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An Exit Seminar: Sharing theInternship Experience at IRRI
11.Mar.2013 6th SSD Division Seminar
Hogeun Park, Intern, SSDMS Candidate, Seoul National University
Supervisor: Dr. Taku W. TsusakaThanks: Dr.Val O. Pede
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AGENDA1)Major Contents
The Linkage between Social Relationship and Behavioral
Spillover, The Case of Irrigated and Rainfed Rice Farmers in
Bohol,
Finding the Effect of Canal Irrigation on Farmers Altruism
and Intolerance, using the Method of Hierarchical LinearModeling,
Preliminary NMRice Simulation Study using IRRI-MICRA
Baseline Survey.
2) Additional Contents
Field experience in Bohol
Life in SSD, IRRI
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
The focus of this paper is the spillover effect throughsocial relationship.
What kind of social relationship transmits
behavioral spillover
Combination of (1) behavioral game experiment,(2) household survey, and (3) spatial econometrictechniques.
Using Spatial econometric technique for
investigating social relationship spill over
effect
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Endogenous Social Effect(or Spatial Lag Effect)
Yi = Behavior(e.g. altruistic behavior)
Yi
Xi
Xi = Individual Profile(e.g. Age)
Xj
Xj
XjXj
YjYj
Yj Yj
i = Residual
Exogenous Social Effect (orCross Effect or ContextualEffect)
Correlated Social Effect (orPerturbation Effect)
j
j
i
j
j
Effects of Social Neighbors
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Social Weight Matrix
Spatial Lag Operator
WX =X =
x1x2x3
xn
x averaged over social neighbors for obs1
x averaged over social neighbors for obs 2x averaged over social neighbors for obs 3
x averaged over social neighbors for obs n
n observations
n
n
00
0
11
0
0
W
row standardization
The construction ofWwill be based on different social relationships
e.g.Kinship Friendship Frequency offace-to-faceinteraction
WorkRelationship
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Dictator Game
P 20ID. P 0
Your partner is in the OtherRoom
P 20P 20
P 20
P 20
Only you receive 100 PesosYour partner does not.
How much do you transfer to your partner
if your partner is someone in your village?
The amount youkeep is your payoffof this game
The amount your partnerreceives is his payoff ofthis game
Total P100
Since your partners payoff is totally dependent on your altruism,
the transferred amount is interpreted as a measure of your altruistic behavior.
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Public Goods Game (Two Rounds)
1
The game is played by groups of 4 people: You and 3 anonymouspartners.
Each member is given P100.
Contributing some amounts to the group. The total amountcontributed will be doubled, and the doubled amount will be
shared equally among all members, regardless of your contribution.
We consider two variables in the analysis Message Receipt Dummy
Free-riding Index (FRI)
Group Members
Average Contribution
Your Contribut
ion
Message
Check
Dummy
Indicator for peer pressure on you
Indicator for degree of awareness of own free-riding
The contributed amount is recorded as the result of the second round, and is in
terpreted as a measure of your contributory behavior to public goods in the presence ofmonitoring mechanism.
with the same partners as in the 1st round.Play the 2nd Round
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Our Study Site
1
Figure by Barkada Tours
Irrigated area (IR) and adjacentRainfed area (RF)
Similar agro-ecological, hydrological, and cultural background.
IR
RF
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Our dataset consists of primary data in the following
categories.
1
Agricultural and Socioeconomic Data (X) 4 crop seasons from 2009 to 2010
Age/Gender Dummy/Years of Schooling Latest Season
Asset/Field Area/Household Size/Household Female Ratio
4-season Average
Social Network Variables (W) Oct. to Dec. 2012
Different Criteria. Different types of social weight matrix
can be defined. Behavioral Game Results (Y)
Sep. 2011
290 randomly selected farmers
Irrigated (N= 144) & Rain-fed (N= 146)
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Constructing Social Weight Matrices
1
Using the social network variables, we define the social weight matrices (W), i.e., whoare the social neighbors and who are not.
For each of the 3 Samples Sample 1: Whole (IR + RF) Sample 2: Irrigated (IR) Sample 3: Rainfed (RF)
How do we choose social neighbors??
