What suicides reveal about gender bias

The Journal of Socio-Economics 37 (2008) 1713–1723

What suicides reveal about gender bias Siddhartha Mitra ∗ , Sangeeta Shroff Gokhale Institute of Politics and Economics, Pune 411004, India Received 15 November 2006; received in revised form 18 July 2007; accepted 5 October 2007

Abstract This article uses some general findings in the medical literature on suicide to suggest how male and female suicide rates in a society can be used to measure the “unfreedom” of women relative to that of men. Our definition of “unfreedom” is similar to that of Amartya Sen and consists of all kinds of suppression of mental or physical freedoms such as physical or sexual abuse, poverty, lack of economic opportunities, and so on, as well as an absence of liberty to voice complaints about the denial of the mentioned elementary freedoms. Though suicides are often associated with mental disease partially attributable to genetic factors, a mental illness is neither necessary nor sufficient for suicide. Rather a suicide is often the result of a multiple coincidence of mental disorders and repression of elementary freedoms. Given that the male and female cohorts in a society have the same genetic background, a major change in the male female ratio of suicide rates can conceivably occur only through a change in the relative incidence of unfreedoms. An application of this inference is attempted for Indian states. © 2007 Elsevier Inc. All rights reserved. Keywords: Suicide rate; Empowerment; Relative wellbeing; Status

1. Introduction Suicide is a complex phenomenon which is often very difficult to comprehend. A number of studies (Durkheim, 1952; Helliwell, 2004, etc.) have probed into the causes of suicides and observed that isolation of the individual from society and lack of social capital are important factors resulting in this extreme step. Further, the suicide literature reveals that causes of suicides vary in different nations and there are large differences in the rates of suicides across countries and over time within countries. A feature that is common to various studies is the existence of significant gender differences in suicide rates, with women displaying a lower suicide rate than men. China, however, is a singular exception to this pattern with female rates exceeding male rates. Some explanations for a lower suicide rate among women is that though women suffer from depression at a higher rate than men, they are more effective in expressing their emotions whereas men suppress their feelings. Hence women are “much more likely to tell a physician how they feel and cooperate in the prescribed treatment. As a result women get better treatment for their depression (www.scienceblog.com)”. Another explanation (Steen and Mayer, 2003) is that women are more likely to hold religious beliefs and negative attitudes towards suicides. Women may also cope better than men because they are expected to successfully deal with multiple roles and they have greater support networks and are more likely to seek professional help whenever required. Finally, men generally tend to adopt violent and assured ∗

Corresponding author. E-mail address: [email protected] (S. Mitra).

1053-5357/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.socec.2007.10.007

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methods for suicide whereas women adopt gentler methods like sleeping pill overdose. The latter group of methods often proves to be unsuccessful in achieving its final objective. Apart from these reasons, another cause for a higher male suicide rate in developing countries might be the fact that in most cases it is men who shoulder the responsibility of being breadwinners in their families and are therefore subjected to greater stress and uncertainty. However, this argument cannot be used to explain the distinctly greater suicide rate of men in the first world where women almost rival men as earners of family income. In the case of China, which is an outlier in this respect, the literature available (taylorandfrancis.metapress.com; www.rfa.org; www.cdc.gov; etc.) reveals that suicides are more common among young females in rural areas because of the relatively low status of women in Chinese society which is still patriarchal. Often deeply frustrating constraints are placed on women and they are not allowed to avail of opportunities. Behaviour towards them is often quite cruel. Suicide is therefore a form of protest and escape from social distress. Data across 89 countries for 2003 (WHO, 2006) reveals that the ratio of male suicide rates to female suicide rates (MENFEM) is always greater than one with the exception of China as explained above and varies from 0.88 to 13.44. The change in this ratio over time can be one indicator of the trend in “unfreedom” (defined in the sense of Sen (1999) as consisting of all kinds of suppression of mental or physical freedoms such as physical or sexual abuse, poverty, lack of economic opportunities, and so on, as well as an absence of liberty to voice complaints about the denial of the mentioned elementary freedoms) of women relative to that of men, as will be explained in Section 2. Accordingly, we have studied the dynamics in this ratio. While the dynamics in this ratio vary across countries, there are also striking differences across regions in the same country. India, a developing country, is one such case which comprises of a large number of States and Union Territories which are at different stages of social and economic development. The relative suicide rates of men and women vary across states and over time for any given state. Accordingly, in this paper we have based our analysis on data on 22 states in India. 2. Theoretical justification for methodology We use certain conclusions that the medical literature on suicide has reached to construct a theoretical index of suicide rates. First, even though 90% of people who commit suicide have a diagnosable mental disorder at the time of death, a very tiny proportion of people with psychiatric illnesses actually attempt suicide. The severity of the mental disorder is also a poor predictor of suicide attempts (Sher, 2004). While a psychiatric condition is observed in most people who commit suicide, this phenomenon is often marked by the coincidence of several triggering phenomena/circumstances such as loss of job, wealth and property, unemployment, history of physical or sexual abuse, hopelessness, etc. These factors act additively as well as synergistically to enhance the risk of suicide by an individual (Sher, 2004 and Gaynes et al., 2004). Koivumaa-Honkanen et al. (2001) concluded on the basis of a 20-year-old study of Finnish Twin Cohorts that people who committed suicide were more likely to be those who obtained “low satisfaction” from life. The incidence of suicide per capita among gender j (male or female) in our theoretical formulation is, therefore, given by1 Smale = A Gmale smale ,

