Aid and fiscal policy in Nicaragua: A fiscal response analysis

Journal of International Development

J. Int. Dev. 22, 483–502 (2010)

Published online 14 April 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/jid.1584

AID AND FISCAL POLICY IN NICARAGUA:A FISCAL RESPONSE ANALYSIS

ROBERTO MACHADO*

Economic Commission for Latin America and the Caribbean, Subregional Headquarters

for the Caribbean, Port of Spain, Trinidad and Tobago

Abstract: This paper estimates a model of fiscal response to analyse the impact of aid on

government consumption and investment, tax revenue and public borrowing in Nicaragua in

1966–2004. This country is an interesting case study since aid flows—i.e. grants and aid

loans—averaged more than 8 per cent of GDP during the analysed period. Results for direct

(structural) effects indicate that the impact of aid on government consumption are more

significant than those on government investment, revealing a higher propensity to consume

than to invest aid flows, presumably reflecting donors and government priorities to finance

social spending. Results also show that aid crowds-out both tax revenue and public borrowing.

Estimates for total (reduced-form) effects are hard to interpret, in some cases showing the

opposite sign than expected or implausible magnitudes. Copyright # 2009 John Wiley &

Sons, Ltd.

Keywords: aid; fiscal response; fiscal policy; Nicaragua

1 INTRODUCTION

The aim of foreign aid is to help developing countries to foster economic growth and to

overcome poverty. But as aid is mainly channelled to the domestic economy through the

public sector, its final effects on growth and poverty will crucially depend on how it

influences fiscal policy: Does aid complement or substitute for tax revenue? Does it mainly

finance public consumption or investment? Does it stimulate or inhibit public borrowing?

As to the first question, aid may stimulate tax revenue when it is given conditional to

domestic counterpart; but it also may deter the tax effort when its availability is taken for

granted by the government. On the other hand, the long-run effects of public expenditure

depend on, among other things, whether it is devoted to consumption or investment.

Therefore, the allocation of aid between the two is relevant. Finally, as aid is another source

*Correspondence to: Roberto Machado, Economic Affairs Officer, Sub-regional Headquarters for the Caribbean,Economic Commission for Latin America and the Caribbean, 1 Chancery Lane, Port of Spain, Trinidad andTobago. E-mail: [email protected]

Copyright # 2009 John Wiley & Sons, Ltd.

484 R. Machado

of fiscal revenue, it would reduce borrowing; but if it can be used as collateral, it could

increase public debt. Although it would be expected that aid crowds-in all expenditure

categories and crowds-out other sources of revenue, it is an issue that needs to be addressed

on empirical grounds.

This paper analyses these affairs in Nicaragua during the last decades. The topic is

relevant as this country is a prominent recipient of foreign aid in the developing world.

Indeed, while official development assistance and official aid (ODA) to low and middle

income countries was between 1 and 1.7 per cent of GNI in 1971–2006—and below

1 per cent of GNI in Latin America and the Caribbean—, the annual average of ODA to

Nicaragua was almost 4 per cent of GNI during the 1970s, near 11 per cent during the

1980s, and just below 30 per cent during the 1990s. In 2001–2006, ODA flows to Nicaragua

amounted to 19.6 per cent of GNI.

The Section 2 shows the behaviour of aid and fiscal revenue and expenditure. The

Section 3 sets out the fiscal response model to analyse the fiscal effects of aid. The Section 4

addresses the methodology and Section 5 presents and discusses the econometric results.

The Section 6 concludes.

2 AID AND FISCAL BEHAVIOUR IN NICARAGUA

Figure 1 shows that tax and other recurrent revenue was above government consumption

from 1960 to 1978, fluctuating around 10.7 and 7.6 per cent of GDP, respectively. This

behaviour changed in 1979—the year of the triumph of the Sandinista revolution—when

government consumption started exceeding tax revenue, especially during the second half

of the 1980s. During this period, government consumption averaged some 40 per cent of

GDP, whereas tax revenue amounted to less than 28 per cent of output. This financial gap

was largely financed by public borrowing that averaged almost 19 per cent of GDP during

1984–1988, as shown in Figure 2.

The impressive increase in government consumption during the 1980s is largely

explained by the growth in military expenses that reached some 28 per cent of GDP in

1989. Military outlays contracted significantly with the return to democracy and the end of

civil war in 1990. By 1996, these totalled just 1.5 per cent of GDP. This trend was reversed

Figure 1. Tax revenue, government consumption and government investment, 1960–2004, (per-centage of GDP in cordovas at 1980 prices)). This figure is available in colour online at www.

interscience.wiley.com/journal/jid

Copyright # 2009 John Wiley & Sons, Ltd. J. Int. Dev. 22, 483–502 (2010)

DOI: 10.1002/jid

-30

-20

-10

0

10

20

30

40

1965

1970

1975

1980

1985

1990

1995

2000

2004

AID/GDP BORR/GDP

Figure 2. Aid and public borrowing, 1965–2004, (percentage of GDP in cordovas at 1980 prices).This figure is available in colour online at www.interscience.wiley.com/journal/jid

Aid and Fiscal Policy in Nicaragua 485

during most of the 1990s, when tax and other recurrent revenue again surpassed

government consumption at lower levels relative to GDP than the 1980s but higher than the

1960s and 1970s, fluctuating between 19 and 18 per cent of GDP since 1992, respectively.

Government investment has been below 10 per cent of GDP over most of the period. Its

evolution has closely followed the ups and downs experienced by tax revenue thus

revealing a pro-cyclical behaviour.

On the other hand, aid to the Nicaraguan government—i.e. grants and aid loans—was

relatively modest until the 1990s, fluctuating below 4 per cent of GDP during the

Sandinista regime in the 1980s (see Figure 2). In 1990, the return to democracy

brought about a pronounced hike in aid disbursed to the government that reached more than

33 per cent of GDP. Thereafter, aid has remained at lower but still very significant levels,

averaging more than 11 per cent of GDP since 1995.

Broadly speaking, it is possible to identify three different periods in public spending and

revenue in Nicaragua since the 1960s. First, up to the final years of the Somoza regime prior

to the inauguration of the Sandinista regime in 1979, tax and other recurrent revenue

financed most government consumption and investment. The financing gap was covered by

aid. During this period, government total expenditure averaged near 13 per cent of GDP

(9 per cent of GDP in consumption and 4 per cent in investment), while tax and other

recurrent revenue amounted to near 11 per cent of output. The second period comprises the

last years of the Somoza regime and the Sandinista rule during the 1980s, which witnessed

a pronounced increase in government spending, particularly consumption that averaged

36.6 per cent of GDP. Tax and other recurrent revenue also increased in this period,

totalling some 25.4 per cent of GDP. However, unlike the previous period, the financing gap

was mainly covered by public borrowing, that averaged 11.6 per cent of GDP in 1980–

1989. Meanwhile, aid flows to Nicaragua amounted to 3.2 per cent of GDP during the

1980s, an increase of 1.2 percentage points from its annual average during 1965–1979.

