Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502 Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 12 Analysis of Sex Ratio in Punjab India (Census 2011) – A Demographic Study Lakshman Rao K and Haragopal V.V.Dept. of Statistics, Osmania University, Hyderabad-500007, AP, INDIAAvailable online at: www.isca.in, www.isca.me Received 26th November 2013, revised 8th January 2014, accepted 9th February 2014 AbstractThe study on the sex ratio of overall population and of children in 0-6 age group for different districts in Punjab concentrates on the following: i. Is there any likely relationship between overall male population and overall female population with respect to rural and urban areas (in particular, are there any significant patterns?). ii. Is there any significant difference between overall male 0-6 population, overall female 0-6 population with respect to rural and urban areas. iii. Is there any significant difference between the proportion of female population and female 0-6 population with respect to all districts (in particular, are there any significant patterns?). iv. Is there any significant difference among the districts with respect to overall male and overall female population (in particular, are there any significant patterns?). v. Is there any significant difference among the districts with respect to overall male 0-6 and overall female 0-6 population (in particular, are there any significant patterns?). vi. Also a scientific arrangement of the district wise path is evaluated by lexisearch method for the first time for demographic data. Keywords: 0-6 sex ratio, rural and urban groups, districts, Punjab, Census 2011, the lexisearch method. Introduction According to the Census of 2011, overall sex ratio at the national level has increased by 7 points since the 2001 census to reach 940 females per 1000 males¸ this is lower than 1961 when the figure stood at 941 females per 1000 males. Despite introducing several laws on female foeticide and schemes to encourage the families to have a girl child, the sex ratio in India has gone down. The child sex ratio has gone to 914 females per 1000 males which is the lowest record since independence. These numbers clearly state that the Indian society still prefers boys over girls such that they could have a security for their future. The ratio has gone down to 914 from 927 when the last census was taken. The monotonic decline in the sex ratio over the last decade, despite the improving socio economic characteristics reinforces the existence of gender discriminatory practices which starts even before birth; which requires the urgent attention of public policy, as improving literacy and economic value of women is necessary but not sufficient for enhancing the relative life chances of girl child. The ratio has gone down to 914 from 927 when the last census was taken. This points to the fact that economic growth and human development seldom moves together, when it comes to improving gender relations. However, this figure conceals the wide variation across the states in India and a distinct geographical pattern. The state of Mizoram has the highest child sex ratio with 971 females per 1000 males, while Meghalaya has 970 per 1000 males. Normally, the states like Punjab and Haryana have lower sex ratio, but in the recent years, an increasing trend has been seen in the states. Haryana has 830 females while Punjab has 846 females per 1000 males. The 2011 census highlighted this issue by devoting a full section on this subject. This distressing state of affairs raised voice of grave concern across all sections of society. It set into motion serious debates and resulted in a series of action on several fronts to curb the menace of female foeticide in certain parts of the country. In this direction, analyzed the data for Andhra Pradesh and found that there is a substantial drop of 0-6 female sex ratio. This was noticed by analyzing the data on village wise/district wise. With reference to this we have analysed the data on Punjab state for the 2011 census. The analysis of results in Punjab district wise data revealed a significant insight into the problem at levels below the state at the national level particularly in certain parts of the country. The rural-urban differentials