University of Neuchâtel · The Impact of Official Development Assistance on Donor Country Exports : Some Empirical Evidence for Switzerland Research project financed by Swiss Agency

The Impact of Official Development Assistance on Donor Country Exports : Some Empirical Evidence for Switzerland

Research project financed by Swiss Agency for Development and Cooperation (SDC)**

Milad Zarin-Nejadan*

in collaboration with : José-Antonio Monteiro* and Sabina Noormamode*

February 2008 * Institute for Research in Economics (irene), University of Neuchâtel, Pierre-à-Mazel 7, 2000 Neuchâtel, Switzerland.

** Opinions and conclusions of the study are the authors’ responsibility.

TABLE OF CONTENTS

ABSTRACT .............................................................................................................................. 1

1. INTRODUCTION............................................................................................................ 3

2. CONCEPTUAL FRAMEWORK ................................................................................... 5 2.1 INFLUENCE OF AID ON EXPORTS............................................................................................ 5 2.2 INFLUENCE OF EXPORTS ON AID............................................................................................ 8 2.3 DIRECTION OF CAUSALITY..................................................................................................... 8

3. SURVEY OF THE LITERATURE.............................................................................. 11

4. DATA............................................................................................................................... 17

5. TIME SERIES ANALYSIS........................................................................................... 21 5.1 GRANGER CAUSALITY METHODOLOGY................................................................................ 21 5.2 COINTEGRATION AND THE ERROR CORRECTION MODEL...................................................... 24 5.3 RESULTS............................................................................................................................... 25 5. 4 COMPARISON WITH GERMANY ............................................................................................ 28

6. ESTIMATION OF A STRUCTURAL MODEL......................................................... 73 6.1 MODEL SPECIFICATION........................................................................................................ 73 6.2 ECONOMETRIC ISSUES ......................................................................................................... 74 6.3 FACTUAL SHEETS................................................................................................................. 78 6.4 FULL-SAMPLE RESULTs ....................................................................................................... 79 6.5 GROUP RESULTS .................................................................................................................. 86 6.6 INDIVIDUAL COUNTRY RESULTS ......................................................................................... 91 6.7 ROBUSTNESS CHECKS........................................................................................................ 105 6.8 COMPARISON WITH GERMANY .......................................................................................... 118

7. CONCLUSION............................................................................................................. 123

8. APPENDICES .............................................................................................................. 127 A: DATA SOURCE AND DESCRIPTION ............................................................................................. 127 B: GROUPS CLASSIFICATION........................................................................................................... 128 C: DESCRIPTIVE DATA.................................................................................................................... 129 D: CORRELATION MATRICES .......................................................................................................... 142

REFERENCES..................................................................................................................... 155

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ABSTRACT This study is a first comprehensive attempt to investigate the relationship between Swiss bilateral official development aid (ODA) and Swiss exports performance. We use data on aid and exports of goods for a large number of recipient countries since the 1960s to study the presumed aid-exports nexus. Apart from stylised facts, the present study combines both strands of the econometric methodology, namely time-series analysis and structural econometrics. The results show that there is no overwhelming evidence in favour of a reverse causality running from exports to aid that would invalidate our econometric modelling. The estimation of a structural econometric model suggests, generally speaking, a positive and quite strong impact of aid on exports. However, the results can vary according to the degree of aggregation, the econometric techniques used and the time period chosen. In particular, the impact of ODA on exports can be quite different across individual recipient countries. There are also clear indications that the full impact of ODA on exports takes time to materialise. This tends to validate the goodwill hypothesis for Switzerland. Finally, while Swiss exports seem not to benefit from other DAC countries aid, they turn out to be highly sensitive to recipient country’s growth. JEL Classifications: F10, F35. Keywords: development aid, exports, goodwill hypothesis, Granger causality, cointegration, error correction, structural model.

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1. INTRODUCTION In 2006 Switzerland gave about CHF 2 billions of bilateral and multilateral aid to developing countries. The overall objective of development aid is to promote sustainable economic growth and to reduce poverty in recipient countries. However, this does not prevent donors from keeping an eye on the impact of aid on their exports to recipient countries. In recent years, many aid agencies worldwide have increasingly focused on commercial benefits for domestic firms when presenting their development aid budgets to their legislative bodies. The positive effects of aid on trade are discussed extensively in the literature. Empirical studies on this subject are however mostly accounting rather than economics-oriented in the sense that they focus on procurements associated with projects that are financed by aid. Jepma (1994) provides a survey of these studies for various donor countries, showing that the return from aid lies usually somewhere between 50 and 80 cents for each dollar given. For Switzerland, a few similar studies have been conducted since 1994 arriving at about the same order of magnitude.1 The main advantage of these studies is that they do not need any theoretical framework and the statistical methodology is quite straightforward which make them easily accessible to a wide audience. Besides, the data can be assembled by the researcher for a given year by means of an ad hoc questionnaire or interview-based study. However, these studies have a narrow focus and suffer from a certain number of shortcomings. Most importantly, they are subject to a measurement risk. First, to the extent that aid is untied or partially tied – which is in fact increasingly the case since the 1990s – these studies may overestimate the beneficial effects of aid on exports. Even tied aid cannot always be directly linked to exports in the sense that – at least for some purchases of goods and services – one cannot rule out the possibility of recipient country importing from the donor country anyway with or without aid. Second, such studies could well underestimate the effect of aid on exports in so far as they do not take into account the impact of aid on recipient country growth which is actually its main motive. To the extent that aid has a positive impact on growth, this further enhances the developing country’s long-term capacity to purchase goods and services from the donor country. This means that empirical studies of the aid-exports nexus should take into account both the short-run and the long-run aspects of this relationship. The long-run aspects are sometimes related to the “goodwill” effect generated by development aid. By goodwill effect is meant the positive predisposition – in the recipient country toward the donor country – resulting from development aid which is favourable to business for exporters. Third, accounting-oriented studies implicitly assume that causality runs unilaterally from aid to exports. But better export performance can also give rise to higher aid. So the direction of 1 See Carbonnier and Zarin-Nejadan (2008) for the most recent of these studies based on 2006 data. According to this study, the so-called “primary” effect – taking into account procurements but also other returns such as salaries of personnel employed by aid projects – is estimated between 84 and 96 centimes for one franc of aid. The primary effect then gets magnified by a factor (Keynesian multiplier) to produce a final impact somewhere between 140 and 164 centimes for each franc given.

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causality is far from being established at the theoretical level. The issue can only be settled at the empirical level by using the appropriate methodology. Failure to take into account the various complex links between aid and exports might give rise to spurious results and false conclusions. Comprehensive empirical studies examining the complex relationship between aid and exports are quite rare. There is no such investigation for Switzerland and the present study tries to fill this unfortunate gap. The lack of interest for this subject is surprising in light of the great importance of exports for the Swiss economy. Also the consensus-based Swiss political process which combines a wide-coalition government and semi-direct democracy renders such a study particularly useful in the sense that significant domestic returns from aid definitely facilitate obtaining business community backing for development aid packages. Finally, the fact that Swiss aid is largely untied and has been so for some time makes Switzerland a particularly interesting case study for the goodwill hypothesis stated above. This study is a first comprehensive attempt to investigate the relationship between Swiss bilateral official development aid (ODA) and Swiss exports. It combines longitudinal (time-series extending backwards to the 1960s) and cross-section (across close to a hundred recipient countries) data on aid and exports of goods to first establish the direction of causality between the two variables and then measure the magnitude of this relationship. The present study combines both strands of the econometric methodology, namely time-series analysis and structural econometrics. The empirical approach is deliberately cautious in the sense that it tries to draw conclusions based on a wide array of evidence. The present report is organised in eight sections. After this short introduction, Section 2 presents the basic conceptual framework of the study. Section 3 provides a selective and focused survey of the literature on the subject. Section 4 presents the dataset pointing out the limits of the statistics used in the empirical study. Section 5 uses the appropriate tools of time-series analysis to study the causal link between aid and exports. Section 6 is devoted to the estimation of a simple structural model through which aid influences exports performance of the donor country while controlling for other relevant variables. Section 7 concludes the study by summarising its main findings and pointing out a few caveats. Finally, Section 8 is devoted to statistical appendices containing data sources and some useful background information such as summary statistics.

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2. CONCEPTUAL FRAMEWORK The objective of this study is to test and measure the relationship between Swiss official development aid (ODA) and the country’s exports performance. This relationship can take different forms from the simple to the complex and the causality can go either way. Two broad points of view can explain why links may exist between aid and exports, each one pointing towards a different direction of causality. 2.1 Influence of Aid on Exports Several arguments explain why foreign aid flows may generate donor exports. The short and long-run effects of aid on the recipient country can induce donor exports. These effects can be stronger if the aid is directly linked to trade or if aid induces trade dependency through technological transfers.

Figure 2.1: Linkages between Aid and Trade

Switzerland

AID

Exports Exports

Other DAC members

Other foreign countries

Exports

Consumption Savings and Investment

Long-Term Impact

Short-Term Impact

AID

Recipient Country

Budget Constraint Reduction

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As shown in Figure 2.1, in order to measure the exports-promoting effects of aid, one should take into account three different aspects (see for example Hyson and Strout (1968) and Tajoli (1999)) listed below. (1) Income growth: The main hypothesis here is that foreign aid enhances economic growth

in the recipient country. Irrespective of whether the recipient country is free or not to decide the allocation of the foreign aid, there is no doubt that its budget constraint will be relaxed in the short run. The additional financial resources can therefore affect the recipient’s trade balance in the short run, for instance by allowing more imports of consumption goods. Moreover, according to traditional macroeconomic theories of aid impact, aid plays in the long run the role of the substitute for domestic savings in the recipient country. As a consequence, more investments can be realised, which leads to an increase in the productive capacity of the country and higher economic growth. The empirical literature on aid effectiveness faces the so-called “macro-micro paradox”. While microeconomic evaluations of aid projects display a positive effect, the empirical evidence on the macroeconomic impact of aid is mixed (see for example White (1992) for a survey of the macroeconomic impact of development aid). Different factors can explain this paradox: First, micro and macro evaluations face measurement errors which can alter the results

obtained. Second, over-aggregation can mislead the macro studies’ results. Third, micro and macro studies do not use the same type of data. The use of economic

(social) data in microeconomic evaluations versus financial (private) data in macroeconomic studies make these two types of studies hardly comparable with each other.

Fourth, because of fungibility, aid can allow government expenditure to be redirected into non-productive activities. As a result, the aid-induced economic growth in the recipient country tends to be lower than expected (Lloyd et al. (1998)).

Finally, aid-financed activities can give rise to backlash effects that affect adversely the private sector. The government expenditures can crowd out private activity, for instance through a displacement of foreign borrowing. As a consequence, aid will have little or no impact on the rate of growth of national income.

However, according to White (1992), the reasons for rejecting a relationship between aid and economic growth are weak.

(2) Impact on exports: Provided the link between development aid and economic growth is

established, a further question is the amount of the additional demand for imports in the recipient economy resulting from the increased amount of financial resources generated by income growth. The economic growth resulting from foreign aid should allow the recipient country to dispose of a greater financial capacity to import goods and services than it would have been the case without aid. Foreign countries, including donor countries, will compete for this additional demand on international markets. Besides, the impact of development aid on exports is boosted if aid is directly linked to trade, as is the case with the currently-implemented Aid for Trade initiative. For example, if aid is linked to the liberalisation of foreign trade regimes (or if economic growth leads to more trade openness), the resulting reduction in trade barriers can improve the access to world markets and indirectly increase the level of imports.

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(3) Donor country’s market share: Assuming the positive impact of development aid on recipient country imports (mainly via economic growth) is established, then a further question arises, namely the share of a particular donor country (i.e. Switzerland) in the increased demand for imports in the recipient economy. Obviously, the market share of the donor country in the recipient’s country imports market will depend on the competitive advantage of the exporting firms with respect to other foreign exporting firms. The more internationally competitive the donor country’s firms are, the more they will export and the higher their market share will be, other things being equal (inter alia the tied or untied nature of aid).

The impact of ODA on donor country exports can be both direct and indirect. – Direct Effect

The most direct link between aid and exports occurs with formally tied aid, where the granting of aid is contingent on purchasing goods and services from the donor. In other words, aid takes the form of goods and services originating from the donor country. As a consequence, the aid itself becomes synonymous to exports. There can also be a direct link between aid and exports with informally tied aid. For instance, a donor may implicitly direct aid towards projects that require supplies from industries in which it has a strong competitive advantage. It could also impose bidding procedures in order to minimise losses due to corruption. These procedures are usually modelled after the donor’s own domestic bidding practices. As a consequence, the donor’s domestic firms, which are more familiar with such procedures, will dispose of an advantage when bidding to supply the project in the recipient country. In the end, the recipient country will have to acquire goods and services procured in the donor country.

– Indirect Effect As argued by Arvin and Baum (1997), untied aid creates cumulatively a stock of “goodwill” which affects positively the demand for donor country’s goods and services. The recipient country may feel more disposed, if not morally obliged, to purchase goods and services from the donor in order to secure more aid in the future. As a result, untied aid has a lingering effect on the donor’s level of exports. Beside this political goodwill, aid associated with technological transfer can also lead to long-term trade dependency. As mentioned earlier, aid often finances projects that require the imports of capital goods and accompanying services which are produced in the donor country. Therefore, when accepting aid, the recipient country implicitly adopts the donor’s technology and corresponding standards. In order to replace, keep up-to-date or expand parts of the equipments, the recipient will have to import goods and services from the donor. Aid can also reduce costs associated with asymmetric information on the recipient’s market. Once a recipient country has imported goods and services from the donor through aid, some of the costs associated with the information barriers have been reduced (market identification, customer relationships development, reputation creation, distribution channels creation, local laws and processes learning, etc.). As a consequence, this reduction of information barriers can have a positive effect on current and future exports.

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Note, however, that the above-made distinction between tied and untied aid with respect to direct and indirect effects is not necessarily relevant (see, for example, Schönherr and Vogler-Ludwig (2002) and Arvin et al. (2003)). The indirect effect does not solely result from untied aid. It can also be related to tied aid. For instance, consider the case where Switzerland gives tied aid associated with a technological transfer. In the short run, the recipient country of this tied aid will purchase the Swiss technology and adopt the corresponding standards, which increases directly Swiss exports. In the long run, there will still be a positive indirect effect on Swiss exports, because the recipient country is now tied to Swiss technology and Swiss standards. Similarly, untied aid can exert a direct effect on exports not related to the goodwill factor.

2.2 Influence of Exports on Aid Aid allocation policies of the donor country constitute the main argument for an influence of exports on aid. Assuming aid allocation policies are the result of different pressures exerted by domestic lobbies, higher exports (or exports potential) can lead to greater aid if the donor country allocates more aid to countries with which it has the strongest commercial links. In other words, the aid the recipient country gets can be viewed as a reward for purchasing the donor country’s goods and services. Business groups concerned with trade promotion can also exert pressure to direct aid towards specific countries in order to expand their market shares in those countries. More generally, to the extent that higher exports result from economic growth in the recipient country, which in turn results from sound economic policies, greater aid flows can be viewed as a means of maximising the effectiveness of aid. According to all these lines of argument, one can expect export flows to influence positively development aid. Note, however, that while exports can influence aid, the impact might be negative. The donor country might decide to use foreign aid to strengthen export ties in countries in which its business is declining. Similarly, it can reduce aid to countries towards which its exports are booming, judging them as lower priority. The negative relationship can also result from the fact that exports constitute a proxy for economic growth. Unfortunately, the literature on aid allocation does not provide a consensus on the nature of the impact of exports on aid flows (Lloyd et al. (1998)). The issue should therefore be handled empirically. 2.3 Direction of Causality The various arguments presented above suggest that the direction and the nature of the relationship between aid and exports is complex, which makes the empirical testing of this link difficult. Causality can run unilaterally in either direction. It can also be bidirectional. Three possibilities can be distinguished. Case 1: Aid causes exports (unidirectional causality) which implies that the former tends to

precede the latter in time, mainly through economic growth and import dependency effects. Its influence is positive.

Case 2: Exports cause aid (unidirectional causality) which implies that export flows tend to

precede aid flows in time. In this case, there is no general agreement on the sign of the impact of exports on aid. It can be either positive or negative.

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Case 3: Aid and exports influence mutually each other (bidirectional causality). Given that the structural model estimated in Section 6 implicitly establishes a causal link running from ODA to exports, before estimating this model, we need to make sure that the causality link between the two variables does not run in the other direction. That is why we apply beforehand in Section 5 Granger-causality tests in order to clarify the issue.

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3. SURVEY OF THE LITERATURE Most of the studies about foreign assistance focus on the effects of foreign aid on the receiving countries or investigate the determinants of foreign aid, namely which donor gives to which recipient and why. But, although less broad, another part of this literature analyses the link between foreign aid and donor’s exports to the recipient country, which is the topic of our research. Some papers treating this question by means of different methodologies are reviewed in this section. Table 3.1 summarises the main features of these studies. The study by Hyson and Strout (1968) shows that, already in the 1960s, the link between foreign aid and the donor’s exports to the recipient country was an important and highly political topic of concern in the United States. On the basis of stylized facts, the authors analyze this relation by an appraisal of the effectiveness of the aid program in inducing economic development, an estimate of the additional demand for imports generated by income growth and an evaluation of the share of the United States in this increased demand. The conclusions of their study of 33 non-communist countries over the period 1960 to 1965 suggest that 60% of the total increase in US commodity exports to the recipient countries was directly or indirectly the outcome of US tied aid. The political implications of these results (in the context of the late 1960s) were that government programs should support the foreign aid system as means to solve the balance of payments deficit and encourage the continuation of US private trade and investment after the end of the aid program. More recently, Arvin, Choudhry and Drewes (1996) have emphasized the lingering effect of untied foreign aid on a donor country’s exports, in the case of Canada. The authors use a linear function of the donor’s level of exports, which depends on its present and past levels of untied aid. The influence of the latter is supposed to decrease through time because recipient countries tend to forget the generosity of a donor over time. The pooled estimation includes 58 developing countries over the period 1982 to 1990 and suggests that current untied aid has a positive effect as well as a lingering impact on Canada’s exports. Finally, they conclude that it is the history of untied aid and not just its contemporaneous amount that cumulatively impacts upon exports. In their study, Arvin and Baum (1997) develop a model of foreign assistance that distinguishes between tied and untied aid, where the latter generates goodwill for a donor in a recipient country, thereby enhancing the donor’s exports. The concept of “goodwill” has been developed in marketing literature and is described as an intangible asset that capitalizes on preferential use by consumers based on certain facts of human nature. Arvin and Baum’s model use an intertemporal maximizing model in order to identify the optimal adjustment paths for tied and untied aid which both increase the exports of a donor country. According to this specification, a donor country chooses its levels of aid over time in order to maximize the present discounted value of its benefits. The authors analysed seventeen OECD countries over the period 1972 to 1990. Their estimations are based on pooled cross-section and time series data set using a nonlinear least squares system estimator. The results suggest that the donor countries maintain a constant flow of untied aid in order to continually replenish the stock of goodwill and adjust the flow of tied aid over time. This shows that a mix of tied and untied aid is necessary in order to maximize the return to a donor. Arvin and Choudhry (1997) tried to find whether untied aid disbursements create goodwill for donor exports. In the case of Canada and for 35 developing countries over the period from

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1982 to 1990, they test three hypotheses. According to the first one, untied aid inflows cause donor exports to increase, reflecting the goodwill a donor creates in recipient countries. The second one reverses the causality by asserting that strong export performance financially enables or politically encourages a donor to increase the level of untied aid to the recipient. Finally, one cannot exclude that both hypotheses are verified, which means that causation runs in both directions (see Section 2. 3). In order to answer theses questions, the authors use a bi- and a trivariate Granger causality procedure. In the multivariate case, the third variable considered is the rate of growth of the recipient country’s GDP. They elaborate pooled OLS estimates for the entire sample but also for sub-samples that are divided according to the different regions, the countries’ income and their relationship with Canada. The results show that, at the aggregate level, no generalization can be made concerning the relationship between untied aid and exports in the case of Canada. Concerning the subsamples, the results are also heterogeneous. It is sometimes difficult to detect an eventual causality between these two variables, which requires a careful analysis of the data. Nevertheless, on the whole, the authors conclude that there are complex links between untied aid and exports and that the former may promote the latter. The impact of tied aid on trade flows between donor and recipient countries has been analysed by Tajoli (1999) in the case of Italy whose tied aid percentage is one of the highest among Development Assistance Committee (DAC) countries. Two effects of tied aid are analyzed: the overall impact on export flows and the influence on the donor’s market share in recipient countries. Through a quite simple microeconomic model, Tajoli shows on a theoretical basis that there can be doubts about the efficiency of tied aid to promote donor’s exports to the recipient country due to the fact that tied aid generates distortions, such as the deterioration of the recipient country’s terms of trade or stronger competition between donor countries, that can overcome tied aid’s positive income effects. This suggests that tying should not be used as a strategic trade policy instrument. Tajoli considers 34 developing countries over the period 1982 to 1991 and undertakes a pooled and a panel analyses, by means of generalized least squares using cross-section weights with countries’ fixed effects. The results show that tied aid does not automatically increase trade flows. The author also examines the influence of the degree of tying on the Italian market shares in the recipient country and shows that tying aid has no role in maintaining it. Vogler-Ludwig et al. (1999) analyze the impact of German aid on its exports to 42 recipient countries over the period 1976 to 1995. They suppose that German exports to the recipient country depend on the level of the latter’s GNP, the aid flows from Germany as well as those from other donor countries. They estimate for each country separately as well as for all countries together a structural equation that allows measuring the exports elasticity with respect to the aid flows. Although the country specific results are heterogeneous, the pooled estimate indicates a positive elasticity. Also by means of a bi- and trivariate Granger-causality procedure, they test the three hypotheses already described and analysed by Arvin and Choudhry (1997). Even if the results of the different estimates vary from one case to another, this analysis suggests a strong relation between untied aid and German exports to the recipient country and shows that the three hypotheses are verified. The authors also mention that other variables may help explain German exports to different recipient countries. In order to determine if there is causality between untied foreign assistance and exports performance, Arvin, Cater and Choudhry (2000) use the Granger causality procedure in the case of Germany. They analyze the three hypotheses mentioned and tested by Arvin and Choudhry (1997). First, they use a bivariate Granger causality test that they then extend in

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order to take into account a third variable (trivariate Granger causality test). Indeed, German tied and partially-tied aid, German export credits and GNP of recipient countries can have an influence on the causality between untied aid and German exports. The authors analyze the aid flows of Germany to 85 developing recipient countries for the period 1973 to 1995 (pooled data). The results are presented for all the different countries together and also for subgroups based on region, income and ties to Germany. It is important to mention that the results are different among the different subsamples. On the whole, this study provides some support for the export promotion hypothesis whereby untied aid disbursements generate goodwill for the donor. Thus, it can be concluded that generating goodwill with untied aid disbursements may be a new direction for the official development assistance of western countries. The main objective of aid to developing countries is to promote economic growth and alleviate poverty in recipient countries. However, donors target also to increase their own exports. Arvin, Piretti and Lew (2003) investigate the three hypotheses analysed by Arvin and Choudhry (1997). In order to answer these questions, they use Granger causality methodology. A trivariate test allows them to take into account the influence of a third variable, a democracy index, on the causality between aid flows and exports. The calculations are made for the entire sample but also for different subsamples, determined according to the region and the income of the recipient countries. The results from the Granger causality tests are mixed among the different subsamples, which suggest that a range of other country-specific characteristics can have an influence on the aid-trade relationship. The authors re-examine for each country individually the causal relationship between aid and exports using an error-correction model. The advantage of such a procedure is that the long-run relationship between the two variables and the short-term dynamics can both be captured. However, like for the standard Granger causality test, the results are heterogeneous among the different countries and no clear common characteristics of recipients exhibiting a particular link can be identified. This study reveals an absence of a consistent causal pattern between aid flows and exports. Finally, the authors suggest that considering specific institutional and country-specific characteristics could be very useful to detect the true nature of the relationship between aid and trade in each developing country. Wagner (2003) supposes that donors make extensive use of their aid programs to promote their own exports. To test this hypothesis, he uses a gravity model of trade to test the link between aid and exports expansion. He then makes a comparison between different donors. This type of analysis can be very useful for governments making decisions on foreign aid policy and budgets. In this study, 20 DAC countries are considered over the period 1970 to 1992 with respect to bilateral aid and no distinction is made between tied and untied aid. By means of this model, it is possible to predict total exports from a donor to a recipient country. Another important contribution is the distinction between direct and indirect effects that aid exerts on export levels. Direct effects include exports realized within the projects financed by aid, regardless of whether the aid is officially tied; indirect effects contain other ways in which aid can give the donor’s exporters an advantage in the recipient’s market. Wagner also considers prior years’ aid to determine how much the trade benefits of aid persist after the year the aid is disbursed (long-term effect). The results suggest that 35 cents out of every dollar of aid comes directly back to the donor for exports of goods related to the aid-financed project and that another 98 cents finds its way back to the donor for exports of goods not directly linked to aid projects. Thus, the indirect effect appears to be more than twice as high as the direct effect. The conclusion also show that the effect of past years’ aid is surprisingly small. Indeed, less than 15% of the trade value of aid comes after the year of donation. In

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addition, the author isolates the aid/trade relationship for Japan to see whether this country really uses aid to gain unfair trade advantages from the recipient countries, as it is often proclaimed by other donors. The results show no evidence that this country exploits aid for commercial advantage more strongly that the average donor.

Table 3.1: Literature survey – Summary

STUDY QUESTIONS COUNTRIES, TIME PERIOD AND TYPE OF DATA

EMPIRICAL APPROACH / METHODOLOGY RESULTS

Hyson and Strout (1968)

Is there an influence of foreign (and tied) aid on donor’s exports to recipient country, in the case of the USA?

USA as donor country and 33 non-communist recipient countries 1960 to 1965 Basic statistics (no econometrics)

Statistical analysis (not econometric). Estimate of: - effectiveness of the aid program in

inducing economic development. additional demand for imports

generated by income growth. share of the USA in the increased

demand.

Approximately 60% of the total increase of US commodity exports to the recipient countries from 1960 to 1965 is directly or indirectly the outcome of US tied foreign aid.

Arvin, Choudhry and Drewes (1996)

Is there lingering effect of untied foreign aid on donor’s exports (in the case of Canada)?

Canada as donor country and 58 recipient developing countries 1982 to 1990 Pooled data

Generalized least squares. Current untied aid has a positive and lingering effect on Canada’s exports.

Arvin and Baum (1997)

How different type of aid (tied and untied) influence donor country’s exports?

17 OECD (donor) countries 1972-1990 Time series and pooled cross-section

Intertemporal maximizing model. (Cobb-Douglas function and current-value Hamiltonian) Nonlinear least square estimator.

Donor countries maintain a constant flow of untied aid over time and adjust the flow of tied aid in order to keep a “goodwill relationship” with recipient countries.

Arvin and Choudhry (1997)

Does untied aid create goodwill for the donor’s (= Canada) exports?

Canada as donor country and 35 developing recipient countries 1982 to 1990 Pooled data

Bi- and trivariate Granger causality tests (estimated by OLS)

There is no generalization of the results, which are different among the different samples. But it can be supposed that there are important and complex link between untied aid and trade and that untied aid may be export promoting.

Tajoli (1999)

What is the impact of tied aid on trade flows between donor and recipient countries? And in the case of Italy?

34 recipient developing countries 1982-1991 Panel data and pooled data

Generalized least squares using cross-section weights and countries’ fixed effects.

Tied aid does not automatically increase trade flows and has no role in maintaining Italian market shares in recipient countries. It can not be used as an export subsidy.

Vogler-Ludwig and Schönherr (1999)

What is the impact of aid flows on German exports to the recipient countries?

43 developing countries 1976-1995 Pooled data and time series

Structural model Bi- and trivariate Granger-causality tests

Mixed results but on the whole the results suggest a strong relation (and a causality) between aid flows and German exports to the recipient country.

STUDY QUESTIONS COUNTRIES, TIME PERIOD AND TYPE OF DATA

EMPIRICAL APPROACH / METHODOLOGY RESULTS

Arvin, Cater and Choudhry (2000)

Is there causality between untied foreign assistance and export performance in the case of Germany?

German as donor country and 85 recipient developing countries 1973 to 1995 Pooled data

Bivariate and trivariate Granger-causality tests.

On the whole, there is (bidirectional) causality between untied aid and donor’s exports. But the results are very heterogeneous among the different subsamples (based on region, income and ties to Germany).

Arvin, Piretti and Lew (2003)

Do aid inflows cause donor exports to increase? Does strong export performance generate an increase of the donor’s aid flows to a recipient? Are aid and exports linked in both directions together?

Italia as donor country and 119 developing recipient countries 1970-1997 Pooled data and time series

Bi- and trivariate Granger causality tests Bivariate Granger causality test with an error-correction model

Mixed results among the different countries and subsamples: for some of them, there is a causality-link between aid flows and exports, for other ones this link is bidirectional but in some cases, there is no relationship between the two variables.

Wagner (2003)

Donors make extensive use of their aid programs to promote their exports. By how much the trade benefits of aid persist after the year the aid is disbursed? Does Japan actually use aid to gain unfair trade advantages from recipient countries?

20 DAC donor countries 1970-1992 Pooled data

Gravity model of trade. OLS regressions. OLS fixed effects. OLS using residuals from imports.

There is a link between foreign aid and donor’s exports. The indirect effects are twice more important than the direct ones and the influence of past years’ aid is relatively small. Japan does not exploit aid for commercial advantage more strongly that than the average donor.

