The impact of the Interaction between institutional quality and aid volatility on growth: theory and evidence

Economic Modelling 29 (2012) 716–724

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The impact of the Interaction between institutional quality and aid volatility on growth: theory and evidence Jay Kathavate, Girijasankar Mallik ⁎ School of Economics and Finance, University of Western Sydney, Locked Bag 1797, Penrith South DC-1797, Australia

a r t i c l e

i n f o

Article history: Accepted 30 January 2012 JEL Classification: F35 O41 O47 Keywords: Foreign aid Volatility Institutional quality

a b s t r a c t We analyze both theoretically and empirically, the effect of aid volatility and its interaction effect with institutional quality on per capita economic growth. Our theoretical model, in which an aid-recipient government, operating in an institutional environment of some given quality (making choices over the distribution of aid), predicts that a negative effect of aid volatility on growth is mitigated by stronger institutional quality. We use panel data covering the period 1984–2004 for 78 countries to test this theoretical prediction. Using Generalised Methods of Moments (GMM) we find the relationship between growth and aid volatility is significantly negative and depends on institutional quality. Our baseline results are robust to various computations of aid volatility and foreign aid, time periods, sub-samples and additional covariates. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Foreign aid effectiveness has been a heavily debated topic in development economics. Since Burnside and Dollar's (2000) influential paper, "Aid, Policies, and Growth", debates on aid effectiveness have centered on the importance of factors such as policy, governance and institutions, politics, and climate. The role of aid volatility in aid effectiveness studies, however, has received scant attention. Foreign aid volatility entails a direct welfare cost for risk-averse individuals, as well as an indirect one through its adverse effect on income growth and development. Only few papers have focused on the output-effects of aid volatility in underdeveloped countries.1 Bulir and Hamann (2008) analyse the effects of increased aid dependence on the relative aid volatilities (with respect to domestic revenue), the pro-cyclicality of aid flows, and the importance of commitments in determining disbursements. Fielding and Mavrotas (2006) highlight the importance of policy and institutions: weaker policy environments hinder aid absorption, in which aid volatility is induced by weak institutions. Hudson and

⁎ Corresponding author. Tel.: + 61 2 9685 9664; fax: + 61 2 9685 9105. E-mail addresses: [email protected] (J. Kathavate), [email protected] (G. Mallik). 1 See, Alessandro and Tressel, 2006; Arellano et al., 2009; Bulir and Hamann, 2008; Fielding and Mavrotas, 2006; Hudson and Mosley, 2008; Lensink and Morrissey, 2000; Neanidis and Varvarigos, 2007 for detail. 0264-9993/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econmod.2012.01.020

Mosley (2008) account for the effects of “upside” and “downside” volatility on growth. They focus on whether the determinants of aid volatility (in the form of global downturns, emergencies, and other exogenous factors) have asymmetric effects on growth. Lensink and Morrissey (2000) stress the importance of separating anticipated and unanticipated aid. They find aid to have a positive effect on growth after controlling for aid uncertainty. Neanidis and Varvarigos (2007) show how productive aid use has growth enhancing effects and unproductive aid use has growth impeding effects, given negligible aid volatility; and conversely, productive aid use has growth impeding effects and unproductive aid use growth enhancing effects, given large aid volatility. They conclude that it is not important whether aid is allocated in a productive or non-productive way, but the volatility of the allocation that impacts growth. Arellano et al. (2009) study the effects of aid volatility on welfare. They show how low aid volatility is beneficial for consumption (welfare enhancing), whereas, high aid volatility leads to consumption volatility (welfare impeding). The authors draw a connection between higher aid volatility, consumption volatility, and investment. Higher volatility, leads to consumption volatility, which leads to changes in investment. An increase in the level of aid however leads to no such change in investment but an increase in consumption. Alessandro and Tressel (2006) show how optimal policy decisions by the government can alleviate the effects of aid volatility on the trade balance. The general argument of most papers is that aid volatility impacts growth negatively due to fiscal uncertainty placed on governments. The work, however, has by and large taken aid-recipient governments to be passive and context-independent – i.e. nominally distributing aid according to some fixed rule, leaving all decision-making to the private sector. The work can be built upon by delineating the effects of

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724

institutional quality on the distribution of aid by recipient governments, where the aid receiving government is a utility maximizing agent. The government can be hypothesized to be an apparatus through which aid volatility is dispersed. A government facing a strong institutional framework faces greater constraints to inappropriate use of aid. Such a government would attempt to smooth foreign aid fluctuations (through savings, investing, etc.) to keep the public content. A government facing poor quality institution, on the other hand, would have no incentive to act in the interest of its citizenry, and instead would inappropriately use money for its own corrupt consumption without attempting to smooth aid fluctuations. These two outcomes would have antithetic effects on a country's growth. Therefore, government behavior in an institutional environment plays an important role in determining the potential impact of aid volatility on growth. The treatment of these interactive effects between incoming foreign aid volatility and a government's behavior has, in the context of aid volatility studies, been absent. In what follows, a theoretical model is developed in which an aidrecipient government, operating in an institutional environment of some given quality, makes choices over the distribution of aid. It is theorized that previously unnoticed differences in output-effects of aid volatility emerge depending on the quality of the institutional environment under which the recipient government makes its choices. Therefore, we would like to test the following hypotheses: i) Does aid volatility reduce per capita economic growth? ii) Does higher institutional quality mitigate the effects of aid volatility? 2. Theoretical model of aid volatility and institutional quality on growth The government can invest in infrastructure, k. which is financed exclusively by aid donations. Total output is determined by Y = Y(k). 2 Further assume ∂Y > 0 and ∂ Y2 b0. ∂k ∂k If the government receives aid of A, then A − k remains after expenditure on public infrastructure. The government consumes the amount A − k for itself and obtains a utility of u(A − k), where u is an increasing and concave utility function. This consumption expenditure is conceptualized here as “corrupt” expenditure in the sense that it is aid that is systematically “siphoned off” by politicians for their own personal benefit rather than being distributed for the
 benefit of citizens. pðx0 þ xÞ Aid donations are stochastic such that A ¼ , ð1−pÞðx0 −xÞ where p is the probability of receiving “high” 2 aid and (1 − p) the probability of receiving “low” 3 aid. x0 is the amount of expected aid, and xacts as a measure of volatility. Since aid is non-negative, we require that x0 ≥ x. It is assumed that p is 0.5, i.e., there is a 50% chance of receiving either x0 + x or x0 − x. If the government invests k then the expected payoff from consumption (“corruption”) is: E½uðA−kÞ ¼

