herding in aid allocation

OECD DEVELOPMENT CENTRE

HERDiNg iN AiD ALLOCATiON

by

Emmanuel Frot and Javier Santiso

Research area:global Development Outlook

July 2009

Working Paper No. 279

Herding in Aid Allocation

DEV/DOC(2009)4

2 © OECD 2009

DEVELOPMENT CENTRE WORKING PAPERS

This series of working papers is intended to disseminate the Development Centre’s research findings rapidly among specialists in the field concerned. These papers are generally available in the original English or French, with a summary in the other language.

Comments on this paper would be welcome and should be sent to the OECD Development Centre, 2 rue André Pascal, 75775 PARIS CEDEX 16, France; or to [email protected]. Documents may be downloaded from: http://www.oecd.org/dev/wp or obtained via e-mail ([email protected]).

THE OPINIONS EXPRESSED AND ARGUMENTS EMPLOYED IN THIS DOCUMENT ARE THE SOLE RESPONSIBILITY OF THE AUTHORS

AND DO NOT NECESSARILY REFLECT THOSE OF THE OECD OR OF THE GOVERNMENTS OF ITS MEMBER COUNTRIES

CENTRE DE DÉVELOPPEMENT DOCUMENTS DE TRAVAIL

Cette série de documents de travail a pour but de diffuser rapidement auprès des spécialistes dans les domaines concernés les résultats des travaux de recherche du Centre de développement. Ces documents ne sont disponibles que dans leur langue originale, anglais ou français ; un résumé du document est rédigé dans l’autre langue.

Tout commentaire relatif à ce document peut être adressé au Centre de développement de l’OCDE, 2 rue André Pascal, 75775 PARIS CEDEX 16, France; ou à [email protected]. Les documents peuvent être téléchargés à partir de: http://www.oecd.org/dev/wp ou obtenus via le mél ([email protected]).

LES IDÉES EXPRIMÉES ET LES ARGUMENTS AVANCÉS DANS CE DOCUMENT SONT CEUX DES AUTEURS ET NE REFLÈTENT PAS NÉCESSAIREMENT CEUX DE L’OCDE OU DES GOUVERNEMENTS DE SES PAYS MEMBRES

Applications for permission to reproduce or translate all or part of this material should be made to: Head of Publications Service, OECD

2 rue André-Pascal, 75775 PARIS CEDEX 16, France

OECD Development Centre Working Paper No. 279

DEV/DOC(2009)4

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .......................................................................................................................... 4

PREFACE ...................................................................................................................................................... 5

ABSTRACT ................................................................................................................................................... 7

RÉSUMÉ ........................................................................................................................................................ 7

I. INTRODUCTION ..................................................................................................................................... 9

II. BEYOND FADS AND FASHIONS: BENEFICIAL HERDING ....................................................... 13

III. HERDING MEASURES ...................................................................................................................... 15

IV. ALTERNATIVE MEASURES ............................................................................................................. 20

V. GEOGRAPHICAL DISTRIBUTION OF HERDING ........................................................................ 24

VI. HERDING DETERMINANTS ........................................................................................................... 25

VII. CORRECTED HERDING MEASURE ............................................................................................. 31

VIII. CONCLUSIONS ................................................................................................................................ 33

REFERENCES ............................................................................................................................................. 35

OTHER TITLES IN THE SERIES/ AUTRES TITRES DANS LA SÉRIE .............................................. 38


DEV/DOC(2009)4

4 © OECD 2009

ACKNOWLEDGEMENTS

The authors wish to thank Torbjörn Becker, Thomas Dickinson, Burcu Hacibedel, Andrew Mold, Elizabeth Nash and Helmut Reisen for their comments and insights.

The errors and views presented in this paper are the sole responsibility of the authors.


DEV/DOC(2009)4

PREFACE

This paper is part of a series of studies on development aid issues published by the OECD Development Centre (following on from Frot and Santiso, 2008) and OECD DAC/DCD (OECD, 2008). It builds on information from some unique databases, combining OECD official statistics on development aid and others on portfolio flows from bond and equity fund managers. The overall objective is also to boost the analysis and policy recommendations related to aid developed by the OECD Development Centre, complementing other work related to the so called “emerging donors” like China (e.g. Reisen and Ndoye, 2008).

The objective is twofold: to contribute to the analysis of the industries on key issues and to foresee the possibility to build in the future an aid efficiency index. For that purpose, this paper offers a potential index on aid herding. Combined with another forthcoming companion paper, where fragmentation and volatility measures and methods are developed, it offers the possibility to build a benchmark and an aggregate index on aid efficiency, from the side of both donor and recipient.

Aid ineffectiveness, fragmentation and volatility have been underlined by many scholars and other OECD studies (OECD, 2008). Far fewer studies have been devoted to another development aid addiction: the herding behaviour of donors. It is precisely what this paper intends to do, presenting the first results on herding in aid allocation.

With this aim, the authors used a herding measure developed for financial investors, a perspective never introduced until now. Taking into account the characteristics of aid allocation, they show that herding is sizeable, and in line with what is observed on financial markets, and that it does not exist in the group of multilateral donors. On the other hand, herding by bilateral donors is always present.

The authors also tried to understand which recipient characteristics create herding, with special attention given to the impact of political transitions. They find that democratic transitions do not create any herding, neither during nor after the transition years. On the other hand, authoritarian transitions create herding out, and the effect is quite large. This result contrasts with previous work done on private bank flows and published by the OECD Development Centre, underlining that private banks tend to increase their lending in the years following democratic transitions1.

1 See Rodríguez, J. and J. Santiso (November 2007). Banking on development: private banks and aid donors in developing countries. OECD Development Centre, Working Paper, 263; and also the chapter by same both authors on


DEV/DOC(2009)4

6 © OECD 2009

This study leaves for future research the fundamental question of the motivations for donors to herd and the costs associated with this herding in and out behaviour. In the future, more efforts will be conducted in that direction in order to contribute to the academic and policy debates on aid efficiency, and help, as Karl Popper would have put it, in the search for a better world.

Javier Santiso

Director and Chief Development Economist

OECD Development Centre

June 2009

private Banks in emerging democracies published in OECD Development Centre, Financing Development 2008 asks: Whose ownership?. Paris, OECD Development Centre, 2008.


DEV/DOC(2009)4

ABSTRACT

Aid ineffectiveness, fragmentation, and volatility have already been highlighted by scholars and OECD studies. Far fewer studies have been devoted to another problem of capital flows: herding behaviour. Building upon a methodology applied to financial markets, where herding is a common feature, this article attempts to measure herding behaviour in the allocation of foreign aid, proposing different indexes that try to capture the specific features of aid allocation. Of course, herding can also be beneficial. When a country faces an earthquake, a tsunami, or any humanitarian disaster, the rush of donors is a positive factor. Excluding such cases of beneficial herding, we attempt to focus on pure herding behaviour, creating pendulum swing effects comparable to those in financial markets. . Our different indexes all detect donor herding, its exact size depending on the measure adopted. Our preferred index, relying on three-year disbursements, indicates a significant level of herding, similar to that which is found on financial markets. We also uncover major differences across different types of donors, with no, or very limited, herding among multilateral donors, in contrast to bilateral donors, always subject to herding behaviour. We then follow by investigating the empirical causes of herding. We find that while political transitions away from democracy are accompanied by herding out, transitions towards democracy do not affect herding levels. Finally, we show that observable determinants actually explain little of the herding levels, leaving a large part of herding unexplained.

RÉSUMÉ

L’inefficacité, la fragmentation et la volatilité de l’aide au développement ont été souvent soulignées dans les travaux académiques comme dans ceux de l’OCDE. Un autre écueil relatif aux flux de capitaux a été beaucoup moins étudié : les comportements moutonniers. Cet article évalue ce comportement dans l’allocation de l’aide. Il s’inspire d’une méthodologie proche de celle utilisée pour les marchés financiers et propose différents indices qui prennent en compte les caractéristiques de l’allocation de l’aide au développement. Nous avons tenté ici de nous concentrer sur les purs évènements moutonniers, en excluant les comportements mimétiques bénéfiques, liés aux afflux d’aide qui suivent les tremblements de terre, des tsunamis ou autres désastres humanitaires : dans de tels cas, le suivisme des donateurs est bénéfique. Peux-t-on pour autant, à l’exclusion de ces cas, détecter des comportements moutonniers des donateurs, qui amplifieraient les mouvements de balanciers des flux, comparables à ce que l’on rencontre dans


DEV/DOC(2009)4

8 © OECD 2009

les marchés financiers ? Nos différents indices détectent tous la présence de comportements moutonniers au sein des donateurs. La magnitude exacte de ces comportements dépend de l’indice utilisé. Notre mesure préférée, basée sur les déboursements tri-annuels, indique des niveaux de comportement moutonnier similaires à ceux trouvés sur les marchés financiers. Nous mettons aussi en évidence d’importantes différences entre les types de donateurs. Le comportement moutonnier n’existe pas, ou très peu, entre les organisations multilatérales, tandis qu’il est présent entre les donateurs bilatéraux. Nous estimons ensuite empiriquement les causes du comportement moutonnier. Nous établissons que les transitions politiques vers moins de démocratie repoussent les pays donateurs de manière coordonnée. Au contraire, les transitions vers plus de démocratie ne modifient pas simultanément les décisions d’allocation de plusieurs donateurs. Enfin, nous montrons que les variables influençant les comportements moutonniers n’en expliquent qu’une faible partie. Il reste donc que la majeure partie de ces comportements reste inexpliquée.

Keywords: aid; herding; volatility; fragmentation.

JEL classification: F34, F35.


DEV/DOC(2009)4

I. INTRODUCTION

Economic literature on foreign aid has so far mainly concerned itself with aid efficiency, attempting to answer the key question of whether aid indeed promotes growth. The motivation for this research agenda has been reinforced by the tough criticisms the aid industry has experienced over recent years. Among others, Paul Collier (2007), after a life devoted to development economics, in his recent book The Bottom Billion, argues that aid is unable to make a real difference to the worlds poor. Others, such as the former World Bank economist William Easterly, regularly point out the deficiencies in aid allocation mechanisms. For instance, Easterly and Pfutze (2008) argue that aid is fragmented between many small donors in a given country, increasing transaction costs and revealing coordination failures.

After many papers evaluating the effect of aid on growth (see Roodman, 2007 for a recent review of the literature) often reaching somewhat disappointing conclusions, some have started to narrow the question and look at the effect of aid on more specific variables (for instance Mishra and Newhouse, 2007 on the effect of aid on infant mortality). Nevertheless, the question still remains firmly focused on the effect of aid on a given growth outcome. Much less has been said about the allocation of aid: while aid determinants have been estimated, donors’ decision process in their choice of recipients, or how one donor’s decisions may affect others’ allocations is still little understood. This is not a completely new concern. Cassen (1986) already mentioned that donors moved in herds, suddenly disbursing money into “star” countries, and that sudden increases were followed by long aid declines. However, while this claim has been made (Riddell 2007 argues that there is a “herd instinct” among donors), no study has yet attempted to measure herding and to determine its causes.

This paper is a first step in this direction. It is also part of a broader research agenda that studies donor allocation policies in order to understand the role of aid relative to capital flows. While herding is now a basic assumption among traders in bonds and equities, much less is known about aid donors. However if the latter also herd, might not such behaviour from both public and private actors compound to create even grater overall herding? Because aid donors are somehow expected to play a different role to that of private investment, we believe it is to compare these different actors’ actual behaviour.2

The concept of herding was originally developed by sociologists following the seminal work of French social scientist Gustave Le Bon, who published his famous La psychologie des

2 For a comparison between aid donors and portfolio funds regarding quantities, volatility and

fragmentation, see Frot and Santiso (2008).


DEV/DOC(2009)4

10 © OECD 2009

foules in 1895 and some years later by George Simmel in another seminal book published in 1903, The Metropolis and Mental Life. The very first economist to refer to this notion was Thorstein Veblen; in his 1899 essay The Theory of the Leisure Class, written while at the University of Chicago, he explained economic behaviour in terms of social influences such as "emulation," where some members of a group mimic other members of higher status. The notion has grown to become used extensively by financial economists over the past decades. Bikhchandani and Sharma (2000), or Hirshleifer and Teoh (2003), provide overviews of the field. The academic study of behavioural finance has identified herding as an important factor in the collective irrationality of investors, particularly the work of Robert Shiller (2000).

This paper offers different measures of herding to test for its presence and evaluate its size. Our results all reject the hypothesis of no herding, with its importance depending on the chosen measure and sample. We develop two indexes and apply them to different data sets. The first one is the most widely used in the finance literature. It was proposed by Lakonishok et al. (1992) and has subsequently been broadly applied in the field. It is based on the simple intuition that there is herding when many more traders (donors) buy or sell a stock (increase or decrease aid to a recipient) than they do on average. The variable on which the herding measure is based is therefore the proportion of traders (donors) buying (increasing aid to) each stock-quarter (recipient-year). Frey et al. (2007) recently showed that this measure was negatively biased and did not consistently estimate herding even for large sample sizes. They proposed an alternative consistent measure based on the same intuition but relying on a simple structural model, which we also apply here.

Nevertheless, both these measures are used in this paper with some variations from the finance literature to capture the specificity of aid allocation. In particular we present results using two types of data frequency. Yearly data is available for aid allocation, but is likely to be contaminated by small, “noisy”, aid movements and may fail to capture the actual donor allocation horizon. Longer time periods are therefore used to account for inertia in aid allocation and eliminate some noise. Our preferred measure uses 3-year data and suggests that herding size is around 10 per cent. This implies that in a world where 50 per cent of all allocation changes are increases, the average recipient experiences 60 per cent of its donors changing their allocation in the same direction. In other words half of recipients see 60 per cent of their donors increase their allocations, and the other half sees a 60 per cent decrease their aid allocations. Our lower bound for herding size, based on measures using yearly data, is around 6 per cent.

