Spatial distribution of socio-economic inequality: evidence from inequality maps of a village in tribal region of Pakistan.
Gul, Ejaz ; Chaudhry, Imran Sharif
The economy of Tribal Region is one in which, in spite of many
initiatives, a high level of socio-economic inequality persists owing to
erratic law and order situation, inaccessibility to commodity markets
and feeble means of earning. Author belongs to village Naryab which is
located in tribal region of Pakistan. A comprehensive research was
undertaken by author to map the spatial distribution of socio-economic
inequality in this small village. Primary data on income and other
socio-economic and environmental factors was collected through survey
method. Using this data, economic modelling of the inequality was done.
This was followed by a prismatic analysis of the inequality of
socioeconomic factors using Gini coefficient for each factor. Higher
income concentration were observed with rising per capita incomes as the
top group's income share rises and the bottom income group's
share falls. This all was mapped using latest mapping software. The maps
revealed spatial distribution of inequality and many other important
aspects which have been discussed in this paper. Based on the results
obtained, policy implications of the inequality of socio-economic
factors for the whole tribal region have been elucidated at the end in
the paper. Inequality mapping is a new subject, hence, paper has wide
application and is very valuable to the readership.
JEL Classification: D31, D63,114,124,132, 018
Keywords: Socio-economic, Factors, Modeling, Spatial, Distribution,
Inequality, Mapping
1. INTRODUCTION
Economic and social inequality is consistently persisting in tribal
region of Pakistan. People in the tribal region of Pakistan are living
in deprived state whereby they lack even basic necessities in their
lives. As described by Gul, the tribal areas are different than the
rural areas because tribal areas are located in far flung mountainous
terrain where accessibility to basic amenities is much lower than the
rural areas [Gul (2013)]. In recent times, the Government of Pakistan
initiated many efforts for provision of basic amenities in tribal areas
as an essential component of development in the context of Millennium
Development Goals (MDGs). However, according to John the desired state
is yet to be achieved in tribal areas [John (2009)]. Tribal life is
characterised by hardship and great insecurity especially for poor
labour. Given the income vulnerabilities, the long run welfare is
forgone for short run securities. Interruption, reduction or loss of
earnings from the contingencies such as unemployment, underemployment,
low wages, low prices and failure to find the market for the produce,
old age, ill-health, sickness, disability etc. are the situations which
call for social security and protection. As concluded by Talbot, this
constant state of deprivation has generated deep rooted inequalities in
the tribal society [Talbot (1998)]. People take rescue measures such as
sending their earners to urban areas and if possible to foreign
countries. Those who have lands and doing agriculture are the blessed
one, although, the earning pattern is distorted due to law and order
situation. To have an assessment of the overall economic inequality in
the tribal region, author conducted a study in a small village Naryab
which is located in the tribal region. Primary data was collected from
the households physically and it was thoroughly analysed to conclude the
pattern of inequality. This inequality was then mapped using latest
mapping software "SURFER". The maps reveal spatial
distribution of inequality and many other important social and
environmental aspects. Based on the results, policy implications have
been described in the paper.
2. LITERATURE REVIEW
Inequality mapping is a new paradigm in economics. Recently many
researchers have made an attempt to map the socio-economic inequalities.
Baulch has worked on the spatial distribution of poverty in Vietnam and
identified the key areas for reduction of poverty [Baulch (2002)].
Alesina has elucidated that to reduce the socio-economic inequality and
poverty, we must first explain and predict its distribution in the area
[Alesina (2005)]. Economists like Li and Zhao have tried to investigate
the extent of income inequality that exists in particular countries and,
even in the absence of satisfactory theories of distribution, to
determine how these relate to various characteristics of the economies
[Li and Zhao (2006)]. There are social researchers like Swinkels worked
on poverty mapping in Vietnam and advocated that if inequality trends
are mapped they can be very beneficial to economic planners and policy
makers [Swinkels (2007)]. This is specifically true in case of Pakistan
since the pattern of inequalities changes with the change of
geographical, environmental and climatic conditions. As explained by
Javaid, Pakistan is blessed with diversified landscape and hence its
poverty and economic inequality landscape is correspondingly diversified
[Javaid (2001)]. In this context, tribal region can be quoted as
example. Trial region consists of the seven tribal agencies which are
located far from urban centers and hence suffering from deprivation of
basic amenities and deep rooted socio-economic inequality. Map of trial
region is shown in Figure 1.
