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No 226 – July 2015

Aid Unpredictability and Economic Growth in Kenya

Elphas Ojiambo, Jacob Oduor, Tom Mburu and Nelson Wawire

Editorial Committee

Steve Kayizzi-Mugerwa (Chair) Anyanwu, John C. Faye, Issa Ngaruko, Floribert Shimeles, Abebe Salami, Adeleke O. Verdier-Chouchane, Audrey

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Salami, Adeleke O.

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Correct citation: Ojiambo,Elphas; Oduor, Jacob; Mburu, Tom and Wawire, Nelson (2015), Aid unpredictability and

Economic Growth in Kenya, Working Paper Series N° 226 African Development Bank, Abidjan, Côte d’Ivoire.

Aid unpredictability and Economic Growth in Kenya

Elphas Ojiambo1, Jacob Oduor2, Tom Mburu3 and Nelson Wawire4

1 Corresponding Author; [email protected], Department of Applied Economics, Kenyatta University 2 Research Department, African Development Bank 3 African Economic Research Consortium 4 Department of Applied Economics, Kenyatta University

AFRICAN DEVELOPMENT BANK GROUP

Working Paper No. 226

July 2015

Office of the Chief Economist

mailto:[email protected]

Abstract

Studies on the impacts of aid on growth

are not limited. Some have found

positive effects, others negative effects,

others no effect and others have found

positive impacts only under certain

conditions. A less examined aspect is the

role of aid unpredictability on growth.

While aid unpredictability may

negatively affect growth when it

disorientates the recipient country’s

consumption and investment planning, it

may also boost economic growth if it is

triggered by responses to economic

shocks. It may also induce strengthening

of systems of aid absorption in the

recipient country that improves aid

effectiveness and economic growth. This

paper assesses the heterogeneous

impacts of aid on growth in a low

income country with different aid

unpredictability episodes and finds that

increased aid unpredictability weakens

economic growth in Kenya. In addition,

aid unpredictability is found to improve

economic growth in an unstable

macroeconomic environment implying

that aid unpredictability forces weak

governments to be more prudent in

managing the limited uncertain

resources at their disposal during

periods of macro instability. We

however find no evidence of different

impacts of aid unpredictability during

periods of shocks.

Key words: Foreign Aid, Aid Unpredictability, Macroeconomic Policy, Economic

Growth

JEL Classification: C22, E02, E61, F35, F43

5

1. Introduction

Foreign aid forms one of the largest components of foreign capital flows to low-income

countries (Radelet, 2006). The question as to how foreign aid affects the economic growth of

developing countries has drawn the attention of many scholars over time. The results of their

studies have been varied.

Several studies (Rosenstein-Rodan, 1961; Chenery and Bruno, 1962; Chenery and

Strout, 1966; Mosley, 1980; Mosley, Hudson and Horrell, 1987; Karras, 2006; among others)

found that foreign aid positively affects economic growth. Using an Autoregressive

Distributed Lag (ARDL) model and various components of foreign aid including loans,

grants, technical cooperation, multilateral and bilateral aid flows to estimate a disaggregated

short-run and long-run relationship between foreign aid and economic growth, Gounder

(2001) found that total aid flows and its various forms had positive and significant impacts on

economic growth in Fiji.

Other studies (Singh, 1985; Snyder, 1993; Burnside and Dollar, 1997; World Bank,

1998; Morrissey, 2001; Bearce and Tirone, 2008; Salisu and Ogwumike, 2010; Herzer and

Morrissey, 2011) have found that foreign aid leads to growth but only under certain

conditions. Singh (1985) found that foreign aid had a strong positive impact on economic

growth in less developed countries (LDCs) for both periods 1960-1970 and 1970-1980, when

state intervention was not taken into account. When the state intervention variable was

included in the regression, the effect of foreign aid got statistically weak over time. Snyder

(1993) argued that when country size was not included, the effects of aid on economic growth

were small and insignificant, but when country size was taken into account, the coefficient of

aid became positive and significant. Burnside and Dollar (1997) and World Bank (1998)

emphasized that good-policy environment was essential for this positive relationship to occur.

Bearce and Tirone (2008) explored the relationship between economic growth and foreign aid

conditioned on the level of democracy in the potential recipient country. Herzer and

Morrissey (2011) found that the effect of aid on GDP depended on a trade-off that is country-

specific: aid had a direct positive effect through financing investment but could have an

indirect negative effect on aggregate productivity.

Yet other studies (Papanek, 1972, 1973; Newlyn, 1973; Knack, 2000; Gong and Heng-

fu, 2001; Boakye, 2008; Mallik, 2008) found a negative relationship between foreign aid and

growth. Knack (2000), for example, observed that high levels of aid had the potential to erode

institutional quality, increase rent-seeking and corruption, thus negatively affecting growth.

Easterly, Levine and Roodman (2003) re-examined works by Burnside and Dollar (1997)

using a larger sample size and found that the results were not as robust as before. Some of the

explanation for the negative relationship was that foreign aid is fungible (Boakye, 2008).

The last strand of studies (Pedersen, 1996; Ekanayake and Chatrna, 2009) noted the

inconclusiveness of the impact of foreign aid on economic growth. Ekanayake and Chatrna

(2009) found that foreign aid had a mixed impact on economic growth of developing

countries.

