In Tables 4, 5 and 6 we present our econometric results based on equations 1 and 3 (or 1 and 2) for the Logit model and equation 4 for the Poisson Regression Model. For all the models, we report marginal effects and standard errors associated with the marginal effects. We control for regional and time effects but for brevity do not report them.
Table 4
Estimation results from logit and poisson regression models
Table 5
Estimation results: coefficients of Indian interactions with the independent variables
Table 6
Estimation results: coefficients of Pakistani interactions with independent variables
The standard errors are clustered at the level of ethnicity and geographic area. Clustering at the level of ethnicity allows for the possibility that the behavior of households originating from the same home country may be correlated due to ethnic-specific norms (Clark 2003). However, as cluster robust standard errors require the number of groups to go to infinity in order to be consistent, we choose to cluster on the interactions of the ethnicity groups reported in Table 1 and geographic area. This strategy allows for the English (or any other ethnic group) that live in London to behave differently compared to the English (or any other ethnic group) that live in other regions. Additionally, this increases our group numbers to 31 rather than 3.
For columns one and two, we estimate the Logit model based on equation 1 and 3 using the General Household Survey dataset. In column one, the model is estimated with the dependent variable being the third-child-male indicator variable for families with at least three children, whereas, in column two we use the male indicator variable at the second parity for families with at least two children. In column three, we estimate the Poisson Regression Model using the same dataset.
In column four, we estimate equations 1 and 2 using the information obtained from the NFHS dataset. Hence we examine the preferences of Indian households that live in India. The dependent variable for this case is the third-child-male indicator for families with at least three children. It is important to note that the coefficients represent directly the preferences and not the differences from other ethnic groups. So the first three columns in each table are the results for the UK; the last column is for India.
As English is the omitted category, the coefficients in Table 4 should be interpreted as: The probability of a male birth at a given parity for an English household, conditional on all previous births being male, mixed-gender or female. The coefficients in Tables 5 and 6 should be interpreted as: The difference in the probability of a male birth at a given parity between an Indian (or Pakistani) and an English household, conditional on all previous births. Similarly, the omitted variable in all three tables is all previous births being male. Consequently, the all girls (or mixed gender) interactions should be interpreted as: The difference in the probability of a male-birth at a given parity between households with all previous children being female (or a mixed set of males and females) and household with all previous children being male, depending on ethnicity.
Looking at the results of column 3, employed, wealthy and more educated English households prefer smaller families. Pakistani families have on average more children than English families. Uneducated and wealthier Pakistani households tend to have even more children. These results and the descriptive statistics we present in Table 2 provide evidence that the practice of sex-selective abortion among Pakistani immigrants may be prevalent among higher order births but unlikely to show up at second and third births as their ‘male-preferring stopping rule’ implies relatively low restrictions on family size. On the other hand, Indian households deviate from English households as follows: Uneducated households prefer larger families, whereas the best educated households prefer smaller families. Owning a house boosts the preferences for larger families. These results provide evidence that the practice of sex-selection among uneducated and wealthy Indian immigrants may be prevalent at the fourth parity. However, as we want to examine the differences in coefficients between ethnic groups, we give emphasis to the second and third parity since 89.5% of English household have three or less children. Regarding the choice of the PRM, the likelihood ratio test fails to reject the null hypothesis of no overdispersion, suggesting that the PRM is an improvement to the NBRM.
Comparing column 1 and column 2, we observe higher coefficients in absolute values for the first column which supports the hypothesis that households are engaging in sex-selection when they have their last child. Thus, it is reasonable to focus on the column 1 results for all English households and non-uneducated Indian households. The results in column 1 possibly provide evidence that Indian families are engaging more in sex-selection compared to English families. On average, uneducated English families seem to engage in sex-selection of male births in case they have two male children. Again, only uneducated English households engage in sex-selection of female births when they have two girls or a mixed group of boys and girls. However, these coefficients are relatively small.
Since we reduce our ethnic groups, it is possible that the English sex-selection arises from the fact that we define “English” as a family where at least one parent was born in the UK. To test that, we apply the model presented in column 1 on the “pure” English families only, and we find that for the uneducated English households the coefficient of the interaction of the university dummy with the dummy of households with two female children is 0.088 (near 0.083) even with the reduced sample. The standard error is 0.039 and it is again statistically significant at the 5% level. In contrast, the best-educated Indian households with two boys seem to undertake sex-selection compared to English households possibly in order to give birth to a daughter. Both uneducated English and best-educated Indian households seem to prefer a balanced household. This is consistent with the results in column 4 for Indians in the home country. For Indian families with two girls, all prefer a third male child. The best educated households have much higher incentives to indulge in sex-selection. These results are in line with the results in column 4 for the best educated households, but this is not evident for the remainder of households. It is possible that living in a developed country with publicly-provided health-care provides all residents with enough information about sex-determination techniques so that even the less educated families have this information.
