metadata: authors: 0: author: Knack, Stephen
1: author: Eubank, Nicholas
count: World
docna: 0: docna: Aid and trust in country systems
docty: Policy Research Working Paper
theme: Poverty strategy, analysis and monitoring
majtheme: Social protection and risk management
subtopic: Macro-Fiscal Policy,Consumption,Fiscal & Monetary Policy,Economic Adjustment and Lending,Development Economics & Aid Effectiveness,Public Sector Economics,Foreign Aid,Economic Stabilization,Public Financial Management,Public Finance Decentralization and
            Poverty Reduction
teratopic: Gender,Macroeconomics and Economic Growth,Social Protections and Labor,Finance and Financial Sector Development
abstracts: cdata!: The 2005 Paris Declaration on Aid
            Effectiveness sets targets for increased use by donors of
            recipient country systems for managing aid. A consensus view
            holds that country systems are strengthened when donors
            trust recipients to manage aid funds, but undermined when
            donors manage aid through their own separate parallel
            systems. This paper provides an analytical framework for
            understanding donors decisions to trust in country systems
            or instead to micro-manage aid using their own systems and
            procedures. Where country systems are sufficiently weak, the
            development impact of aid is reduced by donors reliance on
            them. Trust in country systems will be sub-optimal, however,
            if donors have multiple objectives in aid provision rather
            than a sole objective of maximizing development outcomes.
            Empirical tests are conducted using data from an OECD survey
            designed to monitor progress toward Paris Declaration goals.
            Trust in country systems is measured in three ways: use of
            the recipients public financial management systems, use of
            direct budget support, and use of program-based approaches.
            The authors show using fixed effects regression that a
            donors trust in recipient country systems is positively
            related to (1) trustworthiness or quality of those systems,
            (2) tolerance for risk on the part of the donors
            constituents, as measured by public support for providing
            aid, and (3) the donors ability to internalize more of the
            benefits of investing in country systems, as measured by the
            donors share of all aid provided to a recipient.
repnme: repnme: Aid and trust in country systems
display_title: Aid and trust in country systems
listing_relative_url: /research/2009/07/10839784/aid-trust-country-systems
topicv3: Public Finance Management m1326316 716,Macroeconomic Stability and Growth
            m1326923 1088,Fiscal Policy m1327144 445,Macroeconomic and Structural Policies
            m1327380 439,Humanitarian Aid m1503719 3039
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Gender,Macroeconomics and Economic Growth,Social Protections and Labor,Finance and Financial Sector Development
no_of_pages: 58
model_extractions: WPS5005 Policy Research Working Paper 5005 Aid and Trust in Country Systems _Stephen Knack Nicholas Eubank_ The World Bank Development Research Group Human Development and Public Services Team July 2009
Policy Research Working Paper 5005

# **Abstract**

The 2005 Paris Declaration on Aid Effectiveness sets targets for increased use by donors of recipient country systems for managing aid. A consensus view holds that country systems are strengthened when donors trust recipients to manage aid funds, but undermined when donors manage aid through their own separate parallel systems. This paper provides an analytical framework for understanding donors’ decisions to trust in country systems or instead to micro-manage aid using their own systems and procedures. Where country systems are sufficiently weak, the development impact of aid is reduced by donors’ reliance on them. Trust in country systems will be sub-optimal, however, if donors have multiple objectives in aid provision rather than a sole objective of maximizing development outcomes.

Empirical tests are conducted using data from an OECD survey designed to monitor progress toward Paris Declaration goals. Trust in country systems is measured in three ways: use of the recipient’s public financial management systems, use of direct budget support, and use of program-based approaches. The authors show using fixed effects regression that a donor’s trust in recipient country systems is positively related to (1) trustworthiness or quality of those systems, (2) tolerance for risk on the part of the donor’s constituents, as measured by public support for providing aid, and (3) the donor’s ability to internalize more of the benefits of investing in country systems, as measured by the donor’s share of all aid provided to a recipient.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to determine how aid can be delivered in ways that better promote development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The lead author may be contacted at sknack@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Aid and Trust in Country Systems*

Stephen Knack The World Bank and Nicholas Eubank Stanford Business School JEL: F35, H61, O10, O19

*This research was supported by the World Bank’s Knowledge for Change Program (KCP).  The conclusions of this paper are not intended to represent the views of the World Bank, its Executive Directors, or the countries they represent.  The authors are grateful to Waly Wane for valuable comments are corrections, but they assume full responsibility for any remaining errors.
# **1. Introduction**

Transfers of official development assistance (ODA) from rich to poor countries exceed $100 billion per year, and represent a large share of government spending in many recipients. The Monterrey Consensus (in 2002) committed donor countries to increasing aid further, despite doubts by leading development researchers that aid has been effective in reducing poverty (e.g. Easterly, 2003; Rajan and Subramanian, 2008).

Donor organizations recognize that political support for scaling up aid depends on improving perceptions of its effectiveness in promoting development. Some donors have attempted to increase aid’s impact by allocating a larger share of it to recipients with both high poverty levels and “sound economic management” (World Bank, 1998; Collier and Dollar, 2002). Research findings (Burnside and Dollar, 2000) consistent with this prescription turn out to be fragile (Easterly, Levine and Roodman, 2004; Rajan and Subramanian, 2008). However, the broader message that aid will be used more productively in countries with fewer policy distortions, less rent seeking and more competent government bureaucracies is highly intuitive and continues to influence the allocation decisions of many multilateral and bilateral donors.

In response to concerns regarding the “quality” of aid, the Rome Declaration on Harmonization (in 2003) and Paris Declaration on Aid Effectiveness (in 2005) moved implementation issues to the top of the international aid effectiveness agenda. In these Declarations, donors committed to improving inter-donor coordination and to practices more consistent with the principal of country “ownership” of development strategies. In particular, the Paris Declaration exhorts donors to “base their overall support on partner countries’ national development strategies, institutions and procedures.” Advocacy of the new agenda for aid effectiveness embodied in the Paris Declaration is based primarily on intuition and accumulated anecdotal evidence. Donors’ frequent use of their own separate procurement, reporting and other requirements imposes sizeable transactions costs on aid recipients, that can be substantially reduced by improved “alignment” of aid activities with country programs and management systems. Overlapping donor missions and analytic work are often redundant, so there are potential gains from improved “harmonization” among donors, to eliminate wasteful duplication. A notable example of the inefficiencies motivating the Paris Declaration was described in the _World Development Report 2004_ : the construction of a simple building in Bolivia was paralyzed by the need for three different donor organizations to follow three different sets of procurement rules (World Bank, 2003: 213). In this paper, we provide a more systematic theoretical framework for understanding the incentive problems 2
producing these sorts of outcomes. We also provide empirical evidence largely consistent with the predictions of this theoretical framework.

The Paris Declaration’s call for increased use of recipient systems in managing aid is not oblivious to the risks involved. The document explicitly acknowledges that weak country systems make aid less effective. Recipients, with technical assistance from donors, are urged to strengthen their public financial management (PFM) systems and formulate a credible national development strategy where one does not exist. In the meantime, using those systems, despite their flaws, is believed to strengthen them: “Donors can help build capacity and trust by using country systems to the fullest extent possible, while accepting and managing the risks involved...” (OECD, 2009a: 27).

Donors’ decisions to place trust in country systems or, alternatively, to micro-manage aid using their own parallel systems, are influenced by both donor and recipient characteristics. Where recipient PFM capacity is stronger, the likelihood of a corruption scandal tarnishing the donor agency’s reputation is lowered, and aid funds are more likely to be spent productively in implementing a national development program. Development goals are more likely to be achieved if donors’ use of country systems is at least somewhat responsive to the quality of those systems. However, there are other reasons to believe that donors’ reliance on country systems will be sub-optimal in most cases.

The benefits of using country systems are mostly external (benefiting other donors) and realized only over the long term, while costs are short term and fully internalized by the donor. If donor agency _i_ chooses to help strengthen country systems, for example by providing aid in the form of budget support or technical assistance for PFM reform, it is in effect providing a public good for other donors. The stronger systems that result reduce reputational and fiduciary risks, and increase the developmental impact of aid funds, not only for donor _i_ ’s future aid but also for other donors. Meanwhile, donor _i_ incurs the full costs, in exposing its current aid funds to higher risks than if it bypassed recipient country systems.

Moreover, donor _i_ ’s provision of budget support (or technical assistance for PFM reform) conflicts with political imperatives to show visible achievements, attributable to its own aid funds, to skeptical taxpayers or elected officials at home. If (as is likely the case) officials in donor agency _i_ have short time horizons, incentives to under-invest in aid practices that strengthen rather than weaken country systems are aggravated. Donor _i_ (and other donors) will have an incentive to free ride on the investments of other donors, and manage aid through parallel systems using its own accounting, procurement and other procedures.

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Donors differ from each other, however, in their willingness to use country systems, because they have different mandates and face varying degrees of political pressure from their taxpayers and elected overseers. We provide empirical evidence that multilateral donors exhibit greater trust in country systems than bilateral donors. Among bilateral donors, we show that use of country systems is higher where public opinion on foreign aid provision is more favorable.

For any given donor, trust in country systems varies by recipient country. We show empirically that quality of PFM systems is a strong determinant of trust in country systems. In recipients where a given donor has a larger share of the aid “market,” more of the benefits from its investments in strengthening country systems will be internalized. Consistent with this argument, we show that donor _i_ ’s use of country systems is positively related to its share of all aid received by a given recipient, controlling for quality of recipient systems.