This was due to the assistance of the former Soviet Union that disbursed significant

amounts of aid to the country that more than offset the reduction or cut of aid coming from

western countries, particularly the United States. The last period starts in 1990 with the

return to democracy and the end of the Sandinista regime. This period is characterised by a

drop in both revenue and spending, that nevertheless remained at higher levels that in the

1960s and 1970s. As in the 1960s and 1970s, since 1990 the financing gap has been covered

by grants and aid loans that averaged some 15 per cent of GDP during 1990–2004.


DOI: 10.1002/jid

486 R. Machado

3 THE FISCAL RESPONSE MODEL

The model starts with the utility function of the government:

U ¼ UðGI;GC;TAX;AID;BORRÞ (1)

where GI is government investment, GC is government consumption, TAX is tax and other

recurrent revenue, AID is aid disbursed to the government and BORR is public borrowing.

The five are endogenous choice variables.

This formulation assumes that aid is endogenous. Making aid endogenous does not mean

that the recipient country controls aid made available to the recipient by the donor, but the

amount actually disbursed and spent. As noted by McGillivray and Morrissey (2001), the

justification for treating aid as exogenous is that it is determined by donors only on the basis

of supply-side criteria. However, in practice each year donors set an amount for each

recipient who ultimately decides howmuch is actually disbursed and spent.1 This approach

is followed by Franco-Rodriguez et al. (1998), McGillivray and Ahmed (1999) and

Franco-Rodriguez (2000).

More recently, McGillivray and Ouattara (2005) and Ouattara (2006) have also treated

aid as endogenous. However, as the interest of these two papers is to analyse the interaction

between aid, debt and debt servicing within a fiscal response framework, government

expenditure is disaggregated in debt servicing payments and non-debt servicing spending

instead as in consumption and investment as in the papers mentioned in the previous

paragraph. Therefore their results are not comparable with those found in this paper that is

interested in analysing the impact of aid on government consumption and investment.

It is assumed that each period the government sets targets for each of the five endogenous

variables in its utility function and tries to reach them. If it reaches all targets, then the

utility is maximised. Whenever it fails to reach any of them, the utility diminishes.

This can be represented by a quadratic loss function, which is the formulation used by

Franco-Rodriguez et al. (1998) and Franco-Rodriguez (2000):

U ¼ a0 � a12

GI� GI�ð Þ2� a22

GC� GC�ð Þ2� a32

TAX� TAX�ð Þ2

� a42

AID� AID�ð Þ2� a52

BORR� BORR�ð Þ2(2)

where the asterisks denote exogenous target values of the endogenous variables in

Equation (1), and the a’s are strictly positive. Thus, a0 is the maximum utility, and it is

achieved when all targets are met. The quadratic nature of Equation (2) implies that the

government’s utility function is symmetric in the sense that it is reduced in the same way

independently if it overshoots or undershoots its targets. This could seem restrictive, but

this is not necessarily the case. For instance, if TAX falls short of its target value, then the

government will not have enough resources to finance expenditures or will run a fiscal

deficit. But if tax revenue overshoots its target, then the government will carry the political

costs of collecting taxes or will inhibit private investment. On the other hand, if GC

surpasses GC�, then it might be financing more social programs, but it also might be

accumulating debt or fuelling inflation. In any case, Feeny (2006) introduces a utility

1Prior studies such as Heller (1975) and Gang and Khan (1991) assume that aid is exogenous, so that the fifthterm on the right-hand side of Equation (2) vanishes. Treating aid as exogenous raises several problems. For areview of the different approaches to fiscal response to aid see McGillivray and Morrissey (2001 and 2004).


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function with asymmetric preferences where overshooting expenditure targets (either

consumption or investment) results in less of loss in utility to the government than

undershooting them and where the opposite is true for revenue targets (taxes, aid or

borrowing). His main conclusion is that fiscal response models with asymmetric

preferences provide the same results and conclusions to those based on a utility function

with symmetric preferences as Equation (2). Therefore, this paper follows the symmetric

preferences approach applied in the fiscal response literature.

The government faces a budget constraint that equals total expenditure to total revenue

GIþ GC ¼ TAXþ AIDþ BORR (3)

Maximising Equation (2) subject to Equation (3) alone would imply that GI� and GC�

can always be met provided there is enough revenue. This would imply that there are no

restrictions in the allocation of revenue between consumption and investment, i.e. that

there is total fungibility of revenue, including aid, which is unrealistic. Therefore, another

restriction is imposed limiting the amount of each type of revenue that can be consumed:

GC � r1TAX þ r2AIDþ r3BORR (4)

where the r’s are the maximum share of each category of revenue that can be devoted to

consumption. These parameters are imposed to the government externally by tax payers,

donors and lenders, respectively. For instance, the donors may want the government to

allocate all aid to investment, but as the government can divert some part to consumption,

r2> 0. But the government cannot devote aid to consumption at its complete discretion,

because the donors have some control over it. Thus, the value of r2 is imposed on the

government externally by the donors in the sense that their actions limit the discretion of

the government over the allocation of aid.

Whenever Equation (4) is not binding, then Equation (2) is maximised solely subject to

Equation (3) and a0 can be achieved. Nevertheless, if it is binding, then a0 cannot be

reached because at least one category of expenditure does not meet its target. The analysis

below assumes that the latter is the case.

Thus, Equation (2) is maximised subject to Equations (3) and (4), which gives the

Lagrangean

‘ ¼ a0 � a12

GI� GI�ð Þ2� a22

GC� GC�ð Þ2� a32

TAX � TAX�ð Þ2

� a42

AID� AID�ð Þ2� a52

BORR� BORR�ð Þ2þl1 GIþ GC� TAX� AID� BORRð Þþl2 GC� r1TAX� r2AID� r3BORRð Þ

(5)

Assuming that Equation (4) is binding, then l2> 0 and a0 cannot be achieved.