Amidst these contestations, a less discussed aspect of foreign aid which could have

serious implications on its effects on growth is aid unpredictability. The above studies assume

that foreign aid flows are predictable and therefore the recipient countries can effectively and

timely reflect them in their development planning process. It is also based on the premise that

aid commitments and disbursements are the same. While no country specific study to our

6

knowledge has attempted to determine the effect of aid unpredictability on growth,

unpredictability may have both positive and negative effects on growth depending on what

triggered it. Aid unpredictability may be triggered by the inability of recipient countries to

comply with the conditions of the aid. It may also be triggered by the inability of donors to

disburse in time due to administrative delays. Aid unpredictability may also occur in times of

emergency or shocks to the economy and donors are required to disburse more aid than they

committed at the beginning of the year.

Aid unpredictability in general would jeopardize the recipient’s ability to plan and

effectively execute investments financed through such funds. Such occurrence would

necessitate a re-organization of the recipient’s consumption plans particularly if the activities

that were meant to be funded by donor funds cannot be postponed. This may negatively affect

growth. Lensink and Morrissey (2000) make a similar suggestion, that aid uncertainty may

affect growth negatively. Studies (Bulỉř and Hamann, 2001, 2003, 2005; Pallage and Robe,

2001; Rand and Tarp, 2002 and Chauvet and Guillaumont, 2008) have examined the issue of

aid volatility arguing that it may contribute to macroeconomic instability. According to

Kodama (2011), the effect of aid unpredictability on economic growth is significant and

results in a waste of one-fifth of the aid. Aid unpredictability could also be triggered by the

inability of the recipient country to comply with the conditions of the assistance including

inability to use aid effectively for the intended purposes. Where project implementation

systems are weak in case of project aid, cutting aid may force the recipient country to

strengthen their systems in a way that increases aid effectiveness. This undoubtedly would

have welcome positive impact on growth. In addition, aid unpredictability that is triggered by

donor’s response to emergencies including floods, famine and other natural disasters would

also have positive effects on growth. In this case, some level of aid unpredictability may be

necessary for growth.

The impact of aid unpredictability on growth may therefore be positive or negative

depending on what triggered the unpredictability and may differ from country to country

depending on the recipient country’s adherence to the aid conditions, economic environment

and strength of institutions and policies. The impact may also differ depending on the

economic and political situations in the donor country. An economic downturn or elections in

the donor country may for instance slow down disbursements of aid leading to aid

unpredictability. Unfortunately, poor countries have the weakest systems and therefore are

more likely to be most adversely affected by pull-backs of aid due to non-compliance with aid

conditions. The finding of Celasun and Walliser (2007) that aid unpredictability is more

prevalent in poor countries is therefore not surprising. Vargas (2005) also finds that on

average for sub-Saharan Africa, the difference between commitments and disbursements was

as much as +/- 20 per cent of commitments, and that on average over the period 1975–2002,

disbursements were less than commitments by up to 4.9 per cent. If such unpredictability

were to restrict growth, the poorer countries’ growth would be most affected.

From the preceding discussion, it is clear that at the aggregate level, the impact of aid

unpredictability on economic growth is not obvious. This paper contributes to the debate by

assessing the heterogeneous impact of aid unpredictability in the economy of a low income

country that experiences different aid unpredictability episodes. In particular, we assess the

impact of aid unpredictability on growth under different strengths of institutions,

macroeconomic environments, unexpected shocks and emergencies and sizes of the economy

in the recipient country and conditions in the donor country. We therefore assess whether

there are circumstances when aid unpredictability may be good for economic growth. We use

7

Kenya for this assessment because of the mixed experience that it has had with donors from

good to bad times.

The rest of the paper is organized as follows: Section 2 covers the foreign aid flows and

economic growth in Kenya while the model is presented in section 3. Section 4 presents the

Univariate Analysis while the data and sources are explained in section 5. The results are

presented in section 6 while section 7 presents the conclusions and policy issues.

2. Foreign Aid Flows and Economic Growth in Kenya

Historically, most aid has been given as bilateral assistance directly from one country to

another. Donors also provide aid indirectly as multilateral assistance, whereby resources are

pooled together from many donors. According to Mwega (2004), 78 per cent of Kenya’s aid

has been from the bilateral donors, and that the share of multilateral aid increased moderately

in the 1980s and early 90s, primarily due to the disbursement of the World Bank adjustment

lending under the Structural Adjustment Programme (SAPs). However, the bilateral aid share

rose again since then, with the decline in new adjustment lending after 1991. A summary of

the Official Development Assistance (ODA) disbursements to Kenya over the period 1968-

2011 is shown in Figure A1 in the appendix.

Appendix Figure A1 shows that average flows from multilateral donors were highest in

1990-2000 and 2001-2011 compared to the previous periods. On the bilateral flows, there was

a steady increase in the period 1979-89, a period that recorded the highest foreign aid flows to

Kenya. This was partly due to the flows arising from the need to cushion the economy from

the effects of the SAPs and the need to ensure sustenance of the reforms that the country was

undertaking during this period. This period also witnessed much of the bilateral support being

channeled outside of the Government to the Civil Society Organizations following a not so

good relationship between the government and the development partners during certain

periods. After a dip in the period 1990-2000, there was a noticeable increase in bilateral flows

for the period 2001-2011, possibly due to the regime change and Kenya's commitment to

reform.

The timely flow of aid is important as countries that are dependent on aid become

vulnerable when funds are committed and scheduled, but not disbursed on time, or when there

is insufficient information about donors’ intentions to disburse. In this respect, the Paris

Declaration of 2005 commits donors to “provide reliable indicative commitments of aid over

a multi-year framework, and disburse aid in a timely and predictable fashion according to

agreed schedules” (OECD, 2005, No. 26: 4).

