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    Economic Analysis & Policy Vol.37 No.1, March 2007 41 2007

    FOREIGN AID AND GOVERNMENTS FISCALBEHAVIOUR IN NEPAL: AN EMPIRICAL ANALYSIS *

    Badri Prasad Bhattarai School of Economics and Finance

    University of Western SydneyParramatta Australia

    Using the cointegration technique, this paper examines the revenue and expenditurebehaviour of the Nepalese government in the presence of foreign aid for the period19752002. The results show that aid positively affects both development andnon-development expenditure in the long run. However, the long-run relationshipbetween aid and non-development expenditure is found to be stronger than thatbetween aid and development expenditure. Since aid is generally given fordevelopment expenditure, these results indicate the possibility of diversion of aidto non-development expenditure. Thus, the long-run relationship between aid andnon-development expenditure may indicate aid fungibility. On the other hand,no evidence is found in favour of the hypothesis that aid availability makes thegovernment lazy in terms of domestic revenue mobilisation.

    1. INTRODUCTION

    Despite the constant ow of foreign aid, and decades of aid-nanced developmentefforts, Nepal remains one of the poorest countries in the world, with per capitaincome of about US$ 243. Until the mid 1960s, almost all development projectsin Nepal were nanced by foreign aid. Until 2002, aid as a percentage of GDPwas over 6 per cent, and aid nanced over 50 per cent of Nepals developmentexpenditure.

    Foreign aid has been the principal source of development finance for the majorityof developing countries. The economic rationale for aid as a source of developmentfinance can be traced back to the two-gap model developed by Chenery and Strout(1966). Insufficient savings at an early stage and foreign exchange (required toimport capital goods) at a later stage of economic development were identified as thekey constraints on economic growth by the two-gap model. Within the framework

    * This paper is based on chapter 8 of my PhD dissertation, accepted by the University of Western Sydney. I am grateful to my principal supervisor Prof. Anis Chowdhury for his

    overall guidance and encouragement. I also would like to thank my co-supervisor Dr.Mallik for guiding me with econometrics. Thanks are also due to Prof. P.N. Junankarand my external examiners for their helpful comments. However, any remaining short-comings are mine.

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    of the two-gap models, the role of foreign aid is supposed to boost investment byreducing the savings gap and/or the foreign exchange gap.

    Past (mostly cross-country) studies of aid effectiveness on economic growthhave found conflicting results. However, based on a survey of three generationsof studies, Hansen and Tarp (2000) concluded that, on the whole, there is moreevidence in favour of the positive impact of aid. Nonetheless, these studies sufferfrom methodological problems associated with cross-country analyses, such asheterodoxity of sample countries and the influence of outliers. Among the single-country studies, only a few used econometric techniques, which take account of thepossibility of spurious relationship due to a common time trend in the variables,or of the possibility of simultaneity between aid flows and economic growth orsavings.

    More importantly, only a limited number of studies explicitly recognise that aidinflows go to the public sector of recipient countries and, hence, the ultimate effectof aid on savings or economic growth depends on how governments respond to aidflows. The flow of aid may encourage governments to become profligate or providean incentive to become lax in terms of tax efforts, which may be unpopular. In otherwords, aid flows may encourage governments to pursue populist macroeconomicpolicies, which tend to favour larger budget deficits. This then causes larger savings-investment gaps, which in turn necessitate more aid flows. 1 However, larger budgetdeficits may not necessarily adversely affect economic growth if higher governmentexpenditure made possible by aid is used for developmental purposes. Therefore,

    it is important to analyse how aid influences both the governments expenditureand revenue efforts and its budgetary allocations. A few studies have addressed theissue by using fiscal response models (for example, Gang and Khan, 1991; Packand Pack, 1990 and 1993; Franco-Rodriguez, 2000). However, none of these studiescovered Nepal, one of the highly aid-dependent countries.

    The purpose of the present study is to fill this gap. The paper uses the modeldeveloped by Pack and Pack (1990, 1993) to analyse fiscal behaviour of the Nepalesegovernment in the presence of aid. It also applies cointegration techniques to excludethe possibility of spurious relationships between variables under study. The paper is

    organised as follows: Section 2 discusses aid inflows to Nepal; Section 3 presentsa brief literature review of aid and fiscal behaviour; Section 4 discusses models,methodological procedures and data; the empirical results and analyses are reportedin section 5; and the final section provides a summary and conclusion.

