Explaining Food and Agricultural Policy:

New Data and Hypothesis Tests

Will Masterswmasters@purdue.edu

www.agecon.purdue.edu/staff/masters

Friedman School Seminar24 September 2008

Motivation: the development paradox

Motivation: the development paradox

Why?Why?

Meanwhile…

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/agdistortions

Country coverageNo. of Percentage of world

countries Pop. GDP Ag.GDP

Africa 16 10 1 6

Asia 12 51 11 37

LAC 8 7 5 8

ECA 13 6 3 6

HIC 19 14 75 33

Total 68 91 95 90

Commodity coverage (top 30 products only)

No. of Percentage of world

Products Production Exports

Cereal Grains 10 84 90

Oilseeds 6 79 85

Tropical crops 7 75 71

Livestock products 7 70 88

Total 30 75 85

The method: price distortions from “stroke of the pen” policies

• Tariff-equivalent Nominal Rate of Assistancein 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 deviationsaround trend prices:

tNRA

fd PEmP *)1(

100

)ˆ(

)ˆ()ˆ(

f

df

Psd

PsdPsdSI

f

fd

P

PPNRA

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

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)

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)

-1.0

-0.5

0.0

0.5

1.0

1.5

6 8 10 6 8 10

All Primary Products Tradables

All Primary Products Exportables Importables

NR

A

Income per capita (log)

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 consumersNRA>0

Export taxes with import restrictions = anti-trade bias

Results:A new view of policy change over time

Average NRAs for all products by year, with 95% confidence bands

-10

12

-10

12

1960 1970 1980 1990 2000

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

AFRICA ASIA (excl. Japan) ECA

HIC LAC

All Primary Products (incl. Nontradables)

Heavy taxes on farmers in 1970s

then reformHeavy taxes on consumers in the 1980s, then reform

Increased taxes on consumers in 1990s

Results:A new view of policy change over time

-10

12

-10

12

1960 1970 1980 1990 2000

1960 1970 1980 1990 2000 1960 1970 1980 1990 2000

AFRICA ASIA (excl. Japan) ECA

HIC LAC

Importables Exportables

Average NRAs for importables and exportables by year, with 95% confidence bands

Heavy taxes on exports in 1970s

then reformwith varied

import restrictions

Trend away from taxes on exports,

with rising import restrictions

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*** Africa 0.0651 Asia 0.1404*** Latin Am. & Car. (LAC) -0.1635*** High inc. cos. (HIC) 0.4311*** Importable 0.1650* Exportable -0.2756***Constant -2.6759*** -2.8159*** -2.0352*** -1.9874*** -2.0042***R2 0.28 0.363 0.418 0.827 0.152No. of obs. 2,520 2,269 2,269 2,520 28,118

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

paradoxThe

resource

curse

Some

regional

differencesAnti-trade

bias

Results:Specific hypotheses at the country level

(1) (2) (3) (4) (5) (6) (7)Total NRA for: All Prods. All Prods. All Prods. |All Prods.| Exportables Importables All Prods.

Explanatory variablesIncome (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*** -0.0628 0.0844** Asia 0.1404*** 0.2087*** 0.1355*** 0.1007** -0.1791*** 0.0217 0.1684***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***R2 0.4180 0.45 0.437 0.294 0.373 0.397 0.419No. of obs. 2,269 1,326 2,269 1,631 1,629 1,644 2,269Notes: 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

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** Importable 0.0549 0.0048 -0.0061 0.1106 0.0331Exportable -0.2921*** -0.3028*** -0.2918*** -0.3614*** -0.3414***Land per capita -0.3066*** -0.3352*** -0.3478*** -0.4738*** -0.1746** Africa 0.0553 0.1171 0.0554 0.1236Asia 0.2828 0.2998 0.1833 0.2311LAC -0.0652 -0.0309 -0.1426 -0.0863HIC 0.2605* 0.3388** 0.4837* -0.0298Perennials -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 Area 0.0083Constant -1.8516* -2.0109*** -1.6685* -2.1625** -2.0549* R2 0.1950 0.2100 0.2240 0.3020 0.1940 No. of obs. 25,599 20,063 20,063 15,982 9,932Notes: 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

Results:How much stabilization is achieved?

-20

0-1

00

01

00-2

00

-10

00

100

6 8 10 6 8 10

All Primary Products Exportables

Importables Non Tradables

Africa Rest of the World

SI

Income per capita (log)

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!

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* Importable 6.5568* -7.1127 -9.4289* -10.3265* Exportable 1.5545 -8.4469** -9.5703** -11.6999** Land per capita -9.8402** -9.4037** -9.6186** Income growth variation -444.8959 -547.3185Exchange rate variation 2.0297*** 1.0391Africa 8.2332 1.1559Asia 15.2604** 6.2383Latin America -4.4882 -10.931High income countries -3.0503 -1.5757Constant -37.7412*** 4.6606** -40.9054** -44.9126** -75.4189*** -53.9286R2 0.029 0.005 0.035 0.047 0.032 0.055No. of obs. 757 766 722 722 771 724Dropped obs. 20 11 6 6 6 4Notes: 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

More results: Since 1995, policies have

moved closer to free-trade pricesNational average NRAs by income level, before and after the Uruguay Round agreement

-10

12

3

6 7 8 9 10 6 7 8 9 10 6 7 8 9 10

All Exportables Importables

1960-1994 1995-2005

NR

A

Income per capita (log)

Flatter curves, closer to zero

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-1

01

23

-10

12

3

6 7 8 9 10 6 7 8 9 10 6 7 8 9 10

AFRICA, All AFRICA, Exportables AFRICA, Importables

ASIA, All ASIA, Exportables ASIA, Importables

1960-1994 1995-2005

NR

A

Income per capita (log)

Pro-farm reforms in lower-income Africa

Pro-consumer reform in higher-income Asia

There has been less improvement in E. Europe-Central Asia or Latin AmericaNational average NRAs by income level, before and after the Uruguay Round agreement

-10

12

3-1

01

23

6 7 8 9 10 6 7 8 9 10 6 7 8 9 10

ECA, All ECA, Exportables ECA, Importables

LAC, All LAC, Exportables LAC, Importables

1960-1994 1995-2005

NR

A

Income per capita (log)

Less reform – lines are more similar

-10

12

3-1

01

23

6 7 8 9 10 6 7 8 9 10 6 7 8 9 10

AFRICA, All AFRICA, Exportables AFRICA, Importables

HIC, All HIC, Exportables HIC, Importables

1960-1994 1995-2005

NR

A

Income per capita (log)

22,0003,0001,000 8,000400 22,0003,0001,000 8,000400 22,0003,0001,000 8,000400

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?

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

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 countriesStatus quo bias and price stabilization are not

consistent characteristics of real-life policies; other explanations work better.

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.