The determinants of low fertility in rural and urban West Bengal, India

The determinants of low fertility in rural and urban West Bengal, India 1. Background Sayantani Chatterjee International Institute for Population Sciences, Mumbai India, at the national level, is currently undergoing fertility transition with its Total Fertility Rate falling from a mammoth 5.2 in 1971 to a mere 2.3 in 2013 (Sample Registration System, 2014). However, the fertility levels and patterns vary widely from region to region in India. The northern part of India is characterized by high fertility rates, but the southern part is differentiated by a below replacement level fertility (Sample Registration System, 2014; Guilmoto and Rajan, 2002). West Bengal, lies in the eastern part of the country. The unique geographical features of West Bengal include Himalayas in the extreme north, the Rarh regions in the west, the fertile Gangetic plains in the south-eastern regions and the coastal Sunderbans in the south engulfed by Bay of Bengal. With a population of 91,347,736, West Bengal stands out to be the fourth most populous state in India with 31.87% of them residing in urban areas (Census of India, 2011). Situated in an obscure location, West Bengal lies between the much talked about North- South demographic divide of India (Dyson and Moore, 1983; Bhat 1996). Lying somewhere in the middle of the North-South Demographic Divide, from the very beginning West Bengal shows a fertility rate which is lower than the national level (Bangladesh was a part of undivided Bengal before independence). There was a decline in fertility rates in urban areas which was followed by the reduction in fertility rates in rural areas. Fertility in the urban areas in early 1980s was quite low as compared to the rural areas which started following a downward slope in the late 1980s which still continues. Thus, the urban-rural gap in fertility measures have narrowed down over time in the state. Rural-urban differential in fertility patterns dates back to the early studies on fertility (Robinson, 1961; Kuznets, 1974) and the ideas are still consistent with the contemporary works (Guilmoto and Rajan, 2002; Sample Registration System, 2014; Khan, 2013). Although Kuznetz (1974) found that the gap in fertility was moderate in case of developing countries such as South Asia and Africa when compared to the developed countries. In India, there is moderate difference in rural and urban sectors at the state levels. Not too many studies have investigated the rural-urban diffusion processes within the state boundaries. Even though the fertility levels have remained low in West Bengal, the determinants of low fertility affecting the rural and urban places need not be the same. Say, a factor such as son preference might be more dominant in rural village which might evoke the fertility process in the rural areas but such sex discrimination might be non-existent in the urban places. Age at marriage is a pronounced factor affecting fertility as it raises the mean age at first childbearing following the conjecture that fertility is concentrated within marriages in India (Nag, 1984; Bongaarts, 2001; Das, 2004). Fertility rates provided by Sample Registration System in India (SRS) over the years vividly explain the periodic changes in the fertility indicators in different states, often classifying them in terms of rural-urban or sex differentials. Studies have time and again shown that the districtlevel fertility rates are not homogenous and are often contoured by very specific causes. The districts of India, serving as the bridge between state and household level, provides an extensive scope to identify inherent factors affecting fertility at macro-level. Notably, the state capital Kolkata (formerly known as Calcutta) underwent fertility transition from 1970s and had achieved the lowest Total Fertility Rate (2.0) in the country (Bhat, 1996) before any other place, 1

a rank it maintains to date (in 2011, TFR for West Bengal was 1.7 and for Kolkata it was 1.2) (Bhat, 1996; Guilmoto and Rajan, 2013; Census of India, 2011). There have been considerable efforts to render estimates of fertility rates of districts of India using indirect methods (Bhat, 1996; Guilmoto and Rajan, 2002, 2013). The most commonly used methods of indirect methods for the estimation of fertility rates are P/F ratio method, Arriaga Method, Rele Method, Bougue-Palmore s method and the Reverse Survival (RSV) Method. Among all these, RSV is the most widely used technique. Bhat (1996) used this method to obtain district level estimates of Crude Birth Rates (CBRs) and Total Fertility Rates (TFRs) (the most commonly used rates for understanding fertility) based on population aged 0-6 years using data from 1981 and 1991 censuses of India. Following his footsteps, Guilmoto and Rajan (2002, 2013) used this technique based on 0-6 population to obtain estimates for CBRs for all 594 districts of India. Later, they used the ratio of TFR to CBR to derive estimates of TFR. In the recent times, Mohanty et al. (2016) using 0-6 population and RSV technique estimated CBR and TFR for all districts of India for 2001 and 2011. Das and Mohanty (2012) estimated CBR and TFR using the same technique using data from 0-6 population from Census of India, 2011 and other data from DLHS 3 (2007-08). There are many studies which have outlined the state pattern of fertility transition and what prioritize it but there is still a dearth of studies focusing solely on the levels and patterns of fertility within state borders. The district-level fertility estimates are available from previous studies (Guilmoto & Rajan, 2002, 2013) but the rural urban gaps in fertility estimates were not captured. State-level variations in rural-urban fertility also shown by Sample Registration System (SRS). This present study tries to consolidate these two aspects for West Bengal which shows some interesting characteristics such as relying more on traditional methods of contraception, higher usage of modern contraceptives in the rural place than in urban places, people enlightened with the the knowledge of contraception and trying to limit pregnancies from 1940s onwards. Bearing these ideas, the primary objective of the paper is to compute fertility estimates by place of residence for all districts of West Bengal to understand whether rural-urban differences prevail in fertility rates at district-level. The second objective of the paper is to explore what district-level factors affect low fertility in the state. 2. Methodology 2.1 Sources of Data The present study is based on various sources of data. Primarily, for estimating the fertility rates, data were obtained from Census of India, 2011 (http://censusindia.gov.in). Census is conducted in every ten years in India with the reference date and time as of 1 st March, 12 am. It covers extensive information on different parameters of population and provides scope to analyse fertility, migration, mortality, development etc. According to the census reports of India, 2011, the total population of India is 1,210,854,977 comprising of 623,724,248 males and 586,469,174 females. Since, this present paper deals with West Bengal, we focused on 91,276,115 individuals of which 62,183,113 and 29,093,002 constitute rural and urban populations respectively. The life table populations were derived from the life tables readily available from Sample Registration System Reports (http://www.censusindia.gov.in/vital_statistics/srs_life_table/srs_life_table.html). The questionnaire of Census of India does not focus on fertility preferences of couples, hence the data pertinent to any contraception use by currently married women in the age group 15-49 years were obtained using District Level Household and Facility Survey (DLHS) 3 (2007-08) which provides information on district-level contraception use by currently married women in 2