    2. AID INFLOWS TO NEPAL

    Nepal is an aid-dependent country. Until the mid-1960s, Nepal was almost fullydependent on foreign grants for all its development projects. The rst Five Year

    1 Incidentally, an influential study by Burnside and Dollar (2000) under the auspicesof the World Bank has found that aid works in an environment of good policy whichincludes low or zero budget deficit.

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    Plan (195660) was entirely nanced by foreign aid. Most of these grants were ona bilateral basis and concentrated on the establishment of public enterprises andbuilding physical infrastructures.

    Since then Nepals aid dependency has increased considerably. As can be seenfrom Table 1, the average total aid increased from about 2 per cent of GDP in the1960s to 10 per cent in the 1980s, and aid flows remained at around 10 per centof GDP in the 1990s. Although bilateral aid still dominates, its share has declined.For example, the share of bilateral aid decreased from almost 97 per cent in the1960s to around 62 per cent in the 1990s. With it, the share of grants in total aidalso declined. In the 1960s, almost all aid was grants; this decreased to around 70per cent in the 1990s.

    TABLE 1

    AVERAGE TOTAL AID, BILATERAL AND GRANTS AID, 19602002

    Year Total aid(% of GDP)Bilateral aid

    (% of total aid)Grants aid

    (% of total aid)196069 1.95 96.83 99.79197079 4.34 66.31 72.52198089 10.39 54.22 64.17199002 9.76 62.88 70.2219602002 6.62 69.99 76.85Source: OECD/IDS online database

    Table 2 presents a comparison between Nepal and three South Asian countries(India, Pakistan and Sri Lanka) in terms of government revenue, expenditure andaid. On average, Sri Lanka had the highest revenue/GDP and expenditure/GDPratios, followed by Pakistan, during the period 19752001. Nepals revenueand expenditure as percentages of GDP were the lowest among the South Asiancountries. 2 However, Nepal received higher aid as a percentage of GDP comparedto other South Asian countries. The higher flow of aid perhaps contributed to lowerpublic sector borrowing in Nepal.

    Aid as a percentage of GDP is still over 6 per cent, and aid still finances over

    50 per cent of Nepals development expenditure. Yet slow economic growth persistsand almost 40 per cent of the population lives in absolute poverty. Serious doubtsabout the effectiveness of aid in Nepal have therefore arisen. Casual observers of these facts could easily draw the conclusion that aid to Nepal has not been effective;however, they would not be able to say what would have happened in the absenceof aid.

    It cannot be denied that the Nepalese government lacks the strong politicalwill and coherent foreign aid policy to improve the living standards of the people.Whilst the growth rate faltered continuously, due mainly to the escalation of Maoist

    2 Observers believe that pervasive corruption, a narrow tax structure and inefficient taxadministration have been major obstacles to revenue collection in Nepal.

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    violence and political instability, observers believe that Nepals development effortsin the past five decades have failed to improve living standards by reducing povertyand rural-urban inequality (Panday, 2000). This failure is predominantly due to theelite-dominated political and power structure in Nepal. Thus, there was no seriouspolitical will to address the issue of poverty and inequality. The growing inequalityand a sense of relative deprivation of the lower-class fuelled the recent civil war(Sharma, 2006).

    TABLE 2GOVERNMENT REVENUE AND EXPENDITURE FOR SOUTH ASIAN

    COUNTRIES, AS PERCENTAGE OF GDP, 19752001

    Country/Year Aid/GDP Revenue/GDP Borrowing/GDP Total expenditure/GDPNepal

    19751984 6.93 7.94 1.69 15.0419851994 12.25 9.19 1.89 18.2819952001 8.68 11.18 1.21 18.22

    India19751984 1.19 12.37 5.07 13.4719851994 1.24 13.30 6.48 16.3119952001 0.75 12.49 4.98 15.6

    Pakistan19751984 4.38 15.14 4.52 18.17

    19851994 4.27 17.65 5.71 23.1719952001 2.59 16.41 4.47 22.46

    Sri Lanka19751984 8.36 19.96 6.29 31.8419851994 8.21 20.51 5.46 29.7219952001 3.05 18.01 6.70 26.22

    Note: Bangladesh is not included due to non-availability of data.Source: IMF/IFS online database and Statistical Year Book of Nepal , 1995 and 2003

    3. A BRIEF LITERATURE REVIEW OF FISCAL RESPONSE TO AIDThe issue of scal response has been addressed in the literature through two differentapproaches. The rst approach follows the model developed by McGuire (1978)and is concerned with the question of aid fungibility. Aid is said to be fungible if the recipient uses aid for purposes other than those intended by the donors. Theassumption is that donors intend aid ow to nance specic activities; the questionis whether the ow is diverted to other purposes. In other words, if aid intendedfor investment is actually diverted to government consumption spending, then thepotential growth impact of aid may be reduced. 3 Generally, government investment

    3 Even if aid is used in the intended manner, it allows the government to increase itsconsumption expenditure or fund other projects, which may not be so productive. Inthat case, too, aid effectiveness will be low.

