DETERMINANTS OF TECHNICAL

EFFICIENCY OF RICE FARMS IN NORTH-

CENTRAL AND NORTH-WESTERN REGIONS

IN BANGLADESH

Stefan Bäckman

University of Helsinki, Finland

K.M. Zahidul Islam

University of Helsinki, Finland

John Sumelius

University of Helsinki, Finland

ABSTRACT

This paper estimates a quadratic stochastic frontier production function to examine the

determinants of technical efficiency in rice farming in Bangladesh using the computer program

FRONTIER 4.1. Primary data has been collected using multi-stage random sampling technique

from twelve villages in north-central and north-western regions in Bangladesh. Rice cultivation

displayed much variability in technical efficiency ranging from 0.16 to 0.94 with mean technical

efficiency of 0.83 which suggested substantial gains in output with available resources and existing

technologies. The analysis of the determinants of technical efficiency revealed that the age and

education of the household heads, availability of off-farm incomes, land fragmentation, access to

microfinance, extension visits, and regional variation were the major factors that caused efficiency

differentials among the farm households studied. Hence, the study proposes strategies such as

providing better extension services and farmer training programs, ensuring access to agricultural

microfinance, reducing land fragmentation and raising educational level of the farmers to enhance

technical efficiency.

JEL Classification: C21, Q12, D24

Key Words: Quadratic stochastic frontier production function, Technical efficiency,

Bangladesh, multi-stage random sampling, agricultural microfinance

Corresponding Author’s Email Address: zahidul.islam@helsinki.fi

INTRODUCTION

Agriculture is the most important sector in the economy of Bangladesh as it contributes

about 21% of the gross domestic product (GDP) and 50% of overall employment

(Bangladesh Agricultural Census 2008). The dominant food crop of Bangladesh is rice.

Rice accounts for 94% of the cereals consumed, supplies 68% of the protein in the

national diet, accounts for approximately 78% of the value of agricultural output, and

30% of consumer spending (Ahmed and Haggblade, 2000). It also accounts for 94% of

the total crops produced (Bangladesh Economic Review, 2009) and 76.62% of the

cropped area (BBS, 2006). In Bangladesh, 88.44% of the total households are located in

rural areas and they are more or less dependent on agriculture for a living (Bangladesh

Agricultural Census, 2008). Agriculture provides the basic food for the survival of the

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subsistence farmers in Bangladesh. Subsistence farmers account for the greatest

proportion of those engaged in farming. Bangladesh agriculture already operates at its

land frontier and there is little or no scope to expand the cultivable land to meet the

increasing demand for food requirements for its ever-increasing population (Rahman,

2003). Moreover, high population growth, frequent crop failures resulting from flooding

or droughts put pressure for intensification of land use. So, this country simply cannot

afford any diminution in the productivity of its limited 8.44 million hectare arable lands

(BBS, 2006). We also need to maintain optimum productivity of our existing cultivable

lands in order to get increased yields.

The main agricultural products are rice, jute, sugarcane, potatoes, vegetables,

oilseeds, pulses and tea. Three rice crops are grown during the crop cycle beginning in

April- the 'Aus' (spring) crop, the 'Aman' (summer) crop, and the 'Boro' (winter) crop.

The first two are traditional1, rain-fed crops, whereas the Boro crop is the High Yield

Variety (HYV2). However, there is evidence that actual farm yields for both higher

yielding and traditional varieties show considerable shortfalls in yield from those attained

by experimental station levels; which gives rise to a „yield gap‟(De Datta et al., 1978) of

approximately 40% to 50% (BRRI, 2000; Sattar, 2000). The average rice yield in

Bangladesh is 2.74 tonnes/ha (BBS, 2008), which is much lower than those of other

Asian countries. The potential gain from closing this yield gap is higher for Bangladesh

compared to other Asian countries such as China, Korea, Indonesia, Myanmar, Nepal and

Vietnam (Pingali et al., 1997). This „yield gap‟ indicates a difference in productivity

between „best practice‟ and on other less efficient farms that operate with comparable

resource constraints under similar circumstances (Wadud, 1999; Villano, 2005). The

difference between the actual and technically feasible output for most crops implies great

potential for increasing food and agriculture production through improvements in

productivity. For a resource scarce country such as Bangladesh where opportunities to

develop and adopt new technologies are rare, empirical investigations of technical

efficiency and its determinants in rice farming are a dire necessity. Such studies help to

determine the level at which farmers are using existing technologies, and also explore the

possibility of raising the productivity by increasing the efficiency. A great deal of

empirical studies (e.g. Sharma et al. 1999, Nyemeck et al., 2003; Tzouvelekas et al.,;

Villano, 2005; Kalirajan, 1984; Kumbhkar, 1987; Battese et al., 1996; Coelli and Battese,

1996; Battese and Coelli, 1992 ; Binam et al., 2004; Bravo-Ureta and Pinheiro, 1997;

Wang et al., 1996b) have been conducted in other countries to measure the technical

efficiency by using production function, mathematical programming technique, panel

data, as well as using the cross-section data.

Determinants of inefficiency include some exogenous variables that have some

impacts on efficiency. Examples of such influences are age of the farmer, the education

level of the farmer, the size of the farm, access to credit, land tenure, farmers‟ capabilities

to use the inputs and so forth. The measurement of efficiency entails the determination of

factors influencing the overall efficiency. The most common approach to do this is the

determination of an inefficiency index (considered as the dependent variable) and then

regress the dependent variable against a set of explanatory variables considered to affect

the efficiency levels. Kumbhakar et al. (1991) proposed that the determinants of

inefficiency should be estimated simultaneously by noting that the two-stage procedures

introduce some bias in estimation. In the two-stage approach, efficiency scores

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determined in the first stage regression are regressed by background and production

environment related factors (Pitt and Lee, 1981). This approach contains serious

problems concerning assumptions made for the non-negative random variable, ui.

