Appendix 1

Table A1.1.

Determinants of the Envisaged and Actual Fiscal Adjustment (T−1 to T+1) in IMF-Supported Programs

Note: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. *, **, and *** denote significance at the 90 percennt, 95 percent, and 99 percent confidence levels, respectively.

Table A1.1.

Determinants of the Envisaged and Actual Fiscal Adjustment (T−1 to T+1) in IMF-Supported Programs

	Envisaged		Actual
	ΔGBAL	ΔGPBAL	ΔGBAL	ΔGPBAL
GBALT−1	−0.4609***		−0.5877***
	(−8.52)		(−6.73)
GPBALT−1		−0.4799***		−0.6094***
		(−6.93)		(−6.03)
CABT−1	0.1186***	0.0874*	0.0600*	0.0886**
	(2.10)	(1.78)	(1.88)	(2.42)
EXPT−1	0.0712***	0.1054***	0.0463	0.1226***
	(2.65)	(4.49)	(1.48)	(3.53)
ΔCABT+1	0.1801***	0.2106***	0.0625*	0.1366**
	(4.12)	(4.63)	(1.81)	(2.43)
GrowthT+1	0.0564	−0.0327	0.2099***	0.1906**
	(0.45)	(−0.21)	(2.84)	(2.37)
Transition	−2.079***	−2.151***	0.8949	−1.0238
	(−3.26)	(−3.87)	(1.16)	(−1.51)
Transition*GBALT−1	−0.2425*	−0.1405	0.1001	0.1949
	(−1.85)	(−1.23)	(0.81)	(1.25)
Constant	−1.5420	−0.5875	−3.4334	−2.6590***
	(−1.60)	(−0.54)	(−5.57)	(−3.44)
N	143	142	166	138
F	21.92	19.59	14.21	11.96
Prob > F	0.0000	0.0000	0.0000	0.0000
R-squared	0.6065	0.5799	0.4310	0.4785
Root MSE	2.189	2.245	2.995	2.964

Note: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. *, **, and *** denote significance at the 90 percennt, 95 percent, and 99 percent confidence levels, respectively.

View Table

Table A1.1.

Determinants of the Envisaged and Actual Fiscal Adjustment (T−1 to T+1) in IMF-Supported Programs

	Envisaged		Actual
	ΔGBAL	ΔGPBAL	ΔGBAL	ΔGPBAL
GBALT−1	−0.4609***		−0.5877***
	(−8.52)		(−6.73)
GPBALT−1		−0.4799***		−0.6094***
		(−6.93)		(−6.03)
CABT−1	0.1186***	0.0874*	0.0600*	0.0886**
	(2.10)	(1.78)	(1.88)	(2.42)
EXPT−1	0.0712***	0.1054***	0.0463	0.1226***
	(2.65)	(4.49)	(1.48)	(3.53)
ΔCABT+1	0.1801***	0.2106***	0.0625*	0.1366**
	(4.12)	(4.63)	(1.81)	(2.43)
GrowthT+1	0.0564	−0.0327	0.2099***	0.1906**
	(0.45)	(−0.21)	(2.84)	(2.37)
Transition	−2.079***	−2.151***	0.8949	−1.0238
	(−3.26)	(−3.87)	(1.16)	(−1.51)
Transition*GBALT−1	−0.2425*	−0.1405	0.1001	0.1949
	(−1.85)	(−1.23)	(0.81)	(1.25)
Constant	−1.5420	−0.5875	−3.4334	−2.6590***
	(−1.60)	(−0.54)	(−5.57)	(−3.44)
N	143	142	166	138
F	21.92	19.59	14.21	11.96
Prob > F	0.0000	0.0000	0.0000	0.0000
R-squared	0.6065	0.5799	0.4310	0.4785
Root MSE	2.189	2.245	2.995	2.964

Note: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. *, **, and *** denote significance at the 90 percennt, 95 percent, and 99 percent confidence levels, respectively.

Table A1.2.

Determinants of the Differences Between Envisaged and Actual Fiscal Adjustment

Notes: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. * = significant at the 99 percent confidence level.Definition of variables∆GBAL^A – ∆GBAL^E : Difference between actual and envisaged changes in the fiscal balance from T–1 to T+1.Growth^A_{T+ 1} – Growth^E_{T+ 1}: Differences between actual and envisaged real GDP growth at year T+1.GBAL^A_{T– 1} – GBAL^E_{T– 1}: Difference in the fiscal balance between the WEO (actual) and MONA (envisaged) databases.Transition: Dummy for transition countries.

Table A1.2.

Determinants of the Differences Between Envisaged and Actual Fiscal Adjustment

	∆GBALA – ∆GBALE
$Growt{h}_{T+1}^A-Growt{h}_{T+1}^E$	0.3017*
	(4.01)
$GBA{L}_{T-1}^A-GBA{L}_{T-1}^E$	−0.4798*
	(−4.20)
Transition	1.3868*
	(2.07)
Constant	−0.9863*
	(−3.24)
N	135
F	12.67
Prob > F	0.0000
R-squared	0.2248
Root MSE	3.10

Notes: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. * = significant at the 99 percent confidence level.Definition of variables∆GBAL^A – ∆GBAL^E : Difference between actual and envisaged changes in the fiscal balance from T–1 to T+1.Growth^A_{T+ 1} – Growth^E_{T+ 1}: Differences between actual and envisaged real GDP growth at year T+1.GBAL^A_{T– 1} – GBAL^E_{T– 1}: Difference in the fiscal balance between the WEO (actual) and MONA (envisaged) databases.Transition: Dummy for transition countries.

View Table

Table A1.2.

Determinants of the Differences Between Envisaged and Actual Fiscal Adjustment

	∆GBALA – ∆GBALE
$Growt{h}_{T+1}^A-Growt{h}_{T+1}^E$	0.3017*
	(4.01)
$GBA{L}_{T-1}^A-GBA{L}_{T-1}^E$	−0.4798*
	(−4.20)
Transition	1.3868*
	(2.07)
Constant	−0.9863*
	(−3.24)
N	135
F	12.67
Prob > F	0.0000
R-squared	0.2248
Root MSE	3.10

Notes: Equation estimated through ordinary least squares with White-corrected (heteroskedasticity-consistent) standard errors. * = significant at the 99 percent confidence level.Definition of variables∆GBAL^A – ∆GBAL^E : Difference between actual and envisaged changes in the fiscal balance from T–1 to T+1.Growth^A_{T+ 1} – Growth^E_{T+ 1}: Differences between actual and envisaged real GDP growth at year T+1.GBAL^A_{T– 1} – GBAL^E_{T– 1}: Difference in the fiscal balance between the WEO (actual) and MONA (envisaged) databases.Transition: Dummy for transition countries.

Appendix 2: Code Book for Assessing the Need for Fiscal Adjustment

Do program documents clearly explain the source of the existing or potential balance of payments problem motivating the program?¹

• “Unsatisfactory”: The program document provides no explicit reference to any existing or impending external imbalance either from a flow or stock type that the program aims to correct or prevent.

• “Marginally satisfactory”: The program document makes some quick reference to an existing or possible external imbalance, but does not provide any detailed discussion of the problem. The reader is therefore unclear about whether there is a balance of payments problem, what the nature of the problem is, and how the program is expected to correct it.

• “Satisfactory”: The program document identifies, discusses, and critically analyzes the sources of the balance of payments problem the IMF-supported program is trying to correct. The document clearly explains the nature of the balance of payments problem calling for IMF involvement and the strategy that the program will follow to tackle it.

• “Highly satisfactory”: In addition to the characteristics under “satisfactory,” the program document would clearly identify whether the external financing gap calling for IMF involvement resulted from a current or capital account deficit and whether it stemmed from the public or private sector.

