nutr 215: fundamentals of us agriculture us agricultural policy in a global context will masters 2...
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Nutr 215: Fundamentals of US Agriculture
US Agricultural Policy in a Global Context
Will Masters2 November 2010
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US Agricultural Policy in a Global Context:
What’s ahead today
• A lot of data• Three big ideas:
– The ‘development paradox’ in government choices, which is paradoxical because of…
– The structural transformation in economic activity, and the paradox can be explained by…
– The political economy of policy-making.
• Ample time for discussion
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Source: World Bank data, reprinted from UNEP/GRID-Arendal Maps and Graphics Library (http://maps.grida.no/go/graphic/world-bank-country-income-groups).
Where do we see what types of policy?
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This situation is called “the development paradox”
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Poor countries’ governments tax their farmers, while rich countries’ governments subsidize them
-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)
Average effect of policy on farm product prices, by income level across countries and over time, 1960-2005
Note: Data shown are regression lines and 95% confidence intervals through annual national-average NRAs for over 68 countries, covering more than 90% of world agriculture in each year from 1960 through 2005.
Source: W.A. Masters and A. Garcia, “Agricultural Price Distortion and Stabilization: Stylized Facts and Hypothesis Tests,” in K. Anderson, ed., Political Economy of Distortions to Agricultural Incentives. Washington, DC: The World Bank, 2010.
Support for non-farmers, at the expense of farmers(NRA<0)
Support for farmers, at theexpense of non-farmers (NRA>0 )
≈ $5,000/yr
GDP per person (log scale)
Nominal Rate of Assistance to farmers
(subsidies or taxesas a proportion of domestic prices)
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Average Nominal Rate of Protection for Agricultural Production in East Asia, 1955-2002
Source: K. Anderson (2006), “Reducing Distortions to Agricultural Incentives: Progress, Pitfalls and Prospects.” <www.worldbank.org/agdistortions>
The development paradox also occurs within countries
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Why is this pattern paradoxical?
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As people get richer, what happens to agriculture’s share of employment and earnings?
Source: Reprinted from World Bank, World Development Report 2008. Washington, DC: The World Bank (www.worldbank.org/wdr2008)
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Some of the transition from farming to nonfarm work is within agriculture, to specialized ‘agribusiness’
Source: Reprinted from World Bank, World Development Report 2008. Washington, DC: The World Bank (www.worldbank.org/wdr2008)
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Source: U.S. Economic Report of the President 2007 (www.gpoaccess.gov/eop)
Percent of workforce by sector in the United States, 1800-2005
in 1800, employment was 90% farming
today, about 80% of jobs are in services
in 1930s-70s, industry
reached about
40%
agricultural employment has stabilized
As the U.S. became richer, what’s happened to agriculture’s share of employment and earnings?
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This “structural transformation” out of agriculture, into industry and then services, occurs everywhere
Percent of GDP by sector in Australia, 1901-2000
Source: Government of Australia (2001), Economic Roundup – Centenary Edition, Department of the Treasury, Canberra.
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Agricultural Employment as a Share of Civilian Employment and Real Farm Output as a Share of Real GDP
SOURCE: U.S. Department of Commerce and the Federal Reserve Bank of St. Louis. Reprinted from K.L. Kliesen and W. Poole, 2000. "Agriculture Outcomes and Monetary Policy Actions: Kissin' Cousins?" Federal Reserve Bank of Sf. Louis Review 82 (3): 1-12.
As agriculture’s share of the economy declines, do farmers’ incomes also decline?
Until the 1930s, employment and output fell together
…then employment fell much faster than output
and then both stopped falling
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In the U.S., farm incomes fell and then rose,both absolutely and relative to nonfarm earnings
Source: BL Gardner, 2000. “Economic Growth and Low Incomes in Agriculture.” AJAE 82(5): 1059-1074.
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ollars
per
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Perc
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-farm
in
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Farm income fell…
then caught up
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The same pattern holds across countries: as national income rises, farm incomes fall then rise
-1
-.5
0.5
1
4 6 8 10 12LNGDPpc (Constant US$-2000)
Agri. GDP Share (LCU) Agri. Employment ShareAgri. GDP Share (LCU)minusAgri. Employment Share
Source: C.Peter Timmer, A World without Agriculture: The Structural Transformation in Historical Perspective. AEI Press, 2009 (www.aei.org/book/100002).
