International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 1762-1771, Article ID: IJMET_10_01_175
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=01
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
A FEW DEMOGRAPHIC FACTORS AFFECTING
THE DECLINE OF TOTAL FERTILITY RATE:
AN EMPIRICAL EVIDENCE
D.Srinivasa Kumar
Associate Professor, GMR Institute of Technology, Rajam
K.V.S.Prasad
Sr.Assistant Professor, GMR Institute of Technology, Rajam
A.Vinod Kumar
Scientific Officer "H", Bhabha Atomic Research Centre (BARC), Mumbai
ABSTRACT
This paper investigates the a few demographic factors affecting the decline of Total
Fertility Rate. It is based on survey conducted in Kovvada region, Srikakulam district,
Andhra Pradesh. According to GIS information the study area divided into three zones
with 5km, 15km and 30km radius distance from the Nuclear Plant situated in Kovvada
labeled as core zone, Buffer Zone - I and Buffer Zone - II covering 153 villages. Data
were collected from 11297 household through pre designed questionnaire in these zones
and entered CAPI using DESOFT software and analyze. Children ever born and children
surviving data used to estimate age specific rates. Association between education level
and fertility rates have been established by applying chi square. Results revealed that 61
percent women were illiterate and TFR 2.7. The TFR range 2.7 to 3.4 in all three zones
high and there is significant association between fertility and a few demographic factors
like occupation and education level of women. It may be inferred that literacy rate of
female and women age groups are the most imperative components influencing TFR.
Which proved that the existence of some kind of dependency between level of education
and total fertility rate.
Key Words: Fertility Rate, Women Education, Age Groups, Childbearing, Literacy Rate
Cite this Article: D.Srinivasa Kumar, K.V.S.Prasad and A.Vinod Kumar, A Few
Demographic Factors Affecting the Decline of Total Fertility Rate: an Empirical Evidence,
International Journal of Mechanical Engineering and Technology, 10(01), 2019, pp.1762–
1771
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D.Srinivasa Kumar, K.V.S.Prasad and A.Vinod Kumar
1. INTRODUCTION:
Although fertility decline often correlates with improvements in socioeconomic conditions, many
demographers have found flaws in demographic transition theories that depend on changes in
distal factors such as increased wealth or education (Campbell et.la, 2013).A recent study by Yoo
(2014) estimates completed cohort fertility by utilizing the question in the census that asks the
number of children ever born. Yoo (2014), uses past censuses to estimate completed cohort
fertility for women born between 1926 and 1970. Based on these estimates, Yoo (2014)
concludes that fertility differentials in Korea havebeen diminishing across all educational levels,
arguing that Korea does not conform to Bongaarts’ (2003) finding that educational differentials
in fertility continue to persist in post-transitional societies such as Korea.Education is a standout
amongst the most vital elements affecting fertility behavior. A study on Nigeria investigated a
universal primary education program that took place between 1976 and 1981, and found that it
also influenced fertility behavior (Osili and Long, (2008).This paper analyzes the a few
demographic factors influencing the decline of Total Fertility Rate (TRF), women Education
level of attainment, the fewer children she is probably going to bear. Given that fewer childrenper
women and postponed marriage and childbearing could mean more resources per child and better
wellbeing and survival rates for mother and children this critical connection. Men had
significantly lower fertility awareness than women on almost all parameters (Hammarberg et al,
2013).However, educational uplift along with economic opportunities of women, improved
access to reproductive health information, services at schools, health campaigns, and involvement
of men in family planning decision making have an impact on fertility. In addition, age and
employment; maternal age, level of education, family size and breastfeeding; age of mother, age
at marriage, and education were proved to be significant influencers of birth interval,
(Nadahindawa et al, 2014), (Rasheed and Dabal, (2007), (AI-Almaie, 2003), (Ai-Nahedh, (1999).
One of the significant difficulties in a large portion of the creating nations today is the quick
increment in population, which is in charge of an expansive number of social and economic
problems. Female education has a greater impact on determining the age at marriage and number
of children (Breierva and Duflo, 2002).The fertility rates in creating countries are high versus
developed countries. An extensive number of factors are responsible of high fertility rates in
developing countries of the world. The age at marriage is one of the key determinants of fertility
rate. The lower the age at marriage time, longer the reproductive span, which results in higher
fertility rate. There are numerous reasons behind early marriages in developing countries, which
incorporate both the social and economic factors.Education is considered as a standout amongst
the most vital factor influencing women decision regarding the number of children.
