1. Explaining Food and Agricultural Policy: New Data and Hypothesis Tests Will Masters wmasters@purdue.edu www.agecon.purdue.edu/staff/masters Friedman School Seminar 24 September 2008

2. Motivation: the development paradox

3. Why? Meanwhile…

4. New Data A 3-year project funded through the World Bank involving 100+ researchers and case studies for 68 countries, 77 commodities over 40+ years Project results to be published in six books –Four volumes of country narratives Africa (Anderson & Masters); Asia (Anderson & Martin); LAC (Anderson & Valdes); European Transition (Anderson & Swinnen) –Two global volumes One with regional syntheses and reform simulations One with political economy explanations for policy choices –Results today are mostly from W.A. Masters and A. Garcia (2009), “Agricultural Price Distortion and Stabilization: Stylized Facts and Hypothesis Tests,” in K. Anderson, ed., Political Economy of Distortions to Agricultural Incentives. Washington, DC: World Bank. All available at www.worldbank.org/agdistortionswww.worldbank.org/agdistortions

5. Country coverage No. ofPercentage of world countriesPop.GDPAg.GDP Africa161016 Asia12511137 LAC8758 ECA13636 HIC19147533 Total68919590

6. Commodity coverage (top 30 products only) No. ofPercentage of world ProductsProductionExports Cereal Grains108490 Oilseeds67985 Tropical crops77571 Livestock products77088 Total307585

7. The method: price distortions from “stroke of the pen” policies Tariff-equivalent Nominal Rate of Assistance in domestic prices relative to free trade: Sometimes estimated directly from observed policy: More often imputed by price comparison: We also introduce a new “stabilization index”, for the standard deviations around trend prices:

8. Explaining the data Our approach is to test for: (1)stylized facts –persistent correlations with broadly-defined variables, that could result from many different policymaking mechanisms (2)specific political-economy mechanisms –other correlations with narrowly-defined variables, as implied by particular theories of policymaking –these could explain residuals and add explanatory power to the stylized facts, or explain the stylized facts themselves –most tests are weak; only in one case do we have a strong identification strategy

9. The three stylized facts The three broad influences we capture are: (1)A development paradox from taxation to subsidies as incomes rise, as measured by real GDP per capita at PPP prices (PWT 6.2) (2)An anti-trade bias from taxation of both imports and exports, as measured by whether commodity is importable or exportable in each year (3)A resource curse effect from taxation of natural resources, as measured by arable land area per capita (FAOSTAT)

10. Seven specific hypotheses We test for each standard theory of policy failure: –Rational ignorance when per-person effects are small –Free ridership when groups of people are large (versus more political support from larger groups) –Rent-seeking by unconstrained incumbents (versus checks-and-balances from institutions and markets) –Revenue motives for cash-strapped governments –Time consistency of policy when taxation is reversible but investment is not (as opposed to simultaneous choices) –Status-quo bias from loss aversion or conservative social welfare functions in politics –Rent dissipation from the entry of new farmers (as opposed to free riding among existing farmers)

11. Results: A new view of the development paradox National average NRAs by real income per capita, with 95% confidence bands Notes : Each line shows data from 66 countries in each year from 1961 to 2005 (n=2520), smoothed with confidence intervals using Stata’s lpolyci at bandwidth 1 and degree 4. Income per capita is expressed in US$ at 2000 PPP prices. Our tests aim to account for nonlinearity in these lines, and also dispersion around them, as well as the NRA-income relationship itself (≈$22,000/yr)(≈$400/yr)(≈$3,000/yr) NRA<0 Net taxation of farmers ≈$5,000/yr Net taxation of consumers NRA>0 Export taxes with import restrictions = anti-trade bias

12. Results: A new view of policy change over time Average NRAs for all products by year, with 95% confidence bands Heavy taxes on farmers in 1970s then reform Heavy taxes on consumers in the 1980s, then reform Increased taxes on consumers in 1990s

13. Results: A new view of policy change over time Average NRAs for importables and exportables by year, with 95% confidence bands Heavy taxes on exports in 1970s then reform with varied import restrictions Trend away from taxes on exports, with rising import restrictions

