An Individual Level Test of the “Feminization of Poverty” Hypothesis

Abstract

Feminization of poverty is a hypothesis that postulates that women experience poverty at higher rates than men. Over the years, empirical examination of this hypothesis has relied on the comparison between poverty status of female-headed and male-headed households due to lack of gender disaggregated data in many household surveys. However, the use of poverty among female-headed households as a representative measure of women’s poverty masks the extent of poverty among women. Hence, this study uses individual gender disaggregated data from the Ghana Living Standards Surveys IV and V (GLSS IV and V) and the Foster–Greer–Thorbecke (FGT) classes of poverty measure to empirically test the “feminization of poverty” hypothesis in Ghana. The study also finds out whether this hypothesis is affected by the education level of the individual.

The article finds that “feminization of poverty” is prevalent at all three levels of FGT poverty measures. The result further indicates that when education is taken into consideration, “feminization of poverty” is found to be prevalent only amongst the no education and primary education cohorts while masculinization of poverty is rather found among the secondary and tertiary education cohorts. Generally, in terms of the dynamic changes in “feminization of poverty,” the study finds that over the last two sets of surveys (GLSS IV and V), the phenomenon has reduced. Based on the results, we recommend that measures that target education as a tool for combating poverty should be strengthened amongst females whilst non-educational tools for combating poverty should target males.

Keywords

gender poverty inequality education FGT Ghana

Introduction

The concept of “feminization of poverty” has gained currency over the past few decades. Since the 1980s, studies on the proliferation of female-headed households and research into gender-specific effects of structural adjustment policies among others have led to increased attention to what has become known as “the feminization of poverty” (Moghadam, 2005). Pearce (1978) noted that poverty was becoming “feminized” in the United States. According to Pearce, almost two-thirds of the poor over the age of 16 years were women (McLanahan & Kelly, 1999). This was what Pearce referred to as “feminization of poverty,” a phenomenon where women experience poverty at higher rates than men (Pearce, 1978). Adding to his definition, Northrop (1990, p. 145) stated that “the feminization of poverty is the process through which poverty become more concentrated among individuals living in female-headed households.” Chant (2006) also asserted that the key feature of the “feminization of poverty” is the greater severity of women’s poverty relative to men.

However, over the years, due to lack of gender disaggregated data at individual levels in many household surveys, most studies have laid emphasis on the comparison between poverty status of female-headed and male-headed households as a means of examining the evidence of “feminization of poverty” (see BRIDGE, 2001; Buvinic & Gupta, 1994). Consequently, analyses from household consumption and expenditure surveys of many countries show a high incidence of poverty among female-headed households (Fukuda-Parr, 1999). Particularly, in Ghana, Kyereme and Thorbecke (1987) analyzed income data from the 1974/75 Household Budget Survey and found that female-headed households accounted for disproportionately higher levels of food poverty compared to male-headed households. Similarly, Koster (2008) found that female-headed households were poorer than male-headed households in post-genocide Rwanda. Despite this pervasive conclusion, analyses from household consumption and expenditure surveys of some countries have, however, indicated a high incidence of poverty among male-headed households. For instance, Ghana Statistical Service (GSS, 2007) indicated that since the third round of the Living Standards Survey in Ghana, the incidence of poverty among male-headed households has been greater than that of their female-headed counterpart. Specifically, poverty incidence among male-headed and female-headed households in 1991/92 were estimated as 55 percent and 43 percent respectively while in 1998/99 the figures were 41 percent and 35 percent respectively. In 2005/06, the poverty incidence among male-headed and female-headed households had reduced to 31 percent and 19 percent respectively. Thus, in Ghana, female-headed households have over the years been better off than their male-headed counterparts in terms of the rate of reduction in poverty. This is because, according to Twerefou et al. (2013), most households with male heads are found in the savannah and rural areas, which are prone to poverty.