Criterion 1: How often do you meet with this person? 1 ifevery week or more, 0 otherwise
Criterion 2: How often do you meet with this person? 1 ifevery day or more, 0 otherwise
Criterion 3: Whats the relationship with this person 1 if the answer is close relative, 0 otherwise
Criterion 4: Have you hired him/her for agricultural labor in the past 3 years? 1 ifyes, 0 otherwise
Criterion 5: Have you exchanged agricultural labor with this person in the past 3 years 1 ifyes,
0 otherwise
Criterion 6: Have you ever participated or will you participate in a wedding ceremony of this persons
family? 1 ifyes, 0 otherwise
We will examine the 6 criteria for choosing social neighbors, for each of the 3 samples.
In this paper, we symmetrize the social relationship (e.g., if farmer A says he meets farmer Bevery day, we assume farmer B meets farmer A every day).
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Social-Spatial Model Identification
1
Below are the spatial models suggested for each case
by spatial diagnostic tests (LM tests).
Social Weight Social Weight 1 Social Weight 2 Social Weight 3 Social Weight 4 Social Weight 5 Social Weight 6
Description Meet every week Meet every day Close Relative Hired labor Exchanged labor Wedding Ceremony
Sample Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF
Dictator
GameCross Cross Cross
Lag
&
Cross
Lag
&
Cross
Cross Cross Cross Cross Cross Cross Cross Cross
Lag
&
Cross
Cross Cross Cross Cross
Public Goods
Game, R1
Cross Cross Cross
Lag
&
Cross
Cross Cross Cross
Error
&
Cross
Cross Cross Cross Cross Cross Cross Cross Cross Cross Cross
Public Goods
Game, R2
Lag
&
Cross
Lag
&
Cross
Cross
Error
&
Cross
Lag
&
Cross
Cross
Error
&
Cross
Cross Cross Cross Cross Cross
Error
&
Cross
Lag
&
Cross
Cross Cross
Lag
&
Cross
Error
&
Cross
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Social Spatial Regression Results (1)
1Statistical Significance:*** 1 %, ** 5%, * 10%, 15%.
Dictator GameSocial Weight Social Weight 1 Social Weight 2 Social Weight 3 Social Weight 4 Social Weight 5 Social Weight 6
Relationship Meet every week Meet every day Close Relative Hired labor Exchanged labor Wedding Ceremony
Sample Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF
Model Cross Cross Cross
Lag
&
Cross
Lag
&
Cross
Cross Cross Cross Cross Cross Cross Cross Cross
Lag
&
Cross
Cross Cross Cross Cross
Endogenous
Social Effectn/a n/a n/a
0.220***(0.009)
0.270**(0.012)
n/a n/a n/a n/a n/a n/a n/a n/a0.291***(0.009)
n/a n/a n/a n/a
CorrelatedSocial Effect
n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a
Public Goods Game, Round 1Social Weight Social Weight 1 Social Weight 2 Social Weight 3 Social Weight 4 Social Weight 5 Social Weight 6
Relationship Meet every week Meet every day Close Relative Hired labor Exchanged labor Wedding Ceremony
Sample Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF
Model Cross Cross Cross
Lag
&
Cross
Cross Cross Cross
Error
&
Cross
Cross Cross Cross Cross Cross Cross Cross Cross Cross Cross
Endogenous
Social Effectn/a n/a n/a
0.091
(0.265)n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a
Correlated
Social Effectn/a n/a n/a n/a n/a n/a n/a
0.191
(0.113)n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a
While Meet every week (or more) relationship does not lead to the spillover ofaltruistic behavior, Meet every day relationship seems to do so.
As found in our previous study, no behavioral spillover is found in rainfed areas.
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Social Spatial Regression Results (2)
1Statistical Significance:*** 1 %, ** 5%, * 10%, 15%.
Under the influence of monitoring, contributory behavior spills over through different types of socialrelationship, particularly in irrigated areas.
Peer pressure seems to effectively increase contribution, which is robust to different social weights.
Voluntary correction of contribution is also found in many cases.
