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Florentin Smarandache 



Collected Papers, V 



DETERMINANTS OF POPULATION GROWTH IN RAJASTHAN: 

AN ANALYSIS 

V.V. SINGH, ALKA MITTAL, NEETISH SHARMA, F. SMARANDACHE 



Abstract 

Rajasthan is the biggest State of India and is currently in the second phase of demographic 
transition and is moving towards the third phase of demographic transition with very slow 
pace. However, state’s population will continue to grow for a time period. Rajasthan’s 
performance in the social and economic sector has been poor in past. The poor performance 
is the outcome of poverty, illiteracy and poor development, which co-exist and reinforce each 
other. There are many demographic and socio-economic factors responsible for population 
growth. This paper attempts to identify the demographic and socio-economic variables, which 
are responsible for population growth in Rajasthan with the help of multivariate analysis. 

1. Introduction: 

Prof. Stephan Hawking (Cambridge University ) was on Larry King Live. Larry King called him the 
“most intelligent person in the world”. King asked some very key questions, one of them was: “what 
worries you the most?” Hawking said, “My biggest worry is population growth, and if it continues at 
the current rate, we will be standing shoulder to shoulder in 2600. Something has to happen, and I 
don ’t want it to be a disaster”. 

The importance of population studies in India has been recognized since very ancient times. The 
‘Arthashastra’ of Kautilya gives a detailed description of how to conduct a population, economic and 
agricultural census. During the reign of Akbar, Abul Fazal compiled the Ain-E-Akbari containing 
comprehensive data on population, industry, wealth and characteristics of population. During the 
British period, system of decennial census started with the first census in 1872. 

The population growth of a region and its economic development are closely linked. India has been a 
victim of population growth. Although the country has achieved progress in the economic field, the 
population growth has wrinkled the growth potential. The need to check the population growth was 
realized by a section of the intellectual elite even before independence. Birth control was accepted by 
this group but implementation was restricted to the westernized minority in the cities. When the 
country attained independence and planning was launched, population control became one of the 
important items on the agenda of development. The draft outline of the First Five Year Plan said, “the 
increasing pressure of population on natural resources retards economic progress and limits seriously 
the rate of extension of social services, so essential to civilized existence.” 

India was one of the pioneers in health service planning with a focus on primary health care. 
Improvement in the health status of the population has been one of the major thrust areas for the 
social development programs of the country in the five year plans. India is a signatory to the Alma 



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Ata Declaration (1978) whereby a commitment was made to achieve ‘Health for AH' by 2000 AD. 
We are in the end of the first decade of the 21 st century but still have to go a long way to achieve this 
target. Rajasthan is lagging behind the all India average in the key parameters i.e. CBR, CDR, IMR, 
TFR & CPR. The state has made consistent efforts to improve quality of its people through 
improvement in coverage & quality of health care and implementation of disease control programs 
but the goals remain elusive due to high levels of fertility and mortality. According to the Report of 
the Technical Group on Population Projections, India will achieve the target of TFR = 2.1 (Net 
Reproduction Rate = 1) in 2026. Kerala & Tamilnadu had already achieved it in 1988 & 1993 
respectively but Rajasthan will achieve it in 2048 & Uttar Pradesh in 2100. 

Rajasthan is the largest state of the country with its area of 342239 sq. kms., which constitutes about 
10.41% of the total area of the country. According to 2001 census, its population is 56.51 million. It 
consist 5.5 % population and ranks eighth in the country. In 1901, population of Rajasthan was 10.29 
millions. In 1951, it reached to 15.97 millions with its slow growth during 1901-1951. Figure 1 shows 
that it increased rapidly after 1951. It reached to 34.26 million in 1981 and to 56.51 million in 2001. It 
has multiplied 5.5 times since 1901 and 3.5 times since 1951. Figure 2 shows decennial growth in 
population of the state. Before 1951, it increased by less than 20% growth per decade. In 1971-81, it 
shows the maximum growth rate of 32.97%. In 1981-91, it decreased by 4.53 percentage points and 
grew by 28.44%. The decade of 1991-2001 shows growth of 28.41%. 





The rapid population growth in a already populated state like Rajasthan could lead to many problems 
i.e. pressure on land, environmental deterioration, fragmentation of land holding, shrinking forests, 
rising temperatures, pressure on health & educational infrastructure, on availability of food grains & 
on employment. Figure 3 shows the decennial growth of district-wise population during 1991-2001. 
Jaisalmer shows the maximum growth of 47.45% followed by Bikaner (38.18%), Barmer (36.83%), 
Jaipur (35.10%) and Jodhpur (33.77%). Rajasamand shows minimum growth of 19.88% followed by 
Jhunjhunu (20.90%), Chittorgarh (21 .46%), Pali (22.39%) and Jhalawar (23.34%). 