Such a herding level may be difficult to interpret as many factors induce donors to change their aid allocations along similar lines, and so feed into herding measures. Herding determinants are therefore estimated to better understand what enters into our measures, and the effects of various shocks evaluated using appropriate generalized linear model (GLM) techniques. Predictably, we find that transitions towards less democratic regimes cause donors to simultaneously decrease aid allocations. However, the opposite does not reveal itself to be the case. This asymmetry, while somewhat puzzling, is robust to all our specifications. Natural disasters, unsurprisingly, also create herding, wars, however, do not. This estimation in itself may contribute to the debate on the determinants of aid, suggesting which factors trigger responses from donors, positive or negative. If we consider that donors first choose which


DEV/DOC(2009)4

recipients should receive more aid than in previous years and then, given a fixed aid budget, subsequently decide on precise allocation numbers then our results provide empirical estimates of the first step of the process.

Finally, GLM estimates allow us to calculate the contribution of each determinant to the herding measure. Observable determinants in fact explain little of herding, with a very large share that cannot be solely attributed to political factors, natural disasters or wars. Though we must be careful in interpreting this result, these results suggest that other non-observable factors are at play, and in particular factors that relate directly to “pure” herding such as information cascades or signalling.

Our motivations for studying herding are twofold. First, herding imposes costs and benefits on recipients. It can be regarded positively as the coordination of donors in cases of emergency. Humanitarian needs following a natural disaster or a war naturally call for a greater aid effort from donors. This paper attempts to avoid including such “beneficial” herding in its measures by carefully defining aid.

On the other hand herding is usually associated with sudden swings and an overflow of money that is not always beneficial. In the case of aid, multiple donors implementing many missions in an uncoordinated fashion, or aid fragmentation, has been shown to decrease aid efficiency and may impose an unnecessary burden on already weak administrations in developing countries (see Djankov et al. 2008a; Djankov et al, 2008b; OECD, 2008; Knack and Rahman 2007). By focusing on the proportion of donors increasing aid allocations, and not on actual aid quantities, we also hope to contribute to the debate on the causes of aid fragmentation. Cassen (1986) provides an example of herding leading to fragmentation and a misallocation of resources. He mentions that a large number of donors became involved in the Kenyan rural water supply sector, resulting in an overflow of administrative procedures that the weak Kenyan Ministry of Water Development could not face. Both donors and the Kenyan Government agree that aid to this sector has been a disaster.

The costs of herding also include increased aid volatility. Herding may be an important factor behind the large swings in the levels of aid experienced by recipients, beyond the conscious coordination of aid decisions. Aid volatility has been a major concern for many years (see Bulíř and Hamann, 2006) and its costs have been evaluated to be potentially very high for aid recipients (Arellano et al. 2008). While Frot and Santiso (2008) showed that other types of private capital inflows were more volatile, volatility in foreign aid flows is considered harmful for developing countries, and in particular in low income countries that are aid dependent (for an analysis of fiscal and budget policy sensitivities on aid dependence, see Mejía and Renzio, 2008). An unstable source of finance prevents governments from planning ahead and, as shown by Agénor and Aizenman (2007), may bring aid recipients to fall into a poverty trap by making it impossible to invest in projects requiring a steady flow of funds. Bulíř and Hamann (2006) report that aid volatility has worsened in recent years. Kharas (2008) also finds the cost of volatility to be large and argues that herd behaviour, by creating donor darlings and orphans, accentuates collective volatility, underlining that while a donor can reduce volatility by running counter the overall aid cycle, the herding phenomenon will render this unlikely.


DEV/DOC(2009)4

12 © OECD 2009

A second motivation of the paper is to improve our understanding of donor allocation policies. It has been argued that aid depends on many economic, political and historical determinants. However, the interaction between members of the donor community has been little studied. Given the very large number of actors,3 we expect decisions to depend on various signals (recipient needs, past relationship between donor and recipient, but also other donors’ decisions). While we are not the first to empirically investigate this link between donors, to our knowledge this paper is the first to use herding measures to document it. Past studies have estimated the effect of total donors’ aggregated aid on the aid of a specific, individual donor.

Tezanos Vázquez (2008), using this methodology, finds a “bandwagon effect” of Spanish aid. Berthélemy (2006), Berthélemy and Tichit (2004) and Tarp et al. (1998) use exactly the same approach. It is however quite different from ours, in that it does not look at simultaneous identical decisions from donors and so does not have a great deal to say about interactions. It does not treat donors equally, as the total aid allocated to a recipient often depends on the decisions of a handful of large donors. Finally because of the reflection problem defined by Manski (1993) this effect is not consistently estimated using regressions.

Finally, herding is potentially a force contributing to the emergence of donor darlings and aid orphans. Though her study is based on NGOs rather than on official donors, Reinhardt (2006) provides some evidence that donors do herd. She reports donor agents as saying that “we know other foundations trust Organization X, so we went straight there and told them we wanted a partnership”. An NGO financial director also acknowledges that “I can't get IDB money if I drop the ball with the World Bank”. When repeated this behaviour creates inequalities among aid recipients even if ex ante they share similar characteristics. Marysse et al. (2006) argue that political considerations and donor coordination problems have created such donor darlings and aid orphans in the region of the Great Lakes in Africa.

3 53 donors reported their activities to the Development Assistance Committee of the OECD in 2007, but these do not include some other important donors (Brazil, China, Venezuela, etc.) and non-official donors (NGOs, private foundations and charities).


DEV/DOC(2009)4

II. BEYOND FADS AND FASHIONS: BENEFICIAL HERDING

Any herding measure based on the detection of simultaneous and identical donor decisions must capture aid movements caused by exogenous factors. For instance when a natural disaster hits a country, there is indeed herding. When donors finance urgent humanitarian needs and decide simultaneously to increase their aid allocations to the country, we term this “beneficial” herding. It simply reflects suddenly increased needs that are taken into account by the donor community as a whole. The Asian Tsunami in December 2004 provides an excellent example of such beneficial herding.

Figure 1 plots the proportion of donors disbursing aid that actually increased their gross aid allocations compared to the year before. This proportion is calculated for four countries particularly affected by the tsunami: Indonesia, Maldives, Sri Lanka, and Thailand.

Figure 1: Proportion of donors increasing aid

Source: Authors, 2009; based on OECD data, 2009.


DEV/DOC(2009)4

14 © OECD 2009

During the ten years preceding the tsunami the proportion of donors increasing their gross aid allocation hovers around 50 per cent and remains relatively stable for each recipient, with perhaps the exception of Sri Lanka. In 2005, the year humanitarian aid was actually disbursed, the proportion jumps to about 80 per cent. In this case, coordinated donor actions are beneficial. It is herding in reaction to a clear, identifiable, exogenous shock. Other examples are available: Bosnia-Herzegovina and Timor-Leste faced humanitarian crises and severe reconstruction needs that triggered simultaneous aid flows from most donors. More recently, in 2008, Georgia received a USD 4.5 billion dollar aid pledge by 38 countries and 15 multilateral organisations. Donors also coordinate their actions when they grant debt relief. Many donors tremendously increased aid to Nigeria in 2005 and 2006, though this was through debt forgiveness mechanisms.

Ideally a measure of herding would distinguish between such coordinated moves, sometimes decided in international summits, and herding caused by allocation policies, strategic motives, and competition among donors. A suitable definition of aid allows the elimination of a fair share of the former. Country Programmable Aid (CPA) does not include items that are not predictable by nature: humanitarian aid, debt relief and food aid. This variable has been used recently to study aid fragmentation (OECD 2008) and trends in foreign aid (Kharas, 2007). It is calculated by subtracting humanitarian aid, gross debt relief and food aid from gross aid, as defined by the Development Assistance Committee (DAC). This quantity constitutes the core of aid that finances development in a medium to long term perspective. Though not perfect, a herding measure based on this variable is not subject to the sudden aid swings due to humanitarian emergency and debt relief.


DEV/DOC(2009)4

III. HERDING MEASURES

III.1.Definitions

Evaluating herding in aid allocation is a thorny issue, leading us to turn to the financial literature where herding has been measured and modelled for many years. Lakonishok et al. (1992) developed an index based on the idea that herding occurs when traders (donors) deviate from an “average” behaviour. Their methodology is purely statistical and does not rely on any structural model. It is therefore quite simple and general, but may not be powerful enough to detect and evaluate herding correctly. Their index LSVit is defined as follows

On financial markets pit is the proportion of funds buying stock i in period t. By analogy in our aid study it is the proportion of donors increasing their allocation to recipient i in period t. The basic idea of the measure is that when there is no herding, aid increases and decreases are randomly distributed. If there are an excessive number of increases or decreases then it is interpreted as herding behaviour. πt provides the benchmark against which herding is assessed. It is the average proportion of aid increases in all the decisions taken in year t, where

bit is the number of aid increases and nit is the number of donors active in recipient i in year t. It is the probability that a donor increases its aid to a recipient in year t under the hypothesis of no herding. The first term of the equation is positive even when there is no herding. The second term is an adjustment factor that serves as a correction. LSVit has therefore a zero expected value under the hypothesis of no herding. Herding is measured by averaging LSVit for the desired time period and groups of recipients and we denote this average LSV. This measure has been used by Lakonishok et al. (1992), Grinblatt et al. (1995), and Wermers (1999), amongst others, to estimate herding in mutual funds. Uchida and Nakagawa (2007) applied it to the Japanese loan market, Weiner (2006) to the oil market, and Welch (2000) to financial analysts. Herding behaviour has been particularly pronounced in emerging markets as underlined by many studies on financial crisis spillovers (Ornelas and Alemani, 2008; Bekaert and Harvery, 2000).

The key intuition behind LSVit is the dispersion of increases and decreases around the average proportion πt. This feature makes it neutral with respect to global trends in aid allocation. If donors cut their aid budgets, as they did in the nineties, this is captured by πt and it does not affect the herding measure. The overall increasing trend in aid over the last 50 years does not influence it either. LSVit is also independent of aid concentration at the recipient level.


DEV/DOC(2009)4

16 © OECD 2009

Whether a very small number of donors represent most of the receipts, or whether all donors disburse similar quantities to a recipient does not matter. Herding is here based on the idea of similar decisions, regardless of the quantities involved. For this reason it is also detached from fixed allocation determinants due to historical ties or political economy factors. For example, the fact that a donor favours a particular recipient because it is a former colony is irrelevant here. What matters is the variation in aid from one period to the next, and not the exact quantity allocated each year.

Recently Frey et al. (2007) have shown that this approach may actually fail to measure herding properly. They develop a simple structural model to match the use and interpretation of LSVit and find in simulations that the measure underestimates the true herding parameter. The adjustment factor overcorrects the estimated parameter unless there are a very high number of observations per recipient year. Unfortunately this is not the case in aid where the number of donors never exceeds 53 in our data. Frey et al. (2007) propose an alternative measure Hit not subject to the inherent bias of LSVit:

nit is the number of donors giving aid to recipient i in year t. Hit is then averaged over

recipient-years. For a set A of recipient-years they define

h is a consistent estimator of their herding parameter. Using Monte Carlo simulations

they find that LSV is a good statistic to test for the presence of herding: if LSV is significantly different from zero, then there is herding. However LSV does a poor job at estimating the size of herding. H is also a viable statistic to test the presence of herding and h provides an accurate estimate of herding. In particular it improves significantly with the number of recipient-years, while LSV does not. Since our sample contains at most 5171 observations h is expected to perform particularly well. Frey et al. therefore suggest a two step approach: first, test the existence of herd behaviour using either LSV or H; second if significant herding is found, estimate its level consistently using h.

Our approach follows their suggestion. For each set of recipient-years where herding is measured, LSV is reported. If the hypothesis of no herding is rejected then h is calculated to estimate its size. This approach is not flawless though. It was mentioned above that unlike LSV, h relies on a structural model. It is simple and quite general but it may not be suited to aid allocation or may miss some important characteristics. In that case h may not be a good measure.


DEV/DOC(2009)4

III.2.Which recipient-years? Which donors?

The OECD DAC dataset contains 5837 recipient-years where at least one donor is active.4 Aid activities of 60 donors are reported, though no more than 53 are ever present simultaneously. Herding measures can theoretically be based on all these observations. However there are good reasons to restrict the set of donors and recipients.

Donors enter and exit the aid market. Some donors did not give any aid before a particular year (Greece, New Zealand, Spain, etc.) but some also stopped after a particular year or stopped reporting their activities to the DAC. The fact that a new donor can only increase its aid allocations is not necessarily an issue because the first year a donor is present is by construction not used in the herding measure. Only second year allocations onwards are valid for our purpose since to define aid increases and decreases in year t we need allocations in year t-1. On the other hand a donor that exits can only decrease its allocations. It may do so gradually over time if it has planned to cease its activity. Its behaviour is biased and may hide or exaggerate herding. We consequently compute our herding measure excluding donors that cease their activity. To be on the safe side we additionally estimate herding using a constant set of donors, made of developed countries that have been disbursing aid from 1960 to 2007, the full span of the data. Any donor that enters later or stops its activity earlier is excluded. These two restrictions are quite strong because they limit the number of observations and disregard some potentially useful data.

A similar issue arises with recipients. Some countries do not “exist” before a certain date. That mainly concerns ex-Soviet Republics and regions of former Yugoslavia. There was beneficial herding towards these countries. Their geographic proximity to many important donors and their needs created an influx of aid.5 This type of herding is conspicuous and it is large. It inflates any herding measure. Because we do not want our results to rely on these few particular cases we simply exclude them. Other entries are due to late additions to the OECD DAC recipient list. Aid to China has been recorded only from 1979, and only from 1975 for Mongolia. Whether none was given before or whether it went unreported is unclear. These are also excluded from the set of recipient-years. Any developing country that is not in the dataset in 1960, because it did not exist at that time, or was not on the DAC list of recipients, is not taken into account when computing herding measures. This choice is also quite restrictive as it leaves aside observations where there may be herding.