[FIGURE 1 OMITTED]
The tribal region is bordered by Afghanistan to the north and west
with the border marked by the Durand Line and by Province of Khyber
Pakhtunkhwa to the east. The seven Tribal Areas lie in a north-to-south
strip. The geographical sequence of the seven tribal areas in order from
north to south is: Bajaur, Mohmand, Khyber, Orakzai, Kurram, North
Waziristan and South Waziristan.
The economy of the tribal region is underdeveloped. This region is
the most impoverished and least developed. While this region has 2.4
percent of Pakistan's population, it contributes only 1.5 percent
to Pakistan's economy, making it the smallest contribution to GDP.
As of 2013, the per capita income of tribal region was roughly $663 per
year and very small proportion of households has a sustainable living
standard above the poverty level. Daniel has elucidated the
socio-economic conditions of tribal region in his works on
Pakistan's Tribal Belt and he narrated that being a tribal society,
the local economy is mainly pastoral based, with some practice of
agriculture. Households are involved in primary level activities like
subsistence agriculture, rearing of livestock and small scale
businesses. Many locals seek employment as skilled and unskilled
labourers while some join security agencies and paramilitary forces.
Some are able to travel and migrate to larger cities and urban centres
outside the tribal areas within Pakistan and abroad. A significant
number of these are qualified professionals and in many cases have
settled permanently along with their families outside tribal areas,
contributing to brain drain effect and shortage of skilled workers.
Earners abroad receive their earnings and send remittances to tribal
areas to support their families and relatives at home [Daniel (2008)].
There are abundant natural resources in tribal region such as
marble, copper, limestone and coal which can create a potentially
thriving mining industry, although the current socio-economic situation
has not encouraged their profitable exploitation. Angel has explained
that trade with neighbouring Afghanistan plays an active role in
tribal's economy, and items imported and exported to the country
via trucks pass through supply routes in tribal areas. This has made
tribal region a transitional point for smuggling and trafficking of
goods [Angel (2007)]. Moreover, Tierney has indicated in his works on
national identity that there have been calls to implement greater
measures for integrating tribal region into the mainstream economy. In
this regard, there are several economic, industrial and social
development ventures that have been undertaken by the government
recently in order to aid tribal economy and bring this region in the
mainstream [Tierney (2008)].
According to data available on the official web site of Federally
Administered Tribal Areas (FATA), tribal region has a population of
roughly 3.18 million. Annual population growth is calculated to be 2.19
percent. Population density stands at 117 persons per square kilometre
as a whole. A rough estimate of the population in the tribal region is
given in Table 1.
The average household size is approximately 9 to 10, while the
gender ratio has been estimated as 108 men for every 100 women. The
tribal region has literacy rate of 12 percent, which is well below the
desired rate of above 60 percent. 15.8 percent of men, and only 7.5
percent of women receive education. There is one hospital bed for every
2,179 people, and there is one doctor for every 7,670 people. Only 23
percent of the citizens have access to clean drinking water.
These social conditions in tribal region indicate that tribal
region is deprived and poor. The socio-economic conditions affecting the
life of households have been the main stay of recent social research.
Huong has worked on the socio-economic inequality and major causes of
death in adults of Vietnam. He concluded that socio-economic inequality
generates denial and discontent in the society [Huong (2006)].
