Determinants of Water Price, Contract Choice and Crop Production Inefficiency in Groundwater...
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Transcript of Determinants of Water Price, Contract Choice and Crop Production Inefficiency in Groundwater...
Determinants of Water Price, Contract Choice and Crop Production Inefficiency in Groundwater Irrigation Markets in Bangladesh Md. Saidur RahmanPhD Research Scholar at SSD, AndTeacher, Bangladesh Agricultural University
Supervisory CommitteeSupervisor: Dr. M. A. Sattar MandalProf., Agril. Econ., Former VC of BAU & Former Member, Planning Commission, Bangladesh
Co-supervisors 1. Dr. Humnath Bhandari Scientist, IRRI, Dhaka Office
2. Professor Dr. Kei Kajisa School of International Politics, Economics and Communication, Aoyama Gakuin University, Japan
Area 147 thousand sq kmPopulation 150 millionLand typeCultivable land
Floodplains (80%)8.2 mha
Major crops
Irrigated area
Rice (about 75%), wheat, maize, potato, jute, etc.
6.05 mha (75% of total CL)
Agril. GDP
Govt. priority
18.59% in 2009-10
Self-sufficiency in foodgrain by 2013
Some Features of Bangladesh
Density: 1015/sq km
Staple food: Rice
Irrigation development in BangladeshUp to 1950: Swing baskets, doans, etc. Up to 1959, EPWPDA: Flood control and drainage and supplementary irrigation in the monsoon only.Early 1960s: Modern surface irri. (LLP) introduced.In 1961 EPADC: Groundwater irrigation & DTWTFYP (1980-85): Short-gestation, low capital and quick yielding projects, introduced large scale STW.In 1987 and onward: Massive expansion of STW irrigation through market liberalization.
Motivation:
Foodgrain production: 32.9 million tonsBoro rice: 17.81 million tons
54% of Foodgrain production
57% of total rice production
Surface water irrigation (20%): Dam/barrage, LLP, swing basket, doan, etc. Groundwater irrigation (80%): DTW, STW, TP
DTW and TP cover:
13%
STW covers: 67%
STW alone covers 84%
Motivation contd…
The characteristics of the water market
STW owner/seller: Those who own tubewell and sell water.
Irrigate their own land and partners’ landSell excess water to irrigate plots of their
neighbouring farmersMaximise profit from selling waterBuyer: Those who have no tubewell but use
irrigation water from other tubewells.
Nature of WM: Monopoly >Oligopoly > Competitive
Motivation contd…
Characteristics
Seller Buyer
STW Ownership Owner Non-ownerIncome level Higher LowerNon-farm income
Higher Lower
Tenancy type Landlord/own cultivator
Tenant/ owner cum tenant
Farm size Larger SmallerRisk preference Risk neutral Risk averseCash constrained
Less More
Employment Employer at farm level
Employee at farm level
Credit access More LessCrop diversity More LessFamily labour Less More
Operating system
Mode of payment
Rate of payment
Timing of payment
Price range
Input provider
Popularity
Diesel Crop share
¼ share of harvest
After harvest
18000-25000
Seller Getting less Popular
Diesel Fixed charge
Fixed/ha/ season
Beginning the season
15000-18000
Seller Popular
Diesel Two part tariff
Fixed/ha/ season
Beginning the season
12000-13000
Buyer Emerging
Electric. Fixed charge
Fixed/ha/ season
Beginning the season
14000-16000
Seller Popular
Electric. Crop share
¼ share of harvest
After harvest
18000-25000
Seller Not so popular
Existing contracts in irrigation water marketS
ourc
e:R
ahm
an, 2
008
70%
30%
Crop share payment system
Two part tariff payment method
Buyers pay service charge for using STW and use diesel and other irrigation management of his own.
Service charge
• Irrigation introduced in fifties and ¼ crop share as major payment system for irrigation established from that time and it needs evaluations.
• Due to increase in bargaining power of buyer, the payment system is shifting from crop share to cash payment.
• Due to increase in diesel price and electricity price, and also labour price, the mode of payment of irrigation water is changing (crop share to cash payment).
• After more than 30 years, it is high time to evaluate the payment system of irrigation and examine whether the payment has any negative impact on production efficiency or not. We also need to address reasonable price and contract choice.
Literature reviewedAnswers are already known:Profitability, efficiency in irrigation and command area management, cropping patterns, natures of water market, comparative analysis, existing mode of payments, etc.Research gap: Answer still need to be explored (Research questions):Why are diff. payment system emerging? What would be rational price for irrigation?Which factors determine payment systems?Is there any inefficiency in production due to the variations of payment systems?If commitment fails, what will be inefficiency situation?
Data source: Gisselquist, 1991a and Pitman, 1993 (DTW and STW data from 1973-74 to 1986-87); BADC, 2008 (87-88 to 07-08); DAE, 2011 (08-09 to 10-11).
1980-90: 99% PA
1990-00: 17% PA
2000-10: 12% PA
Hypothesis formulation: Price of irrigation water
1.55 million
Water price mainly depends on:1.Bargaining power of the buyer2.Pumping cost3.Land and soil type4.Relationship with seller5.STW command area6.Parcel size & distance7.Water quality
Hypothesis for research question 1.Hyp. 1. Increasing owner density reduces
the use right price of irrigation water.
Payment choice depends on:
Risk : Crop share Cash payment
Interest rates: Crop share Cash payment
Credit inaccessibility: crop share Cash P.
Commitment level: Crop share Cash P.
Asset position: Cash payment crop share
Hypotheses for research question 2.
Hyp. 2. Higher risk in crop production leads to crop share contract.
Hyp. 2.1. Higher interest rate of credit leads buyer to prefer the payment after harvesting (crop share).
Hyp. 2.2. Inaccessibility to credit or unavailability of microfinance lead buyer to prefer the payment after harvesting (crop share).
Hyp. 2.3. If there is less commitment of seller to buyer, buyer prefers cash payment.
Commitment issue
Groundwater level going down
Drought
MPL(1-α)MPL
QL1 QL*
PLP1
Quantity of lab.
MPL
O
Marshallian Inefficiency analysis (1890)
Hayami and Otsuka, 1993
Cheung, 1969
Johnson and many others studies
Source: Hayami, et al. 1993
Production inefficiency issue
Why are we assuming monitoring and supervision are more difficult in water market?
Land rental mkt.
Water mkt.
LandlordTenant
SellerBuyer
Position as a landlord: StrongPunishment: Land use rightAlternative: Very lessAsymmetric info.: More
Position of water lord: Not so strongPunishment: Water use rightAlternative: MoreMoral strength: WeakAsymmetric info.: Less
ActorsActors
Hypotheses for question 3. Hyp. 3.1. The more difficulty in monitoring and supervision of buyer’s farming irrigation, the more inefficiency in production under crop share payment. Hyp. 3.2. Production inefficiency will be higher if there is less commitment of seller to buyer under crop share payment.
Hypothesis for VDSA data:Hyp. 3.3. Production efficiency of tubewell owners are higher than non-owners over the years in Bangladesh.