In light of the above, do documents explain the country-specific mechanism by which the fiscal adjustment will help improve the balance of payments problem (or more generally the problem that called for the Fund’s involvement)?

• “Unsatisfactory”: The program document makes no reference to the country-specific mechanism through which the envisaged fiscal adjustment will assist in solving or preventing the problems associated with the external imbalance.

• “Marginally satisfactory”: The program documents refer to a possible link between fiscal adjustment and the external problems and imbalances mentioned above but provide virtually no discussion of how the mechanism that links the two will operate.

• “Satisfactory”: The program document clearly describes and explains the mechanism through which the envisaged fiscal adjustment is going to contribute to solve or prevent the existing or possible balance of payments problem.

• “Highly satisfactory”: Same as in previous category, but the program either provides a comprehensive analysis of these questions or includes a medium-term assessment of the relationship between these two variables.

Do documents explain the factors determining the pace and magnitude of the fiscal deficit adjustment, in particular its magnitude relative to the envisaged current account adjustment (e.g., fiscal adjustment as a fraction of the total adjustment)?

• “Unsatisfactory”: Program documents do not compare the direction and size of the change in the fiscal and current account balances over the life of the program.

• “Marginally satisfactory”: Program documents make some connection between how the magnitude of the envisaged fiscal adjustment is related to the magnitude of the envisaged current account adjustment, but provide practically no explanation or analysis of the envisaged joint evolution of these variables. Alternatively, a program document that makes no verbal connection between these two indicators but provides a table with information on the evolution of saving and investment balances of both the public and private sector has also been classified here.

• “Satisfactory”: The program document provides a clear sense of the pace of “burden sharing” between adjustment in the private and public sector.

• “Highly satisfactory”: Same as “satisfactory,” but the document also provides an analysis of the factors affecting the likely evolution of the current account, fiscal deficit, and private savings-investment balance, including a medium-term table with disaggregated data on savings and investment of the public and private sector.

If there are other factors influencing the envisaged fiscal deficit adjustment (other than balance of payments considerations), do documents explain clearly how they influence that adjustment?

• “Unsatisfactory”: The program documents do not point out which macroeconomic imbalances or problems, if any, the envisaged fiscal adjustment is expected to correct, or why a reduction of the fiscal deficit under the program is the appropriate economic policy to follow.

• “Marginally satisfactory”: The program documents give some general reasons why the fiscal adjustment might be necessary (high inflation, debt sustainability, and financing problem) but the language is vague and does not analyze the problem with sufficient detail.

• “Satisfactory”: The program documents provide a clear explanation of the objectives of the fiscal adjustment in terms of some well-defined macroeconomic objective (free resources for the private sector, reduce inflation, and bring the public debt to a sustainable path) and the reader is given a good and unequivocal sense of why the fiscal adjustment is necessary.

• “Highly satisfactory”: The document not only provides a good analysis of why the fiscal adjustment is necessary but also a clear explanation of why the precise magnitude of the envisaged adjustment being proposed (and not some other magnitude) is necessary.

Do documents explain the rationale for the composition of the fiscal deficit adjustment? In other words, is there a good explanation of why the adjustment has to be done through revenues or expenditures or a combination of the two?

• “Unsatisfactory”: The program documents provide a list of expenditure and revenue measures associated with the fiscal deficit reduction, but do not explain why the burden of adjustment has to fall on revenues and expenditures; or how the specific share of adjustment revenue and expenditures has been designed.

• “Marginally satisfactory”: The program documents refer to how the adjustment will be effected (including a sense of the envisaged revenue and expenditure changes), but do not provide a clear rationale of why this specific composition between revenue and expenditures is optimal or necessary.

• “Satisfactory”: The program documents provide a clear sense of why the specific composition of the adjustment (between revenue and expenditures) is the appropriate one. It includes indicators of what percentage of GDP specific revenue and expenditure measures are going to yield.

• “Highly satisfactory”: In addition to providing a good explanation of the envisaged composition of the adjustment, the documents provide some analysis of the structure of revenue and expenditure (aimed at identifying major weaknesses in the structure of public finance) and a relatively detailed analysis of how intra-revenue or intra-expenditure changes are going to contribute to the adjustment.

Appendix 3

Table A3.1.

Levels of Grants in a Sample of Sub-Saharan African Countries

Source: Program documents.

Table A3.1.

Levels of Grants in a Sample of Sub-Saharan African Countries

			Foreign Currency Magnitudes
				Projections			Outturns
Country	YearT	Units	T−1	T	T+1	T+2	T	T+1	T+2
Benin	1996	US$ m	84.6	153.5	138.0	99.4	86.8	108.4	73.2
Burkina Faso	1996	US$ m	102.6	131.2	129.0	124.6	159.1	150.2	175.8
Central African Republic	1998	US$ m	48.1	58.5	64.7	53.6	90.0	86.7	46.2
Congo, Republic of	1996	US$ m	21.4	20.6	5.1	5.0	8.4	2.4	6.1
Côte d’Ivoire	1998	US$ m	75.7	79.1	67.6	68.0	85.6	65.0	47.2
Ethiopia	1996/97	SDR m	248.4	244.3	246.6	244.6	163.5	136.9	171.1
Gambia, The	1998	SDR m	3.8	3.9	4.1	4.4	4.4	3.5	3.6
Ghana	1995	US$ m	40.8	148.3	133.5	144.2	233.3	177.7	93.1
Guinea	1997	US$ m	122.0	127.5	123.9	150.5	116.7	103.3	80.1
Kenya	1995/96	US$ m	103.9	130.3	165.1	136.4	101.8	100.2	87.3
Madagascar	1996	SDR m	60.8	88.9	95.9	98.4	115.9	136.5	95.9
Mali	1996	SDR m	121.7	89.0	88.3	85.8	129.9	104.9	93.0
Mauritania	1995	SDR m	26.7	30.5	22.0	16.5	14.1	16.6	6.2
Mozambique	1996	US$ m	399.0	249.0	249.0	248.0	283.0	313.0	313.0
Niger	1996	US$ m	63.1	82.3	96.6	101.2	81.1	84.3	110.7
Rwanda	1998	US$ m	128.6	164.7	130.3	134.1	105.7	115.3	163.5
Senegal	1998	US$ m	71.8	50.5	46.5	42.5	136.6	100.0	89.9
Tanzania	1995/96	US$ m	128.6	174.9	179.7	182.1	254.0	245.1	350.9
Togo	1994	US$ m	3.5	10.3	38.2	54.9	13.7	17.6	7.6
Uganda	1997/98	US$ m	280.0	301.8	299.2	302.9	345.8	298.8	341.1
			In Percent of GDP
				Projections			Outturns
Country	YearT	Units	T−1	T	T+1	T+2	T	T+1	T+2
Benin	1996	US$ m	4.1	6.9	5.8	3.9	3.9	5.0	3.1
Burkina Faso	1996	US$ m	4.4	5.2	4.8	4.3	6.3	6.3	6.8
Central African Republic	1998	US$ m	4.7	5.4	5.7	4.5	8.8	8.3	4.8
Congo, Republic of	1996	US$ m	1.1	1.0	0.2	0.2	0.3	0.1	0.3
Côte d’Ivoire	1998	US$ m	0.7	0.7	0.6	0.5	0.7	0.5	0.4
Ethiopia	1996/97	SDR m	6.3	6.1	5.9	5.4	3.6	2.8	3.6
Gambia, The	1998	SDR m	1.3	1.2	1.2	1.2	1.4	1.1	1.1
Ghana	1995	US$ m	0.8	1.9	1.3	1.2	3.6	2.6	1.4
Guinea	1997	US$ m	3.1	3.2	2.9	3.3	3.1	2.9	2.3
Kenya	1995/96	US$ m	1.4	1.6	1.9	1.4	1.2	1.0	0.8
Madagascar	1996	SDR m	2.9	3.1	3.3	3.4	4.2	5.3	3.5
Mali	1996	SDR m	7.5	4.9	4.6	4.3	7.1	5.7	4.7
Mauritania	1995	SDR m	3.7	4.1	2.8	1.9	2.0	2.2	0.8
Mozambique	1996	US$ m	11.5	8.5	8.2	7.1	8.8	9.3	8.3
Niger	1996	US$ m	3.3	4.1	4.5	4.5	4.1	4.6	5.3
Rwanda	1998	US$ m	6.9	8.0	5.7	5.3	5.3	5.9	9.0
Senegal	1998	US$ m	1.6	1.0	0.9	0.7	3.0	2.1	2.1
Tanzania	1995/96	US$ m	2.5	3.1	3.0	2.9	3.6	3.0	3.9
Togo	1994	US$ m	0.3	1.2	3.8	5.0	1.5	1.4	0.8
Uganda	1997/98	US$ m	4.6	4.4	3.9	3.6	5.8	5.5	6.4

