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Income inequality within smallholder irrigation schemes in sub-
Saharan Africa 1
Ana Manero
Crawford School of Public Policy, Australian National University, Canberra, Australia
School of Commerce, University of South Australia, Adelaide, Australia
1 This is an Author’s Original Manuscript of an article published by Taylor & Francis in
International Journal of Water Resources Development on 02 March, 2016, available online:
http://dx.doi.org/10.1080/07900627.2016.1152461
Income inequality within smallholder irrigation schemes in sub-
Saharan Africa
Equitable income distribution is recognised as a critical element to achieve
poverty reduction, particularly in developing areas. Most of the existing literature
in this field is based on region or country-wide data, yet fewer empirical studies
exist at community levels. This paper examines income disparities within six
smallholder irrigation schemes in Zimbabwe, Tanzania and Mozambique. Gini
coefficients were used to compare local and national levels of inequality.
By-group and by-source decompositions served to identify the factors that most
contribute to income disparities. The results across the six schemes present
significant contrasts both between schemes and compared to national figures.
The results suggest that, inadvertently, some national strategies may overlook
high levels of income inequality within small communities. Rural development
strategies should recognise the importance of income inequality within the
specific areas of intervention and target growth within the activity sectors that
have the greatest potential to reduce poverty and inequality.
Keywords: income inequality, agriculture, poverty, Gini coefficient, Theil index
Introduction
It is estimated that 1.2 billion people across the world live in extreme poverty, making
this a priority area in international development (UN, 2013). Alongside with growth,
mitigating socio-economic inequalities is widely recognised as a key component of
effective poverty reduction strategies (Groll & Lambert, 2013; Kabubo-Mariara,
Mwabu, & Ndeng’e, 2012). Kuznets (1955) argued that growth may lead to an initial
rise in income inequality, but in the long-term, as the economy develops, inequality
would eventually decline. Nevertheless, without adequate redistribution interventions,
the benefits of development rarely spread spontaneously from the rich to the poor.
Instead, excessive economic disparities often entail serious, long-lasting issues such as
persistent poverty (Ravallion, 1997), violent crime (Hsieh & Pugh, 1993), corruption
(Khagram, 2005), political instability (Alesina, 1996), worsened health (Kawachi &
Kennedy, 1997) and low education levels (De Gregorio & Lee, 2002).
The interconnection between growth, poverty and inequality is particularly
crucial in rural areas, home to 70% of the developing world’s extremely poor (Ferreira,
1996; Ortiz & Cummins, 2011; Watkins, 2013). Sabates-Wheeler (2005) argues that
understanding this relationship is most critical in Sub-Saharan Africa (SSA), where
limited empirical work and data exist. Most of the existing inequality literature is based
on national or regional investigations, yet fewer studies exist within smaller areas, such
as villages or rural communities (Silva, 2013). One of the reasons for this data gap is
that broad studies are frequently based on readily-available governmental census
surveys, whereas localised research requires detailed data collection processes. Given
this gap in the literature, Ostry and Berg (2011) point to the need to further understand
the poverty-inequality nexus within small communities, in order to define more
effective and robust growth strategies.
This study investigates socio-economic inequalities within six smallholder
irrigation schemes in SSA. First, income inequality will be calculated at a local level
and then compared to national figures. Second, income inequality will be decomposed
by economic activity – solely agricultural or diversified incomes - to assess the relative
importance of the between and within-group components. Finally, an analysis by four
different sources of income will determine which are the components that most
contribute to total inequality and which ones have a (un)equalising effect. Lessons
learnt from this investigation will serve to understand how income inequalities within
smallholder agricultural communities compare to country-wide disparities and whether
commonly accepted strategies would be applicable within local contexts.
Growth, poverty and inequality in sub-Saharan Africa
Between 1995 and 2013, SSA experienced a steady increase in economic activity with
an average annual GDP growth of 4.5%, accompanied by a nine percent drop in the
poverty headcount ratio (The World Bank, 2014a). Nevertheless, the benefits have not
reached all, as the sub-continent is still home to 30% of the word’s extreme poor and
undernourished population. Following the global trend, income disparity in the region
has risen compared to 1980’s levels. Cogneau et al. (2007) indicate that SSA is the
second most unequal subcontinent in terms of income, after Latin America and the
Caribbean. Out of all SSA countries, Lesotho, South Africa and Botswana are the most
unequal, with Gini coefficients above 0.63. On the other hand, Niger and Ethiopia have
the lowest incomes disparities, with Gini coefficients below 0.35 (CIA, 2014).