Rangpur
Rajshahi
Dhaka
Sylhet
ChittagongKhulna
Barishal
Survey Design
Sample selection procedure and size:Village level data: 96 villages *48 upazilas *31 districts * 5 divisions. Household level data: 10 farmers from each village. Total HHs: 960
Divisions Districts Upazilas Unions Villages Households
Chittagong 2 2 4 4 40
Dhaka 8 14 28 28 280
Khulna 6 10 20 20 200
Rajshahi 7 11 22 22 220
Rangpur 8 11 22 22 220
Total (5) 31 48 96 96 960
96 villages 960 Households
960 Irrigation sources960 Largest Plots
Data collectionSeason: Last Boro rice 2013 (Jan-May)Collecting period: May to September, 2013Tools: Soft questionnaire at CAPI with SurveyBe softwareMethod used: FGD and personal interviews
Analytical methods: Descriptive statistics Econometric analysis, regression (OLS, Cobb-Douglas) Probit, Multinomial-Probit Stochastic Frontier Analysis (SFA), Sensitivity analysis,
Tobit
Pre-testing
Division
name2013 2003
PS HS Mad Mar Mos Tem Hos PS HS Mad Mar Mos Tem Hos
Chittagong 1 0 1 0 4 0 0 1 0 0 0 2 0 0
Dhaka 2 0 1 1 4 0 0 1 0 0 1 3 0 0
Khulna 2 1 1 1 4 1 1 2 0 1 1 3 1 0
Rajshahi 2 0 1 1 4 1 1 1 0 1 0 3 1 0
Rangpur 3 1 1 1 6 1 1 2 0 1 1 4 1 0
Total 2 0 1 1 5 1 0 1 0 1 1 3 1 0
Table 1. Village-wise infrastructures in the study areas
0% 20% 40% 60% 80% 100%
Chittagong
Dhaka
Kulna
Rajshahi
Rangpur
All
Agriculture
Business
Service
Unemployed
0% 20% 40% 60% 80% 100%
Chittagong
Dhaka
Khulna
Rajshahi
Rangpur
All
Agriculture
Business
Service
Unemployed
Fig 2. Occupation in 2013
Fig 3. Occupation in 2003
Division 2013 2003
Wage labour
Own farm activities
House-wife
Outside work
Wage labour
Own farm
activities
Housewife
Outside work
Chittagong 42.50 57.50 96.00 4.00 50.00 50.00 97.50 2.50
Dhaka 19.14 70.68 93.64 6.00 29.46 63.21 97.14 2.857
Khulna 32.00 59.25 94.65 5.00 37.50 58.25 98.35 1.65
Rajshahi 36.36 50.00 90.23 10.00 45.00 46.14 91.54 8.45
Rangpur 32.18 51.14 89.36 11.00 35.45 52.95 96.59 3.41
Total 29.73** 58.53 92.19* 7.81* 36.93 55.36 96.00 4.00
Table 2. Farm and non-farm activities of the villagers in the study areas (in percent)
Division Figures in 2013 Figures in 2003 % change over 10 years
Arable land
Irrigated land
Irrigated rice land
Arable land
Irrigated land
Irrigated rice land
Arable land
Irrigated land
Irrigated rice land
Chittagong 95.00 64.50 62.00 92.00 63.75 62.50 3 1 -1
Dhaka 90.79 78.39 74.36 82.00 65.00 66.54 9 13 8
Khulna 93.95 91.30 65.5 92.00 73.50 63.00 2 18 3
Rajshahi 97.82 90.68 74.91 93.00 80.91 67.73 5 10 7
Rangpur 93.05 87.73 69.32 88.00 73.41 58.95 5 14 10
All 93.75 85.46 70.97 88.00 72.29 64.17 5 13 7
Table 3. Pattern of arable land, irrigated land and irrigated rice land in 2003 and 2013
Division name
Equipment (No.)
2013 2003Growth
Rate (%)Annual Growth
Rate (%)
ChittagongSTW 20 8 150 15DTW 1 0 0 0LLP 0 0 0 0
DhakaSTW 34 17 100 10DTW 1 1 0 0LLP 1 1 0 0
KhulnaSTW 82 36 128 13DTW 1 1 0 0LLP 0 1 -100 -10
RajshahiSTW 49 36 36 4DTW 2 1 100 10LLP 0 1 -100 -10
RangpurSTW 65 28 132 13DTW 1 0 0 0LLP 5 14 -64 -6
AllSTW 54 28 93 9DTW 1 1 0 0LLP 1 4 -75 -8
Table 4. Number of irrigation equipments by type in the selected divisions in 2003 and 2013
0
20
40
60
80
100Chittagong
Dhaka
Khulna
Rajshahi
Rangpur
All
0200400600800
100012001400
Chittagong
Dhaka
Khulna
Rajshahi
Rangpur
All
Figure 5. Number of STW in 2013 and 2003
6. STW command areas in 2013 and 2003
Division name
No. of owner(No.)
No. of buyer (No.)
Buyers per
tubewell
Owner education
(Class level)
Buyer education
(Class level)
No. of AWD user
(%)
Chittagong 21.50 260.75 20.88 6.00 6.00 0.00
Dhaka 36.07 321.71 12.85 5.79 5.18 24.64
Khulna 83.85 258.00 5.35 6.10 5.20 1.20
Rajshahi 66.27 406.32 9.89 5.05 5.23 4.32
Rangpur 67.86 394.41 9.49 5.86 4.41 5.77
All 59.63 341.95 10.17 5.71 5.05 9.75
Table 5. Village-wise number of irrigator farmers in the study areas
AWD
Division name
2013 2003 Nearby*
Fixed charge
Crop share
Two part tariff
Fixed charge
Crop share
Two part tariff
Fixed charge
Crop share
Two part tariff
Chittagong 80.00 0 20.00 75.00 0 25.00 80.00 0 20.00
Dhaka 38.89 25.00 36.11 26.66 36.67 36.67 37.84 24.32 37.84
Khulna 58.33 8.33 33.34 60.00 15.00 25.00 60.00 8.00 32.00
Rajshahi 51.72 41.38 6.90 40.74 48.15 11.11 46.67 40.00 13.33
Rangpur 48.48 0 51.52 35.72 7.14 57.14 48.57 2.86 48.57
All 49.61 18.11 32.28 40.37 26.61 33.02 48.48 18.18 33.32
Table 6. Types of Contracts are using currently for the payment of irrigation water (Percent)
Figure 7. Depth of shallow tubewell in the selected divisions in Bangladesh in 2003 and 2013 ( in feet)
Fig. 8. Paddy price in different time periods (Price in BD Taka)
0
100
200
300
400
500
600
700
800
900
1000
2003 2008 2012 2013 2014
Chittagong
Dhaka
Khulna
Rajshahi
Rangpur
14% per year
Division name
No. of flood
Flood duration (days)
No. of drought
No. of storm
No. of disease attack
No of rainfall
Chittagong 0.75 12.50 0.50 0.25 0.00 4.50
Dhaka 1.57 21.68 2.51 0.75 1.75 3.61
Khulna 0.65 12.60 1.65 0.60 1.40 2.95
Rajshahi 1.23 17.36 3.68 0.18 1.32 2.36
Rangpur 1.32 7.318 1.64 0.82 1.36 2.82
Total 1.21 15.13 2.32 0.58 1.42 3.04
Table 7. Average occurrence of natural calamities in the study area per season
Arsenic problem
Division name In 2013 In 2003
Yes No Unknown Yes No Unknown
Chittagong 25 75 0 25 75 0
Dhaka 0 100 0 0 96 4
Khulna 20 80 0 15 80 5
Rajshahi 0 100 0 0 91 9
Rangpur 0 100 0 0 100 0
All 5 95 0 4 92 4
Table 8. Awareness about the salinity of irrigation water in 2003 and 2013 (In percent)
Iron is almost everywhere but farmers think iron is not a problem
