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PAPER 3IMPACT ASSESSMENT OF

LBT PARTICIPATION
ON LOCAL PEOPLE IN TANZANIAS. Hanaoka1*, T. Kato*, T. Kawasaki*, T. Tokunaga+*Tokyo Institute of Technology, Japan
1
Corresponding author: Email: hanaoka@tp.ide.titech.ac.jp+
Eight-Japan Engineering Consultants Inc., Japan

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ABSTRACT
Labour-based technology (LBT) can create a number of job opportunities.
In general, more job opportunities contribute to an increase in thehousehold income, and consequently, improve the living standards ofparticipants in the LBT project. However, there has been no research
conducted to assess the impact of LBT participation on the local people at avillage level in Tanzania. This study aims to evaluate the impact ofparticipation on the local people through a comparison between theparticipants and the non-participants of one specific LBT project inTanzania.
A field survey was conducted in the Mbeya region of southwest Tanzania
where the LBT road reconstruction project was implemented in the Mbozi
district. The results revealed that the local people living in the proximity of
the road have a positive impression of the LBT because they recognize that
LBT participation has improved their living standards. Subsequently, theimpact of LBT participation was clarified quantitatively through a multiple
regression analysis. The dependent variables used in the multiple
regression analysis were income and expenditure per head. The resultshows that the coefficient of the LBT participation dummy variable isnegative, which means that the participants basically have a lower
income/expenditure than the non-participants. This result can beinterpreted to signify that the LBT participants were relatively poor
compared to the non-participants.

Keywords: Labour-based technology, impact, living standards, roadreconstruction, Tanzania

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L

abour-based technology (LBT) is a construction technology thatmaximizes the utilization of the labour force to create employment. It
finds application in the construction of small-scale infrastructure projects,
such as feeder roads or irrigation canals, in developing countries. Suchapplications of LBT create job opportunities. More job opportunities
contribute to an increase in the household income, and consequently,
improve the living standards of the participants in the LBT project. In
addition to employment creation, LBT has various benefits, such as low
construction cost and utilization of local resources.
The government of Tanzania introduced a policy to promote LBT in road
construction in rural areas. Hanaoka et al. (2010) revealed the effectivenessof and the problems associated with LBT in Tanzania, based on the naturaland social conditions. However, there is no research that has assessed theimpact of LBT participation on the local people at a village level in
Tanzania. This study aims to evaluate the impact of participation on local
people through a comparison between the participants and thenonparticipants of one specific LBT project in Tanzania.
METHODOLOGY
In order to obtain data of a household's living standards near a LBT projectsite, we carried out a field survey in Vwawa village in the Mbeya region,
Tanzania, from January 12–25, 2011. In this village, a 1-km-long feeder
road was reconstructed using LBT in two months, from the beginning of
October to the end of November, 2010. Local people were employed aslabourers at a basic daily wage of 2.67 USD (4,000 Tanzanian Shilling(TZS)) for women and 3.33 USD (5,000 TZS) for men [1USD= 1500 TZS].
For comparison, we distributed questionnaire sheets among the LBTparticipants as well as the non-participants. We employed a Tanzanian
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surveyor and distributed 50 questionnaire sheets (25 for each group).
Although 47 sheets were collected, some of them contained invalid
answers. Thus, only 27 samples (responses of 11 participants and 16
nonparticipants) were valid.
In order to clarify the differences in income and expenditure between
participants and non-participants, we performed a multiple regression
analysis with income and expenditure as dependent variables. Household
attributes, such as number of household members, age of the household
head, and LBT participation dummy variable were independent variables(see Table 1).

RESULTS
Figure 1 shows how the labourers spent their wages obtained for the work
done in the LBT. More than 90% of the respondents purchased food. Other
common purchases were clothes and farm equipments. Participants spent
their wages mainly for their basic needs; however, they also purchased
goods such as farm equipment and livestock. This spending can beconsidered to be investment and wealth accumulation. Some respondents
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replied as having spent part of their wages to pay for school fees for theirchildren.
(%)
0

20

40

60

80

100

food
clothes
mobile phone
livestockland
farm equipment
othersFigure 1: Expenditure by an LBT participant

All respondents answered “Yes” to the question “Would you like toparticipate if another LBT project is initiated?” The reasons for motivation
for participation in the LBT are categorized in Figure 2. “Improvement oflife” and “development of the community” are the dominant reasons, rather
than “employment creation” and “income generation.”
0

20

(%)

40

development of
the community
income generation
improvement of life
employment creation
economic growthothersFigure 2: Reasons for participating in LBT
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No. of household members