in the sex ratio in the age group 0-6 further sheds light on the spatial analysis of possible adverse impact on the female child due to the spread of the modernization and technological advancement in the villages and urban centers. The Punjab state has 20 districts and this data is analysed and results are reported in the succeeding sections. Child sex ratio district level: From Table 1 it is observed that none of the districts in the state has the 0-6 child sex ratio above 916. It seems to be alarming stage that state should recover comprehensively as per this index is concerned. The district level data on child sex ratio provides further insight into the pattern that exists at this level within a state. Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 13 Keeping this point in view we have analysed the Punjab district level data with urban and rural segregation and found that an alarming situation exists in the districts of Punjab where there is a highest fall in the sex ratio is observed. Results and Discussion On the whole the sex ratio at the district level is below the ideal of 1000. However, as will be seen later in many districts variations in sex ratio are very considerably being quite low in some districts and relatively very high in some other districts. Analysis for the present situation follows: in each district for each of the sub-districts the sex ratio of all children, rural children and urban children as well as entire population including the children and rural and urban separately also are available from Census India, 2011. The following analysis is based on these figures: Table 2 givestotals of 20 districts for overall population, overall male population and overall female population, overall population 0-6, overall male 0-6 population and overall female 0-6 population with respect to rural and urban areas for 2011census data. Tables 3, 5 gives observed and expected frequencies of overall male population and overall female population with respect to rural and urban areas for 2011 census data. Chi-square value for overall population for 2011census data is 2120.8 with 1 d.f from table 6. From these values it can be concluded that there is a significant difference between overall male population and overall female population with respect to rural and urban areas for 2011 census. Tables 7, 8 gives observed and expected frequencies of overall male 0-6 population and overall female 0-6 population with respect to rural and urban areas for 2011 census data. Chi-square value for overall 0-6 population for 2011census data is 15.67 with 1 d.f. From these values it can be concluded that there is a significant difference between overall male 0-6 population and overall female 0-6 population with respect to rural and urban areas for 2011 census. Also, from table 2 we have calculated the proportion of female and female 0-6 population for all 20 districts of Punjab state is calculated which are given in table 9. From this analysis we can understand that there is no difference between rural and urban areas with respect to male population, female population and male 0-6 population, female 0-6 population for 2011 censuses which is quite misleading. To see whether there is any difference within the 20 districts of Punjab we have analysed the data for 2011 census by considering the Chi-square test and observed that there is a difference within the districts with respect to proportion of females in the overall population, in the rural, urban regions and proportion of 0-6 females in the overall population, in the rural, urban regions of the state. Thus, the data findings with respect to chi-square test are tabulated in Table 10 for all the districts of Punjab for 2011 census. From table 10 for 2011 we observe that there are 12 (Gurdaspur, Kapurthala, Jalandhar, Hoshiarpur, Shahib Bhagat Singh Nagar, Ludhiana, Moga, Muktasar, Patiala, Amritsar, Tam Taran, Rupnagar) districts which are differ with respect to proportion of female 0-6 population in overall population. Also there are 13 (Gurdaspur, Kapurthala, Jalandhar, Hoshiarpur, Shahib Bhagat Singh Nagar, Ludhiana, Moga, Patiala, Amritsar, Tam Taran, Rupnagar, Sahibzada Ajit Singh Nagar and Sangrur) districts which are different with respect to proportion of female 