Fig 13. Loan sources of the villagers in 2003
Fig 12. Loan sources of the villagers in 2013
0
20
40
60
80
100
120
140
160
180
Chittagong Dhaka Khulna Rajshahi Rangpur All
Bank
Lender
NGO
0
20
40
60
80
100
120
140
160
180
Chittagong Dhaka Khulna Rajshahi Rangpur All
Bank
Lender
NGO
Figure 14. Loan interest rate in
2013
Figure 15. Loan interest rate in
2003
Division name
Group of village elders/village council
Buyer and seller resolve privately
A single trusted individual
A member of the buyer or seller's family
The court system
Others Total
Chittagong 50.00(2)
0 0 50.00(2)
0 0 100(4)
Dhaka 39.53(17)
48.84(21)
2.33(1)
0 6.98(3)
2.33(1)
100(43)
Khulna 41.67(15)
52.78(19)
2.78(2)
0 2.78(1)
0 100(36)
Rajshahi 26.67(8)
63.33(19)
6.67(2)
3.33(1)
0.00 0 100(30)
Rangpur 52.78(19)
44.44(16)
0 0 2.78(1)
0 100(36)
All 40.94(61)
50.34(75)
2.68(4)
2.01(3)
3.36(5)
0.67(1)
100(149)
Table 9. Dispute resolved by different section of the villagers in the study areas (in percent)
I. Research question-1:
Deter. of irrigation water price:The composite form of the model:Yi = α0 + Xiβi + εi 1,
Where Yi = Price of irrigation water (Tk./ha) in the village iα0 = Intercept βi = Coefficients of the variables of the village iXi = Characteristics of the village i εi = Error term
The empirical models are as follows: Model I. lnYi = α0 + β1lnX1+ β2lnX2+ εi 2,Yi is Price of irrigation, X1 is crop share dummy and X2 is fixed charge dummy Model II for diesel operated tubewell lnYi = α0 + β1lnX1+ β2lnX2+ β3lnX3+ β4lnX4+ β5lnX5+ β6lnX6+ β7lnX7+ β8lnX8+ β9lnX9+ β10lnX10+ β11lnX11+ β12lnX12+ β13 lnX13+ εi 3,whereYi = Price of irrigation water in the village i and i=1 … … … 96X1 = Tubewell owners’ ratioX2 = Command area of a tubewell (decimals)X3 = Percent of people earning remittance (percent)X 4 = Percent of low land area of a village (percent) X5 = Percent of high land area of a village (percent)X6 = Percent of sandy loam soil of a village (percent)X7 = Percent of loam soil of a village (percent)
X8 = Share payment dummy (1= share crop payment, 0=otherwise)X9 = Fixed charge payment dummy (1=fixed charge payment, 0=otherwise) X10 = Number of buyers in a tubewell (number)X11= Number of potential buyers in a tubewell (number)X12 = Buyers’ area of a tubewell (percent)X13 = Tubewell repairing cost (Tk.)εi = Error term Model III for electricity operated tubewell lnYi = α0 + β1lnX1+ β2lnX2+ β3lnX3+ β4lnX4+ β5lnX5+ β6lnX6+ β7lnX7+ β8lnX8+ β9lnX9+ β10lnX10+ β11lnX11+ β12lnX12+ β13 lnX13+ β14 lnX14 + εi 4, Yi = As beforeX1-X13 = As before in model IIX14 = Electricity cost per unit (Tk./kw)εi = Error term
Model selection test:Davidson-Mackinnon J-test for selecting model between OLS and Cobb-Douglas. We run Cobb-Douglas and we get xb (fitted values) and we include it in the OLS model as an explanatory variable. We find this xb is highly significant and thus we get Cobb-Douglas is the best fit model here in this data set.
Test Hypothesis F/Chi2(ᵪ2) values with probability
Decision
1. Omitted variables by using Ramsey RESET test
H0: model has no omitted variables
F(3, 79) = 1.72 and Prob > F =0.1704
We fail to reject null hypothesis
2. Heteroskedasticity test by using Breusch-Pagan/Cook-Weisberg test
Ho: Constant variance
chi2(1)= 0.03Prob > chi2 = 0.8702
We fail to reject null hypothesis
3. Multicolinearity test Ho: VIF values Values are <10 There is no multicolinearity issue
Estimated coefficients of model I
Number of obs = 96F( 2, 93) = 7.55
Prob > F = 0.0009R-squared = 0.1396
Adj R-squared = 0.1211Variables Coefficien
tStd. Err. t P>t
Share crop payment dummy
0.119 0.110 1.08 0.282
Fixed charge payment dummy
-0.197** 0.095 -2.07 0.041
Constant term 9.893(19788.00
)
0.0835 118.55 0
Table 10a-c. Results of first model
Estimated coefficients of model II with diesel operated tubewell
Number of obs = 96F( 13, 82) = 6.33
Prob > F = 0.0000R-squared = 0.5008
Adj R-squared = 0.4216Variables Coefficient Std. Err. t P>tTubewell owner ratio -0.099*** 0.030 -3.26 0.002Command area of a tubewell (decimals) -0.062* 0.033 -1.87 0.065Percent of people earning remittance of a village
0.094*** 0.026 3.69 0.000
Percent of low land area of a village 0.053* 0.027 1.95 0.055Percent of high land area of a village 0.045* 0.028 1.63 0.104Sandy loam soil of a village (percent) -0.018 0.029 -0.63 0.53Loam soil of a village (percent) 0.131*** 0.045 2.89 0.005Share payment dummy 0.091 0.095 0.96 0.342Fixed charge dummy -0.131 0.087 -1.52 0.133Number of buyers in a tubewell -0.104** 0.049 -2.11 0.038Potential buyers in a tubewell (No.) -0.037 0.046 -0.82 0.417Buyers’ area of a tubewell area (percent) 0.027 0.076 0.36 0.723Tubewell repairing cost (Tk) 0.021 0.029 0.72 0.471Constant term 9.572
(14358)0.391 24.51 0
Estimated coefficients of model III with electricity operated tubewell
F( 13, 82) = 5.6Prob > F = 0.0000
R-squared = 0.5357Adj R-squared = 0.4401
Variables Coefficient Std. Err. t P>tTubewell owner ratio -0.117*** 0.034 -3.43 0.001Command area of a tubewell (decimals)
-0.059* 0.034 -1.71 0.093
Percent of people earning remittance of a village
0.087*** 0.027 3.23 0.002
Percent of low land area of a village
0.059** 0.030 1.98 0.052
Percent of high land area of a village
0.036 0.030 1.23 0.223
Sandy loam soil of a village (percent)
-0.048 0.033 -1.46 0.149
Loam soil of a village (percent) 0.079 0.052 1.54 0.129Share payment dummy 0.065 0.105 0.62 0.537Fixed charge dummy -0.155 0.098 -1.59 0.117Number of buyers in a tubewell -0.086 0.053 -1.62 0.11Potential buyers in a tubewell (No.)