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    spending is considered to make a greater contribution to growth than governmentconsumption spending. Thus, aid should not to be diverted to consumption spending.Studies of this approach include Khilji and Zampelli (1991), Pack and Pack (1990,1993), Feyzioglu et al . (1998) and Swaroop et al . (2000). 4

    The second type of model, based on the seminal work of Heller (1975), assumesutility-maximising behaviour for the government, and examines the pattern of differentcomponents of government expenditure when it is faced with a budget constraintwhich is defined by revenue, borrowings and aid. 5 To this end, governments settargets for various expenditures and also set revenue targets for tax and borrowing.Then they maximise their goal (economic growth or social welfare) by attaining theserevenue and expenditure targets. The assumption here is that the realisation of revenueand expenditure targets maximises the goals. The flow of aid can change either thegovernments expenditure targets or revenue/borrowing targets. Government canalso adjust its both expenditure and revenue/borrowing targets in response to aid.Appendix 1 provides a summary of studies of fiscal response models.

    4. THE MODELS, METHODOLOGY AND DATA

    In formulating our empirical model, we follow the work of Pack and Pack (1990,1993), which used a McGuire type theoretical framework. The reason for using thistype of specication is that it does not suffer from the methodological problems of the Heller type models. As White (1994) has pointed out, the Heller type modelssuffer from important estimation problems. These arise from the fact that the Hellertype models use target variables in the utility function of the government. If theavailable revenue (aid plus domestic revenue) fails to meet the target expenditure,then utility is not maximised. However, target variables are not observable and,hence, need to be estimated. The targets are taken to be the tted values from theregression equations, linking target variables to their past values, and some plausibleexogenous factors. White (1994) expressed doubts about whether target valuesthus derived through estimation would necessarily be equal to the values of targetvariables derived from the optimisation exercise. If the target value is very closeto the actual value then one would be regressing the variable on itself, producing R 2 which will be very close to 1. On the other hand, if the R 2 is low, it would be verydifcult to see how the tted values calculated using the estimated coefcients maybe meaningfully interpreted as the values of the targets. Thus, White (1994: 159)notes: the t will be poor either because the wrong variables have been includedin the target equation, or because the outturn was far removed from the target. Inthe latter case, the coefcients will not be those used in the formation of targets.

    On the other hand, Pack and Pack (1990, 1993) specify various types of government expenditure and revenue as functions of GDP and some plausible

    4 See appendix 1 for the results of these studies.5 In later studies, Gang and Khan (1991) and Khan and Hoshino (1992) extended the

    approach. See appendix 1 for details.

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    variables, all of which are observable. Hence, these models do not need anyestimated variables, such as targets as in the Heller type models. Pack and Packused a standard utility function involving public goods to be maximised subject tothe government budget constraint.

    4.1 The models

    Following Pack and Pack (1990, 1993), our model for the scal behaviour of theNepalese government can be written as follows:

    lnGd t = 10 + 11 lnGDPP t + 12 ln AID t + D87 + u t 1 (1)

    lnGnd t = 20 + 21 lnGDPP t + 22 ln AID t + D87 + ut 2 (2)

    ln REV t = 10 + 1 lnGDPP t + 2 ln AID t + D87 + ut 1 (3)

    where Gd = per capita development expenditure, Gnd = per capita non-developmentexpenditure or current expenditure, REV = per capita government revenue (tax plusnon-tax), AID = per capita aid and GDPP = per capita GDP. ln = natural logarithm.All variables are expressed in current prices.

    D87 is a dummy variable (= 0 for 19751986 and 1 for 19872002). The dummycaptures the impact of Structural Adjustment Programs, which have importantbearings on government expenditure and revenue. In particular, the IMF-WorldBank supported Structural Adjustment Program (signed by Nepal in 1987) requiresrevenue effort to increase and expenditure to be contained or cut in order to achievea sustainable budget outcome.

