metadata: authors: 0: author: Grigoli,Francesco
1: author: Mills,Zachary
2: author: Verhoeven,Marinus
3: author: Vlaicu,Razvan
count: World
docna: 0: docna: MTEFs and fiscal performance: panel data evidence
docty: Policy Research Working Paper
subtopic: Consumption,Fiscal & Monetary Policy,Health Care Services Industry,Public Sector Administrative & Civil
            Service Reform,Educational Sciences,Inequality,Democratic Government,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Private Sector Development,Public Sector Development,Health, Nutrition and Population,Finance and Financial Sector Development
abstracts: cdata!: In the last two decades more than 120
            countries have adopted a version of a Medium-Term
            Expenditure Framework (MTEF). These are budget institutions
            whose rationale it is to enable the central government to
            make credible multi-year fiscal commitments. This paper
            analyzes a newly-collected dataset of worldwide MTEF
            adoptions during 1990-2008. It exploits within-country
            variation in MTEF adoption in a dynamic panel framework to
            estimate their impacts. The analysis finds that MTEFs
            strongly improve fiscal discipline, with more advanced MTEF
            phases having a larger impact. Higher-phase MTEFs also
            improve allocative efficiency. Only top-phase MTEFs have a
            significantly positive effect on technical efficiency.
repnme: repnme: MTEFs and fiscal performance: panel data evidence
display_title: MTEFs and fiscal performance: panel
            data evidence
listing_relative_url: /research/2012/09/16698632/mtefs-fiscal-performance-panel-data-evidence
topicv3: Poverty m1326687 695,Education m1327048 470,Macroeconomic and Structural Policies
            m1327380 439,Public Administration m1327723 723,Health m1504661 3480
publishtoextweb_dt: 2012-09-01T00:00:00Z
historic_topic: Private Sector Development,Public Sector Development,Health, Nutrition and Population,Finance and Financial Sector Development
no_of_pages: 44
author_keys: 0: author_key: 1110691
1: author_key: 1047445
2: author_key: 995569
3: author_key: 739082
model_extractions: WPS6186 Policy Research Working Paper 6186

# MTEFs and Fiscal Performance

## Panel Data Evidence

_Francesco Grigoli Zachary Mills Marijn Verhoeven Razvan Vlaicu_ The World Bank Poverty Reduction and Economic Management Network Public Sector & Governance Unit September 2012
Policy Research Working Paper 6186

# **Abstract**

In the last two decades more than 120 countries have adopted a version of a Medium-Term Expenditure Framework (MTEF). These are budget institutions whose rationale it is to enable the central government to make credible multi-year fiscal commitments. This paper analyzes a newly-collected dataset of worldwide MTEF adoptions during 1990–2008. It exploits withincountry variation in MTEF adoption in a dynamic panel framework to estimate their impacts. The analysis finds that MTEFs strongly improve fiscal discipline, with more advanced MTEF phases having a larger impact. Higherphase MTEFs also improve allocative efficiency. Only top-phase MTEFs have a significantly positive effect on technical efficiency.

This paper is a product of the Public Sector & Governance Unit, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at zmills@worldbank.org and mverhoeven@worldbank.org.

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

# Panel Data Evidence<sup>*</sup>

Razvan Vlaicu<sup>¶</sup> Francesco Grigoli<sup>†</sup> Zachary Mills<sup>‡</sup> Marijn Verhoeven<sup>§</sup>

**Abstract**

In the last two decades more than 120 countries have adopted a version of a MediumTerm Expenditure Framework (MTEF). These are budget institutions whose rationale it is to enable the central government to make credible multi-year fiscal commitments. This paper analyzes a newly-collected dataset of worldwide MTEF adoptions during 19902008. It exploits within-country variation in MTEF adoption in a dynamic panel framework to estimate their impacts. The analysis finds that MTEFs strongly improve fiscal discipline, with more advanced MTEF phases having a larger impact. Higher-phase MTEFs also improve allocative efficiency. Only top-phase MTEFs have a significantly positive effect on technical efficiency.

## **JEL Classification:** E62, H51, H62.

**Keywords:** budget institutions, MTEF, fiscal discipline, allocative efficiency, technical efficiency, dynamic panel data analysis.

* We would like to thank Abdul Abiad, Nataliya Biletska, Jim Brumby, Stefania Fabrizio, Richard Hemming, Malcolm Holmes, Sam Ouliaris, Philip Keefer, Steve Knack, Ajay Tandon, and participants at the PREM Knowledge & Learning Forum and the World Bank MTEF Workshop. Haishan Yuan provided excellent research assistance. The views expressed in this paper are those of the authors and should not be attributed to the World Bank or its affiliated institutions, the IMF, its Executive Board, or its management.

† IMF, Fiscal Affairs Department, 1900 Pennsylvania Ave. NW, Washington, DC 20431, USA. E-mail: fgrigoli@imf.org.

‡ The World Bank, Europe and Central Asia Department, 1818 H Street NW, Washington, DC 20043, USA. E-mail: zmills@worldbank.org.

§ The World Bank, Poverty Reduction and Economic Management Vice Presidency, 1818 H Street NW, Washington, DC 20043, USA. E-mail: mverhoeven@worldbank.org.

¶ University of Maryland, Department of Economics, and Department of Government and Politics, 3105 Tydings Hall, College Park, MD 20742, USA. E-mail: vlaicu@econ.umd.edu.
# 1 Introduction

It is generally accepted that ...scal performance is a key factor in a country’s long-run growth prospects.<sup>1</sup> Understanding the determinants of ...scal performance has thus become a central topic of research. A recent strand of literature has emphasized the role of budget institutions in a¤ecting ...scal performance. Budget institutions are the formal rules and procedures according to which budgets are drafted, approved, and implemented. They can take the form of either (i) laws establishing ex ante constraints on the government’s conduct of ...scal policy, such as balanced budget and debt ceiling provisions, or (ii) bargaining rules between the executive and the legislature, such as line-item executive veto or legislative amendment rules.

In the last two decades more than 120 countries have adopted laws instituting multiyear ...scal targets, known as Medium-Term Expenditure Frameworks (MTEFs). First introduced in a small set of developed countries to contain expenditure overruns in the 1980s, MTEFs spread rapidly during the 1990s and 2000s, being in operation in 132 countries at the end of 2008 (see Figure 1 and Table 2). MTEFs translate macro-...scal objectives and constraints into broad budget aggregates as well as detailed expenditure plans by sector. The rationale of this budget institution is to enable the central government to more adequately incorporate future ...scal challenges into the annual budgets, thereby reducing an undue emphasis on short-term goals.