Generally, the use of gender of the household head to examine the evidence of “feminization of poverty” has resulted in mixed results. In view of this, Fukuda-Parr (1999) argued specifically that the use of poverty among female-headed households as a representative measure of women’s poverty masks the extent of poverty among women. This is because the measure is cast in a narrow framework of poverty that focuses on the household as a unit, a focus that leads to ignoring intra-household disparities (Fukuda-Parr, 1999). Moreover, according to Women-in-Development (WID) and Gender and Development (GAD) literature, intra-household inequalities is noted as one of the causes of increased poverty among women (Moghadam, 2005) and thus should be considered when analyzing women’s poverty. Empirical studies have therefore raised conceptual and methodological doubt concerning the relationship between female-headed households and poverty as well as their use as a representative measure of women’s poverty. The conceptual doubt is that female-headed households contain a highly varied universe, “a universe that does not include all poor women and whose members are not all poor’’ (Geldstein 1997, quoted in Feijoó, 1998). In addition, Chant (2003) argued that poverty is rather a function of subjective elements. Hence, it is imperative that “feminization of poverty” is viewed as an individual-level phenomenon rather than a household-level phenomenon. This means that “feminization of poverty” hypothesis should be examined with respect to the gender of the individual household member rather than the gender of the household head. Thus, using individuals aged 16 years old and above as the unit of analysis, this article tests empirically whether “feminization of poverty” is prevalent in Ghana. The article further investigates the effect of educational attainment of the individual on the phenomenon.

Literature Review

Most empirical studies that examine the evidence of “feminization of poverty” have used the gender of the household head as a unit of analysis. For instance, in Botswana, Kossoudji and Mueller (1983) used Rural Income Distribution Survey conducted in 1974–1975 to analyze the demographic and economic status of female-headed households in the rural areas. Their findings indicated that female-headed households were poorer than male-headed households and that on a per adult equivalent basis, the welfare attained by members of female-headed households was nearly 25 percent lower than that attained in male-headed households, which may be regarded as an indication of “feminization of poverty.” McLanahan (1985) also used longitudinal data taken from the Michigan Panel Study of Income Dynamics to address the questions of whether and why off-springs in female-headed households are more likely to experience persistent poverty in adulthood. The findings showed that growing up in a female-headed household increases the risk of poverty. Similarly, Rodgers (1990) concluded that in the United States, male-headed (single-parent) families are less poor than female-headed (single-parent) families, which may be regarded as an indication of “feminization of poverty.” In Tanzania, Katapa (2005) examined the characteristics of female-headed and male-headed households, compared these households, and related them to poverty. He concluded that female-headed households were more likely than male-headed households to be in rural areas, be small in size, have fewer men, not have radios and not have enough food to eat, which may also be regarded as an indication of “feminization of poverty.”

In Ghana, Kyereme and Thorbecke (1987) analyzed income data from the 1974/75 Household Budget Survey of Ghana and found that female-headed households accounted for disproportionately higher levels of food poverty compared to male-headed households, which may be implied as “feminization of poverty.” Furthermore, Codjoe (2010) examined the population–food crop production nexus and within it assessed the differences between male-headed and female-headed households. He concluded that female-headed households in the transitional agro-ecological zone produced more maize, owned more land, earned more from the sale of maize, allowed more years for the land to fallow, used more inorganic fertilizer on their farms, cropped more agricultural land, and cropped maize on soils with better water absorption capacity, compared to male-headed households, meaning female-headed households are not worse off than male-headed households. Though the author did not link the findings directly to poverty, it could be read as evidence against “feminization of poverty.” Ofori (2011) using logistic regression shows that male household heads are about 33.6 percent points likely to be less poor compared to their female counterparts. This therefore supports the “feminization of poverty” phenomenon. However, a related problem with the “feminization of poverty” is its over-concentration on female-headed households (Chant, 2006). Strengthening the case against the undue emphasis on female-headed households as a means of measuring the phenomenon of “feminization of poverty,” Chant (2003) argued that female-headed households are a highly heterogeneous group (including both the poor and rich). In view of this problem, Ofori (2011) focused on only the household head as a unit of analyzing “feminization of poverty.” However, the problem of using the head of the household as a unit of analysis is that not all females and males are household heads. Hence, there is sample selection problem in his study.