Zimbabwe ranks among the ten most unequal SSA countries, with a Gini
coefficient of 0.50 in 2006 (CIA, 2014). Part of Zimbabwe’s economic disparities are
derived from its agrarian socio-economic situation, which still reflects the legacy of the
colonial era, the civil war and the reforms of the late 20th century. Throughout the
1980’s and 1990’s, Zimbabweans’ livelihoods deteriorated significantly, as a result of
repetitive droughts and issues associated with its land reform (Government of
Zimbabwe, 2010; Kinsey, 2010; Mazingi & Kamidza, 2011; Schleicher, 2012).
Tanzania is one of the four most income equal countries in SSA, with a Gini
coefficient of 0.38 in 2007 (CIA, 2014). Its economy is largely dependent on rural
activities, with agriculture, hunting and forestry accounting for 27% of GDP, only
second to the service sector (48%) (NBS, 2013). During the 1980s and early 1990s,
Tanzania experienced significant economic growth, which brought poverty reduction
(14% drop in poverty headcount), but also an increase in economic inequality. In fact,
while the poor benefited from growth, the rich captured a much greater share of
economic improvement (The World Bank, 2011). Over the first decade of the 2000s, the
country experienced an average annual GPD growth of seven percent and a the national
HDI was lifted from 163rd to 151st position, on a world rank of 189 countries (UNDP,
2011). At the national level, the poverty headcount ratio is still over 28%, while in rural
areas it is considerably higher at 38% (Ministry of Planning and Economic Affairs,
2009; The World Bank, 2014b).
In Mozambique, income inequality is relatively high, with a 0.46 Gini index,
above the median of 0.43 across SSA. Between 1995 and 2003, agriculture was the
second largest contributor to GDP growth (1.7% out of 8.6%) and the main driver of
poverty reduction. Over this period, agriculture experienced an average annual growth
of 5.2%, yet this mainly represented a recovery after the 1977-1992 war, with
production returning to pre-conflict levels, rather than resulting from innovation and
investment (Virtanen & Ehrenpreis, 2007). In 1999, the Ministry of Agriculture
launched the first phase of a large-scale reform program, the Agricultural Sector Public
Expenditure Program, towards a market-based agriculture with improved public support
and more effective use of natural resources (The World Bank, 2013). Since then, the
national poverty rate has declined by one-third, although it is still high at 55%.
Worldwide, Mozambique is the tenth least developed nation, with a HDI of 0.393 in
2013 (UN, 2014) .
Research methodology
Data collection
This study is based on six smallholder irrigation schemes in Zimbabwe, Tanzania and
Mozambique. The schemes range in size from 10 to 1,200 hectares and each of them
has between 30 and 1,300 farms. Irrigation water is supplied through gravity-fed
channels and serves to produce a variety of crops, as detailed in Table 1. In addition,
many irrigators also engage in other farming activities (e.g. rainfed crops and livestock)
and non-agricultural businesses, for example through labour and self-employment.
Table 1. Characteristics of the irrigation schemes and surveys undertaken
Country Irrigation scheme, District Area
(ha)
Number of farmers Major crops
Total Surveyed
Zimbabwe
Mkoba, Vungu 10 75 68 Maize, horticulture
Silalabuhwa, Insiza 442 212 100 Maize, wheat, sugar
beans, vegetables
Tanzania Kiwere, Iringa 145 200 100
Tomato, onions,
vegetables, maize, rice
Magozi, Iringa 1,200 1,300 100 Rice
Mozambique
Associacao de 25 Setembro,
Boane 80 38 25 Vegetables
Khanimambo, Magude 16 27 9 Vegetables
Source: Rhodes, Bjornlund, and Wheeler (2014)
For the purpose of this study, the household was used as the unit of analysis.
This is justified because the irrigation schemes are subdivided into farms, each of which
is cultivated by one family, with some families having more than one farm. Given the
association between farm and household, and not farm and individual, the data
collection process was designed using households as the basic unit. The survey
consisted of 65 structured and semi-structured questions, regarding the family members
(sex, age, education, health and occupation), farm characteristics (type of crops, yield),
food security (frequency and type of foods consumed), asset ownership, revenue and
expenses, among other questions.
The surveys were conducted between May and July 2014 with some variation
from country to country. The sampling methods depended on the size of the population
in each scheme. In the three smallest schemes, those in Mozambique, and Mkoba in
Zimbabwe final samples were close to 100% of the defined population. For the three
largest schemes, those in Tanzania and Silalabuhwa in Zimbabwe, the population was
sampled using a stratified approach. Irrigators were categorised according to gender of
the household head and wealth category (poor, medium and well-resourced) and then
randomly sampled (Moyo, Moyo, & van Rooyen, 2014).