Moreover, the second stage specification conflicts with the assumption that uis are

independent and identically distributed. This second stage was criticised by Battese and

Coelli (1995) and Wang and Schmidt (2002). Like Kumbhakar et al. (1991), Battese and

Coelli (1995), Huang and Liu (1994) also proposed similar models for incorporating

technical inefficiency effects.

There have been very few studies undertaken in Bangladesh that measured the

determinants of technical efficiency. Khan et al. (2010) investigated technical efficiency

of a sample of 150 Bangladeshi rice farmers. Separate Cobb-Douglas production frontiers

were estimated for boro and aman rice producers. The mean technical efficiency scores

reported were 95% and 91% respectively and the result indicated that farmers‟ education

had a significant influence on technical efficiency of boro rice producers. Rahman and

Rahman (2009) examined how the land fragmentation and resource ownership (land,

animal power and family labor) affected productivity and technical efficiency of rice

producers in Bangladesh, using survey data from farms. They estimated the mean

technical efficiency to be 91 % and the efficiency differentials were markedly influenced

by land fragmentation and resource ownership. Asadullah and Rahman (2009) examined

the influence of education on farm production efficiency for a large dataset obtained from

141 villages and their analyses revealed that household education significantly reduced

production inefficiencies. Wadud (2003) used both Data Envelopment Analysis (DEA)

and Stochastic Frontier Approaches (SFA) to examine the technical, allocative, and

economic efficiency of a sample taken from 150 farm households and found high level of

technical efficiency. The technical efficiency was explained by land degradation and

irrigation infrastructure. Coelli et al. (2002) used DEA and examined technical, allocative,

costs, and scale efficiencies for the modern Aman and modern Boro rice from a total of

406 sample households. They reported technical efficiency of 66% for Aman rice

whereas a technical efficiency of 69% was reported for Boro rice. Sharif and Dar (1996)

examined how education, growing experience, and farm size influenced technical

efficiency for HYV Boro rice using a two step procedure, and found that education was

positively related to technical efficiency. However, there have been no studies on farm

level technical efficiency and its determinants focusing on financial factor by way of

microfinance. This study introduced a new explanatory variable named access to

agricultural microfinance which was not examined in the previous studies as a potential

determinant of efficiency in rice farming. The study thus attempts to test the hypothesis

that access to agricultural microfinance affects rice production efficiency.

Given that little attention has been devoted to quantify and identify the

determinants of technical efficiency, the present study aims to estimate the determinants

of technical efficiency and each factor‟s contribution to inefficiency. The present study

chooses the appropriate functional form of the inefficiency component and a suitable

production function model that fit the data most based on several empirical hypotheses.

Another justification of this study is the introduction of a flexible production function

rather than the commonly used SFA using Cobb-Douglas and or DEA in estimating

technical efficiency. Further, proposing microfinance as a determinant of technical

efficiency of farmers in Bangladesh is a substantially different policy variable. The

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present paper contributes to the literature in three ways. First, we incorporated the whole

farm rice production in the analysis and in doing so we assumed that the economic

situation of a farmer is better represented by aggregate production of crops, second, we

estimated and identified the determinants of the whole farm rather than for a specific rice

crop and thus gave recommendations to the policy makers to formulate policies that

improve farm productivity, third, our data reinforces some theoretical arguments that

extension visits, education, access to finance and regional variation may have on farm

productivity and efficiency. An understanding of these relationships can provide the

policy makers with information about the nature of the problems facing the rice farms in

Bangladesh and to design programs that improve efficiency.

The rest of the paper is organized as follows: Section two outlines the theoretical

model. Section three describes the methodology, study areas, survey method, and list of

variables of collected data. Section four specifies the models and the results are discussed

in section five. Section six concludes.

ANALYTICAL FRAMEWORK

According to Farrell‟s (1957) model, technical efficiency (TE) is defined as the ability of

a farm to obtain the best production from a given set of inputs (output-increasing

oriented), or alternatively as the measure of the ability to use the minimum feasible

amount of inputs to produce a certain level of output (input-saving oriented) (Greene,

1980; Atkinson and Cornwell, 1994). Consequently, technical inefficiency is defined as

the extent to which firms fail to reach the optimal production. Farrell (1957) proposed to

measure TE of a farm by comparing its observed output to that output which could be

produced by a fully efficient farm, given the same bundle of inputs. Aigner et al. (1977)

and Meeusen and van den Broeck (1977) independently proposed the stochastic frontier

(SF) production function to account for the presence of measurement errors and other

noise in the data, which are beyond the control of managers. Farmers in general operate

under uncertainty and therefore, the present study employs a stochastic production

frontier approach for measuring TE. Following Battese and Coelli (1995), the following

stochastic frontier production function and inefficiency effects model are estimated

simultaneously using single stage with the computer program, FRONTIER 4.1,

developed by Coelli (1996).

