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American International Journal of Research in Humanities, Arts and Social Sciences

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Inter-District Disparity of Neonatal Mortality Rate and Its Major Determinants in Uttar Pradesh, India Dr. Uttam Kumar Sikder Assistant Professor (Stage 3), Department of Economics & Politics, Visva-Bharati, Santiniketan, West Bengal, India Abstract: In this Paper I want to explore the inter-district disparity of neonatal mortality rate and its major determinants in Uttar Pradesh in 2012-13. For this purpose I considered all seventy districts of Uttar Pradesh, India. I have also stratified all seventy districts of Uttar Pradesh into four parts according to value of the Human Development Index of districts of Uttar Pradesh in 2005 and I want to capture the inter-district disparity of these four stratified districts. It is evident that there exist inter-district disparities of neonatal mortality rate in districts of Uttar Pradesh, India. . Our regression results revealed that mean household size, currently married women who are illiterate, sex ratio and the antenatal care are statistically significant determinants in determining neonatal mortality rate in the districts of Uttar Pradesh. Keywords: Neonatal Mortality Rate, Inter-District Disparity, Econometric Model, access to health and Uttar Pradesh.

I. Introduction Neonatal mortality rate is the number of neonatal dying before reaching 28 days of age, per 1000 live births in a given year. In this paper I want to explore the inter-district disparity of neonatal mortality rate and its major determinants in Uttar Pradesh in 2012-13. Actually neonatal mortality rate depends on various socioeconomic and demographic factors. It is influenced by health care facility of neonatal as well as their mother. In this paper I considered all seventy districts of Uttar Pradesh, India. I have stratified all seventy districts into four parts according to value of the Human Development Index of districts of Uttar Pradesh in 2005. First of all I have explored the inter-district disparity of neonatal mortality of all seventy districts of Uttar Pradesh with the help of descriptive statistics. Secondly, I have analyzed the inter-district disparity of neonatal mortality of four stratified districts separately with the help of both descriptive statistics as well as Bar Diagrammatic approach. To explain the determinants of neonatal mortality rate in Uttar Pradesh in 2012-13 I have specified the econometric model in the section III of this article. II. Objective of the Study In keeping with view of neonatal mortality rate as an important determinant of ‘health’, the objectives of the present study may be stated as below. 1. To examine the inter-district disparities of neonatal mortality rates among seventy districts as well as four stratified districts in Uttar Pradesh. 2. To examine the factors which are responsible for the inter-district variation of neonatal mortality rate in Uttar Pradesh as a whole? III. Data, Methodology and Econometric Model The entire data used in my study is secondary source of data collected through ‘Annual Health Survey Fact Sheet’, Government of India, Ministry of Home Affairs, Office of the Registrar General and Census Commission, Government of India. The study is basically based on cross sectional study in 2012-13 where all seventy districts in Uttar Pradesh are considered as a cross sectional units. Finally, we choose some important socio-economic and demographic variables which may affect the neonatal mortality rate in Uttar Pradesh, India in 2012-13. The functional relationship (econometric model) is defined below. NNMRi = 1 + 2 MHHSi + 3 CMWIi + 4 SRi + 5 ANCi + 6 PNCi+ 7 DPi + 8 DGi + 9 DHi + 10 MBLAi + Ui Where, NNMR: Neonatal Mortality Rate MHHS: Mean Households Size CMWI: Percentage of married Illiterate Women of age 15-49 years

