Food and Agricultural Policy: Stylized Facts and Hypothesis Tests

1
Food and Agricultural Policy Stylized Facts and
Hypothesis Tests

• Will Masters
• wmasters_at_purdue.edu
• www.agecon.purdue.edu/staff/masters

Andres Garcia andres_at_purdue.edu www.andresgarcia.
net
28 May 2008
2
What is this?

• Outcome of a 3-year project led by Kym Anderson,
  involving 100 researchers
• Uses case studies for 68 countries, offering
  comparable data for 77 commodities over 40 years
• Project results to be published in six books
• Four volumes of country narratives
• Africa Asia Latin America Caribbean European
  Transition
• Two global volumes
• One with regional syntheses and reform
  simulations
• One with political economy explanations for
  policy choices
• Final version of dataset to be released June 30th

3
Country coverage
No. of Percentage of world Percentage of world 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
4
Commodity coverage (top 30 products only)
No. of Percentage of world 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
5
The data price distortions from stroke of the
pen policies

• Tariff-equivalent Nominal Rates of Protection
• Sometimes estimated directly from observed
  policy
• More often imputed by price comparison
• We also introduce a new stabilization index

6
Our approach

• Use dataset to test for
• stylized facts
• use a few broadly-defined variables to capture
  many different policymaking mechanisms
• specific political-economy mechanisms
• add particular regressors implied by specific
  theories
• these could explain residuals and add explanatory
  power, or explain the stylized facts themselves
• most tests are weak only in one case do we have
  a strong identification strategy

7
The three stylized facts

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

8
Six specific hypotheses

• We test for six possible microfoundations of
  policy failure
• Rational ignorance when per-person effects are
  small
• Free ridership when groups of people are large
  (versus median-voter 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 (versus
  simultaneous choices)
• Status-quo bias from loss aversion or
  conservative social welfare functions in politics

9
One new hypothesis

• Another possible microfoundation could be
• Rent dissipation from the entry of new farmers
  (as opposed to free riding among
  existing farmers)
• Builds on Hillman (1982) and Baldwin (2002), who
  used this idea to explain protection of declining
  industries
• Agriculture is not declining in profitability or
  output, but it does have a natural barrier to
  entry
• except that pop. growth brings new farmers,
  until nonfarm employment is large and
  fast-growing enough.
• Entry of new farmers ended c.1910 in US, c.1950
  Japan, c.1970 Korea, c. 1990 Mexico, etc.

10
Stylized facts the development paradox and
anti-trade bias
National average NRAs and real income per capita,
with 95 confidence bands
We aim to account for nonlinearity in the
regression line, and also dispersion around it,
as well as the NRA-income relationship itself
Notes Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 4. Income per capita is expressed in
I (2000 constant prices).
11
The stylized facts, with land abundance(Our base
regression line)
Stylized facts of the commodity-level NRA
Explanatory variables Model Model Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5) (6) (7)
               
Income (log) 0.2553 0.2277 0.2549 0.2186 0.3383 0.3700 0.2841
Importable 0.1939 0.1136 0.0884
Exportable -0.1685 -0.1859 -0.1875
Land per capita -0.2508 -0.2467 -0.4084 -0.4281

Africa 0.0289 0.0956
Asia 0.2955 0.2253
Latin America -0.0638 -0.1442
High income countries High income countries 0.1856 0.4152
Constant -1.8905 -1.6868 -1.7727 -1.5653 -2.6407 -2.7731 -2.2228
R2 0.0848 0.1152 0.1349 0.1456 0.2705 0.3467 0.3989
No. of obs. 27600 27600 24900 24900 2484 2233 2233
Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent. Notes NRA by commodity is the dependent variable for models 1-4 is and covered total NRA for models 5-7. Results are OLS estimates with robust standard errors and significance levels at 99 (), 95 (), and 90 () levels. Income per capita is expressed in I (2000 constant prices). Europe and Central Asia region is the omitted continent.
12
The stylized facts are (slightly) different
within regions
National average NRAs and real income per capita,
by region
Notes Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 4. Income per capita is expressed in
I (2000 constant prices).
13
The base regression would be (slightly) different
within regions
Stylized facts of the commodity-level NRA, by
region
Explanatory variables Model Model Model Model Model
Explanatory variables Asia Africa LAC ECA HIC
           