1 1 uðx0 þ x−kÞ þ uðx0 −x−kÞ 2 2

3

Higher than expected aid. Lower than expected aid.

government decision-making such as corruption commissions, freedom and independence of the press, freedom and independence of universities, independence and impartiality of the judicial system, democratic accountability and so on. A high quality institutional environment entails greater public accountability than a low quality environment. Thus, the higher the quality of the oversight institutions, the greater the constraint on a government to inappropriately use of aid for its own consumption purposes because there will be, under such conditions, a higher probability of loosing office. On the other hand, a government facing a low quality institutional environment is not worried about re-election. Such a government may be able to remain in office through corrupt means – by bribery, the rigging of elections, military intervention, etc. – and by virtue of the fact that the general public will not be fully aware of the extent of corrupt behavior due to the lack of transparency. In such cases, a government can place greater weight on maximizing utility from expected consumption than the country's per capita GDP. This being so, in order to continue to maximize its utility (from expected consumption), a government must take into consideration the level of consumption (“corruption”) it engages in given the institutional environment in which it makes its decisions over the distribution of aid. Thus, a government must solve the following problem:
  1 1 max fqY ðkÞ þ ð1−qÞE½uðA−kÞg ¼ max qY ðkÞ þ ð1−qÞ uðx0 þ x−kÞ þ uðx0 −x−kÞ 2 2 k k

Where, Y = Y(k) , where q is the institutional quality of the government [q ∈ (0, 1)] can be interpreted as a proxy for the strength of democratic institutions, which effectively weights the trade-off between Y and the government's own consumption (“corruption”).E [u(A − k)] is the expected utility function. The first-order condition for the government's problem reveals that the optimal choices of k,{k = k*} satisfy (optimized variable being asterisked) the following condition: 
  ∂ 1 1 ¼0 max qY ðkÞ þ ð1−qÞ uðx0 þ x−kÞ þ uðx0 −x−kÞ 2 2 k ∂k       1 1 ′  ′  ∴qY ′ ðk Þ þ ð1−qÞ u x0 þ x−k ð−1Þ þ u x0 −x−k ð−1Þ ¼ 0 2 2 i 1 h ′  ′  ′  ∴qY ðk Þ ¼ ð1−qÞ u x0 þ x−k þ u x0 −x−k 2 ð2Þ Re-arranging (2)

h i u′ ðx0 þ x−k Þ þ u′ ðx0 −x−k Þ 2q ¼ ð1−qÞ Y k ðk Þ

ð3Þ

ð1Þ

The government however, faces a trade-off between maximizing expected utility from consumption and increasing its chances of retaining office. The government can improve its chances of staying in office by increasing the country's per capita GDP and thereby improving living standards. In addition, the government's choice vis-à-vis the distribution of aid is constrained by the quality of the institutional environment in which it operates. Institutional quality refers to the extent of institutional “checks and balances” on

2

717

Proposition 1. Assuming the curvature of the utility function becomes less negative as consumption rises (i.e. u‴(⋅) > 0), optimal infrastructure investment (k⁎) is decreasing with respect to volatility (x). Proof. Implicitly differentiating (3) with respect to volatility,x we obtain: From (3), we get

h i u′ ðx0 þ x−k Þ þ u′ ðx0 −x−k Þ 2q ¼ ð1−qÞ Y ′ ðk Þ h  i 2q ′  ′  ′  Y k ¼ u x0 þ x−k þ u x0 −x−k ð1−qÞ

718

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724 

Differenting both sides:

kx ¼

     2q ∂k ∂k ″    ∂k ″  ″  1− −1− Y k ¼ u x0 þ x−k þ u x0 −x−k ð1−qÞ ∂x ∂x ∂x

let; x0 þ x−k ¼ H; and x0 −x−k ¼ L    2q ∂k ″ ∂k ″    ∂k ″ ″ ″ Y k ¼ u ðH Þ− u ðH Þ−u ðLÞ−u ðLÞ ð1−qÞ ∂x ∂x ∂x    2q ∂k ″ ∂k ″    ∂k ″ ″ ″ Y k þ u ðH Þ þ u ðLÞ ¼ u ðH Þ−u ðLÞ ð1−qÞ ∂x ∂x ∂x  ∂k 2q ″  ″ ″ ″ ″ Y k þ u ðHÞ þ u ðLÞ ¼ u ðH Þ−u ðLÞ ∂x ð1−qÞ

Differentiating Eq. (5) with respect to q, we get: 2 ∂kx ∂q

ð4Þ Since, L ≡ x0 − x − k*, H ≡ x0 + x − k*, therefore, H > L. If u‴(⋅) > 0, u″(H) > u″(L), which implies u″(H) − u″(L) > 0 that the numerator is positive; u″(⋅) b 0 implies u″(H), u″(L) b 0, and using Y ″(k*) b 0, we conclude the denominator is negative (since,0 b q b 1).  Therefore, from Eq. (4), ∂k b0 and the optimal infrastructure ∂x investment (k*) is decreasing with respect to volatility (x). Most utility functions are stipulated to possess this property (e.g. log and power utility), implying that we would predict aid volatility to lower infrastructure investment. With respect to infrastructure investment, since higher aid volatility implies greater variation (amplitude) of potential aid donations from period to period, a government moderates its infrastructure investment plans (from large scale to smaller scale) so that such small scale projects can be completed in a given (high aid) period, or if aid in a following period is low, the smaller scale projects can continue or be completed. This is to be contrasted with the alternative: if, in the face of high aid volatility, a government that persisted with larger long-term infrastructure plans, then such projects would take on “start-stop” character that would be both unpopular (aka “white elephants”) and indirectly create uncertainty for private investment which “factors in” and is thus partly dependent on the completion of such projects. Proposition 2. Assuming the curvature of the utility function becomes less negative as consumption rises (i.e. u‴(⋅) > 0) the marginal effect of volatility on optimal infrastructure investment (k⁎x) is negatively related to institutional quality (q). We now posit that a “higher” quality institutional environment implies that the government will engage in greater saving (when aid is high) and borrowing (when aid is low) in order to countervail the uncertainty generated by higher volatility. In short, in a “higher” quality environment, the government can engage in larger scale infrastructure projects without the hazards of “stop-start” expenditure noted above, by “smoothing” expenditure from period to period. Proof. To prove proposition 2,