The key public ...nance problem that MTEFs are intended to overcome is dynamic ...scal ine¢ ciency.<sup>2</sup> Whether it takes the form of strategic obstruction of future political opponents (Alesina and Tabellini 1990), or electoral manipulation through budget cycles (Drazen 2000, Brender and Drazen 2005), dynamic common pool (Velasco 1999), or time-inconsistent voters (Bisin, Lizerri, and Yariv 2011), government spending and borrowing deviates from the social planner level, resulting in suboptimally high de...cits and debt.

Dynamic ine¢ ciency seems particularly inherent in a yearly cycle of budget planning and implementation. Wildavsky (1986, p. 317) makes this point as follows: “Multiyear budgeting has long been proposed as a reform to enhance rational choice by viewing resource allocation in a long-term perspective. One year, it has been argued, leads to short-sightedness, because only the next year’s expendi-

> 1See, e.g., Fischer (1993), Easterly and Rebelo (1993), Easterly, Irwin, and Serven (2008).

> 2Other public ...nance distortions include static common pool problems (Weingast, Shepsle and Johnsen 1982), rent seeking (Persson, Roland and Tabellini 2000, Besley and Smart 2007), and clientelism (Keefer and Vlaicu 2008).

2
tures are reviewed; overspending, because huge disbursements in future years are hidden; conservatism, because incremental changes do not open up large future vistas; and parochialism, because programs tend to be viewed in isolation rather than in comparison with their future costs in relation to expected revenue.” At the basis of an MTEF is a commitment by the budget actors to a medium-term, typically two to four years, ...scal trajectory. Thus, it can be seen either as an ex ante constraint on the government, similar to a balanced budget requirement (Alesina and Perotti 1996), or as a "contract approach" to centralizing the budget process through a broad-based political agreement, as opposed to a "delegation approach" where the executive receives enhanced powers (von Hagen and Harden 1995). While the theoretical underpinnings of this institution are well understood, the empirical evidence on its impact is scarce. The main obstacle has been the shortage of data on MTEF adoption. An additional impediment has been the lack of sources of exogenous variation in national budget institutions (Acemoglu 2005).<sup>3</sup> This paper reports on the ...rst large-sample empirical study of the MTEFs’impacts on ...scal performance. As part of a larger World Bank (2012) study, we collect and analyze MTEF adoption data for a panel of 181 countries over the period 1990-2008, the most comprehensive dataset to date on worldwide MTEF adoption. Following World Bank (2012) we classify MTEFs into three phases, based on their level of sectoral disaggregation, and generate testable hypotheses about the e¤ects of each phase on multiple dimensions of ...scal performance.

The rich time variation in the data allows us to model the dynamics of the ...scal adjustment process, as well as address the endogeneity of MTEF to ...scal performance. We use a Di¤erence Generalized Method of Moments (D-GMM) approach to estimate dynamic panel data models of ...scal performance. These models are designed for "small T large N " panels and, when correctly applied, generate valid internal instruments that hold the promise of overcoming identi...cation issues due to the absence of strong external instruments, a typical hurdle with country-level data.<sup>4</sup> When an MTEF is implemented well we should observe (i) spending that is limited by resource availability (...scal discipline), (ii) budget allocations that re‡ect spending pri-

> 3One way to circumvent econometric identi...cation issues has been to study similar institutions operating at sub-national levels of government. See Besley and Case (2003) for a review of the literature that has employed U.S. state-level data.

> 4The D-GMM approach was ...rst proposed by Holtz-Eakin, Newey, and Rosen (1988) and later developed by Arellano and Bond (1991). Recent re...nements include Windmeijer (2005) and Roodman (2009).

3
orities (allocative e¢ ciency), and (iii) public goods delivery that is cost e¤ective (technical e¢ ciency). We classify MTEFs into three phases: Medium-Term Fiscal Framework (MTFF, which establishes the aggregate resource envelope), Medium-Term Budget Framework (MTBF, which focuses on the allocation of spending across sectors, programs and agencies) and Medium-Term Performance Framework (MTPF, which sets sectoral performance targets). These three MTEF phases are “nested”: an MTPF contains an MTBF, which in turn contains an MTFF.<sup>5</sup> The new data reveal patterns in the timing of MTEF adoption across regions and levels of development. OECD countries were the ...rst to adopt MTEFs, and by the early 1990s most countries in this group had an MTPF in place. The bulk of MTEF reforms in SubSaharan African countries took place in the 1990s, Latin American countries adopted MTEFs in the late 1990s, and Eastern Europe and the former soviet republics joined the trend in the 2000s. We exploit di¤erential patterns of MTEF adoption across regions to construct external instruments that complement the standard GMM-style internal instruments.

The empirical results show that MTEFs strongly improve ...scal discipline and that the e¤ect is larger the more advanced the MTEF phase. The e¤ect varies between 1 and 3 percentage points of central government balance as a percentage of GDP. We also ...nd that MTBFs and MTPFs improve allocative e¢ ciency, measured by the volatility in per capita health spending in purchasing power parity dollars (PPP$), and that MTPFs contribute to technical e¢ ciency, measured as technical e¢ ciency scores from a stochastic frontier model of public health delivery. These results are robust to excluding highly autocratic and highly developed countries.

Our results are more favorable about MTEF e¤ectiveness than the conclusions of prior work. Bevan and Palomba (2000), La Houerou and Talierco (2002), Holmes and Evans (2003), and Oyugi (2008), based on case studies of about a dozen African countries, conclude that the budget process has generally not improved after the adoption of an MTEF, while McNab, Martinez-Vasquez, and Boex (2000) and Oxford Policy Management (2000) raise questions of adequate implementation. However, Gleich (2003) and Ylaoutinen (2004) ...nd that MTEFs in Central and Eastern Europe alleviated the de...cits and debts that emerged in the second half of the 1990s. Wescott (2008) and Filc and Scartascini (2010), using data from Central and Latin America, found mixed results and emphasized the importance of piloting MTEFs in areas where they are likely to deliver the largest payo¤.<sup>6</sup>

> 5This taxonomy follows World Bank (2012) and borrows from Castro and Dorotinsky (2008) with the nesting concept as an added innovation.

> 6Drawing on extensive operational experience with MTEF implementation in developing countries,

4
Alesina, Hausmann, Hommes, and Stein (1999) and Fabrizio and Mody (2006) include MTEFs in aggregate indexes of budget institutions using data from Latin America and Eastern Europe, respectively.<sup>7</sup> Our results complement this empirical literature by providing evidence of MTEF impacts from a worldwide sample. While previous studies rely on small samples and either cross-sectional or static panel models our empirical methodology takes advantage of the time variation to estimate dynamic panel models. We also propose a new classi...cation of MTEF phases, based on the level of disaggregation of the central government’s ...scal objectives.

