Women and men are considered equal in the 21st century. Research in this arena is not rare. However, an article analyzing the poverty scenario of women based on their marital status might trigger curious minds. Married and unmarried women face different economical, political, and cultural challenges. Female-headed households suffer different hurdles compared to their male counterparts.
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Poverty in India is associated with gender, religion, education, geographical location, occupation, and other qualitative factors. A deep dive into the literature is required to understand the dynamics and associations of these categorical variables with Indian poverty.
As per the UNDP, poverty depends on three crucial factors:
1) longevity of life
2) basic education
3) access to essential economic services like safe drinking water, provision of medical and healthcare treatments so on.
To understand what is poverty and how the poverty is being measured please read our previous article:
The data was downloaded from the official website of the Indian Human Development Survey. The website has easy access to the household and individual data sets of 2004-2005 and 2011-2012. Due to the easy availability of the data sets, the two particular years were chosen for comparative studies.
The India Human Development Survey (IHDS) is an organization that conducts a national survey of 41,554 households in 1503 villages and 971 urban neighborhoods across India. The team surveys and collects data that covers multitopic areas like education, income, health, employment, economic status, marriage, fertility, gender relations, social capital, and so on. The survey of 2004-2005 and 2011-2012 both covered all states and Union territories of India. However, there were exceptions:
- Andaman & Nicobar and
The individual data sets were used throughout the study. Merging the individual data sets of 2004-2005 and 2011-2012 were difficult. Both the files had to be sort based on state Id, district id, PSU id, household id, Household split id and personal id. Overlapping data was difficult to extract. However, there are no household records without at least one individual record and no individual records without a household record.
The poverty line used for the survey was also mentioned by IHDS. The poverty line keeps fluctuating and hence, the poverty line was adjusted based on the month of interview by the IHDS team. These records were compiled and stored in STATA, SPSS, and other econometrics compatible formats. Henceforth, running STATA commands, and obtaining analyzed results were easy.
The ratio is not as appealing as an economist expects it to be. The reason behind such a ratio is the primary survey. Indian Government performs Janganana (Population count) accurately but the data we used is a sample collection of the population. Please visit https://censusindia.gov.in/ for more accurate data information. However, the article will proceed with analysis of women poverty based on the 23% of the poor population (2004-2005).
As per 2004-05 individual data, there isn’t much difference between the male and female poverty percentages. But we must not forget that this is sample data and not the actual population data. Hence, biased-ness is possible. The last few decades have seen remarkable progress among the Indian women. The upgraded lifestyle of urban areas, financially independent women have captured a lot of eyes. Today, women have access to education, and the socio-economic standards of women have increased. Apart from education, Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is a perfect example of paid work for rural poor women, an initiative taken by the Indian Government.
The marital status of an individual is a key determinant of Indian poverty. Unmarried women comprises of a higher percentage of the poor population In India. There are many laws and financial benefits which benefit couples rather than unmarried people. 53.78% of the poor women population are unmarried. Now let’s move to the descriptive statistics of 2011-12.
Comparing the data sets of 2004-2005 and 2011-2012, it is evident that there was a drop in the poverty percentage since 2004-2005. Indian Government had taken up plenty measures to eradicate poverty since 2004. The focus of the public expenditure was to provide employment opportunities to the underdeveloped and underprivileged society. Employment opportunities like MNREGA and Pradhan Mantri Rojgaar Yojana, had a dominating role in lowering the poverty.
According to the descriptive statistics, female percentage of poor population was lesser than the male percentage. Again, we must not ignore the fact that the data is a sample collection and not the actual population description. The table below, breaks down the marital status of these 48.61% of (poor) women.
According to the descriptive statistics of 2011-12, the married women contributed higher to the poor population. Unlike the descriptive statistics of 2004-05 unmarried women share a smaller section of the poor women committee. We can attribute this improvement to the fact that single women have better employment possibilities, credit availability, education opportunities. Today women prefer getting educated, employed, stable, and independent. Indian Government has taken plenty of steps to uplift and empower women. The results are evident in the data too.
However, we cannot make a firm comment based on the above descriptive statistics. A PROBIT model is run to understand the probability of a women to fall below the poverty line based on their marital status.
PROBIT Model- Women Marital status outcome.
To understand what are the determinate of poverty in India, a PROBIT model is used. In Econometrics, a Probit model is a regression model where the dependent variable can take numerical values.
Pi = βXi + ei
Where, Pi is the probability that an individual falls below the poverty line.
Pi = Poor
Here, Poor can take two values
0 = not poor (not below the poverty line)
1 = poor (below the poverty line)
Xi = represents the vector of demographic characteristics.
Marital Status: For simplicity purposes, the marital status has been divided into three categories.
Not married= 0
Married = 1
Divorced / Separated = 2
It is evident from graph 1, that married women have a lesser probability compared to unmarried or separated women. An unmarried woman has 0.205% chances of falling below the poverty line. A married woman has 0.20% chances, whereas, a separated or divorced woman has 0.214% chances of falling into the poverty trap. As we know from probability rule, the summation of all probabilities = 1.
Σ P = Σ p1 + Σ p2 + …… +Σ pn
1 = 0.205+0.20+0.214+0.381
0.381 comprises of the women who are either widow or women whose spouse are absent (i.e. women who are victim of polygamy, or whose husband have been on business voyage for years without proper news and money remunerations).
Who is worse off?
Women are stereo-typically concentrated in the precarious part of the tertiary sector. Most jobs are temporary, part-time, or contractual with low paying capacities. Women are unpaid contributors to the family. In a male-headed family, the women bear the household works. However, a female-headed family is managed and handled by the same person. The burden on such a person is a much higher man. Poverty among a single-parent family is four times higher than a two parents family (Sawhill and Thoas 2001).
Poverty is related to change in family structure. A divorce implies a change of great economic significance. The change concerns the loss of a partner’s income. Couples partially pool their incomes during the marriage, and access to this income is prevented post-divorce. Custody of a child is usually given to the mother and the income receipt for raising a child (alone) is insufficient. Hence, most women experience sizable drops in household income. As a consequence, many women fall into poverty following divorce.
India has a population of 135.26 crores. IHDS (I and II) had reported individual data of approximately 2,04,569 individuals. The data could have been a biased or false representation of the Indian population.
More tests could be conducted provided the same individuals were interviewed both in 2004-05 and 2011-12. Merging the data gave us only 1,27,333 observations that might not be the accurate image of the country.
In the end, it will not be incorrect to state that marital status affects the well-being of a woman. It is not easy for a woman to stand steady in a male chauvinist world. So what do you think? Will a single or a divorced woman survive in this complex world? Does the female charisma shine loud irrespective of its marital status?
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