Applied Econometrics and International Development Vol. 16-1 (2016)

AID-FOR-TRADE AND EXPORT PERFORMANCE OF DEVELOPING COUNTRIES

Shankar GHIMIRE1 Debasri MUKHERJEE

Eskander ALVI Abstract. This paper employs system-GMM technique to examine whether foreign-aid specifically targeted for trade promotion (Aid-for-Trade or AFT) helps aid-receiving countries improve their export performance, as measured by export level, export growth, and the change in export-GDP ratio. System-GMM is used to control for the underlying endogeneity problem as well as to capture dynamic behavior of the relation over time. We find positive and significant effect of AFT on multiple measures of export performance. However, the targeted aid is found to exhibit diminishing returns, thus reinforcing the idea of AFT’s important but limited role in the export promotion of aid recipients. JEL Classification: C-23, F-35, O-19 Key Words: Aid-for-Trade, Exports, Dynamic Panel, System-GMM. 1. Introduction:

This paper examines the effectiveness of foreign aid in developing countries by focusing on aid that is specifically targeted to improve trade sector performance of developing countries, referred to in the literature as Aid-for-Trade (AFT). The effectiveness of AFT is examined by studying its impact on the level of total exports, by using five different measures of AFT and employing system-GMM technique that takes care of endogeneity as well as dynamic behavior of the relation over time. Additionally, any possibility of diminishing returns of AFT on export levels is examined. The impacts of AFT on export growth and change in export to GDP ratio are also investigated as robustness checks.

The possible influence of AFT on exports of developing countries is based on three main conceptual arguments. First, developing countries lack access to international markets. To address this problem, donor countries can potentially provide some of the recipient countries preferential access to the donors’ markets. Second, developing countries face supply-side infrastructure constraints and AFT is clearly designed to help the developing countries improve their export capacity and reduce these supply-side constraints by targeting aid to transportation, communication, energy, etc. This is supported by the fact that up to 60% of total AFT is spent on improving infrastructure and a significant amount of AFT (up to 44%) is spent on business promotion and banking services geared towards helping domestic production. 2 Third, aid can also help improve trade policy of the recipient countries. These strategies are

1 Shankar Ghimire, Assistant Professor, Department of Economics and Decision Sciences, Western Illinois University, Macomb,IL 61455L, USA. Contact Email: sp-ghimire@wiu.edu, Debasri Mukherjee and Eskander Alvi, Professors, Department of Economics, Western Michigan University, Kalamazoo, MI, USA. 2 See Appendix A for breakdown of AFT categories.

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expected to have a positive impact on recipient exports and our analysis in-fact shows strong evidence in favor of AFT for improving export performance.

The remainder of the paper is organized as follows: Section 2 presents a brief literature review on aid-exports relation, while Section 3 discusses data, measures of AFT and estimation method that is used. Section 4 explains the empirical findings while Section 5 concludes.

2. Literature Review

Before the systematic discussion on AFT started in 2005, previous academic literature such as Lloyd, McGillivray, and Morrissey (2000), Wagner (2003), and Morrissey (2006) concentrated on examining the relationship between aggregate foreign-aid (Official Development Assistance or ODA) and trade. The studies present mixed results; for example, Wagner (2003) shows that donors’ exports increase more than recipients’ exports due to disbursement of foreign aid (ODA) to developing countries, while Lloyd et al (2000) found the opposite. More recent literature focuses on effects of targeted aid on the specific goal; see for example, Mukherjee and Thomas Kizhakethalackal (2013), Alvi, Mukherjee and Thomas Kizhakethalackal (2013), which investigate the effects of health-aid on infant mortality rate in particular. Pettersson and Johansson (2011) study an aggregate bilateral trade relationship between the donors and recipients with respect to different aspects of foreign aid. While they find that general foreign aid impacts both donor exports and recipient exports positively, AFT benefits donor exports more significantly than it does for recipient exports. At a more regional level, Cali, Razzaque & te Velde (2011) find that AfT does in-fact benefit small Caribbean island countries. Using data on sectoral exports and sector specific AFT, Ghimire, Mukherjee, and Alvi (2013) find significantly positive impact of AFT on exports at the sectoral level.