    4.2 The methodology

    Before testing for cointegration, a unit root test is required to ensure that thevariables under study are nonstationary I(1). The cointegration test is applicableonly if the variables are of the same order I(1). Thus, we employ two types of unitroot tests, the Augmented DickeyFuller (ADF) (Dickey and Fuller, 1979, 1981)and the PhillipsPerron (PP) tests (Phillips and Perron, 1988), with a constant as

    well as a deterministic trend. 6 For the cointegration test, we use Johansens method, first proposed by

    Johansen (1988), and Johansen and Juselius (1990). The Johansen approach iscapable of determining the number of cointegrating vectors for any given numberof nonstationary series of the same order.

    Before applying the Johansen approach, one should first determine the laglength or order of the vector autoregression (VAR). It is a key element in thespecification of the VAR, which forms the basis of inference for the cointegratingrank. Generally, the lag length is chosen on the basis that the equation should pass

    6 Since one can easily find about these test procedures in any standard econometricstextbook, we have not discussed them in detail here to save space.

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    all the diagnostic tests. The most commonly used criteria are the Akaike InformationCriterion (AIC) and Schwarz Bayesian Criterion (SBC). When using the AIC orSBC based on the estimated standard errors, the model with the lowest value forthe AIC or SBC is chosen (see Pesaran and Pesaran, 2003). The dominant practiceis to choose the lag length using one or both of the information criteria plus therequirement that there should be no evidence of serial correlation. One then usesthe Johansen procedure to determine the cointegrating rank (see Johansen, 1988;Johansen and Juselius, 1990).

    The Johansen procedure gives two likelihood ratio tests for the number of cointegrating vectors: (1) the maximum eigenvalue test ( max), which tests the nullhypothesis that there are at least r cointegrating vectors, as against the alternativethat there are r+1; (2) the trace-test ( trace), where the alternative hypothesis isthat the number of cointegrating vectors is equal to or less than r+1.

    Impulse response functionWe also analyse the impulse response function to examine the dynamic stability of therelationships under investigation. The impulse response function shows the responseof dependent variables in the VAR system to shocks in the error terms. Shocks orchanges will alter the dependent variable in the current as well as future periods.In other words, an impulse response analysis demonstrates how long, and to whatextent, variables react to unanticipated changes among them. The transmission of shocks among the variables is investigated using the generalised impulse responseanalysis developed by Koop et al. (1996) and Pesaran and Shin (1996).

    4.3 Data and summary statistics of the variables

    We have used time-series data from 1975 to 2002; no reliable data prior to thisperiod are available. Government revenue and expenditure data are obtained fromthe Central Bureau of Statistics (CBS) publication, Statistical Year Book of Nepal (1981, 1982, 1991, 1999, and 2003). Foreign aid data are obtained from the OECDpublication, IDS online database. For the model estimation, all variables are expressedin per capita form at current prices and transformed into natural logarithms.

    Table 3 presents the correlation among model variables. As can be seen, allvariables are highly correlated, indicating that our model relating them is highlyplausible. We also find that aid growth correlates highly with the growth of bothdevelopment and non-development expenditure as well as with the growth of revenue (Table 3A).

    TABLE 3CORRELATION MATRIX OF MODEL VARIABLES, 19752002

    Variables GDPP AID REV Gd GndGDPP 1.00

    AID 0.93 1.00REV 0.98 0.90 1.00Gd 0.97 0.98 0.95 1.00Gnd 0.98 0.87 0.99 0.92 1.00

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    TABLE 3A

    CORRELATION MATRIX OF VARIABLES, 19752002

    Variables AIDgr Gdgr Gndgr REVgrAIDgr 1.00Gdgr 0.37 1.00Gndgr 0.28 0.06 1.00REVgr 0.21 0.01 0.38 1.00Notes: (a) All variables are expressed as a percentage of GDP.

    (b) gr stands for growth.

    5. THE EMPIRICAL RESULTS

    5.1 Unit root test resultsSince we are using cointegration tests to examine the relationship between aidand various components of government revenue and expenditure, it is necessaryto ensure that all variables under study are of the same order. In other words, thestandard cointegration analysis requires the classication of the variables into I(1);that is, they need to be nonstationary. In order to test for stationarity in the nextstep, therefore, we perform ADF and PP unit root tests.