The gap worsens as incomes rise, then farmers catch up
The farm-nonfarm earnings gap in 86 countries, 1965-2000
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The story so far…
• Poor countries tax farmers and help non-farmers, while rich countries do the reverse– This is paradoxical, because in poor countries
• Most people are farmers (so we’d expect them to be influential)• Farmers are relatively poor (so we’d expect them to be helped)
• The underlying shift is “structural transformation”– Farming’s share of employment & earnings decline– Farmers’ incomes fall and then rise
• What can explain these changes?
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What can explain the structural transformationfrom agriculture to industry and then services?
• Consumers’ income growth?– Engel’s law and Bennett’s law: as income grows,
• demand for food rises less than for other things• demand for staple foods rises less than for higher-value foods
• Farmers’ new technology?– Cochrane’s Treadmill: new farm technologies
• increase output, lower prices and “push” farmers out
• Both of these can explain transformation only when there’s no trade, or for the world as a whole
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When there’s international trade, structural transformation can be explained by:• Consumers’ income growth & new farm technology
(can explain transformation only for the world as a whole)• Non-farm technology?
– The bright lights of the big city• offer an easier life and higher incomes, so “pull” farmers out
• Limited farmland?– When individual farmers succeed, they must either
• buy/rent land from neighbors, or invest in non-farm activity– People are continually choosing how much land to farm
• income from farming is: acres/worker X income/acre
farmers leave ag. ASAP, until incomes equalize
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Slide 18
So does the number of farmers fall over time?
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The number of farmers rises at first, then falls until farm income matches nonfarm earnings
Figure 5-3. Number and average size of farms in the United States, 1900-2002.
The textbook picture of structural transformation within agriculture:
farm numbers stabilized byoff-farm income and rising profits per acre;latest census shows slight rise in no. of farms
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The change in acreage per farmis closely linked to farm income
Th
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Perc
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-farm
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• This is very simple, but very surprising:• Is it because of total population growth?• Yes, but usually urban population growth is even faster.• But rural growth also depends on the initial urbanization level:
• If we divide the total workforce into farmers and nonfarmers:• Lf = Lt – Ln
(Li=no. of workers in sector i)
• And solve for the growth rate of the number of farmers:• %Lf = (%Lt – [%Ln•Sn]) / (Sf)
(Si=share of workers in i)
• We see:
Why does the number of farmers risebefore it falls?
Total pop. growth rate
Urban pop. growth rate
Urbanization level(note: Sf +Sf =100%)
Rural pop. growth rate
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Rate of growth in rural population, by relative size of the sector
proportion of workers who are farmers (Sf): 3/4 2/3 half 1/3
Country is poor but successful: nonfarm employment growth (%Ln) =6%, twice rate of workforce growth (%Lt) = 3%
+2% +1.5% 0.0% -3%
Applying the formula we just derived:%Lf = (%Lt – [%Ln•Sn]) / (Sf)
(Si=share of workers in i)
We see that even if non-farm employment grows very fast,the number of farmers may still rise quickly:
…then this decline continues until farm & nonfarm incomes equalize
This rise continues until cities become large and fast-growing enough to absorb all of the total population growth…
Why does the number of farmers risebefore it falls?
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The rise and eventual fall in number of farmersoccurs faster/earlier in more prosperous regions
Source: Reprinted from W.A. Masters, 2005. “Paying for Prosperity: How and Why to Invest in Agricultural R&D in Africa.” Journal of International Affairs 58(2): 35-64.
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How do governments respond to these changes?
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The structural transformation is closely linked to differences and changes in government policy
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)Source: Kym Anderson et al., 2009. Distortions to Agricultural Incentives database (www.worldbank.org/agdistortions).Notes: Each line shows data from all available countries in each year from 1961 to 2005 (total n=2520), smoothed with confidence intervals using Stata’s lpolyci. Income per capita is expressed in US$ at 2000 PPP prices.
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How do we even know what governments doto influence food prices and farm income?