their reproductive ages. As contraception use forms one of the most important factors determining fertility rates (Bongaarts, 1978), this parameter was used intentionally. 2.2 Analysis Age at marriage is a potential predictor of fertility in any population. Using Census information 2011, singulate mean ages at marriage were computed using Hajnal s technique (1953). The singulate mean age at marriage (SMAM) is the average length of single life expressed in years among those who marry before age 50. The following formula was used to compute SMAM separately for rural, urban and total areas. SMAM= 15+5 45 49 a=15 19 Sa +50 (S45 49+S50 54) 2 1 (S45 49+S50 54) 2 group a., where Sa denotes the proportion single in age 2.2.1 Estimation of CBR and TFR using Reverse Survival Technique In a closed population, children currently aged x are basically the survivors of births that had occurred x years ago. Thus, the number of births occurring x years ago can be estimated by using life-table survivorship probabilities to resurrect numerically those no longer present among the population aged x. Hence, this method of estimation is called reverse survival or reverse projection because the population how aged x is survived or reverse projected to age x-t by moving it with a suitable life table, t years into the past. Here, we took 0-6 years population to undergo this technique to obtain the number of births taken place in the last 6 years prior to the census of India, 2011. The following technique was adopted from the work by Das and Mohanty (2012). Since, the fertility rates correspond to 2008 using Census 2011, we used DLHS 3 (2007-08) data which refers to the same period. The total number of births in six years prior to Census 2011 was obtained by dividing the population aged 0-6 years during 2011 using survival ratio from birth to their age 6 years through survival ratio of the 0-6 population. Step 1: Computation of Survival Ratio for bigger states of India The survival ratios were calculated for all bigger states of India using the following formula, SR0-6 = ((L0-1 + L1-4 + 0.4*(L5-9))/700000 Where, S0-6 is the survival ratio of 0-6 population, L0-1, L1-4, L5-9 are person-years-lived in the age groups 0-1, 1-4 and 5-9 respectively. The life table populations were used from the life tables provided by SRS (http://www.censusindia.gov.in/vital_statistics/appendix_srs_based_life_table.html) which are available only for bigger states and union territories of the country (viz. Andhra Pradesh, Assam, Bihar, Chattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal). Here, in the age group 5-9 years, since we only concentrated on 5 and 6 years, the multiplier 0.4(2/5) was multiplied to the term. Step 2: Computation of Survival ratio in different districts of rural and urban West Bengal 3

In order to compute the regression of SR0-6 on U5MR for the year 2011, the state-specific survival ratios computed were regressed on the state-specific under-five child mortality rates for the given bigger states for 2011 (Registar General of India, 2009b) for rural and urban sectors and also for aggregate level. The regression coefficients thus obtained were used to compute the district specific survival ratios. The regression equations used to compute the district level survival ratios are as follows: SR0-6, rural = 1.0008-0.0009*U5MRrural SR0-6, urban = 0.9996-0.0009*U5MRurban SR0-6, total = 1.0006-0.0009*U5MRtotal Where, SR0-6, rural, SR0-6, urban and SR0-6, total denote the survival ratios of 0-6 population in each district for rural, urban and total areas. U5MRrural, U5MRurban and U5MRtotal denote the underfive mortality rates for each district in rural, urban and total areas of West Bengal. U5MRs for the states were taken from SRS data. The U5MRs were computed using Brass indirect estimation technique based on average Children Ever Born (CEB) and Children Surviving (CS) data available from Census data, using Tables F-1 and F-5 for West Bengal (http://www.censusindia.gov.in/2011census/population_enumeration.html) separately for rural, urban and total sectors. In the process of estimating the district-level under-five child mortality, The United Nations South Asian Model was adopted as it appropriately represents the mortality patterns of countries in South Asia including India (Registrar General of India, 2009). As per the assumptions by United Nations (1983), the child mortality patterns of women in the age groups 20-24 and 25-29 years were used as they appear to be most reliable. Step 3: Estimation of total births in six years before census The number of births in each rural, urban and total sectors were estimated separately using the formula: B= (P0-6/SR0-6) Where, P0-6 and SR0-6 denote the population and survival ratio in the age group 0-6 years in rural, urban and total sectors of each district of West Bengal. Step 4: Estimation of CBR Crude Birth Rates (CBRs) were calculated separately for rural, urban and total sectors of West Bengal using the following formula: CBR= (B/7*P1 Oct,2007)*1000 Where, P1 Oct,2007 refers to the rural, urban and total district-level populations (calculated separately) pointing to the mid-point between March 2011 and March 2005 (the six years in which the births had taken place before census). P1 Oct,2007 was calculated separately for rural, urban and total sectors district-wise using the annual exponential growth rate of 1.384%. Step 5: Computation of TFR using CBR 4