    The ADF test results are presented in Table 4. The results show that onlytwo variables, lnGDPP and lnGnd, are found to be stationary at the 10 per centsignificance level in their first difference form with the assumption of a constantonly. On the other hand, only lnGDPP and lnGd are found to be stationary at the 10and 5 per cent significance levels, respectively, with the assumption of a constantand trend. Thus, a unit root problem exists even in their first difference form forlnAID, lnGnd and lnREV with the assumption of a constant and a time trend; thus,they are nonstationary under the ADF test results.

    TABLE 4

    ADF TEST WITH CONSTANT AND WITH CONSTANT AND TREND(LAG = 2), 19752002

    ADF test with constant only ADF test with constant and trend

    Variables Levels First difference Levels First difference

    lnGDPP 1.94 -2.70*** -2.46 -3.52***lnAID -2.69*** -1.05 -0.17 -2.30lnGd -2.60 -2.05 -0.24 -3.67**lnGnd 0.03 -2.85*** -2.41 -2.79lnREV -0.81 -2.38 -1.22 -2.36

    Notes: (a)** and *** indicate signicant at the 5% and 10% levels respectively.(b) All variables are expressed in natural logarithm form.

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    Since the ADF tests indicate inconclusive results, we perform the PP unit roottest; the results are presented in Table 5. As with the ADF test, we have fixed laglengths of 2 based on the AIC. Almost all variables have a unit root problem in theirlevel form, both with a constant, and with a constant and a time trend assumption.However, they are found to be stationary in their first difference form at the 1 percent significance level. Thus, under both assumptions of a constant only and aconstant with a trend, the variables can be considered nonstationary in their leveland stationary in their first difference forms.

    TABLE 5PP TEST WITH CONSTANT AND WITH CONSTANT AND TREND

    (LAG = 2), 19752002

    PP test with constant and trend

    Variables Levels First difference Levels First difference

    lnGDPP 1.36 -7.41* -2.41 -8.61*

    lnAID -3.89* -5.60* -0.40 -8.35*

    lnGd -3.35** -3.95* -0.05 -5.23*

    lnGnd -0.45 -7.17* -3.09 -7.01*

    lnREV -0.74 -4.88* -1.65 -4.86*Note: * and ** indicate signicant at 1% and 5% levels respectively.

    5.2 Cointegration test resultsAs the variables under study are found to be I(1), we proceed to perform cointegrationtests by applying Johansens Maximum Likelihood approach. We begin with equation(1) relating development expenditure to aid and per capita GDP. Prior to testingfor the cointegrating vectors, the lag length of the vector autoregressive system isdetermined by using the SBC and AIC. The max and trace statistics are reported inTable 6 (Part A). The test results show that there is a signicant long-run relationshipbetween aid and development expenditure.

    TABLE 6COINTEGRATING TEST RESULTS, 19752002

    (equation (1), lnGd, lnGDPP and lnAID), Part AVAR (3) Hypothesis

    Eigenvalues H0 H1 max trace0.86332 r = 0 r = 1 49.75** 64.09**0.55756 r < = 1 r = 2 20.38** 34.35**0.42787 r < = 2 r = 3 13.95** 13.95**

    Normalised cointegrating vectors (normalised on lnGd), Part BlnGd lnGDPP lnAID Trend1.000 -0.397 -0.111 -0.049

    Chi Square [8.56]* [5.15]** [11.87]*Note: * and ** indicate signicant at 1% and 5% levels respectively.

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    The normalised cointegrating vectors are presented in Part B. The results showthat per capita development expenditure is positively associated with both per capitaaid and per capita GDP in the long run. The long-run aid coefficient is significantat the 5 per cent level, but the elasticity of per capita development expenditure withrespect to per capita aid is quite low (0.11). The estimation of equation (1) with adummy for the Structural Adjustment Program did not change the result significantly,and the dummy was not found to be significant. This implies that the StructuralAdjustment Program possibly did not affect development expenditure.

