Chapter

CHAPTER 2 IMF Programs and Growth: Is Optimism Defensible?

Author(s):
Alessandro Rebucci, and Ashoka Mody
Published Date:
April 2006
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Author(s)
Reza Baqir, Rodney Ramcharan and Ratna Sahay1 

IMF-supported programs focus on key objectives (such as growth, inflation, and the external current account) and on intermediate policy targets (such as monetary and fiscal policies) needed to achieve these objectives. In this paper, we use a new, large dataset, with information on 94 programs between 1989 and 2002, to compare programmed objectives and policy targets to actual outcomes. We report two broad sets of results. First, we find that outcomes typically fell short of expectations on growth and inflation but were broadly in line with the programmed external current account objectives. Similarly, programmed intermediate policy targets were generally more ambitious than the policy outcomes. Second, and focusing on growth, we examine the relationship between objectives and policy targets, and find differences in the way ambitious monetary and fiscal targets affected the achievement of the growth objective. On the one hand, more ambitious fiscal targets, even when they were missed, led to better growth performance. On the other hand, more ambitious monetary targets tended to be associated with lower growth performance.

Introduction

IMF-supported programs are often described by those on the left as creating hardships on the population because they are said to be “too tight” (Stiglitz, 2002). Those on the right frequently disparage the objectives that were set in the programs but were not achieved. These criticisms refer to the intermediate targets set in IMF-supported programs in the areas of monetary and fiscal policy, as well as to the macroeconomic outcomes—such as inflation, employment, and growth. Are both groups correct? Is there any validity to these criticisms? Or, are the benchmarks by which IMF programs judged simply misplaced?

Defenders of IMF-supported programs would argue that the programmed objectives and targets should not be viewed as forecasts. The objectives are set high so that countries can aspire to achieve them. Similarly, targets are set tight to ensure that policy slippages are kept to a minimum. If targets are missed either because of negative exogenous shocks or because the programs were set too tight, mechanisms in IMF policies and procedures exist to provide waivers for missing these targets. As a matter of fact, ample evidence exists on the waivers given in IMF-supported programs to ensure that IMF loan disbursements are not interrupted unless a major policy slippage occurs. This raises the question of whether tight policy targets and ambitious objectives are deliberate. Also, if they are deliberate, do they help countries achieve better outcomes than they could otherwise?

In an earlier paper based on a much smaller sample size (Baqir, Ramcharan, and Sahay, 2003), we found that (a) IMF-supported programs were, indeed, optimistic—in particular, programmed objectives on inflation and growth were often not fully achieved; and (b) meeting the fiscal target was associated with meeting the growth target. Given the small sample of 29 countries in that paper, however, we were unable to report conclusive results and, in particular, to explore systematically the relationship between objectives and policy targets.

In this paper, we expand the dataset used in Baqir, Ramcharan, and Sahay (2003) to 94 countries and confirm our previous findings on the optimism on growth projections in IMF-supported programs. We then compare the programmed and actual values of intermediate policy targets and objectives separately, and uncover systematic patterns. We also explore the relationship between the intermediate policy targets and the objectives to understand why there are persistent shortfalls in achieving some objectives. On the latter, we focus on a recurrent finding in reviews of IMF-supported programs—the relatively poor performance on meeting the growth objective—by looking at the main intermediate policy targets in monetary and fiscal policy to explore these questions.

This paper is organized as follows. The next section discusses the IMF’s financial programming framework. The third section describes the data. The fourth section systematically compares programmed objectives and policies with their actual outcomes. We examine the frequency with which program objectives are met simultaneously. We also look at the extent of adjustments that are programmed in different types of IMF-supported programs (Stand-By Arrangements and arrangements under the Poverty Reduction and Growth Facility) to see whether the adjustments differ across these groups. In the fifth section, we examine the relationship between objectives and fiscal and monetary policy targets, respectively. The sixth section concludes the paper.

The IMF’s Financial Programming Framework

The relationship between intermediate policy targets (such as the fiscal balance and monetary aggregates) and macroeconomic outcomes (such as inflation and growth) in IMF-supported programs is derived from the monetary approach to the balance of payments. In turn, this approach produces a framework known as financial programming, which uses a series of macroeconomic accounting identities to link economic growth, inflation, the money supply, the external current account, the budget deficit, and other macroeconomic variables.2

The intermediate policy targets derived within the financial programming framework, such as domestic credit and the fiscal balance, are designed to be consistent with the set of macroeconomic objectives—such as growth, current account adjustment, and inflation—chosen to help resolve the country’s economic difficulties.3 In other words, countries that meet the intermediate policy targets should conditionally expect to achieve the macroeconomic outcomes that underlie these targets.

To illustrate the financial programming approach, consider the classical money equation:

MV = PY

where M is money supply, V is velocity, P is the aggregate price level in the economy, and Y is the aggregate output. Typically, objectives are first established for inflation and growth, yielding P and Y. Next—and importantly—an assumption on velocity is made to arrive at the level of money supply consistent with program objectives. Money creation in excess of this amount would be inflationary. In practice, velocity is often chosen either by examining its historical pattern and making some assumption about how it is likely to be affected by particular factors in the near future and/or by estimating money demand functions. With money supply programmed, and given an external target on the net foreign assets of the country, the banking system’s balance sheet yields the maximum tolerable level of net domestic assets:

ΔNDA = ΔM − ΔNFA.

Given the balance of payments objective underlying the IMF-supported program, the assumption on velocity therefore directly affects the scope for credit creation in the economy. Programming higher velocity reflects an assumption that money demand will be low. In the event that money demand is higher than expected, tight money would drive up interest rates and constrain real activity in the economy, thereby affecting the growth outcome.

Net domestic assets can, in turn, be decomposed into net credit to the private sector (CPS), net credit to the government (NCG), and other items net (OIN):

ΔCPS + ΔNCG + ΔOIN = ΔM − ΔNFA.

This equation gives the other set of relationships between fiscal policy and real activity. Once velocity has been set and the external objective chosen, a higher government deficit financed by the banking system would crowd out credit to the private sector. And to the extent that private sector credit facilitates investment, such crowding out would affect real output.4 We use these relationships to examine, in the empirical section that follows, how assumptions on velocity and programmed fiscal adjustments affect growth outcomes.

