of Rural Labor Market Participation in Tanzania
Abstract: Participation in rural off-farm
activities (outside a household's own farm) is one of the livelihood strategies
among poor rural households in many developing countries. One component
of off-farm activities accessible to the very poor is wage labor because
it does not require any complementary physical capital. A household’s
ability to participate in the rural labor market depends on the characteristics
of the household itself and the local labor markets conditions. This
study examines the factors that determine the number of households supplying
labor to a particular rural local labor market in rural areas of Tanzania
and the share of labor income in total cash income. The study finds
that education level, availability of land, and access to economic centers
and credit are the most important factors in determining the number of households
that participate in a particular rural local labor market and the share of
labor income in total cash income.
Many studies show that participation in rural labor
markets is an important strategy for poverty alleviation and food security
in developing countries.
In Sub-Saharan Africa, rural households commonly depend on off-farm sources
for 30-50 percent of their income. Defined in terms of function, off-farm employment
has two major components, namely wage employment and self-employment. The component of rural
off-farm employment, in which the poor can participate because it does not
require any complementary physical capital, is wage labor (i.e. to supply
their labor for wage in the rural labor markets). A corresponding
Kiswahili saying goes: “mtaji wa maskini ni nguvu zake mwenyewe,”
which translates “the asset of the poor is his/her labor power.” The
report on Tanzania’s Household Budget Survey (HBS) of 2000/01 shows that
the poverty rate of the households that participated in the rural labor
markets were slightly lower than those which did not. However, the
most recent national Labor Force Survey (LFS) in Tanzania shows that only
11 percent of the poor households participated (supplied labor) in the rural
labor markets in 2000/01.
Studies elsewhere have shown that the capacity of
households or individuals to participate in rural off-farm activities varies
significantly across countries and within countries. In their analysis
of 100 farm household studies, Reardon et al. in 2001 find that this
variation is partly due to high entry barriers to certain rural off-farm
activities, which makes certain activities accessible only to higher income
groups. The 'entry
barrier' hypothesis is particularly important in the case of off-farm self-employment.
For the poor rural wage labor supplier, however, the main problem is the
availability of wage employment in areas close to his/her homestead. In other
words, with high entry barriers in off-farm employment such as off-farm self-employment,
very poor households have no other option but to participate in rural off-farm
wage employment, which in turn is only possible if there is sufficient demand
for their labor nearby.
Thus, concerning these very poor households an important
policy question arises: what factors determine the total participation
of individuals/households in the rural labor markets? Equally important
is the question of factors that determine the share of labor income in total
income. Few studies of rural labor markets in Africa focus on the non-farm
sector. although wage employment can be provided by both farm and non-farm
sector. Furthermore, these
studies concentrate on the individual/household level. However, the proponents
of the importance of “spatial targeting” for poverty reduction argue that
most micro-policies first target particular areas/locations and then households
located therein. This paper
answers the question of what determines the number of households in a village
that participate in the rural labor market and the share of income derived
from these markets (in the households total cash income) using a modified
farm household model, aggregated to the village level.
The rest of the paper is organized as follows: Section
2 presents the theoretical framework of the agricultural household model
with transaction costs and liquidity constraints; Section 3 describes the
econometric models and the estimation strategies. Section 4 discusses the
results of the analysis; and Section 5 provides conclusions of the paper.
In finding the determinants of the number of households
which participate in a certain rural local labor market and the share of
labor income in total income, it is important to address the spatial dependence
in the development of one rural market on the other. For example, the study
by Bryceson in 2000 shows that development of other markets such as credit
markets in the rural areas may have significant impacts on rural labor markets. This argument is theoretically
based on the proposition that households participating in rural credit
markets may offer jobs to other households, thus, increasing the number of
households participating in the rural labor markets and the contribution
of income derived from the rural labor markets. As most of the rural credit
is directed to agricultural activities, for example season credit for purchasing
fertilizers and pesticides, the link of credit availability and rural labor
market is likely to work through the farm sector. However, due to transportation and other
transaction costs, the spatial dependence effects dissipate as distance
increases, i.e. the strength of the connection between the two markets is
expected to diminish with distance. This argument is in line with most spatial econometric
analysis and regional sciences in general that indicate spatial dependence
is a declining function of distance. Thus, high transaction costs cause
localization of rural labor markets because it becomes costly to sell labor
to distant places. As
such, transaction costs is one of the factors that may result in disequilibrium
in the rural markets as discussed in the recent study by Kanwar in 2004
for the case of rural India.