Following their specification, we specify the general SF model defined as:

iii xfy );( i = 1, 2, …, N (1)

Where, yi is the revenue from rice for the i

th

farm, xi is a vector of k inputs (or

cost of inputs), β is a vector of unknown parameters to be estimated, )(f is a suitable

functional form for the frontier (Cobb-Douglas, translog or quadratic), εi is an error term,

and N is the total number of observations. The stochastic frontier production is also called

„composite error‟ model, because it postulates that the error term εi is decomposed into

two components: a stochastic random error component (random shocks/white noise) and

a technical inefficiency component defined as follows:

77

εi ii uv

(2)

Where vi is a symmetrical two sided normally distributed random error that

captures the stochastic effects outside the farmers‟ control (for example, weather, natural

disasters, omitted variables, luck, exogenous shocks, measurement errors, and other

statistical noise). It is identically, independently and normally distributed vi~iid N (0, 2

v ), independent of the uis. Thus, vi, allows the production frontier to vary across farms,

or over time for the same farms and therefore, the production frontier is stochastic in

nature. The term ui (asymmetric non-negative error term) is a one sided (u

i ≥ 0) efficiency

component that captures the technical inefficiency of the ith

farm. This may follow a half-

normal, exponential, truncated-normal or gamma distribution (Stevenson, 1980; Aigner et

al., 1977; 1990; Meeusen and Broeck, 1977). In this study we assumed that ui follows the

exponential distribution as was done in various published studies in applied stochastic

frontier literature. It is obtained by the truncation at zero of the normal distribution with

mean μ, and variance (2

u ). If μ is pre-assigned to be zero, then the distribution is half-

normal. The variance parameters of the model are parameterized as:

, 22222

suuvs So that 0 ≤ γ ≤ 1 (3)

The parameter γ must lie between 0 and 1. Here, 2

s denotes the total variation

in the dependent variable due to technical inefficiency (2

u ) and random shocks (2

v )

together. The gamma ( ) parameter explains the impact of inefficiency on output. The

maximum likelihood estimation (MLE) of equation (1) provides consistent estimators for

β, γ, and 2

s parameters. Aigner et al. (1977) expressed the likelihood function in terms

of the two variance parameters, 222

vus and vu / . Battese and Corra

(1977) suggested that the parameter, 22

/ su , be used because it has a value

between zero and 1 and this property permits to obtain a suitable starting value for an

iterative maximization process, whereas the -parameter could be any non-negative

value. A value of closer to zero implies that much of the variation of the observed

output from frontier output is due to random stochastic effects, whereas a value of

closer to one implies proportion of the random variation in output explained by

inefficiency effects or differences in technical efficiency.

SURVEY DATA

The Study Areas and Sampling Methods

Data were collected from twelve villages in north-west and north-central regions in

Bangladesh through a survey conducted in June-August 2009. These regions were

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selected due to their high levels of poverty and good agricultural potential. For

microfinance users, data were collected with the help of Microfinance Institutions‟ (MFIs)

clients‟ lists. Personal interviews were conducted for both the users and non users of

microfinance to collect the data. We interviewed 180 agricultural microfinance borrowers

and 180 non-borrowers (the control group) of agricultural microfinance who operated

farm land between 0.2 to 1 hectare. This land holding criteria was set by the microfinance

institutions while granting agricultural microfinance loan. Non-borrowers are selected

based on similar land holdings and socio-economic background to provide a control

group for comparison with borrowers. As the entire sample is used in explaining

efficiency among the sampled farms, there are no sample-selection issues as well.

Farmers having land more than 1 hectare but taking microfinance exclusively for

agriculture were also considered. Data were collected from the farmers producing Boro,

Aman, and Aus rice crop from the selected areas. As most farmers in Bangladesh are

illiterate, most of them, with some exceptions, do not keep any vouchers or written record

of input prices as well as do not maintain any written documents about input- output data.

With a view to minimizing errors stemming from reliance on farmers‟ memory, data were

collected immediately after the harvest from the three growing seasons.

In conducting the research, multistage sampling technique was used. The first

stage was the purposive selection of two districts (Mymensingh and Sherpur) form north-

central and four districts (Rajshahi, Naogoan, Dinajpur, and Gaibandha) from the north-

west region in Bangladesh. The second stage involved the identification of those farmers

who had taken microfinance specially allocated for agricultural production. Finally, a

multi-stage proportional random sampling method was used to select 60 households (30

from microfinance borrowers and 30 from non-borrowers of microfinance) from each

district, thus a total of 360 households were surveyed.

Description of the Data

Output is defined as the market value of the aggregated rice production in the

survey period. Rice output prices were gathered from individual farms. All rice (Boro,

Aman, Aus) produced on the sample farms were aggregated into one output value (Taka3).

Land represents the total amount of land (own-cultivated land, sharecropping land, and

rented/leased land) used for rice production and was measured in hectare. Labor includes

both family (imputed for hired labor) and hired labor utilized for pre and post planting

operations and harvesting excluding threshing. It was measured in annual labor-days used

for rice production. Fertilizers include all sorts of organic and inorganic fertilizers used

by the farm households for rice production. It represents the total cost of fertilizer

measured by market prices. Seeds included all seeds used in rice production and was

measured in Taka. If seedlings were purchased, it was converted into equivalent amount

of seeds to compute the seed price.

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TABLE 1. DESCRIPTIVE STATISTICS

Variables Unit Mean Std. Dev. Minimum Maximum

Output Taka 88326 98980 4200 795000

Land Hectare 1.25 1.64 0.09 13.47

Seeds Taka 3039 6550 70 102080

Fertilizers Taka 12584 19544 0 180200

Labor Days 199 175 12 1274

Irrigation Taka 7326 11125 0 155000

Pesticides Taka 1334 2670 0 27000

Other variable

costs

Taka 2976 3780 0 36960

Tractor & animal

power

Taka 5922 7688 160 67200

Capital Taka 42117 82303 40 902100

Age of farmer Years 42 13 17 85

Education Years 4.99 5 0 16

Extension No. 6 7 0 24

Off-farm income Taka 61151 78094 0 600000

Experience Years 23 13 1 70

Numbers of plots No. 4 2 1 10

Source: Computed by the authors.