Public Goods Game, Round 2Social
WeightSocial Weight 1 Social Weight 2 Social Weight 3 Social Weight 4 Social Weight 5 Social Weight 6
Relationship Meet every week Meet every day Close Relative Hired labor Exchanged labor Wedding Ceremony
Sample Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF Whole IR RF
Model
Lag
&
Cross
Lag
&
Cross
Cross
Error
&
Cross
Lag
&
Cross
Cross
Error
&
Cross
Cross Cross Cross Cross Cross
Error
&
Cross
Lag
&
Cross
Cross Cross
Lag
&
Cross
Error
&
Cross
Endogenous
Social
Effect
0.222*(0.051)
0.348***(0.003)
n/a n/a 0.171**(0.048)
n/a n/a n/a n/a n/a n/a n/a n/a 0.124*(0.071)
n/a n/a 0.234**(0.017)
n/a
Correlated
Social
Effect
n/a n/a n/a0.150*(0.100)
n/a n/a0.154*(0.052)
n/a n/a n/a n/a n/a0.223***(0.004)
n/a n/a n/a n/a0.224
(0.232)
Message
Receipt
Dummy
7.433**(0.013)
6.622*(0.057)
9.178*(0.089)
7.000**(0.022)
4.349
(0.226)
10.918*(0.053)
7.890**(0.016)
8.105*(0.051)
10.604*(0.053)
9.560**(0.025)
6.851
(0.228)
11.654*(0.078)
12.65***(0.001)
13.03***(0.008)
12.178
(0.105)
7.388**(0.024)
6.690*(0.079)
5.338
(0.298)
Free-
RidingIndex
0.197**(0.034)
0.123
(0.315)
0.247
(0.109)
0.199**(0.034)
0.122
(0.349)
0.176
(0.232)
0.256**(0.012)
-0.058
(0.698)
0.231
(0.132)
0.162
(0.220)
-0.015
(0.944)
0.167
(0.349)
0.203
(0.107)
0.343**(0.022)
0.221
(0.314)
0.243**
(0.033)
0.081
(0.553)
0.207
(0.114)
MRD x
FRI
Interaction
-0.233
(0.256)
0.056
(0.809)
-0.668*(0.096)
-0.183
(0.384)
0.145
(0.556)
-0.739*(0.066)
-0.460*(0.059)
-0.179
(0.487)
-0.595
(0.158)
0.072
(0.811)
1.288***(0.007)
-0.622
(0.168)
-0.010
(0.971)
0.517
(0.118)
-0.355
(0.451)
-0.191
(0.403)
0.153
(0.564)
-0.520
(0.151)
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The Linkage between Social Relationship and Behavioral Spillover,
The Case of Irrigated and Rainfed Rice Farmers in Bohol,
Concluding Remarks
1
I. Altruistic behavior spills over through Meet every day relationship butnot through Meet every week relationship, indicating the role of frequentface-to face communication in the emergence of social norm, i.e., behaveas others behave.
II. On the other hand, LaborHiring relationship does not result in behavior
al spillover, while Labor Exchange does, which may reflect the fact thatthe former relationship is more or less businesslike whereas the latter isbased on a mutual cooperation mindset.
III. As found in our geographical neighborhood effect study, the spillover ofcontributory behavior drastically increases once monitoring system isenforced, implying the importance of such a system in implementing publicwork.
IV. It is also confirmed that behavioral spillover is not found in rainfed farmingsocieties, suggesting the role of irrigation management in the
emergence of social norm.
Fi di h Eff f C l I i i F Al i d I l
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
To investigate the connection between managementof canal (gravity) irrigation and farmers social behavior
(1) measures social behavior through behavioral
game experiments
(2) estimates the effects of irrigation, neighborhood,as well as individual characteristics.
Combination of 1) behavioral game experiments and2) hierarchical level model
The availability of irrigation water in the villagedoes not only improve agricultural productivity but
also enhances social relationship among farmers
2
Fi di th Eff t f C l I i ti F Alt i d I t l
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
Theoretical Framework
Behavioral game experiments are designed so as toquantify participants social behavior under strategic
situations (Gintis 2003).
