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Scholars
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African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 6 (9), pp. 001-010, September, 2018. Available online 
at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s)                                           Author(s) retain the copyright of this article. 
 

 

Full Length Research Paper 

 

Assessing the technical efficiency of commercial 
egg production in Tanzania for improved 

livelihoods 

 
Oleke, J. M.1* and Isinika, A. C.2 

 
1
IITA-Tanzania, Mlalakuwa, (Cocacola) Road, Plot No. 25, Mikocheni Light Industrial Area.  P. O. Box 34441, 

Dar-es-Salaam, Tanzania. 
2
Institute of Continuing Education, Sokoine University of Agriculture, Morogoro, Tanzania. 

 
Accepted 24 April, 2018 

 
This study examined the productivity and technical efficiency (TE) of egg production in Tanzania under the 
intensive system. A multistage random sampling procedure was employed for selecting 80 respondents from 
two districts; Kibaha and Ilala. This study utilizes the most recent developments in stochastic frontier 
modeling as specified for a one-step process in Limdep software. Results indicated that the mean TE of egg 
production is 64%, ranging from 4 to 90%. Egg production was in the rational stage of production (stage II) as 
depicted by the returns to scale (RTS) of about 1.3. Thus, there is room for improving TE, which will raise net 
returns of egg production enterprises, hence, improving livelihoods of farmers and their families. 

 
Key words: Technical efficiency, intensive, egg production, stochastic frontier, Tanzania. 

 
INTRODUCTION 

 
About 94% of the total chicken population in Tanzania is 
kept in villages and in peri-urban areas under traditional 
free range system, in most cases owned by women 
(MAFC, 2008). The traditional poultry system is the 
largest, supplying more than 90% of poultry meat and 
eggs consumed in rural areas, and 20% of the same are 
consumed in urban areas. Despite the predominance of 
local chicken in Tanzania, Paul et al. (1990) argues that 
production of layers is comparatively a better source of 
earning cash, especially in urban and peri-urban areas 
because it offers higher net returns.  

Since the introduction of commercial poultry farming in 
Tanzania during the 1980s, visible growth in the 
production of layers has been observed. Small and 
medium enterprises have increased the numbers of 
layers from 27 million in 2001 to 38 million in 2008 while 
the commercial stock increased from 20 million to 25 
million. On average, 5.5 million hatching eggs and one 
million day old chicks are imported annually to produce  
 
 

 
*Correponding author. E-mail: J.Oleke@cgiar.org or 
jmasahi@yahoo.com. Tel: +255-23-2402370/1, +25578985090, 
+255785635086. Fax: +255-23-2402372. 

 
 
 

 
a total of 25 million day old chicks for commercial purposes 
(MLD, 2008). This figure is low compared to the actual 
requirement of 60 million day old chicks per year. Egg 
production has increased from 790 million in 2002 to 1.8 
billion in 2006 (Msami, 2008). The increase is largely due to 
sensitization on good poultry husbandry practices by farmers 
and increased use of thermo stable vaccine to control the 

New Castle Disease vaccine (MLD, 2008). The per capita 
consumption of eggs has also increased from 23 eggs in 
2002 to 50 eggs in 2008 per person per year. Although, 
the production of eggs has grown rapidly (about 2.5 
million eggs/year), still there is a big gap between the 
demand and supply in the country. Meanwhile, the per 
capita consumption of eggs in Tanzania at 50 
eggs/capita/year is quite low, compared with 106 eggs 
per person per year for Africa and 190 for high income 
countries (Gueye, 2004). The rising demand for eggs 
calls for more investments in the intensive layer 
production and the poultry industry as a whole. This 
improvement could further be sustained with a proper 
analysis of the technical efficiency (TE) of egg production 
so that net income increases, thereby contributing to 
reduce poverty.  

It is important to assess the TE of poultry farms 
especially now that the government is promoting 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


 
 
 

 

commercial oriented agricultural production as declared 
under the Agricultural Sector Development Programme 
(ASDP) and Kilimo Kwanza (URT, 2001, 2009). Technical 
efficiency becomes a critical factor for decision making in 
management and production systems because the cost 
of production is closely related to the productivity and TE 
of the farm. Knowledge on the efficiency of a farm can 
help to identify productivity gaps, including those  
related to socio-economic characteristics and 
management practices, which can subsequently be 
improved. Such knowledge should be used by extension 
workers for advising farmers to improve both the 
technical and economic aspects of their enterprises.  

This study was conducted in Kibaha district, Pwani 
region and Ilala district, Dar-es-Salaam region where 
there is a concentration of poultry farmers to feed the city 
of Dar-es-Salaam, currently estimated to accommodate 
over three million inhabitants. Dar-es-Salaam is the 
commercial center of Tanzania and hence the largest 
market for almost all agricultural commodities in the 
country. It is estimated that the city consumes about 
303,054 eggs per day on average, compared to only 
15,700 in Arusha, 28,730 in Mwanza and 64,118 in 
Mbeya, the other cities in Tanzania (Msami, 2008). 
Considering the difficulty of transporting eggs over long 
distances (in the absence of properly refrigerated and 
cushioned vehicles), most of the poultry farmers in 
Tanzania operate close to their targeted markets. In Ilala 
district, the suburb of Kitunda is famous for producing 
eggs, ferrying over 350,000 eggs to the city center daily 
mostly by bicycle or using small pick up trucks (Msami, 
2008). Kibaha town, which lies about 40 Km from the city 
center, along the highway to Zambia, is also famous for 
egg production to feed the city. The number of egg 
producers in Kibaha and Ilala districts is estimated to be 
around 8,333 and 11,160, respectively. This means, the 
poultry sub-sector plays an important role in providing for 
the livelihoods to over 18,000 families. Any information, 
which would improve the productivity of these poultry 
farms, if used by the extension services and the farmers, 
would contribute to improving farmers’ incomes thereby 
contributing to the goals of the national strategy for 
growth and poverty reduction, popularly known by its 
Kiswahili acronym (MKUKUTA), and the millennium 
development goals (MDGs). 
 