Furthermore, some studies in Ghana have also analyzed poverty without paying attention to its gender dimension. For instance, Adjasi and Osei (2007) examined the nature and correlates of poverty in general. Using the Foster–Greer–Thorbecke (FGT) poverty indicators and a probit regression, the study found that a household is less likely to be poor if the head is educated and urban based. They also conclude that most households in Ghana rely on firewood, do not have access to pipe-borne water and live in rooms rather than full apartments. However, the underlying weakness of their study is that it is based on a static analysis of poverty. Gyimah-Brempong and Asiedu (2009) investigated the effects of international remittances on poverty incidence and severity at the household level using both cross-sectional data from Ghana Living Standards Survey wave 5 (GLSS5) and pseudo-panel data constructed from GLSS3-GLSS5 and a GMM pseudo-panel estimator. The study found that international remittances decrease the probability of a family being poor or chronically poor and increase human capital formation. Ennin et al. (2011) modeled determinants of poverty using logistic regression based on data from three rounds of the Ghana Living Standards Survey (GLSS3, 1991/92; GLSS4, 1998/99; and GLSS5, 2005/06). The results obtained from the analysis indicated that households with large sizes, illiterate heads, and those with heads that have agriculture as their primary occupation are poorer. Their study, however, did not focus on the gender dimension of poverty.

The literature reviewed reveals that not many studies have been undertaken to examine the “feminization of poverty” hypothesis in Ghana using the individual household member as a unit of analysis. Moreover, previous studies have not adequately examined how education influences “feminization of poverty.” Thus, this study tests the hypothesis and contributes to the empirical literature on the phenomenon.

Methodology

Theoretical Framework

Theoretically, this article employs the Individual Theory of poverty to test the “feminization of poverty” hypothesis by Pearce (1978). The Individual theory of poverty postulates that poverty is caused by the individual rather than the community or household in which they live. Under this theory, poverty is due to individual deficiencies and as such, anti-poverty programs in the light of this theory address poverty by focusing on individual characteristics (Bradshaw, 2006). To subject the “feminization of poverty” hypothesis to empirical test, this article employs the decomposable FGT measure of poverty. This is because a useful feature of the FGT measure is that it is additively decomposable with population share weights, specifically, with respect to male and female poverty (Wright, 1992). However, most measures of poverty that incorporate Sen’s axiomatic requirements (including Sen’s own measure) are not decomposable (see Hagenaars, 1987 and Wright, 1992). Therefore, for the purpose of this study, this is problematic since we want to decompose the “total” amount of poverty into male and female “shares.” Thus we employ the poverty measure of Foster, Greer, and Thorbecke expressed as

p (α) = [\frac{1}{n}] {\sum_{i = 1}^{q} [\frac{(I * - I_{i})}{I *}]}^{α}

(1)

Where I* is the income poverty line, I_i is the income of individual i, q is the number of poor individuals in the population (I* < I_i), and n is the total number of individuals in the population. α is a parameter that takes on a value greater than or equal to zero (α ≥ 0).

As α gets larger, the measure becomes more sensitive to the income circumstances of the “poorest poor” (Wright, 1992). If α = 0, then P(0) = H = $\frac{q}{n}$ . This is known as the head count ratio, which is the proportion of the population that has income below the poverty line. If α = 1, then P(l) = HI, where $I = \frac{(I * - \bar{I_{P}})}{I *}$ and ${\bar{I}}_{p}$ is the average income of the poor. This is a renormalization of the “income–gap ratio,” which captures the average income shortfall of the poor. This measures the poverty gap and it captures the resources that would be needed to lift all the poor out of poverty through perfectly targeted cash transfers (see Coulombe and Wodon, 2007).

p (2) = [\frac{1}{n}] {\sum_{i = 1}^{q} [\frac{(I * - I_{i})}{I *}]}^{2}

This is known as the squared poverty gap, which is a measure of the severity of poverty. This study limits itself to the first three measures of the FGT class of poverty measures namely the head count ratio, the poverty gap, and the squared poverty gap.