Sfemale = A CGfemale sfemale ;

C<1

where Gj is an index of average susceptibility to stress or mental disorders (and therefore to suicidal tendencies) due to genetic factors among members of gender j2 and sj captures the impact of the incidence of socio-economic triggers (e.g. mental disorders, unemployment rate, domestic violence, sexual abuse, etc.) on stress/suicidal tendencies among members of gender j and is an increasing function of the incidence of any such trigger. A and C are constants which reflect the fact that ex-ante we can only talk about the extent of risk of suicide but not predict it with any surety. Thus, A and C are used to map the average incidence of stresses leading to suicidal tendencies to actual suicide rates. C < 1 reflects the greater coping ability that women have with respect to stresses.

1

This formulation is based on a medically oriented approach to suicide incidence, as explained above. Durkheim (1897) explains the incidence of suicides on the basis of social integration and economic instability. There is no apparent contradiction between Durkheim’s theory of suicide and the medical approach to suicide as the incidence of negative experiences is often related to the factors mentioned by Durkheim. 2 While on an average genetic material determining behaviour might be the same for males and females in a community, G male is allowed to be different from Gfemale because of the possibility of differential impact of the same genetic material on suicidal behaviour across genders due to physiological differences.

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Dividing the first equation by the second equation we get Gmale smale Smale =C Sfemale Gfemale sfemale Given that genetic attributes of a homogenous community change very slowly over time, the first ratio on the right hand side can be taken to be a constant. As C is also a constant the rate of growth of Smale /Sfemale is also an increasing function of the rate of growth of smale /sfemale i.e. the male–female ratio of suicide rates increases as the ratio of male and female suicidal stresses increases and at a rate which is equal to that of the latter. If we go by the Report of the Surgeon General, Government of USA (2001), genetic susceptibilities to mental disorders do not vary across races. Therefore, it should be possible to compare any two communities, say j and m, as well. Thus Smale,j /Sfemale,j C(Gmale,j /Gfemale,j )(smale,j /sfemale,j ) smale,j /sfemale,j = = Smale,m /Sfemale,m C(Gmale,m /Gfemale,m )(smale,m /sfemale,m ) smale,m /sfemale,m Thus, we can infer that the inter community ratios of male–female ratios of suicide rates do capture the relative exposure of men and women living in different communities to socio-economic triggers that enhance suffering. The male–female suicide rate ratios help to combine various measures of unfreedom (e.g. exposure to violence and abuse) into a natural composite index. Such a procedure cannot be duplicated by any artificial procedure of assigning weights to these measures. Estimation of socio-economic measures of gender unfreedom (such as incidence of domestic violence, physical and sexual abuse) is often fraught with difficulties and errors due to underreporting as this passage from Kishor (2005) illustrates. “There is often a culture of silence around the topic of domestic violence that makes the collection of data on this sensitive topic particularly challenging. Even women who want to speak about their experience with domestic violence may find it difficult because of feelings of shame or fear.” Mary Ellsberg et al. (2001) conclude that the extent of underreporting of domestic violence depends upon the structuring of the questionnaire and how it is administered. More importantly, the degree of underreporting may vary from one cultural/ethnic group/community to another as this passage from a written memorandum by the Southall Black Sisters, a non-profit organization, to the Government of United Kingdom (2006) illustrates: “There is severe underreporting of domestic violence in minority communities, where the incidence of domestic violence, homicides (including honour killings) and suicides are high due to cultural pressures on women to maintain silence. For example, research shows that the suicides rates amongst Asian (subcontinental) women in the UK are up to 3 times the national average especially amongst those aged between 15 and 24. The research shows that pressures which lead to suicide, including the need to maintain cultural identity and tradition are intensified in young Asian women, given their rigidly defined roles in Asian society. ‘Submission and deference to males and elders, the financial pressures imposed by dowries, and ensuing marital and family conflicts have been contributory factors to suicide and attempted suicide in young Indian women’. In our experience, in the face of violence or abuse, many Asian women feel that they have no option but to self harm or kill themselves. Culturally powerful obstacles of izzat (honour) and sharam (shame) prevent women from disclosing violence or leaving their marriage. Many women are rejected and isolated if they break with the cultural norms of their community. In extreme cases they are killed in what are termed ‘honour killings’. Religion and popular culture reinforce the notion that a woman should suffer her fate in silence and that it is preferable to die than to dishonour family name.” Such underreporting of gender related unfreedoms implies that an aggregate measure of relative male and female suffering is best captured by a revealed measure such as the male–female ratio of suicide rates rather than a direct aggregation of statistics related to these separate measures. A low reported incidence of a given unfreedom (say, domestic violence) might be a result of a lack of freedom for voicing complaints and expressing anguish rather than a low actual incidence of this unfreedom. Moreover, lack of freedom to complain about injustice is itself a determinant of aggregate unfreedom. The concept of social unfreedom can also be used to construct a model based on utility theory which determines suicide rates and ratios and is in conformity with some recent findings which relate low life satisfaction to suicide.