-0.040 0.048 -0.83 0.411
Buyers’ area of a tubewell area (%)
-0.006 0.083 -0.07 0.947
Tubewell repairing cost (Tk) 0.018 0.030 0.6 0.548Price of electricity (Tk./kw) 0.381* 0.229 1.66 0.101Constant term 9.457
(12795.00)0.530 17.85 0
II. Research question-2: Contract choice (Probit)General structure of an econometric model is written as follows:Yi
* = Xiβ + ϵi 5,
where Yi* denotes the dependent variable and X denotes
the independent variable of the model. To explain Y 100 percent ϵ is used as an error term and it is assumed that ϵi ~N(0,1).
Models for payment systems:
Yi = αcs + βXics + ϵi where Yi = Share crop payment (1=share crop, 0=otherwise)6,
Yi = αfc + βXifc + ϵi where Yi = Fixed charge payment (1=Fixed charge, 0=otherwise)7,
Empirical probit model for share crop payment system:
Ysc = α0 + β1X1i + β2X2 + β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ε1
9
Where,Ysc Choice of share crop payment system
(1=share crop, 0=otherwise);X1 Flood dummy (1=flooding, 0=otherwise);X2 drought dummy (1=drought, 0=otherwise); X3 arsenic dummy (1=arsenic, 0=otherwise);X4 low land dummy (1=low land, 0=otherwise)X5 interest rate of money lender, and X6 interest rate of NGOα0 intercept;β1-6 coefficients of the independent variables
Empirical probit model for fixed charge payment:
Ycp = α0 + β1X1i + β2X2 + β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ε1 10
Where,
Ycp Choice of share crop payment system (1=cash payment, 0=otherwise);
X1 Likelihood percent of refusing irrigation; X2 diesel price (Tk./lit), X3 electricity price (Tk./unit);X4 drought dummy (1=drought, 0=otherwise)α0 intercept;β1-4 coefficients of the independent variables
Log likelihood = -44.396424 Number of obs = 96LR chi2(6) = 16.92
Prob > chi2 = 0.0096Variables Coef. Std. Err. z P>zFlood dummy 0.740** 0.319 2.32 0.02Drought dummy 0.753* 0.402 1.87 0.061Arsenic dummy -0.661* 0.396 -1.67 0.095Low land dummy -0.703** 0.331 -2.13 0.034Interest rate of money lender
-0.002 0.003 -0.85 0.396
Interest rate of NGO
0.031 0.024 1.29 0.198
Constant term -1.941 0.840 -2.31 0.021
Table 11a_d. Results of probit model of preferring share crop payment system
Hyp. 2.1 Higher risk in crop production leads to crop share contract.
Hyp. 2.2. Higher interest rate of credit leads buyer to prefer the payment after harvesting (crop share).
y = Pr(Share crop payment dummy) (predict)= .19829472
Variables dy/dxStd. Err. z P>z X
Flood dummy 0.207*** 0.088 2.36 0.018 0.48Drought dummy 0.179** 0.079 2.26 0.024 0.73Arsenic dummy -0.160** 0.081 -1.97 0.049 0.27Low land dummy -0.185** 0.081 -2.3 0.022 0.42Interest rate of money lender -0.001 0.001 -0.85 0.397 111.85Interest rate of NGO 0.009 0.007 1.28 0.199 29.77
Marginal effects after probit of preferring share crop payment system
Log likelihood = -58.450951 Number of obs = 96LR chi2(6) = 13.50
Prob > chi2 = 0.0091Variables Coef. Std. Err. z P>z
Likelihood percent of refusing irrigation (%)
0.062* 0.034 1.86 0.063
Diesel price (Tk./lit) -0.057 0.119 -0.48 0.633
Electricity price (Tk./unit)
0.179** 0.094 1.9 0.058
Drought dummy -0.859*** 0.328 -2.62 0.009
Constant term 3.890 8.331 0.47 0.641
Results of probit model of preferring fixed charge payment system
Hyp. 2.3. If there is less commitment of seller to buyer, buyer prefers cash payment.
Commitment
y = Pr(Fixed charge payment dummy) (predict)= .59597877
variable dy/dx Std. Err.
z P>z X
Likelihood percent of refusing irrigation (%)
0.024* 0.013 1.870 0.061 5.62
Diesel price (Tk./lit)
-0.022 0.046 -0.480
0.633 70.18
Electricity price (Tk./unit)
0.069** 0.037 1.890 0.058 3.51
Drought dummy -0.304***
0.101 -3.010
0.003 0.73
Marginal effects after probit of preferring fixed charge payment system
Fig 16. Choice availability
Determinants of contract choice (Village data
It is assumed here on the multinomial model as a series of binary models. That is, evaluate the probability of the alternative j against alternative i for every i≠j. The model started considering the binary model
We get:Pj=F(Xβj)(Pi+Pj)
Application of multinomial probit
Using the expression for
As the response probabilities must sum to 1, we must set the probability of the reference response (j=0) to:
The MNP is obtained through maximum-likelihood estimation:
Mc Fadden (1974) has shown that the log-likelihood function is globally concave, what makes the maximization problem straightforward. The partial effects for this model are complicated. For continuous Xk, we can express like the following equation:
Application of multinomial probit (Village data)Model for choosing any technology:Multinomial Probit Model
Yi,0* = Xi,0β0 + εi,0
Yi,1* = Xi,1β1 + εi,1
Yi,2* = Xi,2β2 + εi,2
Yi =
0 if Crop share; Yi,0* > Yi,1
* & Yi,0* > Yi,2
*
1 if fixed charge; Yi,1* > Yi,0
* & Yi,1* > Yi,2
*
2 if two part tariff; Yi,2* > Yi,0
* & Yi,2* >
Yi,1*
εi ~ type-1 extreme value distribution
Dependent variable(Yi)
Independent variables(Xi)
Contract choices:0 = share crop 1 = fixed charge3 = two part tariff
X1 Yield (kg/ha)X2 Frequent visit (no.)X3 Good relation (no.) X4 Chance of denied irrigation (%)X5 Irrigation cost (Tk./ha)X6 Years of using irrigation (no.)X7 Interest rate of NGOs (Tk.)X8 Flood duration (day)X9 Electricity dummy(1=electricity operated, 0=otherwise)X10 Drought dummy (1=drought, 0=otherwise)X11 Low land dummy(1=low land, 0=otherwise)X12 Distance from upazila dummy (1=10 km, 0=otherwise)