Following Wright (1992), we decompose the FGT class of poverty measures into male and female poverty to examine the evidence of “feminization of poverty” in Ghana. The decomposition is as follows:

p (α) = [\frac{n_{f}}{n}] p {(α)}_{f} + [\frac{n_{m}}{n}] p {(α)}_{m}

(2)

where the subscripts m and f denote male and female, respectively. The ratios $[\frac{n_{f}}{n}]$ and $[\frac{n_{m}}{n}]$ are the population shares of males and females. p (α)_f and p (α)_m are the FGT poverty measures calculated separately for females and males. According to Wright (1992), if p (α) is thought of as being the total amount of poverty in the population, then the female and male shares of this total are:

S {(α)}_{f} = \frac{[\frac{n_{f}}{n}] P {(α)}_{f}}{P (α)}

(3)

S {(α)}_{m} = \frac{[\frac{n_{m}}{n}] P {(α)}_{m}}{P (α)}

(4)

From Equations (3) and (4), if the poverty experience is shared equally between males and females, then S (α)_f = S (α)_m = 0.5. Hence, there is no evidence of “feminization of poverty” at the individual level. Conversely, if S (α)_f > p (α)_m, then poverty is not equally shared, with females being over-represented among the poor and we conclude that there is “feminization of poverty” at the individual level.

Data

The data used in this article are from the fourth and fifth rounds of the GLSS IV and V conducted in 1998/99 and 2005/06, respectively, by the Ghana Statistical Service with the aid of the World Bank. The GLSS IV and V are the two most recent surveys. The fourth round of the GLSS covers 6,000 households in 300 enumeration areas whilst the fifth round of the GLSS covers 8,687 households in 580 enumeration areas. Further, the GLSS IV and V contain 30,000 and 37,128 individuals, respectively. They also contain detailed information on a variety of topics, including community level characteristics, the demographic characteristics of households, education, health, employment, time use, migration, housing conditions, agriculture, and non-farm businesses. Moreover, they contain complete data on incomes of individuals, specifically men and women which are essential for this study. Despite these advantages, they contain a lot of missing data particularly on the education and income levels of individuals. We therefore sampled 1,270 males and 1,576 females, aged 16 years old and above from the GLSS IV. From the GLSS V, we sampled 2,613 males and 3,021 females, who are aged 16 years and above. These sample sizes are selected based on the availability of complete data on individuals’ education and income levels in the two sets of survey. Table 1 therefore shows a breakdown of the sample data from the two sets of survey used in our analysis.

Table 1.

Summary of Sampled Data from GLSS IV and V Used in the Analysis

GLSS IV			GLSS V
Male	Female	Total	Male	Female	Total
1,270	1,576	2,846	2,613	3,021	5,634

Identifying the Poor

Poverty is recognized to be multi-dimensional in its causes and manifestations, which includes lack of income and productive resources sufficient to ensure a sustainable livelihood; hunger and malnutrition; ill health; limited or lack of access to education and other basic services; increasing morbidity and mortality from illness; homelessness and inadequate housing; unsafe environments; and social discrimination and lack of participation in decision making (World Bank, 2000). This study employs the income approach of measuring poverty as adopted by Wright (1992). Specifically, the annual gross income of the individual (which is calculated as a summation of individuals’ income from main and extra occupations, and the value of goods received as income for work done) is used since longer measurement period tends to help “smooth-out” short-term fluctuations in income due to unemployment and overtime payments (Wright, 1992). Gross instead of net income of the individual is used because the datasets (GLSS IV and V) do not contain actual amounts of deductions such as Social Security Contributions, income taxes, and trade union dues.