Often, yield defined in terms of eggs produced per 
laying period, is used as a measure of productivity in 
layers. But, this masks the reality regarding the efficiency 
of using inputs in the production system. Technical 
efficiency has been used for more rigorous economic 
analysis in order to identify specific areas of intervention 
to improve the performance of production systems. This 
study used TE to assess the performance of poultry 
farms in the study area, in order to recommend strategies 
for improvement.  

The main objective of the study is to assess productivity 
and measure the TE of producing eggs under 

 
 
 
 

 

the intensive production systems in urban and peri-urban 
areas of Ilala district in Dar es Salaam region and Kibaha 
district in Pwani region, and determine factors 
contributing to the gap in productivity potential. The 
study’s specific objectives include: 
 
1. To estimate the responsiveness of yield (value of eggs 
produced per annum per unit of variable input) to the 
main factors of production by estimating the elasticity of 
production of the inputs;  
2. To estimate the frontier production function and 
determine levels of technical efficiency for each egg 
producing farms in the sample drawn from Ilala and 
Kibaha districts;  
3. To evaluate the relationship between technical 
efficiency and selected farm characteristics. 
 
 
METHODOLOGY 
 
Conceptual framework 

 
The level of TE of a particular farmer is characterized by the 
relationship between observed production and some ideal or 
potential production (Greene, 1993), often measured as a ratio 
between the output of a particular farmer and the maximum 
possible output obtainable (frontier) using a given set of inputs 
under a given technology. The gap can be closed if the limiting 
technical and socio-economic factors are identified and addressed. 
This study sought to identify these limiting factors in order to 
suggest areas of focus for improvement.  

Analysis of the poultry farms in the study area shows that a small 
scale poultry farm as having up to 2,000 birds; a medium scale farm 
having between 2,001 and 4,999 birds and a large scale farm has 
above 5,000 birds. The deep litter system is used by all the farmers 
in the sample. They also almost exclusively buy feed, medicine and 
feed supplements from similar sources or their outlets. It is 
therefore, safe to assume that they all use very similar technology 
for egg production. In the absence of experimental data, the study 
used the performance of the best farmers in the sample to define 
the frontier against which the performance of all the other farmers 
was compared.  

Primary data for the study were collected between October and 
November 2007. First, Ilala and Kibaha districts were purposively 
selected based on the prominence of egg production relative to 
other districts in the vicinity of Dar-es-Salaam. The second stage 
involved a multistage sampling procedure in which two divisions 
were selected from each district, and two wards from each division. 
Then, 80 farmers (40 from each district) were randomly selected 
from the wards. Data was collected using a structured 
questionnaire, obtaining information on output, inputs and key 
economic and socio-economic variables as subsequently defined 
by the empirical models. 

 
Analytical model 
 
A stock of layers is normally raised for 18 to 24 months before it is 
replaced. Using data from sampled farmers, a stochastic production 
frontier was estimated to compute the TE of each farmer. 
Subsequently, the computed TE of each farmer was regressed 
against a set of socio-economic factors to identify the most limiting 
variable in the production system. A production function was used 
to define the stochastic production frontier as given in general form 
in Equation 1: 



  
 

 

 

(1) zero, with a distribution given as, N (0,δ
2
u) . 

However, ui can also have other distributions such as gamma and 
exponential. It is associated with farm-specific factors. The mean 

values of ui are determined by Equation (5): 

 

 

 

y i   b 0 ∏  x ij
b
 ij   e ε ij

 
  (2) 

where: yi  = output for the i
th

 respondent; for i = 1, 2, ………, n; xij 

= the j
th

 variable input for the i
th

 respondent; for i = 1, 2,., n and j = 1,  
2, ..k; Π = a steady multiplicative symbol; e = natural logarithm; ε = 
error term for the i

th
 respondent and the j

th
 input; b0 = a vector of a 

constant parameter; bij = a vector of parameter estimates for the i
th

 

respondent and the j
th

 variable input.  
The log liner transformation of Equation (2) gives Equation (3), 

which was used for parameter estimation, using frontier regression  

analysis as described by Kumbhakar et al. (1991):  

k  
Y

i  


 
β

0  


 ∑β

ij 

X
ij  


 
ε

i (3) 
j1 

 

where Yi = Ln (yi); β0 =Ln (b0); βij = Ln (bij); Xij = Ln (xij); εi = Ln (ei); 
Ln = natural logarithm 
 

