# **5. Changes in the prevalence of FHHs over time**

How has the prevalence of FHHs evolved over the last 20 years in Africa? What is the relative contribution of various factors in any revealed change? Figure 1 provides an overall picture of trends across the individual countries by macro-regions.<sup>19</sup> The share of a country’s households that are headed by a woman (panel A) and the share of the population living in those households (panel B) are plotted for the country’s earliest DHS survey on the horizontal axis against that for its latest survey. All points above the 45 degree line of equality indicate a rise.

The general picture is of an increase in the prevalence of FHHs for most countries. Declines are evidenced only for Ghana, Chad, Congo and Lesotho, although the change is small for the latter three.<sup>20</sup> Figure 2 disaggregates these data into FHHs that contain an adult male and those that do not. Here too, the overall trend is up for both types of FHHs. It is apparent that the decline in Lesotho is due to fewer FHHs with a male adult. In contrast, the marked change for Ghana arises from a drop in the share of FHHs with no adult male which swamps a smaller increase in FHHs with a male member. In general the patterns of change are quite different across the two types of households.

Finally, Figure 3 provides nonparametric plots of the probability that a woman aged 15 or older heads a household controlling for her age and disaggregating across the four regional country groupings, for the earliest and latest surveys. There are clear level differences across the macro-regions. But clearly, in all regions and across the age distribution, adult women are significantly more likely to be household heads over time.

What explains the rising share of FHHs? Turning to regression analysis, we examine the share of the population living in FHHs over the last 25 or so years by comparing DHSs across countries and years. Thus the observations are country/year DHS based. Since the share is bounded, we use its logit transformation to ensure normally-distributed errors. The dependent variable is thus the natural log of the odds ratio i.e., _ln(S/(1-S))_ where _S_ is the fraction of the population living in FHHs, for each of the DHS surveys, giving 98 total observations (98 surveys for 34 countries, as in Table A3).<sup>21, 22</sup> OLS regressions then examine the role of country

> 19 Refer to Table A2 for the list of countries by macro-region. Here, the sample is restricted to the 26 countries with at least two surveys available and use the earliest and latest years (for a total of 52 surveys).

> 20 Also note that, while for most countries the earliest survey was conducted in the early 1990s and the latest in the 2010s, this is not the case for Chad, Congo, and Lesotho, for which the number of years between earliest and latest is smaller (e.g. Chad 1996 and 2004; Congo 2005 and 2011; Lesotho 2004 and 2009). See Table A2.

> 21  This is not to be confused with a logit regression using a binary dependent variable.

> 22 We drop one observation — the 1994 Central Africa Republic survey — as no HIV prevalence data are available for it from the World Development Indicators.

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