Female household headship and poverty. analysis. in South Africa: an employment-based. Chijioke Nwosu Catherine Ndinda

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1 Female household headship and poverty in South Africa: an employment-based analysis Chijioke Nwosu Catherine Ndinda

2 Outline Introduction Aim & objectives Literature review SA context Analytical methods Data Key variables Results Concluding remarks

3 Introduction (1) Female-headed households (FHHs) are generally more likely to be poor relative to male-headed households (MHHs) Higher dependency ratio Female main earner Women usually have less time for market work (home production, etc) Female heads more likely to face labour & social welfare access discrimination Early parenthood & family instability (due to teenage pregnancy, etc)

4 Introduction (2) Prevalence of FHHs has been increasing globally & in SSA Labour migration by male heads Female labour migration Wars & conflicts Socio-cultural changes (a more permissive culture towards female headship) Poverty largely an earnings problem So increasing earning of FHHs will likely mitigate their vulnerability

5 Introduction (3) FHHs are heterogeneous Cause of female headship (divorce, widowhood, labour migration, teenage pregnancy, etc.) Degree of vulnerability (receipt/non-receipt of remittances/alimony, etc,)

6 Aim & objectives Ascertain the relationship bw female headship, hh employment characteristics, & poverty in SA Are FHHs more likely to be poor relative to MHHs in SA? What role do hh employment characteristics play in mediating the relationship bw female headship & poverty? Caveat: Female headship may be endogenous to poverty Female headship may result from a male head s poverty (divorce) To the extent that this is true in SA, our results are correlations, not causal

7 Literature review (1) No consensus that FHHs are poorer than MHHs, though empirical evidence appears to support higher poverty in FHHs Buvinic et al s (1997) comprehensive review [61 studies: 38 (FHHs are over-represented among the poor); 15 (the r/ship depends on the kind of female headship studied & the poverty measure used); 8 (no relationship)] Heterogeneity among FHHs, especially with respect to the degree of male support & geographical location (e.g. Klasen et al, 2015; Barros et al., 1997) Africa-wide study (Milazzo & van de Walle, 2017) using DHSs Lower prevalence of FHHs is associated with higher GDP But FHHs experienced faster rate of poverty reduction than MHHs

8 Literature review (2) - SA Trend in evolution of female headship (Posel & Rogan, 2012) Role of weather & climate-related disasters in further impoverishing FHHs (Flato et al, 2017) Welfare of children in FHHs (Chant, 2007) Differential poverty & employment patterns in FHHs & MHHs (Posel & Rogan, 2009; 2012) Gaps in SA studies No control for key job quality & employment intensity variables Did not account for gender differences in hh employment patterns

9 SA context (1) Poverty incidence is high and increasing lately Poverty headcounts in South Africa ( ) Poverty headcounts % of population that is UBPL poor % of population that is LBPL poor % of population that is FPL poor Source: Statistics South Africa (2017)

10 Prevalence (%) SA context (2) Generally rising prevalence of female headship Prevalence of female headship in South Africa ( ) Year

11 SA context (3) Poverty is more prevalent among FHHs Percentage of FHHs and MHHs that are in extreme poverty (i.e. FPL) Year MHHs (%) FHHs (%) p-value

12 SA context (4) Many households experience complete household non-employment Distribution of hh employment sizes by gender of hh head Male-headed Female-headed Num of employed hh members N % N % Total number of households

13 SA context (5) FHHs are more likely than MHHs to have only women employed Distribution of households by employment patterns Male-headed Female-headed Household Type No. of hhs % No. of hhs % Only females employed Only males employed Females & males employed Nobody employed Total

14 Analytical methods Regression: POLS / / / pov h,t = αnonemp h,t + X i,tγ + Xh,tβ + Xp,tδ + εi,h,t [1] / / / nonemp h,t = αfemhead h,t + X i,tγ + Xh,tβ + Xp,tδ + εi,h,t [2] / / / pov h,t = αfemhead h,t + X i,tγ + Xh,tβ + Xp,tδ + εi,h,t [3] / / / pov h,t = αonlyfem h,t + X i,tγ + Xh,tβ + Xp,tδ + εi,h,t [4] / / / pov h,t = αonlyfemfhh h,t + X i,tγ + Xh,tβ + Xp,tδ + εi,h,t [5]

15 Data National Income Dynamics Study (wave 1 wave 4) Sample restricted to Africans and coloureds

16 Key variables (1) Poverty=1(Avg monthly hh income<=poverty line) FPL= extreme poverty LBPL=forgo food in order to purchase non-food items UBPL=an unambiguous threshold of relative deprivation below which individuals cannot afford the minimum desired lifestyle in their society