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4. DATA The empirical results presented in this study are based on a sample of longitudinal data on 99 countries2 over the period 1965-2004. In order to avoid applying a special treatment to missing data, a recipient country is selected if it has uninterrupted observations for at least five years. The data emanate from three sources: the Swiss Federal Statistical Office’s (SFSO) Statweb database, the OECD Development Assistance Committee’s (DAC) International Development Statistics database and the World Bank’s World Development Indicators CD-Rom (2006). Because the data exhibit considerable year-to-year fluctuations, they have been smoothed using a weighted three-year moving average3. The data on Swiss exports of goods are extracted from the SFSO database. The official CHF-USD exchange rate (average per year) is taken from the World Development Indicators CD-Rom and is used to express Swiss exports in terms of current (nominal) USD. The data on Gross National Income (GNI) of countries receiving Swiss aid, expressed in millions of current USD, are taken from the World Development Indicators CD-Rom. GNI is an aggregate measure of economic activity close to Gross Domestic Product (GDP). It takes into account all production in the domestic economy (i.e. GDP) plus the net flows of factor income (such as rents, profits, and labour income) from abroad. As far as the aid variable is concerned, it can take many forms. Aid can be either project-related or take place within programmes at sectoral or macroeconomic levels. One can also distinguish between development aid and emergency assistance. Development aid aims at promoting economic growth and poverty reduction (long-run perspective) whereas emergency assistance is destined to provide temporary relief from the aftermaths of conflicts and natural disasters (short-run perspective). Aid flows comprise both grants (concessional) and loans (implying interest payments and reimbursement at maturity) components, the latter having a concessional element. Another relevant distinction is between bilateral and multilateral aid flows. Bilateral aid involves only the donor and the recipient countries whereas multilateral aid takes place via contributions to an international body such as the World Bank. In this study we use OECD’s Official Development Assistance (ODA) statistics. ODA spans over project as well as programme-based aid, comprises both development aid and emergency assistance and makes no distinction between grant and loan components. On the other hand, its coverage is limited to bilateral aid flows. ODA is without doubt the most widely-used definition of aid used in the empirical literature. According to OECD-DAC, to be considered as ODA, aid flows should meet the following requirements: – they must be paid out of public funds; – they must be destined to developing countries or territories; – they must promote economic development and welfare as their main objective; 2 See appendices A and B. 3 Considering a variable tX , its three-year moving average is given by , 1 1

1 1 14 2 4MA t t t tX X X X− += + + .

18

– when aid takes the form of a loan, it must have a grant element of at least 25%. Two measures of aid are used in our empirical analysis: gross and net ODA. According to the OECD-DAC, gross ODA is defined as follows:

Gross ODA = Grants + ODA Loans Extended Gross ODA includes ordinary grants, “debt-forgiveness grants” and concessional loans. This means that gross ODA increases when an ODA loan is cancelled. In fact, the cancellation of an ODA loan is accounted by two offsetting transactions. The first one is a “debt forgiveness grant” which is considered as grant. The second one is an “offsetting entry for debt relief”, which corresponds to the amortization of the immediate return of that grant. It is recorded with a negative sign and can be considered as an ODA loan repayment. Thus, this accounting mechanism allows avoiding double-counting of cancelled ODA loans whose value is fully accounted as aid at disbursement. Since the offsetting entry is considered a reflow, it does not affect gross ODA, but will enter in net ODA. The formula for DAC’s gross ODA contains also two other relevant subtleties. First, any cancellation of a “non ODA” or “Other Official Finance”4 loan is accounted retroactively as a “debt forgiveness grant”. Since the loan was not accounted as ODA at the time of disbursement, there is no concern about double-counting and no need for an offsetting entry. In other words, the cancellation of a “non ODA” loan increases gross ODA. Second, the rescheduling of debt repayment leads to the capitalization of accrued but unpaid interest to be treated as a new aid flow and accounted as “ODA loans extended”. This rule comes from the fact that ODA is a capital flow concept and capitalization of interest is viewed as a capital flow. Following the analogy with the capital flow concept of net foreign direct investment (FDI), where only the return of capital is netted out of net FDI but not the repatriation of earnings, net ODA measures aid flows net of payment of principal but not interest. More precisely, disbursements are adjusted for the loan’s amortization but not the associated interest which can be considered as the donors’ “earnings” on aid investment:

Net ODA = Gross ODA – ODA Loans Received Net ODA constitutes by far the leading measure of foreign aid flows. That is the reason why we will mostly focus our estimations on net ODA. Despite its popularity, the methodology behind the elaboration of net ODA measures suffers from a number of conceptual shortcomings, as identified by Chang et al. (1998). Net ODA does not accurately measure the cost that donors incur in connection with their aid flows, which implies that the evolution of net ODA produces a distorted picture of aid trends. First, there is an underestimation of aid content due to netting out. Second, there is an over-representation of loans with high concessionality and an under-representation of those with

4 “Other Official Finance” loans are those whose concessional element is too small to qualify them as ODA, or

whose objective is military, export-finance or non-development related.

19

low concessionality. Third, a further distorting factor is the inclusion in the ODA figures of official technical assistance grants by their full value, failing to subtract donor benefits from payments received in return. Fourth, for fixed-rate loans, the discount rate used in ODA is fixed (10%) and thus do not correspond to appropriate market rates. The discount rate on loans included in ODA should not be fixed but variable with respect to time, currency and maturity in order to reflect market conditions. Fifth, for variable-rate loans, ODA implicitly assumes that the applicable rates remain constant at their level at the time of disbursement. Finally, net ODA is not adjusted for credit risk, although this constitutes an additional source of donor financial cost. Chang et al. (1998) propose a new and improved valuation approach of foreign aid, called Effective Development Assistance, calculated for 133 developing countries from 1975 to 1995. Unfortunately, this new measure is only available at the recipient country level (aggregated across donors) and therefore cannot be used in the present study. A more recent attempt to correct ODA figures as indicators of donor performance is provided by Roodman (2006). Aware of these shortcomings, in this study we use series on Swiss net/gross ODA to recipient countries. The series, expressed in millions of current USD, are taken from the OECD-DAC’s International Development Statistics Online. Net/gross ODA from DAC members other than Switzerland (used for the estimation of the structural model in Section 6) is constructed as the sum of all net/gross ODA from all DAC members except Switzerland to a given recipient country. Appendix C provides some insights on disparities of Swiss exports, Swiss aid, rest of the world’s (i.e. OECD-DAC members) aid and recipient’s GNI by country and region/income groups, all expressed in millions of current USD. As mentioned previously, the literature usually distinguishes between tied and untied aid. Tied aid obliges recipient countries to reciprocate by buying the donor’s exports, while untied aid allows the recipient country to spend freely the money on goods and services procured by any country. Unfortunately, there is no data on the split of Swiss ODA into tied and untied components at the recipient level. However, in the case of Switzerland, this distinction is less relevant, since Swiss ODA has been almost entirely untied in recent years with a clear rising trend since the 1980s (see Figure 4.1)5.

5 Partially tied aid is defined as aid that is provided on the condition that it is used to procure goods and services

in the donor country or among a restricted group of other countries, which must include substantially all developing countries.

20

Figure 4.1: Tying status of Swiss total bilateral ODA

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%

197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

Year

Pe rcent

untied partially tied tied

The time-series analysis conducted in Section 5 and the estimation of the structural model in Section 6 will be based principally on series expressed in nominal terms. However, as a check of the robustness of the results, some equations will be estimated also in real terms. To convert Swiss exports into millions of constant (real) USD, we apply SFSO’s exports deflator. GNI expressed in real local currency for various base years is taken from the World Development Indicators. We use the official exchange rate (local currency per USD yearly average) taken from the same source in order to express GNI in millions of constant USD. Net ODA in real terms is obtained by applying OECD-DAC’s ODA deflator to net ODA in nominal terms.

21

5. TIME SERIES ANALYSIS We are interested to test whether Switzerland’s ODA has an influence on its exports to the recipient countries and also whether the Swiss exports have an effect on aid flows to these countries. The causality may also run in both directions simultaneously (bilateral causality) (see for example Section 2. 3). We will then determine the influence of the recipient countries’ Gross National Income (GNI) on these relations. In order to elaborate this causality analysis, bivariate as well as trivariate Granger causality tests will be performed, the third variable being the GNI of the recipient countries. However, it is important to mention that this is not a real trivariate causality framework because GNI is treated as an exogenous (and not as an endogenous) variable. After a short description of the methodology and of the different tests to be performed, the results and their interpretation are presented. 5.1 Granger causality methodology Bivariate causality Granger causality is based on the principle that if, after conditioning a variable on its own past values, the addition of another variable’s past values further reduces the prediction error covariance, then the additional variable is said to Granger cause the first. X causes Y if the precision of the estimated current value of Y is improved by controlling for current and past values of X. The use of temporal information enables one to say something about the direction of causality. A symmetric statement can be made for Y causing X. In the case of a bivariate Granger causality test, the regressions for Y and X are the following:

∑∑=

−=

− +++=J

jtjtj

I

iitit uXcYbaY

11

(5.1)

∑∑=

−=

− +++=L

ltltl

K

kktkt vYfXedX

11

(5.2)

where tu and tv are serially uncorrelated zero mean stochastic error terms and X and Y are required to be both stationary. Thus, the test is based on regressions in which the variables are differenced in order to achieve stationarity. The statistical significance of the jc and lf allows us to determine the causality in the Granger sense (by means of a standard F-test, see below). If jc ’s (respectively lf ’s) are jointly significant, then X (Y) can be said to Granger-cause Y (X). If both are significant, then there is evidence of bi-directional causality. On the one hand, development aid from Switzerland may improve its relations with the recipient countries which will then buy more goods and services from Switzerland. Thus, the sign of the sum of the coefficients jc is expected to be positive. On the other hand, the fact that recipient countries’ demand for Swiss goods is higher may induce two types of effects: either an increase of the Swiss aid, in order to develop further exports, or a decrease of the development aid from Switzerland, because the commercial ties between the two countries are

22

sufficiently implemented, which reduces the incentive for Switzerland to give more aid. Thus, the sign of the sum of the coefficients lf may be positive or negative. Trivariate causality Granger causality can be affected by a bias due to the omission of other relevant variables. Therefore, a bivariate test may not reveal the true nature of causality given that both variables may be simultaneously influenced by, for example, a third omitted variable. For this reason, this study uses a trivariate structure in order to introduce a third variable, namely GNI of the recipient country (designated by Z). It allows examining the joint influence of two variables on the third. The regressions for Y and X are:

∑ ∑∑= =

−−=

− ++++=J

jt

M

mmtmjtj

I

iitit ZhXcYbaY

1 11ξ (5.3)

∑ ∑∑= =

−−=

− ++++=L

lt

N

nntnltl

K

kktkt ZgYfXedX

1 11ζ (5.4)

where tξ and tζ are serially uncorrelated zero mean stochastic error terms. The hypotheses being tested are first, whether X causes Y conditional on Z and after controlling for Y’s own lags and second, whether Y causes X in the presence of Z and after controlling for X’s own lags. The null hypothesis that X does not Granger cause Y, given Z, is rejected if the jc ’s are jointly significant, based on a standard F-Test. Y Granger-causes X, conditional on Z, if lf ’s are jointly significantly different than zero. Of course, like in the bivariate case, all the variables must be stationary. Lag lengths It is important to consider the fact that the results of the Granger causality test depend sometimes crucially on the choice of lag lengths selected for the variables. If the number of lags used exceeds the true order, the power of the test is likely to be reduced and if the number of lags used is smaller than the optimal number of lags, the regression estimates will be biased and the residuals will be serially correlated. In this study, the pattern of the lag structure is thus determined statistically rather than assumed arbitrarily, although there is no universal agreement on the criterion to be used. The procedure consists of examining each of the series for its optimal lag length using the following autoregression:

∑=

− ++=P

ptptpt YY

10 ηλλ (5.5)

where tη is a serially uncorrelated zero mean stochastic error term. The procedure involves estimating the above regression using OLS, allowing different values for p, and computing the final prediction error (FPE) as:

NkNSERFPE )()( 2 +

= (5.6)

23

where SER is the standard error of the regression, N the number of observations and k the lag length used in the regression. The optimal lag length corresponds to the value of p that minimizes FPE, which balances the risk of the bias from choosing a lower lag against the risk of an increased variance when a higher order is chosen. Moreover, choosing the lag optimally does not constrain the number of lags to be the same for every regression. Series stationarity As mentioned in the description of the Granger causality methodology, the series must be stationary. In order to determine the order of integration of the series, we use the Phillips-Perron (PP) test (Hamilton, 1994). This method consists of testing the unit root hypothesis from the following equation:

ttt yy ερμ ++=Δ −1 (5.7) where tε is independently and identically distributed. This equation is estimated by OLS and then, the t-statistics on ρ is corrected for the autocorrelation of ε . This last operation is an advantage in comparison with the alternative Augmented Dickey-Fuller (ADF) test as the check of the autocorrelation of the residuals is no more required. We applied this test on the series (in log and transformed by a three-year moving average, see Section 4 for more details) with a constant and a trend for the series in levels and only a constant for the series in first differences. The results show that most of the series are integrated of order 1. Thus, we will use the first difference of the series in log when performing the Granger causality tests. Residuals autocorrelation Before analysing the causality results, we first have to check the autocorrelation of the residuals, by means of the Ljung-Box statistics, which is a test for the null hypothesis that there is no autocorrelation up to order k and is computed as :

∑= −

+=k

j

jLB jN

NNQ1

2

)2(τ

(5.8)

where jτ is the j-th order autocorrelation and T is the number of observations. Q is

asymptotically distributed as a 2χ with degrees of freedom equal to the number of autocorrelations6.

6 If there is no serial correlation in the residuals, the Ljung-Box Q-statistics at all lags should be insignificant with large p-values. Within our calculations, EViews automatically selected an order of lag of 16 for almost all

24

5.2 Cointegration and the error correction model As mentioned above, the Granger causality test requires that X and Y be stationary. But most of time series do not satisfy this condition. Thus, they have to be differenced in order to become stationary, though this has the implication that information on the long-run properties of the series is lost. In order to avoid this problem, an error-correction causal structure could be used provided that the series are cointegrated. The concept of cointegration is relevant to the problem of the determination of long-run relationship between variables. If the difference between two non-stationary series is itself stationary, then the two series are cointegrated. It is important to mention that a necessary condition for variables to be cointegrated is that they be integrated of the same order, and this order has to be at least one. If two or more series are cointegrated, it is possible to interpret these variables as in a long-run equilibrium relationship and an error correction term is added to the modelling procedure in order to capture the short-run dynamics of the model. Lack of cointegration suggests that the variables have no long-run relationship, which means that they can move arbitrarily far away from each other. In our study, we will use the Augmented Engle-Granger (AEG) test for cointegration. This procedure comprises two steps. The first one is to test for the existence of cointegration between two time series integrated of order d (I(d)), tX and tY . This involves running a regression of tX on tY (and vice-versa) and checking if the regression residuals are stationary. The following regressions are estimated using OLS :

ttt eXY 1++= βα (5.9)

ttt eYX 2++= δγ (5.10) where te1 and te2 are the residuals from these regressions. The PP test is then applied to them in order to check if they are stationary. If the null hypothesis is not rejected in both cases, then it is assumed that the error is non-stationary and therefore tX and tY are not cointegrated. If, on the other hand, the null hypothesis is rejected for the residuals of at least one of the above regressions then they are assumed to be stationary and the two variables are cointegrated, which means that there is a long-run relationship between tX and tY . Existence of cointegration has implications for the Granger causality test. If the series are cointegrated of order one, then the first-order difference of each series plus a lagged regression residual has to be introduced in the model. The lagged error residual is the error correction term and the model is called an error-correction model (ECM). It captures both the long-term convergence between the two variables and the short-term dynamics. The equations of the Granger-causality tests now take the following form:

the regressions. The reported Ljung-Box statistics and its p-value in the different tables are obtained from a test that includes 16 lags. The number of lags is mentioned only in the cases where this statistics shows autocorrelation of the residuals at a lag order lower than 16.

25

∑∑=

−−=

− ++Δ+Δ+=ΔJ

jttjtj

I

iitit egXcYbaY

111

1

ˆ ξ (5.11)

∑∑=

−−=

− ++Δ+Δ+=ΔL

lttltl

K

kktkt ehYfXedX

112

1

ˆ ζ (5.12)

where a and d are constants, tξ and tζ represent mutually uncorrelated white noise series and the lag values (i, j, k and l) are determined optimally. 11̂ −te and 12ˆ −te are the error correction terms (residuals) obtained from the regression of tX on tY and from the regression of tY on tX , assuming tY and tX are cointegrated. Then, we apply the same procedure as we did for the standard causality tests (Ljung-Box and F statistics). 5.3 Results General remarks We applied the bi- and trivariate Granger causality tests to each country separately7 but also to the full sample (all the countries together except Malaysia and Trinidad and Tobago, because the series for these two countries are too short to perform trivariate Granger causality tests) as well as to different subgroups of countries determined according to their level of GNI per capita and to their geographical location (see Appendix B) based on region and on income. In the interpretation of the results, we must pay attention to the fact that the sample size of each country separately and of the aggregate samples is not the same. It is also important to remember that we do not perform actual trivariate tests (trivariate vector-autoregressive framework), but bivariate tests with a third exogenous variable, namely GNI. We analyse the relationship between net ODA and exports (nominal and real) and also between gross ODA and exports (only nominal). Gross ODA is more appropriate than net ODA for the detection of causality because it is a better representation of the goodwill effect. Net ODA is, however, the most widely used indicator of aid flows in the literature. This is why both concepts are taken into account in this study. It must also be mentioned that for a few countries, the number of observations is relatively low and the number of lags high (up to 8 years), so the results must be interpreted with caution. It is also important to keep in mind that the link between ODA and exports is not a strong macroeconomic relation, so that the results for causality should give only a rough indication. The trivariate Granger causality tests show whether the causality between ODA and exports is confirmed given GNI of the recipient countries. If the bivariate tests suggest causality between the two variables but that this is not the case according the trivariate tests, this lets us suppose that net ODA or exports are not caused by each other but rather explained by GNI. On the contrary, the situation where there is causality according to the trivariate test but not to the bivariate suggests that GNI allows clarifying the link between ODA and exports and, thus, reveals the possible causality between these two variables. 7 A recipient country is selected if it has uninterrupted observations for at least thirty years.

26

According to the theory, it is expected that ODA positively causes exports. If this relation turns out to be negative, it can be due to some other factors specific to the recipient countries, such as war or corruption. If exports positively cause ODA, this implies that the higher is the demand of the recipient countries for Swiss goods, the more Switzerland tends to give aid to them. If this relationship is negative, the fact that the recipient countries increase their demand for Swiss goods means that there are now wealthier and need less development aid (see Section 2. 3). Granger causality Nominal net ODA A few entries of net ODA series for Brazil and Turkey take negative values. In order to be able to take the log of these series, we applied the transformation suggested by Busse et al.

(2007): log Xt → ( )2log 1t tX X+ + .

The results of the bi- and trivariate Granger causality tests between nominal net ODA and exports are summarized in Table 5.1a. As far as the bivariate tests are concerned, net ODA positively causes exports in the cases of Burkina Faso, Cameroon, Kenya, Madagascar, Indonesia, Bolivia, Trinidad & Tobago, the full sample and East Asia & Pacific (which corresponds to Indonesia). For the Republic of Congo, Tunisia, Paraguay and Turkey, the causality between the two variables is negative as for the aggregate sample of Middle East & North Africa. The results of the bivariate causality tests also show that exports positively cause net ODA for Lesotho and Nepal as well as for the aggregate sample of South Asia. This relationship is negative for Indonesia, which is the only case of bilateral causality. It can be observed that for most of the countries, the causality runs from net ODA to exports but that in five cases, this relationship is negative. The causality tests conditional on GNI confirm the positive relationship between net ODA and exports for Burkina Faso, Cameroon, Kenya and Madagascar and the negative causality between these two variables for Paraguay and Middle East & North Africa. It also confirms the positive impact of exports on net ODA in the cases of Lesotho and South Asia and the negative link for Indonesia. It also shows that net ODA negatively causes exports conditional on GNI for Sudan and Pakistan and that the impact of exports on net ODA conditional on GNI is positive for Algeria, Mauritania, Pakistan, Colombia, Haiti, Honduras and Turkey and negative for Cameroon, Ghana and the aggregate sample for Europe & Central Asia. The causality conditional on GNI runs in both directions in the cases of Cameroon and Pakistan. It can be observed that there are more countries for which exports cause net ODA than the opposite. Nominal gross ODA The results of the bi- and trivariate Granger causality tests between nominal gross ODA and exports are summarized in Table 5.1b. For Trinidad & Tobago, some entries of gross ODA take negative values (even after the application of the moving average), which is incoherent with the definition of gross ODA. As this problem is certainly due to an accounting discrepancy, we do not consider this country in our analysis. In several countries, the value of

27

gross ODA is equal to 0 (even after the application of the moving average). As most of the values are comprised between 0 and 1, the transformation used for negative values of net ODA mentioned in the previous section would affect the evolution of the series and, thereby is not applied. Thus, in order to be able to take the log of the different series, we replace the 0 values by 0.001. The bivariate tests show that gross ODA positively promotes exports in the cases of Cameroon, Kenya, Madagascar and Bolivia and that this causality link is negative for Algeria, Lesotho, Tunisia, Paraguay and Middle East & North Africa. The results also show that exports positively cause gross ODA for Cameroon, Malaysia, Nepal, Pakistan and Ecuador. Cameroon is the only country for which causality runs in both directions. The trivariate regressions confirm the positive impact of gross ODA on exports for Cameroon and Kenya. They also reveal that this causality is positive in the cases of Lesotho and for the aggregate sample of low income countries and that this link is negative for Algeria, Sudan and the aggragat Middle East & North Africa. The causality of exports on gross ODA conditional on GNI is positive for Pakistan, Ecuador, Haiti and Honduras and negative in the cases of Cameroon, Senegal and India. Within the trivariate framework, the causality runs in both directions only for Cameroon. The bivariate tests show that there are more countries for which net ODA cause exports than the contrary, but that in five cases this link is negative. However, according to the trivariate results, there is no evidence in favour of causality running in either direction. If we compare the results for both net and gross ODA, it seems that causality is confirmed, although not necessarily in the same direction, for Cameroon, Kenya, Madagascar, Sudan, Tunisia, Nepal, Pakistan, Bolivia, Colombia, Haiti, Honduras, Paraguay and Middle East & North Africa. Real net ODA The results of the bi- and trivariate Granger causality tests between real net ODA and exports are summarized in Table 5.1c. As for nominal net ODA data, the series for Turkey and Brazil contain some negative values and are again transformed as mentioned above in the section on nominal net ODA. There is no real GNI series for Cote d’Ivoire, Turkey and Nepal. Thus, these countries will not be analysed within the trivariate estimates using real data. For most countries, real series are shorter that the nominal ones. Thus, we do not elaborate the calculations for the full sample and the different subgroups because the smallest common sample size would be too short. The impact of real net ODA on real exports is positive in the cases of Burkina Faso, Cameroon, Kenya, Madagascar, India and Nicaragua and negative for Benin, Republic of Congo, Tunisia, Paraguay, Trinidad & Tobago and Turkey. The results show that causality of real exports on real net ODA is positive for Cameroon and Rwanda and negative for Indonesia. Bilateral causality can be observed only for Cameroon. In most countries, causality runs from real net ODA to real exports rather than the contrary. Conditional on GNI, the impact of real net ODA on real exports is positive for Cameroon, Madagascar and Zimbabwe and negative in the cases of Tunisia, Paraguay and Trinidad & Tobago. The trivariate estimates show only one case of causality of real exports on real net ODA, which is positive, namely for Indonesia. Again, the general evidence is in favour of

28

causality running from real net ODA to real exports but this relationship is negative in three cases. If we compare the results for both nominal and real net ODA, it seems that causality is confirmed, although not necessarily in the same direction, for Burkina Faso, Cameroon, Republic of Congo, Kenya, Madagascar, Tunisia, Indonesia, Paraguay, Trinidad & Tobago and Turkey. Error correction model The integration order of the series for each country is listed in Tables 5.2a, b and c. Unfortunately, for most of the series that are integrated of the same order, the residuals of the regressions (5.9) and (5.10) are not stationary except in the following cases. Nominal net ODA and exports for Guatemala are cointegrated but no causality can be observed between these two variables in either direction. There is also cointegration between gross ODA and exports in the case of the Democratic Republic of Congo but no causality is detected. Nevertheless, the results show that the short-term dynamics of the exports’ effect on gross ODA is significant but negative. Real net ODA and exports are cointegrated in the case of Kenya. There is a positive causality of real net ODA on real exports and the short-term dynamics is also significant but negative. The results also show that real exports do not exert causality on real net ODA but that the coefficient of the error term is significant and negative. Real net ODA and real exports are also cointegrated in the case of Guatemala but in that case there is neither causality nor significant short-term dynamics. On the whole, the general evidence is in favour of unidirectional causality but no global generalization can be made concerning the direction of the causal relationship between ODA and exports. The results from the Granger causality tests are mixed and the nature of the link between aid and trade varies across recipients. This is not surprising given the heterogeneity among the different countries. It can be explained by the fact that variables other than GNI (like institutional, political and country-specific characteristics) also have an influence on the causal relationship between net ODA and exports and, thus, are necessary to determine the true nature of this link between these two variables. The causality may also be influenced by other donors or trade partners. For Cameroon, Kenya, Madagascar and Paraguay causality is confirmed by the three types of “standard” Granger tests. This may suggest that the relationship between aid and exports is relatively strong for these four countries. Furthermore, in the case of Kenya, real exports and real net ODA are cointegrated and the corresponding error-correction model shows a positive causality running from net ODA to exports. 5. 4 Comparison with Germany Vogler-Ludwig et al. (1999) determine the causality between ODA and Germany’s exports also by means of the Granger methodology. Their results for the full sample are stronger than the ones we obtained for Switzerland. Indeed, the bivariate analysis reveal that causality runs in both directions and is positive and the trivariate tests confirm the positive impact of ODA

29

on exports. However, like most of the studies in this field, their results vary among the different subsamples and the causality links are less meaningful for these different groups of countries than for the full sample.

Table 5.1a : Causality results for individual countries and aggregate samples. Nominal net ODA

X Y Z N1) Ljung-Box statistic 2)

F-statistic of the restricted coefficients 3)

Sign of the sum of the restricted

coefficients

Ljung-Box statistic 2)



coefficients

N1) Ljung-Box statistic 2)



coefficients




coefficients

AFRICAAlgeria 6 6 8 34 4.30 1.85 (-) 9.52 1.14 (-) 30 13 1.72 (+) 14.81 3.84 (+)

(0.998) (0.1358) (0.891) (0.3752) (0.673) (0.2219) (0.538) (0.0351)** GNI (-)

Benin 8 2 2 35 13.63 1.11 (-) 12.41 1.59 (+) 35 12.37 0.45 (-) 8.83 1.63 (+)(0.626) (0.3466) (0.716) (0.1806) (0.718) (0.6432) (0.920) (0.1741)

Burkina Faso 2 7 4 33 13.96 2.33 (+) 14.13 0.19 (-) 33 15.02 2.25 (+) 12.35 0.24 (+)(0.602) (0.0576)* (0.589) (0.8306) (0.523) (0.0759)* GNI (-) (0.720) (0.7857)

Sum of the coefficients of GNI not significant

Burundi 2 4 6 36 6.59 0.34 (+) 17.68 0.20 (-) 32 12.82 0.56 (+) 14.93 0.12 (-)(0.980) (0.8499) (0.343) (0.8185) (0.686) (0.6917) (0.530) (0.230973)

Cameroon 3 5 6 35 11.27 2.11 (+) 9.51 2.28 (+) 32 12.30 2.42 (+) 22.70 3.01 (-)(0.793) (0.0954)* (0.891) (0.1034) (0.723) (0.0789)* GNI (-) (0.122) (0.0591)* GNI (+)

Sum of the coefficients of GNI not significant Sum of the coefficients of GNI not significant

Chad 5 3 8 35 6.51 1.32 (+) 14.10 1.42 (-) 30 16.00 1.62 (+) 10.21 1.15 (-)(0.979) (0.2868) (0.591) (0.2495) (0.453) (0.2327) (0.855) (0.3834)

Congo, Rep. 4 7 5 28 10.32 2.52 (-) 15.49 1.44 (-) 28 16.08 1.62 (+) 15.30 1.50 (+)(0.850) (0.0564)* (0.489) (0.2658) (0.448) (0.2267) GNI (-) (0.503) (0.2675)


Congo, Dem. Rep. 6 7 6 33 12.88 0.91 (-) 15.32 0.58 (-) 32 11.53 0.31 (-) 16.46 0.65 (-)(0.681) (0.5199) (0.501) (0.7423) (0.775) (0.9379) (0.422) (0.6909)

net ODA causes exports conditional on GNI

exports cause net ODA conditional on GNI

Trivariate Granger causality

X=f(Y,Z)Y=f(X)

X→Y 4) Y→X|Z 4)Y=f(X,Z)X→Y|Z 4)

X=f(Y)

Y→X 4)

net ODA causes exportsCountry

Optimal lags

Bivariate Granger causality

exports cause net ODA

Table 5.1a : Causality results for individual countries and aggregate samples. Nominal net ODA (continued)




coefficients




coefficients




coefficients




coefficients

Cote d'Ivoire 3 1 6 34 11.52 0.06 (+) 9.39 2.16 (-) 32 7.37 0.27 (-) 10.05 1.57 (-)(0.776) (0.8083) (0.896) (0.1142) (0.965) (0.6115) (0.864) (0.2263)

Egypt 8 2 4 33 18.99 0.37 (-) 9.06 0.30 (+) 33 20.27 0.18 (-) 12.43 0.32 (+)(0.269) (0.6924) (0.911) (0.9587) (0.209) (0.8385) (0.714) (0.9467)

Ghana 2 2 7 34 11.60 0.26 (-) 20.67 0.52 (-) 31 14.25 0.05 (-) 15.47 3.61 (-)(0.771) (0.7703) (0.191) (0.5996) (0.580) (0.9506) (0.491) (0.0468)** GNI (+)


Kenya 5 8 5 32 9.93 2.71 (+) 5.34 0.37 (+) 32 9.77 3.06 (+) 31.48 0.85 (+)(0.870) (0.0355)** (0.994) (0.8621) (0.878) (0.0356)** GNI (-) (0.012)** (0.5358)


Lesotho 2 2 6 34 21.11 0.75 (+) 15.99 5.05 (+) 31 21.19 0.54 (+) 19.82 3.58 (+)(0.174) (0.4830) (0.454) (0.0132)** (0.171) (0.5891) (0.228) (0.0470)** GNI (+)


Madagascar 7 3 8 36 10.18 5.71 (+) 7.67 0.64 (+) 30 21.29 5.45 (+) 20.52 0.84 (-)(0.857) (0.0038)*** (0.958) (0.7195) (0.168) (0.0153)** GNI (+) (0.198) (0.5744)


Mauritania 2 2 8 28 7.25 0.08 (-) 13.56 1.66 (+) 28 19.16 2.83 (-) 21.25 2.82 (+)(0.968) (0.9263) (0.631) (0.2119) (0.085)* (0.0909) (0.169) (0.0910)* GNI (+)

included lags=12 Sum of the coefficients of GNI not significant

Country

Optimal lags

Bivariate Granger causality Trivariate Granger causality

Y→X|Z 4) X→Y|Z 4)

net ODA causes exports exports cause net ODA net ODA causes exports conditional on GNI