Employing dictator game and ultimatum game, whichare developed to explore altruistic and retaliatingbehaviors, respectively
2
Fi di th Eff t f C l I i ti F Alt i d I t l
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
Dictator Game
This game is intended to elicit participants fairness,generosity, or altruism (Hoffman et al., 1996).
2
?100 PHP is equivalent to 2.46 (USD) by Bloomberg currency data, as of 31 January 2013. The Philippines
GDP per capita is $2,370 (2011) as per World Bank data. Given these exchange rate and GDP per capita,100 PHP is considered sufficient to ensure incentive compatibility for the experiment purpose.
Fi di th Eff t f C l I i ti F Alt i d I t l
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
Ultimatum Game
This game is interpreted as an indicator of thereceivers retaliating behavior or unwillingness totolerate the level of distribution (Herbert et al., 2003).
2
?x x100 PHP is equivalent to 2.46 (USD) by Bloomberg currency data, as of 31 January 2013. The Philippines
GDP per capita is $2,370 (2011) as per World Bank data. Given these exchange rate and GDP per capita,100 PHP is considered sufficient to ensure incentive compatibility for the experiment purpose.
Fi di th Eff t f C l I i ti F Alt i d I t l
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
Results for Behavioral Game Experiments
2
Type of Anonymous
Partner(1) Irrigated
Sample
(N=131)(2) Rainfed
Sample
(N=114)(3) t-test for
mean difference
|(1)-(2)|Dictator Game
Someone in Senders
Purok33.97 27.81 6.16**(20.59) (19.04) [0.015]
Someone in Senders
Barangay32.06 27.11 4.96*(21.58) (18.28) [0.053]Ultimatum Game
Someone in Senders
Purok 24.43 34.83 10.40***(15.15) (19.61) [0.000]Someone in Senders
Barangay25.12 34.47 9.36***(16.47) (21.29) [0.000]
Finding the Effect of Canal Irrigation on Farmers Altr ism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
HLM Methodology
2
While ANOVA and OLS analyses are commonlyused in quantitative assessments, care must be takenwhen the data are nested (Raudenbush and Byrk 1993).
Frog-Pond Theory;Robinson(1950) the problem ofcontextual effects
Reference: J. Kyle Roberts., An introduction to HLM with Rhttp://faculty.smu.edu/kyler/training/AERA_overheads.pdf
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,
HLM Methodology
2
Our data set covers randomly selected 238 rice farmerswho reside in 3 municipalities and 18 barangays
Altruistic and retaliating behaviors arise from socialatmosphere; we try to differentiate individual effects
from barangay effects
Employing HLM to account for the barangay-level
characteristics that are expected to affect individuallevel social behaviors
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
Level 1 (Household Level)Variable N Mean SD Min Max
Age 238 51.38 12.06 14 87Schooling Years 238 6.33 3.02 0 14Asset Holding (Log PhP) 238 10.61 1.09 6.21 13.31Household Size 238 5.93 2.32 1 12.5Parcel Size (ha) 238 1.45 1.02 0.12 8.12
Level 2 (Barangay Level)Variable N Mean SD Min Max
Irrigation Dummy 18 0.61 0.5 0 1Age 18 51.3 4.5 43.56 61Schooling Years 18 6.37 0.93 4.46 8Asset Holding (Log PhP) 18 10.57 0.52 9.44 11.53Household Size 18 5.99 1.1 4.65 8.76Parcel Size (ha) 18 1.31 0.46 0.58 2.19
Descriptive Statistics
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
= 00 + +
ICC(Intra Class Correlation) =0
2
(02 +
2)
Random
Coefficient St. Dev.Variance
Component d.f. 2 p-value ICC
Dictator GameIntercept 1, u0 5.830 33.989 17 38.817 0.002 0.085Level-1, r 19.079 364.008
Ultimatum GameINTRCPT1, u0 6.668 44.463 17 49.456
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
[Level-1 Equation]Yij = 0j + 1j (Ageij) + 2j(Schooling Yearsij) + 3j (Assetij) + 4j(Household Sizeij)+ 5j (Parcel Sizeij) + rij
[Level-2 Equation]0j = 00 + u0j, 1j = 10 + u1j, 2j = 20 + u2j, 3j = 30 + u3j, 4j = 40 + u4j, 5j = 50 +u5j
Estimates for Level-1 Equations
Game
Type
0
(Intercept 1)Age
Schooling
YearsAsset
Household
Size
Parcel
Size
Dictator 28.789*** -0.268*** 0.109 -0.658 0.143 0.375
Ultimatum 28.117*** -0.067 -0.578* -1.984* -0.427 0.797