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Let the utility function of an individual be given by U = U(y, a1 , a2 , . . . , an ) where ai is the ith variable which signifies the occurrence of a particular type of negative experience (say domestic violence) in the life of an individual. For the sake of simplicity, we assume that “ai = 1” implies that this negative experience has occurred in the life of an individual whereas “ai = 0” implies that it has not occurred. The expected utility of an individual with income level y is given by   U(y, a1 , a2 , . . . , an ) pi (ai ), ai ∈ {0,1}∀i

∀i

assuming independent probabilities of occurrence of negative experiences. Let us assume that individual threshold utility, denoted by Ij for gender j, follows a normal distribution with mean μj for gender j with the property that μmale > μfemale but with identical coefficients of variation for males and females. If an individual’s utility falls below the threshold level then he or she commits suicide. The threshold level captures the mental condition of the individual with a higher threshold depicting a poorer mental condition (toughness). People with mental illness are therefore represented by the right tail of the distribution of Ij . Let income be distributed normally over the population. The probability of suicide for an individual of income level y is given by  ⎞ ⎛  ¯ U(y, a , a , . . . , a ) p (a ) − U 1 2 n i i ai ∈ {0,1)∀i ∀ii ⎠, F ⎝Z = σU where F(Z) is the distribution function corresponding to the standard normal distribution and U¯ is the expected value of individual utility when the entire population is considered. The term σ U refers to the standard deviation of the distribution of utilities over the population. Thus, the suicide rate for the entire population is given by  ⎞ ⎛  ∞ ¯ ai ∈ {0,1)∀i U(y, a1 , a2 , . . . , an ) ∀ii pi (ai ) − U ⎠ f (y) dy F ⎝Z = σU y=0 where y is assumed to follow a normal distribution with mean μy and standard deviation σ y . In other words we can express y as Zσ y + μy . Thus, the suicide rate for the entire population can be expressed as  ⎞ ⎛  ∞ ¯ U(y, a , a , . . . , a ) p (a ) − U 1 2 n i i a ∈ {0,1)∀i ∀i 1 i i ⎠ f (Z) dz F ⎝Z = σy z=−∞ σU Thus, the male–female ratio of suicide rates is given by     

∞  male F Z= + μmale , a1 , a2 , . . . , an ) ∀ii pmale (ai ) − U¯ /σUmale f (Z) dz σyfemale y i ai ∈ {0,1)∀i U(Zσy z=−∞     

  female ∞ female + μfemale , a , a , . . . , a ) σymale z=−∞ F Z= (ai ) − U¯ /σUfemale f (Z) dz 1 2 n y ai ∈ {0,1)∀i U(Zσy ∀ii pi where the superscripts “male” and “female” are used in the expected manner. Thus, the male–female ratio of suicide rates is dictated by the economic condition and social unfreedom of men relative to that of women. 2.1. Data and methodology The data on Suicide Mortality Rates across states for males and females are taken from Mishra (2006). The data are for the period 1975–2001. We have considered only 22 major states. The growth rates of the ratios of male and female suicide rates over the period 1975–2001 have been estimated. A significantly positive growth rate (in the statistical sense) in this ratio would indicate that the trend growth rate of men’s suicide rate is significantly (again in the statistical sense) greater than that of the female suicide rate and implies that the relative unfreedom of women is falling. If exactly the opposite is true we can infer that their relative unfreedom is rising. If the growth rate of the ratio is statistically insignificant then it indicates no statistically significant change in the relative unfreedom of women.