Source: Program documents.

View Table

Table A3.1.

Levels of Grants in a Sample of Sub-Saharan African Countries

			Foreign Currency Magnitudes
				Projections			Outturns
Country	YearT	Units	T−1	T	T+1	T+2	T	T+1	T+2
Benin	1996	US$ m	84.6	153.5	138.0	99.4	86.8	108.4	73.2
Burkina Faso	1996	US$ m	102.6	131.2	129.0	124.6	159.1	150.2	175.8
Central African Republic	1998	US$ m	48.1	58.5	64.7	53.6	90.0	86.7	46.2
Congo, Republic of	1996	US$ m	21.4	20.6	5.1	5.0	8.4	2.4	6.1
Côte d’Ivoire	1998	US$ m	75.7	79.1	67.6	68.0	85.6	65.0	47.2
Ethiopia	1996/97	SDR m	248.4	244.3	246.6	244.6	163.5	136.9	171.1
Gambia, The	1998	SDR m	3.8	3.9	4.1	4.4	4.4	3.5	3.6
Ghana	1995	US$ m	40.8	148.3	133.5	144.2	233.3	177.7	93.1
Guinea	1997	US$ m	122.0	127.5	123.9	150.5	116.7	103.3	80.1
Kenya	1995/96	US$ m	103.9	130.3	165.1	136.4	101.8	100.2	87.3
Madagascar	1996	SDR m	60.8	88.9	95.9	98.4	115.9	136.5	95.9
Mali	1996	SDR m	121.7	89.0	88.3	85.8	129.9	104.9	93.0
Mauritania	1995	SDR m	26.7	30.5	22.0	16.5	14.1	16.6	6.2
Mozambique	1996	US$ m	399.0	249.0	249.0	248.0	283.0	313.0	313.0
Niger	1996	US$ m	63.1	82.3	96.6	101.2	81.1	84.3	110.7
Rwanda	1998	US$ m	128.6	164.7	130.3	134.1	105.7	115.3	163.5
Senegal	1998	US$ m	71.8	50.5	46.5	42.5	136.6	100.0	89.9
Tanzania	1995/96	US$ m	128.6	174.9	179.7	182.1	254.0	245.1	350.9
Togo	1994	US$ m	3.5	10.3	38.2	54.9	13.7	17.6	7.6
Uganda	1997/98	US$ m	280.0	301.8	299.2	302.9	345.8	298.8	341.1
			In Percent of GDP
				Projections			Outturns
Country	YearT	Units	T−1	T	T+1	T+2	T	T+1	T+2
Benin	1996	US$ m	4.1	6.9	5.8	3.9	3.9	5.0	3.1
Burkina Faso	1996	US$ m	4.4	5.2	4.8	4.3	6.3	6.3	6.8
Central African Republic	1998	US$ m	4.7	5.4	5.7	4.5	8.8	8.3	4.8
Congo, Republic of	1996	US$ m	1.1	1.0	0.2	0.2	0.3	0.1	0.3
Côte d’Ivoire	1998	US$ m	0.7	0.7	0.6	0.5	0.7	0.5	0.4
Ethiopia	1996/97	SDR m	6.3	6.1	5.9	5.4	3.6	2.8	3.6
Gambia, The	1998	SDR m	1.3	1.2	1.2	1.2	1.4	1.1	1.1
Ghana	1995	US$ m	0.8	1.9	1.3	1.2	3.6	2.6	1.4
Guinea	1997	US$ m	3.1	3.2	2.9	3.3	3.1	2.9	2.3
Kenya	1995/96	US$ m	1.4	1.6	1.9	1.4	1.2	1.0	0.8
Madagascar	1996	SDR m	2.9	3.1	3.3	3.4	4.2	5.3	3.5
Mali	1996	SDR m	7.5	4.9	4.6	4.3	7.1	5.7	4.7
Mauritania	1995	SDR m	3.7	4.1	2.8	1.9	2.0	2.2	0.8
Mozambique	1996	US$ m	11.5	8.5	8.2	7.1	8.8	9.3	8.3
Niger	1996	US$ m	3.3	4.1	4.5	4.5	4.1	4.6	5.3
Rwanda	1998	US$ m	6.9	8.0	5.7	5.3	5.3	5.9	9.0
Senegal	1998	US$ m	1.6	1.0	0.9	0.7	3.0	2.1	2.1
Tanzania	1995/96	US$ m	2.5	3.1	3.0	2.9	3.6	3.0	3.9
Togo	1994	US$ m	0.3	1.2	3.8	5.0	1.5	1.4	0.8
Uganda	1997/98	US$ m	4.6	4.4	3.9	3.6	5.8	5.5	6.4

Source: Program documents.

Table A3.2.

Changes in Levels of Grants

Source: Program documents.

Table A3.2.

Changes in Levels of Grants

			Percentage Change
			Foreign currency values		In Percent of GDP
Country		YearT	T/(T−1)	(T+2)/ T	T−(T−1)	(T+2)-T
Benin		1996	81.5	−35.2	2.8	−3.0
Burkina Faso		1996	27.9	−5.0	0.8	−0.8
Central African Republic		1998	21.6	−8.3	0.7	−0.9
Congo, Republic of		1996	−3.8	−75.6	−0.1	−0.8
Côte d’Ivoire		1998	4.4	−14.0	0.0	−0.2
Ethiopia		1996/97	−1.7	0.1	−0.3	−0.7
Gambia, The		1998	1.6	14.5	0.0	0.0
Ghana		1995	263.7	−2.8	1.2	−0.7
Guinea		1997	4.5	18.0	0.2	0.1
Kenya		1995/96	25.4	4.6	0.2	−0.2
Madagascar		1996	46.3	10.6	0.3	0.2
Mali		1996	−26.8	−3.6	−2.6	−0.7
Mauritania		1995	14.3	−45.8	0.4	−2.1
Mozambique		1996	−37.6
Niger		1996	30.4	22.9	0.7	0.4
Rwanda		1998	28.0	−18.6	1.1	−2.7
Senegal		1998	−29.7	−15.8	−0.5	−0.3
Tanzania		1995/96	36.0	4.1	0.6	−0.3
Togo		1994	192.3	431.5	0.9	3.8
Uganda		1997/98	7.8	0.4	−0.2	−0.8
Counts
	Increase		15	7	12	4
	No change		0	2	2	1
	Decrease		4	10	5	14

Source: Program documents.

View Table

Table A3.2.