*** p < 0.01, * p < 0.10 1
23
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
Yij = 00 + 01 (Irrigation Dummyj) + 02 (Agej) + 03 (Schooling Yearj) + 04 (Assetj) +05 (Household Sizej) + 06 (Parcel Sizej) + 10 (Ageij) + 20 (Schooling Yearij) +30 (Assetij) + 40 (Household Sizeij) + 50 (Parcel Sizeij) + u0j + u1j (Ageij) + u2j (Schooling yearij) + u3j (Assetij) + u4j (Household Sizeij) + u5j (Parcel Sizeij)+ rij
Game
Type
00
(Intercept 2)
Irrigation
DummyAge
Schooling
YearsAsset
Household
Size
Parcel
Size
Dictator 23.387*** 9.053* 0.166 -0.259 4.348* -0.724 6.087
Ultimatum 39.092*** -14.012*** -0.697** -1.124 -8.585*** 0.885 -4.964
*** p < 0.01, ** p
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
The result is highly suggestive of the significant socialeffects of canal irrigation schemes.
The positive effect on altruism and the negative effect
on retaliation indicate that the type of social interactionspromoted by the necessity for collective irrigationmanagement leads to inducing the accumulation ofgood social behavior among farmers.
One clue to validating the irrigation effect is to considerthe existence of TSAs (turnout service associations)in the irrigated communities
Concluding Remarks
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
TSA- private canal construction- purchasing machinery- providing micro credit
Compared with the rainfed, irrigated farmers are
exposed to more opportunities to meet and discuss
public arrangements with their neighbors
Dual role: to boost the rural economy throughincreased production, and to accumulate socialcapital among farmers.
Concluding Remarks
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
2
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
Anecdotal Information
( Inday Salaum )
Cultivated cassava before irrigation project
Cultivating Hybrid Rice twice a year
Three children- Crop science- Veterinary- Agronomy
Promoting children back to village foragriculture
Several neighbors children already backto village for their career
Irrigation and modern agricultural technology can prevent
brain drain from rural areas.
Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance
2
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Finding the Effect of Canal Irrigation on Farmers Altruism and Intolerance,
using the Method of Hierarchical Linear Modeling,2
Limitation
Our behavioral game experiments were conducted in 2011 which
was after the construction of irrigation. This survey structure
prevents us from formulating a difference-in-difference
estimator that ensures a more proper impact assessment.
Preliminary NMRice Simulation Study using IRRI-MICRA Baseline Survey
3
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Preliminary NMRice Simulation Study using IRRI MICRA Baseline Survey
3
Introduction
In 2011, IRRI in collaboration with MICRA conducted the baselinehousehold survey on rice farmers over two crop seasons (dry andwet) in four provinces of the Philippines, namely, Bohol, Bukidnon,Pangasinan, and Tarlac. 240 rice and corn farmers that had
irrigated land were randomly selected.
NMRice is being developed on the basis of solid agronomicsciences, it has not been empirically examined to what extent thetool can actually contribute to improving agricultural productivity
and profitability at farm level.
Preliminary NMRice Simulation Study using IRRI-MICRA Baseline Survey
3
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Preliminary NMRice Simulation Study using IRRI MICRA Baseline Survey
3
Methodology
By comparing FP with NM, the sample farmers are divided into twoor three groups (depending on the criterion) in accordance with theproximity of FP to NM
Nitrogen Quantity Applied: FP is defined as NM-Close if the FP quantity is 80-120% of the NM quantity, NM-Mid if 50-80% or 120-150%, and NM-Far if below 50% orabove 150%.
Phosphorus Pentoxide Quantity Applied: FP is defined as NM-Close if the FPquantity is 80-120% of the NM quantity, NM-Mid if 30-80% or 120-170%, andNM-Far if below 30% or above 170%.