Similarly, Pyatt in hi$ work on fundamentals of social accounting
indicated that inequality in socio-economic factors leads to bigotry in
most of the cases [Pyatt (1991)]. Alongside this, many researchers of
social science have investigated the relationship between household
resource allocations and socio-economic inequality. Sow has described
the correlation between household resource allocation and collective
well being. He concluded that justice in household resource allocation
will create equity [Sow (2010)]. The work of Thomas on household
resource allocation in developing countries investigated the income,
expenditure and health outcomes of household resource allocation [Thomas
(1997)]. Household behaviour is also an important aspect in the overall
context of inequality. Few researchers have correlated the living
standard with household behaviour. For example Chiappori in his work on
collective models of household behaviour indicated that household
behaviour has linkage with overall makeup of the society [Chiappori
(1997)]. The household behaviour influences the household welfare as
concluded by Fafchamps in his study on intra household welfare in rural
Ethiopia [Fafchamps (2009)]. Arpino in his investigation on dynamic
multi-level analysis of households' living standards and poverty
has concluded a linkage between household behaviour, living standards
and poverty [Arpino (2007)].
From social science point of view, injustice and corruption
generate socioeconomic inequality. This has been the main stay of
investigation by many social scientists like Glaeser and J. S. You.
Glaeser has deduced that injustice not only generates but also
accelerate inequality [Glaeser (2003)]. J. S. You has elucidated that
relationship between inequality and corruption is casual in nature [You
(2005)].
In short we can say that the retarded growth in rural communities
is due to prevailing poverty which is the outcome of socio-economic
inequality. This vital fact has been investigated by Dercon in his work
on growth and chronic poverty [Dercon (2011)].
3. RESEARCH AREA
Naryab is the village in tribal region at distance of 350
kilometres west of Islamabad, the capital city of Pakistan as shown in
Figure 2. Village Naryab is a bowl amongst the surrounding mountains
having heights up to 100 meters. An off shoot of River Kurram passes
through east of the village. On this off shoot an irrigation dam of 200
cusecs capacity has been constructed in the north of village which
irrigates the fields and has boosted up agriculture activity since its
construction. Besides, this dam also serves a recreational spot. A metal
road goes up to Naryab and ahead in the north as shown in Figure 2. A
zoom in view in Figure 2, shows that village Naryab has scattered
population. There are 2500 houses with a population of approximately
25000 to 30000 at the rate of roughly 10 individuals per house. There
are 10 large shops in the village with few numbers of outlets and
different locations in the village. There is one hospital and a primary,
middle and high school in the village. There is no worthwhile education
system for female education except for one private primary girls school
in the centre of the village.
[FIGURE 2 OMITTED]
Houses are made of mud and bricks with 2 to 3 rooms in each house.
People on the fringes of village have fields where they do their
subsistence agriculture. Electricity is suppose to be available as per
the laid out aerial wire network but it is a hardly available. Clean
drinking water from natural springs is collected in a water tank of 1000
gallons from where it is supplied to houses through a network of pipe
lines.
4. RESEARCH METHODS
Comprehensive research methodology was adopted for this study.
There are about 2500 houses in Naryab and primary data from all the
houses was collected. Broadly, following research design was applied to
complete this study.
* Detail study of the area was done from satellite and Google
imageries to ascertain environmental and climatic aspects of Naryab. The
geographical location of Naryab in relation to urban centres was
particularly focused.
* Field visits were conducted to assess the prevailing
socio-economic conditions of the village in general.
* Survey was carried out to collect primary data about income,
agriculture lands, education, health, earners in urban centres, earners
in foreign countries, drinking water, electricity, lavatory.
* Data obtained was analysed statistically to conclude about the
trend and pattern of inequality with respect to income, agriculture
lands, education health, drinking water, electricity and lavatory.
* Inequality modelling was carried out to have clear picture of
existing and future trend of each socio-economic factor.
* Prismatic assessment of the inequality was done by calculation of
Gini coefficient for each factor.
* Data was transferred to latest mapping software to create maps
for socioeconomic inequality and indicate its spatial distribution.