Irrigation represents the total irrigation costs for rice production. This cost is estimated

from total rice land irrigated and it was measured in Taka. Tilling includes the total land

tilled with tractor and or bullocks. It represents the total cost of tilling measured in Taka.

Other costs include pesticide, seed bed preparation, and crop transportation costs and it

was measured in Taka. Capital is the sum of farm tools, machineries and animal power

used in rice production and was measured in Taka. A large set of data were also collected

about the farmers‟ socio-economic characteristics and other aspects such as the farmer‟s

age, years of schooling, access to credit, numbers of contacts with extension agents,

wealth, investment, institutional constraints to get loan, land ownership etc.

Some basic characteristics of the sample farms are presented in Table 1. It is

evident that farms were small in terms of output and total area farmed. On average each

farm produces rice worth Taka 88326 and it is highly variable ranging from Taka 4200 to

Taka 795000. Farm operators averaged 42 years old and it ranged from 17 years to 85

years. Approximately 98% of the farm households were adult. Their experience in rice

farming was vast and it ranged from 1 year to 70 years while their education level was

moderate.

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MODEL SPECIFICATION

Stochastic Frontier Production (SFP)

We specify a log-quadratic production function as introduced by Chu, Aigner and

Frankel (1970) to estimate the stochastic frontier production function. We used a less

restrictive log-quadratic specification that takes into account both the Cobb-Douglas

specification and translog second order (excluding the cross-term) log-linear form.

The following quadratic model was specified in this study:

8

1

16

9

2

0 )(loglnlnj j

iiijjijji uvxxy (4)

Where yi represents the value of rice output of ith

farm and j is the jth

input used

in production. ln = natural logarithms , X1 = Total Land used for rice production; X2 =

Farm Capital ; X3 = Total labor days used ; X4 = Costs of Fertilizers; X5 = Irrigation costs ;

X6 = Seeds costs ; X7 = Tractor and animal power costs, and X8 = other variable costs .

Inefficiency Model for the Cross Section Data

The technical inefficiency (u

i) could be estimated by subtracting TE from unity. The

function determining the technical inefficiency effect is defined in its general form as a

linear function of socio-economic and management factors. It can be defined in the

following equation:

8

1

0

k

ikkiu (5)

Where, ui is the technical inefficiency effect, δk is the coefficient of explanatory

variables. The Zi variables represent the socio-economic characteristics of the farm

explaining inefficiency and may not be functions of y. We proposed that the technical

inefficiency could be explained by the following determinants:

Zi1

= Age of the household head (years); Zi2

= Education (number of years of schooling of

the farmer); Zi3

= Experience (years); Zi4

= Off-farm income (in Taka); Zi5

= Land

fragmentation (it includes the total number of plots operated); Zi6

= Extension visits

(number); Zi7

= Access to microfinance (A dummy variable to measure the influence of

microfinance on efficiency. Value is 0 if the farmer had cash credit in the last 12 months

prior to the survey from microfinance institutions exclusively for agriculture, otherwise 1)

and Zi8

= Region (A dummy variable. It takes a value of 1 if the region is northwest and 0

otherwise).

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Hypotheses Tests

The following tests have been carried out for testing the functional forms, inefficiency

effects, and determinants of coefficients for rice farmers in the study areas:

(1) Frontier model specification for the data is Cobb-Douglas production function.

That is :0H C-D ( )0............: 4490 H is an adequate representation

of the production Function.

(2) Frontier model specification for the data is a Quadratic production function.

That is 0......: 1690 H is an adequate representation of the production

function. Here 169...... represent the quadratic terms.

(3) Frontier model specification for the data is a Translog production function. That

is 0......: 4490 H is an adequate representation of the production

Function. Here 449...... represent the quadratic terms and also the cross

terms.

(4) There is no inefficiency effect that is 0....: 8100 H

(5) The coefficients of determinants of inefficiency model equals zero that is

0.. 810 H

All the above hypotheses were tested using the generalized Likelihood Ratio

(LR) which is defined as: -2[L(H0)-L(H1)], where L(H0) and L(H1) are the values of

the likelihood function of the frontier model under null hypothesis and alternative

hypothesis respectively. The null hypothesis was rejected when CRL2

.2 . If the null

hypothesis was true, the test statistic had approximately a 2 -distribution or mixed

2 -

distribution with degrees of freedom equal to the difference between the number of

parameters specified in the null hypothesis and alternative hypothesis. If there is no

inefficiency effect, 0....: 810 H , then the test statistic is distributed

like a mixed 2 -distribution with degrees of freedom equal to 9. All the hypotheses are

conducted assuming 05.0 . Thus, if the 2 statistic exceeds the 95% point for the

appropriate 2 -distribution, the null hypothesis would be rejected. The critical value for

the Likelihood ratio for was obtained from Table 1 of Kodde and Palm (1986).