Potassium Oxide Quantity Applied: FP is defined as NM-Close if the FP quantityis 80-120% fo the NM quantity, NM-Mid if 30-80% or 120%-170%, and NM-Far ifbelow 30% or above 170%.
Timing of the First Application: FP is defined as NM-Close if the FP timing iswithin 3 days of the NM timing, and NM-Far if the FP timing differs from the NMtiming by more than 3 days.
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Nitrogen (N) Quantity Applied (kg/hectare)
Average(Standard Deviation)
Region FP NM SampleSize (FP-NM) P-Value
Bohol 25.87(20.80) 60.10(18.47) 48 -34.23*** 0.000
Bukidnon 26.04(14.01)
78.30(30.24) 16 -52.26*** 0.000
Pangasinan 130.30(106.54) 65.58(24.61) 22 64.72*** 0.013
Tarlac 94.97(103.00) 77.62(20.84) 51 7.35 0.237
WeightedAverage 68.38(86.94) 69.63(23.22) 137 -1.25 0.868FP and NM show the average values with the standard deviation in the parentheses. *** p< 0.01 ** p< 0.05
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Phosphorus Pentoxide(P2O5) Quantity Applied (kg/hectare)
Region FP NM SampleSize (FP-NM) P-Value
Bohol 22.96(20.41) 16.99(4.66) 48 5.97*** 0.046
Bukidnon 7.79(10.52) 20.08(8.16) 16 -12.29*** 0.001
Pangasinan 6.85(20.13) 18.40(6.07) 22 -11.55** 0.014
Tarlac 4.28(14.19) 19.64(5.26) 51 -15.36*** 0.000
WeightedAverage 11.65(19.07) 18.56(5.68) 137 -6.91*** 0.000
Average(Standard Deviation)
FP and NM show the average values with the standard deviation in the parentheses. *** p< 0.01 ** p< 0.05
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Potassium Oxide (K2O)Quantity Applied (kg/hectare
Average(Standard Deviation)
Region FP NM SampleSize (FP-NM) P-Value
Bohol 16.70(19.71) 16.99(21.74) 48 -0.29 0.918
Bukidnon 6.54(10.15) 20.08(66.64) 16 -13.54*** 0.000
Pangasinan 3.82(15.08) 17.75(7.19) 22 -13.93*** 0.001
Tarlac 2.06(12.07) 18.90(46.90) 51 -16.84*** 0.000
WeightedAverage 7.99(16.65) 18.18(6.42) 137 -10.19*** 0.000FP and NM show the average values with the standard deviation in the parentheses. *** p< 0.01 ** p< 0.05
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y y g y
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Frequency of Fertilizer Applications
Average(Standard Deviation)
Region FP NM SampleSize (FP-NM) P-Value
Bohol 2.06(0.41) 2.21(0.67) 48 -0.15 0.164
Bukidnon 2.00(0.89) 2.50(0.63) 16 -0.50** 0.041
Pangasinan 1.78(0.52) 2.35(0.49) 23 -0.57*** 0.000
Tarlac 2.29(0.61) 2.51(0.51) 51 -0.22 0.062
WeightedAverage 2.09(0.50) 2.38(0.67) 138 -0.29*** 0.000FP and NM show the average values with the standard deviation in the parentheses. *** p< 0.01 ** p< 0.05
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y y g y
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Timing of the1st Application
Region FP Early Late SampleSize (FP-NMEarly) (FP-NMLate)
Bohol 10.85(6.39) 0.00(0.00) 13.83(0.81) 48 10.85*** -2.98 ***
Bukidnon 16.25(9.03) 5.25(6.15) 14.88(1.02) 16 11.00*** 1.37
Pangasinan 5.30(7.96) 4.70(5.99) 14.26(1.95) 23 0.60 -8.96***
Tarlac 12.02(3.15) 0.00(0.00) 13.06(1.71) 51 12.02*** -1.03**
WeightedAverage
10.99(6.92) 1.39(3.86) 13.74(1.54) 138 9.60*** -2.75***
NM Range
FP and NM show the average values with the standard deviation in the parentheses. *** p< 0.01 ** p< 0.05
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y y g y
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Whose Practice is Close to NM Recommendation?- Coefficient of Correlation bet. Grouping & Factors
Nitrogen (N) Quantity Applied (kg/hectare)
Region SampleSize
PlotSize
CornRotation Age Gender
SchoolingYears
IrrigationCost
Bohol 48 -0.387*** -0.021 0.147 0.130 -0.062 0.386***