Combining the first order conditions the following structural equations are obtained:

GI ¼ g1GI� þ g2GC

� þ g3TAX� þ g4AIDþ g5BORR (6)

GC ¼ g6GI� þ g7GC


TAX ¼ b1GI� þ b2GC


AID ¼ b3GI� þ b4GC

� þ g14TAXþ g15AID� þ g16BORR (9)

BORR ¼ b5GI� þ b6GC

� þ g17TAXþ g18AIDþ g19BORR� (10)


DOI: 10.1002/jid

488 R. Machado

where the g’s are nonlinear combinations of the b’s and the r’s, and the b’s are nonlinear

combinations of the r’s and the a’s (see Appendix A).

The system of structural Equations (6)–(10) expresses each endogenous variable (GI,

GC, TAX, AID, BORR) in terms of both endogenous and exogenous variables (GI�, GC�,

TAX�, AID�, BORR�). Each parameter of the structural model measures the direct impact

of a marginal change in a variable (exogenous or endogenous) on an endogenous one.

However, as the endogenous variables also have effects on other endogenous variables,

there are further (indirect) effects. Thus, to grasp both the direct and indirect effects of a

marginal change in an exogenous variable on an endogenous one, it is necessary to derive

the reduced form model, which expresses the endogenous variables in terms of the

exogenous ones alone. This is shown in Appendix A. Each parameter of the reduced form

model measures the total effect of a marginal change in an exogenous variable on an

endogenous one.

4 METHODOLOGY

The main problem of estimating the model outlined above is to determine the target values

for the revenue and expenditure variables. In the case of taxes, government investment and

government consumption, Franco-Rodriguez et al. (1998), McGillivray and Ahmed (1999)

and Franco-Rodriguez (2000) derive their target values from a co-integrating equation of

vectors of exogenous regressors on each of these variables. Then the fitted values are taken

as approximations of the target values. Not having a well-grounded method to estimate

targets, the rationale of this approach is that targets are taken as the values that the variables

would have had in equilibrium. Alternatively, when there is no evidence of the existence of

any co-integrating relationship or there are no suitable vectors of exogenous regressors, an

auto-regressive formulation is followed so that the relevant variable is regressed against a

constant and its one-year lagged value then taking the series of fitted values as the target

values of that variable. This approach is followed below.2

The shortcomings of estimating the target values for the revenue and expenditure

variables following this procedure are evident. However, as mentioned above, there is no

alternative methodology available in the literature to carry out this task in a more

theoretically grounded manner. Indeed, it is very unlikely that each year policy makers

target the long-run equilibrium values of the revenue and expenditure variables. These

limitations led Osei et al. (2005) to propose an alternative method of estimating the effects

of aid on public spending and revenue that departs from the previous fiscal response

literature. Highlighting that the aim of their paper is merely to investigate the dynamic

effects of aid on the components of the government’s budget, then the estimation of the

reduced form model is sufficient. Being this the case, within a vector auto-regression

(VAR) modelling framework, these authors follow an empirical modelling approach not

maintaining the existence of targets as they are only required for the structural model

representation. In addition, these authors also mention as an advantage of their VAR

methodology the fact that assumptions about exogeneity can be directly tested and

impulse-response analysis can be applied to simulate the effects of inflows of aid.

2The same approach is used by McGillivray and Ouattara (2005) and Ouattara (2006) to estimate the target valuesof tax and other recurrent domestic revenue, debt servicing payments and non-debt servicing outlays.


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However, Osei et al. (2005) also recognise the VAR methodology limitations, especially

the number of observations in their sample (34) that seems too short for time-series

econometric analysis. On the other hand, in the present paper and in the previous fiscal

response literature the interest is not only in the reduced formmodel but also in the structural

model, i.e. not just in the total effect (direct and indirect combined) of aid on the fiscal

variables captured by the formermodel but also in the direct effect captured by the latter. Last

but not least, the aim is also to compare the results for Nicaragua with those found for other

countriesbyother authors that have followed the sameapproachappliedhere,namelyFranco-

Rodriguez et al. (1998) for Pakistan and Franco-Rodriguez (2000) for Costa Rica. The other

papers are not strictly comparable because either they split aid in bilateral andmultilateral—

asMcGillivray andAhmed (1999) in the case of the Phillipines—or they disaggregate public

expenditure in debt servicing payments and non-debt servicing spending—as McGillivray

and Ouattara (2005) and Ouattara (2006) in the cases of Cote d’Ivoire and Senegal,

respectively. For all these reasons, the most standard methodology in the fiscal response

literature is followed, bearing in mind the limitations and shortcomings of estimating the

target values of the fiscal spending and revenue variables in the way outlined above.

As noted above, AID is disbursed aid (grants and aid loans), which is considered a choice

variable for the government. As regards AID�, all previous studies that followed the

selected approach take it as aid commitments on the grounds that it is predetermined with

regards to the recipient countries. Each year aid commitments are set in the previous year,

and any over or under disbursement of these funds result in a loss of utility. This is the

rationale for considering aid commitments as a target.

As regards borrowing, Franco-Rodriguez et al. (1998), McGillivray and Ahmed (1999)

and McGillivray and Ouattara (2005) set BORR� equal to zero, on the grounds that

planning to borrow yields no utility in its own right. However, Franco-Rodriguez (2000)

argues that this can be quite unrealistic, especially if the government has been heavily

indebted in the past. Thus, she uses the co-integrating regression approach to estimate the

target for borrowing. Ouattara (2006) treats the target for borrowing in the same way.

Indeed, setting BORR� equal to zero appears to be incorrect: in practice, each year the

government establishes its expenditure budget and the way it will be financed, which

includes a borrowing line. On the other hand, if the government is to maximise its utility

function, it should be aware of the constraints, thus in setting its targets it should take into

account the budget constraint (3). Therefore, the target for borrowing is calculated as a

residual from the budget restriction (3) using the target values for the other variables.

The second econometric problem is how to estimate the r’s in restriction (4). Their

values must lie between zero and one because they represent the maximum share of each

category of revenue that can be consumed. Franco-Rodriguez et al. (1998), McGillivray

and Ahmed (1999) and Franco-Rodriguez (2000) estimate these parameters within the

structural model (Equations (6)–(10))3 excluding Equation (7) using nonlinear three-stage

least squares (NL3SLS) imposing cross-equation restrictions between the parameters.

However, following this approach there were convergence problems using both NL3SLS

and full information maximum likelihood (FIML). In addition, McGillivray and Ouattara

(2005) highlight a crucial aspect that was totally overlooked by the previous literature,

namely, that the b’s in the structural model must be strictly nonnegative because the a’s in

the government’s utility function (2) must be positive to ensure that utility decreases when

3As McGillivray and Ahmed (1999) split aid into bilateral and multilateral, they have one additional structuralequation.


