Couples’ data to study contraceptive use at last sexual intercourse The Demographic and Health Survey interrogates women of reproductive age (15-49) in

all selected households and men in about one-third of them (Blanc 1993; Becker &

Sayer 2009). The DHS also provides couples’ data, which are created by matching the interviewed men and women who declared being partners. For polygynous unions, the corresponding partner appears several times in the couples data. When considering the set of sub-Saharan African DHS used here, we notice that the total sample is composed by one man for every two women (Table 18). Collecting data on couples adds extra challenges compared to the same exercise for individuals. Ideally, couples should be interviewed in separate spaces and at the same time (Becker & Sayer 2009).

Nonetheless, it is not always possible, and most couples are interviewed on different days (Koffi et al. 2012).

Table 18. Most recent Demographic and Health Survey (2010-17) in sub-Saharan Africa used in the analysis:Women individual file, men individual file and couples file.

(unweighted data)

It is important to note that the couples’ files do not necessarily contain the same variables across countries. For example, the age of the last sexual partner is not systematically asked in all surveys (Kenya, Madagascar, Tanzania, São Tomé &

Principe, and Swaziland). Cameroon and Gabon could not be included because it only contained information on condom use for men at last sex (other methods not recorded).

Congo did not include any information on male contraception at last sex, so it was not included either. Our regional sample is therefore smaller than the one in chapter 4, with data of countries which represent 98% of the West African region, 60% of East Africa, 76% of Middle Africa, and only 7% of Southern Africa (Appendix chapter 4: Table 4.1).

Deducing women’s contraceptive use from males’ contraceptive declarations can only be done with monogamous and sexually exclusive couples. To limit our sample to those couples, we only included the reports of husbands who declared having "just one wife"

and the reports of wives who declared that their husband had "no other wife". In Western Africa, we kept only 64.3% of the original population, whereas in Eastern Africa, Southern and Middle Africa, this share is more important: respectively 88.4%, 88.2% and 72.2%. Indeed, polygyny in sub-Saharan countries is mostly seen in what is called the "polygamy belt", expanding from Senegal to Mozambique , with a higher ⁷¹ concentration in West African countries (Jacoby 1995; Fenske 2013) (Table 19).

We kept only partners who both claimed that their last sexual intercourse took place with their live-in-partner or where the two of them declared that it was with their spouse (i.e., monogamous and sexually exclusive couples in Table 21). It seems like the large majority of couples had their last sexual intercourse with their respective married or cohabiting partner, especially in Eastern Africa, where 88.5% of monogamous couples declared also having monogamous sex. In Middle Africa, the share was the lowest, reaching 74.2% of monogamous couples, it was a bit higher in Western Africa, with a share of 78% and in Southern Africa with 79.7% (Table 19).

The literature mentions Tanzania as the end of the belt; we suggest to replace it with Mozambique

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because the levels of polygyny are even higher there.

Table 19. Total number of couples, of monogamous couples, and of monogamous and sexually exclusive couples. Couples DHS files, latest surveys (2010-17).

(unweighted data)

Next, we need to assess whether the couples data are in sufficient agreement to be used interchangeably. As couples were not necessarily interviewed on the same day, it is only possible to measure the reliability or concordance of couples’ responses for partners who had their last sexual intercourse on the same day or before their respective partner was interviewed (See example Figure 1) (Table 21). Making the necessary adjustments to remove all couples whose last sexual intercourse could not match was not a straightforward task. First, we had to create a continuous variable for the date of the interviews for each country. The DHS provides the year of the interview, the month, and the day. The year can be found in the variable v007 (mv007 for men). The month,

which means "the number of the month since the start of the [XX^th] century". For our surveys it is calculated as follows (DHS 2013): ⁷²

Century Month Code:

CMC = ((YYYY-1900)*12) + MM

MM: month YYYY: year

With this information, we can create a continuous variable with the exact year and month. We crossed this variable with the "day of the month in which the interview took place" (v016/mv016). Since not all months have the same number of days (30 or 31, and on February 28 or 29 depending on the year), knowing exactly when the interviews took place allowed us to create an accurate continuous variable (Table 20).

For Benin (2011-2012) for example:

Table 20. Female interview dates for Benin 2011-12 survey.

*Females were interviewed from the 8^th of December 2011 until the 23^rd of April 2012

Figure 1. Fictitious timeline of last sex & interview date: when male data cannot be included to assess the concordance of data collected about the last sexual intercourse.