According to Celasun and Walliser (2008), whereas aid predictability has been

highlighted as a key issue for aid effectiveness (see also OECD, 2005; IMF, 2007; and World

Bank, 2007), little systematic information is available on the magnitude of the predictability

problem and thus its potential impact on aid recipients. Zimmerman (2005) argued that donors

had not been able to make multi-year financial commitments that would enable the recipients

to rely on during their planning and budgeting process. They have continued to apply

complicated and inconsistent conditionalities, and have in a secretive and slow way been

determining how to allocate the resources. Thus, donors have continued to use different

disbursement mechanisms and monitoring procedures instead of coordinating their activities

(Gómez, 2005; Saasa, 2005). Indeed, creative initiatives such as the global funds run by

mixed groups of public, business and non-profit organizations, have but continued to add to

the confusion (Sagasti, Bezanson and Prada, 2005).

8

Since the 1980s, Kenya has experienced relatively unpredictable flows of international

aid (see also M’Amanja and Morrissey, 2005; Mwega, 2009; Oduor and Khainga, 2009).

According to OECD-DAC statistics, while Kenya experienced a dramatic build-up in nominal

aid flows in the 1980s, the 1990s witnessed a reduction in donor support (Mwega, 2009).

Nominal aid flows increased from US$55.6 million in 1963 to a peak of US$1.2 billion in

1990, before declining to a low of US$309.9 million in 1999, with some recovery thereafter in

response to a new government in December 2002.

Kenya has experienced major standoffs with the donor community, which has

sometimes led to aid freezes (Wawire, 2006). As a result, foreign aid disbursements were

short-lived due to the continued uneasiness amongst the donors with Kenya’s implementation

of aid conditionalities. For example, the World Bank in July 1982 did not release the second

tranche of US$50 million on the basis that Kenya was lax in undertaking policy reforms

(Njeru, 2003). The resumption of funding in 1984 was partly attributable to the humanitarian

gesture of providing large volumes of food aid in response to the devastating drought that

year. Further freezes were experienced in 1992 and 1997.

The extent of aid unpredictability in Kenya is demonstrated in Appendix Figure A2. The

Figure shows that commitments have been higher than disbursements over the years. The only

periods when disbursements were higher than commitments were 1968-70, 1981, 1985, 1992-

95, and 2001-2002. The situation obtaining between 1968 and 1970, up to 1981 could be due

to the effects of the oil shocks that necessitated the need for foreign aid to cushion the Kenyan

economy. Between 1992 and 1995 coincided with the funding of Structural Adjustment

Programmes (SAPs) by International Monetary Fund (IMF) and World Bank. The situation

seen between 2001 and 2002 coincided with the optimism in terms of possible change of

government in 2002 coupled with low commitments on the part of Kenya’s development

partners.

Turning to economic growth performance in Kenya, it is observed that Kenya has had a

chequered history on economic growth since independence. The first period, from 1963 to the

beginning of the 1980s, was characterized by strong economic performance and huge gains in

social outcomes. A second period, from the 1980s to 2002, was typified by slow or negative

growth, mounting macroeconomic imbalances and significant losses in social welfare, notably

rising poverty and falling life expectancy. Failure to reform and the increased role of politics

over policy were at the heart of this structural break (Legovini, 2002). The third period, from

2003 to 2011, was marked by the resurgence in economic growth following the 2002 elections

and regime change thereof. Kenya has also pursued macroeconomic policy reforms over the

period. Some of these reforms have been at the behest of the donor community. Whether these

reforms have led to the desired outcome is an issue worth examination.

Invariably, increased aid should lead to increased growth, but from the above

discussion, even if aid was increasing, this may not lead to increased economic growth as aid

is unpredictable. As has been mentioned earlier, aid flows to Kenya has increased sharply in

the last few years. However, economic growth has remained dismally low. Could aid

unpredictability explain this poor performance in economic growth? This resonates well with

the inherent belief that aid is supposed to promote growth, and therefore, any unpredictability

has a possibility to affect growth negatively. It is for this reason that this study found the

debate on aid unpredictability in Kenya an issue worth examining as most studies (M’Amanja

and Morrissey, 2005; Mwega, 2009; Oduor and Khainga, 2009) had just but mentioned it

without specifically factoring it in their models. The objective of this study is therefore to

9

examine the extent to which output growth is retarded as a result of the unpredictability in aid

flows to Kenya.

3. Model Specification

Lensink and Morrissey (2000) measured aid unpredictability by first estimating a

forecasting equation so as to be able to determine the expected component of the variable

under consideration. Secondly, the uncertainty (unpredictability) proxy was derived by

calculating the standard deviation of the residuals from the forecasting equation. This study

used a predictability indicator defined as the difference between commitments and

disbursements as a percentage of disbursements (also known as the predictability per a unit of

aid dollar) (see Celasun and Walliser, 2008). This measure of aid predictability is independent

of the scale of aid as a share of GDP.

100*)/)(( tttt DDCAid (1)

where χAidt is a predictability indicator, Ct is Commitments and Dt is disbursements.

An increase shows that aid is becoming more unpredictable. If the variable is decreasing

it means aid is becoming more predictable. A negative sign means that donors disbursed more

than they committed as may be the case during periods of economic shocks. Thus, an aid

predictability indicator (XAid) and an interactive variable linking predictability to

macroeconomic policy environment (POLICY) (see Appendix and Table A1 for computation

of the Policy variable) and other relevant factors were used in the regressions. The functional

relationship is given as:

,,,,,,,,, 11 tttttttttt PRIPAIPXAIDBPOLICYFdebtTaxAidPinvYpfYp (2)

where Yp is real per capita GDP, Tax is the total tax revenue as a share of GDP, Pinv is

private investment as a share of GDP, Fdebt is Foreign debt as a share of GDP, AIP is an

interactive variable of AID and Policy (see appendix for computation of the policy variable).

