Vulnerability of Poverty between Male and Female Headed Household in Tanzania

Abstract

The study analyzes how the male and female headship of households affects poverty vulnerability in Tanzania. The study uses a sample for the 2017–18 HBS covered the population residing in private households in Tanzania Mainland. A representative probability sample of 9,552 households was selected. Probit regression with instrument variables for the endogenous variable (education) is used for estimation. The results imply that, in general, female-headed households are less likely to face extreme poverty than their male counterparts. The study also reveals that extreme poverty is less likely with the female head when divorced or widowed. Finally, the results imply that extreme poverty varies across different regional zones in the country. Therefore, female in Tanzania can shield their households from extreme poverty.

Keywords

extreme poverty male-headed household female-headed households ivprobit

Introduction

Tanzania reached a significant milestone in 2020 when it officially graduated from a low-income country to a lower-middle-income country. But Tanzania’s rapid growth of population has steadily increased the number of people below the national poverty line. In 2020, the pandemic influenced economic slowdown and caused the poverty rate to rise to an estimated 27.2%, compounding the impact of population growth on the absolute number of poor people (World Bank, 2021). Because a large portion of Tanzania’s population is near the poverty line, even a slight economic shock can push several households into poverty. The food poverty line is the level at which households use total spending to meet their food needs. It is also often referred to as the extreme poverty line. Individuals who fall below this level are classified as extremely poor (Alkire and Santos, 2014). In Tanzania, the Bureau of Statistics measures poverty by comparing households' consumption per adult equivalent to the national poverty line using Household Budget Survey (HBS) data. The basic needs poverty (headcount poverty rate) measures the population whose monthly consumption per adult equivalent is below the basic needs poverty line. The basic needs approach is used to measure absolute poverty in Tanzania’s Mainland. It attempts to define the minimum resources necessary for long-term physical well-being in terms of consumption of goods (National Bureau of Statistics, 2019). In Tanzania, especially in rural areas, tribal norms and customs promote a division of labor by gender. Women take care of the household chores, small children, livestock, and plant and weed the agricultural fields. Men are the breadwinners and make critical financial decisions for the family. In several modern households in Tanzania, husbands and wives equally participate in income generation (Mclaughlin, 2020). This gender dynamic affects how households are resilient towards extreme poverty in the country. Therefore, this study empirically analyzes whether male or female-headed households are more vulnerable to extreme poverty.

Literature Review

Various studies with different methodology and countries have addressed the vulnerability between male and female-headed households in poverty. Klasen et al. (2015), and Klasen et al. (2011) investigated whether different types of female-headed households in Vietnam and Thailand are worse off than men-headed households. They found that female-headed households have higher current consumption than counterparts in both countries. Also, their study found that singles and widows are mostly worse off than female household heads with a migrated husband. These results abide with another study by the same researchers in 2015 as they investigated whether heterogenous subgroups of female-headed households are worse off than households headed by men in Vietnam and Thailand. They found that households headed by a single female are less vulnerable to poverty than men-headed households in Thailand. However, in Vietnam, these households are impoverished and susceptible to poverty. Smajic and Ermacora (2007) studied whether households with female heads are over-represented amongst the poor compared to households with male heads in Bosnia and Herzegovina, measured in terms of the consumption dimension of poverty. Their analysis did not find evidence of female head households being disadvantaged in terms of household consumption. Kwabena, Senadza, & Owusu-Afriyie (2014) studied the poverty trend between male and female head households in Ghana. They found that poverty incidence among female-headed households is lower than Male-Headed Households, which conflicts with the “feminization of poverty” theory. But another study also conducted in Ghana by Kpoor (2019) examined the social, human, economic, and financial assets and livelihoods of male and female-headed households. He found that male-headed households have a better livelihood and more asset endowment than female-headed counterparts. Owusu-Afriyie and Nketiah-Amponsah (2014) also conducted their study in Ghana by empirically tested the term “feminization of poverty” theory found that the hypothesis is affected by the education level of the individual. They also found that when education is considered, “feminization of poverty” is predominant only amongst the no and primary education associates. In contrast, masculinization of poverty is relatively found among the secondary and tertiary education associates. Milazzo and Walle (2015) study the current prevalence and characteristics of households with female heads and whether poverty has fallen in proportionately for female and male-headed households. They found that female-headed households have mostly seen faster poverty reduction. Rajaram (2009) estimated whether male-headed households are poorer than their female-headed counterparts. Employing logit and probit estimations, he found that the relationship between poverty and female-headed households hinges on the choice of poverty measure. Precisely, poverty measures according to the wealth indices and housing conditions show that male-headed households are poorer than female-headed households. It is vice versa when poverty is measures based on the standard of living index. The results abide with another study by Kapata (2006) in Tanzania by applying logit estimations. Boudet et al. (2021) focuses on the relationship between sex, age, and poverty and found that females of reproductive age have a higher probability of living in a poor household than males. She found that 122 females between 25 and 34 years of age live in poor households compared to 100 males of the same age group. Also, they found that nuclear family households of married couples and children make up 41% of poor households.