As in many studies of rural economies, the starting
point of our theoretical framework is the Farm Household Model (FHM). This model is preferred
to, for example, the occupational choice models, because of its flexibility
to analyze economic aspects for a range of different household types --
from pure subsistence to commercial farm households. The FHM can readily
be extended to accommodate incomplete markets and market imperfections such
as differential accessibility to rural labor markets and other off-farm
activities due to differences in transaction costs, rationing, and entry
Some studies of rural labor markets assumed that
the rationing and transaction costs apply to each household differently. When the emphasis is on
spatial targeting, this assumption may not be realistic because households
in one village are likely to be rationed in wage labor markets by their
access to infrastructure, information costs, and credit availability. This
is particularly important when modeling village labor markets because farmers
in Tanzania’s villages are not
fully integrated into urban wage labor markets. Thus, the paper adopts the non-separable
farm household model (with transaction costs, rationing in labor markets,
and credit constraints) used in the 2000 work of Woldehanna and extended
by Mduma in 2003. However,
the paper moves further by considering market outcomes at the village level.
Normally, we consider the importance of the rural labor markets in terms
of the number of households which sold their labor in the rural labor markets
and the share of labor income in total cash income.
We impose some regularity conditions, namely the
quasi-concavity of the household preference, convex agricultural production
frontier, and linearity in all constraints in the model. Woldehanna and Mduma
show that, under these assumptions, the Kuhn-Tucker first order conditions
for utility maximization are both necessary and sufficient for a household’s
utility maximization problem. They also show that the propensity to
participate in the rural labor markets declines with the increase in marginal
value of time, the extent of rationing in the rural labor markets, and the
transaction costs of participating in the rural labor markets. Furthermore, from the
assumptions of rationing, search, and other transaction costs, the model
implies that access to information and markets will influence participation
rate in the village. Also from the assumptions of household characteristics,
labor endowment and stock of human capital in the villages are some of the
factors that have bearing on the rural labor markets.
Although the focus of this study is mainly on the
supply side of the rural labor market, the comparative statics behavior
discussed above needs to be qualified by also looking at some aspects of
the demand side in order to enable a village level analysis. This is particularly
needed because the presence of 8.4% of unemployment rate in rural areas
of Tanzania points to some of
the demand side factors. On the demand side of the rural labor markets, the
amount of labor that a household can buy increases with relaxation of the
cash constraint. In a 2003 study Mduma shows that relaxing the cash constraints
increases the demand for labor by shifting the labor demand curve upwards.
In other words, in the village labor markets, households that can access
credit are likely to offer opportunities for wage employment to other households.
With respect to agricultural land, we note that land
is likely to be complimentary to wage labor. However, even though large
landholdings may reduce the need for seeking for wage employment, households
with large amount of land (relative to their labor endowment) are likely
to demand labor from the rural labor markets. Thus, at the village
level, the effect of the land availability on rural labor markets is also
likely to manifest itself through inequality in landholdings. In other words,
those with relatively large amount of land will demand labor in the rural
labor market and those with relatively low amount of land will sell their
labor in the rural labor markets. The same argument is made with respect
to the per capita village income: households with relatively higher per
capita income are likely to be employers in the rural labor markets. Thus,
it is likely that for the kind of off-farm employment we analyze, the village
income inequality is an important factor in influencing the availability
of wage employment to the relatively poor households. In the next section
we describe how these theoretical implications were operationalized for empirical
VARIABLES AND ECONOMETRIC ESTIMATIONS
(a) Dependent variables
As in Isigut’s 2004 work, we use the share of village
income from labor markets over total cash income (SHARE) as the first dependent
variable that indicates the extent of participation in the rural labor markets.