Data used in this study includes household revenues and expenditure over the
12-month period prior to the interview. These figures were reported in each country’s
local currency, i.e. US dollar in Zimbabwe (USD), shilling in Tanzania (TSH) and
metical in Mozambique (MZN). The data was collected according to the source of
revenue and type of expenditure, and was then aggregated into on-farm and off-farm
categories, following the classification in Table 2.
Table 2. Revenue and Expenditure categories used in household survey
Revenue Expenditure
On-farm
Rainfed crops Crop inputs
Irrigated crops Harvesting/transport
Livestock sales Livestock inputs
Milk sales Hired labour
Other Irrigation
Other
Off-farm
Agricultural labour Food
Non-agricultural labour Education
Regular employment Health
Business/self-employment Social events
Remittances Housing
Seasonal work Personal transport
Other
Analytical framework
Economic inequality can be defined in many ways, but it is typically considered to be
the uneven distribution of wealth, income and/or assets among individuals of a group, or
between groups of individuals (McKay, 2002).
While the literature is consistent in affirming that there is no ideal unit of
measurement for wealth, money-metrics, i.e. income or consumption expenditure, tend
to be the preferred indicators of poverty and living standards (Sahn & Stifel, 2003).
Alternative non-monetary measures of poverty and inequality exist, such as those based
on asset ownership (Filmer & Pritchett, 2001; McKenzie, 2005) and the
Multidimensional Poverty Index, defined by a combination of education, health and
living standards indicators (Alkire & Santos, 2010; Kovacevic & Calderon, 2014). In
this paper, monetary indicators were used so as to evaluate how various income sources
contribute to total inequality.
There are a number of methods and indices to estimate economic inequality. The
section below presents a brief summary of the most common ones.
Gini coefficient
The Gini coefficient measures the extent to which the distribution of wealth
within a group deviates from a perfectly equal distribution (The World Bank, 2011). It
is the most commonly used inequality measure and is typically applied to income and
expenditure. Its advantages include being relatively easy to calculate; having a visual
representation and allowing for comparison between different size populations.
It can be estimated based on the representation of the Lorenz curve, plotting
cumulative income vs. cumulative population. The Gini coefficient can also be
mathematically calculated as follows:
𝐺 = 𝐶𝑜𝑣(𝑦, 𝐹(𝑦))2
�̅� (1)
where Cov is the covariance between income levels y and the cumulative
distribution of the same income F(y) and ȳ is average income (Bellù & Liberati, 2006b).
Lerman and Yitzhaki (1985) developed a method to decompose the Gini
coefficient as the sum of the inequality contribution of each income source. This is
obtained as a product of its own inequality, its share of total income and its correlation
with total income. Hence, the Gini coefficient for total income, G, can be formulated as
follows:
𝐺 = ∑ 𝑅𝑘𝑘𝑘=1 𝐺𝑘𝑆𝑘 (2)
where, Sk is the share of income source k in total income, Gk is the Gini
coefficient of income source k and Rk is the Gini correlation of income from source k
with the distribution of total income.
By calculating partial derivatives of the Gini coefficient with respect to a percent
change e in income source k, it is possible to estimate the percent change in total
inequality resulting from a small percent change in income source k:
𝜕𝐺𝜕𝑒⁄
𝐺=
𝑅𝑘𝐺𝑘𝑆𝑘
𝐺− 𝑆𝑘 (3)
This property is particularly useful when identifying which sources of income
have an “equalising” or “un-equalising” effect on total income (López-Feldman, 2006).
Theil Index
The Theil is a specific case of the generalised entropy indices (Bellù & Liberati, 2006a).
Its lower value is zero (perfect equality) and it has no upper limit. The index is defined
as follows:
𝑇 =1
𝑛∑ (
𝑦𝑖
�̅�)𝑖 ln (
𝑦𝑖
�̅�) (4)
where yi is the i observation and �̅� is the average income.