PRIP is the interaction term between aid predictability and the policy environment.

As a robustness check, we use H-P filter (Hodrick et Prescott, 1997) to extract the trend

and cycle components of foreign aid in line with Bulir and Hamann (2001, 2003, 2005),

Pallage and Robe (2001) and Rand and Tarp (2002). The H-P filter decomposes a series,

xt(where xt is the logarithm of the observed series Xt in a cycle, xtc and a trend xt

g by

minimizing the following equation:

(3)

Where ʎ is the smoothing parameter of xtg .The choice of the value of λ depends on the

frequency of observations. Pallage and Robe (2001) use λ equals 100 for annual data. Bulir

and Hamann (2001) use λ equals 7 while Ravn and Uhlig (2002) show that on annual data, λ

should be of the order of 6.25. We experimented with each of these values of λ to arrive at the

best results. Through this process, we opted to use λ equals 100 in line with Pallage and Robe

(2001) as it represented a better fit (see Appendix Figures A3 and A4).

10

Our choice of the HP filter is further driven by the conclusion from previous studies on

aid volatility that more often than contra-cyclical, aid is pro-cyclical, at best not correlated

with the cycles of national income or fiscal revenues (Bulir and Hamann, 2001, 2003, 2005;

Pallage and Robe, 2001). The foregoing argument resonates well with our concern about aid

unpredictability thus our resolve to use the filter as a robustness check.

4. Univariate Analysis

We use an autoregressive distributed lag (ARDL) model developed by Pesaran and Shin

(1995) to examine the direction of causation between the variables. The main advantage of

using this approach to examine long-run relationships among variables over other approaches

including Johansen cointegration test is that the ARDL approach does not necessarily require

pre-testing of the variables, implying that the test is possible even if the underlying regression

is purely I(0), purely I(1) or a mixture of the two.

The ARDL approach is conducted in two steps (Pesaran and Pesaran, 1997). In the first

stage, the hypothesis of no cointegration is tested. The null hypothesis in this case is that the

coefficients on the lagged regressors (in levels) in the error-correction form of the underlying

ARDL model are jointly zero. The approach uses the F-test, although the asymptotic

distribution of the F-statistic in this context is non-standard irrespective of whether the

variables are I(0) or I(1). We use the critical values provided by Narayan (2004), due to their

appropriateness for small samples (Boakye, 2008). Two sets of values are tabulated. The first

assumes that all the variables are I(1) and the second that they are I(0). This band allows for

the fact that variables may be stationary, integrated of order one, or even fractionally

integrated. In this respect, when the calculated F-statistic is above the upper value of this

band, the null hypothesis will be rejected, indicating cointegration between the variables

irrespective of whether they are I(1) or I(0). If the F-statistic falls below the band, then the

null hypothesis of no cointegration cannot be rejected. A value within the band implies the

test is inconclusive.

In the second stage, the lag orders of the variables are chosen using Schwarz Bayesian

Criteria (SBC). The importance of this step is that an appropriate lag selection enables us to

identify the true dynamics of the models. We further check on the performance of the

estimated model and present the associated diagnostic tests of serial correlation, functional

form and heteroscedasticity.

Further, we conducted two crucial stability tests - CUSUM (Cumulative Sum) and

CUSUMSQ (CUSUM of Squares) of recursive residuals – based on Brown et al. (1975). The

CUSUM test does not require the variables to be I(1) and is based on the cumulative sum of

recursive residuals based on the first set of n observations. The estimated coefficient is said to

be stable if the CUSUM statistic stays within 5 per cent significance level. The CUSUMSQ

on the other hand is based on the squared recursive residuals and is more suitable for

detecting systematic changes in the regression coefficients and is useful in situations where

the departure from the constancy of the regression coefficients is haphazard and sudden.

The long and short-run parameters are estimated using the ARDL method. We set the

initial lag length at two periods on all variables in the general ARDL equation. Once

cointegration is established, the conditional ARDL (p, q1, q2, q3, q4, q5, q6, q7, q8) long-run

model for Ypt in equation (2) is specified as:

11

,lnln

lnlnlnlnln

7 8654

321

0 0

98

1

7

0

6

0

5

0

4

0

3

0

2

1

10

t

q

i

it

q

i

iiti

q

i

iti

q

i

iti

q

i

iti

it

q

i

iit

q

i

iit

q

i

i

p

i

itit

PRIPAIPFdebtXAIDBPOLICY

AidPinvTaxYpYp

(4)

where p, q1, q2, q3, q4, q5, q6, q7, q8 are the lag lengths for each of the variables.

We obtained the short-run dynamic parameters by estimating an error correction model

associated with the long-run estimates. This was specified as follows:

,ln

lnlnlnlnlnln

1

9

1

8

1

7

1

6

5

1

4

1

3

1

2

1

10

tit

n

i

iti

n

i

iti

n

i

iti

n

i

iti

n

ii

itiit

n

i

iit

n

i

iit

n

i

i

n

i

itit

ecmPRIPAIPFdebtXAIDB

POLICYAidPinvTaxYpYp

(5)

where φ1, φ2, φ3, φ4, φ5, φ6, φ7, φ8, φ9 are the short-run dynamic coefficients of the

model’s convergence to equilibrium, and π is the speed of adjustment to long-run equilibrium

following a shock to the system.