We also take the number of households in a village who reported to have
supplied their labor to the rural labor markets (ACT_W) in the survey year
as another dependent variable. From a policy perspective, while the former dependent
variable is relevant in indicating the extent to which rural poor households
depend on the income derived from the rural labor markets, the latter gives
an overview of the participation rate in the supply side of these markets.
(b) Predictor variables
(i) Development in other rural markets:
To capture the interconnectedness of rural labor
markets with other markets, we focus on the rural credit market because it
is assumed to relax the cash constraints at the village level. The indicator
of the village cash constraint is measured by the proportion of households
which have at least one member who participated in formal financial and/or
informal financial arrangements (CREDIT). As derived in the theoretical framework,
the relaxation of cash constraints reduces participation in rural wage employment
at household level. However, we note that the relaxation of cash constraints
may have an opposite effect on labor supply in case of unemployment, through
its positive effects on the labor demand. Therefore, the square of
this variable (CREDIT_SQ) is included to capture this complex relationship.
As pointed out in the section 2, labor allocation
is likely to be affected by land availability. Thus, we include the per
capita cultivated land in the survey year (PER_LAND). According to Yao’s
study of rural China, agricultural land is
both a wealth variable and production factor, and therefore, it is expected
to have multiple effects on participation in rural labor markets. First, as a wealth variable,
land may induce confidence to households with a relatively large amount
of land (confidence in what they can produce with their land). This effect,
which Yao termed “insurance effect of land,” reduces participation in wage
employment in the rural areas. Secondly, as a factor of production, land
can be either complimentary to labor or a substitute for labor. The complimentary
nature of land and labor is expected to have dominated in rural Tanzania due to the form of
technology used in small-scale farming (hand hoe cultivation). However,
at village level, per capita landholding may have negative effects on the
labor supplied off-farm while at the same time inequality in landholding
is likely to have positive impacts on the rural labor markets (because households
with low land endowment are employed by those with relatively large per
capita land). Thus, we include the gini coefficient in land holding (GINI_LAND)
as one of the explanatory variables, in addition to per capita cultivated
land in the survey year.
(iii) Infrastructure development and transaction
We include proximity to important economic centers
(including main roads) to capture costs related to marketing, transaction,
rationing, and information. We tried two proxies to capture these aspects.
The first proxy used was the average distance, in kilometers, to the nearest
major/essential economic center, such as shops, market place, main road,
and health centers. The
alternative proxy is the average travel time (DISTC_HRS) to the nearest economic
center. In the course of estimation, the travel time was found to be more
appropriate than distance measured in kilometers. The travel time approach
tended to fit the data better than distance in kilometers because travel
time captures both the differences in terrain and the quality of the paths/roads.
(iv) Human capital stock and labor resources:
We use education indicators as proxies for the stock
of skills in the village. For most studies in Sub-Saharan Africa, the cut
off point has been primary education (and above) vs. no education. Thus
we use the proportion of those who have primary education and above (PR_EDUC)
in the population of individuals above 15 years of age. The square of this
variable (EDUC_SQ) has also been included to capture the nonlinearity of
the relationship, for the kind of wage employment analyzed here is expect
to decline with the increase in education (as households shift to other preferred
form of off-farm employment, e.g. self-employment). Thus, we expect the
square of education to have significant negative effects on the participation
and the share of labor income in the total cash income.
in the theoretical framework at household level, we include the average
age (AGE) of the population between 15 and 65 years in the village. We have
also included the square of age (AGE_SQ) to capture the lifecycle in the
participation in the rural labor markets. Moreover, the average household
size (HH_TOTAL) in the village was included as an indicator of the available
labor resources in the village. However, to account for a non-working population
in the village, we also included the average dependency ratio (DEP_RATIO)
within the village, which is expected to reduce the rate of participation
in rural labor markets. The dependency ratio was computed as the ratio of
the population below 5 years plus above 65 years to the population between
15 and 65 years. The square of dependency ratio (DRATIO_SQ) was also included
to capture possible non-linearity.