The Theil index has certain key advantages such as being decomposable and
additive into groups, thus allowing distinction of between and within sub-group
inequality components. Assuming m groups, the Theil Index is decomposed as follows:
𝑇 = ∑ (𝑛𝑘
𝑛
𝑦𝑘̅̅ ̅̅
�̅�) 𝑇𝑘 + ∑
𝑛𝑘
𝑛(
𝑦𝑘̅̅ ̅̅
�̅�) ln (
𝑦𝑘̅̅ ̅̅
�̅�)𝑚
𝑘=1𝑚𝑘=1 (5)
where the first term of the equation is the within component, and the second
term is the between component. Similarly to by group analysis, the Theil index can be
decomposed by source of income, following the expression for m sources:
𝑇 = ∑ 1
𝑛∑ (
𝑦𝑖𝑘
�̅�) ln (
𝑦𝑖
�̅�)𝑛
𝑖=1𝑚𝑘=1 (6)
In this study, the decomposition of the Theil index in between/within sub-groups
and by income source was calculated by computing equations (5) and (6).
The Theil Index has also some drawbacks, such as, not having an intuitive
representation and not being suitable to compare populations of different sizes. Also, it
does not support non-positive values, as log 𝑥 is undefined if 𝑥 < 0. As explained by
Bellù and Liberati (2006a) and Vasilescu, Serebrenik, and van den Brand (2011), this
limitation can be overcome by replacing zeros with very small values ε > 0, such that
ITheil (x1, . . . ,xn−1, 0) ≡ ITheil (x1, . . . ,xn−1, ε). In this paper, ε was taken equal to 10-10.
The Atkinson index
Atkinson (1970) developed inequality measures based on the concept of Equally
Distributed Equivalent (EDE) income. EDE is that level of income that, if obtained by
every individual in the income distribution, would enable the society to reach the same
level of welfare as actual incomes (Bellù & Liberati, 2006c). The Atkinson index is,
hence, a welfare-based measure of inequality that can be used to address questions
around transfers of income from better-off to poorer sections of the population.
In this study, the Atkinson index will not be used as welfare considerations
specific to each of the six communities are out of the scope of this paper. Therefore,
cross-regional inequality comparisons and income decomposition analysis will be
carried out using the Gini coefficient and the Theil index.
Negative incomes and measures of inequality
In agricultural economies, two common measures of income are net cash income and
net farm income. The former compares cash receipts to cash expenses (Schnepf, 2012),
while the latter is a value of production including cash and non-cash transactions, such
as value of commodities consumed or stored by the household, depreciation or
inventory changes (Edwards, 2013). In this study, net cash income has been chosen as
the measure of household income.
Across the six irrigation schemes, 30% of the households reported higher
on-farm expenses than on-farm revenues, thus resulting in negative net (farm) cash
incomes. This poses a major constraint in the study of inequality given that the most
robust and commonly used tools, such as the Gini coefficient and Theil index, are not
defined for negative values. This issue had been discussed in the literature with different
authors adopting different approaches.
Walker and Ryan (1990) and Möllers and Buchenrieder (2011) note the
existence of negative incomes in their data, yet neither discuss the method of calculation
of household incomes or ways of dealing with non-positive incomes.
Schutz (1951) and Stich (1996) indicate that negative incomes are usually
excluded from the measurements of income inequality. This approach can be found, for
example, in Cowell (2009). Cribb, Hood, Joyce, and Phillips (2014) and Sanmartin et al.
(2003).
Nonetheless, disregarding households with negative net (farm) cash incomes is
not ideal in this study as it would ignore almost one-third of the households in the
sample. This is in line with the consideration made by Rawal, Swaminathan, and Dhar
(2008, p. 232): “Such (negative incomes) exclusion not only narrows down the database
but also misses out on a key feature of household incomes”. Moreover, Allanson (2005)
argues that negative incomes cannot be ignored in the analysis of agricultural
redistribution policies given that it is normal for farms to record losses.
A modified Gini coefficient can be calculated including zero and negative
values, based on the geometric properties of the Lorenz curve, yet its values are no
longer limited between 0 and 1. Chen, Tsaur, and Rhai (1982) reformulated the Gini
coefficient to correct this issue, yet this alternative measures still has major limitations,
such not allowing for an accurate decomposition by income source (Mishra, El-Osta, &
Gillespie, 2009).
The Australian Bureau of Statistics (ABS, 2006) notes that negative incomes are
not necessarily a good indicator of economic inequality and that it is inappropriate for
them to have a disproportionate influence on summary inequality measures. It is argued
that negative incomes often reflect the households’ business and investment
arrangements or, in other cases, incomes may be accidentally or deliberately
underreported. Thus, another common method of handling negative incomes is through
a process referred to as equivalisation, by which the individual components of market
income showing negative values are set to zero before computing the total income of
each household (OECD, 2014). The process of equivalisation has been defined by the
OECD and is used by government agencies such as the Australian Bureau of Statistics
(ABS, 2006) and the UK Department for Work & Pensions (2014).