While most previous studies have examined the impact of bilateral AFT on bilateral exports (such as Petterson and Johansson, 2011), this study focuses on the effect of total (bilateral + multilateral) AFT on the exports of developing countries to the rest of the world, by using various measures of AFT. We also distinguish between the effects of bilateral versus multilateral AFT in the context. Since exports is considered to be an “engine of economic” growth (Awokuse, 2006; Hausmann & Rodrik, 2006), it is more crucial for the developing countries to be able to export, than which country exports are oriented to.

It is well-known in the aid literature that aid is endogenous to the outcome variable - aid is supposed to affect the goal, while the disbursement of the aid itself depends on the severity of the factor it is disbursed for. Unlike bilateral aid, it is hard to find proper instrumental variables (addressing endogeneity) for the total or the multilateral aid. However, studies such as Dalgaard, Hansen, and Tarp (2004) have shown that the lag of aid serves as the best instrument for it. System-GMM technique uses lagged values of the endogenous variables as instruments, and helps researchers choose the lag-length (for panel data) in a scientific way, producing efficient estimators. Along these lines, one of the key contributions of our paper is the use of system-GMM approach which is new to AFT-export literature.

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3. Data and Empirical Method

This study analyzes an annual level longitudinal dataset of 121 AFT-recipient countries over a period of 16 years [1995-2010]. Although discussion on AFT began with a design to help the least developed countries, there are a number of middle income countries benefitting as well.3 As such, this study considers countries categorized as low-income and middle-income by the World Bank. The AFT data are obtained from OECD’s Creditor Reporting System (CRS); detailed discussion on AFT measurement is presented below. The export data are obtained from United Nation’s Conference on Trade and Development (UNCTAD). Trade openness as measured by trade freedom is obtained from Heritage Foundation. Other control variables included in the analysis are obtained from the World Bank: GDP per capita, money supply, exchange rate from the World Development Indicator (WDI) database; and the institutional variables - control of corruption and regulatory quality - from the World Governance Indicator (WGI) database, which are produced by Kaufmann, Kraay, and Mastruzzi (2010).

A summary statistics of the data is presented in Table (1), where exports, aid, income, exchange rate, and money supply are measured in real (2005) US Dollars and are reported in their natural log form. Trade openness is an index that ranges between 0 and 100 with higher value representing more open economies. Similarly, control of corruption and regulatory quality range between -2.5 and +2.5, with higher values indicating higher quality of institutions and vice versa.

Measuring Aid-for-Trade (AFT):

It should be noted that AFT is not a separate category of foreign aid. Following the guidelines from the Organization for Economic Cooperation and Development (OECD)4, the AFT measure is constructed by summing the amount of aid flowing into sectors that directly enhance economic infrastructures and other services expected to promote exports. These data are obtained from the CRS database maintained by OECD under different headings.5 From these broader headings, total AFT reflects the sum of aid that is categorically spent for: (i) trade policy and regulations, (ii) trade related infrastructure, and (iii) productive capacity building.

In this paper, the impact of AFT on exports is analyzed with respect to the broader measure, total AFT, as well as some narrower measures. Based on the end use of AFT, the narrower measure (AFT1) reflects the amount of aid that is spent solely on trade policy and regulations. A slightly broader measure, AFT2, consists of AFT1 plus the aid that is provided for trade-related infrastructures – such as transport and storage, communications, and energy. Finally, the broadest measure, total AFT, consists of AFT2 plus the aid provided for productive capacity buildings. Further details on AFT construction are presented in Appendix A. This paper also distinguishes the impact of bilateral versus multilateral AFT. 3 See Appendix B for list of countries included in the study. 4 OECD: http://www.oecd.org/document/17/0,3746,en_2649_34665_46582545_1_1_1_1,00.html 5 See Appendix A for further details.