0-6 population in rural region. While there are 9 (Gurdaspur, Kapurthala, Jalandhar, Shahib Bhagat Singh Nagar, Ludhiana, Muktasar, Mansa, Amritsar, and Sangrur) districts which are differing with respect to proportion of female 0-6 population in urban region. And there are 16 (Kapurthala, Jalandhar, Hoshiarpur, Shahib Bhagat Singh Nagar, Fatehgarh Sahib, Ludhiana, Faridkot, Bathinda, Mansa, Patiala, Amritsar, Tam Taran, Rupnagar, Sahibzada Ajit Singh Nagar, Sangrur and Barnala) districts which differ with respect to proportion of female population in overall population. All the districts which are differ with respect to proportion of female population in rural region. There are 17 (Gurdaspur, Jalandhar, Hoshiarpur, Shahib Bhagat Singh Nagar, Fatehgarh Sahib, Ludhiana, Moga, Firozpur, Muktasar, Bathinda, Mansa, Patiala, Tam Taran, Rupnagar, Sahibzada Ajit Singh Nagar, Sangrur and Barnala) districts which are differ with respect to proportion of female population in urban region. We found that in case of overall 0-6 female population there are 12 districts in 2011 census which are different. And in case of overall female population 16 districts which are different. Also, with respect to proportion of female 0-6 in the rural region for 2011 census data it was found that there ar13 districts which are different. And in case of proportion of female in the rural region 20 districts which are different. Whereas, with respect to proportion of female 0-6 population in the urban region for 2011 census data it was found that there are 9 districts which are different. And in case of proportion of female in the urban region 17 districts which are different. Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 14 This justification can be seen in the figure 1 and figure 2 that is how the chi-square values are changing is displayed for 2011 census data. Further, to know which district differs significantly with respect to all the other districts is analysed by considering the district data for 2011 census. The Chi – square values have been calculated for six characteristics of the population for 2011 census and found that all the districts are different keeping some districts left with no difference. That is, this analysis shows that there is no improvement over these six characteristics from 2011 census. We can understand that the 0-6 child sex ratio is quite different in almost all the districts of Punjab. In the next section we want to study that whether these districts are similar or not with respect to 0-6 sex ratio etc is explored by the technique of clustering. Cluster analysis is computed for finding the districts similarities for 2011 census with respect to six characteristics of the population separately and found that there is a difference within the districts and among the districts with respect to the six characteristics. As there is no meaningful similarity obtained through the clustering approach, the data is analysed by a technique called Min-Maxion method to evaluate the data for the possible path with respect to the six characteristics of the population of 20 districts by taking chi-square values as distance matrix, which is non-metric and Min-maxion technique is applied to the distance matrix7, 8. The path obtained is the optimal one in arranging them as changes of sex ratio. This can help in trying to link the possible causes of difference in sex ratio with those factors which change in similar way among the districts. For example Education, Transportation facilities, Industrialization etc Thus, it is an exploratory tool which arranges districts according to gradual changes in sex ratios and suggesting to explore whether any other characteristics (like Education, Welfare groups etc) about the districts show a similar ordering. Also, by comparing all the paths it is observed that all the paths differ drastically with each other. Thus, the causes operating on the sex ratios may not be the same but differ from path to path for the 2011 data. Path for overall male population and overall female populationHoshiarpur Shahid Bhagat Singh Nagar Rupnagar Jalandhar Kapurthala Tarn Taran Muktsar Firozpur Patiala Amritsar Moga Faridkot Sangrur Mansa Sahibzada Ajit Singh Nagar Barnala Fatehgrh Sahib Ludhiana Path for rural male