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Table 1 Time trend in ratio of male and female suicide rate over time State

Time trend over 1975–2001

Number of years required for MENFEM to change by unity

Tamil Nadu Meghalaya Orissa Madhya Pradesh Bihar Uttar Pradesh Himachal Pradesh Assam Jammu & Kashmir Haryana Karnataka Rajasthan Kerala Andhra Pradesh West Bengal Goa Gujarat Maharashtra Nagaland Punjab Sikkim Tripura

Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Decreased Increased Increased Increased Increased Increased Increased No statistically significant change No statistically significant change No statistically significant change No statistically significant change No statistically significant change No statistically significant change No statistically significant change

142.9 125.0 125.0 92.6 52.6 45.5 27.8 18.9 14.9 27.8 66.7 66.7 83.3 166.7 166.7 NA NA NA NA NA NA NA

Source: Derived from Mishra (2006).

3. Results In Table 1 the time trend in ratio of male and female suicide rates is indicated. It can be observed that out of the 22 states as many as 9 states fall in the “decreased” (corresponding to a rate of decrease which is statistically significant) category i.e. women’s relative unfreedom shows a significant increase over time. In the case of five states this ratio exhibited a statistically significant increase indicating a greater percentage increase in male suicide rates than in female suicide rates over time and hence a decreased relative unfreedom of women. There were seven states where no statistically significant change in the ratio over time was observed. With respect to states which showed a statistically significant decrease in the ratio over time, it was observed that Jammu and Kashmir showed the highest rate of decrease with respect to time (as given by the coefficient on time). It would take barely 15 years for MENFEM to fall by 1 from its present level of 1.12 in Jammu Kashmir to .12. In other words, at the present rate female suicides would be 8.33 times the male suicide rate in 15 years time. This alarming rate of increase in the relative unfreedom of women, as indicated by the values of MENFEM for this state at different points of time, surely necessitates policy interventions. At the other end we have states like Meghalaya and Orissa, where 125 years or more would be required for MENFEM to decline by 1 while in case of Tamil Nadu 143 years would be required. Moreover, the present value of MENFEM exceeds 2 for Meghalaya and 1.5 for Tamil Nadu (see Table 2), indicating that the situation in these states is not as serious as Jammu and Kashmir. If we consider a period of 15 years starting from 2001 (as we have for Jammu and Kashmir), in the case of Tamil Nadu MENFEM would decline over this period by just .12 to 1.38. In Meghalaya the same period would see a decline by just 0.1 to 2.32. These decreases are not very serious given that MENFEM in this states is already quite high and portrays a low unfreedom of women relative to men. The state of Tamil Nadu shows a mixed picture in terms of development, with per capita income exceeding the national average only marginally. Though it is the most urbanized state in the country further urbanization seems to be occurring rapidly. In 1981, 32.95% of the population was residing in urban areas and this percentage increased to 43.86 in 2001 (www.indiastat.com). The literacy rate in the state is above the national average. The urban female literacy rate was 75.64% which was 12.5% points less than the corresponding male literacy rate. The gap between rural male and rural female literacy rates was however much higher at 37% points. Work participation rates however showed a

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Table 2 States in descending order of ratio of male suicide rate female suicide rate State

Average of MENFEM over 1999–2001

Kerala Nagaland Meghalaya Punjab Assam Goa Haryana Karnataka Himachal Pradesh Tamil Nadu Sikkim Maharashtra Andhra Pradesh Rajasthan Tripura Madhya Pradesh Gujarat Orissa Jammu & Kashmir West Bengal Uttar Pradesh Bihar

2.57 2.48 2.42 2.35 2.06 1.95 1.91 1.76 1.63 1.55 1.51 1.50 1.48 1.43 1.27 1.23 1.13 1.13 1.12 1.06 0.98 0.96

Source: Derived from Mishra (2006).