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490 R. Machado

the government does not meet its targets. It is worth noting that should the d’s and the b’s

satisfy the theoretical restrictions, then the direct effects of aid on TAX and on BORR are

strictly negative (see Equations (A3) and (A5) in Appendix A), i.e. aid crowds-out the other

revenue categories.4 The signs of the direct effects of aid on GC and on GI are ambiguous,

and depend on the relative magnitudes of these parameters (see Equations (A1) and (A2)).

The latter is also true in the case of the total effects of aid (see Equations (A11) to (A15)).

Bearing these considerations in mind, a two step approach is followed: first the binding

restriction (4) is estimated by ordinary least squares (OLS), and then the structural

Equations (6) and (8)–(10) are simultaneously estimated by NL3SLS imposing the cross-

equation restrictions in the parameters and the estimated values of the r’s found in the firststep.5 Not estimating the binding restriction (4) simultaneously with the structural

Equations (6) and (8)–(10) will reduce the efficiency of the estimates given that the

estimation of the structural equations will not take into account the information contained

in the binding restriction. In case the estimates of the parameters do not meet the theoretical

restrictions, then the model needs to be re-estimated imposing that the r’s lie between 0 and

1 and that the b’s are nonnegative.

The model is estimated using annual data for Nicaragua in the period 1966–2004 based

on official information from the Central Bank of Nicaragua. All variables are expressed in

local currency at 1980 prices.6 AID is grants and aid loans to the central government.

Franco-Rodriguez et al. (1998) and McGillivray and Ahmed (1999) take the data for this

variable from national sources, whereas Franco-Rodriguez (2000) does so from the

Geographical Distribution of Financial Flows to Developing Countries published by

the OECD. McGillivray and Ahmed (1999) use this latter source to fill some gaps found in

the national sources of the Philippines. In the case of Senegal, Ouattara (2006) obtains aid

data as a residual from budget restriction (3), whereas McGillivray and Ouattara (2005)

obtained aid data for Cote d’Ivoire from the OECD online database. In the absence of

information on aid loans from national sources, aid data for Nicaragua was obtained from

the latter source and corresponds to ODA.

Finally, as in the case of its target, borrowing is simply obtained by difference from the

budget constraint (3), which is similar to the treatment given by Franco-Rodriguez (2000)

andMcGillivray and Ouattara (2005). Franco-Rodriguez et al. (1998) andMcGillivray and

Ahmed (1999) define BORR as domestic borrowing, including all financing from external

sources in AID. On the contrary, Ouattara (2006) define BORR as domestic and external

borrowing net of aid loans that are included in AID along with grants.

5 ECONOMETRIC RESULTS

5.1 Targets

Given the high sensitivity of tax revenue to economic activity and the importance of taxes

on international trade, the existence of long-run relationships between TAX, GDP, imports

4This is what McGillivray and Ouattara (2005) and Ouattara (2006) find.5The computer program used is TSP 5.0. See Hall and Cummins (2005) for details on the implementation of theestimation method.6Government consumption and investment were taken from the national accounts in cordovas at 1980 prices. Totransform the revenue variables from current prices taken from the central government accounts to constant prices,the deflator of total government spending was used. The observations for 2003 and 2004 were obtained usinggrowth rates at 1994 prices.


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(M) and exports of goods and services (X) is tested. All variables exhibit a unit root

according to standard Augmented Dickey–Fuller tests whereas the trace test reveals the

existence of a unique co-integrating vector (see Appendix B). The estimated long-run

relationship is:7

log TAXð Þ ¼ �4:869þ 1:575 log GDPð Þ þ 1:439 log Mð Þ � 1:803 logðXÞ (11)

Apart from the sign of the estimated long-run elasticity of exports, which is hard to

explain, the other parameters seem reasonable.

On the other hand, GC is modelled as being related to GDP alone. However, no

evidence can be found of co-integration between the two variables.8 It seems that

government consumption is better predicted by its own past values as one of the main

features of public consumption is its rigidity, given that it is basically compounded by

salaries and other recurrent expenditure. The regression estimated by OLS is (t-values in

parenthesis)

logðGCÞ ¼ 0:422þ 0:951 logðGCÞ�1

1:58ð Þ 28:6ð ÞR2 ¼ 0:95

(12)

The high value of the R2 indicates that on average GC is very close to GC�. In

terms of the theoretical model, it implies that the relevant term in Equation (2) is

almost zero.

In the case of GI, previous studies have included private investment in the co-integration

relationship.9 However, this would lead to misspecification of the estimated relationship, as

theory suggests that GI is a determinant of private investment (crowding-in or crowding-

out) and not the other way around.10 Therefore, an auto-regressive approach is followed

where GI is regressed on its lagged value and a constant. The estimated relationship using

OLS is (t-values in parenthesis)

log GIð Þ ¼ 2:022þ 0:722 log GIð Þ�1

3:30ð Þ 8:40ð ÞR2 ¼ 0:63

(13)

As shown, the value of the R2 is much lower than the one found in the GC regression

(12). This is not surprising as GI tend to be more flexible than government consumption.

Indeed, public investment is often reduced whenever governments need to undertake a

fiscal adjustment, while the curtailment of public consumption is hard to implement given

the political and social costs associated.

7Franco-Rodriguez et al (1998) and McGillivray and Ahmed (1999) include both GDP and imports in the co-integration equation of TAX. Franco-Rodriguez (2000) includes GDP and a time trend in her estimation. Ouattara(2006) includes GDP per capita, import revenues and export revenues in his long-run relationship, whereasMcGillivray and Ouattara follow an auto-regressive approach (including GDP) to estimate TAX�.8Franco-Rodriguez (2000) finds a similar result, whereas Franco-Rodriguez et al (1998) and McGillivray andAhmed (1999) find co-integration relationships between government consumption, GDP, primary and secondaryschooling. However, the inclusion of schooling rates may prove wrong if they exhibit stationarity, perhaps arounda deterministic trend.9Franco-Rodriguez et al (1998) and McGillivray and Ahmed (1999) include both private investment and GDP inthe co-integration relationship of GI, whereas Franco-Rodriguez (2000) includes the former but not the latter.10This was pointed out by one referee.


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Table 1. Estimates of structural parameters

Estimate t-statistic

r1 0.68�� 9.66

r2 0.83�� 6.12

r3 1.00�� 8.85

b1 0.4183�� 1.86

b2 0.2145� 2.46

b3 0.9167�� 4.68

b4 0.6136�� 11.90

b5 1.4784�� 3.57

b6 1.3698�� 13.97

Source: Author’s estimations.�Significant at the 5% level.��Significant at the 1% level.��Significant at the 10% level.