A similar approach was adopted by Seidl, Pogorelskiy, and Traub (2012) who
truncated their data in a way that negative incomes were reported as zeros. Bray (2014,
p. 443) also used this technique in a comparison with four other treatment methods, thus
showing that, when negative incomes are set to zero, the resulting Gini coefficients
were consistent with the figures obtained through the alternative methods.
When it comes to adopting one method or another, Deaton (1997, p. 140) notes
that the choice between various inequality measures is sometimes made on the grounds
of practical convenience and others on the grounds of theoretical preference. Similarly,
Smeeding, O'Higgins, and Rainwater (1990, p. 11) state that “each researcher is left to
deal with zero and negative incomes as he or she sees fit”.
Given the preference to maintain all households in the sample and the choice of
Gini and Theil measures of inequality, the author deemed that the most suitable
approach to deal with negative incomes is adopt the equivalisation process described
above. Thus, negative farm incomes were converted to zero, before being added to
other income components to obtain the total.
In order to test the adequacy of the chosen method, a sensitivity analysis was
conducted (see Appendix A). The levels of income inequality calculated using the
equivalisation method are coherent with those obtained through alternative approaches,
such as excluding households with negative incomes.
The use of non-parametric tests
Non-parametric tests of statistical significance were used to analyse differences in the
distribution of incomes in different population sub-groups. This choice is justified by
the fact that commonly used parametric tests make assumptions on parameters
characterising the population’s distribution, which was not possible given the data in
this study.
The Wilcoxon rank-sum test (hereafter WRS, and also known as Wilcoxon-
Mann-Whitney) is a non-parametric test analog to the independent samples t-test that
can be used when the variables are not normally distributed (UCLA). In addition, the
two-sample Kolmogorov-Smirnov (hereafter KS) is another non-parametric test that can
be used to test the hypothesis that two populations have the same distribution. The KS
test is sensitive to any differences in the distributions (e.g. shape, spread or median) and
has more power to detect changes in the shape, whereas WRS tests on location and
shape, and has more power to detect a shift in the median (GraphPad Software, 2015).
Limitations
This study has two major limitations. First, the populations of study consist only
of members of irrigation schemes, but not the entire agricultural communities. This is
due to the fact that the data used in this study was collected as part of a research project
focusing on increasing irrigation water productivity (ACIAR, 2013). Given the nature of
this research, the population of study was limited to irrigation farmers and, hence, did
not include other members of the rural community, such as dryland farmers and non-
farmers. Studying income inequality across the entire community would not have been
possible because there is no comprehensive list of all its members that would allow
adequate probability sampling. Conversely, Irrigation Organisations keep up-to-date
lists of all their members, from which population samples were drawn. Future research
could be extended to analyse income and inequality within the entire rural community
and also investigate the potential differences between irrigators a non-irrigators.
The second limitation is the large proportion of households reporting negative
farm incomes. As previously discussed, this entails difficulties in the study of inequality
analysis, as most analysis tools are restricted to positive values. It is possible that during
the interviews, farm incomes were underreported and expenses overreported, either
accidentally or deliberately. Therefore, an improvement to this study could have been
identifying negative farm income during the interviews to then question famers about
their financial losses. This method would have improved the accuracy of the records
and also would have provided greater insight into why certain households experience
negative incomes.
Results and discussion
Income inequality at scheme and national levels
This section describes the levels of expenditure and income inequality within six
smallholder agricultural communities and compares them to their respective national
figures.
As shown in Table 3, inequalities measured by expenditure are smaller than by
income. This tendency is quite common (Aguiar & Bils, 2011; Finn, Leibbrandt, &
Woolard, 2009; Krueger & Perri, 2006) and is mainly a result of consumption
expenditure being more evenly distributed than income.
Table 3: Inequality at scheme and national levels
Scheme level National level
Consumption
expenditure
Gini
Income
Gini Income Gini
Mkoba 0.54 0.60 0.50
Silalabuhwa 0.47 0.48 0.50
Kiwere 0.54 0.60 0.38
Magozi 0.39 0.56 0.38
Ass.e 25 Setembro 0.59 0.65 0.46
Khanimambo 0.55 0.58 0.46
Source: author’s computations for scheme level and CIA (2014) for national levels.
Except for Silalabuhwa (Zimbabwe), income inequalities at scheme level are
higher than at national levels. The greatest difference is in Tanzania, where Gini income
coefficients within the agricultural communities (0.56 in Magozi and 0.60 in Kiwere)
are in the order of 50% to 60% higher than at the national scale (0.38).
