Professor Finn Tarp Director, UNU-WIDER. Aid, Growth and Development

Transcription

1 Professor Finn Tarp Director, UNU-WIDER Aid, Growth and Development

2 Meeting in the Danish Economic Association Copenhagen, Denmark 10 October 2012

3 Part I Introduction and Motivation Boserup (1966): Are We Helping?

4 Revisiting the Past After >45 years -easy to criticise aid in hindsight Aid has many roles and is working for many masters Emergency relief Social sectors Economic growth Purely political motives (e.g. Camp Davis Egypt) Aid is only one (composite) player and failures of aid often confounded with failures of development policy Development is not so easy after all and development failures have often been confounded with aid failures Unrealistic expectations but does that mean aid has played no role? And that we can t document it?

5 One Key (Macro) Question of Interest Does foreign aid boost economic growth on average in developing countries? Much debated both in the academic and popular literature (influencing perceptions) The notion that aid can alleviate systemic poverty, and has done so, is a myth. Millions in Africa are poorer today because of aid; misery and poverty have not ended but have increased. (Dambisa Moyo, 2009) A reasonable estimate is that over the last thirty years [aid] has added around one percentage point to the annual growth rate of the bottom billion. (Paul Collier, 2007)

6 Outline What have we learnt about aid, growth and development over the last years? What does recent UNU-WIDER research (ReCom) contribute? What are present trends and future challenges?

7 Part II Four Generations of Empirical Macroliterature A Tale of Moving Goal Posts

8 Aid and Growth 1970s and 1980s Early optimism Papanek s high-profile articles using simple cross-country regressions But increasing disappointment with traditional (Harrod- Domar and two gap) models Aid may work at micro but its impact is not only smaller than predicted (for many reasons) it also somehow evaporates on its way to the macro level (micro-macro paradox) Eventually widespread perception of failure reported in influential so-called summary studies by Mosley, Anne Krueger, Howard White etc But what did the simple cross-country research actually show? No impact??

9 Aid Effectiveness Disputed Hansen and Tarp Journal of International Development (2000) and Routledge (2000)

10 The Early Literature Hansen and Tarp (2000) 131 early (simple) cross-country regression studies.. Some studies showed aid associated with decreased domestic savings BUT only one study (and one regression) (Gupta & Islam, 1983) shows negative impact is greater than the aid Aid increases investment! Not a single study contradicts Only one study (and one regression) (Mosley, 1987) shows negative impact on growth (and the insignificant studies overwhelmingly dominated by one study with misspecified model ) Aid seemed to work on average But then the goal posts moved

11 Aid and Growth in the 1990s (Panel Data Cross-country Regressions) New data: panel data New theory (introducing economic policy and institutions directly) Taking account of the endogeneity of aid Taking non-linearity serious New econometric methods dynamic panels (GMM) Boone (1994): Aid is down the rathole But Boone soon started fading.

12 Aid and Growth: Burnside-Dollar Burnside-Dollar: aid works But only in good policy countries Burnside-Dollar cut the Gordian knot introducing an aid x policy interaction term in the statistical analysis alongside aid itself (aid insignificant, interaction significant at 10%) Note underlying development paradigm and key policy implication: selectivity (not conditionality) Discussion about how one defines good policy

13 Aid and Growth Regressions Hansen and Tarp Journal of Development Economics (2000)

14 A More Convincing Story Hansen and Tarp (2001) there is a more convincing story/better description of the data: Aid works, but diminishing returns The interaction term, aid x policy, looses out to aid squared! And policy also works Easterly: New data, new doubts! But Burnside-Dollar continued influential (although gradually undermined in practice)

15 On the Empirics of Foreign Aid and Growth Dalgaard, Hansen and Tarp Economic Journal (2004)

16 Structural Characteristics Key No empirical support for the hypothesis that aid only works in association with good policy Previous research has confused poor policy and governance with complex underlying structural characteristics Aid impact heterogenous But then the goal posts moved again

17 Pessimistic Contributions after 2000 (Long Run Cross Section Averages) Leading example: Rajan and Subramanian 2008 (RS08) Long-run cross-section averages rather than dynamic panel methods RS08: no robust positive systematic effect of aid seems to hold for: different types of aid and alternative time periods The return of the micro-macro paradox Anecdotal background what drove the story?