Based on the datasets, an individual is classified as “poor” if his or her annual gross income, I_i, is below the poverty line, I*. According to Hagenaars and Van Praag (1985), there are no well-defined rules for selecting the appropriate poverty line. This article uses the so-called “individuals below average income” (IBAI) approach, where the poverty line is set at a fraction, β, of the mean level of income. That is: I*= b × $\bar{I_{}}$ . Therefore, an individual is poor if he or she earns an income below this level. The implicit assumption underlying this approach is that the economic resource of the individual is solely a function of his or her income. In estimating the income poverty line, we set b to be equal to 48.6 percent and 37.8 percent representing upper and lower poverty lines respectively. This approach of poverty line definition is adapted from GSS (2007). Thus, two income poverty lines are defined by this article. The upper income poverty line is 48.6 percent of sample mean of income whilst the lower income poverty line is set at 37.8 percent of sample mean of income. Our objective of fixing two poverty lines is to find out whether our result is robust to the choice of poverty line.

Results and Discussion

In this section, we present estimates of poverty indices separately for males and females based on the three FGT poverty measures; the head count poverty P(0), the poverty gap P(1), and the squared poverty gap P(2) (see Tables 2, 3 and 4 respectively).

Table 2.

Male–Female Poverty Index: Index is P(0) × 100

	No Education		Basic Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	53.85	45.83	44.43	50.00	32.17	32.35	43.84	21.21	43.78	48.73
2005/06	37.50	55.00	51.12	55.42	55.26	47.85	51.69	50.00	53.43	60.94
β = 0.378
1998/99	44.23	36.46	32.63	41.45	21.68	26.47	24.66	12.12	32.05	40.16
2005/06	37.50	55.00	43.90	47.20	49.21	39.71	44.92	45.65	46.08	54.35

Source: Authors’ Computation from GLSS IV and V.

Table 3.

Male–Female Poverty Index: Index is P(1) × 100

	No Education		Basic Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	28.60	22.60	20.03	25.18	8.700	19.11	12.66	9.42	20.36	24.90
2005/06	28.73	37.79	32.40	34.90	35.97	29.27	30.77	27.03	36.53	42.63
β = 0.378
1998/99	22.72	17.76	14.71	19.34	40.81	14.64	6.53	6.75	15.31	19.10
2005/06	26.22	32.87	28.20	30.16	31.25	24.87	25.84	21.29	32.76	38.23

Source: Authors’ Computation from GLSS IV and V.

Table 4.

Male–Female Poverty Index: Index is P(2) × 100

	No Education		Primary Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	19.45	14.98	12.43	16.23	7.82	14.10	5.35	5.96	12.96	16.08
2005/06	22.02	28.58	25.70	27.50	28.06	21.89	22.70	19.44	30.51	35.34
β = 0.378
1998/99	15.29	11.50	9.16	12.17	163.74	12.12	2.60	4.60	9.73	12.09
2005/06	18.36	23.98	22.60	24.13	24.34	18.44	18.94	16.18	27.77	31.93

Source: Authors’ Computation from GLSS IV and V.

The Head Count Poverty Ratio – P(0)

This is a measure of the incidence of poverty. It is also known as the poverty head count ratio. Table 2 shows the poverty head count ratio separately for males and females in 1998/99 and 2005/06 respectively. We also carried out an analysis of this measure based on highest level of education completed by individuals (males and females) in 1998/99 and 2005/06 respectively. Using the head count poverty measure, the all-adult sample indicates that females suffer poverty at higher rate than males in the respective survey years regardless of educational level (see Table 2). This means that if the effect of education is held constant, females suffer poverty at higher rate than their male counterparts. However, the tertiary-education cohort indicates that males suffer poverty at higher rate than females (see Table 2). This means that females having tertiary education as highest level of education completed suffer poverty at a lesser rate than their male counterparts. Further, this could imply that tertiary education is more effective in reducing poverty among females than males. Thus education is an effective tool for combating poverty among females. Table 2 further indicates that generally, the head count poverty ratio for males and females has increased over the respective years. Specifically, the all-adult head count poverty increased by approximately 10 and 12 percentage points for males and females, respectively. The results are robust to the choice of poverty line (b).