Income (log) 0.3049 0.0479 -0.0917 0.0261 0.2872
Importable 0.4250 0.0854 0.3652 0.5642 -0.1642
Exportable -0.2603 -0.2447 -0.0225 0.3361 -0.2902
Land per capita -0.2647 0.0147 -0.1836 -0.6287 -0.2683
Constant -2.1702 -0.3576 0.7973 -0.1454 -1.8368
R2 0.2371 0.0882 0.1363 0.1438 0.0475
No. of obs. 3124 5471 2666 1646 12000
Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is NRA by commodity and year. Results are OLS estimates, with significance levels shown at the 99 (), 95 (), and 90 () levels.
14
There is also (some) time-series variation we can
exploit
National average NRA over time, by region
Note Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 2.
15
Can we explain trend changes in the stylized
facts, e.g. less anti-trade bias?
National average NRA over time, by region and
trade direction
Note Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 2.
16
Some of our hypotheses will be tested through the
absolute value of NRA
Distortions (absolute value of NRA) and real
income
Note Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 2.
17
We find strong support for rational ignorance,as
larger costs (benefits) per capita are linked to
smaller (larger) NRA values
Rational ignorance and group size effects among
rural and urban constituents
Explanatory variables Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5)

Policy transfer cost per rural person -0.3685 -0.3919 -0.3746 -0.3926 -0.3901
Policy transfer cost per urban person -1.4710 -1.4956 -1.4561 -1.4816 -1.4673
Rural population -0.0091
Urban population -0.8613

Income (log) 0.1969 0.1846 0.1722 0.1895 0.1982
Land per capita -0.2054 -0.2191 -0.2183 -0.2276 -0.2288
Asia 0.1969 0.2775 0.3120
Africa 0.1128 0.2467 0.2580
LAC -0.0865 -0.0009 0.0135
HIC 0.2487 0.2341 0.2409
Constant -1.4559 -1.2536 -1.3766 -1.4941 -1.5570
R2 0.7297 0.6922 0.7509 0.7064 0.7086
No. of obs. 2233 1328 2233 1328 1328
Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Models 2, 4, and 5 only use positive NRAs. Results are OLS estimates with significance shown at the 99 (), 95 (), and 90 () levels.
18
We find larger groups get more favorable
policies median-voter effects outweigh
free-ridership
Group size effects among rural and urban
constituents
  Model Model Model Model
Explanatory variables (1) (2) (3) (4)

Rural Population 1.0870 0.3759 1.8685 1.2747
Urban Population -2.4613 -2.0620 -4.4979 -3.6532

Income (log) 0.2827 0.2440 0.4212 0.3428
Importable 0.1165 0.0682
Exportable -0.1682 -0.1973
Land per capita -0.2568 -0.2496 -0.4060 -0.4238
Asia 0.3452 0.2564
Africa 0.0361 0.1449
LAC -0.0393 -0.0924
HIC 0.1741 0.3990
Constant -1.9911 -1.7373 -3.1573 -2.6962
R2 0.1395 0.1493 0.3743 0.4163
No. of obs. 24900 24900 2233 2233
Notes Commodity level NRA is the dependent variable for models 1 and 2. Covered average NRA is the dependent variable for models 3 and 4. Rural and urban population are expressed in millions. Results are OLS estimates with significance levels shown at 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1 and 2. Covered average NRA is the dependent variable for models 3 and 4. Rural and urban population are expressed in millions. Results are OLS estimates with significance levels shown at 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1 and 2. Covered average NRA is the dependent variable for models 3 and 4. Rural and urban population are expressed in millions. Results are OLS estimates with significance levels shown at 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1 and 2. Covered average NRA is the dependent variable for models 3 and 4. Rural and urban population are expressed in millions. Results are OLS estimates with significance levels shown at 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1 and 2. Covered average NRA is the dependent variable for models 3 and 4. Rural and urban population are expressed in millions. Results are OLS estimates with significance levels shown at 99 (), 95 (), and 90 () levels.
19
We find both rent seeking and revenue motives
More constraints on government and a more
monetized economy are linked to less distortion
Rent-seeking and revenue-motive effects
Explanatory variables Model Model Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5) (6) (7)

WB Governance Indicators -0.0799 0.0000 -0.0778
Freedom Hse Econ. Fr. Index -0.0341 -0.0703 -0.1008
Monetary depth (M2/GDP) -0.0014 -0.0589 -0.0361
Monetary depth Income 0.0002 0.0060 0.0038