Economic development and diversification:
time used per week on the primary activities (ACT_HRS1) and the secondary
activities (ACT_HRS2) in the village are included as measure of rural economy
diversification. The primary activities are essentially farm and livestock
activities. The secondary activities are mainly off-farm activities such
as fishing, mining, tourism, construction, and employment in the government
and parastatals. However, a large proportion of the secondary activities
take the form of off-farm self-employment. As an alternative way of including
the proxies for economic diversification in the village, we also include
the ratio of average time per week used in the primary activities to average
time per week used in the secondary activities (denoted as ACT1_to_2). Furthermore,
we include the number of petty traders in the villages (TRADE) and its square
(TRADE_SQ), to capture wage employment generated by expansion in other off-farm
activities, particularly off-farm self-employment.
To control for differences in well-being of the people
across villages, we include the proportion of households in the village
that have access to safe water (SAF_WATE) as computed by Tanzania’s National
Bureau of Statistics. The square of this variable (WATER_SQ) is included
to capture the nonlinearity of this relationship. We also include the proportion
of households in the village that are connected to the national power grid
(ELEC_PW), expecting that the villages with high proportions of their households
connected to the national power grid will have better developed rural labor
markets. Further, we include the square of this variable (ELEC_SQ) to capture
nonlinear relationship. By including access to water and electricity as proxies
of the village development level, we avoid the endogeneity problem that would
be caused by the direct inclusion of per capita income (because income reported
in the survey is necessarily a function of labor allocation in that year).
However, we have included the gini coefficient of per capita expenditure
(GINI_EXP) to capture possible wage employment provided by relatively rich
households to the poor households in the village. This is a particularly
important aspect when a substantial portion of the inequality is generated
by incomes derived from activities located in the rural areas (thus increased
labor demand). However, if a substantial portion of the inequality is generated
by incomes from outside the rural areas, the effects of inequality on the
rural labor markets remain ambiguous.
Data source and descriptive statistics
The data used in this study represents 519 ‘true
rural’ villages (the enumeration areas) used in the 2000/01 Household Budget
Survey (HBS) in Tanzania. According to Tanzania’s National Master
Sample (NMS), a true rural enumeration area shares the same boundary with
the village in which it is defined. The 2000/01 HBS was a nationally-representative
survey whereby the fieldwork was conducted between May 2000 and June 2001.
Between 12 and 24 households were surveyed in each sampled village. More
details of this survey can be found in the HBS main report published by in
2002 by Tanzania's National Bureau
of Statistics. Table 1 provides the descriptive statistics of the data
used in this study.
It is apparent from Table 1 that about 8% of total
cash income comes form the rural labor markets. This is a substantial amount
as most of the rural wage labor incomes are intended to relax cash constraints
when other sources of cash income, e.g. from selling agricultural products,
are not available (as noted by the latest USAID report on the state of food
security in 2004). Table 1 also shows that on average, 10% of the households
interviewed reported participating in off-farm wage employment. In general,
this is a relatively low rate of participation as compared to in some other
countries in Sub-Saharan Africa as shown in a recent study by Mduma and
Wobst published in 2005. Figure 1 also shows geographical distribution
of the importance of labor income in the rural areas. The lowest shares
are in Dodoma, Lindi, Mtwara, Ruvuma, and Rukwa regions. These regions are
known for their relative lag in many economic aspects such as transport
infrastructure. The relatively large share in the Coastal region (Pwani)
could probably be explained by the influence of being close to Dar es Salaam.
It is important to emphasize that rural labor markets
in Tanzania are mainly agricultural
based. The 2000/01 Integrated Labor Force Survey, which was conducted parallel
to the 2000/01 HBS, shows that 97.8% of the households in rural Tanzania were involved in agriculture.
