Download AGEC 640 * Agricultural Policy

Nutr 215: Fundamentals of US Agriculture
US Agricultural Policy
in a Global Context
Will Masters
2 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
Where do we see what types of policy?
Source: World Bank data, reprinted from UNEP/GRID-Arendal Maps and Graphics Library
(http://maps.grida.no/go/graphic/world-bank-country-income-groups).
This situation is called “the development paradox”
Poor countries’ governments tax their farmers,
while rich countries’ governments subsidize them
0.5
-0.5
NRA
(subsidies or taxes
as a proportion of
domestic prices)
Support for farmers, at the
expense of non-farmers
(NRA>0 )
0.0
Nominal Rate of
Assistance
to farmers
1.0
1.5
Average effect of policy on farm product prices, by
income level across All
countries
and over time, 1960-2005
Primary Products
Tradables
-1.0
≈ $5,000/yr
Support for non-farmers,
at the expense of farmers
(NRA<0)
6
8
10
6
8
10
GDP
per person
(log scale)
Income per capita (log)
Note: Data shown are regression lines and 95% confidence intervals through annual national-average NRAs for over 68
All Primary
Products
Exportables
Importables
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.
The development paradox also occurs within countries
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>
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)
As the U.S. became richer, what’s happened to
agriculture’s share of employment and earnings?
Percent of workforce by sector in the United States, 1800-2005
today, about 80% of
jobs are in services
in 1800,
employment
was 90%
farming
in 1930s-70s,
industry
reached
about
40%
agricultural
employment has stabilized
Source: U.S. Economic Report of the President 2007 (www.gpoaccess.gov/eop)
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.
As agriculture’s share of the economy declines,
do farmers’ incomes also decline?
Agricultural Employment as a Share of Civilian Employment and
Real Farm Output as a Share of Real GDP
Until the 1930s,
employment and
output fell together
and then both
stopped falling
…then employment fell
much faster than output
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.
Source: BL Gardner, 2000. “Economic Growth and Low Incomes in Agriculture.” AJAE 82(5): 1059-1074.
Thousands of 1992 dollars per farm
Percent of non-farm income
In the U.S., farm incomes fell and then rose,
both absolutely and relative to nonfarm earnings
The same pattern holds across countries:
as national income rises, farm incomes fall then rise
-.5
0
.5
1
The farm-nonfarm earnings gap in 86 countries, 1965-2000
-1
The gap worsens as incomes rise, then farmers catch up
4
6
8
10
LNGDPpc (Constant US$-2000)
12
Agri. GDP Share (LCU)
Agri. Employment Share
Agri. 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 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 transformation
from 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
So does the number of farmers fall over time?
Slide 18
The number of farmers rises at first, then falls
until farm income matches nonfarm earnings
The textbook picture of structural transformation within agriculture:
farm numbers stabilized by
off-farm income and rising profits per acre;
latest census shows slight rise in no. of farms
Figure 5-3. Number and average size of farms in the United States, 1900-2002.
Thou. of 1992 dollars per farm
Percent of non-farm income
The change in acreage per farm
is closely linked to farm income
Why does the number of farmers rise
before it falls?
• 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:
Rural pop.
growth rate
Total pop.
growth rate
Urban pop.
growth rate
Urbanization level
(note: Sf +Sf =100%)
Why does the number of farmers rise
before it falls?
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:
Rate of growth in rural population, by relative size of the sector
proportion of workers
who are farmers (Sf):
Country is poor but successful:
nonfarm employment growth (%Ln) =6%,
twice rate of workforce growth (%Lt) = 3%
This rise continues until cities become
large and fast-growing enough to absorb
all of the total population growth…
3/4
2/3
half
1/3
+2% +1.5% 0.0%
-3%
…then this decline
continues until farm &
nonfarm incomes equalize
The rise and eventual fall in number of farmers
occurs 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
ASIA (excl. Japan)
ECA
-1
0
1
2
AFRICA
1960
1980
1990
2000
LAC
-1
0
1
2
HIC
1970
1960
1970
1980
1990
2000
1960
1970
1980
1990
2000
Source: Kym Anderson et al., 2009. Distortions toAllAgricultural
Incentives database (www.worldbank.org/agdistortions).