different picture. The female work participation in urban areas was only 23% (www.indiastat.com) as against 61% for urban males despite a high urban female literacy rate of 76%. Further, Tamil Nadu had the lowest household size in the state at 4.26 (even lower than highly progressive Kerala at 4.73) falling short of the national average of 5.31 by more than 1 (Census of India, 2001). A study by Steen and Mayer (2004) observed that with female empowerment, that is improved literacy, falling birth rates and so on, suicides increase. The suicide rate is also correlated to size of the household. Declining size of household can lead to isolation due to weakening of traditional social support networks and an erosion of orthodox principles and values. The study further noted that increased opportunities for females often expose them to pressures similar to those in the competitive male working environment which may also increase their vulnerability to suicide. Perhaps the state of Tamil Nadu falls in the category where such socio-economic factors can explain an increase in suicide rates among females. The female suicide rate in Tamil Nadu showed a growth rate of 2.6% per annum (1975–2001) as against a corresponding rate of 2.2% per annum for males (Mishra, 2006). Tamil Nadu also had a policy of paying cash compensation to women who underwent sterilization after having two daughters. In some cases, in order to get this compensation, men first got their wives sterilized, pocketed the compensation money and then remarried (Pande, 2003). This could leave a number of women in isolation leading to depression. In the case of Orissa, which also revealed an increasing relative unfreedom of women over time through these ratios, it is observed that the growth rate in male suicide rates (1975–2001) was 0.7% per annum while in the case of female suicide rates it was 1.5% for the same period (Mishra, 2006). The per capita income of the state is about 55% of the national average which reflects the economic backwardness of the state. According to the National Family Health Survey-2 about 48% of the women population in Orissa suffers from nutritional deficiency. Prevalence of anemia is also very high in the age group of 15–49 years and consequently these women are also more vulnerable to diseases (www.rc.orissa.gov.in). Such factors could be an important cause of depression among women leading to suicidal tendencies. As in the case of China, there are two states in India, namely, Uttar Pradesh and Bihar where the male to female suicide ratio is less than 1. Both Uttar Pradesh and Bihar are poor states with per capita income much below the national average. In terms of the Human Development Index these states rank very low and work participation rates as well as literacy rates for females are much lower than the national average. The female literacy rate in Bihar is only

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33.57% while in Uttar Pradesh it is 43% (Census of India, 2001). The female work participation rate in rural areas in Bihar is 12.3% against the national average of 28.8% while in Uttar Pradesh it is 18%. In urban areas the work participation rate is 8.7% in Bihar and 10% in Uttar Pradesh. Such low levels of literacy and work participation rates are an important social and economic issue. It is possible that low male–female suicide ratios in Bihar and Uttar Pradesh3 can be explained by reasons that are similar to those in the Chinese case, such as the high unfreedom of women and deeply frustrating constraints on opportunities. The growth rate of the male suicide rate in Bihar (1975–2001) is 0.5% per annum as against the corresponding female rate of 2% per annum. In Uttar Pradesh during the same period the male growth rate in suicide was negative while in the case of females it was 1% per annum (Mishra, 2006). As against nine states which fall in the “decreased” category thereby showing a rising unfreedom of women, there are six states which fall in the “increased” category (see Table 1) i.e. women’s unfreedom relative to men has exhibited a statistically significant decrease over time in these states. For states with falling relative unfreedom of women the fall is most impressive for Haryana and least impressive for West Bengal. It is well known that while declining sex ratios is observed in several states in India, the problem is more pronounced in Haryana. This adverse trend in the sex ratio is mainly attributed to female foeticide and infanticide due to son preference. The sex ratio which was 865 in 1991, further declined to 861 in 2001 and in a number of districts the sex ratio was below the 800 mark. This adverse sex ratio, being an important cause of concern in the demography of the state, led to policy intervention. The State and the Central government and other Planning agencies made all round efforts to minimize the sex gap in the population. The Pre-Natal Diagnostic Techniques Act (PNDT), 1994 was enforced by states and Central governments to check misuse of medical techniques. In Haryana the government claimed that due to its pro-women programmes like Apni Beti Apna Dhan (My Daughter, My Treasure), enforcement of PNDT act, etc. there was a check on declining sex ratio (www.alwayson-network.com). Surveys in the state revealed that some women genuinely believed that if their number declines, their value would increase as men would not find suitable brides (www.hinduonnet.com). Women are also aware of their secondary unfreedom in society and learn to survive oppression. The mentioned factors obviously played a role in lowering the female suicides rate in Haryana which declined from 9.4 per 1000 in 1999 to 7.3 per 1000 in 2001. It was also observed that while the male suicide rate in Haryana (1975–2001) grew at 4.5% per annum, the corresponding figure for females was 2% per annum (Mishra, 2006). This implies a rate of growth of the ratio of 2.5% per annum i.e. that ratio would increase by 1 from its present level of 1.91 in just 17 years time. Punjab is another state with a practice of female foeticide and infanticide and a consequent low sex ratio. In 2001 Punjab had a sex ratio of 874. Unlike Haryana, no significant change was observed in the ratio of male and female suicide rates over time. However, the male to female suicide ratio (Table 2) was one of the highest in the country and of the order of 2.35 which is puzzling given the high incidence of female foeticide and infanticide. It was noted in a study (Datta, 2006) that it was not uncommon in Punjabi families to keep only the first girl child. Subsequent female children were killed at birth as the birth of a female child was associated with dowry expenses which had to be borne by the bride’s side. However, once the girl child was accepted and allowed to live, she was closely guarded by the members of the household. She was closely watched and chaperoned on all occasions as her parents feared any possible gossip, which might damage the good name of their daughter and endanger any future marriage negotiations. It is possible that such factors led to low suicide rates of women relative to men. Thus, the male–female suicide ratio in this case might be an understatement of the unfreedom of women relative to men. Rajasthan, though an economically poor state with per capita income well below the national average, shows a female work participation rate which is above the national average in rural areas and close to the national average in urban areas. The literacy rate for females is 10% points below the national average while male literacy rate is more or less the same as the national average. The state is characterized by a wide gap between male and female literacy rates and while male literacy rate is 76.5%, the literacy rate for females is only 44.34 (Census of India, 2001). The ratio of male to female suicide rate has however showed an increasing trend over time. As discussed earlier, suicide is a complex phenomenon explained by many factors. Household size was observed to be above the national average and the large size of household could be a source of social support for women, which gives protection from suicide. Feudal customs and values are also a part of the culture of this state and belief in these customs and values can lower suicidal tendencies.