The Tanzanian Ministry of Planning and Economic Affairs (2009) argues that,
given the country’s relatively low levels of inequality, redistribution of incomes is not
likely to be effective in achieving significant reductions in poverty. Instead, it suggests
that continued high rates of economic growth over the long-term will be required. By
contrast, the results of this study show that significant income inequalities exist at
smaller scales (e.g. irrigation schemes), which are currently being overlooked by
country-wide statistics.
Income dualism between agricultural and diversified sources
In rural developing areas, non-agricultural earnings represent an important part of
households’ incomes (Barrett, Reardon, & Webb, 2001; Escobal, 2001; Reardon, 1997).
While this can contribute to improved living standards, entry barriers to non-farm
activities may result in a greater divide between those who are able to diversify and
those who are not.
The aim of this section is to analyse income differences between and within two
households groups: i) those earning incomes exclusively from agriculture (Ag),
including farm incomes and agricultural labour; and ii) those having diversified income
sources (Div), including non-agricultural labour, regular, seasonal or self-employment,
business, remittances or other. Statistical significance tests were used in the
comparisons between agricultural and diversified income households.
In Zimbabwe, the vast majority of households have diversified incomes, while in
Tanzania and Mozambique, only half obtain earnings outside of agriculture. One
common characteristic to all six communities is that households who make a living
exclusively from agriculture had consistently lower mean and median incomes than
those with diversified incomes. The results of the WRS and the KS tests (Table 4)
conclude that the distribution of income is not the same in both groups and that
exclusively agricultural households rank lower in the overall income distribution. The
WRS test (p<0.10) indicated that the null hypothesis that incomes of agricultural
households are not different from diversified-income households could be rejected.
Similarly, the KS test concluded that (p<0.10) the hypothesis that both groups have the
same distribution was also rejected in all schemes, except for Magozi (Error!
Reference source not found.). Caution should be taken when interpreting the results
from the Mozambican schemes, since the power of statistical significance tests is low
when applied to such small samples (n<50).
Table 4: Income statistics by type of income
Scheme n
Mean HH
Income*
Median HH
Income*
Wilcoxon
rank-sum test
Kolmogorov-
Smirnov test
Ag Div Ag Div Ag Div Z p D p
Mkoba 6 62 179 1,098 67 475 -2.52 0.012 0.66 0.009
Silalabuhwa 20 80 411 940 180 700 -3.55 0.000 0.48 0.001
Kiwere 56 44 1,006 2,026 436 1,203 -3.29 0.001 0.43 0.000
Magozi 48 51 1,500 2,905 1,007 1,458 -1.79 0.074 0.20 0.217
As. 25
Setembro 14 11 40,634 187,707 27,930 84,000 -2.63 0.009 0.55 0.030
Khanimambo 4 5 5,250 177,610 0 173,200 -2.49 0.013 1.00 0.016
M: male-headed household; F: female-headed household
* Mkoba, Silalabuhwa in USD; Kiwere, Magozi in ‘000 TSH; As. 25 Setembro, Khanimambo in MZN,
Despite the remarkable contrast between agricultural and diversified income
households, the Theil index decomposition reveals that disparities within these two
groups are actually the main contributor to overall inequality (Table 5). The only
exception is Khanimambo, yet statistics from this scheme are subject to a very small
sample size.
Table 5. Household income analysis and decomposition by activity group
Scheme Percentage
of Ag HH
Gini Theil
Ag Div Within Between
Mkoba 9% 0.59 0.58 92% 8%
Silalabuhwa 20% 0.49 0.45 91% 9%
Kiwere 56% 0.59 0.69 90% 10%
Magozi 48% 0.55 0.59 92% 8%
As. 25 Setembro 56% 0.64 0.43 72% 28%
Khanimambo 44% 0.56 0.54 27% 73%
Ag: exclusively agricultural income household; Div: diversified income households
The results of this section suggest that promoting agricultural households to
diversify their income sources could potentially have a poverty reduction effect, as
households with a variety of incomes are consistently better-off.
Relative importance of income sources in total inequality
An extensive literature review undertaken by Senadza (2011) concluded, that to better
understand the effects of income on inequality, it is important to distinguish between the
various components of non-farm income. Hence, this section is dedicated to analysing
the effect on total inequality derived from four distinct income sources : i) Agricultural,
including on-farm income and agricultural labour; ii) Wages, including non-agricultural
labour, regular employment and seasonal work; iii) Business and self-employment and
iv) Other, including remittances and other unspecified sources.
