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Do the Strong Receive What They Can? Explaining the Allocation of Environmental Aid. Chris Marcoux The College of William and Mary Christian Peratsakis University of Texas. Augmenting Available Data. Improving the breadth of coverage
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Do the Strong Receive What They Can?Explaining the Allocation of Environmental Aid Chris Marcoux The College of William and Mary Christian Peratsakis University of Texas
Augmenting Available Data • Improving the breadth of coverage • Adding multilateral and bilateral donors not reporting to OECD DAC • Moving beyond ODA by including other types of aid flows • Adding additional years of data for existing donors (e.g. IDA) • Improving the depth of coverage • Adding more detail for existing project records • Documents • Descriptions • Co-financiers
Getting the Data • OECD CRS • Donor Documents: Annual Reports, Project Factsheets • Historical Data • Often not digitized • Webscraping: Online donor data • Reliable; Quick; Automatically updated • New information captured readily • Direct from Donors: Phone; Email; Site Visits • Official; Primary source • Difficulties of winning donor cooperation
Total Development Flows in AidData by Year Millions (2000 USD)
List of FieldsBlue = New in AidData AidData 1.0 has 67 variables: • Donor Project ID • Donor Code/Name • Beneficiary • Location • Recipient Code/Name • Source • Source Detail • Source Type • Contacts/Role of Contact • Financing Agency • Implementing Agency • Other Organization • Commitment Date (not available in online version of CRS) • End Date • Start Date • Year • Commitment Original Currency • Disbursement Original Currency • Total Cost • Commitment Constant • Commitment Current • Flow Code • Grace Period • Grant Element • Interest Rate • Investment Marker • Date of first/last repayment • Number of repayments per year • Type of repayment • Status • Tied Aid, Partially Tied Aid, Untied Aid • Description (long) • Description, original language • Short description • Title • Title, original language • Biodiversity Marker • Climate Change Marker • CRS Purpose Code/Name (partially new, we imputed values for the data we added) • Environmental Impact Assessment Marker • Freestanding Technical Cooperation • Gender Equality Marker • PDGG Marker • Sector Name/Code • Sector Programme Aid • AidData Activity Codes/Descriptions • AidData Dominant Sector Code/Name • AidData Feasibility Study Marker • AidData Technical Assistance Marker • Notes
Aid From Recipient Perspectives • When Small Donors Matter: • Small donors can still have a big impact in specific countries • Example: Mauritania in 2007 • Existing sources of data misses 61% of the flows Mauritania received.
Composition of Flows to Africa • 0%=All Aid from Traditional Donors • 100%=All Aid from • Non-Traditional Donors
Explaining the Allocation of Environmental Aid Annual reports and websites of donor agencies emphasize the high levels of environmental degradation experienced by recipient countries. Recipient governments complain of donor-dominated environmental agendas that focus on regional and global threats and neglect development (as well as local environmental needs). Who is right?
Categorizing Environmental Assistance • 5-point ordinal scale • Environmental, Strictly Defined (ESD) • Environmental, Broadly Defined (EBD) • Neutral (N) • Dirty, Broadly Defined (DBD) • Dirty, Strictly Defined (DSD)
Categorizing Environmental Benefit All environmentally friendly projects (ESD or EBD) are further coded by scope: Green Global or Regional Environmental Problemsex: climate, ozone depletion, biodiversity Brown Local / National Environmental Problemsex: drinking water treatment, soil erosion
Next Steps • Develop and test a model of environmental aid allocation that accounts for recipients’ interests and power. • Since these may vary by issue, I focus on environmental transfers related to biological diversity – one of the two major treaties negotiated at UNCED. • Examine how much aid is given under the umbrella of MEAs (financial transfers) and assess success of financial transfers in building capacity (completeness of nat’l reporting)
“Greening Aid” allocation model • Ecofunctionalism • Aid correlates with environmental significance of recipients • Donors will target recipients with poor environmental quality • Institutionalism • Donors will target recipients based on revealed preferences • Donors will favor governments that provide credible/verifiable information about environmental performance • Realpolitik • “Loyal” recipients will receive more aid • Donors will disproportionately favor large recipients • Liberalism • Donors will favor trading partners