– We can imagine two possible approaches:• Add up influence of observed tariffs, subsidies and other transfers
– This is the OECD’s “Producer Subsidy Equivalent” approach– Works well for industrialized countries that are subsidizing agriculture
• Infer influence from observed market prices– This is the World Bank’s tariff-equivalent “distortions” approach– Needed to compare large numbers of developing and developed countries
– Both approaches lead to the same answer:• Policy effects are differences between domestic and foreign prices
– Policy effects = domestic prices – foreign prices ± cost of transport etc.– Domestic prices may be raised or lowered by policy– Foreign prices are each product’s opportunity cost in trade
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Notation for the tariff-equivalence approachto policy measurement, used by World Bank
for Distortions to Agricultural Incentives
• Tariff-equivalent ‘Nominal Rate of Assistance’in domestic prices relative to free trade:
• Occasionally estimated directly from observed policy:
• More often imputed by price comparison:taxesNRA
worldfree PExchRateMktingCostP *)1(
free
freedom
P
PPNRA
-º
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Procedures and results fromDistortions to Agricultural Incentives
• A 3-year project:– 100+ researchers and case studies for 68 countries, 77 commodities over 40+ years; in
total have over 25,000 policy measurements.
• Project results published in six books – Four volumes of country narratives
• Africa, Asia, LAC and European Transition– Two global volumes
• Regional syntheses and simulations• Political economy explanations for policy choices
– Some of today’s results are from W.A. Masters and A.F. Garcia (2010), “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 as e-books at www.worldbank.org/agdistortions
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Countries covered by Distortions to Agricultural Incentives
No. 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
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Commodities covered by Distortions to Agricultural Incentives
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
Note: Totals above are for the top 30 global commodities; An additional 47 other products also appear in the dataset.
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The results: Distortions have grown and shrank
-200
-100
0
100
200
300
1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07
-200
-100
0
100
200
300
1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07
Developing countries (no averages for periods 1955-59 and 2005-07)
High-income countries and Europe's transition economies
Net, global (decoupled payments are included in the higher, dashed line)
Con
stan
t 200
0 U
S$
(bil
lion
s)
Source: Anderson, K. (forthcoming), Distortions to Agricultural Incentives: A Global Perspective, 1955 to 2007, London: Palgrave Macmillan and Washington DC: World Bank.
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-50
-30
-10
10
30
50
70
90
1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04
Import-competing Exportables Total
Importables
Total
Exportables
High-income countries plus Europe’s transition economies
Per
cent
0
Policy reforms have reduced both anti-farm and anti-trade biases
Importables
TotalExportables
Developing countries
Per
cent
Source: Anderson, K. (forthcoming), Distortions to Agricultural Incentives: A Global Perspective, 1955 to 2007, London: Palgrave Macmillan and Washington DC: World Bank.
This gap is anti-trade bias
This level is anti-farm bias
High-income countries’ biases have also shrunk
0
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On average, Africa has had very large and sustained reforms since the 1990s
Source: K.Anderson and W. Masters (eds), Distortions to Agricultural Incentives in Africa. Washington, DC: The World Bank, 2009.
Importable products
Exportable products
All farm products
Smaller anti-trade bias since 1990s
Smaller anti-farm bias
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Source: K.Anderson and W. Martin (eds), Distortions to Agricultural Incentives in Asia. Washington, DC: The World Bank, 2009.
Importable products
Exportable products
All farm products
Asia has large pro-farm shift; ending net export taxes in 1990s, net support to ag. since 1980s
No more anti-farm bias since 1980s
No more net export taxation since 1990s
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Source: K.Anderson and A. Valdes (eds), Distortions to Agricultural Incentives in Latin America. Washington, DC: The World Bank, 2009.
Importable products
Exportable products
All farm products
Latin America has had similar trends at a slower pace,supporting ag. since 1990s
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Reform paths vary within regionsExamples in Africa
Countries’ total NRA for all tradable farm products, 1955-2004
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Reform paths vary within regionsExamples in Asia
Countries’ total NRA for all tradable farm products, 1955-2004
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Reform paths vary within regionsExamples in Latin America
Countries’ total NRA for all tradable farm products, 1955-2004
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Reform paths vary within regionsExamples among High-Income Countries
Countries’ total NRA for all tradable farm products, 1955-2004
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-1
.0-0
.50
.00
.51
.01
.5
6 8 10 6 8 10
All Primary Products Tradables
All Primary Products Exportables Importables
NR
A
Income per capita (log)
National average NRAs by real income per capita, with 95% confidence bands
Source: Author’s calculations, from data available at www.worldbank.org/agdistortions. 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.
