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Do IMF and World Bank Influence Voting in the UN General Assembly?

Do IMF and World Bank Influence Voting in the UN General Assembly?. Axel Dreher and Jan-Egbert Sturm International Political Economy Society Inaugural Conference November 17-18, 2006. Literature overview. G7. UN Voting. a. d. b. c. IMF / WB. Hypotheses.

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Do IMF and World Bank Influence Voting in the UN General Assembly?

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  1. Do IMF and World Bank Influence Votingin the UN General Assembly? Axel Dreher andJan-Egbert Sturm International Political Economy Society Inaugural Conference November 17-18, 2006

  2. Literature overview G7 UN Voting a d b c IMF / WB

  3. Hypotheses • Cultural and political proximity increases voting coincidence • Countries depending on foreign support are more likely to vote in line with G7 countries • () Bilateral foreign aid increases the probability that a recipient country votes in line with the donor • Trade flows increase/decrease the probability that a country votes in line with its partner country • () IMF and World Bank loans increase the probability that a recipient country votes in line with the institutions’ major shareholders

  4. Main Hypothesis • () IMF and World Bank loans increase the probability that a recipient country votes in line with the institutions’ major shareholders • IBRD: International Bank for Reconstruction and Development • IDA: International Development Association

  5. Main Hypothesis • () IMF and World Bank loans increase the probability that a recipient country votes in line with the institutions’ major shareholders • World Bank • Number of technical loans programs starting • Number of adjustment programs starting • Number of other programs starting • IMF • Start of EFF and SBA program (non-conc.) • Start of SAF and PRGF program (conc.)

  6. Data • Years: 1970-2002 • Recipient-countries: up to 188 • Donor-countries: each individual G7 country & weighted-average G7

  7. G7 0.75 0.95 0.71 0.96 0.77 0.99 0.71 0.92 0.74 0.97 0.73 0.92 Comparing inline voting behavior across donor countries, 1970-2002 • Variables corrected for country- and year-specific effects • Each cell based on ca. 5,000 country/year observations CAN FRA GBR DEU ITA JPN USA Canada (CAN) 0.95 0.96 0.92 0.99 0.97 France (FRA) 0.98 0.91 0.97 0.91 UK (GBR) 0.92 0.97 0.92 Germany (DEU) 0.93 0.89 Italy (ITA) 0.96 Japan (JPN) United States (USA) 0.79

  8. Rest of the world 0.45 0.42 0.36 0.35 0.44 0.48 0.20 0.36 Europe, Western 0.73 0.71 0.64 0.65 0.73 0.71 0.46 0.65 Asia, Central & Western 0.60 0.60 0.55 0.54 0.60 0.62 0.37 0.54 Oceania 0.61 0.57 0.52 0.52 0.60 0.62 0.39 0.52 Europe, Central & Eastern 0.59 0.59 0.53 0.51 0.59 0.60 0.34 0.52 South America 0.57 0.53 0.46 0.46 0.55 0.60 0.30 0.46 Central & Middle America 0.55 0.52 0.45 0.45 0.54 0.58 0.31 0.46 Caribbean 0.53 0.50 0.45 0.43 0.52 0.56 0.30 0.44 North America 0.54 0.50 0.44 0.43 0.52 0.58 0.26 0.43 Africa 0.51 0.48 0.43 0.41 0.50 0.55 0.27 0.42 Middle East 0.52 0.48 0.42 0.41 0.50 0.55 0.27 0.42 Asia, Eastern & Southern 0.52 0.48 0.43 0.41 0.50 0.56 0.25 0.41 Voting coincidence across different regions CAN DEU FRA GBR ITA JPN USA G7 • Regional classification based on CIA World Fact Book

  9. Extreme Bounds Analysis • Estimate equation of the following formYit = Mit + Fit + Zit + uit • Re-specify Z-vector holding M and F constant • Go through all combinations of the Z vector • Examine all estimated  coefficients • Levine and Renelt (1992): look at extremes • Sala-i-Martin (1997): look at distribution • How to specify baseline model (M-vector)? • General-to-specific methodology (Temple, 2000)

  10. CAN FRA GBR DEU ITA JPN USA G7 p-value Hausman 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 Adj. R 0.86 0.89 0.89 0.86 0.87 0.83 0.82 0.89 #Obs 4286 4286 4286 4286 4285 4286 4286 4286 #Cnt 177 177 177 177 177 177 177 177 Period 73-01 73-01 73-01 73-01 73-01 73-01 73-01 73-01 National capability -7.147 -7.506 -7.610 -6.476 -7.304 -7.064 -7.932 -7.686 (-9.98) (-11.29) (-10.62) (-7.34) (-10.28) (-10.26) (-8.90) (-10.70) Democracy [t-1] 0.013 0.012 0.012 0.013 0.013 0.010 0.010 0.012 (16.43) (15.64) (14.95) (12.90) (16.09) (12.68) (9.73) (14.50) Results for baseline model • Voting coincidence increases when • cultural and political proximity increases (hypothesis 1) • dependence upon foreign support increases (hypothesis 2)