Of those, 5.8% had hired employees, which is relatively high as compared
to only 1.5% for those working in the non-farm rural sector. Table 2 provides
other dimensions of the rural labor markets, including the gender dimension
where it is apparent that more males are reported to be working for a wage
For the first dependent variable SHARE (the share
of labor income in total cash income), we use a truncated regression because
the distribution of the variable is bounded between zero and one. Furthermore,
given the discrete (count) nature of the second dependent variable (the
number of households which sold labor in the rural labor markets, ACT_W),
the empirical model was estimated using a negative binomial model. The reason
for using the negative binomial regression is that, although ordinary least
squares (OLS) could be used, the preponderance of zeros and small and discrete
values of the dependent variable poses econometric problems. Alternatively, we could
have used standard Poisson regression. However, one restrictive assumption
of the standard Poisson model is that the mean and the variance are equal. Often, this restriction
may not agree with sample data and may cause an “over-dispersion” problem
(i.e. the mean deviates from the variance).
RESULTS AND DISCUSSION
(a) Model diagnostics:
The overall results of our estimations are presented
in Table 3. The last three rows in the table show the model diagnostics.
The two estimations, namely the share of labor income and the number of households
supplying labor in rural labor markets, fit the data fairly well. The null
hypothesis that all coefficients are zero is rejected at one percent significance
level in the two estimations. Furthermore, several regressors are
individually significant at the conventional levels as indicated by an asterisk
(*) in Table 3. The test for over-dispersion shows that there is significant
over-dispersion, which justifies the use of the negative binomial model.
(b) Estimated coefficients and their implications
It is apparent from Table 3 that average household
size (HH_TOTAL) in the village, age (AGE), and the dependency ratio (DEP_RATIO)
are among the variables that are not significant. Although this could be
a result of aggregation to the village level, the most likely reason is the
theoretical indeterminacy of some of these variables, for example household
size. However, many regressors have the expected signs and are significant.
As expected, costs related to marketing, transaction,
rationing, and information (DISTC_HRS) — the average travel time to the
nearest economic center — has a negative effect on the number of households
participating in the rural labor markets. Although it has the expected sign,
it is not significant in influencing the share of rural labor income in total
cash income. The non-significant effects on the share of labor income could
mean that this variable will have similar effects on other sources of cash
income (e.g. off-farm self-employment, leaving the composition of different
sources unchanged). Thus, this result is supported by the findings of many
other studies that conclude access to markets plays a significant role in
enhancing development in off-farm employment and in improving the welfare
of the rural poor.
Relaxation of the cash constraint (CREDIT) has a
positive and significant effect on the number of households participating
in rural labor markets. However, this variable is not significant in influencing
the share of labor income in the cash income of the village. Even though
the participation rate increases, this finding could imply that relaxation
of cash constraints increases income from off-farm self-employment faster
than it increases the wage income. In general therefore, this finding is
contrary to Woldehanna’s 2000 findings at household level, which show that
the relaxation of cash constraints reduced participation in the rural labor
markets in Ethiopia. The reason for this difference is that
our analysis at village level captures the effects of liquid households
in providing employment to cash constrained households. However, the square
cash constraint (CREDIT_SQ) is negative, which indicates that the relationship
is not linear and that after some time the increase in cash flow in the
village may reduce the number of households that participated in the rural
labor markets. This could result from the ability of some households, who
initially were wage workers, to overcome entry barriers in self-employment.
The indicator of wellbeing at village level, safe
water availability (SAF_WATE), is positively associated with the number
of households supplying labor in rural labor markets in a village and the
share of labor income in total income. The square of this variable also
is negative and significant, indicating that wage employment and safe water
availability are not necessarily linearly related. Furthermore, the other
wellbeing-related indicator, access to electrical power (ELEC_PW) is positively
related to the number of households supplying labor in the rural labor markets
and the share of income derived from these markets in the total cash income.
We find that, contrary to our expectation, inequality in per capita expenditure
has negative effects on the share of labor income in the total cash income.