This procedure allowed us to create a continuous time-lapse variable in days. So, the lower the value of the variable, the earlier the interview happened in time. By subtracting the number of days since last sexual intercourse to our continuous interview variable, we could find out when the last sexual intercourse occurred, within that lapse of time. If the date of last sex mentioned by the respondents happened to be after the date of the interview of their partner, they would have to be removed from the sample

Year 2011

Year

2012 Month Days*

CMC: 1344 / December 1-31 / CMC: 1345 January 1-31 / CMC: 1346 February 1-29

/ CMC: 1347 March 1-31

/ CMC: 1348 April 1-30

Female Female Male Male

Last sex Interview day Last sex Interview day

——————|———————|————————————|———————————|——————

The formulae changes for XXI^th century data.

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because it is impossible for their responses to match. Thankfully, except for Eastern ⁷³ Africa, this situation did not concern a significant share of the monogamous and sexually exclusive sample. Indeed, out of our remaining monogamous and exclusive couples 5.2%, 20%, 8.9% and 10.7% had to be dropped out for this reason from respectively West, East, Middle Africa and Southern Africa (Table 21).

Altogether, compared to the original sample, our final sample has shrunk to more than half its original size in most regions. Indeed, we now only have 47.5% of couples in West Africa, 37.8% in East Africa, 49.1% in Middle Africa and 62.8% of Southern Africa (Table 22). It is therefore critical to verify whether this sub-group is highly biased. For this reason, we compared our final sample to the original one, in order to interpret the results accordingly. Since polygyny is more common and more likely to be considered economically advantageous in rural areas, one could expect to have a higher representation of urban dwellers, wealthier and highly educated individuals in our reduced population (Boserup 1985; Jacoby 1995; Greenaway & Trinitapoli 2014). We will therefore compare socio-economic variables such as residency, level of education and wealth within the two samples. It will be also important to have a look at the non-cohabitation status and sexual activity status in the original couples data and our final sample and contrast it with the married sample of women in the individual DHS file.

This comparison is key, as our results are likely to differ substantially from the findings of chapter 4. Indeed, since the DHS obviously cannot interview couples who are living apart, levels of sexual activity in this chapter are expected to be much higher than what was previously recorded.

Nonetheless, comparing our final sample of couples with DHS’s initial population, the differences do not seem to be as important as expected. Regarding the area of residence, in West Africa, we now have 36% of our sample living in urban areas, which is just somewhat higher compared to the initial population (30%). In Southern and in East Africa the differences are smaller and in Middle Africa they are almost null. As for the wealth index in West Africa, we can observe an overrepresentation in our final data of the wealthiest quintile (22.1% instead of 17% in the original file), and an underrepresentation of the poorer and poorest quintiles, but the differences are not huge.

In Southern Africa we also observe a slight overrepresentation of the wealthiest, while in East and Middle Africa the results remain very similar. Regarding the level of education, in the West, we observe fewer women with no education (56.6% instead of 65.1%) and more women holding a secondary education, while in the other regions the proportions remain largely unchanged (Table 23. Appendix chapter 6: Tables 6.1, 6.2, 6.3).

Codes available upon request.

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Table 21. Number of monogamous and sexually exclusive couples, and number and share of couples excluded because the date of the last sexual intercourse of one partner falls after the interview of the other. Couples DHS files, latest surveys (2010-17).

(unweighted data)

In essence, despite the numerous adjustments made, our reduced sample remains quite loyal to the DHS’s original couples’ data, in socio-economic terms. Nonetheless, the couples’ file contains crucial differences in relation to the individual woman’s file.

Indeed, the couples’ data can only be constructed if both partners are present for the interview, which means that the couples’ DHS file concerns only cohabiting ones.

Indeed, we confirm that 99.1% of couples in our sample declare to be living together, compared to a maximum of 92.3% of married couples in the individual woman’s file.

Moreover, this situation also changes the levels of sexual inactivity. In our reduced sample, we noticed that 10% of women are postpartum abstaining and about 8%

declared they did not have sexual activity in the last month; while in the DHS’s women individual file, even though the levels of postpartum abstinence do not substantially

Country Survey 


differ (8.6%), around 26% of women declared being sexually inactive outside postpartum abstinence (see chapter 4, Table 4).

Table 22. Total number of original couples, number of couples in our final sample, and relative size of the final sample in comparison to original one. Couples DHS file, latest surveys (2010-17).

(unweighted data)

This last comparison is very important, as it means that we are going to generate unmet need results taking for the first time into consideration sexual inactivity outside of the postpartum period, but for the share of couples who are sexually inactive despite living together. On the one hand, it means that we are not going to be able to assess the contraceptive needs of the share of sexually inactive couples, which are to a large extent (as we saw in chapter 4) partners who are separated by migration. On the other hand, if our data turns out to be reliable, our results are likely to be quite relevant so as to study contraceptive needs of married women who are sexually inactive, as they are portraying couples who are likely to be at higher risk of having unprotected sexual activity

Table 23. Relative distributions of couples by area of residency, wealth quintiles and education in DHS’s original couples file and in our reduced sample, by region. Couples DHS file, latest surveys (2010-17).

(unweighted data)