The effectiveness of foreign financial aid on economic growth – A Meta-analysis

• Thesis title in Czech: The effectiveness of foreign financial aid on economic growth – A Meta-analysis
• Thesis title in English: The effectiveness of foreign financial aid on economic growth – A Meta-analysis
• English key words: meta-analysis, foreign financial aid, economic growth, effectiveness, publication bias, Bayesian model averaging
• Academic year of topic announcement: 2023/2024
• Thesis type: Bachelor's thesis
• Thesis language: angličtina
• Department: Institute of Economic Studies (23-IES)
• Supervisor: Mgr. Martina Lušková
• Author: hidden - assigned by the advisor
• Date of registration: 23.05.2024
• Date of assignment: 23.05.2024

References

Alesina, A. and Dollar, D. (2000). Who Gives Foreign Aid to Whom and Why? Journal of Economic Growth, 5(1), pp.33–63.
Amini, S.M. and Parmeter, C.F. (2012). COMPARISON OF MODEL AVERAGING TECHNIQUES: ASSESSING GROWTH DETERMINANTS. Journal of Applied Econometrics, 27(5), pp.870–876.
Andrews, I. and Kasy, M. (2019). Identification of and Correction for Publication Bias. American Economic Review, 109(8), pp.2766–2794.
Bom, P.R.D. and Rachinger, H. (2019). A kinked meta‐regression model for publication bias correction. Research Synthesis Methods, 10(4), pp.497–514.
Doucouliagos, H. and Paldam, M. (2008). Aid effectiveness on growth: A meta study. European Journal of Political Economy, 24(1), pp.1–24.
Egger, M., Smith, G.D., Schneider, M. and Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), pp.629–634.
Fasanya, I.O. and Onakoya, A.B.O. (2012). Does Foreign Aid Accelerate Economic Growth? An Empirical Analysis For Nigeria. International Journal of Economics and Financial Issues, 2(4), pp.423–431.
Furukawa, C. (2019). Publication Bias under Aggregation Frictions: Theory, Evidence, and a New Correction Method. SSRN Electronic Journal.
Hansen, B.E. (2007). Least Squares Model Averaging. Econometrica, 75(4), pp.1175–1189.
Ioannidis, J.P.A., Stanley, T.D. and Doucouliagos, H. (2017). The Power of Bias in Economics Research. The Economic Journal, 127(605), pp.F236–F265.
Irsova, Z., Bom, P.R.D., Havranek, T. and Rachinger, H. (2023). Spurious Precision in Meta-Analysis.
Juselius, K., Møller, N.F. and Tarp, F. (2013). The Long-Run Impact of Foreign Aid in 36 African Countries: Insights from Multivariate Time Series Analysis. Oxford Bulletin of Economics and Statistics, 76(2), pp.153–184.
Khan, M.A. and Ahmed, A. (2007). Foreign Aid—Blessing or Curse: Evidence from Pakistan. The Pakistan Development Review, 46(3), pp.215–240.
Mekasha, Tseday & Tarp, Finn. (2019). A Meta-Analysis of Aid Effectiveness: Revisiting the Evidence. Politics and Governance, 7(2), pp.5–28.
Rahnama, M., Fawaz, F. and Gittings, K. (2017). The Effects of Foreign Aid on Economic Growth in Developing Countries. The Journal of Developing Areas, 51(3), pp.153–171.
Stanley, T.D. (2005). Beyond Publication Bias. Journal of Economic Surveys, 19(3), pp.309-345.
Steel, M.F.J. (2020). Model Averaging and Its Use in Economics. Journal of Economic Literature, 58(3), pp.644–719.
Van Aert, R. and Van Assen, M. (2019). P-uniform*: A new meta-analytic method to correct for publication bias.

Preliminary scope of work

Research question and motivation

Foreign aid, a twentieth-century innovation involving the voluntary transfer of resources from one country to another, has become a familiar and often anticipated element in international relations. Countries may decide to provide aid to others facing hardships for humanitarian, altruistic, or diplomatic reasons, primarily to foster their economic growth. However, foreign aid has faced substantial criticism for its potential unintended consequences, such as disincentives for labor and production, distortion of social safety nets, price structures, trade, and others, questioning overall the effectiveness of foreign aid in promoting economic growth. It remains a contentious issue, debated both theoretically and empirically. Therefore naturally, numerous studies have been conducted over the years on such topic, yielding different assessments of the correlation between aid and economic growth.