An intermediate restriction is also applied. Recipients and donors entering after 1960 are included but their first five years of presence are not. It leaves some time for donors to scale aid up to new countries and reach a stabilised regime. It also allows donors to increase their allocations in their first years of presence without this affecting the herding measures.

4 A donor is defined as active in recipient i in year t if it changes its allocation to recipient i from year t-1 to

year t. 5 It is debatable whether there was herding given their characteristics: other countries under similar

conditions might not have enjoyed similar attention.


DEV/DOC(2009)4

18 © OECD 2009

Finally both LSVit and Hit can be computed as soon as one donor is active in the recipient-year. Yet to talk about herding when there is only one donor does not make much sense. There must be a herd to follow in order to have herd behaviour. Recipient-years with fewer than five active donors are not considered.

To sum up, donors that stop their activity are never included, those that enter after 1960 are not included, or only after their first five years. Countries whose receipts were not recorded in 1960 are excluded, or are included only after their first five years. Recipient-years with fewer than five active donors are always excluded. That leaves us with a dataset that contains at most 5171 recipient-years.

LSVit and Hit are computed for different groups of donors. Activities either from all donors are considered, or only from DAC donors, or only from multilateral donors. Indeed one might think that bilateral donors herd but that multilateral donors take independent decisions. In that case the latter are merely adding some noise that makes herding more difficult to detect.

III.3.Results

Table 1 presents herding measures for different groups of donors. Herding is significant in all groups, regardless of the restrictions imposed, except among multilateral donors. Those do not herd.6 DAC donors on the other hand, do. LSV is always significant at the 1 per cent level. Having passed the existence test, h is computed to find herding levels.

Table 1: LSV and h measures

All donors DAC donors Multilateral donors

LSV h LSV H LSV h

Donors present until 2007, recipients present since 1960 1.07

(5171)

6.27 1.05

(4866)

6.53 0.43

(3998)

4.36

Donors present 1960-2007, recipients present since 1960 1.07

(4788)

6.65 1.08

(4640)

7.08 n.a .

5 year presence threshold 1.02

(5098)

6.03 1.11

(4865)

6.61 0.45

(3803)

4.64


Both LSV and h have the same interpretation. Paraphrasing Lakonishok et al. (1992), a measure of x implies that if π, the average fraction of changes that are increases, was 0.5, then 50+x per cent of the donors were changing their allocations to an average recipient in one direction and 50-x per cent in the opposite direction. As emphasised by Frey et al. (2007) LSV 6 It must be clear that multilaterals do not herd among themselves. It does not imply that some of them do

not herd with bilateral donors.


DEV/DOC(2009)4

underestimates herding and is always much smaller than h. According to the LSV measure, there is statistically significant herding but not economically. Only about 1 per cent of the changes can be attributed to herding. In other words only 1 per cent of the changes in an average recipient-year cannot happen by chance and so constitute herding. The adjustment factor overcorrects LSV to the point that it cannot distinguish between randomness and herding. h does not suffer from the same bias. Its size implies that if the average fraction of changes that are increases is 0.5, on average around 56 per cent of donors take similar decisions in a recipient-year.

Herding on financial markets using the LSV measure is usually higher. Lakonishok et al. (1992) find a value of 2.7, Wermers (1999) of 3.40. Herding in aid allocation would be roughly a third of what is observed in finance according to this measure.


DEV/DOC(2009)4

20 © OECD 2009

IV. ALTERNATIVE MEASURES

The two previous measures rely on the idea of dispersion relative to an average behaviour in aid allocation. The benchmark is the same for all countries in a given year. It is a cross country reference, implicitly assuming that all countries are expected to be treated similarly in the absence of herding.

An alternative is to use past changes within countries. The expected proportion of increases in recipient i in year t would not be the proportion of increases in that year but instead the average proportion of increases in i from year t-5 to year t-1. The 5 year window is arbitrary. This measure does not assume equal treatment across countries but rather that under the assumption of no herding today’s proportion of increases is expected to be close to what occurred in the last five years. The two measures are complementary as they are based on two different conceptions of herding. Like LSVit it should be made neutral to changes in the global trend in aid. A proportion of increases can be far from its moving average because of herding or because the general policy in the current year is to decrease (or increase) aid. In order to shield the measure against such variations, the variable of interest is not the proportion of increases but instead the difference between this proportion and the average proportion of increases in that year. The within measure Wit is defined as:

where the upper bar designates the 5-year moving average. The analogue of Hit is Hitw:

Hw and hw are then defined in a similar fashion to H and h. An example may serve to illustrate the difference between the two measures. Assume that the proportion of increases in year t is 0.5. 80 per cent of the donors active in a given recipient i in year t increase their allocations. LSVit interprets this deviation as herding. In the past five years the average proportion of increases for recipient i has been 0.75. The within measure does not see any significant difference between year t and the past and returns a non-significant herding value.


DEV/DOC(2009)4

The two measures do not necessarily disagree though. Had the past proportion of increases been 0.5 then both measures would return the exact same herding level.

Wit and Hitw are computed for the same group of donors and recipients as LSVit and Hit. In order to be included a recipient-year and each of the past five years must have at least 5 active donors. If fewer than five years are available then the observation is discarded.

Table 2: W and hw measures

All donors DAC donors Multilateral donors

W hw W hw W hw

Donors present until 2007, recipients present since 1960 1.45

(4485)

6.96 1.77

(4162)

8.42 0.65

(3234)

5.21

Donors present 1960-2007, recipients present since 1960 1.90

(4113)

8.95 1.90

(3933)

9.31 n.a

5 year presence threshold 1.57

(4656)

7.80 1.86

(4370)

8.86 0.69

(3066)

5.26


Table 2 reports the results using the within measures. Herding is more pronounced when

the benchmark is based on past allocations. hw indicates values around 7-9 per cent. This means that if in the past five years on average 50 per cent of all active donors increased their aid allocations then in the current year 57-59 per cent take a similar decision. Values for multilateral donors are still very small.

LSV uses yearly data to detect herding. It makes sense because donor disbursements are allocated on a yearly basis and these are expected to be influenced by herding. On the other hand, donors, unlike traders, commit to future disbursements over several years. Many projects have a longer horizon than a year. Even if they herd, donors might find it difficult to stop programs currently running and shifts in allocation may take some years before taking effect. Year-on-year changes may fail to capture such movements and on the contrary be oversensitive to small variations that do not reflect herding but rather marginal changes due to project progression. Indeed many aid changes are quite small: the median absolute change for all donors is USD 0.80 million but the average is 8.53 million. The distribution of changes is strongly skewed towards small values. It is difficult to argue that such small variations always reflect donor choices. However LSV (or h) treat changes regardless of their sizes. It could be argued that these limited variations inflate herding measures artificially by putting some weight on random variations. These also create noise in the data that makes herding more difficult to detect.


DEV/DOC(2009)4

22 © OECD 2009

Two solutions are proposed to address this issue. First “small” changes are not taken into account. More precisely, focusing on the most stable group of donors and recipients that includes only donors present continuously from 1960 to 2007 and recipients present since 1960, 25 per cent of all changes are smaller than USD 0.24 million (the median is 1.52, the average 11.99). If a donor changes its allocation by less than USD 0.24 million in absolute terms then it is not used to compute the two herding measures. However, the requirement that at least 5 valid donors must be active in a recipient year is not changed. Since a valid donor must change its disbursement by at least 0.24 million this condition is stronger. Second random variations are smoothed away by using 3-year periods. Instead of using year-on-year changes, disbursements are added up over a period of three years and a donor is said to increase its aid to a recipient between two periods if its disbursements over three years are higher than over the three precedent years (here again a period is valid only if the donor disbursed aid during each of the three years of the period). Collapsing the data in such a way drastically reduces the number of observations but increases the median size of an absolute change from 1.52 to 4.87 million. Lengthening the period takes into account the medium term perspective of development aid. The exact length is arbitrary, and results using 5-year periods are also presented.

Table 3: LSV and h measures excluding small changes and with 3 and 5-year periods

Small changes

excluded

3-year

periods

5-year

periods

LSV h LSV h LSV h

Donors present until 2007, recipients

present since 1960

1.32

(4573)

7.21 3.37

(1670)

11.20 4.44

(974)

12.92

Donors present 1960-2007, recipients

present since 1960

1.14

(4056)

7.26 3.37

(1582)

11.98 4.25

(935)

13.61


The first two columns of Table 3 indicate that the exclusion of small changes does not have a big impact on herding size. It is only slightly higher, suggesting that small variations tend to compensate each other on average. The next two columns use three-year periods to detect herding. Its presence is confirmed and its size is above what has been found in Table 1. For the same donor and recipient category, h measured on yearly data yielded a value of 6.27. 3-year data reveal a herding level of 11.20. 5-year data provide a similar and even stronger conclusion. If one prefers to use the LSV measure for both detection and size to avoid relying on any structural model, then results are even stronger with size multiplied by three. Magnitudes are now similar to those observed on financial markets. If we are willing to adopt a slightly longer term perspective, herding appears to be more pronounced. Given the way aid is disbursed with


DEV/DOC(2009)4

commitments and tranche disbursements as projects progress, this perspective seems well suited and avoids building measures on often small yearly variations.

Table 3 confirms that there is significant herding in aid allocation and that its size is actually more important than what is derived from yearly data. Longer periods yield levels above or similar to what has been measured on financial markets with quarterly data. A high frequency makes sense in finance where investors are quick to respond and modify their portfolio choices, but much less in development. It is difficult to identify the optimal time span, but a few years are likely to match allocation policies’ time frame.

The different measures we have used all point in the same direction. Herding in aid allocation is present. However its exact size depends on how we think it should be evaluated. Yearly allocations in a pure cross country framework return a limited size. Within measures improve on the cross country ones by having the advantage of taking into account country fixed effects. Within herding is somewhat higher. Finally, year-on-year changes might not reflect decisions but be contaminated by random variations. Longer periods reveal larger herding levels that have important consequences for aid recipients. Using all the different approaches, the measure h estimates herding to be between 6 and 12 per cent.


DEV/DOC(2009)4

24 © OECD 2009

V. GEOGRAPHICAL DISTRIBUTION OF HERDING

Even though we focus on the average level of herding across developing countries, we show here how herding differs across countries. The following map indicates herding levels in each country. Herding is computed using the LSV index with 3-year periods, donors present until 2007, and with more than 5 donors per recipient-period. Unlike the previous tables, we do not exclude developing countries that entered later than 1960, since the point here is to provide as comprehensive a picture as possible. Interval bounds are chosen such that each category includes the same number of countries.

Figure 2: LSV herding, 3-year data

Source: Authors’ calculations, 2009.


DEV/DOC(2009)4

VI. HERDING DETERMINANTS

Which factors cause donors to act similarly? By subtracting debt relief, humanitarian and food aid from official development assistance, some of them have already been excluded from our analysis. Others are expected to influence donors in a similar fashion: political transitions that promote or jeopardise democracy, armed conflicts, income shocks, etc. These are determinants we can take into account but must leave aside more subtle ones related to strategic behaviour or informational cascades that are more difficult to identify. This section quantifies the effect of those observable shocks that create herding.

As argued in Section II, such allocation changes can be regarded positively. Following a democratic transition, donors may all respond to better governance and more transparent institutions with increased aid flows to further foster democracy. A more nuanced view would still caution against herding even in these situations. While it makes no doubt that these constitute valid reasons to increase aid, donors might still herd and overreact all together. Donors that do not participate in the aid splurge may fear being left out and missing some future investment or diplomatic opportunities. They may follow the crowd, increasing aid fragmentation in the country and inflating aid disbursements above the recipient country’s absorptive capacity. The border between legitimate, well planned aid increases (or decreases) and herding is usually difficult to delimit. This section does not attempt this difficult exercise but provides a first study of herding determinants.

We also see this estimation as a valuable result in its own right. Beyond the issue of herding proper, this result sheds a new light on aid allocation to what has been done in the past. Researchers have always related aid quantities to recipients’ characteristics (see Alesina and Dollar, 2000, Alesina and Weder, 2002, Berthélemy, 2006, and Berthélemy and Tichit, 2004). The approach taken here is more basic as it considers the proportion of donors increasing aid, regardless of quantities. Donor decisions can be decomposed in two steps. First, they have to decide which recipients should receive more aid, and this is what is investigated here. Second, once where to increase and decrease aid is known, actual quantities are decided upon, and this is what the aid allocation literature has studied so far.

The dependent variable in the estimations is pit. Because it is a proportion it only takes values between zero and one. OLS estimation is not well suited for this type of bounded dependent variable because predicted values cannot be ensured to lie in the unit interval. Papke and Wooldridge (1996) provide suitable estimators based on quasi-maximum likelihood methods. They propose a method using a generalised linear model with a logit link, the binomial family and robust standard errors. More specifically, they assume that:


DEV/DOC(2009)4

26 © OECD 2009

where G(.) is the logistic function and xit is a vector of explanatory variables. The herding

measures suggest a slightly different approach because the variable of interest is pit-πt, and not pit. To take the benchmark into account, however poses no difficulty. Instead of G being the logistic

function , it is chosen to be . That is equivalent to changing

the exposure of the dependent variable, or to have an offset . The method developed by

Papke and Wooldridge (1996) can readily be applied using this function G.

Results following this estimation technique are complemented by more standard techniques using OLS with and without country fixed effects. These are not our preferred estimators due to the dependent variable being a proportion but we provide them as a further robustness check. Country fixed effects are added to remove any time-invariant unobserved characteristics that would affect herding (for instance Cuba may not fit our general model; a fixed effect removes its particular status).