The Poverty Gap – P(1)

This poverty index measures the depth of poverty and it also captures the resources that would be needed to lift all the poor out of poverty through perfectly targeted cash transfers (Coulombe and Wodon, 2007). Table 3 shows the poverty gap rates calculated separately for males and females in 1998/99 and 2005/06 respectively. Similarly, using the poverty gap measure, the all-adult sample indicates that females have higher depth of poverty than males in the respective years regardless of educational level (see Table 3). This implies that when the effect of education is held constant, females’ depth of poverty is higher than that of their male counterparts. On the contrary, the secondary and tertiary education cohorts indicate that females’ depth of poverty is lower than that of females with the exception of 1998/99 (see Table 3). This is an indication that secondary and tertiary education is more effective in reducing the depth of poverty among females than males. These results are also robust to the choice of poverty line (b).

The Squared Poverty Gap – P(2)

This index is a measure of severity of poverty and takes into account the inequality among the poor (Coulombe and Wodon, 2007). Table 4 shows the poverty severity index of males and females in 1998/99 and 2005/06 respectively. From Table 4, the all-adult cohort indicates that when the effect of education is held constant, poverty is more severe among females than males in the respective years. Conversely, the secondary and tertiary education samples indicate that poverty is more severe among males than females (see Table 4). In other words, this could imply that secondary and tertiary education is very effective in reducing the severity of poverty among females than among males. These results are also robust to the choice of poverty line (b).

From Tables 2, 3, and 4, all three measures of poverty; the head count ratio, the poverty gap, and the squared poverty gap indicate that poverty is higher among females than among males but with secondary or tertiary education this trend reverses. Moreover, the three measures of poverty calculated separately for males and females are robust to the choice of poverty line (b).

Male–Female Poverty Shares Based on the FGT Poverty Measures – S(α)

This section provides the estimated male–female poverty shares based on the three FGT classes of poverty measure. Based on the estimated poverty shares, we conclude whether “feminization of poverty” is prevalent in Ghana or not. Moreover, to test the statistical significance of the male–female poverty share differences, we apply the paired samples T-test. We also analyze the effect of education on the male-female poverty shares. This is done to investigate the effectiveness of the various levels of education in reducing poverty among males and females. Tables 5, 6, and 7 indicate the poverty shares of men and women based on the three FGT classes of poverty measure – the head count ratio, the poverty gap, and the squared poverty gap respectively.

Table 5.

Male–Female Poverty Shares Based on P(0) – S(0) × 100

	No Education		Basic Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	38.75	61.25**	48.05	51.95*	80.91	19.09***	68.88	31.12***	42.37	57.63***
2005/06	21.78	78.22**	47.98	52.02*	68.20	31.80***	72.67	27.33***	42.75	57.25***
β = 0.378
1998/99	39.51	60.49**	43.06	56.94*	77.74	22.26***	81.91	18.09***	39.50	60.50***
2005/06	21.78	78.22**	48.19	51.81*	69.71	30.29***	71.67	28.33***	41.94	58.06***

Source: Authors’ Computation from GLSS IV and V.

Notes: *** male–female poverty share difference is significant at 1%; ** male–female poverty share difference is significant at 5%; * male–female poverty share difference is significant at 10%.

Table 6.

Male–Female Poverty Shares Based on P(1) – S(1) × 100

	No Education		Basic Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	40.53	59.47**	45.29	54.71*	66.00	34.00*	74.95	25.05***	40.08	59.92***
2005/06	23.07	76.93**	48.14	51.86*	69.53	30.47*	74.54	25.46***	42.20	57.80***
β = 0.378
1998/99	40.79	59.21**	42.22	57.78*	109.19	–9.19*	68.29	31.71***	39.61	60.39***
2005/06	24.57	75.43**	48.32	51.68*	70.00	30.00*	75.73	24.27***	42.20	57.80***

Source: Authors’ Computation from GLSS IV and V.