Farell (1957) and Bylee (1991) disaggregated economic 
efficiency into allocative efficiency and technical efficiency (TE). In a 
perfectly competitive market, allocative efficiency occurs if the 
marginal physical product is equal to the ratio of the product price to  
 

the corresponding input prices (  . Allocative  

 

inefficiency is the failure for a farm to meet the conditions for profit 
maximization. Meanwhile, TE is defined as the ratio of the observed 
output to the corresponding output on the frontier, as estimated 
from the composed error term. In estimating the stochastic frontier 
production function, effects of the socio-economic characteristics of 
the household on the variation of the dependent variable (Yi) are 
often lumped together in the error term, which accounts for the 
component of variation due to random, unsystematic and 
unexplained noise. The error term (εi) in Equation (3) has two 
components ui and vi such that: 

 

ε i    vi   − ui (4) 

 

where νi represents a random error associated with random factors, 

over which the farmer has no control. It has a zero mean and a 

variance equal to δ
2
ν such that its distribution is given as N (0,δ

2
v). 

Meanwhile, ui represents the inefficiency component of the error 

term. It is a non-negative half normal random variable truncated at 

 

 
 

ui    ρi Zi (5) 
 

where Zi represents inefficiency variable for the i
th

 respondent; i = 

1, 2…, n; ρi = parameter estimates for the i
th

 respondent; δ
2
v and 

δ
2
u are the variances of vi and ui respectively. 

 

The inefficiency variables are represented by farm 
characteristics such as age, marital status, employment status, 
education of a farmer, family size, sex, location of the farm, access 
to credit and management because these traits account for 
performance differences between farmers, some being more 
inefficient that others. 
 

Equation (3) was estimated using the maximum likelihood 
method, on the basis of which, the TE of a production function was 
obtained from the conditional expectation of ui given εi as shown by 
Zaibet and Dharmapala (1999) and represented in Equation (6): 
 

E − ui 
 

 
δ u δ v  f 

*
 λε i  / δ   ε i λ 

 
 

   
 

ε i 
 

 

     

− 
 

 

 
 

δ  − F 
*
 λε  / δ  δ  

 

   
1 

i 
   

 

           (6)  
             

 

 

where δu = standard error of u, the random component of the error 

term; δv = standard of ν, the inefficiency component of the error  
 

term; λ = ratio of the standard deviation of the error terms, (  ); f*  
 

= value of a standard normal density; F* = value of the distribution 

function; δ 
2
  δ v

2
  δ u

2
 , components of variance of the error 

term, and  E− ui ε i  = the conditional mean of ui given εi, which 
  

measures expected value of the random component of the error 
term. 
 

Technical efficiency is measured as the mean of the negative 
value of the inefficiency component of the error term (-u) or  

), also as given by Equation (7) such that  0 ≤ TE ≤ 1: 
 

 

TEi    expE− ui 
 

ε i . 
 

 
 

   (7) 
 

 

This inefficiency component is influenced by many factors which are 
discussed next and their effect is determined subsequently. 
 

 

Empirical estimation 

 

To estimate the stochastic frontier, a two step process was 
followed. First, using a linear transformed Cobb-Douglas type 
production function as derived in Equation (3), the value of eggs 

produced per farmer over 24 months of production (Yi) was 
regressed against independent variables including the cost of 
production, bird stock size and feed intake as given in Equation (8): 
 

Yβ βX βX  βX βX  β lnX  (v −u ) 
i o 1 1ij 2 2ij 3 3ij 4 4ij 5 5ij ij ji 

 

(8) 
 

where Yi  = value of egg produced over 24 months period (TShs); X1 

where; Yi = total value of eggs produced over 24 months for the i
th

 
respondent; S = average value for stock of chicken over 24 months; 
F = total cost of feed used for production over 24 months; C = total 
cost of all the other (non-feed) inputs over 24 months. 

A rational producer strives to maximize profit by maximizing output 
(Y) while minimizing cost (F + C), thus obtaining economic efficiency. 
To establish the production frontier, a Cobb-Douglas type translog 
functional form with constant elasticity of supply (CES) was used to 
estimate parameter estimates of Equation 1, as derived further in 
Equations 2 to 4. A stochastic production frontier is based on the 
premise that a production system is bounded by a set of smooth and 
continuously differentiable concave transformation functions for 
which the frontier is the limit to the range of all production 
possibilities given the technology and the set of inputs used. 
Following from Zaibet and Dharmapala (1999), the multiplicative 
form of the production function is given in Equation 

(2
): 

Yi=f{S, F, C…} 



 
 
 

 
Table 1. Categorization of farm size by district.  

 
 

Category 
 Kibaha  Ilala  Sample 

 

 

(N) (%) (N) (%) (N) (%) 
 

  
 

 100-2000 28 70 40 100 68 85 
 

 2001-4099 8 20 0 0 8 10 
 

 2001-4099 4 10 0 0 4 5 
 

 
Source:  Oleke (2008). 

 
 
 
 
Table 2. Descriptive statistics on input use and output (N = 80).  
 