VI.1.Variables

The dependent variable in the estimations is based on the 3-year period herding. Yearly data maximize the sample size but are very noisy. As argued before, they are based on many small allocation changes and are unlikely to correspond to donor time horizons, but results using yearly data are still presented as a robustness check. The 3-year herding measure is calculated for two samples: one only with donors present from 1960 to 2007, the other with all the donors that do not exit the market. Both only include recipients present from 1960. The first sample offers the advantage of a stable group with no entry, but misses some large donors and may fail to capture some herding. The 3-year measure also allows for some time before a new donor actually enters the data and so measures are unlikely to be contaminated by periods of portfolio increases. To use the maximum amount of information we prefer the second measure but we also present results using the stable set of donors.

Independent variables are constructed from four different categories: economy, politics, conflict, and natural disasters. Economic variables include GDP growth and GDP per capita, from the World Development Indicators of the World Bank. Political variables are constructed from the Polity IV Project dataset. We exploit political transitions that result in more democratic or authoritarian regimes. A dummy variable is defined for each type of transition if it occurs in any of the three years of the period. Because of the 3-year structure of the data it is unclear that donors react during the same period. It might be the case when the transition is short and occurs at the beginning of the period, but not when the transition takes place in the last year of the period. To avoid missing such effects we create another dummy variable equal to 1 if there was a transition last period and not in the current period. We also use dummies for “new” countries, that is countries that gained independence7 and for foreign interventions. Because 3-year periods 7 Countries that have not been present since 1960 are excluded from the data. Some countries gained

independence later but aid flows had been recorded as early as 1960.


DEV/DOC(2009)4

are used, dummy variables take a value of 1 if the event occurred in any of the three years. GDP growth and GDP per capita are averages over the three years.

The number of deaths in a country caused by natural disasters is provided by the Emergency Events Database (EM-DAT). Figures for each year of the period under consideration are added to proxy for natural disaster intensity during the period. This number is then divided by the average population size in thousands during the period. The unit of measure is therefore the number of deaths due to a natural disaster by thousands of people. Aid in this paper does not include emergency aid but natural disasters are still expected to affect the number of aid increases for various reasons. First, humanitarian aid is not reported before 1995 in the data, and so enters our aid variable before that date. Second a natural disaster causes an influx of humanitarian aid and more long term investments that do not necessarily enter into this category. It also attracts attention to the affected country and may trigger simultaneous aid flows from many donors. Armed conflict data comes from the UCDP/PRIO Armed Conflict Dataset, as described in Gleditsch et al. (2002). The war dummy takes a value of 1 if there was a conflict in any of the three years of the period.

VI.2.Results

The main empirical question is to estimate the effect of political, natural, and conflict shocks that cause similar allocation changes by donors. The first set of estimates uses 3-year data and is presented in Table 4. The first sample used includes all donors present until 2007. Column (1) uses the GLM estimator. Coefficients reported are marginal effects at the means to make them comparable with OLS estimates.

GDP per capita is significant but the size of the coefficient is extremely small given that income is measured in thousands of dollars. A transfer of USD 1000 per capita, arguably a very large change, reduces pit by 1.1 per cent. The inclusion of GDP per capita is not directly linked to any shock but rather controls for different treatments towards rich and poor countries. Growth, on the other hand, is not related to herding. The coefficient has the expected sign but is far from being significant.

The variable “new polity” is very large and significant, implying that “new” countries receive aid from 20 per cent more donors than the average recipient. Political transitions offer interesting results. Democratic transitions do not trigger simultaneous positive responses from donors neither during nor afterwards. On the other hand donors do react to authoritarian transitions and reduce their allocations during transitions. The asymmetry between the two types of transitions is rather unexpected. We would expect donors to punish transitions towards authoritarianism but to reward those towards democracy. It is only mildly the case.


DEV/DOC(2009)4

28 © OECD 2009

Table 4: Herding determinants, 3-year data

Note: Standard errors clustered at the country level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. GLM estimation is done using a logit link and a binomial family. In columns (1) and (4) the dependent variable is pit and πt is included as an offset. Estimates are marginal effects estimated at the mean pit. For dummy variables the marginal effect is for discrete change from 0 to 1. In columns (2), (3), (5) and (6) the dependent variable is pit-πt. Source: Authors, 2009.

Donors present until 2007 Donors present 1960-2007 (1) (2) (3) (4) (5) (6) GLM OLS FE GLM OLS FE Real GDP per capita -0.011***

(0.0023) -0.011***

(0.0023) -0.012

(0.0085) -0.0097***

(0.0020) -0.0095***

(0.0019) -0.0099 (0.010)

Real GDP growth -0.0019

(0.0012) -0.0019(0.0012)

-0.0011(0.0014)

-0.0011(0.0014)

-0.0010 (0.0013)

0.00012 (0.0014)

New polity 0.20***

(0.050) 0.17*** (0.043)

0.15*** (0.048)

0.21*** (0.062)

0.18*** (0.052)

0.17*** (0.058)

Foreign intervention 0.038

(0.065) 0.038

(0.068) -0.11 (0.10)

0.0024 (0.057)

0.0013 (0.058)

-0.22*** (0.038)

Democratic transition 0.0043

(0.016) 0.0045(0.016)

-0.00043(0.018)

0.021 (0.019)

0.021 (0.019)

0.016 (0.021)

Democratic transition, post year 0.011

(0.018) 0.011

(0.018) 0.0044(0.019)

0.011 (0.021)

0.012 (0.021)

0.0067 (0.024)

Authoritarian transition -0.062***

(0.018) -0.060***

(0.018) -0.058***

(0.019) -0.079***

(0.020) -0.076*** (0.019)

-0.068*** (0.020)

Authoritarian transition, post year -0.015

(0.019) -0.015(0.018)

-0.012 (0.019)

-0.028 (0.022)

-0.027 (0.021)

-0.022 (0.022)

Natural disaster 0.033**

(0.013) 0.027***

(0.0093) 0.022*

(0.011) 0.029** (0.013)

0.024** (0.0099)

0.018 (0.012)

Conflict -0.011

(0.0099) -0.011

(0.0097) 0.0015(0.014)

-0.0096(0.012)

-0.0093 (0.012)

0.0073 (0.017)

Conflict, post year 0.0050

(0.015) 0.0049(0.014)

0.019 (0.016)

0.010 (0.019)

0.0099 (0.018)

0.025 (0.020)

Observations 1377 1377 1377 1376 1376 1376 Adjusted R2 0.045 0.021 0.028 0.017


DEV/DOC(2009)4

Table 5: Herding determinants, yearly data

Donors present until 2007 Donors present 1960-2007 (1) (2) (3) (1) (2) (3) GLM OLS FE GLM OLS FE Real GDP per capita -0.0051***

(0.00087) -0.0051***

(0.00087) -0.0064(0.0049)

-0.0051***

(0.0010) -0.0050*** (0.0010)

-0.0077 (0.0057)

Real GDP growth 0.000015

(0.00041) 0.000014(0.00040)

0.00026(0.00041)

0.00026(0.00052)

0.00026 (0.00051)

0.00053 (0.00050)

New polity 0.14***

(0.028) 0.13*** (0.024)

0.13*** (0.024)

0.15*** (0.028)

0.13*** (0.024)

0.14*** (0.024)

Foreign intervention 0.037***

(0.0037) 0.037*** (0.0037)

0.029* (0.016)

0.052*** (0.013)

0.052*** (0.014)

-0.070*** (0.010)

Democratic transition 0.0040 (0.012)

0.0040 (0.012)

0.0023 (0.012)

0.0078 (0.015)

0.0077 (0.014)

0.0047 (0.015)

Democratic transition, post year 0.0055

(0.012) 0.0054 (0.012)

-0.00010(0.012)

0.0047 (0.014)

0.0047 (0.014)

-0.0043 (0.014)

Authoritarian transition -0.035***

(0.012) -0.034***

(0.012) -0.033***

(0.012) -0.038***

(0.014) -0.038*** (0.013)

-0.039*** (0.014)

Authoritarian transition, post year -0.0080

(0.012) -0.0079(0.012)

-0.0080(0.012)

-0.027* (0.014)

-0.026* (0.014)

-0.027* (0.014)

Natural disaster 0.026*

(0.013) 0.024** (0.012)

0.022* (0.012)

0.020 (0.012)

0.019* (0.012)

0.017 (0.013)

Conflict 0.0089*

(0.0051) 0.0088* (0.0051)

0.013* (0.0077)

0.014** (0.0064)

0.013** (0.0064)

0.017* (0.0090)

Observations 3784 3784 3784 3784 3784 3784 Adjusted R2 0.032 0.027 0.022 0.020

Note: Standard errors clustered at the country level in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01. GLM estimation is done using a logit link and a binomial family. In columns (1) and (4) the dependent variable is pit and πt is included as an offset. Estimates are marginal effects estimated at the mean pit. For dummy variables the marginal effect is for discrete change from 0 to 1. In columns (2), (3), (5) and (6) the dependent variable is pit-πt. Source: Authors, 2009.

Contrary to what Rodríguez and Santiso (2008) found for private bank flows, there is no democratic premium in donor herding behaviour. Donors are not attracted by a democratic transition, though they shy away from authoritarian transitions. This result is consistent with previous ones underlined by Easterly and Pfutze (2008) and Knack (2004) that found no evidence that aid rewards democracy. Kalyvitis and Vlachaki (2008) found an even more disturbing result


DEV/DOC(2009)4

30 © OECD 2009

and robust evidence that aid flows are negatively associated with the likelihood of observing a democratic regime in the recipient country.

Natural disasters have a very significant effect. The number of deaths per 1000 persons due to natural disasters has a standard deviation of 0.46. A one-standard deviation from the mean increases the proportion of donors allocating more aid by 1.5 per cent. Finally, armed conflicts do not affect herding. Column (2) replicates the results using OLS. Estimates are surprisingly similar to those using GLM. Column (3) includes country fixed effects and confirms most results, with similar magnitudes. Once country time invariant characteristics are controlled for, GDP does not enter significantly in the regression. The significance levels of the GDP coefficient in column (1) was due to cross-section regression, and might have captured country fixed characteristics.

Columns (4), (5), and (6) use the same specification but restrict the sample to the fixed set of donors present from 1960 to 2007. Results are very similar. The negative impact of authoritarian transitions is even larger than with the first specification. Though we think 3-year data constitutes a better and less volatile indicator of herding, we now present results using yearly data to check whether only aggregation drives the findings. Some definitions are slightly changed to accommodate for the new frequency. The new polity dummy takes a value of 1 during the first three years of the new regime. This is to allow for a longer time span than the exact year the polity is created. Similarly, post transition dummies are equal to 1 in the two years following the last year of a transition, unless there is a transition in that specific year. Results do not depend on the exact time frame.

Table 5 essentially confirms the results from Table 4. Yearly data exhibits the same pattern, except for a less precisely estimated effect of natural disasters. Both tables show consistent results and make us confident that they are quite robust.

Having identified herding determinants, we now evaluate to what extent they explain the results of Sections IV and V.


DEV/DOC(2009)4

VII. CORRECTED HERDING MEASURE

GLM estimation ensures that predicted values of pit are within the interval [0,1]. That allows us to compute hypothetical proportions had some events not happened and compute the “corrected herding measure” using the predicted proportions. OLS estimations usually provide predicted values smaller than 0 or larger than 1 and would make the exercise inconsistent. Consider a recipient year whose real proportion is pit and is characterised by the vector xit. We want to find the proportion had the characteristics been zit instead of xit. Using the definition of the function G, we obtain that:

The quantity pit(zit) can be calculated by using the estimate value of beta from the regressions. pit(zit) is the proportion of donors that would have increased aid to recipient i in year t had its characteristics been zit instead of xit. The functional form adopted ensures, unlike a linear specification, that the number obtained can be interpreted as a proportion because it is between 0 and 1.

Table 4: Effects of each determinant on the 3-year herding measure

Original measure

New polity

Foreign intervention

Democratic transition

Authoritarian transition

Natural disasters

Conflicts

LSV 3.37 3.35 3.35 3.35 3.30 3.26 3.24

h 11.20 10.85 10.87 10.87 10.75 10.66 10.63Source: Authors, 2009.

All the dummy variables are to be successively switched off to zero to see how much they account for herding. Natural disasters will also be assumed away. Once the new proportions are obtained, it is only a small step to obtain the corrected herding measures. The only remaining issue concerns the benchmark to be used for these new measures. The observed benchmark is affected by recipients’ characteristics. Changing these necessarily implies that the benchmark would have been different. To find the new benchmark we convert proportions in number of positive changes by multiplying them by the number of active donors in the recipient-year. That


DEV/DOC(2009)4

32 © OECD 2009

implicitly assumes that the number of donors would have been the same under the two sets of characteristics xit and zit. While this is not necessarily the case, this assumption provides a natural way to find the new benchmark proportion and should not greatly affect the results. The new benchmark is then computed using these hypothetical allocation changes and herding measures are calculated as in Section III.

The sample and estimates using the 3-year period data with donors present until 2007 is used. Table 4 shows the effect of each variable on the herding measure.

Starting from the original herding measure found in Table 3, each column removes the effect of a variable. For instance, deducting herding caused by new polities reduces h from 9.83 to 9.76. Foreign intervention and democratic transition hardly change this result. Authoritarian transition and natural disasters have a larger effect. Conflicts also reduce herding but this should be taken with caution given that the regressions in Table 4 did not show any significant effect of conflicts. All the identified factors reduce h from 11.20 to 10.63, or LSV from 3.37 to 3.24. It is a modest fall (5 and 3 per cent respectively) and although some of these factors have been found to be significantly correlated with herding, they do not explain it well. In the absence of other easily identifiable factors a tentative conclusion is that the corrected herding levels reveal “irrational” herding due to some unobservable characteristics or strategic donor behaviour.