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50.00 

40.00 

30.00 

20.00 

10.00 

ll|l||||l|ll||||l|l||||■|■||l|l|| 

Source: Government of India, Registrar General, India, see the website www.censusindia.net 



Fig. 3 : POPULATION GROWTH (1991-2001) 




Rajasthan is currently in the second phase and is moving towards the third phase of demographic 
transition with very slow pace. The changes in the population growth rates in Rajasthan have been 
relatively slow, but the change has been steady and sustained. We are aware of the need for birth 
control, but too many remain ignorant of contraception methods or are unwilling to discuss them. 
There is considerable pressure to produce a son. However, the state’ s population will continue to grow 
for a time period. 



Rajasthan is the second state in the country to formulate and adopt its own Population Policy in 
January 2000. State Population Policy 5 has envisaged strategies for population stabilization and 
improving health conditions of people specially women and children. The policy document has 
clearly presented role and responsibilities of different departments actively contributing in 
implementation of population policy. Family Welfare Program was linked with other sectors and 
demands intervention and efficient policies in these sectors so that changes can be brought in the 
social, economic, cultural & political environment. The State Population Policy envisages time bound 
objectives as mentioned in table 1 : 



Table 1: Objectives of Population Policy of Rajasthan 



Indicators 


1997 


2001 


2004 


2007 


2011 


2013 


2016 


Total Fertility Rate 


4.11 


3.74 


3.41 


3.09 


2.65 


2.43 


2.10 


Birth Rate 


32.1 


29.2 


27.5 


25.6 


22.6 


20.9 


18.4 


Contraceptive Prevalence Rate 


38.5 


42.2 


48.2 


52.7 


58.8 


61.8 


68.0 


Death Rate 


8.9 


8.7 


8.4 


7.9 


7.5 


7.2 


7.0 


Infant Mortality Rate 


85.0 


77.4 


72.7 


68.1 


62.2 


60.1 


56.8 



Rajasthan’s performance in the social and economic sector has been poor in past. The poor 
performance is the outcome of poverty, illiteracy and poor development which co-exist and reinforce 
each other. State Government has taken energetic steps in last few years to assess and fully meet the 
unmet needs for maternal & child health care and contraception through improvement in availability 
and access to family welfare services but still remains a long path. The progress in these indicators 
would determine the year and size of the population at which the state achieves population 
stabilization. 



2. Objectives and Methodology: 



There is a major data difficulty regarding availability of annual statistics, calculations & comparisons 
of Crude Birth Rate (CBR), Total Fertility Rate (TFR) and Females’ Mean Age at Gauna (FMAG) 
over time for district level study of any state and which is applied to Rajasthan also. This data 
problem distorts the calculations and negates the usefulness of making comparisons over time. Due to 
this data information problem, we use the information for different years (as per the availability of 
latest data, taking 2000-01 as base year) in this paper. This data problem at district level is a constraint 



5 Government of Rajsthan (1999), “Population Policy of Rajasthan”, Department of Family Welfare, Jaipur. 



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that creates a limitation in the selection of study objectives and hypotheses. This paper attempts to 
identify the demographic and socio-economic variables, which are responsible for population growth 
in Rajasthan. The main objectives of the study are: 

♦♦♦ To observe the characteristics of indicators of population growth in Rajasthan. 

♦♦♦ To identify the various demographic & socio-economic variables which have causal 
relationship with population growth. 

♦♦♦ To analyze the inter-relationship between the indicators of population growth and 
demographic & socio-economic variables. 

For achieving the above objectives, the a priori hypotheses are as follows: 

♦♦♦ Positive impact of infant mortality & total fertility rate and negative impact of income 
equality on population growth. 

♦♦♦ Positive impact of infant mortality and negative impact of female’s age at gauna and female 
literacy on crude birth rate. 

♦♦♦ Negative impact of couple protection rate, income equality, female literacy and positive 
impact of infant mortality on total fertility rate. 

❖ Positive impact of female literacy & income equality on female’s age at gauna. 

❖ Positive impact of female literacy, females age at gauna and income equality on couple 
protection rate. 

To rummage the inter-relationship between indicators of population growth and demographic & 
socio-economic variables, a social sector model is proposed. The model is estimated by the use of 
Multiple Regression Analysis (Method of Ordinary Least Squares). The general form of the Multiple 
Regression Equation Model is as follows: 

Yi = pi + p2 X 2 ; + P', X 3 , + ••• + |3kXld + Ui 

where i = 1, 2, 3, ... , n. 

In this multiple regression equation model, Y; is dependent variable and X 2 , X 3 , ... , X k are 
independent explanatory variables. (3i is the intercept, shows the average value of Y, when X 2 , X 3 , ... , 
X k are set equal to zero; (3 2 . (3 3 , ..., (3 k are partial regression/slope coefficients; Ui is the stochastic 
disturbance term; i is the i lh observation and n is the size of population. 

