




































 Agricultural Science; Vol. 2, No. 1; 2020 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v2n1p133 

133                             Published by IDEAS SPREAD 
 

Rural Livelihood Diversification in Bangladesh: Effect on Household 
Poverty and Inequality 

Shakila Salam1 
1 Associate Professor, Institute of Agribusiness and Development Studies, Bangladesh Agricultural University, 
Mymensingh, Bangladesh 
Correspondence: Shakila Salam, Associate Professor, Institute of Agribusiness and Development Studies, 
Bangladesh Agricultural University, Mymensingh, Bangladesh. E-mail: shampa.bau@gmail.com 
 
Received: April 1, 2020   Accepted: April 18, 2020   Online Published: April 26, 2020 
 
Abstract 
The pervasiveness of poverty continues to be a daunting challenge for Bangladesh. This study attempts to examine 
the effect of different livelihood diversification on rural household poverty and income inequality. A panel dataset, 
which is used in this study, was collected in the three different years (1988, 2000 and 2008) from 62 villages across 
57 districts of Bangladesh. Besides, 153 households from three districts of Bangladesh were also randomly selected 
as primary data. Multidimensional poverty index (MPI) was used to measure poverty whereas Gini coefficients 
and decomposition of Gini coefficients were used to measure inequality and to identify marginal effects of certain 
livelihood income source on total inequality. The results reveal that diversifying livelihood through income source 
changes has an impact on the poverty level and inequality among rural households. Following these changes, the 
overall poverty situation has been improving and income distribution has been worsening over the years. 
Households drastically reduce their poverty by diversifying their livelihood from only agriculture to part-time 
farming. Among different non-farm income sources, only self-employment has a positive contribution in 
decreasing income inequality since 2000. Incomes from migration and wage-employment widen income inequality 
in rural Bangladesh. Therefore, policy options should strive to expand rural industry and scope of self-employment 
in the rural areas along with agricultural sector development. 
Keywords: livelihood diversification, poverty, inequality, multidimensional poverty index (MPI), gini coefficient, 
Bangladesh 
1. Introduction 
The pervasiveness of poverty continues to be a stubborn problem in Bangladesh, where about 22 million people 
still living below the poverty line (World Bank, 2018). Though the country has made significant progress in 
decreasing poverty levels over the past two decades, it still has a sizeable proportion of its population living in 
poverty or extreme poverty. The situation going to worst as levels of inequality continues to rise steadily. Poverty 
rate has been reduced by 1.2% during 2010 to 2016 which was 1.7% during 2005 to 2010 (Alam, 2019). In the last 
report of HIES (HIES, 2019), the headcount poverty rates under the upper and lower poverty line have been found 
24.3% and 12.9% respectively in 2016. These rates have further decreased to 21.0% and 10.4% in 2018 (Alam, 
2019). However, in the meantime, the income Gini coefficient has been increased from 0.45 to 0.48, which implies 
deteriorating condition of income inequality in the country. Poverty in rural areas is more severe than in urban 
areas. According to the HIES (2019) estimation, about 18.90 percent of urban poverty rate in comparison to 26.40 
percent of rural indicates the existence of a wide gap within these areas.  Moreover, a strong negative correlation 
between land ownership and incidence of poverty was also found where landless, marginal (cultivating less than 
0.20ha) and small farm households (cultivating 0.21-1ha) are the main victims of poverty. In 2016, about 34.4 
percent marginal and 23.8 percent small landholders lived under the poverty line. So, small-scale landholder’s 
survival and improvement of their economic condition play an important role for poverty reduction, living 
conditions and overall welfare of the society (Salam et al., 2019). Government also prioritized the poverty 
reduction of these extremely poor households by considering it as one of the goals to achieve within next five 
years (FYP, 2015). Moreover, poverty and inequality mostly relates to the income of the households, which they 
receive from a diverse portfolio of activities. Hence, the economic situation and the standard of living of the 
households cannot be fully described by only on-farm income (Castagnini et al., 2004). Therefore, rural households 
are diversified their livelihood from single occupation (farming) to multiple occupation. Rural non-farm sector is 
one of the major sources of these multiple occupation. Participation in rural non-farm activities has two different 