Changes in Levels of Grants

			Percentage Change
			Foreign currency values		In Percent of GDP
Country		YearT	T/(T−1)	(T+2)/ T	T−(T−1)	(T+2)-T
Benin		1996	81.5	−35.2	2.8	−3.0
Burkina Faso		1996	27.9	−5.0	0.8	−0.8
Central African Republic		1998	21.6	−8.3	0.7	−0.9
Congo, Republic of		1996	−3.8	−75.6	−0.1	−0.8
Côte d’Ivoire		1998	4.4	−14.0	0.0	−0.2
Ethiopia		1996/97	−1.7	0.1	−0.3	−0.7
Gambia, The		1998	1.6	14.5	0.0	0.0
Ghana		1995	263.7	−2.8	1.2	−0.7
Guinea		1997	4.5	18.0	0.2	0.1
Kenya		1995/96	25.4	4.6	0.2	−0.2
Madagascar		1996	46.3	10.6	0.3	0.2
Mali		1996	−26.8	−3.6	−2.6	−0.7
Mauritania		1995	14.3	−45.8	0.4	−2.1
Mozambique		1996	−37.6
Niger		1996	30.4	22.9	0.7	0.4
Rwanda		1998	28.0	−18.6	1.1	−2.7
Senegal		1998	−29.7	−15.8	−0.5	−0.3
Tanzania		1995/96	36.0	4.1	0.6	−0.3
Togo		1994	192.3	431.5	0.9	3.8
Uganda		1997/98	7.8	0.4	−0.2	−0.8
Counts
	Increase		15	7	12	4
	No change		0	2	2	1
	Decrease		4	10	5	14

Source: Program documents.

Table A3.3.

Aid Flows Under IMF-Supported Programs, 1995–2001

Sources: Program documents, and IEO staff estimates.

Table A3.3.

Aid Flows Under IMF-Supported Programs, 1995–2001

Panel A. Medium-Term Projections of Aid Flows in ESAF/PRGF-Supported Programs, 1995-2001
(Change between initial and third program year)
Direction and Magnitude of Change			Count	Share of Total (In percent)	Mean Change (In percent of GDP)
Decrease			74	76	−1.1
	By more than 2 percent of GDP		10	10	−3.7
	Between 1 and 2 percent of GDP		17	18	−1.4
	By less than 1 percent of GDP		47	48	−0.5
Increase			23	24	0.6
		Total	97	100

Panel B. Deviation of Outturns from Projected Aid Flows for the First Year of the Program (T)
Direction and Magnitude of Change			Count	Share of Total (In percent)	Mean Projection Shortfall (In percent of GDP)
Projections exceed actuals
	By more than 1 percent of GDP		3	8	2.6
	By less than 1 percent of GDP		17	42	0.6
Projections below actuals
	By less than 1 percent of GDP		12	30	−0.4
	By more than 1 percent of GDP		8	20	−1.4
		Total	40	100

Panel C. Deviations of Outturns from Projected Aid Flows for the Outer Years in a Sample of 20 Sub-Saharan African Countries
(Aid flows measured in U.S. dollars)
			Number of Cases
			T	T+1	T+2
Projected exceeded outturns by more than 20 percent			6	6	9
Projected exceeded outturns by less than 20 percent			2	6	2
Projected below outturns by less than 20 percent			7	6	4
Projected below outturns by more than 20 percent			5	2	5
	Total		20	20	20

Sources: Program documents, and IEO staff estimates.

View Table

Table A3.3.

Aid Flows Under IMF-Supported Programs, 1995–2001

Panel A. Medium-Term Projections of Aid Flows in ESAF/PRGF-Supported Programs, 1995-2001
(Change between initial and third program year)
Direction and Magnitude of Change			Count	Share of Total (In percent)	Mean Change (In percent of GDP)
Decrease			74	76	−1.1
	By more than 2 percent of GDP		10	10	−3.7
	Between 1 and 2 percent of GDP		17	18	−1.4
	By less than 1 percent of GDP		47	48	−0.5
Increase			23	24	0.6
		Total	97	100

Panel B. Deviation of Outturns from Projected Aid Flows for the First Year of the Program (T)
Direction and Magnitude of Change			Count	Share of Total (In percent)	Mean Projection Shortfall (In percent of GDP)
Projections exceed actuals
	By more than 1 percent of GDP		3	8	2.6
	By less than 1 percent of GDP		17	42	0.6
Projections below actuals
	By less than 1 percent of GDP		12	30	−0.4
	By more than 1 percent of GDP		8	20	−1.4
		Total	40	100

Panel C. Deviations of Outturns from Projected Aid Flows for the Outer Years in a Sample of 20 Sub-Saharan African Countries
(Aid flows measured in U.S. dollars)
			Number of Cases
			T	T+1	T+2
Projected exceeded outturns by more than 20 percent			6	6	9
Projected exceeded outturns by less than 20 percent			2	6	2
Projected below outturns by less than 20 percent			7	6	4
Projected below outturns by more than 20 percent			5	2	5
	Total		20	20	20

Sources: Program documents, and IEO staff estimates.

Appendix 4: Explanatory Variables and Methodological Issues in the Analysis of Social Spending in IMF-Supported Programs

In order to appropriately assess the impact of the IMF on social spending using a multivariate regression framework, we need to take into account at least three methodological problems: (1) missing variable bias, (2) serial correlation and nonstationarity, and (3) the endogeneity of IMF-supported programs (for a more extensive discussion of these methodological issues, including an analysis of alternative estimating techniques such as the Generalized Evaluation Estimator, see Martin and Segura-Ubiergo (forthcoming).

To avoid a missing variable bias, the following control variables were defined using data from the World Bank’s World Development Indicators and the IMF’s World Economic Outlook (see Table A4.4 of this appendix for the summary statistics, including means for the “with IMF” and “without IMF” groups). Two other control variables (health_priv and ca_y) had insignificant coefficients and were excluded from the final regressions.

gdpusdpc	=	GDP per capita in U.S. dollars
health_priv	=	private expenditures in health as share of GDP (percent)
pop95young	=	share of the population aged 0-4(percent)
pop95old	=	share of the population 65 years or older (percent)
growth	=	annual rate of real growth (percent)
grw_neg	=	annual rate of growth, when it is negative (= 0 otherwise)
grw_sd	=	variability (standard deviation) on the rate of growth
ca_y	=	current account deficit, share of GDP (percent)
devaluation	=	annual change on the real exchange rate (percent)
democracy	=	index of democracy from Gurr’s Polity III data.1

View Table

gdpusdpc	=	GDP per capita in U.S. dollars
health_priv	=	private expenditures in health as share of GDP (percent)
pop95young	=	share of the population aged 0-4(percent)
pop95old	=	share of the population 65 years or older (percent)
growth	=	annual rate of real growth (percent)
grw_neg	=	annual rate of growth, when it is negative (= 0 otherwise)
grw_sd	=	variability (standard deviation) on the rate of growth
ca_y	=	current account deficit, share of GDP (percent)
devaluation	=	annual change on the real exchange rate (percent)
democracy	=	index of democracy from Gurr’s Polity III data.1

The above control variables explain some of the differences in spending between countries, but there may be residual country differences in spending not captured by them. To account for that, the empirical model was also estimated with country dummies (fixed effects), that is, which allowed for a different level of average spending for each country.

To address the problem of serial correlation and nonstationarity we used a dynamic model that clearly separates short- and medium-term effects. Although there are different models that can serve this purpose, we decided to use an Autoregressive Moving Average process (ARIMA), which seemed to fit the data rather well. A first-order process on the dependent and independent variables was enough to obtain residuals without further detectable serial correlation or unit roots. The following equation was used:

where S_it denotes social spending in country “i” and period “t”, X_it is the vector of exogenous variables defined above, and IMF_it measures the presence of an IMF-supported program as proxied by the instruments defined below. L is the lag operator (i.e., $LZ\equiv Z_{t-1}$ , for any variable Z), D is the first-difference operator ($D{Z}_t\equiv Z_t-Z_{t-1}$ ) and u_it are the residuals.

