Ferility Transition in Uttar Pradesh and Bihar: A district level analyses

MALAY DAS* – SANJAY K. MOHANTY**

Spatial pattern of fertility transition in Uttar Pradeshand Bihar: a district level analysis

1. BACKGROUND

Since independence, stabilizing the population has been a top priority inIndia’s development agenda. Empirical studies in India highlighted the socie-tal benefits of limiting population growth (Coale and Hoover 1958; Dyson,2004; Bhat, 2004; Datta and Mohanty, 2005). Studies from other developingcountries also suggest that the slower population growth may indeed acceler-ate economic growth and advance overall economic well-being (Srinivasan,1988; Kelley, 1988). Despite various efforts, the population of India in the lastsix decades has increased more than threefold, from 361 million in 1951 to1210 million in 2011, an additional 849 million people (Registrar General ofIndia, 2011a), largely due to natural increase. Though the country has achievedhigher economic growth in recent decades (Bhattacharya and Sakthivel, 2004)and improved health and education (Planning Commission, 2011; Nava-neetham and Dharmalingam, 2011), the overall state of human developmentremained low (Planning Commission, 2011). In the composite index of humandevelopment in 2011, India ranked 134th among 187 countries (UNDP, 2011).This argues for further efforts to reduce population growth so as to improve thestate of human development in the country.

In 1952, India was the first country in the world to launch a family plan-ning program with the objective of reducing birth rates (MOHFW, 2000).During the first five decades of implementation, the family planning programin India underwent several changes, starting with a clinical approach (1951-1961), then subsequently moving to an extension education approach (1962-1969), Health department operated, Incentive based, Target-oriented, Time-bound and Sterilization-focused program (HITS) approach (1969-1975),coercive approach (1976-1977), recoil and recovery phase (1977-1994) andthe reproductive and child health approach (Srinivasan, 1998). All theseapproaches differ with respect to targets, choices of methods and implemen-tation strategy. The reproductive and child health (RCH) approach (the cur-rent RCH approach), which was implemented following the 1994 Internation-

GENUS, LXVIII (No. 2), 81-106

* International Institute for Population Sciences (IIPS), Mumbai, India.** Department of Fertility Studies, International Institute for Population Sciences (IIPS),Mumbai, India.Corresponding author: Malay Das; e-mail: nbumkd@gmail.com.

MALAY DAS – SANJAY K. MOHANTY

al Conference on Population and Development (ICPD), postulated that popu-lation policies should be viewed as an integral part of women’s reproductivehealth (Srinivasan, 1998). Additionally, the National Population Policy 2000 inIndia was introduced with its medium term objective of achieving replacementlevel fertility by 2010 and a long term objective of achieving population stabi-lization by 2045 (MOHFW, 2000). This, essentially, called for immediateneeds of reducing fertility throughout the country.

The fertility transition and population stabilization in India is of globalsignificance as it is home to 17% of the world’s population (Planning Com-mission, 2008) with a low level of human development. The total fertility rate(TFR) in India had declined from 3.6 in 1991 to 2.6 in 2008; by 2008, half ofthe states had reached the replacement level of fertility (Registrar General ofIndia, 2009a; 2009b). Though all states of India are experiencing fertility tran-sition, the pace of change is not uniform across the states. The fertility levelin two of the larger states, namely, Uttar Pradesh and Bihar remains high. TheTFR of Bihar declined from 4.4 in 1991 to 3.9 in 2008 whereas in UttarPradesh, during the same period, it declined from 5.1 to 3.8 (Registrar Gen-eral of India, 2009a; 2009b). These states are also lagging in many of the keysocio-economic and health indicators and ranked at the bottom of the humandevelopment index (Planning Commission, 2011; Mohanty and Ram, 2011).There have been concerted efforts to increase the reproductive and childhealth services in these states, but they seem to have little effect on loweringfertility. Moreover there is large variations in the level of socio-economicdevelopment and population characteristics among the districts of UttarPradesh and Bihar (Planning Commission, 2011; Chaudhuri and Gupta, 2009;Ram and Mohanty, 2002; Ram et al., 2005). In this context, the paper exam-ines the pattern of fertility transition in the districts of Uttar Pradesh andBihar. It also explores the factors determining fertility variations across thosedistricts.