Number of observations = 96
Table 12a_d. Results of mprobit Wald chi2 (24) =24
Probability> chi2 = 0.0725
Contract choice: 0 Base outcome = Share cropPayment system Coefficients Std. Err. z P>z
1 Fixed chargeYield (kg/ha) 0.00108*** 0.00044 2.46000 0.01400Frequent visit (no.) -0.01912 0.01998 -0.96000 0.33900Good relation 0.00011 0.02269 0.00000 0.99600Chance of denied irrigation (%) 0.11020 0.07384 1.49000 0.13600Irrigation cost (Tk./ha) -0.00010** 0.00005 -2.24000 0.02500Years of using irrigation (year) -0.13750*** 0.04389 -3.13000 0.00200Interest rate of NGOs (Tk.) -0.07803* 0.04276 -1.82000 0.06800Flood duration (day) -0.02760** 0.01236 -2.23000 0.02600Electricity dummy 1.91391** 0.94990 2.01000 0.04400Drought dummy -2.07133*** 0.72280 -2.87000 0.00400Low land dummy 1.57627*** 0.62687 2.51000 0.01200Distance from upazila dummy 0.45375 0.54716 0.83000 0.40700Constant term 2.51065 3.01869 0.83000 0.40600
2 Two part tariff
Yield (kg/ha) 0.00070 0.00046 1.53000 0.12600
Frequent visit (no.) -0.04767** 0.02295 -2.08000 0.03800
Good relation 0.05144** 0.02616 1.97000 0.04900
Chance of denied irrigation (%) -0.03440 0.08295 -0.41000 0.67800
Irrigation cost (Tk./ha) -0.00007 0.00005 -1.38000 0.16900
Years of using irrigation (year) -0.02453 0.04666 -0.53000 0.59900
Interest rate of NGOs (Tk.) -0.02196 0.04773 -0.46000 0.64600
Flood duration (day) -0.00132 0.01327 -0.10000 0.92100
Electricity dummy -0.19003 0.77525 -0.25000 0.80600
Drought dummy -0.17991 0.76776 -0.23000 0.81500
Low land dummy -0.21757 0.77941 -0.28000 0.78000
Distance from upazila dummy 0.37720 0.62261 0.61000 0.54500
Constant term -0.80239 3.53152 -0.23000 0.82000
Marginal effects
Case: Crop share payment
Variables
y = Pr(Payment method=0) (predicted outcome of crop share) = .18634181
dy/dx Std. Err. z P>z X
Yield (kg/ha) -0.00020*** 0.00008 -2.48 0.01 6286.90Frequent visit (no.) 0.00515 0.00374 1.38 0.17 45.66Good relation (no.) -0.00232 0.00423 -0.55 0.58 28.62Chance of denied irrigation (%) -0.01586 0.01345 -1.18 0.24 5.62Irrigation cost (Tk./ha) 0.00002** 0.00001 2.39 0.02 19348.70Years of using irrigation (year) 0.02280*** 0.00841 2.71 0.01 31.96Interest rate of NGOs (Tk.) 0.01330 0.00831 1.60 0.11 29.77Flood duration (day) 0.00442* 0.00234 1.88 0.06 15.13Electricity dummy -0.27838 0.20389 -1.37 0.17 0.86Drought dummy 0.25437*** 0.08115 3.13 0.00 0.73Low land dummy -0.21723** 0.09457 -2.30 0.02 0.42Distance from upazila dummy -0.09292 0.10988 -0.85 0.40 0.68
Marginal effects
Case: Fixed charge
Variables
y = Pr(Payment method=1) (predicted, outcome of fixed charge) = .68922906
dy/dx Std. Err. z P>z X
Yield (kg/ha) 0.00021** 0.00010 2.1 0.035 6286.90Frequent visit (no.) 0.00020 0.00468 0.04 0.967 45.66Good relation 0-.00576 0.00553 -1.04 0.297 28.62Chance of denied irrigation (%) 0.03367* 0.01760 1.91 0.056 5.62Irrigation cost (Tk./ha) -0.00002* 0.00001 -1.9 0.057 19348.70Years of using irrigation (year) -0.03442*** 0.01075 -3.2 0.001 31.96Interest rate of NGOs (Tk.) -0.01863* 0.01075 -1.73 0.083 29.77Flood duration (day) -0.00732** 0.00317 -2.31 0.021 15.13Electricity dummy 0.55805*** 0.19105 2.92 0.003 0.86Drought dummy -0.41381*** 0.09850 -4.2 0.000 0.73Low land dummy 0.40726*** 0.11397 3.57 0.000 0.42Distance from upazila dummy 0.08361 0.13574 0.62 0.538 0.68
Marginal effects
Case: Two part tariff
Variables
y = Pr(Pay_method1=2) (predicted outcome of two part tariff) = .12442913
dy/dx Std. Err. z P>z X
Yield (kg/ha) -0.00001 0.00006 -0.19 0.847 6286.90Frequent visit (no.) -0.00535* 0.00323 -1.66 0.098 45.66Good relation 0.00808** 0.00394 2.05 0.04 28.62Chance of denied irrigation (%) -0.01781 0.01206 -1.48 0.14 5.62Irrigation cost (Tk./ha) 0.00000 0.00001 0.05 0.96 19348.70Years of using irrigation (year) 0.01161* 0.00716 1.62 0.104 31.96Interest rate of NGOs (Tk.) 0.00533 0.00717 0.74 0.457 29.77Flood duration (day) 0.00290 0.00208 1.4 0.163 15.13Electricity dummy -0.27967 0.18668 -1.5 0.134 0.86Drought dummy 0.15944** 0.07003 2.28 0.023 0.73Low land dummy -0.19002** 0.08602 -2.21 0.027 0.42Distance from upazila dummy 0.00931 0.08740 0.11 0.915 0.68
Payment methods Observation Mean Std. Dev. Min Max
Crop share, p0 96 0.246 0.243 1.39E-05 0.958907
Fixed charge, p1 96 0.578 0.342 0.001017 0.999854
Two part tariff, p2 96 0.176 0.202 3.65E-09 0.817899
Table 13. Probability of choosing payment methods : Model estimated
Payment systems Frequency Percent Cumulative percent
Crop share ( 0) 23 23.96 23.96
Fixed charge (1) 56 58.33 82.29
Two part tariff (2) 17 17.71 100
Total 96 100 -
Table 14. Frequency distribution of existing payment methods
Research question-3: HH level data
Production inefficiency issue
Production inefficiency estimation:
Qi = F(Li, Ii) (1)
Where Qi is output per hectare i = inputs provided by the farmer i under different payment systems.Li = Labour (man-day) per hectareIi = Irrigation (hour) per hectare, and
F exhibits production function with positive first and second derivatives (F1,F2>0; F11, F22<0). Farmers maximise productivity by using labour and irrigation along with others factors of production but assumed to be constant here in this model.