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Table:1 Summary Statistics Variable Mean SD Min Max Exports 21.71098 2.306137 15.27629 33.27481 Export Growth 0.0018119 0.2942804 -3.41573 2.391069 Share Change 0.0018304 0.0590197 -0.3885911 0.4082006 AFT1 13.03644 2.435642 2.98017 20.49552 AFT2 16.76738 2.680089 7.942404 26.6331 AFT 18.11981 1.849304 11.62564 28.4408 Bi-AFT 17.17084 2.17415 8.268145 27.20644 Multi-AFT 16.56545 2.632257 2.230593 28.09686 Income 7.325953 1.252625 3.592018 17.27936 Openness 61.49815 16.95288 0 95 Exchange Rate 4.135976 2.537803 0.3551887 10.81656 Money Supply 32.57938 3.213639 24.61211 41.97512 Corruption Control -0.499709 0.6381646 -2.489213 1.563225 Regulatory Quality -0.502213 0.7356837 -2.481155 1.587131 Number of Aid Recipients: 121

Note: Monetary values represent the natural log of real USD using 2005 as the base year.

Measuring Export Performance:

The paper primarily examines the impact of AFT on the export levels. Export levels are measured by the dollar value (adjusted for inflation) of total exports: goods and services added together. We also use exports growth and export to GDP ratio, defined as:

Where, X denotes the export volume measured in US Dollar, ‘i’ represent 121 AFT-receiving developing countries and ‘t’ represent yearly observations from 1995 through 2010.

Model Specification and Estimation Technique: Since current exports are likely to depend heavily on past exports, the paper

uses a dynamic panel specification while analyzing the impact of AFT on the level of exports. The specification also includes important control variables that are part of most trade regressions. The empirical model takes the following form:

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Where, represents the log of export level from an aid recipient country ‘i’ to the rest of the world at time period ‘t’. denotes the log of Aid-for-Trade, the main variable of interest impacting exports. Our control variables are chosen from the existing literature (see Ghimire et al., 2013 for example). is the log of real GDP per capita of the exporting country. represents a measure of financial development (real broad money supply as a proxy), which plays a crucial role in financing export promoting businesses (Beck, 2002). represents the real exchange rate of domestic currency vis-à-vis US Dollar, which is an important determinant of exports (Hsing & Guisan, 2011). and represent control of corruption and regulatory quality, respectively. These institutional measures are relevant in this type of study as their quality determines the effectiveness of foreign aid (Burnside and Dollar, 2000). Finally, represents the trade openness measured by trade freedom of that country. This is an index measuring import tariff and quota, voluntary export restraint, etc. So, it is expected to proxy for both import and export openness. All of these variables are expected to impact exports positively. In the model presented above, represents the unobserved heterogeneity across individual countries and represents the idiosyncratic error.

As argued earlier, aid is endogenous to the outcome variable (exports in our case). Also the presence of the lagged dependent variable along with country-specific heterogeneity adds to the endogeneity problem, as is well known (See, Arellano, 2003 for details). To address these issues, system Generalized Method of Moments (GMM) is used for estimation (See Arellano and Bover 1995). 6 This technique uses instrumental variables from the system that consist of the exogenous and predetermined (lagged) variables in the regression model. Sargan Test and second order autocorrelation tests performed for each of the regressions fail to reject the validity of the instrumental variables, hence upholding the application of System-GMM for our data. This method provides efficient estimators and has been a popular method of analysis in studies where the problem of endogeniety is pervasive (see for example - Osakwe, 2007; Elhiraika & Mbate, 2014, etc.). While analyzing the effect of AFT on export growth (change compared to the previous year) and change in export-GDP ratio, we use explanatory variables in both level and growth (change compared to the previous year) terms alternatively.

4. Empirical Results

Table (2) presents an analysis of the impact of AFT on export levels. We find that all three specifications based on the end use (AFT1, AFT2, and AFT) have positive and highly significant coefficients on the level of total exports. Similarly, classifications of AFT based on their origin (bilateral AFT and multilateral AFT) also have a similar impact on the recipient’s total exports - a positive and statistically significant impact. One striking observation is the favorable impact of total AfT with large and strong statistical significance.