Two extreme views are available to interpret Table 4. The deviations from the benchmark must be interpreted as herding and these events only serve as triggers, without any rationale. The other view is that these events cause “rational” deviations from the benchmark. They merely reflect conditions that cause similar allocation changes. Donors react similarly to natural disasters, not because they herd but because they all agree natural disasters call for increased aid flows. The reality is likely to stand between these two extreme views. The former seems too strong as shocks are highly unlikely to be mere triggers that provoke aid surges for no good reason. On the other hand the latter may be too optimistic. As Section VI has already argued, even if donors follow motivations based on hard facts (natural disasters, political transitions, etc.) it does not prevent them from herding when these events occur. Their response is likely to be based on a mixture of herding and sound motivations. Exactly which share herding represents remains a complex question to address.


DEV/DOC(2009)4

VIII. CONCLUSIONS

This paper proposes different ways to measure herding in aid allocation. We chose to use two measures initially developed in finance and adapted them to the specifics of foreign aid. Our different estimates all reject the hypothesis of no herding.

Its size however varies according to the measure used. Our preferred measure, using 3-year data and correcting for the bias inherent to the LSV measure, finds a herding level around 11 per cent. That implies that in a world where 50 per cent of all allocation changes are increases, the average recipient experiences 61 per cent of its donors changing their allocation in the same direction. In other words, half of the recipients see 61 per cent of their donors increase their allocations, and the other half sees 61 per cent decrease their aid allocations. The determinants of aid allocation, common to many donors, warn us against interpreting this quantity as “pure” herding, instead of similar responses to similar factors.

We therefore moved on to estimate herding determinants. Shocks are expected to create swings in aid allocations and we primarily focused on these. Their influence has been shown to be relatively limited. It therefore remains that a large share of the measured herding cannot solely be explained by these shocks. We also see this estimation as a supplementary contribution of the paper, as previous research on aid allocation has mainly focused on aid quantities but not on increased generosity from many donors simultaneously. The asymmetry we found between democratic and authoritarian transitions is a novel result in the literature.

Our strategy for measuring herding in aid allocation is a first step in an otherwise unexplored field. It is still unclear which measure would best suit our purpose. A structural model would clearly help but here again such models do not yet exist. The fact that all our indicators point in the same direction makes us confident that herding is present in aid allocation. Finding that herding does not seem to occur for observable reasons leads us to believe that some unobserved motives are driving the results. This is what we would expect if donors did not herd “rationally” and followed what others did in an informational cascade fashion with no clear rationale.

This paper suggests there is still a lot to learn about donor allocation policies. It also shows that beneficial herding is unlikely to explain herding levels, which might be worrisome in a world of globalised flows. Aid allocation decisions are not pro or counter-cyclical with respect to many variables (growth, democratic transitions and wars). It implies that large aid variations are not necessarily due to identifiable factors. Donor coordination would help to prevent such variations in cases where they stood to be harmful, and perhaps boost them when they were useful.


DEV/DOC(2009)4

34 © OECD 2009

This study leaves for future research the fundamental question of the motivations for donors to herd. It also leaves unanswered questions that we plan to investigate in the future. We have not investigated herding at the sector level. The analysis realised at the country level could be completed with a focus on sectors (education, infrastructure, water sanitation, etc.) in order to underline donor herding behaviour at that level and identify the shifting fashions that drive the aid industry, or, in another words, to identify both donor darling countries and donor darling sectors. We have not estimated the costs of herding. These could be evaluated in terms of higher volatility since the costs of volatility have already been estimated. They could also be related to overcrowding in countries or sectors, and so to inefficiencies due to aid fragmentation.


DEV/DOC(2009)4

REFERENCES

AGÉNOR, P.-R. and J. AIZENMAN (September 2007), Aid volatility and poverty traps, Working Paper 13400, National Bureau of Economic Research.

ALESINA, A. and D. DOLLAR (2000), “Who Gives Foreign Aid to Whom and Why?,” Journal of Economic Growth, 5, 33-63.

ALESINA, A., B. WEDER (2002), “Do Corrupt Governments Receive Less Foreign Aid?,” The American Economic Review, 92(4), 1126-1137.

ARELLANO, C., A. BULÍŘ, T. LANE, and L. LIPSCHITZ (2008), “The dynamic implication of foreign aid and its variability,” Journal of Development Economics, forthcoming.

BEKAERT, G. and C.R. HARVERY (2000), “Foreign speculators and emerging equity markets,” Journal of Finance, 55, pp. 565-613.

BERTHÉLEMY, J-C. (2006), “Bilateral Donors’ Vested Interest vs. Recipients’s Development Motives in Aid Allocation: Do All Donors behave the Same?,” Review of Development Economics, 10(2), 179-194.

BERTHÉLEMY. J-C. and A. TICHIT (2004), “Bilateral Donors’ Aid Allocation Decisions – a Three-Dimensional Panel Analysis,” International Review of Economics and Finance, 13, 253-274.

BIKHCHANDANI, S. and S. SHARMA (2000), Herd Behavior in Financial Markets, IMF Staff Papers 47 (3), 279–310.

BULÍŘ, A. and J. HAMANN (2006), Volatility of development aid: From the frying pan into the fire?, IMF Working Paper, International Monetary Fund, 06/65.

CASSEN, R. (1986), Does Aid Work?: Report to an Intergovernmental Task Force, Oxford University Press, Oxford, UK.

COLLIER, P. (2007), The bottom billion, Oxford University Press, Oxford, UK.

DJANKOV, S., J.G. MONTALVO, and M. REYNAL-QUEROL (2008), Aid with Multiple Personalities. Journal of Comparative Economics, forthcoming.

EASTERLY, W. and T. PFUTZE (2008), “Where does the money go? Best and worst practices in foreign aid,” Journal of Economic Perspectives, Vol. 22 (2), pp. pp. 29-52. See also: http://www.nyu.edu/fas/institute/dri/Easterly/File/Where_Does_Money_Go.pdf

FIELDING, D. and G. MAVROTAS (2005), The volatility of aid, UNU-WIDER Discussion Paper, 6.


DEV/DOC(2009)4

36 © OECD 2009

FREY, S., P. HERBST, and A. WALTER (2007), Measuring Mutual Fund Herding – A Structural Approach, Working Paper, Tübingen University, Germany.

FROT, E. and J. SANTISO (2008), Development aid and portfolio funds: Trends, volatility, and fragmentation, OECD Development Centre, Working Paper, 275.

GLEDITSCH, N.P.; P. WALLENSTEEN, M. ERIKSSON, M. SOLLENBERG, and H. STRAND (2002), “Armed Conflict 1946–2001: A New Dataset,” Journal of Peace Research 39(5), 615–637.

GRINBLATT, M., S. TITMAN, and R. WERMERS (1995), “Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behaviour,” The American Economic Review, 85 (5), pp, 1088-1105.

HIRSHLEIFER, D. and S. H. TEOH (2003), “Herd Behaviour and Cascading in Capital Markets: A Review and Synthesis,” European Financial Management 9 (1), 25–66.

KALYVITIS, S. and I. VLACHAKI (2008), More Aid, Less Democracy? An Empirical Examination of the Relationship between Foreign Aid and the Democratization of Recipients, Athens University of Economics and Business (unpublished).

KHARAS, H. J. (2008), Measuring the cost of aid volatility, Working Paper, Wolfensohn Center for Development, The Brookings Institution. See http://www.brookings.edu/experts/kharash.aspx

KHARAS, H. J. (2007), Trends and issues in development aid, Working Paper, Wolfensohn Center for Development, The Brookings Institution. See http://www.brookings.edu/experts/kharash.aspx

KNACK, S. and A. RAHMAN (2007), “Donor Fragmentation and Bureaucratic Quality in Aid Recipients,” Journal of Development Economics, 83, 176-197.

LAKONISHOK, J., A. SHLEIFER, and R. W. VISHNY (1992), “The impact of institutional trading on stock prices,” Journal of Financial Economics, 32 (1), pp. 23-43.

MARYSSE, S., A. ANSOMS, and D. CASSIMON (2006), The Aid “Darlings” and “Orphans” of the Great Lakes Region in Africa, Discussion Paper, 2006/10, Institute of Development Policy and Management.

MEJÍA ACOSTA, A. and P. de RENZIO (2008), Aid, rents and the politics of budget process, IDS Working Paper, Institute of Development Studies, Sussex University. See: http://www.ids.ac.uk/UserFiles/File/governance_team/people/Andr/AMA-Renzio-aidrents.pdf

MISHRA, P. and D. NEWHOUSE (2007), Health Aid and Infant Mortality, IMF Working Paper, 07/100, International Monetary Fund, Washington.

OECD (2008), Scaling up: Aid fragmentation, aid allocation and aid predictability, OECD Development Co-Operation Directorate, Paris.

ORNELAS, J. R. H. and B. ALEMANNI (2008), Herding Behaviour by Equity Foreign Investors on Emerging Markets, Banco Central do Brasil Working Paper, No. 125, Brasil.


DEV/DOC(2009)4

PAPKE, L.E. and J.M. WOOLDRIDGE (1996), “Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates,” Journal of Applied Econometrics, 11(6), 619-632.

REINHARDT, G. Y. (2006), “Shortcuts and signals: An analysis of the micro-level determinants of aid allocation, with case study evidence from Brazil,” Review of Development Economics, 10 (2), 297-312.

REISEN, H. and S. NDOYE (2008), Prudent versus imprudent lending to Africa. From debt relief to emerging lenders, Working Paper N° 268, OECD Development Centre, Paris.

RIDDELL, R. (2007), Does foreign aid really work?, Oxford University Press, USA.

RODRÍGUEZ, J. and J. SANTISO (November 2007), Banking on development: private banks and aid donors in developing countries, Working Paper N° 263, OECD Development Centre, Paris.

RODRÍGUEZ, J. and J. SANTISO (2008), “Banking on Democracy: The Political Economy of International Private Bank Lending in Emerging Markets,” International Political Science Review, 29, pp. 215-246.

ROODMAN, D. (2007), Macro Aid Effectiveness Research: a Guide for the Perplexed, Working Paper Number 134, Center for Global Development, Washington.

SHILLER, R. J. (2000), Irrational Exuberance, Princeton University Press, Princeton.

TARP, F., BACH, C. F., HANSEN, H., and BAUNSGAARD, S. (1998), Danish aid policy: Theory and empirical evidence, Discussion Paper, vol. 98/06, Copenhagen: Institute of Economics, University of Copenhagen.

TEZANOS VÁZQUEZ, S. (2008), The Spanish Pattern of Aid Giving, Working Paper 04/08, Instituto Complutense de Estudios Internacionales.

UCHIDA, H. and R. NAKAGAWA (2007), “Herd behavior in the Japanese loan market: Evidence from bank panel data,” Journal of Financial Intermediation 16 (4), pp. 555-583.

WEINER, R.J. (2006), Do Birds of a Feather Flock Together? Speculator Herding in the World Oil Market, Discussion Paper 06-31, Resources for the Future, Washington.

WELCH, I. (2000), “Herding among security analysts,” Journal of Financial Economics, 38, pp. 369-396. See also: http://welch.econ.brown.edu/academics/journalcopy/2000-jfe.pdf

WERMERS, R. (1999), “Mutual fund herding and the impact on stock prices,” The Journal of Finance, 54 (2), pp. 581-622.


DEV/DOC(2009)4

38 © OECD 2009

OTHER TITLES IN THE SERIES/ AUTRES TITRES DANS LA SÉRIE

The former series known as “Technical Papers” and “Webdocs” merged in November 2003 into “Development Centre Working Papers”. In the new series, former Webdocs 1-17 follow

former Technical Papers 1-212 as Working Papers 213-229.

All these documents may be downloaded from: http://www.oecd.org/dev/wp or obtained via e-mail ([email protected]).

Working Paper No.1, Macroeconomic Adjustment and Income Distribution: A Macro-Micro Simulation Model, by François Bourguignon, William H. Branson and Jaime de Melo, March 1989. Working Paper No. 2, International Interactions in Food and Agricultural Policies: The Effect of Alternative Policies, by Joachim Zietz and Alberto Valdés, April, 1989. Working Paper No. 3, The Impact of Budget Retrenchment on Income Distribution in Indonesia: A Social Accounting Matrix Application, by Steven Keuning and Erik Thorbecke, June 1989. Working Paper No. 3a, Statistical Annex: The Impact of Budget Retrenchment, June 1989. Document de travail No. 4, Le Rééquilibrage entre le secteur public et le secteur privé : le cas du Mexique, par C.-A. Michalet, juin 1989. Working Paper No. 5, Rebalancing the Public and Private Sectors: The Case of Malaysia, by R. Leeds, July 1989. Working Paper No. 6, Efficiency, Welfare Effects and Political Feasibility of Alternative Antipoverty and Adjustment Programs, by Alain de Janvry and Elisabeth Sadoulet, December 1989. Document de travail No. 7, Ajustement et distribution des revenus : application d’un modèle macro-micro au Maroc, par Christian Morrisson, avec la collaboration de Sylvie Lambert et Akiko Suwa, décembre 1989. Working Paper No. 8, Emerging Maize Biotechnologies and their Potential Impact, by W. Burt Sundquist, December 1989. Document de travail No. 9, Analyse des variables socio-culturelles et de l’ajustement en Côte d’Ivoire, par W. Weekes-Vagliani, janvier 1990. Working Paper No. 10, A Financial Computable General Equilibrium Model for the Analysis of Ecuador’s Stabilization Programs, by André Fargeix and Elisabeth Sadoulet, February 1990. Working Paper No. 11, Macroeconomic Aspects, Foreign Flows and Domestic Savings Performance in Developing Countries: A ”State of The Art” Report, by Anand Chandavarkar, February 1990. Working Paper No. 12, Tax Revenue Implications of the Real Exchange Rate: Econometric Evidence from Korea and Mexico, by Viriginia Fierro and Helmut Reisen, February 1990. Working Paper No. 13, Agricultural Growth and Economic Development: The Case of Pakistan, by Naved Hamid and Wouter Tims, April 1990. Working Paper No. 14, Rebalancing the Public and Private Sectors in Developing Countries: The Case of Ghana, by H. Akuoko-Frimpong, June 1990. Working Paper No. 15, Agriculture and the Economic Cycle: An Economic and Econometric Analysis with Special Reference to Brazil, by Florence Contré and Ian Goldin, June 1990. Working Paper No. 16, Comparative Advantage: Theory and Application to Developing Country Agriculture, by Ian Goldin, June 1990. Working Paper No. 17, Biotechnology and Developing Country Agriculture: Maize in Brazil, by Bernardo Sorj and John Wilkinson, June 1990. Working Paper No. 18, Economic Policies and Sectoral Growth: Argentina 1913-1984, by Yair Mundlak, Domingo Cavallo, Roberto Domenech, June 1990. Working Paper No. 19, Biotechnology and Developing Country Agriculture: Maize In Mexico, by Jaime A. Matus Gardea, Arturo Puente Gonzalez and Cristina Lopez Peralta, June 1990. Working Paper No. 20, Biotechnology and Developing Country Agriculture: Maize in Thailand, by Suthad Setboonsarng, July 1990. Working Paper No. 21, International Comparisons of Efficiency in Agricultural Production, by Guillermo Flichmann, July 1990.