Household attributes are shown in Figures 3 to 7. According to these figures,the LBT participants tend to have a smaller family size, a younger
household head, more workers, and less owned land than the
nonparticipants. It is notable that more than half of the participant
households are female-headed, and there is not much difference between theparticipants and the nonparticipants in terms of their occupation andeducation; that is, most respondents are farmersand are not highly educated.
10
9
8
7
6
5
4
3
0

2

Whole

(Avg. 5.44)

Participants

4

6

8

10

Nonparticipants(Avg. 5.94)(Avg. 4.73)

Age of household head

Figure 3: No. of household members

51~60
41~50
31~40
~30

0

5

10

Whole (Avg. 41.30 years old)
Nonparticipants (Avg. 43.81 years old)
Participants (Avg. 37.64 years old)

Figure 4: Age of household head
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No. of workers

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3
2
1
0

0

5

10

Whole (Avg. 2.04)
Participants

15
Nonparticipants

20(Avg. 1.81)(Avg. 2.36)

Owned land (acre)

Figure 5: No. of workers

6
5
4
3
2
~1
0

5
10
Whole (Avg. 2.13 acre)
Nonparticipants (Avg. 2.25 acre)
Participants (Avg. 1.95 acre)

15

Figure 6: Area of owned land

Farmer, Not high education, Male household head
Nonfarmer, High education, Female household head
Farmer/nonfarmer
Whole

Gender of household head
Farmer/nonfarmer
Nonparticipants

High/Not high…

Gender of household head
Farmer/nonfarmer
Participants

24
22

High/Not high education

High/Not high education
Gender of household head

17
14
13
12
10
1
9
2
5
6

3
5
10
2
3
4

Figure 7: Farmer, Highly educated and Female headed household
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Table 2 shows the monthly income and expenditure per head for the
respondents. The average expenditure of the participants is approximately
1.2 USD higher than that of the nonparticipants; however, the incomes ofthe participants and the nonparticipants are almost the same. The level ofincome/expenditure of the respondents seems slightly below the average
Tanzanian expenditure figure, which is 10.95 USD/month/head in rural
areas, according to the National Bureau of Statistics, Tanzania (2008).
Table 2: Monthly expenditureand income per head
Item
M onthly expenditure per head
(Unit:USD)

M onthly income per head
(Unit:USD)

No. of Samples

M ax

M in

Ave

Std. Deviation

Whole

27

20.90

3.02

8.24

4.38

Participants

11

20.90

5.22

8.96

5.26

Nonparticipants

16

16.67

3.02

7.75

3.77

Whole

27

16.67

3.50

7.83

3.61

Participants

11

14.68

3.84

7.77

3.59

Nonparticipants

16

16.67

3.50

7.87

3.73

Prior to the regression analysis, correlation analysis was performed, and its
result is shown in Table 3. Overall, the independent variables are not highly
correlated with each other. The largest absolute value of the correlation
coefficient among the independent variables is 0.509 (work and land). The
second largest value is 0.419 (family and land). Therefore, multicollinearity
is not significant in this study.
Table 3: Correlationmatrix

family
fem
age
edu
work
farm
land
LBT
I
E

family1.000-0.2820.118-0.2850.016-0.0900.419-0.340-0.516-0.286

fem

age

edu

work

farm

land

LBT

I

E

1.000
-0.231
0.029-0.038-0.271
0.073
0.301
0.283
0.072

1.000
0.106
0.102
-0.1060.241-0.398-0.476-0.396

1.000
0.232
0.135
0.254
-0.007
0.361
0.327

1.000
-0.176
0.509
0.364
0.044
0.024

1.000
-0.317-0.053
0.257
0.398

1.000
-0.115-0.216-0.0871.000
0.138-0.014

1.000
0.846

1.000

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The result of the regression analysis is shown in Table 4. The dependent
variable in models I-1 and I-2 is monthly income per head, while in models
E-1 and E-2, it is monthly expenditure per head. I-1 and E-1 contain all eightvariables collected by the field survey. Model I-1 is 1% significant in F-test,
but the significant independent variables are only age and the LBTparticipation dummy variable, as determined by t-test. On the other hand,model E-1 is 10% significant and its significant independent variables are
age and farm size.
Table 4: M odel Results
Variables
family
fem
age
edu
work
farm
land
LBT

