Development Aid and portfolio Funds: Trends, Volatility and Fragmentation
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Development Aid and portfolio Funds: Trends, Volatility and Fragmentation

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Presentation by Emmanuel Frot and javier Santiso in Stockholm, February 2009.

Presentation by Emmanuel Frot and javier Santiso in Stockholm, February 2009.

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  • lundi 8 juin 2009

Development Aid and portfolio Funds: Trends, Volatility and Fragmentation Development Aid and portfolio Funds: Trends, Volatility and Fragmentation Presentation Transcript

  • Development Aid and Portfolio Funds: Trends, volatility and fragmentation Paris February 2009 Emmanuel Frot and Javier Santiso Stockholm Institute of Transition Economics OECD Development Centre
  • Research agenda in development finance
    • Compare flows along several dimensions
      • Quantity: are portfolio capital flows more important than ODA?
      • Volatility: is ODA a more stable source of income than portfolio capital flows?
      • Efficiency: is the fragmentation of ODA increasing?
    • Complementarity between flows
      • Pro or counter-cyclical role of ODA (with respect to private capital flows)
      • Can we expect ODA to insure against capital flows shortfall in the future?
      • In the current crisis, what role is there for ODA if we consider the sudden stop of private flows?
    • Towards an aid efficiency index?
    Different sources of finance for developing countries
  • ODA and capital flows quantities
    • Growth of all inflows
      • FDI and remittances increased much faster
      • FDI and remittances are more important than ODA.
      • Equity flows almost as high as ODA recently.
    • Any fall in capital flows expected to severely harm developing countries.
    • Role for ODA
    ODA has been less important than other external income sources in the past 15 years Source: Authors based World Bank and OECD data.
    • ODA is accused of being too volatile
    • But it is much less than capital flows.
    • However:
      • Its volatility has increased while those of private capital flows fell.
      • In recent years ODA has actually been more volatile than remittances (during the year 2008 trend different).
    Volatility ODA is less volatile than capital flows, hence a potential role for cushioning against shocks
  • A shock in capital flows does not trigger an ODA inflow. ODA is not countercyclical. Does ODA compensate for capital flow shortfalls?
    • In the paper:
      • ODA and capital flows are substitutes across countries: redistributive role of ODA.
      • They are neither complements nor substitutes at the country level.
    • A country experiencing a capital flow shock does not see a change in its ODA.
    • No observed risk insurance against capital flow shocks of ODA.
  • Fragmentation has become more severe: aid efficiency is reduced 1970-1979 1990-1999 1980-1989 2000-2006 Aid fragmentation for recipients: Hirschman-Herfindahl index Source: Authors based on OECD DAC data. 1,00 0,75 0,50 0,25 0,15 0,10 0,00 Non-developing country / missing data
    • DAC uses a different fragmentation measure for recipients (DAC10): number of donors that represent less than 10% of total receipts.
    • Normalizing the Hirschman-Herfindahl index to make it comparable to DAC10 actually yields very similar results.
    Average recipient fragmentation 1960-2006 Aid fragmentation for recipients Source: Authors based on OECD DAC data.
    • ODA has a role to play in a globalized world of development finance with large quantities of private capital flows.
    • The current crisis reinforces the need for countercyclical mechanisms, particularly because of the importance of sudden stops of private flows.
    • Aid fragmentation is severe whereas it increases transaction costs and reduces the value of aid.
    • There are gains to be made by implementing coordination and adopting the recommendations of the Paris declaration.
    • However ODA is a potentially more efficient tool in the development process. It does not currently use opportunities to smooth away variations harmful to developing countries. Even worse, it often adds to them.
    ODA is not exploited at its potential value Conclusion and policy implications
  • Herding in Development Aid Allocation Paris February 2009 Emmanuel Frot and Javier Santiso Stockholm Institute of Transition Economics OECD Development Centre
  • Are aid donors herding?
    • Herding has been suspected for years.
      • Cassen (1986): donors move in herd, suddenly disbursing money into star countries, and sudden increases are followed by long aid declines.
      • Riddell (2007): “herd instinct” among donors.
    • But no study has ever quantified its size or its causes.
    • Herding is defined as the tendency for donors to follow the crowd, a trend, to mimic each others’ decisions.
      • We look at simultaneous decisions about aid increases and decreases.
      • Even without herding this is expected because donors react to similar variables, and we will control for this.
      • Other reasons are closer to those identified in finance: informational cascades, strategic behaviors.
    Many claims that they are, and that this decreases aid efficiency.
    • Herding sometimes creates benefits for recipients (humanitarian aid following an emergency).
    • But also costs
      • It provokes aid swings and so contributes to volatility.
      • It increases fragmentation if it results in many uncoordinated missions.