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SR: Sex Ratio ANC: Mother who had full Antenatal check-up (%) PNC: mothers who received post-natal check-up within 48 hours of delivery (%) DP: Delivery in private Institution (%) DG: Delivery in Government Institution (%) DH: Delivery at home conducted by skilled health personnel (%) MBLA: Marriage among female below legal age (18 years) (%) U: Error term IV. Hypothesis Our hypothesis is as follows Null hypothesis (Ho): i = 0, i= 1,2,………………,10 Against the alternative hypothesis (H1) : i  0 To estimate our model I shall use Ordinary Least Squares (OLS) technique. For the purpose of the test of statistical significance I shall use‘t’- statistics. V. Results and Discussion Descriptive statistics I shall try to capture the inter-district disparity of neonatal mortality rate in Uttar Pradesh, India in 2012-13. There are seventy districts in Uttar Pradesh. I have stratified these seventy districts into four parts according to district human development profile in 2005 of Uttar Pradesh. Out of these four stratification there is 17 districts bearing high human development index (HDI above 0.60) which is represented by Table 1 in appendix. Twenty three districts have medium human development index (HDI between 0.55 to 0.59). Again nineteen districts have low human development index (HDI between 0.50 to 0.54). Further it is found that ten districts bear very low human development index (HDI below 0.50). So Uttar Pradesh is one of the backward states in India relative to other states in terms of human development index in 2005. Health is one of the indicators of human development index. I shall explore the situation of health in Uttar Pradesh in 2012-13. First of all I would like to explore the situation of NNMR in all 70 districts in Uttar Pradesh. It is seen that mean NNMR in Uttar Pradesh is 48.05714 when all districts taken together. This mean value of neonatal mortality is quite satisfactory as it is below 50. The value of the skewness and Kurtosis point to the fact that the distribution of NNMR is not symmetrical. The value of standard deviation (11.39144) points out the existence of inter-districts disparities of NNMR and it not so pronounced. The NNMR is as high as 70 in the district of Siddharth Nagar followed by the districts shrawasti (68), Budaun (65) and Fanidabad (65). Whereas NNMR is as low as 24 in Kanpur Nagar followed by the districts Hamirpur (26), Lucknow (29) and Jhansi (29). Now in order to supplement the descriptive statistical framework in Tables 3-6 a more revealing picture at a glance, a bar diagrammatic approach through Bar Diagram 1-4 were followed by me. It is found that except the districts of Saharanpur, Varanasi, Jalun and Bluandshahar the inter-district disparity is not so pronounced when I considered the districts bearing high HDI. In the case of districts bearing high HDI it is seen that the inter-district disparity of NNMR is much pronounced which is also applicable for districts bearing medium and low level of human development index. These inter-district disparity of NNMR is caused by the disparity of access to health care facilities like improve sources of drinking, institutional delivery opportunity, education, antenatal care, post natal care, improved toilet facility etc. I shall explore which factors are responsible for the variation NNMR in Uttar Pradesh in 2012-13. Inter Correlation Profile among Different Explanatory Variable Before running regression it is necessary to check whether dependent variable is correlated with its explanatory variables or not. It is also required to check the inter correlation among the explanatory variables because it provides the presence or absence of multicollinerity in our multiple regression model. I would also test empirically statistical significance of these correlation coefficients. The results of correlation coefficients are presented in Table – 7. I computed the triangular matrix of Correlation Coefficients considering all socio-economic (explanatory) variables and the neonatal mortality rate. It is seen that there is significant and poor degree of association (0.2929) exists between neonatal mortality rate (NNMR) and mean household size (MHHS). High MHHS leads to low per capita family income which makes a deficiency of wealth for nursing newborn baby. The Correlation Coefficient between NNMR and ANC is as high as -0.6101 and it is statistically significant at 5% level. It is expected that NNMR will decrease with increase in ANC. The neonatal mortality is highly as well as positively correlated with CMWI and SR and they are statistically significant at 5% level. It is seen that neonatal mortality is negatively correlated with delivery in private institution, delivery with governmental institution and delivery at home with skill health Personnel and these values of Correlation Coefficient are -0.3484, -0.1778, -0.5515 respectively. This can be explained by the fact that all these three typed of delivery might have been occurred by skilled doctors and by using modern equipment’s. Here all these three Correlation Coefficients are statistically significant at 5% level. It is observed that neonatal mortality rate is positively related to percentage of marriage below legal age and it is statistically significant at 5% level.

