Contextual Influence on Age at Marriage in Uttar Pradesh-India

Female age at marriage in Uttar Pradesh is amongst the lowest in India. Previous studies on female age at marriage in an Indian context primarily focused on the individual level factors impacting female age at marriage. Community level factors that influence the female age at marriage in Uttar Pradesh were non-existent. In this study, the impact of community presence of mass media is studied using Hierarchical Linear Models. Thus using the data from NFHS-2, the study explores the within community presence of any forms of print, electronic or local mass media and its impact on female age at marriage in Uttar Pradesh. Total of 7175 currently married individual women were nested in 317 rural communities across Uttar Pradesh. Hierarchal linear modeling using SAS PROC MIXED was used in the study. The intra-class correlation of 0.13 indicates fair bit of clustering of female age at marriage within communities. Thirteen percent of the differences in female age at marriage are due to between community differences in Uttar Pradesh. Communities’ exposure to any forms of mass media has a positive impact on female age at marriage in Uttar Pradesh.

marriage, especially of females, has been prevalent for a long time and cohabitation outside marriage is uncommon. In this situation mother's age at birth of her first child is primarily linked with her age at marriage and its effects on the timings of subsequent childbearing within the reproductive period. To achieve reduction of fertility one of the measures proposed in the World Population Plan of Action (1974) [1] was to establish an appropriate lower limit for age at marriage, for it was taken as an important factor influencing fertility. It was pointed that raising the age at marriage, at least in high fertility countries, would dampen future population growth.
It is well recognized, that India's high population growth rate primarily is due to its low ages at marriage, resulting in high levels of fertility, as women get to spent more number of years in cohabitation due to a stretched reproductive life span. In not so recent past schooling of girl was considered a low priority due to prevailing patriarchy and limited number of schools available locally. In the absence of schooling and limited skills to be gainfully employed, marriage was the only socially legitimate option available for young women [2] [3]. In their study from Uttar Pradesh using three rounds of NFHS data [4] have documented increasing female age at marriage trends and slowing rate of nuptiality in Uttar Pradesh however, the current marriage levels are still low in comparison to most of the low fertility countries. Further there is lot of variation in the age at marriage among the states, and within states among castes, communities and across other social stratifications.
Achievements specially in raising age at marriage through legislation seemed to be limited in an Indian context if one looks at the implementation of Child Marriage Restraints Act (CMRA). Raising age at marriage through legislation has worked only in the regions where there has been rapid social, economic and political change. None the less a legislation relating to the minimum age at marriage was enacted by the Parliament in February 1978. Contravention of the child marriage Act became cognizable offence from October 1978. The Act rose the marriageable age from 15 to 18 years for girls and from 18 to 21 for boys. Though the child marriage was considered cognizable offence under the CMRA, the act did not provide the legal framework for the annulment of such marriages. Provisions of CMRA were modified in 2006 and a new child protection officer, with the required set of legal powers was introduced. This child protection officer is given powers to arrest the violators in order to stop the child marriage from being solemnized, unlike CMRA were investigating officer had wait and get the arrest warrant from the magistrate by the time such marriages usually got solemnized which couldn't be annulled under the CMRA law. Pathak [5] had shown that despite CMRA being in place, early marriages are prevalent in the Indian society.
Variations in the female age at marriage in India have primarily been explored from the individual perspective. Desai et al. [6] in their 2010 study on age at marriage using their own dataset have used the district level data to analyze the female marriage patterns from the construct of the prevailing societal gender