The specific model is as follows:
Qi =α0+βiXi+ϵ i (2)
Where Qi is output per hectare of the farmer i in a season
X1 is labour (man-day/ha)X2 is irrigation (hour/ha)X3 is seed (kg/ha)X4 is tillage (hour/ha)X5 is chemical fertilizer (kg/ha)X6 is other fertilizer (kg/ha)X7 is insecticide and herbicides (kg or lit/ha)X8 is crop share dummy (1=crop share, 0=otherwise)X9 is fixed charge dummy (1= fixed charge, 0=otherwise)X10 is two part tariff dummy (1= two part tariff, 0=otherwise)
Socioeconomic and socio-demographic factors
X11 is main soil type dummy (sandy loam) (1= sandy loam, 0=otherwise)X12 is main soil type dummy (clay loam) (1= clay loam, 0=otherwise)X13 is main soil type dummy (clay) (1=clay, 0=otherwise)X14 is main land type dummy (medium high land) (1= clay, 0=otherwise)X15 is main land type dummy (high land) (1= high land, 0=otherwise)X16 is farm size (hectare)X17 is family kinship (1=yes, 0=otherwise)X18 is household head education (years of schooling)X19 is irrigation source distance (meter)β1-19 are the unknown parameters to be estimatedAnd, ϵi is error term
Inclusion of supervision from seller:
Qi =α+βXi+γZ1i+ϵ i (3)
Where,
Xi= As beforeZ11 = Share payment*No. of supervision by the sellerZ12 = Fixed charge payment*No. of supervision by the sellerϵi is the sum of two error terms
Inclusion of commitment from seller: Qi =α+βXi+ γZ1i + θZ2i+ϵ i (4)Where,Xi = As beforeZ1i = As beforeZ21 = Crop share payment*Likelihood percent of refusing irrigation water by the sellerZ22 = Fixed charge payment*Likelihood percent of refusing irrigation water by the sellerϵi is error term
Inclusion of transaction cost in irrigation water markets:
Qil= α+βXi+ γZ1i + θZ2i+γW1i+ϵi (6)
WhereXi= As beforeZ1i = As beforeZ2i = As beforeW11 = Share price dummy*Times talk by the user-seller)W12 = Fixed price dummy*Times talk by the user-seller)ϵi is the sum of two error terms
Division name Irrigation (Hrs)* Labour (Man-day)
Own paym
ent
Crop share
Fixed charge
Two part tariff
Own payment
Crop share
Fixed charge
Two part tariff
Chittagong 365 99 276 325 101 128 157 149
Dhaka 373 372 289 325 107 122 111 103
Khulna 561 480 430 451 131 128 130 124
Rajshahi 313 323 171 217 104 98 101 93
Rangpur 393 0 293 373 106 0 107 113
All 409 354 294 363 111 109 115 112
Table . Division-wise inputs use under different payment system (Figure per hectare)
Division name
Likelihood percent of refusing irrigation water*
Users’ plot visit by the seller(No.)
Crop share
Fixed charge
Two part tariff
Crop share Fixed charge
Two part tariff
Chittagong 0 11.9 2.3 12 35 33
Dhaka 4.2 6.7 5.0 57 46 36
Khulna 4.7 5.9 3.2 66 48 44
Rajshahi 7.4 5.9 2.8 43 46 49
Rangpur - 8.7 2.7 - 49 46
All 6.0 7.1 3.7 50 46 41
Table 15 Likelihood percent of refusing irrigation water and users’ plot visit by the seller in different payment systems (Figure per season)
Payment
types
Target variables
Irrigation Labour Yield
(Hrs/ha) %∆ Man-
day/ha
%∆ Kg/ha %∆
Crop share 382 16.6 118 15.3 6075 -2.2
Fixed charge 317 15.4 119 5.6 6576 6.9
Two part
tariff 435 36.1 112 0.7 6187 -0.4
All 368 22.2 117 5.8 6344 2.5
Table 16. Changes situation of driving variables if visit by the seller to the user’s plot increase (25%) in different payment systems
Sensitivity analysis
Payment
types
Target variables
Irrigation Labour Yield
(Hrs/ha) %∆ Man-
day/ha
%∆ Kg/ha %∆
Crop share321 -2.0 102 0.5 6223 0.2
Fixed charge271 -1.3 112 -0.4 6153 -0.03
Two part
tariff 319 0.02 112 0.3 6190 -0.4
All298 -0.9 110 -0.01 6179 -0.1
Table 17. Changes situation of driving variables if visit by the seller to the user’s plot decrease (25%) in different payment systems (Figure per season)
Payment types Target variables
Irrigation Labour Yield
(Hrs/ha) %∆ Man-day/ha %∆ Kg/ha %∆
Crop share225 -43.9 106 -4.7 6167 -0.3
Fixed charge213 -32.9 112 -5.2 6204 -1.3
Two part tariff334 -10.5 109 -4.0 6246 1.4
All245 -31.1 110 -4.5 6206 -0.2
Table 18. Changes situation of driving variables if likelihood percent of refusing irrigation decrease (6% to 12%) in different payment systems (Figure per season)
Payment
types
Target variables
Irrigation Labour Yield
(Hrs/ha) %∆ Man-
day/ha
%∆ Kg/ha %∆
Crop share150 -62.6 111 -0.2 6043 -2.3
Fixed charge220 -30.8 118 0.0 5990 -4.7
Two part
tariff 316 -15.2 125 9.8 5979 -2.9
All221 -37.8 118 2.3 5998 -3.5
Table 19. Changes situation of driving variables if likelihood percent of refusing irrigation increase (6% to 18%) in different payment systems (Figure per season)
Payment types Likelihood of refusing Visit Irrigation
(Hrs)
Labour
(Man-day)
Yield
No. % ∆ No. %∆ No. %∆ No. %∆ No. %∆
Crop share 5.1 -0.9 45.8 0.9 355 0.4 110 0.1 6228 -0.3
Fixed charge 6.5 -0.5 42.3 0.9 291 2.3 117 0.2 6340 0.2
Two part tariff 3.6 1.1 35.2 1.5 346 1.3 114 -0.4 6227 0.3
All5.2 -0.4 40.7 1.0 322 1.6 115 0.0 6279 0.1
Table 20. Changes situation of driving variables if number of talk times increase 20% (27 to 36) in different payment systems (Figure per season)
Payment
types
Likelihood of refusing Visit Irrigation
(Hrs)
Labour (Man-day) Yield
No. % ∆ No. %∆ No. %∆ No. %∆ No. %∆
Crop share 5.2 0.0 45.4 0.0 354 0.0 110 0.0 6246 0.0
Fixed
charge
6.5 -0.7 42.3 0.9 283 -0.3 116 -0.3 6337 0.2
Two part tariff
3.6 2.3 34.7 -0.1 346 1.4 114 -0.3 6266 0.9
All 5.3 0.5 40.5 0.6 318 0.3 114 -0.3 6295 0.4
Table 21. Changes situation of driving variables if number of talk times decrease 20% (27 to 14) in different payment systems (Figure per season)
Dependent variable: Yield (kg/ha)Name of variables
Values of the coefficients in different models
I II III IV
Irrigation (hour) 0.34** 0.20 0.17 0.17Labour (Man-day) -0.75 -0.14 -0.08 -0.24Seed (kg) -5.08** -4.78* -4.79* -5.20*
Tillage (hour) 3.41 8.75 10.87 12.09Fertilizer (kg) 0.85*** 0.77** 0.77** 0.78**
Other fertilizer (kg) 0.02 0.03 0.03 0.03Insecticide & herbicides (kg/lit) 4.27 5.37 6.68 6.57Crop share payment dummy -215.21 70.72 129.04 212.10Fixed charge dummy -9.55 -84.37 -34.57 11.78Two part tariff dummy -162.81Crop share payment dummy* No. of visit by the seller 4.52** 4.49** 4.90**