3

The absolute levels of reported suicide rates in both U.P. and Bihar are low. This could be a result of poor recording as these states have a history of poor governance.

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Kerala ranks first in the Human Development Index in the country with a literacy rate of 90.92% and sex ratio of 1058. It is another state where the male to female suicide ratio has increased over time and while male suicide rate was 48.3 per 1000 in 2001, the corresponding rate for females was 18.5 (Mishra, 2006). A study on suicides among women in Kerala (www.maithrikochi.org) also observed that the ratio of female to male suicide rates has been steadily declining over the years. The decline in the relative female suicide rate is attributed to movements addressing gender issues which have gained momentum in the recent past. There are an increasing number of crisis-intervention centers (which help women in distress), women friendly development initiatives and family courts which consider the grievances of women. Social capital as explained by Helliwell (2004) has positive effects on well-being and thus reduces suicide rates. Perhaps in the case of Kerala, the decline in the rate of suicide among women relative to men can be attributed to the protective effect of social capital. In the case of West Bengal the ratio of the male to the female suicide rate has increased over time, showing lower unfreedom of women. It is around 1.06 (Table 2) at present which shows that the two rates are almost equal. While female literacy rate in the state is above the national average, the female work participation rate is 16.7%, much below the national average indicating poor economic empowerment of women. Indicators like literacy however indicate that social empowerment of women is adequately high. It is when a balance is obtained between social empowerment and economic empowerment that the male to female suicide ratio should approach its biological level of 2–3. Perhaps, West Bengal is progressing in that direction, as is evident from the rising secular trend of the ratio. Seven states fall in the “no statistically significant change” category. It is necessary to point out that “lack of statistical significance” implies that there is not enough evidence with us to say that MENFEM is moving in either an upward or downward direction. Neither does it constitute enough evidence that it is not moving at all. The magnitude of MENFEM however does give us an idea of the relative unfreedom of women. These seven states include states like Gujarat and Tripura where MENFEM is low and is therefore, indicative of a high relative unfreedom of women. From Tables 1 and 2 it can be observed that in some states, where the relative unfreedom of women is high such as Bihar, Orissa and Uttar Pradesh, the trend is for the relative unfreedom to rise further. This is cause for concern. On the other hand in Kerala and to a lesser extent in Karnataka the relative unfreedom is low and decreasing over time. In other states such as West Bengal and Andhra the unfreedom, as revealed by the ratio, is high but falling over time. In yet other states such as Meghalaya and Assam the unfreedom is low but increasing. Thus, we can conclude that both the statics and dynamics of relative unfreedom of women, as captured by the ratio of male and female suicide rates, exhibit a very different picture in different parts of the country. This is illustrative of the truism that different social and economic conditions often give rise to differential levels of pathological behaviour, such as suicides. The various Indian states form interesting case studies of how the mentioned ratio is influenced by the social and economic milieu. Given that the institutions of governance in these states are similar (being part of the same federation), there is enough scope for them to learn from each other’s experiences. Note that following McCloskey (1985) and McCloskey and Ziliak (1996), the size of the impact of time on the male–female suicide ratio has been highlighted (see last column of Table 1). The more substantive the rate of change over time, the smaller is the number. 3.1. Socio-economic and policy determinants of male–female suicide ratios In this section we try to trace the socio-economic causes of differences in male–female suicide ratios across regions and over time. This is similar to the exercise conducted by Durkheim (1952, 1897) for overall suicide rates. We use a panel of data on suicide ratios, per capita incomes and overall, male and female literacy rates for five equally spaced out points of time starting from 1977 to 2001 (the included years being 1977, 1983, 1989, 1995, 2001). While suicide ratios are obtained from the data in Mishra (2006), data on literacy4 and per capita income are obtained from www.indiastat.com. Per capita income can affect the male–female suicide ratios through various channels. As economic development proceeds, institutions to which women can turn to in times of distress, improve. On the other hand, women might be exposed more to professional pressures. Theoretically, the impact of per capita income on the male–female suicide ratio remains ambiguous. Fixed effect and random effect panel regressions (see Table 3) of the male–female suicide 4