Javed and Asif (2011) analyzed the relationship between poverty and the gender of household heads in the two Tehsils of District Faisalabad. They found that consumption, family size, income, and household headship gender play an important role in determining poverty. They found that there is a negative relationship between poverty and head of households. Female-headed households have lower earning capacity, fewer assets than male-headed ones. These results abide with another study by Brown, Ravallion, and van de Walle (2017), Brown and van de Walle (2019) who analyzed the common welfare and poverty comparisons between male-headed households and female-headed households. They found that the female-headed households are poorer than male-headed households except when the married female head. Bose-Duker, Henry, & Strobl (2020) did a comparative study of children’s resource shares in male and female-headed households in Jamaica. They found that children receive considerably larger resource shares in households headed by females than their counterparts. The results abide with the study by Oginni, Ahonsi, & Ukwuije (2013) who analyzed whether female-headed households are more impoverished than male-headed households in Nigeria. They found that female-headed households are less likely to be poor than male-headed households. The results abide with another study in South Korea by Nam and Hyesun (2017). Fuller and Lain (2019) compared the resilience of male and female-headed households by interviewing a sequence of 16 assessments of rural development projects in 12 countries across Asia, Latin America, and Africa. They found that, on average female-headed households, have significantly lower resilience to poverty than male-headed households. This result contradicts another study conducted in Latin America by Liu, Albert, & Rocío (2017) who investigated the rise in female headship and its relationship with changing living arrangements and household living conditions. They found that female-headed householders are, in reality, less likely to exist in impoverished households after governing for union status as the results from another study by Agnes et al. (2001). In Egypt, Lobna, Ramadan, Abdel Latif, and Elbakry (2019) applied a Gender-Based Poverty Detection Model to provide a good detection of household poverty. They show that the vulnerable characteristics of females could be more influenced by the general household’s poverty than females’ headed households. They found that not all female-headed households are poor and that female headship is not always correlating with poverty. Lebni et al. (2020) explore the opportunities and challenges confronting female-headed households in Iran. They found that individual problems, intra-family problems, social problems, and positive outcomes. Kim et al. (2020) analyzed psychological welfare and related factors among female-headed households in Korea. They found that 39.8% of households headed by female workers were psychologically unhealthy. They found that living alone; low education level, low income, fatigue, musculoskeletal pain, and anxiety/depression were negatively related to psychological well-being. Ede’o, Haji Ketebo, and Wolteji Chala (2020) examined poverty feminization in urban areas across sub-Saharan Africa countries, namely, Tanzania, Ethiopia, Rwanda, and Malawi. They found that male-headed households were less poor than female-headed households. The results abide another study in Northern Ethiopia by Debela (2017), who investigated gender differences in livestock holding. He found that households headed by females own significantly fewer livestock compared to their male counterparts. The results abide with another study by Cheryl et al. (2015) in ownership and controlling of land in Africa. Julka and Das (2015) reviewed existing literature and found evidence on associations between gender of household head and poverty in two agro-biodiversity flashpoints in Odisha and Tamil Nadu. They found that the gender of the household head has a significant impact on poverty in Tamil Nadu. Schalkwyk (2019) uncovered the strengths and challenges of a household headed by a female living in a South African high-risk community. They found the spill-over effect of mothers' previous successions of difficulties upon their families’ present functioning and structure. This result abides with another study in South Africa by Cheteni, Khamfula, Mah, & Casadevall (2019) and Slavchevska (2015) in Tanzania on agricultural productivity. They also found that females residing in farm and rural areas had a higher probability of being in poverty than male counterparts. Nwosu and Ndinda (2018) analyzed the relationship between female household headship and higher poverty incidence relative to male headship in South Africa. They also found that female headship is positively linked with complete household non-employment, while male headship is positively associated with poverty. Goebel, Dodson, & Hill (2010) explored situations of the female-headed households in Msunduzi Municipality. They found important patterns about the incidences around HIV/AIDS- related deaths, lack of health services, and poverty. Montoya et al. (2017) applied Multidimensional Poverty Index to study single-mother and bi-parental families in Nicaragua. They found that poverty is more dominant in male-headed families than single-mother and married female-headed families. Brown and Walle (2020) argued that female-headed households, on average, have lower poverty rates compared to male-headed households in Africa. Nevertheless, they also argued that female heads households are poorer than male-headed households except when the female head is married. Another study was done in Kenya by Wanjiru et al. (2020) also found that female-headed households in Nairobi are less likely to escalate through the wealth index classes’ contrast to the male-headed households. Also, households in North Eastern and Western Kenya are the least likely to move up to the wealth index categories compared to other regions. Mwawuda and Nyaoke (2015) analyzed the causes of poverty among female-headed households in Nyatike constituency (Migori County) in Western Kenya. The study sought to assess the poverty and food security situation in Nyatike Constituency using a cross-sectional descriptive research design, which adopted both qualitative and quantitative methods. The study population consisted of female-headed households with the unit of analysis based on individual, female respondents. The result abides with another study by Bilenski, Gungor, & Tapsin (2015) in Turkey. However, it is critical to point out that these studies did not focus specifically on the vulnerability of male and female-headed households in Tanzania. Therefore, in a case study of Tanzania, the impact of household head gender and poverty is empirically analyzed to see if it parallels with the results on empirical literature reviews.