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Let us look at paired Correlation Coefficients among the explanatory variables (socio-demographic variables). From correlation matrix it is found that there is positive and statistically significant (at the 5% level) correlation exists between mean household size and sex ratio, post natal care and delivery in private health institution. Whereas, there is negative and statistically significant correlation exists between antenatal care, delivery in government health institution, delivery at home by the skilled health personnel and currently married women who illiterate. There is poor but positive statistically significant correlation between currently married women who are illiterate and sex ratio. Whereas currently married women who are illiterate is negatively and statistically significantly correlated with the antenatal care, post-natal care, delivery at private health institution and deliver at home by the skilled health personnel .The currently married women is positively correlated with marriage among female below legal age. However sex ratio is negatively related with antenatal care and delivery at home with skilled health personnel and these two Correlation Coefficients are statistically significant at 5% level. It is also found that the Correlation Coefficient between sex ratio and post natal care is positive and statistically significant at 5% level. There is poor but statistically significant (at 5% level) correlation exists between antenatal care and postnatal care and marriage among female below the legal age. On the other hand the high and statistically significant correlation (0.7271) exists between antenatal care and delivery at home by the skilled health personnel. It is also found that antenatal care and delivery at government institution are positively correlated and statistically significant at 5% level. Next the post natal care is negatively related with delivery of government institution and delivery at home by skilled health personnel. The Correlation Coefficient between postnatal care and delivery in private health institution is positive and it is statistically significant at 5% level. It is found that DP is negatively correlated with DG and MBLA and they are significant at 5% level. Whereas DG is positively correlated with DH and DH is negatively correlated with MBLB and all paired correlation coefficient is statistically significant at 5% level. Eventually I would bring out attention to the fact that this subsection of my study helps me to identifying the variables ,which are responsible for creating neonatal mortality in the Uttar Pradesh in 2012-13.Once I have diagnosed and identified the variables/determinants of neonatal care, I shall be examine how these determinants affects the neonatal mortality as it was found that there is inter-correlation among the paired explanatory variables, so there shall be possibility of high degree of multicollinearity. The severity of multicollinearity is captured by the running step with regression procedure. Results of Robust Multiple Regression Model of Neonatal Mortality Rate I have run a robust multiple regression model of neonatal mortality rate by experimenting with the independent variables adopted. Taking neonatal mortality rate as a dependent variable the best performing ordinary least square (OLS) found to be that which included mean household size, currently married woman who are illiterate, sex ratio, antenatal care, and delivery at private health institute. In terms of Multiple Co-efficient of determination (RSquared) the overall goodness- of- fit of the chosen model is quite satisfactory as it is observed to be 0.5920. Hence about the 59% of the neonatal mortality (dependent variable) can be explained in terms of explanatory variables included in our model. The observed F value also points to an overall satisfactory performance of the multiple regression models. Prob>F (5, 64) =0.0000 indicates null hypothesis is rejected at high level of significance. Further as the mean variance inflation factor (VIF) falls to 2.12 (less than 10), the model also suffers from less multicollinearity effect. In General the incidence of neonatal mortality is found to be close association with socio-demographic as well as economic variables. Out of these the education of currently married woman seems to be major factor. One might expect that education of the woman to be producing a significant effect on incidence of NNMR. A priori we expect that NNMR is positively related to MHHS, CMWI, SR and negatively related to ANC and DP. Our results found that all estimated coefficients are found to be negative/positive according to our general expectation. However on the basis of t- statistics and p-values we found that the explanatory variables MHHS, CMWI, SR and ANC are satisfactorily significant at 10%, 5%, 1% and 5% level of the significance. The positive relationship between the NNMR and MHHS is explained by the fact that the increase in mean household size leads to fall in standard of living for each family member for the developing country took India through fall in per capita income, number of required living room and other amenities which directly and indirectly affects neonatal mortality rate. Minimum level per capita family income is necessary for maintaining and nursing a new born baby. I have already explained the role of education for currently married women. It is found that estimated co-efficient of CMWI is positive which is explained by the fact that currently married illiterate women has less awareness about the access to the health care facilities which ultimately leads to an increase in neonatal mortality. Next estimated co-efficient of the sex ratio is found to be positive which means a sex ratio increases leads to an increase in neonatal mortality. It is explained by the fact that if number of female of a society increases than distinguished unemployment also increases in the country look like India. As a result the females are unable to provide sufficient time for her new born baby which ultimately leads to increase in neonatal mortality.

