scale subsidy for
schooling. The problem of course is that for studying the implications of this type of
policy, the constraints resulting from the sizes of schools are central, and any model
that does not recognize these constraints will not be helpful in such a study. This
emphasizes that the proper use of economic theory does not mean writing down a
specific model; instead, it requires that we incorporate the appropriate constraints
and margins of adjustments, that we develop the case that economic theory robustly
leads to the estimating equation in question, and that we clarify which important
economic mechanisms and effects are being excluded from the model.5
Another advantage of structural reasoning based on theory is that once we go
through the process of explicitly justifying the equation we are estimating, either
using economic theory or other theoretical or empirical arguments, we may realize
that such an equation cannot easily be defended. In such cases, it has to be interpreted with greater caution, or perhaps it has to be modified or abandoned. This
advantage becomes particularly important in contexts where general equilibrium
and political economy effects are present. Finally, economic theory provides the
best way of interpreting what the estimates from an equation, such as the one we
started with, mean. For example, when this equation is derived from the economic
model above, we understand that a = 1/
1/s
s is a function of the elasticity of the
human capital production function.
The structural approach also faces major challenges, however. First, as already
emphasized, writing down a model like the one described above is clearly not
sufficient for achieving external validity. That model itself made several assumptions which are restrictive and may not provide a good approximation to the
economic phenomena in which we are interested. This is again illustrated by the
above example, which showed that one might end up deriving the same estimating
equation from a theoretical model, and thus reach the same conclusions about
the implications of a counterfactual policy change, as one might have done by just
specifying a reduced-form equation.
Second, we may in fact question whether there is any ground for assuming a
constant elasticity a between years of schooling and costs of schooling. After all,
we know that all theories are abstractions and approximations, so there is little
reason to believe that a parameter such as a—or the intertemporal elasticity of
5
The online appendix available with this paper at ?http://www.e-jep.org? discusses some issues that
arise in thinking about how we could develop such robust predictions and how we could try to map
them to data. This discussion also highlights that in certain cases one could achieve counterfactual
validity without much theory. For example, we need only the most basic theory in interpreting a
controlled experiment designed to evaluate the effectiveness of a drug. In this case, we can say that
common sense and a very limited amount of medical theory are sufficient to interpret the results of the
controlled experiment and decide whether they are informative about the effectiveness of the drug in
question beyond the experimental setting. It should also be noted that the evaluation of the effectiveness of a drug in this example has a clear parallel to “modeling individual behavior” in economics. As
further discussed below, the role of economic theory becomes even more central when our focus shifts
to “modeling equilibrium behavior.”
22
Journal of Economic Perspectives
substitution, or the Frisch elasticity of labor supply, or the elasticity of substitution
between two factors, or any other Marschakian preference or technology parameter—should be really constant. But without such constancy, there are severe limits
to external validity.
Finally, one may even question the existence or usefulness of “structural
parameters” altogether. What we take as a structural parameter for one theory
will naturally become an endogenous object in another. So a particular model can
serve us well as an abstraction for a series of counterfactual experiments, but there
will exist other experiments for which it will be much less informative. For example,
an elasticity of substitution or certain technology parameters may be constant with
respect to certain variations, but would change in response to others. This is almost
by necessity: a precondition for external validity is that key factors relevant for the
outcome of the counterfactual should be included in the model, and models as
abstractions have to exclude several relevant factors, so no single model can include
all of the relevant factors for all possible counterfactual exercises.
These challenges notwithstanding, it is clear that economists often have to
take a position about the parameters being estimated corresponding to structural
parameters (at least for a well-defined though perhaps limited set of variations in
environment and policy). Otherwise, we will have no way of performing counterfactual exercises and making predictions about policy changes (Imbens, 2009).
But this necessitates a claim to external validity (even if it is only implicit), and
economic theory is our best guide for formulating the appropriate models and
justifying such claims to external validity. These issues become only more central
in the presence of general equilibrium effects and political economy factors, to
which I turn next.
The Centrality of General Equilibrium
The bulk of empirical work using microdata, particularly in development
economics, engages in partial equilibrium comparisons. Depending on magnitudes of various effects, general equilibrium interactions can offset or even reverse
sensible partial equilibrium conclusions. However, most empirical strategies do not
directly estimate general equilibrium effects.6
Economic theory nonetheless provides some guidance in assessing the importance of general equilibrium effects. Three types of general equilibrium effects,
which are usually not estimated in partial equilibrium comparisons, are potentially
important. First, in response to large policy interventions or shocks, imperfect
substitution between factors and diminishing returns imply that factor productivities
6
See also Townsend (forthcoming) for a complementary discussion of the role of general equilibrium
analysis in development economics, with special emphasis on credit market issues; Heckman, Lochner,
and Taber (1998) for a discussion of general equilibrium issues in the analysis of the effects of technology on wage inequality; and Duflo (2004a) for a discussion of other difficulties in “scaling up”
policy interventions evaluated using microdata.
Daron Acemoglu
23
and prices will change. Second, the same policy interventions or shocks can lead to
endogenous technology responses. Third, there may be composition effects resulting
from equilibrium substitution of some factors or products for others (whereby the
composition of micro units changes differently in response to different types of interventions). Theory generally implies that the first and the third eff …
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