Data

The data for this paper have been assembled from an internal IMF database on IMF-supported programs. In the sampling methodology, a unit of observation is defined as a program country-year: a calendar year in which disbursements were made to a particular country. Before disbursements are made, a document known as a staff report is issued and discussed at a meeting of the Executive Board, the body that decides IMF policy and approves IMF-supported programs. As their name suggests, staff reports contain the IMF staff’s assessment of a country’s economic situation and policies. They include the program’s intermediate policy targets and their macroeconomic counterparts that are meant to correct the particular problem(s) that prompted the country to seek IMF assistance. After each such Executive Board meeting, the data in the staff report on the key macroeconomic indicators are recorded in the database.

Typically there are several Board meetings on a country’s program in a given year. The staff report issued for each successive meeting contains an updated set of historical and programmed/projected data on key macroeconomic indicators. As such, there are several vintages of the programmed values for any variable of interest. We make use of the information in the evolving forecasts/programs by recording the programmed values for a variable xt in years t, t —1, t —2, and t —3 from the most recent staff report in that particular year.

Data on outcomes are generally not released until after the end of the year. We therefore define the within-year horizon as the forecast made for xt in year t. Similarly, a one-year horizon is defined as the value programmed for xt in year t— 1. For most empirical work, we focus on up to two-year horizons, since the number of observations declines sharply as the horizon length increases. We measure the actual as the most recent historical observation available on a particular variable for the entire set of staff reports for a country. For example, we record the actual fiscal balance for 1995 as that contained in a staff report dated 1998 if that particular report is the most recent available in the database for that country.

Conceivably, we could expand our data on actual outcomes by combining these data with other popular databases, such as the IMF’s Government Finance Statistics (GFS) or International Financial Statistics (IFS). However, aside from growth and inflation, which are generally measured in the same way across databases, nearly all other variables of interest in the areas of monetary, fiscal, and external policies can potentially be measured in different ways across databases. This is particularly true for fiscal policy targets—indeed, staff report data on fiscal measures are often somewhat different from those reported in GFS. Hence, to avoid contaminating our data, we focus only on actual outcomes as recorded in the staff reports.

To facilitate our analysis by type of program, we divide all programs into three groups—the Stand-By Arrangements (SBAs), a subset of SBAs that we call “high-profile” SBAs, and arrangements under the Poverty Reduction and Growth Facility (PRGFs). Borrowings under the SBAs are typically for shorter periods and carry higher rates of charge than those under the PRGF. The high-profile SBAs are distinguished from other SBAs by the greater amounts of access they provide to the IMF’s resources—they are also typically covered prominently by the media. We defined “large access” as all programs in the database with access exceeding two billion Special Drawing Rights (SDRs).5 The list of large-access countries in our sample consists of Argentina, Brazil, Indonesia, the Republic of Korea, Mexico, the Russian Federation, Thailand, Turkey, and Uruguay.

The universe of our data consists of 94 countries for the years 1989–2002. The number of observations varies by country for each variable. Table 1 shows the distribution of available observations on actuals for key variables we use in the empirical work. On average, we have about 7–8 observations per country, which allows us to capture significant variation, both across countries and within countries, over time. We exploit both dimensions of this variation in the empirical work discussed later in this paper. The corresponding number of observations available for forecasts is considerably smaller. For example, a one-year growth forecast is available for 495 country-years, compared with 776 country-years for actuals.

Table 1Country List and Number of Observations for Key Variables
Number of Observations for Actuals on
Country IDCountry NameReal GDP growthInflationCurrent account balanceFiscal balanceBroad money
ALBAlbania109101010
ALGAlgeria77777
ARGArgentina12125128
ARMArmenia11116106
AZEAzerbaijan101091010
BELBelarus33333
BENBenin131391313
BOLBolivia99990
BOSBosnia and Herzegovina61055
BRABrazil66162
BULBulgaria121271110
BURBurkina Faso1212101211
CAMCameroon1211121211
CAPCape Verde53433
CENCentral African Republic99999
CHAChad1111111010
CMBCambodia11119108
COLColombia66362
CONCongo, Republic of88588
COSCosta Rica66555
COTCôte d’Ivoire75665
CROCroatia1010497
CZECzech Republic44423
DJIDjibouti77637
DOMDominican Republic33333
ECUEcuador77570
EGYEgypt77777
ELSEl Salvador88888
EQUEquatorial Guinea33333
ESTEstonia1010899
ETHEthiopia119111111
GABGabon99998
GAMGambia, The44324
GEOGeorgia1075109
GHAGhana1010101010
GUBGuinea-Bissau55555
GUIGuinea88588
GUYGuyana1111111111
HAIHaiti44444
HONHonduras1111111111
HUNHungary77777
INDIndonesia77472
JAMJamaica77777
JORJordan1111111111
KAZKazakhstan88487
KENKenya99999
KORKorea, Republic of66664
KYRKyrgyz Republic13128127
LAOLao People’s Democratic Rep.111110118
LATLatvia1111101111
LESLesotho97798
LITLithuania12123119
MACMacedonia, FYR of88888
MADMadagascar1010101010
MALMali1411131313
MAUMauritania131191111
MEXMexico88888
MLWMalawi99999
MOLMoldova10107108
MONMongolia1110111111
MOZMozambique99988
NEPNepal44444
NGRNigeria33333
NICNicaragua88677
NIGNiger1212111011
PAKPakistan1310111212
PANPanama88888
PAPPapua New Guinea88888
PERPeru10106109
PHIPhilippines99999
POLPoland55555
ROMRomania10108108
RUSRussian Federation77777
RWARwanda66564
SAOSão Tomé and Príncipe33333
SENSenegal1111111111
SIESierra Leone66666
SLOSlovak Republic55444
SRISri Lanka44444
TAJTajikistan66664
TANTanzania88675
THAThailand66635
TOGTogo66666
TURTurkey1111987
UGAUganda99999
UKRUkraine99787
URUUruguay10109107
UZBUzbekistan33333
VENVenezuela, República Bolivariana de33333
VIEVietnam10107109
YEMYemen88877
YUGYugoslavia44423
ZAMZambia10105109
ZIMZimbabwe101010108
Total776748649735665
Average number of observations per country8.38.06.97.87.1

Objectives and Targets: Programmed Versus Actual

To evaluate IMF-supported programs, it is of central interest to know whether both the objectives and the policy targets were met. If the objectives were not met (in either direction), it could suggest that programs were either not sufficiently ambitious or too ambitious. If the policy targets were not met (in either direction), it suggests either that policy efforts by the borrowing countries were insufficient or that the government exceeded its targets. If the policy targets were met but objectives were not (and vice versa), it may imply that the IMF program design was faulty or that the targets and objectives were inconsistent.6

Table 2 and Figures 13 summarize the programmed and actual outcomes for the main economic objectives in IMF-supported programs—the IMF’s Articles of Agreement suggest that the most important goals include inflation, growth, and external current account balance (see Baqir, Ramcharan, and Sahay, 2003 for a detailed discussion). The tables compare the programmed outcomes with the actual ones. For each of the three objectives, the rows indicate values for all programs, PRGFs, SBAs, and high-profile SBAs.