We include a dummy variable to show the periods when the country experienced major

standoffs with the donor community (the country was out of the IMF programme). We are

cognizant of the fact that such period of standoff provided an opportunity for foreign aid to be

disbursed outside the regular government channels, the so called bypass. We do this to

account for such unexpected aid shocks.

To test for the robustness of the results as discussed above, we introduce two new

variables AIDRES and AIDRESP derived from the HP filter and an interaction of the filter

with the policy variable respectively. These two variables are used in the above two models in

place of XAIDB and PRIP respectively. A graphical representation of the foreign aid

disbursement and HP trend is shown in Figure A3 in the appendix. Figure A4 shows the aid

cycle as derived from the HP filter. This is a confirmation of the pro-cyclical nature of foreign

aid and could be an indication of aid unpredictability.

5. Data

The study used secondary sources of data covering the period 1966 – 2010. The data on

real GDP, Inflation, degree of openness, Final Government Consumption and private

investment were obtained from the World Bank, Africa Development Indicators database.

Other complimentary sources were Economic Survey, and Statistical Abstract. Foreign aid

data (commitments and disbursements) were from the Organization for Economic

Corporation and Development (OECD), OECD.Stat online database. This data was

supplemented with those from the World Bank, Africa Development Indicators. Data on tax,

external debt variables were from Economic Survey (various issues).

Real Per capita income is defined as real income divided by the population. Per capita

income is often considered an indicator of a country's standard of living. The economic

growth rate is measured in this study as the growth of real GDP per capita in constant (2000)

U.S. dollars. Private Investment is proxied by the gross capital formation less public

investment, and was measured as a share of GDP. Foreign Aid (Aid) is defined as Official

Development Assistance (ODA), which includes all loans with a grant component above 25

per cent measured as a share of GDP. Aid Predictability Indicator is defined as the difference

12

between commitments and disbursements as a percentage of disbursements (also known as

the unpredictability per a unit of aid dollar). Tax is defined as the financial charge or other

levy charged by the state to a taxpayer and is an important source for government revenue. In

this study the tax variable is measured as a share of GDP. External Debt is the stock of

resources borrowed externally by the Government and measured as a share of GDP. Openness

to trade refers to the degrees to which countries or economies permit or have trade with other

countries or economies. The trading activities include import and export, foreign direct

investment (FDI), borrowing and lending, and repatriation of funds abroad. It was measured

as a summation of exports and imports as a percentage of GDP. Inflation is defined as a

percentage change in consumer price index. Final Government Consumption Expenditure

includes all government current expenditures for purchases of goods and services (including

compensation of employees). It also includes most expenditure on national defense and

security, but excludes government military expenditures that are part of government capital

formation. It is measured as a share of GDP. Macroeconomic Policy Index is constructed

from selected macroeconomic variables such as inflation, degree of openness and final

government consumption. An increase in the policy index is an indication of unstable

macroeconomic policy environment.

6. Empirical Results

The cointegration results are shown in Table A2 in the Appendix. The results show that

the calculated F-statistic (4.948) was higher than the upper bound critical value at 1 per cent

level of significance (4.832) using a restricted intercept and no trend. The calculated F-

statistic was also higher than the upper bound critical value at 5 per cent level of significance

(4.004) using restricted intercept and trend. Thus, the null hypothesis of no long-run

relationship between the above variable was rejected irrespective of the order of their

integration implying that there was a long-run relationship between the variables.

We estimate Equation (4) and use SBC in selecting the lag length on each of the first

differenced variable. The estimates of the ARDL representation are summarised in Appendix

Table A3. The requisite diagnostic tests of the primary model and the robustness check model

are also provided.

The table shows that there was no evidence of autocorrelation in the disturbance of the

error term with p-values of 0.785. The study used Breuch-Godfrey Lagrange Multiplier (LM)

to test for serial correlation. The ARCH tests suggest the errors were homoskedastic and

independent of the regressors as evidenced by the p-values of 0.198. The model passed the

Jarque-Bera normality tests (p-value of 0.943), suggesting that the errors are normally

distributed. The RESET test indicated that the model was correctly specified with p-values of

0.192.

Appendix Figures A4 and A5 show the plots of both the CUSUM and CUSUMSQ for

the primary model. It can be seen from the figures that the plot of CUSUM stays within the

critical 5 per cent bound for all equations, and CUSUMSQ statistics does not exceed the

critical boundaries that confirms the long-run relationships between economic growth and the

variables. It also shows that the stability of co-efficient plots lie within the 5 per cent critical

bound, thus providing evidence that the parameters of the model do not suffer from any

structural instability over the period of study. From the foregoing diagnostic tests, it is clear

that the model passed all the required tests and thus paving way for interpretation of estimates

of both the long-run and short-run coefficients as required in an ARDL approach. It is

13

therefore on the basis of these tests that it was reasonable to claim that the model had a good

statistical fit.

The empirical results of the short run error-correction model (ECM) results are

presented in Appendix Table A4. The estimated long-run coefficients are presented in

Appendix Table A5. Appendix Table 4 shows that the coefficient of the adjustment term (ecm

(-1)) is significant at the 5per cent level and carries the expected negative sign. The

coefficient is found to be -0.390, suggesting that that the deviation from the long-term in

economic growth is corrected by 39 per cent in the coming year. This means that an

exogenous shock to the economy dissipates completely within two and half years. The

relatively slow speed of adjustment could be attributed to the structural rigidities - lack of

access to productive land, lack of capital, problem of enclave formal economies, export of raw

materials, policy distortions - that are inherent in developing countries such as Kenya, which

slows down the adjustment process as intimated by M'Amanja and Morrissey (2005).