14. Results: The stylized facts in OLS regressions Table 1. Stylized facts of observed NRAs in agriculture Explanatory variables Model (1)(2)(3)(4)(5) Income (log)0.3420***0.3750***0.2643***0.2614***0.2739*** Land per capita-0.4144***-0.4362*** Africa0.0651 Asia0.1404*** Latin Am. & Car. (LAC)-0.1635*** High inc. cos. (HIC) 0.4311*** Importable0.1650* Exportable-0.2756*** Constant-2.6759***-2.8159***-2.0352***-1.9874***-2.0042*** R2R2 0.280.3630.4180.8270.152 No. of obs.2,5202,269 2,52028,118 Notes: Covered total NRA is the dependent variable for models 1-4, and NRA by commodity for model 5. Model 4 uses country fixed effects. Results are OLS estimates, with significance levels shown at the 99% (***), 95% (**), and 90% (*) levels from robust standard errors (models 1-4) and country clustered standard errors (model 5). The omitted region is Europe and Central Asia. Source for all tables and charts: W.A. Masters and A. Garcia (2009), “Agricultural Price Distortion and Stabilization: Stylized Facts and Hypothesis Tests,” in K. Anderson, ed., Political Economy of Distortions to Agricultural Incentives. Washington, DC: World Bank. The development paradox The resource curse Some regional differences Anti-trade bias

15. Results: Specific hypotheses at the country level (1)(2)(3)(4)(5)(6)(7) Total NRA for:All Prods. |All Prods.|ExportablesImportablesAll Prods. Explanatory variables Income (log) 0.2643*** 0.1234***0.3175***0.1913***0.2216***0.1142***0.2461*** Land per capita -0.4362*** -0.2850***-0.4366***-0.4263***-0.7148***-0.6360***-0.4291*** Africa 0.0651 0.1544***0.0964**0.2612***-0.1071***-0.06280.0844** Asia 0.1404*** 0.2087***0.1355***0.1007**-0.1791***0.02170.1684*** LAC -0.1635*** -0.0277-0.1189***-0.0947***-0.2309***-0.1780***-0.1460*** HIC 0.4311*** 0.2789***0.4203***0.3761***1.0694***0.8807***0.4346*** Policy transfer cost per rural person-0.0773* Policy transfer cost per urban person -1.2328*** Rural population 1.4668*** Urban population -3.8016*** Checks and balances-0.0173*** Monetary depth (M2/GDP)-0.0310***-0.0401*** Entry of new farmers-0.0737* Constant -2.0352*** -0.9046**-2.4506***-1.2465***-1.5957***-0.4652*-1.8575*** R2R2 0.4180 0.450.4370.2940.3730.3970.419 No. of obs. 2,2691,3262,2691,6311,6291,6442,269 Notes: Dependent variables are the total NRA for all covered products in columns 1, 2, 3 and 7; the absolute value of that NRA in column 4, and the total NRA for exportables and importables in columns 5 and 6, respectively. For column 2, the sample is restricted to countries and years with a positive total NRA. Monetary depth is expressed in ten-thousandths of one percent. Results are OLS estimates, with robust standard errors and significance levels shown at the 99% (***), 95% (**), and 90% (*) levels. Table 2. Hypothesis tests at the country level Rational ignorance Number of people Governance Revenue Motives Rent dissipation

16. Results: Specific hypotheses at the product level Explanatory variables Model (1)(2)(3)(5)(6) Income (log)0.2605**0.2989***0.2363**0.3160**0.2804** Importable0.05490.0048-0.00610.11060.0331 Exportable-0.2921***-0.3028***-0.2918***-0.3614***-0.3414*** Land per capita-0.3066***-0.3352***-0.3478***-0.4738***-0.1746** Africa0.05530.11710.05540.1236 Asia0.28280.29980.18330.2311 LAC-0.0652-0.0309-0.1426-0.0863 HIC0.2605*0.3388**0.4837*-0.0298 Perennials-0.1315**-0.1492*** Animal Products 0.2589***0.2580*** Others-0.1764**-0.1956** Lagged Change in Border Prices-0.0025*** Lagged Change in Crop Area0.0083 Constant -1.8516* -2.0109***-1.6685*-2.1625**-2.0549* R2R2 0.1950 0.21000.22400.30200.1940 No. of obs. 25,59920,063 15,9829,932 Notes: The dependent variable is the commodity level NRA. Observations with a lagged change in border prices lower than -1000% were dropped from the sample. Results are OLS estimates, with clustered standard errors and significance levels shown at the 99% (***), 95% (**), and 90% (*) levels. Table 3. Hypothesis tests at the product level Time consistency Status-quo bias