The model is estimated by using cross-sectional data of all 32 districts of the state (at that time, the no. 
of districts was 32). In this paper, we also calculated the Mean, Standard Deviation and Coefficient of 
Variation of the variables. The variables used in this paper, their reference year and 
abbreviations/identification code are given in the Appendix I (Table 9). Firstly, we regress the 
dependent variables with all the variables, which have theoretical relationship and then choose the 
appropriate variables for multiple regressions. The dependent and independent variables for the model 
are as follows: 



Table 2: Functional Form of the Model 



Dependent Variable 


Independent Variables 


POPGWR 


CBR, TFR, FMAG, CDR, CPR, IMR, CIMM, MRANC, PWRSAP, PWETVR, MIPLP, 
BPGH, PCEMPH, LIT, LIT,,,, LIT,, PCEEE, PCNDDP, PPBPL, ROADSK, PHDW, PCEWS 


CBR 


POPGWR, FMR, EMR (0 . 6) , PURPOP, FMAG, CPR, IMR, PWETVR, PCEMPH, PCEFW, 
LIT, LIT m , LIT,, PCEEE, PCNDDP, PPBPL 


TFR 


PURPOP, FMAG, CPR, CDR, IMR, MRANC, PWRSAP, PWETVR. PCEMPH, LIT, LIT m , 
LIT,, PCEEE. PCNDDP, PPBPL. PCESCS 


FMAG 


PURPOP, PWETVR, LIT, LIT,,,, LIT,, PSER, PSER,„ PSER,, DORPS, DORPS,,,, DORPS,. 
PCEEE, PCNDDP, PPBPL 



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Dependent Variable 


Independent Variables 


CPR 


PURPOP, FMAG, IMR, PWETVR, MIPLP, PCEMPH, PCEFW, LIT, LIT m , LIT,, PCEEE, 
PCNDDP, PPBPL, IDI, PCESCS 



In this paper, we have taken 32 variables (appendix-I). All the 32 variables are relating to Population; 
Fertility, Reproductive Health and Mortality; Public Health and Health Infrastructure; Education and 
Educational Infrastructure; and Economic Growth and Infrastructure. Data used in this paper have 
taken from website of Census Department, State Human Development Report (Rajasthan), Various 
Administrative Reports of Medical, Health & Family Welfare Department, Government of Rajasthan 
and Plan Documents of Planning Department, Government of Rajasthan. 

3. Multivariate Analysis 

3.1 Mean, Standard Deviation & Coefficient of Variation 

Mean, standard deviation and coefficient of variation of all the 32 variables for all 32 districts along 
with the figures of all Rajasthan are at appendix I (table 9). The Mean, measures the average value of 
the variables for all 32 districts. The Standard Deviation, measures the absolute variation in the mean 
and the Coefficient of Variation, measures the percentage variation in mean. The variables are divided 
in to five categories according to the range of Coefficient of Variation for the analysis of Standard 
Deviation and Coefficient of Variation. 



Table 3: Range-wise Variables according to the Coefficient of Variation 



Range 


Variables 


Less than 25% 


POPGWR (19.92), FMR (5.28), FMR <0 . 6 >(3.26), CBR (7.02), TFR (10.20), FMAG (3.66). CPR 
(14.73). CDR (10.44), IMR (20.60), MIPLP (18.59), LIT (12.64), LIT m (8.31), LIT, (21.19), 
PSER (9.39), PSER,„ (11.17), PSER, (14.27), DORPS (12.94), DORPS m (12.89), DORPS, 
(17.01), PCNDDP (24.34), PHDW (21.33) 


25% to 50% 


MRANC (38.95), BPGH (30.61), PCEFW (38.55), PCEEE (44.54), PPBPL (46.88), PCESCS 
(48.75) 


50% to 75% 


PURPOP (53.79), PWETVR (66.94), PCEMPH (58.63), IDI (55.05) 


75% to 100% 


- 


More than 100% 


PCEWS (158.62) 



Table 3 shows that variability is higher in the variables of public health & health infrastructure and 
economic growth & infrastructure head. There is need to reduce disparities on this front. 

3.2 Regression analysis 

To rummage the interrelationship between indicators of population growth and various demographic 
and socio-economic variables, we regress the dependent variable with the independent variables 
individually (independent variables are those variable which have causal relationship with dependent 
variable in theoretical and behavioral terms) and then pick the most influential variables and regress 
with the help of step-wise method and get best fitted multiple regression equation of them. Some 
variables with insignificant coefficients have also been kept in the model because theoretically their 
importance has been proved. Figures below the coefficients are ‘t’ values. Significance of variables 
with the level of significance is denoted as follows: 

* Significant at 1% level of significance 

** Significant at 2% level of significance 

*** Significant at 5% level of significance 

**** Significant at 10% level of significance 



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Efforts have been made to avoid the problem of multicollinearity (as it presents commonly in the 
analysis of cross-sectional data) but at some places, it is difficult to avoid it. 