In summary, a donor’s trust in a recipient’s aid management systems is determined by three sets of variables:

- Trustworthiness of those systems, as measured e.g. by PFM quality or corruption ratings

- Trust in aid’s effectiveness in general, on the part of its domestic constituents

- Confidence it will reap sufficient benefits from investing in recipient country systems

The next section elaborates on these arguments and presents a formal model of a donor’s decision to manage aid using its own systems or recipient country systems. Section 3 describes the data used for empirical testing, and summarizes hypotheses to be tested. Detailed results are reported in section 4. The final section summarizes and briefly discusses policy responses.

# **2. Theory**

Aid delivery entails a long chain of principal-agent relationships, each one with the potential to weaken the development impact of aid. Taxpayer funds are allocated by elected officials to aid agencies, sometimes earmarked for particular uses or tied to employment of donor-country contractors.<sup>1</sup> Projects are implemented by contractors hired by aid agencies or recipient government officials (if aid is in the form of budget support), typically under incomplete contracts with uncertain costs and imperfectly observable outputs (Martens et al., 2002: ch. 3). Government officials in recipient countries, in turn, are imperfectly accountable to their citizens, and may pursue other goals conflicting with development and poverty reduction objectives (Svensson, 2000; World Bank, 2003: ch. 6). Recipient governments may steer

> 1 Interest by USAID staff in improved coordination with other donors is often stymied by Congressional earmarks and directives.  The U.S. Congress micro-manages its aid agencies more than other donor country parliaments, at least in part due to relatively strong separation of powers and weak party discipline (OECD, 2006: 21-22, 64; Lancaster, 2007: 99-100).

4
projects or funds to favored constituencies in attempting to strengthen their hold on power. Public funds including aid could be diverted to private uses including overseas bank accounts.

More problems arise if we relax the assumption that a donor agency or a recipient government constitutes a single decision unit. For example, a donor agency official responsible for the success of a particular project will have an incentive to bypass weak country systems, even if doing so conflicts with the agency’s objective of using country systems more frequently. Within a recipient government, central ministries typically prefer aid be managed via country systems, but line ministries often face different incentives. A donor and line ministry may collude to bypass central ministries in their efforts to shift resources toward particular sectors, or to produce more visible outputs such as hospital or school buildings (Wuyts, 1996: 742-3).

Project modalities with parallel funding and management mechanisms generate multiple material and non-material benefits for the ministers and civil servants in whose sectors they are located, including salary top-ups, allowances, vehicles, training and travel opportunities and prestige. Ministers, parliamentarians and local authorities are interested in the political credit they get from attracting a stand-alone project to a specific sector or area. (Williamson and Agha, 2008: 35).

For simplicity, we abstract from most of the principal-agent problems outlined above, and focus on only a small subset of the links in the aid delivery chain, selected on the basis of analytical tractability and feasibility of empirical testing. Specifically, we analyze how a donor agency’s trust in country systems is affected by: (1) the commitment and capacity of a recipient government to spend aid funds productively, (2) political constraints on donors associated with their particular mandates and domestic constituencies, and (3) the donor’s ability to internalize the benefits of its investments in country systems. “Trust” in our terminology does not necessarily imply an absence of perceived risk, i.e. a belief that a recipient is particularly trustworthy. Nor does it necessarily imply the presence of significant risk. Rather, trust - as reflected in a donor’s decision to use country systems – is a behavior, not a belief. Trust is facilitated by low perceived risk, a high tolerance for risk, and ability to internalize the benefits from investing in country systems.

The quality of country systems varies by recipient, and so will donor perceptions of risk. The Paris Declaration recognizes that weaknesses in country systems sometimes justify donors’ decisions to bypass them. Its targets include improvement in quality of recipients PFM systems (including procurement systems), and the formulation of national development strategies with priorities linked to the budget. The developmentally-optimal level of trust by donors in country systems varies positively with the quality of those systems. Jansen (2009: 23) reports on rampant corruption in a donor-funded natural resource management project in Tanzania, where 5
“the financial management system which the Norwegians chose to trust functioned very badly.” In this case, trust may have been inefficiently high, but more often it is likely to be too low.

The Paris Declaration’s high numerical targets for several indicators of aid harmonization and alignment, to be achieved by 2010, reflect a view that donors currently exhibit too little trust in recipient countries’ systems, even taking their flaws into account. The use of country systems can readily be portrayed as a prisoners’ dilemma game among donors, with trust as an efficient but non-equilibrium outcome. Standardization of aid management procedures could substantially reduce transactions costs for recipients, at a relatively small cost to each donor. Taking procurement as an example, an individual donor’s first preference would be for all other donors to be bound by a set of harmonized regulations that did not favor any particular donor country’s contractors, but to remain free itself to use its own procurement rules. Any other single donor would have the same preference, so the equilibrium outcome is non-harmonization. In the absence of any enforcement mechanism, a donor has an incentive to “defect” and use its own procurement systems. However, a donor would prefer a harmonized set of rules binding all donors, including itself, to the fully non-harmonized outcome if the savings in transactions costs to recipients were sufficiently high.

Moreover, it is generally acknowledged that using recipients’ aid management systems strengthens them, while avoiding them undermines them, by diffusing accountability and fragmenting policy and planning processes (OECD, 2009b; Mokoro Ltd. 2008a). When donors bypass country systems they often staff their own parallel aid management systems by “poaching” the most talented government officials. Trusting in country systems rather than bypassing them therefore increases the productivity of public funds including aid, but an individual donor typically will under-invest in strengthening country systems, as the benefits accrue mostly to other donors and in the future while it bears the full (and immediate) costs.

The Paris Declaration, along with its follow-up “Accra Agenda for Action” in 2008, can be viewed as an agreement among donors acknowledging these collection action problems, and creating a modest level of peer pressure to “cooperate” rather than “defect” on the optimal outcome of increased investment in the strength of country systems.<sup>2</sup> The OECD-DAC, in cooperation with the UN and World Bank, conducts “Paris Declaration Monitoring Surveys” that measure progress toward numerical targets for harmonizing aid and aligning it with country systems. The DAC’s periodic “peer reviews” of the aid systems of donor countries now include sections assessing progress towards better-harmonized and better-aligned aid.

> 2 International donor conferences on harmonization bring senior managers of aid agencies “in close contact with colleagues from other agencies, pushing them to align with recognized international best practice and not be seen as laggards” (de Renzio, 2005: 11).

6
Peer pressure is unlikely to be the most important explanation for some donors’ willingness to use country systems, even when using them entails significant risks. Donors’ tolerance of risk will depend heavily on their domestic constituencies and institutional mandates. Aid management practices of some donors are constrained by the need to convince their sometimes-skeptical principals (elected officials and voters) that aid produces visible and measurable results. Even if its domestic constituents were concerned solely with maximizing development outcomes, a donor agency’s need to provide them with tangible evidence of results, directly attributable to its funding, can make it more reluctant to delegate aid implementation to recipient systems, regardless of their quality.

Aid agencies want to be able to identify their own contributions, often through distinct projects, to facilitate feedback to taxpayers and sustain political support for aid flows. A new hospital is easier to showcase than the outcome of policy reform or budget support. (World Bank, 2004: 216) Donor agencies benefit from the visibility associated with separately managed and “branded” projects. They assist in defending the aid budget to parliamentary committees and audit authorities...In contrast, where more programmatic multidonor ventures are introduced, visibility is lost and the attribution of development results to the particular donor’s support becomes problematic. (Williamson and Agha, 2008: 34).

These political imperatives of donors may not always distort aid delivery in ways that reduce its development effectiveness, but they will be more easily satisfied when donors micro-manage their aid projects. To this point we have assumed donor agencies and their domestic constituents are concerned only with development effectiveness.<sup>3</sup> When bilateral donors use aid to advance diplomatic or commercial objectives, incentives to rely on their own parallel systems for aid delivery will be further aggravated. For example, using their own procurement rules will likely advantage donor-country contractors.

All donor agencies face some combination of political and bureaucratic incentives to pursue objectives that may conflict with the goal of increased use of country systems. However, donors do not have homogeneous mandates and constituencies. From the standpoint of development effectiveness, trust in country systems is likely to be most sub-optimal for a bilateral donor representing constituents who are particularly skeptical of aid’s effectiveness. Aid from a global multilateral donor will more closely approximate the developmentally-optimal level of trust in country systems.

> 3 Public opinion surveys in donor countries suggest humanitarian and development motives are far more important than diplomatic or commercial objectives in explaining popular support for aid (McDonnell, Lecomte and Wegimont (2003).  Lower support for foreign aid among Americans is apparently due to perceptions that very little of it reaches the poor with much of it devoted to political objectives or diverted to corrupt officials in recipient countries (Lancaster, 2007:97; OECD, 2006: 22-23).

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# _A Model of Trust in Country Systems_

These incentives facing aid agencies can be captured in a simple model. A representative donor agency _i_ maximizes its value function<sup>_V_</sup> _i_<sup>by allocating its aid budget between donor-</sup> managed ( _Dij_ ) and recipient-managed ( _Rij_ ) activities in recipient country _j_ , so _Aij_ = _Dij_ + _Rij_ . _D R_ Outputs _Qij_ are produced solely by _Dij_ . Outputs _Q j_ are produced by _Rij_ and by _R_ − _ij_ = _R j_ − _Rij_ contributed by all other donors − _i_ operating in recipient _j_ .<sup>4</sup> The link between _Di_ .spending and _Q D_ output is more observable than the link between<sup>_R_</sup> _i_<sup>and</sup> _Q R_ , because the latter is jointly produced with aid provided by other donors. By definition, the donor-managed funds _Di_ used to produce _Q D_ are better insulated than<sup>_R_</sup> _i_<sup>from weaknesses in the recipient’s</sup> public financial management systems, e.g. risk of diversion of funds, inability to track expenditures, or rigged procurement bidding benefiting cronies of a government official.