Therefore, Eq. (4) can be re-written as: ∂k Φ ¼ 2q ∂x Γ þ Ψ 1−q

−2ΦΨ ¼  2 2q Γ þΨ ð1−qÞ2 ð1−qÞ ⇒

h i ∂kx ″ ″ ″  > 0 ∵Φ; −2Ψ > 0; as u ðHÞ−u ðLÞ ¼ Φ and Y k ¼ Ψb0 ∂q

Therefore, marginal effect of volatility on optimal infrastructure investment (k⁎x) is negatively related to institutional quality (q). 3. Methodology To empirically test our main hypothesis we construct an interaction term combining quality and volatility. We construct a quality index by computing an equally weighted average of institutional corruption, democratic accountability and political rights. Since there is no strong apriori evidence that any one of these measures contributes to an institutional framework's quality more than the other, we find it reasonable to take equally weighted averages. Indeed, the theoretical framework indicates all three measures to be important: political processes may be free of corruption, but unless the government has a certain degree of accountability and unless the people have means to fair elections, the overall institutional quality may be assessed as poor. Aid volatility is obtained by use of the Hodrick-Prescott filter, which separates the trend and cyclical components of our aid series. Taking standard deviations of the cyclical component over three year time intervals forms our aid volatility series.4 The main hypothesis we test is by employing the following model: 

Aid GDPpc it m n X X þβ3 V it þ β4 Q it þ β5 ðQ it  V it Þ þ β1j X jit þ β2l Dlt þ εit

g it ¼ α 0 þ βt þ β1 lnðinitialGDPpcit Þ þ β2

ð6Þ

l¼1

Where, git is the average per capita GDP growth in country i at time t. initial GDPpcit is the initial GDP per capita at the beginning of the relevant time period. Vit is aid volatility and Qit is the relevant quality index for country i at time t, while (Qit × Vit) is the product of aid volatility and quality. Xk, it is the vector of variables used by

i h i ″ ″ ″ ″ ″  u ðHÞ−u ðLÞ ¼ Φ; u ðH Þ þ u ðLÞ ¼ Γ; andY k ¼ Ψ



7 ∂ 6 Φ 6  7 5 2q ∂q 4 ΓþΨ ð1−qÞ

j¼1

Let,

kx ¼

¼

3

   Φ ∂ 2q ¼ ð−1Þ  Γ þΨ  2  ð1−qÞ ∂q 2q Γ þΨ ð1−qÞ   −Φ 2ð1−qÞ−2qð−1Þ ¼  2  Ψ 2 2q ð1−qÞ Γ þΨ ð1−qÞ   −Φ 2 ¼  2  Ψ 2q ð1−qÞ2 Γ þΨ ð1−qÞ

∂k u″ ðH Þ−u″ ðLÞ ¼ 2q ∂x ″  Y k u″ ðH Þ þ u″ ðLÞ þ ð1−qÞ i h u″ ðH Þ−u″ ðLÞ ∂k
 ∴ ¼

″  2q ∂x u ðLÞ þ u″ ðH Þ þ Y ″ ðk Þ 1−q

h

Φ   Γ þ Ψ 2qð1−qÞ−1

ð5Þ

4 Bulir and Hamann (2008) used similar (detrained the natural logarithm of the series using HP filter) method for calculating aid volatility.

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724 Table 1 Summary Statistics of the variables under study.

Table 2 Impact of Volatility & Interaction Effects on Growth, OLS & GMM Estimations (Dependent Variable is Average Annual Growth of Per Capita GDP).

Variable

Mean

St.dev.

Min

Max

GDP growth per capita Institutional Quality Aid volatility Interaction Investment Initial Income Aid per capita Initial life Expectancy (log) Fertility N = number of observations

0.010 2.538 0.286 0.688 20.947 7.043 5.864 4.145 3.712 492

0.058 1.079 0.402 1.142 6.759 1.245 9.151 0.152 1.711

− 0.406 0.000 0.006 0.000 3.2465 4.416 0.000 3.644 1.120

0.122 5.000 2.954 12.3003 57.394 10.459 67.929 4.379 7.134

most cross country growth studies which have been able to explain a significant portion of the variation in real per capita GDP. They are fertility rate, tropical land area, log of initial life expectancy as a proxy for the health conditions prevalent in a country, and investment which is measured by gross fixed capital formation. Dit are regional dummies (Asia, Europe, Sub-Saharan Africa and The Americas) to capture variation in the dependent variable due to regional differences.α0 is a constant and βt captures common deterministic trends by incorporating dummies for different time periods. 5 The inclusion of the interaction term reduces the partial effect of volatility on growth to the following: ∂GDPpc ¼ β3 þ β5 Q ∂V