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Now the inverse relationship between antenatal care (ANC) and neonatal mortality is explained by the fact that the ANC will develop the reproductive health of women which ultimately decrease in neonatal mortality rate. In the present case the estimated value of ANC is -0.6514189. Finally, we find that NNMR is inversely related to the delivery at private institution(DP) which explained by the fact that delivery at private institution is more efficient as well as more scientific relative to delivery at home is without skilled healthy personnel. The very skilled healthy personnel and modern equipment for operation /delivery are available in elite private healthy institution. VI. Conclusion Of the above analysis it is evident that there exist inter-district disparities of neonatal mortality rate in the districts of Uttar Pradesh, India. This disparity arises due to the variation of amenities of access to health care facility. This access to health care facility is not uniform across the districts of Uttar Pradesh. Our regression results revealed that mean household size, currently married women who are illiterate, sex ratio and the antenatal care are statistically significant determinants in determining neonatal mortality rate in the districts of Uttar Pradesh. Hence the government of India as well as government of Uttar Pradesh should regulate these variables so that neonatal mortality should be reduced Acknowledgements I indebted to my father Bodhisattva Makhan Lal Sikder and my mother Srimati Labanya Prova Sikder who continuously taught me about courage in my life in each and every dimension.

References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11].

Annual Health Survey 2012-13, “Government of India”, Ministry of Home Affairs. Sikder, U.K. (2015), “Interstate Disparity in Infant Mortality Rates and Important Determinants in North East India”, American International Journal of Research in Humanities, Arts Social Science, Volume 1, Issue 11, pp 21-28. Kumar, C., Singh, P.K. and Rai, R.K. (2012), “Under-Five Mortality in High Focus States in India: A District Level Geospatial Analysis’, PLOS/ONE, DOI: 10.1371/ journal. pone 0.035515. Malhotra, C. and Do, Y.K. (2012), “Socio-economic disparities in health system responsiveness in India”, Health policy and planning, Oxford Journals, DOI 10.1093/ heapol/ CZS051, Vol. 28, pp. 197-205. Mani, K.; Dwivedi, S.N. and Pandey, R.V. (2012), “Determine ants of Under-Five Mortality in Rural Empowered Action Group States in India : An application of Cox Frailty Model”, International Journal of MCH and AIDS, Vol. 1, Issue 1, pp. 60-72. Sayem, A.M. et al (2011), “Achieving the Millennium Development Goal for Under-Five Mortality in Bangladesh: Current status and lessons for issues and challenges for further improvements”, J Health Popul Nutr, Vol. 29, Issue 2, pp. 92-102. Sen, R.P. and Sikder, U.K. (2015), “Interstate Disparity of infant Mortality Rate and Its Determinants in India: Evidence from cross sectional. Data in 2012-13”, IOSR Journal of Humanities and Social Science (IJOR-JHSS), Vol. 20, Issue 7, pp. 130.136. Sikder, U.K. (2012), “Access to Health and Medical Service of Rural Poor and West Bengal: A Case Study of a Village of Birbhum District”, Artha Bickshan, Vol. 21, No. 3, pp. 81-91. Sikder, U.K. and Roy, M.S. (2015), “Interstates Disparities in Infant Mortality Rates and their major determinants in India : Study Based on Latest Cansus, 2011”, IOSR Journal of humanities and Social Science (IOSR-JHSS), Vol. 20, Issue 8, pp. 11-16. Willis, J.R. et al (2011), “Utilization and perceptions of neonatal health care providers in rural Uttar Pradesh, India”, Public Health Journals, Oxford University Press Journals, Vol. 23, Issue 4, pp. 487-494. Sikder, U.K. (2015), “Inter-District Disparity of Under-Five Mortality Rate and Its Major Determinants in Uttar Pradesh”, American International Journal of Research in Humanities , Arts Social Science, Volume 3,Issue 11, pp 242-251.

Appendix Table 1: Inter-District Disparities Neonatal Mortality in Uttar Pradesh, India in 2012-13 Sl. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Sl. No. 1 2 3 4 5 6 7

DISTRICTS OF UTTAR PRADESH District having high HDI(Above 0.60) Gautam Buddha Nagar Gagiyabad Kanpur Nagar Lucknow Baghpat Meerut Agra Jhansi Saharanpur Mathura Hathras Etawah Kanpur Dehat Auraiya Varanasi Jalaun Bluandshahar Districts having Medium HDI( 0.55 to 0.59) Mazaffanagar Mau Chitrakoot Mainpuri Chandauli Firozabad Bijnor