 

Standard measure Value of Stock 
Cost of Other Transport 

Feed intake Feed cost Total cost  

 
drugs operating cost  

 of  sample eggs (TShs) size (Kg) (TShs) (TShs)  

 (TShs) cost (TShs) (TShs)  

       
 

 Mean 56,601,289 1,071.8 563,925.6 3,339,139 440,491.8 38,938.9 10,881,347 15,586,883 
 

 Mode 30,960,000 300 713,750.0 7,626,000 120,000 7,118,500 7,118,500 20,262,652 
 

 Standard deviation 106,445,285    1,265.5416,530.3 4,111,895 2,093,954 16,296,275 16,296,275 1,832,590 
 

 Minimum 2,314,500 200 33,600 355,800 0 634,000 634,000 119,628 
 

 Maximum 753,857,260 6,000 1,592,000 22,650,500 18,602,575 127,342,569 12,7342,569 15,586,883 
 

 
Source: Oleke (2008). 
 
 

 

= cost of drugs (TShs); X2  = transport cost (TShs); X3  = feed intake FINDINGS AND DISCUSSION 
 

(Kg);  X4   =  stock  of  birds  (Number);  X5   =  other  operating  cost  
 

(TShs);vi  random  errors and ui represents  technical  inefficiency Description of farms  

effects.                
 

                
 

The  TE  of  each  respondent  was  computed  as  the  deviation  
 

between the antilog of Yi  and YP, (Yp-Yi) where Yp  is the maximum For this study, farms were classified by size according to 
 

potential output obtained by the best performing farmer and Yi is the the number of birds. Boki (2000) classified small farms as 
 

output of the i
th

 farmer. Then in the second step, based on Equation having up to 2,000 birds, medium sized farms had from  

(5),  using the Limdep  software,  the  maximum  Likelihood  method  

2,001 to 4,999 birds and large farms had 5,000 birds or  

was used to assess the effect of various socio-economic factors on  

more.   On   average,   farms   were   larger   in   Kibaha 
 

the variation of TE. The inefficiency component of the error term (ui) 
 

was  regressed  against  factors  that  are  thought  to  influence compared  to  Ilala  with mean  stock  sizes of  624.9  and 
 

deviation of observed output (Yi) from the potential output level (Yp). 518.7  birds,  respectively  (Table  1).  However,  for  the 
 

They are all represented in Equation (9):      entire  sample,  smaller  sized  farms  dominated  with  a 
 

                 sample  mode  of  300  birds.  About  85%  of  the  poultry 
 

u  ρ ρZ ρ Z ρ Z ρ Z ρ Z ρ Z   farms were small while 10% were medium and only 5% 
 

i 0  1 1  2 2 3 3 4 4 5 5 6 6  
were  classified  as  large.  The  large  and  medium  sized  

ρ Z ρ Z ρ Z ρ Z  ρ Z  ρ Z  ρ Z  ρ Z 
 

farms were found only in Kibaha district, which is located 
 

7 7 8 8  9 9  10  10 11  11 12  12  13  13 14  14 
further from the city center where there is more room for 

 

                (9) 
 

                 farm activities. 
 

where; Z1  = location of the farm (Urban = 1, Peri-urban = 0); Z2 = The  value  of  output  per  farm  over  a  period  of  24 
 

farmer’s marital status (Married = 1, Otherwise = 0); Z3 = education months was obtained by adding cash receipts from the  

level of a  farmer  (years);  Z4  = experience  of a  farmer  squared 
 

sale of eggs produced, value of eggs consumed by the  

(squared term); Z5 = Use of credit (use of credit =1, no use of credit 
 

farmers’ households and the value of manure. The mean  

= 0); Z6  = family size (Number of people); Z7  = experience of the  

value of  egg produced  was 56,601,290 TShs per  farm 
 

farm attendant (years); Z8= level management (represented by the 
 

technology  for   lighting  and  temperature  (a  dummy  variable: (Table  2),  and  a  mean  total  cost  of  15,586,883  TShs; 
 

Electricity = 1, charcoal/kerosene = 0); Z9 = housing type (Concrete thus, net returns of 41,014,406 TShs per farm per stock 
 

walls and iron roof =1, mud walls and iron roof = 0); Z10 = farmer’s or  about  38,696  TShs  per  bird  on  average  over  24  

employment (formal employment = 1, no formal employment = 0);  

months. The mean cost of drugs per farmer was 563,927  

Z11  = education level of farm attendant (years); Z12  = age of  the  

TShs with a  standard  deviation of  416,530 TShs while  

farm attendant (years); Z13 = sex of the household head (Male=1,  

other operating cost had a mean of 3,339,139 TShs with 
 

Female=0); Z14 = age of a farmer (years), and ρo, ρ1,   ρ2 ,…  ρ14   = 
 

parameter estimates.           a standard deviation of 4,115,895 TShs in  the  entire  life 
 



 
       

 

 Table 3. Respondents’ personal characteristics.      
 