Output Elasticity ( j )

The rice output elasticity for land, labour, fertilizers, irrigation, seeds, tractor hours,

capital, and other variable costs are included in the regression of interest. The output

elasticity ( j ) with respect to inputs were computed for the quadratic model as follows:

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y

x

x

xf ij

ij

ij

j

),ˆ( ) (6)

For quadratic terms, we may represent the above elasticity in the following equation:

j =y

xx

ij

ijjj )ˆ2ˆ( )8( (7)

Where, ijx is the mean of thj input, y is the mean production estimated at mean

inputs, j is the estimated coefficient of the X term and )8(ˆ

j is the estimated

coefficient of the 2X term.

Returns to Scale (RTS)

Returns to scale is equal to the sum of marginal production elasticises of each input. It is

defined in the following equation:

RTS =

8

1j

j (8)

RESULTS AND DISCUSSIONS

The MLE of the parameters of the Cobb-Douglas stochastic frontier production function,

the quadratic production function, and the translog model were obtained using computer

program NLOGIT 4.0 (Greene, 2007). The results are presented in Table 2. To select the

most suitable model (Cobb-Douglas, quadratic, or translog) we tried all models with

different distribution assumptions of the error component (ui) and tested all models with

the results of Log likelihood at the predetermined critical value ( )71.295.0),1(2 to reject

or accept one model over another. First, we tested the Cobb-Douglas with the translog to

determine whether Cobb-Douglas fitted the data by using the likelihood ratio test4. We

rejected the null hypothesis and excluded this model from further consideration. Finally,

we compared the translog model with the quadratic function and found that linear-

quadratic model fitted the data well with the expected signs for production coefficients

and with the results of hypothesis, which assumed the error term to be exponential. The

estimates of the quadratic stochastic frontier production are presented in Table 2. The

result revealed that with the exception of fertilizer all the explanatory variables conform

to prior expectation of signs of the coefficients for the quadratic production function with

nine coefficients significant at different significance levels and suggesting that model

fits the data well.

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TABLE 2. MAXIMUM LIKELIHOOD ESTIMATES OF COBB-DOUGLAS AND

QUADRATIC STOCHASTIC FRONTIER PRODUCTION FUNCTION

Variables Parameters Cobb-Douglas ML Estimaste Quadratic ML Estimates

Intercept

0 4.665*** (21.59) 1.329 (1.13)

Ln (Land)

1 0.526*** (14.21) 0.742***(3.04)

Ln (Capital)

2 -0.016** (-2.00) 0.075 (1.14)

Ln (Labor)

3 0.160*** (4.21) 1.079***(3.07)

Ln (Fertilizer)

4 0.021 (1.00) -0.085**(2.36)

Ln (Irrigation)

5 0.030*** (2.73) 0.017 (1.06)

Ln (Seed)

6 0.103*** (3.96) 0.198(0.188)

Ln (Tractor and bullock)

7 0.1020*** (3.52) 0.631***(2.67)

Ln (Other variable costs)

8 0.102*** (4.86) 0.011(0.31)

[Ln (Land)]2

9 -0.023(-1.00)

[Ln (Capital)]2

10 -0.005 (0.004)

[Ln (Labor)]2

11 -0.068***(-2.72)

[Ln (Fertilizer)]2

12 0.010*** (3.33)

[Ln (Irrigation)]2

13 0.0037* (1.95)

[Ln (Seed)]2

14 -0.008(0.66)

[Ln (Tractor)]2

15 -0.034**(-2.27)

[Ln (Other variable costs)]2

16 0.008***(2.67)

Variance parameters

)/( 222vuu

0.475 0.2529

)(222

vus 0.20

0.119

Log Likelihood -152.87 -125.843

2

v 0.0889

2

u 0.0301

Inefficiency effects

Constant

0 -2.184 (-0.54)

Age

1 0.003 (0.17)

Education

2 0.017 (1.13)

Experience

3 -0.057***(-2.71)

Off-farm Income

4 0.001 (1.00)

84

Number of plots

5 0.004**(2.00)

Extension Visits

6 -0.024 (-0.88)

Access to microfinance

7 0.132 (0.62)

Region

8 0.154 (0.38)

Source: Computed by the authors.

Notes: t-statistics are in parentheses; *, **, *** indicate significance at 10%, 5% and 1% level, respectively, Log Likelihood

under OLS estimates is -127.790

The significant positive coefficients of land, labour, and tractor imply that as

each of these variables is increased, rice production also increases. One explanation of

negative coefficient of fertilizer may be due to the wrong application leading to excessive

use of urea as source of N fertilizer since it is relatively cheap and use very little of

expensive fertilizers like P and K. Lack of farmers‟ knowledge about the need for

balancing the application of fertilizer is another plausible reason of this negative sign.

Government subsidy for fertilizer in Bangladesh may also encourage the farmers to use

too much urea and it may have long term damaging effects on the long-term productivity

of soil. Since the fertilizer dealers are more responsive than the government to local

fertilizer requirements and preferences, government may encourage the dealers to guide

and motivate the farmers in maintaining an optimum nutrient balance on the farms while

selling fertilizers to the farmers. Other independent variables such as seeds, irrigation,

and other variables costs have positive coefficients but are insignificant under quadratic

production function. For labor, the poor performance is attributed to high average man

days of labour (199). This is an indication of over-utilization of labor as is typical of

developing countries.

Analysis of Productive Efficiency

The result of the TE estimates is presented in Table 3. The TE analysis revealed that

technical efficiency score of sample farms varied from 16.22% to 94.47%, with the mean

efficiency level being 83%. The mean technical efficiency implies that the average farm

produces 83% of the maximum attainable with given input levels. This variation is also

confirmed by the value of gamma ( ) that is 0.25. The gamma value of 0.25 suggests

that 25% variation in output is due to the differences in technical efficiencies of farm

household in Bangladesh. This finding establishes the fact that inefficiencies exist in the

sampled farmers. Moreover, the corresponding variance-ratio parameter5,

* , implies that

11% differences between observed and maximum frontier output for rice farming is due

to the existing differences in efficiency among the sample farms.