5. DATA COLLECTION
Comprehensive data collection process was adopted for evaluation of
inequality in village Naryab. Primary data about income, agriculture
lands, education, health, earners in urban centres, earners in foreign
countries, drinking water, electricity, lavatory was collected from the
village. For this purpose, village was divided into five zones; North,
South, East, West and Central. These zones are shown in figure 3. Five
data collection teams were made and one team was sent for each zone.
Isolated dwellings on the fringes of village and mountain terraces were
ignored. A panoramic view of the landscape of each zone is shown in
figure 4. North zone is hilly in nature with numerous plateaus available
on mountains. People have made their houses on plateaus, terraces and
valleys. It's a fairly populated zone. Primary school of Naryab is
located in this zone. There is considerable agriculture activity in this
zone. East zone is located on the sloping mountain terraces. Small
agriculture fields are located on these terraces for subsistence
farming. A high school and rural health centre are located in this zone.
Naryab agriculture dam is located in the north east of this zone.
Central zone is the most populated zone and located on plain surface.
Shops and markets are mostly located in this zone. South zone has
scattered population with size of the houses bigger than rest of the
zones. Metal road passes through this zone and goes north. Inhabitants
of this zone remain busy in agriculture activity on large fields, cattle
farming and house poultry. West zone has congested dwellings, however,
has huge agriculture fields on its west. The area in this zone is
generally sloping from west to east.
[FIGURE 3 OMITTED]
[FIGURE 4 OMITTED]
After zoning of the research area, selection of suitable
socio-economic factors for analysis of the inequality was the most
important step of the research. Based on detail study of prevailing
conditions in the village, a socio-economic factors matrix was developed
in which relevant nine factors were included for assessment of the
inequality. Factors matrix is shown in Table 2.
Door to door survey was conducted in all the zones to record the
data about the factors mentioned in Table 2. Field visits and interviews
were also conducted by the author. Each factor of the evaluation matrix
was discussed with experts. This survey took about three months.
6. DATA ANALYSIS
Data collected from the households was summarised and average of
selected nine socio-economic factors was calculated for each zone.
Descriptive statistics were also calculated to know about the trends.
Data along with descriptive statistics is tabulated in Table 3.
Some of the relevant aspects can be concluded from table 3. For
example the mean per capita income of village Naryab is just $453.4
which is much below the desired level. However, the mean per capita
income of south zone is $719 which is relatively acceptable, albeit not
desirable. This indicates the inequality in per capita income
distribution across the different zones of village Naryab. Similar is
the case with, other socio-economic factors. For example the possession
of agriculture lands by households is more in the west zone, educated
males per house are more in the east zone due to availability of Naryab
high school is near vicinity. However, there are no educated females in
the entire east and west zones. The number of patients per house are
more in the north zone due to non availability of doctor and health
facilities in the zone and also due to less number of clean water taps.
Electricity hours are more in the south zone due to its proximity to
grid station and less line losses. Lavatories are not available in the
houses of north and west zones. The most congested living was observed
in the central zone.
7. INEQUALITY MODELING
After statistical analysis, digital model for each selected
socio-economic factor was developed using latest computer assisted
qualitative data analysis software (CAQDAS). The digitised models
indicated existing trends and future tendency with a best fit trend
line, econo-mathical representative equation and value of R2 as shown in
Figure 5. Econo-mathical equations were obtained after digital iteration
and attenuation by the software. These equations caters for the errors
in the variables. Coefficient of determination, R2 is a statistic that
gives information about the goodness of fit of a curve. In regression,
R2 is a statistical measure of how well the regression line approximates
the real data points. An R2 of 1 indicates that the regression line
perfectly fits the data as indicated by Brown in his work on multiple
membership multiple classification models [Browne (2001)]. For example
for the per capita income, the equation of the best fit polynomial trend
lines is quadratic in nature with R2 value as 0.75, which makes the
arrangement acceptable. As per the trend line, south zone will continue
to hold maximum per capita income followed by central zone. There are
two reasons for this; south zone has maximum agriculture activity and
number of earners in urban centres and foreign countries while central
zone is the hub of all business activities in village Naryab.