M odel I-1
Coefficients
Beta
t-Value
Coefficients-1371.64-0.372-1.8653183.00
0.238
1.379-451.04-0.534-3.243***4067.94
0.245
1.422
2697.740.311
1.526
3745.60
0.182
1.132-801.34-0.156-0.682-5148.70-0.392-2.005*R square0.642
Adjusted R square
0.483
F-Value
4.031***
M odel I-2
Variables Coefficients
Beta
t-Value
Coefficientsfamily-1991.10-0.540-3.400***age-431.90-0.511-3.140***
LBT-3266.97

R squareAdjusted R square
F-Value-0.2490.487
0.420
7.283***-1.448

Variables
family
fem
age
edu
work
farm
land
LBT

M odel E-1
Coefficients
Beta
Coefficients-835.72-0.275957.220.087-359.81-0.5172543.830.186
1561.160.219
5930.030.351
226.650.54-4243.491-0.393t-Value-1.1650.425-2.652**
0.912
0.905
1.838*
0.198-1.694R square0.496
Adjusted R square
0.273
F-Value
2.219*
M odel E-2
Variables Coefficients
Beta
t-Value
Coefficientsfamily-1025.97-0.338-1.813*age-336.47-0.4842.532***
LBT-3469.93-0.321-1.591R squareAdjusted R square
F-Value

0.293
0.201
3.449**

LBT participation dummy variable is insignificant. To improve andsimplify the models, some insignificant variables were removed one by one,which resulted in the models I-2 and E-2. In these models, all variables havenegative coefficients, and the LBT participation dummy variable isinsignificant. The coefficients of the LBT participation dummy variable arenegative throughout all models. The interpretation of these regression
results is that a smaller family, a younger household head, and non LBT
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participants are likely to have larger income and expenditure.
DISCUSSION
The average expenditure of the participants is higher than that of thenonparticipants by about 1.2 USD. However, this does not mean that mostparticipants have a higher income than the nonparticipants. Figure 8 showsthat most participants, except two outliers, are located at relatively low
income and expenditure levels, while nonparticipants are located in a wide
range from low to high.

Expenditure /head /month(USD)

The income level of the participants is comparatively low because thelabourers employed in this project were those who were interested in LBT
participation and were selected by the community leaders. It is believed thatpeople, particularly young people, without an avenue for earning cash
income were preferentially selected. Here, subsistence agriculture was notconsidered as a job providing cash income and the people engaged in it wereprioritized as labourers in LBT.
20
15

10
5

0
0

5

10

15

20

25

Income / head / month(USD)

Non-participants (16samples)

Participants (11samples)

Figure 8: Distribution of Incomeand Expenditure

Before conducting the regression analysis, we expected that LBT would
have resulted in an increase in the income and expenditure of the
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participants; however, participants have reported overall lower
income/expenditure than those of the nonparticipants. In addition, the R
square value is not high enough. Thus, these models are inadequate to
explain the participants' income and expenditure. One interesting result isthat a remarkable difference between the income and the expenditure of theparticipants was not observed, regardless of the high wage of LBT whencompared to the average income of the respondents. The daily wage
provided in this LBT was 2.67-3.33 USD, which is equivalent toapproximately one third of the average monthly income per head. For
example, according to a record of construction, male and female workers
received an average of 78.42 USD and 55.76 USD, respectively, in themonth of October. We can say that this is an extremely high extra income for
local people.
A woman managing a local shop near the LBT project road mentioned anincrease in sales by approximately 20% as a result of the project. This is agood example of how LBT can create income opportunities and how
spending by labourers can stimulate the local economy.
CONCLUSION
In this research, we analyzed a few tendencies of the LBT participants andthe nonparticipants, and the differences between them in terms of theirincomes and expenditures, and other household attributes. This wasachieved by conducting a field survey at Vwawa village in the Mbeya
region, Tanzania. Responses to the survey indicated how local people
evaluated the benefits of LBT. For a quantitative analysis, a moresophisticated method is required to clarify the impact of LBT on the income
and expenditure of the participants.

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ILO REGIONAL SEMINARACKNOWLEDGMENT:We would like to express our deep gratitude to Mr. Noriaki Nishimiya andMr. Yukihide Katsuta, Japan International Cooperation Agency, forsupporting the field survey in Tanzania.
REFERENCES
Hanaoka, S., Kawasaki, T. and Tokunaga, T. (2009), Economic impact of
the implementation of LBT in Tanzania and cost comparison between LBT
and EBT, 13th ILO Regional Seminar for Labour-Based Practitioners,
Kampala, Uganda.
National Bureau of Statistics Tanzania (2008): Household Budget Survey
2007.

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