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directions, namely demand-pull and distress-push processes (Barrett et al., 2001; Reardon et al., 2007). In demand-
pull process, agricultural labors participate in the most lucrative employment opportunities of the rural non-farm 
sector. According to distress-push process, insufficient agricultural income and other factors push agricultural 
labors into the low paid rural non-farm sector. According to Möllers & Buchenrieder (2005), based on theory, both 
of these processes have the potentiality to reduce poverty. Participation in different rural non-farm activities exerts 
a positive impact on rural poverty if return per labor unit from the non-farm sector is higher than those in agriculture 
(Chuta & Liedholm, 1979). In Bangladesh, about 40 percent of poverty reduction between 2000 and 2005 was due 
to rural non-farm income growth, whereas only 21 percent in the same period was accounted for by farm income 
growth (World Bank, 2013). However, the poverty-decreasing effect of the non-farm sector is found in many 
research works, but the effect of a combination of farm and non-farm sector on poverty and income-inequality is 
still unclear. Malek & Usami (2009b) found participation in the non-farm sector in Bangladesh has an income 
poverty reducing effect but not educational poverty. In China, non-farm employment was found as a solution of 
reducing poverty, its depth and income inequality as well (deJanvry et al., 2005). Likewise, inequality-decreasing 
effects of non-farm employment was also found in Pakistan, as it can provide an opportunity to the landless or 
marginal farmers to earn more and thus reducing the gap of the income distribution (Adams, 1995). Contrarily, 
Rahman (1999) found non-farm income as a major contributor to increasing of overall income-inequality. Rich 
literatures drawing on a number of developing countries concluded that the overall impact of non-farm earning on 
income distribution is mixed (Haggleblade et al., 2009; Lanjouw & Lanjouw, 2001). In the context of Bangladesh, 
the relevant previous studies identified rural non-farm income as a key force behind uneven income distribution 
(Hossain et al., 2000; Malek & Usami, 2009a). But these analyses do not consider different livelihood 
diversification, and even income from remittance or other transfers, which is one of the important sources of non-
farm income. Besides, the effect of a combination of farm and non-farm sector on poverty especially using a 
modern method like MPI is still unavailable in the existing literatures.  For policy purposes, it is more worthy to 
know which type of livelihood diversification has a greater contribution to poverty reduction. Therefore, 
considering these importance, the present study attempts to explore the role of livelihood diversification on poverty 
and income distribution in Bangladesh.   
2. Materials and Methods 
2.1 Data Sources 
Both primary and secondary data were used in this research. As a secondary source, an eight years panel data set 
was used which was collected in 1987-1988 by the Institute of Development Studies (BIDS) from 62 villages 
across 57 districts. The data set was further revised by International Rice Research Institute (IRRI) in 2000-2001 
and Bangladesh Rural Advancement Committee (BRAC) in 2008 in the same households to collect relevant 
information. This whole data set was collected from Research and Evaluation Division (RED) of BRAC in 2014. 
The total numbers of used observations in this research are 510, which were collected and balanced from three 
years panel data set (1988, 2000 and 2008). Though this dataset is quite out dated, during research this was the 
latest data set of that panel. For updating information, a cross-sectional primary data set was collected through a 
field survey of 153 households located in 3 districts during July to November 2014. Following panel data set, same 
villages and households from the corresponding districts were chosen as sample. Moreover, other required 
secondary information for this study was obtained through reviewing literatures such as publications, research 
articles, working paper etc. Besides, some important information gathered from different authentic websites (FAO, 
OPHI, VDSA, etc.) and experts of the relevant field through email communication. 
2.2 Analytical Techniques 
2.2.1 Multidimensional Poverty Index (MPI) 
Traditionally, poverty can be measured by two methods: (1) direct method and (2) indirect method. A set of selected 
basic needs and rights, whether satisfied or not is indicated by the direct approach. On the other hand, people’s 
income drop below the basic needs identified poverty line or not is determined by the indirect approach. Although 
both methods have been largely applied in different aspects, but direct method is popularly used nowadays due to 
some limitations of income approach. For example, as the consumption pattern is not always same, earning 
specified money for moving over the poverty line does not guarantee that he or she can fulfil basic needs (Sen, 
1981). Moreover, considering only one factor like income or expenditure may not necessarily explain the real full 
situation of poverty. A lot of factors like education level, health or nutrition situation, living standard can affect 
simultaneously a person or household’s poverty situation. Considering all of these and for implementing direct 
approach, Multidimensional Poverty Index (MPI) was developed by Alkire & Foster (2007). In the MPI, Adjusted 
Headcount Ratio (Mo) is measured instead of simple Headcount Ratio for estimating poverty. Actually, adjusted 