    Next, we estimate equation (2) relating per capita non-development expenditureto per capita GDP, per capita aid and a dummy (D87). The results (Part A of Table7) show that the variables under study are cointegrated at the 5 per cent significancelevel. More importantly, there exists a long-run relationship between per capita aidand per capita non-development expenditure. All the normalised cointegrating vectorsare found to be positive except for the dummy (Part B). Because aid is generallygiven for development purposes, the positive and significant association betweenper capita aid and per capita non-development expenditure may be considered adiversion of development aid to non-development expenditure. The significant andnegative coefficient for the dummy implies that the Structural Adjustment Programhad a negative impact on non-development expenditure. That is, the conditionalityimposed by the World Bank and the IMF through the Structural Adjustment Programseemed to have been effective in changing the governments spending patterns. 7

    TABLE 7COINTEGRATING TEST RESULTS, 19752002

    (equation (2), lnGnd, lnGDPP, lnAID and Dummy (D87)), Part A

    VAR (3) Hypothesis

    Eigenvalues H0 H1 max trace0.76756 r = 0 r = 1 36.47** 47.81**

    0.33819 r < = 1 r = 2 10.31 11.33

    0.03971 r < = 2 r = 3 1.01 1.01

    Normalised cointegrating vectors (normalised on lnGnd), Part B

    lnGnd lnGDPP lnAID D87 Trend

    1.000 -0.221 -0.613 0.423 -0.074

    Chi Square [0.56] [18.84]* [17.49]* [27.66]*

    Notes: (a) * and ** indicate signicant at 1% and 5% levels respectively.(b) Since the dummy is placed at Part B for convenience, the sign of the coefcient is takenas positive in Table 7, which is actually negative.

    7 The government reduced expenditure through the privatisation of public enterprises,which were running with huge losses. The government had already cut subsidies tosome enterprises, thus maintaining a low budget deficit and domestic borrowing.

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    Interestingly, the elasticity of per capita non-development expenditure withrespect to per capita aid is found to be relatively larger (0.61) than that for developmentexpenditure. In other words, a 1 per cent increase in per capita aid leads to anapproximately 0.6 per cent increase in the per capita non-development expenditure,whereas it leads to only a 0.11 per cent increase in per capita development expenditure(see Table 6). Since aid is generally given for development expenditure, these resultsindicate the possibility of diversion of aid to non-development expenditure. As isthe case with other developing countries, diversion of development aid is not anew phenomenon in Nepal. In practice, due to political reasons, one can find manyexamples of diversion of development aid to non-development expenditure.

    TABLE 8

    COINTEGRATING TEST RESULTS, 19752002

    (equation (3), lnREV, lnGDPP and lnAID), Part A

    VAR (3) Hypothesis Eigenvalues H0 H1 max trace

    0.65678 r = 0 r = 1 26.73** 51.35**

    0.41795 r < = 1 r = 2 13.52 24.61***

    0.35827 r < = 2 r = 3 11.08 11.08

    Normalised cointegrating vectors (normalised on lnREV), Part B

    lnREV lnGDPP lnAID Trend

    1.000 -0.123 -0.482 -0.057

    Chi Square [10.09]* [12.67]* [15.39]*Note: *, ** and *** indicate signicant at 1%, 5% and 10% levels respectively.

    Table 8 (Part A) presents the results of cointegration between per capita revenue,per capita GDP and per capita aid. The results show that there is a long-run relationshipbetween all three variables. All the long-run normalised coefficients are found to bepositive (Part B). The long-run normalised coefficient of per capita aid is found tobe positive and significant at the 1 per cent level. A 1 per cent increase in per capitaaid contributes to an almost 0.5 per cent increase in per capita revenue. Thus, theresults indicate that aid did not lead to a reduction in revenue raising efforts. Thismay be due to the influence of aid (through technical assistance) on improvingadministrative and institutional quality. 8 The finding of a positive influence of aidon revenue is contrary to earlier studies such as Heller (1975), Franco-Rodriguez(2000) and Pack and Pack (1993).

    8 There has been considerable technical assistance for the preparation and the imple-mentation of VAT, as well as for broadening the structural base of the Nepalese taxationsystem.

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    5.3 Impulse response function

    To measure the time prole of the effect of shocks at a given point in time on theexpected future values of variables in a dynamic system, we have used the generalisedimpulse response function. Here, we present the impulse response analysis of threevariables at a time. All three variables are in natural logarithms, measured on a percapita basis. Since all the variables under study are found to be I(1), we proceedwith our analysis in the context of a cointegrated VAR(3) model with unrestrictedintercepts and restricted trend coefcients.

    Aid and government expenditureFigures 1 to 3 indicate the generalised impulse response over a 25-year period.Figure 1A shows the response of per capita GDP (DlnGDPP) and per capita aid(DlnAID) to a once for all one standard error shock to per capita development

    expenditure (DlnGd). We nd that the shock has a larger and more persistenteffect on per capita development expenditure itself followed by per capita aid.This is as expected since development projects are of longer duration and, oncecommenced, cannot be abandoned. The shock response of per capita aid to per capitadevelopment expenditure shows a cyclical pattern over 16 years and then tends todie out. Interestingly, the shock response of aid ows follows the response patternof development expenditure. It implies that the larger the development expenditure,the larger will be the aid ow.