Primary Products (incl. Nontradables)
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 do
to 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 approach
to policy measurement, used by World Bank
for Distortions to Agricultural Incentives
• Tariff-equivalent ‘Nominal Rate of Assistance’
Pdom- P free
in domestic prices relative to free trade: NRA 
P free
• Occasionally estimated directly from observed policy:
NRA  taxes
• More often imputed by price comparison:
Pfree  (1  MktingCost)  ExchRate*  Pworld
Procedures and results from
Distortions 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
Africa
No. of
countries
16
Percentage of world
Pop.
GDP
Ag.GDP
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
Cereal Grains
No. of
Products
10
Percentage of world
Production
Exports
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.
300
200
The results:
Distortions have grown and shrank
300
Constant 2000 US$ (billions)
100
200
0
100
-100
0
-200
-100
1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07
-200
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)
economies
transition
Europe's
and
High-income
Developingcountries
countries (no
averages
for periods
1955-59
and 2005-07)
High-income
countries payments
and Europe'sare
transition
economies
in the higher, dashed line)
included
(decoupled
Net, global
Net, global (decoupled payments are included in the higher, dashed line)
Source: Anderson, K. (forthcoming), Distortions to Agricultural Incentives: A Global Perspective,
1955 to 2007, London: Palgrave Macmillan and Washington DC: World Bank.
Policy reforms have reduced both
anti-farm and anti-trade biases
Percent
Developing
countries
0
Importables
Total
Exportables
This gap is anti-trade bias
This level is anti-farm bias
90
Percent
70
50
High-income countries plus
Europe’s transition economies
High-income countries’
biases have also shrunk
Importables
30
Total
10
Exportables
0
-10 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04
Source: Anderson, K. (forthcoming), Distortions to Agricultural Incentives: A Global Perspective,
-30
1955 to 2007, London: Palgrave Macmillan and Washington DC: World Bank.
On average, Africa has had very
large and sustained reforms since
the 1990s
Importable
products
All farm
products
Exportable
products
Smaller
anti-trade bias
since 1990s
Smaller
anti-farm bias
Source: K.Anderson and W. Masters (eds), Distortions to Agricultural Incentives in Africa. Washington,
DC: The World Bank, 2009.
Asia has large pro-farm shift;
ending net export taxes in 1990s,
net support to ag. since 1980s
Importable
products
All farm
products
No more net export taxation
Exportable since 1990s
No more anti-farm bias since 1980s
products
Source: K.Anderson and W. Martin (eds), Distortions to Agricultural Incentives in Asia. Washington, DC:
The World Bank, 2009.
Latin America has had
similar trends at a slower pace,
supporting ag. since 1990s
Importable
products
All farm
products
Exportable
products
Source: K.Anderson and A. Valdes (eds), Distortions to Agricultural Incentives in Latin America.
Washington, DC: The World Bank, 2009.
Reform paths vary within regions
Examples in Africa
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regions
Examples in Asia
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regions
Examples in Latin America
Countries’ total NRA for all tradable farm products, 1955-2004
Reform paths vary within regions
Examples among High-Income Countries
Countries’ total NRA for all tradable farm products, 1955-2004
To explain and predict policy change,
we’ll need to merge regions and test hypotheses
A key variable will be per-capita income
National average NRAs by real income per capita, with 95% confidence bands
Tradables
0.0
-0.5
NRA
0.5
1.0
1.5
All Primary Products
Anti-trade bias
ends above
$12,000/yr
-1.0
Anti-farm bias
ends at about
$5,000/yr
6
(≈$400/yr)
8
10
6
Income per capita (log)
All Primary Products
Exportables
8
(≈$3,000/yr)
10
(≈$22,000/yr)
Importables
Source: Author’s calculations, from data available at www.worldbank.org/agdistortions. Each line shows
data from 66 countries in each year from 1961 to 2005 (n=2520), smoothed with confidence intervals using
Stata’s lpolyci at bandwidth 1 and degree 4. Income per capita is expressed in US$ at 2000 PPP prices.
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
Income (log)
Land per capita
Africa
Asia
Latin Am. & Car. (LAC)
High inc. cos. (HIC)
Importable
Exportable
Constant
R2
No. of obs.