An alternative and equivalent way of writing (1) is:

where $(1-\gamma )·{\alpha }_2={\alpha }_{1}and(1-\gamma )·{\beta }_2={\beta }_1$. In this specification, changes in the dependent variables, D S_it, can be seen as the result of two effects: contemporaneous change in the explanatory variables (with an impact determined by the coefficients ${\alpha }_{1}and{\beta }_1$) ; and gradual adjustment to an “equilibrium” level of spending, determined by the coefficients ${\alpha }_{2}and{\beta }_2$ . Transitory changes in the independent variables do not change the long-run “equilibrium” level, so that the effect decays geometrically at the rate $(1-\gamma )$ after the second period.

To address the endogeneity issue, the following instruments were used to “predict” the presence of an IMF-supported program:

• current account deficit as fraction of GDP in the previous year (as proxy of external crisis);

• growth in the previous year (proxy of unsustainable expansion);

• income per capita (IMF-supported programs less likely in high-income countries);

• presence of an IMF-supported program in the previous year;

• government balance as share of GDP in the previous year; and

• democracy index (as in the control variables).

To explore the robustness of the result we compared the results with those obtained with alternative estimation methods and with different subsamples of countries (see Table A4.2 and Table A4.3).

Table A4.1.

ARIMA Model with Control Variables and Endogenous IMF-Supported Programs

Note: See the text for variable definitions. An initial L indicates a lagged value and D the first difference. IMF(predicted) is the estimated value of the IMF variable with the following instruments: lagged values of IMF, growth, CA/GDP, government balance/GDP, democracy index, and GDP per capita in U.S. dollars. The actual estimated equation is $\begin{array}{ccccccc}\mathit{IMF}\mathit{\left(}\mathit{predicted}\mathit{\right)}=& 0.148+0.696\mathit{IMF}(-1)& -0.003\mathit{growth}(-1)& +0.001{\mathit{ca}}_{\mathit{-}}\mathit{y}(-1)& +0.001\mathit{cgbal}(-1)& -0.043\mathit{democracy}& -\mathrm{0.011.}\mathit{gdpusdpc}\\ & \begin{array}{c}\left({41.94}^{***}\right)\end{array}\hfill & \begin{array}{c}(-{2.58}^{***})\end{array}\hfill & \begin{array}{c}(-0.69)\end{array}\hfill & \begin{array}{c}\left(0.60\right)\end{array}\hfill & \begin{array}{c}(-{3.26}^{***})\end{array}\hfill & (-{4.85}^{***})\hfill \end{array}$

N=1.916

R²=0.522

Table A4.1.

ARIMA Model with Control Variables and Endogenous IMF-Supported Programs

	Health				Education
	GDP	Total Exp			GDP	Total Exp
	(In percent)		US$ pc	DP pc	(In percent)		US$ pc	DP pc
L. Depend. Var.	0.577***	0.548***	0.748***	0.688***	0.604***	0.559***	0.662***	0.743***
L.IMF(predicted)	0.179***	0.492*	0.390*	4.593	0.251**	0.681*	0.168	4.157
D.IMF(predicted)	0.206***	0.636**	0.395**	9.736***	0.228***	0.748**	0.333	6.027**
L.gdpusdpc	−0.030*	−0.027	0.014	−0.164	0.021	0.070	0.517	1.406
D.gdpusdpc	−0.080***	−0.093	1.101***	−2.761**	−0.034	0.125	2.144***	0.178
L.devaluation	0.002**	0.012***	0.010***	0.109***	−0.001	0.001	0.011***	0.007
D.devaluation	0.001	0.008***	0.005***	0.046*	−0.001	0.000	0.005**	−0.025
L.year	0.011***	0.068***	−0.002	1.219***	0.012*	0.104***	−0.012	0.686***
L.democracy	0.061	0.342	0.221*	2.917	0.142	0.620*	0.114	4.969
D.democracy	0.009	0.308	0.072	1.784	0.035	0.428	0.056	2.852
L.pop95young	−0.031**	−0.015	−0.190	0.059	0.023	0.211***	−0.190	1.593***
L.pop95old	−0.129*	−0.120	−1.980***	−1.528	−0.116	−0.119	−3.745***	3.560
L.growth	0.013*	0.028	0.073**	1.521***	−0.010	−0.047	0.050	0.779***
D.growth	0.005	0.019	0.033	0.895***	−0.021***	−0.035	0.025	0.320
L.grw_neg	−0.049***	−0.060	−0.078*	−1.736***	−0.024	0.022	−0.045	−0.399
D.grw_neg	−0.035**	−0.025	0.000	−1.027**	0.004	0.036	0.060	0.236
L.grw_sd	0.047***	0.000	0.386***	−0.029	0.050**	−0.118	0.955***	−0.831*
Number of obs.	992	1,001	992	992	989	1,001	989	989
R-squared	0.931	0.894	0.985	0.544	0.918	0.881	0.987	0.626
Root MSE	0.408	1.375	1.209	20.569	0.597	1.952	1.761	15.591

Note: See the text for variable definitions. An initial L indicates a lagged value and D the first difference. IMF(predicted) is the estimated value of the IMF variable with the following instruments: lagged values of IMF, growth, CA/GDP, government balance/GDP, democracy index, and GDP per capita in U.S. dollars. The actual estimated equation is $\begin{array}{ccccccc}\mathit{IMF}\mathit{\left(}\mathit{predicted}\mathit{\right)}=& 0.148+0.696\mathit{IMF}(-1)& -0.003\mathit{growth}(-1)& +0.001{\mathit{ca}}_{\mathit{-}}\mathit{y}(-1)& +0.001\mathit{cgbal}(-1)& -0.043\mathit{democracy}& -\mathrm{0.011.}\mathit{gdpusdpc}\\ & \begin{array}{c}\left({41.94}^{***}\right)\end{array}\hfill & \begin{array}{c}(-{2.58}^{***})\end{array}\hfill & \begin{array}{c}(-0.69)\end{array}\hfill & \begin{array}{c}\left(0.60\right)\end{array}\hfill & \begin{array}{c}(-{3.26}^{***})\end{array}\hfill & (-{4.85}^{***})\hfill \end{array}$

N=1.916

R²=0.522

View Table

Table A4.1.

ARIMA Model with Control Variables and Endogenous IMF-Supported Programs

	Health				Education
	GDP	Total Exp			GDP	Total Exp
	(In percent)		US$ pc	DP pc	(In percent)		US$ pc	DP pc
L. Depend. Var.	0.577***	0.548***	0.748***	0.688***	0.604***	0.559***	0.662***	0.743***
L.IMF(predicted)	0.179***	0.492*	0.390*	4.593	0.251**	0.681*	0.168	4.157
D.IMF(predicted)	0.206***	0.636**	0.395**	9.736***	0.228***	0.748**	0.333	6.027**
L.gdpusdpc	−0.030*	−0.027	0.014	−0.164	0.021	0.070	0.517	1.406
D.gdpusdpc	−0.080***	−0.093	1.101***	−2.761**	−0.034	0.125	2.144***	0.178
L.devaluation	0.002**	0.012***	0.010***	0.109***	−0.001	0.001	0.011***	0.007
D.devaluation	0.001	0.008***	0.005***	0.046*	−0.001	0.000	0.005**	−0.025
L.year	0.011***	0.068***	−0.002	1.219***	0.012*	0.104***	−0.012	0.686***
L.democracy	0.061	0.342	0.221*	2.917	0.142	0.620*	0.114	4.969
D.democracy	0.009	0.308	0.072	1.784	0.035	0.428	0.056	2.852
L.pop95young	−0.031**	−0.015	−0.190	0.059	0.023	0.211***	−0.190	1.593***
L.pop95old	−0.129*	−0.120	−1.980***	−1.528	−0.116	−0.119	−3.745***	3.560
L.growth	0.013*	0.028	0.073**	1.521***	−0.010	−0.047	0.050	0.779***
D.growth	0.005	0.019	0.033	0.895***	−0.021***	−0.035	0.025	0.320
L.grw_neg	−0.049***	−0.060	−0.078*	−1.736***	−0.024	0.022	−0.045	−0.399
D.grw_neg	−0.035**	−0.025	0.000	−1.027**	0.004	0.036	0.060	0.236
L.grw_sd	0.047***	0.000	0.386***	−0.029	0.050**	−0.118	0.955***	−0.831*
Number of obs.	992	1,001	992	992	989	1,001	989	989
R-squared	0.931	0.894	0.985	0.544	0.918	0.881	0.987	0.626
Root MSE	0.408	1.375	1.209	20.569	0.597	1.952	1.761	15.591