3.2.1 Population Growth (Decennial) 

Population Growth (POPGWR) is regressed with different variables such as CBR, TFR, FMAG, 
CDR, CPR, IMR, CIMM, MRANC, PWRSAP, PWETVR, MIPFP, BPGH, PCEMPH, FIT, FIT m , 
FIT f , PCNDDP, PPBPF, ROADSK, PHDW, PCEWS. 



Table 4: Regression Equations of Population Growth (Decennial) 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 


1. 


10.0934 


+ 


0.5643 


CBR 


0.0514 


31 








1.2745 








2. 


8.1549 


+ 


4.1092 


TFR*** 


0.1325 


31 








2.1408 








3. 


49.0047 


- 


1.1555 


FMAG 


0.0156 


31 








0.6903 








4. 


50.0223 


- 


0.5750 


CPR* 


0.3245 


31 








3.7963 








5. 


46.2904 


- 


2.0212 


CDR**** 


0.1119 


31 








1.9442 








6. 


36.6587 


- 


0.0979 


IMR**** 


0.0946 


31 








1 .7709 








7. 


34.4649 


- 


0.1670 


CIMM*** 


0.1413 


31 








2.2217 








8. 


278632 


+ 


0.0061 


MRANC 


0.0007 


31 








0.1473 








9. 


13.8825 


+ 


0.1462 


PWRSAP 


0.0497 


31 








1.2532 








10. 


30.8793 


- 


0.1961 


PWETVR**** 


0.097 


31 








1.8019 








11. 


24.9574 


+ 


0.1171 


MIPLP 


0.0118 


31 








0.5995 








12. 


21.9702 


+ 


0.0768 


BPGH**** 


0.1167 


31 








1.9906 








13. 


29.1035 


- 


0.0453 


PCEMPH 


0.0079 


31 








0.4881 








14. 


32.8303 


- 


0.0768 


LIT 


0.0106 


31 








0.5664 








15. 


40.5194 


- 


0.1629 


LIT m 


0.0328 


31 








1.0084 








16. 


31.2097 


- 


0.0696 


LIT, 


0.0124 


31 








0.6137 








17. 


26.7674 


+ 


0.0346 


PCEEE 


0.0138 


31 








0.6478 








18. 


32.6477 


- 


0.0003 


PCNDDP 


0.0361 


31 








1.0604 








19. 


27.1559 


+ 


0.0285 


PPBPL 


0.0057 


31 








0.4138 








20. 


35.2376 


- 


0.2308 


ROADSK*** 


0.1539 


31 








2.3363 








21. 


31.0646 


- 


0.0465 


PHDW 


0.0114 


31 








0.5872 








22. 


28.6427 


- 


0.0134 


PCEWS 


0.0122 


31 








0.6075 









Fit of the equations is with the expected signs. TFR, CPR, CDR, IMR, CIMM, PWETVR, BPGH and 
ROADSK have significant coefficients. PCEEE appears with opposite sign as of expected sign. In the 
step-wise regression, PPBPF is found more relevant in spite of PCNDDP for multiple regression. 



POPGWR = 12.5485 + 5.6405 TFR* - 0.1477 IMR* + 0.0246 PPBPL 

(3.0425) (2.7565) (0.4075) 

R 2 = 0.3196 d.f. = 29 



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In the multiple regression analysis the coefficients of TFR and IMR are significant at 1% level of 
significance. This indicates that TFR influences POPGWR positively. IMR shows negative influence 
to POPGWR in mathematical/statistical terms but in actual terms this leads to birth to more children 
due to less survival. The variable PPBPL does not affect POPGWR significantly. 

3.2.2 Crude Birth Rate 

Crude Birth Rate (CBR) is regressed with different variables such as POPGWR, FMR, FMR (0 _ 6) , 
PURPOP, FMAG, CPR, IMR, PWETVR, PCEMPH, PCEFW, LIT, LIT m , LIT f , PCEEE, PCNDDP, 
PPBPL. 



Table 5: Regression Equations of Crude Birth Rate 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 


1. 


29.6053 


+ 


0.0910 


POPGWR 


0.0514 


31 








1.2745 








2. 


39.4165 


- 


0.0079 


FMR 


0.0286 


31 








0.9391 








3. 


25.6612 


- 


0.0072 


FMR10-6) 


0.0088 


31 








0.5163 








4. 


32.1109 


+ 


0.0032 


PURPOP 


0.0002 


31 








0.0856 








5. 


50.5045 


- 


1.1004 


FMAG**** 


0.0879 


31 








1.7004 








6. 


38.4234 


- 


0.1650 


CPR*** 


0.1657 


31 








2.4406 








7. 


30.0598 


+ 


0.0247 


IMR 


0.0372 


31 








1.0766 








8. 


32.2562 


- 


0.0059 


PWETVR 


0.0006 


31 








0.1291 








9. 


31.1170 


+ 


0.0563 


PCEMPH 


0.0755 


31 








1.5657 








10. 