719

ð7Þ

The interaction term is included to capture the belief that better quality institutions have a mitigating effect on the negative impact of aid volatility on a country's growth. A negative value for β3 and a positive value for β5 would confirm this. Indeed, the term β5Q probably does not outweigh β5 for all observations on quality, and thus, we expect ∂GDPpc b0. This would be in line with previous findings ∂V of the negative effect of aid volatility on growth (Arellano et al., 2009; Hudson and Mosley, 2008; Lensink and Morrissey, 2000; Neanidis & Varvarigos, 2009). Our benchmark model is estimated with OLS and with difference and system GMM. Many researchers used OLS and 2SLS, 6 but there are advantages of GMM over IV and OLS. If heteroskedasticity is present, the GMM estimator is more efficient than the simple IV estimator. According to Baum et al. (2003), page 11-“————if heteroskedasticity is not present, the GMM estimator is no worse asymptotically than the IV estimator”. OLS estimation pools observations across cross sections and by using all the variation in the data tends to be more efficient than performing individual OLS on repeated cross sections. The pooled OLS, however, fails to account for the potential endogeneity of the right hand side variables. Specifically, it fails to account for potential country specific variations which are unmodelled and unobserved. Any significant correlation between unobserved country specific factors and the right hand side regressors renders the OLS estimations inconsistent (Hansen & Tarp, 2001). In general, the variables measured with an error term tend to display a bias toward zero and OLS does not account for standard errors from the first stage estimator (see Arellano et al., 2009). Moreover, GMM addresses potential endogeneity concerns

5 The autocorrelation tests and robust standard errors reported later in the paper assume absence of autocorrelation across groups in the error term. Thus, including time dummies increases the chances of this assumption holding (Roodman, 2006). 6 For example, Chervin and Wijnbergen (2010) used OLS and 2SLS while estimating growth equation using aid volatility.

(1)

(2)

(3)

(4)

OLS

OLS

GMM-SYS

GMM-DIFF

Interaction

0.008⁎⁎ (0.025) − 0.096⁎⁎⁎ (0.000) 0.028⁎⁎⁎

0.007 (0.120) − 0.099⁎⁎⁎ (0.000) 0.028⁎⁎⁎

0.012 (0.165) − 0.137⁎⁎⁎ (0.005) 0.046⁎⁎⁎

0.020⁎⁎ (0.041) − 0.094⁎⁎ (0.027) 0.029⁎⁎

Investment

(0.000) ———

Initial GDP per capita (ln)

———

Aid per capita

——— ———

Fertility

———

Countries/N adj. R2 Number of instruments AR(1) test (p-value) AR(2) test (p-value) Hansen J-statistic

78/527 0.245

(0.006) 0.003⁎⁎⁎ (0.010) − 0.017⁎⁎ (0.042) 0.001 (0.503) − 0.039 (0.591) − 0.014 (0.233) 78/492

(0.031) 0.001 (0.165) − 0.073⁎ (0.078) 0.002⁎⁎

Life expectancy (ln)

(0.002) 0.001⁎⁎ (0.011) − 0.009⁎⁎⁎ (0.003) 0.001 (0.111) 0.049 (0.122) − 0.008⁎⁎⁎ (0.008) 78/492 0.344

(0.043) − 0.351 (0.239) 0.048 (0.119) 78/412

61 0.000 0.56 0.16

72 0.005 0.88 0.43

Quality Volatility

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) Instrumented variables appear in bold type. iv) ***,** and * represents the significance at 1%, 5% and 10% respectively.

between the set of cross-country regressors and other country specific characteristics. Further, our model consists of more moment conditions than model parameters, and our panel dataset consists of a short time

Table 3 Impact of Volatility & Interaction Effects on Growth with Different Smoothing Parameters (Dependent Variable is Average Annual Growth of Per Capita GDP). (1)

(2)

(3)

(4)

GMM-DIFF

GMM-SYS

GMMDIFF

GMMSYS

0.006 (0.536) − 0.150*** (0.002) ———

0.018⁎ (0.052) ———

0.010 (0.227) ———

Interaction (λ = 7)

0.015 (0.133) − 0.110** (0.012) ——— − 0.122⁎⁎⁎ (0.001) 0.041⁎⁎⁎

———

Interaction (λ = 100)

0.055⁎⁎⁎ (0.000) ———

———

(0.004) ———

0.032⁎⁎⁎ (0.009) 0.001 (0.233) − 0.072** (0.043) 0.002** (0.046) − 0.139 (0.579) 0.042* (0.093) 78/412 72 0.001 0.90 0.54

0.044⁎⁎⁎ (0.000) 0.0024** (0.021) − 0.016* (0.076) 0.001 (0.364) 0.004 (0.965) − 0.017 (0.134) 78/492 61 0.000 0.81 0.27

Quality Volatility (λ = 7) Volatility (λ = 100) (0.011)

Investment

0.001 (0.152) Initial GDP per capita − 0.067* (ln) (0.067) Aid per capita 0.002** (0.022) Life expectancy (ln) − 0.173 (0.519) Fertility 0.040 (0.147) 2 Countries/N adj. R 72/412 Number of instruments 72 AR(1) test (p-value) 0.000 AR(2) test (p-value) 0.99 Hansen J-statistic 0.55

0.002** (0.025) − 0.019** (0.041) 0.001 (0.471) 0.020 (0.795) − 0.016 (0.204) 78/492 61 0.000 0.81 0.12

− 0.091⁎⁎

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) Instrumented variables appear in bold type. iv) ***,** and * represents the significance at 1%, 5% and 10% respectively.

720

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724

Table 4 Impact of Volatility & Interaction Effects on Growth with Additional Controls, GMM Estimations(Dependent Variable is Average Annual Growth of Per Capita GDP).

