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
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
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?
This situation is called “the development paradox”
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)
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
Why is this pattern paradoxical?
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)
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)
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?
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.
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
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.
Th
ou
san
ds o
f 1
99
2 d
ollars
per
farm
Perc
en
t of
non
-farm
in
com
e
Farm income fell…
then caught up
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
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?
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
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
Slide 18
So does the number of farmers fall over time?
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
The change in acreage per farmis closely linked to farm income
Th
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. of
19
92
dollars
p
er
farm
Perc
en
t of
non
-farm
in
com
e
• 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
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?
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.
How do governments respond to these changes?
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.
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
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
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
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
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.
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.
-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
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
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
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
Reform paths vary within regionsExamples in Africa
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regionsExamples in Asia
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regionsExamples in Latin America
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regionsExamples among High-Income Countries
Countries’ total NRA for all tradable farm products, 1955-2004
-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
To explain and predict policy, we’ll need to think carefully about who benefits
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.
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?
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!
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)
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…
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
Rentseeking
Revenue Motives
Rent
dissipation
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
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
-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
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.
Comparison of policy measures over time
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
Comparison of PSEs across countries
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
Composition of PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
Composition of PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
EU and US PSEs by policy instrument
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
EU and US PSEs by commodity
Source: OECD (2010), Agricultural Policies in OECD Countries: At A Glance. Paris: OECD.
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?