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TABLE 3. SUMMARY OF TECHNICAL EFFICIENCY OF THE RICE

FARMERS

Efficiency level Frequency Percentage

0.10-0.19 1 0.28

0.2-0.29 0 0

0.3-0.39 4 1.11

0.4-0.49 2 0.56

0.5-0.59 6 1.67

0.6-0.69 13 3.61

0.7-0.79 65 18.05

0.8-0.89 222 61.67

0.9-0.99 47 13.05

Total farms 360 100

Mean 82.65

Standard Deviation 9.84

Min. 16.22

Max. 94.47

Source: Computed by the authors.

The indices of TE indicates that if the average farmer of the sample could

achieve the TE level of its most efficient counterpart, then average farmers could increase

their output by 12% approximately [that is, 1-(83/94)]. Similarly the most technically

inefficient farmer could increase the production by 83% approximately [that is, 1-(16/94)]

if he/she could increase the level of TE to his/her most efficient counterpart. Since the

mean TE is 83%, it can be deduced that 17% of the output is lost due to the inefficiency

in rice producing system or in the inefficiency among the sampled farmers or both

combined. It also indicates that small farms in the study area, on average, can gain output

growth at least by 12% through the improvements in the technical efficiency. For a land

scarce country like Bangladesh this gain in growth will help much to ensure food security

in the country. These findings may invite attention of the policy makers to improve the

efficiency of the farmers through adoption of right policies. Results of the Hypotheses Test

The formulation and results of different hypotheses (model selection, inefficiency effect,

determinants of coefficients) are presented in Table 4. All the hypotheses are tested by

using generalized likelihood-ratio (LR). The first hypothesis relates to the

appropriateness of the Cobb-Douglas functional form in preference to translog model.

The computed LR statistic exceeded the tabulated value of 2 at 5% significance level.

So, we rejected the null hypothesis by indicating that the translog functional form is a

better representation of the data.

86

TABLE 4. SUMMARY OF HYPOTHESES FOR PARAMETERS OF

STOCHASTIC FRONTIER AND INEFFICIENCY EFFECTS MODELS

Null Hypotheses L(H0 L(H1 ) LR 2 critical

value

Decision

1.Production Function is

Cobb-Douglas

( )0............: 4490 H

-152.867 -111.849 82.04 49.23 Reject H0

2. Production Function is

Cobb-Douglas

( 0......: 1690 H )

-152.867 -125.843 54.05 14.85 Reject H0

3. Production function is

Quadratic

0......: 4490 H

-125.843 -111.849 27.99 41.98 Accept H0

4. There is no inefficiency

effect

(H0: =0)

-127.790 -125.843 3.89 2.71 Reject H0

5. The coefficients of

determinants of inefficiency

model equals zero

0.... 8100 H

-116.24 -125.843 19.21 16.27 Reject H0

Source: Computed by the authors.

The second hypothesis relates to the appropriateness of the Cobb-Douglas in

preference to the quadratic functional form. This hypothesis was also rejected at 5% level

of significance and indicated that quadratic functional form is a better formulation than

the Cobb-Douglas functional form. The third hypothesis relates to the appropriateness of

the quadratic functional form in preference to the translog functional form. The computed

LR statistic fell below the tabulated value of 2 at 5 % significance level and we failed

to reject the null hypothesis indicating that the quadratic functional form was the best fit

for the data. Therefore, we selected this functional form in our analysis. The fourth

hypothesis stated that 0 , is rejected at the 5% level of significance confirming that

inefficiencies exist and are indeed stochastic (LR statistic 3.89> 71.22

95.0,1 ). The fifth

hypothesis that dd 0...00 , which means that the technical inefficiency

effects were not related to the variables specified in the inefficiency effect model, is also

rejected at the 5% level of significance (LR statistic 19.21> 27.162

95.0,9 ). Thus the

observed inefficiency among the rice farmers in Bangladesh can be attributed to the

variables specified in the model and the variables play a significant role in explaining the

observed inefficiency.

87

Elasticities and Returns to Scale

Table 5 reports output elasticity estimates with respect to eight production inputs used

and were evaluated at the sample means. Furthermore, the last column of the same table

gives the scale elasticities for combined inputs. Scale elasticity exceeds unity thus leading

to the conclusion that rice producers operate in the region of increasing returns to scale.

The sample mean of RTS, 1.04, indicates that the farmers could be made scale efficient

by providing more input to produce more output with the exception of capital (tractors,

buildings, machineries).

TABLE 5. ELASTICITIES AND RETURN TO SCALE OF THE QUADRATIC

FRONTIER PRODUCTION FUNCTION

Independent Variable Mean Value Elasticities RTS

Land 1.25 ha 0.505 1.04

Labor 199 labor days 0.106

Fertilizer Taka 12584 0.093

Irrigation Taka 7326 0.076

Seeds Taka 3039 0.080

Tractors and animal power Taka 5922 0.070

Capital Taka 42117 -0.016

Other variable costs Taka 2976 0.128

Source: Computed by the authors.