(1)
(2)
0.3420***
0.3750***
-0.4144***
-2.6759***
0.28
2,520
-2.8159***
0.363
2,269
Model
(3)
0.2643***
-0.4362***
0.0651
0.1404***
-0.1635***
0.4311***
-2.0352***
0.418
2,269
(4)
(5)
0.2614***
0.2739***
-1.9874***
0.827
2,520
0.1650*
-0.2756***
-2.0042***
0.152
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.
Results:
Specific hypotheses at the country level
Table 2. Hypothesis tests at the country level
(1)
(2)
Total NRA for: All Prods. All Prods.
Explanatory variables
Income (log)
0.2643*** 0.1234***
Land per capita
-0.4362*** -0.2850***
Africa
0.0651
0.1544***
Asia
0.1404*** 0.2087***
LAC
-0.1635*** -0.0277
HIC
0.4311*** 0.2789***
Policy transfer cost per rural person
-0.0773*
Policy transfer cost per urban person
-1.2328***
Rural population
Urban population
Checks and balances
Monetary depth (M2/GDP)
Entry of new farmers
Constant
-2.0352*** -0.9046**
R2
0.4180
0.45
No. of obs.
2,269
1,326
(3)
(4)
(5)
(6)
(7)
All Prods. |All Prods.| Exportables Importables All Prods.
0.3175***
-0.4366***
0.0964**
0.1355***
-0.1189***
0.4203***
0.1913***
-0.4263***
0.2612***
0.1007**
-0.0947***
0.3761***
0.2216***
-0.7148***
-0.1071***
-0.1791***
-0.2309***
1.0694***
0.1142***
-0.6360***
-0.0628
0.0217
-0.1780***
0.8807***
0.2461***
-0.4291***
0.0844**
0.1684***
-0.1460***
0.4346***
1.4668***
-3.8016***
-0.0173***
-0.0310*** -0.0401***
-2.4506*** -1.2465*** -1.5957***
0.437
0.294
0.373
2,269
1,631
1,629
-0.4652*
0.397
1,644
-0.0737*
-1.8575***
0.419
2,269
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.
Results:
Specific hypotheses at the product level
Table 3. Hypothesis tests at the product level
Explanatory variables
Income (log)
Importable
Exportable
Land per capita
Africa
Asia
LAC
HIC
Perennials
Time
Animal Products
Others
Lagged Change in Border Prices
Lagged Change in Crop Area
Constant
R2
No. of obs.
(1)
0.2605**
0.0549
-0.2921***
-0.3066***
0.0553
0.2828
-0.0652
0.2605*
consistency
(2)
0.2989***
0.0048
-0.3028***
-0.3352***
-0.1315**
0.2589***
-0.1764**
Model
(3)
0.2363**
-0.0061
-0.2918***
-0.3478***
0.1171
0.2998
-0.0309
0.3388**
-0.1492***
0.2580***
-0.1956**
Loss aversion
-1.8516*
0.1950
25,599
-2.0109***
0.2100
20,063
-1.6685*
0.2240
20,063
(5)
0.3160**
0.1106
-0.3614***
-0.4738***
0.0554
0.1833
-0.1426
0.4837*
(6)
0.2804**
0.0331
-0.3414***
-0.1746**
0.1236
0.2311
-0.0863
-0.0298
-0.0025***
-2.1625**
0.3020
15,982
0.0083
-2.0549*
0.1940
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.
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
Exportables
Importables
1
0
-1
NRA
2
3
All
Flatter curves, closer to zero
6
7
8
9
10
6
7
8
9
10
6
Income per capita (log)
1960-1994
1995-2005
7
8
9
10
AFRICA, All
AFRICA, Exportables
AFRICA, Importables
-1
0
1
2
3
The biggest change has been
in high-income countries
National average NRAs by income level, before and after the Uruguay Round agreement
HIC, All
HIC, Exportables
HIC, Importables
1
0
-1
NRA
2
3
US, EU and Japan: reforms and WTO commitments
6
7
8
9
400
1,000
3,000
8,000
10
22,000
6
7
8
9
400
1,000
3,000
8,000
10
22,000
6
7
8
9
400
1,000
3,000
8,000
Income per capita (log)
1960-1994
1995-2005
10
22,000
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?