The long-run coefficients for the equation are reported in Table A5. Results show that

the elasticity of foreign aid was positive and statistically significant at 5 per cent in the long-

run. The implication of this is that a 1 per cent increase in foreign aid would result in a 0.16

per cent increase in economic growth in the long-run. The study finding is in line with other

studies such as Mosley (1980), Singh (1985), Mosley et al. (1987), Snyder (1993), Murthy,

Ukpolo and Mbaku (1994), Gounder (2001), Lloyd, Morrissey and Osei (2001), Mavrotas

(2002), M’Amanja and Morrissey (2005), Karras (2006), Bhattarai (2009), and Kargbo

(2012).

From the results reported, the coefficient of the aid predictability indicator is negative

and statistically significant at 1 per cent. This implies that increased aid unpredictability

weakens economic growth in Kenya. A one per cent increase in aid unpredictability decreases

economic growth by 0.7 per cent. This is expected and is in line with the general hypothesis in

the literature that increased aid unpredictability reduces economic growth. This is also

consistent with the findings of Lensink and Morrissey (2000) and Kodama (2011) who found

that aid uncertainty negatively affects economic growth.

Results further show that coefficient of the log of macroeconomic policy environment

was negative and statistically significant at 1 per cent. This implies that a 1 per cent increase

in the macroeconomic policy environment (instability) leads to a decrease in economic

growth by 1.2 per cent and points to the fact that Kenya has experienced macroeconomic

instability for a greater part of the study period.

The coefficient of the interaction term between aid unpredictability and macroeconomic

policy environment is found to be positive and statistically significant at 1 per cent level in

both the short and long-run. Thus, a one per cent increase in aid unpredictability could

increase economic growth by 0.26 per cent in the long run under unstable macroeconomic

policy environment. This implies that aid unpredictability could be beneficial for economic

growth in both the short run and long run during periods of unstable macroeconomic policy

environment. Uncertain aid inflow has the potential to increase prudence in the use of limited

resources a country has thus improving economic growth.

The foregoing findings are confirmed when a robustness check is conducted. The results

of this model are presented in Appendix Table A6 and A7 for the Short run and Long run

respectively.

14

We assess the impact of aid unpredictability on growth when a country is experiencing

some shock emanating from the donor community. Kenya has experienced episodes of frosty

relationship with her development partners, particularly the World Bank and IMF resulting in

aid freeze and some episodes of abundant aid flows. For example, the World Bank in July

1982 did not release the second tranche of US$50 million on the basis that Kenya was lax in

undertaking policy reforms (Njeru, 2003). Further freezes were experienced in 1992 and

1997. To assess the impact during these periods, we introduce a dummy variable to account

for periods when Kenya was not on the IMF/World Bank programme (periods of standoff).

The additional set of models in which the periods are disaggregated to take care of IMF=1 and

0 otherwise and vice versa was constructed and interacted with the aid predictability indicator.

The results reported in Appendix Table A8 and A9 show that there was no statistically

significant effect when this variable was taken into consideration. This could be attributed to

the fact that while there was a standoff between the Government of Kenya and the Bretton

Woods institutions, the bilateral donors opted to channel their funds through the Non-

Governmental Organizations (NGOs) or established Project Management Units (PMUs)

through which they were able to ensure that their aid flows continued. In addition, it could be

argued that the periods of standoff where short and therefore the magnitude of their effects

was not drastically felt given that foreign aid from the bilateral donors was flowing albeit

through a bypass means.

7. Conclusion and Policy Issues

Aid-Growth literature is awash with studies that either support or find no evidence for

the importance of aid as a driver of growth. This study examines a related but different aspect

of this debate; whether aid unpredictability has any positive or negative effects on economic

growth. Proponents have argued that some level of aid unpredictability may force recipient

countries to clean up their on systems and this may improve aid effectiveness and economic

growth. Critics argue that aid unpredictability disorientates the recipient country’s

consumption and investment plans and may adversely affect economic growth. They also

argue that unpredictability of aid may increase pro-cyclicality of growth as aid may be

disbursed during good times and slowed down during hard times.

Using an autoregressive distributed lag model, the findings show that increased aid

unpredictability reduces economic growth in Kenya. In addition, aid unpredictability is found

to improve economic growth during periods of macroeconomic instability implying that aid

unpredictability forces governments to be more prudent in managing the limited uncertain

resources at their disposal during periods of macro instability.

On the part of donors, the finding that aid unpredictability reduces the pace of economic

growth should be a pointer to the need for the trade-off between strict enforcement of

conditionalities and the need to ensure that aid is useful for growth particularly during periods

of macro stability. However, the donors need to be strict and withdraw aid if the

macroeconomic environment is weak since this is found to instill prudence among the weak

economies that increases economic growth.

While appreciating the number of studies that have been done on the foreign aid debate,

there are still new areas that have not been examined. This study is among the few that have

examined the issue of aid unpredictability. The emerging interest on the nexus between

foreign aid and trade (aid for trade) on one hand and aid and private sector development are

other areas that still require examination.

15

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Appendix

Source: Based on data from OECD/DAC, March, 2013

Figure A1: Average ODA flows to Kenya from multilateral and bilateral donors

Source: Based on data from OECD/DAC March, 2013

Figure A2: Foreign aid commitments and disbursements (1966-2011)

Constructing the Macroeconomic Policy Index

Burnside and Dollar (1997, 2000), Feeny (2005) and Javid and Qayyum (2011) all

assumed that distortions affect growth that will determine the effectiveness of aid. Thus, they

assigned the weights to the policy variables according to their correlation with growth.