Methodology

Data and Model Specification

The study uses a sample for the 2017-18 HBS covered the population residing in private households in Tanzania Mainland. A representative probability sample of 9552 households was selected. This sample is designed to allow separate estimates for each of the 26 regions of the Tanzania Mainland, also urban and rural areas separately at the national level. The 2017-18 HBS adopted a two-stage cluster sample design. The main variables are extreme poverty, gender of household head, age of household head, number of children under the age of 14, and number of adults over the age of 65 years as shown in Table 1. The endogenous variable is the education level of the household head, and the instrumental variables are parents’ education. The model is specified as follows

expo v_{i} = α_{0} + α_{1} femal e_{i} + α_{2} hhag e_{i} + α_{3} child 14_{i} + α_{4} elder 65_{i} + α_{5} hhedu c_{i} + ε_{i}

(1)

where hheduc is a vector endogenous variable and α_i is a vector of structural parameters, the following is the reduced-form equation (first stage) to obtain the estimated values of hheduc

expo v_{i} = α_{0} + β_{1} fnoedu c_{i} + β_{2} fprimar y_{i} + β_{3} fsecondar y_{i} + β_{4} fcolleg e_{i} + β_{5} femal e_{i} + β_{6} hhag e_{i} + β_{7} child 14_{i} + β_{8} elder 65_{i} + β_{9} hhedu c_{i} {+ µ}_{i}

(2)

whereby, βi is the matrix of the reduced-form parameter. The following is the second stage regression estimating the original equation (1) with the predicted value of hheduc replacing the actual value

expo v_{i} = α_{0} + α_{1} femal e_{i} + α_{2} hhag e_{i} + α_{3} child 1 4_{i} + α_{4} elder 6 5_{i} + α_{5} (h h e d u c_{i} = β_{1} fnoedu c_{i}) + β_{2} fprimar y_{i} + β_{3} fsecondar y_{i} + β_{4} fcolleg e_{i} + ε_{i}

(3)

Results and Discussion

From Table 2, starting with the first column, the results show that female-headed households are less likely to fall into extreme poverty than male-headed ones when all other factors remain constant. These results abide with priori hypothesis and other studies by (Oginni et al., 2020; Liu, 2017; Klasen et al., 2011; Bose-Duker, 2020; Walle, 2015; Lobna, 2019; Ndinda, 2018). The second and third column shows how the impact of household head gender differs between urban and rural. The results imply that the probability of extreme poverty in the household is lower with the female-headed household than the male-headed household head in urban. Still, there is no significant difference between male and female-headed households in rural areas. The results correspond with priori grounds because women are more empowered in urban than rural, giving the female-headed household capacity to become the family’s breadwinner. This propels them to edge over male-headed households for the vulnerability of extreme poverty in the household.