The states of Uttar Pradesh and Bihar were selected with followingrationale. First, they are among the bigger states in India and lagging in manysocioeconomic and demographic indicators. Secondly, the population stabi-lization of India is contingent on the future fertility scenario in these states asthese states constitute one-fourth of India’s population. Of all the bigger statesof India, the decadal growth rate of population during 2001-2011 was thehighest in the state of Bihar (25.07%), followed by Uttar Pradesh (20.09%)(Registrar General of India, 2011a). Without a faster fertility decline in thesetwo states, the replacement level fertility and population stabilization in Indiacannot be achieved (Planning Commission, 2008). Third, in the wake ofdecentralized planning, demographic indicators are often sought for policyand program implementation at the district level. Most studies in India havebeen carried out at the national and state level and thus do not provide esti-mates at the district level.

SPATIAL PATTERN OF FERTILITY TRANSITION IN UTTAR PRADESH AND BIHAR ...

2. DETERMINANTS OF FERTILITY CHANGE: SELECTED REVIEW

The factors contributing to fertility change has been extensively addressedin demographic literature. Davis and Black (1956) identified a set of 11 “interme-diate variables” through which various socio-economic factors affect fertility, butthese variables were difficult to measure. Bongaarts (1978) provided a compre-hensive model and outlined four proximate determinants, namely; proportionmarried, use of contraception, induced abortion and postpartum infecundability,which explain large variations in fertility. There are a number of studies thatexamined the role of socio-economic factors on proximate determinants of fertil-ity in India (Jain and Adlakha, 1982; Jain, 1985; Irudaya Rajan, 2005). Thesestudies attributed the increase in age at marriage and use of modern contraceptionas the key proximate determinants of fertility change. In the 1970s and 1980s, themost significant factors factors of fertility change were female literacy, age atmarriage, and reduction in childhood mortality (Jain and Adlakha, 1982; Drezeand Murthi, 2001; Murthi et al., 1995). These findings are also consistent with astudy of India’s fertility decline between 1961 and 1991 (Arokiasamy, 1997).Studies outlined women’s education as a significant predictor of small familynorms and fertility decline regardless of religion, culture and level of develop-ment (Vaidyanathan, 1988; Jejeebhoy, 1995; UN, 1995; Parasuraman et al., 1999;Dreze and Murthi, 2001). In recent years, the role of space, use of maternal healthservices and diffusion of contraception were added in explaining the variation inproximate determinants of fertility (Guilmoto, 2000; Mohanty and Ram, 2011).For instance, about two-fifths of the reduction in TFR in Chhattisgarh - a statewhose level of socioeconomic development is similar to that of Bihar - wasamong the poor, while the TFR has not shown any change in Bihar (Mohanty andRam, 2011). Evidence also suggests that fertility reduction in recent years in Indiais largely due to fertility reduction among uneducated and poor women (Bhat,2002; McNay et al., 2003; Arokiasamy, 2009).

There are few studies that attempted to explain the variation of fertility inthe districts of India, mainly using data from the Indian census. Bhat (1996),using 1991 census data of selected districts of India, found that joint family(other than nuclear family), the proportion of Muslims, the proportion ofscheduled tribes, child mortality, unmet need for contraception, and agricultur-al and child labor have strong positive effects, while female age at marriage,female literacy, media exposure, and population density and number of banksper 100,000 people have strong negative effects on fertility. Dreze and Murthi(2001), using district level data from the 1981 and 1991 census, showed thatfemale education and child mortality are important factors in explaining fertil-ity differentials in districts of India. While districts with a higher proportion ofMuslims tend to have significantly higher fertility, it was not so with respect toscheduled tribes. Their study shows that region is an important factor in

explaining fertility differentials. A study based on 358 districts of India indi-cates that fertility at district level tends to vary by level of socioeconomicdevelopment and gender biases in kinship structure and it is significantly asso-ciated with child mortality and female labour force participation (Malhotra etal., 1995). Dommaraju (2012), using data from the National Family HealthSurveys (1992-1993 and 2005-2006), showed that the decline in fertility inIndia over the last two decades was due to changes in marital fertility resultingfrom a longer birth interval among younger women. He also added that the ageat marriage has a significant influence on fertility, particularly, in countrieswhere child-bearing occurs predominantly within marriage.