Figure 1Projection Errors, by Program Horizon: Growth

(Error = projected minus actual, mean, and 95 percent confidence interval)

Figure 2Projection Errors, by Program Horizon: Inflation

(Mean and 95 percent confidence interval)

Figure 3Projection Errors, by Program Horizon: Current Account

(Mean and 95 percent confidence interval, percentage of GDP)
Table 2Objectives in IMF Programs: Program Versus Actual
Program HorizonDifference

(program minus actual)
Two yearsOne yearWithin-yearActualTwo yearsOne yearWithin-year
Real GDP growth (in percent)
All program years5.24.63.51.83.42.81.7
PRGFs5.75.34.73.32.42.01.4
SBAs4.53.82.00.34.23.51.7
High-profile SBAs4.12.91.31.13.01.80.2
CPI inflation (percent, end of period)
All program years5.06.08.010.3-5.3-4.3-2.3
PRGFs4.35.07.08.4-4.1-3.4-1.4
SBAs6.07.09.113.2-7.2-6.2-4.1
High-profile SBAs6.06.36.68.9-2.9-2.6-2.3
Current account balance (percentage of GDP)
All program years-8.6-9.1-9.4-9.40.80.30.0
PRGFs-11.4-12.4-13.2-13.92.51.50.7
SBAs-4.1-4.7-4.6-4.50.4-0.2-0.1
High-profile SBAs-2.1-1.3-1.3-1.0-1.1-0.3-0.3
Sources: IMF; Authors’ calculations.Notes: Table reports means by group except for inflation, for which medians due to outliers are reported. All observations are used for each sample. The same general pattern is preserved if sample size is kept constant across columns. The last three columns report the difference between the program columns and the actual columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.
Sources: IMF; Authors’ calculations.Notes: Table reports means by group except for inflation, for which medians due to outliers are reported. All observations are used for each sample. The same general pattern is preserved if sample size is kept constant across columns. The last three columns report the difference between the program columns and the actual columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.

Objectives

Table 2 indicates that for all types and subsets of programs, programmed real GDP growth was consistently higher than actual outcomes. Moreover, programmed growth was progressively higher, the longer was the horizon of the forecasting period (Figure 1). When we compare the forecast errors in absolute terms, we see that the errors were higher in SBAs than in PRGF programs. It is notable, however, that the errors in the high-profile SBAs were lower than in the SBAs and even lower than those in the PRGF programs. This suggests that growth projections are more optimistic in SBAs than in PRGF programs, with one caveat: the projections in the high-profile SBAs were more realistic than in other SBAs and PRGFs, although the direction of the bias was the same in all types of program.

In the second set of rows in Table 2, the programmed and actual inflation rates are compared. As in our results on real GDP growth, programmed inflation is lower than the actual outcomes in all types of program. And as in our results on growth forecasts, the errors decrease as the horizon of the forecasting period becomes smaller (Figure 2). Comparing across programs, the inflation objectives are more optimistic in the SBAs than in the PRGFs. Within SBAs, the high-profile ones had more realistic programmed inflation, although differences between actuals and program objectives were less for the PRGFs. Again, the direction of the bias was the same across programs, which points to optimism toward achieving inflation objectives.

The results on the current account objectives are qualitatively different from those obtained on the growth and inflation objectives. Although the forecasting error falls with the length of the forecasting horizon, as in the previous cases, there is no bias, on average, in all programs. There are some differences across the types of program. In PRGF programs, on the one hand, the programmed current account balance is somewhat optimistic relative to the realized values; on the other hand, in the SBAs, the realized values were higher than the programmed ones. The high-profile SBAs performed best, since this group had the smallest bias compared with other SBAs and PRGFs.

We also explored the unconditional probability of meeting all three objectives at the same time (Figure 4). The figure shows that when all programs are considered, the probability of achieving all three objectives at the same time is about 10 percent. As is to be expected, this probability rises as the horizon of the forecast shortens, but only marginally. Figure 4 also indicates that the probability of meeting the current account objective is the highest, followed by the inflation and growth objectives, respectively. This should not be surprising, since the core function of IMF-supported programs is stabilization and restoration of balance of payments viability.

Figure 4Unconditional Probability of Meeting Program Objectives

In summary, all three objectives—growth, inflation, and the current account—are unlikely to be met at the same time. Second, the inflation and growth objectives consistently reflect optimism in the formulation of IMF-supported programs, while the current account balance is met more frequently. Optimism about inflation and growth is highest in SBAs, followed by PRGFs and high-profile SBAs, respectively. Third, the extent to which the targets for the current account balance are exceeded is greatest in high-profile SBAs, followed by other SBAs and PRGFs, respectively. These results indicate that when they are judged by the values of the programmed objectives, the high-profile SBAs appear to have performed best, since either the bias is smaller than for other programs or the targets are exceeded. A question that arises is whether the IMF does a better job of designing programs in high-profile cases or simply sets them more realistically in such instances because, almost by definition, external scrutiny is greater.

Fiscal Policy Targets

Table 3 compares the fiscal policy targets set in programs with those realized. From top to bottom, the first two sets of rows relate to measures of fiscal balance, the next two to revenues, and the last two to expenditures.