    Figure 1B shows the responses of per capita GDP and per capita developmentexpenditure to a once for all one standard error shock to per capita aid. The effectsof shock to per capita aid decreased from a positive 13 per cent to an almost negativefour per cent within a one-year period, and responses of aid to a shock to itself dieout quickly. This implies that changes in aid flows cannot be sustained for long.That is, aid flows follow an average trend. Once again, we find that responses of development expenditure to shocks in aid flows follow the same pattern as theresponses of aid themselves. This implies a close connection between aid anddevelopment expenditure.

    Responses of per capita GDP and per capita aid to a once for all one standarderror shock to per capita non-development expenditure are presented in Figure2A. It shows that responses of non-development expenditure to a shock to itself die out relatively faster than was the case for per capita development expenditure.Aids response to a shock in non-development expenditure follows the responsesof non-development expenditure. We also find that responses of per capita non-development expenditure to a one standard error shock to aid flows do not persist(Figure 2B), as aids response to a shock to itself dies out quickly.

    In sum, we find that, in general, aid responds to shocks to both developmentand non-development expenditure. Additionally, shocks to aid flows cannot besustained for long.

    Aid and government revenueFigure 3A demonstrates the responses of per capita GDP and per capita aid to a onestandard error shock to per capita revenue. The response of revenue to shocks to itself

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    FIGURE 1A

    GENERALISED IMPULSE RESPONSE(S) OF DlnGDPP AND DlnAID TOONE S.E. SHOCK IN DlnGD

    FIGURE 1B

    GENERALISED IMPULSE RESPONSE(S)OF DlnGd AND DlnGDPP TOONE S.E. SHOCK IN DlnAID

    FIGURE 2A

    GENERALISED IMPULSE RESPONSE(S)OF DlnGDPP AND DlnAID TOONE S.E. SHOCK IN DlnGnd

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    initially begins with a larger positive effect of over 8 per cent and then declines tonegative 6 per cent. It then converges quickly towards zero. Thus, it suggests thatper capita revenue does not have much cyclical effect from shocks. Interestingly,when revenue response is negative in the early period, the aid response is found tobe positive. That is, aid is needed to ll the short-fall in revenue.

    The responses of per capita GDP and per capita revenue to a one standarderror shock to per capita aid are displayed in Figure 3B. Generally, the responsesof revenue to the shock in aid flows follow the opposite pattern of responses of aid to the shock to itself. This is largely in line with the pattern of responses of aidto a shock to revenue (Figure 3A). That is, aid is needed to cover the short-fall inrevenue.

    FIGURE 3B

    GENERALISED IMPULSE RESPONSE(S) OF DlnGDPP AND

    DlnREV TO ONE S.E. SHOCK IN DlnAID

    FIGURE 3AGENERALISED IMPULSE RESPONSE(S) OF DlnGDPP AND

    DlnAID TO ONE S.E. SHOCK IN DlnREV

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    6. SUMMARY, CONCLUSION AND POLICY IMPLICATION

    We have investigated the revenue and expenditure behaviour of the Nepalesegovernment in the presence of aid ows. We have found that per capita aid, per

    capita revenue and per capita development and non-development expenditure areall cointegrated. The results also show that aid positively affects both developmentand non-development expenditure in the long-run. However, since aid is mainlygiven for development expenditure, the positive long-run relationship between aidand non-development expenditure may indicate aid fungibility. This is in line withndings for most developing countries.

    However, contrary to most of the early studies, we have found that aid ispositively related to revenue in the long-run. Relevant to this may be aid in theform of technical assistance to improve tax administration and the efficiency of the tax system.

    The analysis of the impulse response function shows that aid responds positivelyto shocks in both development and non-development expenditure as well as to shocksin revenue. That is, government expenditure programs influence aid disbursement,and aid is needed to cover the shortfall in revenue. This implies that aid is generallyused as revenue in the government budget. That is, aid flows can relax governmentbudget constraint, and there is no evidence that aid flows reduce revenue efforts.

    Finally, while foreign aid is an important source of revenue, Nepal should beable to improve and broaden its domestic sources of revenue. Aid can help in manyways. For example, technical assistance can help to improve and extend the tax baseas well as create a more efficient tax administration. Most significantly, Nepals aiddependency can be reduced through linking aid-financed projects to an improveddomestic revenue mobilisation capacity.