6 Econometric estimation is performed using STATA-11

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Table 2: Level of Export (System-GMM estimation) (1) (2) (3) (4) (5) Income 0.4515*** 0.5887*** 0.9974*** 0.4767*** 1.0580*** (0.0199) (0.0190) (0.0232) (0.0262) (0.0386) Openness -0.00407*** -0.00249*** -0.000906** -0.00188*** -0.00298*** (0.000619) (0.000634) (0.000377) (0.000312) (0.000298) Money Supply 0.08832*** -0.1053*** -0.1090*** -0.01501 0.1184*** (0.0166) (0.0243) (0.0257) (0.0110) (0.0215) Exchange Rate 0.06165** 0.1574*** 0.3263*** 0.07723*** 0.2850*** (0.0251) (0.0162) (0.0199) (0.0139) (0.0238) Corrupt. Control -0.1324*** -0.03116 0.02914 -0.1337*** -0.1992*** (0.0336) (0.0296) (0.0334) (0.0332) (0.0261) Regulation (RQ) 0.2618*** 0.2383*** 0.02565 0.3155*** 0.04014 (0.0455) (0.0385) (0.0182) (0.0304) (0.0343) AFT1 0.003599*** (0.000913) AFT2 0.005032*** (0.00149) AFT 0.03499*** (0.00270) Bi-AFT 0.01545*** (0.00329) Multi-AFT 0.004261*** (0.000919) N 537 504 600 600 456 Sargan_Test 0.9997 0.9986 0.9202 0.9989 0.9998 AR2_Test 0.9930 0.7293 0.4775 0.9385 0.8157 Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

These results demonstrate that developing countries are indeed benefitting from comprehensive assistance as shown by an increase in their level of exports to the global market.7

7 We emphasize the impact of AFT, but there are other control variables that also contribute to the analysis of export levels. Income of exporting countries exhibits a positive and significant impact on exports in all regressions, as expected. Similarly, an increase in the exchange rate (i.e. - devaluation of domestic currency vis-à-vis USD) is found to improve exports of total goods and services. This is consistent with the general theoretical assumptions of currency devaluation. The impact of financial development, measured by the level of money supply on the level of total exports, or that of control over corruption as a measure of institutional quality display mixed results. Finally, as expected regulatory quality measuring the ability of the government to create business-friendly policies and regulations has, for the most part, a positive and significant impact.

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Table (3) presents a negative coefficient on AFT-squared implying that AFT exhibits diminishing returns in its effectiveness on exports. A deeper analysis to identify the most appropriate amount of AFT that produces optimum results can be an interesting future line of research.

Table 3: Level of Exports (checking for diminishing returns). (1) Income 0.7479*** (0.0372) Openness -0.002109*** (0.000414) Money Supply -0.004011 (0.0268) Exchange Rate 0.1920*** (0.0262) Corruption Control -0.02513 (0.0195) Regulation (RQ) 0.1507*** (0.0244) AFT 0.1330** (0.0525) AFT-Squared -0.003395** (0.00153) N 600 Sargan_Test 0.9998 AR2_Test 0.8993

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

Robustness checks:

We perform robustness checks of our results by considering two other measures of export performance – export growth and change in export to GDP ratio which are presented in Tables 4 and 5 respectively.

Our results show significantly positive impacts of both AFT and AFT-growth on export growth as well as change in export-to-GDP ratio, suggesting strong evidence in favor of AFT in enhancing export performance.

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Table 4: Export Growth (System-GMM estimation) (N=415) (1) (2) Income 0.6940*** (0.0388) Exchange Rate 0.1939*** (0.0285) Money Supply -0.08962*** (0.0280) Openness -0.003853*** -0.004734*** (0.000611) (0.000363) Regulation (RQ) -0.1407*** 0.1578*** (0.0518) (0.0345) Corruption Control -0.003521 -0.02633 (0.0446) (0.0221) Income Growth 0.9062*** (0.0768) Exchange Rate Growth -0.08354 (0.106) Money Supply Growth 0.1872*** (0.0320) AFT 0.03341*** 0.006759 (0.00517) (0.00660) AFT Growth 0.01113*** (0.00410) Sargan_Test 0.4915 0.9975 AR2_Test 0.9873 0.1952