The results in Table 6 show the relative importance of each source in terms of its
contribution to the scheme’s combined income and to its total income inequality. In
Tanzania, agriculture is the most important source of income, accounting for
three-quarters of total earnings and circa 80% of inequality. Conversely, Zimbabwean
schemes rely more heavily on remittances and other sources, which also drive the
largest portion of the schemes’ income disparities. In Mozambique, incomes and
inequalities are mainly split between agriculture and wages.
Table 6: Income and inequality decomposition by source
Scheme Agriculture Wages Business and
self-employment Other
Share of
Income
Share of
Inequality
Share of
Income
Share of
Inequality
Share of
Income
Share of
Inequality
Share of
Income
Share of
Inequality
Mkoba 19% 2% 15% 23% 14% 17% 52% 58%
Silalabuhwa 34% 14% 17% 42% 5% 3% 44% 42%
Kiwere 79% 83% 7% 6% 11% 9% 3% 1%
Magozi 66% 43% 9% 15% 23% 42% 2% 0%
As. 25 Setembro 46% 10% 47% 86% 6% 4% 1% 0%
Khanimambo 52% 48% 43% 47% 5% 5% 0% 0%
As noted by Shariff and Azam (2009), ‘A key rational for studying
decompositions by source is to learn how changes in particular income source will
affect overall inequality. What impact does a marginal increase in a particular income
source have on inequality?’ In order to answer this question, a Gini decomposition
following equations (2) and (3) was carried out for each of the six irrigation schemes.
For each income source, the results summarised in Table 7 indicate the marginal impact
in total inequality due to a 1% increase in that particular source, whilst holding income
from all other sources constant. The direction and magnitude of the marginal impact are
given by the % Change. A negative sign indicates a tendency of that particular
component to reduce total inequality, while a positive sign reveals an un-equalising
effect. Logically, the larger the absolute value, the greater marginal the impact.
Table 7: Gini decomposition by income source
Scheme Agriculture Wages Business and
self-employment Other
Gini % Change Gini % Change Gini % Change Gini % Change
Mkoba 0.76 -0.07 0.93 0.02 0.92 0.01 0.76 0.04
Silalabuhwa 0.68 -0.07 0.94 0.10 0.91 -0.01 0.70 -0.01
Kiwere 0.66 0.01 0.94 0.00 0.92 -0.01 0.92 -0.01
Magozi 0.57 -0.09 0.95 0.02 0.91 0.08 0.96 -0.01
As 25 Setembro 0.54 -0.13 0.90 0.13 0.91 0.01 0.90 -0.01
Khanimambo 0.61 -0.06 0.69 0.06 0.75 -0.01 N/A N/A
In five out of six schemes, agriculture has an equalising effect. In fact, the
distribution of agricultural income is skewed towards the bottom, meaning that poorer
households rely more heavily on farming and would benefit the most from growth in
this sector. The exception to this trend is Kiwere, in Tanzania, where even the highest
income households (top two quintiles) earn a significant portion (80%) of their incomes
from agriculture.
Wage incomes have an un-equalising effect across the six schemes, while the
effect of business and self-employment is mixed, i.e. equalising three scheme (one in
each country), yet unequalising in the rest. Unequalising effects could be explained by
low wages received by poor famers and entry barriers preventing poorer households
from venturing into entrepreneurship. The fact that neighbouring schemes experience
contrasting effects reveals the existence of significant intra-country and even intra-
region differences. The policy implication is that one same strategy targeting growth in
a certain activity sector could have a positive, equalising effect in some groups, yet the
exact opposite (unequalisng) in other nearby communities.
Conclusions
This paper analyses income inequality within six smallholder irrigation schemes in
Zimbabwe, Tanzania and Mozambique using household survey data from 2014. The
Gini and Theil indices are used to measure income inequalities and decompose
inequalities by gender and source.
The results indicate that income inequalities within the irrigation communities
are considerably higher (20% to 60%) than their respective country-wide figures.
Comparisons between agricultural and diversified income households showed that those
relying exclusively on farming activities earn consistently lower incomes than their
counterparts. In five out of six schemes, agricultural income was found to have an
equalising effect, i.e. a marginal increase in agricultural income will lead to decrease in
overall inequality. Conversely, a marginal increase in wages would lead to an increase
in inequality in all schemes, while the impact of business and self-employment was
mixed across the three countries.
These findings have important policy implications. First, it is crucial to
recognise the existence of significant levels of income inequality at small scales.