Interpolations have been used to calculate figures for literacy in inter-census years.

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Table 3 Regression resultsa with male female suicide ratio as dependant variable Regression

1

2

3

4

5

6

F Statistic/chi2 Type of regression

.0272 Random effects

.10** Pooled

.22*** Random effects

.002* Random effects

.04* Random effects

.21*** Cross-sectional (2001)

0.900

.10** (+)

.11*** (.000058) .09** (−.017)

0.57

0.329 (.000036) 0.01 (−.032) 0.02* (.019)

0.984

Coefficients Per capita income Time Overall literacy Male literacy Female literacy Beds per 1000

0.001* (−.06) 0.000* (+.053) 0.198*** (.4311)

Figures reported outside parentheses are p values. *, ** and *** represent significance at 5%, 10% and 25% level, respectively. Magnitudes of significant coefficients are indicated in parentheses. a All figures reported are p values.

ratio on per capita income (at 1993–1994 prices) turn out to be entirely statistically insignificant (as revealed by the F-test). Pooled regressions, with the same independent and dependent variable, yield significance of per capita income, but only at 10% (see Table 3). The results imply that an increase in per capita income increases the male–female suicide ratio, thereby implying that the downward pressure on female suicide rates exerted by better institutions overwhelms the upward thrust from greater professional pressures. It might be the case that our model is not correctly specified. Increases in the male–female suicide ratio might be due to changes in male, female or overall literacy. Moreover, literacy might not be perfectly correlated with per capita income. While correlation exists for certain sub-groups of states in the Indian case, it often breaks down for others. For example, in 2001, Kerala with a per capita income which was less than 70% of that of the leader, Punjab, had literacy rates far exceeding those of any state in the country. In that year Kerala had a literacy rate of 91% whereas Punjab had a literacy rate of 70%. Thus, literacy might have effects on male–female suicide ratios which are independent of those of per capita income. Female literacy might be empowering and might increase the ratio. Male literacy might result in more compassion, an improvement in attitudes and a decrease in domestic violence and sexual abuse—a fact which might also result in an increase in this ratio. Overall literacy increase might be enlightening and lead to an increase in the ratio. Table 3 captures all the regression results in the section. The numerical values and signs of coefficients are indicated only if they are statistically significant. The p-value (lowest possible level of significance) and significance at 5%, 10% and 25% are indicated appropriately. Following McCloskey (1985) and McCloskey and Ziliak (1996), we study the substantive significance of coefficients. A random effects regression of male–female suicide ratios on time and per capita income leads to the conclusion that both explanatory variables are statistically significant, though at differing levels, with time having a negative effect and per capita income having a positive effect. This implies that after controlling for the effect of per capita income the suicide rates for women are going up faster than those for men over time. This is quite disturbing. Quantitatively, the effect of per capita income on male–female suicide ratios is not very large; Table 3 implies that an increase in per capita income by Rs. 1000 would increase the male–female suicide ratio by 6% points. In other words, an increase in per capita income per annum by around Rs. 17,000 would be required to raise MENFEM by 1. This required increase is considerable given that many states have a per capita income which is below Rs. 17,000. The effect of time is substantial—a decrease in male–female suicide ratio at the rate of 1.7% points per year. In regression 4, male literacy increase actually tends to lead to a statistically significant decrease in the male–female suicide ratio (this might be due to the fact that it increases the importance of men in the household and hence, their relative bargaining power) and female literacy tends to increase the ratio (this might be due to the empowering effect it has on women which enables them to fight evils such as domestic violence and sexual abuse). Quantitatively, every percentage point increase in male literacy brings down the male–female suicide ratio by 6% points and every percentage point increase in the female literacy rate increases this ratio by 5.3% points. An increase of 1 in MENFEM can be brought about by an increase in female literacy by 19% points. This result is very encouraging given that over the years female literacy is expected