Based on the state-level time series data (1981-2011) of West Bengal available from SRS of India (http://www.censusindia.gov.in/2011-common/sample_registration_system.html), TFRs were regressed on CBRs separately for rural, urban and total sectors to obtain the regression coefficients. The derived regression equations for rural, urban and total sectors of West Bengal for 1981-2011 are as follows: TFRrural = 0.1488*CBRrural 0.8707 TFRurban = 0.1266*CBRurban 0.238 TFRtotal = 0.1421*CBRtotal 0.6497 It should be noted that the TFRs so obtained are based on births in the last 6 years preceding the census, hence the estimates should ideally refer to the midpoint between 2005 and 2011. Thus, these estimates derived from 2011 census basically correspond to 2008. 2.2.2 Multivariate Analysis For the multivariate analysis, multiple linear regression was used to study what factors explain the district-level fertility rates in West Bengal with TFRtotal as the dependent variable and corresponding to the time period of 2008. Since, the estimates computed using RSV refers to 2008, for the information on any contraceptives used by currently married women in the age group 15-49 years was extracted from DLHS 3 (2007-08). Use of any modern contraception was higher among currently married women in their reproductive age groups in rural places (53.3%) to those in urban places (49.1%) but on the whole use of contraception among them was higher in urban areas than those in rural places (DLHS, 2007-08). The independent variables used for the study were proportion of currently married women in the age group 15-49 years using any contraceptive method, Singulate Mean Age at Marriage, under-five child mortality rate, proportion of urban population, female literacy rate and proportion of nonworkers in the age group 15-59 years. The covariates used for the statistical analysis to some extent could be inter-linked. The association between female education and fertility is usually understood as causal. Spending more years in studying raises age at marriage, women get well versed with using contraception for spacing and limiting unwanted childbirth. Simultaneously, the linkages between female education and child mortality are also presumed to be causal (Bicego & Boerma, 1993; Caldwell, 1979; Cleland & van Ginneken, 1988). Each of the four models used captures various socio-economic demographic characteristics affecting district-level fertility separately so that collinearity or multicollinearity could be taken care of and rule out the chance of getting misleading results. Along these lines fertility has been treated as a function of contraceptive use by women in their reproductive ages in Model 1 and in Model 2 it has been observed as the function of age at marriage and child mortality. Thus Model 1 is as follows- TFRtotal = constant+ β1*proportion of currently married women in the age group 15-49 years using any contraceptive method + εi, where, εi ~N (0, 1) i=1(1)19. In Model 2, two other important demographic features- logarithm of Under-five child mortality and Singulate Mean Age at Marriage were incorporated. Thus, Model 2 is TFRtotal = constant+ β1* Proportion of currently married women in the age group 15-49 years using any contraceptive method + β2*logarithm of SMAM + β3*logarithm of U5MR+ εi, where, εi ~N (0, 1) i=1(1)19. In Model 3, further to the existing model, female literacy rate and proportion of urban population were added. Thus, Model 3 could be written as TFRtotal = constant+ constant+ β1*proportion of currently 5

Total Fertility Rates married women in the age group 15-49 years using any contraceptive method + β2*logarithm of SMAM + β3*logarithm of U5MR+ β4*female literacy rate+ β5*proportion of urban population+ εi, where, εi ~N (0, 1) i=1(1)19. Proportion of non-workers in the age group 15-59 years was included in the next model. Thus, Model 4 is TFRtotal = constant+ β1**proportion of currently married women in the age group 15-49 years using any contraceptive method + β2*logarithm of SMAM + β3*logarithm of U5MR+ β4*female literacy rate+ β5*proportion of urban population+ β5*proportion of non-workers in the age group 15-59 years+ εi, where, εi ~N (0, 1) i=1(1)19. 3. Results Reverse Survival Method SRS 2.1 2.1 1.7 2 1.9 1.3 Rural Urban Total Place of Residence Figure 1: Estimated Total Fertility Rates using Reverse Survival Method and that of SRS of West Bengal, 2008 The estimated TFR for rural sectors of West Bengal (2008) using RSV technique and that provided by SRS-2008 Report happened to be the same. A very negligible gap of 0.1 was found in the total TFR whereas a gap of 0.4 prevailed in the urban sector of West Bengal. Table 1: Estimated Singulate Mean Ages of West Bengal by place of residence, 2011 Districts SMAM Rural SMAM Urban SMAM Total Murshidabad 18.9 20.4 19.2 Birbhum 19 20.4 19.2 East Midnapore 19.2 20.3 19.3 West Midnapore 19.2 21.5 19.4 Dakshin Dinajpur 19.3 21 19.5 Maldah 19.3 20.6 19.5 Nadia 19.3 20.4 19.6 Bankura 19.5 21.1 19.6 6