3. DATA AND METHODS

In recent decades, there has been growing interest in providing the esti-mates of fertility in districts of India using indirect methods (Registrar Generalof India, 1989; 1997; Bhat, 1996; Prakasam et al., 2000; Guilmoto and IrudayaRajan, 2002). The indirect methods that were used to provide district level esti-mates of fertility in India are: P/F ratio method (ratio of reported average pari-ties (P) to average parity equivalent (F)) Arriaga Method, Rele Method,Bougue-Palmore’s method and the Reverse Survival (RSV) method. The P/Fratio method suggested by William Brass (Brass and Coale, 1968) adjusts thelevel of observed age-specific fertility rates to the level of fertility indicated bythe average parities of women below 35 years of age. The reported children everborn (CEB) are transformed into the estimated age-specific fertility rate(ASFRs) and the current fertility rate is adjusted by using the P/F ratios. TheArriaga method (1983) is the modified Brass P/F ratio method when fertility ischanging. The Registrar General of India, using the P/F ratio and Arriaga meth-ods, provided district level estimates of fertility in India for 1981 and 1991,respectively (Registrar General of India, 1989; 1997). The estimated TFRshowed considerable variation across districts in India. The Rele method andBogue-Palmore’s method are regression-based methods used to estimate fertil-ity. The Rele method (1967) assumes that for a given level of mortality, theGross Reproduction Rate (GRR) is linearly related to the child-woman ratio(CWR) (ratio of children under 5 years to women of childbearing ages) and thebirth rate has a curvilinear relationship with CWR. The Bogue-Palmore methodis a regression technique of estimating fertility from a number of predictorssuch as the child-women ratio (ratio of 0-4 children to women aged 15-49), theinfant mortality rate, mean age at marriage and the index of fertility age com-position (Bogue and Palmore, 1964). Prakasam et al. (2000), using both theRele and Bogue-Palmore methods, provided comparable estimates of fertility indistricts of India for 1991. The RSV method is the most widely used indirectmethod of estimating fertility. In RSV method, the number of children enumer-

ated in a defined age (say 0-6 years) is reverse survived by the appropriate sur-vival ratio to obtain the number of births in the last 6 years preceding the dateof survey. The estimated births are divided by the estimated population to obtainthe birth rate.

Bhat (1996), using the RSV method, provided district level estimates ofCBR and TFR based on population aged 0-6 years from 1981 and 1991 census-es. Guilmoto and Irudaya Rajan (2002) also used the RSV method on the 0-6population from the 2001 census of India to estimates the CBR in districts ofIndia. They used the ratio of TFR to CBR to derive the estimates of TFR. Theage-specific fertility schedules from the National Family Health Survey of1998-1999 (NFHS-2) of each state was used together with the age distributionof women of reproductive age (15-49) from the 1991 census to convert the esti-mated CBR to TFR. Beside these studies, Ram et al., (2005) using the regres-sion method, estimated TFR in districts of India based on birth order statistics.They used the combined percentage of first and second order births from theDistrict Level Household Survey of 2002-2004 (DLHS-2) to derive the esti-mates of TFR for districts in India. Since 1981, the census of India has also beencollecting data on the number of births occurred during the one-year periodprior to the date of enumeration. However, the fertility estimates based on theone-year births reported at the census are not consistent and tend to be underes-timated (Registrar General of India, 2009c). More recently, Mohanty et al.,(2012), based on the data on 0-6 population and by using RSV method, estimat-ed the CBR and TFR for all the districts of India for 2001 and 2011.

In this paper, the RSV method is used to estimate the CBR in the districtsof Uttar Pradesh and Bihar. To understand the spatial pattern of fertility tran-sition in Uttar Pradesh and Bihar, the TFR at district level is considered forthree periods of time, namely, 1991, 1998 and 2008. The district level esti-mates of TFR for the period 1991 are borrowed from the census of India esti-mates (Registrar General of India, 1997). For 1998, we used Guilmoto andIrudaya Rajan’s (2002) estimates. The TFR of 2008 (TFR2008) is estimatedfrom the estimated CBR of 2008 (CBR2008) using regression method. The dataon the 0-6 population are collected from the census of India website(http://www.censusindia.gov.in). The methods of estimating CBR and TFRare described below.

3.1 Estimation of CBR

In order to estimate the CBR2008 using the RSV method, the total num-ber of births in six years preceding the survey is obtained by dividing the pop-ulation aged 0-6 years by the survival ratio from birth to age 6 years, i.e. thesurvival ratio of the 0-6 population.