Fixed charge dummy* No. of visit -2.49 -2.71 -1.99Share payment dummy*Likelihood percent of refusing irrigation
-7.31 -6.58
Fixed charge dummy* Likelihood percent of refusing irrigation
-8.25 -5.31
Share cropping dummy*No. of talk between user and seller
-2.40
Table 22. Summary of the variables and coefficients in different models
Inpu
ts on
ly
Supe
rvisi
on
Com
mitm
ent
Tran
sacti
on
Fixed renting dummy* No. of talk between user and seller
-4.87
Sandy loam soil dummy -283.89** -275.75** -267.48** -251.66*
Clay loam soil dummy -68.70 76.76 97.42 94.76Clay soil dummy 34.49 158.11 153.42 168.99Medium high land dummy -148.04 60.78 58.54 58.05High land dummy 31.77 226.10 239.68 252.06Farm size (ha) 231.71* 522.36** 536.13** 561.43**
Household head education (years schooling) 30.20*** 32.99** 33.57** 33.06**
Kinship dummy 191.32* 230.82* 233.44* 236.68**
Irrigation distance (meter) -0.13 -0.18 -0.17 -0.18Constant term 5935.73 5427.50 5390.53 5383.78
No. of observation 958 716 716 716Probability of F value 0.0000 0.0001 0.0002 0.0002R2 0.0570 0.0713 0.0736 0.0771Adjusted R2 0.0379 0.0445 0.0442 0.0450
There is a possibility to increase yield by increasing irrigation hours
III. Determinants of technical inefficiency:
Functional form of the model:
ii
n
jijji vXY
1
lnln 0
where: ln = natural logarithmic formYi = rice production (yield) in tons ha-1
k = number of input variablesβ0 = intercept or constant termβj = unknown parameters to be estimatedXij = vector of production inputs (j) of the farmer i
vi = random error termui = inefficiency component
Translog production function:
We can generalized it in the following form like as,
lnYi = β0 + β1lnX1i + β2lnX2i +0.5 β11(lnX1i)2 + 0.5 β22(lnX2i)2 + β12lnX1ilnX2i + vi - μi
ii
k
jiij
k
i
k
iiii vXXX jY
lnlnln
1110 2
1ln
Where,μi = technical inefficiencyδ0 = intercept or constant termδj = parameters to be estimatedZj = determinants of inefficiency
k
iijiji Z
10
Technical inefficiency model
Likelihood ratio (LR) test for selecting model between Cobb-Douglas & Translog since they belong nested
one another
Empirical models specification: Cobb-Douglas
lnYi =β0 + β1lnX1i + β2 lnX2i + β3 lnX3i + β4lnX4i + β5lnX5i + β6lnX6i + β7lnX7i + β8lnX8i + β9 lnX9i + β10lnX10i + β11lnX11i + vi – μi .. ... ... ...(11)Where,Yi = Yield (kg)X1i = Seed (kg/ha)X2i = Human labour (man-day/ha)X3i = Tillage (hour/ha)X4i = Irrigation (hour/ha)X5i = Chemical fertilizer (kg/ha)X6i = Insecticide & herbicides (kg or lit/ha)X7i = Other fertilizer dummy (1=use other fertilizer, 0= otherwise)X8i = Other cost dummy (1=use other cost, 0=otherwise)X9i = Share payment dummy (1=under share payment, 0=otherwise)X10i = Fixed charge dummy (1=under fixed charge payment, 0=otherwise)X11i = Two part dummy (1=under two part tariff payment, 0=otherwise)
Input variables Interaction factor variables1. Seed 12. 0.5*Seed2, 13. Seed*Human labour, 14. Seed*Tillage, 15. Seed*Irrigation, 16. Seed*Chemical fertilizer,
17. Seed* Insecticide & herbicides, 18. Seed* Other fertilizer dummy, 19. Seed* Other cost dummy, 20. Seed* Share payment dummy, 21. Seed* Fixed charge dummy, 22. Seed* Two part dummy
2. Human labour 23. 0.5*Human labour2, 24. Human labour*Tillage, 25. Human labour*Irrigation, 26. Human labour*Chemical fertilizer, 27. Human labour*Insecticide & herbicides, 28. Human labour*Other fertilizer dummy, 29. Human labour*Other cost dummy, 30. Human labour*Share payment dummy, 31. Human labour* Fixed charge dummy, 32. Human labour*Two part dummy
3 . Tillage 33. 0.5*Tillage2, 34. Tillage*Irrigation, 35. Tillage*Chemical fertilizer, 36. Tillage*Insecticide & herbicides, 37. Tillage* Other fertilizer dummy, 38. Tillage*Other cost dummy, 39. Tillage* Share payment dummy, 40. Tillage*Fixed charge dummy, 41. Tillage* Two part dummy
4. Irrigation 42. 0.5*Irrigation2, 43. Irrigation* Chemical fertilizer, 44. Irrigation* Insecticide & herbicides, 45. Irrigation*Other fertilizer dummy 46. Irrigation*Other cost dummy, 47. Irrigation* Share payment dummy, 48. Irrigation*Fixed charge dummy, 49. Irrigation* Two part dummy
5. Chemical fertilizer 50. 0.5*Chemical fertilizer2, 51. Chemical fertilizer*Insecticide & herbicides, 52. Chemical fertilizer*Other fertilizer dummy, 53. Chemical fertilizer*Other cost dummy, 54. Chemical fertilizer* Share payment dummy, 55. Chemical fertilizer* Fixed charge dummy, 56. Chemical fertilizer* Two part dummy
6. Insecticide & herbicides 57. 0.5*Insecticide & herbicides2, 58. Insecticide & herbicides* Other fertilizer dummy, 59. Insecticide & herbicides*Other cost dummy, 60. Insecticide & herbicides* Share payment dummy, 61. Insecticide & herbicides* Fixed charge dummy, 62. Insecticide & herbicides* Two part dummy
7. Other fertilizer dummy 63. Other fertilizer dummy*Other cost dummy, 64. Other fertilizer dummy*Share payment dummy, 65. Other fertilizer dummy*Fixed charge dummy, 66. Other fertilizer dummy*Two part dummy
8. Other cost dummy 67. Other cost dummy*Share payment dummy, 68. Other cost dummy* Fixed charge dummy, 69. Other cost dummy*Two part dummy
9. Share payment dummy -10. Fixed charge dummy -11. Two part dummy -
Empirical models specification: Translog
lnYi = β0 + β1lnX1i + β2lnX2i +0.5 β11(lnX1i)2 + 0.5 β22(lnX2i)2 + β12lnX1ilnX2i + ... + vi - μi ...(6)
Table 2. List of variables and interaction factors:
Table 23. Results from Translog modelDept: Yield (kg/ha)
Number of observation =958Wald chi-square =121.52Probability > chi-square = 0.0001 Log likelihood = -9.745668
Input variables and integration variables
Coefficient. Std. Err. z P>z