32.3631 


- 


0.1645 


PCEFW 


0.0010 


31 








0.1744 








11. 


32.4349 


- 


0.0043 


LIT 


0.0002 


31 








0.0792 








12. 


30.2455 


+ 


0.0256 


LIT m 


0.0050 


31 








0.3897 








13. 


32.9059 


- 


0.0172 


LIT, 


0.0047 


31 








0.3753 








14. 


32.2458 


- 


0.0016 


PCEEE 


0.0002 


31 








0.0748 








15. 


34.6147 


- 


0.0002 


PCNDDP 


0.0689 


31 








1.4901 








16. 


31.1572 


+ 


0.0321 


PPBPL 


0.0447 


31 








1.1847 









FMAG and CPR have significant coefficients. PURPOP, PCEMPH and LIT m are with opposite signs 
as of expected signs. 



CBR = 50.2161 - 1.0819 FMAG**** + 0.0114 IMR - 0.0234 LIT f 
(1.7123) (0.4421) (0.4701) 

R 2 = 0.1099 d.f. = 29 



Fit of the multiple regression equation is with the expected signs Coefficient of FMAG is significant 
at 10% level of significance. This indicates that FMAG influences CBR negatively. The coefficients 
of IMR and LIT f are insignificant but included due to their importance in the determination of CBR. 



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3.2.3 Total Fertility Rate 



Total Fertility Rate (TFR) is regressed with different variables such as PURPOP, FMAG, CPR, CDR, 
IMR, MRANC, PWRSAP, PWETVR, PCEMPH, LIT, LIT m , LIT f , PCEEE, PCNDDP, PPBPL, 
PCESCS. 



Table 6: Regression Equations of Total Fertility Rate 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 


1 . 


4.9033 


- 


0.0006 


PURPOP 


0.0002 


31 








0.0744 








2. 


9.2697 


- 


0.2629 


FMAG**** 


1.1031 


31 








1.8573 








3. 


6.5736 


- 


0.0445 


CPR* 


0.2471 


31 








3.1379 








4. 


3.6639 


+ 


0.1375 


CDR 


0.0659 


31 








1.7123 








5. 


4.2069 


+ 


0.0079 


IMR 


0.0797 


31 








1.6123 








6. 


5.2989 


- 


0.0064 


MRANC**** 


0.1017 


31 








1.8431 








7. 


5.0179 


- 


0.0033 


PWRSAP 


0.0032 


31 








0.3124 








8. 


51792 


- 


0.0073 


PWETVR 


0.0175 


31 








0.7304 








9. 


4.9694 


- 


0.0011 


PCEMPH 


0.0006 


31 








0.1367 








10. 


4.9951 


- 


0.0033 


LIT 


0.0025 


31 








0.2719 








11. 


5.4818 


- 


0.0139 


LIT m 


0.0305 


31 








0.9720 








12. 


5.0591 


- 


0.0039 


LIT, 


0.0051 


31 








0.3932 








13. 


4.9577 


- 


0.0016 


PCEEE 


0.0036 


31 








0.3288 








14. 


5.6749 


- 


0.00006 


PCNDDP*** 


0.1465 


31 








2.2693 








15. 


4.9662 


+ 


0.0023 


PPBPL 


0.0051 


31 








0.3906 








16. 


5.1543 


- 


0.0014 


PCESCS 


0.0664 


31 








1.4618 









FMAG, CPR, MRANC and PCNDDP are with significant coefficients. All the variables show the 
expected signs. 



TFR = 5.9697 



- 0.0412 CPR* 
(2.9361) 

- 0.0031 LITf 
(0.3446) 



R- = 0.4305 



+ 0.0104 IMR*** 
(2.3704) 

- 0.00004 PCNDDP**** 
(1.8641) 
d.f. = 28 



Coefficient of CPR is significant at 1% level of significance, IMR at 2% and PCNDDP at 10%. This 
indicates that CPR & PCNDDP influence TFR positively and IMR influences TFR negatively. LIT f 
appears with insignificant coefficient but it has major influential role in the determination of TFR. 

3.2.4 Females’ Mean Age at Gauna 

Females’ Mean Age at Gauna (FMAG) is regressed with different variables such as PURPOP, 
PWETVR, LIT, LIT m , LIT f , PSER, PSER m PSER f , DORPS, DORPS m , DORPS r , PCEEE, PCNDDP, 
PPBPL. 



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Collected Papers, V 



Table 7: Regression Equations of Females’ Mean Age at Gauna 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 


1 . 


16.1794 


+ 


0.0060 


PURPOP 


0.0118 


31 








0.5994 








2. 


15.0223 


+ 


0.0273 


PWETVR*** 


0.1619 


31 








2.4070 








3. 


15.7873 


+ 


0.0190 


LIT 


0.051 


31 








1.3231 








4. 


14.9522 


+ 


0.0305 


LIT m **** 


0.0981 


31 








1.8061 








5. 