Step 1: Computation of Survival ratio for bigger states of India In order to compute the survival ratio of the 0-6 population for each dis-

trict in the respective states, it was first calculated for all bigger states of India(with a population equal or larger than 10 million) as:

S0-6 = ((L0-1 + L1-4 + 2/5*(L5-9))/700000 where, S0-6 is the survival ratio of the 0-6 population, and L0-1, L1-4, and L5-9are the life table populations in the age groups 0-1, 1-4, and 5-9 years of age,respectively, in the bigger states. The survival ratio of the 0-6 year old popu-lation for each bigger state is computed from state-specific life tables gener-ated by using the age-specific death rates (ASDRs) of the respective statesavailable in the SRS report of 2008 (Registrar General of India, 2009a). Sincethe life table population is calculated for five year age intervals (except in thefirst two age groups), the average of the life table population in the age group5-9 is multiplied by 0.4 (2/5) to get the population in the 5-6 year age group.Here, it should be mentioned, that, in the SRS report of 2008, the ASDRs areavailable for the 20 bigger states of India. Thus, the survival ratio of the 0-6population is computed for bigger states only. Step 2: Computation of Survival Ratio in districts of Uttar Pradesh and Bihar

The state-specific survival ratios of the 0-6 population are regressed on thestate-specific under-five mortality rates (U5MRs) of 2008. The state-specificU5MRs of 2008 are obtained from the SRS of India (Registrar General of India,2009b). The survival ratios of the 0-6 population are regressed on the U5MRsavailable for the same states. The regression coefficients obtained at the statelevel are then used to obtain the district specific survival ratio. The regressionequation used to obtain the survival ratio in the districts is as follows:

SR0-6 = 1.00226 - 0.00121*U5MRwhere, SR0-6 is the survival ratio of the 0-6 population in each district. The coef-ficient values in actual decimal points have been used to calculate the survivalratio of the 0-6 population in the districts. However, for the current purpose, thecoefficient values are rounded up to five decimal points. The U5MRs for thedistricts of the respective states are derived by using Brass2 indirect estima-

1 The state-specific abridged life tables are constructed based on age-specific death rates to obtainthe survival ratio of the 0-6 population at the state level. The survival ratio for any specific agegroup of the population is derived from an abridged life table and is relatively accurate (Shryocket al., 1980).2 The Brass indirect estimation technique is a technique for estimating infant and child mortalityfrom survey data on the survival of children ever born. In this method, the probability of dyingbefore attaining certain exact childhood ages is determined by using average children ever bornand children surviving by age group of mother. William Brass was the first to propose this tech-nique (Brass and Coale, 1968).

tion technique based on CEB and CS data by age group of mother. The dataon CEB and CS for the districts are obtained from the District Level House-hold Survey-3 (i.e. DLHS-3), 2007-2008. The United Nations (UN) SouthAsian model life table (Shryock et al., 1980) is followed to estimate theU5MRs for the districts because it adequately represents the mortality pat-terns of countries in the South Asian region, including India. The UN SouthAsian model is most commonly accepted for providing the estimates of childmortality in India (Registrar General of India, 2009c). The child mortality pat-terns for women in the age groups 20-24 and 25-29 are used under theassumption that the births and deaths of children ever born, reported bywomen in these age groups, are reliable (UN, 1983). The estimated U5MRand survival ratio of the 0-6 population for the districts of Uttar Pradesh andBihar are presented in Appendix 2 and Appendix 3, respectively.Step 3: Estimation of total births in six year prior to survey

The number of births (B) in each district of the respective states is esti-mated as:

B = (P0-6/SR0-6)where, P0-6 is the population in the 0-6 year age group in each district and SR0-6is the survival ratio of the 0-6 population in each district.Step 4: Estimation of CBR

Once the total number of births for each district is obtained, the districtlevel CBR is estimated as:

CBR = (B/7* P1 October, 2007)*1000where, P1 October, 2007 refers to the district-level population in the respec-tive states as of 1st October, 2007 which is the midpoint between March 2011and March 2005. The mid-year population in the districts and respectivestates is computed using the annual exponential growth rate.

3.2 Derivation of TFR from CBR

The TFR2008 was derived by using the regression method (excluding inter-cept) based on state level time series data of CBR and TFR for the 1981-2008period available from the SRS of India (Registrar General of India, 2009b). TheTFR is regressed on CBR separately for both the states to derive the regressioncoefficients. The state specific regression coefficient is then applied to the esti-mated CBR to derive the TFR for each district in both states. In the case of UttarPradesh, the regression equation used to estimate the TFR is:

TFR = 0.144*CBR

In the case of Bihar, the regression equation used to estimate the TFR is:

TFR = 0.143*CBRSince the estimates of TFR are based on the births in the 6 years prior

to census, the estimates correspond to the mid-period. It may be mentioned that the estimates derived from the 2011 census

are referred to that of 2008. The estimates of TFR for the districts createdduring 1991-2001 and 2001-2011, are not available from publishedsources. Therefore, the estimates of TFR for newly created districts for theperiods 1991 and 1998 are assumed to be the same as in the parent districts.The newly created districts during 1991-2001 and 2001-2011 in both UttarPradesh and Bihar are presented in Appendix 1.