Our paper is also related to the broader empirical literature on budget institutions.<sup>8</sup> The most studied institutions have been balanced budget amendments, debt ceilings, tax and expenditure limitations, and supermajority requirements for tax increases. Examples include: Bayoumi and Eichengreen (1995), von Hagen and Eichengreen (1996), Stein, Talvi, and Grisanti (1999), Hallerberg and von Hagen (1999), Perotti and Kontopoulos (2002), Fatas and Mihov (2003), von Hagen and Wol¤(2006), and Hallerberg and Ylaoutinen (2010), all using country-level data; and Poterba (1994), Bohn and Inman (1996), Kiewert and Szakaly (1996), Poterba and Rueben (1999), Knight (2000), Knight and Levinson (2000), and Fatas and Mihov (2006) using state-level data. Important lessons from this literature are that numerical constraints have limited e¤ectiveness because they can be circumvented, that the e¤ect of reduced ...scal discretion on macroeconomic volatility remains an open question, and that the political environment matters for the e¤ectiveness of budget institutions.

We contribute to this literature by proposing a dynamic panel approach (di¤erence GMM) that models the ...scal adjustment process while at the same time addressing the issue of institutional endogeneity. Apart from employing standard internal instruments, we also propose external instruments that improve estimation e¢ ciency, based on the time-varying degree of MTEF penetration in a country’s geographic region. Also, in addition to macroeconomic e¤ects, which have been the focus of this literature, we provide evidence of sectoral e¤ects.

The structure of the paper is as follows. Section 2 provides a background discussion of the MTEFs, their adoption trends, and their expected e¤ects on ...scal performance. Section 3 discusses the data and the empirical strategy. Section 4 presents the empirical results. Section 5 summarizes the paper and suggests directions for future research.

> Schiavo-Campo (2009) puts forward conceptual arguments supporting a gradual introduction of these institutions and emphasizes the potential downsides of instant reform.

> 7In these two papers the MTEF component of the index is weighted by 1/10 and 1/12, respectively.

> 8See the NBER volume edited by Poterba and von Hagen (1999), as well as von Hagen (2006), for reviews of the budget institutions literature.

5
# 2 Background

This section takes a ...rst look at the worldwide MTEF adoption data collected for this paper by presenting stylized facts of MTEF global growth and regional penetration during 1990-2008. It also discusses the rationale behind MTEFs as budget institutions designed to improve ...scal performance. This discussion helps generate theoretical expectations. We subject these conjectures to empirical scrutiny in the next section.

## 2.1 MTEF Phases

We classify MTEFs into three phases, based on the following criteria.<sup>9</sup>

- Medium-Term Fiscal Framework (MTFF): the government has rolling aggregate, expenditure, revenue, and other ...scal forecasts. Features include the availability of a macro-...scal strategy, macroeconomic and ...scal forecasts, and debt sustainability analysis.

- Medium-Term Budgetary Framework (MTBF): budget, spending agency or other reports explain aggregate and sectoral expenditure objectives and strategies, budget circulars detail medium-term expenditure ceilings and revenue forecasts, and budget documents contain some detail about medium-term estimates.<sup>10</sup>

- Medium-Term Performance Framework (MTPF): budget, spending agency or other reports explain program objectives and strategies, and list speci...c agency and/or program output or outcome targets, as well as results.

These three phases are "nested" in the sense that a higher-phase MTEF contains the lowerphase MTEF just below it.

## 2.2 Stylized Facts

Although some forms of medium-term expenditure projections existed in OECD countries as early as the 1960s, the ...rst application of a coherent system of multiyear budgeting occurred in Australia, where an MTEF was introduced in the 1980s (see Folscher 2007). MTEFs

> 9We use these de...nitions to code each country-year in our sample as falling into each of these mutually exclusive categories; see Section 3 and the Data Appendix for a description of variable construction.

> 10We coded countries that introduced a "pilot" MTBF in a few sectors as MTFF since the health sector, our focus in the analysis below, might not be one of the piloted sectors.

6
have since been adopted by a large number of low and middle-income countries as a central element of public ...nancial management reform.

While MTEFs began to spread across industrial countries and Africa in the early 1990s, it was not until the late 1990s and 2000s that they took o¤ in emerging market economies; see Figure 1. An average of 10 countries per year introduced an MTEF between 1996 and 2008. By the end of 2008, 132 countries, or about two-thirds of the globe, had an MTEF. Figure 2 shows temporal patterns of MTEF growth by continent. The regional trends are evident, with Europe leading the pack, followed by Africa and the Americas.

Initially, most MTEFs were of the ...rst phase, or MTFF, and until recently about twothirds of the increase in MTEFs has been in the form of new MTFFs. However, there has been a recent uptick in the number of MTBFs and MTPFs. In 2008 there were 71 MTFFs, 42 MTBFs, and 19 MTPFs. Table 2 shows that the shift to MTBFs and MTPFs has been mainly through transitions from a lower MTEF phase to a higher one.<sup>11</sup> MTEF coverage varies signi...cantly. Advanced economies had achieved almost complete coverage (96%) by the end of our sample period. MTEF adoption in advanced countries occurred in two waves. In the late 1980s and early 1990s only a few advanced economies followed Australia’s lead in MTEF adoption. Then, in the late 1990s MTEFs were introduced in the European Union to support budgetary targets set as pre-conditions for monetary union. By the end of 2008, 46% of the MTEFs in advanced economies were MTPFs. The relatively low fraction of the second-phase MTEF (the MTBFs) in these countries suggests that when advanced economies decide to move beyond an MTFF introducing a performance focus is a natural development, re‡ecting their more sophisticated budgeting systems.

MTEFs have also achieved broad coverage in Europe and Central Asia. The spread was more rapid and consistent in Central and Eastern Europe than in the Former Soviet Union. This may re‡ect e¤orts made in Central and Eastern Europe for quick integration with Western Europe.<sup>12</sup> Building on an early start in Botswana and Uganda, MTEFs spread rapidly across Sub-Saharan Africa in the 2000s. MTEFs are more numerous in Francophone Africa than Anglophone Africa. MTEFs have also been adopted by most countries in South Asia, with Nepal and Sri Lanka having implemented MTBFs.