Therefore, broad-based strategies should be carefully examined before being applied
within local contexts, as they could overlook existing disparities and thus perpetuate, or
even worsen, economic inequalities. Policies incorporating income distribution
considerations would be more effective in achieving substantial and long-lasting
poverty reduction, rather than those targeting only economic growth.
Second, strategies aimed to lessen inequality levels within smallholder
irrigation schemes should be two-fold. On the one hand, lifting incomes from sources
having equalising effects (e.g. agriculture) could act as a poverty reduction intervention
at the same time as it would reduce income inequality. On the other hand, it is also
important to create new opportunities for poor households to diversify into more gainful
activities (e.g. skilled employment or entrepreneurship), rather than relying exclusively
on agricultural incomes.
Acknowledgments
I thank my PhD supervisors, Henning Bjornlund, Sarah Wheeler, Quentin
Grafton and Jaime Pittock for their advice and insightful comments. This study is based
on data collected through a large research project funded by the Australian Centre for
International Agricultural Research. I kindly thank my research colleagues Andre van
Rooyen, Martin Moyo, Nuru Mziray, Makarius Mdemu, Paiva Munguambe and Wilson
de Sousa for their efforts in collecting the data used in this study.
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Appendix A
This Appendix provides the results of a sensitivity analysis using various
methods of estimating income inequality. The purpose of this appendix is to verify that
results obtained through the chosen methodology are consistent with the other possible
alternatives.
Income Gini coefficients for the six communities are summarised in Table A 1
following four different methods. The first column reflects the method used in this
study, which consists of converting negative farm incomes to zero, before being added
to the rest of household income components. The second column represents HH income
Gini coefficients excluding those households with negative farm incomes. The third
column reflects HH income, excluding households with negative incomes. The fourth
column indicates the values of the Gini coefficients for household earnings, without
taking into consideration farm expenses. The last column indicates modified Gini
coefficients including negative incomes, which are calculated based on the geometric
properties of the Lorenz curve. The differences shown in this sensitivity analysis are a
result of the different calculations methods and are consistent with previous studies
(Bray, 2014) also comparing various approaches to treat negatives incomes.
Table A 1: Gini coefficient sensitivity analysis
Income Gini
Adjusting for
negative farm
incomes
Income Gini
Excluding HHs
with negative
Farm Income
Income Gini
Excluding HHs
with negative
HH income
Gini for HH
Revenue
(without
considering
farm expenses)
Modified
income Gini
including
negative
incomes
Mkoba 0.60 0.51 0.58 0.57 0.63
Silalabuhwa 0.48 0.44 0.46 0.42 0.52
Kiwere 0.60 0.52 0.52 0.53 0.93
Magozi 0.56 0.55 0.55 0.51 0.66
As. 25 Setembro 0.65 0.62 0.62 0.64 0.85
Khanimambo 0.58 0.55 0.59 0.57 0.59
As discussed in this paper, methods that exclude households with negative
incomes (Columns 2 and 3) tend to underestimate income inequalities as the bottom part
of the distribution is not taken into account. The exception is the Khanimambo scheme
where there are no households with negative incomes. Estimating economic inequality
based only on revenue (Column 4) also provides lower Gini coefficients, which
indicates that gross revenue (earnings without considering expenses) is more evenly
distributed than net income (revenue minus farm expenses). Finally, modified Gini
coefficients including negative incomes are consistently higher than those treating
negative incomes.
Similarly to the Gini coefficient sensitivity analysis, Table A 2 summarises the
results of the Theil index sensitivity analysis.
Table A 2: Theil index sensitivity analysis
Income Theil
Adjusting for
negative farm
incomes
Income Theil
Excluding
HHs with
negative and
zero Farm
Income
Income Theil
Excluding
HHs with
negative and
zero HH
income
Theil for HH
revenue
(without
considering
farm
expenses)
Mkoba 0.64 0.45 0.58 0.55
Silalabuhwa 0.41 0.31 0.35 0.27
Kiwere 0.63 0.47 0.46 0.46
Magozi 0.60 0.56 0.58 0.46
As. 25 Setembro 0.89 0.73 0.74 0.66
Khanimambo 0.66 0.17 0.31 0.36
Calculations excluding households with negative and zero incomes (Columns 2
and 3) provide lower values of the Theil index. However, it should be noted that the
Theil index does not allow direct comparisons across populations of different sizes.
Lastly, Theil indices calculated based on earnings are lower than those based on net
income, as in the case of the Gini coefficients.