Table 3Fiscal Policy Targets in IMF Programs: Program Versus Actual(Percentage of GDP)
Program HorizonDifference

(program minus actual)
Two yearsOne yearWithin-yearActualTwo yearsOne yearWithin-year
Fiscal balance, broadest coverage
All program years-2.5-3.0-3.5-4.72.21.71.2
PRGFs-3.1-3.7-4.3-5.62.51.91.3
SBAs-1.3-2.0-2.5-3.82.51.81.3
High-profile SBAs-1.9-3.0-3.8-3.31.40.3-0.5
Primary balance (excluding grants)
All program years-2.1-2.5-2.9-3.81.71.30.9
PRGFs-3.5-4.2-5.2-6.12.61.90.9
SBAs1.81.00.8-0.72.51.71.5
High-profile SBAs0.7-0.40.0-0.51.20.10.5
Revenues (excluding grants)
All program years20.120.621.021.4-1.3-0.8-0.4
PRGFs17.717.817.617.8-0.10.0-0.2
SBAs26.726.727.127.3-0.6-0.6-0.2
High-profile SBAs22.621.520.421.70.9-0.2-1.3
Revenues (including grants)
All program years22.823.523.924.2-1.4-0.7-0.3
PRGFs20.721.221.321.3-0.6-0.10.0
SBAs27.027.327.627.9-0.9-0.6-0.3
High-profile SBAs21.621.521.121.10.50.40.0
Total expenditures
All program years25.226.327.028.2-3.0-1.9-1.2
PRGFs23.824.424.725.9-2.1-1.5-1.2
SBAs28.229.330.131.3-3.1-2.0-1.2
High-profile SBAs23.224.324.123.4-0.20.90.7
Primary expenditures
All program years22.823.523.925.3-2.5-1.8-1.4
PRGFs21.822.022.223.1-1.3-1.1-0.9
SBAs25.125.826.428.0-2.9-2.2-1.6
High-profile SBAs21.720.819.920.90.8-0.1-1.0
Sources: IMF; Authors’ calculations.Notes: Table entries report means by group. All available observations are used for each sample. The same general pattern is preserved if sample size is left constant across columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.
Sources: IMF; Authors’ calculations.Notes: Table entries report means by group. All available observations are used for each sample. The same general pattern is preserved if sample size is left constant across columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.

The table indicates that both the fiscal-balance and primary-balance targets (shown in first two sets of rows) are missed consistently in all types of program; and, as expected, the forecast errors shrink as the forecast horizon declines. Three results are noteworthy. First, the targets in SBAs were missed by smaller margins than in PRGFs and in the one- and two-year horizons. Second, the targets in SBAs and PRGFs were missed by larger margins than in high-profile SBAs. Third, and finally, the bias in the overall fiscal balance is in the opposite direction in high-profile SBAs, compared with PRGFs and other SBAs for the within-year forecast horizon. That is to say, the actual outcomes on overall fiscal balance in high-profile SBAs were better than the ones programmed the previous year.

Regarding revenue targets and performance, the pattern is unexpected and striking. The actual revenue outcomes—whether measured with or without grants—are consistently better than the programmed targets for all programs and across almost all time horizons. This pattern is unexpected because we have seen that the growth outcomes were far worse than programmed, which should lead us to believe that the revenue performance would be worse than programmed. The second notable feature is that contrary to our expectations, errors in forecasting do not necessarily fall over time when revenues are measured without grants. It almost seems as if programs were made tighter over time when the targets came close to being reached early in their implementation.

The pattern of expenditure (programmed and actual values) is similar to that of the fiscal balance. Actual expenditures were higher than the programmed ones across all types of program. Also, as expected, forecast errors generally became smaller with the shortening of the forecast horizon. The only puzzling result is for high-profile SBAs: the programmed total expenditures were higher than the actuals, though this result did not hold when primary expenditures were considered. It appears that the interest costs were overestimated for the high-profile SBAs—the interest rate spreads turned out to be smaller than expected, perhaps owing to better performance, as we saw earlier, or to the credibility of the IMF programs themselves that IMF staff members did not fully take into account when the programs were designed.

In summary, the fiscal targets appear to have been met more often in the high-profile SBA programs, although, in general, more fiscal targets were achieved in PRGFs than in SBAs. Although it is generally true that the forecasting errors improved as the horizon shortened, this result did not necessarily hold for revenue projections, which did not change very much with the forecast horizon.

Monetary Policy Targets

Table 4 compares the programmed monetary policy targets with the actual outcomes under IMF-supported programs. To analyze adjustments under programs and to facilitate comparisons across countries, we look at the first differences (rather than the actual levels) of broad money, net domestic assets, and net foreign assets. In addition, the absolute values of velocity are compared across program types.

Table 4Monetary Policy Targets in IMF Programs: Program Versus Actual(Percentage of GDP)
Program HorizonDifference

(program minus actual)
Two yearsOne yearWithin-yearActualTwo yearsOne yearWithin-year
Broad money
All program years22.723.423.525.9-3.2-2.5-2.4
PRGFs20.120.219.621.9-1.8-1.7-2.3
SBAs38.437.432.134.34.13.1-2.2
High-profile SBAs41.054.540.336.14.918.44.2
Increase in broad money
All program years3.33.73.65.9-2.6-2.2-2.3
PRGFs2.82.72.64.0-1.2-1.3-1.4
SBAs6.17.26.78.2-2.1-1.0-1.5
High-profile SBAs6.37.36.89.9-3.6-2.6-3.1
Increase in net domestic assets
All program years1.92.12.12.9-1.0-0.8-0.8
PRGFs1.41.41.31.6-0.2-0.2-0.3
SBAs3.34.23.74.8-1.5-0.6-1.1
High-profile SBAs3.85.85.47.7-3.9-1.9-2.3
Increase in net foreign assets
All program years1.41.71.81.9-0.5-0.2-0.1
PRGFs1.31.41.61.9-0.6-0.5-0.3
SBAs1.72.22.01.9-0.20.30.1
High-profile SBAs1.21.30.90.50.70.80.4
Velocity
All program years4.44.34.33.90.50.40.4
PRGFs5.05.05.14.60.40.40.5
SBAs2.62.73.12.9-0.3-0.20.2
High-profile SBAs2.81.82.52.80.0-1.0-0.3
Sources: IMF; authors’ calculations.Notes: Table reports medians by group. The median is a better indicator of the central tendency for monetary variables owing to several outliers in the monetary series. All observations are used for each sample. The same general pattern is preserved if sample size is kept constant across columns. The last three columns report the difference between the program columns and the actual columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.
Sources: IMF; authors’ calculations.Notes: Table reports medians by group. The median is a better indicator of the central tendency for monetary variables owing to several outliers in the monetary series. All observations are used for each sample. The same general pattern is preserved if sample size is kept constant across columns. The last three columns report the difference between the program columns and the actual columns. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.