Note: See the text for variable definitions. An initial L indicates a lagged value and D the first difference. IMF(predicted) is the estimated value of the IMF variable with the following instruments: lagged values of IMF, growth, CA/GDP, government balance/GDP, democracy index, and GDP per capita in U.S. dollars. The actual estimated equation is $\begin{array}{ccccccc}\mathit{IMF}\mathit{\left(}\mathit{predicted}\mathit{\right)}=& 0.148+0.696\mathit{IMF}(-1)& -0.003\mathit{growth}(-1)& +0.001{\mathit{ca}}_{\mathit{-}}\mathit{y}(-1)& +0.001\mathit{cgbal}(-1)& -0.043\mathit{democracy}& -\mathrm{0.011.}\mathit{gdpusdpc}\\ & \begin{array}{c}\left({41.94}^{***}\right)\end{array}\hfill & \begin{array}{c}(-{2.58}^{***})\end{array}\hfill & \begin{array}{c}(-0.69)\end{array}\hfill & \begin{array}{c}\left(0.60\right)\end{array}\hfill & \begin{array}{c}(-{3.26}^{***})\end{array}\hfill & (-{4.85}^{***})\hfill \end{array}$

N=1.916

R²=0.522

Table A4.2.

Summary of Robustness Analysis

Table A4.2.

Summary of Robustness Analysis

		Subsamples According to Total Time Under IMF-Supported Programs During 1985-2000
	S0: Complete Sample (N = 146 countries)	S1: one to five years (N = 53)	S2: one to ten years (N = 88)	S3: five or more years (N = 64)
Time series analysis
R1. Regressions by countries	For most countries no significant difference between years with and without IMF-supported programs. In countries with significant differences it was found that years with programs show lower spending in U.S. dollars, but higher spending measured in domestic prices.	Small number of countries with significant results.	Similar to the overall sample (S0), but with a smaller number of countries with non- significant difference with and without IMF- supported programs.	Significant difference between years with and without IMF- supported programs in half of the countries; among them, when there is an IMF-supported program, half have higher education spending and two-thirds have higher health spending.
Pooled cross-section and time series data
R2. No correction for serial correlation or endogeneity of IMF- supported programs	No significant difference with and without an IMF-supported program, except for health/expend (+) and education per capita in U.S. dollars (−). High level of serial correlation in the residuals.	No significant difference. High level of serial correlation in the residuals.	No significant difference except for education per capita in U.S. dollars (−). High level of serial correlation in the residuals	No significant difference with and without an IMF-supported program, except for health/ expend (+) and education per capita in U.S. dollars (−). High level of serial correlation in the residuals.
R3. No correction for endogeneity of IMF- supported programs	Health: significant positive impact in all definitions. Education: significant positive impact for GDP and domestic prices measures.	Health: no significant effects. Education: positive effect as share of GDP; others no significant effects.	Health: significant positive impact in all definitions. Education: no significant effects.	Health: significant positive impact in all definitions. Education: significant positive impact in all definitions.
R4. Base case. ARIMA model and instrumental var. (Table A4.1)	All 16 coefficients for contemporaneous and lagged effects positive and all but 4 significant.	No significant coefficient.	All 16 coefficients for contemporaneous and lagged effects positive and all but 6 significant.	All 16 coefficients for contemporaneous and lagged effects positive and all but 2 significant; smaller in magnitude than in the base case.
R5. Probit model for IMF- supported programs	All 16 coefficients for contemporaneous and lagged effects positive and all but 3 significant; smaller in magnitude than in the base case.	No significant coefficient.	All 16 coefficients for contemporaneous and lagged effects positive and all but 6 significant; smaller in magnitude than in the base case.	All 16 coefficients for contemporaneous and lagged effects positive and all but 2 significant; smaller in magnitude than in the base case.

View Table

Table A4.2.

Summary of Robustness Analysis

		Subsamples According to Total Time Under IMF-Supported Programs During 1985-2000
	S0: Complete Sample (N = 146 countries)	S1: one to five years (N = 53)	S2: one to ten years (N = 88)	S3: five or more years (N = 64)
Time series analysis
R1. Regressions by countries	For most countries no significant difference between years with and without IMF-supported programs. In countries with significant differences it was found that years with programs show lower spending in U.S. dollars, but higher spending measured in domestic prices.	Small number of countries with significant results.	Similar to the overall sample (S0), but with a smaller number of countries with non- significant difference with and without IMF- supported programs.	Significant difference between years with and without IMF- supported programs in half of the countries; among them, when there is an IMF-supported program, half have higher education spending and two-thirds have higher health spending.
Pooled cross-section and time series data
R2. No correction for serial correlation or endogeneity of IMF- supported programs	No significant difference with and without an IMF-supported program, except for health/expend (+) and education per capita in U.S. dollars (−). High level of serial correlation in the residuals.	No significant difference. High level of serial correlation in the residuals.	No significant difference except for education per capita in U.S. dollars (−). High level of serial correlation in the residuals	No significant difference with and without an IMF-supported program, except for health/ expend (+) and education per capita in U.S. dollars (−). High level of serial correlation in the residuals.
R3. No correction for endogeneity of IMF- supported programs	Health: significant positive impact in all definitions. Education: significant positive impact for GDP and domestic prices measures.	Health: no significant effects. Education: positive effect as share of GDP; others no significant effects.	Health: significant positive impact in all definitions. Education: no significant effects.	Health: significant positive impact in all definitions. Education: significant positive impact in all definitions.
R4. Base case. ARIMA model and instrumental var. (Table A4.1)	All 16 coefficients for contemporaneous and lagged effects positive and all but 4 significant.	No significant coefficient.	All 16 coefficients for contemporaneous and lagged effects positive and all but 6 significant.	All 16 coefficients for contemporaneous and lagged effects positive and all but 2 significant; smaller in magnitude than in the base case.
R5. Probit model for IMF- supported programs	All 16 coefficients for contemporaneous and lagged effects positive and all but 3 significant; smaller in magnitude than in the base case.	No significant coefficient.	All 16 coefficients for contemporaneous and lagged effects positive and all but 6 significant; smaller in magnitude than in the base case.	All 16 coefficients for contemporaneous and lagged effects positive and all but 2 significant; smaller in magnitude than in the base case.

Table A4.3.

Summary of Regression Results

Note: IMF variable measured as proportion of the years under an IMF-supported program. The number of asterisks indicates the significance level for the test that the coefficient is different from zero: *** for 99 percent, ** for 95 percent, and * for 90 percent.

¹Estimate of serial correlation of the regression.

²CONC = Stand-By or EFF programs; NONCONC = SAF, ESAF, or PRGF programs.