We assume that both _Q D_ and _Q R_ contribute to development outcomes such as poverty reduction and progress on broad health, education and other social indicators. “Development outcomes” produced using aid from donor _i_ in recipient _j_ can be expressed as _D D R O j_ = _f_ ( _Qi_ [ _Di_ ], _Q_ − _i_ [ _D_ − _i_ ], γ _Q_ [ _Ri_ , _R_ − _i_ ]) (1) The “leakage” parameter γ (with 0 ≤γ ≤ 1) reflects potential reductions in the value of _Q R_ associated with weaknesses in recipient government systems, such as corruption or diversion of aid funds to lower-valued uses. A higher γ indicates country systems are more trustworthy. Donor-managed funds are less subject to these losses, and for simplicity we assume no leakages.<sup>5</sup> Donors value the development outcomes produced by their own aid and by the aid efforts of other donors. Separately from any impact on outcomes, however, donors place a positive valuation on visible outputs that can be directly linked to their own aid inputs. Donor therefore _i_ allocates a given aid budget<sup>_A_</sup> _i_<sup>between</sup> _Di_ and<sup>_R_</sup> _i_<sup>to maximize the following value function:</sup>

> 4 For simplicity the subscript _j_ indexing recipients will be suppressed henceforth.

> 5 Alternatively γ could be interpreted as the difference in the “leakage” rate between recipient-managed and donormanaged funds.

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In equation (3), α , is a “skepticism” parameter ( ≥ 0) that varies by donor. Higher values indicate a donor agency accountable to domestic constituents that are relatively skeptical of aid’s development effectiveness. This skepticism can reflect doubts regarding the motives or competence of officials in the aid agency and/or in recipient countries. Greater aid skepticism _D_ (higher α ) leads a donor agency to place a higher valuation on _Qi_ , independently of its impact on actual development outcomes. Conversely, a lower α can be interpreted as indicating greater trust, on the part of the donor’s constituencies, in aid’s effectiveness in general.

The benefits to donor agencies (in the form of prestige to agency staff, higher agency budgets, etc.) from producing better development outcomes will vary, we argue, by the relative size of its activities in a given recipient. A donor that is not operating in recipient _j_ will receive none of the credit from aid-financed improvements in development outcomes; conversely if there is a single donor it will receive all of the credit.<sup>6</sup> As an approximation, we assume that donor _i_ ’s valuation of aid outcomes is proportional to its share of the aid market in the recipient country, or _Ai_ /( _Ai_ + _A_ − _i_ ) =<sup>_A_</sup> _i_<sup>. Where</sup><sup>_A_</sup> _i_<sup>is higher, the donor has more of a “reputational</sup> stake” in the country’s development in general (Knack and Rahman, 2007). More specifically, when<sup>_A_</sup> _i_<sup>is higher the donor internalizes more of the current and future benefits from its</sup> investment in strengthening recipient country aid management systems.<sup>7</sup> In allocating<sup>_A_</sup> _i_<sup>between</sup><sup>_R_</sup> _i_<sup>and</sup> _Di_ , the donor thus weighs several factors. Its contributions<sup>_R_</sup> _i_<sup>to financing</sup> _Q R_ are indistinguishable in their results from those of other donors, so donor _i_ equally values an increment to _Q R_ whether it is financed by<sup>_R_</sup> _i_<sup>or by any</sup> other donor. Moreover, the benefits from producing _Q R_ are discounted by 1 - γ (“leakages”) and by the fact other donors will reap some or most of the benefits (if<sup>_A_</sup> _i_<sup>< 1). The</sup> development-related benefits of financing _QiD_ similarly will accrue in part to other donors, but it

> 6 The U.S. has been credited with aid successes in Western Europe (the Marshall Plan), Korea and Taiwan during a period when it was the only significant donor (e.g. DeLong and Eichengreen, 1993; Brautigam, 2000).

> 7 There is no explicit time dimension in the model, so we are implicitly assuming<sup>_A_</sup> _i_<sup>in time</sup><sup>_t_is a good proxy for</sup>

> <sup>in</sup><sup>_t+1_,</sup><sup>_t+2_, etc.</sup>

> <sup>_A_</sup> _i_

9
yields additional rewards specific to the donor financing it, in the form of tangible evidence of aid’s impact useful in appeasing a donor agency’s skeptical domestic constituencies.

The necessary first-order condition for maximizing equation (3) requires the donor to equate the marginal benefits from its donor-managed and recipient-managed aid activities: ∂ _<u>QiD</u>_ Making the reasonable assumption that is increasing in<sup>_R_</sup> _i_<sup>,8</sup> the donor’s optimal choice of ∂ _Di_

* _Di_ , _Di_ , increases with α .  It is inversely related to γ and to the donor’s aid share<sup>_A_</sup> _i_<sup>.</sup> * Conversely,<sup>_R_</sup> _i_<sup>decreases withαand increases withγand</sup><sup>_A_</sup> _i_<sup>.</sup>

Development outcomes are maximized in the model when α = 0 and<sup>_A_</sup> _i_<sup>= 1 and the</sup> donor sets the ratio of the marginal products of _Di_ and<sup>_R_</sup> _i_<sup>equal toγ. Trust in country systems</sup> _R_ ( _i_ ) is sub-optimal from a development standpoint if α > 0 or<sup>_A_</sup> _i_<sup>< 1.</sup> _A i_ We assume for simplicity that donor responses to γ are not inconsistent with maximizing development outcomes. This assumption could be relaxed, for example, by altering the model so ∂<sup>_R_</sup> _i_ * that varies positively with α , if donors with more skeptical domestic constituents are ∂γ thought to be more sensitive to corruption and mis-management in recipient countries. We also abstract from the possibility that aid volumes may be related to α or<sup>_A_</sup> _i_<sup>. For example,</sup><sup>_R_</sup> _i_ *<sup>could</sup> be increased by greater geographic specialization among donors:<sup>_A_</sup> _i_<sup>would increase to one if</sup> two donors, each with an aid share of 1⁄2 in each of two recipients, agreed to an aid “trade.” However, the visibility of a donor agency’s activities may decline if concentrated in fewer recipients, with potentially adverse impacts in turn on political support for aid provision.

A dynamic extension of the model could endogenize γ so that it improves with<sup>_R_</sup> _i_<sup>and</sup> with targeted technical assistance, and deteriorates with _Di_ . A donor with a higher aid share and longer-term commitment to aiding the country, i.e. a donor with a more “encompassing interest” in Olson’s (1982) terminology, would have stronger incentives to invest in reforms intended to reduce leakages, i.e to increase γ .

> 8 This is a sufficient but not necessary condition for this comparative statics result.  If it is not satisfied, other parameter restrictions would be necessary.  See Appendix 1 for the comparative statics analysis.

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The remainder of this paper is devoted to empirical tests of the model’s predictions. The _R_ next section describes how we operationalize _i_ , γ , α and<sup>_A_</sup> _i_<sup>using data from the Survey on</sup> _A i_ Monitoring the Paris Declaration and other sources.

# **3. Data and Hypotheses**

_Measuring trust in country systems_ _R_ We operationalize _i_ , the share of recipient-managed aid, using the OECD DAC’s 2008 _A i_ Survey on Monitoring the Paris Declaration (SMPD). This survey provides indicators of aid delivery practices not included in the DAC’s standard aid reporting systems for donors. Survey indicators cover the number of missions and country analytic studies donors undertake jointly, the share of technical assistance that is coordinated with recipient governments’ capacity building programs, and other aid management practices. Most of these indicators are beyond the scope of the present study, and we focus on the few that address most directly donors’ use of country financial management systems for implementing aid projects and programs.

The SMPD is designed to measure progress toward a set of specific targets for 2010 agreed by donors and recipients on delivering aid in ways believed to enhance its development effectiveness. A baseline survey was conducted in 2006, and a report by the DAC (OECD, 2008a) summarizes progress toward the targets comparing the 2006 and 2008 survey results. The report’s conclusions regarding progress are highly tentative, however, because of data quality issues concerning the 2006 survey.

Based on lessons learned, guidance on definitions was substantially strengthened for the second round survey, and the comparability of data reported by donors and by recipients was improved significantly relative to the 2006 survey (OECD, 2008a). Moreover, the number of recipient countries participating increased from 34 in 2006 to 54 in 2008. For purposes of this study, therefore, we treat 2006 as a pilot exercise, and use data only from the 2008 survey.<sup>9</sup> We measure trust in country systems using three variables constructed from the SMPD: 1) use of recipients’ public financial systems for the management of aid funds ( _PFM_ ), 2) direct budget support ( _DBS_ ), including sector budget support, and

- 3) aid disbursed through program-based approaches ( _PBA_ ), inclusive of budget support.

> 9 A third and final survey is scheduled for 2010, and comparisons over time with the 2008 survey should be reasonably valid.

11
Use of public financial management systems ( _PFM_ ) is constructed in turn as a simple average of four other variables: use of national (i) budget execution procedures, (ii) financial reporting procedures, (iii) auditing procedures, and (iv) procurement systems. Detailed criteria for these four PFM dimensions are provided in Appendix 2. Correlations among these four variables average .66 (ranging from .54 to .77).

