International Journal of Climate Change Strategies and Management Gender dimension of vulnerability to climate change and variability: Empirical evidence of smallholder farming households in Ghana Article information PDF

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International Journal of Climate Change Strategies and Management Gender dimension of vulnerability to climate change and variability: Empirical evidence of smallholder farming households in Ghana Suhiyini I. Alhassan, John K.M. Kuwornu, Yaw B. Osei-Asare, Article information: To cite this document:...

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International Journal of Climate Change Strategies and Management Gender dimension of vulnerability to climate change and variability: Empirical evidence of smallholder farming households in Ghana Suhiyini I. Alhassan, John K.M. Kuwornu, Yaw B. Osei-Asare,

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Article information: To cite this document: Suhiyini I. Alhassan, John K.M. Kuwornu, Yaw B. Osei-Asare, (2018) "Gender dimension of vulnerability to climate change and variability: Empirical evidence of smallholder farming households in Ghana", International Journal of Climate Change Strategies and Management, https:// doi.org/10.1108/IJCCSM-10-2016-0156 Permanent link to this document: https://doi.org/10.1108/IJCCSM-10-2016-0156 Downloaded on: 24 April 2018, At: 13:17 (PT) References: this document contains references to 56 other documents. Access to this document was granted through an Emerald subscription provided by All users group

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Gender dimension of vulnerability to climate change and variability

Climate change and variability

Empirical evidence of smallholder farming households in Ghana Suhiyini I. Alhassan Department of Agricultural Economics and Agribusiness, College of Basic and Applied Science, University of Ghana, Legon, Ghana

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John K.M. Kuwornu

Received 30 October 2016 Revised 10 May 2017 30 December 2017 12 February 2018 Accepted 27 February 2018

Department of Food, Agriculture and Bioresources, School of Environment, Resource and Development, Asian Institute of Technology, PathumThani, Thailand, and

Yaw B. Osei-Asare Department of Agricultural Economics and Agribusiness, College of Basic and Applied Science, University of Ghana, Legon, Ghana

Abstract Purpose – This paper aims to investigate farmers’ vulnerability to climate change and variability in the northern region of Ghana.

Design/methodology/approach – The study assessed the vulnerability of male-headed and femaleheaded farming households to climate change and variability by using the livelihood vulnerability index (LVI) and tested for significant difference in their vulnerability levels by applying independent two-samplestudent’s t-test based on gender by using a sample of 210 smallholder farming households.

Findings – The results revealed a significant difference in the vulnerability levels of female-headed and male-headed farming households. Female–headed households were more vulnerable to livelihood strategies, socio-demographic profile, social networks, water and food major components of the LVI, whereas maleheaded households were more vulnerable to health. The vulnerability indices revealed that female–headed households were more sensitive to the impact of climate change and variability. However, female-headed households have the least adaptive capacities. In all, female-headed farming households are more vulnerable to climate change and variability than male-headed farming households. Research limitations/implications – The study recommends that female-headed households should be given priority in both on-going and new intervention projects in climate change and agriculture by empowering them through financial resource support to venture into other income-generating activities. This would enable them to diversify their sources of livelihoods to boost their resilience to climate change and variability.

Originality/value – This is the first study that examined the gender dimension of vulnerability of smallholder farmers in Ghana by using the livelihood vulnerability framework. Female subordination in northern region of Ghana has been profound to warrant a study on gender dimension in relation to climate change and

© Suhiyini I. Alhassan, John K.M. Kuwornu and Yaw B. Osei-Asare. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

International Journal of Climate Change Strategies and Management Emerald Publishing Limited 1756-8692 DOI 10.1108/IJCCSM-10-2016-0156

IJCCSM

variability, especially as it is a semi-arid region with unpredictable climatic conditions. This research revealed the comparative vulnerability of male- and female-headed households to climate change and variability.

Keywords Ghana, Gender, Livelihood vulnerability, Smallholder farmers, Climate change and variability