Quality

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

GMM

GMM

GMM

GMM

GMM

GMM

GMM

GMM

0.015⁎⁎

Interaction

0.013 (0.158) − 0.136⁎⁎⁎ (0.006) 0.045⁎⁎⁎

0.011 (0.240) − 0.145⁎⁎⁎ (0.005) 0.047⁎⁎⁎

0.012 (0.1) − 0.138⁎⁎⁎ (0.003) 0.048⁎⁎⁎

(0.029) − 0.076⁎⁎ (0.012) 0.027⁎⁎⁎

0.013 (0.134) − 0.130⁎⁎⁎ (0.004) 0.040⁎⁎

0.001 (0.920) − 0.250⁎⁎⁎ (0.000) 0.076⁎⁎⁎

0.010 (0.2) − 0.160⁎⁎⁎ (0.005) 0.056⁎⁎⁎

0.001 (0.978) − 0.240⁎⁎ (0.015) 0.077⁎⁎

Investment

(0.007) 0.003⁎⁎

(0.008) 0.004⁎⁎⁎ (0.004) − 0.017⁎⁎ (0.029) 0.001 (0.538) 0.024 (0.787) − 0.018 (0.157)

(0.004) 0.003⁎⁎ (0.011) − 0.018⁎⁎ (0.033) 0.001 (0.391) − 0.021 (0.813) − 0.019⁎ (0.09)

(0.004) 0.001 (0.540) − 0.011 (0.390) − 0.001 (0.985) 0.110 (0.510) − 0.016 (0.262)

(0.011) 0.003⁎⁎ (0.022) − 0.017⁎⁎ (0.033) 0.001 (0.261) 0.080 (0.458) − 0.016 (0.205)

(0.000) 0.001 (0.138) − 0.014 (0.120) 0.001 (0.460) − 0.011 (0.901) − 0.014 (0.210)

(0.006) 0.002 (0.197) − 0.022⁎⁎ (0.049) 0.001 (0.192) − 0.001 (0.991) − 0.022⁎ (0.077)

(0.017) 0.002 (0.211) − 0.002\ (0.783) 0.001 (0.517) 0.016 (0.851) − 0.090 (0.443)

Volatility

(0.037) − 0.016⁎ (0.068) 0.001 (0.457) − 0.039 (0.608) − 0.009 (0.494) 0.001 (0.494)

Initial GDPPC(ln) Aid PC Life exp(ln) Fertility Land area

− 0.002 (0.188)

Population Trade Share

0.013 (0.523) − 0.001 (0.265)

Schooling Ethnic Frac

0.079 (0.233)

Openness

0.001 (0.585) − 0.005 (0.478)

Inflation(ln) M2/GDP 2

Countries/N adj. R Number of instruments AR(1) test (p-value) AR(2) test (p-value) Hansen J-statistic

78/492 61 0.000 0.591 0.120

78/492 61 0.000 0.466 0.230

78/485 71 0.000 0.650 0.215

78/363 61 0.039 0.699 0.297

78/492 66 0.000 0.468 0.196

78/473 71 0.000 0.227 0.328

78/461 71 0.000 0.335 0.296

− 0.001* (0.086) 78/469 71 0.000 0.326 0.242

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) Instrumented variables appear in bold type. iv) ***,** and * represents the significance at 1%, 5% and 10% respectively.

dimension (T=21) and a larger country dimension (N =78).7 Therefore the use of GMM in this paper is appropriate as it addresses potential endogeneity problems of the regressors and incorporates fixed effects. Arellano and Bond (1991)) pioneered the difference-GMM estimator while the system-GMM estimator is a product of the work done by Blundell and Bond (1998). Identification in both types of estimators is based on first-differencing and using lagged values of the endogenous variables as instruments. In the difference-GMM estimator (GMMDIFF), lagged levels are used to instruments for the differenced right hand side variables, while for the system-GMM estimator (GMM-SYS) the estimated system is composed of a difference equation instrumented with lagged levels and additionally a level equation, which is estimated using lagged differences as instruments (Bond et al., 2001; Raghuram and Subramanian, 2008).8 We report results using both the GMM-DIFF and GMM-SYS. Additionally macroeconomic policy variables such as inflation, M2 financial development (GDP ), have been shown to be correlated with 7 If the time dimension is large, then dynamic panel bias becomes insignificant – in such a case, a fixed estimator is recommended (see Roodman, 2006). Further, as the time dimension of the panel increases, the number of instruments in the GMM-SYS and GMM-DIFF tends to explode; additionally, as the cross-sectional dimension increases, the Arellano-Bond autocorrelation test may become unreliable. 8 We use the xtabond2 command in STATA 10 to conduct all GMM-DIFF & GMM-SYS regression analyses while the GMM estimations were implemented using STATA 10's in-built xtabond command.

country specific cultural, socioeconomic characteristics such as fertility, distribution of income, social capital, etc. (Alesina & Rodrik, 1994; Easterly & Ross, 1997; Temple, 1998). Furthermore, the fluctuations of aid may be a result of changes in the level of income distribution: a rise in income may result in decreased aid flows (Alesina & Dollar, 2000; Trumbull & Wall, 1994). This implies that aid volatility and income volatility share a negative correlation. Ramey & Ramey (1995)9 have shown that income volatility has a strong negative correlation with developing country growth. Thus, aid volatility may also be a potentially endogenous regressor. Empirically it has been shown that a country's income per capita, distribution of income, efficiency of its tax system and the educational attainment of its population, ethnic fractionalization and endowment of natural resources are correlated with its institutional quality (Alonso & Garcimartín, 2010). Therefore, variables such as ethnic fractionalization and natural resources will be affected by changes in institutional quality, which will impact income per head and tax revenues respectively, where changes in income per head will have an effect on growth. The instruments we use for difference-GMM are the potentially endogenous variables themselves. The system-GMM uses second

9 See also Aizenman & Marion, 1999 . “Volatility & Investment: Interpreting Evidence From Developing Countries”.

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724 Table 5 Impact of Volatility & Interaction Effects on Growth with Different Period Averages, Fixed Effects (FE) & Random Effects (RE) Estimations. (Dependent Variable is Average Annual Growth of Per Capita GDP). (1)

(2)

(3)

(4)

FE-11 year average

RE-11 year average

FE-7 year average

RE-7 year average

− 0.011 (0.113) Volatility − 0.080** (0.012) Interaction 0.022** (0.034) Investment 0.002*** (0.006) Initial GDP per − 0.058*** capita (ln) (0.009) Aid per capita − 0.001 (0.282) Life expectancy 0.692** (ln) (0.030) Fertility − 0.0156 (0.479) N 131 F 3.087 R2 0.10 Chi-squared

− 0.005 (0.412) − 0.112*** (0.000) 0.033*** (0.001) 0.001** (0.013) − 0.012*** (0.007) 0.001 (0.662) 0.111* (0.052) − 0.005 (0.225) 131

− 0.003 (0.628) − 0.112*** (0.000) 0.030*** (0.001) 0.002*** (0.000) − 0.079*** (0.000) − 0.001** (0.043) − 0.159 (0.344) − 0.014 (0.275) 205 14.04 0.043

− 0.001 (0.905) − 0.125*** (0.001) 0.035*** (0.005) 0.002*** (0.000) − 0.022*** (0.001) − 0.001 (0.774) 0.025 (0.814) − 0.014* (0.056) 205

Quality

0.488 59.22

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Table 6 Impact of Volatility & Interaction Effects on Growth with Different Measure of Aid (Dependent Variable is Average Annual Growth of Per Capita GDP).