population and rural female populationSahibzada Ajit Singh Nagar Bathinda Barnala Mansa Fatehgarh Sahib Moga Patiala Ludhiana Faridkot Muktsar Firozpur Tarn Taran Amritsar Rupnagar Kapurthala Jalandhar Shahid Bhagat Singh Nagar Hoshiarpur Path for urban male population and urban female populationFatehgarh Sahib Gurdaspur Bathinda Barnala Firozpur Kapurthala Faridkot Amritsar Jalandhar Sangrur Sahibzada Ajit Singh Nagar Moga Tarn Taran Muktsar Mansa Rupnagar Shahid Bhagat Singh Nagar Hoshiarpur Path for overall 0-6 child male population and overall 0-6 child female population Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 15 Shahid Bhagat Singh Nagar Jalandhar Kapurthala Hoshiarpur Rupnagar Moga Bathinda Faridkot Firozpur Barnala Fatehgarh Sahib Sahibzada Ajit Singh Nagar Sangrur Mansa Muktsar Amritsar Tarn Taran Gurdaspur Path for rural 0-6 child male population and 0-6 rural child female populationJalandhar Shahid Bhagat Singh Nagar Hoshiarpur Moga Rupnagar Faridkot Bathinda Barnala Mansa Fatehgarh Sahib Muktsar Patiala Sangrur Amritsar Sahibzada Ajit Singh Nagar Gurdaspur Path for urban 0-6 child male population and urban 0-6 child female populationKapurthala Shahid Bhagat Singh Nagar Rupnagar Sangrur Jalandhar Hoshiarpur Ludhiana Sahibzada Ajit Singh Nagar Fatehgarh Sahib Moga Firozpur Faridkot Tarn Taran Barnala Muktsar Amritsar Mansa Gurdaspur Table-1 Distribution of districts by range of 0-6 child sex ratio of Punjab state: 1991, 2001, 2011 Child sex ratio (0-6) Number of Districts 1991 2001 2011 880 & below 8 17 19 881-915 4 0 1 916-950 0 0 0 951-985 0 0 0 986 &above 0 0 0 * Data source: Data C. D’s from Census India-1991, 2001 and 2011 Table-2 Totals of 20 districts for each of six groups (for 2011 data) Population Male Female Pop 0-6 Male 0-6 Female 0-6 Total 27743338 14639465 13103873 3076219 1665994 1410225 Rural 17344192 9093476 8250716 1945502 1055297 890205 Urban 10399146 5545989 4853157 1130717 610697 520020 * Data source: Data C. D’s from Census India-2011 Table-3 Observed frequencies of overall population (for 2011 data) Male Female Rural 9093476 8250716 Urban 5545989 4853157 Calculation of Expected frequencies Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 16 Expected frequency = For example: Expected frequency of the value 9093476 (from Table 4) = (17344192 * 14639465)/27743338 = 9152096 Similarly the other Expected frequencies computed and are tabulated in Table 5. The Pearson chi-square test statistic to summarize the difference between observed and expected counts is given by Oi = Observed frequency of the given data, Ei = Expected frequency r = Number of rows, s = Number of columnsTable-4 Observed frequencies of overall population (for 2011 data) along with row and column totals Male Female Row Total Rural 9093476 8250716 17344192 Urban 5545989 4853157 10399146 Column Total 14639465 13103873 27743338 Table-5 Expected frequencies of overall population (for 2011 data) Male Female Rural 9152096 8192096 Urban 5487369 4911777 Table-6 Computation of Chi-square value for overall population (for 2011 data) Observed frequency (Oi) Expected frequency (Ei) 9093476 9152096 375.4659 8250716 8192096 419.4653 5545989 5487369 626.22 4853157 4911777 699.6043 (Chi-square) = 2120.8 *Observed and Expected frequencies from Tables 3, 4Table-7 Observed frequencies of overall 0-6 population (for 2011 data) Male Female Rural 1055297 890205 Urban 610697 520020 Table-8 Expected frequencies of overall 0-6 population (for 2011 data) Male Female Rural 1053629 891872.6 Urban 612364.6 518352.4 Table-9 Proportion of female, female 0-6 of 20 districts (for 2011 data) Male Female Male 0 - 6 Female 0 - 6 Total 0.5277 0.4723 0.5424 0.4576 Rural 0.5416 0.4584 0.5333 0.4667 Urban 0.5243 0.4757 0.5401 0.4599 Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 17 Table-10 Chi-square values for the proportion of overall female population, female 0-6 population of 20 districts with respect to rural, urban areas (for 2011 data) Districts Proportion of females pop in the pop. Proportion of female 0-6 in the pop. proportion of females in the rural region proportion of female 0-6 in the rural region proportion of females in the urban region proportion of female 0-6 in the urban region Gurdaspur 0 59.1* 30.6* 33.4* 165.4* 23.4* Amritsar 