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Does Happiness Affect the Bilateral Aid Flows Between Donor and Recipient Countries?

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Abstract

The literature on the economics of well-being and happiness is currently growing at a remarkable rate. In separate studies, happiness has been linked to income, health, age, political and economic freedom, unemployment, inflation, self-employment, voluntary work, marriage, and even watching television. None of these studies have linked happiness to foreign aid disbursements. Using data from the Organisation for Economic Co-operation and Development, the World Bank and the World Database of Happiness, we construct an empirical model of aid, in which a host of donor interest and recipient need motives impact on the level of assistance a donor country gives. However, unlike other studies of aid, we concentrate on three factors in donor aid disbursement: the degree of happiness of the donor and the recipient; geographical proximity between the two countries; and competition between donors in providing aid. Using a number of different specifications, we test, in particular, the validity of the hypothesis that donor and recipient happiness are important determinants of levels of aid.

La littérature sur l’économie du bien-être s’accroît à une vitesse remarquable. Différentes études ont montré le lien entre le bonheur et les revenus, la santé, l’âge, la liberté économique et politique, le chômage, l’inflation, le travail indépendant, le bénévolat, le mariage et même l’acte de regarder la télévision. Aucune de ces études n’a fait le lien entre le bonheur et les dépenses d’aides à l’étranger. À partir de données provenant de l’OCDE, de la Banque Mondiale et de la Base de Données Mondiales sur le Bonheur, nous développons un modèle empirique d’aide dans lequel un grand nombre de motivations relatives aux intérêts des pays donateurs et aux besoins des pays receveurs ont un impact sur le niveau d’aide fournie par le pays donateur. Cependant, contrairement à d’autres études sur l’aide, nous nous concentrons sur trois facteurs influençant les dépenses d’aide réalisées par les pays donateurs: le degré de bonheur du pays donateur et du pays bénéficiaire, la proximité géographique des deux pays, la concurrence à laquelle se livrent les donateurs pour fournir l’aide. En mobilisant un ensemble de spécifications, nous testons notamment la validité de l’hypothèse selon laquelle le bonheur du pays donateur et celui du bénéficiaire sont d’importants déterminants du niveau d’aide.

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Notes

  1. A number of explanations have been offered for this finding, such as the notion that people's or countries’ happiness simply adapts to higher levels of income – what Layard (2007) calls a ‘phenomenon of addiction’ – or that agents or countries care about their income relative to others. See Frey and Stutzer (2002) for a discussion.

  2. Considering the trade-off between inflation and unemployment, and covering 12 European countries for the period 1975–1991, Di Tella et al (2001) find that a one percentage point increase in the unemployment rate compensates for a 1.7 percentage point increase in inflation.

  3. The common finding in this group of studies is that happiness is u-shaped in age, minimizing around the mid-40s.

  4. Studies of aid motivations date back to the 1960s, and include Mikesell (1968) and Levitte (1968).

  5. Good governance may be defined using various criteria such as the quality of policies, performance of public institutions, absence of corruption and so on.

  6. The gravity model is also used in other areas, including the literature on migration, commuting and foreign direct investment. It is founded on Newton's Law of Universal Gravitation, namely that two bodies attract each other in proportion to the product of their masses and inversely to the square of their distances apart.

  7. As is clear from the above, the only paper examining the issue of aid and happiness, Arvin and Lew (2009b), uses aggregate aid flows.

  8. That is, our data do not consist of a true panel of time-series observations. Hence, the results are not time-series findings. In addition, our ‘panel’ is unbalanced.

  9. To be clear, a note on the sample size is appropriate. We have more data on bilateral aid flows than we have for happiness. We have 165 countries receiving bilateral aid flows and 29 countries sending bilateral aid flows, though Turkey is a member of both groups (although not in the same years). There are, therefore, a total of 193 countries covered as either donor or recipient. We have happiness data for at least a subset of years for all 29 aid donors. We have happiness data for at least a subset of years for only 52 recipients of bilateral aid flows. These are the 52 recipient countries listed in Table 3. When our regressions include only donor happiness, we could cover up to 165 recipient countries, though the sample might be reduced as we add in other independent variables. However, when our regressions include recipient happiness, we could only cover up to 52 recipient countries, and again the actual sample differs by regression depending on which other variables are included. In addition, Turkey, for which happiness is available, is a recipient of aid in some years and a donor in other years. Therefore, we have a total of 80 unique countries with happiness data. Table 3 lists these countries.