Review
In the population based household surveys, the subjects are often individuals, usually nested within the household and households nested within the communities and the regions. When the data is clustered so, the clustering effect needs to be taken into account while analyzing such data. This study therefore takes in to account the community context in analyzing the female age at marriage using hierarchical linear models. It is not surprising to see that the body of literature that emanate from India analyzed age at marriage mainly as an individually driven phenomenon.
Historically changes in nuptiality pattern have played very significant roles in many of the European demographic transitions [7]. In Western Europe late marriage and wide spread celibacy have been the main mechanisms through which its fertility was brought to a low level. However, in other parts of Europe (eastern and central), marriages generally occurred early, and were also nearly universal and decline in fertility was achieved mainly through reduction in marital fertility. However, changes in marriage pattern in terms of higher age at marriage and lower proportion ever married at different ages has characterized several early demographic transitions in many other developed countries [8] [9] [10]. As per Matra's [11] classification, shift towards late marriage is characteristic of a mid transitional stage in the course of fertility decline ever occurred among nations. Confirming to this, Coale's [8] analysis revealed that in the initial phase of demographic transition early and universal marriage was very common which slowly paved the way to later marriage and fairly common spinsterhood and then finally to a decline in marital fertility.
Evidences are on the rise in recent years too, to support the argument that nuptiality reductions produce a retarding effect on fertility. Analysing the data from several Asian countries, Cho and Retherford [12], demonstrated that marital factors have contributed substantially towards the reduction of fertility in these countries. In West Malaysia about two thirds of its decline in crude birth rate during the sixties has been attributed to change in the marital structure. The significant decline in CBR of Sri Lanka was also partly due to changes in age at marriage. Jones [13]  Lestheghe [14] analyzed the impact of nuptiality on fertility and growth rates of a series of populations from developing nations where extra marital fertility was negligible and found that nuptiality changes can produce the same effect on birth and growth rates as changes in marital fertility. The study concluded that an overall fertility reduction initiated by decreasing marital fertility alone would fall considerably short of the targets in several developing nations. Apart from family size being reduced through reduced exposure to pregnancy risk [15], the delayed marriage can lead to significant decline in birth rates through its effect on length of generation [10]. According to Ridley and Sheps [16] age at marriage affects fertility by changing the fertility schedule and family building pattern. Thus due to its combined effect through several routes, age at marriage can be termed the best single predictor of fertility. Goode's [17] modernization theory emphasizes the impact of industrialization on marriage patterns. Modernization operates at both societal and individual levels by affecting marriage timing. According to him, expansion of educational opportunities, changes in work force and occupational activities, and urbanization are the most important "modem forces". In the process of modernization individuals with higher social status (more education, modem occupational roles etc.) want more freedom and thus tend to marry later in life. Place of residence is another factor-people reared in urban areas are exposed to more diverse life-styles and to weaker social controls than those who are reared in rural areas or small towns. As a result, those growing up in an urban environment are more likely to marry late than those living in the rural areas. Empirical studies in Asian countries support Goode's [17] [18] modernization theory. The effect of these modernization factors may be found in any population, although the degree influence of each factor may not be the same across countries and time or across provinces and country.
In addition to modernization, there are other influential factors affecting the pattern of age at marriage, for example, religion and caste or ethnicity, in certain developing countries. In this context, Dixon [19] in her sociological framework emphasized the effect of social institutions, such as the family system and marriage norms and customs as well as factor such as warfare, which may affect the age-sex ratio. While marriage squeeze (availability of marriage partner) is less likely to have effect on the age at marriage in most of the Indian states (there being no severe imbalances in the age-sex ratio of the marriageable population during the recent past) the other factors such as the family system, social pressure, marriage norms and customs as well as individual motivations to marry and financial and social conditions are again likely to be influenced by the modernization forces.
Objectives  To study the factors influencing the female age at marriage at individual level in Uttar Pradesh  To study the relative influence of communities exposure to any form of mass

Data & Method
National Family Health Survey round-2 [20] dataset is used to study the relative influence of community level exposure to any form of mass media such as films, drama or puppetry shows on female age at marriage in Uttar Pradesh. The MIXED procedure in SAS is used to fit the NFHS-2 data that is clustered/ correlated and has non constant variability. Since the data is clustered the response is not necessarily normally distributed. It is therefore necessary to use generalized linear mixed models (GLMM) or random effect models to analyze such data.