Bukidnon 16 0.190 -0.289 0.011 -0.372 0.236 0.400
Pangasinan 22 -0.181 -0.224 -0.284 n/a -0.111 -0.003
Tarlac 51 0.080 -0.157 0.072 0.030 -0.215 0.148
WeightedAverage137 -0.012 -0.223*** 0.015 -0.015 -0.126 0.125
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Whose Practice is Close to NM Recommendation?- Coefficient of Correlation bet. Grouping & Factors
Phosphorus Pentoxide(P2O5) Quantity Applied (kg/hectare)
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Whose Practice is Close to NM Recommendation?- Coefficient of Correlation bet. Grouping & Factors
Potassium Oxide (K2O) Quantity Applied (kg/hectare)
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Whose Practice is Close to NM Recommendation?- Coefficient of Correlation bet. Grouping & Factors
Timing of the 1st Application
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Would NM Practice lead to Better Productivity?- Coefficient of Correlation bet. Grouping & Factors
Nitrogen (N) Quantity Applied (kg/hectare)
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Would NM Practice lead to Better Productivity?- Coefficient of Correlation bet. Grouping & Factors
Phosphorus pentoxide(P2O5) Quantity Applied (kg/hectare)
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Would NM Practice lead to Better Productivity?- Coefficient of Correlation bet. Grouping & Factors
Potassium oxide (K2O)Nitrogen Quantity Applied (kg/hectare)
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Would NM Practice lead to Better Productivity?- Coefficient of Correlation bet. Grouping & Factors
Timing Of the 1st Fertilizer Application
n/a cannot be computed because at least one of the variables is constant*** p < 0.01, ** p
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Regression Model, Profitability
VariablesStandardizedCoefficients
P-Value
FP-NM Proximity: N Quantity 0.178* 0.07FP-NM Proximity: P2O5 Quantity 0.029 0.893FP-NM Proximity: K2O Quantity -0.083 0.695FP-NM Proximity: Timing of
the 1st Application 0.227** 0.031Parcel Size 0.051 0.628Corn Rotation 0.079 0.479Age (Household Head) 0.146 0.162Gender Dummy (Household Head) 0.007 0.944Years of Schooling (Household Head) 0.037 0.73
Bukidnon Dummy 0.054 0.648Pangasinan Dummy -0.311** 0.018Tarlac Dummy -0.163 0.244Observations 121R2 0.179Adjusted R2 0.071F-Statistic (P-Value) 1.656 (0.076)
**: p < 0.05, *: p < 0.10
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Regression Model, Yield
VariablesStandardizedCoefficients
P-Value
FP-NM Proximity: N Quantity 0.005 .964FP-NM Proximity: P2O5 Quantity -.066 .767FP-NM Proximity: K2O Quantity 0.265 .217FP-NM Proximity: Timing of
the 1st Application 0.043 .921Parcel Size -.052 .684Corn Rotation 0.243*** .256Age (Household Head) -.095 .629Gender Dummy (Household Head) -.126 .033Years of Schooling (Household Head) 0.004 .367
Bukidnon Dummy 0.098 .185Pangasinan Dummy -0.106 .970Tarlac Dummy 0.220 .413Observations 125R2 0.158Adjusted R2 0.047F-Statistic (P-Value) 1.418 (0.157)
**: p < 0.05, : p < 0.15
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Quantity of nitrogen applied per area, is correlatedpositively with farm profitability and negatively withoverall fertilizer cost per area.
Timing of the first application, the FP-NM proximity isagain positively and highly significantly correlatedwith farm profitability on aggregate, and particularlyin Pangasinan.
NM-generated nitrogen quantity and timing of the firstapplication would be beneficial for improving farmprofitability
Concluding Remarks
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Thank you for your attention