151910 


+ 


0.0126 


LIT, 


0.0346 


31 








1.0371 








6. 


16.0412 


+ 


0.0160 


PSER 


0.0456 


31 








1.1974 








7. 


16.9408 


- 


0.0028 


PSER m 


0.0027 


31 








0.2861 








8. 


15.8440 


+ 


0.0166 


PSER, 


0.0775 


31 








1.5871 








9. 


17.3288 


- 


0.0225 


DORPS 


0.0796 


31 








1.6104 








10. 


18.1252 


- 


0.0268 


DORPS,,,**** 


0.1050 


31 








1.8765 








11. 


17.2214 


- 


0.0099 


DORPS, 


0.0147 


31 








0.6701 








12. 


16.7143 


+ 


0.0014 


PCEEE 


0.0018 


31 








0.2328 








13. 


16.4417 


+ 


0.00002 


PCNDDP 


0.0074 


31 








0.4713 








14. 


16.6888 


- 


0.0010 


PPBPL 


0.0006 


31 








0.1374 









PWETVR, LIT m and DORPS m are with significant coefficients. Except PSER m , coefficients of all are 
with expected Signs. 



FMAG=13.7224 +0.0279 PWETVR*** +0.0039 LIT f **** +0.00003 PCNDDP 
(2.1774) (1.8126) (1.0034) 

R 2 = 0.1912 d.f. = 29 

All the variables are with expected signs. Coefficient of PWETVR is significant at 5% level of 
significance & coefficient of LITf is significant at 10% level of significance. This indicates that 
PWETVR & LITf influence FMAG positively. Coefficient of PCNDDP is insignificant means the 
variable PCNDDP does not affect FMAG significantly. 

3.2.5 Couple Protection Rate 

Couple Protection Rate (CPR) is regressed on different variables such as PURPOP, FMAG, IMR, 
PWETVR, MIPLP, PCEMPH, PCEFW, LIT, LIT m , LIT f , PCEEE, PCNDDP, PPBPL, IDI, PCESCS. 



Table 8: Regression Equations of Couple Protection Rate 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 


1 . 


40.2031 


- 


0.1133 


PURPOP 


0.0511 


31 








1.2713 








2. 


18.8647 


+ 


1.1404 


FMAG 


0.0155 


31 








0.6876 








3. 


34.1252 


+ 


0.0435 


IMR 


0.0190 


31 








0.7628 








4. 


35.2834 


+ 


0.1922 


PWETVR**** 


0.0956 


31 








1.7811 








5. 


35.3518 


+ 


0.0892 


MIPLP 


0.0069 


31 



247 





Florentin Smarandache 



Collected Papers, V 



S.No. 


Intercept 


Coefficient 


R 2 


d.f. 








0.4596 








6. 


36.7349 


+ 


0.0597 


PCEMPH 


0.0139 


31 








0.6522 








7. 


33.9617 


+ 


3.4369 


PCEFW 


0.0726 


31 








1.5325 








8. 


35.5692 


+ 


0.1294 


LIT 


0.0306 


31 








0.9726 








9. 


29.9513 


+ 


0.1606 


LIT m 


0.0325 


31 








1.0032 








10. 


36.5518 


+ 


0.0869 


LIT, 


0.0197 


31 








0.7762 








11. 


39.5813 


+ 


0.0402 


PCEEE**** 


0.0189 


31 








1.7607 








12. 


31.0288 


+ 


0.0005 


PCNDDP**** 


0.0889 


31 








1.7108 








13. 


39.0037 


- 


0.1215 


PPBPL**** 


0.1051 


31 








1.8769 








14. 


38.5776 


+ 


0.0077 


IDI 


0.0050 


31 








0.3894 








15. 


36.6705 


+ 


0.0093 


PCESCS 


0.0250 


31 








0.8785 









PWETVR, PCEEE, PCNDDP and PPBPL are with significant coefficients and expected signs. Sign 
of coefficient of PURPOP is opposite of the expected. 

CPR = 20.6541 + 0.4813 FMAG + 0.1388 LIT f **** + 0.0006 PCNDDP**** 

(0.2922) (1.8065) (1.9266) 

R 2 = 0.1433 d.f. = 29 



All the variables are with expected signs of coefficients. Coefficients of LIT f and PCNDDP are 
significant at 10% level of significance. This indicates that LITf and PCNDDP influence CPR 
positively. Coefficient of FMAG is insignificant means the variable FMAG does not affect CPR 
significantly. 

4. Conclusion 

The model is fit good with the expected signs. Estimated equations confirm the a priori hypotheses of 
positive impact of infant mortality & total fertility rate and negative impact of income equality on 
population growth; positive impact of female literacy & income equality on female’s age at gauna; 
positive impact of infant mortality and negative impact of female’s age at gauna and female literacy 
on crude birth rate; negative impact of couple protection rate, income equality, female literacy and 
positive impact of infant mortality on total fertility rate, positive impact of female literacy, females 
age at gauna and income equality on couple protection rate. Literacy, especially female literacy and 
per-capita income appeared as most influential variables to attack the poor status of socio-economic & 
demographic variables. There is need to emphasize on the improvement of these two variables. 