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head count ratio, Mo is an approach that combines the incidence of poverty measurement and the intensity of 
poverty measurement. 
 
Table 1. Dimensions, indicators and cut-offs used in MPI calculation 

Dimension Indicator Deprived if… 

Education 
(0.33) 

Years of schooling 
(0.33) 

At least 1 household member aged more than 14 years has 
not completed 5 years of schooling 

Health 
(0.33) 

Per capita per day calorie intake 
(0.33) 

Per capita per day food calorie intake of AEU in Kcal< 2122
(Note 1)  

Living standard 
(0.33) 

House condition (0.11) - House has mud floor and tin or straw roof 

Electricity (0.11) - Household has no electricity service or solar energy or 
battery 

Land asset (0.11) - Household owns less than 0.20ha of land 
Note: AEU = Adult Equivalent Unit; Figures in the parentheses indicate respective weights. 
Source: Own compilation after modification of Alkire & Santos (2013) 
 
Only five indicators were used in this measurement due to limitation of data. In a particular dataset, if it has less 
than ten indicators, same weighting principle can be applied (Alkire & Santos 2013). Thus as detailed in Table 1, 
natural weights, adopting equal importance of the dimensions and indicators (within dimensions) are used. That 
means each indicator within one dimension is equally weighted like as equal weight of dimensions. According to 
MPI, sample households are classified into poor and non-poor groups based on selected indicators in this study. 
Deprivation cut-offs are selected in this way so that it assures the sufficiency to be non-deprived in each indicator 
and dimension, and vice versa. Then, the sum of each deprivation multiplied by its respective weight indicates the 
multidimensional deprivation score, C. Finally, following Alkire & Santos (2013) MPI calculation procedure, a 
poverty cut-off of 33.3 percent is used to differentiate between poor and non-poor households based on 
multidimensional indicators. This implies that, a household having more than 33.3 percent deprivation score is a 
poor household, whereas holding less than or equal to 33.3 percent deprivation score is a non-poor household. 
Adjusted head count ratio or MPI (Mo) represents the product of two measures: (1) the incidence of poverty or 
multidimensional headcount ratio (H) and (2) the intensity of poverty (A). The proportion of the poor in the total 
population is calculated by the multidimensional Headcount ratio, H. This percentage also indicates the incidence 
of multidimensional poverty. Thus, head count ratio (H) was calculated by the following ratio:                              

                                    𝐻 =                                                (1) 

where, ‘q’ denotes multidimensional poor households and ‘n’ indicates the sample population. Moreover, average 
proportion of poor households weighted indicators is called the intensity of multidimensional poverty (A). This 
intensity is computed in two steps. At first, adding up all the weighted deprivation scores of the poor. Then dividing 
this score by the total number of poor households. Therefore, intensity of poverty (A) is computed by: 

q

C
A

q

i


== 1                                                 (2)  

where, C represents the deprivation score of poor households from ith to qth. 