Table 3 shows how the impact of gender of household heads in extreme poverty varies across different regional zones in the country. This is because each zone has its culture, tradition, and social characteristics that can affect how the gender of the household head plays a role in poverty vulnerability. The results imply that when other factors remain constant, female-headed households are less likely to experience extreme poverty than their counterparts in the West, South, Southwest, and Lake Zone regions. The results abide with the priori expectation because the remaining regional zones are characterized by demeaning women and practicing men’s superiority. Women are born to bear children and housekeeping with little to no knowledge or experience in generating income. Table 4 presents the results, which show how the impact of gender of household heads in extreme poverty varies according to the marital status of the household head. The results imply that when other factors remain constant, female-headed households are less likely to experience extreme poverty than male-headed ones when the female is divorced, or her husband has passed away (widow). The result contradicts with that of another study by (Walle, 2020) who argued that female-headed households are poorer than male-headed households except when the female head is married.

Table 5 presents the correlation analysis, which shows the relationship between variables in the model. The results suggest that female-headed households are less vulnerable to extreme poverty than their male counterparts (Oginni, 2020; Liu, 2017; Klasen, 2011; Bose-Duker, 2020; Walle, 2015; Lobna, 2019; Ndinda, 2018). Other variables that are also a positive and statistically significant relationship with extreme poverty are the number of children below 14 years and the age of household head while education and household living in urban have a negative relationship with extreme poverty.

Conclusion

The paper analyzes the vulnerability of poverty between male and female-headed households in Tanzania. Therefore, the results suggest that, in general, female-headed households are less likely to experience extreme poverty than their male-headed households in Tanzania. Female-headed households naturally are more mature households. They have fewer young children and older adults, thus smaller household size. Also, they are excessively urban, where living standards are significantly higher than in rural areas. This result is encouraging as they reveal that female heads in Tanzania can shield their households from extreme poverty. However, the study calls for the government to put more effort and policy measures targeting to empower female-headed households, especially in rural areas, as they are still vulnerable in our society.

Footnotes

Table A1. Variable Name,Definition,and Priori Hypothesis.

Variable	Definition	Priori Expectations
Extreme poverty	A dummy variable where 1 represent household living below the poverty line and 0 if otherwise. It is the level at which households use total spending to meet their needs for food.
Female	Is the dummy variable whereby 1 if the household head is female and 0 if otherwise	-
Age	This is a continuous variable represent the age of household head	-
Child14	A continuous variable represents the number of children under the age of 14 years in a household	+
Elder65	A continuous variable represents the number of elders with the age above 65 years in the household.	+
Hheduc	An endogenous continuous variable represents the education level of household head	+
Fnoeduc	A dummy variable represents fathers with no education	+
Fprimary	A dummy variable represents fathers with primary education	+
Fsecondary	A dummy variable represents fathers with secondary education	+
Fcollege	A dummy variable represents fathers with college education	+

Probit Regression (with Instrument Variables) Approach: Dependent Variable is Extreme Poverty.

	(Ivprobit)	(Urban)	(Rural)
Variables	expov	expov	expov
female	−0.140*	−0.315***	−0.0663
	(0.0756)	(0.0998)	(0.0937)
hhage	−0.00173	−0.00796***	0.000375
	(0.00295)	(0.00289)	(0.00398)
child14	0.122***	0.0531	0.149***
	(0.0255)	(0.0511)	(0.0251)
elder65	−0.193**		−0.201**
	(0.0779)		(0.0909)
hheduc	−0.138***	−0.163***	−0.124***
	(0.0171)	(0.0117)	(0.0377)
First stage instruments
fnoeduc	−0.894***	−1.033***	−0.374*
	(0.201)	(0.388)	(0.203)
fprimary	0.776***	0.682**	0.828***
	(0.212)	(0.345)	(0.220)
fsecondary	5.640***	6.305***	3.443***
	(0.817)	(1.203)	(0.643)
fcollege	8.726***	8.242***	7.359***
	(1.118)	(1.434)	(1.288)
Constant	0.931*	2.537***	0.356
	(0.561)	(0.509)	(0.952)
Observations	7381	2517	4864

Robust standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Estimations Results across Regional Zones: Dependent Variable is Extreme Poverty.