Two part dummy -1.028** 0.437 -2.350 0.019Seed-tillage -0.080*** 0.027 -2.960 0.003Seed-irrigation 0.033** 0.016 2.000 0.046Seed-two part tariff dummy 0.057* 0.032 1.780 0.075Labour-irrigation 0.076** 0.037 2.090 0.037Labour-chemical fertilizer 0.112** 0.068 1.640 0.102Tillageha2 -0.063* 0.038 -1.640 0.101Tillage-other fertilizer -0.099** 0.037 -2.700 0.007Tillage-two part tariff dummy 0.130*** 0.046 2.820 0.005Irrigation-other fertilizer -0.039* 0.022 -1.780 0.074Irrigation-share payment dummy -0.069** 0.034 -2.050 0.040Chemical fertilizer-other fertilizer 0.107** 0.050 2.150 0.031Constant term 12.232 1.633 7.49 0.00
Payment methods Technical efficiency TE Rank Technical inefficiency TI Rank
Own payment 0.767 2 0.232 3
Crop share 0.763 4 0.237 1
Fixed charge 0.768 1 0.231 4
Two part tariff 0.766 3 0.234 2
All 0.767 - 0.233 -
Table 24. Technical efficiency, inefficiency and rank
020
4060
80Fr
eque
ncy
0 .2 .4 .6 .8Inefficiency level
Inefficiency distribution of own payment system
010
2030
Freq
uenc
y
0 .2 .4 .6Inefficiency level
Inefficiency distribution of share payment system0
2040
6080
100
Freq
uenc
y
0 .2 .4 .6 .8Inefficiency level
Inefficiency distribution of fixed payment system
020
4060
80Fr
eque
ncy
0 .2 .4 .6 .8 1Inefficiency level
Inefficiency distribution of two part tariff payment system
Fig 22a-d. Frequency distribution of technical inefficiency
Tobit model for determining technical inefficiency:
lnYi =β0 + β1lnX1i + β2 lnX2i + β3 lnX3i + β4lnX4i + β5lnX5i + β6lnX6i + β7lnX7i + β8lnX8i + β9 lnX9i + β10lnX10i + μi ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... (11)Where,Yi = Technical inefficiency [Censored values, ll(o) & ul(1)]X1i = Sandy loam soil type dummy (1=sandy loam soil, 0=otherwise)X2i = Clay loam soil type dummy (1=clay loam soil, 0=otherwise)X3i = Clay soil type dummy (1=clay soil, 0=otherwise)X4i = Medium high land type dummy (1=medium high land, 0=otherwise)X5i = high land type dummy (1=high land, 0=otherwise)X6i = Farm size (ha)X7i = Kinship dummy (1=kinship, 0= otherwise)X8i = Family head age (year)X9i = Family head education (year of schooling)X10i = Distance from plot to tubewell (meter)X11i = Asset position of the farmer (Tk.)X12i = Loan dummy (1=loan receiver, 0=otherwise)μi = Error term
Determinants of inefficiency Coefficients Std. Err. t P>tSandy loam soil type dummy 0.0209** 0.0105 1.99 0.047Clay loam soil type dummy 0.0163 0.0132 1.23 0.219Clay soil type dummy -0.0058 0.0115 -0.51 0.611Medium high land type dummy 0.0121 0.0091 1.33 0.185High land dummy 0.0082 0.0134 0.62 0.538Farm size (ha) -0.0068 0.0066 -1.03 0.301Respondent’s age 0.0162 0.0153 1.06 0.29Respondent’s education -0.0128*** 0.0046 -2.78 0.006Kinship dummy -0.0178* 0.0095 -1.88 0.061Distance from plot to tubewell 0.0021 0.0024 0.89 0.373Asset position of the farmer -0.0083* 0.0049 -1.7 0.089Loan dummy 0.0003 0.0084 0.03 0.975
Table 25. Determinants of technical inefficiency in irrigated HYV boro rice by using Tobit model
Summary and conclusions:Major determinants for price of irrigation: Cobb-
DouglasFixed charge and two part tariff are less costly, tubewell
owner ratio, number of buyers, command area of a tubewell, remittance earning person, low land area, loam soil area, fuel priceMajor determinants of contract choice: Probit model
Crop share: Flood, Drought, (-) arsenic, low landFixed charge: Commitment level, Fuel price, (-) drought
Findings from multinomial probitCrop share: Irrigation cost, years with irrigated rice, flood,
droughtFixed charge: Yield, commitment level, fuel price
(electricity), low landTwo part tariff: Transaction cost (good relation), years with
irrigated rice production
Production efficiency/inefficiency issues: Sensitivity analysis:Labour and irrigation usages are less in crop share payment
system. Increasing interaction can increase it but will not be as like as land tenancy markets due to the different nature of water markets.
YieldFrom OLS: Irrigation, fertilizer, supervision (crop share),
farm size, farmers’ education, kinshipFrom Translog: Seed-irrigation, labour-irrigation, tillage-
two part tariff, chemical fertilizer-other fertilizer
TE: Higher in fixed charge payment and lower in crop share system
TI: Lower in fixed charge system and higher in crop share system
Determinants of inefficiency: Tobit model
Increase: Sand soil, high land, distance of the irrigation source
Decrease: Farmers’ education level, kinship, asset position
Some policy suggestions:Credit availability needs to be taken care more and interest rate of the NGOs needs to be lower and it needs monitoring from the government.
Paddy price at harvesting time is a big concerned for the farmers. The paddy procurement price can be declared at the beginning of the harvesting season otherwise farmers may shift in alternatives crop.
Irrigation water contracts is verbal. If water market works rationally, there will have less possibility of disputes. Market can enforce to increase the commitment level. Groundwater is scarce resource. It needs deep thinking to use groundwater efficiently. AWD is one of the alternatives and we can get its application more by expanding two part tariff payment.
Reasonable irrigation water price per hectare can be suggested by the Ministry of Agriculture but it will be an intervention. Better try to improve the bargaining power of the users so that they can negotiate more rationally at local level.
New form of cash payment, two part tariff has more flexibility for both user and seller’s point of views and buyer has more benefits in this payment system.
Encouraging farmers for talking between user and seller for their activities that can increase the use of water and labour for increasing yield.
Acknowledgement
Fund support:1. GRiSP, IRRI2. ICRISAT3. VDSA
PhD admission & leave permission:Dept. of Agri. EconomicsBangladesh Agricultural University
Prof. Dr. Randolph BarkerDr. Elizabeth Liz, CESD
Dr. David Spielman, IFPRIDr. Uttam Deb, ICRISATDr. Motaleb, CIMMYT
Orlee, ADB, Anylin, Floilan, TC
Dr. Khan, BAU Dr. Akhlas, BRRI Rouselle, Zilhas,
Robert, UPLB
Finally hospitality at SSD is gratefully acknowledged
Data Source Team
Thanks for patience hearing