MTEFs are less widespread in other regions, despite a recent spurt of adoptions in East Asia and the Paci...c, including MTBFs in Cambodia and Thailand. The picture is similar

> 11Three countries (Bulgaria, Canada and Norway) performed a full transistion - from an MTFF to an MTBF to an MTPF - during this period.

> 12The countries remaining without an MTEF in this region are: Azerbaijan, Belarus, Montenegro, and Turkmenistan.

7
in Latin America and the Caribbean, where a number of countries have introduced MTFFs following years of managing ...scal policy under IMF programs. Only four countries have moved beyond an MTFF and introduced an MTBF: Argentina, Colombia, Nicaragua and St. Lucia, although Brazil’s budgeting system has recognizable MTBF characteristics. In the Middle East and North Africa MTEFs are a very recent innovation. Only Algeria and Jordan have an MTBF, while major oil exporting countries such as Saudi Arabia and United Arab Emirates, as well as Egypt, had not yet adopted MTEFs.

Despite pronounced di¤erences between regions patterns of MTEF adoption have been relatively uniform across income and development levels. Apart from the widespread adoption of MTEFs in high-income countries, there is little di¤erence in penetration across upper middle, lower middle, and lower-income countries. MTEF adoption does not appear to follow a monotonic relationship with respect to income per capita or the human development index; see Figures 3 and 4.

# 2.3 MTEF Objectives

MTEFs represent a multiyear approach to budgeting that addresses the shortcomings of annual budgeting noted above in the Introduction. Most public programs require funding and yield bene...ts over a number of years, but annual budgeting largely ignores future costs and bene...ts. Annual budgets take as their starting point the previous year’s budget and modify it in an incremental manner, making it di¢ cult to re-prioritize policies and spending.<sup>13</sup> MTEFs take a strategic forward-looking approach to establishing spending priorities and resource allocation. They also look across sectors, programs and projects to see how spending can be restructured to best serve national objectives, which contrasts with the narrow selfinterest of spending agencies and bene...ciaries that dominates resource allocation under annual budgeting (World Bank 1998).

Insofar as an MTEF constrains spending to resource availability, makes budget allocations re‡ect spending priorities, and generates cost e¤ectiveness in the delivery of public goods and services, it should contribute directly to ...scal discipline, allocative e¢ ciency, and technical e¢ ciency.<sup>14</sup> Moreover, there are synergies among these three dimensions of ...scal

> 13While incremental budgeting can work well in times of revenue growth, it comes under particular pressure when revenue falls, becomes more volatile, or reaches its natural limit. In these instances expenditure prioritization takes on increased importance.

> 14There is also a link to broader economic development. With improved ...scal outcomes, growth should be higher, in‡ation lower, and macroeconomic volatility reduced. Moreover, as the quality of spending improves, higher incomes should be accompanied by lower poverty rates, while better infrastructure should contribute to even higher growth and further poverty reduction.

8
performance. With ...scal discipline secured, governments should be free to focus on the microeconomic challenges of improving spending e¢ ciency and not preoccupied with having to address the adverse macroeconomic consequences of persistent ...scal imbalances.<sup>15</sup> It should also be easier to maintain ...scal discipline when improvements to both allocative and technical e¢ ciency reduce abuse and waste. Moreover, against a background of ...scal discipline, new expenditure needs are more likely to prompt spending reallocations as opposed to requests for additional funding. Finally, both ...scal discipline and expenditure e¢ ciency create ...scal space for productive spending on economic and social infrastructure, and for responding to ...scal risks.

MTFFs can promote ...scal discipline by addressing the root causes of de...cit bias. By specifying an overall "top-down" resource constraint, an MTFF reins in the political tendency to over-commit resources (the common pool problem). By imparting a medium-term perspective to budgeting and taking into account the future ...scal costs of government policies and programs, an MTFF can ...ll information gaps that allow politicians to renege on commitments to implement a¤ordable policies (the time consistency problem). A medium-term perspective also encourages governments to conduct discretionary stabilization in a symmetric, counter-cyclical manner, rather than asymmetrically which leads to rising de...cits and debt (Kumar and Ter-Minassian 2007).<sup>16</sup> Since MTBFs and MTPFs incorporate an MTFF, they should have a stronger e¤ect on ...scal discipline compared to an MTFF alone. This is in part because countries that have the administrative capacity to implement these higher phases will likely also have greater ...scal discipline. But it is also a consequence of better prioritization and more emphasis on performance, which can bring the payo¤ to ...scal discipline into sharper focus.

Prioritization guided by longer-term sector strategies should improve allocative e¢ ciency. Insofar as spending agencies prepare sector strategies, identify their resource needs, and allocate their budgets according to strategic priorities, this "bottom-up" prioritization should produce a shift to spending with higher economic and social returns. However, the full payo¤ to prioritization requires that choices are also made as to how resources should be

> 15It can be argued that, in fact, large ...scal imbalances prompt better expenditure prioritization; however, the lessons from ...scal adjustments around the world is that spending cuts are borne disproportionately by high-priority spending, and especially public investment in infrastructure, with adverse consequences for future growth (Easterly, Irwin, and Serven 2008). Lewis and Verhoeven (2010) report that the growth of social spending has dipped as the global ...nancial crisis has put ...scal positions under pressure, which risks setting back achievement of human development goals, because these depend on the rapid spending increases achieved in the 1990s and the earlier part of the 2000s.

> 16On the downside, if spending agencies view MTEFs as minimum entitlements, rather than constraints, ceilings, or forward estimates, MTFFs could actually be a source of ...scal indiscipline and de...cit bias (Schick 2010).

9
allocated across sectors, which is done as part of the reconciliation between the "top-down" and "bottom-up" approaches involving a lead agency, normally the Ministry of Finance, and spending agencies, and in connection with which less strategic guidance may be available, especially in the absence of national medium-term planning.<sup>17</sup> The outcome of e¤ective prioritization should be a change in the allocation of spending. In the short term, spending volatility by sector may increase following MTEF implementation as spending is reallocated to more productive sectors and programs. Thereafter, insofar as spending decisions are guided by strategic priorities with a longer-term focus, sectoral spending should become less volatile, especially in the high-priority areas of health and education. The payo¤coming from an MTBF should be even higher with an MTPF since this last phase goes further by setting within-sector and within-program performance targets.<sup>18</sup> A third dimension of ...scal performance is technical e¢ ciency. The better the economic and social outcomes achieved by spending programs from a given amount of budget resources, or the fewer resources used to achieve given outcomes, the more technically e¢ cient is government spending. Improved technical e¢ ciency may follow from an MTFF, but is more likely a consequence of an MTBF and MTPF, with the latter possibly having the largest e¤ect as budgets are linked to results in the form of outcomes or outputs.