Regional Zones	(Northeast)	(West)	(South)	(Southwest)	(Central)	(Lakezone)	(Northcoast)
Variables	expov	expov	expov	expov	expov	expov	expov
female	0.104	−0.368**	−0.349*	−0.232**	−0.331	−0.337***	0.0255
	(0.236)	(0.171)	(0.207)	(0.111)	(0.463)	(0.125)	(0.176)
hhage	−0.00290	−0.00528	0.000149	−0.0107**	0.0136	−0.00283	−0.00757
	(0.00810)	(0.00765)	(0.00878)	(0.00483)	(0.0166)	(0.00444)	(0.00577)
child14	0.133*	0.0588	0.0722	0.00662	0.247*		0.128
	(0.0800)	(0.0958)	(0.0940)	(0.0510)	(0.132)		(0.125)
elder65	0.138	−0.346**	−0.128	−0.231
	(0.211)	(0.175)	(0.193)	(0.243)
hheduc	−0.134***	−0.209***	−0.169***	−0.180***	−0.105	−0.124***	−0.163***
	(0.0468)	(0.0372)	(0.0438)	(0.0145)	(0.100)	(0.0328)	(0.0256)
First stage instruments
fnoeduc	−1.798***	−1.445**	−1.234	−0.322	−0.370	−0.490	−0.735
	(0.562)	(0.604)	(1.257)	(0.303)	(0.639)	(0.336)	(0.749)
fprimary	−0.357	−0.286	0.0493	1.072**	1.217	1.659***	0.728
	(0.584)	(0.824)	(1.031)	(0.496)	(0.770)	(0.406)	(0.532)
fsecondary	2.034	1.079	3.535	2.373***	10.84***	4.744***	7.137***
	(1.351)	(1.775)	(2.974)	(0.822)	(3.698)	(0.982)	(1.493)
fcollege	6.092***	6.319	1.285	9.603***	11.81***	10.05***	11.08***
	(2.068)	(5.743)	(1.490)	(3.129)	(3.243)	(2.089)	(1.737)
Constant	0.769	3.059**	1.862	3.231***	−1.235	1.090	1.865
	(1.824)	(1.489)	(1.625)	(0.598)	(3.346)	(0.828)	(1.653)
Observations	855	608	446	1050	474	1834	1598

Robust standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Estimation Results across Marital Status: Dependent Variable is Extreme Poverty.

Marital Status	(Married)	(Divorced)	(Widowed)	(Never Married)
Variables	expov	expov	expov	expov
female	−0.0183	−1.026**	−0.692***	−0.519
	(0.0880)	(0.445)	(0.197)	(0.378)
hhage	−0.00448	0.0135	−0.00228	0.0302*
	(0.00329)	(0.0119)	(0.00603)	(0.0176)
child14	0.114***	0.156**	0.0836	0.122*
	(0.0315)	(0.0718)	(0.0554)	(0.0651)
elder65	−0.161*	−0.359	−0.199	−0.841**
	(0.0942)	(0.426)	(0.145)	(0.412)
hheduc	−0.145***	−0.0597	−0.136***	−0.0922
	(0.0216)	(0.0446)	(0.0301)	(0.100)
First stage instruments
fnoeduc	−0.813***	−1.902**	−1.766***	−0.918
	(0.233)	(0.841)	(0.669)	(0.959)
fprimary	0.781***	0.717	0.767	0.206
	(0.246)	(1.041)	(0.732)	(0.850)
fsecondary	5.582***	4.547	8.432***	3.594*
	(0.964)	(3.181)	(2.531)	(1.926)
fcollege	8.268***	19.40***	6.453***	9.504**
	(1.188)	(3.775)	(2.480)	(4.211)
Constant	1.195*	−1.397	1.066	−1.501
	(0.700)	(1.200)	(1.057)	(2.623)
Observations	5282	338	1038	381

Correlation Matrix.

Variables	expov	female	hhage	child14	elder65	hheduc	Urban
expov	1
female	−0.0376**	1
hhage	0.0271*	0.0524***	1
child14	0.205***	−0.112***	−0.0178	1
elder65	−0.00504	0.0114	0.572***	−0.0551***	1
hheduc	−0.0850***	−0.0513***	−0.181***	−0.128***	−0.115***	1
Urban	−0.0760***	0.0853***	−0.0642***	−0.207***	−0.0625***	0.277***	1

*p < 0.05 ** p < 0.01 *** p < 0.001.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Enock Mwakalila

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