Poverty Shares of Males and Females Based on P(0)

This relates to the shares of men and women in poverty based on the head count ratio – P(0). From Table 5, when the effect of education is held constant, women are found to be more represented among the poor than men. Specifically, the shares of men and women in poverty in 1998/99 are 42.37 percent and 57.63 percent respectively (see Table 5). However, in 2005/06, the poverty shares of men increased to 42.75 percent whilst that of women reduced to 57.25 percent (see Table 5). Moreover, though the poverty share of women has been greater than that of men in both years of the living standards survey, Table 5 indicates that the gap in their poverty shares has reduced from 15.26 percent in 1998/99 to 14.50 percent in 2005/06. Therefore, this indicates “feminization of poverty” but has not worsened over the years. However, when educational level is considered, the share of men in poverty with secondary and tertiary education is greater than that of women with secondary and tertiary education. Specifically, when b = 0.486 (upper poverty line), the share of men in poverty with secondary education is 80.91 percent in 1998/99, which plummets to 68.20 percent in 2005/06, whereas the share of women in poverty with secondary education is 19.09 percent in 1998/99, which increases to 31.80 percent in 2005/06. This indicates that though the share of women in poverty with secondary education has increased over the respective years, there is no evidence for “feminization of poverty” among this cohort but rather what Wright (1992) termed as “masculinization of poverty.” Table 5 also indicates that in all the two respective years (1998/99 and 2005/06), men with tertiary education have a higher share of poverty than their women counterparts. For instance, the shares of men with tertiary education in poverty are 68.88 percent and 72.67 percent in 1998/99 and 2005/06 respectively whilst the shares of women in poverty with tertiary education are 31.12 percent and 27.33 percent, respectively. This indicates that poverty share of men with tertiary education has increased by 3.79 percent, whereas that of women with tertiary education has reduced by the same percentage points. The result also shows that there is no evidence for “feminization of poverty” among the tertiary education cohort. Again, these results are robust to the choice of poverty line (b).

Poverty Shares of Males and Females Based on P(1)

Table 6 indicates the shares of men and women in poverty based on the poverty gap index – P(1). We observe from our analysis that holding educational effect constant, the percentage share of women who are in poverty based on the second measure of poverty, P(1) is greater than that of men. This indicates “feminization of poverty” at the P(1) level of poverty measure. For instance, when the upper poverty line (i.e., b = 0.48) is considered, regardless of the level of education, the shares of men in poverty are estimated at 40.08 percent and 42.20 percent in 1998/99 and 2005/06 respectively whereas the shares of women in poverty are estimated as 59.92 percent and 57.80 percent in 1998/99 and 2005/06, respectively (see Table 6). Table 6 also indicates that the gap between male and female poverty shares declines from 19.84 percent in 1998/99 to 15.60 percent in 2005/06. This indicates that though “feminization of poverty” is found to be evident in 1998/99, it is found to be less pronounced in 2005/06.

However, when educational level of the individual is factored into the analysis, our results indicate that the share of men in poverty is greater than that of women in poverty. For instance, when we consider the upper poverty line (i.e., b = 0.48), the secondary education cohort indicates that the shares of men in poverty based on P(1) are estimated as 66.00 percent and 69.53 percent respectively in 1998/99 and 2005/06 whilst that of women are estimated as 34.00 percent and 30.47 percent, respectively, in 1998/99 and 2005/06 (see Table 6). For the tertiary education cohort, the shares of men in poverty are estimated as 74.95 percent and 74.54 percent, respectively, in 1998/99 and 2005/06 whilst those of women are estimated as 25.05 percent and 25.46 percent, respectively, in 1998/99 and 2005/06 (see Table 6). Therefore, our results show that there is no evidence of “feminization of poverty” among the secondary and tertiary education cohorts based on this poverty measure. This result is also robust to the choice of poverty line (β).