17 Key variables (2) SA poverty lines, (Rand) Year FPL LBPL UBPL Source: Statistics South Africa (2017)

18 Key variables (3) Female headship Survey-determined Asked of the oldest woman and/or any adult knowledgeable about the hh s living arrangements & spending patterns Some authors doubt the survey-determined female headship & resort to common-sense author definitions usually based on economic responsibility So-called de facto FHHs (with non co-resident male heads who support the family) & de jure FHHs (absence of a live-in male partner or lack of economic support from a male partner) But economic responsibility doesn t necessarily confer headship (some female breadwinners are not hh heads) Not sure why doubt survey headship designation but believe other survey responses (e.g. hh income & expenditure)

19 Key variables (4) Employment Engaged in economic activity usually for money over the past month (employees, self-employed, participated in family business, etc)

20 Results (1) Descriptive statistics MHHs FHHs Variable N Mean SD Variable N Mean SD poor (fpl) poor (fpl) poor (lbpl) poor (lbpl) head sch head sch african african coloured coloured male male

21 Results (2) Descriptive statistics MHHs FHHs Variable N Mean SD Variable N Mean SD num. of u num. of u num. over num. over grant grant hh size hh size head marr/cohab head marr/cohab hh hrs hh hrs

22 Results (3) Descriptive statistics MHHs FHHs Variable N Mean SD Variable N Mean SD num. empl num. empl hh hh num. unsk num. unsk hh hh num. semsk hh num. semsk hh num. sk hh num. sk hh rhh inc pc rhh inc pc head emp head emp

23 Results (4) R/ship bw poverty & complete hh non-employment Dep variable: individual comes from a poor hh complete household nonemployment (1) (2) fpl lbpl 0.262*** 0.303*** (0.005) (0.005) Controls: head s gender; own gender; own yrs of schooling; hh head s yrs of schooling; race; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; provincial unemployment rate; time dummies

24 Results (5) R/ship bw headship & complete hh non-employment Dependent variable: complete hh non-employment belongs to a FHH 0.095*** (0.006) Controls: own yrs of schooling; hh head s yrs of schooling; race; own gender; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; belongs to a grantreceiving hh; provincial unemployment rate; time dummies

25 Results (6) R/ship between poverty and gender-based hh employment Dep variable: individual (1) (2) (3) (4) is from a poor hh covariates fplnoemp lbnoemp fplall lball fhh has employed 0.014*** 0.024*** member(s) (0.004) (0.006) (0.004) (0.006) Controls: own yrs of schooling; hh head s yrs of schooling; race; own gender; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; provincial unemployment rate; time dummies: Columns 3 & 4 include: hh s total work hrs; num of empl hh members; num of unskilled hh members; num of semi-skilled hh members; num of skilled hh members

26 Results (7) R/ship bw pov & gender-based empl when hh empl varies by gender Dependent variable: (1) (2) (3) (4) individual is from a poor household Covariates fplnoemp lbnoemp fplall lball only females empl in fhh 0.029*** 0.036*** 0.024*** 0.026*** (vs only males employed in mhh) (0.007) (0.009) (0.007) (0.009) Controls: own yrs of schooling; hh head s yrs of schooling; race; own gender; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; provincial unemployment rate; time dummies: Columns 3 & 4 include: hh s total work hrs; num of empl hh members; num of unskilled hh members; num of semi-skilled hh members; num of skilled hh members

27 Results (8) R/ship bw pov & hh head by gender of employed hh member(s) (1) (2) (3) (4) VARIABLES fplnoemp lbnoemp fplall lball only females empl in fhh (vs at least a male empl in mhh) 0.060*** 0.105*** 0.025*** 0.037*** (0.006) (0.009) (0.006) (0.008) Controls: own yrs of schooling; hh head s yrs of schooling; race; own gender; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; provincial unemployment rate; time dummies: Columns 3 & 4 include: hh s total work hrs; num of empl hh members; num of unskilled hh members; num of semi-skilled hh members; num of skilled hh members

28 Results (9) R/ship bw pov & hh head by gender of employed hh member(s): FHHs (1) (2) (3) (4) VARIABLES fplnoemp lbnoemp fplall lball only females empl in fhh (vs at least a male empl in fhh) 0.078*** 0.138*** 0.027*** 0.058*** (0.007) (0.008) (0.008) (0.009) Controls: own yrs of schooling; hh head s yrs of schooling; race; own gender; hh s avg age; location; hh size; num. of u-14 children in hh; number of over-60 in hh; hh head s marital status; provincial unemployment rate; time dummies: Columns 3 & 4 include: hh s total work hrs; num of empl hh members; num of unskilled hh members; num of semi-skilled hh members; num of skilled hh members

29 Concluding remarks FHHs are more likely to be poor relative to MHHs This is due to higher rates of complete hh non-employment & only female employment in FHHs relative to MHHs Also, due to fewer number of employed hh members, fewer hours supplied, and lower skills of employed hh members FHHs are not more likely to be poor relative to MHHs with similar hh employment characteristics Poverty eradication should not target hhs based on the gender of the hh head. Targeting should be based on features associated with economic vulnerability HH s employment status Gender of employed hh members Skill level of employed hh members