26 May 2016 Lauren M. Phillips

1. Global Debt Dynamics Initiative University of Sussex The political utility of multilateral borrowing: MICs and continued demand for development finance 26 May 2016 Lauren M. Phillips

2. Outline Background, puzzle and the literature The political economy of MDB borrowing Initial evidence from Mexico Next steps

3. Stylized facts The percent of total debt middle income countries borrow from MDBs has been declining since the 1990s Official borrowing, which increased during the financial crisis, has declined dramatically vis-a-vis private borrowing since 2009

4. Reasons for the decline Strong availability of private finance • Historically low interest rates • Demand from developed investors looking for returns • Strong growth and solid macroeconomic performance / stability in many UMICs • Investment grade credit in a number of previous “lemons” High transaction costs of MDB borrowing • Safeguards for social and environmental issues complicate and delay disbursement • Loans are often tightly tied to specific project / geography

5. Why do UMICs borrow from MDBs at all? Answers from MDBs / IFIs: • Technical advisory / knowledge • Investment in public goods / areas with marginal economic gains • Innovative products (e.g. on climate finance) • Cost advantages (non-concessional finance still concessional vis-à-vis market terms) Answers from the political economy literature have mostly looked in the other direction – supply side, however: • Credible commitment / tying hands • Political gains with key creditors • Demand for borrowing varies between institutions depending on factors such as governance structures

6. Hypothesis Layered political economy rationale for UMIC borrowing from MDBs / IFIs • Tightly targeted investments constrain borrowers, but also enable them to reach precise populations that may deliver electoral or other political advantages • This money is more likely to reach target populations than private borrowing or state resources because the approval process permits less political interference, and safeguards are in place to make sure money is spent where designated • Finally easy to get credit when outcomes are positive gains, and deniability to external actor when things go wrong

7. Note: Project identification process While MDBs / IFIs have specific criteria to help them prioritize projects and target populations, the government usually provides the first indication of where project finance should go and what it should cover Thus, the tying to specific areas and sectors is more often a product of government preference rather than MDB priorities This is especially true in UMICs, where the government has defined development strategies and a strong voice / capacity to negotiate

8. Where to look for evidence Better to look at UMICs than LMICs because access to private markets is stronger Better to look at competitive democracies than other systems because political incentives and payoffs are transparent / easier to model and measure Latin American UMICs provides an excellent universe of cases • Long history of both private and multi-lateral borrowing • Many are UMICs / HICs • Consolidated democracies in most countries with active opposition parties

9. First tests: Mexico Determine the extent to which electoral volatility - net percentage of voters who change parties between two elections (Pedersen Index) – determines allocation of MDB / IFI loans State level data • Corresponds to the data structure of most MDBs / IFIs • Data for other IDs also available • Requires assumptions to be made that distribution of loan across states is equal (i.e. $100 million loan over 10 states = $10 million per state)

10. Variables Dependent Variable: Allocation of IFI lending to Mexican state (World Bank, IFAD, combined) IDs: population, poverty & extreme poverty, climate change vulnerability, dummy for government control of state government (governor of ruling party = president’s party) Electoral Volatility: calculated for legislative elections on a state level between 1997 (first fully competitive election) and 2015 and averaged • Only for legislators elected through FPTP rule in their district (not state wide deputies assigned through PR) – 300 of 500 Mexican members of lower house

11. Results 1: MDB / IFI lending Dependent Variable: Total IFI financing (23 loans) 32 observations; R-squared = 0.19

12. Results 2: World Bank • Dependent Variable: World Bank Financing (17 loans: 2005-16) • 32 observations; R-squared = 0.28

13. Results 3: IFAD Dependent Variable: IFAD financing (6 loans, 2000-15) 32 observations; R-squared = 0.63

14. Implications Political logic is at play in the allocation of borrowed development finance to individual Mexican states • In states in which party turnover of legislators is high, and therefore electoral outcomes are uncertain, allocation is higher • In states in which the governor is from the ruling party, allocation is higher • This logic varies between the two institutions tested Core targeting criteria also matters • Climate change vulnerability and extreme poverty strong influence on IFAD allocations • Less for WB, possibly because of its wider mandate (including urban transport, energy, etc.)

15. Next steps Expand dataset to include other UMICs from Latin America (Brazil, Colombia, Peru, Venezuela) Expand DV to look at allocation of IADB lending Multi-faceted testing strategy which compares the political utility of MDB lending vs. similar test for national budget Test other elements of the hypothesis: • Insulated approval process: measure debate or amendments on IFI loans vs. national budget • Claims for gains / deniability for losses: soft capture through press