2.2.2 Gini Coefficient and Gini Decomposition 
In case of the inequality measurement, this study focuses on income distribution instead of consumption. The main 
reason behind this is that income inequality can clearly visualize the change in overall distribution rather than 
consumption inequality. Gini ratio for income inequality would provide policy guidance that the ratio for 
consumption components would not provide (Khan, 2005). According to Möllers & Buchenrieder (2011), there 



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are two ways to measure a specific income source’s impact on total income distribution. The first and most well-
known way is to compare the values of Gini coefficient for total incomes and Gini coefficients for excluding the 
income sources of interest. If Gini based on excluded income’s value, is greater (smaller) than the Gini based on 
total income, that excluded income source positively (negatively) contribute to the income inequality reduction. 
The decomposition of Gini coefficient is the second method to identify marginal effects of certain sources on total 
inequality.    
Among different instrument to measure inequality, the Gini coefficient is the most popular and frequently used 
measure of statistical distribution. The range of the Gini coefficient lies between 0 and 1. The value of the 
coefficient 1 indicates maximal inequality while 0 indicates perfect equal state. Gini coefficient can be measured 
by a number of indexes or methods such as Robin Hood index, Hoover index, Atkinson’s index, Theil index etc. 
In this study, Gini coefficient is measured by: 

                          
−=

=
++ +−−=

1

1
11 ))((1

nk

k
kkkk YYXXG                                 (3) 

Where G, X and Y denote the Gini value, percentage of households and percentage of their corresponding income 
respectively. Computation of Gini index as a ratio of two areas in the Lorenz curve is easy to understand and apply. 
There are also some limitations of the Gini index to measure inequality as the same Gini index can hold two very 
different distributions of income. Though this situation is not a common case, it is difficult to understand the actual 
situation of inequalities between two groups.  
Decomposing inequality needs overall inequality to be subdivided into its all components or sources. By assessing 
the contribution of each component to total inequality, policy makers can make effective policies and design tools 
that can reduce divergence in the distribution of their interested sectors. Therefore, decomposition of the Gini 
coefficient of total income can easily be used for identifying the equalizing or dis-equalizing effect of different 
sources of income. 
If household income is categorized into k sources and Y1 … Y2… Yk indicate those sources of income, its overall 
income (Y) is 

                                       
=

=
k

i
iYY

1

                                         (4) 

Following Pyatt et al. (1980) and Stark et al. (1986), the decomposed Gini coefficient for total income (G) can be 
represented as follows: 

                                      
=

=
k

k
kkk SGRG

1
                                   (5) 

Where,  
Sk = the share of kth income source in total income, which ultimately implies the importance of kth income source 

regarding total income 
Gk = the Gini coefficient of kth income source, that indicates the degree of equal or unequal distribution of that 

income source 
Rk = the correlation between kth income source and the total income distribution 
Following L´opez-Feldman (2006), marginal effect of income change can be estimated using this Gini 
decomposition technique. That means, find out how much total income inequality will change for 1 percent change 
in the kth source of income, holding income from all other sources constant. If change of kth income source is 
indicated by ek, the partial derivative of the Gini-coefficient with respect to ek is equal to: 

                                 )( GRGS
e
G

kkk
k

−=
∂
∂

                                   (6) 

where, G implies the Gini coefficient of total income inequality before making any change to the income. Marginal 
effect relative to the overall Gini is calculated by dividing equation (6) by G: 



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                                 k
kkkk S

G
RGS

G
eG −=∂∂                                   (7) 