Based on these considerations we state the expected MTEF e¤ects on ...scal performance in the following hypotheses (see also World Bank 2012): (H1) MTFF, MTBF, and MTPF improve ...scal discipline, with higher-phase MTEFs having larger e¤ects.

(H2) MTBF and MTPF improve allocative e¢ ciency, with MTPF having a larger e¤ect. (H3) MTPF improves technical e¢ ciency.

The rest of the paper examines the evidence for these conjectures.

# 3 Data and Empirical Strategy

This section discusses the choice of variables for the empirical analysis and takes a ...rst look at the statistical properties of our data. It then outlines our empirical strategy for identifying and estimating the MTEFs’e¤ects on ...scal performance.

> 17Moreover, di¢ cult decisions have to be made to cut low-priority, but often politically sensitive, spending.

> 18A shift away from unproductive spending should also be observed. Poor-quality investment, distortionary and untargeted subsidies, bloated civil services, and the like should not survive scrutiny under the MTEF, while productive spending on economic and social infrastructure, health and education services, and other growth- and development-promoting activities should be favored.

10
# 3.1 Data

The dataset contains both cross-sectional and time-series variation in MTEF presence. The sample consists of 181 countries over the period 1990-2008. The country sample re‡ects data availablility on MTEF status. The period sample re‡ects data availability on public ...nances. Here we brie‡y discuss the key variables. Section A.3 of the Appendix contains the complete list of variables together with their data sources.

The construction of the MTEF indicators relied upon an extensive data collection e¤ort as no single type of document su¢ ciently describes the existing institutional arrangements for all countries or even individual countries. Thus, the data were compiled from a large number of sources, including IMF Article IV country reports, IMF Reports on the Observance of Standards and Codes (ROSC), ...scal transparency modules, World Bank Public Expenditure Reviews (PERs), World Bank Country Financial Accountability Assessments (CFAAs), OECD documents, donor case studies, and country websites. Additionally, World Bank and IMF public sector specialists supplemented the above information with technical details.<sup>19</sup> We measure ...scal discipline, an indicator of macro ...scal performance, by the central government’s overall balance. Although the literature suggests alternative indicators, e.g., primary balance and debt, data availability limited the choice to the overall balance. Moreover, by including government borrowing, the overall balance is a good indicator of the state of public ...nances.<sup>20</sup> Allocative e¢ ciency does not have a universally accepted de...nition. Potential proxies for allocative e¢ ciency are budget composition volatility and volatility of core spending (health and education). Since volatility in these sectors jeopardizes long-term objectives, health care and public education spending should be largely una¤ected by short-term ‡uctuations in GDP. In other words, allocative e¢ ciency implies that spending in core sectors where needs are fairly constant does not behave in a volatile manner. Given data constraints and the requirement that the public good category should be reasonably comparable across countries, we choose to work with the volatility of per-capita health spending, in PPP$. We de...ne volatility of a time series yi;t for country i as the absolute yearly growth rate of the

> 19For the purposes of this paper, we refrain from making judgments to distinguish between an MTEF present in the law (de jure) and a well-functioning MTEF (de facto). Such a distinction would introduce a signi...cant amount of subjectivity into the analysis.

> 20It could be argued that the overall balance does not account for the e¤ect of in‡ation on interest payments and that interest payments are a function of the accumulated debt and not the present ...scal stance.

11
detrended series: ~ T where yi;t = yi;t � t � T<sup><u>1</u></sup> Pk=1<sup>k</sup> is the detrended series for yi;t: � � Technical e¢ ciency is typically measured using technical e¢ ciency scores from a Stochastic Frontier Analysis (SFA). This is the approach we adopt. The SFA approach relies on a reduced-form relationship between inputs and outputs. The country with the highest health output after controlling for inputs is the most e¢ cient, and the e¢ ciency level of the other countries is measured with respect to the most e¢ cient country.<sup>21</sup> In particular, we compute technical e¢ ciency scores in the health sector using a parsimonious version of the model estimated in Greene (2005). The outcome of interest is life expectancy, and the input is health spending per capita. The model is: where � t’s are year ...xed e¤ects, vi;t � N (0; �v), and ui;t = jUi;tj � Exp(0; �u): The controls are population density and OECD membership.<sup>22</sup> The parameters are estimated by maximum likelihood. The estimates of vi;t � ui;t are translated into an estimate of ui;t using the standard Jondrow, Materov, Lovell, and Schmidt (1982) formula. Technical e¢ ciency then is simply: ^ where ui;t is the maximum likelihood estimate of ui;t: Table 4 presents the estimation results. The coe¢ cients follow the same pattern noticed in prior work using di¤erent data. The asymmetry parameter � = �u=�v is also within the range of variation reported previously. Estimated mean e¢ ciency is 86.48.

Following Baltagi, Demetriades, and Law (2009), who study the e¤ect of ...nancial openness on banking sector development, we introduce MTEF regional penetration as external

> 21The SFA was inspired by Farrell (1957), who de...ned technical e¢ ciency as the ability to produce the maximum possible output from a given set of inputs, and measured it as the di¤erence between maximum attainable output and observed output. Ine¢ ciencies might arise from waste or because the most coste¤ective set of programs is not implemented.

> 22Greene (2005) also includes education spending per capita, as an input, and controls for government voice and accountability, government e¤ectiveness, share of government ...nancing, the Gini coe¢ cient, and GDP per capita.

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instruments to supplement the internal GMM-style instruments. In a region consisting of n countries, penetration for country i is de...ned as the fraction of countries in the surrounding region that already have an MTEF: for each of the MTEF phases. We use the classi...cation of the world into the twenty-two geographic regions de...ned by the United Nations Statistics Division.

Table 1 reports summary statistics. All variables display considerable variation both between and within countries, justifying the use of panel estimation techniques. An exception is MTPF, which has small within variation due to the few adoptions of this top phase, and OECD membership. Table 3 reports pairwise correlations between the main variables. The correlation coe¢ cients are within plausible ranges and support our choices of regressors in the next subsection.

# 3.2 Empirical Strategy

Estimating the impact of a budget institution on ...scal performance requires that we address several identi...cation challenges: reverse causality, omitted variable bias, and errors-invariables.