Quality Volatility Interaction Investment Initial GDP per capita (ln) Net aid per capita Life expectancy (ln) Fertility Countries/N Number of instruments AR(1) test (p-value) AR(2) test (p-value) Hansen J-statistic

0.441 114.3

GMM-DIFF

GMM-SYS

(1)

(2)

0.030⁎⁎⁎ (0.003) − 0.156⁎⁎ (0.019) 0.050⁎⁎

0.024⁎⁎ (0.008) − 0.175⁎⁎ (0.022) 0.052⁎⁎

(0.034) 0.002 (0.446) − 0.102⁎⁎ (0.023) 0.001 (0.559) − 0.478 (0.260) − 0.050⁎⁎⁎ (0.000) 78/412 54 0.000 0.989 0.090

(0.032) − 0.001 (0.535) − 0.037⁎⁎⁎ (0.002) − 0.001 (0.564) 0.049 (0.696) − 0.036⁎⁎⁎ (0.010) 78/492 61 0.000 0.558 0.173

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) ***,** and * represents the significance at 1%, 5% and 10% respectively.

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) ***,** and * represents the significance at 1%, 5% and 10% respectively.

4. Data order and deeper lags in differences and levels of the potentially endogenous regressors. With the instrumental variable approach our model becomes:  Aid Δg it ¼ ðβt −βt−1 Þ þ β1 Δð ln initial GDPpcit−1 Þ þ β2 Δ GDPpc it−1    þβ3 ðV it −V it−1 Þ þ β4 ðQ it −Q it−1 Þ þ β5 ΔðQ  V Þit−1 ′ m

X  þ β1j X j;i;t −X j;i;t−1 þ ∑ðεit −εit−1 Þ

ð8Þ

Our data set is composed of 78 aid receiving countries over the period 1984–2004. 10 Aid data was collected from the OECD's DAC and CRS databases in the form of official development assistance (ODA). Data on GDP, investment and additional controls was taken from the World Development Indicators (WDI) database. Though the data are based on annual observations, we use the series’ longrun information by taking averages over three year time intervals 11 (3 year time intervals: 1984–1986, 1987–1989, and so on till 2002–2004).

j¼1

5. Results and discussions By construction the regressors are correlated with the error term (e.g. Δgit − 1 with εit − 1). There are three specification tests we apply to test our main hypothesis: i) We test the instrument validity by using Hansen's J statistic of over-identifying restrictions. The Hansen's J statistic is used in place of the Sargan test of over-identifying restrictions because of its consistency in the presence of autocorrelation and heteroscedasticity (Neanidis & Varvarigos, 2009; Roodman, 2007). We make sure we check whether deeper lags of the instrumented variables are correlated with deeper lags of the disturbances. ii) We use the Arellano and Bond (1991) AR (1) & AR (2) tests for first and second order serial autocorrelation. For system-GMM we only check for the absence of second order serial autocorrelation as Eq. (2) is in first differences. iii) We restrict the number of instruments to be less than the number of countries in our sample. Where the number of instruments exceed the number of countries we report the robustness results to reducing the instrument count. Note that too many instruments can lead to over fitting the exogenous variables, where the instruments fail to extract the endogenous components (Roodman, 2006).

Table 1 shows the summary statistics of the variables under study. We begin by estimating a simpler version of (5) with OLS. Table 2 shows the results of our baseline model. Moving to the right of the columns we subsequently add more right hand side controls and m P allow variables in equation βj X j;i;t to be endogenous. Column (1) illusj¼1

trates the effects of institutional quality, aid volatility and the influence of institutional quality on the impact of aid volatility on growth. The interaction term is positive and strongly significant. This illustrates that a one unit increase in quality (for a given level of aid volatility) mitigates the negative impact of volatility on growth. Column (2) shows the result when we add the control variables mentioned earlier. These variables have been shown to have a significant impact on growth (Zhang and Li, 2007). The coefficient on initial GDP captures cross country convergence and is negative as expected. We should note that the sign 10 The starting year is restricted by the availability of the ICRG dataset and the end year is a function of the lack of availability of aid data for Armenia, Azerbaijan, Belarus, Estonia, Latvia, Lithuania, Moldova, Kazakhstan, Kyrgyzstan, Tajikistan, and Ukraine from 2005 onwards. Excluding these countries, which are of the same region, may bias the results. Thus, we have included them and as a consequence our series is limited to the year 2004. 11 We use three year averages rather than conventional 4 year averages which appear in most growth literature because of the length of our series being divisible by 3 – making panel data analysis amenable to the given time series.