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

Table 5: Export to GDP ratio (System-GMM estimation) (N=377) (1) (2) Income 0.05076***(0.00774) Exchange Rate 0.04479***(0.00458) Money Supply -0.04062***(0.00285) Openness -0.0006651***(0.000137) -0.0009648***(0.000116) Regulation (RQ) -0.002370 (0.00527) 0.02373**(0.0105) Corruption Control 0.008316

(0.00800) 0.02346** (0.0115)

Income Growth -0.1003***(0.0198) Exchange Rate Growth -0.1959***(0.0183) Money Supply Growth 0.1362***(0.00771) AFT 0.009409*** -0.002536 (0.00109) (0.00292) AFT Growth 0.006606*** (0.00148) Sargan_Test 0.7896 0.8918 AR2_Test 0.5223 0.4004

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

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5. Conclusions:

This paper contributes to the literature by employing system-GMM technique to scrutinize the effectiveness of AFT on various export performance measures of aid recipient developing countries. While the rationale of the econometric method employed is based on the fact that (i) aid is inherently endogenous, and a (ii) dynamic panel analysis with cross-country heterogeneity adds to the problem of endoegeneity; the appropriateness of the system GMM method used is verified by our Sargan and AR(2) test results, supporting the validity of the instruments chosen. Thus system-GMM not only produces consistent estimators, it also provides efficient estimators. We consider several different measures of AFT as well as export performance for a comprehensive overview of the AFT-export nexus. Overall, this study finds that AFT has a positive impact on export performance, which further calls for increased export-targeted foreign-aid to developing countries since many countries are yet to realize the widespread benefits of trade. The study also finds diminishing returns to AFT calling for a deeper analysis as to how much aid is optimal for the purpose.

References:

Arellano, M. (2003). Panel Data Econometrics. Oxford: Oxford University Press. Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and Application to Employment Equations. The Review of Economic Studies. 58: 277 – 297. Arellano, M. & Bover. O. (1995). Another Look at the Instrumental Variable Estimation of Error Components Models. Journal of Econometrics. 68:1. 29-51. Awokuse, T. (2006). Causality between Exports, Imports, and Economic Growth: Evidence from Transition Economics. Economic Letters. 94(3): 389-395. Beck, T. (2002). Financial Development and International Trade – Is there a Link? Journal of International Economics. 57: 107 – 131. Burnside, C. & Dollar, D. (2000). Aid, Policies, and Growth. American Economic Review. 4 (90): 847–868. Cali, M., Razzaque, M. & te Velde, D. (2011). Effectiveness of Aid for Trade in Small and Vulnerable Economies. Economic Paper. 91. Dalgaard C.J., Hansen, H. and Tarp, F. (2004) “On the empirics of foreign aid and growth” Economic Journal. 114: F191-F216. Elhiraika, A. & Mbate, M. (2014). "Assessing the Determinants of Export Diversification in Africa," Applied Econometrics and International Development, Euro-American Association of Economic Development. 14(1): 147-160. Ghimire, S., Mukherjee, D. and Alvi, E. (2013). “Sectoral Aid-for-Trade and Sectoral Exports: A Seemingly Unrelated Regression Analysis”. Economics Bulletin. 33(4): 2744-2755. Hausmann, R. & Rodrik, D. (2006). What You Export Matters. Journal of Economic Growth. 12(1): 1-25. Heritage Foundation (2012). Economic Freedom Index. http://www.heritage.org/index/ . Accessed May 2012. Hsing, Y. & Guisan, M.C. (2011)."Real Exchange Rate, Foreign Trade And Real Output Growth: The Case Of Spain, 1970-2009," Applied Econometrics and