South Twenty Four Parganas 19.4 20.6 19.7 Koch Bihar 19.6 21.8 19.8 Barddhaman 19.3 21.4 20.1 Puruliya 19.9 21.3 20.1 West Bengal 19.5 21.6 20.1 Uttar Dinajpur 20.2 21.8 20.4 Hugli 19.8 21.5 20.4 North Twenty Four Parganas 19.4 21.9 20.7 Haora 20 21.4 20.9 Jalpaiguri 21.3 21.4 21.3 Darjiling 21.7 22.7 22.1 Kolkata - 23.1 23.1 ***There are no rural places in Kolkata district, hence we ignored it. 23.5 23.0 22.5 22.0 21.5 21.0 20.5 20.0 19.5 19.0 22.7 22.1 21.9 21.8 21.8 21.7 21.6 21.5 21.3 21.4 21.5 21.4 21.4 21.0 21.1 21.3 21.3 20.9 20.7 20.6 20.6 20.4 20.4 20.4 20.3 20.4 20.4 20.2 20.1 20.1 20.1 20.0 19.9 19.8 19.8 19.6 19.6 19.7 19.6 19.5 19.5 19.3 19.2 19.3 19.3 19.4 19.4 19.3 19.4 19.5 19.5 19.3 19.2 19.2 19.2 19.0 18.9 23.1 23.1 18.5 SMAM Rural SMAM Urban SMAM Total Figure 2: District-wise Singulate Mean Ages at Marriage of West Bengal by place of residence, 2011 3.1 Spatial pattern of SMAM in West Bengal (2008) by place of residence West Bengal displayed an overall SMAM of 20.1 years combining rural and urban places. The differences in SMAM were not outstanding (mean gap was 1.5 years) when it came to rural and urban areas (Table 1).The urban SMAM was slightly (21.6 years) higher than that of the rural areas (19.5 years). Kolkata showed the highest SMAM (23.1 years) whereas the lowest was observed in Murshidabad and Birbhum (19.2 years). In the rural settings, the highest 7

SMAM was observed in Darjiling (21.7 years) and it was lowest in Murshidabad (18.9 years). On the other hand, in the urban settings, SMAM was lowest in East Midnapore (20.3 years) and highest in Kolkata (23.1 years). Gaps more than 2 years in rural-urban SMAM were noted in Koch Bihar, Bardhhaman, North Twenty Four Parganas and South Twenty Four Parganas. North Twenty Four Parganas exhibited the highest gap in rural-urban SMAM (2.5 years) whereas the lowest was observed in Jalpaiguri (0.1 years). Table 2: Estimated under-five mortality rates, Population in Census 2011 and Total Fertility Rates (TFRs) of West Bengal, 2008 by place of residence Districts Rural Urban Total U5MR P2011 TFR U5MR P 2011 TFR U5MR P2011 TFR Kolkata - - - 89 4486679 1.3 89 44,86,679 1.1 North Twenty Four Parganas 63 4275724 1.8 69 5807128 1.4 65 1,00,82,852 1.5 Hugli 55 3388395 1.5 64 2131994 1.5 58 55,20,389 1.5 Nadia 58 3730897 1.7 60 1437591 1.4 59 51,68,488 1.6 Darjiling 68 1123859 1.8 61 718175 1.6 66 18,42,034 1.7 Barddhaman 63 4644079 1.7 66 3079584 1.9 64 77,23,663 1.8 Haora 62 1776970 1.9 70 3064668 1.9 66 48,41,638 1.8 Dakshin Dinajpur 74 1434856 1.9 81 236075 1.4 75 16,70,931 1.9 Bankura 57 3295613 1.9 61 300679 1.6 57 35,96,292 1.9 West Midnapore 58 5228308 1.9 71 714992 1.7 59 59,43,300 1.9 East Midnapore 61 4500770 1.9 53 593468 1.9 60 50,94,238 1.9 Jalpaiguri 69 2825001 2.1 62 1044674 1.9 68 38,69,675 2 West Bengal 67 62213676 2.1 69 29134060 1.7 67 91347736 2 Koch Bihar 65 2533480 2.1 64 289300 1.5 65 28,22,780 2.1 Birbhum 74 3054019 2.3 59 448368 1.9 72 35,02,387 2.2 South Twenty Four Parganas 74 6065179 2.3 70 2087997 1.9 73 81,53,176 2.2 Puruliya 63 2554584 2.5 56 373381 2.2 62 29,27,965 2.5 Murshidabad 74 5697224 2.5 73 1405206 2.7 74 71,02,430 2.6 Maldah 86 3446056 2.8 75 551914 2.7 85 39,97,970 2.8 Uttar Dinajpur 86 2638662 3.2 57 362187 2.1 83 30,00,849 3 ***There are no rural places in Kolkata district, hence we ignored it. 8