The negative sign of capital implies low marginal increments to total output if more

capital is provided. One explanation may be that farmers in Bangladesh are mostly

subsistence farmers and operate very small size of land (Table 1). This leads to increasing

the opportunity cost of capital items like tractor and other expensive cultivating and

harvesting machineries. The elasticity of output with respect to land is the highest among

all the inputs, which demonstrates the importance of scarce land in boosting rice

production in Bangladesh. It is concluded that land had the major effect on the total value

of rice production. The policy implication of this finding is that government could give

incentives and encouragement to the farmers to keep their existing arable land and bring

the remaining fallow land under cultivation, if any. Elasticity of labor is the third highest

but excess use of the labor exerts negative impacts on output as is observed from the

second order of labor. Both fertilizers and irrigation should be utilized efficiently to

ensure optimum growing conditions of land since their inappropriate utilization may have

far reaching impacts through degradation of land and its soil quality.

88

Factors Explaining Inefficiency

The parameters of the explanatory variables in the inefficiency model were

simultaneously estimated in a single stage using computer program, FRONTIER 4.1. The

dependent variable of the model was inefficiency and the negative signs imply that an

increase in the explanatory variable would decrease the corresponding level of

inefficiency. Lower part of Table 2 shows the coefficients of explanatory variables in the

inefficiency model. The results show that most of the signs related to inefficiency

determinants were as expected. The parameter estimates showed that factors such as age,

education, number of plots, region (dummy variable), access to microfinance (dummy

variable), and off-farm income were positively related with inefficiency while extension

visits and experience were negatively related to inefficiency.

The age coefficient is positive but insignificant, which indicates that younger

farmers are more efficient than older farmers. This conforms to the results obtained by

Coelli and Battese (1996) and Battese and Coelli (1995). A possible explanation might be

that the adoption of new technology and managerial capability to carry out farming

activities decreases with age. However, its inclusion in the inefficiency model improved

the model‟s explanatory power.6 The analysis revealed that education, measured in terms

of years of schooling, had a statistically insignificant effect on technical inefficiency.

This result conforms to those obtained by Wadud (2003) and Coelli and Battese (1996). It

can be deduced that five or more years of formal education are required before increases

in efficiency can be observed. The off-farm income variable is positively and

insignificantly related with technical inefficiency. This indicates that higher off-farm

income increases the technical inefficiency of rice farmers. It also implies that the more

off-farm hours a producer works, the less time is devoted to farming, thus resulting in

higher technical inefficiency. This result is consistent with Abdulai and Eberlin (2001)

and Coelli et al. (2002). The estimated coefficient of farming experience had a significant

negative impact on technical inefficiency, which implies that rice farmers‟ expertise

assists them in ensuring the optimal timing and use of inputs and thereby reduces their

technical inefficiency. Another probable reason for the significant negative contribution

of experience on technical inefficiency could be that farmers with more years of

experience tend to gain more proficiency through „learning-by-doing‟ in uncertain

production environment. Several other empirical studies have also reported similar results

(Bozoglu and Chehan, 2007; Huffman 2001; Kalirajan and Flinn, 1983).

The estimated co-efficient of the number of plots operated by the farm

household is positive and significant. It implies that the more the lands are fragmented,

the more the technical inefficiency increases. That is farmers with less fragmented land

will operate at higher technical efficiency levels. This result is also consistent with those

of Wadud (2003), Wadud and White (2000) and Coelli and Battese (1996). Higher

technical efficiency associated with less fragmented land can be attributed to adopting

modern technologies and better farm practices such as the use of irrigation (Wadud,

1999). Extension visits were negatively related with technical efficiency. Although not

significant, however, the extension visits may be an important policy instrument by

which the government could raise agricultural productivity since the agricultural

extension visits enable the farmers to learn better farm management methods and more

efficient uses of limited resources. The policy implication of this finding is that the

89

government could support further the agricultural extension network in order to make the

interactions between the farm and extension agent more participative and field oriented

through practical demonstrations rather than just conveying some recommendations.

The coefficient of the access to microfinance (dummy variable) was positively

related to technical inefficiency. It indicates that those farmers who did not have

agricultural microfinance, tended to have higher technical inefficiency levels than their

peers. It implies that access to microfinance reduces the technical inefficiency of the

sample farms as the estimated average technical efficiency of microfinance borrowers

was 84% and for the non-borrowers the average technical efficiency was 81%. The

difference in mean technical efficiency is also significant at 5% significance level.7 The

surveyed farms in the study areas faced acute shortage of working capital for farming.

Average loan obtained by the microfinance borrowers for agriculture was Taka 16673

while the average demand was Taka 39383.8 The shortage of working capital due to the

increasing price of inputs as well as the low returns to farm produce resulted in high level

of technical inefficiency. Most rice farms faced negative cash flow during the planting

and growing period due to the time lag of purchasing the inputs and receiving the returns

long after the crops are harvested. Credit thereby helps to mitigate the financial constraint

and to reduce inefficiency. This finding also conforms to the results of Binam et al.

(2004). Credit also helps the farmers to increase farm revenue while lack of credit

decreases the efficiency of the farmers by limiting their adoption of high yielding

varieties and acquiring of information for increased productivity, a view supported by

Wozniak (1993).

Thus, improved access to agricultural microfinance remains an important issue

for improving the rural farm production efficiency in Bangladesh. Another implication of

this finding is that farmers who are indebted need to meet their repayment obligations and

this puts more pressure on the farmers to produce more output to repay the loan by

generating more cash. For microfinance borrowers, the future possibility of getting loan

depends on current repayment behaviour and this implicit pressure to repay the loan acts

as a catalyst to optimize the resources to produce more. The dummy variable region is

positively related with technical inefficiency. Thus farmers operating in the north western

region perform less efficiently compared to those of the north central region. This finding

reinforces the argument that regional concentration is a vital policy instrument that

should be addressed in formulating agricultural policy in Bangladesh.