Income (log) 0.1311 0.1157 0.2254 0.2130 0.1697 0.0629 0.0757
Importable 0.3421 0.3898 0.3797 0.4212 0.5018 0.4018 0.4840
Exportable 0.1406 0.1796 0.1971 0.2333 0.1470 0.1851 0.1954
Land per capita -0.2837 -0.2373 -0.2212 -0.2101 -0.3083 -0.3607 -0.3554
Asia 0.3384 0.3900 0.3401 0.2840
Africa 0.3060 0.3033 0.1150 0.1626
LAC -0.0750 0.0087 -0.1530 -0.0630
HIC -0.2318 0.0748 (0.0409) 0.3821
Constant -0.8937 -0.5637 -1.8551 -1.4007 -1.0946 (0.1352) 0.1403
R2 0.0774 0.0692 0.1128 0.0849 0.1470 0.2052 0.1807
No. of obs. 4112 6365 4112 6365 17100 2995 4416
Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes The dependent variable is the absolute value of national average value-weighted NRAs over all covered products. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.
20
We find a time consistency effect in that
perennials are taxed more
Time consistency and the wheat/rice pudding effect
Explanatory variables Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5)

Perennials -0.0744 -0.0913 0.0365
Animal Products 0.3310 0.3372 0.3776
Others -0.4133 -0.4439 -0.2619
Sugar -1.1052 -1.2347 -1.4911
Rice -0.5977 -0.6812 -0.9267
Milk -4.6251 -4.4799 -5.0371
Wheat -0.9518 -0.9770 -1.2089
Other Cereals 0.7328 0.7429 0.4236
SugarIncome 0.1833 0.1992 0.2448
RiceIncome 0.0725 0.0796 0.1234
MilkIncome 0.6012 0.5867 0.6237
WheatIncome 0.1088 0.1128 0.1553
OtherIncome -0.0839 -0.0831 -0.0309
Income (log) 0.2206 0.2012 0.2113 0.1839 0.1471
Importable 0.0743 0.0607 0.1577 0.1438 0.0940
Exportable -0.2225 -0.2102 -0.2057 -0.1960 -0.2334
Land per capita -0.2580 -0.2515 -0.2389 -0.2327 -0.2354
Asia 0.3843 0.3377 0.4103
Africa 0.1420 0.0791 0.1617
LAC (0.0021) -0.0497 0.0097
HIC 0.2369 0.1988 0.2602
Constant -1.5047 -1.5268 -1.4904 -1.4009 -1.2357
R2 0.1642 0.1765 0.2083 0.2199 0.2411
No. of obs. 24700 24700 24700 24700 24700
Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.
Other crops with specific investments are also
treated differently wheat and the rice pudding
crops are subsidized less in poor countries than
in rich ones
21
We find status quo bias to be weak no remaining
correlation between policies and lagged changes
in either world prices or domestic acreage
The (absence of) status quo bias
  Model Model Model Model
Explanatory variables (1) (2) (3) (4)

Lagged Change in Border Prices 0.0000 0.0000
Lagged Change in Crop Area 0.0039 -0.0015

Income (log) 0.1702 0.1687 0.1601 0.2129
Importable 0.2277 0.1918 0.1137 0.0992
Exportable -0.2353 -0.2624 -0.1916 -0.1969
Land per capita -0.3718 -0.2227 -0.1349 -0.1060
Asia 0.1822 0.3184
Africa -0.0663 0.0770
LAC -0.1223 -0.0619
HIC 0.0000 -0.0578
Constant -1.1262 -1.1105 -1.1280 -1.6235
R2 0.1996 0.2235 0.1417 0.1648
No. of obs. 10800 10800 13300 13300
Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.90 () levels. Notes Commodity level NRA is the dependent variable. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.90 () levels.
22
Our new hypothesis entry of new farmers

• In rich countries, land blocks entry and helps
  incumbents organize
• In poor countries, expansion dissipates any gains
  from lobbying
• Furthermore, entry of new farmers is largely
  exogenous

• Rural population growth, 1955-2050

Continuous entry of new farmers

• At first Ltotal grows faster than Lm so La rises
  then falls
• Annual change is heavily constrained by pop.
  growth and urban shares