Rapid population growth retards the economic, social and human development. Enhancement of 
women’s status and autonomy has been conclusively established to have a direct bearing on fertility 
and mortality decline, which indirectly affects the population growth. More specifically, inter- 
relationships between women’s characteristics and access to resources are the mechanisms through 
which human fertility is determined. Education is highly correlated with age at the marriage of the 
females and thus helps in the reduction of the reproductive life, on an average, and helps in the 
conscious efforts to limit the family size. The early marriage of the daughter in rural areas is an 
expected rational behavior, as long as there is mass illiteracy and poverty. The age at marriage for 
females cannot be raised by mere, legislation unless the socio-economic conditions of the rural people 
is improved and better educational facilities and occupational alternatives for the teenage girls are 
provided near their homes. 



248 





Florentin Smarandache 



Collected Papers, V 



Reproductive and public health have their importance in determination of population stabilization. 
National Rural Health Mission (NRHM) and Rajasthan Health System Development Project 
(RHSDP) are ongoing programs which can improve the situation. There is need of effective 
monitoring of activities under these programs. Effective implementation of family welfare program 
will create opportunities for better education and improvement in nutritional status of family through 
check on population growth, which will turn in better health of mother and child and there will be less 
infant and maternal mortality. 

References 



• Government of Rajasthan (2005), “District-wise Performance of Family Welfare Programme- 2004”, Directorate of Family 
Welfare, Jaipur. 

• Government of Rajasthan, “Various Plan Documents”, Planning Department, Jaipur. 

• Government of Rajsthan (1999), “Population Policy of Rajasthan”, Department of Family Welfare, Jaipur. 

• Kulkarni, Sumati and Minja Kim Choe (1997), “State-level Variations in Wanted and Unwanted Fertility Provide a Guide 
for India’s Family Planning Programmes”, NFHS Bulletion, UPS, Mumbai. 

• Mittal, Alka (2004), “Billion Plus Population: Challenges Ahead”, Paper submitted to Academic Staff College, University 
of Rajasthan, Jaipur during 57 th Orientation Course. 

• Mohanty, Sanjay K. and Moulasha K. (1996), “Women’s Status, Proximate Determinants and Fertility Behaviour in 
Rajasthan”, Paper Presented at National Seminar on Population and Development in Rajasthan at HCM-RIPA, Jaipur. 

• Murthy, M.N. (1996), “Reasons for Low Contraceptive Use in Rajasthan”, Paper Presented at National Seminar on 
Population and Development in Rajasthan at HCM-RIPA, Jaipur. 

• Radhakrishan, S., S. Sureender and R. Acharya (1996), “Child Marriage: Determinants and Attitudes Examined in 
Rajasthan”, Paper Presented at National Seminar on Population and Development in Rajasthan at HCM-RIPA, Jaipur. 

• Ramesh, B.M., S.C. Gulati and Robert D. Retherford (1996), “Contraceptive Use in India”, NFHS Subject Report, UPS, 
Mumbai. 

• Retherford, Robert D. and Vinod Mishra (1997), “Media Exposure Increases Contraceptive Use”, NFHS Bulletin, UPS, 
Mumbai. 

• Retherford, Robert D., M.M. Gandotra, Arvind Pandey, Norman Y. Luther, and Vinod K. Mishra (1998), “Fertility in 
India”, NFHS Subject Report, UPS, Mumbai. 

• Retherford, Robert D., P.S. Nair, Griffith Feeney and Vinod K. Mishra (1999), “Factors Affecting Source of Family 
Planning Services in India”, NFHS Subject Report, IIPS, Mumbai. 

• Roy, T.K., R. Mutharayappa, Minja Kim choe and Fred Arnold (1997), “Son Preference and its Effect on Fertility in India”, 
NFHS Subject Report, IIPS, Mumbai. 

• Shariff, Abusaleh (1996), “Poverty and Fertility Differentials in Indian States: New Evidence from Cross-Sectional Data”, 
Margin, October-December, Vol. 29, No.l, pp. 49-67. 

• Sinha, Narain and Assakaf Ali (1999), “Econometric Analysis of Socio-Economic Determinants of Fertility: A Case Study 
of Yemen”, Paper Presented at the Conference of the India Econometric Society, Jaipur. 

• Society for International Development (1999), “Human Development Report: Rajasthan”, Rajasthan Chapter, Jaipur. 

• Visaraia, Pravin and Leela Visaria (1995), “India’s Population in Transition”, Population Bulletin, 50(3), Population 
Reference Bureau, Washington, D.C. 