3. Results and Discussion 
3.1 Multidimensional Poverty Status Over Time 
A household is identified as poor if it is deprived in at least one third of the weighted multidimensional indicators 
that are described in Table 1. Multidimensional poverty rate (MPI) and its two components (incidence of poverty 
and average intensity of deprivation) are presented in Table 2. Based on the household’s involvement in livelihood 
earning activities over the years, households are categorized into two groups: stable households and diversified 
households (Note 2). The result shows that overall, both incidence of poverty (H) and average intensity across the 
poor (A) are declining over the years. The multidimensional poverty rate decreased from 0.43 in 2000 to 0.30 in 
2008. Cross-sectional data (collected in 2014) also indicate that the poverty rate follows the decreasing trend in 
Bangladesh, as MPI is found 0.26 in 2014 (Note 3). OPHI (2011) and OPHI (2015) reported that MPI values for 
Bangladesh were 0.29 in 2007 and 0.25 in 2011. This type of small difference in result is not surprising, as the 
sampled households are not same. Moreover, estimated intensity of poverty in 2008 implies that households are 
deprived in 67.92 percent of the weighted indicators, which is smaller than the deprivation value of 2000 (71.35 
percent).  
The next concern of this study is to look deeper into the changing pattern of poverty among different diversified 
groups. As in the overall poverty situation, poverty rates of stable households also decreased during the period 
2000-2008. But their poverty reduction rate depends on which types of activities the households are involved in.  
For instance, household involvement in only agricultural activities reduces 27.54 percent poverty (MPI), whereas 
non-farm households reduce 32.89 percent poverty by doing only non-farm activities during 2000-2008. However, 
the highest reduction of poverty (43.33 percent) is observed in part-time farming households - involved in a 
combination of agricultural activities and some sort of non-farm activities - during this period.  
The poverty rate of part-time farming households is also found to be smaller than the agricultural households in 
2014 (Table 2). In the case of diversified households, households drastically reduced their poverty by shifting to 
part-time farming from only agricultural work during 2000-2008. Specifically, diversifying into a combination of 
non-farm and agricultural activities contribute to a decrease of 41.19 percent poverty value of only agriculture 
based households. Even a diversification to part-time farming from a pure non-farm group is found to be a more 
poverty reducing measure than leaving farming activities and fully joining the non-farm sector. Salam & Bauer 
(2018) also found that a very small percentage of households are shifted to the only agriculture group from non-
farm or part-time farming groups during this time.  
Comparatively, lower effect on poverty may be one of the reasons for their indolence in agricultural activities. 
Therefore, movements from only agriculture to part-time farming and the non-farm sector are more welfare 
generating strategies (by reducing poverty) for rural households in Bangladesh. 
 
 
 
 
 
 
 
 
 
 
 
 
 



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Figure 1. Percentage of the MPI Poor household in different years (2000-2008-2014) 

Source: Author’s own computation. 
 
The proportions of the households that are poor and deprived in each indicator over time are presented by a 
pentagon shaped radar diagram (Figure 1). Deprivation of the non-poor households is not included in this 
calculation. Figure 1 shows that patterns of deprivation across indicators vary from time to time in rural areas. In 
the case of panel data interpretation, housing conditions and electricity facilities greatly improved during the period 
2000 to 2008. It is found that deprivation of the poor in calorie intake and years of schooling also decreased, while 
land deprivation increased. As farm size decreased day by day in Bangladesh, the increasing trend of land 
deprivation is expected in the case of rural-poor. On the other hand, cross-sectional data shows that the incidence 
for each deprivation is reduced in 2014 from the previous periods except land deprivation. 
3.2 Income Inequality Over Time 
Using panel and cross-sectional data, Gini coefficients from 1988 to 2014 are calculated and shown in Table 3. 
This distribution of income is also calculated by excluding total non-farm income and different sources of non-
farm incomes from total income. The value of the Gini coefficient for the selected sample increased from 0.48 in 
1988 to 0.51 in 2000, which slightly decreased to 0.50 in 2008. These figures indicate the existence of large income 
inequality in the country. Compared to the result of previous years, the Gini coefficient value for the year 2014 
(0.36) indicates that income distribution is markedly less unequal.  As this value is calculated using cross-
sectional data (not the previous same sampled households), it does not show the actual change of previous 
households but it can show a general trend of change.  Though similar pattern of change is also found in inequality 
estimation at national level, but rural income Gini slightly increased to 0.431 in 2010 from 0.428 in 2005 (HIES, 
2010).  
For identifying the effect of income generation from different sources, at first total income is categorized into 
agricultural income and non-farm income. Gini coefficients are calculated by excluding total non-farm income, 
even different forms of non-farm income separately over time for understanding their impact on income inequality. 
In the case of total non-farm income, overall slightly smaller values of the Gini coefficients are found in 1988 and 
2008. This implies that in those periods, non-farm income had more unequal distribution in rural areas. This similar 
finding is also found in some other developing countries like Ghana, Uganda and Jordan (Canagarajah et al., 2001; 
Senadza, 2011). However, income from self-employment activities has a positive contribution for equalizing 
income distribution from 2000 to 2014. This result is also affirmed by the partial coefficients analysis using 
decomposition of Gini coefficients.  
 