Y→X 4) X→Y 4)X=f(Y) Y=f(X) X=f(Y,Z) Y=f(X,Z)





coefficients




coefficients




coefficients




coefficients

Morocco 4 2 8 34 18.80 2.27 (-) 13.38 0.06 (+) 30 14.86 0.55 (+) 15.94 1.83 (+)(0.279) (0.1217) (0.645) (0.9933) (0.535) (0.5896) (0.457) (0.1763)

Niger 5 6 7 26 12.95 1.25 (-) 7.36 1.54 (+) 26 23.91 0.71 (+) 13.40 1.14 (+)(0.677) (0.3346) (0.833) (0.2405) (0.091)* (0.6566) (0.643) (0.4198)

Rwanda 4 4 2 36 24.25 0.61 (-) 10.82 1.44 (+) 36 24.80 0.71 (+) 10.31 1.32 (+)(0.084)* (0.6578) (0.820) (0.2479) (0.073)* (0.5948) -0.85 (0.2901)

Senegal 5 7 4 28 10.52 0.26 (+) 19.12 1.56 (+) 28 11.21 0.43 (+) 23.47 2.11 (+)(0.838) (0.9622) (0.263) (0.2312) (0.796) (0.8657) (0.102) (0.1402)

Sudan 8 7 4 24 11.847 0.62 (-) 6.1231 0.80 (+) 24 20.455 13.49 (-) 37.44 2.58 (+)(0.754) (0.7320) (0.987) (0.6230) (0.200) (0.0121)** GNI (+) (0.002)*** (0.1880)

Togo 2 2 7 39 19.52 1.20 (-) 11.74 2.13 (+) 31 17.14 1.61 (-) 18.27 0.75 (+)(0.243) (0.3135) (0.761) (0.1354) (0.376) (0.2258) (0.309) (0.4879)

Tunisia 8 2 8 35 7.15 4.56 (-) 21.11 0.99 (+) 30 34.71 4.79 (-) 27.43 0.50 (+)(0.970) (0.0205)** (0.174) (0.4658) (0.004)*** (0.0319) GNI (-) (0.037)** (0.8320)


Country

Optimal lags

Bivariate Granger causality Trivariate Granger causality X=f(Y) Y=f(X) X=f(Y,Z) Y=f(X,Z)

Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients




coefficients




coefficients




coefficients

Uganda 6 1 6 31 8.15 0.78 (+) 14.73 1.28 (-) 31 11.42 0.73 (+) 8.29 1.29 (+)(0.944) (0.3851) (0.544) (0.3039) (0.783) (0.4042) (0.940) (0.3128)

Zambia 7 2 4 33 11.10 0.51 (+) 9.34 0.61 (+) 33 5.30 0.19 (+) 14.10 1.00 (+)(0.803) (0.6070) (0.899) (0.7423) (0.994) (0.8271) (0.591) (0.4636)

Zimbabwe 3 6 3 25 18.24 0.59 (+) 13.35 1.22 (+) 25 16.72 0.99 (+) 14.18 1.34 (+)(0.310) (0.7338) (0.647) (0.3379) (0.404) (0.4711) (0.585) (0.3081)

ASIABangladesh 5 6 8 26 18.87 1.38 (-) 7.64 0.36 (-) 22 56.60 33.98 (+) 67.37 6.27 (-)

(0.275) (0.2861) (0.959) (0.8699) (0.000)*** (0.0289) (0.000)*** (0.1432)

India 5 4 7 36 11.92 2.04 (+) 10.59 0.43 (+) 31 20.18 2.20 (+) 12.84 1.04 (+)(0.749) (0.1169) (0.834) (0.8245) (0.212) (0.1215) (0.685) (0.4335)

Indonesia 7 2 5 35 10.70 2.61 (+) 10.32 2.60 (-) 31 23.44 2.01 (+) 12.82 3.66 (-)(0.828) (0.0928)* (0.849) (0.0365)** (0.102) (0.1658) GNI (+) (0.686) (0.0150)** GNI (+)


Jordan 7 2 5 33 11.91 0.03 (+) 12.15 0.83 (-) 33 13.26 0.69 (-) 18.83 1.75 (+)(0.750) (0.9687) (0.733) (0.5724) (0.654) (0.5158) (0.278) (0.1609)

Country

Optimal lags

Bivariate Granger causalityX=f(Y) Y=f(X)

Y→X 4) X→Y 4)

net ODA causes exports

Trivariate Granger causality X=f(Y,Z) Y=f(X,Z)Y→X|Z 4) X→Y|Z 4)

exports cause net ODA net ODA causes exports conditional on GNI






coefficients




coefficients




coefficients




coefficients

Malaysia 8 8 6 22 10.73 1.00 (+) 10.91 1.58 (+) insufficient number of observations insufficient number of observations(0.826) (0.5168) (0.815) (0.3177)

Nepal 3 6 8 30 7.8121 0.81 (+) 9.0657 3.55 (+) 30 21.173 0.66 (-) 32.497 6.74 (-)(0.954) (0.5754) (0.911) (0.0330)** (0.172) (0.6861) (0.009)*** (0.0064) GNI (+)

Pakistan 4 7 8 29 14.42 0.47 (-) 12.17 1.28 (+) 29 12.43 3.16 (-) 11.56 3.27 (+)(0.567) (0.8423) (0.732) (0.3180) (0.714) (0.0557)* GNI (-) (0.774) (0.0644)* GNI (-)


Sri Lanka 8 7 4 25 11.61 0.58 (+) 18.70 0.29 (-) 25 17.03 2.10 (+) 24.57 1.25 (+)(0.770) (0.7602) (0.284) (0.9506) (0.383) (0.2149) (0.078)* (0.4214)

LATIN AMERICABolivia 2 4 6 32 15.79 2.50 (+) 13.03 0.20 (-) 27 13.68 1.56 (-) 24.08 0.25 (-)

(0.467) (0.0671)* (0.671) (0.8221) (0.623) (0.2383) GNI (+) (0.088)* (0.7809)

Brazil 5) 6 6 8 34 12.19 0.57 (-) 22.33 1.27 (-) 30 4.05 5.27 (-) 16.81 0.58 (-)(0.731) (0.7516) (0.133) (0.3110) (0.044)** (0.0136) (0.398) (0.7393)

included lag=1

Chile 4 6 4 29 14.53 1.90 (+) 10.91 0.60 (+) 29 17.43 1.40 (+) 12.02 0.36 (+)(0.559) (0.1332) (0.815) (0.6670) (0.359) (0.2821) (0.743) (0.8298)

X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4)

Country





coefficients




coefficients




coefficients




coefficients

Colombia 8 6 6 31 5.52 0.42 (+) 13.64 1.14 (+) 31 27.29 1.76 (+) 12.32 2.39 (+)(0.992) (0.8568) (0.626) (0.3918) (0.038)** (0.2046) (0.722) (0.0984)** GNI (+)

Costa Rica 6 3 5 33 8.98 0.42 (+) 7.06 0.46 (-) 33 8.25 0.23 (+) 8.98 1.44 (-)(0.914) (0.7380) (0.972) (0.8279) (0.941) (0.8763) (0.914) (0.2536)

Ecuador 4 8 7 27 14.48 0.39 (-) 16.05 2.14 (+) 27 24.55 0.58 (+) 27.54 3.17 (+)(0.563) (0.9113) (0.450) (0.1296) (0.078)* (0.7678) (0.036)** (0.0876)

Guatemala 8 8 6 30 8.31 1.00 (+) 15.67 0.86 (-) 30 24.49 3.06 (+) 30.39 4.21 (-)(0.939) (0.4753) (0.476) (0.5744) (0.079)* (0.0789) (0.016)** (0.0370)

Haiti 6 2 4 36 12.66 0.43 (-) 6.07 1.23 (+) 34 10.10 0.72 (+) 12.18 3.90 (+)(0.698) (0.6561) (0.987) (0.3209) (0.861) (0.5891) (0.732) (0.0089)*** GNI (-)

Honduras 8 4 6 25 7.71 0.98 (-) 17.13 0.45 (+) 25 27.14 3.60 (-) 13.97 3.95 (+)(0.957) (0.4531) (0.377) (0.8714) (0.040)** (0.0793) (0.601) (0.0557)* GNI (+)

Nicaragua 4 2 4 31 11.63 1.24 (+) 10.13 1.18 (+) 31 17.64 2.12 (+) 8.68 1.14 (+)(0.769) (0.3071) (0.860) (0.3435) (0.345) (0.1462) (0.926) (0.3648)

Y→X|Z 4) X→Y|Z 4)


exports cause net ODA conditional on GNICountry

Optimal lags


Y→X 4) X→Y 4)





coefficients




coefficients




coefficients




coefficients

Paraguay 4 2 6 32 13.96 3.62 (-) 8.38 0.99 (+) 32 15.75 2.69 (-) 10.54 0.31 (+)(0.601) (0.0409)** (0.937) (0.4331) (0.471) (0.0937)* GNI (+) (0.837) (0.8661)


Peru 5 3 3 37 4.37 0.58 (+) 17.71 1.17 (+) 35 7.32 1.55 (+) 16.91 1.24 (+)(0.998) (0.6318) (0.341) (0.3477) (0.967) (0.2284) (0.391) (0.3218)

5 3 3 20 11.03 2.91 (+) 9.09 1.02 (-) 22 13.64 1.35 (+) 15.73 2.31 (+)(0.808) (0.0749)* (0.910) (0.4513) (0.626) (0.3133) GNI (+) (0.472) (0.1400)


EUROPETurkey 5) 4 1 8 39 8.36 3.28 (-) 17.16 0.30 (-) 27 22.60 1.00 (-) 10.95 3.08 (+)

(0.937) (0.0790)* (0.375) (0.8746) (0.125) (0.3360) GNI (-) (0.813) (0.0546)* GNI (+)

FULL SAMPLE AND SUBGROUPS 6)

Full sample 6) 7 6 4 23 13.17 3.21 (+) 12.02 0.64 (+) 23 15.53 1.44 (+) 11.14 0.89 (+)(0.660) (0.0502)* (0.742) (0.7169) (0.486) (0.3544) GNI (+) (0.801) (0.5718)


4 4 8 25 12.09 5.07 (+) 11.71 0.56 (-) 22 18.13 16.26 (+) 27.97 3.53 (+)(0.738) (0.0071)*** (0.763) (0.6949) (0.079)* (0.0045) GNI (+) (0.032)** (0.0994)

included lags=11

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)

East Asia and Pacific 7)

Trinidad and Tobago





coefficients




coefficients




coefficients




coefficients

2 5 8 24 10.32 0.68 (-) 10.49 1.03 (-) 22 17.36 0.11 (-) 17.55 4.29 (-)(0.850) (0.6421) (0.840) (0.3785) (0.363) (0.9848) (0.351) (0.0697)* GNI (+)

8 7 4 22 12.05 0.69 (+) 12.40 1.26 (-) 22 45.41 0.68 (+) 51.47 106.93 (-)(0.741) (0.6833) (0.716) (0.4016) (0.000)*** (0.7066) (0.000)*** (0.0093)

6 2 5 25 11.07 4.89 (-) 9.24 0.26 (+) 25 11.52 3.35 (-) 11.90 0.37 (-)(0.805) (0.0210)** (0.903) (0.9487) (0.776) (0.0729)* GNI (-) (0.751) (0.8804)


South Asia 4 5 5 24 17.41 0.78 (+) 16.27 2.70 (+) 24 22.28 0.30 (+) 18.81 5.26 (+)(0.360) (0.5794) (0.434) (0.0737)* (0.134) (0.8998) (0.279) (0.0183)** GNI (-)

7 4 7 24 14.57 0.64 (+) 9.20 1.63 (+) 23 22.79 6.76 (-) 17.36 0.28 (+)(0.556) (0.6460) (0.905) (0.2192) (0.089)* (0.0455) (0.363) (0.9332)

included lags=15

Low income 4 7 5 22 19.02 2.23 (+) 14.74 0.73 (+) 22 3.50 10.55 (+) 16.19 2.14 (-)(0.267) (0.1124) (0.544) (0.5891) (0.061)* -0.0098 (0.440) (0.2134)

included lags=1

6 4 6 25 12.78 0.41 (+) 18.06 1.66 (+) 24 16.44 2.30 (+) 23.62 1.27 (-)(0.689) (0.7958) (0.320) (0.2046) (0.423) (0.1588) (0.098)* (0.3779)

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)

Europe and Central Asia 8)

Latin America and Caribbean 9)

Middle East and North Africa

Sub-Saharan Africa

Lower middle income





coefficients




coefficients




coefficients




coefficients

2 5 3 24 7.59 0.34 (-) 23.71 0.38 (+) 24 18.01 2.34 (-) 15.52 0.15 (-)(0.960) (0.8818) (0.096)* (0.6929) (0.323) (0.1002) (0.487) (0.8621)

1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hypothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss net ODA and Z is recipient country's GNI.5) Transformed net ODA series because of negative values (see description in text).6) All countries except Trinidad and Tobago and Malaysia (see description in text). Sample size: 1974 to 2004.7) East Asia and Pacific without Malaysia. Thus, in the case of our sample, only Indonesia.8) In the case of our sample, only Turkey (transformed, see note 5)).9) Latin America and Caribbean without Trinidad and Tobago.10) Upper middle income without Trinidad and Tobago and Malaysia.

X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4)

Upper middle income 10)

Country

Table 5.1b : Causality results for individual countries and aggregate samples. Nominal gross ODA




coefficients




coefficientsN1) Ljung-Box

statistic 2)



coefficients




coefficientsAFRICAAlgeria 6 2 8 37 15.97 5.19 (-) 14.34 1.30 (-) 30 9.82 6.79 (-) 16.66 1.69 (-)

(0.455) (0.0118)** (0.573) (0.2917) (0.876) (0.0096)*** GNI (-) (0.408) (0.2005)

Benin 8 3 2 35 12.34 0.74 (-) 12.74 1.31 (+) 35 11.82 0.42 (-) 10.48 1.25 (+)(0.720) (0.5366) (0.692) (0.2856) (0.756) (0.7400) (0.841) (0.3191)

Burkina Faso 2 8 4 32 10.10 1.86 (+) 32.66 0.18 (-) 32 15.47 2.05 (+) 32.35 0.49 (+)(0.861) (0.1194) (0.008)*** (0.8391) (0.490) (0.1022) (0.009)*** (0.6186)

Burundi 2 6 6 35 8.01 0.57 (+) 13.79 0.39 (-) 32 11.06 0.46 (+) 18.24 0.23 (-)(0.949) (0.7499) (0.614) (0.6816) (0.806) (0.8283) (0.310) (0.7986)

Cameroon 3 4 6 36 13.77 3.22 (+) 8.77 3.71 (+) 32 9.66 3.31 (+) 23.47 4.85 (-)(0.616) (0.0265)** (0.923) (0.0229)** (0.884) (0.0338)** GNI (-) (0.102) (0.0121)** GNI (+)


Chad 5 7 8 31 10.79 1.92 (+) 7.88 1.56 (-) 30 18.43 0.55 (+) 12.76 1.54 (-)(0.822) (0.1229) (0.952) (0.2226) (0.299) (0.7756) (0.691) (0.2695)

Congo, Rep. 4 3 5 32 7.55 1.67 (+) 7.52 1.15 (+) 32 19.25 0.60 (+) 12.57 0.81 (+)(0.961) (0.1988) (0.962) (0.3588) (0.256) (0.6201) (0.704) (0.5339)

Congo, Dem. Rep. 6 4 6 33 10.91 1.60 (-) 10.78 0.46 (+) 32 7.09 0.33 (+) 5.65 0.49 (-)(0.815) (0.2084) (0.823) (0.8276) (0.971) (0.8566) (0.991) (0.8048)

Trivariate Granger causality Y=f(X)

Optimal lags

Country

X=f(Y)

Y→X 4)

gross ODA causes exports

Bivariate Granger causalityY=f(X,Z)X→Y|Z 4)X→Y 4)

exports cause gross ODA

X=f(Y,Z)Y→X|Z 4)

gross ODA causes exportsconditional on GNI

exports cause gross ODA conditional on GNI

Table 5.1b : Causality results for individual countries and aggregate samples. Nominal gross ODA (continued)




coefficients





statistic 2)



coefficients




coefficients

Cote d'Ivoire 3 2 6 33 10.18 0.33 (+) 9.43 1.56 (-) 32 7.63 0.13 (-) 11.29 0.86 (-)(0.857) (0.7210) (0.895) (0.2219) (0.959) (0.8785) (0.791) (0.4782)

Egypt 8 2 4 33 19.53 0.34 (-) 8.63 0.33 (+) 33 21.06 0.24 (-) 14.26 0.38 (+)(0.242) (0.7148) (0.928) (0.9470) (0.176) (0.7918) (0.579) (0.9180)

Ghana 2 2 7 34 10.95 0.37 (+) 18.05 0.64 (-) 31 14.18 0.01 (+) 15.48 0.23 (-)(0.812) (0.6965) (0.321) (0.5358) (0.585) (0.9875) (0.490) (0.7962)

Kenya 5 8 5 32 15.39 2.59 (+) 8.66 0.45 (-) 32 14.93 3.10 (+) 23.08 0.70 (-)(0.496) (0.0421)** (0.927) (0.8067) (0.530) (0.0340)** GNI (-) (0.112) (0.6334)


Lesotho 2 8 6 28 14.27 2.52 (-) 26.70 0.93 (+) 28 18.74 4.95 (+) 15.05 1.04 (+)(0.579) (0.0494)** (0.045)** (0.4129) (0.282) (0.0085)*** GNI (-) (0.521) (0.3871)


Madagascar 7 6 8 34 8.60 2.60 (+) 12.39 0.47 (-) 30 31.96 3.19 (+) 18.05 0.77 (+)(0.929) (0.0481)** (0.717) (0.8455) (0.010)** (0.0665) GNI (+) (0.321) (0.6252)


Mauritania 2 2 8 28 6.71 0.17 (-) 12.37 1.76 (+) 28 22.35 4.00 (-) 19.85 2.62 (+)(0.979) (0.8455) (0.718) (0.1945) (0.099)* (0.0405) (0.227) (0.1055)

Y→X|Z 4) X→Y|Z 4)

gross ODA causes exports exports cause gross ODA gross ODA causes exportsconditional on GNI


Trivariate Granger causality X=f(Y) Y=f(X) X=f(Y,Z) Y=f(X,Z)

Country

Optimal lags


Y→X 4) X→Y 4)





coefficients





statistic 2)



coefficients




coefficients

Morocco 4 2 8 34 18.80 2.27 (-) 13.38 0.06 (+) 30 14.86 0.55 (+) 15.94 1.83 (+)(0.279) (0.1217) (0.645) (0.9933) (0.535) (0.5896) (0.457) (0.1763)

Niger 5 6 7 26 12.95 1.25 (-) 7.36 1.54 (+) 26 23.91 0.71 (+) 13.40 1.14 (+)(0.677) (0.3346) (0.833) (0.2405) (0.091)* (0.6566) (0.643) (0.4198)

Rwanda 4 4 2 36 26.24 0.49 (-) 13.79 1.27 (+) 36 25.94 0.47 (+) 11.77 0.86 (+)(0.051)* (0.7443) (0.614) (0.3067) (0.055)* (0.7590) (0.759) (0.4990)

Senegal 5 8 4 27 14.09 0.31 (+) 16.71 1.36 (+) 27 15.11 0.35 (+) 14.64 2.69 (-)(0.592) (0.9509) (0.405) (0.3000) (0.516) (0.9241) (0.551) (0.0932)* GNI (-)

Sudan 8 7 4 4 11.72 0.58 (-) 5.60 0.81 (+) 24 19.02 17.51 (-) 35.90 2.19 (+)(0.763) (0.7580) (0.992) (0.6105) (0.268) (0.0075)*** GNI (+) (0.003)*** (0.2345)

Togo 2 8 7 32 15.55 1.19 (-) 13.31 0.88 (+) 31 15.15 1.26 (+) 33.05 0.98 (-)(0.485) (0.3470) (0.650) (0.4310) (0.514) (0.3404) (0.007)*** (0.4014)

Tunisia 8 2 8 35 7.54 4.47 (-) 21.97 0.96 (+) 30 31.65 4.76 (-) 27.84 0.46 (+)(0.961) (0.0219)** (0.144) (0.4870) (0.011)** (0.0324) GNI (-) (0.033)** (0.8574)


Y→X|Z 4) X→Y|Z 4)


exports cause gross ODA conditional on GNICountry

Optimal lags


Y→X 4) X→Y 4)





coefficients





statistic 2)



coefficients




coefficients

Uganda 6 1 6 31 8.15 0.78 (+) 14.73 1.28 (-) 31 11.42 0.73 (+) 8.29 1.29 (+)(0.944) (0.3851) (0.544) (0.3039) (0.783) (0.4042) (0.940) (0.3128)

Zambia 7 2 4 33 11.10 0.51 (+) 9.34 0.61 (+) 33 5.30 0.19 (+) 14.10 1.00 (+)(0.803) (0.6070) (0.899) (0.7423) (0.994) (0.8271) (0.591) (0.4636)

Zimbabwe 3 6 3 25 17.60 0.62 (+) 14.19 1.28 (+) 25 16.63 1.05 (+) 16.51 1.51 (+)(0.348) (0.7139) (0.584) (0.3174) (0.410) (0.4401) (0.418) (0.2611)

ASIABangladesh 5 6 8 26 18.57 1.40 (-) 7.51 0.34 (-) 22 58.31 35.77 (+) 66.93 6.56 (-)

(0.292) (0.2792) (0.962) (0.8819) (0.000)*** (0.0274) (0.000)*** (0.1375)

India 5 2 7 38 7.74 2.00 (+) 16.66 0.62 (-) 31 29.70 0.88 (+) 16.91 4.17 (-)(0.956) (0.1525) (0.408) (0.6844) (0.020)** (0.4328) (0.391) (0.0129)** GNI (+)


Indonesia 7 1 5 36 8.34 2.22 (+) 9.60 0.64 (-) 31 18.23 1.29 (+) 9.68 1.16 (+)(0.938) (0.1475) (0.887) (0.717) (0.310) (0.2724) (0.883) (0.3753)

Jordan 7 2 5 33 11.84 0.03 (+) 10.92 0.74 (-) 33 13.40 0.85 (-) 17.22 1.87 (+)(0.755) (0.9747) (0.814) (0.6416) (0.644) (0.4433) (0.372) (0.1355)

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)





coefficients





statistic 2)



coefficients




coefficients


Nepal 3 6 8 30 7.8121 0.81 (+) 9.0657 3.55 (+) 30 21.173 0.66 (-) 32.497 6.74 (-)(0.954) (0.5754) (0.911) (0.0330)** (0.172) (0.6861) (0.009)*** (0.0064) GNI (+)

Pakistan 4 7 8 29 15.31 0.67 (-) 8.59 7.08 (+) 29 11.09 2.48 (-) 10.62 23.15 (+)(0.502) (0.6977) (0.929) (0.0015)*** (0.804) (0.1029) (0.832) (0.0001)*** GNI (-)

Sri Lanka 8 7 4 25 13.83 0.88 (+) 21.77 0.40 (+) 25 19.51 1.61 (+) 32.22 1.29 (+)(0.611) (0.5521) (0.151) (0.8939) (0.243) (0.3114) (0.009)*** (0.4075)

LATIN AMERICABolivia 2 4 6 32 14.98 2.78 (+) 12.37 0.12 (+) 27 12.73 1.48 (-) 21.96 0.42 (-)

(0.526) (0.0481)** (0.719) (0.8876) (0.692) (0.2623) GNI (+) (0.145) (0.6658)

Brazil 6 8 8 32 13.61 0.66 (+) 17.36 0.84 (-) 30 30.13 5.33 (+) 11.76 0.56 (-)(0.628) (0.7172) (0.363) (0.5593) (0.017)** (0.0200) (0.761) (0.7490)

Chile 4 4 4 31 12.17 1.47 (+) 14.05 0.72 (+) 31 17.11 1.55 (+) 20.89 0.16 (+)(0.732) (0.2444) (0.595) (0.5861) (0.378) (0.2306) (0.183) (0.9537)

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)





coefficients





statistic 2)



coefficients




coefficients

Colombia 8 6 6 31 5.52 0.42 (+) 13.64 1.14 (+) 31 27.29 1.76 (+) 12.32 2.39 (+)(0.992) (0.8568) (0.626) (0.3918) (0.038)** (0.2046) (0.722) (0.0984)** GNI (+)

Costa Rica 6 3 5 33 8.98 0.42 (+) 7.06 0.46 (-) 33 8.25 0.23 (+) 8.98 1.44 (-)(0.914) (0.7380) (0.972) (0.8279) (0.941) (0.8763) (0.914) (0.2536)

Ecuador 4 8 7 27 19.66 0.82 (-) 7.63 3.32 (+) 27 19.18 1.86 (-) 11.47 7.68 (+)(0.236) (0.5952) (0.959) (0.0411)** (0.259) (0.2150) (0.780) (0.0106)** GNI (+)

Guatemala 8 8 6 30 16.81 1.94 (+) 10.92 0.59 (-) 30 25.62 1.20 (+) 7.41 1.65 (-)(0.968) (0.1329) (0.814) (0.7724) (0.060)* (0.4101) (0.965) (0.2618)

Haiti 6 4 4 34 11.01 1.08 (+) 8.90 1.82 (+) 34 8.57 0.33 (+) 17.11 4.46 (+)(0.809) (0.3869) (0.918) (0.1392) (0.930) (0.8574) (0.379) (0.0056)*** GNI (-)

Honduras 8 4 6 25 8.98 1.09 (-) 15.72 0.44 (+) 25 27.68 3.37 (-) 13.70 3.03 (+)(0.914) (0.4002) (0.473) (0.8767) (0.034)** (0.0898) (0.621) (0.0966)* GNI (+)

Nicaragua 4 7 4 26 9.10 1.73 (+) 9.07 0.83 (+) 26 13.46 1.70 (+) 13.18 1.81 (+)(0.909) (0.1762) (0.910) (0.5263) (0.639) (0.2161) (0.660) (0.2027)

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)





coefficients





statistic 2)



coefficients




coefficients

Paraguay 4 2 6 32 13.96 3.62 (-) 8.38 0.99 (+) 32 15.75 2.69 (-) 10.54 0.31 (+)(0.601) (0.0409)** (0.937) (0.4331) (0.471) (0.0937)* GNI (+) (0.837) (0.8661)


Peru 5 3 3 37 4.37 0.58 (+) 17.71 1.17 (+) 35 7.32 1.55 (+) 16.91 1.24 (+)(0.998) (0.6318) (0.341) (0.3477) (0.967) (0.2284) (0.391) (0.3218)

5 x 3

EUROPETurkey 4 2 8 38 8.23 0.49 (-) 12.85 0.68 (-) 27 22.15 0.61 (-) 9.10 1.37 (-)

(0.942) (0.6169) (0.684) (0.6098) (0.138) (0.5593) (0.909) (0.3013)


Full sample 6) 7 8 4 21 20.13 2.84 (-) 12.64 0.19 (+) 12.72 5.32 (+) 12.72 0.13 (-)(0.214) (0.1103) (0.699) (0.9753) (0.693) (0.3240) (0.693) (0.9709)

4 8 8 21 15.29 1.43 (+) 12.67 0.65 (+) insufficient number of observations insufficient number of observations(0.504) (0.3022) (0.697) (0.6457)

Y→X|Z 4) X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4)

Trinidad and Tobago 5)






coefficients





statistic 2)



coefficients




coefficients

2 5 8 24 10.32 0.68 (-) 10.49 1.03 (-) 22 17.36 0.11 (-) 17.55 4.29 (-)(0.850) (0.6421) (0.840) (0.3785) (0.363) (0.9848) (0.351) (0.0697)* GNI (+)

8 7 4 22 12.05 0.69 (+) 12.40 1.26 (-) 22 45.41 0.68 (+) 51.47 106.93 (-)(0.741) (0.6833) (0.716) (0.4016) (0.000)*** (0.7066) (0.000)*** (0.0093)

6 2 5 25 11.03 4.72 (-) 8.96 0.26 (+) 25 11.34 3.22 (-) 11.29 0.37 (-)(0.808) (0.0235)** (0.915) (0.9496) (0.788) (0.0792)* GNI (-) (0.791) (0.8823)


South Asia 4 8 5 21 10.79 1.29 (+) 26.59 4.39 (+) 21 51.09 4.81 (-) 45.58 2.56 (-)(0.822) (0.3557) (0.046)** (0.0360) (0.000)*** (0.1119) (0.000)*** (0.2334)

7 3 7 24 13.75 0.64 (+) 9.20 1.98 (+) 23 52.38 0.61 (-) 16.51 0.56 (-)(0.617) (0.6044) (0.905) (0.1372) (0.000)*** (0.6369) (0.418) (0.7687)

Low income 4 8 5 22 12.56 2.09 (+) 9.46 0.53 (+) 21 23.25 180.28 (+) 81.81 4.57 (-)(0.704) (0.1459) (0.893) (0.7165) (0.107) (0.0006)*** GNI (+) (0.000)*** (0.1210)

6 4 6 25 7.02 0.90 (+) 16.23 1.75 (+) 24 14.70 2.79 (+) 22.71 1.67 (-)(0.973) (0.4899) (0.437) (0.1825) (0.547) (0.1117) (0.091)* (0.2578)

included lags=15

X→Y|Z 4)



Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4)

Sub-Saharan Africa


Lower middle income



Country





coefficients





statistic 2)



coefficients




coefficients

2 5 3 24 7.37 0.24 (+) 26.50 0.06 (-) 24 17.15 2.06 (+) 17.46 0.53(0.965) (0.9381) (0.047)** (0.9402) (0.376) (0.1357) (0.356) (0.6006)

1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hpyothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss gross ODA and Z is recipient country's GNI.5) Accounting discrepancy in gross ODA series (negative values).6) All countries except Trinidad and Tobago and Malaysia (see…). Sample size: 1974 to 2004.7) East Asia and Pacific without Malaysia. Thus, in the case of our sample, only Indonesia.8) In the case of our sample, only Turkey.9) Latin America and Caribbean without Trinidad and Tobago.10) Upper middle income without Trinidad and Tobago and Malaysia.