20

The macroeconomic policy index is constructed using the principal component analysis

with SPSS 17.0. The advantage of this construction is that it helps to conserve the degrees of

freedom arising from the reduction in the number of variables in the model while at the same

time helping to avoid the possibilities of high correlation among the macroeconomic variables

(Kargbo, 2012). Sricharoen and Buchenrieder (2005:2) consider this approach as 'indicator

reduction procedure to analyze observed variables that would result in a relatively small

number of interpretable components (group of variables), which account for most of the

variance in a set of observed variables’. The variables used in this study were inflation (INF)

as a proxy for monetary policy, final government consumption expenditure (FGC) as a proxy

for fiscal policy and degree of openness (OPEN) as a proxy for trade policy.

The Principal Components is a method that enables identification of patterns in data and

expressing them in a way that takes into consideration their similarities and differences. In

other words, it is a technique which involves a data reduction process in which the variables

are scored without much loss of information. The Eigen values for shows that 46 per cent of

the variance is explained by the first principal component, while the second and third account

for the remaining 54 per cent. The implication of this is that the first principal component

alone explains the variation of the dependent variable better than any other combination of the

three variables used.

We therefore considered the first principal component as an appropriate macroeconomic

policy index measure. The scores obtained in the construction of the variable are -0.160 for

inflation, 0.322 for final government consumption expenditure and 0.122 for degree of

openness. The respective contribution of the components of the macroeconomic policy index

is shown as:

where α1, α2 and α3 are the weights for inflation, final government consumption and

openness. The signs attached to the weights are essential in the construction of the policy

index such that α1< 0, α2> 0 and α3>0.

Policy index = -0.160INF+ 0.322FGC+0.122OPEN.

Index Policy 3 2 1 INF + OPEN FGC

21

Table A1: Principal Component Analysis for the Generation of the Policy Index

Variable

Principal component Eigenvalues % of variance Cumulative %

1 1.384 46.121 46.121

2 1.005 33.489 79.610

3 0.612 20.390 100.000

Variable Component loadings Component score

INF -0.160 0.588

FGC 0. 322 -0.116

OPEN 0.122 -0.026

Figure A3: Foreign aid disbursements and HP Trend(1966-2011)

22

Figure A4: HP Aid Cycle (1966-2011)

Table A2: The F-Statistic of Cointegration Relationship

Test Statistic Value Lag Significance

Level

Bound Critical

Values* (restricted

intercept and no

trend)

Bound Critical

Values* (restricted

intercept and trend)

F-statistic 4.948 1

1%

5%

10%

I(0) I(1) I(0) I(1)

3.383

2.504

2.131

4.832

3.723

3.223

3.595

2.643

2.238

5.225

4.004

3.461

Note: * Based on Narayan (2004)

23

Table A3: ARDL Estimations

Primary model

Robustness Check

Coefficient t-ratio p-value Coefficient t-ratio p-value

Constant 1.541** 4.379 0.000 1.142** 3.1599 0.004

Log Real per capita GDP (-1) 1.106** 9.640 0.000 1.190** 10.350 0.000

Log Real per capita GDP (-2) -0.496** -3.850 0.001 -0.485** -3.953 0.001

Log Private investment 0.027 0.658 0.516 0.042 1.203 0.240

Log Private investment (-1) 0.087 1.502 0.145

Log Private investment(-2) -0.096* -2.313 0.029

Log Foreign aid -0.011 -0.499 0.657 -0.058 -1.423 0.167

Log Foreign aid(-1) 0.074** 2.855 0.008 0.133** 3.774 0.001

Macro policy -0.199** -5.488 0.000 -0.170** -4.629 0.000

Macro policy(-1) -0.27* -1.934 0.064

Aid interacted with policy 0.001 1.666 0.107 0.003* 2.457 0.021

Aid interacted with policy(-1) -0.004** -5.381 0.000 -0.007** -5.801 0.000

Aid interacted with policy(-2)

0.002* 2.649 0.014

Aid predictability indicator -0.263** -3.442 0.002 1.94E-04 1.638 0.114

Aid predictability indicator (-1)

-4.62E-04** -4.386 0.000

Aid predictability indicator (-2)

-9.01E-05* -2.342 0.027

Predictability interacted with

policy 0.100**

3.223 0.003 -8.92E-06 -1.609 0.120

Predictability interacted with

policy

2.28E-05** 4.325 0.000

Log External debt (-1) 0.607** 3.992 0.000 0.390** 7.643 0.000

Log External debt(-2) -0.112** -4.379 0.000 -0.171** -4.262 0.000

R -Bar Squared 0.959

0.94593

SE of Regression 0.020

0.020911

F-Stat (15, 27) = 66.909**

(16,25) = 45.8274**

Diagnostic Tests

Serial correlation

F[1,26] = 0.045

(0.834)

F(1,24) = 0.055 (0.817)

Heteroscedasticity

F[1,41] = 1.642

(0.207)

F(1,40) = 3.410(0.072)

Functional form

F[1,26] = 1.071

(0.310)

F(1,24) = 2.676(0.115)

Normality (CHSQ 2)

CHSQ[2] =

0.118(0.943)

CHSQ(2) =

1.392(0.498)

Testing for existence of a level relationship among the variables in the ARDL model

F-statistics 2.537

3.854

95% Bound (Lower, Upper) (3.1065, 4.4903)

(3.122, 4.500)

90% Bound (Lower, Upper) (2.6196, 3.8401)

(2.612, 3.857)

* Significant at 5 per cent and ** indicate significant at 1 per cent.