DEV/DOC(2009)4

Working Paper No. 22, Unemployment in Developing Countries: New Light on an Old Problem, by David Turnham and Denizhan Eröcal, July 1990. Working Paper No. 23, Optimal Currency Composition of Foreign Debt: the Case of Five Developing Countries, by Pier Giorgio Gawronski, August 1990. Working Paper No. 24, From Globalization to Regionalization: the Mexican Case, by Wilson Peres Núñez, August 1990. Working Paper No. 25, Electronics and Development in Venezuela: A User-Oriented Strategy and its Policy Implications, by Carlota Perez, October 1990. Working Paper No. 26, The Legal Protection of Software: Implications for Latecomer Strategies in Newly Industrialising Economies (NIEs) and Middle-Income Economies (MIEs), by Carlos Maria Correa, October 1990. Working Paper No. 27, Specialization, Technical Change and Competitiveness in the Brazilian Electronics Industry, by Claudio R. Frischtak, October 1990. Working Paper No. 28, Internationalization Strategies of Japanese Electronics Companies: Implications for Asian Newly Industrializing Economies (NIEs), by Bundo Yamada, October 1990. Working Paper No. 29, The Status and an Evaluation of the Electronics Industry in Taiwan, by Gee San, October 1990. Working Paper No. 30, The Indian Electronics Industry: Current Status, Perspectives and Policy Options, by Ghayur Alam, October 1990. Working Paper No. 31, Comparative Advantage in Agriculture in Ghana, by James Pickett and E. Shaeeldin, October 1990. Working Paper No. 32, Debt Overhang, Liquidity Constraints and Adjustment Incentives, by Bert Hofman and Helmut Reisen, October 1990. Working Paper No. 34, Biotechnology and Developing Country Agriculture: Maize in Indonesia, by Hidjat Nataatmadja et al., January 1991. Working Paper No. 35, Changing Comparative Advantage in Thai Agriculture, by Ammar Siamwalla, Suthad Setboonsarng and Prasong Werakarnjanapongs, March 1991. Working Paper No. 36, Capital Flows and the External Financing of Turkey’s Imports, by Ziya Önis and Süleyman Özmucur, July 1991. Working Paper No. 37, The External Financing of Indonesia’s Imports, by Glenn P. Jenkins and Henry B.F. Lim, July 1991. Working Paper No. 38, Long-term Capital Reflow under Macroeconomic Stabilization in Latin America, by Beatriz Armendariz de Aghion, July 1991. Working Paper No. 39, Buybacks of LDC Debt and the Scope for Forgiveness, by Beatriz Armendariz de Aghion, July 1991. Working Paper No. 40, Measuring and Modelling Non-Tariff Distortions with Special Reference to Trade in Agricultural Commodities, by Peter J. Lloyd, July 1991. Working Paper No. 41, The Changing Nature of IMF Conditionality, by Jacques J. Polak, August 1991. Working Paper No. 42, Time-Varying Estimates on the Openness of the Capital Account in Korea and Taiwan, by Helmut Reisen and Hélène Yèches, August 1991. Working Paper No. 43, Toward a Concept of Development Agreements, by F. Gerard Adams, August 1991. Document de travail No. 44, Le Partage du fardeau entre les créanciers de pays débiteurs défaillants, par Jean-Claude Berthélemy et Ann Vourc’h, septembre 1991. Working Paper No. 45, The External Financing of Thailand’s Imports, by Supote Chunanunthathum, October 1991. Working Paper No. 46, The External Financing of Brazilian Imports, by Enrico Colombatto, with Elisa Luciano, Luca Gargiulo, Pietro Garibaldi and Giuseppe Russo, October 1991. Working Paper No. 47, Scenarios for the World Trading System and their Implications for Developing Countries, by Robert Z. Lawrence, November 1991. Working Paper No. 48, Trade Policies in a Global Context: Technical Specifications of the Rural/Urban-North/South (RUNS) Applied General Equilibrium Model, by Jean-Marc Burniaux and Dominique van der Mensbrugghe, November 1991. Working Paper No. 49, Macro-Micro Linkages: Structural Adjustment and Fertilizer Policy in Sub-Saharan Africa, by Jean-Marc Fontaine with the collaboration of Alice Sindzingre, December 1991. Working Paper No. 50, Aggregation by Industry in General Equilibrium Models with International Trade, by Peter J. Lloyd, December 1991. Working Paper No. 51, Policy and Entrepreneurial Responses to the Montreal Protocol: Some Evidence from the Dynamic Asian Economies, by David C. O’Connor, December 1991. Working Paper No. 52, On the Pricing of LDC Debt: an Analysis Based on Historical Evidence from Latin America, by Beatriz Armendariz de Aghion, February 1992. Working Paper No. 53, Economic Regionalisation and Intra-Industry Trade: Pacific-Asian Perspectives, by Kiichiro Fukasaku, February 1992. Working Paper No. 54, Debt Conversions in Yugoslavia, by Mojmir Mrak, February 1992. Working Paper No. 55, Evaluation of Nigeria’s Debt-Relief Experience (1985-1990), by N.E. Ogbe, March 1992. Document de travail No. 56, L’Expérience de l’allégement de la dette du Mali, par Jean-Claude Berthélemy, février 1992. Working Paper No. 57, Conflict or Indifference: US Multinationals in a World of Regional Trading Blocs, by Louis T. Wells, Jr., March 1992. Working Paper No. 58, Japan’s Rapidly Emerging Strategy Toward Asia, by Edward J. Lincoln, April 1992. Working Paper No. 59, The Political Economy of Stabilization Programmes in Developing Countries, by Bruno S. Frey and Reiner Eichenberger, April 1992. Working Paper No. 60, Some Implications of Europe 1992 for Developing Countries, by Sheila Page, April 1992.


DEV/DOC(2009)4

40 © OECD 2009

Working Paper No. 61, Taiwanese Corporations in Globalisation and Regionalisation, by Gee San, April 1992. Working Paper No. 62, Lessons from the Family Planning Experience for Community-Based Environmental Education, by Winifred Weekes-Vagliani, April 1992. Working Paper No. 63, Mexican Agriculture in the Free Trade Agreement: Transition Problems in Economic Reform, by Santiago Levy and Sweder van Wijnbergen, May 1992. Working Paper No. 64, Offensive and Defensive Responses by European Multinationals to a World of Trade Blocs, by John M. Stopford, May 1992. Working Paper No. 65, Economic Integration in the Pacific Region, by Richard Drobnick, May 1992. Working Paper No. 66, Latin America in a Changing Global Environment, by Winston Fritsch, May 1992. Working Paper No. 67, An Assessment of the Brady Plan Agreements, by Jean-Claude Berthélemy and Robert Lensink, May 1992. Working Paper No. 68, The Impact of Economic Reform on the Performance of the Seed Sector in Eastern and Southern Africa, by Elizabeth Cromwell, June 1992. Working Paper No. 69, Impact of Structural Adjustment and Adoption of Technology on Competitiveness of Major Cocoa Producing Countries, by Emily M. Bloomfield and R. Antony Lass, June 1992. Working Paper No. 70, Structural Adjustment and Moroccan Agriculture: an Assessment of the Reforms in the Sugar and Cereal Sectors, by Jonathan Kydd and Sophie Thoyer, June 1992. Document de travail No. 71, L’Allégement de la dette au Club de Paris : les évolutions récentes en perspective, par Ann Vourc’h, juin 1992. Working Paper No. 72, Biotechnology and the Changing Public/Private Sector Balance: Developments in Rice and Cocoa, by Carliene Brenner, July 1992. Working Paper No. 73, Namibian Agriculture: Policies and Prospects, by Walter Elkan, Peter Amutenya, Jochbeth Andima, Robin Sherbourne and Eline van der Linden, July 1992. Working Paper No. 74, Agriculture and the Policy Environment: Zambia and Zimbabwe, by Doris J. Jansen and Andrew Rukovo, July 1992. Working Paper No. 75, Agricultural Productivity and Economic Policies: Concepts and Measurements, by Yair Mundlak, August 1992. Working Paper No. 76, Structural Adjustment and the Institutional Dimensions of Agricultural Research and Development in Brazil: Soybeans, Wheat and Sugar Cane, by John Wilkinson and Bernardo Sorj, August 1992. Working Paper No. 77, The Impact of Laws and Regulations on Micro and Small Enterprises in Niger and Swaziland, by Isabelle Joumard, Carl Liedholm and Donald Mead, September 1992. Working Paper No. 78, Co-Financing Transactions between Multilateral Institutions and International Banks, by Michel Bouchet and Amit Ghose, October 1992. Document de travail No. 79, Allégement de la dette et croissance : le cas mexicain, par Jean-Claude Berthélemy et Ann Vourc’h, octobre 1992. Document de travail No. 80, Le Secteur informel en Tunisie : cadre réglementaire et pratique courante, par Abderrahman Ben Zakour et Farouk Kria, novembre 1992. Working Paper No. 81, Small-Scale Industries and Institutional Framework in Thailand, by Naruemol Bunjongjit and Xavier Oudin, November 1992. Working Paper No. 81a, Statistical Annex: Small-Scale Industries and Institutional Framework in Thailand, by Naruemol Bunjongjit and Xavier Oudin, November 1992. Document de travail No. 82, L’Expérience de l’allégement de la dette du Niger, par Ann Vourc’h et Maina Boukar Moussa, novembre 1992. Working Paper No. 83, Stabilization and Structural Adjustment in Indonesia: an Intertemporal General Equilibrium Analysis, by David Roland-Holst, November 1992. Working Paper No. 84, Striving for International Competitiveness: Lessons from Electronics for Developing Countries, by Jan Maarten de Vet, March 1993. Document de travail No. 85, Micro-entreprises et cadre institutionnel en Algérie, par Hocine Benissad, mars 1993. Working Paper No. 86, Informal Sector and Regulations in Ecuador and Jamaica, by Emilio Klein and Victor E. Tokman, August 1993. Working Paper No. 87, Alternative Explanations of the Trade-Output Correlation in the East Asian Economies, by Colin I. Bradford Jr. and Naomi Chakwin, August 1993. Document de travail No. 88, La Faisabilité politique de l’ajustement dans les pays africains, par Christian Morrisson, Jean-Dominique Lafay et Sébastien Dessus, novembre 1993. Working Paper No. 89, China as a Leading Pacific Economy, by Kiichiro Fukasaku and Mingyuan Wu, November 1993. Working Paper No. 90, A Detailed Input-Output Table for Morocco, 1990, by Maurizio Bussolo and David Roland-Holst November 1993. Working Paper No. 91, International Trade and the Transfer of Environmental Costs and Benefits, by Hiro Lee and David Roland-Holst, December 1993. Working Paper No. 92, Economic Instruments in Environmental Policy: Lessons from the OECD Experience and their Relevance to Developing Economies, by Jean-Philippe Barde, January 1994. Working Paper No. 93, What Can Developing Countries Learn from OECD Labour Market Programmes and Policies?, by Åsa Sohlman with David Turnham, January 1994.