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J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724

Table 7 Impact of volatility and interaction effects on growth with different subsamples using GMM estimation (dependent variable-average per capita GDP annual growth). W/o Africa

W/o Europe

W/o Asia

W/o Americas

Low Income

(1)

(2)

(3)

(4)

(5)

Interaction

0.011 (0.254) − 0.131⁎⁎⁎ (0.008) 0.044⁎⁎⁎

0.008 (0.560) − 0.170⁎ (0.060) 0.056⁎

0.014 (0.146) − 0.123⁎⁎ (0.026) 0.027⁎

0.0121 (0.165) − 0.124⁎⁎⁎ (0.007) 0.044⁎⁎⁎

0.009 (0.638) − 0.247⁎⁎ (0.022) 0.082⁎⁎

Investment

(0.009) 0.004⁎⁎⁎

(0.073) 0.001 (0.168) − 0.007 (0.470) − 0.001 (0.496) − 0.021 (0.823) − 0.004 (0.745) 64/413 61 0.000 0.093 0.261

(0.079) 0.002 (0.221) − 0.022⁎⁎ (0.013) 0.001 (0.203) 0.091 (0.441) − 0.015 (0.146) 56/358 61 0.002 0.701 0.686

(0.007) 0.003⁎⁎⁎ (0.008) − 0.021⁎⁎ (0.020) 0.001 (0.151) − 0.022 (0.773) − 0.017 (0.103) 63/395 61 0.000 0.548 0.227

(0.044) 0.002 (0.217) − 0.021 (0.195) 0.001 (0.850) − 0.058 (0.556) − 0.021 (0.184) 51/332 61 0.000 0.679 0.779

Quality Volatility

(0.004) − 0.009 (0.395) − 0.001 (0.470) − 0.137 (0.582) − 0.009 (0.436) 52/317 61 0.001 0.488 0.556

Initial GDP per capita (ln) Aid per capita Life expectancy Fertility Countries/N Number of instruments AR(1) test (p-value) AR(2) test (p-value) Hansen J-statistic Variable description Variable

Definition

Source

Corruption; accountability; bureaucratic quality

International Country Risk Guide (ICRG)1

GDP per capita growth rate Initial GDP per capita (log) Investment Volatility Aid per capita Life expectancy (log) Interaction Fertility Volatility (λ = 7) Volatility (λ = 100) Interaction (λ = 7) Interaction (λ = 100) Land Area

Ordinal data measured on a scale between 1 and 7. High corruption, low accountability and bureaucratic quality are represented by a number at the lower end of the scale; conversely, low corruption and high accountability and high bureaucratic quality are indicated by a number at the higher end of the scale. Annualized average growth rate of GDP per capita GDP per capita in constant 2000 USD for the first year of each period Gross Fixed Capital Formation (%) in constant 2000 Volatility of foreign aid for each period Official Development Assistance Life expectancy at birth, total (years) Product of volatility and institutional quality each period Fertility rate, total (births per woman) Volatility of foreign aid for each period obtained for a smoothing parameter of 7 Volatility of foreign aid for each period obtained for a smoothing parameter of 100 Product of volatility and institutional quality each period for a smoothing parameter of 7 Product of volatility and institutional quality each period for a smoothing parameter of 100 Square kilometers

Population Trade share Schooling Ethnic Fractionalization Real openness

Total Ratio of international trade to GDP in constant 2000 USD School enrollment, secondary (% gross) Degree of ethnic, linguistic and religious heterogeneity Nominal trade divided by PPP GDP

Log inflation M2/GDP Net aid per capita

Natural logarithm of 1 + consumer price inflation Money and quasi money (% of GDP in current USD) Gross disbursements minus aid repayments

World Bank, WDI World Bank, WDI World Bank, WDI Authors’ calculations OECD,2 DAC (online) World Bank, WDI Authors’ calculations World Bank, WDI Authors’ calculations Authors’ calculations Authors’ calculations Authors’ calculations World Bank, Global Development Network World Bank, WDI Penn World Tables World Bank, WDI Alesina et.al (2003) Alcalá and Ciccone (2001)/using Penn World Tables World Bank, WDI World Bank, WDI OECD, DAC (online)

Notes: i) p-values in parentheses based on robust standard errors. ii) Constant term, country and time dummies not reported. iii) Instrumented variables appear in bold type. iv) ***,** and * represents the significance at 1%, 5% and 10% respectively. 1 I'm grateful to Sambit Bhattacharyya for institutional quality data from the ICRG databases. 2 I'm grateful to Sambit Bhattacharyya for aid data from the OECD databases.

of the interaction term in column (2) remains strongly significant at the 1 per cent level. Columns (3) & (4) estimate our baseline equation via GMM estimations. Column (3) shows the results for GMM-system estimation and column (4) displays them for GMM-difference estimation. Columns (3) & (4) strongly support our theoretical result. The magnitude of the coefficient for the interaction term increases in magnitude and remains significant at least at the 5 per cent level. The Arellano and Bond (1991) AR (1) test for the presence of first order serial autocorrelation cannot be rejected based on the significant p-values in (3) & (4). The insignificant p-value for the AR (2) test, however, reveals absence of second order serial autocorrelation. The Hansen J-statistic for over identifying restrictions indicates that our instrument choice for both

GMM models is valid. Thus, our results of these procedures continue to support our hypothesis. We now carry out robustness exercises to investigate the strength of our findings. Easterly (2003), Roodman (2007) and Varvarigos & Neanidis (2009) have conducted robustness tests to show the applicability of their basic findings to a variety of changes in specification, periodization, variable definitions and data subsets. In line with these papers our robustness tests account for i) different measures of aid and volatility; ii) additional controls; iii) changes in period averages; iv) various sub-samples. Following Afonso and Fureri (2008) we test the sensitivity of our key variables by incorporating different measures of volatility. We