In the SMPD, donors report total aid disbursements for the calendar year 2007, excluding humanitarian aid and debt relief. They also report how much of this aid was “for the government sector.” The latter includes aid disbursed to NGOs, parastatals or private companies if and only if it is provided in the context of an agreement with officials authorized to act on behalf of central government. Aid to the government sector reported in the SMPD, aggregated over all recipients, was roughly $37 billion, or 82.7% of total aid.

Following OECD (2008a), we measure _PFM_ as a share of aid to the government sector, while budget support ( _DBS_ ) and program-based approaches ( _PBA_ ) are measured as a share of total aid. Aid not for the government sector clearly does not use country PFM systems, so _PFM_ alternatively could be calculated as a share of total aid. However, providing aid to NGOs or other private entities in the absence of an agreement with the government does not always reflect a donor’s desire to avoid weak country systems. Governments of some middle-income nations are not very concerned about obtaining aid or interfering with its provision to NGOs, and a sizeable share of aid may go directly from donors to NGOs. For these recipients, donors’ use of PFM systems as a share of total aid would be a misleading indicator of trust in country systems. In any case, results reported below are unchanged if we replace government sector aid with total aid in the denominator of _PFM_ .

Some aid projects may be provided in support of program-based approaches, even if they are not part of an agreement with governments. Hence the DAC monitoring indicators measure _PBA_ and _DBS_ (a subset of _PBA_ ) as a share of total aid, not as a share of aid to the government sector. Again, however, our empirical findings are not sensitive to this choice of denominator.

We classify aid delivered through program-based approaches as recipient-managed because the programs are led by government and reflect its priorities, and include processes for harmonizing donor procedures and using some country systems (see Appendix 2). Subject to those conditions, aid delivered in the form of projects can qualify as program-based, even if it does not use country PFM systems. As the case of _PBA_ illustrates, the distinction between donor-managed and recipient-managed aid is more accurately depicted on a continuum than as a 12
dichotomy.<sup>10</sup> Despite more rigorous definitions in the 2008 SMPD compared to the 2006 survey, donors still use some subjective judgment in determining whether or not project aid is program-based, so _PBA_ may contain more measurement error than _DBS_ or _PFM_ .

All three dependent variables measure (to some degree) donors’ investment in the “public good” of improving recipient country systems, as well as donors’ trust in countries’ existing systems to use aid funds productively. Budget support and program-based aid usually are premised on policy dialogues with government, and are often explicitly or implicitly conditioned on certain policy reforms. Thus, _DBS_ and _PBA_ also measure (in part) donors’ trust in a common approach to development that may be country led, but influenced by the World Bank, IMF or other donors with leading roles in policy dialogues.

In the SMPD sample, all three dependent variables range from 0% to 100%. Means for _PFM_ , _DBS_ and _PBA_ respectively are 34%, 11.9% and 31%. Table 1 provides summary statistics for these and other variables in the analysis.

# _Independent variables_

Independent variables mostly fall into one of three groups:

- Trustworthiness of country systems ( γ in the model)

- Trust in aid’s effectiveness (in general) on the part of the donor’s constituents ( α )

- Donor’s ability to benefit from investments in country systems (<sup>_A_</sup> _i_<sup>)</sup>

We define _donor aid share_ as the percentage of total aid (inclusive of aid not to the government sector) to recipient _j_ accounted for by donor _i_ . This variable corresponds to<sup>_A_</sup> _i_<sup>in</sup> the model in Section 2, and is predicted to increase use of country systems. A donor providing a larger share of aid to a recipient has a larger reputational stake in the country’s development (Knack and Rahman, 2007) and internalizes more of the benefits of investments in strengthening country systems. The sample mean for _donor aid share_ is 6.4%, with a minimum value of 0.01%<sup>11</sup> (three observations) and a maximum of 70.4% (Australia in Papua New Guinea).<sup>12</sup> Donors’ reputational stake in a recipient’s development may also be greater, other things equal, in countries it once colonized. For example, media accounts of genocide, civil war and

> 10 “Even budget support...may not be fully aligned to the country budgeting process” if it is not committed or disbursed in time to be incorporated fully into policy and planning frameworks (OECD 2008: 13).  Conditions often attached to budget support also may be inconsistent with the principle of country ownership.

> 11 Values of 0 are not present in the sample because the dependent variables are all undefined for donor-recipient pairs with no aid transfers.

> 12 Larger donors at the global level do not necessarily have higher average aid shares at the recipient level, as some donors concentrate their aid in fewer countries.  For example, Portugal’s average aid share (14%) exceeds the average for the U.S. (12.9%), although the U.S. provided more than 100 times as much total aid to countries in the sample as Portugal.

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corruption in Rwanda and the Democratic Republic of Congo often implicate Belgium’s exploitative and misguided colonial policies in these territories between 1901 and 1962. Former colonial powers may also retain, even many years after independence, a stronger sense of responsibility for the development of ex-colonies. Colonial ties are shown to influence aid allocations by donors across recipients in Alesina and Dollar (2000).<sup>13</sup> We hypothesize that the former colonial power will be more willing than other donors to trust country systems. The dummy variable _colonial tie_ is set equal to 1 for all donor-recipient pairs (such as UK-Ghana) where the recipient was once part of the donor’s colonial empire.<sup>14</sup> A colonial tie is present in about 5% of the SMPD observations. Although _colonial tie_ is not explicitly in the model in Section 2, intuitively it can be considered a proxy for the term<sup>_A_</sup> _i_ .

Recipient country characteristics comprise a second set of independent variables. Most of these measure, in one way or another, the trustworthiness of recipient country systems, corresponding to γ in the model. The most direct measures of the quality of country systems are from the World Bank’s “Country Policy and Institutional Assessments” (CPIA). For brevity we re-name the CPIA’s “Quality of Budgetary and Financial Management” as _PFM Quality_ . Higher ratings reflect a comprehensive and credible budget linked to policy priorities, effective financial management systems to ensure that the budget is implemented as intended, and timely and accurate accounting and fiscal reporting. “Transparency, Accountability and Corruption in the Public Sector” is re-named _Transparency_ ; it assesses the extent to which the executive can be held accountable for its use of funds and the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for the use of resources, administrative decisions, and results obtained. A third variable we use is _Quality of Macro/Fiscal Policy_ , constructed as the simple average of two CPIA indicators on “Macroeconomic Management” and “Fiscal Policy.”<sup>15</sup> These two indicators assess the quality of the monetary/exchange rate and aggregate demand policy framework, and the short- and medium-term sustainability of fiscal policy, taking into account monetary and exchange rate policy and the sustainability of the public debt **.** Full definitions for all of these CPIA indicators are provided in Appendix 3.

Weaker country systems as measured by lower scores on _PFM Quality_ and _Transparency_ reflect increased risks to donors of corruption scandals, diversion of funds to lower priority uses,

> 13 However, Berthelemy and Tichit (2004) find this pattern has weakened in recent years, and the correlation between _donor aid share_ and a dummy for former colonial ties is only .14 in the SMPD.

> 14 Colonies are assigned only to their last colonial master; e.g. Rwanda and Burundi are assigned only to Belgium, which occupied those parts of German East Africa in 1916.

> 15 These two variables are correlated at .71 for the 54 countries in the SMPD sample.  Results below obtained using _Quality of Macro/Fiscal Policy_ are very similar, but slightly weaker, if either of its two components is used instead.

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or inability to account for how funds were spent. We therefore expect coefficients to be positive for these two variables, in our use-of-country-systems regressions. We expect _Quality of Macro/Fiscal Policy_ to be associated with greater use of budget support and other programbased approaches, because they depend for their success on the ability to plan and determine budget priorities in a meaningful way (Foster and Leavy, 2001). As Mosley and Eeckhout (2000) assert: “A certain degree of macro stability is a precondition for any planning.” The CPIA indicators are produced annually by World Bank staff for aid allocation purposes, for approximately 135 developing countries. Assessments are on a 1 to 6 scale, including half-point increments. For example, a 3.5 rating would be assigned to a country meeting some of the criteria for a rating of 3 and some of the criteria for a rating of 4. We use the CPIA ratings from 2006, just prior to the 2007 calendar year covered by the SMPD.<sup>16</sup> In our sample both _PFM Quality_ and _Transparency_ range only from 2 to 4.5, with means of 3.4 and 2.9 respectively. The range for _Quality of Macro/Fiscal Policy_ (the simple average of two CPIA indicators) is 2.25 to 5, and the mean is 3.9.

Use of country systems is expected to be greater in countries with stronger “national development strategies” as assessed in World Bank (2007).17 These qualitative assessments were based on three criteria (OECD, 2008a):

- Existence of an authoritative country-wide development policy

- Realism of the development policy with clearly-identified priorities

- Well-costed policies that can be funded

No country in the 2008 SMPD sample received the top grade of A. Eight were graded B, 27 as C, 6 as D and 1 as E. Only low-income countries eligible for the World Bank’s IDA aid were graded, so 12 middle-income countries in the SMPD are missing data. We code _Strategy_ on a 1- 4 scale with B grades equal to 4 and E grades equal to 1.<sup>18</sup> We expect use of country systems to be greater in countries that have fulfilled donors’ requirements for debt relief eligibility under the HIPC initiative. Donors’ engagement with these countries has been unusually intensive, including technical assistance aimed at improving PFM systems and enabling donors and citizens to track public expenditures more effectively. The

> 16 The CPIA includes 12 other questions in addition to the ones we use, but most of them (e.g. policies for gender equality, environmental sustainability) are not relevant to this study.  Despite moderate to high inter-correlations among the CPIA variables, most of them do not produce significant results in our use-of-country-systems regressions, when substituted for the more theoretically-relevant variables we use.  The CPIA indicators we use are designed specifically to assess public sector systems for managing public funds including aid.  Other well-known “governance” indicators are designed to assess risks to foreign investors (e.g. the International Country Risk Guide) or protection of individual rights (e.g. Freedom House).