3.3 Multilevel analysis

In order to determine the variation in fertility attributable to the differencesbetween district level characteristics within the states, a multilevel linear regres-sion (MLLR) analysis was carried out for the period 2008. In the present case,the analysis has been conducted using data of 109 districts in Uttar Pradesh andBihar, where the TFR2008 is the dependent variable. The independent variablesused in the analysis are: proportion of women marrying below 18 years of ageas a proxy indicator of age at marriage, proportion of women using any moderncontraceptive method, mean years of schooling for women, proportion of poor(i.e. level of poverty), under-five mortality rate, proportion of scheduled tribepopulation, proportion of Muslim population, and proportion of urban popula-tion (i.e. level of urbanization). The TFR and women’s years of schooling aretransformed into a logarithmic form and under-five mortality is transformed intoa logit scale (Bhat, 1996). The state dummy (1 for districts in Uttar Pradesh and0 for districts in Bihar) has been used to determine the variance components atdifferent levels (state level and district level).

The estimates for all the variables (except proportion of urban population);proportion of women marrying below 18 years, proportion of women using anymodern contraceptive method, mean years of schooling for women (individuallevel), proportion of poor, under-five mortality rate, proportion of scheduledtribe population and proportion of Muslim population, are derived from DLHS-3. The proportion of poor is derived from a set of household economic proxies(household assets and amenities). Principal component analysis (PCA)3 is used

3 The principal component analysis (PCA) is a method of computing weights for individual indi-cators used to compute a composite index. In the recent past, some authors have used PCA forcomputing wealth index based on household assets and amenities (Gwatkin et al., 2000; Filmerand Pritchett, 2001).

in deriving a composite wealth index separately for rural and urban areas ashealth estimates differ significantly when separate wealth indices for ruraland urban areas are used rather than a national wealth index (Mohanty, 2009).The state-specific poverty estimate provided by the Planning Commission ofthe Government of India (2007) is applied to the composite index in derivingthe poor. The under-five mortality rates for the districts are estimated by usingthe Brass indirect method as explained in section 4.1. The proportion of theurban population is obtained from the 2011 census of India.

4. RESULTS

4.1 Levels in CBR and TFR

The estimated CBR2008 for Uttar Pradesh, derived from RSV method,was 27 compared to 29.1 of SRS of 2008. Similarly, the estimated CBR2008for Bihar derived from RSV method was 31.6 compared to 28.9 of SRS2008(see Figure 1).

On the other hand, the estimated TFR2008 for Uttar Pradesh derived fromthe RSV method was 3.9, close to that of SRS (3.8). However, the estimatedTFR2008 for Bihar derived from RSV method was found to be higher (4.5)than the SRS (3.9) (see Figure 2). Our analysis shows that the pace of fertil-ity transition during 1991-2008 was substantially lower in districts of Bihar

Figure 1 – Comparison of estimated crude birth rate derived from reverse survival method and that of SRS in Uttar Pradesh and Bihar, 2008

compared to that in districts of Uttar Pradesh. Moreover, some districts ofBihar (Madhepura, Saharsha and Supaul) have shown marginal increases inTFR, whereas all districts of Uttar Pradesh have experienced a reduction infertility during the same period. The lower pace of the fertility transition inthe districts of Bihar may be responsible for the relatively higher fertility inthis state.

The estimated CBR2008 and TFR2008 for the districts of Uttar Pradesh andBihar are shown in Appendix 2 and Appendix 3, respectively. In UttarPradesh, the estimated CBR2008 was the highest in the district of Bahraich(37.7), followed by Balrampur and Siddharthnagar (34.1 in both), Budaun(33.1) and Sonbhadra (32.9), and the lowest in the district of Kanpur Nagar(18.4), preceded by Lucknow (20.9) and Jhansi (21.7). Similarly, in Bihar, theestimated CBR2008 was the highest in the district of Kishanganj (37.2), fol-lowed by Khagaria (37.1), Katihar and Purnia (36.6 in both), and the lowestin the district of Siwan (26.4), preceded by Patna (27.2), Saran (28) andMunger (28.3). While the majority of the districts in Uttar Pradesh (56 out of71 districts) had a CBR between 18 and 30, the majority of districts in Bihar(26 out of the 38 districts) had a CBR of 30 and above.