Y→X|Z 4) X→Y|Z 4)



Trivariate Granger causality X=f(Y) Y=f(X) X=f(Y,Z) Y=f(X,Z)


Country

Optimal lags


Y→X 4) X→Y 4)

Table 5.1c : Causality results for individual countries. Real net ODA




coefficients





statistic 2)



coefficients




coefficientsAFRICAAlgeria 6 4 6 36 3.87 1.90 (-) 23.62 1.69 (-) 32 18.36 1.61 (-) 23.23 1.07 (-)

(0.999) (0.1412) (0.098)* (10.12379) (0.303) (0.2244) (0.108) (0.4240)

Benin 8 8 8 30 6.94 2.35 (-) 11.21 1.20 (+) 30 31.94 4.63 (-) 10.50 6.37 (+)(0.974) (0.0771)* (0.796) (0.3691) (0.010)** (0.0541) GNI (-) (0.033)** (0.0283)

Sum of the coefficients of GNI not significant included lags=4

Burkina Faso 8) 2 7 x 33 16.36 2.29 (+) 12.85 0.34 (-) insufficient number of observations insufficient number of observations(0.428) (0.0618)* (0.684) (0.7186)

Burundi 2 4 7 36 7.71 0.28 (+) 16.08 0.37 (-) 29 10.93 0.50 (+) 20.54 0.36 (+)(0.957) (0.8878) (0.447) (0.6917) (0.814) (0.7358) (0.197) (0.7046)

Cameroon 3 4 3 36 9.76 2.70 (+) 5.71 3.48 (+) 35 4.65 3.62 (+) 8.60 2.15 (-)(0.879) (0.0502)* (0.991) (0.0289)** (0.997) (0.0192)** GNI (-) (0.929) (0.1205) GNI (+)


Chad 4 3 8 34 8.61 1.30 (+) 13.51 1.89 (-) 30 12.33 1.45 (+) 10.03 1.98 (-)(0.929) (0.2949) (0.635) (0.1422) (0.721) (0.2713) (0.865) (0.1529)

Congo, Rep. 5) 4 1 3 34 9.71 3.40 (-) 7.24 1.42 (+) 27 10.22 0.02 (+) 29.96 1.27 (-)(0.882) (0.0755)* (0.968) (0.2541) (0.855) (0.9036) GNI (-) (0.018)** (0.3180)


Congo, Dem. Rep. 3 2 1 38 14.54 0.74 (+) 7.51 2.09 (-) 35 14.80 0.18 (-) 5.94 1.33 (-)(0.559) (0.4856) (0.962) (0.1217) (0.539) (0.8327) (0.989) (0.2856)

Y→X 4)


Trivariate Granger causality

net ODA causes exports conditional on GNI


Y→X|Z 4)Y=f(X,Z)X→Y|Z 4)


Country

X=f(Y,Z)Y=f(X)

X→Y 4)

exports cause net ODAOptimal lags

X=f(Y)

Table 5.1c : Causality results for individual countries. Real net ODA (continued)




coefficients





statistic 2)



coefficients




coefficients

Cote d'Ivoire 9) 3 2 x 33 9.84 0.06 (-) 9.28 1.90 (-) No real data for GNI No real data for GNI (0.875) (0.9425) (0.902) (0.1533)

Egypt 8 2 4 33 17.14 0.57 (-) 11.18 0.17 (+) 33 21.88 0.28 (-) 14.72 0.34 (+)(0.377) (0.5757) (0.798) (0.9926) (0.147) (0.7616) (0.040)** (0.9369)

included lags=7

Ghana 2 2 7 34 11.99 0.32 (-) 17.22 0.87 (-) 31 11.77 0.08 (+) 10.54 0.25 (-)(0.745) (0.7313) (0.371) (0.4307) (0.760) (0.9198) (0.837) (0.7815)

Kenya 8 8 5 32 11.79 3.01 (+) 6.14 0.81 (+) 32 25.50 3.53 (+) 14.54 0.92 (+)(0.758) (0.0287)** (0.986) (0.6031) (0.062)* (0.0330) GNI (-) (0.558) (0.5396)


Lesotho 8 8 6 28 14.38 0.79 (-) 23.02 1.04 (+) 28 19.80 0.77 (+) 10.28 0.86 (-)(0.571) (0.6199) (0.084)* (0.4608) (0.229) (0.6489) (0.036)** (0.5948)

included lags=4

Madagascar 7 6 5 34 7.90 2.08 (+) 18.62 0.52 (-) 33 12.45 2.72 (+) 19.93 0.94 (-)(0.952) (0.0991)* (0.289) (0.8098) (0.713) (0.0578)* GNI (+) (0.224) (0.5089)


Mauritania 5) 2 2 7 28 7.39 0.03 (-) 14.41 0.83 (+) 23 31.26 0.76 (-) 10.74 1.83 (-)(0.965) (0.9737) (0.569) (0.4484) (0.012)** (0.4891) (0.825) (0.2068)

Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients





statistic 2)



coefficients




coefficients

Morocco 4 2 8 34 13.78 1.84 (-) 14.71 0.17 (-) 25 17.03 1.05 (+) 10.21 1.66 (+)(0.615) (0.1783) (0.546) (0.9532) (0.048)** (0.3860) 0.856 (0.2356)

included lags=9

Niger 5) 7 7 4 25 11.19 0.69 (-) 14.12 2.14 (+) 20 52.21 0.93 (+) 52.21 4.39 (+)(0.797) (0.6813) (0.590) (0.1327) (0.000)*** (0.6659) (0.000)*** (0.3525)

Rwanda 7 4 7 33 18.64 0.28 (+) 15.96 2.44 (+) 28 6.88 0.28 (-) 21.79 1.93 (+)(0.288) (0.8876) (0.456) (0.0537)* (0.975) (0.8815) (0.150) (0.1772) GNI (-)


Senegal 5 7 8 28 8.25 0.30 (+) 12.92 0.99 (+) 27 24.07 0.57 (-) 2.78 5.42 (-)(0.941) (0.9454) (0.679) (0.4548) (0.088)* (0.7638) (0.095)* -0.0315

included lag=1

Sudan 8) 8 7 x 24 11.88 0.61 (-) 8.61 0.58 (+) insufficient number of observations insufficient number of observations(0.752) (0.7391) (0.929) (0.7677)

Togo 2 2 7 38 17.98 1.25444 (-) 15.04 1.39 (+) 31 13.96 1.05 (-) 10.13 0.22 (+)(0.325) (0.2981) (0.522) (0.2630) (0.601) (0.3680) (0.860) (0.8080)

Tunisia 8 2 8 35 6.25 6.59 (-) 20.47 0.69 (+) 30 13.82 6.40 (-) 25.69 0.57 (+)(0.985) (0.0050)*** (0.200) (0.6935) (0.612) (0.0144)** GNI (-) (0.059)* (0.7829)


Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients





statistic 2)



coefficients




coefficients

Uganda 2 1 8 31 8.03 1.36 (+) 7.13 0.46 (-) 13 35.39 0.17 (+) 35.39 10.62 (-)(0.948) (0.2532) (0.971) (0.6362) (0.000)*** (0.7527) (0.000)*** (0.2120)

Zambia 2 2 6 33 11.09 0.90 (+) 10.14 0.00 (+) 32 8.63 1.40 (+) 14.49 0.54 (+)(0.804) (0.4195) (0.860) (0.9952) (0.928) (0.2686) (0.562) (0.5896)

Zimbabwe 2 6 2 25 25.98 1.26 (+) 8.96 0.32 (+) 25 13.41 2.44 (+) 9.33 0.30 (+)(0.054)* (0.3243) (0.915) (0.7278) (0.642) (0.0792)* GNI (+) (0.899) (0.7424)

ASIABangladesh 4 6 5 26 15.20 1.01 (-) 8.88 0.97 (-) 17 123.44 11.19 (+) 123.44 19.36 (-)

(0.510) (0.4532) (0.918) (0.4545) (0.000)*** (0.2250) (0.000)*** (0.1687)

India 8 7 5 33 8.46 3.71 (+) 15.56 0.74 (+) 28 7.16 1.40 (+) 45.83 0.45 (+)(0.934) (0.0118)** (0.484) (0.6533) (0.970) (0.3346) GNI (+) (0.000)*** (0.8568)


Indonesia 7 2 8 35 12.28 1.87 (+) 7.93 1.97 (-) 28 14.09 2.66 (+) 11.89 3.10 (+)(0.725) (0.1747) (0.951) (0.0996)* (0.592) (0.1185) (0.751) (0.0518)* GNI (-)

Jordan 7 4 8 31 10.78 0.10 (+) 19.50 0.67 (-) insufficient number of observations insufficient number of observations(0.823) (0.9813) (0.244) (0.6976)

Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients





statistic 2)



coefficients




coefficients


Nepal 9) 2 6 x 29 5.43 0.82 (+) 15.82 1.72 (-) no real data for GNI no real data for GNI(0.993) (0.5701) (0.465) (0.2042)

Pakistan 3 7 6 28 15.62 0.55 (-) 12.68 0.54 (+) 28 14.27 1.34 (-) 7.24 1.36 (+)(0.480) (0.7862) (0.696) (0.6635) (0.579) (0.3179) (0.968) (0.3045)

Sri Lanka 8 5 8 23 12.36 1.12 (+) 12.51 0.25 (+) insufficient number of observations insufficient number of observations(0.719) (0.4111) (0.708) (0.9669)

LATIN AMERICABolivia 2 4 1 32 13.21 2.08 (+) 10.25 0.32 (-) 32 13.66 6.93 (+) 9.39 0.07 (-)

(0.658) (0.1118) (0.853) (0.7274) (0.034)** (0.0007) (0.897) (0.9285)included lags=6

Brazil 6) 5 8 1 32 17.01 1.00 (+) 31.12 1.86 (-) 32 13.21 0.91 (+) 39.45 2.27 (-)(0.385) (0.4655) (0.013)** (0.1513) (0.040)** (0.5276) (0.001)*** (0.0940)

included lags=6

Chile 4 4 2 31 11.66 0.59 (+) 13.92 0.18 (-) 31 11.10 0.19 (+) 12.51 0.50 (-)(0.767) (0.6752) (0.605) (0.9444) (0.804) (0.9427) (0.709) (0.7359)

Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients





statistic 2)



coefficients




coefficients

Colombia 8 8 6 29 8.83 0.65 (+) 9.95 1.53 (-) 29 15.55 1.24 (-) 25.21 11.33 (-)(0.920) (0.7264) (0.869) (0.2428) (0.485) (0.4076) (0.066)* (0.0041)

Costa Rica 5 3 2 33 11.17 0.28 (+) 7.19 1.04 (-) 33 10.97 0.28 (+) 8.47 1.16 (-)(0.799) (0.8365) (0.969) (0.4188) (0.811) (0.8376) (0.934) (0.3595)

Ecuador 4 8 7 27 13.33 0.48 (-) 16.61 1.18 (+) 27 18.19 0.63 (-) 23.74 1.97 (+)(0.648) (0.8514) (0.411) (0.3613) (0.313) (0.7382) (0.095)* (0.2043)

Guatemala 8 8 6 30 8.77 0.67 (-) 15.30 0.98 (-) 30 10.60 1.47 (+) 15.47 0.89 (-)(0.922) (0.7075) (0.503) (0.4898) (0.833) (0.3113) (0.491) (0.5662)

Haiti 8) 6 2 x 36 14.03 0.27 (-) 12.05 0.62 (+) insufficient number of observations insufficient number of observations(0.596) (0.7650) (0.740) (0.7143)

Honduras 8 4 8 25 8.47 2.10 (-) 18.65 0.73 (+) 24 33.72 5.70 (-) 54.47 16.05 (+)(0.934) (0.1393) (0.287) (0.6661) (0.006)*** (0.0922) (0.000)*** (0.0217)

Nicaragua 8) 7 7 x 26 18.43 4.12 (+) 14.73 1.25 (+) insufficient number of observations insufficient number of observations(0.299) (0.0157)** (0.545) (0.3548)

Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)







coefficients





statistic 2)



coefficients




coefficients

Paraguay 4 2 5 32 17.65 5.06 (-) 6.89 1.09 (+) 32 20.31 5.76 (-) 6.81 0.72 (+)(0.345) (0.0139)** (0.975) (0.3813) (0.206) (0.0106)** GNI (+) (0.977) (0.5913)


Peru 4 3 2 37 5.31 0.19 (+) 22.17 1.79 (+) 36 11.09 0.87 (+) 21.01 0.37 (-)(0.994) (0.9027) (0.138) (0.1574) (0.804) (0.4702) (0.178) (0.8306)

4 3 4 26 7.34 4.49 (-) 13.39 0.88 (+) 26 16.20 2.58 (-) 14.36 0.90 (+)(0.966) (0.0153)** (0.644) (0.4975) (0.439) (0.0924)* GNI (+) (0.572) (0.4916)


EUROPETurkey 6), 9) 7 1 x 31 16.26 4.38 (-) 18.58 0.66 (+) No real data for GNI No real data for GNI

(0.435) (0.0475)** (0.291) (0.7011)

1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hpyothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss net ODA and Z is recipient country's GNI.5) Basis year = 1990.6) Transformed net ODA series because of negative values (see description in text).7) No estimation because common sample size too short.8) GNI series too short.9) No real GNI series available.

Trinidad and Tobago 5), 6)

Country

Optimal lags


Y→X 4) X→Y 4) Y→X|Z 4) X→Y|Z 4)



Table 5.2a : Causality results for individual countries and aggregate samples with error-correction. Nominal net ODA

X Y X Ynet ODA

regressed on exports

exports regressed on gross ODA




coefficients

F-statistic of the error term

coefficient 3)

Sign of the error term coefficient




coefficients

F-statistic of the error term coefficient 3)


AFRICAAlgeria 6 6 1 1 non-stationary non-stationary

Benin 8 2 2 1

Burkina Faso 2 7 1 1 non-stationary non-stationary

Burundi 2 4 1 1 non-stationary non-stationary

Cameroon 3 5 2 1

Chad 5 3 1 1 non-stationary non-stationary

Congo, Rep. 4 7 1 3

Congo, Dem. Rep. 6 7 1 1 non-stationary non-stationary

Cote d'Ivoire 3 1 1 1 non-stationary non-stationary

Egypt 8 2 2 1

Ghana 2 2 1 1 non-stationary non-stationary

Country

Optimal lags

Integration order Error 1 Error 2

Y→X 4)


Y=f(X)

X→Y 4)


Bivariate Granger causalityX=f(Y)

Table 5.2a : Causality results for individual countries and aggregate samples with error-correction. Nominal net ODA (continued)

X Y X Ynet ODA






coefficients


coefficient 3)





coefficients



Kenya 5 8 0 2

Lesotho 2 2 0 1

Madagascar 7 3 1 1 non-stationary non-stationary

Mauritania 2 2 1 1 non-stationary non-stationary

Morocco 4 2 2 1

Niger 5 6 1 0

Rwanda 4 4 1 1 non-stationary non-stationary

Senegal 5 7 1 2

Sudan 8 7 1 1 non-stationary non-stationary

Togo 2 2 1 1 non-stationary non-stationary

Country

Optimal lags



Y→X 4) X→Y 4)

net ODA causes exports exports cause net ODA


X Y X Ynet ODA






coefficients


coefficient 3)





coefficients



Tunisia 8 2 1 1 non-stationary non-stationary

Uganda 6 1 1 1 non-stationary non-stationary

Zambia 7 2 0 1

Zimbabwe 3 6 1 2

ASIABangladesh 5 6 0 1

India 5 4 2 0

Indonesia 7 2 1 2

Jordan 7 2 1 1 non-stationary non-stationary

Malaysia 8 8 1 1 non-stationary non-stationary

Nepal 3 6 0 2

Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Ynet ODA






coefficients


coefficient 3)





coefficients



Pakistan 4 7 0 1

Sri Lanka 8 7 0 1

LATIN AMERICABolivia 2 4 1 1 non-stationary non-stationary

Brazil 5) 6 6 2 1

Chile 4 6 1 1 non-stationary non-stationary

Colombia 8 6 1 1 non-stationary non-stationary

Costa Rica 6 3 1 1 non-stationary non-stationary

Ecuador 4 8 2 1

Guatemala 6 8 2 2 stationary stationary 29 11.61 1.18 (+) 1.41 (-) 17.92 0.64 (-) 0.00 (+)(0.770) (0.3733) (0.2540) (0.328) (0.6993) (0.9499)

Haiti 6 2 1 1 non-stationary non-stationary

Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Ynet ODA






coefficients


coefficient 3)





coefficients



Honduras 8 4 1 2

Nicaragua 4 2 1 1 non-stationary non-stationary

Paraguay 4 2 1 1 non-stationary non-stationary

Peru 5 3 2 1

5 3 0 0

EUROPETurkey 5) 4 1 1 1 non-stationary non-stationary


Full sample 6) 8 6 2 2 non-stationary non-stationary

4 3 2 2 non-stationary non-stationary



Trinidad and Tobago




Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Ynet ODA






coefficients


coefficient 3)





coefficients



6 2 0 1

South Asia 3 5 2 2 non-stationary non-stationary

7 4 0 2

Low income 8 8 2 2 non-stationary non-stationary

6 4 0 2

2 5 2 1

1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hpyothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss net ODA and Z is recipient country's GNI.5) Transformed net ODA series because of negative values (see description in text).6) All countries except Trinidad and Tobago and Malaysia (see description in text). Sample size: 1974 to 2004.7) East Asia and Pacific without Malaysia. Thus, in the case of our sample, only Indonesia.8) In the case of our sample, only Turkey (transformed, see note 5)).9) Latin America and Caribbean without Trinidad and Tobago.10) Upper middle income without Trinidad and Tobago and Malaysia.


Sub-Saharan Africa

Lower middle income


Country

Optimal lags



Y→X 4) X→Y 4)


Table 5.2b : Causality results for individual countries and aggregate samples with error-correction. Nominal gross ODA

X Y X Y

gross ODA regressed on

exports





coefficients






coefficients




Benin 8 3 2 0

Burkina Faso 2 8 1 1 non-stationary non-stationary


Cameroon 3 4 2 1

Chad 5 7 1 1 non-stationary non-stationary

Congo, Rep. 4 3 1 3

Congo, Dem. Rep. 6 4 1 1 stationary non-stationary 33 8.72 1.27 (-) 0.90 (-) 7.7693 0.93 (-) 4.12 (-)(0.924) (0.3101) (0.3531) (0.955) (0.4963) (0.0552)*

Cote d'Ivoire 3 2 1 0

Egypt 8 2 2 1

Ghana 2 2 1 1 non-stationary non-stationary

Error 2X=f(Y)

Y→X 4)Optimal

lagsIntegration

order Error 1

gross ODA causes exportsCountry

Y=f(X)Bivariate Granger causality

X→Y 4)

exports cause gross ODA

Table 5.2b : Causality results for individual countries and aggregate samples with error-correction. Nominal gross ODA (continued)

X Y X Y


exports





coefficients






coefficients



Kenya 5 8 0 1

Lesotho 2 8 0 1


Mauritania 2 2 1 1 non-stationary non-stationary

Morocco 4 7 2 1

Niger 5 6 1 1 non-stationary non-stationary


Senegal 5 8 1 0

Sudan 8 7 1 1 non-stationary non-stationary


Country

Optimal lags



Y→X 4) X→Y 4)

gross ODA causes exports exports cause gross ODA


X Y X Y


exports





coefficients






coefficients




Uganda 6 5 1 1 non-stationary non-stationary

Zambia 7 8 0 1

Zimbabwe 3 6 1 2

ASIABangladesh 5 6 0 1

India 5 2 2 0

Indonesia 7 1 1 2

Jordan 7 2 1 2


Nepal 3 6 0 2

Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients



Pakistan 4 7 0 1

Sri Lanka 8 7 0 0


Brazil 6 8 2 0

Chile 4 4 1 1 non-stationary non-stationary



Ecuador 4 8 2 1

Guatemala 8 8 2 1

Haiti 6 4 1 1 non-stationary non-stationary

Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients



Honduras 8 4 1 0

Nicaragua 4 7 1 1 non-stationary non-stationary


Peru 5 3 2 0

5 0

EUROPETurkey 4 2 1 1 non-stationary non-stationary


Full sample 6) 8 8 2 2 non-stationary non-stationary







Trinidad and Tobago 5)

Country

Optimal lags



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients



6 2 0 1

South Asia 4 8 2 1

7 3 0 2

Low income 8 8 2 2 non-stationary non-stationary

6 4 0 1


1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hpyothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss gross ODA and Z is recipient country's GNI.5) Accounting discrepancy in gross ODA series (negative values).6) All countries except Trinidad and Tobago and Malaysia (see description in text). Sample size: 1974 to 2004.7) East Asia and Pacific without Malaysia. Thus, in the case of our sample, only Indonesia.8) In the case of our sample, only Turkey 9) Latin America and Caribbean without Trinidad and Tobago.10) Upper middle income without Trinidad and Tobago and Malaysia.



Sub-Saharan Africa

Lower middle income

Country

Optimal lags



Y→X 4) X→Y 4)


Table 5.2c : Causality results for individual countries with error-correction. Real net ODA

X Y X Y

net ODA regressed on

exports





coefficients






coefficients




Benin 8 8 2 1

Burkina Faso 8) 2 7 1 1 non-stationary non-stationary


Cameroon 3 4 2 1

Chad 4 3 2 1

Congo, Rep. 5) 4 1 1 3

Congo, Dem. Rep. 3 2 1 1 non-stationary non-stationary

Cote d'Ivoire 9) 3 2 2 1

Egypt 8 2 2 1

Ghana 2 2 2 1

Country

Optimal lag

Integration order

X=f(Y) Y=f(X)Error 1 Error 2

Y→X 4)

net ODA causes exportsX→Y 4)



Table 5.2c : Causality results for individual countries with error-correction. Real net ODA (continued)

X Y X Y


exports





coefficients






coefficients



Kenya 7 8 2 2 non-stationary stationary 31 11.79 3.16 (+) 8.94 (-) 12.11 0.96 (-) 4.86 (-)(0.758) (0.0241)** (0.0086)*** (0.737) (0.4979) (0.0447)**

Lesotho 8 8 1 1 non-stationary non-stationary


Mauritania 5) 2 2 2 1

Morocco 4 2 2 1

Niger 5) 7 7 1 1 non-stationary non-stationary


Senegal 5 7 1 1 non-stationary non-stationary

Sudan 8) 8 7 1 1 non-stationary non-stationary


Country

Optimal lag



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients




Uganda 2 1 2 1

Zambia 2 2 1 1 non-stationary non-stationary

Zimbabwe 2 8 2 2 non-stationary non-stationary

ASIABangladesh 4 6 1 1 non-stationary non-stationary

India 8 7 2 0

Indonesia 6 2 2 1 non-stationary non-stationary

Jordan 7 4 1 1 non-stationary non-stationary


Nepal 9) 2 6 1 1 non-stationary non-stationary

Country

Optimal lag



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients



Pakistan 3 7 2 1

Sri Lanka 8 5 1 1 non-stationary non-stationary


Brazil 6) 5 8 2 1

Chile 4 4 2 1



Ecuador 4 8 2 1

Guatemala 7 8 2 2 non-stationary stationary 29 8.77 0.73 (-) 0.83 (-) 14.87 0.71 (+) 0.00 (-)(0.922) (0.6640) (0.3775) (0.534) (0.6648) (0.9698)

Haiti 8) 6 2 1 1 non-stationary non-stationary

Country

Optimal lag



Y→X 4) X→Y 4)



X Y X Y


exports





coefficients






coefficients



Honduras 8 4 1 0

Nicaragua 8) 7 7 1 1 non-stationary non-stationary


Peru 4 3 1 0

4 3 1 2

EUROPETurkey 6), 9) 7 1 1 1 non-stationary non-stationary

1) Number of observations after adjustments.2) Ljung-Box statistics. If no indication: included lags = 16. In parentheses: probability of being wrong when rejecting the null hypothesis of no-autocorrelation. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.3) F-statistics and probability of null hpyothesis of no-causality in parentheses. ***, ** and * denote, respectively, significance at the 1%, 5% and 10% levels.4) → indicates Granger causality, X is Swiss exports, Y is Swiss net ODA and Z is recipient country's GNI.5) Basis year = 1990.6) Transformed net ODA series because of negative values (see description in text).7) No estimation because common sample size too short.8) GNI series too short.9) No real GNI series available.

Trinidad and Tobago 5), 6)

Country

Optimal lag



Y→X 4) X→Y 4)


73

6. ESTIMATION OF A STRUCTURAL MODEL 6.1 Model Specification Based on the discussion in Section 2 and the results of the time-series analysis presented in Section 5, the flow of Swiss exports to the recipient country i can be expressed as a function of its past level and the level of financial resources in the country i, which includes its output/income, the aid received from Switzerland and the aid from the rest of the DAC members:

( ), 1 , , ,, , ,it i t i t a i t b i t bEXP f EXP GNI ODA RESTODA− − − −= (6.1) where itEXP stands for Swiss exports to the recipient country i at period t, ,i t aGNI − is the Gross National Income (GNI) of the recipient country i at time t a− for [ ]0;1a∈ , ,i t bODA − represents bilateral aid flows from Switzerland to the recipient country i at time t b− for [ ]0;3b∈ and

,i t bRESTODA − denotes bilateral aid received by the country i from all DAC members other than Switzerland. The number of lags a and b are determined empirically (see Section 6.2). Following Vogler-Ludwig et al. (1999), we consider a log-linear specification where itε is the idiosyncratic error which varies over countries and time:

( ) ( ) ( ) ( )

( )0 1 , 1 2 , 3 ,

4 ,

log log log log

logit i t i t a i t b

i t b it

EXP EXP GNI ODA

RESTODA

α β β β

β ε− − −

−

= + + + +

+ (6.2)

This logarithmic specification allows us to interpret the different coefficients 1β , 2β , 3β and

4β as elasticities. In other words, 3β represents the elasticity of Swiss exports to the recipient country i with respect to Swiss aid to the same country, i.e. if Swiss aid to the country i changes by 1%, then Swiss exports to this country will vary by 3β % in the short run, other things being equal. We expect the parameter associated with lagged Swiss exports (β1) to be less than unity in absolute value (condition for the stability of the model). If it is positive (i.e. last year’s exports has a positive impact on this year’s exports), this means that transaction costs (those associated with dealing with new customers or suppliers) tend to fall as trade develops. A negative coefficient (e.g. a surge in exports last year is followed by a fall this year) would be a manifestation of the “correction” phenomenon, reflecting the mere fact that exports sometimes evolve in “spurts” rather than smoothly. Note that β1 allows us to measure the “lingering effect” of the other variables appearing on the right-hand side of the equation (6.2), implying a distinction between short-run and long-run elasticities. For example, the long-run elasticity of exports with respect to ODA is measured as ( )3 11β β− %. A positive (respectively negative) β1 implies that the long-run effect is larger (smaller) than the short-run effect.

74

The sign of the coefficient associated with the variable GNI (β2) is also assumed to be strictly positive. This comes from the budget constraint of the recipient country: the more financial resources are available, the more the country is able to import goods from abroad. For various reasons discussed in Section 2 including the “goodwill” hypothesis, the impact of Swiss aid on exports (β3) should be positive. We therefore expect Swiss bilateral aid to developing countries to favour Switzerland’s exports to these countries. The coefficient of aid received from all DAC members other than Switzerland (β4) can take a positive or negative sign. If it turns out to be positive, this would mean that there is a “complementarity” effect between Swiss aid and other DAC countries’ aid on Swiss exports. On the contrary, if it is negative, this would imply a “substitution” effect between Swiss aid and other DAC members’ aid as far as the impact on Swiss exports is concerned. According to the results presented in Section 5, time-series on exports, GNI and aid tend to be non-stationary. Regressions involving the levels of non-stationary variables usually yield misleading results in terms of standard significance tests (t-test, F-test) and therefore tend to reject the null hypothesis of the absence of any relationship, when in reality there might be none. In most cases, stationarity can be achieved by applying a first-order difference. That is why we prefer to express equation (6.2) in terms of first differences rather than levels of variables involved:

( ) ( ) ( ) ( )( )

0 1 , 1 2 , 3 ,

4 ,

log log log log


i t b it

EXP EXP GNI ODA

RESTODA

α β β β

β ε− − −

−

Δ = + Δ + Δ + Δ +

Δ + Δ (6.3)

where “ Δ ” denotes the change between two adjacent periods. Although under this specification, all variables represent growth rates of the original ones, the expected signs and the interpretations of different coefficients remain the same. 6.2 Econometric Issues Basic Specifications Estimation of equations (6.2) and (6.3) faces two main econometric problems: the use of logarithms when variables can take zero or negative values and the problem of multicollinearity. Because net ODA can take null or negative values and exports can be null, the use of logarithms is ruled out. However, there are different ways to deal with this technical problem: – Treat the observations with negative or zero values as missing data. This solution has the

advantage of being easy to implement. Its main disadvantage is to reduce the sample and give up part of the available information.

75

– Transform the variables with negative or zero values using the transformation already used

in Section 5. 3 : log Xt → ( )2log 1t tX X+ + .

We adopt the second solution which carries the advantage of using all the available information. Note that, in spite of this transformation, the expected sign of the coefficient remains unchanged. Unfortunately, due to a high correlation between ( ), 1log i tEXP − and ( ),log i t aGNI − (see Appendix D), equations (6.2) and (6.3) cannot be estimated at country and group levels without facing a problem of GNI being crowded out by lagged exports (i.e. GNI becomes non-significant in the presence of lagged exports which does not make much economic sense). That is why we have to estimate the following equations:

( ) ( ) ( ) ( )0 2 , 3 , 4 ,log log log logit i t a i t b i t b itEXP GNI ODA RESTODAα β β β ε− − −= + + + + (6.4)

( ) ( ) ( ) ( )2 , 3 , 4 ,log log log logit i t a i t b i t b itEXP GNI ODA RESTODAβ β β ε− − −Δ = Δ + Δ + Δ + Δ (6.5) In order to specify the correct lags for GNI ( [ ]0;1a∈ ) and the aid variables ( [ ]0;3b∈ ), we run equations (6.4) and (6.5) with different lags. The selection of the model is based on the sole condition that the GNI coefficient is positive and significant. When different specifications give approximately the same results, we retain the model which yields a better fit for the equation (higher adjusted-R2) and a higher degree of significance for the GNI coefficient. As mentioned previously, due to non-stationarity, we will focus our attention on the first-order difference specification. When equations (6.4) and (6.5) show in addition signs of multicollinearity, i.e. the coefficient of GNI is not significant or negative and significant; we turn to the following specifications:

( ) ( ) ( ) ( )0 1 , 1 3 , 4 ,log log log logit i t i t b i t b itEXP EXP ODA RESTODAα β β β ε− − −= + + + + (6.6)

( ) ( ) ( ) ( )1 , 1 3 , 4 ,log log log logit i t i t b i t b itEXP EXP ODA RESTODAβ β β ε− − −Δ = Δ + Δ + Δ + Δ (6.7)

In this case, we can distinguish between short-run ( 3β ) and long-run ( ( )3 11β β− ) elasticities of exports with respect to aid. Panel Setting Before estimating the structural equations presented above individually for each recipient country, we will consider several possibilities of exploiting panel data. A panel dataset comprises a mix of cross-section and time series data. It consists of repeated measures (through time) of one or more variables on a set of statistical units (individuals, firms, countries, etc.). Baltagi (2001) discusses some of the benefits and limitations in using panel data. The advantages are the following:

76

– First, they are more informative compared to pure time-series or cross-section data. Panel data present more variability, less collinearity among the variables and, most importantly, more degrees of freedom. Moreover, estimates based on panel data are more efficient.