  11. conc. flows 0.094 IDA flows (conc.) -0.110 (0.92) (-1.39) conc. flows agreed -0.011 (-0.28) non-conc. flows 0.197 IBRD flows (non-conc.) 0.861 (2.38) (4.75) non-conc. flows agreed 0.208 (3.41) SAF & PRGF (conc.) 0.002 techn. projects 0.004 (0.59) (2.04) EFF & SBA (non-conc.) 0.012 adjust. projects 0.005 (4.75) (3.22) other projects 0.001 (2.28) Testing the main hypothesis for the G7 IMF World Bank

  12. Main hypothesis: IMF & World Bank support (4,089 regressions per cell) IMF conc. flows 0.91 0.75 0.73 0.82 0.91 0.90 0.73 0.74 IMF non-conc. flows 0.81 0.82 0.81 0.80 0.83 0.84 0.80 0.79 IMF conc. flows agreed 0.62 0.62 0.67 0.67 0.66 0.69 0.66 0.60 IMF non-conc. flows agreed 0.89 0.92 0.91 0.87 0.93 0.81 0.72 0.92 IMF SAF & PRGF (conc.) 0.82 0.85 0.86 0.66 0.85 0.83 0.81 0.84 IMF EFF & SBA (non-conc.) 0.96 0.93 0.93 0.89 0.97 0.93 0.76 0.93 IDA flows (conc.) 0.80 0.78 0.71 0.84 0.77 0.81 0.79 0.74 IBRD flows (non-conc.) 0.82 0.90 0.92 0.87 0.86 0.89 0.99 0.93 WB techn. projects 0.86 0.97 0.89 0.91 0.94 0.74 0.80 0.85 WB adjust. projects 0.99 1.00 1.00 0.98 1.00 0.96 0.89 0.98 WB other projects 0.81 0.77 0.77 0.83 0.80 0.90 0.91 0.75 CDF(0) Results of EBA CAN FRA GBR DEU ITA JPN USA G7 Baseline variables (4,525 regressions per cell) National capability 0.93 0.99 0.99 0.90 0.96 1.00 0.99 0.97 Democracy [-1] 1.00 0.98 0.98 1.00 1.00 1.00 0.94 0.96

  13. USA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.82 0.86 0.86 0.86 0.84 0.82 0.79 0.86 0.82 3578 3578 3578 3578 3578 3578 3578 3578 4249 154 154 154 154 154 154 154 154 177 73-00 73-00 73-00 73-00 73-00 73-00 73-00 73-00 73-01 0.011 0.008 0.009 0.009 0.010 0.007 0.005 0.008 (4.28) (3.67) (3.54) (3.30) (4.17) (2.89) (1.51) (3.29) 0.701 0.636 0.717 0.770 0.720 0.620 0.783 0.772 0.851 (3.94) (3.91) (4.02) (3.81) (4.09) (3.69) (3.49) (4.33) (3.90) 0.004 0.005 0.005 0.004 0.004 0.002 0.003 0.005 (2.46) (3.34) (3.10) (1.97) (2.72) (1.17) (1.53) (3.03) Testing the extended models CAN FRA GBR DEU ITA JPN USA G7 p-value Hausman test 2 Adj. R #Obs #Cnt Period IMF EFF & SBA IBRD flows WB adjust. projects

  14. Robust to changes in dependent variable? • How to define voting coincidence? • Barro & Lee = BothYes + BothNo + Abs. • Thacker: = BothYes + BothNo + ½ Abs. • Kegley & Hoock: = BothYes + BothNo • Which votes to count? • Also almost unanimous votes? • All votes or only Keyvotes? • Include “dominant” topics? (Israel: 20% of all votes) • How to weigh the past? • No weights, or slowly decaying weights

  15. Conclusions Hypotheses 1 to 4 The probability that a recipient country votes in line with the donor … • significantly increases with cultural proximity • significantly increases with dependency on foreign support • is not significantly related to bilateral foreign aid • is somewhat related to bilateral trade flows

  16. Conclusions Main Hypothesis • Concessional loans are not correlated with voting coincidence in the UN General Assembly • Non-concessional IMF and World Bank programs and loans matter for voting in the UN General Assembly • number of IMF EFF and SBA programs • flow of World Bank IBRD loans • Also the start of World Bank adjustment programs matter • For the US, only IBRD loans matter for the voting behavior of recipient countries

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