> 17 These assessments are not done by the same Bank staff responsible for the CPIA.

> 18 Testing dummy variables for each grade (and for the missing data countries) does not change any of the conclusions yielded from our use of the single cardinal indicator _Strategy_ .

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HIPC countries were required to formulate and implement national development strategies and achieve macroeconomic stability. Some of these national strategies - including those for Mozambique, Tanzania and Uganda - express an explicit preference for budget support (Williamson and Agha, 2008). Debt relief funds are excluded from the SMPD, but the same strategies and reforms that justified provision of debt relief can also justify increased use of country systems, including budget support. Also, the HIPC Initiative’s success demonstrates that for this set of aid recipients, donors have managed to act collectively to overcome the usual parochial interests limiting aid’s developmental impact. Donors’ experience with HIPC can be expected to have some residual influence over their aid management decisions in HIPC countries for at least several years following implementation of debt forgiveness. Our _HIPC completion_ dummy variable is coded 1 for the twenty countries in the SMPD sample that had completed the process by 2007, the year for which the survey measures use of country systems.

Budget support is sometimes used as a means of disbursing aid more quickly where aid levels are high (de Renzio, 2005). Budget support is typically accompanied by a policy dialogue between donor agencies and recipient governments: donors are more confident that nonearmarked aid will be used productively if they have had some input into development policy choices. Where aid levels are lower, however, donors have less leverage to engage government on policies, and may in any event choose not to incur the costs of achieving policy consensus (Foster and Leavy, 2001). Aid thus tends to take the form of projects, where aid is relatively low. Accordingly, our _DBS_ and _PBA_ regressions control for the _aid share of GDP_ for recipients, with the expectation of positive coefficients.<sup>19</sup> The _aid share of GDP_ in the sample averages 10.8%, with a low of 0.2% for the Dominican Republic and a high of 56.3% for Liberia.

Donor characteristics comprise a third set of determinants of trust in country systems. A first-level distinction, between multilateral and bilateral (i.e. national) donors, reflects their differing mandates. Multilateral aid agencies were established in part to resolve collective action problems plaguing bilateral donors. They are better insulated from political pressures to demonstrate short-term visible results to elected officials and taxpayers. Multilaterals “are cases of joint delegation from multiple principals” that may “enable the agency to commit itself to procedures that would not be easy to implement for a bilateral donor, such as transparent and competitive procedures for tendering and procurement” (Martens et al., 2002: 21). They also have a comparative advantage in aid activities that “involve spillover effects” which bilaterals

> 19 Aid levels may not be entirely exogenous to the existence of a policy dialogue, and ideally we would be able to measure the existence and strength of policy dialogues more directly.  Also note that policy dialogues on particular issues typically involve multiple donors, so we use total aid/GDP to a recipient from all donors, and not each donor’s aid as a share of recipient GDP.

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“might have difficulty internalizing” (Martens et al., 2002: 65). Some multilaterals view donor coordination as part of their mandates. For example, the World Bank and UN partner with the OECD-DAC in its harmonization agenda and in the Paris Declaration monitoring effort.<sup>20</sup> The World Bank has taken a lead role in promoting the principle of “country ownership” since it adopted its “Comprehensive Development Framework” in 1999.<sup>21</sup> For these reasons, we expect use of country systems to be greater for multilateral than for bilateral donors.

Among multilaterals, we further differentiate the MDBs (multilateral development banks including the World Bank, IMF and several regional development banks) from other multilateral agencies including the UN system and European Commission (EC). The non-MDB multilaterals are a heterogeneous group, but for multiple reasons we expect their use of country systems to be lower than the MDB’s use. In the EC, “responsibility rests in the hands of serving politicians from member states,” so its decisions are less apolitical than other multilateral agencies that “have genuinely delegated their management to an executive board” (Martens et al., 2002: 47). National representatives in EC foreign aid decision-making committees devote considerable effort to pursuing opportunities for their own nation’s aid contractors (Martens et al., 2002: 193). Also, much EC and UN aid is in the form of technical assistance, reducing the share of aid provided in the form of direct budget support.

Among bilateral donors, we differentiate between OECD-DAC donors and non-DAC donors. Use of country systems is expected to be higher for DAC donors, because of the DAC’s leading role in donor harmonization initiatives, and peer reviews of members’ aid programs that now include assessments of their consistency with Paris Declaration principals and objectives.

The DAC donors can be divided further, between the “Nordic Plus” group and others. Nordic Plus donors include Denmark, Finland, Norway, Sweden, Ireland, the Netherlands, and the United Kingdom. The group’s purpose is to improve complementarities among its members, through division of labor based on comparative advantages (NORAD, 2006; de Renzio, 2005). By reducing the number of sectors and countries each donor operates in, transactions costs for recipients can be reduced, at the price of reduced visibility for the donors. We take membership in the Nordic Plus group as a proxy for low “skepticism” of aid effectiveness among the domestic constituencies of these bilateral donors. Empirical support for this hypothesis could be interpreted as merely indicating that donors committed to certain parts of the Paris Declaration agenda tend to be committed to other parts of it. At a minimum, however, tests of the Nordic

> 20 The OECD-DAC is itself a multilateral agency, representing most of the OECD’s bilateral donor countries.  The OECD-DAC is not a donor agency, but conducts peer reviews of its members’ aid programs, maintains aid databases, and pursues research and advocacy work on improving aid effectiveness.

> 21 However, “ownership” is sometimes criticized as a euphemism for developing countries’ adoption of policies advocated by the Bank and other donors (OECD, 2008b).

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Plus dummy can help show whether or not there is a striking degree of variation among the DAC bilateral in use of country systems.

“Vertical funds” (sometimes called “global funds”) comprise a last set of donors. These donors have limited sector-specific mandates, such as the environment, primary education, or particular diseases. In the SMPD, most aid from vertical funds is accounted for by the Global Fund to Fight AIDS, Tuberculosis and Malaria. It disbursed 1.9% of the aid represented in the survey (about the same amount as Spain, Denmark or Sweden), in 47 of the 54 SMPD recipient countries. Only five donors disbursed funds in more countries in the survey. Vertical-fund programs, particularly in health, are generally viewed as having weak country “ownership,” driven predominantly by concerns over global public goods (World Bank, 2006). They are often criticized for using parallel implementation units outside normal government structures, with overlapping or redundant “reporting systems, procurement policies and procedures not aligned to national guidelines..” (World Bank, 2006: 22). We therefore expect vertical funds to be associated with lower use of country systems in general. Vertical funds are not generally mandated with providing budget support, but they are often designed to be compatible with sector strategies and programs (in health, education or other relevant sectors).

For the DAC bilateral donors, we can go beyond these donor group dummies and attempt to measure domestic constituents’ trust in aid effectiveness in general ( α in the model) using data from public opinion surveys. We expect stronger public support for development aid to increase a bilateral donor agency’s use of country systems. Where support for aid is relatively weak, aid agency officials will be under more pressure to show that the funds they are provided produce visible results directly attributable to its efforts. These “results” may include not only development objectives but also commercial or national security objectives for the donor country. Project aid, often administered using parallel procurement and other systems, is more conducive than budget support for linking aid funds to visible, attributable results, including employment of donor-country aid contractors.

Data for testing this hypothesis are available from three different public opinion surveys: Gallup International’s 2002 “Voice of the People” survey (equations 1 and 2), the 1995-1998 round of World Values Surveys (equations 3 and 4), and the 2004 Eurobarometer. These surveys each cover a somewhat different sample of donor countries, as shown in Table 2. Thirty donors are represented in one or more of the surveys, but only four (Finland, Germany, Spain and Sweden) are included in all three. The question inquiring about support for development aid is worded somewhat differently in each survey. The percentage of respondents indicating greater support for aid in the WVS is correlated at .85 with the corresponding percentage from Gallup 18
International, and is correlated at .49 with the percentage supporting aid in Eurobarometer. Support for aid in Gallup and Eurobarometer, however, are correlated at only .18. The two donors with the weakest support for foreign aid in both the WVS and Gallup International are the U.S. and Japan; neither of them is included in Eurobarometer.

# _Sample Composition_

The DAC survey includes only 54 aid recipients, but coverage on the donor side is fairly comprehensive. Appendixes 4a and 4b, respectively, list the percentage of all aid reported in the SMPD accounted for by donor and by recipient. All DAC donors, bilateral and multilateral, are included, as well as vertical funds (e.g. the Global Fund and the GAVI Alliance) and several non-DAC bilateral donors. The various UN agencies are treated as a single donor in the survey, with their data collected and reported by the UNDP. Aid volumes to the 54 recipients as reported in the DAC survey closely approximate the corresponding aid totals in the DAC’s official aid database, compiled from an entirely separate reporting system.

Recipient governments decide whether or not to volunteer their country for the survey. The 54 self-selected recipients potentially differ systematically from other aid recipients. For larger countries, the costs of government participation in data collection may be relatively low. Countries more dependent on development aid are likely to have an interest in monitoring their progress on Paris Declaration provisions over time and in comparison to other countries. We therefore expect inclusion in the survey to be associated with lower income per capita and higher aid per capita. Countries already intensively engaged with donors to qualify for debt relief may have a greater interest in results, as well as lower participation costs, so a dummy for countries reaching the HIPC completion point should be positively related to survey participation.