Growth lt 0, no more entry
23
Observed policies do become more favorableto
farmers when farm population stops growing
Demographic influences on agricultural price
distortion
Continuous entry of new farmers
No more entry
Note Smoothed line and 95 confidence interval
computed with Statas lpolyci using bandwidth 1
and degree 2.
24
We find a strong entry effect, especially when
we also control for other effects
Demographic influences on agricultural price
distortion
  Model Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5) (6)

Entry of new farmers -0.0663 -0.0860 -0.2126 -0.0781 -0.0804 -0.1188
Rural Population 1.1127 0.4104 -1.7045 1.8950 1.2960 0.0431
Urban Population -2.4650 -2.1058 2.7575 -4.5010 -3.6824 (0.5673)
World Bank Goverance Indicators 0.1254 0.1627
Perennial -0.1041
Lagged Change in Crop Area (0.0599)
Lagged Change in Border Prices -0.0037

Income (log) 0.2620 0.2230 0.1458 0.3958 0.3217 0.1880
Land per capita -0.2525 -0.2423 -0.6585 -0.4015 -0.4163 -0.3653
Importable 0.1154 0.0665 0.1463
Exportable -0.1673 -0.1959 -0.1899
Africa 0.0600 0.1156 0.1701 0.1861
Asia 0.3738 0.3704 0.2880 0.3070
LAC (0.0136) -0.2998 -0.0670 -0.0782
HIC 0.1822 . 0.4108 -0.1154
Constant -1.7831 -1.5356 -0.7120 -2.9018 -2.4981 -1.3749
R2 0.1400 0.1501 0.2779 0.3756 0.4176 0.5014
No. of obs. 24900 24900 1141 2233 2233 346
Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Commodity level NRA is the dependent variable for models 1-3. Covered average NRA is the dependent variable for models 4-6. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.
25
We are also interested in the (lack of)
stabilization
Stabilization index and real income, by trade
status
Notes Income per capita is expressed in I (2000
constant prices) and represents the average for
the 1960-2004 period. Data shown for All Primary
Products are country averages, while all other
panels show individual products. Smoothed line
and 95 confidence interval computed with Statas
lpolyci using bandwidth 1 and degree 1.
26
On average from 1960-2004, policies destabilized
domestic prices in Africa and among exportables
Stabilization index by trade status and region
Stabilization Index Stabilization Index Stabilization Index Stabilization Index
Region All Non-Tradables Imports Exports
Asia 1.6063 -0.8211 6.8570 -0.3123
Africa -2.9289 0.1510 -4.7060 -3.2535
LAC 2.2851 0.0902 3.6796 1.4549
ECA 1.9191 33.3068 3.4658 -2.6902
All Regions 0.3192 1.7330 1.8960 -1.6422
27
But the average is misleading, due to a
composition effect (more exportables in poorer
countries)
Stabilization index and real income
Notes Income per capita is expressed in I (2000
constant prices) and represents the average for
the 1960-2004 period. Observations with SI
greater than 100 and lower than -100 were dropped
from this figure.
28
Stabilization is a feature only of richer and
more land-scarce countries
Stylized facts of the stabilization index
Explanatory variables Model Model Model Model Model
Explanatory variables (1) (2) (3) (4) (5)
           
Income (log) 5.4164 6.6017 4.3250 5.0814
Importable 4.0610 -2.4951 -2.5934
Exportable 3.2746 -1.4733 -1.7609
Land per capita -14.2430 -18.5638
Asia -0.5162 -6.0855
Africa -3.8713 -5.5526
Latin America -4.2289 -7.4799
Constant -40.7127 0.23 -43.4193 -29.7508 -25.1963
R2 0.0502 0.0030 0.0681 0.0556 0.0790
No. of obs. 560 560 560 560 560
Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels. Notes Dependent variable for all regressions is the Stabilization Index by country and product. Stabilization Index data not available for high income countries. Influential outliers were dropped from the sample based on the Cook's distance criteria. Results are OLS estimates with significance levels shown at the 99 (), 95 (), and 90 () levels.
29
Some conclusions

• The three stylized facts w.r.t. income level,
  trade direction and land abundance are robust but
  explain only a small fraction of variance
• The six specific political economy mechanisms
  generally add explanatory power to the stylized
  facts, rather than explaining them. (Only
  governance could account for a large fraction of
  the income effect.)
• Main novelty here is effect of new farmers
  entry effect is same or larger when
  instrumented, and it adds explanatory power as
  well as helping to accounts for income effect
• Main surprise here is (lack of) stabilization,
  except in the richer and more land-scarce
  countries