       
 

 Standard measure of the sample Kibaha Ilala Whole sample  
 

 
Sex 

Male (%) 34 52.5 38.5   
 

 
Female (%) 66 47.5 61.5 

  
 

    
 

 Household size Mean number of members 7.9 7.7 7.8   
 

 
Age 

Mean age (Farmer) 47.7 37.3 41.3   
 

 
Mean age (Attendant) 21.4 35.4 22.4 

  
 

    
 

 
Marital status 

Married (%) 85 95 91   
 

 
Single (%) 15 5 9 

  
 

    
 

 
Educational level 

Mean years education (Farmers) 11.4 7.6 7.3   
 

 
Mean years education (Attendants) 7.1 6.7 7 

  
 

    
 

 Experience Farmers’ experience (years) 7.8 7.4 7.6   
 

  Employed male (%) 66 4 33   
 

 
Employment 

Employed female (%) 37 11 23   
 

 
Not employed male (%) 33 96 66 

  
 

    
 

  Not employed female (%) 63 89 77   
 

 
Quality of housing 

Iron roof and concrete wall (%) 97.5 17.5 55.5   
 

 
Iron roof and mud walls (%) 2.5 85.5 45.5 

  
 

    
 

 
Use of credit 

Had access to credit (%) 35.2 5 19   
 

 
Did not use credit (%) 67.5 95 81 

  
 

    
 

 Flock size Mean number of birds 1,624.9 518.7 1,071.8   
 

 
Source: Oleke (2008). 

 
 

 

of the stock. The study further reveals that on average, 
farmers incurred transport cost amounting to 440,499 
TShs, with a standard deviation of 2,093,954 TShs during 
the entire life of stock. Feed consumption had a mean of 
10,881,347 TShs, constituting the major components of 
poultry production cost in both study areas, accounting 
for about 70% of production cost. On average, about 
38,939 kg of feed was consumed per farm during the life 
of stock of bird. 
 

 

Respondents’ characteristics 

 

Information was collected regarding the personal 
characteristics of the farm managers and the attendants 
who carry out routine jobs on the farm (Table 3). Some of 
the information that is relevant for this analysis is 
presented here. For the sample as a whole, majority of 
the respondents were women (61.5%). However, there 
was male dominance in Ilala, where men constitute 
52.5% of the respondents compared to 47.5% for women. 
In Kibaha, women constitute 66% of the 

 
 
 

 

respondents compared to 34% for men. The mean 
household size for the entire sample was 7.8 people 
being 7.9 in Kibaha and 7.7 in Ilala. The distribution of 
respondents by age gave a mean of 41.3 years for 
farmers and 22.4 for farm attendants. Farmers tended to 
be younger in Ilala at 37.3 years on average compared to 
45.7 years in Kibaha. On the other hand, farm attendants 
were older in Ilala at a mean of 35.4 years relative to 21.4 
years in Kibaha. Both the oldest farmer (61) and the 
youngest (23) were found in Kibaha district. Majority of 
the respondents (91%) were married, more so in Ilala 
(95%) compared to Kibaha (85%). The educational level 
of both farmers and attendants was low. Farmers had 7.3 
years of formal education on average being higher in 
Kibaha (11.4 years) compared to Ilala (7.6 years) with a 
minimum of 7 years in both districts. The mean education 
of attendants was lower (7 years) but not significantly 
different from that of farmers. The attendants in Kibaha 
tended to be more educated (7.1 years) compared to Ilala 
(6.7 years), and the education of attendants in Kibaha 
was lower than that of their employees while the 
educational difference between attendants and farmers in 



 
 
 

 
Table 4. The production frontier and technical efficiency.  

 
 

Variable 
 Proxy Expected Estimated Standard 

t- Value  

  
parameter sign coefficient error  

    
 

 Production frontier parameters(Equation 8)      
 

 Constant  β0 +/- 7.18** 2.05 2.80 
 

 Cost of drugs  β1 + 0.02 0.12 0.67 
 

 Transport cost  β2 + 0.06 0.10 0.09 
 

 Amount of feed  β3 + 0.37** 0.23 1.21 
 

 Stock size  β4 + 0.54*** 0.15 3.25 
 

 Operating cost  β5 + 0.26* 0.16 1.75 
 

 Variance parameter (Equation 8)      
 

 Ratio of Standard error changes (δu/δv) λ  17.65* 996.40  
 

 Sum of standard error changes ( ) δ  10.18* 573.89  
 

 Variance of inefficiency error (u)    103.30   
 

 Variance of random error (v)    0.33**   
 

 Log likelihood function    - 91.18   
 

 Variance  (δ
2
u/δ

2
u +δ

2
v)  γ  0.99   

 

 Mean technical efficiency  TE(mean)  64%   
   

*** = α = 0.1; ** = α = 0.05; and * = α = 0.001 representing significance levels. 
 
 
 
Ilala is not significant. Some of the farm attendants in 
both districts had no formal education, but the lowest 
education of farmers in both districts was 7 years.  

The respondents had been farming poultry for 7.6 years 
on average, being slightly higher in Kibaha (7.8 years) 
than Ilala (7.4 years) with a sample mode of 8 years. 
Majority of respondents are not employed in the formal 
sector, implying that their poultry enterprise is probably 
one of their main sources of livelihood. Only about 33 and 
23% of the male and female respondents respectively 
had other employment. However, in Kibaha, more than 
half of the male respondents (66%) were employed 
elsewhere but only 37% of their female counterparts had 
the same status. In Ilala district, more of the male 
respondents (96%) were not employed elsewhere 
compared to 89% of the female respondents. Assessing 
the quality of the respondents housing, about 55.5% of 
the respondents had improved houses made of concrete 
walls and iron roofs but 45.5% had houses were made of 
mud walls with iron roofs. There is a significant difference 
between districts on this variable. More than 97% of the 
respondents in Kibaha had concrete walled houses 
whereas in Ilala district, more than 85% of the 
respondents had houses made of mud walls, with iron 
roofs.  