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International Development, Euro-American Association of Economic Development, vol. 11(1). Thomas Kizhakethalackal, E. , Mukherjee, D. & Alvi, E. (2013). Quantile Regression Analysis of Health-aid and Infant Mortality: A Note. Applied Economics Letters. 20(13): 1197-1201. Lloyd, T., McGillivray, M., Morrissey, O. & Osei, R. (2000). ‘Does aid create trade?. European Journal of Development Research. 12(1): 107-123 Morrissey, O. (2006). Aid or Trade, or Aid and Trade? The Australian Economic Review. 39(1): 78–88. Mukherjee, D. and Thomas Kizhakethalackal, E. (2013). The Empirics of Health-aid, Education and Infant Mortality: A Semiparametric Study. Applied Economics. 45(22): 3137-3150. OECD (2006). Aid for Trade: Making it Effective. The Development Dimension. OECD(2012).Creditor Reporting System. Accessed May 2012. http://stats.oecd.org/Index.aspx?DatasetCode=CRSNEW . Osakwe, P. N. (2007). Export Diversification and the Dilemma of African Development. Applied Econometrics and International Development, Euro-American Association of Economic Development. 7(2): 143-154. Pettersson, J. & Johansson, L. (2011). Aid, Aid for Trade, and Bilateral Trade: And Empirical Study. The Journal of International Trade and Economic Development: An International and Comparative Review. DOI:10.1080/09638199.2011.613998 StataCorp. 2011. Statistical Software: Release 11.0. College Station. TX: Stata Corporation. Wagner, D. (2003). Aid and Trade – An Empirical Study. Journal of the Japanese and International Economics. 17: 153-173. World Bank (2011). World Development Indicators. http://data.worldbank.org/data-catalog/world-development-indicators . Accessed May 2012. World Bank (2011). World Governance Indicators. Accesed May 2012 http://info.worldbank.org/governance/wgi/sc_country.asp

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Appendix A: Official Development Assistance

Foreign Aid Categories (Official Development Assistance) Not included in AFT Included in AFT

Education Category 1 Health Trade Policies & Regulations Food Aid Population Policies Category 2 Water and Sanitation Transport & Storage Government and Civil Society Communications Conflict Resolution and Peace Energy Social Infrastructure Environmental Protection Category 3 General Budget Support Banking & Financial Services Debt Relief Business & Other Services Humanitarian Aid Agriculture Emergency Relief Forestry Disaster Prevention Fishing Administrative Costs Industry Support to NGOs Mineral Resources & Mining Refugee Management Tourism

AFT as a Percent of ODA for Select Years

2002 2006 2010 AFT/ODA 24% 18% 29%

AFT Categories as a percent of total AFT Category 1 4% 4% 3% Category 2 52% 52% 60% Category 3 44% 44% 37%

Broad and Narrow Measures of Aid: AFT1 = Category 1; AFT 2 = Category 1 + Category 2; Total AFT = Category 1 + Category 2 + Category 3 Source: OECD

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Appendix B: List of Countries Included in the Study

Afghanistan Ghana Panama Albania Grenada Papua New Guinea Algeria Guatemala Paraguay Angola Guinea Peru Argentina Guinea-Bissau Philippines Armenia Guyana Rwanda Azerbaijan Haiti Samoa Bangladesh Honduras Sao Tome and Principe Belarus India Senegal Belize Indonesia Serbia Benin Iran, Islamic Rep. Seychelles Bhutan Iraq Sierra Leone Bolivia Jamaica Solomon Islands Bosnia and Herzegovina Jordan South Africa Botswana Kazakhstan Sri Lanka Brazil Kenya St. Lucia Burkina Faso Kosovo St. Vincent and the Gren Burundi Kyrgyz Republic Sudan Cambodia Lao PDR Suriname Cameroon Lebanon Swaziland Cape Verde Lesotho Syrian Arab Republic Central African Republic Liberia Tajikistan Chad Macedonia, FYR Tanzania Chile Madagascar Thailand China Malawi Timor-Leste Colombia Malaysia Togo Comoros Maldives Tonga Congo, Dem. Rep. Mali Tunisia Congo, Rep. Mauritania Turkey Costa Rica Mauritius Uganda Cote d'Ivoire Mexico Ukraine Djibouti Moldova Uruguay Dominica Mongolia Vanuatu Dominican Republic Montenegro Venezuela, RB Ecuador Morocco Vietnam Egypt, Arab Rep. Mozambique Yemen, Rep. El Salvador Namibia Zambia Ethiopia Nepal Zimbabwe Fiji Nicaragua Panama Gabon Niger Papua New Guinea Gambia, The Nigeria Paraguay Georgia Pakistan Peru

Journal published by the EAAEDS: http://www.usc.es/economet/eaat.htm