Table A4.3.

Summary of Regression Results

		Health				Education
		GDP	Total Exp			GDP	Total Exp
	(In percent)		US$ pc	DP pc	(In percent)		US$ pc	DP pc
R0. Without control variables
IMF		−0.156*	0.170	−5.721***	0.795	−0.440***	0.267	−11.983***	−2.968*
Const		2.27	7.20	9.14	99.74	4.31	14.18	16.27	100.99
R1. Regressions by country
Number of countries where the IMF variable is:
	Signif. Positive	8	13	3	10	7	11	1	8
	Nonsignificant	78	76	83	75	83	76	86	71
	Signif. Negative	7	4	6	7	5	8	6	14
R1a. Regressions by country-with GROWTH as control variable
Number of countries where the IMF variable is:
	Signif. Positive	7	12	2	10	b	10	1	9
	Nonsignificant	80	77	80	76	82	78	84	72
	Signif. Negative	5	3	10	6	4	4	7	11
R2. With control variables and country dummies (fixed effects)
	IMF	0.074	0.355*	0.064	1.793	−0.074	0.090	−0.771***	−2.898
	Est. serial corr1	0.497***	0.329***	0.505***	0.439***	0.574***	0.523***	0.617***	0.651***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.148***	0.512***	0.240*	7.056***	0.112*	0.365	0.087	3.969**
	Delta IMF	0.042	0.224	0.017	2.855	−0.017	−0.072	−0.095	1.352
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.179***	0.492*	0.390*	4.593	0.251**	0.681*	0.168	4.157
	Delta IMF(pred)	0.206***	0.636**	0.395**	9.736***	0.228***	0.748**	0.333	6.027**
R4b. With limited dependent model for endogenous IMF-supported programs (Tobit model;ARIMA, fixed effects)
	Lagged IMF(pred)	0.058***	0.159*	0.131*	1.488	0.083***	0.223*	0.061	1.398
	Delta IMF(pred)	0.065***	0.198**	0.126**	3.071***	0.073***	0.237**	0.116*	1.993**
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.042***	0.115*	0.096*	1.079	0.061***	0.161**	0.046	1.020
	Delta IMF(pred)	0.047***	0.142**	0.091**	2.216***	0.053***	0.171**	0.087*	1.450**
R5. With concessionary/nonconcessionary IMF-supported programs (instrumental variables, ARIMA, fixed effects)2
	Lagged CONC(pred)	0.506***	1.083*	1.804***	14.476**	0.382**	0.837	0.704	5.194
	Delta CONC(pred)	0.274**	0.638	0.798**	9.328**	0.251	0.936*	0.520	4.096
	Lagged NONCONC(pred)	0.060	0.270	0.099	1.545	0.042	0.327	−0.006	2.317
	Delta NONCONC(pred)	0.195**	0.739**	0.073	11.477*	0.036	0.091	−0.032	4.746
	F-test of CONC = NONCONC	3.44**	1.22	4.52**	2.05	1.13	0.59	0.92	0.25
Sample 1. At least 1 year of IMF-supported program but not more than 6 years (53 countries)
R0. Without control variables
IMF		0.095	−0.533	−1.626*	−9.342***	−0.075	−1.636***	−4.535***	−10.641***
_cons		2.20	7.29	5.52	102.76	4.30	15.31	10.32	103.20
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	3	3	1	2	2	2	0	3
	Nonsignificant	34	36	36	33	38	37	37	30
	Signif. Negative	3	1	2	4	1	2	3	7
R2. With control variables and country dummies (fixed effects)
	IMF	0.092	0.152	0.242	−1.932	0.083	−0.083	−0.406	−1.623
	Est. serial corr. coeff.	0.695***	0.663***	0.786***	0.751***	0.788***	0.851***	0.868***	0.837***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.103	−0.066	0.151	1.862	0.184*	−0.285	0.047	3.520
	Delta IMF	−0.052	−0.270	−0.134	−2.533	0.026	−0.539	−0.232	1.432
R4. [Base case] With instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.121	0.193	0.119	−3.355	0.143	0.063	−0.270	−1.388
	Delta IMF(pred)	0.190	0.097	0.399	4.375	0.124	−0.031	0.185	2.390
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.030	0.047	0.033	−0.631	0.034	0.015	−0.052	−0.269
	Delta IMF(pred)	0.038	0.008	0.090	0.795	0.026	−0.010	0.049	0.623
Sample 2. At least 1 year of IMF-supported program but not more than 10 years (88 countries)
R0. Without control variables
IMF		0.135	0.260	−1.155**	−1.491	−0.140	−0.609	−3.503***	−6.430***
const		2.12	7.22	5.13	100.60	4.11	14.72	8.99	102.62
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	7	10	2	8	4	9	0	6
	Nonsignificant	62	62	66	60	67	61	68	56
	Signif. Negative	5	2	5	5	4	5	6	12
R2. With control variables and country dummies (fixed effects)
IMF		0.049	0.229	0.048	−0.102	−0.077	0.085	−0.554***	−3.032
Est. serial corr. coeff.		0.598***	0.681***	0.833***	0.776***	0.711***	0.911***	0.897***	0.837***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.138***	0.439**	0.247**	5.508**	0.088	0.295	0.033	2.887
	Delta IMF	0.001	0.093	−0.049	0.427	−0.020	−0.021	−0.133	0.868
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
Lagged IMF(pred)		0.263***	0.627**	0.537**	6.737	0.264**	0.656	0.049	3.685
Delta IMF(pred)		0.269***	0.764**	0.452*	11.058***	0.199*	0.653	0.193	5.053*
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
Lagged IMF(pred)		0.062***	0.147**	0.125**	1.612	0.064**	0.162	0.016	0.917
Delta IMF(pred)		0.061***	0.172**	0.102*	2.520**	0.047*	0.156	0.048	1.236*
Sample 3. Five or more years of IMF-supported program (64 countries)
R0. Without control variables
IMF		0.184	0.888**	−0.022	6.196**	0.031	0.742*	−0.692	−0.662
	_cons	1.96	6.67	3.96	96.46	3.87	13.84	6.05	100.38
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	7	12	2	8	4	9	1	5
	Nonsignificant	47	43	53	46	51	45	55	48
	Signif. Negative	4	3	3	4	4	5	3	6
R2. With control variables and country dummies (fixed effects)
IMF		0.105	0.467*	0.031	3.373	−0.030	0.324	−0.544***	−0.953
	Est. serial corr. coeff.	0.395***	0.717***	0.926***	0.817***	0.734***	0.939***	0.928***	0.905***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.168***	0.702***	0.276**	8.919***	0.163**	0.662**	0.252**	5.862***
	Delta IMF	0.085*	0.435**	0.098	5.058**	0.019	0.115	0.057	1.742
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.225**	0.730*	0.542**	7.521	0.392***	1.339***	0.493***	9.584**
	Delta IMF(pred)	0.235***	0.826**	0.321	12.823***	0.382***	1.123***	0.386*	10.523***
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.056**	0.180*	0.136**	1.832	0.097***	0.333***	0.123***	2.353**
	Delta IMF(pred)	0.058***	0.205**	0.078	3.214***	0.096***	0.281***	0.097**	2.650***

Note: IMF variable measured as proportion of the years under an IMF-supported program. The number of asterisks indicates the significance level for the test that the coefficient is different from zero: *** for 99 percent, ** for 95 percent, and * for 90 percent.

¹Estimate of serial correlation of the regression.

²CONC = Stand-By or EFF programs; NONCONC = SAF, ESAF, or PRGF programs.

View Table

Table A4.3.