On the other hand, the estimated TFR2008 in Uttar Pradesh was the high-est in the district of Bahraich (5.4), followed by Balrampur and Siddharthna-gar (4.9 in both), Budaun (4.8) and Chitrakoot, Kheri, Shrawasti andSonbhadra (4.7 in each), and the lowest in the district of Kanpur Nagar (2.6),preceded by Lucknow (3.0) and Jhansi (3.1). Moreover, out of 71 districts in

Figure 2 – Comparison of estimated total fertility rate derived from reversesurvival method and that of SRS in Uttar Pradesh and Bihar, 2008

Uttar Pradesh, 42 districts had a TFR of between 3.0 and 3.9 and 28 districtshad a TFR of 4 and above. None of the districts had reached the replacementlevel of fertility. Similarly, in Bihar, the TFR2008 was the highest in the districtof Kishanganj and Khagaria (5.3 in both), followed by Araria, Katihar and Pur-nia (5.2 in each) and Madhepura (5.1), and the lowest in the district of Siwan(3.8), preceded by Patna (3.9) and Munger and Saran (4.0 in both). It should benoted that 36 of the 38 districts in Bihar had a TFR of 4 and above. The coeffi-cient of variation in TFR for districts, indicative of variability in fertility levels,was 12.8 in Uttar Pradesh, compared to 9.2 for districts in Bihar.

4.2 Spatial pattern of fertility transition in Uttar Pradesh and Bihar

In order to understand the spatial pattern of fertility transition in UttarPradesh and Bihar, the district level estimates of TFR for the three periods - 1991,1998 and 2008 - are plotted in Figure 3 (Uttar Pradesh), and Figure 4 (Bihar).

The trends in the estimates of TFR in the districts of Uttar Pradesh andBihar indicate that the transition in fertility was more apace in districts ofUttar Pradesh, while it was observed to be slow in districts of Bihar. Howev-er, the pace of transition in fertility was not uniform across the districts inthese states. In Uttar Pradesh, the decline in TFR between 1991 and 2008was the highest in the district of Kanpur Dehat (45.2%), followed by Firoz-abad (43.3%), Deoria (41.4%) and Bijnor (41.3%), and the lowest in the dis-trict of Bahraich (3.6%), preceded by Kheri (7.8%), Sitapur (9.8%) andSonbhadra (11.3%). Moreover, between 1991 and 2008, out of 71 districts inUttar Pradesh, 3 districts had experienced decline in TFR of less than 10%,9 districts had experienced a decline in TFR of 10-20% and 59 districts hadexperienced a decline in TFR of more than 20%. On the other hand, 35 of the38 districts in Bihar had experienced a reduction in TFR between 1991 and2008, except the districts of Madhepura, Saharsa and Supaul (shown margin-al increases in TFR during the same period). Also, the decline in TFR wasthe highest in the district of Munger (35.5%), followed by Siwan (29.6%)and Lakhisarai and Jamui (27.4% in both), and the lowest in the district ofAraria (1.9%), preceded by Sitamarhi (2%) and Khagaria and Kishanganj(3.6% in both) during the same period. However, the level of TFR in the dis-trict of Sheohar remains stagnant at 5.0 during this period. Results also indi-cate that the pattern of fertility transition in districts of Bihar was not simi-lar to that of Uttar Pradesh. In Bihar, half of the districts (19 out of 38) hadexperienced a reduction in TFR by more than 10% during 1991-2008, com-pared to more than 90% of the districts (68 out of 71) in Uttar Pradesh.

Figure 3 – Total fertility rate in districts of Uttar Pradesh

Figure 4 – Total fertility rate in districts of Bihar

4.3 Factors determining fertility differentials

The MLLR analysis is carried out to examine the factors determiningfertility as well as to capture the variation in fertility attributable to differ-ences in state level and district level.