Educated women exercise higher command over their reproduction, as even after controlling
husband’s education, advanced women education is positively associated with the use of modern
contraceptives (Omariba, 2006). Another argument is that women’s higher education empowers
them to make decisions on their fertility. In fact, women’s empowerment could be the driving
force for the effect of education on fertility (Chicoine, 2012).Women’s education level could
influence fertility through its effect on women’s health and their physical ability to give birth,
children’s health, the quantity of children wanted, and women’s ability to control birth and
knowledge of different birth control methods. There was no significant relationship between
demographic characteristics and gender (Srinivasa& Prasad, 2018).
TFR is the number of children a women can expect to have over her lifetime given current
rates of age specific fertility. The figure is demonstrates TFR drifts after some time in core zone,
Buffer Zone - I and Buffer Zone - II in study area among women varying levels of education
accomplishment. What it appears for each of the three zones is that there are striking contrasts in
TFR between women with no schooling and women with a higher education
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A Few Demographic Factors Affecting the Decline of Total Fertility Rate: an Empirical Evidence
2. STUDY AREA:
The study area is defined as 30 km radius of proposed Nuclear Power Plant in Kovvada Region
(NPPKR) of Ranasthalam Mandal, Andhra Pradesh, India. In this connection, we made GIS
based village generation thematic maps in Kovvada region. According to those maps 0-5 Km
radius named as Core Zone where we identified 13 villages; 5-15 km radius named as Buffer
Zone – I, we identified 101 villages and 15-30 radius named as Buffer Zone – II, where we
identified 283.
3. OBJECTIVES:
To assess the Sex Ratio
To assess the Literacy rate
To assess the Age groups as per gender
To assess the occupation of participants
To assess the women fertility rate
4. METHODOLOGY:
Before commencing the door to door survey we collected the required data from the available
agencies (Census, Municipality, Panchayath office, Mandal Revenue office, Mandal
Development offices etc.) as it is a field intensive work. In the core zone 13 villages, Buffer Zone
- I 72 villages and Buffer Zone - II 68 villages were selected and we made door to door survey in
these zones through well-structured questionnaire from households (HHs). The responses of
household heads information was entered in their laptops by using the Data Entry Software
(DSOFT).
According to GIS information, Buffer Zone -I consists of 101 villages and we made it into 72
PSUs (Primary Sample Units) with its hamlet villages. Buffer Zone -II consists of 283 villages
but we selected 74 PSUs in this zone. We selected 10 PSUs in urban area and 64 PSUs in rural
area and data collected from 45 households in each PSU. Data analysis was carried out by using
statistical tools like SPSS (Statistical Package for Social Science) for statistical analysis simple
averages, percentages and chi-square tests are used. The hypotheses are tested at both 0.05% and
0.01% significant levels.
Hypotheses:
H0: Women education level and total fertility rate are independent
H1: Women education level and total fertility rate are significantly dependent
4.1. Sex Ratio Distribution:
The number of males and females in study area,the distribution may refer to how many men or
women presented in table-1.
Table 1: Sex Wise Distribution of Participants
Sl.No.
1
2
Sex
Male
Female
Total
Core Zone
9092 (51.78)
8466 (48.22)
17558
Buffer Zone - I
6400 (51.40)
6052 (48.60)
12452
Buffer Zone - II
6407 (51.49)
6035 (48.51)
12442
Total
21899 (51.59)
20553 (48.41)
42452
Data Format: Number (%)
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D.Srinivasa Kumar, K.V.S.Prasad and A.Vinod Kumar
Total 42452 populations were covered during survey from all the three zones. Of these,
21899(51.59%) are males and 20533 (48.41%) are females. In all the three zones, male to female
population ratio is constant. In core zone 9092 (51.78%) population is male and 8466 (48.22%)
are females. In Buffer zone - I, 6400 (51.40%) population is male and remaining 6052 (48.60)
are females. In Buffer Zone - II, 6407 (51.49) population is male and remaining 3035 (48.51) are
females. As per the census 2011, at national level male population is 51.47%and 48.52% females.