492 R. Machado

5.1.1 Structural Model

As noted above, first restriction (4) is estimated by OLS to get the r�s, which in turn are usedin the estimation of the structural model. Following Franco-Rodriguez et al. (1998),

McGillivray and Ahmed (1999) and Franco-Rodriguez (2000), the estimated simultaneous

Equations are (6) and (8)–(10). The estimation method is NL3SLS and the sample period is

1966–2004. Then these estimations are used to calculate the parameters in Equation (7).

The estimates of the parameters are shown in Table 1.

As can be seen, all parameters’ estimates satisfy the theoretical restrictions. In addition,

the estimated parameters are statistically significant at the 1 per cent level, except for those

that measure the marginal impact of a change in GI� and GC� on tax revenue (b1 and b2),

which are significant at the 5 per cent and at the 10 per cent level, respectively. Regarding

the limits imposed to the government in the allocation of different revenue categories to

consumption, the estimates of the r�s indicate that 68 per cent of tax and other recurrent

revenue is consumed. This figure is almost equal to the estimate ofMcGillivray and Ahmed

(1999) for the Philippines (67 per cent), higher than that estimated by Franco-Rodriguez

(2000) for Costa Rica (54.9 per cent), and lower than the estimate of Franco-Rodriguez

et al. (1998) for Pakistan (85 per cent). The estimates of McGillivray and Ouattara (2005)

for Cote d’Ivoire and of Ouattara (2006) for Senegal are much lower (28.5 and 14 per cent,

respectively).

Table 1 also shows that 83 per cent of disbursed aid is allocated to consumption, which

may seem high, given that the donors tend to favour investment over consumption.

However, many social programs financed through aid have a high component of

consumption in the form of salaries and the purchase of goods and services. Franco-

Rodriguez et al. (1998) estimates it at 51 per cent, McGillivray and Ouattara (2005) at

62 per cent, Ouattara (2006) at 41 per cent, and McGillivray and Ahmed (1999) at a

surprisingly high 97 per cent for bilateral aid, and 75 per cent for multilateral aid.11

Finally, results indicate that the Nicaraguan government borrows exclusively to finance

consumption. The estimate of 1.00 for r3 reveals that lenders do not impose any significant

11Franco-Rodriguez (2000) estimate is 186 per cent, which violates the theoretical restrictions, since theupper limit for the share of aid that can be consumed is 100 per cent. The same happens with her estimateof r3 (137 per cent), which represents the maximum share of borrowing that can be devoted to consumption.


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Table 2. Direct effects of aid

Direct effect of aid on

Government investment 0.09

Government consumption 0.66

Taxes and other recurrent revenue �0.25

Public borrowing �1.39

Source: Author’s estimations.


restriction to the country regarding the use of the resources borrowed. The opposite is true

in the cases of Cote d’Ivoire and Senegal, where McGillivray and Ouattara (2005) and

Ouattara (2006), respectively, estimate a value of zero for this parameter. Franco-

Rodriguez et al. (1998) estimate it at 54 per cent, whereas McGillivray and Ahmed (1999)

estimate it at 85 per cent.

Considering the direct impact of aid on different expenditure and revenue categories,

Table 2 shows that any 1000 extra cordovas disbursed to the Nicaraguan government

decreases tax and other recurrent revenue in 250 cordovas. This result validates the

hypothesis that aid inhibits tax efforts, and its magnitude seems plausible. The estimation

also reveals that aid crowds-out borrowing significantly, reducing it by 1.39 cordovas for

each extra cordova disbursed. However, being alternative revenue sources, it would be

expected that aid reduces borrowing at most one-to-one.

As regards government spending, results indicate that the direct impact of aid is

important on consumption: any extra cordova of aid increases government consumption by

66 cents. In the case of investment, this additional aid causes it to augment by only 9 cents.

Again, the fact that any extra aid disbursed to the Nicaraguan government is mainly

consumed rather than invested can be explained by the social programs financed by the

donor community which have a high component of consumption.

5.1.2 Reduced form Model

The structural model parameter estimates discussed above represent the direct effects of

aid on different categories of government expenditure and revenue. However, from an

economic policy perspective, it is more useful to look at the overall impact (direct and

indirect) of changes in aid. This calls for the estimation of the reduced formmodel outlined

in Equations (A6)–(A10) in Appendix A. The parameters of interest are derived from the

estimates of the parameters of the structural model based on Equations (A11)–(A15).

These figures are shown in Table 3.

Estimates of the reduced form parameters are disconcerting and are radically different

from those of the structural parameters as most of them exhibit either the opposite expected

sign or implausible magnitudes. Indeed, it is hard to explain why 1000 additional cordovas

in targeted aid (aid commitments) would reduce GC in almost 1500 cordovas and BORR in

3600 cordovas as total effect. In addition, any extra cordova in targeted aid generates an

increase of more than three cordovas in disbursed aid, which is also hard to explain. An

estimated total effect that seems plausible both in sign and in magnitude is on GI (0.47) that

is much higher than the estimated direct effect (0.09). Finally, according to the estimates,

aid crowds-in TAX as total effect, increasing it by 17 cents for each extra cordova of aid. An

interpretation for this could be that aid increases the tax take either improving tax

administration or fostering economic growth.


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Table 3. Total effects of aid

Total effect of aid on

Government investment 0.47

Government consumption �1.47

Taxes and other recurrent revenue 0.17

Disbursed aid 2.42

Public borrowing �3.60

Source: Author’s calculations.

Table 4. Direct and total effects of aid

GI GC TAX AID BORR

Franco-Rodriguez et al. (1998)

Pakistan 1956–1995

Direct effect 0.05 �1.97 �2.91 �1.06

Total effect 0.05 �2.36 �3.59 0.41 0.88

Franco-Rodriguez (2000)

Costa Rica 1971–1994

Direct effect �0.36 2.47 1.10 �1.27

Total effect �0.02 0.07 0.05 0.08 �0.08

Machado

Nicaragua 1966–2004

Direct effect 0.09 0.66 �0.25 �1.39

Total effect 0.47 �1.47 0.17 2.42 �3.60

Source: Franco-Rodriguez et al. (1998), Franco-Rodriguez (2000) and Tables 2 and 3.

494 R. Machado

5.1.3 Comparison to other studies

How do the above results compare to those of other studies? Basically, the two studies that

use the same methodology are Franco-Rodriguez et al. (1998) for Pakistan and Franco-

Rodriguez (2000) for Costa Rica.12 Notwithstanding, strictly speaking, not even these

studies are comparable, inasmuch their results are inconsistent with the theoretical

restrictions, i.e. they exhibit either r’s greater than one or b’s with negative values. For

illustrative purposes only, Table 4 shows the results of these studies together with those

shown in Tables 2 and 3.