This partly implies that the inequality in the rural areas (Gini coefficient
of 0.39) is a result of incomes generated from outside the rural areas such
as remittances. Note, however, that there is significant evidence that land
inequality is positively associated with the number of households that supplied
labor in the rural labor markets. Moreover, as expected, per capita land
has negative effects on the participation in rural labor markets as a supplier.
Thus, these results in general imply that past policies in Tanzania that
have favored egalitarian land holding/distribution partly explain the relatively
low development of rural labor markets in Tanzania as compared to some other
countries in the region, for example Malawi.
Indicators of economic diversifications in the rural
economy, the hours worked in the main activity (ACT_HRS1) and in the secondary
activity (ACT_HRS2) do have the expected effects. We find that the number
of hours worked on the main activities in rural Tanzania (which are generally
farming and livestock activities), increases the share of wage labor income
in total cash income. However, number of hours worked on secondary activities
in rural Tanzania (which are generally off-farm self-employment)
reduces the number of households supplying labor in the rural labor markets.
Similar results are obtained when these variables are replaced by their
ratio in the regression. It is established at 5% level of significance that the increase in the ratio
of hours worked in primary activities to hours worked in secondary activities
increases the share of labor income in total cash income. However, the effect
of this ratio was not significant in determining the number of households
selling labor in the rural labor markets.
The implication of this finding is that agriculture
still has the dominating role in the rural economy of Tanzania
as compared to other forms of off-farm self-employment. This is because
most of the rural off-farm self-employment enterprises are generally small
and provide employment only to their proprietors. This argument is
also reflected in the findings which show that villages with larger a number
of petty traders (TRADE) are associated with a low share of labor income
in total cash income. This last finding could mean that the kind of wage
labor analyzed here is considered to be an inferior option as compared to
other off-farm self-employment. This argument is in line with the argument
of distress-pushed participation in wage labor discussed in the 1997 work
of Islam who identified the major factor for distress-pushed participation
as successive droughts that depress agricultural income and hence increase
the need for alternative sources of income. Furthermore, these results show that
off-farm self-employment is a substitute for distress participation in rural
wage employment. Thus, while promotion of off-farm self-employment may be
an end unto itself, it is likely to reduce distress wage labor participation
and increase wage in rural labor markets. In this case, promotion of off-farm
self-employment can be welfare enhancing for both sub-sectors of rural off-farm
employment, namely wage employment and self-employment.
The results with respect to education are as expected.
We find that this type of labor market participation declines with education
(the square of education is negative). As the type of wage labor analyzed
here is predominately for the poor and less educated, our results indicate
that education empowers rural households in their search and participation
in other off-farm employment such as self-employment. Furthermore, a positive
and significant coefficient of education implies that even for this inferior
form of rural wage labor, education is important. It emphasize that villages
with a relatively educated population will offer more wage opportunities
than villages a with relatively uneducated population.
SUMMARY AND CONCLUDING REMARKS
This paper has considered factors that determine
the number of households that supply labor in the rural labor markets at
village level in Tanzania and the share of labor
income (derived from supplying labor to these markets) in total cash income.
Due to high transaction and supervision costs (involved in the rural labor
markets) as well as poor transport infrastructure in rural areas of Tanzania, each village was
considered to constitute a local labor market of its own.
We have shown that the factors significant in determining
the development of village labor markets are access to credit, education
level, per capita agricultural land, and market access. Thus, interventions
that relax cash constraints through increased access to credit for some households
are likely to indirectly increase the participation of other households
in the rural labor markets. For the case of education, we however noted
that even though education is important for the development of rural labor
markets, relatively high education (in the rural context) is likely to induce
participation in self-employment because it is generally considered superior
to rural wage employment.