The use of credit for poultry enterprises was low in both 
districts, but especially in Ilala where only 5% of the 
respondents used credit during the study period 
compared to 35.2% in Kibaha and 19% for the whole 
sample. This can be attributed to a number of reasons 
including a higher level of education of farmers in Kibaha 
and a higher mean number of birds raised (1,624.9) 

 
 
 
 
nearly three times the mean for Ilala (518.7). Some of 
these personal characteristics were then used to estimate 
the production frontier of the technology used by farmers 
in the study area, hence determine performance 
differences (technical efficiency) among farmers, and 
then to identify factors that influence the variation of TE. 
 

 

The production frontier and technical efficiency 

 

The production frontier was estimated using Equation (8) 
as previously described, regressing the value of 
production (y) against a set of variables that influence 
variation of the dependent variable, using the maximum 
likelihood analytical model. Table 4 presents a summary 
of parameter estimates of the production function and the 
parameters for measuring the degree of variance. 
Equation 8 was tested and found to be free of 
multicollearity, heteroskedacity and autocorrelation 
(Oleke, 2008). The sum of standard errors (δ = 10.18) is 
significantly different from zero, indicating good fit and 
correct specification of the model. The value of Gamma 
(ϒ = δ

2
u/δ

2
u +δ

2
v) measures the relationship between 

random variation in the production of eggs and 
inefficiency in the use of inputs. The computed value of 
0.99 implies 99% of the random variation in egg 
production is explained by inefficiency in resource 
utilization. Furthermore, a high value of the natural log for 
the likelihood functions (-91.18), which is always 
negative, means the observed results were more likely to 
occur, again implying a high predictive ability of the 
model. 



  
 
 

 
Table 5. Factor price and marginal value product.  

 

Variable APP Elasticity of production MPP VMP (TShs) Factor cost (Px) TShs 

Cost of drugs (X1) 196.9 0.024 4.72 708 526.1 

Transport cost (X2) 252.1 0.06 15.1 2,265 410.9 

Amount of feed (X3) 2,851.7 0.37 1,055.1 158,265 260.0 

Stock size (X4) 103,606.4 0.544 56,361.8 8,454,270 1000.0 

Other operating cost (X5) 33.3 0.26 8.6 1,290 3,115.0 
 

Source: Oleke (2008). 
 

 

Most of the variables affecting the efficiency of the 
production system have parameters that are statistically 
significantly different from zero. For example, estimates 
of the ratio of changes in the standard error due to 
inefficiency to corresponding changes in the error term 
due to random error (λ = 17.65), implying that variation in 
output comes more from differences in farmers practices 
rather than from random variation. This is supported by 
the variance of the random component of the error term   
( )  being  significantly  different  from  zero  but   
relatively small in magnitude, while changes in the 
variance of the inefficiency component of the error term   
( ) is not significant but relatively large in  
magnitude. These values indicate the relative magnitude 
of the variance with the inefficiency effects constituting a 
larger share. The mean technical efficiency of the 
production frontier is estimated at 64%.  

The sign of the constant and all the independent 
variables are positive as expected implying that an 
increase in any of the variables will result in an increase 
in the value of production. The parameter estimates of 
the variables represent partial elasticities of production. 
Changes in the stock size have a highly significant effect 
on the value production such that if the stock of 
production increased by 1% the value of production 
would increase by 0.54%. The amount of feed used by a 
farmer also has a significant effect at α = 0.05, increasing 
production by 0.37% for every 1% increase in this 
variable input. The other operating cost of the poultry 
enterprise also have a significant influence on the value 
of production (at α = 0.10). If the operating cost increases 
by 1% the value of production would increase by 0.26%. 
The cost of drugs and transportation has a positive effect 
on the value of production, but it is not significant. A 1% 
increase in the cost of drugs and transport cost would 
increase the value of production by only 0.02 and 0.06%, 
respectively.  

The summation of all the partial elasticities of 
production with respect to every input is 1.25, 
representing the total output elasticity or the function 
coefficient, also referred to as returns to scale. If all 
factors are varied by the same proportion, the function 
coefficient indicates the percentage by which output 
would increase. In this case, it means if all of the 
variables were to increase by 1%, output would increase 

 
 

 

by 1.26% representing increasing return to scale. This 
means there is still room for improving the productivity of 
poultry enterprises in the study area by increasing stock 
size, which is consistent with the finding of Nair and 
Ghadoliya (2000) who reported that larger flocks sizes 
(>20000 birds) yielded higher returns per bird compared 
to smaller flocks. The findings also conform to similar 
findings by Ramrao et al. (2008), who reported that 
farmers who maintained a flock size of 10,000 layers 
were able to recover their fixed invested capital from 
production of layer in about two years compared to those 
who keep smaller flock sizes. The results also show that 
for all the factors of production, there is room for 
improvement because the respective value marginal 
product is higher than the corresponding factor cost 

 implying there is room for adding more   
variable inputs (number of birds, feed, drugs, transport)   
up to the point where    as can be seen in   
Table 5. It seems that the most gain would come from 
increasing the stock size and the amount of feed. 
However, in the case of other operating cost, which 
included labor, water and lighting, the VMP is less than 
the factor cost implying the need to reduce them down to 
the profit maximizing point.  