For instance, a study examining the effects of foreign aid (ODA) on key macroeconomic variables in 36 sub-Saharan African countries from the mid-1960s to 2007 concluded that there is a significantly positive long-term impact on investment and GDP in the vast majority of cases (Juselius, Møller, and Tarp, 2013). However, as Alesina and Dollar (2000) pointed out, a significant portion of foreign aid from developed to developing countries is wasted and only increases unproductive public consumption. Factors such as poor institutional development, corruption, inefficiencies, and bureaucratic failures in the recipient countries are often cited as reasons for these disappointing outcomes. Rahnama, Fawaz, and Gittings (2017) analyzed data from developing countries spanning 1970 to 2010, considering variables such as the unemployment rate, capital formation, budget surplus, inflation rate, degree of openness to international trade, and corruption. They found that financial aid had an adverse effect on economic growth. Their study demonstrated that poor institutional quality and corruption hinder the potential benefits of foreign aid.

The effectiveness of foreign aid in promoting economic growth is influenced by the specific conditions of each recipient country. For instance, a study on Pakistan covering the period from 1972 to 2006 found that financial aid acted more as a curse than a blessing due to the country's poor state and ineffective methods used (Khan and Ahmed, 2007). Conversely, the situation in Nigeria during 1970-2010 was different. Research by Fasanya and Onakoya (2012) provides evidence that foreign aid positively impacted economic growth in Nigeria, highlighting the importance of context in determining the outcomes of foreign aid.

Given this background, I will meta-analytically examine the following question in my bachelor thesis: Is publication bias present in the literature about the effect of foreign financial aid on economic growth and does it exaggerate the mean value of the estimated effect? What factors influence this relationship?

Contribution

Investigating the efficiency of foreign financial aid is crucial as it forms the basis for policy implications. The primary aim of this study is to update the latest meta-analysis on the effectiveness of foreign aid, as conducted by Mekasha and Tarp (2019) and Doucouliagos and Paldam (2008). Since the sample of these analyses only extends until 2011, and more than a decade has passed since then, I will include newly emerged studies from 2011 to 2024. In addition to expanding and updating the sample coverage, I will focus on comparing factors that contribute to or detract from the success of financial aid. These factors may include the GDP of the recipient country, the level of corruption, and other relevant variables. Such detailed analyses were not included in the latest meta-analysis, as they primarily focused on differences between years. However, it has been demonstrated that factors such as these play a significant role in the effectiveness of foreign financial aid, making it important to include such analysis in this study.

Moreover, while previous studies did incorporate the examination of publication bias and heterogeneity, I intend to employ additional modern methodologies, including linear, non-linear and methods allowing for endogeneity, that could potentially yield different outcomes. These variations in methods can enhance the depth of analysis, giving clearer and more accurate conclusions. This approach can help to draw a deeper understanding of the topic and provide valuable insights for policymakers aiming to optimize financial support mechanisms for genuine impact.

Methodology

Collecting data is a critical and challenging aspect of conducting a meta-analysis. The first step involves defining a search query and using the Google Scholar database to perform a full-text search of relevant studies to build a dataset of primary research. I will also review existing meta-analyses on this topic, such as those by Mekasha and Tarp (2019) and Doucouliagos and Paldam (2008) and incorporate their findings into my study. Additionally, I will include more recent studies to update the evidence in the dataset. Careful selection of studies is essential, as I will need to decide which studies to include and which to exclude, along with the reasons for these decisions. The focus will be on collecting estimates of comparable effect, along with corresponding standard errors and other relevant data. Once the dataset is compiled, I will examine it for publication bias and heterogeneity.

To examine publication bias, I will employ both linear and non-linear techniques. For a visual assessment, I will use a funnel plot (Egger et al., 1997), though it can be subject to interpretation. Therefore, I will also apply the funnel asymmetry test (Stanley, 2005). For non-linear techniques, where publication bias is not a linear function of the standard error, I will utilize several methods. These include statistical power and bias analysis (Ioannidis et al., 2017), the selection model (Andrews & Kasy, 2019), the stem-based method (Furukawa, 2019), the kinked meta-regression model (Bom & Rachinger, 2019), the p-uniform* method (Aert & Assen, 2019) and Instrumental Variable Estimator (MAIVE), which employs inverse sample size as an instrument for reported variance. In the second part, I will address model uncertainty using Bayesian model averaging (BMA), as outlined by Steel (2020). Additionally, I will apply the frequentist model averaging technique following Hansen (2007) and Amini & Parmeter (2012). Furthermore, I will report several robustness checks with different priors and weights for BMA.

Outline
1. Introduction
2. Literature review
• the topic and its significance
• existing meta-analysis and previous literature
• approaches to estimating the effect
3. Data
• inclusion criteria and final data set
• summary statistics
4. Publication bias
• graphical method: Funnel plot
• linear methods
• non-linear methods
• methods allowing for endogeneity
5. Heterogeneity
• coding of variables
• estimation
6. The best practice estimate
7. Conclusion