Table 3 reports probit regression results, with the dependent variable coded 1 for countries participating in the SMPD and coded 0 for all other aid recipients. As expected, countries that are larger, more aid dependent, poorer, and that have completed the HIPC process have significantly higher probabilities of inclusion in the SMPD.

Controlling for these four variables, survey participation is unrelated to other plausible determinants, such as political openness (measured by the well-known Freedom House indexes). Nor does geography matter. Although 63% of aid-recipient countries in Sub-Saharan Africa are in the SMPD, compared to only 26% from other regions, this difference is accounted for by income, aid and HIPC status. An Africa dummy, if added to the probit regression, produces a small and insignificant coefficient.

The fact that larger, poorer, more aid dependent and HIPC-completion countries are better represented in the SMPD suggests caution in interpreting the findings presented below.

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Results from our analyses of the 54-country SMPD sample may not fully generalize to all aid recipients, despite the fact they account for $45 billion in ODA (not including debt relief and humanitarian aid), more than half of the total ODA delivered to all aid recipients.

# **4. Results**

Donors choose whether or not to use country systems. Their choices, however, reflect some combination of donor and recipient country characteristics. Both sources of variation turn out to be important, but as shown in Table 4, recipient characteristics matter somewhat more than donor characteristics. Each of the three dependent variables (arranged across columns) is regressed on, alternatively, (1) a full set of donor dummy variables, (2) a full set of recipient dummies, and (3) both sets together. Recipient dummies alone explain 28% of the variation in _PFM_ , compared to 21% for donor dummies. Donor and recipient dummies explain an equal share (21%) of the variation in _DBS_ . Recipient dummies explain 23% of the variation in _PBA_ , compared to only 14% for donor dummies. The importance of recipient characteristics has implications for the possible inclusion of SMPD-derived indicators in rankings of donor performance. Namely, if donor aggregates on use of country systems are not adjusted for recipient characteristics, donors can climb in the rankings merely by avoiding riskier countries.

The remainder of this section tests more substantive hypotheses regarding donor and recipient characteristics affecting use of country systems. We estimate regressions of the form: _yij_ = β _Zij_ + δ _X i_ +η _M j_ + _uij_ (6) where _Z_ is a vector of<sup>_y_</sup> _ij_<sup>is the share of donor</sup><sup>_i_’s aid to recipient</sup><sup>_j_that is recipient-managed,</sup> _ij_ regressors that vary by donor and recipient, while _X i_ and _M j_ respectively vary only by donor and by recipient.

The dataset can be treated as an unbalanced panel, with anywhere between 1 and 54 observations per donor. We can exploit this structure of the data to conduct stronger tests of _Zij_ that control for donor and recipient fixed effects, with regressions of the form: _yij_ = β _Zij_ + _vi_ + _w j_ + ε _ij_ (7) Similarly, tests of _X i_ and _M j_ respectively can control for recipient effects or donor effects: 20
In regressions of the form (7) and (8), we correct for non-independence of errors within clusters of observations pertaining to each donor. In focusing on recipient-level determinants, in (9), we correct instead for non-independence of errors within recipient clusters.

Table 5 tests two variables that vary across both donors and recipients, so we are able to control for donor and recipient dummies as in (7). Use of country systems is not significantly associated with _colonial tie_ in any of the regressions reported in Table 5. It is similarly insignificant if included in tests reported in subsequent tables, and its inclusion does not materially affect any other estimates. We therefore drop it from those subsequent tables, in the interests of space and simplicity.<sup>22</sup> Results on _donor aid share_ however are consistent with the theory in section 2. Its coefficient is positive and highly significant for each of the three dependent variables in equations 1-3. Each 1-percentage-point increase in _donor aid share_ is associated with an increase _PFM_ of about 0.65 percentage points, e.g. from the mean of 26% to 26.65%. A 3- percentage-point increase in _donor aid share_ is associated with an increase in _DBS_ of about 1 percentage point, e.g. from the mean of 12% to 13% of aid. If we did not control for donor fixed effects, results such as these could be interpreted as merely showing that larger donors such as the World Bank and EC make more use of country systems. Because donor dummies are included, however, these results imply that a given donor makes more use of country systems in those recipients where its share of aid is larger.

Despite the inclusion of donor and recipient dummies, coefficients for _donor aid share_ in equations 1-3 may be biased upwards. Its numerator,<sup>_A_</sup> _i_<sup>, is the sum of donor-managed aid</sup> _Di_ and recipient-managed aid<sup>_R_</sup> _i_<sup>. The latter is also in the numerator of the dependent variables,</sup> _PFM_ , _DBS_ and _PBA_ . Measurement error in _donor aid share_ may thus be correlated with measurement error in use of country systems.<sup>23</sup> Moreover, an omitted variable such as donorvarying perceptions of corruption in a recipient could produce a positive bias: if donor is more _i_ pessimistic than other donors about corruption in recipient _j_ , it may respond by reducing both its aid levels and its reliance on country systems. Corruption indicators, or recipient dummies in

> 22 We experimented with different definitions for _colonial tie_ and none were found to be related to use of country systems.  In one variation, all ex-colonies of EU members were coded as “colonies” for purposes of the EU aid program.  In another variation, the ACP (African, Caribbean and Pacific) countries with favored status for EU development aid were coded as EU “colonies.”  Other ties (such as the U.S. in Afghanistan) may be more important than many colonial ties, but we refrained from creating our own _ad hoc_ indicator of donors’ reputational stake in countries’ development.

> 23 Measurement error in<sup>would create an upward bias in the correlation between</sup><sup>_donor aid share_and use of</sup>

> <sup>_R_</sup> _i_

> country systems.  Measurement error in _Di_ however would create a downward bias.

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regressions of the form (7) or (8) above, can control only for donors’ common perceptions of corruption in recipient countries.

In equations 4-6 of Table 5 we address this problem by substituting _donor aid share_ values from 2005 for the 2007 values used in equations 1-3. Regressing use of country systems in 2007 on 2005 _donor aid share_ values should reduce, if not eliminate, any positive bias. The 2007 _donor aid share_ values are from the SMPD, while the 2005 values are from the OECDDAC’s Creditor Reporting System (CRS). Some donors included in the SMPD did not report data in the 2005 CRS, so numerous observations are lost. Results in equations 4-6 are therefore not directly comparable to those in equations 1-3. Despite the smaller sample, _donor aid share_ retains its positive and significant coefficients in equations 4-6. In the _PFM_ regressions, its coefficient drops only from .647 (equation 1) to .63 (equation 4). Coefficients decline by more than one third in the _DBS_ and _PBA_ regressions but remain highly significant.

Admittedly, measuring _donor aid share_ two years prior to use of country systems does not fully resolve the potential problem of an upward bias in coefficient estimates. Subject to this caveat, our results are consistent with the prediction that donors are more likely to rely on country systems when they have a larger reputational stake in a recipient’s development outcomes, and when they internalize more of the benefits of investing in those systems..

Recipient characteristics, including trustworthiness of country systems, are tested in Tables 6 and 7. The dependent variable in Table 6 is _PFM_ , while results for _DBS_ and _PBA_ are reported in Table 7. Donor fixed effects are included in all of these regressions, and standard errors are adjusted for non-independence within recipient clusters of observations.

As in Table 5, _donor aid share_ is positive and significant in Table 6, equation 1. So is _HIPC completion_ : use of PFM systems is 9.3 percentages points higher, other things equal, in countries completing the HIPC qualifying process.

Both _PFM Quality_ and _Transparency_ are associated with greater use of PFM systems, although _Transparency_ is only marginally significant.<sup>24</sup> Each half-point increment in _PFM Quality_ is associated with an increase of 5 percentage points in use of PFM systems. A similar increase in _Transparency_ has a slightly smaller 3.5 percentage-point effect.<sup>25</sup> Equation 2 substitutes _Strategy_ for the two CPIA indicators. Each one-grade increment in the quality of countries’ national development strategies is associated with an increase of more than 8 percentage points in use of PFM systems. This effect is only marginally significant,

> 24 These two variables are correlated at .53, and omitting either one of them increases the coefficient and significance of the other one.

> 25 These findings for _PFM_ change very little if any one of its four components is analyzed instead, as shown in the Appendix 5 table.

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however. Nor is it robust to the inclusion of the CPIA variables, in equation 3, where the coefficient for _Strategy_ is negative but not significant.<sup>26</sup> Although quality of national development strategies is one of the Paris Declaration monitoring indicators, the indicator has been criticized for its close identification with certain donors. The World Bank and IMF have encouraged or required formulation of strategies for recipients of concessional loans and debt relief, and the quality of the strategies is assessed by World Bank staff. If strategies are often Bank-driven, their Bank-assessed quality may have little influence on decisions of some other donors advocating different approaches to development. When the specification in Table 6, equation 2 is tested separately for the 39 World Bank observations and for all others, _Strategy_ is estimated to increase use of PFM systems by 21 percentage points for the Bank sample, but by only 8 percentage points for non-Bank observations.<sup>27</sup> The inclusion of _Strategy_ reduces the sample size by about one fifth, as the quality of development strategies was not assessed for middle-income countries. For this low-income sample, the coefficient for _PFM Quality_ in equation 3 is nearly double its magnitude in equation 1. Because _Strategy_ is not a robust predictor of trust in country systems, and its inclusion cuts the sample size substantially, we omit it from subsequent tests.