Table 6 presents the predicted TE values for each 
district and for the whole sample. It is evident that the 
farmers operate within a wide spectrum of TE ranging 
from 90 to 4% and a sample mean of 64.8% with a 
standard deviation of 15.8%. Kibaha district exhibited a 
higher level of TE at 69.8% compared to 59.8% for Ilala 
(Table 6). The scope of a production frontier measures 
existing room for improving from the current level of 
productivity up to the maximum potential. For the sample 
as a whole, the scope for improvement is 35.2%, being 
higher in Ilala (40%) relative to 30.2% in Kibaha, which is 
consistent with relatively better efficiency exhibited by 
farms in Kibaha. 
 

 

The effect of socio-economic characteristics on 
technical efficiency 

 

In the second stage of the study, the TE of each farmer 
was regressed against their corresponding socio-
economic characteristics as derived in Equation 9. 



    

   Table 6a. Technical efficiency.    
       

   Standard measure of the sample Kibaha Ilala Whole sample 

   Number of respondents 40.0 40.0 80.0 

   Mean TE 69.8 59.8 64.8 

   Standard deviation 11.4 15.7 15.7 

   Minimum TE 30.0 3.9 3.9 

   Maximum TE 90.0 80.0 90.0 

   % Scope 30.2 40.0 35.2 
 

Source: Oleke (2008). 
 

 
Table 6b. Technical efficiency by farm size.  

 

% Technical 
  Farm size    

Total 
 

100-2000 2001-4999 
 

≥5000 
 

 

efficiency    
 

(N) % (N) % (N) % (N) % 
 

 
 

<30 5 8.33 0 0 0 0 5 6.25 
 

30-39 2 2.99 0 0 0 0 2 2.50 
 

40-49 1 1.4 0 0 0 0 1 1.25 
 

50-59 9 13.2 0 0 0 0 9 11.25 
 

60-69 30 44.11 2 25 0 0 32 40.00 
 

70-79 20 29.41 4 50 4 100 30 37.50 
 

≥80 4 5.88 2 25 0 0 6 7.50 
 

Total 67 100 8 100 4 100 80 100 
 

 
Source: Oleke (2008). 

 

 
Table 7a. Percentage technical efficiency by sex.  

 
 

Sex 
 District 

Average  

 

Kibaha Ilala 
 

   
 

 Females 75.0 62.0 68.0 
 

 Males 64.0 57.0 60.5 
 

 Mean 69.8 59.8 64.8 
 

 
Source; Oleke (2008). 

 

 

Results as presented in Table 7a show a condition 
index of 12.78 and a Durban Watson index of 1.72 
indicating the model is free of multicollinearity and 

autocorrelation. The predictive ability of the model (R
2
) is 

high showing that 78.1% of the variation in TE is 
attributed to the socio-economic variables presented in 
Table 7b. A negative sign on an efficiency parameter 
means that the variable increases TE, while a positive 
sign reduces TE. All the independent variables are 
expected to have a negative sign, which implies a 
positive sign for any quadratic term, as is the case for the 
farmers’ experience. The sign for location (+0.5023) had 
a positive sign as expected implying that farms that were 
further from the input and product market would tend to 
reduce efficiency. Likewise, the quadratic term of the 
farmer’s experience was positive (+ 0.622), implying a 
negative linear sign. The quadratic term of experience 

 
 

 

was also positive, which implies that farmers who have 
been raising layers for more years tend to have higher 
TE. The remaining parameters were expected to have 
negative signs, however, exceptions were observed in 
the case of; attendants’ years in business (+0.048), the 
attendants level of education (+0.0002), the farmers’ type 
of housing (+0.3243) and the farmers’ age (+0.0001). 
These had a positive sign implying that a unit increase in 
each of the variables would reduce the farm’s technical 
efficiency.  

The sign indicate that farm owners ought to provide 
more supervision to attendants who have worked for 
them for longer periods and those who are more 
educated because they may gloss-over the job they are 
assigned to do since they command more trust from the 
farm owner. As regards the farmers housing type where 
farmers with better houses and farmers who are older 



      
 

Table 7b. Determinants of technical inefficiency.       
 

       
 

Variable 
Proxy Expected Estimated Standard error 

t - Value  

coefficient sign coefficient (SE)  

   
 

Constant (Z0) ρ0 +/- -1.0222* 0.0323  34.21 
 

Location (Z1) ρ1 + + 0.5023*** 0.3043  1.67 
 

Marital status (Z2) ρ2 - - 0.9813*** 0.4314  22.22 
 

Education of farmer (Z3) ρ3 - - 0.5117** 0.0244  0.74 
 

Farmers years in business (squared) (Z4)
2
  + + 0.622** 0.2778  2.27 

 