The results of MLLR analysis for all the districts of Uttar Pradesh andBihar are presented in Table 1. In MLLR analysis, four different regressionmodels are used and the dependent variable is in logarithmic form in all mod-els. Different models are developed in order to capture the interaction effects,if any, between the variables. In Model 1, the proportions of the Muslim pop-ulation, scheduled tribe population, urban population, poor and under-fivemortality rate are included. In Model 1, the level of urbanization has a signif-icant negative effect on the TFR, with a 10% increase in the level of urbaniza-tion leading to a 2% decline in TFR. The proportion of the Muslim population,proportion of scheduled tribe population, poverty and under-five mortality ratehave significant positive effects on the TFR. The variance components (ran-dom effect parameters) indicate that 54% of the variation in TFR is attributa-ble to the state level, while 45.6% of the variation in TFR is attributable to thedistrict level. In Model 2, use of any modern contraception is added to the vari-ables already used in Model 1. All six variables used in Model 2 are seen tohave a significant effect on TFR. The effect of modern contraceptive use,though not strong, is found to be negative. In Model 3, the proportion ofwomen marrying before 18 years of age is included along with the six vari-ables used in Model 2. In Model 3, level of poverty and use of modern contra-ception are not significant. The proportion of women marrying before age 18is significant and positively associated with TFR, indicating that an increase inthe proportion of women marrying below age 18 may lead to an increase in fer-tility. Moreover, the inclusion of this variable in Model 3 has accounted forsome of the variance in TFR and, as a result, the variance component corre-sponding to the random intercept decreases by 23% from Model 2 to Model 3(from 0.00706 in Model 2 to 0.00544 in Model 3). The inter-class correlationfrom model 3 implies that about 50% of the variation in TFR occurs due tovariation in the district level characteristics of the states under consideration.In Model 4, women’s years of schooling is added to all the covariates in Model3. In this model, all variables except use of any modern contraception and levelof poverty are significant. From this model, it is also observed that the valueof the random intercept decreases further by an additional 27.4%, reflectingthe fact that the inclusion of women’s years of schooling must have accountedfor most of the variance in the dependent variable. Furthermore, the randomparameters in Model 4 indicate that 54% of the variance in TFR is attributableto the district level differences, while 46% of the variance in TFR is attributa-ble to the state level differences, controlling for all variables.

This analysis demonstrates that factors such as women’s years ofschooling, proportion of women marrying below age 18, proportion of Mus-lim population, proportion of scheduled tribe population, level of urbaniza-tion and under-five mortality rate have a significant effect on TFR, indicat-ing that these variables are important predictors of fertility. Moreover, thedifferences in district level characteristics account for major variation inTFR; thus, these variables are significant predictors of fertility variationsacross the districts in the states under consideration.

Table 1 – Results of multilevel linear regression based on the data of 109 districts in Uttar Pradesh and Bihar

@Used in logit form. #Used in logarithmic form. -- Not included in the model. ***Significant at 1%level. **Significant at 5% level. *Significant at 10% level. Notes: The dependent variable is TFR2008, which is in logarithmic form. Figures in parentheses correspon-ding to the coefficients of Fixed-effects parameters represent absolute values of z-statistics. Figures in paren-theses corresponding to the random intercept and variance represent the amount of percentage variance.

5. DISCUSSION AND CONCLUSION

The fertility transition and population stabilization in India is of global sig-nificance due to its size and regional diversity in the level of socio-economicdevelopment. Though fertility transition began in early 1970s, it is uncertainwhen India can achieve the replacement level of fertility. While half of the statesof India have reached replacement level of fertility, the four larger states of India(Uttar Pradesh, Bihar, Madhya Pradesh and Rajasthan) continue to have high fer-tility. The population stabilization in India is largely contingent on the future fer-tility scenarios in these states. Moreover, though demographic research hasextensively dealt with the factors governing fertility change at the micro level,there are a limited number of studies that examine the variation in fertility at thedistrict level in India. There seems to be a great degree of variation in fertilitylevels among the districts within the two demographically-speaking largest statesof India, Uttar Pradesh and Bihar. The primary aim of this study was to under-stand the fertility transition in the districts of Uttar Pradesh and Bihar in the lasttwo decades. Taking advantage of the results of the 2011 census of India, thisstudy estimated two indicators of fertility, namely CBR and TFR, with theutmost care. It should be noted that, although large-scale surveys bridged thedata gap in many population and health parameters at the state level, the fertili-ty and mortality estimates are still not available for districts of India. For exam-ple, the DLHS-3 did not collect information on birth history and the fertility esti-mates are not available in published reports. Some researchers provided the indi-rect estimates of TFR for districts of India using birth order statistics from theDLHS-2 (Ram et al., 2005). Comparing the districts level estimates of TFRbased on birth order statistics from DLHS-2 with those from the 2001 censusproduced by Guilmoto and Irudaya Rajan (2002), it was found that the estimatesderived from the DLHS-2 data were relatively higher. Alongside with the decen-tralized planning and limited resources, fertility estimates are often required foreffective program intervention.

Results indicate that fertility reduction in districts of Uttar Pradesh during1991-2008 was at varying degree, whereas it was slow in most districts of Bihar.However, the fertility levels, as of 2008, remain higher in many districts in thesestates. In Uttar Pradesh, 42 of the 71 districts had a TFR in the range of 3.0-3.9and 28 districts had a TFR of 4 or more. On the other hand, 36 of the 38 districtsin Bihar had a TFR of 4 or more. Surprisingly, none of the districts in these stateshad reached the replacement level of fertility.