First, reverse causality arises because ...scal stress, e.g., a ...nancial crisis, may have prompted a country to restrain spending, adopt an MTEF, or strengthen an existing one. Von Hagen (2006, p. 474) notes that "Historical experience suggests that governments make e¤orts to centralize the budget process to overcome sharp ...scal crises." If MTEFs have positive e¤ects on ...scal performance, and poor ...scal performance increases the probability of adopting an MTEF, then the reverse causality bias is probably negative. In this case, the estimates are still useful as a lower bound for the actual e¤ect.<sup>23</sup> Second, omitted variable bias arises due to the failure to account for a factor that a¤ects both the adoption of an MTEF and ...scal performance. For instance, strong economic growth may reduce the pressure on a government to reform budget institutions, and, at the same time, improve the government’s ...scal outcomes, thus leading to negative omitted variable bias. As suggested by Fabrizio and Mody (2006), a partial solution to this problem is to

> 23The endogeneity of budget institutions with respect to ...scal performance is extensively discussed in the cross-country literature (see Alesina and Perotti 1999, Stein, Talvi, and Grisanti 1999, Perotti and Kontopoulos 2002, and Fabrizio and Mody 2006) yet none of these papers proposes an instrument that in‡uences the probability of ...scal reform while being exogenous to ...scal performance.

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use within-country variation in ...scal institutions. This approach, in e¤ect, eliminates the unobservable country speci...c ...xed e¤ects that may in‡uence budget de...cits. The problem of omitted variables is thus alleviated; however it is not eliminated.<sup>24;25</sup> Finally, if some of the variables in the analysis are not measured accurately, there is the potential for errors-in-variables bias, which usually dampens the e¤ect of interest. Although in our empirical model the primary explanatory variable, MTEF status, can be observed with a reasonably high degree of precision, there is still scope for measurement error in the other explanatory variables.

Our empirical strategy exploits within-country variation in ...scal institutions. Annual data allow us to account for the possibility that observed ...scal performance in a given year may not represent long-run equilibrium values, because of incomplete adjustment in other variables. For example, as revenues cannot be perfectly anticipated budget balance in a given year ‡uctuates around the equilibrium balance level. To allow for the possibility of partial adjustment, we use a dynamic speci...cation with a lagged dependent variable: where yi;t is a measure of ...scal performance, L is the number of lags of the dependent variable, MTFFi;t; MTBFi;t; MTPFi;t are indicators of the three MTEF phases, xi;t is a vector of covariates, and "i;t is an error term that contains country and year ...xed e¤ects: with the idiosyncratic error �i;t assumed to be mean zero.

While this dynamic panel formulation allows for a richer model of ...scal adjustment, the presence of a lagged dependent variable introduces new sources of endogeneity. First, without controlling for the ...xed e¤ects, the model in equation (5) has a built-in positive bias in the ...rst lag of the dependent variable.<sup>26</sup> Second, the di¤erenced version of the equation eliminates this bias, but has a built-in negative bias due to the fact that yi;t<sup>�=</sup> �yi;t is negatively correlated with yi;t<sup>�</sup> �1<sup>= �yi;t�1:Thusanunbiasedestimateofthelagged</sup>

> 24Most studies have not been able to use this method because either budget institutions do not change much over time or because changes are di¢ cult to measure. When it has been implemented with U.S. statelevel data (e.g., Knight and Levinson 2000) the results are typically di¤erent, indicating that the problem of omitted variables is relevant.

> 25Additional omitted variables could include political institutions. Evidence from Europe shows that institutional design responds to political factors and events (Hallerberg, Rolf, and von Hagen 2009).

> 26Nickell (1981) has shown that this "dynamic panel bias" disappears only when T approaches in...nity.

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dependent variable coe¢ cient should lie in the range between the FE estimate and the OLS estimate. This bracketing range thus provides a natural speci...cation check (Bond 2002).

The D-GMM solution to this endogeneity problem is to instrument for �yi;t�1 using yi;t�2 and possibly earlier lags. Notice that the lagged values of the dependent variable are useful instruments unless yi;t is close to a random walk, in which case past levels convey little information about future changes. Table 5 presents unit root test results for the three measures of ...scal performance. The IPS test statistic safely rejects the null of unit root in each case.<sup>27</sup> In the same fashion, one can instrument �MTFFi;t, which is also endogenous to �yi;t, with MTFFi;t�2 and possibly earlier lags (and similarly for the higher MTEF phases). In this way D-GMM generates internal instruments for the budget institutions. To improve estimation e¢ ciency we supplement the internal instruments with external instruments based on MTEF penetration in a country’s geographic region, computed according to equation (4). The external instruments are inspired by the regional penetration patterns noted in the previous section; see Subsection 2.2.<sup>28</sup> Because our panel is unbalanced, we use the orthogonal deviations transform (Arellano and Bover 1995) in the baseline speci...cations: yi;t<sup>�=</sup> q TTi;ti;+1t �yi;t � T1i;t Ps>t<sup>yi;s</sup> �, where Ti;t is the number of available future observations; we also report estimates based on the di¤erence transform yi;t<sup>�= yi;t�yi;t�1in the alternative speci...cations.Orthogonal deviations,</sup> instead of subtracting the previous observation from the current observation, subtracts the average of all future available observations from the current observation. This maximizes sample size in panels with gaps.

The moment conditions are based on orthogonality between the transformed errors and the lagged values of the dependent variable. To test for this condition one can run two diagnostics, namely ...rst-order and second-order serial correlation in the idiosyncratic error �i;t: The test should reject the null of no ...rst-order serial correlation, and not reject the absence of second-order serial correlation. We include year ...xed e¤ects in all speci...cations to strengthen the case for the assumption of no correlation in the idiosyncratic errors across countries.

> 27An alternative approach would be to use the System GMM estimator proposed by Arellano and Bover (1995) and Blundell and Bond (1998); this estimator requires an additional identifying assumption, namely that ...rst di¤erences of the instrumenting variables are exogenous to the ...xed e¤ects. This is the strategy we employ in World Bank (2012). There we also include a larger set of covariates which should mitigate omitted variable bias, but may also increase the possibility of additional endogeneity bias. The consistency in ...ndings between the two approaches adds to the robustenss to our results.

> 28By themselves the external instruments are not strong enough to justify a di¤-in-di¤s IV strategy. The lack of strength is driven in particular by insu¢ cient variation in MTPF adoption.

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The number of moment conditions increases with T: To test for over-identifying restrictions we use a Hansen J test. Too many moment conditions introduce bias while increasing e¢ ciency. Thus, it is important to keep the number of internal instruments in check. In the baseline speci...cations we use one lag for each lagged dependent variable, and two lags for each MTEF indicator. In the alternative speci...cations we also report results with collapsed instrument matrix, as recommended by Roodman (2009).<sup>29</sup> To restrain the number of instruments we only include covariates that satisfy two conditions: (i) have the potential to act as conduits in transmitting regional trends, and (ii) are not endogenous to the dependent variable, at least in the short run. The ...rst condition is needed because our external instruments are regional variables. The second condition simply insures that we do not have to introduce internal instruments for the covariates as well. Openness and con‡ict are one economic and one political variable that satisfy these criteria and are available for our full sample, thus xi;t = (Opennessi;t; Conflicti;t) in equation (5).