If using country systems strengthens them while bypassing them weakens them (OECD, 2008a), then _PFM Quality_ and _Transparency_ may be subject to endogeneity bias. We address this issue in Table 6, equation 4 by using lagged (2004, instead of 2006) values of _PFM Quality_ and _Transparency_ . Coefficients do not change much from their equation 1 magnitudes and _PFM Quality_ retains its significance, but _Transparency_ is no longer significant at the .10 level.28 Results for _PFM Quality_ and _Transparency_ are potentially weaker for donors other than the World Bank. The CPIA assessments are done by Bank staff, so they are particularly likely to affect decisions by the Bank on aid implementation. Equation 5 (Table 6) replicates the base specification of equation 1, but dropping the 49 World Bank observations. Results for the CPIA variables (and for the other regressors) are unaffected by dropping World Bank observations.

Equation 6 drops a different set of observations, those for middle-income aid recipients. The World Bank publicly releases the CPIA ratings only for low-income (“IDA eligible”) countries. Equations 1-5 include non-public CPIA ratings for 15 middle-income countries representing about 18% of the observations in equation 1. Results based only on publicly-

> 26 _Strategy_ is correlated with _PFM Quality_ and _Transparency_ at .77 and .53 respectively.

> 27 In a full set of donor-by-donor tests, _Strategy_ is significantly associated with greater reliance on country PFM systems only for France, Japan, Norway and the United Kingdom.

> 28 Data on CPIA are available for 41 fewer observations in equation 4 than in equation 1, but the sample change is not responsible for the weaker result on _Transparency_ .  The 2006 _Transparency_ value, tested for the smaller 2004 sample, produces a coefficient of 8.2 with a t-statistic of 2.16.

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available data for _PFM Quality_ and _Transparency_ , presented in Equation 6, are stronger, compared to results for the full sample in equation 1. The coefficient for _PFM Quality_ is about two-fifths larger, and statistical significance improves for both _PFM Quality_ and _Transparency_ , despite the reduction in sample size. In this low-income sample, however, the coefficient for _HIPC completion_ is smaller and no longer significant.

Budget support by definition uses country PFM systems. Equation 7 returns to the full sample of observations, but differs from equation 1 in adding _DBS_ as a regressor. Conceivably, the other right-hand-side variables increase _PFM_ in equations 1-6 only by increasing _DBS_ . As expected, _DBS_ is strongly related to _PFM_ in equation 7. More importantly, _donor aid share_ , _HIPC completion_ and _PFM Quality_ retain their effects, indicating that they are associated with greater use of PFM systems for project aid and other non-budget-support aid. The coefficient for _Transparency_ is cut in half, however, and it is no longer significant when controlling for _DBS_ .

Table 7 replaces _PFM_ with the other two dependent variables, _DBS_ and _PBA_ . Righthand-side variables are somewhat different. For reasons explained above in Section 3, we add the regressors _aid/GDP_ and _Quality of Macro/Fiscal Policy_ . Rather than including three (somewhat collinear) CPIA variables, we drop _PFM Quality_ from the _DBS_ and _PBA_ regressions, where it is not significant. In equations 1 and 2, _donor aid share_ is positively and significantly associated with _DBS_ and _PBA_ , as in the two-way fixed effects tests of Table 3. In _HIPC completion_ countries, _DBS_ is 4.4 percentage points higher, and _PBA_ is nearly 16 points higher. Each 1-point increase in _Quality of Macro/Fiscal policy_ and in _Transparency_ is associated with, respectively, 7.3 and 5.9 percentage point increases in _DBS_ . However, their coefficients are not significant for _PBA_ in equation 2. Aid levels are significantly related to budget support (equation 1), but not to program-based aid (equation 2). Each 3 percentage-point increase in _aid/GNP_ is associated with a 1 percentage point increase in budget support.

The model’s explanatory power for _PBA_ is somewhat lower than for _DBS_ , likely due to greater measurement error in program-based aid. Although the criteria for determining whether aid qualifies as _PBA_ are more explicit in the 2008 SMPD than in the 2006 survey, there is still more room for subjective judgment in classifying aid as _PBA_ than in classifying it as _DBS_ .

Equations 3 and 4 in Table 7 drop the World Bank observations, but otherwise are identical to (respectively) equations 1 and 2. Results change only trivially.

Equations 5 and 6 drop middle-income countries, but otherwise are identical to (respectively) equations 1 and 2. The most notable change is that _Quality of Macro/Fiscal Policy_ and _Transparency_ are significantly related to program-based aid in this low-income sample (equation 6); their coefficients were somewhat smaller and not significant in equation 2.

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Donor characteristics, including proxies for domestic constituents’ trust in aid’s effectiveness, are tested in Tables 8 and 9. These tests control for recipient fixed effects, and correct for non-independence of standard errors within donor clusters of observations.

Based on the discussion in Section 3, donors are classified into six groups: _Nordic Plus_ , _other DAC bilateral_ s, _non-DAC bilaterals_ , _MDBs_ (multilateral development banks), _non-MDBs_ (other non-bank multilaterals), and _Vertical funds_ . Dummy variables are tested for five of these groups in Table 8, with _other DAC bilaterals_ as the reference category.

Large differences are observed across the groups. In equation 1, _PFM_ is 16 percentage points higher on average for _Nordic Plus_ members than for the reference group. The _Nordic Plus_ donors also deliver much more aid in the form of _DBS_ (equation 2) and _PBA_ (equation 3) than most other donor groups. Use of country systems is lower for non-DAC bilaterals as expected, although the difference with DAC bilaterals is significant only for _PBA_ (equation 3).

Results for the two multilateral-donor dummies are generally consistent with the proposition that these donor agencies were created in part to resolve exactly the sort of collective action problems leading to sub-optimal use of country systems by bilaterals. Use of country systems is significantly higher for _MDBs_ than for _other DAC bilaterals_ . The _MDBs_ coefficient is similar in magnitude to the _Nordic Plus_ coefficient in the case of _DBS_ (equation 2), but somewhat smaller in the case of _PFM_ (equation 1) and _PBA_ (equation 3). The _non-MDBs_ – a category including the EC and UN agencies – differ very little from the reference category: _PBA_ is 4.6 percentage points higher (equation 3), but _PFM_ and _DBS_ are not significantly different from the _other DAC bilaterals_ .

The _Vertical funds_ are very similar to the _Nordic Plus_ group in PFM and PBA. Although _Vertical funds_ provide very little budget support (2.9%)<sup>29</sup> , they do not differ significantly from the reference category on _DBS_ in equation 2.

The donor groups thus exhibit substantial variation, and in general in ways consistent with the predictions from Section 3. Some important variation may still be hidden within some of these groupings, however. In Equations 4-6, we explore this possibility by adding dummy variables for four prominent donors. The two largest _non-MDBs_ – the UN and EC – differ in important respects. The EC is more like a bilateral agency, with decisions influenced more directly by its member governments (Martens et al. 2002: ch. 2; Degnbol-Martinussen and Engberg-Pedersen, 2003: ch. 7). It is a leader in providing budget support, as confirmed by the EC dummy in equation 5. Some UN agencies specialize in humanitarian aid, which is not

> 29 The only two instances of budget support from vertical funds in the SMPD are to Jordan from the Global Fund to Fight AIDS, Tuberculosis, and Malaria, and to Bolivia from IFAD.

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included in the SMPD. Other UN agencies providing mostly technical assistance; it does not count as budget support and rarely uses PFM systems, consistent with the large negative coefficient for the UN dummy in equation 4 of Table 8.

The DAC bilaterals are already split into two groups in equations 1-3, the _Nordic Plus_ group and all others. Equations 4-6 add dummies for two large bilateral donors - the U.S. and Japan - that are well-known for favoring project-based aid over budget support. Corruption concerns are voiced often in U.S. debates on foreign aid, e.g. in Congress and in Wall Street Journal editorials. “Congress insists that aid is spent on identifiable sets of measureable activities” and its earmarks and reporting requirements severely restrict the use of budget support or reliance on recipients’ procurement and other PFM systems in managing project aid (OECD, 2006). Negotiators for the U.S. insisted on weakening or eliminating Paris Declaration targets for budget support and use of country procurement systems (Mokoro Ltd., 2008b: 23).<sup>30</sup> Results in equations 4-6 confirm the expectation that Japan and (particularly) the U.S. use country systems less than do other bilaterals.

For bilateral donors, Table 9 tests three different survey measures of domestic constituents’ trust in aid’s effectiveness, from _Gallup International_ , the _WVS_ , and _Eurobarometer_ . As expected, public support for aid is associated with greater use of country systems, as shown in Table 9. Coefficients on the public opinion variables are all positive, and significant in most cases, with t-values ranging from 1.43 to 3.75. For each 5-point increase in the percentage of _WVS_ respondents who are strongly in favor of their country increasing economic aid to poorer countries, _PFM_ increases by nearly 4 percentage points and _DBS_ increases by 1 percentage point. The U.S., unsurprisingly, is somewhat of an outlier in terms of weak support for aid, but results are not dependent on inclusion of its observations in the sample. The strongest effects of public opinion on _DBS_ are obtained using _Eurobarometer_ (equation 6), which excludes U.S. observations.