At state level 50.17% are males and 49.82% are females. At district level, Srikakulam district is
having 49.65% of males and 50.34% females. In the given area population distribution is similar
to the National level.
4.2. Age Wise Distribution:
Age composition by residence were broadly classified by ages groups like Up to 5, 6 to 15, 16 to
49, 50 to 60 years and above 60 years in my study area is shown in the table-2.
Sl.No.
1
2
3
4
5
TABLE 2: AGE WISE DISTRIBUTION OF PARTICIPANTS
Age Groups
Core Zone
Buffer Zone - I
Buffer Zone - II
Up to 5
1682 (9.58)
1062 ((8.53)
848 (6.82)
6 to 15
3154 (17.96)
2106 (16.91)
1958 (15.74)
16 to 49
9454 (53.84)
6579 (52.83)
6621 (53.21)
50 to 60
2133 (12.15)
1825 (14.66)
1972 (15.85)
>60
1135 (6.46)
880 (7.07)
1043 (8.38)
Total
17558
12452
12442
Total
3592 (8.46)
7218 (17.00)
22654(53.36)
5930 (13.97)
3058 (7.20)
42452
Data Format: Number (%)
Age wise distribution of participants is as following. In core zone 1682 (9.58%) participants are
below five years of age, and in the Buffer Zone - I and Buffer Zone - II was 1062 (8.53%) and
848 (6.82%) participants are below five years age, respectively. Total 3592 (8.46%) participants
are below five years of age from all three zones. 22654 (53.36%) participants are from the 16 to
49 years of age group from all three zones. Participants over sixty years of age are 3058 (7.20%)
from all two zones. In this study indicates that more fertility age group peoples were living in
the proposed plant area.
4.3. Mean Age of Marriage:
Marriage is for bliss not for misery. Studies have also revealed that an average marriageable age
in India for men is 26 years and for women 22.2 years. Rural and urban India shows sharp
difference between the ages at marriage. Overall the age in urban areas is 21 years whereas in
rural areas it is 19 years in study area Average age of marriage showed in table - 3
Table 3: Average Age of Marriage
Sl.No.
Gender
Core Zone
1
Boys
24.50
2
Girls
19.35
Data Format: Number (%)
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Buffer Zone-I
25.30
19.60
1765
Buffer Zone-II
26.44
18.98
Total
25.41
19.31
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A Few Demographic Factors Affecting the Decline of Total Fertility Rate: an Empirical Evidence
Average age of boys who married in study area was 25.41 years and girl’s average age was
19.31. Average age of marriage is steadily increasing in all parts of state. In 2011 census average
age of marriage for boys and girls was 24.3 and 19.8 years in Andhra Pradesh, respectively.
4.4. Total Fertility Rate:
The cumulative value of the Age Specific Fertility Rates at the end of the child bearing age gives
a measure of fertility known as Total Fertility Rate (TFR). TFR indicates the average number of
children expected to be born per women during her entire span of reproductive period assuming
that the age specific fertility rates to which she exposed to continue to be the same and there is
no mortality. Delayed childbearing is not necessarily an informed decision-making but could be
a less conscious choice associated with lack of knowledge regarding the impact of female age on
fertility (Cooke et al, 2010).The TFRs worked out on the basis of average specific fertility rate in
the core zone 3.37 children are born per women, 2.82 children in Buffer Zone - I and 2.67 children
in Buffer Zone - II. The TFR in India is 2.4 and in the Andhra Pradesh are 1.8 children per
women, implying that the state has reached below replacement level of fertility.
Mean age of childbearing in core zone at the age of 24.38 years, inBuffer Zone - I and Buffer
Zone - II is 23.08 and 23.67 years respectively. At national level mean age of childbearing was
26.6 years and in state level was 24.3 years, in study area it exactly matches with state level
childbearing value was presented in table-4.
Table 4: Age specific fertility rates derived from CEB data* for Core, Buffer I and II zone areas of KOVADA
NPP area
Sl.No.