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to register massive increases over its level of 54% in the 2001 Census. It is instructive to note that India had a female literacy rate of just 15% in 1961. Given that female literacy has increased by 39% points in 40 years we should expect a rate of increase of roughly 1% point every year or 19% points in 19 years. Clearly, female literacy is a key driver of change. Encouragingly regression 5 shows that overall literacy increase, which is also a statistically significant determinant, itself increases the ratio i.e. this might indicate that a more literate society is less tolerant of women’s sufferings. The rate of increase is however, understandably smaller at 1.9% point per percentage point increase in literacy, the reason being the difference in the direction of impact of male and female literacy rates on the ratio. Next we look at policy variables. Once we control for hospital beds per 1000 population (data on hospital beds in different states are obtained from the Tenth Five Year Plan Document, Government of India) literacy itself becomes statistically insignificant. However, the former has a positive and statistically significant effect on the ratio. Given the statistical significance of literacy in regression 5, it seems that a high figure for hospital beds per 1000 should be associated with a high level of literacy as well. This is true as they have a correlation of .68 for 2001—the year for which regression 6 is conducted. A higher figure for hospital beds per 1000 might have a direct effect as better care of women attempting suicide is made possible. It also might be indicative of a more welfare oriented and enlightened society. Quantitatively, the effect is significant as an increase in the number of beds per 1000 by 1 increases the male–female suicide ratio by 0.43% points. Note, however, that this inference is based on a regression on the cross-sectional data of a single year. The results, therefore, are not very reliable. Some regressions were also tried for health expenditure per capita (again deduced from the data on health expenditure in the Tenth Five Year Plan Document, Government of India) but were not reported as this variable turned out to be statistically insignificant in all regressions. This might be due to the fact that health expenditure performs both preventive and curative functions. A higher incidence of suicides among women might be a cause for undertaking higher health expenditure. On the other hand, it is quite possible that higher health expenditure performs the function of checking suicide rates. Moreover, health expenditure has two components—expenditure on physical health programmes and expenditure on mental heath programmes. An increase in overall health expenditure cannot be considered to be always associated with an increase in mental health expenditure. To that extent, the level of health expenditure will not have a systematic effect on the incidence of mental health problems in a state. The decomposition of health expenditure is not known for any state in India, thus limiting the scope and usefulness of our analysis. 4. Conclusion In this paper we have used time trends in the ratio of male and female suicide rates to capture secular trends in the unfreedom of women relative to men in different parts of India. We find widely different results with states like Kerala, Haryana and West Bengal showing a significant improvement over time whereas others like Uttar Pradesh and Bihar showing a significant deterioration from an already gloomy picture. Our results for these ratios and the implications we draw for the relative unfreedom of women are consistent with other well known facts. For example, Meghalaya and Kerala have been known to have matriarchal societies and therefore a low unfreedom of women. In our study we find that the current value of the mentioned ratio for these two states is in fact among the lowest for our sample. However, caution should be exercised before reading too much into these rankings as comparisons across regions at a point of time are less reliable than those for a given region over time. The reason is that the implication of the biological advantage, that females have over males in terms of the propensity to resist suicide, for the male–female suicide ratio might vary from culture to culture. For examples, cultures in which more social interaction is allowed or encouraged among women will tend to reinforce this biological advantage of women. The unfreedom of women relative to men can be attributed to socio-economic and policy variables. Regressions reveal that the relative unfreedom of women, as revealed by the male–female suicide ratio, is increasing over time if we control for the effect of per capita income. While female literacy generates pressures which tend to lower the relative unfreedom of women, male literacy with unchanged female literacy generates pressures in the other direction, most probably by increasing the bargaining power of men in the household. Overall literacy on the other hand is found to decrease the relative unfreedom of women, by resulting in a more tolerant society. Variables that indicate how welfare oriented a society is, such as beds per 1000 people, also bring about a fall in the relative unfreedom of women. A higher

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