J. Kathavate, G. Mallik / Economic Modelling 29 (2012) 716–724

employ the Baxter King bandpass filter to compute an alternative measure of aid volatility. We also explore how changes in the smoothing parameter contained in the Hodrick-Prescott affect our key variables. Traditionally, researchers have used lamda set to 6.25 (Ravn and Uhlig, 2002), 7 (Bulir and Hamann, 2008), 100 (Backus and Kehoe, 1992), a value between 6 and 14 (Maravall and Del Rio, 2001), while Baxter & King (1999) suggest a value of 10. Our hypothesis is that if our results are robust to employing the two extreme values in the literature, such as 7 and 100, then results should be robust to values occurring between these figures. 12 In this vein, we employ lambda equal to 7 and lambda equal to 100. Table 3 shows our results for the two extreme smoothing parameter values of 7 and 100. The interaction term is strongly significant, suggesting our benchmark findings are not biased by the choice of lambda(λ). We now turn to testing the strength of our key explanatory variables by addressing the issue of omitted variable bias. Though we have included regressors in that have been found to have a significant impact on growth. The set of additional controls we have chosen is not a comprehensive set. They include land area, population, trade shares, education, ethnic fractionalization, real openness, inflation and financial development (e.g. Arellano et al., 2009; Barro and Sala-i-Martin, 1995; Burnside and Dollar, 2000; Clemens et al., 2004; Lensink and Morrissey, 2006); Varvarigos & Neanidis, 2009. The results appear in Table 4. Controlling for potential omitted variable bias does not alter our conclusion in any way. The interaction term is still significant at least at the 5 per cent level in all cases. Cross country growth regressions typically use four year or five year averages to capture the long run effects of explanatory variables on economic growth. Easterly (2003) and Roodman (2007) show how different period averages significantly alter baseline results. Our baseline sample uses three year period averages. We test the significance of our key variables by taking seven year and eleven year period averages. We use fixed effects (FE) and random effects (RE) to test the robustness of the seven and eleven year period averages. We resort to FE and RE estimations due to an insufficient number of observations to employ GMM estimations. Table 5 presents these findings. Columns (1) & (2) are results based on FE and RE for seven year averages and columns (3) & (4) are for eleven year averages. These results support our baseline findings – the volatility and interaction terms are significant at least at the 5 per cent level. Since the majority of the aid-growth literature utilizes some variant of net aid as a measure of per capita aid flows, it behooves us to compare our baseline results with the significance of aid volatility and interaction terms computed from a measure of net aid. Here, aitnet = aitgross − aitrepayments. Table 6 illustrates our findings. Column (1) displays results obtained from GMM-diff and column (2) shows results via GMM-system. Volatility remains negative and significant and the interaction term remains positive and significant at the 5 per cent level. The final robustness test involves comparing baseline results with various sub-samples. Table 7 presents our findings. Columns (1) to (4) present findings for sub samples without Africa, Europe, Asia and the Americas respectively. Column (5) presents findings for a low to lower middle income group of countries. Our key terms survive the test though for the sub sample without Asia and Europe significance is only at 10 per cent. This may be because omitting European and Asian countries excludes a sample of highly corrupt countries which may be experiencing high aid volatility.

12 We do not report results for values of lambda chosen between 7 and 100. They are available upon request.

723

6. Conclusion The main objective of our paper was to evaluate the relationship between aid volatility and economic growth while accounting for the strength of a country's institutions. Our theoretical and empirical findings predict that a negative and significant effect of aid volatility on growth is mitigated by stronger institutional quality. Theoretically, we have developed a model in which changes in institutional quality affect the distribution of aid allocated by a government toward investment and “corruption” expenditure in the face of aid volatility. Our empirical results confirm this theoretical prediction over various computations of aid volatility, foreign aid, time periods, sub-samples and additional covariates. In all empirical tests save for the sub-sample test, our interaction term is found to be significant at least at the 5 per cent level. In the sub-sample test it is significant at the 1 per cent level. From policy perspective, foreign aid would be more effective if the volatility of aid was reduced and the quality of institutions strengthened. Therefore, it is suggested that the donor countries should provide consistent aid and the recipient countries should improve their institutional environments. It is required that the actual level of aid given to developing countries be more consistently aligned with the expected levels of aid, while at the same time, developing stronger democratic, accountable and less corrupt systems of governance. In this way, the recipient countries which have a more predictable pattern of aid flow, and who are situated in increasing levels of qualitative governance, can thus spend aid money in a more effective manner through infrastructure improvement and other long term investments. Acknowledgement We would like to acknowledge the helpful comments of anonymous referees. We would also like to acknowledge the helpful contribution of Chris Bidner, who helped us formalize the theoretical model; while Edward Mariyanni Squire offered helpful suggestions to Propositions 1 and 2. References Afonso, A., Fureri, D., 2008. Government Size, Composition, Volatility an Economic Growth. European Central Bank, Working Paper Series, No.849. Aizenman, J., Marion, N., 1999. Volatility and Investment: Interpreting Evidence from Developing Countries. Economica, 66, 157–1179. Alcalá, F., Ciccone, A., 2001. Trade and Productivity, CEPR Discussion Paper No.3095, London. Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., Wacziarg, R., 2003. Fractionalization. Journal of Economic Growth, 8, 155–194. Alesina, A., Dollar, D., 2000. Who Gives Foreign Aid to Whom and Why? Journal of Economic Growth, 5, 33–63. Alessandro, P., Tressel, T., 2006. Aid Volatility and Dutch Disease: Is There a Role for Macroeconomic Policies? IMF Working Papers with number 06/145. Alonso, J.A., Garcimartín, C., 2010. The determinants of institutional quality. More on the debate. Journal of International Development. doi:10.1002/jid.1710. Alesina, A., Rodrik, D., 1994. Distributive Politics and Economic Growth. The Quarterly Journal of Economics 109, 465–490. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–297. Arellano, C., Bulir, A., Lane, T., Lipschitz, L., 2009. The Dynamic Implications of Foreign Aid and its Variability. Journal of Development Economics, 88, 87–102. Backus, D., Kehoe, P., 1992. International Evidence of the Historical Properties of Business Cycles. American Economic Review, 82, 864–888. Barro, R.J., Sala-i-Martin, X., 1995. Economic growth. McGraw Hill, New York. Baum, C.F., Schaffer, M.E., Stillman, S., 2003. Instrumental variables and GMM: Estimation and Testing, Boston College, Department of Economics. Working Paper No. 545. Baxter, M., King, R.G., 1999. Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series. The Review of Economics and Statistics, 81, 575–593. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models”. Journal of Econometrics, 87, 115–143. Bond, S.R., Hoeffler, A., Temple, J., 2001. GMM Estimation of Empirical Growth Models. CEPR Discussion Papers 3048, C.E.P.R. Discussion Papers. Bulir, A., Hamann, A.J., 2008. Volatility of Development Aid: From the Frying Pan into the Fire? World Development, 36, 2048–2066.

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