Age Group of
Women
1
2
3
4
5
6
15 – 20
20 – 25
25 – 30
30 – 35
35 – 40
40 – 45
7
45 – 50
Mean Age of Child
bearing:
Total Fertility Rate:
Core Zone
Fertility
Children
consistent
Ever Born
with CEB
(CEB)
A.S.F.R *
0.64935
0.23610
1.31055
0.08677
2.0445
0.14299
2.40704
0.02783
2.67994
0.09334
2.96606
0.06411
3.23746
Buffer Zone - I
Fertility
Children
consistent
Ever Born with CEB
(CEB)
A.S.F.R *
0.56522
0.21865
1.32391
0.10893
2.00416
0.11927
2.28795
0.01435
2.41718
0.05184
2.58868
0.03787
0.02358
2.98396
0.01404
Buffer Zone - II
Fertility
Children
consistent
Ever Born
with CEB
(CEB)
A.S.F.R *
0.62963
0.21542
1.09350
0.05452
1.76225
0.13002
1.99097
0.00689
2.12127
0.07339
2.27838
0.05005
2.39773
0.01849
24.38500
23.08345
23.67504
3.37358
2.82477
2.674972
*Arraiga's approach for estimation of ASFR for one point in time CEB (MORTPAK)
4.5. Occupation:
Agriculture is the backbone of the economy of the district. More than half of its population is
engaged in agriculture in order to earn their livelihood.Formalization, decentralization and
communication indicate that there is very highly significant variation of mean percentage when
analysis is made according to occupations of the staffs (Kangjam & Devi, 2018).Occupation of
people in study area presented in the table – 5
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D.Srinivasa Kumar, K.V.S.Prasad and A.Vinod Kumar
Table 5: Occupation of the Households
Sl.No.
1
2
Occupation
Cultivation
Agriculture Labour
Non Agricultural
3
Labour
Traditional
4
Occupation
5
Self-Business
6
Service (Govt.)
7
Service (Pvt.)
8
Others
9
Student
10
House Wife
11
No Work
Total
Data Format: Number (%)
Core Zone
595 (3.39)
2775 (15.80)
Buffer Zone-I
564 (4.53)
1921 (15.43)
Buffer Zone-II
715 (5.75)
2246 (18.05)
Total
1874 (4.41)
6942 (16.35)
1569 (8.94)
1631 (13.10)
1160 (9.32)
4360 (10.27)
984 (5.60)
105 (0.84)
210 (1.69)
1299 (3.06)
1320 (7.52)
83 (0.47)
307 (1.75)
440 (2.51)
3976 (22.64)
2572 (14.65)
2937 (16.73)
17558
379 (3.04)
102 (0.82)
484 (3.89)
415 (3.33)
3023 (24.28)
2015 (16.18)
1813 (14.56)
12452
434 (3.49)
149 (1.20)
376 (3.02)
65 (0.52)
3018 (24.26)
2106 (16.93)
1963 (15.78)
12442
2133 (5.02)
334 (0.79)
1167 (2.75)
920 (2.17)
10017 (23.60)
6693 (15.77)
6713 (15.81)
42452
Occupation of the participants is as following. 1874 (4.41%) participants are farmers and
engaged exclusively in cultivation. Core Zone 595 (3.39%) participants are engaged in cultivation
which is less than Buffer Zone-I and II, which is 564 (4.53%) and 715 (5.75%) respectively. 6942
(16.35%) participants are engaged in agriculture related labor, in the Core Zone 2775 (15.80%)
participants are engaged in the agricultural labor, in Buffer Zone-I and II, 1921 (15.43%) and
2246 (18.05%), respectively . 334 (0.79%) participants are in government services only.
Percentage of participants in government services is less compared to other occupations. As per
census 2011, total work force population is 29.86% in India and 64.43% in Andhra Pradesh. In
Srikakulam district is 47.73% , in study area work force population more than the national level
but less than the state level.
4.6. Literacy Status:
Literacy is an essential aspect of our everyday lives that is embedded in our activities, social
interactions and relationships. It is the ability to read and write, in this research the literacy rates
shown in the table – 6.
TABLE 6: GENDER WISE LITERACY STATUS
Sl.No.