18 Part III UNU-WIDER Foreign Aid Research ReCom Research and Communication on Foreign Aid

19 Point of Departure Aim of empirics is to falsify a prior so what is our prior? First: prior from growth theory Rajan and Subramanian (2008): 10% Aid/GDP 1% increase in per capita growth rate (but might be higher) Second: time dimension is important due to long run cumulative effects of aid Education & health (Ashraf et al. 2008; Acemoglu & Johnson 2007) One reason to opt for long-run cross-section averages rather than dynamic panel method

20 Aid Impact in the Aggregate Arndt, Jones and Tarp (AJT) Journal of Globalization and Development (2010)

21 Have We Come Full Circle Arndt-Jones-Tarp (AJT) (2010) Start from RS08 (same data and instrument), i.e. we retain focus on long-run cross-section but then: 1) Improve the instrumentation strategy 2) Strengthen the growth equation specification 3) Introduce a new treatment/control estimator Quick review of results: Cannot reject the theoretical prior of an aid-growth parameter = 0.1 (only in simple OLS is the result insignificant) In fact it appears 10% aid gives 1.3 percentage point additional growth (significant at 1%). We can reject a no impact hypothesis

22 Summary Results [ ] Instrument Specification RS08 Estimator AJT RS08 AJT RS * AJT ** RS * 0.21* AJT 0.25** 0.13***

23 1 st ReCom Conclusion: Arndt, Jones and Tarp (2010) Cross-country Analysis On average and in the long run, aggregate aid contributes positively to growth at levels predicted by growth theory aid has been associated with a growth bonus So, there is no micro-macro paradox

24 The Long Run Impact of Aid on Macro-variables in Africa Juselius, Møller and Tarp Oxford Bulletin of Economics and Statistics (forthcoming)

25 Evidence From Time-series Data Many different conclusions in literature based on the use of basically the same publicly available data bases Such differences have to be due to the choice of econometric/statistical methods: Exogeneity/endogeneity assumptions Data transformations Single equation contra a system approach

26 Our Purpose and Method Using a cointegrated VAR approach as econometric method: To offer an econometrically coherent and transparent picture of how aid has worked in 36 countries in Sub-Saharan Africa To assess previous results in the literature within a econometrically broad framework To address the widespread misuse of statistical insignificance as an argument for aid ineffectiveness

27 2 nd ReCom Conclusion: Juselius, Møller and Tarp (forthcoming) Time-series Analysis Aid has a positive long-run effect on key macrovariables (GDP, investment, consumption) for the vast majority of countries In only 3 out of 36 countries is there a negative effect of aid on GDP or investment The transmission of aid to the macro economy quite heterogeneous. Hence a country-specific approach is vital in further analysis

28 Unpacking the Aggregate Impact of Aid Arndt, Jones and Tarp World Development (special issue)

29 Motivation Many studies ask: does aid increase growth? Focus on a single outcome/result Answers the question: should we give aid? BUT many possible paths linking aid to growth Which ones matter? What should we give aid for? Thus we want to open the black box Identify key drivers linking aid to growth Non-growth outcomes important per se e.g., poverty reduction, human capital etc. (MDGs).

30 What Did We Do? 1. Replicate AJT aid-growth result with extended dataset ( ) Cross-check with other final outcomes (e.g., poverty) 2. Quantify causal impact of aid on a range of intermediate outcomes Example: aid education 3. Decompose aggregate aid effectiveness [1] into key channels via intermediate variables [2] Example: aid health growth

31 Results: Impact of Aid Outcome Baseline +$25 p.c./year GDP per capita growth Poverty headcount at $1.25 / day Agriculture (% GDP) Investment (% GDP) Av. years total schooling, Life expectancy at birth (years) Note: baseline is the observed median of the outcome variables