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Paper type Research paper

1. Introduction There is a growing global concern on climate change and variability given its impact on the environment and agriculture. The recent increases in temperatures, erratic rainfall leading to floods, droughts and water scarcity are all evidences of climate change and variability (Adger et al., 2003; Asante and Amuakwa-Mensah, 2015; Intergovernmental Panel on Climate Change [IPCC], 2007; Adu et al., 2017). Odada et al. (2008) revealed that climate change and variability is a serious challenge to future development, especially in semi-arid areas. About 85 per cent of the world farmers are smallholders and earn their livelihood through rain-fed agriculture (IPCC, 2014; Morton, 2007; Harvey et al., 2014). Thus, the concern is how climate change and variability is impacting ecosystem, agriculture and livelihoods. Ghana’s economy is basically agrarian, and the agricultural sector is dominated by small-scale farmers who cultivate on two-hectare farm lands or less (Ministry of Food and Agriculture, MoFA, 2010). In Ghana and other parts of the tropical region, climate change and variability is predicted to unduly distress smallholder farmers, making their livelihoods more precarious (IPCC, 2014). Unfortunately, to the best of our knowledge, information on the extent of their vulnerability and adaptation is scanty or non-existent in the literature. Poverty and food insecurity in Northern Ghana continue to be high as a result of climate change and variability (Amikuzuno and Donkoh, 2012). The Ministry of Environment, Science, Technology and Innovation (2013) noted in the Ghana National Climate Change Policy that the savannah zone is the most vulnerable to climatic stresses in Ghana. Northern region is most vulnerable to climatic stresses because of its high poverty incidence, high rural population, poor agro-climatic systems and predominance of subsistence farmers relative to the southern part of Ghana (GSS, 2014; Nti, 2012). Stanturf et al. (2014) stated that northern Ghana is relatively more vulnerable to climate change and variability compared to other parts of Ghana because of its high illiteracy rate and underdeveloped infrastructure. In short, most studies on climate change in Ghana have portrayed the northern region as the most exposed and vulnerable region to climatic stresses with the least adaptive capacities (Etwire et al., 2013; Kuwornu et al., 2013; Al-Hassan et al., 2013; Nti, 2012; START, 2013). According to Boko et al. (2007), the effect of climate change and variability is expected to differ based on agro-ecological regions, spatial features and across socio-economic groups such as gender differentials. Though both male-headed and female-headed farming households within the same geographical location are exposed to the same climatic conditions, the extent of effect of the climatic stresses varies between men and women, because of differences in their levels of adaptive capacities and sensitivity. Thus, vulnerability to climate change is worsened by gender disparity (World Bank, 2010). Women constitute about 50.4 per cent of the northern region’s population (GSS, 2012). Yet, female farmers’ agricultural activities lack the needed resources relative to male farmers (Asare, 2000; Food and Agricultural Organisation, FAO, 2011). In the northern region, female-headed households have less tenures and access to land and other production

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resources compared to male-headed households (Blackden and Wodon, 2006; Doss and Morris, 2000; Koru and Holden, 2008). Numerous studies in the climate change vulnerability literature have examined spatial and sector vulnerability with little emphasis on the gender dimension of vulnerability to climate change. Therefore, the objective of this study is to examine the vulnerability to climate change and variability for female-headed and male-headed farming households in the northern region of Ghana. The authors postulate that there is a significant difference in the vulnerability levels of male-headed and female-headed households. Apart from adding to the climate change literature, the findings of this study will provide specific gender vulnerability levels, sensitivity and adaptive capacities to climate change and variability. This will be instrumental in formulating policies to address the specific needs of gender groups in reducing vulnerability to climate change and variability as a way of achieving Ghana’s National Climate Change Policy objective of gender equity. 1.1 Vulnerability to climate change and variability The term “vulnerability” has been used to portray different interpretations in different disciplines and does not lend itself to a precise and concise definition. Turner et al. (2003) defined vulnerability as the extent of injury likely to be caused to a system as a result of its exposure to a hazard. Cutter et al. (2008) and Nelson et al. (2010) view vulnerability as the predisposition of any group of people, location or system to disorders determined by exposure and sensitivity to distresses, including their adaptive capacity. The Third and Fourth Assessment Reports of the IPCC (2014) defined vulnerability as the level to which a system is susceptible to, or incapable of coping with the adverse effects of climate change, climate variability and extremes. In other words, vulnerability is an embodiment of the character, magnitude and degree of exposure of a system to climate change and variability, its sensitivity and adaptive capacity. According to IPCC (2007), adaptive capacity of a system is its ability to reduce the possible consequences of climate variability through prevailing opportunities or using measures to deal with these consequences; sensitivity is the extent to which a system is affected by climate-related stimuli either positively or negatively; covertly or overtly; and exposure is the extent to which a system is unshielded from major climate-related events. In the context of this study, vulnerability is the extent to which a farming household is susceptible to, or unable to adapt to, the negative effects of climatic stresses. 2. Methodology The kind of research designed to be used by a researcher is greatly influenced by research question(s). Qualitative research seeks to understand a phenomenon based on the opinions of the population or people experiencing it (Mack et al., 2005). According to Kothari (2004), quantitative research is a process that involves measurement of phenomenon to obtain numerical data and is often applied to phenomenon that can be measured in terms of quantity. Thus, the quantitative method solicits information from respondents by the use of structured questionnaire, which provides numerical data at the end. Between qualitative and quantitative research methods is the mixed method, which is a process where a researcher uses a qualitative method at one phase of the research and then uses a quantitative method at the other phase to validate the results of the qualitative assessment. In this study, the authors adopted the mixed method. This made it possible to for us to compute vulnerability levels for households from the data gathered from the questionnaire administered to respondents while providing explanations to the vulnerability levels of households through the information gathered from focus group discussions.