Thus, it is clear that, despite the observed reduction in fertility, many dis-tricts in these states continue to have very high fertility. This is a worrying signas the increased population nullifies the developmental efforts in these states.The high fertility may not only affect the average progress at the household andindividual level but also affect the average progress at the macro level. Theresults of the MLLR analysis indicate that factors such as women’s years ofschooling, age at marriage for women, proportion of Muslim population, propor-

tion of scheduled tribe population, level of urbanization and under-five mortali-ty rate are significant predictors of fertility differentials across the districts ofUttar Pradesh and Bihar. Though many of the districts in these states haverecorded significant increases in female literacy in last two decades, TFR did notdecline as expected. Emphasis on the use of modern family planning methods,increase in age at marriage of girls and reducing early childhood mortality mayhelp in reducing fertility in these states. From our analysis, it is evident that theeffect of use of modern contraception on TFR is insignificant when controllingfor age at marriage (Models 3 and 4 in Table 1). This is most likely due to its cor-relation with age at marriage of women or because of the aggregate analysis.However, past studies demonstrated that the increased use of modern contracep-tion among women was one of the major causes of fertility decline in districts ofIndia (Jain and Adlakha, 1982; Jain, 1985; Irudaya Rajan, 2005; Dreze andMurthi, 2001; Murthi et al., 1995; Arokiasamy, 1997). Based on our analysis, wesuggest to reposition family planning and to increase the accessibility and avail-ability of contraception in all districts in Uttar Pradesh and Bihar. There is agreater need for strong political commitment to make family planning success-ful. While both the states had seen substantial economic growth and increases infemale literacy over the last two decades, there is a greater need to push forincreasing the use of contraception and promoting later marriage among girls.Involvement of community leaders is essential to generate demand for contra-ception. The investment in family planning is likely to yield more progress at thestate and country levels in the coming years.

Though our study is an update on recent estimates and the determinants offertility in districts of Uttar Pradesh and Bihar, it is required to put forward thereliability of our study. We have compared our state level estimates of CBR andTFR with those of SRS (Registrar General of India, 2009a) and district level esti-mates of CBR with that of Annual Health Survey (AHS) (Registrar General ofIndia, 2011b) to understand the reliability in the estimates of CBR and TFR. Wefound that our state level estimates of fertility are close to the SRS estimates. Thecorrelation coefficient of CBR derived from the RSV method and the CBR fromthe AHS is 0.75 for Uttar Pradesh and 0.78 for Bihar. This suggests that our esti-mates are fairly reliable. Despite these, we acknowledge some limitations in ourstudy. First, we have used the provisional results of the 2011 census (the 0-6 pop-ulation and total population) for the estimation of the CBR. Over the censusyears, the difference between the provisional and final population has been verysmall. For example, in the 2001 census, while the provisional population of 0-6age group was 30.5 million in Uttar Pradesh and 16.2 million in Bihar, the finalpopulation of the same age group was 31.6 million in Uttar Pradesh and 16.8million in Bihar. If the final estimates of the 0-6 population are significantlyhigher than that of the provisional estimates, our fertility estimates are likely tobe lower. Second, potential biases may arise due to age misreporting in 2011 cen-sus. Researchers underscored the reliability of the proportion of the populationin the 0-6 age group for estimating the fertility indicators preferring that age

group to the age groups 0-4 and 5-9 years (Bhat, 1996; Gulimoto and IrudayaRajan, 2002). Researchers also acknowledged that age misrepresentation inIndia is rapidly decreasing due to increased literacy levels (Gulimoto and Iru-daya Rajan, 2002). If the under-count of children aged 0-6 years still persists, itis likely to underestimate the fertility indicators. Third, we have used the Brassmethod to estimate U5MR for the districts of Uttar Pradesh and Bihar and linkedit to the survival ratio of population aged 0-6 years. If mortality continues to beas high for the population aged 5-6 as that of the under 5-year age group, it islikely to bias the estimates upward. In other words, it may overestimate the num-ber of births and birth rate. Though, theoretically, these biases are possible, webelieve that such errors are minimal and that our estimates are reliable.

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Appendix 1

Districts created during 1991-2001 and 2001-2011 in Uttar Pradesh and Bihar

Appendix 2Estimated under-five mortality rate, survival ratio of 0-6 population,

and estimated crude birth rate (CBR) and total fertility rate (TFR) in 2008for districts of Uttar Pradesh

...Cont’d...

Appendix 2– Cont’d

...Cont’d...

Appendix 3Estimated under-five mortality rate, survival ratio of 0-6 population, and estimated crude birth rate (CBR) and total fertility rate (TFR)

in 2008 for districts of Bihar

...Cont’d...

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