Figure 3: District-wise total fertility rates of West Bengal by place of residence, 2008 9

3.2 Spatial pattern of fertility in West Bengal (2008) by place of residence Table 2 shows the estimated under-five mortality rates, district-wise populations in 2011 and Total Fertility Rates (TFRs) corresponding to 2008 for rural, urban and total sectors of all districts of West Bengal (Figure 3). In general, West Bengal displayed a TFR of 2.0. Uttar Dinajpur showed the highest fertility with a TFR of 3 followed by Maldah with a TFR of 2.8. The lowest fertility was observed in Kolkata with a TFR of 1.3. Even at the aggregate level, 12 out of 19 districts exhibited below replacement fertility (TFR <2.1). The rural sectors of West Bengal exhibited a TFR of 2.1. It was observed that in the rural settings, fertility was highest in Uttar Dinajpur with a TFR of 3.2 in 2008. It was followed by Maldah district with a TFR of 2.8. 10 districts showcased below replacement level fertility (TFR <2.1). However, the lowest fertility was observed in Hugli district with a TFR of 1.5. TFR of 1.7 was found in urban West Bengal. In the urban settings, highest fertility was noted in Murshidabad with a TFR of 2.7 and Maldah with a TFR of 2.7. The lowest urban fertility rate was noticed in Kolkata with a TFR of 1.3 (lowest-low fertility). Interestingly, 15 out of 19 districts in the urban sector exhibited below replacement level fertility (TFR <2.1). As it is evident from the study, West Bengal, both in the rural and urban settings exhibited fertility rates which are not very high as compared to the national level. Rural-urban differentials existed even at district-level in West Bengal, although the patterns were not very consistent. Fertility was relatively higher in the regions of Uttar Dinajpur, Maldah and Murshidabad and Puruliya. Lowest fertility was observed in the regions of south-east in districts like Kolkata, Hugli and North Twenty Four Parganas. Rural and urban TFRs in West Bengal differed by 0.4. Koch Bihar, Uttar Dinajpur and Dakshin Dinajpur showed very high gaps in rural-urban fertility (>0.5). Uttar Dinajpur with an overall TFR of 3 exhibited the highest gap of 1.1 in rural and urban TFRs. Haora, Hugli, Maldah and East Midnapore showed almost no variations in fertility rates in rural and urban areas. Leaving Hugli, North Twenty Four Parganas (TFR 1.5) and Nadia (1.6) showed quite high gaps (around 0.4) in TFRs between rural and urban places. Most of the districts with moderately low TFRs (1.7-2.1) showed moderate gaps of around 0.2. Except Uttar Dinajpur, districts like Malda and Murshidabad showed low gaps around 0.1-0.2. Interestingly, districts exhibiting TFRs around 2.2-2.5 were observed to have quite high gaps around 0.4. Table 3: Results of multiple linear regression of TFR on different parameters in West Bengal, Census 2011 Model 1 Model 2 Model 3 Model 4 Beta Beta Beta Coefficie Coeffic Coeffic Coefficent nts Coefficents ients Coefficents ients s Beta Coefficie nts Parameters Coefficents Proportion of currently married women using any contraceptive method -5.192*** -0.693-4.462*** -0.595-2.365*** -0.316-2.316*** -0.309 (-5.49) - (-6.34) (-3.1) (-3.03) Singulate Mean Age at Marriage@ - - -5.634*** -0.581-1.766-0.182-1.648-0.170 - - (4) (-1.32) (-1.23) Under-five mortality @ - - 0.959** 1.244** -0.343 1.253** 0.345 - - (2.14) 0.264 (2.72) (-2.83) Female literacy rate - - - - -0.99-0.194-1.31-0.257 - - - - (-1.58) (-1.38) Proportion of urban population - - - - -0.861* -0.423-0.923* -0.453 - - - - (-1.88) (-2.02) Proportion of non-workers (15-59 years) - - - - - - 1.192 0.101 - - - - - - (0.63) 10