32 What Does This Mean for a Typical Low Income Country? Assume a country of 30 million people (1970) receiving US$ 1.25 billion/year over 37 years (Vietnam as example) Our estimates imply: Growth effect of aid delivers a 16% internal rate of return (IRR) [income gain net of the cost of aid] 70 million poverty years avoided [1.9 million fewer poor per year on average] 16 million schooling years added by 2007 Note: average effects variance of impact across countries

33 Impact Channels Aid Investment Growth (75%) Aid Education Growth (0%) Aid Health Growth (25%) Channel (Y) Aid Y Y Growth Aid Growth Investment Education Health Overall

34 3 rd ReCom Conclusion: Arndt, Jones and Tarp (forthcoming) - Unpacking Consistent and coherent pattern of results across meso- and macro-outcomes Cumulative (long-run) impact of aid, NO (average) quick wins (although variance across countries) Internal rate of return from aid (to growth) = 16% Ambiguous link from education to growth is found elsewhere Remember: positive impact of aid on education Aid supports key building-blocks for growth: physical investment human capital (health)

35 Aid and Growth: What Meta-Analysis Reveals Mekasha and Tarp Journal of Development Studies (forthcoming)

36 Background Back to the goal posts story Meta-analysis a commonly applied approach in medical science research (contested in social sciences) Main idea: to quantitatively combine empirical results from a range of independent studies & get a single effect estimate In doing so, one can either allow for or ignore the heterogeniety (differences) among studies

37 Data and Methodology A database of 68 aid-growth empirical studies identified by Doucouliagos and Paldam (2008) henceforth DP08... DP08, using a (fixed effect) meta-analysis of the 68 aid-growth studies reach a pessimistic conclusion... We make a careful assessment of their analysis and fully replicate their results We then proceed to make three key analytical improvements: economic model, statistical choices and data

38 4 th ReCom Conclusion: Mekasha and Tarp (forthcoming) Meta-Analysis DP08 s fixed effect does not address heterogeneity problematic for theoretical reasons (many papers argue the effect of aid non-linear so a function not a single constant) They mis-measure the partial effect of aid for those papers which include an interaction term with the aim of capturing the non-linearity in the aid-growth relation The assumption of heterogeniety in the true effect of aid on growth across studies is confirmed Statistical tests + graphical tools Controlling for heterogeniety (random effect model), the weighted average effect of aid on growth is found to be positive & statistically significant

39 Part IV Present Trends Big Picture Evidence and Context

40 So Growing body of up-to-date academic evidence using different methods aid has worked, but impact is heterogeneous Supported by many studies: CGD: Economic Journal Case studies (Mozambique) Large health gains And the non-econometric evidence

41 CGD IDA Working Group Observations (Soft Lending with Fewer Poor Countries) Within 10 years 36 of 68 current IDA recipients will graduate out of IDA Especially the large countries will graduate India, Vietnam, Pakistan, Nigeria, Ghana and Kenya Population in IDA eligible countries will fall from 3 to 1 billion IDA evolving into a small country fragile states facility The changing geography of poverty 20 years ago 90% of the poor in low income, today 75% in middle income

42 Other Notable Trends More diverse financing (FDI etc) and better macroeconomic management in many countries Several hard (fragile) cases remain and small & poor countries still vulnerable to shocks New needs: global public goods (climate, health) New sources of supply the emergence of new donors: countries and private foundations Overall: Much more complexity

43 Part V Conclusion and Future Challanges Declare Success and Shrink Or?

44 Should We Worry about Aid? Aid s critics would say NO (some even say growth will rise if aid is eliminated, others say aid has no effect) Weight of empirical evidence: Aid s aggegate impact conforms to priors from modern growth theory (i.e. 10% aid/gni gives 1.3% additional growth) and no evidence aid is in general harmful It would appear present financial climate (where private flows are under pressure) not a good time to experiment with Dambisa Moyo s proposal to kill aid especially for small, fragile, resource-poor countries

45 But Aid must adapt to emerging national and global contexts, including how to deal with challenges such as: Increased complexity/competition (on supply side) The development of exit strategies Dealing with a hard core of fragile states (including building state capability) The new geography of poverty Global public goods (climate, health) Much of this is unknown territory

46 Helsinki, Finland