Other factors that are significant in determining
the development of village labor markets are diversification of economic
activities in the village and inequality in both per capita expenditure and
landholding. Economic diversification occurs mainly in the form of off-farm
self-employment. As most of these self-employment enterprises do not have
employees apart from their sole proprietors, we found that they have negative
impacts on the number of participants in wage labor markets. As such, we
found that in the current rural setting in Tanzania, rural labor markets
are mainly tied to the farming sector (including the issue of land inequality
discussed above) as opposed to the off-farm sector.
Therefore, we argued that since a substantial portion
of labor supplied in the rural labor markets is a result of economic distress,
the promotion of off-farm self-employment is likely to reduce distress-push
participation in the wage labor markets. If the promotion of the rural self-employment
can bid up wages in the rural labor markets, then it can be welfare enhancing
for both sub-sectors of rural off-farm employment (i.e. wage and self-employment).
 Islam, 1997.
L. (2003). “Spatial Externalities.” International Regional Science Review,
Vol. 26, No. 2, pp. 147-152.
E.G. (1997). The impact of HIV/AIDS on rural communities and the need
for multi-sectoral prevention and mitigation strategies to combat the epidemic
in rural areas. Rome: Food and Agriculture Organization of the
United Nations (FAO).
Barrett, C. B., T. Reardon, and P. Webb (2001). “Nonfarm
income diversification and household livelihood strategies in rural Africa:
concepts, dynamics, and policy implications.” Food Policy, Vol. 26,
No. 4, pp. 315-331.
D. and H. Fofock (2000). Geographical Targeting for Poverty Alleviation:
Methodology and Applications. Washington DC: World Bank.
Bright, H. et al. (2000). “Rural Non-Farm
Livelihoods in Central and Eastern Europe and Central Asia and the Reform
Process: A Literature Review.” World Bank Natural Resources Institute Report
No. 2633. http://www.nri.org/work,
accessed on May 6, 2004.
Bryceson, D. (2000). “Rural Africa at the crossroads:
Livelihood practices and policies.” Natural Resources Perspective No. 52.
London: Overseas Development Institute.
(1998). “The State of Food and Agriculture. Part 3: Non-farm income in developing
countries.” Rome: Food and Agriculture Organization of the United
Nations (FAO). www.fao.org, accessed
on January 20, 2003.
A., H. Tchale and P. Wobst (2004). “The Impact of Labour Market Liberalization
on Maize Productivity and Rural Poverty in Malawi.” Submitted to Journal
of Development Studies.
L. (1996). “Poverty and Inequality During Structural Adjustment in Rural Tanzania.” World Bank Policy
Research Working Paper 1641.
W. (2001). Econometric Analysis. Upper Saddle River, NJ: Prentice-Hall.
Isigut, A. (2004). “Nonfarm income and employment
in rural Honduras: Assessing the role
of locational factors.” Journal of Development Studies, Vol. 40,
No. 3, pp. 59-86.
N. (1997). “The non-farm sector and rural development – review of
issues and evidence.” Food, Agriculture and the Environment Discussion Paper
22. IFPRI, Washington D.C.
S. (2004). “Seasonality and Wage Responsiveness in a Developing Agrarian
Economy.” Oxford Bulletin of Economics and Statistics, Vol.
66, No. 2, pp. 189-204.
P. (1998). “Rural Nonfarm Employment and Poverty: Evidence from Household
Survey Data in Latin America.” Development Research Group, World Bank, Washington,
J. and H. White (2003). “Rural labor markets and poverty in Sub-Saharan
Africa.” Working paper, Institute of Development Studies, University of
H. and S. Robinson (1999). “To Trade or not to Trade: Non-separable Farm
Household Models in Partial and General Equilibrium.” TMD Discussion Paper
37. IFPRI, Washington, DC.
J. (2003). “Village Level Analysis of the Factors Affecting Households'
Participation in Rural Labor Markets in Tanzania.” PASAD Working Paper,
Center for Development Research, University of Bonn. www.pasad.uni-bonn.de, accessed
on April 13, 2004.
J. and P. Wobst (2005). Village Level Labor Market Development in Tanzania: Evidence from Spatial
Econometrics. ZEF-Discussion Papers on Development Policy No. 96,
Center for Development Research (ZEF), Bonn.