Poverty Shares of Males and Females Based on P(2)

Table 7 shows the shares of men and women in poverty based on the squared poverty gap – P(2). In other words, it shows the shares of men and women in severe poverty. When the effect of education is isolated, the share of women who suffer from severe poverty is higher than the share of men who suffer from severe poverty (see Table 7). This according to Chant (2006) is a feature of “feminization of poverty.” Similarly, Table 7 indicates that the shares of men in severe poverty are greater than those of women in severe poverty. Specifically, under b = 0.486 (upper poverty line), the estimated shares of men in severe poverty are 70.28 percent and 70.42 percent respectively in 1998/99 and 2005/06 for the secondary education cohort whilst women’s estimated shares of severe poverty in 1998 and 2005/06 are 29.72 percent and 29.58 percent respectively. For the tertiary education cohort, under b = 0.486, men’ shares of severe poverty in 1998/99 and 2005/06 are estimated as 66.64 percent and 75.02 percent, respectively, for the secondary education cohort whilst women’s shares of severe poverty in 1998 and 2005/06 are estimated as 33.36 and 24.98 percent respectively. Thus, at this level of poverty measure, there is no evidence of “feminization of poverty” amongst the secondary and tertiary education cohorts.

Table 7.

Male–Female Poverty Shares Based on P(2) – S(2) × 100

	No Education		Primary Education		Secondary Education		Tertiary Education		All Adults
	Male	Female	Male	Female	Male	Female	Male	Female	Male	Female
β = 0.486
1998/99	41.15	58.85**	44.36	55.64*	70.28	29.72**	66.64	33.36**	39.74	60.26***
2005/06	23.94	76.06**	48.31	51.69*	70.42	29.58**	75.02	24.98**	42.38	57.62***
β = 0.378
1998/99	41.72	58.28**	41.97	58.03*	98.29	1.71**	55.71	44.29**	39.70	60.30***
2005/06	23.82	76.18**	48.36	51.64*	71.03	28.97**	75.06	24.94**	42.56	57.44***

Source: Authors’ Computation from GLSS IV and V.

Based on the estimated male–female poverty shares in respect of the three FGT poverty measures (as indicated in Tables 5, 6 and 7), it is evident that generally there is “feminization of poverty” in Ghana. However, when we examine the educational effect on this conclusion, our estimates show that “feminization of poverty” is prevalent only amongst the no education and primary education cohorts. However, amongst the secondary and tertiary cohorts, what we rather find is what Wright (1992) termed as “masculinization of poverty.” Generally, we also observe from our estimated poverty shares in Tables 5, 6 and 7 that as the educational level of males increases, their poverty share does not reduce. This means that education is not a sufficient “weapon” for combating poverty amongst males. However, as the educational levels of females increase, we observe a reduction in poverty share. This means that education is rather a potent “weapon” for combating poverty amongst females.

Policy Recommendation and Conclusion

In conclusion, our analysis of the two sets of household survey data (GLSS IV and V) using the three FGT classes of poverty measures supports the claim of “feminization of poverty” made by Pearce (1978). The first is that, in general, all three classes of poverty measures indicate that women are more represented in the ranks of the poor than men. This is contrary to the findings of Wright (1992), which lends no support to the “feminization of poverty” claim. Our analyses also indicate that over the last two sets of household surveys (1998/99 and 2005/06), the head count poverty ratio for males in general has increased significantly from 43.78 percent to 53.43 percent whilst that of females has increased significantly from 48.73 percent to 60.94 percent (see Table 2). Similarly, the poverty depth index for males in general has increased from 20.36 percent to 36.53 percent, whereas that of females has increased significantly from 24.90 percent to 42.63 percent (see Table 3). Furthermore, the poverty severity index for males has increased from 12.96 percent to 30.51 percent, whereas that of females has also increased significantly from 16.08 percent to 35.34 percent. Hence, over the two survey periods, all three FGT indices of poverty have increased. Our results further indicate that though there is evidence of “feminization of poverty” in both years of the survey, the trend reverses with high levels of education. Specifically, at lower level of education (primary education) of the individual household member, “feminization of poverty” is found to be prevalent but at higher levels of education (secondary and tertiary), “masculinization of poverty” is rather found to be prevalent. Therefore, further studies should be carried out in order to investigate this. In terms of gender-specific policies and strategies for combating poverty, we recommend that since females’ poverty shares reduce with increasing level of education, measures that target education as a tool for combating poverty should be strengthened amongst females whilst education in tandem with complementary interventions such as entrepreneurial training should be pursued in combating poverty among males.

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