A priori, it is expected that aid fosters both categories of expenditure as direct and as total

effect. The study on Pakistan finds small but positive effects of aid on government

investment both as direct and as total effect. This is consistent with the estimates for

Nicaragua, though in this case the magnitudes are much larger, especially in the total effect

(0.47). On the contrary, the study on Costa Rica find negative effects of aid on GI, although

the low value of the total effect can be taken as zero. As regards government consumption,

Franco-Rodriguez et al. (1998) find surprisingly high negative estimates both for direct and

for total effect in Pakistan. On the contrary, Franco-Rodriguez (2000) finds positive

12The studies of McGillivray and Ouattara (2005) and Ouattara (2006) are not comparable because they splitgovernment expenditure in debt servicing and non-debt servicing spending.


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impacts of aid on GC, though direct effect’s magnitude is substantially higher than the total

effect one. In the Nicaraguan case, this paper findmixed results, with a positive direct effect

and a negative total effect, the latter exhibiting a relative high absolute value .47).

Regarding revenue, it is expected that aid crowds-out both TAX and BORR at least as

direct effect.13 In the case of tax revenue, this is what is found in Pakistan and in Nicaragua,

though in the former country the absolute value seems abnormally high (2.91), as one

would expect that aid crowds-out the other revenue categories at most one-to-one.

Surprisingly, the direct effect in Costa Rica is positive. The sign of the total effect of aid on

the other revenue categories is ambiguous as, for instance, it can be argued that aid fosters

tax revenue if it is devoted to improve the tax administration system. The results of the three

sets of estimates under consideration are mixed in this regards.

Finally, the effect of aid commitments on disbursed aid appears to be abnormally high in

Nicaragua whereas it seems low in Costa Rica. In the Pakistani case, the magnitude of this

effect is in an intermediate position.

5.1.4 Econometric results and fiscal policy

How do the econometric results fit with fiscal policy evolution in Nicaragua during 1966–

2004? Unfortunately, during the analysed period the country experienced a major event

that was determinant in most aspects of Nicaragua’s life, including fiscal policy. Indeed, the

irruption of the Sandinista revolution, the civil war and the return to democracy were all

prominent factors that governed policy making, thus obscuring the effect(1s of aid on

government revenue and expenditure variables.

As highlighted in Section 2, the analysed period can be divided in before, during and

after the Sandinista regime. Up to 1976, aid complemented tax revenue in the financing of

government spending leaving no major role for public borrowing in a context of moderate

fiscal deficits that averaged 1 per cent of GDP since 1960. Nevertheless, around 1977

government consumption started to increase as the armed conflict escalated until the

triumph of the Sandinista revolution in 1979 and the economic and political situation that

followed until the end of the 1980s, including the civil war. Tax revenue closely followed

the evolution of GC during this period, though at a lower level. As both government

investment and aid did not show dramatic increases during this phase, public borrowing

financed the gap between GC and TAX. Obviously, the continuous expansion in GC was

mainly led by the escalation in military spending. The increasing financing needs led to

reforms that multiplied the tax take as a share of GDP by a factor of 2.5 during the 1980s as

compared to the 1970s and to massive public borrowing. These outcomes did not seem to

have anything to do with the slightly increase in aid observed in the 1980s relative to the

1970s.

Notwithstanding, the impressive hike in aid in 1990 associated to the return to

democracy was accompanied by a collapse in public borrowing, which is consistent with

the finding that AID crowds-out BORR. As tax and other recurrent revenue remained

stable, this also suggests that aid flows did not have major effect on the tax effort, which is

not consistent with the estimates. Thereafter, aid flows declined but still remained at high

levels as compared to previous decades whereas public borrowing posted negative values

during almost all the remainder years until 2004.

On the other hand, the end of the civil war prompted a significant contraction in

government consumption that remained close to tax revenue until 2004. Meanwhile,

13As noted above, this is always the case when the values of the r’s and the b’s satisfy the theoretical restrictions.


DOI: 10.1002/jid

496 R. Machado

government investment remained stable as a share of GDP. Thus, during 1990–2004 the

fiscal behaviour of Nicaragua seems to be consistent with the econometric results regarding

the effect of aid on BORR—i.e. that it crowds-out public borrowing—but not on TAX.

Finally, GC was governed by the end of the civil war, whereas government investment did

not reacted much with the impressive surge in aid. The latter is consistent with the direct

effect but not with the total effect estimate.

6 CONCLUSIONS

This paper presents a model of fiscal response to analyse the impact of aid on government

consumption and investment, tax and other recurrent revenue, and public borrowing. The

model is estimated for Nicaragua with annual data during the period 1966–2004. Results

indicate that the direct and total effects of aid on government investment are positive,

whereas in the case of government consumption the results are mixed, with aid crowding-in

GC as direct effect but crowding it out as total effect. The latter finding is puzzling, as it

does not appear consistent with neither donors nor government priorities, given that social

programs contain a high component of consumption spending in the form of salaries (of

teachers, nurses, and so on) and purchases of goods and services. As regards the other

sources of government revenue, the estimates exhibit also mixed results for the effect of aid

on TAX, with a negative direct impact but positive total effect. In the case of public

borrowing, a clear crowding-out impact of aid on this source of revenue is found both as

direct and as total effect but with magnitudes difficult to explain, especially in the latter

case. Finally, increases in targeted aid (aid commitments) generate a surprisingly high

impact on disbursed aid, highlighting a difficult to believe capacity of the Nicaraguan

government to absorb aid from the donor community.

Confronting these results to fiscal policy in Nicaragua during the analysed period is

difficult as the Sandinista revolution and its aftermath exerted immense effects on all

aspects of the Nicaraguan life, including fiscal affairs. Notwithstanding, after 1990 the

econometric results appear to be consistent with the effects of aid on public borrowing but

not on tax revenue. However, as in 1977–1989, government consumption was led by the

armed conflict, in this case the end of the civil war. Meanwhile, government investment did

not seem to have been significantly affected by the swings in aid inflows to the country

which is also not in tune with the econometric results.

On the other hand, direct effect estimates on different categories of government spending

and revenue found in this paper seem more plausible in terms of signs and magnitudes than

those from other studies that have used a similar approach. This can be partly due to the

treatment given to public borrowing. In this paper, as in Franco-Rodriguez (2000), this

variable is calculated as a residual, so that the budget constraint of the government—i.e.

