Spatial Decentralization and Programme Evaluation: Theory and an Example
| DOI | http://doi.org/10.1111/obes.12265 |
| Author | Mark M. Pitt,Nidhiya Menon |
| Date | 01 June 2019 |
| Published date | 01 June 2019 |
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©2018 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 81, 3 (2019) 0305–9049
doi: 10.1111/obes.12265
Spatial Decentralization and Programme Evaluation:
Theory and an Example*
Mark M. Pitt,† and Nidhiya Menon‡
†Professor Emeritus of Economics and Research Professor of Population Studies, Brown
University, Box 1836, 02912 Providence RI, USA (e-mail: mark pitt@brown.edu.)
‡Department of Economics & IBS, Brandeis University, MS 021, 02454 Waltham MA, USA
(e-mail: nmenon@brandeis.edu)
Abstract
This paper proposes an instrumental variable method for programme evaluation that only
requires a single cross-section of data on the spatial intensity of programmes and outcomes.
The instruments are derived from a simple theoretical model of government decision-
making in which governments are responsive to the attributes of places, rather than to the
attributes of individuals, in making allocation decisions across space, and have a social
welfare function that is spatially weakly separable, that is, that the budgeting process
behaves as if it is multi-stage with respect to administrative districts and sub-districts.The
spatial instrumental variables model is then estimated and tested with a single cross-section
of Indonesian census data. The results offer support to the identification strategy proposed
but also highlight some issues affecting validity.
I. Introduction
Governments in developing countries earmark significant proportions of their budget to-
wards establishing programmes that seek to alter the behaviour of target populations. By
influencing fertility, health and schooling outcomes, these programmes are often the gov-
ernment’s main tools for spreading economic well-being and for spurring economic growth.
A fundamental problem in programme evaluation is that the coverage of programmes and
the timing of programme initiatives – programme placement – are not likely to be random
to the extent that governmental decision rules are responsive to attributes of the targeted
populations that are not measured in the data. Simple measured associations between pro-
grammes and programme outcomes, anticipated or unanticipated,will therefore not provide
correct estimates of programme effects. Data on the spatial distribution of programmes and
population characteristics at more than one point in time can be used to identify programme
JEL Classification numbers: C21, H44, O12, C50.
*Thanks to Moshe Buchinsky and Andrew Fosterand to participants at the Nor thAmerican Summer Meeting of
the Econometric Society at Duke, the Northeast Universities DevelopmentConsortium Conference at MIT, the Royal
Economic Society Meetings at Cambridge University, and the 2014 AEA meetings. Thanks also to the Editor and
two referees whose comments haveimproved the paper. The usual disclaimer applies.
512 Bulletin
effects with relativelysimple methods (fixed effects) when programme placement depends
on unmeasured time-persistent or permanent characteristics of locations but varies as a
function of aggregate economy-wide trends or shocks.1The longitudinal data required for
fixed effects estimation are not always available in developing countries, or are too closely
spaced so that programme change is small relative to noise, and the assumption of the time
invariance of the confounding unobservable component may not always hold. This paper
illustrates an application of an instrumental variable method for programme evaluation
that only requires a single cross-section of data on the spatial intensity of programmes and
outcomes. The instruments are derived from a theoretical model of government decision-
making that requires that the government’s social welfare function is spatially weakly
separable, that is, that the budgeting process behaves as if it is multi-stage with respect to
administrative districts and sub-districts. The spatial instrumental variables model is then
estimated and tested with a cross-section of Indonesian census data to illustrate how spatial
methods may be used to address research in public finance.
The spatial instruments illustrated in this paper can be derived from models that opti-
mize household and government behavioursubject to resource and information constraints.
The idea is that the nature of the budgetary process with spatially defined administrative
districts generates a set of exclusion restrictions that can be used as instruments for the ob-
served allocation of public programmes across space. Governmentsare, rather innocuously,
assumed to be responsive to the attributes of places and their populations, rather than to the
attributes of individuals, in making allocation decisions across space. If the attributes of a
district rather than individual characteristics decide programme placement, and the means
(or higher moments) of outcomes for all ‘competing’ districts enter into the government’s
social welfare function, then the district means of individual and district exogenous deter-
minants of these outcomes in other districts may be used as instruments for the placement of
programmes in any particular district.The intuition underlying this is the same as the notion
of strategic interaction betweendecentralized bodies of government in the public finance lit-
erature where spatial attributes of places matter in decision-making (Besley and Case, 1995;
Brueckner, 2003). The assumption of weakseparability of a social welfare function having
as argument the mean outcomes of every administrativeunit (district) is sufficient to gener-
ate spatially decentralizedbudgeting , an allocation process that yields restrictions sufficient
for identification. The separability assumption says that the marginalrate of substitution in
human capital between any two locations is independent of all other locations, thus allow-
ing for the existence of aggregator functions by area. With separability, the process can be
thought of as a multi-stage budgeting process where allocation decisions are made sequen-
tially at progressively lower levels of government. Decision-making of this type will arise
as a consequence of the costliness of acquiring and processing information on the returns
to and health status of every single uniquely identified household or spatial aggregation of
households. If characteristics of locations (which may be defined bypolitical boundaries or
other criteria as we discuss below) enter the government’s operand to influence programme
placement, with budget constraints, competition for resources will arise at increasingly
1Other approaches to identification with single-cross sections of data make use of natural or quasi-experiments
(e.g. Pitt and Khandker, 1998), regression discontinuity designs (e.g. van der Klaauw, 2002) or the time history of
program placement (Duflo, 2001).
©2018 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd
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