Climate change and variability

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IJCCSM

2.1 Approaches to measuring vulnerability There are two main approaches to measuring vulnerability to climate change and variability: the indicator and econometric approaches (Deressa et al., 2009). While the econometric approach uses regression analysis, the indicator approach involves choosing components which a researcher considers as indicators of vulnerability and then computing indices for these components. The econometric approach to measuring vulnerability is limited by the setback of testing several econometric assumptions concerning confidence intervals, standard errors and hypotheses. On the other hand, the major shortfall of the indicator approach is the subjectivity on the part of the researcher in selecting the indicators of vulnerability to be incorporated in computing the vulnerability index. In spite of this criticism, the indicator approach is still preferred over the econometric approach because it is easier to compute and comprehend by readers with low mathematical inclination. In fact, the authors have explored the econometric approach and realized that, in this context, the results of the indicator approach were more appealing and intuitive than those of the econometric approach. Moreover, unlike the econometric approach, the indicator approach (especially the livelihood vulnerability index [LVI]), in addition to determining households’ present vulnerability to drought, bushfires and floods, also provides projections of future vulnerability for effective planning (Hahn et al., 2009). Though the indicator approach is subjective, it is possible to compare the vulnerability of a given system to climatic stresses at a particular geographical location within a given time period. In this respect, this study used the indicator approach in measuring vulnerability of female-headed and male-headed farming households to climate change and variability. In the literature, several indicator methods have been developed by several authors to measure vulnerability. These indicator methods have been dependent on the discipline in which it is used and also the objective of the research. Table I presents a list of indicator methods applicable in measuring vulnerability in the literature. The LVI approach developed by Hahn et al. (2009), which is an indicator method, was used in this study to examine farming households’ vulnerability to climate change and variability. The selected indicators have to be contextual and relevant to the local communities in which the investigation is being conducted (Asare-Kyei et al., 2014). Therefore, in this study, the authors have chosen indicators that are contextual and relevant to the local communities in which the study was conducted. 2.2 Testing for difference in means of livelihood vulnerability indices Given that the computed vulnerability indices are averages, there is a need to test for statistical difference in the means of the LVIs for both gender groups (i.e. female-headed and male-headed households). In the literature, the Student’s t-test and the Mann–White U test are some of the statistical methods for testing for differences in means of two samples. According to Ruxton (2006a, 2006b), the Mann–White t-test is best applicable for smaller sample (N < 30) with unequal population variance and non-normal t distribution. The Student’s t-test on the other hand is suitable for larger samples (N
30) where equal variance (homogenous population) and normal t distribution are assured (Sokal and Rohlf, 1987). This study used the independent two-sample student’s t-test (two-tailed) to test for significant differences in the means of the LVI major components, overall LVI, Intergovernmental Panel on Climate Change (IPCC) vulnerability contributory factors and the LVIIPCC indices. The t-statistic is calculated using equation (1):

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Index

Authors (year)

Assumption

Limitation

Social vulnerability

Lee (2014)

Social vulnerability index (SVI)

Ge et al. (2013)

Climate vulnerability index (CVI)

Pandey and Jha (2012)

Indicator based (in terms of capital) study Zero-mean normalization was applied to standardize the indicator values Application of projection pursuit cluster (PPC) model. Hazard-loss assessment by using economic variables (GDP and PCI) Primary data-based index Useful tool for assessing spatio-temporal scale differences in vulnerability

Vulnerability index

Gbetibouo et al. (2010)

All indicators (variables) showed same (positive) direction to vulnerability Considered only single hazard (flood) Absence of exposure indicator(s) No algebraic solution of PPC and hence no global optimal solution Suitable only for mountainous areas Weightage of different subcomponents were data sensitive Likelihood of paradoxical weight assigning to indicators due to poor data structure

Livelihood effect index (LEI)

Urothody and Larsen (2010)

LVI

Hahn et al. (2009)

Vulnerability as expected poverty

Deressa et al. (2009)

Social vulnerability index (SVI)

Vincent (2004)

Social vulnerability index (SVI)

Cutter et al. (2008)

Large spatial base (nine South African provinces) for data collection Principal component analysis for weighing indicators Primary data were used Comparison between LVI and LEI

Good dataset/primary data Diversified components were considered for vulnerability Measures farmers’ vulnerability to drought, floods and other climatic extremes Estimates the probability that a household’s consumption will fall below a minimum level due to the occurrence of a climatic shock Different weights were used for different sub-indices Multi-country analysis data problem due to us age of secondary data County-level socio-economic and demographic data were used Principal component analysis was applied for data reduction

Source: Authors’ compilation from literature, 2016

Climate change and variability

Perception on climate change and assigning importance (weights) to contributing factors by the illiterate respondents might not be accurate Equal weights for all components is not feasible Measures only the tendency to be poor (vulnerability) in future due to climatic extremes and not current vulnerability

For multi-country analysis the relative importance(weights) of sub-indices were likely to be different Missing data problem due to usage of secondary data Variables related to exposure to na...