Our baseline speci...cation computes two-step D-GMM estimates with standard errors corrected with the Windmeijer (2005) procedure.<sup>30</sup> We also report alternative speci...cations with one-step D-GMM estimates, in which case we report cluster-robust standard errors, i.e., robust to heteroskedasticity and arbitrary patterns of correlation within countries.

# 4 Estimation Results

The main results of the paper are contained in Tables 6 through 11. The tables contain estimates of ...scal discipline (Tables 6 and 7), allocative e¢ ciency (Tables 8 and 9), and technical e¢ ciency (Tables 10 and 11) regressions using the dynamic panel model discussed in the previous section. In each case the ...rst table presents the baseline results, namely twostep D-GMM with Windmeijer corrected standard errors. In these speci...cations we treat the MTEF indicators as endogenous. The second table presents alternative speci...cations, namely one-step robust standard errors, collapsed instruments, di¤erence transform, and treatment of the MTEF variables as lagged and predetermined instead of contemporaneously endogenous. We preserve the structure of both the baseline and the alternative speci...cations across all three measures of ...scal performance.

> 29In large samples collapsing the instruments reduces statistical e¢ ciency, however in small samples it may alleviate the bias created when the number of instruments times the number of periods approaches the number of panel units.

> 30The two-step standard error correction is needed because the original formula for the variance produces standard errors that are severely downward biased when the number of instruments is large.

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# 4.1 Fiscal Discipline

Table 6 presents the baseline regressions for ...scal discipline, measured as the central government’s budget balance as a percent of GDP. Columns (1) and (2) start with OLS and ...xed e¤ects estimates, which determine the bracketing range for the lagged dependent variable coe¢ cient, namely 0.379�0.481. As expected, government balance follows a distinct adjustment process as evidenced by the highly signi...cant coe¢ cients on the two lags of the dependent variable.

Columns (3) and (4) present the D-GMM estimates of the MTEF e¤ects. The internal instruments are the second and third lags of the budget balance and of the MTEF indicators. Column (4) augments column (3) with the external regional penetration instruments. In both speci...cations the e¤ect of each MTEF phase is positive and statistically signi...cant at conventional levels. Moreover, the e¤ects are economically meaningful, ranging between 1.305 and 4.577 percentage points. In our sample the average ...scal balance among countries without an MTEF is �2:87%. Taking the estimates in column (4) at face value implies that only introducing the top-phase MTPF will put this average country in the black. The increase in the coe¢ cients with more advanced MTEF phases lends support to hypothesis (H1).

These baseline speci...cations have lagged dependent variable coe¢ cients in the FE-OLS bracketing range, not raising any speci...cation issues. The model with external instruments is somewhat more precise than the one without. The D-GMM estimates of the MTEF e¤ects are larger than the FE and OLS estimates, suggesting that the latter are depressed by a potential negative reverse causality bias, as when countries tend to adopt an MTEF in response to a ...scal crisis.<sup>31</sup> For both models the diagnostic tests are satisfactory. The absence of ...rst-order serial correlation in errors is rejected, while the absence of second-order serial correlation is not. The Hansen test does not reject the over-identi...cation restrictions. We conclude that D- GMM is an internally consistent estimator in this model, and can be relied upon to carry out statistical inference for the hypotheses of interest.

Table 7 reports alternative speci...cations. Overall the results uphold the conclusions drawn from the baseline speci...cations of Table 6 columns (3) and (4). One exception is the model in column (3) that uses the di¤erence transform instead of the orthogonal deviations

> 31This is consistent with the fact that international assistance organizations such as the World Bank, the UK’s Department for International Development (DFID), the Asian Development Bank (ADB), and to a lesser extent the IMF, have recommended these reforms as part of a sound public ...nancial management strategy.

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transform. The lagged dependent variable coe¢ cient is below the bracketing range, suggesting that just di¤erencing the data produces a model with speci...cation problems. The estimates are also very stable across speci...cations, with the exception of column (4) where the MTEFs enter lagged, increasing the size of their coe¢ cients by a factor of between 1.5 and 2. As expected, collapsing the lagged dependent variable instruments in column (2) reduces statistical e¢ ciency, yet the coe¢ cients remain close to the baseline. We also note that in some speci...cations the con‡ict variable has a signi...cant and large negative coe¢ - cient, consistent with the notion that armed con‡ict is costly on the budget. The openness variable has a very small e¤ect and does not attain statistical signi...cance.

# 4.2 Allocative E¢ ciency

Table 8 presents the baseline regressions for allocative e¢ ciency, measured as the volatility in per-capita health spending in PPP$; see equation (1) above. The table structure mimics the previous ...scal discipline regressions. The sample period shortens due to the lack of health spending data in the ...rst half of the 1990s, as well as the loss of one year to compute the volatility measure. The FE-OLS bracketing range for spending volatility, based on columns (1) and (2), is 0.009�0.308: None of the MTEF coe¢ cient estimates in these ...rst two columns are signi...cant, moreover they seem small relative to the sample average spending volatility of 6.69%. Based on hypothesis (H2) above, we expect the advanced micro MTEF phases (MTBF and MTPF) to reduce spending volatility, with the top-phase reducing it more.<sup>32</sup> Columns (3) and (4) present the D-GMM estimates of these e¤ects. The internal instruments are the second lag of budget balance and the second and third lags of the MTEF indicators. Compared to the previous two columns, the estimated MTEF e¤ects become more negative and increase in magnitude. The MTBF e¤ect is around �6 percentage points and statistically signi...cant at conventional levels. The MTPF e¤ect is about twice larger, although only weakly signi...cant. Overall, these results provide support for hypothesis (H2).<sup>33</sup> The statistical properties of these baseline models do not raise model speci...cation issues. The lagged dependent variable coe¢ cient estimate is in the bracketing range, although not reaching the signi...cance threshold. The ...rst-order and second-order serial correlation tests support the model’s assumptions about the idiosyncratic error term. The Hansen test does

> 32Some countries chose to pilot an MTBF in the health sector before extending it to other sectors. We have been unable to systematically identify the countries that follow this particular sequencing of reform.

> 33This result can be seen as a sectoral counterpart of the ...nding in Fatas and Mihov (2003) that "discretionary" ...scal policy, i.e., variation in spending unrelated to economic fundamentals, increases aggregate output volatility.