Constant 5.739 18.102 4.703 3.983 R-square 0.48 0.792 0.879 0.884 Note: ***, ** and * indicate significance at 1%, 5% and 10% respectively, @ indicates included in logarithmic form.. Figures in parentheses corresponds to the coefficients of parameters representing t-values. 3.3 Factors determining fertility differentials in West Bengal The results of multiple linear regression for all districts of West Bengal are presented in Table 4. Each of the four different regression models were used to capture separate socio-economic and demographic dimensions regulating low fertility at district-level in West Bengal with total TFR as the dependent variable. Each of the subsequent models gave a better fit than the previous one. In Model 1, proportion of currently married women in the age group 15-49 years using any method of contraceptive method was included which alone could explain 48% of the total variation. One unit increase in proportion of currently married women in the age group 15-49 years using any method of contraceptive method was associated with 5 units decrease in total TFR (p<0.05). In Model 2, logarithm of Singulate Mean Age at Marriage and logarithm of under-five mortality rate were incorporated. Here, all the predictors had significant associations with total TFR. Like earlier, proportion of currently married women in the age group 15-49 years using any method of contraceptive method had a negative effect on TFR. Similarly, the negative association of proportion of SMAM with TFR indicated that a higher age at marriage would have a diminishing effect on total TFR. Increase in under-five child mortality tended to increase total TFR. Model 2 could explain 79% of the total variation in total TFR. In Model 3, further female-literacy rate and proportion of urban population were annexed. Increase in female literacy elevates age at marriage and as well as creates awareness among women regarding the benefits of using contraception which was much established through the negative association between contraception use or SMAM with total TFR. The level of urbanization was likely to diminish total TFR (p<0.05). Model 3 explained around 88% of the total variation. Level of urban population, child mortality and contraception stood out to be the most important predictors in Model 3. Lastly in Model 4, proportion of non-workers in the age group 15-59 years was incorporated for controlling economic status. A negative relationship was noted between proportion of male non-workers and fertility rates. In this model contraception usage and level of urbanization had significant negative associations with fertility rates in West Bengal whereas child mortality was positively linked to total TFR. In both Model 3 and 4, the mod values of beta coefficients of proportion of urban population are respectively 0.423 and 0.453 which are greater than the other beta values of predictors in these two models. Thus, the highest values of level of urbanization in Model 3 and 4 suggest greater impact on the criterion variable than the rest suggesting higher impact on district-level fertility than any other predictors. 4. Discussion Numerous studies have extensively dealt with factors affecting fertility differentials in India and its states focusing on micro-levels. Yet, there is a dearth of studies connecting fertility to different socio-economic factors at the district-level. Interestingly, the districts of West Bengal too show variations in fertility both in rural and urban sectors. The main objective of this study was to compute district-level fertility rates in rural and urban sectors. Age at marriage being an important predictor of fertility was also computed at district-level for rural and urban places. The present study also tried to throw some light on various factors which affect district-level fertility measures. The present study regards use of contraception among women, level of urbanization and child mortality as important predictors of fertility in West Bengal. 11

The likelihood of limiting unwanted births existed as early as in the 1940s in West Bengal. There are clear indications from a study undertaken by Chandrasekaran and George (1962) that those localities of Calcutta (during 1947-49) marked by upper class and higher education tended to use some kind of birth control which is quite interesting. As pointed out by Basu and Amin (2000), the effects of historical and cultural factors play an important role in shaping up reproductive changes through diffusion. Many earlier studies pointed that embracing and relying on methods of contraception among women were important factors to bring down fertility (Jain and Adlakha, 1982; Murthi et al., 1995; Dreze and Murthi, 2001; Rajan, 2005; Arokiasamy, 1997). Using of contraception could alone explain 48% of the total variations of fertility in West Bengal. In fact, it retained its significant negative association with fertility in all the models used (Table 4). From the reports of West Bengal, DLHS 3 (2007-08), it was discerned that currently married women in their reproductive ages in urban areas had higher inclinations towards using traditional methods of contraceptives than modern methods although contraceptive prevalence rates were higher in urban sectors. Nevertheless, urban populations too experienced lower fertility than rural sectors. As pointed out by Mohanty et al. (2016), the recent fertility decline in the states of India in the absence of increased contraceptive use urges for understanding the distal determinants of fertility change. Level of urbanization play a crucial role in shaping up of fertility patterns through ancillary pathways. This has motivated us to assess whether the level of urbanization truly had effects in a state which already has a low fertility level. Although the level of urbanization have remained lower in West Bengal than many parts of the country, but our results suggests that it indeed had the maximum significant effect on fertility decline in the state. Child mortality tends to be lower in urban places (Sastry, 1997; Fink and Hill; 2013). Murthi et al. (1995) acknowledged the close association between child mortality and fertility. Even though child mortality rate is moderate in West Bengal (38 per 1000 lives in 2008), it forms an important ingredient of distal determinants of observed fertility in West Bengal. Social development is always kindled by an economic advancement. An overall upsurge in economic level encourages individuals to embrace newer ideas viz. modernization which summons a demographic transition (Nag, 1984). Following societal changes, age at marriage for both men and women has risen up owing to engagement of more women in higher education or employment. The late marriages promote reduction in fertility, mainly due to the curtailment of higher parity births. Das (2004) showed that female literacy was the second most important factor characterizing fertility transition in India after age at marriage and followed by degree of urbanization. Recent studies also pointed out that age at marriage has a major role to play in reducing fertility rates (Khan, 2013; Dharmalingam et al., 2014). Age at marriage has considerably risen in West Bengal in the near past. This study concludes that an increase in SMAM (Singulate Mean Age at Marriage) has a negative influence on fertility rates. The indivisible association of SMAM with female education and contraception use in the above aggregate analysis might be a probable cause of these predictors to be insignificant compared to contraception use, child mortality or level of urbanization. To control for economic conditions, proportion of non-workers in the working age-groups (15-59 years) at different districts was taken. Our analysis suggests that fertility tended to be higher if the numbers of non-workers are higher. Globally over the years, a lot of studies have established the close relationship between education and fertility. Education is considered as a basic element for fertility reduction. Especially, female literacy has a crucial role to play in order to curb fertility rates in India as well as West Bengal (Murthi et al., 1995; Dreze and Murthi, 2001; Das and Mohanty, 2012; Mohanty et al., 2016). Education among women kind of generates a sense of autonomy and 12