 
 



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Table 3. Gini coefficients of rural income based on income sources 

Income 
Gini coefficients 

1988 2000 2008 2014
Total income 0.48 0.51 0.50 0.36

Income from total non-farm excluded 0.46 0.53 0.48 0.38
 - Income from wage employment excluded 0.47 0.51 0.48 0.40
 - Income from migration excluded 0.43 0.48 0.47 0.32
 - Income from self-employment excluded 0.38 0.56 0.55 0.40

Source: Author’s calculation based on panel data (1988-2008) and field survey (2014) 
 
The contribution of different sources of income on inequality of rural households depends largely on which type 
of income source the households are involved in. Each income source’s contribution to total inequality is calculated 
using Gini decomposition technique following the methodology explained in previous section. These contributions 
of different income sources for four specified years are shown in Table 4. In this study, marginal contribution of 
agricultural income to total income inequality is found negative for all selected years and self-employment income 
is found negative since 2000. That means, self-employment activities have helped to reduce income inequality gap 
since 2000. This negative sign implies a positive contribution to income inequality, which is also expressed by 
decreasing Gini coefficient value (Gk). In 2000, by holding other source’s income as constant, a 1 percent increase 
in agricultural and self-employment income reduce about 0.11 percent and 0.03 percent of total income inequality 
of rural households respectively.  
 
Table 4. Inequality decomposition by income source for rural households (1988-2014)  

Ye
ar

 

Income source 
Income 

share (Sk)
Gini coeffici-

ent (Gk)

Gini correlation 
with total income 

(Rk)

Relative 
share of 
income 

inequality  

Marginal 
effect (% 
change) 

19
88

 

Agriculture 0.56 0.47 0.77 0.42 -0.14
Wage employment 0.12 0.87 0.62 0.14 0.01
Remittance 0.10 0.97 0.94 0.20 0.10
Self-employment 0.21 0.70 0.70 0.24 0.03

20
00

 

Agriculture 0.43 0.53 0.71 0.32 -0.11
Wage employment 0.16 0.88 0.81 0.14 0.10
Remittance 0.15 0.79 0.56 0.19 0.04
Self-employment 0.25 0.69 0.53 0.35 -0.03

20
08

 

Agriculture 0.40 0.55 0.72 0.36 -0.09
Wage employment 0.15 0.86 0.68 0.13 0.02
Remittance 0.23 0.89 0.88 0.32 0.09
Self-employment 0.21 0.66 0.55 0.19 -0.02

20
14

 

Agriculture  0.60 0.41 0.63 0.48 -0.13
Wage employment 0.10 0.83 0.43 0.08 0.02
Remittance 0.17 0.86 0.78 0.31 0.13
Self-employment 0.13 0.67 0.31 0.14 -0.02

Source: Author’s calculation based on panel data (1988-2008) and field survey (2014) 
 