DEV/DOC(2009)4

Working Paper No. 94, Trade Liberalization and Employment Linkages in the Pacific Basin, by Hiro Lee and David Roland-Holst, February 1994. Working Paper No. 95, Participatory Development and Gender: Articulating Concepts and Cases, by Winifred Weekes-Vagliani, February 1994. Document de travail No. 96, Promouvoir la maîtrise locale et régionale du développement : une démarche participative à Madagascar, par Philippe de Rham et Bernard Lecomte, juin 1994. Working Paper No. 97, The OECD Green Model: an Updated Overview, by Hiro Lee, Joaquim Oliveira-Martins and Dominique van der Mensbrugghe, August 1994. Working Paper No. 98, Pension Funds, Capital Controls and Macroeconomic Stability, by Helmut Reisen and John Williamson, August 1994. Working Paper No. 99, Trade and Pollution Linkages: Piecemeal Reform and Optimal Intervention, by John Beghin, David Roland-Holst and Dominique van der Mensbrugghe, October 1994. Working Paper No. 100, International Initiatives in Biotechnology for Developing Country Agriculture: Promises and Problems, by Carliene Brenner and John Komen, October 1994. Working Paper No. 101, Input-based Pollution Estimates for Environmental Assessment in Developing Countries, by Sébastien Dessus, David Roland-Holst and Dominique van der Mensbrugghe, October 1994. Working Paper No. 102, Transitional Problems from Reform to Growth: Safety Nets and Financial Efficiency in the Adjusting Egyptian Economy, by Mahmoud Abdel-Fadil, December 1994. Working Paper No. 103, Biotechnology and Sustainable Agriculture: Lessons from India, by Ghayur Alam, December 1994. Working Paper No. 104, Crop Biotechnology and Sustainability: a Case Study of Colombia, by Luis R. Sanint, January 1995. Working Paper No. 105, Biotechnology and Sustainable Agriculture: the Case of Mexico, by José Luis Solleiro Rebolledo, January 1995. Working Paper No. 106, Empirical Specifications for a General Equilibrium Analysis of Labor Market Policies and Adjustments, by Andréa Maechler and David Roland-Holst, May 1995. Document de travail No. 107, Les Migrants, partenaires de la coopération internationale : le cas des Maliens de France, par Christophe Daum, juillet 1995. Document de travail No. 108, Ouverture et croissance industrielle en Chine : étude empirique sur un échantillon de villes, par Sylvie Démurger, septembre 1995. Working Paper No. 109, Biotechnology and Sustainable Crop Production in Zimbabwe, by John J. Woodend, December 1995. Document de travail No. 110, Politiques de l’environnement et libéralisation des échanges au Costa Rica : une vue d’ensemble, par Sébastien Dessus et Maurizio Bussolo, février 1996. Working Paper No. 111, Grow Now/Clean Later, or the Pursuit of Sustainable Development?, by David O’Connor, March 1996. Working Paper No. 112, Economic Transition and Trade-Policy Reform: Lessons from China, by Kiichiro Fukasaku and Henri-Bernard Solignac Lecomte, July 1996. Working Paper No. 113, Chinese Outward Investment in Hong Kong: Trends, Prospects and Policy Implications, by Yun-Wing Sung, July 1996. Working Paper No. 114, Vertical Intra-industry Trade between China and OECD Countries, by Lisbeth Hellvin, July 1996. Document de travail No. 115, Le Rôle du capital public dans la croissance des pays en développement au cours des années 80, par Sébastien Dessus et Rémy Herrera, juillet 1996. Working Paper No. 116, General Equilibrium Modelling of Trade and the Environment, by John Beghin, Sébastien Dessus, David Roland-Holst and Dominique van der Mensbrugghe, September 1996. Working Paper No. 117, Labour Market Aspects of State Enterprise Reform in Viet Nam, by David O’Connor, September 1996. Document de travail No. 118, Croissance et compétitivité de l’industrie manufacturière au Sénégal, par Thierry Latreille et Aristomène Varoudakis, octobre 1996. Working Paper No. 119, Evidence on Trade and Wages in the Developing World, by Donald J. Robbins, December 1996. Working Paper No. 120, Liberalising Foreign Investments by Pension Funds: Positive and Normative Aspects, by Helmut Reisen, January 1997. Document de travail No. 121, Capital Humain, ouverture extérieure et croissance : estimation sur données de panel d’un modèle à coefficients variables, par Jean-Claude Berthélemy, Sébastien Dessus et Aristomène Varoudakis, janvier 1997. Working Paper No. 122, Corruption: The Issues, by Andrew W. Goudie and David Stasavage, January 1997. Working Paper No. 123, Outflows of Capital from China, by David Wall, March 1997. Working Paper No. 124, Emerging Market Risk and Sovereign Credit Ratings, by Guillermo Larraín, Helmut Reisen and Julia von Maltzan, April 1997. Working Paper No. 125, Urban Credit Co-operatives in China, by Eric Girardin and Xie Ping, August 1997. Working Paper No. 126, Fiscal Alternatives of Moving from Unfunded to Funded Pensions, by Robert Holzmann, August 1997. Working Paper No. 127, Trade Strategies for the Southern Mediterranean, by Peter A. Petri, December 1997. Working Paper No. 128, The Case of Missing Foreign Investment in the Southern Mediterranean, by Peter A. Petri, December 1997. Working Paper No. 129, Economic Reform in Egypt in a Changing Global Economy, by Joseph Licari, December 1997.


DEV/DOC(2009)4

42 © OECD 2009

Working Paper No. 130, Do Funded Pensions Contribute to Higher Aggregate Savings? A Cross-Country Analysis, by Jeanine Bailliu and Helmut Reisen, December 1997. Working Paper No. 131, Long-run Growth Trends and Convergence Across Indian States, by Rayaprolu Nagaraj, Aristomène Varoudakis and Marie-Ange Véganzonès, January 1998. Working Paper No. 132, Sustainable and Excessive Current Account Deficits, by Helmut Reisen, February 1998. Working Paper No. 133, Intellectual Property Rights and Technology Transfer in Developing Country Agriculture: Rhetoric and Reality, by Carliene Brenner, March 1998. Working Paper No. 134, Exchange-rate Management and Manufactured Exports in Sub-Saharan Africa, by Khalid Sekkat and Aristomène Varoudakis, March 1998. Working Paper No. 135, Trade Integration with Europe, Export Diversification and Economic Growth in Egypt, by Sébastien Dessus and Akiko Suwa-Eisenmann, June 1998. Working Paper No. 136, Domestic Causes of Currency Crises: Policy Lessons for Crisis Avoidance, by Helmut Reisen, June 1998. Working Paper No. 137, A Simulation Model of Global Pension Investment, by Landis MacKellar and Helmut Reisen, August 1998. Working Paper No. 138, Determinants of Customs Fraud and Corruption: Evidence from Two African Countries, by David Stasavage and Cécile Daubrée, August 1998. Working Paper No. 139, State Infrastructure and Productive Performance in Indian Manufacturing, by Arup Mitra, Aristomène Varoudakis and Marie-Ange Véganzonès, August 1998. Working Paper No. 140, Rural Industrial Development in Viet Nam and China: A Study in Contrasts, by David O’Connor, September 1998. Working Paper No. 141,Labour Market Aspects of State Enterprise Reform in China, by Fan Gang,Maria Rosa Lunati and David O’Connor, October 1998. Working Paper No. 142, Fighting Extreme Poverty in Brazil: The Influence of Citizens’ Action on Government Policies, by Fernanda Lopes de Carvalho, November 1998. Working Paper No. 143, How Bad Governance Impedes Poverty Alleviation in Bangladesh, by Rehman Sobhan, November 1998. Document de travail No. 144, La libéralisation de l’agriculture tunisienne et l’Union européenne: une vue prospective, par Mohamed Abdelbasset Chemingui et Sébastien Dessus, février 1999. Working Paper No. 145, Economic Policy Reform and Growth Prospects in Emerging African Economies, by Patrick Guillaumont, Sylviane Guillaumont Jeanneney and Aristomène Varoudakis, March 1999. Working Paper No. 146, Structural Policies for International Competitiveness in Manufacturing: The Case of Cameroon, by Ludvig Söderling, March 1999. Working Paper No. 147, China’s Unfinished Open-Economy Reforms: Liberalisation of Services, by Kiichiro Fukasaku, Yu Ma and Qiumei Yang, April 1999. Working Paper No. 148, Boom and Bust and Sovereign Ratings, by Helmut Reisen and Julia von Maltzan, June 1999. Working Paper No. 149, Economic Opening and the Demand for Skills in Developing Countries: A Review of Theory and Evidence, by David O’Connor and Maria Rosa Lunati, June 1999. Working Paper No. 150, The Role of Capital Accumulation, Adjustment and Structural Change for Economic Take-off: Empirical Evidence from African Growth Episodes, by Jean-Claude Berthélemy and Ludvig Söderling, July 1999. Working Paper No. 151, Gender, Human Capital and Growth: Evidence from Six Latin American Countries, by Donald J. Robbins, September 1999. Working Paper No. 152, The Politics and Economics of Transition to an Open Market Economy in Viet Nam, by James Riedel and William S. Turley, September 1999. Working Paper No. 153, The Economics and Politics of Transition to an Open Market Economy: China, by Wing Thye Woo, October 1999. Working Paper No. 154, Infrastructure Development and Regulatory Reform in Sub-Saharan Africa: The Case of Air Transport, by Andrea E. Goldstein, October 1999. Working Paper No. 155, The Economics and Politics of Transition to an Open Market Economy: India, by Ashok V. Desai, October 1999. Working Paper No. 156, Climate Policy Without Tears: CGE-Based Ancillary Benefits Estimates for Chile, by Sébastien Dessus and David O’Connor, November 1999. Document de travail No. 157, Dépenses d’éducation, qualité de l’éducation et pauvreté : l’exemple de cinq pays d’Afrique francophone, par Katharina Michaelowa, avril 2000. Document de travail No. 158, Une estimation de la pauvreté en Afrique subsaharienne d’après les données anthropométriques, par Christian Morrisson, Hélène Guilmeau et Charles Linskens, mai 2000. Working Paper No. 159, Converging European Transitions, by Jorge Braga de Macedo, July 2000. Working Paper No. 160, Capital Flows and Growth in Developing Countries: Recent Empirical Evidence, by Marcelo Soto, July 2000. Working Paper No. 161, Global Capital Flows and the Environment in the 21st Century, by David O’Connor, July 2000. Working Paper No. 162, Financial Crises and International Architecture: A “Eurocentric” Perspective, by Jorge Braga de Macedo, August 2000. Document de travail No. 163, Résoudre le problème de la dette : de l’initiative PPTE à Cologne, par Anne Joseph, août 2000. Working Paper No. 164, E-Commerce for Development: Prospects and Policy Issues, by Andrea Goldstein and David O’Connor, September 2000.


DEV/DOC(2009)4

Working Paper No. 165, Negative Alchemy? Corruption and Composition of Capital Flows, by Shang-Jin Wei, October 2000. Working Paper No. 166, The HIPC Initiative: True and False Promises, by Daniel Cohen, October 2000. Document de travail No. 167, Les facteurs explicatifs de la malnutrition en Afrique subsaharienne, par Christian Morrisson et Charles Linskens, octobre 2000. Working Paper No. 168, Human Capital and Growth: A Synthesis Report, by Christopher A. Pissarides, November 2000. Working Paper No. 169, Obstacles to Expanding Intra-African Trade, by Roberto Longo and Khalid Sekkat, March 2001. Working Paper No. 170, Regional Integration In West Africa, by Ernest Aryeetey, March 2001. Working Paper No. 171, Regional Integration Experience in the Eastern African Region, by Andrea Goldstein and Njuguna S. Ndung’u, March 2001. Working Paper No. 172, Integration and Co-operation in Southern Africa, by Carolyn Jenkins, March 2001. Working Paper No. 173, FDI in Sub-Saharan Africa, by Ludger Odenthal, March 2001 Document de travail No. 174, La réforme des télécommunications en Afrique subsaharienne, par Patrick Plane, mars 2001. Working Paper No. 175, Fighting Corruption in Customs Administration: What Can We Learn from Recent Experiences?, by Irène Hors; April 2001. Working Paper No. 176, Globalisation and Transformation: Illusions and Reality, by Grzegorz W. Kolodko, May 2001. Working Paper No. 177, External Solvency, Dollarisation and Investment Grade: Towards a Virtuous Circle?, by Martin Grandes, June 2001. Document de travail No. 178, Congo 1965-1999: Les espoirs déçus du « Brésil africain », par Joseph Maton avec Henri-Bernard Solignac Lecomte, septembre 2001. Working Paper No. 179, Growth and Human Capital: Good Data, Good Results, by Daniel Cohen and Marcelo Soto, September 2001. Working Paper No. 180, Corporate Governance and National Development, by Charles P. Oman, October 2001. Working Paper No. 181, How Globalisation Improves Governance, by Federico Bonaglia, Jorge Braga de Macedo and Maurizio Bussolo, November 2001. Working Paper No. 182, Clearing the Air in India: The Economics of Climate Policy with Ancillary Benefits, by Maurizio Bussolo and David O’Connor, November 2001. Working Paper No. 183, Globalisation, Poverty and Inequality in sub-Saharan Africa: A Political Economy Appraisal, by Yvonne M. Tsikata, December 2001. Working Paper No. 184, Distribution and Growth in Latin America in an Era of Structural Reform: The Impact of Globalisation, by Samuel A. Morley, December 2001. Working Paper No. 185, Globalisation, Liberalisation, Poverty and Income Inequality in Southeast Asia, by K.S. Jomo, December 2001. Working Paper No. 186, Globalisation, Growth and Income Inequality: The African Experience, by Steve Kayizzi-Mugerwa, December 2001. Working Paper No. 187, The Social Impact of Globalisation in Southeast Asia, by Mari Pangestu, December 2001. Working Paper No. 188, Where Does Inequality Come From? Ideas and Implications for Latin America, by James A. Robinson, December 2001. Working Paper No. 189, Policies and Institutions for E-Commerce Readiness: What Can Developing Countries Learn from OECD Experience?, by Paulo Bastos Tigre and David O’Connor, April 2002. Document de travail No. 190, La réforme du secteur financier en Afrique, par Anne Joseph, juillet 2002. Working Paper No. 191, Virtuous Circles? Human Capital Formation, Economic Development and the Multinational Enterprise, by Ethan B. Kapstein, August 2002. Working Paper No. 192, Skill Upgrading in Developing Countries: Has Inward Foreign Direct Investment Played a Role?, by Matthew J. Slaughter, August 2002. Working Paper No. 193, Government Policies for Inward Foreign Direct Investment in Developing Countries: Implications for Human Capital Formation and Income Inequality, by Dirk Willem te Velde, August 2002. Working Paper No. 194, Foreign Direct Investment and Intellectual Capital Formation in Southeast Asia, by Bryan K. Ritchie, August 2002. Working Paper No. 195, FDI and Human Capital: A Research Agenda, by Magnus Blomström and Ari Kokko, August 2002. Working Paper No. 196, Knowledge Diffusion from Multinational Enterprises: The Role of Domestic and Foreign Knowledge-Enhancing Activities, by Yasuyuki Todo and Koji Miyamoto, August 2002. Working Paper No. 197, Why Are Some Countries So Poor? Another Look at the Evidence and a Message of Hope, by Daniel Cohen and Marcelo Soto, October 2002. Working Paper No. 198, Choice of an Exchange-Rate Arrangement, Institutional Setting and Inflation: Empirical Evidence from Latin America, by Andreas Freytag, October 2002. Working Paper No. 199, Will Basel II Affect International Capital Flows to Emerging Markets?, by Beatrice Weder and Michael Wedow, October 2002. Working Paper No. 200, Convergence and Divergence of Sovereign Bond Spreads: Lessons from Latin America, by Martin Grandes, October 2002. Working Paper No. 201, Prospects for Emerging-Market Flows amid Investor Concerns about Corporate Governance, by Helmut Reisen, November 2002. Working Paper No. 202, Rediscovering Education in Growth Regressions, by Marcelo Soto, November 2002.