CONCLUSION This study uses a quadratic stochastic frontier production function on survey data (2009)

to determine the technical efficiency and its determinants in rice production in north-

central and north-western region of Bangladesh. First, we draw conclusion on the

methodology choice of production technology. This was based on some selected

hypotheses and we concluded that traditional production function model was not

adequate for farm level analysis. Consequently, we proposed the quadratic stochastic

production function. Second, the results revealed that mean technical efficiency of farms

was 0.83, indicating that there are opportunities to gain substantial additional output or

decreases the inputs, given the existing technology and resource endowments of rice

farmers in the study areas.

90

The empirical results revealed that inefficiency exists in the rice production

systems and we found farmers‟ experience and extension visits negatively affected

technical inefficiency whereas factors such as access to microfinance, regional dummy,

off-farm income, age, education, and land fragmentation positively affected technical

inefficiency. In particular policies leading to granting access to microfinance, raising the

educational level of farmers, ensuring land preservation for agricultural purposes, and

ensuring sufficient returns to the farmers could be beneficial for reducing inefficiency in

rice production in Bangladesh. The findings of the relationship between microfinance and

technical efficiency suggest that improving greater access of farmers to agricultural

microfinance will improve production efficiency. Consequently, streamlining the

microfinance to the credit constraints farmers would be vital factor in increasing farm

technical efficiency and revenue. However, this is a multi-disciplinary work that needs to

be addressed out more rigorously by the government policy makers in collaboration with

Non Government Organizations (NGOs) and the donor agencies.

To improve farmers‟ access to microfinance, at first policies geared towards

addressing the features of agricultural microfinance products are vital. These policies

should include substantial modifications to conventional operational methodologies of

agricultural microfinance and should take into account the seasonality of crops and farm

incomes. Such modifications should match the heterogeneous households‟ demands and

enable farmers to afford credit by devising flexible repayment schedules. Second,

effective linkages between the rural MFIs with liquidity constraints and mainstream

banks with excess liquidity may minimize the demand-supply gap and ensure greater

access to microfinance for those farmers that are largely excluded or untapped by the

MFIs. Third, the establishment of „poor-friendly‟ microfinance banks to improve the

access of farmers to finance without collateral and at reasonable cost is suggested. The

delivery of such tailor-made agricultural microfinance that is backed up by direct support

from the government through regulatory framework and institutional innovations, would

improve farmers‟ access to microfinance. This in turn, may lead to more efficient

allocation of resources and increased production through improved efficiency.

Policies leading to the improvement of farm education and land holding will be

favorable for improving the technical efficiency of farmers. More investments in

education in rural areas through private and public partnerships, initiating programs to

encourage those at school-going age and „food for education‟ programs may be harnessed

as a central ingredient in the development strategies. Moreover, the farmer field schools

(FFS) program, promoted by different development agencies including the World Bank,

may be rigorously implemented and practiced. This would help farmers develop their

„learning by doing‟ practices and improve their analytical and decision making skills that

contribute to adapting to improved farming technologies. Initiating well-designed adult

literacy programs that have direct impact on household production could also contribute

to ensuring basic literacy and numeracy skills for the farmers.

The land fragmentation problems in Bangladesh should be directed through

addressing the law of inheritance of parental property, developing the land market and

tracing the causes of such fragmentation. The broad policy and legal measures that may

be devised should include, inter alia, revising the laws of inheritance and land tenancy

91

and motivating the small and marginal farmers to consolidate their lands through creating

viable farms. Enlargement of farms through forming cooperatives, encouraging voluntary

exchange of plots to form larger unitary plots, motivating farmers to buy and enlarge

contiguous plots by selling discrete distant plots, passing legislation that supports such

consolidation and formulating national land use policy will restrict land fragmentation

and are recommended.

These measures, if addressed in national agricultural policy formulation, may

direct the farmers‟ production frontier upward in the long run, which may in turn, reduce

technical inefficiency on the one hand and lead to food security through increased

production on the other hand.

ENDNOTES

1Aus and Aman are local breed crops and typically known as traditional rice crop.

2Boro is the irrigated rice crop and typically known as High Yielding Variety rice.

3USD 1= Taka 69.15 approximately; Euro 1=Taka 93.52 (as of April 13, 2010).

4The likelihood-ratio test statistic, -2{ln[likelihood (H0)]-ln[likelihood(H1)]}, has

approximately chi-square distribution with parameter equal to the number of parameters assumed

to zero in the null hypothesis, (H0), provided.

5 is not equal to the ratio of variance of inefficiency to total residual variance. This is because

the variance of ui )(2

u is equal to [2]/)2( not

2 . The relative contribution of the

inefficiency effect of the total variance term )]2/)1(/[* (Coelli et al.,

1998).

6Age and experience are generally interrelated but their impacts on technical inefficiency are not

necessarily identical. In this analysis, the coefficient of age is positive while that of experience is

negative. This finding is in line with Coelli et al. (2002) and Bozoglu and Chehan (2007), who

found experience to be a better predictor of technical inefficiency than age for farm household

production efficiency.

7 The critical of t358 (0.05) is 1.96 and the t-test statistic is 3.56 and thereby suggesting significant

differences in averages of technical efficiency between microfinance borrowers and non-

borrowers of microfinance.

8 The results are not reported here but available on request from the authors.

92

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