  10. As is evident from the discussion in the previous section, our empirical model uses a number of clusters that are defined as bilateral country pairs. The model assumes (quite sensibly) that observations are independent across different clusters, but not within clusters. As is clear, although our data are not contiguous in time, there may be persistency in determinants of aid for a bilateral pair (say, United States–Egypt) over time not explained by our model, causing correlation in the observations. That is, as we have several observations for each country pair over time, the cluster option allows for possible correlation of aid flows between the two countries over time. For example, we might expect that aid flows from the United States to Mexico may be correlated over time because there is some underlying relationship between the two countries not explained by the independent variables that persist over time. The aid flow from the United States to Mexico, though, would neither be influenced by nor would it influence the aid flow from, say, the United States to Panama. Therefore, it is sensible to assume that there is no intergroup correlation. We utilize an option in Stata, the statistical package we use, to include an adjustment for intragroup correlation in the calculation of standard errors. This ensures that the standard errors reported in our tables are more robust, and the inferences made about the relationships are more conservative.

  11. None of our tables report the value and statistical significance of the four time dummies. These are available from the authors upon request.

  12. We also tried including a measure of the degree of inequality in the recipient country (the Gini coefficient) as an additional explanatory variable. However, the sample size dropped remarkably and the coefficient of Gini was statistically insignificant when we tried it. We also attempted to use additional control variables such as unemployment and inflation to characterize the donor. However, none was statistically significant.

  13. A note on the change in the sample size across the four specifications in Table 4 is in order. Specification A included only donor happiness, whereas Specification C included recipient happiness too. We reran Specification A with the same sample size as in Specification C and obtained very modest changes in most of the coefficients. In particular, the coefficient of happ_donor changes insignificantly (that is, statistically, as well as in terms of the size of the coefficient). The inclusion or exclusion of happ_recip in Specification C has no effect on the coefficient of happ_donor. This means that Specification A is presented simply to show the larger sample result. Therefore, what we conclude is that happ_recip is insignificant without further control variables included (as in Specification D). We do not have sufficient observations on happ_recip to test it with as large a sample. If we compare Specification C as is with Specification C without happ_recip, happ_donor is unaffected.

  14. We would also like to comment on the changes in the coefficients of happ_recip because of a change in sample size as we move from Specification C to D. We wanted to know the impact of adding in the variables for inflation and unemployment versus the change in sample size on the change in the coefficient of happ_recip. Thus, we reran Specification C with the sample size of Specification D to test Specification C's coefficients using the smallest sample size. It turns out that the sample size is not the issue. Using the smaller sample size from Specification D makes no significant difference to the coefficients of Specification C. Thus, it is the addition of unemployment and inflation in Specification D, not the smaller sample, which accounts for the significance of the coefficient on happ_recip (and the smaller coefficient on happ_donor).

  15. As is obvious, with FDI and trade covariates, our sample size declines precipitously. Undeniably, there is a trade-off between more explanatory variables and larger sample size.

  16. Once again, we tested the consequences of changing the sample size (see a similar discussion in footnotes 13 and 14). The impact of a change in sample size (on its own) was inconsequential with respect to the results.

  17. The nature of the lag is that, for example, we use real per capita GDP in 2003 (not in 2000) as the instrument for real per capita GDP in 2004.

  18. Obviously, happiness may also be endogenous to the aid received. Thus, it is also possible econometrically to instrument for happiness, using its lagged values. However, happiness surveys are not conducted annually, but at irregular intervals. Consequently, for example, the lagged value for happiness for 1990 would be the 1982 value, and the lagged value for 2004 would be the 2000 value. Hence, given that the happiness data are not of the same quality as the other data, the typical solution of using lags is not possible.

  19. As is clear, we have two measures of a colonial relationship. The coefficient of the ‘colony’ variable is uniformly statistically significant across all the specifications. The coefficient of the other measure is not. We experimented with leaving the ‘col45’ variable out of the regressions, but this made no difference to the statistical significance of the ‘colony’ coefficient, although its size did change. Thus, there is no change to any inferences or conclusions by including or excluding ‘col45.’ We erred on the side of including it in our regressions.

  20. In this sense, aid by donors follows more of a cooperative model than a competitive one.

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Acknowledgements

An earlier version of this article was presented at the 43rd annual conference of the Canadian Economics Association at the University of Toronto, May 2009. We thank the session participants and our discussant, Hideki Ariizumi, for helpful comments. Thanks are also due to Marisa Scigliano and an anonymous referee of this Journal for many useful suggestions on earlier versions of this article.

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Arvin, B., Lew, B. Does Happiness Affect the Bilateral Aid Flows Between Donor and Recipient Countries?. Eur J Dev Res 22, 546–563 (2010). https://doi.org/10.1057/ejdr.2010.28

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