– Second, they allow studying individual dynamics. – Third, panel data give information on the time-ordering of events, since it consider both

the temporal and the individual dimensions. – Finally, panel data allow controlling for individual unobserved heterogeneity. On the other hand, the use of panel data often implies more complicated design and data collection problems than in the case of time-series or cross-section data. Also, measurement errors as well as selectivity problems (self-selectivity, non-response, attrition, and new entries) may produce distortions in the inferential procedures. As a result, the model to be estimated based on panel data is defined along two dimensions (country and time). Equation (6.4) then becomes:

( ) ( ) ( ) ( )0 2 , 3 , 4 ,log log log logit i t a i t b i t b itEXP GNI ODA RESTODA uα β β β− − −= + + + + (6.8) where itu represents the composite residual that can be expressed as the sum of two error terms:

it i itu v ε= + (6.9) where iv is the country-specific error, which does not change over time. Every recipient country has a fixed value on this latent variable (fixed effects). It represents country-specific time-invariant unobserved heterogeneity. itε is the idiosyncratic error, which varies across countries and over time. It fulfils the usual OLS assumptions for regression residuals. Given the general framework defined by (6.8) and (6.9), we can now consider various panel estimation procedures. 1) Cross-Section OLS Estimator The cross-section OLS regression for the full sample considers the sample mean over the period for each recipient country:

( ) ( ) ( ) ( )0 2 3 4log log log logi i i i i iEXP GNI ODA RESTODA vα β β β ε= + + + + + (6.10)

The cross-section OLS estimator will be biased because of unobserved heterogeneity as it relies totally on a between-country comparison. This can be misleading because countries are self-selected and not chosen randomly. It is therefore likely that the error term iv will be correlated with the explanatory variables contrary to the standard OLS assumption.

77

2) Time-Series OLS Estimator The time-series OLS regression for the full sample consists of summing each variable across all recipient countries for each year:

( ) ( ) ( ) ( )( )

0 1 1 2 3

4

log log log log

logt t t a t b

t b t

EXP EXP GNI ODA

RESTODA

α β β β

β ε− − −

−

= + + + +

+ (6.11)

The OLS estimator is still biased because of unobserved heterogeneity. This is due to the fact that the time-series OLS estimator relies only on a between-time comparison. 3) Pooled OLS Estimator A common first step with panel data – using the entire information provided by the data – is pooled OLS:

( ) ( ) ( ) ( )( )

0 1 , 1 2 , 3 ,

4 ,

log log log log


i t b it

EXP EXP GNI ODA

RESTODA

α β β β

β ε− − −

−

= + + + +

+ (6.12)

The pooled OLS estimator relies on the assumption of homogeneity, across both time periods and cross-sectional units. In other words, it imposes the assumptions that (i) all the errors are drawn from the same distribution, and (ii) the slope coefficients are the same irrespective of whether one is looking across countries or over time within in a country. In practice, these assumptions are rarely met. The estimate is therefore still biased because of unobserved heterogeneity. This comes from the fact that the error term itε is correlated with the explanatory variables. However, compared with the cross-section and time-series OLS, the bias will be lower because pooled-OLS estimation takes into account the within variation. 4) First-Difference Estimator Applying a first-order difference in a panel setting allows to “difference out” the country-specific error:

( ) ( ) ( ) ( )2 , 3 , 4 ,log log log logit i t a i t b i t b itEXP GNI ODA RESTODAβ β β ε− − −Δ = Δ + Δ + Δ + Δ (6.13) This new specification has the advantage of cancelling out the fixed-effects ( iv ). The first-difference estimator no longer relies on the between-country comparison. It only considers within-country changes. Therefore, we do not need the assumption that iv is uncorrelated with the explanatory variables. However, this first-difference OLS estimator is not efficient, because of the serial correlation in itεΔ . In this first-difference specification, error terms across observations are correlated. Therefore, OLS estimation will produce biased standard errors. To tackle this problem, one can apply GLS or Huber-White sandwich estimators.

78

5) Fixed-Effect Estimator An alternative to applying a first-order difference is to “time-demean” the data through a within-transformation. We rewrite equation (6.8) as the deviation of each variable from its mean:

( ) ( ) ( ) ( )( )( ) ( )( )( ) ( )( )( ) ( )( )

1 , 1

2 ,

3 ,

4 ,

log log log log

log log

log log

log log

iit i i t

i t a i

ii t b

i ii t b it

EXP EXP EXP EXP

GNI GNI

ODA ODA

RESTODA RESTODA

β

β

β

β ε ε

−

−

−

−

− = − +

− +

− +

− + −

(6.14)

This equation includes fixed effects for each recipient country in order to capture the potential country heterogeneity biases like geographical location. The model can now be estimated by OLS (fixed-effects estimator), since the error term iv has disappeared and we no longer need the assumption that iv is uncorrelated with the explanatory variables. Since we are using all the available information, the fixed effect estimator is more efficient than the first-difference estimator. 6.3 Factual Sheets Before commenting on the results obtained for our unbalanced panel data, it is interesting to have a look at Figure 6.1 which illustrates the relationship between the log of Swiss exports and the log of Swiss net ODA in different panel settings.

79

Figure 6.1: Scatter diagram of exports and net ODA

-4-2

02

4lo

g(ex

p)

-2 0 2 4 6log(ODA)

95% CI Fitted values(mean) logoda

Cross-Section

-100

-50

050

100

log(

exp)

0 100 200 300log(ODA)

95% CI Fitted values(sum) logoda

Time-Series level

-10

-50

510

15D

elta

log(

exp)

-10 -5 0 5 10 15Deltalog(ODA)

95% CI Fitted values(sum) Deltalogoda

Time-Series First-Difference

-4-2

02

4lo

g(ex

p)

-5 0 5 10log(ODA)

95% CI Fitted valueslogoda

Pooled level

-4-2

02

4D

elta

log(

exp)

-2 -1 0 1 2 3Deltalog(ODA)

95% CI Fitted valuesDelta log(oda)

Panel First-Difference

-4-2

02

4lo

g(ex

p)

-6 -4 -2 0 2 4log(ODA)

95% CI Fitted valuesdmlogexp

Panel Time-Demean level

Although the slope of the linear fit varies between specifications, there seems to be a positive relationship between Swiss exports and Swiss net ODA, except in the cross-section specification. With time-series data, the relationship is much stronger than in the pooled and panel data setting, suggesting that the real effect of aid on exports lies somewhere between these two specifications. 6.4 Full-Sample Results Table 6.1 presents the estimation results for cross-section, time-series (level and first-difference) and panel (pooled, fixed effect and first-difference) specifications. For each specification, the first line reports for each variable the associated estimated coefficient and

80

the number of lags involved. The second line indicates the value of White’s standard errors in parentheses. The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively. The observations made when looking at Figure 6.1 are confirmed by the estimation results. All specifications find a positive relationship between aid and exports. The cross-section estimation is the only one to find a positive but non significant relationship. All other specifications, which produce less biased estimations, find a positive effect. The effect is, however, much stronger in the time-series specification than in the panel specifications. Note also that the difference between the pooled and the fixed effect specifications is small suggesting that the bias associated with the pooled estimation is negligible. The other major difference between the panel and the time-series estimations is the lag structure. The panel estimation is characterised by the absence of lags for the aid variables. This suggests that the impact of aid on exports is more limited to the current year only and confirms previous empirical evidence (Wagner (2003)). The impact of the aid from the rest of DAC members is negative and significant in the time-series setting, but it is no longer significant in the panel specifications. When this elasticity is significant the effect is more important on the exports than the effect of Swiss aid on the exports. This should not come as a surprise, since Swiss aid is less tied than other DAC members’ aid on average. Moreover, during the observed period, the share of Swiss aid in the recipient country varies between 0.048% for Bhutan and 7.79% for Nigeria. The impact of the recipient country GNI on Swiss exports is always positive. The fact that the coefficient is larger than one in the cross-section estimation suggests that it is overestimated. The real impact of GNI lies between 0.6 and 0.9, although the lag structure of GNI is different between the time-series and the panel estimations. In terms of goodness of fit, the adjusted R2 lies between 0.57 and 0.99 for the level estimations, suggesting that the structural model is able to account for most of the variability in the data. For the first-difference estimations, the adjusted R2 drops significantly. This is a common feature of first-difference estimations. In order to allow for differences among recipient countries in the panel setting, Table 6.2 displays the results of the panel estimation including fixed effects when there is no correction for autocorrelation.8 Most recipient country dummies (70 over 98) are significant and range from –2.5 (Lesotho) to +2.7 (Liberia). Among the significant dummies, half are positive (mostly upper and lower middle-income countries) while the other half is negative (mostly low income and some lower middle-income countries). In other words, high income recipient countries can use their higher economic growth to import more goods from Switzerland. This is consistent with the macroeconomic theory that states that the economic growth should

8 The elasticities of Swiss aid and the rest of DAC members’ aid are similar to the ones found when a first-order autoregressive (AR(1)) correction is applied, except for the coefficient of GNI. In fact, the elasticity of GNI is greater when we neglect the fact that the disturbance term is first-order autoregressive. This over-estimation is consistent with the fact that GNI is usually non-stationary and persistent over time.

81

allow the recipient country to dispose of a greater financial capacity to import goods and services.

Table 6.1: Full sample regression results 1966-2003

Estimation Constant Swiss

Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Cross-Section -6.486 1.055 - 0.025 - -0.052 - 0.840 1966-2003 (-11.19)*** (20.23)*** (0.29) (-0.43)

-3.434♦ 0.968 0 0.348 3 -0.424 3 0.997 1966-2003 Time-Series Level OLS (-2.04)* (8.40)*** (2.26)** (-2.08)**

-0.002 0.991 0 0.36 3 -0.407 3 0.569 1966-2003 Time-Series FD OLS (-0.07) (5.15)*** (2.79)*** (-2.30)** Pooled OLS -4.403♦ 0.817 0 0.045 0 0.008 0 0.984 1966-2003 (-8.25)*** (17.28)*** (3.18)*** (0.90) FE Panel -3.17♦ 0.651 0 0.045 0 0.003 0 0.815 1966-2003 (-50.83)*** (13.18)*** (3.14)*** (0.32) FD Panel 0.024 0.705 0 0.056 0 0.009 0 0.074 1966-2003 (3.54)*** (12.80)*** (3.93)*** (0.94)

Note: The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively. ♦ indicates that a first-order autocorrelation correction has been applied and the associated AR(1) coefficient is at least significant at 10 percent.

Table 6.2: Fixed effect panel regression results 1966-2003

Variable Coefficient t-statistic Constant -6.37 (-28.11)*** Recipient GNI (t) 0.99 (42.33)*** Swiss Net ODA (t) 0.044 (3.35)*** Rest of DAC's Net ODA (t) 0.016 (0.8) Dummy Albania 0.3 (1.59) Dummy Argentina -0.21 (-1.13) Dummy Armenia -0.569 (-2.78)*** Dummy Azerbaijan -0.379 (-1.8)* Dummy Burundi -0.56 (-3.64)*** Dummy Benin 0.221 (1.46) Dummy Burkina Faso -1.73 (-11.55)*** Dummy Bangladesh -1.044 (-6.68)*** Dummy Bulgaria 1.286 (6.49)*** Dummy Bosnia and Herzegovina 0.697 (3.17)*** Dummy Belarus -0.31 (-1.44) Dummy Bolivia 0.006 (0.04) Dummy Brazil -0.319 (-1.8)* Dummy Bhutan -0.834 (-4.44)*** Dummy Chile 0.111 (0.67) Dummy China -0.701 (-3.66)*** Dummy Côte d'Ivoire 0.44 (2.91)*** Dummy Cameroon -0.313 (-2.12)** Dummy Republic of the Congo -0.447 (-2.93)*** Dummy Colombia 0.232 (1.49) Dummy Cape Verde -0.509 (-2.78)*** Dummy Costa Rica 0.37 (2.42)** Dummy Dominican Republic -0.557 (-3.04)*** Dummy Algeria 0.224 (1.44) Dummy Ecuador 0.519 (3.43)*** Dummy Egypt 0.909 (5.78)*** Dummy Eritrea -0.356 (-1.71)* Dummy Estonia 0.374 (1.88)* Dummy Ethiopia -0.119 (-0.72) Dummy Georgia -0.989 (-4.86)*** Dummy Ghana 0.526 (3.51)*** Dummy Guinea 0.153 (0.87) Dummy Guinea-Bissau 0.318 (1.83)* Dummy Guatemala 0.091 (0.6) Dummy Honduras 0.733 (4.72)*** Dummy Croatia 0.993 (4.44)*** Dummy Haiti -0.383 (-2.56)*** Dummy Hungary 1.51 (7.62)*** Dummy Indonesia -0.428 (-2.72)*** Dummy India -0.942 (-5.81)*** Dummy Iran 0.34 (1.55) Dummy Jordan 1.197 (7.9)***

84

Table 6.2: Fixed effect panel regression results 1966-2003 (continued)

Variable Coefficient t-statistic Dummy Kazakhstan -0.17 (-0.64) Dummy Kenya 0.478 (3.24)*** Dummy Kyrgyz Republic -0.633 (-3.07)*** Dummy Lao PDR -1.799 (-9.84)*** Dummy Lebanon 1.815 (9.79)*** Dummy Liberia 2.723 (10.92)*** Dummy Sri Lanka 0.018 (0.12) Dummy Lesotho -2.496 (-16.13)*** Dummy Lithuania 0.879 (4.13)*** Dummy Latvia 0.937 (4.43)*** Dummy Morocco 0.519 (3.33)*** Dummy Moldova 0.963 (4.37)*** Dummy Madagascar -0.473 (-3.18)*** Dummy Mexico -0.372 (-1.87)* Dummy Macedonia 1.704 (7.77)*** Dummy Mali -0.897 (-4.74)*** Dummy Mozambique -0.48 (-2.91)*** Dummy Mauritania 0.043 (0.27) Dummy Malaysia 0.319 (1.91)* Dummy Namibia -1.109 (-6.01)*** Dummy Niger -0.826 (-5.36)*** Dummy Nigeria 0.621 (3.21)*** Dummy Nicaragua 0.261 (1.72)* Dummy Nepal -1.315 (-8.78)*** Dummy Pakistan 0.209 (1.38) Dummy Peru 0.061 (0.41) Dummy Philippines 0.131 (0.77) Dummy Papua New Guinea -1.807 (-10.17)*** Dummy Poland 0.741 (3.69)*** Dummy Paraguay 0.059 (0.39) Dummy Romania 0.59 (3.08)*** Dummy Russian Federation -0.399 (-1.77)* Dummy Rwanda -0.703 (-4.56)*** Dummy Sudan 0.073 (0.48) Dummy Senegal 0.085 (0.57) Dummy Slovak Republic 1.068 (5.46)*** Dummy Syrian Arab Republic 0.762 (3.07)*** Dummy Chad -1.591 (-10.5)*** Dummy Togo 0.596 (3.94)*** Dummy Thailand 0.54 (3.31)*** Dummy Tajikistan -0.764 (-3.69)*** Dummy Trinidad and Tobago -0.351 (-2.12)** Dummy Tunisia 0.531 (3.57)*** Dummy Turkey 0.591 (3.53)*** Dummy Tanzania -0.61 (-3.36)*** Dummy Uganda -0.592 (-3.88)***

85

Table 6.2: Fixed effect panel regression results 1966-2003 (continued)

Variable Coefficient t-statistic Dummy Ukraine 0.079 (0.37) Dummy Uruguay 0.465 (2.68)*** Dummy Uzbekistan -0.211 (-1.02) Dummy Venezuela, RB -0.424 (-1.54) Dummy Vietnam -0.175 (-0.95) Dummy Yemen 0.462 (2.26)** Dummy South Africa 0.487 (2.28)** Dummy Democratic Republic of Congo -0.177 (-1.17) Dummy Zambia 0.125 (0.82) Dummy Zimbabwe -0.009 (-0.06) Adjusted R2 0.941 Period 1966-2003 Note: Dummy Angola naturally omitted. The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1

percent level respectively.

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6.5 Group Results Regional Groups Before presenting the results of the estimations at the individual recipient country level, it is interesting to distinguish between different groups according to region and income. First, the different countries are geographically divided into the following categories (regions): East Asia & Pacific, Europe & Central Asia, Latin America & the Caribbean, Middle East & North Africa, South Asia, and finally Sub-Saharan Africa (see Appendix B).

Figure 6.2: Regional split of Swiss net ODA for 1965-2004

8.424%

14.24%

15.1%

3.864%18.77%

39.6%

East Asia & Pacific Europe & Central AsiaLatin America & Caribbean Middle East & North AfricaSouth Asia Sub-Saharan Africa

Swiss net ODA 1965-2004

Table 6.3 reports the estimation of time-series level and first-difference OLS at a regional level. All regional groups exhibit a positive impact of Swiss net ODA on exports. However, for some regions the coefficient is not significant. This is the case for the first-differenced OLS estimation for Latin America & the Caribbean, Middle East & North Africa as well as South Asia, although in the case of Middle East & North Africa and South Asia the coefficient is almost significant. One interesting result is that for a majority of regions, the aid from other DAC members has no impact on Swiss exports. The absence of significant results for Middle East & North Africa could be explained by the fact that during the period 1966-2003, less than 4 percent of the total sum of Swiss aid went to this part of the world (see Figure 6.2). In general, the equations show a remarkable fit. The adjusted R2 varies mostly between 0.5 and 0.9. This is quite high for first-difference estimations. However, one of the shortcomings of this geographical classification is to aggregate countries which do not necessary dispose of the same level of economic development. As a

87

consequence, the effect of aid on exports could be masked. The income-based classification considered below is deemed more relevant.

Table 6.3: Regional groups regression results 1966-2003

Sample Estimation Constant Swiss

Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net ODA

Lag (t-x)

Adjusted R2

Time Period

Level OLS -5.408♦ 0.766 0 0.224 0 0.158 0 0.995 1969-2003 East Asia & Pacific (-6.31)*** (8.36)*** (3.01)*** (0.99) FD OLS -0.001 0.834 0 0.177 0 -0.008 0 0.617 1969-2003 (-0.02) (5.92)*** (2.17)** (-0.04)

Level OLS 1.204♦ 0.607 0 0.09 0 -0.103 0 0.995 1970-2003 Europe & Central Asia (0.54) (5.5)*** (3.11)*** (-1.97)* FD OLS 0.079 0.604 0 0.084 0 -0.108 0 0.609 1970-2003 (3.62)*** (5.45)*** (2.92)*** (-2.05)**

Level OLS -1.779♦ 0.739 1 0.17 1 -0.233 1 0.989 1968-2003 (-1.5) (4.67)*** (2.16)** (-1.28)

Latin America & Caribbean FD OLS -0.008 0.882 0 0.013 0 0.221 0 0.514 1967-2003 (-0.37) (5)*** (0.21) (1.14)

Level OLS -4.331♦ 0.614 1 0.051 0 0.392 0 0.988 1967-2003 (-2.6)** (5.24)*** (0.7) (3.41)***

Middle East & North Africa FD OLS -0.017 1.132 0 0.05 0 0.341 0 0.915 1967-2003 (-1.18) (14.16)*** (1.45) (5.2)***

South Asia Level OLS 1.382♦ 0.734 1 0.272 3 -0.09 3 0.991 1970-2003 (1.32) (8.03)*** (2.05)** (-0.57) FD OLS 0.026 0.55 1 0.164 3 0.062 3 0.357 1970-2003 (1.2) (3.82)*** (1.62) (0.44)

Level OLS -7.019♦ 1.237 0 0.118 0 -0.265 0 0.994 1967-2003 Sub-Saharian Africa (-5.12)*** (9.33)*** (0.74) (-1.25) FD OLS -0.039 1.413 0 0.36 0 -0.129 0 0.824 1967-2003 (-1.59) (10.2)*** (2.22)** (-0.62)


89

Income Groups Empirical studies show that foreign aid is usually determined by poverty (initial income per capita), trade openness, democracy as well as colonial past and political alliances (Alesina and Dollar (2000)). In the case of Switzerland, one can safely assume that aid is likely to be determined by the economic needs and policy performance of the recipient countries. Three groups are distinguished according to their GNI per capita: low-income, lower middle-income and upper middle-income countries (see Appendix B).

Figure 6.3: Swiss net ODA split by income categories for 1965-2004

57.18%32.73%

10.08%

Low income Lower middle incomeUpper middle income

Swiss net ODA 1965-2004

The estimation results reported in Table 6.4 show a positive and significant effect of aid on exports for low-income and lower middle-income countries. This is interesting because during the period 1966-2003 almost 90 percent of Swiss aid went to low income (57 percent) and lower middle-income (33 percent) countries (see Figure 6.3). Moreover, unlike the regional classification, the lag structure of Swiss aid is different for each income group. As was the case with the regional classification, it seems that, whatever the income group, other DAC members’ aid does not exert a significant positive or negative impact on Swiss exports. Also, the adjusted R2 is relatively high (between 0.5 and 0.7) for first-difference estimations.

Table 6.4: Income groups regression results 1966-2003

Sample Estimation Constant

Swiss Exports

Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Low Income Level OLS -4.593♦ 0.713 0 0.392 0 -0.024 0 0.994 1967-2003 (-2.72)** (4.2)*** (2.5)** (-0.11) FD OLS -0.044 1.381 0 0.216 3 0.111 3 0.556 1970-2003 (-1.81)* (5.16)*** (1.82)* (0.78)

Level OLS 2.686♦ 0.352 0 0.333 0 -0.136 0 0.997 1967-2003 Lower middle Income (1.24) (2.66)** (2.83)*** (-0.94) FD OLS 0.017 0.454 0 0.362 0 0.053 0 0.59 1967-2003 (0.77) (3.38)*** (2.95)*** (0.39)

Level OLS -5.555♦ 0.881 1 0.054 3 0.114 3 0.987 1970-2003 Upper middle income (-6.74)*** (10.21)*** (1.58) (1.16) FD OLS 0.028 0.927 0 0.008 2 -0.075 2 0.703 1969-2003 (1.35) (8.57)*** (0.47) (-1.17)


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6.6 Individual Country Results In this section, each recipient country is considered individually. In this way we are able to uncover more fundamental differences among recipient countries. Among the 99 countries of the sample, we consider only those which have at least 30 observations. As a result, only 47 countries are analysed individually. Table 6.5 displays the times-series level and first-difference OLS estimations corrected for first-order autocorrelation (AR(1)) when necessary. In case the AR coefficient is not significant at least at the 10% level, we re-run the regression without any AR correction. The adjusted R2 is systematically smaller in the first-difference estimation than in the level estimation. This is normal given the fact that the economic variables considered here follow a trend and are non-stationary. As a result, the correct R2 is given by the first-difference OLS estimation. The results at the country level do not allow us to draw clear patterns in terms of income or region. About half low-income countries of the sample present a positive and significant effect of net ODA on exports, while the other half show a positive but non-significant effect. The same is true for lower middle-income countries. Four upper middle-income countries have a positive and significant effect, while two others have a positive but non-significant effect. Only one low-income country (Mauritania) exhibits a negative yet non-significant coefficient. In most cases, the lag structure remains the same in the level and first-difference estimations, which constitutes an encouraging sign in terms of robustness. Most recipient countries display a positive and significant coefficient for current Swiss ODA. The remaining recipients exhibit lags of one, two or three years for ODA. The impact of Swiss aid on Swiss exports lies between 0.083 and 1.014. This difference can be attributed to the heterogeneity among countries and to the different forms of the relationship between aid and exports. In any case, whenever the impact of Swiss aid is positive and significant, it is systematically smaller than the impact of GNI or lagged exports and that of the aid from the rest of DAC members. This result seems plausible since Swiss aid is less tied than the other DAC members’ aid on average. For a small number of countries (Algeria, Bangladesh, Republic of the Congo, Ghana, Haiti, Sri-Lanka, Mauritania, Nepal, Paraguay, Pakistan, Chad, Togo, Uganda, Democratic Republic of Congo), the only positive and significant variable is GNI or lagged exports. This suggests that, for these recipient countries, imports from Switzerland are determined by other economic and political factors. For most recipients, the constant term is not significant, which is consistent with a first-difference specification. Only five recipient countries (Guatemala, Honduras, Trinidad and Tobago, Turkey and Uganda) have a significant positive or negative constant. This could indicate that, for these countries, some important structural change took place during the period considered which helps explain their imports from Switzerland.

Table 6.5: Recipient countries’ regression results 1966-2003


Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Burundi Level OLS -11.653♦ 1.977 0 0.083 2 -0.372 2 0.943 1969-2003 (-7.88)*** (6.78)*** (0.9) (-2.27)**

FD OLS -0.018 1.696 1 0.148 1 -0.321 1 0.326 1968-2003 (-0.44) (3.92)*** (1.31) (-1.77)*

Benin Level OLS -6.667♦ 1.846 1 0.114 0 -1.009 0 0.971 1968-2003

(-1.82)* (3.36)*** (0.93) (-3.16)***

FD OLS 0.023 2.215 1 0.412 0 -0.595 0 0.257 1968-2003 (0.32) (3.36)*** (2.66)** (-1.51)

Level OLS 3.655♦ 0.498 1 0.349 1 -0.741 1 0.966 1968-2003 Burkina Faso (1.95)* (3.33)*** (3.4)*** (-2.31)**

FD OLS 0.045 0.564 1 0.364 1 -0.719 1 0.44 1968-2003 (1.09) (4.04)*** (3.41)*** (-2.25)**

Bengladesh Level OLS 5.215♦ 0.661 1 0.133 1 -0.598 1 0.974 1976-2003

(2.91)*** (3.18)*** (1.4) (-2.42)** FD OLS 0.036 0.567 1 0.143 1 -0.443 1 0.142 1976-2003 (1.27) (2.72)** (1.37) (-1.56)

Bolivia Level OLS 2.042 0.857 1 0.267 1 -0.394 1 0.744 1972-2003

(3.06)*** (9.59)*** (2.28)** (-2.48)** FD OLS -0.043 0.527 1 0.328 1 0.095 1 0.394 1973-2003 (-0.99) (3.59)*** (2.14)** (0.27)

Table 6.5: Recipient countries’ regression results 1966-2003 (continued)


Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Brazil Level OLS 1.605♦ 0.697 1 0.31 2 -0.009 2 0.986 1969-2003 (2.11)** (4.98)*** (2.6)** (-1.2) FD OLS 0.017 0.676 1 0.234 2 -0.009 2 0.506 19692003 (0.89) (5.61)*** (1.97)* (-1.27)

Chile Level OLS 1.188♦ 0.722 1 0.074 3 0.014 3 0.987 1973-2003

(1.04) (2.88)*** (1.41) (1.11) FD OLS 0.019 0.635 1 0.084 3 0.014 3 0.518 1973-2003 (1.09) (4.85)*** (1.72)* (1.1)

Level OLS -3.43♦ 0.879 1 0.149 3 -0.21 3 0.801 1971-2003 Côte d’Ivoire

(-0.83) (1.71)* (1.08) (-1.02) FD OLS -0.016 1.254 1 0.191 3 -0.22 3 0.253 1973-2003 (-0.31) (3.31)*** (1.76)* (-0.99)

Cameroon Level OLS -12.589♦ 1.365 0 0.22 2 -0.132 2 0.977 1969-2003

(-0.59) (4.11)*** (2)* (-0.7) FD OLS -0.031 1.37 0 0.217 2 -0.128 2 0.555 1969-2003 (-0.95) (5.33)*** (2.03)* (-0.69)

Level OLS 0.727 0.923 1 0.109 3 -0.086 3 0.801 1972-2003 Republic of

the Congo (1.21) (8.68)*** (0.98) (-0.82) FD OLS 0.044 0.429 1 0.079 2 0.363 2 0.176 1972-2003 (0.84) (2.59)** (0.46) (1.68)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Colombia Level OLS 1.807♦ 0.656 0 0.147 0 -0.063 0 0.987 1969-2003 (2.14)** (3.79)*** (1.88)* (-1.05) FD OLS 0.009 0.592 0 0.166 0 -0.057 0 0.421 1969-2003 (0.45) (4.51)*** (1.9)* (-0.7)

Costa Rica Level OLS -4.057♦ 0.759 1 0.136 0 0.026 0 0.974 1970-2003

(-2.65)** (4.55)*** (1.56) (1.53) FD OLS -0.001 0.698 1 0.132 0 0.026 0 0.297 1970-2003 (-0.03) (3.34)*** (1.72)* (1.63)

Level OLS 0.576 0.839 1 -0.054 3 0.024 3 0.957 1969-2003 Algeria

(1.15) (26.27)*** (-1.42) (0.24) FD OLS 0.012 0.735 1 0.008 3 0.1 3 0.54 1970-2003 (0.5) (6.02)*** (0.18) (0.58)

Ecuador Level OLS -0.119♦ 0.75 1 -0.088 0 0.231 0 0.976 1971-2003

(-0.27) (5.75)*** (-1.65) (2.28)** FD OLS -0.008 0.727 1 0.052 0 0.313 0 0.603 1971-2003 (-0.38) (6.51)*** (0.65) (2.5)**

Egypt Level OLS -0.41♦ 0.825 1 0.03 0 0.179 0 0.983 1971-2003

(-0.52) (10.62)*** (1.02) (2.07)** FD OLS 0.012 0.614 1 0.048 0 0.137 0 0.493 1971-2003 (0.55) (4.99)*** (1.64) (2.08)**



Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Ghana Level OLS 1.051 0.842 1 0.045 3 -0.108 3 0.729 1971-2003 (2.35)** (9.35)*** (1.07) (-1.43) FD OLS 0.044 0.636 1 0.017 3 -0.354 3 0.362 1972-2003 (0.95) (4.4)*** (0.2) (-1.32)

Guatemala Level OLS -13.175♦ 0.859 0 0.092 2 0.492 2 0.932 1969-2003

(-0.83) (2.14)** (1.37) (2.11)** FD OLS -0.089 0.979 0 0.159 1 0.515 1 0.474 1968-2003 (-2.27)** (2.96)*** (2.82)*** (2.68)**

Level OLS 0.674♦ 0.684 1 0.231 3 -0.03 3 0.946 1979-2003 Honduras

(0.66) (5.27)*** (2.78)** (-0.17) FD OLS 0.073 0.395 1 0.086 2 -0.669 2 0.412 1978-2003 (2.36)** (2.94)*** (1.37) (-3)***