NNMR 36 30 24 29 35 35 35 29 54 31 38 41 41 41 50 48 48 37 58 45 34 56 36 47

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Kannuauj Ballia Farrukhabad Gorakpur Allahabad Aligarh Jyotiba Phule Nagar Sant Ravidas Nagar(Bhadohi) Ghazipur Mahoba Hamirpur Sonbhadra Ambedkar Nagar Jaunpur Faridabad Mirzapur

55 52 54 46 60 51 57 59 64 31 26 52 47 59 65 57

Sl. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sl. No. 1 2 3 4 5 6 7 8 9 10

District Having LOW HDI (0.50 TO 0.54) Banda Khri Deoria Azamgarh Unnao Sultanpur Pilibhit Eata Latipur Fatehpur Bareilly Barabanki Pratapgarh Moradabad Rae Bareli Kaushmbi Sitapur Shahjahanpur Hardoi Kushinnagar Districts Having Very Low HDI (Below 0.50) Basti Rampur Mahrajgang Sant Kabir Nagar Gonda Siddhartha Nagar Budaun Balrampur Bahraich Shrawasti

36 55 54 57 37 31 50 48 53 39 52 52 64 46 35 61 54 58 52 61 60 45 61 49 54 70 65 60 44 68

Source: Annual Survey of Health, Ministry of Home Affairs, Government of India 2012-13 Table 2: Descriptive Statistics of NNMR in Uttar Pradesh, India in 2012-13

NNMR 1% 5% 10% 25% 50% 75% 90% 95% 99%

Percentiles 24 29 31 37

Smallest 24 26 29 29

50 57 61 65 70

Largest 65 65 68 70

Obs Sum of Wgt.

70 70

Mean Std. Dev.

48.05714 11.39144

Variance Skewness Kurtosis

129.7648 -.2420472 2.080379

Source: Authorsâ&#x20AC;&#x2122; own Computation from Stata- 11.1, Portable based on Annual Health Survey 2012-13 data

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Table 3: Descriptive Statistics of NNMR in the Districts of Uttar Pradesh Having High HDI, India in 2012-13

NNMR

1% 5% 10% 25%

Percentiles 24 24 29 31

50%

75% 90% 95% 99%

41 50 54 54

Smallest 24 29 29 30 Largest 48 48 50 54

Obs Sum of Wgt.

17 17

Mean Std. Dev.

37.94118 8.39993

Variance Skewness Kurtosis

70.55882 .3215505 2.207273

Source: Authors’ own Computation from Stata- 11.1, Portable based on Annual Health Survey 2012-13 data Table 4: Descriptive Statistics of NNMR in the Districts of Uttar Pradesh Having Medium HDI, India in 2012-13

NNMR

1% 5% 10% 25% 50% 75% 90% 95% 99%

Percentiles 26 31 34 45

Smallest 26 31 34 36

52 58 60 64 65

Largest 59 60 64 65

Obs Sum of Wgt.

23 23

Mean Std. Dev.

49.91304 10.76152

Variance Skewness Kurtosis

115.8103 -.7123164 2.523298

Source: Authors’ own Computation from Stata- 11.1, Portable based on Annual Health Survey 2012-13 data Table 5: Descriptive Statistics of NNMR in the Districts of Uttar Pradesh Having Low HDI, India in 2012-13

NNMR 1% 5% 10% 25% 50% 75% 90% 95% 99%

Percentiles 31 33 35.5 42.5

Smallest 31 35 36 37

52 56 61 62.5 64

Largest 58 61 61 64

Obs Sum of Wgt. Mean Std. Dev. Variance Skewness Kurtosis

20 20 49.75 9.51384 90.51316 -.5469866 2.224343

Source: Authors’ own Computation from Stata- 11.1, Portable based on Annual Health Survey 2012-13 data

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NNMR of Uttar Pradesh Having Very Low HDI, India in Table 6: Descriptive Statistics of NNMR in the Districts 2012-13 1% 5% 10% 25%

Percentiles 44 44 44.5 49

50%

75% 90% 95% 99%

65 69 70 70

Smallest 44 45 49 54

Obs Sum of Wgt.

Largest 61 65 68 70

10 10

Mean Std. Dev.