Equation (4)—holds. This is not the case either in Franco-Rodriguez et al. (1998) or

McGillivray and Ahmed (1999). In addition, these two papers set the target for borrowing

at zero, which seems mistaken as noted by Franco-Rodriguez (2000), especially when the

government has been highly indebted in the past. But the latter author estimates public

borrowing targets following the same co-integration approach applied in the estimation of

the targets of government consumption and investment, and tax and other recurrent

revenue. On the contrary, in this paper the target for public borrowing is estimated as a

residual from the budget constraint of the government substituting the target values of the

other variables. The rationale for this treatment is that should the government is to


DOI: 10.1002/jid


maximise its utility function, then it should take into account the restrictions it faces.

Therefore, the targets set should be consistent with the budget constraint of the government

i.e. the target for total spending should be equal to the target for total revenue. It is worth

noting that the comparison with the other studies is for illustrative purposes only, as some

of their parameter estimates do not conform with the theoretical model either in signs (b’s

with negative values) or in magnitudes (r’s greater than one).

Despite its usefulness, the estimates of fiscal response models should be taken with

prudence and not be used to derive mechanical policy recommendations. This is the case

because the overall econometric exercise relies upon the estimation of the targets of the

different categories of government spending and revenue. Should these estimated targets

are in fact wrong, the results are not valid. The development of a rigorous method for

estimating target values is a pending issue for further research.

Last, but not least, these models that are based on a utility function that the government

seeks to maximise are prone to exhibit structural breaks, i.e. that the estimated parameters

are not constant. This is very likely because the preferences of governments do change,

especially in developing countries where there is a succession of administrations with quite

different views about the economy and the goals the government should accomplish.

Indeed, this is true for Nicaragua in the analysed period. Therefore, this needs to be taken

into account when deriving policy implications from the estimates of the parameters.

ACKNOWLEDGEMENTS

The author thanks Jose Roberto Sanchez-Fung, Rodrigo Cubero, Valpy FitzGerald and two

anonymous referees for comments on earlier drafts; Julian Caballero, Julieta Caunedo and

Alejandro Tamola for competent collaboration; and Manuel R. Agosin for suggesting him

to write this paper.

REFERENCES

Doornik JA, Hendry DF. 2001. Modelling Dynamic Systems Using PcGive 10. Timberlake Con-

sultants Ltd.: London.

Gang IN, Khan HA. 1991. Foreign aid, taxes and public investment. Journal of Development

Economics 24: 355–369.

Hall BH, Cummins C. 2005. TSP Reference Manual version 5.0. TSP International: Palo Alto.

Heller PS. 1975. A model of public fiscal behavior in developing countries: aid, investment and

taxation. American Economic Review 65: 429–445.

Feeny S. 2006. Policy preferences in fiscal response studies. Journal of International Development

18: 1167–1175.

Franco-Rodriguez S, McGillivray M, Morrissey O. 1998. Aid and public sector in Pakistan: evidence

with endogenous aid. World Development 26: 1241–1250.

Franco-Rodriguez S. 2000. Recent developments in fiscal response with an application to Costa Rica.

Journal of International Development 12: 429–442.

McGillivray M, Ahmed A. 1999. Aid, adjustment and the public sector fiscal behaviour in the

Philippines. Journal of the Asia Pacific Economy 4: 381–391.


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McGillivray M, Morrissey O. 2001. A review of evidence on the fiscal effects of aid. CREDIT

Research Paper No. 01/13. Centre for Research in Economic Development and International

Trade, University of Nottingham, Nottingham.

McGillivray M,Morrissey O. 2004. Fiscal Effects of Aid. In: Fiscal Policy for Development. Poverty,

Reconstruction and Growth, Addison T, Roe A (eds). WIDER, Palgrave Macmillian: London.

McGillivray M, Ouattara B. 2005. Aid, debt and government fiscal behaviour in Cote d’Ivoire.

Journal of African Economies 14: 247–269.

Osei R, Morrissey O, Lloyd T. 2005. The fiscal effects of aid in Ghana. Journal of International

Development 17: 1037–1053.

Ouattara B. 2006. Aid, debt and fiscal policies in Senegal. Journal of International Development 18:

1105–1122.

APPENDIX A. STRUCTURAL AND REDUCED FORM MODELS OF FISCAL

RESPONSE TO AID

The structural model—Equations (6)–(10)—is

GI ¼ b1 1� r1ð ÞGI� þ b2 1� r1ð ÞGC� þ 1� r1ð Þ 1� 1� r1ð Þb1 � r1b2½ �TAX�

þ 1� r2ð Þ � 1� r1ð Þ 1� r2ð Þb1 � 1� r1ð Þr2b2½ � AIDþ 1� r3ð Þ � 1� r1ð Þ 1� r3ð Þ b1 � 1� r1ð Þr3b2½ � BORR

(A1)

GC ¼ r1b1GI� þ r1b2GC

� þ r1 1� 1� r1ð Þb1 � r1b2½ � TAX�

þ r2 � r1 1� r2ð Þ b1 � r1 r2 b2½ � AIDþ r3 � r1 1� r3ð Þ b1 � r1r3b2½ �BORR(A2)

TAX ¼ b1GI� þ b2GC

� þ 1� 1� r1ð Þ b1 � r1b2½ �TAX� � 1� r2ð Þ b1 þ r2b2½ �AID� 1� r3ð Þb1 þ r3 b2½ � BORR

(A3)

AID ¼ b3GI� þ b4GC

� � 1� r1ð Þb3 þ r1b4½ �TAXþ 1� 1� r2ð Þb3 � r2b4½ �AID�

� 1� r3ð Þb3 þ r3 b4½ � BORR(A4)

BORR ¼ b5GI� þ b6GC

� � 1� r1ð Þb5 þ r1 b6½ �TAX� 1� r2ð Þb5 þ r2b6½ �AIDþ 1� 1� r3ð Þb5 � r3b6½ �BORR�

(A5)

where

b1 ¼�a1 a4 a5 r1 � 1ð Þ � a1 a2 a5 r1 � r2ð Þ r2 � a1 a2 a4 r1 � r3ð Þ r3½ �

D

b2 ¼�a2 a4 a5 r1 þ a1 a2 a5 r1 � r2ð Þ � a1 a2 a5 r1 � r2ð Þ r2 þ a1 a2 a4 r1 � r3ð Þ � a1 a2 a4 r1 � r3ð Þ r3½ �

D

b3 ¼a1 a2 a5 r1 r1 � r2ð Þ � a1 a3 a5 r2 � 1ð Þ � a1 a2 a3 r2 � r3ð Þ r3½ �

D


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b4 ¼�a1 a2 a5 r1 � r2ð Þ þ a1 a2 a5 r1 r1 � r2ð Þ � a2 a3 a5 r2 þ a1 a2 a3 r2 � r3ð Þ � a1 a2 a3 r2 � r3ð Þ r3½ �

D

b5 ¼a1 a2 a4 r1 r1 � r3ð Þ þ a1 a2 a3 r2 r2 � r3ð Þ � a1 a3 a4 r3 � 1ð Þ½ �

D

b6 ¼�a1a2a4ðr1 � r3Þ þ a1a2a4r1ðr1 � r3Þ � a1a2a3ðr2 � r3Þ þ a1a2a3r2ðr2 � r3Þ � a2a3a4r3½ �

D

and

D ¼ �a2a4a5r21 þ a1 a2a5ðr1 � r2Þ2 þ a4 a5ðr1 � 1Þ2 þ a2ðr1 � r3Þ2

h in

þa3 a5ðr2 � 1Þ2þa2ðr2 � r3Þ2þa4ðr3 � 1Þ2h io

�a3 a2a5r22 þ a4ða5 þ a2r

23Þ

� �

On the other hand, the reduced form model has the following form:

GI ¼ P11GI� þP12GC

� þP13TAX� þP14AID

� þP15BORR� (A6)

GC ¼ P21GI� þP22GC



TAX ¼ P31GI� þP32GC



AID ¼ P41GI� þP42GC



BORR ¼ P51GI� þP52GC



where the parameters of interest are:

P14 ¼g5 d1 þ d2g8 � g7ðd1g3 � d2g2Þ½ �

1� g6g8 � g2ðg4 þ g6g7Þ � g3ðg4g8 þ g7Þ(A11)

P24 ¼g5 d3 þ d4g8 � g7ðd3 � d4g2Þ½ �


P34 ¼g5ðg2 þ g3g8Þ


P44 ¼g5ð1� g3g7Þ


P54 ¼g5ðg8 þ g2g7Þ


where

d1 ¼ ð1� r2Þ � ð1� r1Þ ð1� r2Þb1 � ð1� r1Þ r2b2½ �

d2 ¼ ð1� r3Þ � ð1� r1Þ ð1� r3Þb1 � ð1� r1Þ r3b2½ �

d3 ¼ r2 � r1ð1� r2Þb1 � r1r2b2½ �; d4 ¼ r3 � r1ð1� r3Þb1 � r1r3b2½ �


DOI: 10.1002/jid

500 R. Machado

and

g2 ¼ � ð1� r2Þb1 þ r2b2½ �; g3 ¼ � ð1� r3Þb1 þ r3b2½ �

g4 ¼ � ð1� r1Þb3 þ r1b4½ �; g5 ¼ 1� ð1� r2Þb3 � r2b4

g6 ¼ � ð1� r3Þb3 þ r3b4½ �; g7 ¼ � ð1� r1Þb5 þ r1b6½ �

g8 ¼ � ð1� r2Þb5 þ r2b6½ �:

APPENDIX B. UNIT ROOTS AND CO-INTEGRATION TESTS FOR

ESTIMATION OF TARGET VALUES OF TAX REVENUE

Table B1 shows standard unit root Augmented Dickey–Fuller (ADF) tests of TAX, GDP,

exports (EXP) and imports (IMP) of goods and services. All variables are in cordovas at

1980 prices and expressed in logarithms. Critical values at the 5 per cent level of

significance are shown in parenthesis. The estimated ADF model is:

DYt ¼ aþ bYt�1 þXs

i¼1

diDYt�i þ mT

where T is a time trend.

Following Doornik and Hendry (2001), the lag length was selected according to the

highest s with a significant last ds according to conventional t-values. As usual, the tests

were conducted with and without the deterministic trend. The numbers in parenthesis are

the critical values at the 5 per cent level of significance.

As noted in the last column of Table B1, all variables exhibit a unit root and therefore are

I(1). Next, the existence of a co-integration relationship between the four variables was

tested using the trace test. The results are shown in Table B2.

According to the trace test there is a unique co-integration relationship between

log(TAX), log(GDP), log (IMP) and log(EXP) at the 1 per cent level of significance. Taking

exponential to the fitted values of this estimation we get the target values for TAX.

Figure B1 plots TAX and TAX�, showing that tax revenue do not systematically overshoots

or undershoots its target value.

In order to address the issue of potential breaks in the series of variables included in the

co-integration relationship that can obscure the long-run relationship estimated, Figure B2

plots the residuals of the estimation, i.e. LTAXminus LTAX� (the fitted values for LTAX in

Equation (11)). Defining as abnormal a residual observation that is above or below the

Table B1. Augmented Dickey–Fuller unit root tests

Model with constant Model with constant and time trend Lag Inference

log(TAX) �2.34 (�2.93) �2.76 (�3.51) 0 I(1)

log(GDP) �2.73 (�2.93) �2.63 (�3.52) 1 I(1)

log(IMP) �0.88 (�2.93) �2.47 (�3.52) 3 I(1)

log(EXP) �1.45 (�2.93) �1.61 (�3.51) 0 I(1)

Source: Author’s calculations.


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Table B2. Trace test for co-integration

Eigen values Null hypothesis Trace statistic p-value

0.59175 r� 0 65.950�� 0.000

0.42664 r� 1 27.427 0.093

0.065035 r� 2 3.509 0.932

0.014244 r� 3 0.617 0.432

Source: Author’s calculations.��Significant at the 1% level.

Figure B1. Actual and target values of tax and other recurrent revenue, 1960–2004. (Millions ofcordovas at 1980 prices) Source: Central Bank of Nicaragua and author’s estimations. This figure is

available in colour online at www.interscience.wiley.com/journal/jid

Figure B2. Residuals of LTAX co-integration relationship. Source: Author’s calculations. Thisfigure is available in colour online at www.interscience.wiley.com/journal/jid


mean value plus/minus two standard deviations, there are 2 years where the residuals seem

to break, namely 1964 and 1988.

Table B3 shows the trace test for the existence of co-integration relationships between

log(TAX), log(GDP), log(IMP) and log(EXP), including dummy variables for 1964 and

1988. As shown, the existence of a unique co-integration relationship is verified.


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Table B3. Trace test for co-integration

Eigen values Null hypothesis Trace statistic p-value

0.64574 r� 0 66.177�� 0.000

0.35890 r� 1 21.555 0.334

0.05412 r� 2 2.439 0.980

0.00109 r� 3 0.047 0.829

Source: Author’s calculations.��Significant at the 1% level.


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502 R. Machado

Documents

Aid and fiscal policy in Nicaragua: A fiscal response analysis