24

Table A4: The Error Correction Representation for the Selected ARDL model

Dependent variable is Δ Log real per capita GDP Coefficient p-value

Δ Log real per capita GDP1 0.496**

(3.850)

0.001

Δ Log private investment 0.027

(0.658)

0.516

Δ Log foreign aid -0.011

(-0.449)

0.657

Δ Log macro policy -0.199**

(-5.488)

0.000

Δ Aid interacted with policy 0.001

(1.667)

0.106

Δ Aid predictability indicator -0.262**

(-3.442)

0.002

Δ Predictability interacted with policy 0.100**

(3.223)

0.003

Δ Log foreign debt(-1) 0.607**

(3.992)

0.000

Δ Log foreign debt(-2) -0.112

(-4.286)

0.000

ecm(-1) -0.390**

(-4.477)

0.000

R-Bar-Squared 0.752

Akaike Info. Criterion 100.714

Schwarz Bayesian Criterion 86.625

DW-statistic 2.025

F-statistic F(11, 31) 12.929** 0.000

Note: (1) * Significant at 5 per cent and ** indicate significant at 1 per cent. (2) Δ Log Real per capita GDP1 = Log Real per

capita GDP(-1)-log Real per capita GDP (-2). (3) Δ is first difference estimator. (4) t-statistics in parenthesis.

25

Table A5: Long-Run Estimates

Dependent variable is log real per

capita GDP

Coefficient p-value

Constant 3.956**

(7.017)

0.000

Log private investment 0.046

(0.388)

0.701

Log foreign aid 0.161*

(2.016)

0.054

Log macro policy -1.204**

(-5.162)

0.000

Aid interacted with policy -0.007**

(-2.875)

0.008

Aid predictability indicator -0.674**

(-2.620)

0.009

Predictability interacted

with policy

0.258**

(2.483)

0.020

Log foreign debt(-1) 1.558**

(6.256)

0.000

Log foreign debt(-2) -0.289**

(-2.983)

0.006

Note: * Significant at 5 per cent and ** indicate significant at 1 per cent. t-statistic in parenthesis.

26

Table A6: The Error Correction Representation for the Selected ARDL model (Robustness

Check Model)



(3.953)

0.000

Δ Log private investment 0.042

(1.203)

0.239

Δ Log foreign aid -0.058

(-1.423)

0.165


(-4.629)

0.000

Δ Aid interacted with policy 0.003**

(2.457)

0.020

Δ Aid interacted with policy1 -0.002**

(-2.649)

0.013

Δ Aid predictability indicator 0.193E-3**

(1.638)

0.112

Δ Aid predictability indicator1 0.901E-4**

(2.342)

0.026

Δ Predictability interacted with policy -0.892E-5**

(-1.609)

0.118


(7.643)

0.000


(-4.262)

0.000

ecm(-1) -0.295**

(-4.271)

0.000

R-Bar-Squared 0.840



DW-statistic 1.881

F-statistic F(12, 29) 10.899 0.000



27

Table A7: Long-Run Estimates (Robustness Check Model)


capita GDP

Coefficient p-value

Constant 3.870**

(8.289)

0.000

Log private investment 0.143

(1.040)

0.308

Log foreign aid 0.255**

(2.747)

0.011


(-3.074)

0.005


(-2.458)

0.021

Aid predictability indicator (HP Residual

Filter)

-0.001**

(-2.222)

0.036

Predictability interacted

with policy

0.4715E-4*

(1.994)

0.057


(4.289)

0.000


(-2.774)

0.010

Note: * Significant at 5 per cent and ** indicate significant at 1 per cent. t-statistic in parenthesis.

28

Table A8: The Error Correction Representation for the Selected ARDL model (Accounting for

external shocks)



(3.807)

0.000

Δ Log foreign aid 0.0124

(0.442)

0.661


(-3.661)

0.001

Δ Aid interacted with policy 0.001

(1.265)

0.215

Δ Aid predictability indicator -0.056

(-0.739)

0.465

Δ IMF Dummy -0.018

(-0.290)

0.774

Δ Predictability interacted with IMF dummy 0.034

(0.451)

0.655


(3.668)

0.001


(-3.115)

0.004

ecm(-1) -0.465**

(-4.587)

0.000

R-Bar-Squared 0.623



DW-statistic 2.147

F-statistic F(10, 31) 8.072 0.000



29

Table A9: Long-Run Estimates (accounting for external shocks)


capita GDP

Coefficient p-value

Constant 3.711**

(11.711)

0.000

Log foreign aid 0.157**

(2.498)

0.019


(-4.831)

0.000

Aid predictability indicator -0.120

(-0.736)

0.468


(-2.850)

0.008

IMF Dummy -0.038

(-0.291)

0.773

Predictability interacted with IMF

Dummy

0.074

(0.450)

0.656


(5.873)

0.000


(-2.556)

0.016

Note: * Significant at 5 per cent and ** indicate significant at 1 per cent. t-statistic in parenthesis

30

-20

-10

0

10

20

1968 1979 1990 2001 2010

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Recursive Residuals

Figure A5: Cumulative Sum of Recursive Residuals

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1968 1979 1990 2001 2010


Plot of Cumulative Sum of Squares of Recursive Residuals

Figure A6: Cumulative Sum of Squares of Recursive Residuals

31

-20

-10

0

10

20

1969 1980 1991 2002 2010


Plot of Cumulative Sum of Recursive Residuals


-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1969 1980 1991 2002 2010


Plot of Cumulative Sum of Squares of Recursive Residuals


32

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