Finally, although the best educated Indian families with a mixed-gender set of previous children in the home country use the techniques we describe above to have a second boy, this does not occur in the host country possibly because of varied cultural effects. Overall, the effect of education on the incentives for sex-selection among son-preferring families is clearly positive, endorsing our initial conjecture and the findings by Jha et al. (2006) that educated households are generally more aware of the availability of gender determination techniques and so more likely to use them 3. Lastly, for the Pakistani coefficients reported in Table 6, we need to mention that they might not be in line with the results in Table 1, but this occurs because we do not present the regional dummy coefficients; for example, the interaction of IndianXtwogirlXLondon is 0.37, statistically significant, and over 60% of Indian households with the first two being female children live in London.
Regarding the Oaxaca-Blinder decomposition using equation 5, the difference in average male birth probabilities between the English (group 1) and the Indians (group 2) in the UK is −0.0166. The ethnic effect is found to be 86.95%. Intuitively, this represents the proportion of the average difference in male birth likelihoods which are explained by giving Indians an English ethnicity, conditional on both groups having the same attributes. The balance percentage is the attribute effect which represents the proportion of the relevant difference that is explained by giving Indians English attributes, conditional on both groups having the same ethnicity.
By using equation 6, we estimate the number of ‘missing women’ in the UK among Indian immigrants. The estimation is carried out for two time periods: 1970–2006 and 1993–2006. For our main results provided in Tables 4, 5 and 6 column 1, we assume that the coefficients are the same for all time periods. However, this is not true as we know that sex-determination was not possible before the 25th week of pregnancy until the late 1980s (Dubuc and Coleman 2007). Also in the period 1980–1992 consumers were uncertain about the quality of the “new product” and the prices of sex-determination procedures were significantly higher. In the last period consumers overcame their hesitation regarding the safety of the procedure and prices fell significantly. Consequently, the latter period 1993–2006 captures the peak of the use of the technology for sex-selective abortion.
We construct Table 7 where Panel (A) contrasts the numbers of boys and girls that Indian families actually gave birth to (Actual), with the corresponding number in the case where they are endowed with English characteristics (Predicted), noting how many Indian households would have essentially changed their fertility decision from boys to girls, holding the number of children constant. Equivalently, Panel (B) depicts the relevant figures for English families.
Table 7
Estimating the number of Indian 'missing women'
Panel (A) suggests that, for the whole period, there would have been 3.00% fewer boys among Indian families, had they been given English preferences or equivalently 3.37% more girls. In the subset, these percentages are slightly higher, consonant with enhanced sex-selective abortion availability after 1990.
From Panel (B), we can infer that, had English families had the Indian son-preferring characteristics, they would have had 5.39% less boys in the whole sample but a striking 11.42% additional boys during the final period (or equivalently 751 missing women). The results for the full sample come from the fact that the Indian coefficients were estimated for earlier periods, and because 9170 English households have two boys. This is about 15.6% more households compared to English households with two girls, as shown in Table 1.
By accepting the evidence of Dubuc and Coleman (2007) regarding the national number of Indian third- parity male births (about 8000 for our critical period), we conclude that there are 914 missing women for that period 4. Abrevaya (2009) finds “a conservative estimate” of 1,300 missing women among Indian families in the USA during a similar period 1992–2004. Our results seem consistent with the Abrevaya study if we take into account the difference between immigrant population sizes: there are approximately 1.5 times as many Indian Americans as there are British Indians (US Census and UK Census). Abrevaya’s corresponding number for our case is 867, which belongs to the closed interval of [751,914].
Two important caveats that affect the validity of our findings need to be acknowledged. Firstly, despite the efforts made to expand our sample size by pooling over all survey years, any causal and quantitative claims that concern the determinants of sex-selection decisions deduced from the marginal effects of three-variable interaction terms, must be advanced very cautiously, as the sample size becomes rather small. Indeed, the best we can do is to provide qualitative conclusions about these effects. Secondly, even though we include all controls available in the dataset and account for time, region and ethnic fixed effects, it is still not possible to control for unobserved heterogeneity across households, individuals and time.