thus help them in decision-making and in being capable in bargaining. It as well creates awareness regarding the importance of small family size and norms. They learn about the benefits of family planning methods, hence reducing the risks of giving births to unwanted children. Active participation in the labour-force by the women give them lesser time to bear and rear children. Mohanty et al. (2016) concluded that a minimum level of economic advancement is required to trigger social development such as female literacy for undergoing of fertility transition. But once it gets initiated, it follows its own course. Social development is largely incited by a surge in female literacy. This study has also put forward the urban-rural differentials that exist in observed fertility rates. Fertility rates were higher in the rural sectors whereas urban fertility was quite lower. The state capital Kolkata holds the record of experiencing the lowest TFR in India. As pointed out by Robinson (1961) the first serious effort to handle urban-rural fertility differentials in India was done by Kingsley Davis (1951) where he concluded that larger cities are associated with lower fertilities and there exists an inter-city and rural-urban differential in fertility in India. Using Census data 1921-51, Robinson concluded that the large rural-urban fertility ratio differentials had diminished with time and the rural-urban ratios existed particularly in terms of the marital fertility ratios. He surmised that this happened as infant mortality reduced in the rural sectors; and the heavy inflows of the people from rural to urban had plausibly caused demographic changes in urban places. To obtain reliability, the estimates were compared to state-level estimates of TFR obtained from SRS and the district level estimates of TFR by Guilmoto and Rajan (2013). The high correlations indicate that the findings are decent enough. 4.1 Limitations Many researchers question the data quality of census data in India. Census data is accompanied by certain potential biases such as age misreporting, recall bias etc. Researchers accentuated the reliability of 0-6 population for estimating different fertility measures to the age groups such as 0-4 years or 5-9 years (Bhat, 1996; Guilmoto and Rajan, 2002; Das and Mohanty, 2012). Guilmoto and Rajan (2002) suggested that increased literacy levels create awareness and attenuate age misreporting. ). It should be kept in mind that if the final estimates of the 0-6 population are higher than the provisional estimates, the derived fertility rates tend to be under-estimated. Over time, the gaps between the provisional and final population have declined (Das and Mohanty, 2012). Also, the information on using of modern contraceptives was extracted from DLHS 3 (2007-08) as census questionnaire do not provisions for incorporating such questions. Lastly, child mortality was indirectly estimated using Brass method and linked to the survival ratio of 0-6 population which might overestimate the number of births and birth rate if mortality continues to be as high for children aged 5-6 years as that of under-five age group. 5. Conclusion This study was aimed at estimating fertility rates in rural and urban sectors of West Bengal. It also gives the rural-urban differences in SMAM. Although, the gaps in rural urban SMAMs were found to be trifling but modest gaps existed in rural-urban fertility rates. Rural-urban differential exists even at district-level in West Bengal, although the patterns are not very consistent. Fertility was relatively higher in the regions of Uttar Dinajpur, Maldah and Murshidabad and Puruliya. Lowest fertility was observed in the regions of south-east in districts like Kolkata, Hugli and North Twenty Four Parganas. Maldah, Puruliya and Murshidabad experienced high fertility rates in the state but showed lower gaps in fertility rates in rural and urban. Districts such as Koch Bihar, Dakshin Dinajpur, Birbhum and South Twenty Four Parganas showed higher gaps in fertility rates in rural and urban sectorsnadia, North 13