Let's now see the scenario with regard to possession of
agriculture lands. Figure 5 shows that the best fit polynomial dotted
trend line for agriculture has a representative equation of degree two,
quadratic in nature and the R2 value is 0.721. The maximum land
possession is in west zone owing to the open agriculture spaces
available. As per the trend, south and west zones will have maximum
agriculture activity while it may reduce in the east zone owing to
construction of new houses and shifting of population in the vicinity of
Naryab Dam in south zone. Average number of educated males per house are
more in the east zone due to availability of public and private schools
in this zone. Opposite conditions exist in the west zone, where due to
minimum education facilities, number of educated males per house is
less. In this case the best fit polynomial dotted trend line has a cubic
equation with R2 value of 0.923 which means an accurate representation
of the trend. As shown by the dotted trend line, east zone will have
greater number of the educated males but the situation in west zone is
also likely to improve with development of new educational facilities.
There are very less number of educated females in village Naryab. In the
east and west zones there are no educated female at all. However, in the
north, central and south zones there is one educated female per house.
There is only one private female primary education school in the centre
zone. The best fit polynomial dotted trend line representing female
education in village Naryab has a cubic equation with R2 value of 0.892.
As shown by the trend line, the situation is likely to remain the same
unless construction and operation of new female education schools. The
number of patients per house are minimum in the south zone owing to
better sanitation facility. Number of patients per house are more in the
north zone where the sanitation facility is not good and there are no
lavatories in the houses.
[FIGURE 5 OMITTED]
The best fit polynomial dotted trend line representing average
number of patients per house in village Naryab has a cubic equation with
R2 value of 0.891. As shown by the trend line, the number of patients
per house will remain minimum in the central and south zones while it
will increase in the east and west zones. The best fit polynomial dotted
trend line representing average number of clean drinking water taps per
house in village Naryab has a quadratic equation with R2 value of 0.809.
As shown by the trend line, the number of clean drinking water taps per
house will improve in the south zone in the future. The average number
of electricity hours per day are more in the south zone owing to better
electricity network and closeness to electricity grid station. In this
case, the representative equation of the best fit polynomial dotted
trend line is cubic in nature with R2 value of 0.784. As shown by the
trend line situation of electricity is likely to remain the same in
future. Average number of lavatories per house is an indicator of
sanitation facilities. In north and west zones there is no lavatory in
any of the house. Fields and orchards are being used as lavatories. The
representative equation of the best fit polynomial dotted trend line is
quadratic with R2 value of 0.90. No change in the existing trend is
expected unless sanitation measures and awareness drive is launched. In
village Naryab, the most congested houses are located in the central
zone while the most spacious houses are located in the south zone.
However, the data exhibited huge fluctuation due variations in the
dimensions of houses in all the zones. The representative equation of
the best fit polynomial dotted trend line is quartic with R2 value of
0.0.83. The whole argument regarding the trend of inequality is
summarised in Table 4.
8. PRISMATIC ASSESSMENT OF INEQUALITY
There is very high and most probably rising inequality in the
distribution of income and other social factors in tribal region as
indicated from the statistics of a small village Naryab. This is rooted
in extreme economic imbalances which is directly and indirectly creating
uneven income earning opportunities as indicated by Gul in his study on
unknown tribal economy [Gul (2013)]. As a result, inequality was found
in the distribution of socio-economic amenities. In support of this
argument, I took help of Gini coefficient which is a measure of
inequality of a distribution of a factor. It has value between 0 and 1
with 0 corresponds to complete equality while 1 corresponds to complete
inequality. For prismatic assessment of inequality in village Naryab,
Gini coefficients were found for all the nine socio-economic factors The
graphical representation of distribution of socio-economic factors is
shown in figure 6. Equality line (45 degree line representing perfect
distribution) and Lorenz Curve are also shown on in Figure 6.