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not reject the over-identi...cation restrictions.

Table 9 reports alternative speci...cations of the allocative e¢ ciency regressions. Overall the results uphold the conclusions drawn from the baseline speci...cations of Table 8 columns (3) and (4). The MTBF and MTPF coe¢ cient estimates maintain their prior patterns, although again the di¤erence transform model in column (3) performs somewhat poorly. The covariates openness and con‡ict do not display any signi...cant e¤ects on health spending volatility.

# 4.3 Technical E¢ ciency

Table 10 presents the baseline regressions for technical e¢ ciency, measured as e¢ ciency scores from a stochastic frontier model of health delivery; see equations (2), (3), and Table 4. As expected, this indicator of ...scal performance is much more persistent than the previous ones. The FE-OLS bracketing range, based on columns (1) and (2), climbs to 0.858�0.999, indicating strong persistence. National health delivery is a complex system that may take decades to fully internalize the bene...ts of a given reform. The MTEF coe¢ cient estimates are all negative, small and far from statistically signi...cant. Hypothesis (3) above predicts that the top-phase MTEF (the MTPF) increases technical e¢ ciency.

Columns (3) and (4) present the D-GMM estimates of the MTEF e¤ects. The internal instruments are the second lag of technical e¢ ciency and the second and third lags of the MTEF indicators. Compared to the previous two columns, the estimated MTEF e¤ects turn positive and increase in magnitude. The speci...cation with external instruments is more precise. The MTPF e¤ect is 1.015 and statistically signi...cant at the 10 percent level. The lack of precision should be expected since the within-country variation in technical e¢ ciency is much smaller relative to the ...rst two measures of ...scal performance (1.67 vs. 9.59, 6.35). As before, the diagnostic tests perform well, increasing con...dence in the model speci...cation. Overall, these baseline results provide moderate support for hypothesis (H3).

Table 11 reports alternative speci...cations of the technical e¢ ciency regressions. The Hansen test suggests that the di¤erence transform and predetermined MTEFs in columns (3) and (5) respectively are not adequate speci...cations. The remaining three models have satisfactory diagnostics and display a pattern of coe¢ cients similar to the baseline. Among the alternative speci...cations only the speci...cation with collapsed instruments attains statistical signi...cance in the MTPF coe¢ cient (p-value 0.059). As in the baseline results, the covariates openness and con‡ict do not display measurable e¤ects on technical e¢ ciency.

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## 4.4 Democracy and Development

Finally, to rule out the possibility that the results are driven by subgroups of countries with extreme characteristics, we restrict the sample in two ways. First, we exclude highly autocratic countries, de...ned as those whose Polity IV score in 1990 takes the extreme value –10 ("strongly autocratic" in the language of the score producers). Second, we exclude highly developed countries, de...ned as those that are classi...ed by UNDP in 1990 as having “very high human development”based on the country’s Human Development Index (HDI). The two subgroups of countries are listed in Table 14. Note that the most developed countries are also the most democratic.

Tables 12 and 13 present the results with the restricted samples. The tables report the two early D-GMM baseline speci...cations for each of the three measures of ...scal performance. Overall, the prior patterns are preserved, strengthening the support for hypotheses (H1)(H3). The government balance regression in Table 12 column (2) shows stronger MTEF e¤ects both in magnitude and in statistical signi...cance. This result seems consistent with the notion that MTEFs as commitment devices are particularly well suited for democratic settings which Mueller and Stratmann (2003) argue are more prone to ...scal indiscipline.<sup>34</sup> The results in Table 13 excluding the most developed countries are comparable to the baseline results in Tables 6, 8, and 10, both in magnitude and statistical signi...cance, providing evidence that the MTEFs’ positive e¤ects on ...scal performance are not a phenomenon speci...c only to the most developed countries.<sup>35</sup>

# 5 Conclusion

In the last two decades more than 120 countries have moved toward a multiyear budget process. Although there has been much debate in the literature as to whether MTEFs are a worthwhile budget institution, a systematic empirical analysis of their impacts has been lacking due to insu¢ cient data on MTEF adoption around the world. This paper is the ...rst to empirically investigate the MTEFs’impacts on ...scal performance in a large sample of

> 34Ideally, to study how democracy and development condition MTEF impacts we would have interacted each MTEF indicator with measures of democracy and development. However, that would introduce three new endogenous variables, increasing the number of internal instruments above the number of available countries, and biasing the estimates toward their OLS counterparts.

> 35We also explored how the results change when we exclude more countries. Generally, the results are more sensitive when excluding more autocratic countries than when excluding more developed countries. The estimation strategy limits the degree to which we can restrict the sample through the requirement that the number of instruments times the number of periods remain below the number of panel units. See Roodman (2009).

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countries. In order to disentangle the e¤ects of the di¤erent MTEF phases (MTFF, MTBF, and MTPF) from other factors and to correct for reverse causality we apply a dynamic panel data approach to a newly-collected panel dataset of 181 countries over the period 1990-2008, the most comprehensive dataset to date on worldwide MTEF adoption.

The econometric ...ndings suggest that, unlike in previous small-sample and case-study analyses, MTEF adoption is associated with strong improvement in ...scal discipline, the effects increasing with each successive MTEF phase. The adoption of an MTBF and an MTPF decrease the volatility of health spending per capita, which we interpret as an improvement in allocative e¢ ciency. Finally, the MTPF seems to be the only MTEF phase that exerts a signi...cant e¤ect on technical e¢ ciency in the health sector, although due to insu¢ cient within-country variation in technical e¢ ciency this e¤ect is less precisely estimated. Overall these results are more favorable than the conclusions of prior work, and suggest that budget institutions that restrain short-term incentives to manipulate the budget can have measurable bene...ts for ...scal performance.

Our analysis may be limited by the fact that an MTEF could be in place only in law (de jure) and not in practice (de facto). However, if this phenomenon were widespread it would induce an attenuation bias and our estimates could still be regarded as a lower bound on the actual e¤ect. Being in e¤ect commitment mechanisms, transparency and enforcement are critical components of MTEFs. Studying which features of the broader civic, juridical, and political environment enhance MTEF e¤ectiveness may lead to a better understanding of these institutions. Also, our analysis of MTEF impacts on allocative e¢ ciency and technical e¢ ciency is limited to the health sector. Whether the results carry over to other categories of productive spending is an interesting question for future research. To create more precise measures of these dimensions of ...scal performance one needs to identify disaggregated categories of spending that are comparable across a broad range of countries.

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