• website www.censusindia.net 



249 




Florentin Smarandache 



Collected Papers, V 



Appendix - 1 

Table 9: All Rajasthan Figures, Mean, Standard Deviation & Coefficient of Variation of Variables 



S. No. 


Variable & Year 


Code 


Unit 


All 

Rajasthan 


Mean 


S. D. 


CoV 


1 . 


Population Growth (Decennial) 1991-2001 


POPGWR 


Per cent 


28.33 


28.25 


5.63 


19.92 


2. 


Female-Male Ratio 200 1 


FMR 


Nos. 


921 


922.03 


48.65 


5.28 


3. 


Female-Male Ratio (0-6 years) 2001 


FMRio-f,, 


Nos. 


909 


909.00 


29.59 


3.26 


4. 


Percentage of Urban Population to Total 
Population 200 1 


PURPOP 


Per cent 


23.38 


20.69 


11.13 


53.79 


5. 


Crude Birth Rate 1997 


CBR 


Per ‘000 


32.90 


32.18 


2.26 


7.02 


6. 


Total Fertility Rate 1997 


TFR 


Nos. 


4.9 


4.89 


0.50 


10.20 


7. 


Females Mean Age at Gaunal 996-97 


FMAG 


Years 


17.7 


16.66 


0.61 


3.66 


8. 


Couple Protection Rate 2001 


CPR 


Per cent 


37.00 


37.86 


5.58 


14.73 


9. 


Crude Death Rate 1997 


CDR 


Per ‘000 


8.9 


8.93 


0.93 


10.44 


10. 


Infant Mortality Rate 1997 


IMR 


Per ‘000 


87 


85.81 


17.67 


20.60 


11. 


Percentage of Mothers Receiving Total Ante- 
Natal Care 1996-97 


MRANC 


Per cent 


72.3 


63.38 


24.69 


38.95 


12. 


Percentage of Women having Exposure to TV 
& Radio 1996-97 


PWETVR 


Per cent 


13.1 


13.40 


8.97 


66.94 


13. 


Medical Institutions Per-Lakh of Population 
1997-98 


MIPLP 


Nos. 


27 


28.13 


5.23 


18.59 


14. 


Beds Per-Lakh Population in Govt. Hospitals 
1997-98 


BPGH 


Nos. 


85 


81.81 


25.04 


30.61 


15. 


Per-Capita Expenditure on Medical & Public 
Health 2000-01 


PCEMPH 




19.00 


18.82 


11.04 


58.63 


16. 


Per-Capita Expenditure on Family Welfare 
2000-01 


PCEFW 




0.97 


1.13 


0.44 


38.55 


17. 


Literacy Rate 2001 


LIT 


Per cent 


60.41 


59.58 


7.53 


12.64 


18. 


Literacy Rate (Male) 2001 


LIT,,, 


Per cent 


75.70 


75.31 


6.26 


8.31 


19. 


Literacy Rate (Female) 2001 


LIT, 


Per cent 


43.85 


42.51 


9.01 


21.19 


20. 


Primary School Enrolment Ratio 1997-98 


PSER 


Per cent 


86.50 


86.75 


8.15 


9.39 


21. 


Primary School Enrolment Ratio (Male) 1997- 
98 


PSER m 


Per cent 


99.78 


100.51 


11.22 


11.17 


22. 


Primary School Enrolment Ratio (Female) 
1997-98 


PSER, 


Per cent 


71.91 


71.65 


10.22 


14.27 


23. 


Drop-Out Rates at Primary Level 1 996-97 


DORPS 


Per cent 


56.60 


59.13 


7.65 


12.94 


24. 


Drop-Out Rates at Primary Level (Male) 1 996- 
97 


DORPS,,, 


Per cent 


54.72 


57.07 


7.36 


12.89 


25. 


Drop-Out Rates at Primary Level (Female) 

1996-97 


DORPS, 


Per cent 


56.96 


62.68 


10.66 


17.01 


26. 


Per-Capita Expenditure on Elementary 
Education 2000-01 


PCEPEE 




47.00 


42.86 


19.09 


44.54 


27. 


Per-Capita Net District Domestic Product 
1999-2000 


PCNDDP 




12752 


12831.88 


3122.8 

0 


24.34 


28. 


Population Below Poverty Line 1999-2000 


PPBPL 


Per cent 


30.99 


31.74 


14.88 


46.88 


29. 


Infrastructure Development Index 1994-95 


IDI 


Nos. 


100.00 


93.46 


51.45 


55.05 


30. 


Percentage of Villages with Safe Drinking 
Water 1998-99 


PHDW 


Per cent 


64.30 


60.54 


12.91 


21.33 


31. 


Per-Capita Expenditure on Social & 
Community Services 2000-01 


PCESCS 




245.62 


194.69 


94.92 


48.75 


32. 


Per-Capita Expenditure on Water Supply 
2000-01 


PCEWS 




39.95 


29.19 


46.30 


158.62 



250