DEV/DOC(2009)4

44 © OECD 2009

Working Paper No. 203, Incentive Bidding for Mobile Investment: Economic Consequences and Potential Responses, by Andrew Charlton, January 2003. Working Paper No. 204, Health Insurance for the Poor? Determinants of participation Community-Based Health Insurance Schemes in Rural Senegal, by Johannes Jütting, January 2003. Working Paper No. 205, China’s Software Industry and its Implications for India, by Ted Tschang, February 2003. Working Paper No. 206, Agricultural and Human Health Impacts of Climate Policy in China: A General Equilibrium Analysis with Special Reference to Guangdong, by David O’Connor, Fan Zhai, Kristin Aunan, Terje Berntsen and Haakon Vennemo, March 2003. Working Paper No. 207, India’s Information Technology Sector: What Contribution to Broader Economic Development?, by Nirvikar Singh, March 2003. Working Paper No. 208, Public Procurement: Lessons from Kenya, Tanzania and Uganda, by Walter Odhiambo and Paul Kamau, March 2003. Working Paper No. 209, Export Diversification in Low-Income Countries: An International Challenge after Doha, by Federico Bonaglia and Kiichiro Fukasaku, June 2003. Working Paper No. 210, Institutions and Development: A Critical Review, by Johannes Jütting, July 2003. Working Paper No. 211, Human Capital Formation and Foreign Direct Investment in Developing Countries, by Koji Miyamoto, July 2003. Working Paper No. 212, Central Asia since 1991: The Experience of the New Independent States, by Richard Pomfret, July 2003. Working Paper No. 213, A Multi-Region Social Accounting Matrix (1995) and Regional Environmental General Equilibrium Model for India (REGEMI), by Maurizio Bussolo, Mohamed Chemingui and David O’Connor, November 2003. Working Paper No. 214, Ratings Since the Asian Crisis, by Helmut Reisen, November 2003. Working Paper No. 215, Development Redux: Reflections for a New Paradigm, by Jorge Braga de Macedo, November 2003. Working Paper No. 216, The Political Economy of Regulatory Reform: Telecoms in the Southern Mediterranean, by Andrea Goldstein, November 2003. Working Paper No. 217, The Impact of Education on Fertility and Child Mortality: Do Fathers Really Matter Less than Mothers?, by Lucia Breierova and Esther Duflo, November 2003. Working Paper No. 218, Float in Order to Fix? Lessons from Emerging Markets for EU Accession Countries, by Jorge Braga de Macedo and Helmut Reisen, November 2003. Working Paper No. 219, Globalisation in Developing Countries: The Role of Transaction Costs in Explaining Economic Performance in India, by Maurizio Bussolo and John Whalley, November 2003. Working Paper No. 220, Poverty Reduction Strategies in a Budget-Constrained Economy: The Case of Ghana, by Maurizio Bussolo and Jeffery I. Round, November 2003. Working Paper No. 221, Public-Private Partnerships in Development: Three Applications in Timor Leste, by José Braz, November 2003. Working Paper No. 222, Public Opinion Research, Global Education and Development Co-operation Reform: In Search of a Virtuous Circle, by Ida Mc Donnell, Henri-Bernard Solignac Lecomte and Liam Wegimont, November 2003. Working Paper No. 223, Building Capacity to Trade: What Are the Priorities?, by Henry-Bernard Solignac Lecomte, November 2003. Working Paper No. 224, Of Flying Geeks and O-Rings: Locating Software and IT Services in India’s Economic Development, by David O’Connor, November 2003. Document de travail No. 225, Cap Vert: Gouvernance et Développement, par Jaime Lourenço and Colm Foy, novembre 2003. Working Paper No. 226, Globalisation and Poverty Changes in Colombia, by Maurizio Bussolo and Jann Lay, November 2003. Working Paper No. 227, The Composite Indicator of Economic Activity in Mozambique (ICAE): Filling in the Knowledge Gaps to Enhance Public-Private Partnership (PPP), by Roberto J. Tibana, November 2003. Working Paper No. 228, Economic-Reconstruction in Post-Conflict Transitions: Lessons for the Democratic Republic of Congo (DRC), by Graciana del Castillo, November 2003. Working Paper No. 229, Providing Low-Cost Information Technology Access to Rural Communities In Developing Countries: What Works? What Pays? by Georg Caspary and David O’Connor, November 2003. Working Paper No. 230, The Currency Premium and Local-Currency Denominated Debt Costs in South Africa, by Martin Grandes, Marcel Peter and Nicolas Pinaud, December 2003. Working Paper No. 231, Macroeconomic Convergence in Southern Africa: The Rand Zone Experience, by Martin Grandes, December 2003. Working Paper No. 232, Financing Global and Regional Public Goods through ODA: Analysis and Evidence from the OECD Creditor Reporting System, by Helmut Reisen, Marcelo Soto and Thomas Weithöner, January 2004. Working Paper No. 233, Land, Violent Conflict and Development, by Nicolas Pons-Vignon and Henri-Bernard Solignac Lecomte, February 2004. Working Paper No. 234, The Impact of Social Institutions on the Economic Role of Women in Developing Countries, by Christian Morrisson and Johannes Jütting, May 2004. Document de travail No. 235, La condition desfemmes en Inde, Kenya, Soudan et Tunisie, par Christian Morrisson, août 2004. Working Paper No. 236, Decentralisation and Poverty in Developing Countries: Exploring the Impact, by Johannes Jütting, Céline Kauffmann, Ida Mc Donnell, Holger Osterrieder, Nicolas Pinaud and Lucia Wegner, August 2004. Working Paper No. 237, Natural Disasters and Adaptive Capacity, by Jeff Dayton-Johnson, August 2004.


DEV/DOC(2009)4

Working Paper No. 238, Public Opinion Polling and the Millennium Development Goals, by Jude Fransman, Alphonse L. MacDonnald, Ida Mc Donnell and Nicolas Pons-Vignon, October 2004. Working Paper No. 239, Overcoming Barriers to Competitiveness, by Orsetta Causa and Daniel Cohen, December 2004. Working Paper No. 240, Extending Insurance? Funeral Associations in Ethiopia and Tanzania, by Stefan Dercon, Tessa Bold, Joachim De Weerdt and Alula Pankhurst, December 2004. Working Paper No. 241, Macroeconomic Policies: New Issues of Interdependence, by Helmut Reisen, Martin Grandes and Nicolas Pinaud, January 2005. Working Paper No. 242, Institutional Change and its Impact on the Poor and Excluded: The Indian Decentralisation Experience, by D. Narayana, January 2005. Working Paper No. 243, Impact of Changes in Social Institutions on Income Inequality in China, by Hiroko Uchimura, May 2005. Working Paper No. 244, Priorities in Global Assistance for Health, AIDS and Population (HAP), by Landis MacKellar, June 2005. Working Paper No. 245, Trade and Structural Adjustment Policies in Selected Developing Countries, by Jens Andersson, Federico Bonaglia, Kiichiro Fukasaku and Caroline Lesser, July 2005. Working Paper No. 246, Economic Growth and Poverty Reduction: Measurement and Policy Issues, by Stephan Klasen, (September 2005). Working Paper No. 247, Measuring Gender (In)Equality: Introducing the Gender, Institutions and Development Data Base (GID), by Johannes P. Jütting, Christian Morrisson, Jeff Dayton-Johnson and Denis Drechsler (March 2006). Working Paper No. 248, Institutional Bottlenecks for Agricultural Development: A Stock-Taking Exercise Based on Evidence from Sub-Saharan Africa by Juan R. de Laiglesia, March 2006. Working Paper No. 249, Migration Policy and its Interactions with Aid, Trade and Foreign Direct Investment Policies: A Background Paper, by Theodora Xenogiani, June 2006. Working Paper No. 250, Effects of Migration on Sending Countries: What Do We Know? by Louka T. Katseli, Robert E.B. Lucas and Theodora Xenogiani, June 2006. Document de travail No. 251, L’aide au développement et les autres flux nord-sud : complémentarité ou substitution ?, par Denis Cogneau et Sylvie Lambert, juin 2006. Working Paper No. 252, Angel or Devil? China’s Trade Impact on Latin American Emerging Markets, by Jorge Blázquez-Lidoy, Javier Rodríguez and Javier Santiso, June 2006. Working Paper No. 253, Policy Coherence for Development: A Background Paper on Foreign Direct Investment, by Thierry Mayer, July 2006. Working Paper No. 254, The Coherence of Trade Flows and Trade Policies with Aid and Investment Flows, by Akiko Suwa-Eisenmann and Thierry Verdier, August 2006. Document de travail No. 255, Structures familiales, transferts et épargne : examen, par Christian Morrisson, août 2006. Working Paper No. 256, Ulysses, the Sirens and the Art of Navigation: Political and Technical Rationality in Latin America, by Javier Santiso and Laurence Whitehead, September 2006. Working Paper No. 257, Developing Country Multinationals: South-South Investment Comes of Age, by Dilek Aykut and Andrea Goldstein, November 2006. Working Paper No. 258, The Usual Suspects: A Primer on Investment Banks’ Recommendations and Emerging Markets, by Sebastián Nieto Parra and Javier Santiso, January 2007. Working Paper No. 259, Banking on Democracy: The Political Economy of International Private Bank Lending in Emerging Markets, by Javier Rodríguez and Javier Santiso, March 2007. Working Paper No. 260, New Strategies for Emerging Domestic Sovereign Bond Markets, by Hans Blommestein and Javier Santiso, April 2007. Working Paper No. 261, Privatisation in the MEDA region. Where do we stand?, by Céline Kauffmann and Lucia Wegner, July 2007. Working Paper No. 262, Strengthening Productive Capacities in Emerging Economies through Internationalisation: Evidence from the Appliance Industry, by Federico Bonaglia and Andrea Goldstein, July 2007. Working Paper No. 263, Banking on Development: Private Banks and Aid Donors in Developing Countries, by Javier Rodríguez and Javier Santiso, November 2007. Working Paper No. 264, Fiscal Decentralisation, Chinese Style: Good for Health Outcomes?, by Hiroko Uchimura and Johannes Jütting, November 2007. Working Paper No. 265, Private Sector Participation and Regulatory Reform in Water supply: the Southern Mediterranean Experience, by Edouard Pérard, January 2008. Working Paper No. 266, Informal Employment Re-loaded, by Johannes Jütting, Jante Parlevliet and Theodora Xenogiani, January 2008. Working Paper No. 267, Household Structures and Savings: Evidence from Household Surveys, by Juan R. de Laiglesia and Christian Morrisson, January 2008. Working Paper No. 268, Prudent versus Imprudent Lending to Africa: From Debt Relief to Emerging Lenders, by Helmut Reisen and Sokhna Ndoye, February 2008. Working Paper No. 269, Lending to the Poorest Countries: A New Counter-Cyclical Debt Instrument, by Daniel Cohen, Hélène Djoufelkit-Cottenet, Pierre Jacquet and Cécile Valadier, April 2008. Working Paper No.270, The Macro Management of Commodity Booms: Africa and Latin America’s Response to Asian Demand, by Rolando Avendaño, Helmut Reisen and Javier Santiso, August 2008.


DEV/DOC(2009)4

46 © OECD 2009

Working Paper No. 271, Report on Informal Employment in Romania, by Jante Parlevliet and Theodora Xenogiani, July 2008. Working Paper No. 272, Wall Street and Elections in Latin American Emerging Democracies, by Sebastián Nieto Parra and Javier Santiso, October 2008. Working Paper No. 273, Aid Volatility and Macro Risks in LICs, by Eduardo Borensztein, Julia Cage, Daniel Cohen and Cécile Valadier, November 2008. Working Paper No. 274, Who Saw Sovereign Debt Crises Coming?, by Sebastián Nieto Parra, November 2008. Working Paper No. 275, Development Aid and Portfolio Funds: Trends, Volatility and Fragmentation, by Emmanuel Frot and Javier Santiso, December 2008. Working Paper No. 276, Extracting the Maximum from EITI, by Dilan Ölcer, February 2009. Working Paper No. 277, Taking Stock of the Credit Crunch: Implications for Development Finance and Global Governance, by Andrew Mold, Sebastian Paulo and Annalisa Prizzon, March 2009. Working Paper No. 278, Are All Migrants Really Worse Off in Urban Labour Markets? New Empirical Evidence from China, by Jason Gagnon, Theodora Xenogiani and Chunbing Xing, June 2009.

herding in aid allocation

Documents

france oecd development

oecd development centreherding

dveloppement documents

france ou

rue andr pascal

rue andrpascal

paris cedex

locde ou des gouvernements