(≈$22,000/yr)(≈$400/yr) (≈$3,000/yr)
Anti-farm bias ends at about
$5,000/yr
Anti-trade bias ends above $12,000/yr
To explain and predict policy change, we’ll need to merge regions and test hypotheses
A key variable will be per-capita income
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To explain and predict policy, we’ll need to think carefully about who benefits
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Farm policy is not a pretty sight!
Note this cartoon is from the U.S. in 2002; similar farm policies are supported by all political parties.
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Modern political economy:Two main theories
• ‘Positive’ or ‘neoclassical’ political economy focuses on:– government as a marketplace for competing interests, where
• observed policies reveal the balance of power, and• power results from people (or firms) deciding to invest in politics
• Why would some invest more than others in politics?– If the answer were just income and wealth, we’d see free trade!
• To explain what we actually see, two of the main theories are:– Size of potential gains from politics (per person or per firm)
• Larger impact = more incentive to invest• Small impacts = ‘rational ignorance’? (Downs 1954)
– Size of interest group (number of people or firms)• Larger group = more ‘free-ridership’ (Olson 1965)• Smaller group = easier cooperation, but offset by fewer votes?
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Modern political economy:Size of gains and the rational ignorance of losers
• The basic idea of “rational ignorance” is that– learning about and participating in political action is costly,– so people won’t, unless it’s worthwhile to do so
• Some implications of this model are that: – only those with relatively large stakes will participate in politics;– if people have similar and large stakes, they can lobby together;– the costs of participation can have a decisive influence;
• if political information is easier to get, and• if political participation is easier to do, • then outcomes will be more economically efficient
– …but participants in politics may deliberately choose confusing and ambiguous policies, to raise the costs of participation!
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Modern political economy:Size of interest groups and free ridership
• The basic idea of the “interest-group” approach is that– policy choices are inherently collective actions, – so obtaining desired policies requires limiting free-ridership
• Some implications of the interest-group approach are that people will invest more in politics if they:– are few in number (so each is less likely to free-ride)– are fixed in number (so new entrants won’t free-ride)
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Modern political economy:Five other theories
• Rent seeking – Mainly due to Anne Krueger (1974) on Turkish trade policy – Checks and balances can offset interest groups
• Time consistency – Due to Kydland and Prescott (1977) on inflation and central banks– Government can do only what it can credibly commit to keep doing
• Loss aversion – Due to Kahnemann and Tversky (1979) in psychology– People hate losses more than they love gains
• The resource curse– Many authors, mainly from experience with minerals and oil– Governments exploit what’s abundant
• Anti-trade bias and revenue motives– Many authors: governments tax what they can
• Of course there are also plenty of other, less influential theories…
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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
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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
Rentseeking
Revenue Motives
Rent
dissipation
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Results:Specific hypotheses at the product level
Explanatory variablesModel
(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 9,932
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
Loss aversion
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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
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-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
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To focus on high-income countries, we use the OECD ‘PSE’ data
OECD members are:
Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, UK and US.
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Comparison of policy measures over time
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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Comparison of PSEs across countries
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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Composition of PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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Composition of PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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EU and US PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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EU and US PSEs by commodity
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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Some conclusions• Where and when do we see what types of policy?
– The ‘development paradox’: as countries get richer, governments switch from taxing to subsidizing farmers;
– despite structural transformation that makes farmers become both fewer and richer than non-farmers;
– the political economy of policy-making can explain this, as structural transformation changes the stakes:• Once the number of farms stops growing (eg 1914 in the US)• each farmer’s stake in policymaking rises sharply,
so they become very actively engaged• each non-farmer’s stake in policy outcomes declines
so they don’t object and may like it for cultural reasons
• Of course, this is just one set of data & approaches!– What else is going on?
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