[FIGURE 6 OMITTED]
Gini coefficients for different factors were calculated using
calculus for area under a curve using Lorenz Curve, equality line and
Gini coefficient curves of the socioeconomic factors [Zhang (2006)].
These values are shown in Table 5.
As shown in Figure 7, the highest value of Gini coefficient (0.765)
is for female education. There is no facility available for female
education in village Naryab except for the girls who go to urban areas
and educate themselves. Very few girls are permitted to go to urban
areas for education. The Gini coefficient for availability of
lavatories, which is the basic element of hygiene and sanitation, is
also very high (0.725). There is no lavatory in any house in the north
and west zones (32 percent of the population). Similarly, only 8 percent
of the population possesses 88 percent of the per capita income in
village Naryab which results into a high Gini coefficient (0.712). The
lowest Gini coefficient (0.367) is for possession of agriculture land as
people from all zones possess agriculture lands barring central zone
where the business activity is more than agriculture. Overall Gini
coefficient for socio-economic factors in village Naryab is 0.639 which
is undesirably high.
9. INEQUALITY MAPPING AND SPATIAL DISTRIBUTION OF INEQUALITY
After assessment of the inequality, its spatial distribution across
the entire village Naryab was mapped using SURFER software which is a
latest state of the art mapping software. SURFER uses a systematic
process to create a map. Firstly, the data grid was created for each
factor on excel sheet. Secondly, this data grid was converted to grid
map by the software using the values of latitudes and longitudes.
Thirdly, grid map was converted into contour map by the software showing
spatial distribution of particular socio-economic factor. Separate
contour map was created for each socio-economic factor. Key indicating
the values of socio-economic factor is available with each map which
makes the map comprehendible.
[FIGURE 8 OMITTED]
Figure 8 shows the spatial distribution of selected socio-economic
factors. As indicated by the map for per capita income, contours of the
map are increasing from north zone to south zone. The distribution of
per capita income is such that north and west zone are poor compared to
east and south zone. The central zone has moderately high per capita
income while the higher per capita incomes are mostly concentrated in
south zone. Thus, from the map it is clear that there is inequality in
the per capita income of the households in village Naryab. Similarly,
the map for spatial distribution of inequality in agriculture land shows
that less land is possessed by the people living in the central zone and
surroundings. The people living in the west zone have a greater
possession of agriculture followed by inhabitants of the east zone who
own moderate quantity of agriculture land. For educated males, the
contours of the map are increasing from west to east zone. Contours of
the map are comparatively straight which indicate clear division amongst
the zones and hence the inequality. Educated males are mostly
concentrated in east zone due to presence of educational institutes. Map
for female education indicates that it is highly ignored aspect of
village Naryab. There is hardly any female education in the village. As
indicated by the map of average number of patients per house, number of
patients are less in the south zone due to availability of hospital in
the zone while patients are more in the north zone due to non
availability of health facilities in the zone. Moreover, due to distance
between north and south zones, patients are reluctant to visit hospital
and as a compulsion use the home made herbal medicines. As for as
spatial distribution of clean drinking water facility is concerned, the
north and west zones are at disadvantage having less number of clean
water taps compared to central zone where number of clean water taps per
house are more. Due to non availability of clean water, the number of
patients per house are comparatively more in the north and west zones.
As shown in the electricity map, more electricity hours are available in
the south zone due to proximity of grid station and less line losses.
Very less electricity hours are available to people living in central
and east zones. Sanitation and hygiene conditions are represented by
lavatories map. There are no lavatories in the north and west zones. Due
to this bad conditions of sanitation, there are more patients in the
north and west zones. The map for size of houses shows that houses in
the central zone are congested while houses in the south zone are
specious.
10. POLICY IMPLICATIONS
Spatial distribution of selected socio-economic factors for village
Naryab reflects inequality of sizeable magnitude. If this situation is
generalised to complete tribal region, the overall milieu may give
extremely worse picture. This perspective has certain policy
implications which are shown in Figure 9.