Several broad patterns emerge in comparing the programmed and actual values of the monetary policy targets. First, targets for broad money and domestic asset growth were generally missed in all types of program. Second, targets for foreign assets were met with greater precision, which is consistent with our earlier finding that external current account objectives are generally met in IMF-supported programs. Third, the errors in forecasting monetary targets were similar across PRGFs and SBAs, but higher for high-profile SBAs.

Interpreting the results on the income velocity of money is not a trivial task. We find that programmed velocity, relative to the realized values, is highest for PRGFs, followed by all SBAs and high-profile SBAs, respectively. In fact, for the high-profile SBAs, the forecasting error (programmed minus actual value) was negative. One interpretation of this result is that IMF-supported programs underestimated the pickup in the demand for money in PRGFs and most SBAs but overestimated the increase in the demand for money in the high-profile SBAs. Another interpretation is that the monetary programs were looser for the high-profile SBAs, compared with the other two types of program.

Were Objectives Less Optimistic and Fiscal Targets Less Tight for High-Profile SBAs?

One stylized fact that emerges from the previous subsections is that fiscal outcomes were closer to targets in high-profile SBAs than in other types of program. This could indicate that either program targets were not ambitious—so that it was easier to attain them—or that programs were designed better, so that outcomes were close to expectations. In this subsection, we examine evidence for the first of these two possible interpretations. Table 5 shows programmed fiscal adjustment by type of program and by type of the fiscal measure. Here, instead of comparing actuals to program values, as we did before, we summarize programmed fiscal effort (measured as the fiscal measure programmed for next year minus this year’s actual outcome). The results are striking and systematic: first, the adjustment planned in all SBAs is always more than in high-profile SBAs. The adjustments programmed for high-profile SBAs, however, are not only always less than for other SBAs but also less than for the PRGFs. In fact, virtually all fiscal targets are relaxed in the within-year horizon in the high-profile SBAs.

Table 5Programmed Fiscal Adjustments, by Program Type(Percentage of GDP)
Programmed Change in Fiscal Measure
SBAs
AllPRGFsAllHigh-profile
Fiscal balance, broadest coverage0.540.550.53-0.84
Primary fiscal balance excluding grants, broadest coverage0.550.410.800.46
Revenue0.530.670.330.02
Revenue excluding grants0.360.360.36-0.95
Expenditure0.100.29-0.19-0.18
Primary expenditure0.070.27-0.21-1.06
Notes: Table entries report the fiscal measure programmed for one year ahead less this year’s actual. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.
Notes: Table entries report the fiscal measure programmed for one year ahead less this year’s actual. PRGFs denotes arrangements under the Poverty Reduction and Growth Facility. SBAs denotes Stand-By Arrangements.

Program Objectives and Intermediate Policies

IMF-supported programs are designed to set policies that are consistent with achieving certain objectives. As part of this exercise, the IMF’s staff produces a “program scenario,” which quantifies the objectives (growth, inflation, and others) and the intermediate policies (fiscal balance, monetary expansion, and others) consistent with these objectives. Our approach to examining the link between intermediate policy targets and objectives is to ask whether achieving the intermediate policy targets helps to achieve program objectives. To address this question, we focus on the deviation of the outcomes from the programmed values (which we will refer to as “projection errors” for lack of a better term).7 For example, the question posed is “does growth fall further short of its programmed value when the growth-consistent policy falls further short of its programmed value?” If there is no such relationship, or the relationship is in the opposite direction, it would cast serious doubt on the validity of the framework underlying program design. Conversely, the empirical relationship may turn out to be in the expected direction yet growth outcomes may still fall systematically short of programmed values even after controlling for the extent to which policy targets are achieved. That would suggest that there are other elements missing in the programming framework and/or that the optimism in setting growth targets is greater than could be justified by policy shortfalls.

We examine the relationship between the growth objective and two types of macro policies: fiscal and monetary.

Fiscal Policy

We start our investigation by recapitulating the statistics presented earlier on the systematic shortfall in growth outcomes compared with the programmed values. The equation shown in Table 6’s second column regresses the projection error in growth on a constant, reflecting the normal approach to examining the extent of bias in a projection. Projection errors are defined as programmed values minus actual values. Such errors can be presented at different time horizons. For the sake of brevity, we present the results with the one-year horizon.8 Thus, the figure in the first column indicates that, on average, actual growth is about 0.9 percentage points less than what was programmed a year earlier.9

Table 6Regressions for Projection Errors in Growth and Fiscal Targets
Dependent Variable
Proj. error in growthProj. error in growthProj. error in growthActual growthProgrammed growthProj. error in growth
Fiscal measure = Broad fiscal balance
Country fixed effectsNoNoYesYesYesYes
Constant0.890***

(0.000)
0.736***

(0.002)
-4.717

(0.233)
4.995

(0.268)
2.734

(0.242)
-2.538

(0.519)
Proj. error in fiscal measure0.251***

(0.000)
0.471***

(0.000)
Actual fiscal measure0.559***

(0.000)
-0.512***

(0.000)
Programmed fiscal measure0.106**

(0.018)
0.431***

(0.000)
Wald test (p-value)0.59
No. of observations R-squared313

0.000
287

0.057
287

0.309
735

0.398
445

0.417
287

0.310
Fiscal measure = Broad primary fiscal balance, excluding grants
Country fixed effectsNoNoYesYesYesYes
Constant0.890***

(0.000)
0.599**

(0.023)
4.439

(0.207)
-10.465**

(0.045)
7.892***

(0.006)
2.849

(0.458)
Proj. error in fiscal measure0.298***

(0.000)
0.276***

(0.009)
Actual fiscal measure0.502***

(0.000)
-0.345***

(0.007)
Programmed fiscal measure0.112**

(0.023)
0.210*

(0.090)
Wald test (p-value)0.33
No. of observations313207207584361207
R-squared0.0000.0610.4300.4530.4440.434
Notes: Projection error is defined as the programmed value minus the realized value. This table presents results for programmed values at the one-year horizon (see text). “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percentage of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent. The Wald test corresponds to the null hypothesis that the sum of the coefficients on the actual and programmed fiscal measure in the last specification equals zero.
Notes: Projection error is defined as the programmed value minus the realized value. This table presents results for programmed values at the one-year horizon (see text). “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percentage of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent. The Wald test corresponds to the null hypothesis that the sum of the coefficients on the actual and programmed fiscal measure in the last specification equals zero.