17. Results: How much stabilization is achieved? When stabilizing, SI>0 SI<0 if gov’t is destabilizing Stabilization index over the 1961-2005 period, by income level Many governments actually destabilize prices Not much!

18. Results: Richer countries stabilize more Explanatory variables Model (1)(2)(3)(4)(5)(6) Income (log)5.6507***7.0059***7.4730***9.4113***8.8422* Importable6.5568*-7.1127-9.4289*-10.3265* Exportable1.5545-8.4469**-9.5703**-11.6999** Land per capita-9.8402**-9.4037**-9.6186** Income growth variation-444.8959-547.3185 Exchange rate variation2.0297***1.0391 Africa8.23321.1559 Asia15.2604**6.2383 Latin America-4.4882-10.931 High income countries -3.0503-1.5757 Constant-37.7412***4.6606**-40.9054**-44.9126**-75.4189***-53.9286 R2R2 0.0290.0050.0350.0470.0320.055 No. of obs.757766722 771724 Dropped obs.20116664 Notes: Dependent variable for all regressions is the Stabilization Index by country and product. Influential outliers were dropped from the sample based on the Cook's distance criteria [( K -1)/ N]. Results are OLS estimates, with clustered standard errors and significance levels shown at the 99% (***), 95% (**), and 90% (*) levels. Table 4. Determinants of the stabilization index Exportable crops and land-abundant countries have less stabilization Asia has more imports and less land, which explains high stabilization Another development paradox

19. More results: Since 1995, policies have moved closer to free-trade prices National average NRAs by income level, before and after the Uruguay Round agreement Flatter curves, closer to zero

20. Low-income Africa taxes farmers less, Higher-income Asia taxes consumers less National average NRAs by income level, before and after the Uruguay Round agreement Pro-farm reforms in lower-income Africa Pro-consumer reform in higher- income Asia

21. There has been less improvement in E. Europe-Central Asia or Latin America National average NRAs by income level, before and after the Uruguay Round agreement Less reform – lines are more similar

22. The biggest change has been in high-income countries National average NRAs by income level, before and after the Uruguay Round agreement US, EU and Japan: reforms and WTO commitments But current events could change the pattern: …will return of high food prices cause policy reversals? …how will the 2008 credit crisis affect policy choices?

23. Some conclusions Three stylized facts help explain policy choices: –A development paradox from taxing farmers to taxing consumers as incomes rise –An anti-trade bias from taxation of both imports and exports –A resource abundance effect against natural resources Three mechanisms help explain the income effect: –Rational ignorance when per-person costs are small –Improved governance from more checks and balances –Revenue motives for import taxes when financial systems are deeper

24. More conclusions Four other mechanisms help add to the income effect: –More people in the sector leads to more favorable policies –An end to entry of new farmers leads to more farm support –Crops with more sunk costs (perennials) are taxed more –Policy changes try to reverse the last year’s price changes Two widely-held views are not supported: –Policy changes do not try to reverse changes in area planted –Policy provides little price stabilization in poor countries  Status quo bias and price stabilization are not consistent characteristics of real-life policies; other explanations work better.

25. Finally… Policy relationships have changed over time –Relative to income levels, prices are now much closer to free trade than in the past, especially in Africa, Asia and the high income countries. The recent move to freer trade could be reversed –In particular, a return of 1970s-style food prices could easily cause a return to 1980s-style food policies. Policy outcomes are far from predetermined! –Our models explain less than half of the variation we see.