57.6 9.252027

Variance Skewness Kurtosis

85.6 -.2506482 1.745401

Source: Authors’ own Computation from Stata- 11.1, Portable based on Annual Health Survey 2012-13 data Bar Diagram 1: Inter-District Disparities in NNMR of the Districts of Uttar Pradesh Having High HDI in 2005 District having high HDI(Above 0.60) NNMR 54

50 48 48

50 40

35 35 35

30 24

38 41 41 41

20 District having high HDI(Above 0.60) NNMR

10 0

Sources: Author Derives from Annual Health Survey Data on 2012-13 Bar Diagram 2: Inter-District Disparities in NNMR of the Districts of Uttar Pradesh Having Medium HDI in 2005 Districts having Medium HDI( 0.55 to 0.59) NNMR 58

56 47

45 37

55 52 54

60 46

57 59

64 52 31

65 57

Mazaffanagar Mau Chitrakoot Mainpuri Chandauli Firozabad Bijnor Kannuauj Ballia Farrukhabad Gorakpur Allahabad Aligarh Jyotiba Phule Nagar Sant Ravidas… Ghazipur Mahoba Hamirpur Sonbhadra Ambedkar Nagar Jaunpur Faridabad Mirzapur

70 60 50 40 30 20 10 0

Districts having Medium HDI( 0.55 to 0.59) NNMR

Sources: Author Derives from Annual Health Survey Data on 2012-13

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Bar Diagram 3: Inter-District Disparities in NNMR of the Districts of Uttar Pradesh Having Low HDI in 2005 District Having LOW HDI (0.50 TO 0.54) NNMR 70 60 50 40 30 20 10 0

64 55 54 57

50 48 37

61 54

52 52

61 52

District Having LOW HDI (0.50 TO 0.54) NNMR

Sources: Author Derives from Annual Health Survey Data on 2012-13 Bar Diagram 4: Inter-District Disparities in NNMR of the Districts of Uttar Pradesh Having Very Low HDI in 2005 Districts Having Very Low HDI (Below 0.50) NNMR 80 70 60 50 40 30 20 10 0

70 61

68 60 44

Districts Having Very Low HDI (Below 0.50) NNMR

Sources: Author Derives from Annual Health Survey Data Table 7: Pair Wise Correlation Coefficients among Explanatory variables & Also With Dependent Variables in Uttar Pradesh, India

. pwcorr nnmr mhhs cmwi sr anc pnc dp dg dh mbla,star(5) nnmr nnmr mhhs cmwi sr anc pnc dp dg dh mbla

1.0000 0.2929* 0.5571* 0.5728* -0.6101* 0.2369* -0.3484* -0.1778 -0.5515* 0.4319* dg

dg dh mbla

mhhs 1.0000 -0.0125 0.2488* -0.4444* 0.5118* 0.5020* -0.4974* -0.4984* -0.0786

cmwi

1.0000 0.2829* -0.4591* -0.0932 -0.6015* -0.0706 -0.4499* 0.5805*

1.0000 -0.4025* 0.4945* -0.2254 0.0310 -0.2677* 0.2320

anc

1.0000 -0.2392* 0.1686 0.4248* 0.7271* -0.2791*

pnc

1.0000 0.3918* -0.2462* -0.2607* 0.0133

1.0000 -0.4556* 0.0900 -0.4264*

mbla

1.0000 0.4354* 1.0000 -0.1199 -0.2708* 1.0000

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Table8: Robust Multiple R egression Model of Neonatal Mortality Rate in Uttar Pradesh, India Linear regression Number of obs = 70 F( 5, 64) = 32.88 Prob > F = 0.0000 R-squared = 0.5920 Root MSE = 7.5553

nnmr

Coef.

mhhs cmwi sr anc dp _cons

5.865968 .2321655 .0382332 -.6570662 -.2437601 -22.65626

Robust Std. Err. 3.271755 .1092646 .010959 .2839457 .1648549 16.52713

t 1.79 2.12 3.49 -2.31 -1.48 -1.37

P>|t| 0.078 0.037 0.001 0.024 0.144 0.175

[95% Conf. Interval] -.6701137 .0138843 .0163401 -1.224313 -.5730957 -55.673

12.40205 .4504466 .0601262 -.0898195 .0855755 10.36049

. vif Variable

VIF

1/VIF

dp mhhs cmwi anc sr

2.91 2.44 2.06 1.86 1.33

0.343792 0.410266 0.486433 0.538306 0.753538

Mean VIF

2.12

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