In the second specification, we regress the projection error in growth on the projection error in the overall fiscal balance:10

where, for any variable x(g and f are growth and the fiscal balance, respectively) for country i, et − s(xt) denotes the projection error based on a projection made s periods ahead and defined as ets(xt)ts0xtxt In our notation, t − sxt denotes the s-period-ahead forecast, and xt simply denotes the outcome for x in period t.

There are two points worth noting in the regression results. First, the coefficient of the projection error on the fiscal balance is consistent with the financial programming framework. That framework implies that with other factors remaining the same, a smaller fiscal deficit creates more room for private sector credit while respecting overall conditions for money growth. To the extent that private sector credit is conducive to financing investment and growth, this is expected to allow a greater expansion of output. The coefficient suggests that a 1 percentage point improvement in the extent to which the fiscal target is met is associated with a ¼of 1 percentage point improvement in the extent to which the growth target is met.

The second notable point is that the growth objective is not met, on average, even after controlling for the extent to which the intermediate policy target is met. This is indicated by the continued statistically significant coefficient on the constant term—the conventional measure of bias. When the programmed fiscal balance is exactly equal to the actual fiscal balance, actual growth performance remains systematically less than programmed, though the magnitude of the shortfall is somewhat less than the unconditional bias when we do not control for the extent to which policy targets are met. Systematically being optimistic in setting growth objectives can have serious consequences for other aspects of program design, particularly for debt dynamics (Helbling, Mody, and Sahay, 2003). Taken together, these two points suggest that although programs get the direction of the framework right, their growth assumptions are more optimistic than can be justified.

In the third data column of Table 6, we allow for country-specific heterogeneity by including a complete set of country fixed effects in the equation. The coefficient on the projection error on the fiscal balance strengthens, suggesting that programs usefully use country-specific information in program design. In terms of bias, in this specification there is one estimated constant per country. The joint test for all country-specific constants being equal to zero is not rejected, suggesting that one constant could have been estimated.11

A potential issue of interpretation in the previous specification is that a relationship estimated in the form of projection errors may be suppressing useful information in the respective relationships between actual growth and actual fiscal balance, and between programmed growth and programmed fiscal balance. The next two specifications in Table 6 essentially unravel this relationship. We first regress actual growth on actual fiscal balance and then do the same for the programmed values:

In each case, we get a significant relationship, although the magnitude is somewhat stronger for the relationship estimated in actuals. We formally test for whether actuals and programmed values can be pooled in the next column, where we regress the projection error in growth on both the actual and programmed levels of the fiscal balance:

If β12 = β and the errors are uncorrelated, we would simply get (1).12Table 6 shows the proximity between the estimated coefficients on β1 and β1. A Wald test for β12 is not rejected, vindicating our original approach.

The measure of fiscal balance we have used so far is the overall balance. There are two potential problems with it. First, to the extent that some revenue consists of fully funded grants—for instance, from official donors—an expansion of the deficit may not crowd out private sector credit and may not adversely affect growth. Hence, a more appropriate measure of fiscal balance in the context of the program framework may be one that excludes grants. Second, it may be more appropriate to look at the primary fiscal balance to more appropriately measure fiscal effort by a country. The bottom panel of Table 6 repeats the above set of specifications for the primary fiscal balance excluding grants. We get thesame pattern, with very similarly sized estimated coefficients, and again the Wald test is not rejected.13

Implicit in the preceding discussion is the assumption that an improvement in the fiscal balance leads to an improvement in growth. In reality, growth outcomes may well affect the realized fiscal balance. In particular, such endogeneity could arise in two forms. First, buoyancy in revenues may yield procyclical movements in the revenue-to-GDP ratio. Second, government spending may react to external shocks to stabilize output. Externally driven slowdowns in growth may cause the government to increase public outlays. Similarly, in good times, the government may let the private sector take the lead and roll back its own spending. We address each of these potential problems in turn.

As a first step toward reducing potential bias in the previously estimated equations, we start by first differencing our data. Hence, we look at how the change in growth is correlated with the change in the fiscal balance. Although this automatically gets rid of country fixed effects, it allows us to additionally control for country-specific trends. Some countries may be on a “good path” with rising growth and fiscal balances. Using first differences and a complete set of country fixed effects allows us to control for such differences among countries. The first two rows of Table 7 show that the previously estimated relationships in levels survive when estimated in first differences, with and without country fixed effects. For example, an improvement of 1 percent of GDP in the fiscal balance is associated with an 0.5 percentage point increase in growth. The next two rows of Table 7 show that this relationship is not validated from the revenue side. There is no relationship between changes in the revenue ratio (including or excluding grants) and changes in growth. Thus, buoyancy is probably not contaminating our results. The last two rows show that the relationship between the fiscal balance and growth emanates from the expenditure side. A 1 percentage point increase in expenditure is associated with about an 0.3 percentage point reduction in growth.

Table 7Regressions for Growth and Fiscal Targets, First Differences
Dependent Variable = First Difference of Growth Rate
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)
Country fixed effectsNoYesNoYesNoYesNoYesNoYesNoYes
Fiscal balance, broadest coverage

(first difference)
0.526***

(0.000)
0.532***

(0.000)
Primary fiscal balance excluding grants, broadest coverage

(first difference)
0.451***

(0.000)
0.458***

(0.000)
Revenue

(first difference)
0.046

(0.658)
0.047

(0.700)
Revenue, excluding grants

(first difference)
0.103

(0.425)
0.106

(0.499)
Expenditure (first difference)-0.319***

(0.000)
-0.312***

(0.000)
Primary expenditure

(first difference)
-0.280***

(0.000)
-0.263***

(0.002)
Constant0.217

(0.425)
0.713

(0.916)
0.180

(0.534)
1.758

(0.786)
0.485

(0.108)
-0.328

(0.960)
0.383

(0.232)
2.306

(0.720)
0.451

(0.121)
1.343

(0.832)
0.488

(0.106)
1.157

(0.855)
No. of observations609609459459407407349349414414385385
R-squared0.0860.2010.0880.1660.0000.0660.0020.0910.0470.1180.0380.126
Notes: The table reports results from regressions of the change in the growth rate on the change in the fiscal measure listed in the first column. “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percent of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.
Notes: The table reports results from regressions of the change in the growth rate on the change in the fiscal measure listed in the first column. “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percent of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.