Haiti Level OLS 0.033 0.968 1 -0.039 0 0.001 0 0.934 1967-2003

(0.18) (15.91)*** (-1.32) (0.02) FD OLS 0.012 0.545 1 0.015 0 0.096 0 0.233 1968-2003 (0.5) (3.21)*** (0.16) (0.16)

Indonesia Level OLS -6.987♦ 0.865 1 0.229 0 0.206 0 0.983 1970-2003

(-4.85)*** (8.65)*** (3.43)*** (1.37) FD OLS 0.006 0.677 1 0.154 0 0.009 0 0.334 1970-2003 (0.18) (2.99)*** (1.92)* (0.05)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

India Level OLS -11.495♦ 1.288 0 0.2 3 0.035 3 0.991 1970-2003 (-6.19)*** (8.4)*** (2.36)** (0.23) FD OLS -0.023 1.511 0 0.159 3 0.044 3 0.481 1970-2003 (-0.85) (4.79)*** (2.28)** (0.32)

Jordan Level OLS -6.329♦ 0.949 1 0.041 1 0.299 1 0.992 1971-2003

(-3.57)*** (5.16)*** (1.62) (3.89)*** FD OLS 0.017 0.985 1 0.041 1 0.357 1 0.667 1971-2003 (0.72) (5.36)*** (1.47) (5.06)***

Level OLS -2.526♦ 0.426 0 0.075 0 0.276 0 0.976 1967-2003 Kenya

(-2.56)** (2.99)*** (1.2) (2.43)** FD OLS -0.03 0.866 0 0.068 0 0.284 0 0.495 1967-2003 (-1.35) (4.21)*** (1.36) (2.31)**

Sri-Lanka Level OLS -1.7♦ 0.79 1 -0.03 0 0.396 0 0.959 1972-2003

(-1.08) (5.09)*** (-0.2) (1.46) FD OLS 0.023 0.41 1 0.038 0 0.374 0 0.184 1972-2003 (0.51) (2.42)** (0.28) (1.24)

Lesotho Level OLS -16.444♦ 3.568 1 0.06 3 -2.26 3 0.642 1972-2003

(-1.79)* (2.13)** (0.12) (-2.14)** FD OLS -0.081 3.372 1 0.217 3 -2.51 3 0.081 1972-2003 (-0.4) (1.93)* (0.44) (-2.1)**



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Morocco Level OLS -0.384♦ 0.627 1 0.045 3 -0.252 3 0.977 1972-2003 (-0.18) (3.22)*** (1.85)* (-3.39)*** FD OLS -0.023 1.568 0 0.059 0 -0.149 0 0.485 1969-2003 (-0.86) (5.68)*** (1.92)* (-1.47) Madagascar Level OLS 1.435 0.709 1 0.184 0 0.014 0 0.935 1967-2003 (3.83)*** (7.16)*** (2.58)** (-3.3)*** FD OLS -0.017 0.415 1 0.302 0 0.054 0 0.342 1968-2003 (-0.44) (2.93)*** (2.62)** (0.3)

Level OLS 7.084♦ 0.653 1 -0.107 3 -1.304 3 0.822 1978-2003 Mauritania (1.67) (3.39)*** (-0.64) (-1.67) FD OLS 0.03 0.582 1 -0.018 3 -0.829 3 0.299 1978-2003 (0.41) (3.27)*** (-0.11) (-1.51) Malaysia Level OLS -10.316♦ 1.446 0 0.142 0 0.003 0 0.994 1970-1999 (-6.52)*** (9.83)*** (2.54)** (0.38) FD OLS 0.025 1.197 0 0.133 0 0 0 0.543 1970-1999 (0.76) (4.71)*** (2.58)** (0.03) Niger Level OLS -2.321♦ 1.267 0 0.142 2 -1.168 2 0.870 1974-2003 (-0.39) (1.96)* (0.26) (-2.17)** FD OLS 0.036 1.406 0 0.678 2 -0.784 2 0.221 1975-2003 (0.48) (2.69)** (1.75)* (-1.68)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Nicaragua Level OLS 2.183♦ 0.626 1 0.108 2 -0.31 2 0.88 1974-2003 (1.96)* (2.26)** (1.72)* (-2.03)* FD OLS -0.033 0.551 1 0.157 2 -0.067 2 0.291 1974-2003 (-0.73) (3.4)*** (2.24)** (-0.38)

Nepal Level OLS -7.231♦ 0.963 0 0.107 2 -0.024 2 0.925 1969-2003

(-2.19)** (1.71)* (0.44) (-0.06) FD OLS -0.025 2.722 0 0.107 2 -0.577 2 0.079 1969-2003 (-0.34) (2.3)** (0.38) (-1.34)

Level OLS -7.933♦ 1.089 0 0.054 0 0.108 0 0.985 1967-2003 Pakistan

(-2.97)*** (4.34)*** (1.29) (0.84) FD OLS -0.006 1.013 0 0.049 0 0.109 0 0.308 1967-2003 (-0.21) (3.61)*** (1.25) (0.88)

Peru Level OLS 2.25 0.707 1 0.21 3 -0.262 3 0.837 1969-2003

(3.24)*** (7.07)*** (2.31)** (-2.14)** FD OLS 0.007 0.543 1 0.308 3 -0.295 3 0.349 1970-2003 (0.24) (3.67)*** (2.51)** (-1.19)

Paraguay Level OLS -0.769 0.792 1 0.02 0 0.302 0 0.971 1971-2003

(-1.73)* (10.71)*** (0.27) (2.1)** FD OLS 0.017 0.691 1 0.051 0 0.052 0 0.496 1972-2003 (0.73) (4.89)*** (0.79) (0.33)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Rwanda Level OLS -13.683♦ 1.629 0 1.071 0 -0.161 0 0.958 1967-2003 (-3.04)*** (4.08)*** (4.16)*** (-0.43) FD OLS -0.059 1.298 0 1.014 0 -0.421 0 0.448 1967-2003 (-1.03) (3.75)*** (3.85)*** (-1.24)

Sudan Level OLS -4.642♦ 0.772 1 0.137 0 -0.015 0 0.853 1975-2003

(-2.71)** (3.85)*** (1.6) (-0.11) FD OLS -0.024 0.763 1 0.132 0 -0.021 0 0.499 1975-2003 (-1.01) (4.06)*** (1.77)* (-0.17)

Level OLS 1.270♦ 0.884 1 0.092 1 -0.186 1 0.972 1971-2003 Senegal

(2.73)** (4.57)*** (2.08)** (-1.64) FD OLS 0.022 0.518 1 0.114 1 -0.15 1 0.319 1971-2003 (1.24) (3.2)*** (2.65)** (-1.31)

Tchad Level OLS 1.672 0.72 1 0.299 3 -0.437 3 0.742 1969-2003

(1.02) (5.75)*** (1.36) (-1.15) FD OLS -0.041 0.357 1 0.403 0 0.359 0 0.195 1968-2003 (-0.53) (2.3)** (0.91) (0.71)

Togo Level OLS 1.289 0.937 1 0.101 3 -0.225 3 0.81 1969-2003

(1.18) (10.37)*** (0.81) (-1.03) FD OLS 0.042 0.579 1 0.037 3 -0.117 3 0.258 1970-2003 (0.57) (3.75)*** (0.27) (-0.3)



Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Level OLS 0.191 0.931 1 0.354 2 -0.031 2 0.979 1969-1996 Trinidad and Tobago (3.17)* (34.37)* (0..61) (-0.88)

FD OLS 0.047 0.351 1 -0.035 3 -0.137 3 0.347 1971-1996 (2.20)** (2.1)** (-0.03) (-3.5)***

Tunisia Level OLS -6.279♦ 1.072 1 0.086 0 -0.001 0 0.993 1968-2003

(-3.34)*** (5.46)*** (2.67)** (-0.01) FD OLS -0.009 1.331 1 0.083 0 0.01 0 0.54 1968-2003 (-0.34) (5.45)*** (2.72)** (0.11)

Level OLS -0.324♦ 0.64 0 0.029 0 -0.04 0 0.986 1970-2003 Turkey (-0.1) (2.85)*** (0.75) (-1.08)

FD OLS 0.062 0.682 0 0.025 0 -0.039 0 0.201 1970-2003 (2.29)** (3.2)*** (0.65) (-1.04)

Uganda Level OLS 1.291 0.487 1 0.108 0 -0.119 0 0.539 1973-2003

(3.1)*** (3.25)*** (2.44)** (-2.13)** FD OLS -0.004 0.305 1 0.143 0 -0.093 0 0.082 1974-2003 (-0.08) (1.68)* (1.74)* (-0.38)

Level OLS 0.261♦ 0.76 1 -0.146 1 0.087 1 0.921 1968-2003

(0.3) (3.37)*** (-1.47) (0.63) Democratic Republic of the Congo

FD OLS 0.021 0.578 1 0.055 2 -0.014 2 0.264 1969-2003 (0.37) (3.89)*** (0.31) (-0.06)



Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Zambia Level OLS -3.644♦ 1.1 1 0.248 3 -0.541 3 0.905 1973-2003 (-1.52) (3.28)*** (2.87)*** (-2.71)** FD OLS 0.001 1.099 1 0.245 3 -0.463 3 0.303 1973-2003 (0.03) (3.51)*** (2.96)*** (-2.64)**

Zimbabwe Level OLS -0.423♦ 0.277 1 0.115 0 0.426 0 0.966 1975-2003 (-0.78) (2.35)** (1.72)* (3.41)*** FD OLS -0.029 0.435 1 0.143 0 0.252 0 0.552 1975-2003 (-0.69) (3.14)*** (1.55) (2.1)**

Note: The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively.

♦ indicates that a first-order autocorrelation has been applied and the associated AR(1) coefficient is at least significant at 10 percent.

102

The estimation of the elasticities of Swiss exports with respect to Swiss aid and the other DAC members’ aid allows us to distinguish 6 different groups (see Table 6.6).

Table 6.6: Elasticities of exports with respect to aid from other DAC countries

Positive Swiss aid coefficient Null Swiss aid coefficient

Positive rest of the world aid elasticity

coefficient

Null rest of the world aid elasticity

coefficient

Negative rest of the world aid

elasticity coefficient

Positive rest of the world aid elasticity

coefficient

Null rest of the world aid elasticity

coefficient

Negative rest of the world aid

elasticity coefficient

− Guatemala − Benin − Burkina Faso − Ecuador − Bengladesh − Burundi − Bolivia − Zambia − Egypt − Honduras − Brazil − Jordan

− Republic of the Congo − Lesotho

− Chile − Kenya − Algeria − Côte d'Ivoire − Zimbabwe − Ghana

− Trinidad and Tobago

− Cameroon − Haiti − Colombia − Sri-Lanka − Costa Rica − Mauritania − Indonesia − Nepal − India − Pakistan − Morocco − Paraguay − Madagascar − Tchad − Malaysia − Togo − Niger − Turkey − Nicaragua − Peru − Rwanda

− Democratic Republic of Congo

− Sudan − Senegal − Tunisia − Uganda

According to Table 6.6, the aid received from other DAC members has no significant impact on Swiss exports for a majority of recipient countries. More precisely, when the coefficient associated with Swiss aid is positive and significant, the coefficient of the aid from the rest of DAC donors is non-significant in almost all cases; only one case of complementarity and two cases of substitutability of Swiss and other DAC countries’ aid are detected. When the elasticity of exports with respect to Swiss aid is non-significant, the impact of aid from other countries is still mostly non-significant; in five cases, however, Swiss exports seem to benefit from other donors’ aid with roughly the same number of cases showing a deleterious effect. The fact that in an overwhelming number of cases Swiss exports do not benefit from other donors’ aid could be related to the fact that on average the other countries’ aid has been more tied during most of the period under observation (see Figure 6.4).

103

Figure 6.4: Swiss tied aid vs. rest of DAC members’ tied aid

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1979 1984 1989 1994 1999 2004

Year

Tied

Aid

/ To

tal A

id

b

SwitzerlandRest of DAC members

Finally, following Arvin et al. (1996), we compute the long-term effect of aid on exports. This is possible only for the recipient countries with a specification incorporating lagged exports and whose aid variable is significantly different from zero. Table 6.7 reports the associated long-run elasticities as compared to short-run elasticities. The results suggest that for some recipient countries the lingering effect of Swiss aid on exports tends to be more than twice as big as the short-run elasticity. This is the case for most Latin American countries as well as Burkina Faso and Senegal.

104

Table 6.7: Short-run vs. long-run elasticities of exports with respect to aid

Sample Estimation Short-Run

Swiss Net ODA Long-Run Swiss

Net ODA

Short-Run Rest of DAC's Net

ODA

Long-Run Rest of DAC's Net

ODA Burkina Faso Level OLS 0.349 0.695 -0.741 -1.476 FD OLS 0.364 0.835 -0.719 -1.649 Bolivia Level OLS 0.267 1.867 -0.394 -2.755 FD OLS 0.328 0.693 0.095 0.201 Brazil Level OLS 0.31 1.023 FD OLS 0.234 0.722 Chile FD OLS 0.084 0.230 Colombia Level OLS 0.147 0.427 FD OLS 0.166 0.407 Ecuador Level OLS 0.231 0.231 FD OLS 0.313 1.252 Egypt Level OLS 0.179 1.023 FD OLS 0.137 0.355 Honduras Level OLS 0.231 0.731 FD OLS -0.669 -1.106 Madagascar Level OLS 0.184 0.632 0.014 0.048 FD OLS 0.302 0.516 Nicaragua Level OLS 0.108 0.289 -0.310 -0.829 FD OLS 0.157 0.350 Peru Level OLS 0.21 0.717 -0.262 -0.894 FD OLS 0.308 0.674 -0.295 -0.646 Senegal Level OLS 0.092 0.793 FD OLS 0.114 0.237 -0.150 -0.311 Uganda Level OLS 0.108 0.211 -0.119 -0.232 FD OLS 0.143 0.206 -0.093 -0.134 Zimbabwe Level OLS 0.115 0.159 0.426 0.589 FD OLS 0.252 0.446 Note: The long-run elasticity of exports with respect to Swiss aid and aid from other DAC members is computed

respectively as follows: ( )3 11 1β β⋅ − , ( )4 11 1β β⋅ − .

105

6.7 Robustness Checks Robustness checks are done in three ways. First, we re-estimate the full-sample equation using Swiss gross ODA rather than net ODA. Second, we consider two sub-periods to make sure there is non fundamental structural change during the period. Third, we re-estimate the model with variables expressed in real instead of nominal terms (for recipient countries whose real GNI was available). Table 6.8 displays the results based on Swiss gross ODA. These results confirm to a large extent those found previously with net ODA. The lag structure is the same for time-series level and first-difference OLS using net ODA. It is, however, different for the panel estimation (current values instead of one-year lagged). As for the impact of aid on exports, it turns out to be larger with gross ODA than with net ODA. This can be explained by the fact that gross ODA does not include any loans reimbursement, implying a smaller budget constraint. Note that the adjusted R2 associated with gross ODA is quite close to the one obtained with net ODA, confirming the strong overall fit of the structural model. In order to check for time-consistency in our estimations, we consider two sub-periods 1966-1991 and 1992-2003. In 1991, the OECD recommended its members to untie their ODA arguing that tied aid would distort the market mechanism by forcing the recipient countries to purchase rather expensive goods from donor countries. The push for tighter rules of international organisations, such as OECD, IMF and World Bank to make this form of export subsidy more costly to government has lowered the extent of tying over time. Following this recommendation, DAC members agreed to virtually untie all aid to the Least-Developed Countries in 2001. However, most members including Switzerland have untied their aid beyond the requirement of the recommendation.9 Other things being equal, the sub-period 1966-1991 should be characterized by more tied aid than the ensuing period. Assuming that the impact of tied aid on exports is stronger than that of untied aid, we should expect a stronger magnitude for the coefficient of the Swiss aid variable during 1966-1991 than during 1992-2003. We apply the Chow test in order to test for the presence of a structural break (Gujarati (1970)). The null hypothesis is that there is no structural break. Interestingly, the results presented in Table 6.9 suggest the opposite for most specifications, except for the time-series level OLS estimation. The effect of aid on exports turns out to be much stronger in 1992-2003 than in 1966-1991. Moreover, the optimal lag structure is different between sub-periods. This result is confirmed by the Chow test which rejects in the pooled and panel settings the null hypothesis of the absence of a structural break.

9 Australia, Finland, France, Germany, Ireland, Japan, the Netherlands, Norway, Portugal, Sweden, United

Kingdom and Switzerland.

106

Another interesting result is the change of sign, from one sub-period to another, of the impact of aid from other DAC members. For instance, in the fixed-effect panel, the elasticity is positive and significant in the second sub-period, which is consistent with the use of more untied aid. In the time-series level setting, today’s exports are determined by 3-year lagged aid for 1966-1991 while they are determined by current year’s aid for 1992-2003. This could be explained by the fact that untied aid allows the recipient to use the available funds to purchase the most appropriate goods at the best price it wants. The economic growth in the recipient country is then more stimulated, and the country can absorb more imports. Table 6.10 displays the results at the recipient country level when all variables are expressed in real terms. For most countries which have a positive and significant effect of Swiss ODA in the nominal-value specification, the real-value equation yields basically the same result. For these countries, the lag structure remains the same in nominal and real specifications. However, the effect of ODA is usually stronger in real terms than in current terms. Six recipient countries (Cameroon, Colombia, Costa Rica, Morocco, Niger and Uganda) have a positive and significant effect for Swiss net ODA in current terms, but this effect is no longer significant in real terms. On the other hand, four countries (Bangladesh, Egypt, Mauritania as well as Zimbabwe) which did not show a significant effect in current terms display a positive and significant effect in real terms. According to the adjusted R2, the relative predictive power of the structural model in first-difference is quite high for most recipient countries. The R2 lies between 0.51 and 0.88 for 22 countries. In only 5 countries, it falls below 0.4. Overall, the structural model seems appropriate for recipient countries at the individual level.

Table 6.8: Full-sample regression results with gross ODA 1966-2003


Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Gross ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Cross-Section -6.818 1.014 - -0.068 - 0.071 - 0.849 1966-2003 (-11.43)*** (18.91)*** (-0.78) (0.57)

-4.67♦ 0.990 0 0.257 3 -0.286 3 0.997 1970-2003 Time-Series Level OLS (-3.74)*** (8.80)*** (2.24)** (-1.82)*

0.000 0.984 0 0.255 3 -0.244 3 0.559 1970-2003 Time-Series FD OLS (0.98) (5.02)*** (2.63)** (-1.71)* Pooled OLS -1.716♦ 0.571 1 0.048 0 0.009 0 0.982 1966-2003 (-2.81)** (10.07)*** (2.81)*** (0.98) FE Panel -2.619♦ 0.583 0 0.047 0 0.003 0 0.813 1966-2003 (-41.70)*** (11.71)*** (3.08)*** (0.25) FD Panel 0.019 0.714 0 0.055 0 0.011 0 0.079 1996-2003 (2.83)*** (12.79)*** (3.83)*** (1.12) Note: The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively.

♦ indicates that a first-order autocorrelation correction has been applied and the associated AR(1) coefficient is at least significant at 10 percent.

Table 6.9: Full-sample regression results 1966-1991 and 1992-2003


Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Chow Test

-2.594♦ 1.014 0 0.458 3 -0.632 3 0.994 1966-1991 7.06 Time-Series Level OLS (-1.04) (4.68)*** (2.41)** (-2.34)** [0.22] -10.492 0.936 0 0.429 0 0.24 0 0.99 1992-2003 (-7.95)*** (28.32)*** (3.22)** (1.24)

-0.021 1.138 0 0.46 3 -0.59 3 0.487 1966-1991 4.29 Time-Series FD OLS (-0.53) (3.92)*** (2.80)** (-2.58)** [0.37] 0.007 0.866 0 0.476 0 0.047 0 0.892 1992-2003 (0.56) (6.45)*** (2.40)** (0.20) Pooled OLS -4.887♦ 0.835 0 0.041 0 0.063 0 0.981 1966-1991 22.43 (-4.89)*** (12.69)*** (2.07)** (2.14)*** [0.00] 0.605♦ 0.484 1 0.057 0 0.001 0 0.985 1992-2003 (0.31) (5.84)*** (2.62)*** (0.15) FE Panel -3.005♦ 0.64 0 0.038 0 -0.002 0 0.814 1966-1991 1.60 (-20.79)*** (7.88)*** (1.74)*** (-0.19) [0.17] -4.825♦ 0.788 0 0.044 0 0.076 0 0.794 1992-2003 (-50.08)*** (11.48)*** (2.05)** (2.50)** FD Panel 0.026 0.433 1 0.051 0 -0.001 0 0.029 1966-1991 2.10 (2.99)* (5.30)*** (2.34)** (-0.06) [0.04] 0.026 0.758 0 0.061 0 0.035 0 0.076 1992-2003 (2.45)** (9.85)*** (1.59)*** (1.59)

Note: The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively. ♦ indicates that a first-order autocorrelation correction has been applied and the associated AR(1) coefficient is at least significant at 10 percent. The value in brackets for the Chow test is the probability of not rejecting the null hypothesis associated with the F-statistic that there is no structural break in 1992 (dummy variable test).

Table 6.10: Recipient countries regression results in real terms 1966-2003


Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Algeria Level OLS -0.618 0.912 1 -0.013 3 0.184 3 0.948 1970-2002 (-1.18) (15.18)*** (-0.26) (3.06)*** FD OLS 0.008 0.928 1 0.033 3 0.131 3 0.76 1971-2002 (0.5) (8.31)*** (0.67) (1.02)

Bengladesh Level OLS 3.195♦ 0.738 1 0.096 1 -0.361 1 0.986 1976-2002

(1.64) (3.9)*** (0.95) (-1.72)* FD OLS -0.004 0.857 1 0.2 1 -0.542 1 0.417 1976-2002 (-0.19) (4.6)*** (1.98)* (-2)*

Level OLS 1.52♦ 0.839 1 -0.046 3 -0.202 3 0.984 1971-2002 Benin (0.67) (3.48)*** (-0.41) (-0.65)

FD OLS 0.021 0.857 1 -0.03 3 -0.268 3 0.76 1971-2002

(0.67) (8.37)*** (-0.29) (-0.86) Bolivia Level OLS 0.287♦ 0.833 1 0.376 1 -0.151 1 0.917 1971-2002

(0.14) (5.1)*** (2.62)** (-0.47) FD OLS -0.031 0.774 1 0.332 1 0.062 1 0.608 1971-2002 (-1.2) (6.81)*** (2.8)*** (0.23)

Brazil Level OLS 1.053♦ 0.822 1 0.107 2 -0.008 2 0.984 1970-2002

(0.73) (3.57)*** (2.06)** (-1.32) FD OLS 0.013 0.751 1 0.102 2 -0.008 2 0.617 1970-2002 (0.89) (6.8)*** (2.19)** (-1.29)

Table 6.10: Recipient countries regression results in real terms 1966-2003 (continued)


Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Burkina Faso Level OLS 5.59♦ 0.558 1 0.351 1 -1.086 1 0.975 1969-2002 (2.83)*** (4.5)*** (3.58)*** (-3.09)***

FD OLS 0.014 0.648 1 0.412 1 -0.96 1 0.674 1969-2002 (0.57) (6.14)*** (3.79)*** (-2.86)***

Burundi Level OLS -4.545♦ 0.959 1 0.016 2 -0.499 2 0.942 1970-2001

(-2.44)** (2.31)** (0.14) (-1.41)

FD OLS 0.081 1.253 1 0.037 2 -0.538 2 0.247 1970-2001 (2.34)** (3.48)*** (0.33) (-1.69)

Level OLS -9.95♦ 1.247 0 0.119 2 0.149 2 0.968 1970-2002 Cameroon (-2.45)** (3.58)*** (0.82) (0.47)

FD OLS 0.037 1.355 0 0.139 2 0.298 2 0.399 1970-2002 (1.28) (4.02)*** (0.93) (0.92)

Chad Level OLS -2.838♦ 0.55 0 0.67 0 0.298 0 0.872 1976-2002

(-2.06)* (3.31)* (1.94)* (0.99) FD OLS -0.031 0.481 1 0.572 0 0.506 0 0.562 1976-2002 (-0.63) (3.31)* (1.26) (1.17)

Chile Level OLS 1.059♦ 0.745 1 0.126 3 0.015 3 0.989 1974-2002

(1.65) (5.34)*** (2.21)** (2.35)** FD OLS 0.016 0.721 1 0.156 3 0.013 3 0.673 1974-2002 (1.27) (6.83)*** (2.79)*** (2.01)*



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Colombia Level OLS 1.252♦ 0.766 1 0.097 2 -0.041 2 0.981 1971-2002 (1.02) (3.11)*** (0.87) (-0.54) FD OLS 0.007 0.718 1 0.077 2 -0.06 2 0.531 1971-2002 (0.42) (4.92)*** (0.67) (-0.67)

Costa Rica Level OLS 561.251♦ 0.564 0 0.068 0 0.017 0 0.979 1970-2002 (0.01) (4.96)*** (0.83) (0.36)

FD OLS 0.108 0.565 0 0.068 0 0.017 0 0.453 1970-2002 (6.18)*** (5.1)*** (0.88) (0.39)

Level OLS -0.826♦ 0.916 1 0.073 3 0.159 3 0.98 1972-2002 (-2.72)** (8.38)*** (0.98) (2.12)**

Democratic Republic of the Congo

FD OLS -0.038 0.55 1 0.11 3 0.095 3 0.877 1972-2002 (-2.67)** (14.62)*** (1.54) (1.1)

Level OLS 0.306♦ 0.728 1 0.02 3 0.138 3 0.848 1976-2002 Ecuador

(0.28) (2.06)* (0.16) (0.76) FD OLS -0.008 0.746 1 0.084 3 0.201 3 0.647 1976-2002 (-0.47) (4.44)*** (0.67) (1.15)

Level OLS -545.614♦ 0.269 1 0.104 0 0.262 0 0.979 1971-2002 Egypt

(0) (1.8)* (2.15)** (3.46)*** FD OLS 0.055 0.269 1 0.104 0 0.261 0 0.466 1971-2002 (3.15)*** (1.85)* (2.2)** (4.2)***



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Ghana

Level OLS 2.251♦ 0.747 1 0.031 3 -0.269 3 0.885 1973-2002

(1.07) (1.89)* (0.4) (-1.16) FD OLS 0.01 0.752 1 0.044 3 -0.376 3 0.603 1973-2002 (0.34) (6.26)*** (0.66) (-1.46)

Guatemala Level OLS -4.7♦ 0.579 0 0.376 1 0.313 1 0.95 1969-2002

(-2.21)** (3.98)*** (6.33)*** (2.14)** FD OLS 0.012 0.517 0 0.322 1 0.262 1 0.507 1969-2002 (0.59) (2.17)** (5.64)*** (1.54)

Level OLS -0.096 0.856 1 -0.099 1 0.050 1 0.963 1969-2002 Haiti

(-0.29) (4.60)* (-1.62) (0.72) FD OLS -0.009 0.683 1 -0.036 3 0.118 3 0.532 1971-2002 (-0.60) (5.31)*** (-0.52) (1.49)

Honduras Level OLS 0.352♦ 0.87 1 0.179 3 -0.054 3 0.952 1980-2002 (0.32) (6.24)*** (1.68) (-0.3)

FD OLS 0.002 0.577 1 0.103 3 -0.106 3 0.324 1980-2002 (0.07) (3.03)*** (1.72) (-0.72)

India Level OLS -6.368♦ 0.89 0 0.38 3 0.108 3 0.986 1973-2002

(-1.39) (2.71)** (3.3)*** (0.42) FD OLS 0.092 1.106 0 0.421 3 -0.115 3 0.422 1973-2002 (4.38)*** (3.13)*** (3.42)*** (-0.44)



Exports Lag (t-x)

Recipient'GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Indonesia Level OLS -1.76♦ 0.565 1 0.059 1 0.232 1 0.978 1971-2002 (-0.44) (2.7)** (0.65) (0.98) FD OLS 0.085 0.498 1 0.159 0 -0.131 0 0.33 1971-2002 (3.62)*** (2.67)** (2.14)** (-0.6)

Jordan Level OLS -3.178♦ 0.852 0 0.044 3 0.037 3 0.932 1979-2002

(-1.22) (5.09)*** (1.37) (0.35) FD OLS 0.041 0.851 0 0.046 3 0.018 3 0.564 1979-2002 (2.61)** (5.3)*** (1.52) (0.18)

Level OLS -0.227♦ 0.473 1 0.047 3 -0.172 3 0.916 1971-2002 Kenya

(-0.08) (1.85)* (0.36) (-0.67) FD OLS 0.06 0.955 0 0.042 3 0.238 3 0.473 1971-2002 (3.26)*** (4.83)*** (0.71) (1.15)

Lesotho Level OLS 2.169 0.895 1 -0.043 2 -0.537 2 0.738 1971-2002

(1.43) (9.23)*** (-0.26) (-1.6) FD OLS 0.022 0.675 1 0.197 2 -0.89 2 0.332 1972-2002 (0.19) (4.2)*** (0.42) (-0.82)

Madagascar Level OLS -2.39♦ 0.468 1 0.359 1 -0.207 1 0.941 1969-2002 (-1.29) (2.17)** (2.33)** (-1.43) FD OLS 0.032 0.541 1 0.401 1 -0.199 1 0.449 1969-2002 (1.11) (2.82)*** (3.72)*** (-1.38)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Malaysia Level OLS -20.276♦ 2.322 0 0.223 0 0.014 0 0.996 1971-1998 (-10.1)*** (12.71)*** (3.75)*** (2.18)** FD OLS 0.031 1.656 0 0.187 0 0.006 0 0.729 1971-1998 (1.6) (7.48)*** (3.48)*** (1.03) Mauritania Level OLS -16.814♦ 3.115 0 0.332 2 -0.819 2 0.928 1978-1997 (-3.4)*** (3.97)*** (3.37)*** (-1.96)* FD OLS 0.094 3.089 0 0.337 2 -0.658 2 0.47 1978-1997 (2.05)* (4.07)*** (3.53)*** (-2.17)**

Level OLS -8.217♦ 1.134 0 0.028 0 0.228 0 0.97 1975-2002 Morocco (-3.81)*** (6.56)*** (0.9) (1.4)