Literacy
Status
1
2
Core Zone
Buffer Zone - I
Buffer Zone - II
Total
Male
Female
Male
Female
Male
Female
Male
Female
Literates
4324
(47.56)
3028
(35.77)
3443
(53.80)
2531
(41.82)
3657
(57.08)
2527
(41.87)
11424
(52.17)
8086
(39.34)
Illiterates
4768
(52.44)
5438
(64.23)
2957
(46.20)
3521
(58.18)
2750
(42.92)
3508
(58.13)
10475
(47.83)
12467
(60.66)
Total
9092
8466
6400
6052
6407
6035
21899
20553
Data Format: Number (%)
*Excluding Up to 5 years population
In this study male literacy is 52.17% and female literacy is 39.24%, in the core zone male
literacy is 47.56% and female literacy is 35.77%. In Buffer Zone - I, male literacy is 53.80% and
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A Few Demographic Factors Affecting the Decline of Total Fertility Rate: an Empirical Evidence
female literacy is 41.82%. In Buffer Zone - II, male literacy is 57.08% and female literacy is
52.17%.At the district level total literacy is 61.74%, in this 71.61% are male and 52.08% are
females. In the given study overall literacy status is less compared to district, state and national
average. The level of education can be classified in three groups.
4.7. Educational Levels in Different Age Groups of Females:
Educational attainment or level of education has become basic necessity of life. The status of
educational levels of residence in the study area as per age groups was presented in table – 7.
Table 7 : Educational Levels of Females as per Age Groups
Name of the
Zone
Core Zone
Buffer Zone I
Buffer Zone II
Age Group
in Years
Educational Levels
Primary
Upper Primary
High School
Higher
Education
15-25
439 (41.07)
288 (51.89)
418 (46.29)
206 (41.12)
25-35
254 (23.76)
102 (18.38)
269 (29.79)
164 (32.73)
35-50
376 (35.17)
165 (29.73)
216 (23.92)
131 (26.15)
Total
1069
555
903
501
15-25
257 (30.09)
170 (40.96)
343 (45.01)
266 (53.20)
25-35
291 (34.07)
101 (24.34)
222 (29.13)
160 (32.00)
35-50
306 (35.83)
144 (34.70)
197 (25.85)
74 (14.80)
Total
854
415
762
500
15-25
230 (32.08)
110 (32.64)
291 (38.14)
281 (39.58)
25-35
191 (26.64)
106 (31.45)
256 (33.55)
233 (32.82)
35-50
296 (41.28)
121 (35.91)
216 (28.31)
196 (27.61)
Total
717
337
763
710
Data Format: Number (%)
In the age group 15-25 years women having highest fertility rate, in a core zone upper primary
level of education have 51.89% followed by high school was 46.29%, higher education was
41.12%, but primary level of education was 41.07% shown as lowest level of education in core
zone. In Buffer Zone - I higher education level was highest shown as 53.20%, lowest was primary
education level is lowest shown as 30.39%. In Buffer Zone - II higher education also shown as
highest 39.50%, compare to remaining levels, primary level also shown as lowest 32.08%.
5. RESULTS AND DISCUSSION:
The major objective of present study was to find out the direct and indirect impact of females
education on TFR in study area. TFR is treated as dependent variable; level of education an
important explanatory variable has been limited to primary, upper primary, higher school and
higher education. This paper analyze the TFR is significant influences on female level education,
in this connection independent variables expected frequencies calculated in different age groups
like 15-25, 25-35 and 35-50 years are considered for analysis. The expected frequency values of
female educational level in all three zones at different age groups present in table – 8.
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Table 8: Expected Frequency (fe) Values of Educational Levels at different Age Groups
Core Zone
Educational Status
Buffer Zone - I
Buffer Zone - II
15-25
25-35
35-50
15-25
25-35
35-50
15-25
25-35
35-50
Primary
476.95
278.55
313.50
349.56
261.16
243.28
258.77
223.02
235.22
Upper Primary
247.62
144.62
162.76
169.87
126.91
118.22
121.62
104.82
110.56
High School
402.89
235.29
264.82
311.91
233.03
217.07
275.37
237.32
250.31
Higher Education
223.53
130.54
146.92
204.66
152.90
142.43
256.24
220.84
232.92
The chi-square values are calculated in all three zones and test the 0.05% and 0.01%
significance levels and presented in the table – 9.