To test whether expenditure, and hence our fiscal-balance measures, may be reacting to output shocks owing to countercyclical fiscal policy, we present results from instrumental variable regressions in Table 8.14 In this specification, we identify the actual change in the fiscal balance by using the programmed change in the fiscal balance and export growth. Since adjustment programmed one year in advance is predetermined relative to the actual realization of the shock in period t, we think it is a good instrument for identifying the exogenous variation in the actual change in the fiscal balance. In addition, export growth may capture external shocks to which fiscal policy may react. We run this specification both with and without country fixed effects. In each case, we find that the improvement in the fiscal balance, as identified, likely increases growth. We also test whether we should instead have these variables directly in the regression as right-hand-side variables by running a test of overidentifying restrictions. In each case, the test is not rejected, corroborating our approach.

Table 8Instrumental Variable Regressions for Growth and Fiscal Targets, First Differences
Dependent Variable = First Difference of Growth Rate
(1)(2)(3)(4)
Country fixed effectsNoYesNoYes
Fiscal balance, broadest coverage1.274***1.188***
(first difference)(0.000)(0.000)
Primary fiscal balance excluding grants,
broadest coverage0.399***0.418**
(first difference)(0.008)(0.016)
Constant0.261

(0.541)
2.345

(0.723)
0.735*

(0.072)
2.440

(0.688)
Test of overidentifying restrictions (p-value)
Sargan test0.400.280.620.39
Basmann’s test0.400.200.620.48
No. of observations268268199199
R-squared0.1410.0600.272
Notes: The table reports the results from instrumental variable regressions of the change in the growth rate on the change in the fiscal balance measure. The change in the fiscal balance is instrumented with the change in the fiscal balance programmed 1–2 years ago and with export growth. The test of overidentifying restrictions is the test of the joint hypothesis that the instruments are valid and correctly excluded from the estimated equation. A rejection of the test casts doubt on the validity of the instruments. “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percentage of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.
Notes: The table reports the results from instrumental variable regressions of the change in the growth rate on the change in the fiscal balance measure. The change in the fiscal balance is instrumented with the change in the fiscal balance programmed 1–2 years ago and with export growth. The test of overidentifying restrictions is the test of the joint hypothesis that the instruments are valid and correctly excluded from the estimated equation. A rejection of the test casts doubt on the validity of the instruments. “Growth” refers to growth of real GDP in percentage points. Fiscal measures are in percentage of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.

Monetary Policy

We now turn to examining the relationship between growth and monetary policy in the context of IMF-supported programs. The approach we follow is similar to the one we followed for fiscal policy. The key relationship examined is between growth and velocity. An assumption on velocity is one of the first and integral assumptions made as part of program design. After the growth and inflation objectives have been set, an implicit assumption regarding money demand is made by projecting a specific velocity. Alternatively, a money demand function is estimated and an estimate for velocity is then derived. Setting the amount of monetary expansion under the program is key, since it establishes the overall “tightness” of the program. As discussed in the previous section on financial programming, after the monetary growth and the net foreign asset (NFA) targets have been set, the maximum tolerable expansion in net domestic assets is determined as a residual. Programming higher velocity would systematically lead to tighter monetary objectives, which, in turn, with other things held constant, would constrain total credit to the economy and, hence, output.15

Table 9 shows the results of the specifications we ran. One problem we encountered was the significant large volatility in the monetary aggregates typically observed in the early years in the transition countries, when many systemic changes and structural transformations took place. Under such circumstances, money demand was virtually impossible to predict. To be on the safe side, we therefore excluded all transition countries from the regressions in this subsection. Since this exclusion reduces our sample size, we use the within-year horizons in this section to maximize available observations. The first column regresses the projection error in growth on a constant. The second regression adds the projection error in velocity:

where v denotes velocity. The positive estimated coefficient suggests that programming higher velocity drives actual growth performance below the programmed value. The next specification adds a complete set of country fixed effects. Controlling for country-specific heterogeneity strengthens the relationship between the projection errors in velocity and growth. To reduce the scope for contemporaneous correlation between velocity and growth, the next specification lags the projection error in velocity. Although the number of observations drops, the coefficient is still significant at 10 percent. The next specification removes constraints on the coefficients on actual and programmed velocity and shows that the two coefficients are close in magnitude and opposite in sign, as hypothesized. A Wald test for β1= β2 is not rejected, indicating that the regression could be run in terms of projection errors.

Table 9Regressions for Growth and Velocity
Dependent Variable = Programmed Less Actual GDP Growth
(1)(2)(3)(4)(5)(6)
Country fixed effectsNoNoYesYes
Programmed velocity0.398**0.635***0.643***
less actual velocity(0.014)(0.003)(0.002)
Lagged programmed velocity

0.438*
less actual velocity(0.061)
Programmed velocity0.645***
(0.003)
Actual velocity-0.603**
(0.013)
Fiscal balance projection error0.144***
(broadest available measure)(0.010)
Constant0.1380.1851.0334.770**-2.072-1.498
(0.333)(0.168)(0.648)(0.023)(0.413)(0.510)
No. of observations332279279176279275
R-squared0.0000.0210.2590.2940.2590.287
Notes: Projection error is defined as the programmed value minus the realized value. The table presents results for programmed values at the within-year horizon (see text). “Growth” refers to growth of real GDP in percentage points. Fiscal balance is in percent of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.
Notes: Projection error is defined as the programmed value minus the realized value. The table presents results for programmed values at the within-year horizon (see text). “Growth” refers to growth of real GDP in percentage points. Fiscal balance is in percent of GDP. Parentheses report p-values for the estimated coefficients. An asterisk (*) denotes significance at 10 percent, ** at 5 percent, and *** at 1 percent.