FD OLS 0.032 1.142 0 0.025 0 0.271 0 0.631 1975-2002 (2.9)*** (6.75)*** (0.83) (1.73)*

Niger Level OLS 5.537♦ 0.906 1 -0.179 0 -0.913 0 0.953 1974-2002 (3.37)*** (6.98)*** (-0.81) (-3.22)*** FD OLS 0.018 0.797 1 0.32 0 -0.959 0 0.649 1974-2002 (0.41) (7.05)*** (1.03) (-2.47)** Pakistan Level OLS -6.88♦ 1.296 0 0.012 3 0.16 3 0.986 1971-2002 (-1.47) (4.84)*** (0.26) (1.19) FD OLS 0.102 1.341 0 0.033 3 0.131 3 0.44 1971-2002 (5.18)*** (4.99)*** (0.73) (1.01)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Paraguay Level OLS -0.599♦ 0.666 1 -0.087 3 0.333 3 0.982 1975-2002 (-0.81) (3.15)*** (-1.05) (2.48)** FD OLS -0.006 0.832 1 -0.037 3 0.386 3 0.76 1975-2002 (-0.37) (6.98)*** (-0.48) (2.85)***

Peru Level OLS 2.986♦ 0.604 1 0.307 3 -0.34 3 0.763 1971-2002

(2.82)*** (2.59)** (2.49)** (-2.57)** FD OLS -0.001 0.602 1 0.376 3 -0.337 3 0.531 1971-2002 (-0.06) (4.69)*** (3.17)*** (-1.95)*

Level OLS 0.229 0.929 1 0.1 2 0.007 2 0.822 1972-2002 Republic of

the Congo (0.45) (11)*** (0.74) (0.06) FD OLS 0.771 1 -0.038 3 -0.295 3 0.46 1974-2002 (4.97)*** (-0.24) (-1.35)

Rwanda Level OLS -9.825♦ 1.272 0 0.738 0 -0.251 0 0.972 1968-1999 (-2.46)** (3.55)*** (2.4)** (-0.57)

FD OLS 0.044 1.367 0 0.759 0 -0.18 0 0.547 1968-1999 (1.3) (5.06)*** (2.49)** (-0.47)

Senegal Level OLS 1.823♦ 0.666 1 0.081 1 -0.195 1 0.972 1972-2002

(2.23)** (3.37)*** (1.91)* (-1.82)* FD OLS 0.012 0.625 1 0.103 1 -0.156 1 0.528 1972-2002 (1.08) (4.84)*** (2.75)** (-1.44)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Sri-Lanka Level OLS -1.064♦ 0.594 1 -0.027 0 0.475 0 0.971 1972-2002 (-0.49) (3.17)*** (-0.2) (1.24)

FD OLS 0.034 0.559 1 -0.032 0 0.575 0 0.411 1972-2002 (1.13) (3.73)*** (-0.24) (1.66)

Togo Level OLS 3.473 0.982 1 0.174 3 -0.697 3 0.837 1970-2002

(2.57)** (12.41)*** (1.35) (-2.49)** FD OLS 0.024 0.77 1 0.063 3 -0.446 3 0.514 1971-2002 (0.47) (5.84)*** (0.42) (-0.99)

Level OLS 0.166♦ 0.928 1 -0.017 2 -0.011 2 0.979 1971-1992 Trinidad and

Tobago (0.92) (7.15)*** (-0.23) (-0.33) FD OLS 0.027 0.685 1 0.008 2 -0.001 2 0.46 1971-1992 (1.46) (2.8)** (0.08) (-0.02)

Tunisia

Level OLS -7.075♦ 1.285 0 0.093 0 -0.07 0 0.996 1968-2002

(-3.91)*** (7.39)*** (2.16)** (-1.04) FD OLS 0.072 1.441 0 0.088 0 -0.058 0 0.691 1968-2002 (6.46)*** (8.14)*** (1.87)* (-0.79)

Uganda Level OLS 0.959 0.631 1 0.075 0 -0.095 0 0.719 1975-2002

(2.49)** (4.29)*** (1.85)* (-2.11)** FD OLS -0.005 0.387 1 0.116 0 -0.004 0 0.146 1976-2002 (-0.15) (2.04)* (1.34) (-0.02)



Exports Lag (t-x)

RecipientGNI

Lag (t-x)

Swiss Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Level OLS 0.531♦ 0.844 1 0.077 3 -0.02 3 0.978 1974-2002 Zambia (0.37) (2.73)** (1.49) (-0.09)

FD OLS 0.005 0.893 1 0.09 3 -0.096 3 0.617 1974-2002 (0.23) (5.64)*** (1.82)* (-0.49)

Level OLS 4.222♦ 0.295 1 0.217 1 -0.791 1 0.859 1980-2002 (2.06)* (2.66)** (2.34)** (-1.89)*

Zimbabwe

FD OLS 0.115 0.634 1 0.157 0 0.333 0 0.621 1980-2002 (2.16)** (2.54)** (1.97)* (1.56)

Note: The asterisks *, ** and *** indicate that the coefficient is significant at the 10, 5 and 1 percent level respectively. ♦ indicates that a first-order autocorrelation has been applied and the associated AR(1) coefficient is at least significant at 10 percent.

118

6.8 Comparison with Germany Table 6.11 compares some of the country-level results for Switzerland with those for Germany reported by Vogler-Ludwig et al. (1999) for the period 1976-1995 based on a similar model. Both countries share a lot of common features, although German aid is more tied than Swiss aid (see Figure 6.5).

Figure 6.5: Swiss tied aid vs. German tied aid

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1979 1984 1989 1994 1999 2004

Year

Tied

aid

/ To

tal a

id

.

SwitzerlandGermany

This could at least partly explain why the elasticity of German exports with respect to aid is usually greater than the Swiss one. More fundamentally, given the more diversified nature of the German economy, its exports benefit more from aid compared to Switzerland. Interestingly, when German aid to a given recipient country has a negative impact, the effect is in most cases non-significant for Swiss exports.

Table 6.11: Comparison between Switzerland and Germany

Donor Recipient Donor

Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Donor Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Switzerland Full Sample 0.968 0 0.348 3 -0.424 3 0.997 1966-2003 (8.40) (2.26) (-2.08)

Germany Full sample 0.811 0 0.366 0 - - 0.93 1976-1995

(4.55) (2.34)

Switzerland Bengladesh 0.661 1 - - -0.598 1 0.974 1976-2003 (3.18) (-2.42)

Germany Bengladesh 1.155 3 0.316 3 - - 0.882 1976-1995

(10.08) (2.99)

Bolivia 0.857 1 0.267 1 -0.394 1 0.744 1972-2003 Switzerland (9.59) (2.28) (-2.48)

Germany Bolivia 0.288 3 0.805 0 -0.544 0 0.931 1976-1995

(3.29) (8.61) (-4.27)

Switzerland Brazil 0.697 1 0.31 2 - - 0.986 1969-2003 (4.98) (2.6))

Germany Brazil 1.508 0 -0.282 3 - - 0.907 1976-1995

(11.74) (-2.66) Switzerland Chile 0.722 1 - - - - 0.987 1973-2003 (2.88)

Germany Chile 0.491 0 0.334 2 - - 0.949 1976-1995

(6.09) (5.34)

Table 6.11: Comparison between Switzerland and Germany (continued)


Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Donor Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Switzerland Colombia 0.656 0 0.147 0 - - 0.987 1969-2003 (3.79) (1.88)

Germany Colombia 0.288 3 -0.223 2 0.538 2 0.951 1976-1995

(2.4) (3.14) (7.98)

Switzerland Costa Rica 0.759 1 - - - - 0.974 1970-2003 (4.55)

Germany Costa Rica 0.689 1 0.202 3 0.089 3 0.927 1976-1995

(5.52) (3.39) (2.51)

0.76 1 - - - - 0.921 1968-2003 Switzerland

Dem. Rep of Congo (3.37)

Germany 0.187 0 - - 0.383 0 0.93 1976-1995

Dem. Rep of Congo (3.09) (2.34)

Switzerland Egypt 0.825 1 - - 0.179 0 0.983 1971-2003 (10.6) (2.07)

Germany Egypt 0.609 0 -0.235 0 0.247 0 0.894 1976-1995

(6.21) (-3.48) (2.31) Switzerland Ghana 0.842 1 - - - - 0.729 1971-2003 (9.35)

Germany Ghana 0.441 2 -0.539 1 0.621 1 0.8 1976-1995

(4.09) (-5.03) (6.59)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Donor Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Switzerland Guatemala 0.859 0 - - 0.492 2 0.932 1969-2003 (2.14) (2.11)

Germany Guatemala 0.693 0 -0.344 3 0.436 3 0.71 1976-1995

(2.4) (-2.19) (3.51)

Switzerland India 1.288 0 0.2 3 - - 0.991 1970-2003 (8.4)) (2.36)

Germany India 0.676 0 0.715 3 -0.436 3 0.91 1976-1995

(2.42) (3.52) (-2.14)

Indonesia 0.865 1 0.229 0 - - 0.983 1970-2003 Switzerland (8.65) (3.43)

Germany Indonesia 0.753 0 - - 0.521 1 0.943 1976-1995

(4.56) (5.26)

Switzerland Kenya 0.426 0 - - 0.276 0 0.976 1967-2003 (2.99) (2.43)

Germany Kenya 0.645 0 0.335 0 -0.291 0 0.524 1976-1995

(2.63) (2.23) (-2.44) Switzerland Morocco 0.627 1 0.045 3 -0.252 3 0.977 1972-2003 (3.22) (1.85) (-3.39)

Germany Morocco 1.343 0 0.252 0 - - 0.894 1976-1995

(2.37) (7.4)



Exports Lag (t-x)

Recipient GNI

Lag (t-x)

Donor Net ODA

Lag (t-x)

Rest of DAC's Net

ODA Lag (t-x)

Adjusted R2

Time Period

Switzerland Nepal 0.963 0 - - - - 0.925 1969-2003 (1.71)

Germany Nepal 0.527 0 0.748 0 - - 0.856 1976-1995

(2.37) (7.4)

Switzerland Pakistan 1.089 0 - - - - 0.985 1967-2003 (4.34)

Germany Pakistan 0.735 0 0.444 1 0.257 1 0.971 1976-1995

(8.53) (5.89) (2.3)

Tunisia 1.072 1 0.086 0 - - 0.993 1968-2003 Switzerland (5.46) (2.67)

Germany Tunisia 0.569 3 - - -0.112 3 0.889 3 0.651 1976-1995

(5.17) (-2.22) (6.63)

Note: OLS results for both donor countries. The sign “-“ indicates that the coefficient is not significant. Source: present study for Switzerland and Vogler-Ludwig et al. (1999) for Germany.

123

7. CONCLUSION In this study we investigated the relationship between Swiss bilateral official development assistance (ODA) and Swiss exports to recipient countries by applying various methodologies, namely stylised facts, time-series analysis and estimation of a structural model. Given that these methodologies all have their advantages and shortcomings, the approach pursued in this study is deliberately cautious and tries to draw conclusions based on a wide array of empirical evidence. The data used cover a large number of developing countries having received aid from Switzerland over a certain number of years, sometimes since the 1960s. The main results are summarised below. At the conceptual level, one can establish a clear link between bilateral ODA and exports. Aid, whether tied or untied, tends to have a positive impact on exports both in the short run and in the long run. However, the relationship between the two variables is far from being straightforward and one cannot exclude a (positive or negative) causality running in the opposite direction, namely from exports to aid. Also, the significance and the magnitude of the impact cannot be inferred from the theory. As a consequence, the issue can only be settled on empirical grounds. One needs to elaborate and estimate a theoretically-founded econometric model in order to test the significance and measure the magnitude of the presumed relationship between aid and exports. One should, however, investigate beforehand the exact nature of the causality between the two variables to make sure that the direction of causality corresponds to the one underlying the econometric model being estimated. The empirical literature on the subject uses either time-series analysis or structural econometrics to investigate the relationship between ODA and exports, but rarely both approaches within the same study. In this sense, our conclusions appear more robust than those established by the majority of the studies found in the literature. Although no unique pattern can be detected in these studies, most of them show a significant and strong effect of aid on exports. The results can however be quite different from one donor country to another and – for the same donor – across recipient countries. For Switzerland, simple graphical representations of raw as well as differenced data on net ODA and exports to recipient countries show a positive correlation between the two variables. More precisely, Swiss aid and Swiss exports seem to be positively – and rather strongly – related when the data are represented in time-series and panel settings. However, no clear pattern emerges with aggregate cross-section data. Of course, simple correlations are hardly a proof of a significant economic relationship between the two variables. The existence and the magnitude of such a link can only be established by means of regression analysis. This can be done first via time-series techniques which have the advantage of imposing little theoretical constraints on the data. According to the time-series analysis conducted in this study, on the whole, the general evidence is in favour of unidirectional causality between ODA and exports but no global generalization can be made concerning the direction of the causal relationship. The results from bi-variate and tri-variate Granger causality tests are mixed and the nature of the link between aid and exports varies across recipients. This is not surprising given the heterogeneity among the different countries. It can be explained by the fact that variables other than Gross National Income (GNI) – such as institutional, political and country-specific characteristics – also have an influence on the causal relationship between net ODA and exports and, thus, are necessary to determine the true nature of the link between these two variables. The causality may also be influenced by aid from other donors.

124

The main conclusion we draw from this analysis is that there is no overwhelming evidence in favour of a causality running from exports to aid that would invalidate our econometric modelling. The estimation of a structural econometric model through which Swiss exports of goods to developing countries are explained inter alia by Swiss bilateral ODA suggests, generally speaking, a positive impact of aid on exports. The results can however vary according to the degree of aggregation (countries grouped together or analysed individually), the econometric techniques used and the time period. In particular, the impact of ODA on exports can be quite different across recipient countries. This can be attributed to the heterogeneity among countries and to the different forms the relationship between aid and exports can take. It might be useful to illustrate the type of results we obtained by means of an example. By considering all recipient countries lumped together over the period 1966-2003, a specification explaining Swiss exports to these countries by Swiss net bilateral ODA as well as two other explanatory variables, namely the recipient country’s GNI and the aid flows from other DAC member countries, produces quite satisfactory results in terms of the general fit of the regression as well as the significance and the sign of the coefficients estimated. The equation (in first differences) explains about 58% of the variance of the dependent variable. This is relatively high for a first-differenced equation and given that many potentially important determinants of exports are absent from this “parsimonious” model. According to the same equation, the elasticity of Swiss exports with respect to Swiss net ODA is estimated at around 0.36. This is lower than the estimated elasticity with respect to GNI of the recipient country (0.99). As for the elasticity of Swiss exports with respect to other DAC countries’ aid, it turns out to be negative (-0.41) which is an indication of the “substitution” effect between Swiss and other countries’ aid. More generally, our results show that Swiss exports tend not to benefit from other DAC countries’ aid. This can be related to the more tied nature of other countries’ aid on average compared to Swiss aid during the period under observation. In order to judge the magnitude of the measured impact of Swiss ODA on Swiss exports, a tentative comparison with another country would be useful. A reasonably comparable study for Germany conducted for the period 1976-1995 (Vogler-Ludwig et al. (1999)) can be used for this purpose. According to this study, the elasticity of German exports with respect to German ODA is almost the same as the one estimated for Switzerland (0.37), but the elasticity with respect to GNI is clearly lower (0.81). In comparison to Germany, the impact of Swiss net ODA on Swiss exports looks quite strong by taking into account the relatively untied nature of Swiss aid during the period under study and the less diversified economic structure of the Swiss economy. There are also differences in term of the lag distribution of the impact. Our results show that, in several cases, the full impact of Swiss ODA on Swiss exports takes time to materialise. This is a clear indication of the validity of the goodwill hypothesis for Switzerland. Depending on the recipient country, the long-run impact turns out to be between 1.5 and 7 times higher than the short-run impact. Finally, our econometric estimations seem to resist well to the use of real rather than nominal flows or gross rather than net ODA. The results appear less robust with respect to the choice

125

of the time period in the sense that the impact of ODA on exports seems to have become stronger through time. This finding speaks in favour of untied aid. At this stage a few caveats are in order. Future research should concentrate on the study of the relationship between ODA and exports in a multi-donor and multi-recipient perspective in order to take full account of all the complex interactions that might exist among these variables. Time series analysis should therefore be carried out through a vector auto-regression framework and the structural specification should take the form of a simultaneous-equations model. Extensions of this study could also integrate other relevant variables determining exports. Variables such as the distance between Switzerland and the recipient country could be used to capture the impact of transport costs. Also, the inclusion of variables reflecting inward FDI stock as well as the commercial policy of the recipient country might help better explain the flow of exports between Switzerland and its developing partners.

127

8. APPENDICES A: Data Source and Description

Table A: Data Source and Description

Variable Definition Source

Exports Swiss exports to countries

receiving Swiss ODA in millions of current USD

Swiss Federal Statistical Office (Statweb)

Exports Deflator Deflator associated with

Swiss exports Swiss Federal Statistical Office

Total net ODA Swiss total net ODA to

recipient countries in millions of current USD

DAC/OECD, International Development Statistics Online (www.oecd.org/dac/stats/idsonline)

Total gross ODA Swiss total net ODA to

recipient countries in millions of current USD

OECD, (new.sourceoecd.org)

Total Net RESTODA Total net ODA from DAC’s

members except Switzerland in millions of current USD

DAC/OECD, International Development Statistics Online (www.oecd.org/dac/stats/idsonline)

Total net ODA deflator Deflator associated with total

net ODA DAC/OECD, International Development Statistics Online (www.oecd.org/dac/stats/idsonline)

GNI Gross National Income of

countries receiving Swiss ODA in millions of current USD

World Development Indicator (2006)

GNI Deflator Deflator associated with GNI World Development Indicator (2006) Exchange Rate Official Swiss Exchange rate

(CHF per USD average per year)

World Development Indicator (2006)

http://www.oecd.org/dac/stats/idsonline




128

B: Groups Classification

Table B: Country Groups Classification

Low income Lower middle income Upper middle income

Europe &

Central Asia

Kyrgyz Republic; Tajikistan; Uzbekistan;

Albania; Armenia; Azerbaijan; Bosnia and Herzegovina; Belarus; Georgia; Moldova; Macedonia; Ukraine;

Bulgaria; Estonia; Croatia; Hungary; Kazakhstan; Lithuania; Latvia; Poland; Romania; Russian Federation; Slovak Republic; Turkey;

East Asia &

Pacific

Laos; Papua New Guinea; Vietnam;

China; Indonesia; Philippines; Thailand;

Malaysia;

South Asia Bangladesh; India; Nepal; Pakistan;

Bhutan; Sri Lanka;

Middle East & North Africa

Yemen; Algeria; Egypt, Arab Republic; Iran; Jordan; Morocco; Syrian Arab Republic; Tunisia;

Lebanon;

Sub-Saharan Africa

Burundi; Benin; Burkina Faso; Côte d'Ivoire; Eritrea; Ethiopia; Ghana; Guinea; Guinea-Bissau; Kenya; Liberia; Madagascar; Mali; Mozambique; Mauritania; Niger; Nigeria; Rwanda; Sudan; Senegal; Chad; Togo; Tanzania; Uganda; Democratic Republic of Congo; Zambia; Zimbabwe;

Angola; Cameroon; Republic of the Congo; Cape Verde; Lesotho; Namibia;

South Africa;

Latin America & the Caribbean

Haiti; Bolivia; Colombia; Dominican Republic; Ecuador; Guatemala; Honduras; Nicaragua; Peru; Paraguay;

Argentina; Brazil; Chile; Costa Rica; Mexico; Trinidad and Tobago; Uruguay; Venezuela;

Source: The World Bank (www.worldbank.org/data/countryclass/classgroups.htm.)

129

C: Descriptive Data

Table C: Countries’ Descriptive Data in millions of current USD

Country Variable Period Mean Standard

Error Min Max Full sample Swiss Exports 1966-2003 3997.83 3741.3 102.81 12972.68 GNI 1966-2003 2262249 2059537 102023.2 6676967 Swiss Net ODA 1966-2003 221.98 190.8 4.1 517.44 Other DAC's Net ODA 1966-2003 22732.92 15257.4 2556.35 46228.81

Swiss Exports 1969-2003 1089.87 1344.93 27.12 4816.47 GNI 1969-2003 325798.6 394941 18504.09 1286411

Europe & Central Asia Swiss Net ODA 1969-2003 31.42 43.45 -0.86 121.48 Other DAC's Net ODA 1969-2003 2440.48 2994.25 59.85 8089.77

Swiss Exports 1966-2003 986.3 731.19 52.01 2309.55 Latin America & Caribbean GNI 1966-2003 774428.8 640358.4 33102.42 1802448

Swiss Net ODA 1966-2003 33.81 28.53 0.44 82.69 Other DAC's Net ODA 1966-2003 2224.58 1501.95 350.14 4525.58

Swiss Exports 1966-2003 467.18 359.76 4.09 1312.51 Middle East & North Africa GNI 1966-2003 142392.9 123157.2 4056.44 406557.9 Swiss Net ODA 1966-2003 8.83 9.82 0.39 38.78 Other DAC's Net ODA 1966-2003 3196.78 1737.66 190.21 7672.55 South Asia Swiss Exports 1966-2003 340.75 254.63 38.16 873.73 GNI 1966-2003 303596.5 194461.5 56342.84 756930.4 Swiss Net ODA 1966-2003 42.94 27.8 1.61 90.3 Other DAC's Net ODA 1966-2003 3955.69 1638.05 1159.57 6653.73

Swiss Exports 1966-2003 306.38 268.42 4.3 850.73 Sub-Saharan Africa GNI 1966-2003 133093.6 111801.8 8412.35 384077.9 Swiss Net ODA 1966-2003 88.2 76.01 1.42 210.17 Other DAC's Net ODA 1966-2003 7484.74 5897.18 305.03 20163.44 Low income Swiss Exports 1966-2003 531.78 373.32 42.9 1288.61 GNI 1966-2003 398694.1 260970 64390.45 990432.4 Swiss Net ODA 1966-2003 127.55 102.36 2.76 288.15 Other DAC's Net ODA 1966-2003 11181.25 7348.53 1757.69 24671.1

Swiss Exports 1966-2003 1472.64 1259.08 24.46 4642.17 Lower middle income GNI 1966-2003 849913.9 839213.4 11883.45 2886257 Swiss Net ODA 1966-2003 72.87 65.31 0.91 177.32 Other DAC's Net ODA 1966-2003 9220.46 5991.71 294.35 18451.51

130

Table C: Countries’ Descriptive Data in millions of current USD (Continued)


Error Min Max Upper middle income Swiss Exports 1966-2003 1993.41 2152.63 31.84 7041.9 GNI 1966-2003 1013641 974857 25749.29 2800277 Swiss Net ODA 1966-2003 21.56 27.02 0.02 73.34 Other DAC's Net ODA 1966-2003 2331.21 2228.2 258.53 6685.04

Swiss Exports 1991-2003 7.24 3.7 3.52 16.49 GNI 1991-2003 2953.02 1500.19 923.72 6032.73

Albania Swiss Net ODA 1991-2003 7.22 3.42 0.64 11.65 Other DAC's Net ODA 1991-2003 275.36 66.74 176.83 378.71

Swiss Exports 1966-2003 72.42 38.11 2.66 143.12 Algeria GNI 1966-2003 36101.18 20426.59 3045.44 66342.55


Swiss Exports 1986-2003 12.72 7.17 4.53 29.13 Angola GNI 1986-2003 6522.61 2373.94 2703.24 12661.24 Swiss Net ODA 1986-2003 3.84 1.82 1.07 6.7 Other DAC's Net ODA 1986-2003 318.94 123.57 120.29 629.65

Swiss Exports 1978-2003 180.16 56.15 105.55 292.2 Argentina GNI 1978-2003 160896.2 82785.47 60099.58 286458.5 Swiss Net ODA 1978-2003 0.18 0.07 0.04 0.33 Other DAC's Net ODA 1978-2003 109.09 68.46 31.14 256.73

Swiss Exports 1994-2003 2.9 2.64 0.51 7.27 Armenia GNI 1994-2003 1953.32 469.17 1299.57 2853.37 Swiss Net ODA 1994-2003 1.14 0.74 0.15 2.44 Other DAC's Net ODA 1994-2003 219.67 27.3 177.32 258.75 Azerbaijan Swiss Exports 1995-2003 9.37 8.09 1.02 22.99 GNI 1995-2003 4714.17 1224.68 3125.91 6834.48 Swiss Net ODA 1995-2003 1.75 1.14 0.79 3.71 Other DAC's Net ODA 1995-2003 185.54 69.24 118.43 304.08

Swiss Exports 1974-2003 22.09 13.84 4.47 50.97 Bangladesh GNI 1974-2003 29321.98 13440.67 10626.92 54717.47 Swiss Net ODA 1974-2003 9.83 5.15 1.79 18.18 Other DAC's Net ODA 1974-2003 1254.92 347.45 618.61 1954.93

Swiss Exports 1995-2003 17.82 3.13 13.23 23.21 Belarus GNI 1995-2003 14360.51 1674.98 12507.69 18265.76 Swiss Net ODA 1995-2003 1.47 0.81 0.47 2.85 Other DAC's Net ODA 1995-2003 61.88 41.95 38.28 158.19

131



Error Min Max Benin Swiss Exports 1966-2003 5.32 5.33 0.03 18.61 GNI 1966-2003 1348.44 789.66 298.64 3446.11 Swiss Net ODA 1966-2003 3.92 3.71 0.02 11.96 Other DAC's Net ODA 1966-2003 134.76 99.23 13.19 290.51 Bhutan Swiss Exports 1981-2003 0.24 0.27 0 1.08 GNI 1981-2003 283.02 129.84 120.33 597.61 Swiss Net ODA 1981-2003 3.78 1.72 0.78 6.94 Other DAC's Net ODA 1981-2003 44.77 19.2 8.87 70.76

Swiss Exports 1971-2003 7.74 3.23 2.94 14.02 Bolivia GNI 1971-2003 4443.71 2374.11 1432.61 8171.42


Swiss Exports 1996-2003 18.86 5.01 10.18 27.08 GNI 1996-2003 4818.64 1470.59 2549.12 7333.4

Bosnia and Herzegovina Swiss Net ODA 1996-2003 13.75 1.28 11.77 15.58 Other DAC's Net ODA 1996-2003 758.86 150.28 562.01 915.65 Brazil Swiss Exports 1966-2003 351.63 253.15 31.84 859.81 GNI 1966-2003 308593.2 221818.4 25749.29 778837.5 Swiss Net ODA 1966-2003 1.18 0.93 -0.12 3.61 Other DAC's Net ODA 1966-2003 175.57 67.86 -40.53 287.96

Swiss Exports 1993-2003 85.28 23.09 65.53 147.71 Bulgaria GNI 1993-2003 12613.36 2807.93 10246.41 19401.62 Swiss Net ODA 1993-2003 7.2 1.65 5.02 9.29 Other DAC's Net ODA 1993-2003 243.5 101.65 128.44 437.53 Burkina Faso Swiss Exports 1966-2003 0.72 0.46 0.03 1.67 GNI 1966-2003 1843.62 978.47 431 4093.53 Swiss Net ODA 1966-2003 5.18 5.48 0.02 19.4 Other DAC's Net ODA 1966-2003 234.57 155.19 16.58 506.72

Swiss Exports 1966-2003 0.84 0.6 0.12 2.3 Burundi GNI 1966-2003 721.33 340.59 159.14 1149.2 Swiss Net ODA 1966-2003 1.64 1.52 0.04 6.84 Other DAC's Net ODA 1966-2003 121.06 87.66 7.51 279.92 Cameroon Swiss Exports 1966-2003 9.93 7.76 0.61 28.93 GNI 1966-2003 6481.25 3848.65 824.18 11991.31 Swiss Net ODA 1966-2003 3.01 2.35 0.3 9.59 Other DAC's Net ODA 1966-2003 292.86 216.31 36.41 804

132



Error Min Max Cape Verde Swiss Exports 1987-2003 0.52 0.23 0.25 1.09 GNI 1987-2003 456.94 137.59 253.91 778.33 Swiss Net ODA 1987-2003 3.18 1.33 1.09 5.36 Other DAC's Net ODA 1987-2003 104.29 13.81 81.17 126.87 Chad Swiss Exports 1966-2003 0.5 0.35 0.07 1.39 GNI 1966-2003 1141.74 487.82 428.99 2336.89 Swiss Net ODA 1966-2003 3.76 3.33 0.06 10.54 Other DAC's Net ODA 1966-2003 135.32 87.3 18.39 277.34

Swiss Exports 1969-2003 60.69 37.39 13.57 111.68 Chile GNI 1969-2003 33538.75 24033.05 7655.34 76492.86

Swiss Net ODA 1969-2003 1.11 0.87 0.1 3.96 Other DAC's Net ODA 1969-2003 59.01 54.48 -15.33 167.1

Swiss Exports 1982-2003 589.88 409.96 125.91 1859.19 China GNI 1982-2003 661935.2 446952.1 206572.3 1658330 Swiss Net ODA 1982-2003 6.72 4.08 0.08 13.9 Other DAC's Net ODA 1982-2003 1892.32 808 547.76 3227.66 Colombia Swiss Exports 1967-2003 81.72 48.61 13.39 156.82 GNI 1967-2003 42416.53 30383.39 5603.8 100101.6 Swiss Net ODA 1967-2003 2.21 2.57 0.04 10.95 Other DAC's Net ODA 1967-2003 147.73 119.74 60.34 629.38

Swiss Exports 1968-2003 13.35 10.1 2.05 36.23 Costa Rica GNI 1968-2003 6288.44 5149.24 757.22 16889.88 Swiss Net ODA 1968-2003 0.89 0.78 0.04 2.68 Other DAC's Net ODA 1968-2003 78.79 85.65 -4.89 241.75 Côte d'Ivoire Swiss Exports 1969-2003 22.22 13.65 1.31 65.78 GNI 1969-2003 7599.67 3251.78 1325.39 12883.46 Swiss Net ODA 1969-2003 1.05 1.58 0.04 6.35 Other DAC's Net ODA 1969-2003 379.6 347.22 49.15 1286.93

Swiss Exports 1996-2003 100.48 30.98 69.01 156.92 Croatia GNI 1996-2003 21167.44 2924.49 18728.99 27749.17 Swiss Net ODA 1996-2003 1.83 0.72 1.08 3.22 Other DAC's Net ODA 1996-2003 82.19 31.65 40.87 122.64

Swiss Exports 1966-2003 11.02 6.49 0 19.88 Democratic Republic of Congo GNI 1966-2003 7683.26 3093.89 3677.63 14689.9 Swiss Net ODA 1966-2003 2.08 3.58 0.12 19.67 Other DAC's Net ODA 1966-2003 413.41 601.83 81.82 3438.16