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Another point is that the calculated Gini coefficients for different income sources are higher than the coefficient 
for total income. As all households did not earn income from each of the income sources, the value of calculated 
Gini coefficient is comparatively higher. During last 3 decades, minimum Gini coefficient value is found in case 
of agricultural income in 2014 (0.41) and maximum value is found for remittance in 1988 (0.97). Agricultural 
income, as might be expected, provides the highest share of total income and also highly contributes to the overall 
inequality during the study period. For instance, agricultural income accounted for 56 percent of total income and 
was responsible for 42 percent of income inequality in 1988.  The share of remittances has been increasing 
steadily, while it is mostly unequally distributed (0.89 in 2008). Even the Gini correlation between remittance and 
total income is higher compared to other income sources. This implies that remittances make rich people richer. 
However expectedly, wage income influences income inequality positively. That means a small group of 
households earn higher income from this source, except all households in the rural area. This result confirms the 
findings of Canagarajah et al. (2001) and Senadza, (2011), where wage employment and self-employment were 
also identified as income inequality increasing and decreasing sources respectively. 
As wage employments, namely high-return activities require higher education, special skills and knowledge; all 
rural poor people cannot participate in these activities due to these barriers. Comparatively richer households can 
afford to overcome these barriers and enjoy higher income from that sector. Some poor households participate in 
low-return wage-employment activities but this generated income is not sufficiently high to reduce overall income 
inequality. On the other hand, self-employment may not have strict requirements like wage-employment, which 
resulted more participation of poor households in these activities and ultimately reduce income inequality.  
In general, it is clear from this decomposition of income sources that agriculture and self-employment contribute 
positively to reduce overall income inequality in rural areas. On the other hand, migration increases this income 
inequality gap in rural Bangladesh.  
4. Conclusions and Policy Recommendation 
The rural economy of Bangladesh shows indications of growing inequality between resource-poor and rich 
households, in spite of progress in poverty reduction. The poverty reduction rate of household depends on which 
types of activities households are involved in.  However, the highest reduction of poverty is observed in part-time 
farming households (43.33 percent) and following non-farm households (32.89 percent) during 2000-2008. 
Households drastically reduce their poverty by diversifying their economic activity from only agricultural work to 
part-time farming. It is often assumed that participation in non-farm employment would worsen income and asset 
distribution. This existing concept is somewhat confirmed by the results of this study. Agricultural activities and 
only self-employment (in different form of non-farm activities) have positive contribution to income equality. 
Incomes from migration and wage-employment widen income inequality in rural Bangladesh. 
Importance of non-farm activities like self-employment and wage based activities cannot be denied in the rural 
economic growth of Bangladesh. These activities not only absorb excess rural labor but also reduce poverty and 
improve agricultural status through earning cash income. Therefore, policy options should strive to combine 
development of the agricultural sector, and the expansion of industry and other sectors. Establishment of small and 
medium industries by both public and private entrepreneur should be promoted. On the other hand, easy 
accessibility of short-term and long-term cheap credit schemes will be made available which will motivate 
households to become involved in self-employment based activities. Besides, it is required to make rural people 
more eligible to participate in the non-farm sector by providing higher education in the rural areas. 
Acknowledgement 
Authors acknowledged her thanks to The German Academic Exchange Service (DAAD) for providing logistic and 
financial support to conduct this study. 
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Notes  
Note 1. The conversion factor was used for converting consumed food to calorie content, which was found in the 
study of Shaheen et al. (2013) and Nahar et al. (2013) cited in Akhter (2015). For calculating per capita calorie 
intake, at first all members of households of different ages were converted into adult male equivalent scales using 
calorie based adult equivalent ratio scales. This scale was adopted from Bermudez et al. (2012) cited in Akhter 
(2015). 
Note 2. Those households involved in the same type of livelihood activities in 2008 as 2000 are termed as a stable 
households group. On the other hand, those households that shifted from one activity group to another within the 
2000-2008 periods are treated as diversified households. 
Note 3. About 40 percent of the sample of the field survey (2014) was drawn from the samples of the panel survey 
(2000-2008). 
 
Copyrights 
Copyright for this article is retained by the author(s), with first publication rights granted to the journal. 
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution 
license (http://creativecommons.org/licenses/by/4.0/). 
















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    /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke.  Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.)
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    /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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    /UKR <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>
    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