The last specification in Table 9 regresses the projection error in growth on both the projection error in velocity and the projection error in the broad fiscal balance. These results suggest that even after controlling for the projection error in the fiscal balance, higher-than-actual programmed velocity depresses growth; and conversely, even after controlling for the tightness of the monetary program, a higher fiscal surplus is associated with greater growth.

Conclusion

In this paper, we have attempted to analyze several aspects of IMF program design. We have documented systematically the relationship between programmed values and outcomes for key program objectives and the intermediate policies designed to achieve them. We find that IMF-supported programs achieve the objectives set for external current account adjustment more frequently than those set for inflation and growth. All three objectives are met simultaneously in about 10 percent of the programs. Likewise, the programmed values on intermediate policy targets on the fiscal and monetary variables were generally more ambitious than those actually achieved in the programs.

Second, we have explored the relationship between errors in growth objectives, on the one hand, and errors in fiscal and monetary policy targets, on the other hand. The evidence suggests that an improvement in the fiscal balance is associated with better growth outcomes, and that programming more ambitious fiscal targets helps to achieve higher growth. Fiscal targets are more often missed than met, however. Recognizing the difficulty in meeting fiscal targets, programs may tend to overcompensate by being tougher on the monetary policy side. Programming tight velocity may protect the country against missing the fiscal objective but does so at the cost of dampening growth.16

Third, we find systematic biases in growth and inflation projections even after controlling for policy implementation.17 To the extent that ambitious objectives are used to spur authorities into action, this may not, in itself, be a problem. To the extent that the bias is more than what could be justified on grounds of inadequate policy implementation, however, there is cause for concern. One example of the costs of getting growth projections wrong is in the context of debt dynamics where IMF-supported programs may predict much lower debt-to-GDP ratios than are actually achieved.

One question we were not able to address is whether, in a constrained world where fiscal targets are likely to be missed, overcompensating by having tighter monetary programs is the best strategy for designing programs to achieve more ambitious objectives. Although a tighter monetary program is likely to entail output costs, it may be necessary to promote fiscal discipline, ensure inflation stability, and restore external current account balance (two other key objectives that we do not explore in greater depth in this paper).

Returning to the broader questions that we began with in this paper, we note that it is indeed the case that IMF-supported programs are ambitious with respect to their objectives and intermediate policy targets. In that sense, both those on the right and those on the left are correct: most program objectives are rarely fully achieved, and fiscal and monetary policy targets are ambitious. On the more interesting question of whether such ambition is defensible, this paper has attempted to substantiate that it is justifiable for the fiscal targets, because it helps achieve higher growth objectives than would otherwise be possible. There is also evidence, however, that growth objectives are ambitious to an extent that exceeds what can be explained by the need to ensure consistency with ambitious intermediate policy targets. The latter can, and have tended to, have unwarranted side effects: when growth is programmed too high, IMF-supported programs end up projecting lower debt-to-GDP ratios than those actually realized.

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1The paper was presented at the IMF’s Annual Research Conference held in Washington on November 4–5, 2004, the proceedings of which are to be published in IMF Staff Papers. The authors are grateful to the conference discussant, Michael Clemens, for his comments; Russell Kincaid, Alessandro Rebucci, and Carlos Végh for helpful discussions; Manzoor Gill for excellent research assistance; and Maansi Sahay Seth for her editorial suggestions.
2Underlying these identities are several behavioral relationships. Depending on data availability, IMF country desk economists estimate relationships—the typical ones include money demand functions, export and import functions, and investment and saving functions.
3Additional performance criteria are often set on structural reforms. These are not derived directly from the financial programming framework but are meant to be consistent with, and support, the policy targets.
4The trade-off with private sector credit would be correspondingly less if the deficit were financed from nonbank or external financing.
5The SDR is an international reserve asset created by the IMF in 1969 to supplement the existing official reserves of member countries. SDRs are allocated to member countries in proportion to their IMF quotas. The SDR also serves as the unit of account of the IMF and some other international organizations. Its value is based on a basket of key international currencies. The SDR equaled roughly US$1.55 in December 2004.
6Of course, these inferences can be drawn only after taking into account exogenous shocks that could not have been anticipated when the program was designed and the targets and objectives were set. We assume that shocks are randomly distributed across the programs.
7As discussed previously, it is not quite right to think of the program numbers as projections in the sense that this term is generally used. Program numbers are best understood as the IMF staff’s projections of outcomes conditional on the member country’s achieving certain policy targets and adequate implementation of other elements of the program.
8While a within-year horizon may be too short for a meaningful test of program design, a two-year horizon may be too long, in that ensuing events can seriously weaken the assumptions on which targets were based. Thus, in general, we focus on the one-year horizon, although we conducted robustness checks for other lengths of horizon. The results for different horizon lengths were generally consistent.
9The slight variations from the summary statistics presented earlier were due to small differences in the sample sizes.
10We use the broadest available measure of the fiscal balance throughout.
11The test may be compromised owing to the limited number of observations per country, however; in this specification, there are, on average, only 3–4 observations for each country. Since time-invariant, country-specific heterogeneity can be an important source of bias—which could contaminate our results—we include a complete set of fixed effects in all subsequent specifications.
12We address issues of endogeneity later in this subsection.
13We repeated these regressions for all possible permutations of the fiscal measures along the following dimensions: level of coverage (central government versus broadest available), treatment of grants (excluded from versus included in revenues), and interest expenditure (excluded from versus included in fiscal balance). We found the same general pattern of results reported previously.
14Kaminsky, Reinhart, and Végh (2004) find that fiscal policy is, in fact, procyclical for non-industrial countries.
15As an alternative, one could also focus on the projection errors in net domestic assets. We found considerable instability in the measures of net domestic assets in our database, however. In part this is due to cases of very high inflation in the sample during which the relationships among monetary aggregates become particularly unstable.
16Tighter monetary programs may be designed to bring down inflation, which may necessarily entail output costs. In this paper, we do not explore the relationship between intermediate policy targets and the inflation objective.
17Our results contrast with those of Musso and Phillips (2002), which does not find statistical bias in growth projections under IMF-supported programs. We note, however, that their sample, consisting of 54 countries, was much smaller than ours.

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