133



Error Min Max Swiss Exports 1989-2003 11.6 1.82 9.57 15.24 Dominican

Republic GNI 1989-2003 12931.75 4754.29 6139.74 20031.97 Swiss Net ODA 1989-2003 0.94 0.51 0.22 1.74 Other DAC's Net ODA 1989-2003 93.04 29.61 29.73 137.34 Ecuador Swiss Exports 1969-2003 31.66 15.09 3.84 53.95 GNI 1969-2003 11291.87 6649.43 1646.51 25816.99 Swiss Net ODA 1969-2003 3.07 2.66 0.21 7.33 Other DAC's Net ODA 1969-2003 123.22 71.28 21.72 231.07

Swiss Exports 1969-2003 169.1 90.76 13.69 313.4 Egypt GNI 1969-2003 39839.39 29571.94 6827.5 99210.96


Swiss Exports 1994-2003 0.96 0.57 0.45 2.28 Eritrea GNI 1994-2003 666.63 65.96 529.2 756.48 Swiss Net ODA 1994-2003 2.63 1.44 1.38 5.55 Other DAC's Net ODA 1994-2003 184.04 56.28 128.18 279.67 Estonia Swiss Exports 1993-2003 15.04 8.55 3.51 32.13 GNI 1993-2003 5389 1393.12 3878.04 8638.2 Swiss Net ODA 1993-2003 1.43 1.34 0.06 4.02 Other DAC's Net ODA 1993-2003 66.16 13.4 44.41 89.55

Swiss Exports 1982-2003 13.52 11.41 5.48 55 Ethiopia GNI 1982-2003 6815.14 1081.04 5524.62 9347.99 Swiss Net ODA 1982-2003 5.27 2.17 1.47 9.35 Other DAC's Net ODA 1982-2003 826.26 315.05 243.95 1572.12 Georgia Swiss Exports 1994-2003 2.62 1.42 0.55 5.43 GNI 1994-2003 3270.68 475.52 2542.02 4195.49 Swiss Net ODA 1994-2003 3.22 1.66 0.91 6.59 Other DAC's Net ODA 1994-2003 237.32 34.25 169.07 283.1

Swiss Exports 1968-2003 14.67 10.52 3.85 54.48 Ghana GNI 1968-2003 4622.79 1671.14 1710.39 7436.47 Swiss Net ODA 1968-2003 2.12 2.72 0.06 7.62 Other DAC's Net ODA 1968-2003 366.12 278.84 44.06 973.8 Guatemala Swiss Exports 1966-2003 14.64 7.57 2.74 30.75 GNI 1966-2003 9295.56 6514.09 1375.7 24720.95 Swiss Net ODA 1966-2003 0.97 1.11 0.01 4.53 Other DAC's Net ODA 1966-2003 130.29 93.5 11.98 268.83

134



Error Min Max Guinea Swiss Exports 1987-2003 6.91 3.67 2.88 15.89 GNI 1987-2003 3057.03 511.95 2033.92 3704.6 Swiss Net ODA 1987-2003 1.27 0.7 0.46 2.58 Other DAC's Net ODA 1987-2003 312.33 68.02 205.38 417.15 Guinea-Bissau Swiss Exports 1978-2003 0.6 0.52 0.07 2.22 GNI 1978-2003 186.79 42 113.62 248.36 Swiss Net ODA 1978-2003 1.99 1.67 0.1 7.45 Other DAC's Net ODA 1978-2003 88.99 30.49 47.25 146.36 Haiti Swiss Exports 1966-2003 1.87 0.88 0.41 3.42 GNI 1966-2003 1852.63 1150.74 350.37 3931.76 Swiss Net ODA 1966-2003 1.94 2.03 0.03 8.16 Other DAC's Net ODA 1966-2003 154.45 141.25 3.53 595.5

Swiss Exports 1975-2003 14.71 7.65 2.02 27.26 Honduras GNI 1975-2003 3554.25 1453.86 1125.25 6646.5 Swiss Net ODA 1975-2003 4.44 2.52 0.04 8.33 Other DAC's Net ODA 1975-2003 290.27 161.57 41.58 591.87 Hungary Swiss Exports 1991-2003 367.63 97.62 274.21 606.14 GNI 1991-2003 46298.46 12244.58 32935.17 78644.81 Swiss Net ODA 1991-2003 4.03 1.7 0.13 6.22 Other DAC's Net ODA 1991-2003 210.61 111.96 -28.38 381.1 India Swiss Exports 1966-2003 212.56 164.35 21.4 582.91 GNI 1966-2003 234900 148615.9 48214.88 596822.8 Swiss Net ODA 1966-2003 15.75 9.06 1.12 35.52 Other DAC's Net ODA 1966-2003 1503.26 400.58 748.1 2290.5 Indonesia Swiss Exports 1968-2003 124.97 100.61 9.33 353.84 GNI 1968-2003 94202.72 63205.32 7475.99 225384.3 Swiss Net ODA 1968-2003 7.87 7.85 0.08 26.06 Other DAC's Net ODA 1968-2003 1052.5 492.87 302.38 1894.82 Iran Swiss Exports 1994-2003 243.15 67.63 178.12 395.46 GNI 1994-2003 106612.9 17753.73 72699.23 140279 Swiss Net ODA 1994-2003 0.63 0.46 0.06 1.3 Other DAC's Net ODA 1994-2003 150.52 23.93 117.28 181.85 Jordan Swiss Exports 1969-2003 32.2 22.27 1.4 90.92 GNI 1969-2003 4702.38 2742.01 667.38 10483.43 Swiss Net ODA 1969-2003 2.14 3.82 0.1 16.93 Other DAC's Net ODA 1969-2003 530.48 280.08 55.49 1228.36

135



Error Min Max Kazakhstan Swiss Exports 1999-2003 33.85 20.12 14.08 62.15 GNI 1999-2003 22158.86 5082.92 17914.44 29937.6 Swiss Net ODA 1999-2003 0.25 0.05 0.2 0.32 Other DAC's Net ODA 1999-2003 195.92 31.91 167.93 248.78 Kenya Swiss Exports 1966-2003 18.29 9.09 2.64 29.97 GNI 1966-2003 6725.89 4027.97 1106.63 14692.07 Swiss Net ODA 1966-2003 2.51 1.75 0.01 5.28 Other DAC's Net ODA 1966-2003 420.35 298.14 45.3 1080.05 Kyrgyz Republic Swiss Exports 1994-2003 1.98 1.47 0.62 5.2 GNI 1994-2003 1582.56 188.52 1300.69 1844.1 Swiss Net ODA 1994-2003 7.67 1.83 5.58 10.37 Other DAC's Net ODA 1994-2003 216.36 27.98 176.56 249.01 Lao PDR Swiss Exports 1989-2003 0.72 0.81 0.06 2.83 GNI 1989-2003 1452.6 374.4 721.2 2035.32 Swiss Net ODA 1989-2003 2.86 1.4 1.09 5.75 Other DAC's Net ODA 1989-2003 239.02 67.23 125.59 320.35 Latvia Swiss Exports 1995-2003 38.68 23.44 10.03 79.49 GNI 1995-2003 7530.68 1909.36 5302.71 11237.22 Swiss Net ODA 1995-2003 1.62 1.13 0.11 3.15 Other DAC's Net ODA 1995-2003 88.57 16.37 59.7 116.54

Swiss Exports 1990-2003 119.52 14.91 88.42 142.03 Lebanon GNI 1990-2003 12843.37 5537.27 3788.67 18976.01 Swiss Net ODA 1990-2003 0.95 0.41 0.48 1.95 Other DAC's Net ODA 1990-2003 216.8 60 118.81 342.41 Lesotho Swiss Exports 1968-2003 0.2 0.3 0 1.4 GNI 1968-2003 729.65 419.41 78.34 1306.81 Swiss Net ODA 1968-2003 0.88 0.71 0.02 2.21 Other DAC's Net ODA 1968-2003 72.65 41.5 12.22 136.09 Liberia Swiss Exports 1998-2003 10.07 1.41 8.97 12.5 GNI 1998-2003 375.26 41.26 309.73 416.53 Swiss Net ODA 1998-2003 1.55 0.69 0.79 2.63 Other DAC's Net ODA 1998-2003 74.73 23.63 48.21 116.85 Lithuania Swiss Exports 1995-2003 43.99 10.03 32.21 65.96 GNI 1995-2003 11354.47 3169.45 7484.11 17796.57 Swiss Net ODA 1995-2003 0.79 0.74 0.06 1.97 Other DAC's Net ODA 1995-2003 145.46 56.34 106.32 280.64

136



Error Min Max Macedonia Swiss Exports 1996-2003 43.08 19.9 20.12 64.45 GNI 1996-2003 3840.22 382.03 3523.83 4586.44 Swiss Net ODA 1996-2003 5.28 1.75 3.19 7.31 Other DAC's Net ODA 1996-2003 196.46 72.6 93.78 260 Madagascar Swiss Exports 1966-2003 3.63 2.13 0.28 7.93 GNI 1966-2003 2734.36 1046.18 890.72 4852.16 Swiss Net ODA 1966-2003 7.6 6.94 0.03 23.18 Other DAC's Net ODA 1966-2003 238.42 170.04 43.44 666.15 Malaysia Swiss Exports 1969-1999 128.76 145.82 3.36 472.39 GNI 1969-1999 35395.08 27410.34 3934.97 92953.32

Swiss Net ODA 1969-1999 0.17 0.14 0.02 0.52 Other DAC's Net ODA 1969-1999 112.72 127.63 -261.77 341.12 Mali Swiss Exports 1991-2003 2.05 0.32 1.62 2.54 GNI 1991-2003 2647.52 482.56 2144.5 4030.46 Swiss Net ODA 1991-2003 7.36 1.97 5.9 13.09 Other DAC's Net ODA 1991-2003 421.32 56.09 347.43 521.27 Mauritania Swiss Exports 1974-2003 2.06 2.46 0.38 11.53 GNI 1974-2003 872.62 261.8 388.11 1420.12 Swiss Net ODA 1974-2003 0.39 0.22 0.09 0.98 Other DAC's Net ODA 1974-2003 209.85 47.34 71.74 298.07 Mexico Swiss Exports 1983-2003 372.37 180.98 112.96 707.7 GNI 1983-2003 340583.3 165638.5 138195.3 639134.5 Swiss Net ODA 1983-2003 0.41 0.22 0.13 0.84 Other DAC's Net ODA 1983-2003 177.29 119.69 -0.73 412.82 Moldova Swiss Exports 1996-2003 8.08 2.63 3.64 12.18 GNI 1996-2003 1705.34 308.14 1343.68 2283.27 Swiss Net ODA 1996-2003 1.57 0.81 0.71 2.98 Other DAC's Net ODA 1996-2003 93.52 34.36 49.88 128.53 Morocco Swiss Exports 1968-2003 56.82 32.56 4.57 109.98 GNI 1968-2003 19522.95 11337.51 3266.92 42695.52 Swiss Net ODA 1968-2003 0.78 1.07 0.02 5.02 Other DAC's Net ODA 1968-2003 496.52 266.61 80.59 1109.25 Mozambique Swiss Exports 1981-2003 4 1.84 1.21 9.11 GNI 1981-2003 3086.63 846.85 1870.7 4710.04 Swiss Net ODA 1981-2003 16.53 8.88 1.56 27.8 Other DAC's Net ODA 1981-2003 837.83 405.06 161.46 1570.45

137



Error Min Max Namibia Swiss Exports 1990-2003 1.92 1.06 1.27 5.51 GNI 1990-2003 3322.89 538.59 2351.03 4586.72 Swiss Net ODA 1990-2003 0.38 0.2 0.18 0.74 Other DAC's Net ODA 1990-2003 154.37 19.88 118.81 181.59 Nepal Swiss Exports 1966-2003 1.75 1.25 0.12 4.57 GNI 1966-2003 2855.96 1632.53 793.77 5981.45 Swiss Net ODA 1966-2003 8.46 5.77 0.29 16.95 Other DAC's Net ODA 1966-2003 227.17 164.42 11 439.8 Nicaragua Swiss Exports 1971-2003 5.73 3.98 1.81 17.26 GNI 1971-2003 2288.76 971.01 763.86 4026.51

Swiss Net ODA 1971-2003 5.01 4.52 0.01 14.43 Other DAC's Net ODA 1971-2003 325.42 277.16 18.94 841.06 Niger Swiss Exports 1972-2003 2.15 2.41 0.17 11.1 GNI 1972-2003 1810.16 460.51 778.82 2662.52 Swiss Net ODA 1972-2003 4.78 3.19 0.1 9.53 Other DAC's Net ODA 1972-2003 242.16 96.22 49.43 429.97 Nigeria Swiss Exports 1989-2003 88.56 21.38 59.34 128.08 GNI 1989-2003 31482.42 8550.61 21878.74 50107.11 Swiss Net ODA 1989-2003 0.11 0.07 0.04 0.25 Other DAC's Net ODA 1989-2003 233.44 53.99 166.62 371.88 Pakistan Swiss Exports 1966-2003 92.74 69.92 11.05 211.38 GNI 1966-2003 35312.24 22521.41 6729.05 80838.48 Swiss Net ODA 1966-2003 6.57 4.92 0.03 15.45 Other DAC's Net ODA 1966-2003 856.58 366.52 325.07 1804.49

Swiss Exports 1987-2003 1.06 0.42 0.42 1.61 Papua New Guinea GNI 1987-2003 3714.36 724.56 2747.95 4808.05 Swiss Net ODA 1987-2003 0.22 0.08 0.04 0.34 Other DAC's Net ODA 1987-2003 322.55 65.55 207.09 413.25 Paraguay Swiss Exports 1970-2003 9.5 6.98 1.01 25.6 GNI 1970-2003 4833.05 2694.08 592.04 9561.54 Swiss Net ODA 1970-2003 0.75 0.31 0.16 1.54 Other DAC's Net ODA 1970-2003 64.58 30.01 19.38 117.43 Peru Swiss Exports 1966-2003 42.12 15.34 11.79 73.74 GNI 1966-2003 26059.2 17908.4 5627.06 59398.02 Swiss Net ODA 1966-2003 5.71 4.69 0.2 16.56 Other DAC's Net ODA 1966-2003 256.8 157.16 46.56 489.37

138



Error Min Max Philippines Swiss Exports 1980-2003 116.06 56.62 40.11 202.52 GNI 1980-2003 54951.68 21390.88 29576.02 84717.2 Swiss Net ODA 1980-2003 2.78 2.97 0.07 11.84 Other DAC's Net ODA 1980-2003 758.73 329.1 309.61 1483.15 Poland Swiss Exports 1991-2003 543.52 195.92 270.75 868.09 GNI 1991-2003 142731.8 44807.5 71364.02 209941.3 Swiss Net ODA 1991-2003 7.65 8.38 0.22 23.2 Other DAC's Net ODA 1991-2003 1447.79 520.67 935.95 2628.29

Swiss Exports 1969-2003 1.71 1.36 0.24 6.59 Republic of the Congo GNI 1969-2003 1517.44 704.25 258.95 2710.36


Swiss Exports 1991-2003 128.31 51.28 55.52 245.62 Romania GNI 1991-2003 36639 8338.08 26275.57 57223.52 Swiss Net ODA 1991-2003 6.53 2.35 2.11 11.5 Other DAC's Net ODA 1991-2003 342.8 146.21 176.22 622.48

Swiss Exports 1995-2003 431.91 161.43 291.01 779.91 Russian Federation GNI 1995-2003 330223.1 72368.2 222128.8 435931.3 Swiss Net ODA 1995-2003 12.25 1.86 9.83 15.69 Other DAC's Net ODA 1995-2003 1321.37 212.06 971.79 1621.72 Rwanda Swiss Exports 1966-2003 1.62 1.77 0.13 6.16 GNI 1966-2003 1260.56 733.7 139.45 2438.66 Swiss Net ODA 1966-2003 7.3 5.75 0.56 20.78 Other DAC's Net ODA 1966-2003 206.83 160.64 10.17 622.65 Senegal Swiss Exports 1969-2003 6.37 2.82 1.59 11.95 GNI 1969-2003 3359.47 1628.92 845.91 6784.24 Swiss Net ODA 1969-2003 3.89 3.43 0.02 11.95 Other DAC's Net ODA 1969-2003 383.27 215.82 48.35 727.07 Slovak Republic Swiss Exports 1991-2003 108.89 60.25 0 205.36 GNI 1991-2003 19579.43 5563.06 11896.4 32388.39 Swiss Net ODA 1991-2003 2.94 2.24 1.11 8.29 Other DAC's Net ODA 1991-2003 122.5 54.62 55.07 223.48 South Africa Swiss Exports 1994-2003 411.72 87.47 321.6 560.61 GNI 1994-2003 135242.6 12263.2 116729.3 159463 Swiss Net ODA 1994-2003 8.07 1.81 6.12 10.83 Other DAC's Net ODA 1994-2003 456.43 87.1 304.49 596.65

139



Error Min Max Sri Lanka Swiss Exports 1970-2003 18.01 13.66 1.72 44.57 GNI 1970-2003 8056.08 4999.24 2231.23 17972.66 Swiss Net ODA 1970-2003 2.38 1.84 0.01 7.01 Other DAC's Net ODA 1970-2003 392.13 206.79 50.95 779.52 Sudan Swiss Exports 1973-2003 18.4 6.08 5.91 27.38 GNI 1973-2003 9717.75 3710.26 3104.78 17466.86 Swiss Net ODA 1973-2003 3.43 2.15 0.09 8.46 Other DAC's Net ODA 1973-2003 505.13 283.28 72.86 967.53

Swiss Exports 1998-2003 62.85 22.88 39.57 91.97 Syrian Arab Republic GNI 1998-2003 17727.97 2416.43 14621.9 21014.01


Swiss Exports 1994-2003 1.08 0.52 0.44 2.12 Tajikistan GNI 1994-2003 1150.89 177.01 987.68 1525.27 Swiss Net ODA 1994-2003 4.65 3.43 0.77 12.27 Other DAC's Net ODA 1994-2003 117.99 36.63 55.25 164.51 Tanzania Swiss Exports 1989-2003 7.25 2.55 3.94 13.93 GNI 1989-2003 6695.29 2310.47 4239.64 10252.8 Swiss Net ODA 1989-2003 18.95 1.98 15.93 24.13 Other DAC's Net ODA 1989-2003 1068.7 190.09 869.65 1575.71

Swiss Exports 1972-2003 283.25 242.43 21.38 716.52 Thailand GNI 1972-2003 73651.63 51457.09 8598.93 166862.5 Swiss Net ODA 1972-2003 1.95 1.77 0.06 7.47 Other DAC's Net ODA 1972-2003 432.31 292.97 -400.86 856.9 Togo Swiss Exports 1966-2003 4.23 4.88 0.28 20.95 GNI 1966-2003 952.08 467.74 208.83 1711.77 Swiss Net ODA 1966-2003 0.47 0.71 0.02 3.74 Other DAC's Net ODA 1966-2003 93.95 62.93 11.73 228.08

Swiss Exports 1967-1996 3.83 2.18 0.71 7.71 Trinidad and Tobago GNI 1967-1996 3887.95 2209.35 683.14 7679.28 Swiss Net ODA 1967-1996 0.07 0.04 -0.02 0.14 Other DAC's Net ODA 1967-1996 8.29 6.32 1.23 23.85 Tunisia Swiss Exports 1966-2003 33.67 25.51 1.43 72.13 GNI 1966-2003 9733.12 6670.77 1011 23726.15 Swiss Net ODA 1966-2003 1.1 1.11 0.21 6.01 Other DAC's Net ODA 1966-2003 205.96 81.85 75.07 365.97

140



Error Min Max Turkey Swiss Exports 1969-2003 416.56 341.65 27.12 1255.37 GNI 1969-2003 106122 64330.63 18504.09 240128.3 Swiss Net ODA 1969-2003 1.92 2.03 -0.86 5.79 Other DAC's Net ODA 1969-2003 326.68 272.03 15.86 1168.24 Uganda Swiss Exports 1972-2003 3.41 0.99 2.15 5.76 GNI 1972-2003 3886.28 1731.62 1481.84 6347.41 Swiss Net ODA 1972-2003 2.28 2.86 0.03 9.05 Other DAC's Net ODA 1972-2003 396.05 319.21 22.19 963.14 Ukraine Swiss Exports 1995-2003 85.03 29.59 62.12 148.06 GNI 1995-2003 41836.85 6684.93 32173.26 51268.54

Swiss Net ODA 1995-2003 5.05 1.91 1.06 6.87 Other DAC's Net ODA 1995-2003 426.69 84.57 330.44 536.13 Uruguay Swiss Exports 1982-2003 29.84 13.92 12.55 57.35 GNI 1982-2003 12662.35 5876.73 4523.79 21478.75 Swiss Net ODA 1982-2003 0.12 0.06 0.04 0.25 Other DAC's Net ODA 1982-2003 33.41 25.53 3.04 91.26

Swiss Exports 1994-2003 17.93 4.16 11.45 23.82 Uzbekistan GNI 1994-2003 13123.29 1934.14 10073.59 15562.57 Swiss Net ODA 1994-2003 1.48 1.96 0.06 6.27 Other DAC's Net ODA 1994-2003 138.22 49.49 49.88 199.73

Swiss Exports 1999-2003 101.95 9.97 91.46 113.75 Venezuela, RB GNI 1999-2003 101739.8 10061.12 89600.54 112232.8 Swiss Net ODA 1999-2003 0.29 0.2 0.1 0.51 Other DAC's Net ODA 1999-2003 58.29 5.4 51.33 65.68 Vietnam Swiss Exports 1990-2003 43.04 27.44 3.89 92.68 GNI 1990-2003 22665.65 10391.98 6887.83 39268.15 Swiss Net ODA 1990-2003 8.53 3.92 0.61 13.65 Other DAC's Net ODA 1990-2003 968.48 496.94 177.62 1647.65 Yemen Swiss Exports 1991-2000 14.84 3.81 9.41 20.49 GNI 1991-2000 5513.58 1481.78 3673.99 8354.04 Swiss Net ODA 1991-2000 1.38 1.07 0.04 2.55 Other DAC's Net ODA 1991-2000 293.65 72.56 183.81 386.63 Zambia Swiss Exports 1969-2003 5.83 2.52 1.39 9.62 GNI 1969-2003 2827.85 662.43 1655.61 4253.23 Swiss Net ODA 1969-2003 0.56 0.56 0.03 2.37 Other DAC's Net ODA 1969-2003 426.21 339.42 16.98 1345.45

141



Error Min Max Zimbabwe Swiss Exports 1973-2003 14.92 9.1 1.91 35.05 GNI 1973-2003 7345.44 3469.22 3251.39 20542.07 Swiss Net ODA 1973-2003 2.67 3.19 0.04 10.38 Other DAC's Net ODA 1973-2003 234.86 169.84 0.99 606.66

142

D: Correlation Matrices

Table D: Countries’ Correlation Matrices

Full Sample Swiss Exports(t)

Swiss Exports(t-1) GNI(t)

Swiss Net ODA(t)

Swiss Gross ODA(t)

Other DAC’s Net ODA(t)

Swiss Exports(t) 1 Swiss Exports(t-1) 0.99* 1

GNI(t) 0.99* 1* 1 Swiss Net ODA(t) 0.92* 0.9* 0.9* 1

Swiss Gross ODA(t) 0.9* 0.87* 0.87* 1* 1 Other DAC’s Net ODA(t) 0.91* 0.9* 0.9* 0.99* 0.99* 1

East Asia & Pacific Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.94* 0.94* 1 Swiss Net ODA(t) 0.82* 0.78* 0.64* 1


Europe & Central Asia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.98* 0.98* 0.97* 0.96* 1 Other DAC’s Net ODA(t) 0.97* 0.96* 0.97* 0.98* 0.95* 1

Latin America & Caribbean

Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)


Swiss Exports(t) 1 Swiss Exports(t-1) 1* 1



Middle East & North Africa

Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





143

Table D: Countries’ Correlation Matrices (Continued)

South Asia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Sub-Saharan Africa Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Low income Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Lower middle income Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Upper middle income Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





144


Algeria Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.34* 0.38* 0.55* 1* 1 Other DAC’s Net ODA(t) 0.23 0.22 0.57* 0.57* 0.57* 1

Bengladesh Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.61* 0.6* 0.75* 1* 1 Other DAC’s Net ODA(t) 0.19 0.15 0.23 0.36* 0.36 1

Benin Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.73* 0.71* 1 Swiss Net ODA(t) 0.27 0.23 0.79* 1

Swiss Gross ODA(t) 0.28 0.23 0.79* 1* 1 Other DAC’s Net ODA(t) 0.45* 0.41* 0.9* 0.95* 0.95* 1

Bolivia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.09 0.11 1 Swiss Net ODA(t)| -0.13 -0.2 0.79* 1

Swiss Gross ODA(t) | -0.11 -0.19 0.8* 1* 1 Other DAC’s Net ODA(t)| -0.02 -0.04 0.95* 0.9* 0.91* 1

Brazil Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.9* 0.86* 0.86* 1* 1 Other DAC’s Net ODA(t) 0.26 0.29 0.24 0.14 0.16 1

145


Burkina Faso Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Burundi Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Cameroon Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.59* 0.61* 0.77* 1* 1 Other DAC’s Net ODA(t) 0.16 0.21 0.78* 0.49* 0.49* 1

Chad Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Chile Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





146


Colombia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Costa Rica Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.69* 0.66* 0.68* 1* 1 Other DAC’s Net ODA(t)| -0.3 -0.36* -0.29 0.1 0.09 1

Côte d’Ivoire Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.04 0.04 0.39* 1* 1 Other DAC’s Net ODA(t) 0.43* 0.33 0.64* 0.67* 0.67* 1

Democratic Republic of Congo

Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.7* 0.68* 1 Swiss Net ODA(t)| -0.28 -0.25 -0.26 1

Swiss Gross ODA(t) | -0.31 -0.25 -0.28 1* 1 Other DAC’s Net ODA(t)| -0.06 -0.05 -0.04 0.92* 0.91* 1

Ecuador Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





147


Egypt Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.61* 0.55* 0.71* 1* 1 Other DAC’s Net ODA(t) 0.29 0.21 0.17 0.55* 0.55* 1

Ghana Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) | -0.09 -0.14 0.46* 1* 1 Other DAC’s Net ODA(t) 0.01 0.01 0.92* 0.55* 0.55* 1

Guatemala Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Haiti Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.29 0.35* 1 Swiss Net ODA(t) 0.37* 0.45* 0.72* 1


Honduras Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.64* 0.7* 1 Swiss Net ODA(t) 0.66* 0.55* 0.3 1

Swiss Gross ODA(t) 0.66* 0.55* 0.3 1* 1 Other DAC’s Net ODA(t) 0.8* 0.85* 0.91* 0.53* 0.53* 1

148


India Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)

Other DAC’sNet ODA(t)




Indonesia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Jordan Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.98* 0.98* 1 Swiss Net ODA(t) 0.19 0.16 0.18 1

Swiss Gross ODA(t) 0.19 0.16 0.18 1* 1 Other DAC’s Net ODA(t) 0.39* 0.29 0.39* 0.02 0.02 1

Kenya Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.65* 0.55* 0.21 0.96* 1 Other DAC’s Net ODA(t) 0.86* 0.85* 0.55* 0.76* 0.7* 1

Lesotho Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.23 0.23 1 Swiss Net ODA(t) 0.09 0.11 0.87* 1

Swiss Gross ODA(t) 0.1 0.12 0.87* 1* 1 Other DAC’s Net ODA(t) 0.31 0.33 0.74* 0.81* 0.82* 1

149


Madagascar Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.69* 0.72* 0.33* 1* 1 Other DAC’s Net ODA(t) 0.29 0.37* 0.77* 0.6* 0.6* 1

Malaysia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.97* 0.97* 1 Swiss Net ODA(t) 0.43* 0.35 0.45* 1

Swiss Gross ODA(t) 0.37* 0.29 0.39* 0.99* 1 Other DAC’s Net ODA(t)| -0.39* -0.48* -0.29 0.28 0.34 1

Mauritania Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.39* 0.44* 1 Swiss Net ODA(t) 0.12 0.27 -0.09 1

Swiss Gross ODA(t) 0.12 0.26 -0.08 1* 1 Other DAC’s Net ODA(t) 0.47* 0.38* 0.78* 0.17 0.18 1

Morocco Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Nepal Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





150


Nicaragua Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) | -0.28 -0.14 0.38* 1* 1 Other DAC’s Net ODA(t)| -0.43* -0.37* 0.57* 0.89* 0.89* 1

Niger Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.33 0.31 0.66* 1* 1 Other DAC’s Net ODA(t) 0.43* 0.53* 0.77* 0.88* 0.88* 1

Pakistan Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Paraguay Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.91* 0.94* 1 Swiss Net ODA(t) 0.08 0.11 0.26 1

Swiss Gross ODA(t) 0.07 0.1 0.27 1* 1 Other DAC’s Net ODA(t) 0.86* 0.78* 0.81* -0.11 -0.1 1

Peru Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





151


Rwanda Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Senegal Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Sri Lanka Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Sudan Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Togo Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.32 0.29 1 Swiss Net ODA(t) 0.02 0.04 0.57* 1

Swiss Gross ODA(t) 0.03 0.05 0.58* 1* 1 Other DAC’s Net ODA(t) 0.04 0.05 0.71* 0.54* 0.54* 1

152


Trinidad and Tobago Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.2 0.26 -0.02 0.86* 1 Other DAC’s Net ODA(t) 0.49* 0.54* 0.14 0.47* 0.31 1

Tunisia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)





Turkey Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)




Swiss Gross ODA(t) 0.52* 0.5* 0.61* 0.96* 1 Other DAC’s Net ODA(t)| -0.05 -0.07 0.07 0.08 0.16 1

Uganda Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t)| -0.13 -0.11 1 Swiss Net ODA(t) 0.47* 0.45* 0.26 1

Swiss Gross ODA(t) 0.45* 0.46* 0.36* 0.97* 1 Other DAC’s Net ODA(t) 0.01 0.01 0.79* 0.49* 0.57* 1

Zambia Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.06 -0.01 1 Swiss Net ODA(t) 0.04 0.14 -0.01 1

Swiss Gross ODA(t) 0.05 0.14 0.01 1* 1 Other DAC’s Net ODA(t)| -0.11 0 0.52* 0.47* 0.47* 1

153


Zimbabwe Swiss Exports(t)


Swiss Net ODA(t)

Swiss Gross ODA(t)



GNI(t) 0.14 0.06 1 Swiss Net ODA(t) 0.82* 0.83* 0.01 1

Swiss Gross ODA(t) 0.82* 0.83* 0.01 1* 1 Other DAC’s Net ODA(t) 0.88* 0.95* 0.17 0.88* 0.88* 1

Note: The asterisk * indicates that the correlation is significant at the 10 percent level.

155

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University of Neuchâtel · The Impact of Official Development Assistance on Donor Country Exports : Some Empirical Evidence for Switzerland Research project financed by Swiss Agency