It might be objected that public opinion is partly endogenous to how aid is delivered. If use of country systems improves aid’s effectiveness in reducing poverty, support for aid may increase. We find this argument implausible for several reasons. At this point, the assertion that use of country systems increases aid effectiveness is an untested hypothesis, and any positive effects would likely be too small to account for much of the large cross-donor variation in public support for aid. Moreover, public opinion in each of the three surveys is measured at least

> 30 In contrast, the U.S. led the effort to include a Paris Declaration provision on “managing for results” because “reporting on results is critical to demonstrating aid effectiveness and to sustaining public and Congressional support for U.S. assistance” (USAID, 2006).  However, USAID’s own reporting system “focuses mainly on physical deliverables” such as number of schools or clinics, rather than on outcomes (OECD, 2006).

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several years prior to the dependent variables. The strongest results are found using _WVS_ , from surveys conducted mostly in 1995 and 1996, more than a decade prior to the SMPD, when use of country PFM systems and direct budget support were much less prevalent for donors in general.

To this point, we have included donor and/or recipient fixed effects in all of our tests. This method most effectively captures the influence of otherwise unmeasured determinants of use of country systems. Depending on the specification, however, Hausman tests do not always indicate fixed effects are preferred over random effects. In such cases, it is common to use random effects instead of fixed effects, on efficiency grounds. We therefore present random effects results for _PFM_ , _DBS_ and _PBA_ in equations 1-3 (respectively) of Table 10. Donor and recipient characteristics can be included together in these tests. However, we do not include the public opinion variables from Table 9, because they cut the sample size by half or more.

Most results obtained using fixed effects tests change very little in the random effects tests. Coefficients for _donor aid share_ are somewhat smaller using random effects, for _PFM_ and _PBA_ (equations 1 and 3 in Table 10), but not for _DBS_ (equation 2). Results for _HIPC completion_ , _aid/GNP_ , and the three CPIA variables change very little in most cases. One exception is that _Transparency_ is highly significant in the _PBA_ regression in Table 10; the corresponding t-stat using fixed effects was only 1.52 (equation 2 of Table 7). The only notable change in results for the donor groupings dummies is for the _non-MDBs_ : their coefficients in _DBS_ and _PBA_ random effects regressions (Table 10, equations 2 and 3) remain positive, but are smaller in magnitude. When recipient fixed effects were included (Table 8, equation 3), _PBA_ was significantly higher for the _non-MDBs_ than for the omitted category of _other DAC bilaterals_ but in random effects the difference is smaller and not significant.

Equations 4-6 in Table 10 report random effects tobit models for each of the three dependent variables, which are bounded by 0% and 100%.<sup>31</sup> For example, of the 782 observations in equation 1, _PFM_ equals 0% for 203 cases and 100% for 37 others. All results significant at conventional levels in equations 1-3 remain significant in their respective counterparts using tobit in equations 4-6, and no coefficients change sign.<sup>32</sup> For space reasons we do not report a table of results for random effects versions of the Table 9 (fixed effects) tests showing public support for aid increases trust in country systems.

> 31 Fixed-effects tobit estimates are biased, so we report tobit results only for random effects models.  The value added of tobit estimation is relatively small (Angrist and Pischke, 2009), so we report tobit results only to show our main results from linear (fixed and random effects) regressions are robust to the most obvious nonlinear alternative. 32 Coefficient magnitudes are not directly comparable between random effects and random effects tobit: in tobit, a coefficient measure the marginal effects of a one-unit change in a regressor, evaluated at the means of the other regressors, on the latent (unobserved) dependent variable.

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However, results (available on request) changed only trivially using random effects or random effects tobit instead of fixed effects.

# **5. Conclusions**

The Paris Declaration has placed the use of recipient country systems firmly on the international aid effectiveness agenda. This paper identifies and tests three broad explanations for donor agencies’ inability or unwillingness to rely more on country systems. First, and most fundamentally, country systems are often not very trustworthy so their use implies significant reputational and fiduciary risks to donors. Where PFM and other aid management systems are sufficiently weak, donors’ use of parallel systems for managing aid can be fully consistent with maximizing development objectives. We provide empirical evidence supporting the proposition that use of country systems is strongly related to their quality, contradicting findings from more casual analyses that the _PFM Quality_ indicator from the World Bank’s CPIA only weakly predicts donors’ use of PFM systems (OECD, 2009a, 2009b).

Second, donors’ trust in country systems is influenced by their mandates and by their constituents’ faith in the development effectiveness of aid. Multilateral donors (particularly the development banks) use country systems more than bilaterals. Among bilateral donors, we show that popular support for aid is associated with greater use of country systems. Where voters are more skeptical of aid, we argue, the bilateral agency is compelled to micro-manage aid expenditures to produce tangible, visible outputs that can be plausibly attributed to its funds.

Third, we find that a donor’s use of country systems is greater when it has a more “encompassing interest” in a recipient’s development. When a donor’s share of all aid provided to the recipient is higher, it internalizes more of the benefits of its use of country systems.

As a general rule, donors will incur the full costs of their own decisions to trust in country systems, in the form of increased risks to their aid funds. In contrast, the benefits – in the form of positive development results agencies can cite when lobbying to maintain or increase their budgets – will be shared among many donors. In the absence of an effective coordination mechanism, therefore, use of country systems is likely at sub-optimal levels for most donors.

The donors’ own harmonization initiatives, led by the OECD-DAC and World Bank, are a partial solution to the under-provision of recipient-managed aid. Other possible policy responses address the trustworthiness of country systems, tolerance for risk among donor country voters, or donors’ ability to internalize more of the benefits from their use of country systems.

The Paris Declaration itself emphasizes the importance of strengthening weak country systems, and technical assistance from donors can contribute importantly to this objective. The 28
multi-donor Public Expenditure and Financial Accountability program (www.pefa.org) replaces numerous uncoordinated donor projects supporting diagnostic and analytic work, financing for reform, and technical support for reform implementation in PFM. Progress in reforming PFM systems however tends to occur only over very long time periods, and involves political as well as technical challenges, with resistance from certain public officials who benefit from maintaining less-transparent systems and procedures (OECD, 2009b; Allen, 2009; IEG, 2008).

Bilateral donor agencies can attempt to increase the tolerance of risk in aid programs by highlighting the development benefits of using country systems, as part of their public and parliamentary relations efforts (OECD, 2009b: 41). Understandably, the Paris Declaration targets and aid management practices will never resonate as much with non-specialists as debt relief or even the Millennium Development Goals. However, donors might be able to enlist some opinion leaders (including prominent entertainers or economists with experience in aid advocacy) to help sell the principle of “country ownership” and stressing the damaging effects of donors’ use of parallel systems.

Concentrating a typical donor’s aid in fewer countries and sectors would reduce the enormous transactions costs imposed on recipient governments. It would also tend to increase donors’ use of country systems, as the leading donors in a recipient would internalize a greater share of the benefits. Even if donors’ aid shares were all unchanged in a country, but divisions of labor by sector were sharpened, donors would have less incentive to micro-manage aid. The lead donor for education, for example, would have a strengthened reputational stake in education outcomes, and similarly for the lead donor in health. Incentives for them would shift away from delivering donor-managed, successful-looking projects, and toward working with governments to deliver improved sector-wide development outcomes. Concentrating aid in fewer countries or sectors, however, potentially reduces a donor agency’s visibility, with possibly adverse consequences for its budget.

Representatives of aid constituencies who visit aid-receiving countries may easily get the impression that the aid from their own country or international organization is making footprints everywhere in the country, at least everywhere they happen to appear, in accordance with a well-designed traveling program prepared by local authorities in cooperation with respective embassies or agency residential representations. (Andersen, 2000: 193).

Visibility of aid, of this sort, would run into decreasing returns much more quickly if aid were concentrated more by sector and by country instead of fragmented.

Bilateral donors with tenuous domestic support for aid face a tradeoff between meeting the Paris Declaration goals and the Monterrey Consensus goal of increasing aid levels. Nominal 29
earmarks can sometimes reduce these tradeoffs; e.g. budget support can be disguised by a donor as aid targeting a particular health issue, as a useful fiction to help sustain domestic support for aid provision.

Another means of making progress toward the Paris goals, without reducing support for the donor’s aid budget, is simply to shift aid allocations so that recipients with stronger management systems are favored even more than they are now. This response could create aid “orphans,” a result not envisioned or advocated in the Paris Declaration. It also has implications for the increasingly-popular practice of ranking donors (e.g. Roodman, 2006; Easterly and Pfutze, 2008). Use of country systems and other Paris Declaration monitoring indicators, aggregated to the donor level, are obvious candidates for inclusion in future rankings (Center for Global Development, 2007). We show in Table 4, however, that recipient characteristics explain more variation in use of country systems than do donor characteristics. Any ranking of donor performance based on the SMPD data should adjust for differences in risk across donors’ aid portfolios. For example, rankings could be based on donor dummy coefficient estimates from two-way fixed effects models that net out recipient effects. If donors were ranked on the unadjusted aggregates, the surest way to move up in the rankings would be to cease giving any aid to countries with less trustworthy systems.

Other possible policy responses to facilitate progress toward the Paris Declaration’s goals go beyond the scope of our model. In particular, we have ignored incentives within donor agencies, but there are ways to raise the reputational stakes for staff members in a country’s development – in effect internalizing more of the benefits of using country systems within the agency. Staff members in many donor agencies arguably are rotated too frequently, shortening the time period in which they need to produce measurable outputs for their annual performance reviews. When staff do need to be re-assigned from one country or region to work on another one, their ongoing projects should remain part of their work programs until they are completed. Even completed projects can be evaluated with respect to their sustained benefits, feeding into current and future performance evaluations.

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