Table 8: Summarizing the data for Calculating Chi-square Values
Name of the Zone
Core Zone
Buffer Zone - I
Buffer Zone - II
Observed (fo)
439
254
376
288
102
165
418
269
216
206
164
131
(fo-fe)2
1440.56
602.57
3906.50
1630.23
1816.06
5.01
228.29
1136.17
2383.04
307.33
1119.26
253.60
257
291
306
170
101
144
343
222
197
266
160
174
8567.91
890.42
3934.18
0.02
671.34
664.60
966.89
121.57
402.77
3762.33
50.35
996.42
230
191
296
110
106
121
291
256
216
281
233
196
Expected (fe)
fo-fe
476.95
-37.95
278.55
-24.55
313.50
62.50
247.62
40.38
144.62
-42.62
162.76
2.24
402.89
15.11
235.29
33.71
264.82
-48.82
223.53
-17.53
130.54
33.46
146.92
-15.92
χ2
349.56
-92.56
261.16
29.84
243.28
62.72
169.87
0.13
126.91
-25.91
118.22
25.78
311.91
31.09
233.03
-11.03
217.07
-20.07
204.66
61.34
152.90
7.10
142.43
31.57
χ2
258.77
-28.77
223.02
-32.02
235.22
60.78
121.62
-11.62
104.82
1.18
110.56
10.44
275.37
15.63
237.32
18.68
250.31
-34.31
256.24
24.76
220.84
12.16
232.92
-36.92
χ2
α = 0.01, df = (4-1)(3-1) = 6 the critical value is 16.812
α = 0.05, df = (4-1)(3-1) = 6 the critical value is 12.592
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827.54
1025.04
3694.59
135.12
1.39
109.09
244.35
348.79
1177.00
613.03
147.89
1363.12
(fo-fe)2/ fe
3.02
2.16
12.46
6.58
12.56
0.03
0.57
4.83
9.00
1.37
8.57
1.73
62.89
24.51
3.41
16.17
0.00
5.29
5.62
3.10
0.52
1.86
18.38
0.33
7.00
86.19
3.20
4.60
15.71
1.11
0.01
0.99
0.89
1.47
4.70
2.39
0.67
5.85
41.59
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A Few Demographic Factors Affecting the Decline of Total Fertility Rate: an Empirical Evidence
The above table indicates that significance dependence is clearly seen between different levels
of female education on the TFR in a study area. At 1% level of significance in a core zone χ2 is
62.89 > 16.812, Buffer Zone – I χ2 is 86.19 > 16.812 and Buffer Zone - IIχ2 is 41.59 > 16.812
and also at 5% level of significance In a core zone χ2 is 62.89 > 12.592, Buffer Zone – I χ2 is
86.19 > 12.592 and Buffer Zone – II χ2 is 41.59 > 12.592, hencethere is enough statistical
evidence torejectthe null hypothesis (H0) and accepted the H1. Comparing the level of education
on fertility rates all three zones, it can be observed that the highest TFR 3.37 in core zone because
the literacy rate in female is lowest and level education is upper primary is highest 51.89% in the
age group of 15-25 years. TFR is lowest in Buffer Zone - I and Buffer Zone - II is 2.82 and 2.67
respectively. Because the literacy rate 41.82% in Buffer Zone - I and 41.87% in Buffer Zone - II
it is almost all similar, that is reason the TFR is also similar in these zones. The level of education
in Buffer Zone - II is higher education level is 53.20%, which proved that the existence of some
kind of dependency between level of education and total fertility rate. So that level of education
also shows impact on fertility rate.
6. CONCLUSION
The present examination analyzed there is a huge impact demographic factors on TFR in stud
area based on results obtained from chi-square test it can be concluded that mean age at marriage
of male and female, education levels of women and occupation of male are most important factors
affecting the TFR, it very well may be inferred that literacy rate of female and women age groups
are the most imperative components influencing TFR in the examination all three zones. Women
education can be effective if it is at higher education level. An inverse relation between the TFR
and education level suggest that higher the women education the lower will be TFR. In addition,
in case of male, primary education has significant impact on the TFR. On the basis of present
investigation focus on high rate female education will enable them as part decision making with
regarding to the number of children. Finally, I conclude that TFR is decreases when women
educational levels are increased.
ACKNOWLEDGMENT:
The authors would like to extend their sincere gratitude to the Board of Research in Nuclear
Science (BRNS), Mumbai for funding of this research project with grant No.36 (4)/14/59/2014BRNS/10133.
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