27.8* 43.2* 4.6* 16.0* 1.2 33.1* Firozpur 1.8 0 11.7* 1.1 4.0* 0.8 Ludhiana 536.0* 24.3* 49.7* 13.0* 217.0* 6.4* Jalandhar 276.3* 58.9* 607.0* 59.7* 28.0* 7.7* Kapurthala 72.3* 17.8* 85.1* 4.7* 1 18.7* Hoshiarpur 2006.1* 18.7* 1517.8* 21.0* 197.3* 1.5 Rupnagar 83.5* 6.5* 21.1* 4.5* 46.2* 3 Patiala 10.4* 7.3* 91.6* 12.4* 58.9* 0 Sangrur 52.1* 2.6 191.4* 12.5* 25.1* 5.8* Bathinda 319.0* 2.8 304.9* 2.1 49.3* 0.7 Faridkot 4.4* 0.4 6.8* 1.8 0.3 0.5 Fatehgarh Sahib 112.6* 0.4 67.1* 1 72.2* 0.2 Moga 1 7.1* 40.4* 10.4* 16.9* 0 Muktsar 0.4 8.5* 15.1* 3.2 18.5* 5.6* Mansa 34.0* 3.5 127.1* 0.3 12.2* 6.5* Shahid Bhagat Singh Nagar 630.6* 30.5* 421.9* 26.4* 94.0* 6.2* Tarn Taran 7.3* 33.3* 11.2* 27.5* 9.3* 1.5 Sahibzada Ajit Singh Nagar 78.5* 1.1 226.5* 9.8* 35.0* 1.1 Barnala 72.4* 0.3 86.4* 0.2 8.0* 2.2 * indicates difference in the characteristics considered, in the table which are given in the above table. 0 10 20 0 500 1000 1500 2000 2500 0 10 20 0 20 40 60 0 10 20 0 500 1000 1500 2000 0 10 20 0 20 40 60 0 10 20 0 50 100 150 200 250 0 10 20 0 10 20 30 40 Figure-1 X – Axis: Districts, Y – Axis: Chi – square values (For 2011) Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 18 0 2000 4000 0 5 10 15 0 50 100 0 2 4 6 8 0 1000 2000 0 5 10 15 0 50 100 0 2 4 6 8 10 0 200 400 0 2 4 6 8 10 0 20 40 0 5 10 15 Figure-2 X – Axis: Chi – square values, Y – Axis: Districts, (For 2011) Indicates the histograms of the valuesConclusion From the analysis of the data for 2011 census of Punjab state, we could find that there is a difference between the six characteristics by applying Chi-square test. Through cluster analysis we could find the similarities among the districts with respect to the six characteristics for 2011 census. We have explored by applying min-maxion technique the possible path for the district wise patterns with respect to the six characteristics. From the analysis we could find that drastic changes have taken place in Punjab during 2011census and specifically we found that alarming changes has occurred in 0-6 child sex ratio during 2011 census. Since in overall comparisons 0-6 child sex ratio is found to be lower in rural areas than in urban communities, reason for this anomaly needs looking in to. Does it imply larger female infant mortality in rural areas or is there a selective migration of families from rural to urban setting over a period of time. An investigation about possible different mortality ratio of girl – infants in the rural and urban areas is perhaps in order. Also, the distribution of ‘last child’s sex and of the birth sequence, by sex in the families, and socio economic status of families may throw some light on this matter. Therefore, continued monitoring of Sex Ratio can be of help in formulating and implementing policies to overcome the adverseness in the Sex Ratio. Hence, a five year sample survey for this sort of data should also be undertaken to take the stock of the situation for corrective action.References 1.Anwesha Sen., Fall in Sex Ratio: A National Shame, Social Research Assistant and Internship Coordinator, SANLAAP, 38 B, Mahanirban Road, Kolkata (2011)2.Lekha S Chakraborty and Darshy Sinha., New Delhi, Determinants of Declining Child Sex Ratio in India: An Empirical Investigation MPRA paper No. 7602, posted 10. March 2008 http://mpra.ub.uni-muenchen.de/7602 B (2008) 3.Srinivasan K.,Economic and Political Weekly, Sex Ratios - what they Hide and what they Reveal, 30(51), 323–334 (1994) 4.Registrar General of India,Census of India, www.censusindia.com (2011) Research Journal of Recent Sciences ______________________________________________________________ ISSN 2277-2502Vol. 3(ISC-2013), 12-19 (2014) Res. J. Recent. Sci. International Science Congress Association 19 5.Haragopal V.V and Pandit S.N.N. andProc. A.P. Akademi of Sciences, Hyderabad, Analysis of Sex Ratio in Andhra Pradesh (Census 2001), 10(2), 289-301 (2006)6.Agnihotri S.B., New Delhi, Sage Publications,Sex Ratio Patterns in the Indian Population: A Fresh Exploration,(2000) 7.Pandit S.N.N.,I.R.E Transactions on Circuit Theory, Minaddition and an Algorithm to find most reliable paths in a network, CT-9, 190-191 (1961)8.Pandit S.N.N.,journal of the society of Industrial and Applied Mathematics, A New Matrix Calculus,9, 632-639(1961)