[FIGURE 9 OMITTED]
11. CONCLUSION
The socio-economic conditions in the tribal region are not very
encouraging due to prevailing inequality. There are host of reason for
this inequality, the major being the deprivation and discontent over
distribution of resources. This has, and is still, affecting the life of
commoners in the tribal region. Spatial distribution maps indicate that
spread of inequality can be controlled if appropriate remedial measures
are taken. There is a need to launch major socio-economic initiatives in
this region, particularly fields of education, health and energy should
be focused. If clean water and sanitation facilities are provided to
people of tribal region, the health will improve manifold.
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Ejaz Gul <
[email protected]> is affiliated with the
Department of Economics, Bahauddin Zakariya University, Multan. Imran
Sharif Chaudhry <
[email protected]> is Chairman, Department of
Economics, Bahauddin Zakariya University, Multan.
Table 1
Population in Tribal Region, Pakistan
Area
(Square
Agency Kilometers) Population
Complete Tribal Region 27,220 3,176,331
Bajaur 1,290 595,227
Mohmand 2,296 334,453
Khyber 2,576 546,730
Orakzai 1,538 225,441
Kurram 3,380 448,310
North Waziristan 4,707 361,246
South Waziristan 6,620 429,841
Population Density Annual
(Persons per Square Growth
Agency Kilometers) Rate (%)
Complete Tribal Region 117 2.19
Bajaur 461 4.33
Mohmand 146 4.28
Khyber 212 3.92
Orakzai 147 2.69
Kurram 133 2.50
North Waziristan 77 2.46
South Waziristan 65 1.95
Source: Official web site of Federally Administered Tribal Areas
(FATA) at http://www.fata.gov.pk
Table 2
Socio-economic Factors Matrix for Evaluation of the Inequality
Socio-economic factors Description
Per Capita Income (I) Average per capita income per year in a zone
from agriculture, cattle farming, house
poultry, local business, earners in urban
centres and foreign countries (US$)
Agriculture land (AL) Average agriculture land per household in a
zone (acres)
Male Education (Em) Average number of educated male per house in
a zone
Female Education (Ef) Average number of educated female per house
in a zone
Health (H) Average number of patients per house per
month in a zone
Clean drinking water Average number of clean drinking water taps
(Cw) per house in a zone
Electricity (Elec) Average number of electricity hours per day
in a zone
Lavatory (L) Average number of lavatories per house in a
zone
Size of house (SoH) Average area of house in a zone (square
meters)
Table 3
Summary of the Data Collectedfrom the Households
Average Average Average
Average Average Number of Number of Number of
Income Per Agriculture Educated Educated Patients
Year in US Lands Male Per Female Per Per House
Dollars (Acres) House House Per Month
Zones I AL Em Ef H
North Zone 254 0.4 2 1 6
East Zone 378 0.8 5 0 4
Central 603 0.2 3 1 5
Zone
South Zone 719 1.03 2 1 1
West Zone 313 1.5 1 0 5
Descriptive Statistics
Mean 453.4 0.786 2.6 0.6 4.2
Median 378 0.8 2 1 5
Standard 178 0.461 1.356 0.49 1.720
Deviation
Variance 32045 0.17 1.53 0.19 3.03
Kurtosis -2.014 -0.843 1.456 -3.33 2.608
Skewness 2.235 -1.487 1.239 -2.08 0.209
Average
Number of
Clean Average Average
Drinking Number of Average Area of
Water Electricity Number of House in
Taps Per Hours Per Lavatories Square
House Day Per House Meters
Zones Cw Elec L SoH
North Zone 2 3 0 300
East Zone 5 2 1 515
Central 6 2 2 245
Zone
South Zone 3 6 2 567
West Zone 2 4 0 313