Summary of Regression Results

		Health				Education
		GDP	Total Exp			GDP	Total Exp
	(In percent)		US$ pc	DP pc	(In percent)		US$ pc	DP pc
R0. Without control variables
IMF		−0.156*	0.170	−5.721***	0.795	−0.440***	0.267	−11.983***	−2.968*
Const		2.27	7.20	9.14	99.74	4.31	14.18	16.27	100.99
R1. Regressions by country
Number of countries where the IMF variable is:
	Signif. Positive	8	13	3	10	7	11	1	8
	Nonsignificant	78	76	83	75	83	76	86	71
	Signif. Negative	7	4	6	7	5	8	6	14
R1a. Regressions by country-with GROWTH as control variable
Number of countries where the IMF variable is:
	Signif. Positive	7	12	2	10	b	10	1	9
	Nonsignificant	80	77	80	76	82	78	84	72
	Signif. Negative	5	3	10	6	4	4	7	11
R2. With control variables and country dummies (fixed effects)
	IMF	0.074	0.355*	0.064	1.793	−0.074	0.090	−0.771***	−2.898
	Est. serial corr1	0.497***	0.329***	0.505***	0.439***	0.574***	0.523***	0.617***	0.651***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.148***	0.512***	0.240*	7.056***	0.112*	0.365	0.087	3.969**
	Delta IMF	0.042	0.224	0.017	2.855	−0.017	−0.072	−0.095	1.352
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.179***	0.492*	0.390*	4.593	0.251**	0.681*	0.168	4.157
	Delta IMF(pred)	0.206***	0.636**	0.395**	9.736***	0.228***	0.748**	0.333	6.027**
R4b. With limited dependent model for endogenous IMF-supported programs (Tobit model;ARIMA, fixed effects)
	Lagged IMF(pred)	0.058***	0.159*	0.131*	1.488	0.083***	0.223*	0.061	1.398
	Delta IMF(pred)	0.065***	0.198**	0.126**	3.071***	0.073***	0.237**	0.116*	1.993**
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.042***	0.115*	0.096*	1.079	0.061***	0.161**	0.046	1.020
	Delta IMF(pred)	0.047***	0.142**	0.091**	2.216***	0.053***	0.171**	0.087*	1.450**
R5. With concessionary/nonconcessionary IMF-supported programs (instrumental variables, ARIMA, fixed effects)2
	Lagged CONC(pred)	0.506***	1.083*	1.804***	14.476**	0.382**	0.837	0.704	5.194
	Delta CONC(pred)	0.274**	0.638	0.798**	9.328**	0.251	0.936*	0.520	4.096
	Lagged NONCONC(pred)	0.060	0.270	0.099	1.545	0.042	0.327	−0.006	2.317
	Delta NONCONC(pred)	0.195**	0.739**	0.073	11.477*	0.036	0.091	−0.032	4.746
	F-test of CONC = NONCONC	3.44**	1.22	4.52**	2.05	1.13	0.59	0.92	0.25
Sample 1. At least 1 year of IMF-supported program but not more than 6 years (53 countries)
R0. Without control variables
IMF		0.095	−0.533	−1.626*	−9.342***	−0.075	−1.636***	−4.535***	−10.641***
_cons		2.20	7.29	5.52	102.76	4.30	15.31	10.32	103.20
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	3	3	1	2	2	2	0	3
	Nonsignificant	34	36	36	33	38	37	37	30
	Signif. Negative	3	1	2	4	1	2	3	7
R2. With control variables and country dummies (fixed effects)
	IMF	0.092	0.152	0.242	−1.932	0.083	−0.083	−0.406	−1.623
	Est. serial corr. coeff.	0.695***	0.663***	0.786***	0.751***	0.788***	0.851***	0.868***	0.837***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.103	−0.066	0.151	1.862	0.184*	−0.285	0.047	3.520
	Delta IMF	−0.052	−0.270	−0.134	−2.533	0.026	−0.539	−0.232	1.432
R4. [Base case] With instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.121	0.193	0.119	−3.355	0.143	0.063	−0.270	−1.388
	Delta IMF(pred)	0.190	0.097	0.399	4.375	0.124	−0.031	0.185	2.390
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.030	0.047	0.033	−0.631	0.034	0.015	−0.052	−0.269
	Delta IMF(pred)	0.038	0.008	0.090	0.795	0.026	−0.010	0.049	0.623
Sample 2. At least 1 year of IMF-supported program but not more than 10 years (88 countries)
R0. Without control variables
IMF		0.135	0.260	−1.155**	−1.491	−0.140	−0.609	−3.503***	−6.430***
const		2.12	7.22	5.13	100.60	4.11	14.72	8.99	102.62
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	7	10	2	8	4	9	0	6
	Nonsignificant	62	62	66	60	67	61	68	56
	Signif. Negative	5	2	5	5	4	5	6	12
R2. With control variables and country dummies (fixed effects)
IMF		0.049	0.229	0.048	−0.102	−0.077	0.085	−0.554***	−3.032
Est. serial corr. coeff.		0.598***	0.681***	0.833***	0.776***	0.711***	0.911***	0.897***	0.837***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.138***	0.439**	0.247**	5.508**	0.088	0.295	0.033	2.887
	Delta IMF	0.001	0.093	−0.049	0.427	−0.020	−0.021	−0.133	0.868
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
Lagged IMF(pred)		0.263***	0.627**	0.537**	6.737	0.264**	0.656	0.049	3.685
Delta IMF(pred)		0.269***	0.764**	0.452*	11.058***	0.199*	0.653	0.193	5.053*
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
Lagged IMF(pred)		0.062***	0.147**	0.125**	1.612	0.064**	0.162	0.016	0.917
Delta IMF(pred)		0.061***	0.172**	0.102*	2.520**	0.047*	0.156	0.048	1.236*
Sample 3. Five or more years of IMF-supported program (64 countries)
R0. Without control variables
IMF		0.184	0.888**	−0.022	6.196**	0.031	0.742*	−0.692	−0.662
	_cons	1.96	6.67	3.96	96.46	3.87	13.84	6.05	100.38
R1. Regressions by country. Number of countries where the IMF variable is:
	Signif. Positive	7	12	2	8	4	9	1	5
	Nonsignificant	47	43	53	46	51	45	55	48
	Signif. Negative	4	3	3	4	4	5	3	6
R2. With control variables and country dummies (fixed effects)
IMF		0.105	0.467*	0.031	3.373	−0.030	0.324	−0.544***	−0.953
	Est. serial corr. coeff.	0.395***	0.717***	0.926***	0.817***	0.734***	0.939***	0.928***	0.905***
R3. With correction for serial correlation (ARIMA, fixed effects)
	Lagged IMF	0.168***	0.702***	0.276**	8.919***	0.163**	0.662**	0.252**	5.862***
	Delta IMF	0.085*	0.435**	0.098	5.058**	0.019	0.115	0.057	1.742
R4. [Base case] with instrumental variables for IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.225**	0.730*	0.542**	7.521	0.392***	1.339***	0.493***	9.584**
	Delta IMF(pred)	0.235***	0.826**	0.321	12.823***	0.382***	1.123***	0.386*	10.523***
R4c. With PROBIT model for endogenous IMF-supported programs (ARIMA, fixed effects)
	Lagged IMF(pred)	0.056**	0.180*	0.136**	1.832	0.097***	0.333***	0.123***	2.353**
	Delta IMF(pred)	0.058***	0.205**	0.078	3.214***	0.096***	0.281***	0.097**	2.650***

Note: IMF variable measured as proportion of the years under an IMF-supported program. The number of asterisks indicates the significance level for the test that the coefficient is different from zero: *** for 99 percent, ** for 95 percent, and * for 90 percent.

¹Estimate of serial correlation of the regression.

²CONC = Stand-By or EFF programs; NONCONC = SAF, ESAF, or PRGF programs.