      • It potentially creates aid darlings and orphans:
        • Reinhardt (2006): “I can't get IDB money if I drop the ball with the World Bank”.
    • Additionally, we identify which variables prompt donors to increase aid and so we improve our understanding of donor allocation policies.
    Motivation Why is it important to learn about herding mechanisms?
  • Different types of herding
    • Natural disasters cause donors to react similarly
      • Though beneficial, it may cause an overflow.
    • Debt relief is often granted in a coordinated fashion.
    • Current crisis: sudden stops in capital flows should trigger simultaneous decisions by donors.
    Beneficial herding: exogenous shocks trigger simultaneous reactions Source: Authors based and OECD data. Proportion of donors increasing aid to countries hit by the 2004 Asian tsunami
    • A fall in capital flows does not induce more donors to increase aid.
    Beneficial herding following capital flow sudden stops No evidence of beneficial herding shows no counter-cyclical decisions from donors Source: Authors based and OECD and WDI data.
  • Herding is detected by exploiting deviations from an average behaviour.
    • Carefully define aid (gross ODA net of debt relief, humanitarian and food aid) and carefully select a group of donors and recipients.
    • Adopt a time horizon suitable for aid:
      • Look at 3 and 5-year periods to avoid capturing small changes.
    • Idea behind the measure:
      • If “many” donors simultaneously increase or decrease aid to a recipient there is herding.
      • How to define “many”? We need a benchmark: Global proportion of aid changes in a period that are increases
      • If in a recipient-year the proportion of increases is far from the benchmark then it is interpreted as herding.
    How to measure herding in aid allocation
  • Finance provides some guidance about herding measurement
    • Use two measures from the finance literature that use this intuition.
      • LSV developed by Lakonishok, Shleifer and Vishny (1992).
      • h proposed by Frey, Herbst and Walter (2007) to correct for the downward bias present in LSV.
    • Results
      • All the measures adopted in the paper indicate that herding is present.
      • Using 3-year periods: LSV has a value around 3% (similar to financial markets), h has a value close to 10%.
      • It means that if 50% of all allocation changes are increases then 60% (or 53%) of donors take the same decisions for each recipient.
    Two herding measures Source: Authors based on OECD DAC data.
  • When do donors simultaneously decide to increase aid to a recipient?
    • Estimate the effect of various shocks on aid allocation decision:
      • Economic growth
      • Political transitions
      • Natural disasters
      • Armed conflicts
    • Results:
      • No effect of growth (decisions are neither pro nor counter-cyclical)
      • Positive effect of “new polity”
      • No effect of democratic transitions
      • Negative effect of authoritarian transitions
      • Positive effect of natural disasters
      • No significant effect of armed conflicts
    Herding determinants Asymmetry in political transitions
    • Netting out the effect of these determinants leaves the herding measures quite unaffected.
    • The “corrected” levels are due to herding caused by strategic behaviours, informational cascades, etc.
    • Such behaviours are more than anecdotal: Marysse et al. (2006) argue that political considerations and donor coordination problems have created aid darlings and orphans in the region of the Great Lakes in Africa.
    “ Rational”, observables causes explain very little of herding How much do these determinants account for herding? Source: Authors based and OECD data.
    • Beneficial herding occurs for natural disasters but not for other expected reasons: capital flow stops, democratic transitions, recessions.
      • Beneficial herding may also be harmful: humanitarian aid overflow is no panacea.
    • Once again aid allocation decisions are not found to be pro or counter-cyclical for many variables: growth, democratic transitions, wars, capital flow stops.
      • Nancy Birdsall argued this week that because of capital flight, credit drying up, and declining remittances, most developing countries will experience big shortfalls in revenue this year, and called for $1 trillion to be urgently unlocked. Given past experiences, this is unlikely to happen.
    • Overall it seems herding does not occur for observable reasons, but more because of some unobserved motives: there is still a lot to understand about donor allocation policies.
    • The aggregation of donors’ individual behaviours potentially leads to large aid swings and donor darlings/orphans.
    • Coordination in the donor community would help to prevent such outcomes , or to create swings when they are most needed.
    Herding was expected to be caused by external shocks, but it is only weakly so. Policy Implications
    • Volatility and fragmentation have so far been measured at the country level: extend the analysis at the sector level.
      • Is fragmentation more pronounced in some sectors? If yes, why?
      • Is volatility influenced by some sector specificities? What can be done about it?
    • Similarly, herding is likely to be stronger in sectors
      • Fads and fashions
    • These intermediate characteristics of donor allocation will ultimately be combined to build an aid efficiency index.
    These results call for further investigation Future research