Findings
Let Y ij be the female age at marriage of i th individual in the j th community. Then the unconditional means model (model without any covariates) to estimate the intra-class correlation accounting for clustering in the data can be expressed by (1) has one fixed effect ( µ ) and two variance components. The variation between community-means is represented by 00 τ while 2 σ represents the variation among individuals within communities.
The above model can be expressed in a simpler form as two levels hierarchical models one each at an individual (level-1) and community level (level-2). The level-1 model or the individual level model expresses the individual outcome as the sum of an intercept for the individual's community ( 0 j β ) and a random error ( ij r ) associated with the i th individual in the j th community:  τ & 2 σ from the appendix Table 1 we get: The intra-class correlation ( ρ ) predicts fair bit of clustering of female age at marriage within communities (0.87) while thirteen percent of the total variance in female age at marriage was due to between community difference. This is also suggests that an ordinary least square analysis of this data would likely yield misleading results. The value of the intercept (15.78) in the unconstrained model predicts that the mean age at marriage for females in rural Uttar Pradesh is 15.78 years. The confidence limit shows that the female age at marriages in rural Uttar Pradesh varies from at-least 15.67 years to at-most 15.89 years.
As a next step we introduce the individual's exposure to various forms of mass media including films, drama and puppetry within community as a level-2 pre- is a level-2 variable whether or not community is exposed to films, drama and puppetry. This variable is arrived at by combining responses on three different set of questions asked, such as, whether or not community is exposed to film screening, any kind of drama/natak and puppetry shows. The three different variables are combined into one for keeping the model simple and the variance covariance matrix to converge. Exposure being coded as 0 (no) and 1 (yes), the female age at marriage can now be estimated using Equation ( This predicts communities that are not exposed to any form of mass media (film, drama, or puppetry) for them the female age at marriage is 15.72 years which is the community mean female age at marriage. Communities that are exposed to media (film, drama or puppetry) have female age at marriage as 16.23 years. This clearly shows the positive impact of community's exposure to mass media in breaking cultural barriers, taboos and influencing decision making τ has reduced to 0.6656. This indicates that the level-2 predictor "exposure" explains some proportion of community to community variation in female age at marriage in Uttar Pradesh. In order to assess how much variation in communities is explained by "exposure" we compute The SAS output gives the output for Equation (5) .21 0.01 0.04 0.01 0.11 0.58 The fixed effects in Table 5 are all significantly different from 0 (p < 0.05) except for respondents current age. As the variable exposure is a dummy variable indicating whether community is exposed to films, drama & puppetry or not, the pair for equation for variable exposure can be written as: For communities exposed to film, drama, puppetry and individuals belonging to Hindu religion and either other backward caste and general category for them the average female age at marriage is given by Equation (7 For communities not exposed to film, drama, puppetry and individuals belonging to religion other than Hindus and SC/ST caste for them the average female age at marriage is given by Equation (8  In order to assess how much within community variations in female age at marriage is explained by the introduction of the individual level predictors in the model. We compare the value of residual from unconstrained models to that of the model Equation (4). This is calculated as

Conclusions
The community's exposure to media (films, drama or puppetry) does influence female age at marriage positively. Compared to the communities that are not exposed to such kind of media, those exposed to such media have higher female age at marriage and are statistically significant. At individual level the educational levels of both respondents as well as their husbands has a positive impact and raises the female age at marriage in Uttar Pradesh and is statistically significant. Similarly, Hindu female have lower age at marriage compared to other religious groups, this is primarily due to the prevalent return marriage (gauna) ceremonies among Hindus which is considered as the effective age at marriage.
Being from general caste and other backward caste pulls the mean age at marriage upwards than SC/ST.
It is therefore imperative for the government not only to strengthen the rural educational opportunities for females in Uttar Pradesh but also to strengthen the reach and access to informative and educative films/snippets and other forms of educative media in these areas targeting rural adolescent's females using local dialect. This will enable the young adolescents in making informed decisions in the matters relating to their marriage age and not to marry below the legal minimum of 18 years.
NFHS-2 is the only dataset providing information at community level. In order to capture the digital revolution of the recent times in true sense within the community in rural areas, breaking the stereotypes, the future rounds of NFHS (proposed NFHS-5 in 2018-19) should incorporate some of these variables that capture the impact of social media revolution in the country and it's within community impacts on social indicators.