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    APPENDIX 1

    SUMMARY OF EMPIRICAL STUDIES OF AID ANDFISCAL BEHAVIOUR

    Study Sample Methodology Findings CommentsHeller (1975) 11 African

    countries (19601970)

    Used cross-section time-series data, andGLS and 2SLS

    Aid increasedinvestment butreduced taxesand borrowings

    Seminal work onscal responsemodel and usedgovernmentsutilitymaximisationframework

    Pack and Pack(1990)

    Indonesia,(19701990)

    Time-series dataand used SUR

    Aid did not leadto a reductionin domestic

    revenue efforts,but stimulatedtotal publicexpenditure

    Model derivedfrom medianvoter model.

    Focusedmore on aidfungibilityrather than scalimpact

    Khilji andZampelli (1991)

    Pakistan (19601986)

    Time-series dataand used FIMLtechnique

    Aid was foundto be fullyfungible

    McGuiretype model.Examined onlythe US aid

    Gang and Khan(1991)

    India (19611984)

    Used time-series dataand estimatedfull system of simultaneousequation with3SLS procedure

    Grants, loansand multilateralaid had nosignicant effecton governmentconsumption

    Heller typemodel. Due tomisspecicationof modelthere existproblems in theinterpretation of results

    Khan andHoshino (1992)

    5 South andSouth EastAsian countries(19561976)

    Pooled time-series and cross-section data,non-linear 3SLS

    Loans werefound morepositive forinvestmentthan grants, andwhile grantsreduced taxburdens, loansincreased it

    Extension of Heller model.Failed to showtotal effects(direct andindirect) andthus ignoredfeedback effects

    Pack and Pack(1993)

    DominicanRepublic (19681986)

    Time-series dataand used SUR

    Found adivergence of aid away fromits intendedpurpose

    Model derivedfrom medianvoter model.The results aredifferent fromtheir ndingsfor Indonesia.Thus, fungibilitydepends on

    country specicfactors

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    Feyzioglu et al.(1998)

    14 and 38developingcountries (19711990)

    Panel data, OLSand GMM

    Aid was notfungible at theaggregate levelin a sample of 14 countries butaid was foundto be fungible in38 countries

    McGuire (1978)type model. Aidwas found to bemore fungiblein agriculture,education andenergy sector

    Franco-Rodriguez et al.(1998)

    Pakistan (19561995)

    Time-seriesdata, non-linear3SLS

    Slightly positiveimpact on publicinvestment andnegative impacton tax effort

    Extended theHeller modelby allowingborrowing onboth capital andconsumptionexpenditure andtreating aid as

    an endogenousvariableSwaroop et al.(2000)

    India (19701995)

    Time-seriesdata, and usedOLS and 2SLS

    Foreign aid didnot inuencethe internallydeterminedpattern of resourceallocation

    McGuire (1978)type model.Aid fungibilityinvestigated inboth federal andstate levels

    Franco-Rodriguez(2000)

    Costa Rica(19711994)

    Time-seriesdata, non-linear3SLS

    A very smallimpact of aidinows on

    public sectorscal behaviour

    Heller typemodel. Notconclusive

    result; it couldbe due toinappropriatetarget variablesand countryspecic factors

    McGillivray(2000)

    Pakistan (19561995)

    Time-seriesdata, non-linear3SLS

    Aid associatedpositively withboth publicinvestment andconsumptionexpenditureand aid hadno impact ontaxation

    Heller typemodel.Disaggregatedaid into grantsand loan aid,but aid was notendogenised inthe model

    McGillivray(2002)

    Philippines(19601997)

    Time-seriesdata, non-linear3SLS

    Almost allmultilateralaid has beenallocated toconsumptionexpenditure andalmost 100 percent domestic

    borrowingallocated to theconsumptionbudget

    Heller typemodel.Ambiguousresults ashe foundmultilateralaid was alsoallocated to

    consumption

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    McGillivray andOuattara (2003)

    Cote dIvoire(19751999)

    Time-seriesdata and appliedscal responsemodel as amaximisingutilityframework, non-linear 3SLS

    Large portion of aid is used fordebt servicingand it doesnot induce areduction inborrowing; alsoborrowing isused for bothinvestment andconsumption

    Heller typemodel. Thendings suggestthat borrowingshould beallowed forboth capital andconsumptionexpenditure inthe model

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