Use of credit (Z5) ρ5 - - 0.3308* 0.1803  1.84 
 

Family size (Z6) ρ6 - - 0.0014* 0.0016  -1.82 
 

Attendant years in business (Z7) ρ7 - + 0.048 0.974  0.12 
 

Lighting in poultry house (Z8) ρ8 - -0.1656 0.5689  -0.28 
 

Farmers’ house type (Z9) ρ9 - + 0.3243 0.1512  1.35 
 

Farmer’s employment  (Z10) ρ10 - - 0.0003 0.0156  -0.9 
 

Education of farm attendant (Z11) ρ11 - + 0.0001 0.0043  0.56 
 

Age of attendant (Z12) ρ12 - -0.0002 0.0019  -0.76 
 

Sex of farmer (Z13) ρ13 - -0.0702 0.3066  -0.61 
 

Age of farmer (Z14) ρ14 - + 0.0001 0.0052  0.19 
 

Adjusted R
2
    78.1%   

 

Durbin Watson statistics    1.72   
 

Condition index number    12.78   
 

Degrees of freedom    73   
   

*** = α = 0.1;  ** = α = 0.05; and * = α = 0.001 reflect levels of significance. 
 

 

tend to have lower TE, may be explained by the fact that 
often such farmers have alternative sources of income 
such that they do not pay much attention to the 
performance of the poultry farm. The positive sign on the 
parameter for location implies that farms that are further 
away from the city center tend to have a lower technical 
efficiency.  

There was a negative sign on the famer’s; marital 
status (- 0.9813), level of education (- 0.5117), use of 
credit (- 0.3308) and family size (- 0.0014). All these 
parameter estimates were significantly different from zero 
at the level of significance of 99, 95 and 90%, 
respectively. The coefficients on farmers’ sex (- 0.0702), 
farmers’ alternative employment (- 0.0003), age of farm 
attendants (- 0.0002) and use of lighting in the chicken 
houses (- 0.3243) were also negative but not significantly 
different from zero. The negative sign on all these 
variables means an increase in the variables also 
increased TE. Thus, married farmers tended to be more 
technically efficient, probably reflecting more availability 
of labor, which is consistent with larger families having 
more labor at their disposal, thus contributing to higher 
TE. Similarly, farmers with more education are more likely 
to use farm resources more efficiently especially in 
relation to feed mix and use medicine, and they are more 
likely to use credit for acquiring the inputs. Such farmers 
also tend to have alternative employment, and the 
salaries can be used to finance poultry enterprises. The 
negative sign on lighting of the chicken house reflects the 

 
 

 

relationship between lighting and feeding. As birds feed 
for longer periods in a day, they tend to be more 
productive (Ryan, 2004).  

The results also imply that female farmers tend to have 
higher TE than their male counterparts, which is 
consistent with the finding that the mean TE of female 
farmers was 68% compared to 60.5 for male farmers 
(Table 7a). Younger farm attendants also contribute to 
higher TE of the farm, probably because they tend to 
adhere more to instructions they are given on feeding and 
general hygiene of the poultry houses. This confirms the 
finding reported earlier that farm attendants who have 
worked for the farm longer tended to reduce TE which as 
previously argued may reflect their tendency to put less 
than their best effort, banking on the trust upon them, 
hence, less likely to be supervised as closely as newer 
attendants who should be expected to be relatively 
younger. All these findings show that there is much room 
for improving the performance of egg production 
enterprises in Kibaha and Temeke district by focusing on 
improving the technical efficiency and hence the net 
returns farmers receive for their capital and labour 
investment. These findings could also be extrapolated to 
other parts of Tanzania where similar poultry productions 
systems are found. 

 

Conclusions 
 
The  study  established  that  there  are  differences in the 



 
 
 

 

performance of farmers in the study area as measured by 
the technical efficiency of their egg production 
enterprises. The stock size has the most significant effect 
in the variation of the value of production, followed by the 
amount of feed and other operating cost. Variation in the 
transport cost and the cost of drugs do not contribute as 
much to variation in the dependent variable. Majority of 
the farms are small (85%) having flock sizes in the range 
of 100 to 1,000 birds and a mode of 300. The results 
however, revealed that bigger flock sizes are associated 
with higher TE implying the need to expand flock size. 
The overall mean technical efficiency is estimated at 
64.8% implying that, there is a 35.2% scope for 
increasing egg production by using the present 
technology  

Performance differences between farmers in Kibaha 
and Temeke districts have been attributed to education 
and levels of investment. Farmers in Kibaha district were 
more educated on average; they also operate larger 
farms (flock sizes) which provide for economies of scale. 
The high cost of production has been shown to reduce 
the competitiveness of poultry farmers. One of the most 
important avenues for reducing production cost is to 
increase output, which would lower average cost of 
production and hence improve technical efficiency. Farm 
owners should also set up management systems that 
provide for better supervision of their farm attendants. 
Farm owners should not relax and bank on their long 
term relationship with employees. Poor management 
reduces yield and hence increases the average cost of 
producing each egg. Credit, which had a positive and 
significant effect on technical efficiency, is necessary to 
encourage technical innovation and timely availability of 
necessary inputs. The government should influence 
borrowing rates on credit in order to spur more and faster 
transformation of the poultry industry in Tanzania thereby 
contribution to realizing national development goals to 
overcome food insecurity, improve nutrition and 
overcome poverty. These findings can be used by 
agricultural extension agents to develop training 
programmes for poultry farmers that aim at improving at 
farm performance by raising their technical efficiency so 
that majority of farmers operate close to the production 
frontier. 

 
 
 
 

 
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