Twenty Four Parganas, Bankura West Midnapore and East Midnapore in south Bengal showed lesser rural-urban differentials in fertility rates. Those districts which are marked by very high rural population showed higher gaps in rural and urban fertility. Multivariate analysis showed that the level of urbanization, under-five child mortality rate and proportion of currently married women (15-49 years) using any contraception were the most important predictors of low fertility in West Bengal. The urban-rural fertility gaps in West Bengal have narrowed down over the last few decades and it is likely that the gap would further diminish. Most of the districts are currently facing very low or lowest low fertility rates. The present situation in urban areas and in a few years in rural areas of West Bengal resemble the current situations of fertility patterns in the developed countries. There are ongoing debates whether declining fertility patterns will show a reverse direction in the developed countries relying on pro-natalist policies or through immigration to solve the issues of de-population. Although West Bengal is likely to reduce its fertility and most likely to attain lowest low fertility in the upcoming years, India at the aggregate level is yet to achieve below replacement level fertility rates. West Bengal is now set to enjoy the window of demographic dividend and continue having sufficient working population for some more years henceforth. Government should grasp this opportunity and utilize this demographic window of opportunities and take proper care of the plausible unemployment situation. 6. References Basu A M and Amin S. (2000). Conditioning factors for fertility decline in Bengal: History, language identity, and openness to innovations. Population and Development Review, 26(4): 761-794. Bhat P M. (1996). Contours of fertility decline in India: A district level study based on the 1991 census. Population policy and reproductive health. Bicego G T and Boerma, J T. (1993). Maternal education and child survival: a comparative study of survey data from 17 countries. Social Science & Medicine, 36(9): 1207-1227. Bongaarts J. (1978). A framework for analyzing the proximate determinants of fertility. Population and development review, 105-132. Caldwell J C. (1979). Education as a factor in mortality decline: an examination of Nigerian data. Population Studies, 33(3): 395-413. Chandrasekaran C and George M V. (1962). Mechanisms underlying the differences in fertility patterns of Bengalee women from three socio-economic groups. The Milbank Memorial Fund Quarterly, 40(1): 59-89. Cleland J G and van Ginneken J K. (1988). Maternal education and child survival in developing countries: the search for pathways of influence. Social Science & Medicine, 27(12): 1357-1368. Das A. (2004). Fertility Transition and Threshold Estimation: A District-Level Analysis in India. Journal of Social and Economic Development. Das M and Mohanty S K. (2012). Spatial pattern of fertility transition in Uttar Pradesh and Bihar: a district level analysis. Genus, 68(2): 81-106. Davis J S. (1951). The Population of India and Pakistan Kingsley Davis. 14

Dharmalingam A, Rajan S and Morgan S P. (2014). The determinants of low fertility in India. Demography, 51(4): 1451-1475. Drèze J and Murthi, M. (2001). Fertility, education, and development: evidence from India. Population and development review, 27(1): 33-63. Dyson T and Moore M. (1983). On kinship structure, female autonomy, and demographic behavior in India. Population and development review, 35-60. Fink, G and Hill K. (2013). Urbanization and Child Mortality Evidence from Demographic and Health Surveys. Background paper prepared for Commission on Investing in Health. Harvard School of Public Health, Cambridge, MA. Guilmoto C Z and Rajan S I. (2002). District level estimates of Fertility from India's 2001 Census. Economic and Political Weekly, 665-672. Guilmoto C Z and Rajan S I. (2013). Fertility at the District Level in India. Economic and Political Weekly, 48(23): 59-70. Hajnal J. (1953). Age at marriage and proportions marrying. Population studies, 7(2): 111-136. Jain A K and Adlakha A L. (1982). Preliminary estimates of fertility decline in India during the 1970s. Population and Development Review, 589-606. Khan A A. (2013). Rural-urban Fertility Gap and Fertility Adaptation by Rural to Urban Migrants in Punjab: A Case of Bahawalpur District. South Asian Studies, 28(2): 445. Kuznets S. (1974). Rural-urban differences in fertility: An international comparison. Proceedings of the American Philosophical Society, 118(1): 1-29. Mandal N K, Mallik S, Roy R P, Mandal S B, Dasgupta S and Mandal A. (2007). Impact of religious faith & female literacy on fertility in a rural community of west Bengal. Indian Journal of Community Medicine, 32(1). Mohanty S K, Fink G, Chauhan R and Canning D. (2016). Distal determinants of fertility decline: Evidence from 640 Indian districts. Demographic Research, 34: 373-406. Murthi M, Guio A C and Dreze J. (1995). Mortality, fertility, and gender bias in India: A district-level analysis. Population and development review, 745-782. Nag, M. (1984). Fertility differential in Kerala and West Bengal: Equity-Fertility hypothesis as explanation. Economic and Political Weekly, 33-41. ORGI (2011). Census of India. Ministry of Home Affairs, Govt. of India, New Delhi. http://censusindia.gov.in/2011-common/censusdataonline.html accessed on 20.11.2016. ORGI (2014). Sample Registration System. SRS Bulletin, Ministry of Home Affairs, Govt. of India, New Delhi. http://www.censusindia.gov.in/2011common/sample_registration_system.html accessed on 20.11.2016. Rajan S I. (2005). District level fertility estimates for Hindus and Muslims. Economic and Political Weekly, 437-446. Robinson W C. (1961). Urban-rural differences in Indian fertility. Population Studies, 14(3): 218-234. 15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Sastry N. (1997). What explains rural-urban differentials in child mortality in Brazil?. Social science & medicine, 44(7): 989-1002. Appendix Table: Demographic profile of West Bengal by place of residence Description Rural Urban Total Total Population 6,21,83,113 2,90,93,002 9,12,76,115 Population Growth 7.68% 29.72% 13.84% Total Child Population 78,20,710 27,60,756 1,05,81,466 Sex Ratio 953 944 950 Child Sex Ratio 959 947 956 Literacy 72.13% 84.78% 76.26% Male literacy 78.44% 88.37% 81.69% Female literacy 61.98% 76.01% 70.54% Source: Census of India, 2011 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 - Total Rural Urban *** Source: Sample Registration System, 2014 Figure: TFRs in West Bengal by place of residence (1981-2013) at five years interval 16