Descriptive Statistics
Mean 3.6 3.4 1 388
Median 3 3 1 313
Standard 1.625 1.497 0.894 128
Deviation
Variance 2.38 2.05 0.57 20166
Kurtosis -2.231 0.536 -3.000 -2.655
Skewness -1.697 -0.286 -2.181 2.234
Table 4
Summary of Inequality Trendfrom Modelling Calculus
Socio-economic Equation of the
Factors Trend Line Trend Line
Per Capita Polynomial Quadratic
Income (I)
Agriculture Land Polynomial Quadratic
(AL)
Male Education Polynomial Cubic
(Em)
Female Polynomial Cubic
Education (Ef)
Health (H) Polynomial Cubic
Clean Drinking Polynomial Quadratic
Water (Cw)
Electricity (Elec) Polynomial Cubic
Lavatory (L) Polynomial Quadratic
Size of House Polynomial Quartic
(SoH)
Socio-economic Future Trend
Factors (Under Existing Conditions) [R.sup.2]
Per Capita South and central zone will continue 0.75
Income (I) to have concentration of per
capita income
Agriculture Land South and west zones will have 0.72
(AL) maximum agriculture activity while
it may reduce in east zone
Male Education East zone will have greater number
(Em) of the educated males. Situation 0.92
in west zone is likely to improve
with development of new
educational facilities
Female The situation is likely to remain 0.89
Education (Ef) the same
Health (H) Number of patients per house will
remain minimum in central and 0.89
south zones while it will increase
in the east and west zones
Clean Drinking Number of clean drinking water taps
Water (Cw) per house will improve in the 0.80
south zone while it will decrease
in the west zone
Electricity (Elec) Situation is likely to remain the 0.78
same
Lavatory (L) Situation is likely to remain the 0.90
same
Size of House Situation in the east and south 0.83
(SoH) zones is likely to improve
Table 5
Assessment of Inequality in Village Naryab by Using Gini Coefficient
North East Central South West
Factors N Zone Zone Zone Zone Zone
Population (%) 20 15 45 8 12
Cumulative Population (%) 20 35 80 88 100
Per Capita Income (I)
Income (%) 3 10 22 60 5
Cumulative income (%) 3 13 35 95 100
Gini Coefficient 0.712
Agriculture Lands per
Household (AL)
Agriculture Land (%) 10 30 5 20 35
Cumulative Agriculture Land 10 40 45 65 100
(%)
Gini Coefficient 0.367
Number of Educated Male per
House (Em)
Educated Males per House (%) 10 55 20 10 5
Cumulative Educated Males per 10 65 85 95 100
House (%)
Gini Coefficient 0.651
Number of Educated Female per
House (Ef)
Educated Females per House (%) 33 0 33 34 0
Cumulative Educated Males per 33 33 66 100 100
House (%)
Gini Coefficient 0.795
Number of Patients per House
(H)
Number of Patients per House 60 20 8 4 8
(%)
Cumulative Number of Patients 60 80 88 92 100
per House (%)
Gini Coefficient 0.639
Number of Clean Drinking
Water Taps per House (Cw)
Clean Drinking Water Taps per 5 30 40 20 5
House (%)
Cumulative Clean Drinking 5 35 75 95 100
Water Taps / House (%)
Gini Coefficient 0.426
Number of Electricity Hours
per Day (Elec)
Electricity Hours per Day (%) 12 4 4 60 20
Cumulative Electricity Hours 12 16 20 80 100
per Day (%)
Gini Coefficient 0.603
Number of Lavatories per
House (L)
Lavatories per House (%) 0 20 40 40 0
Cumulative Lavatories per 0 20 60 100 100
House (%)
Gini Coefficient 0.725
Area of House in Square
Meters (SoH)
Area of House (%) 8 30 7 45 10
Cumulative Area of House (%) 8 38 45 90 100
Gini Coefficient 0.633
Fig. 7. Gini Coefficients for Selected Socio-economic Factors
I 0.712
AL 0.367
Em 0.651
Ef 0.765
H 0.639
Cw 0.426
Elec 0.603
L 0.725
SoH 0.633
Socio-economic Factors
Note: Table made from bar graph.