(2002a). The Integrated Labor Force Survey, 2000/2001. Tanzania National Bureau of
Statistics, Dar es Salaam.
(2002b). Household Budget Survey 2000/01. Tanzania National Bureau of
Statistics, Dar es Salaam.
T. (1997). “Using evidence of household income diversification to inform
study of the rural nonfarm labor market in Africa.” World Development,
Vol. 25, No. 5, pp. 735-748.
T., J. Berdegué, and G. Escobar (2001). “Rural Nonfarm Employment
and Incomes in Latin America: Overview and Policy Implications.” World
Development, Vol. 29, No. 3, pp. 395-409.
M.R. (1988). “Labor Markets in Low Income Countries.” In H. Chenery and
T.N. Srinivasan (eds), Handbook of Development Economics, Volume 1. Amsterdam: Elsevier Science Publisher B.V. pp 713-762.
R. and M. Berg (2001). “Nonfarm employment and poverty alleviation of rural
farm households in Honduras.” World Development,
Vol. 29, No. 3, pp. 549- 560.
I., L. Squire, and J. Strauss (1986). Agricultural Household Models:
Extensions, Applications, and Policy. Baltimore and London: Johns
Hopkins University Press.
J. E. and I. Adelman (2003). “Agricultural Household Models: Genesis, Evolution,
and Extensions.” Review of Economics of the Household, Vol. 1 No.
1-2, pp. 33 – 58.
A. E., M. Mwachang'a, and K. Kilima (2001). “Agriculture Development Intervention
and Smallholder Farmers Credit in Southern Tanzania: An Assessment of Beneficiaries.”
African Review of Money, Finance and Banking, Supplement: pp.119-41.
(2004). “Tanzania Food Security Monthly
Report.” Famine Early Warning Systems Monthly Reports for January 2004.
US Agency for International Development. http://www.reliefweb.int, accessed
on March 31, 2004.
T. (2000). “Economic Analysis and Policy Implications of Farm and Off-farm
Employment: A Case Study in the Tigray Region of Northern Ethiopia.” Ph.D.
Thesis, Wageningen University.
J.M. (2001). Econometric Analysis of Cross Section and Panel Data.
Cambridge, MA: MIT Press.
Bank (2000). “Plenary Session Materials to the Internet Forum.” Africa Poverty
Forum on Poverty Reduction Strategies, Yamoussoukro, Cote d'Ivoire, June 29, 2000.
accessed on March 31, 2004.
Y. (2001). “Egalitarian Land Distribution and Labor Migration in Rural China.” China Center for Economic
Research, Beijing University, http://www.ccer.edu.cn,
accessed on December 9, 2003.
K. Mduma is a lecturer of Economics at the Economics Department, University
of Dar es Salaam & Junior Research Fellow at the Center for Development
Research (ZEF), University of Bonn. Peter Wobst is a Senior Research
Fellow at ZEF & Research Fellow at the International Food Policy Research
Institute (IFPRI), Washington, D.C. The research was made possible
by the Robert Bosch Foundation under the Policy Analysis for Sustainable
Agricultural Development (PASAD) Project at ZEF.
John K. Mduma is a lecturer of Economics at
the Economics Department, University of Dar es Salaam & Junior Research
Fellow at the Center for Development Research (ZEF), University of Bonn.
Peter Wobst is is a Senior Research Fellow
at ZEF & Research Fellow at the International Food Policy Research Institute
(IFPRI), Washington, D.C. The research was made possible by the Robert
Bosch Foundation under the Policy Analysis for Sustainable Agricultural
Development (PASAD) Project at ZEF.
Reference Style: The
following is the suggested format for referencing this article: John K.
Mduma and Peter Wobst. " Determinants
of Rural Labor Market Participation in Tanzania " African Studies Quarterly 8, no.2: (2005) [online] URL: http://web.africa.ufl.edu/asq/v8/v8i2a2.htm