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Environment Systems and Decisions

, Volume 38, Issue 1, pp 6–22 | Cite as

Food security in Africa: a cross-scale, empirical investigation using structural equation modeling

  • Riva C. H. DennyEmail author
  • Sandra T. Marquart-Pyatt
  • Arika Ligmann-Zielinska
  • Laura Schmitt Olabisi
  • Louie Rivers
  • Jing Du
  • Lenis Saweda O. Liverpool-Tasie
Article

Abstract

Despite consistent gains in global agricultural productivity in the last 50 years, lack of food security persists in many regions of the world. Addressing this issue is especially pertinent in Africa where 39 of the nearly five dozen nations most at risk of food insecurity are located. We draw from interdisciplinary research to develop an empirical model that outlines the four interconnected aspects of food security—availability, access, utilization and stability. Given the complexity of this issue, we develop a model that considers agricultural, socio-political, and economic factors as drivers of food security and its manifestations, related in a complex system of relations that includes both direct and indirect paths. We use structural equation modeling with latent variables to specify a model that seeks to determine the primary drivers of food security over 55 years in Africa, West Africa as a region, and for a group of 5 West African countries: Burkina Faso, Ghana, Mali, Niger, and Nigeria. Empirical results reveal the critical importance of availability and accessibility for mitigating food insecurity.

Keywords

Food security Undernutrition Africa Structural equation modeling 

1 Introduction

Persistent food insecurity is a challenge for the global community. In the face of global wealth gains, economic growth, and increased food production since at least the 1950s, populations remain vulnerable to shifts in food availability and access. Food insecurity is the norm rather than the exception in the developing world and is a particularly critical challenge in Africa where recent work estimates 1 in 4 people go hungry (UNDP 2017). Since the factors shaping food security are complicated and interrelated, encompassing social, economic, political, and environmental issues from poverty and inequality to health, temperature, and rainfall, developing an empirical model to test these proposed paths requires careful and rigorous specification, assessment, and validation. In this study, we build a model of food security that specifies multiple paths from social, economic, political, and agricultural factors and tests these proposed direct and indirect relations for African countries, with a West African regional emphasis, and for a five-country West African sub-sample, over 55 years using structural equation modeling with latent variables (SEMLV).

For decades scholars have investigated food security as an important component of well-being, since answering the question of who goes hungry and why is a topic of great importance to populations, governments, and policymakers worldwide. Food security is recognized as a fundamental right and is a central goal of development efforts and international organizations including the Food and Agriculture Organization (FAO) of the United Nations, the World Bank, and the International Food Policy Research Institute (IFPRI). Famines and disasters draw episodic international media attention to this recalcitrant problem, such as in Niger in 2004/2005 (BBC 2005), and, as of this writing in 2017, in Somalia (Gettleman 2017), yet food insecurity reflects deeper structural challenges related to inequality and distributional issues related to resource access. Ecological factors contributed to both crises, yet played out via a slightly differing confluence of events. Although the poor harvest the preceding fall was the proximate cause of the 2005–2006 famine in Niger, the lack of rain during the rainy season in the month of August the prior year—typically when the heaviest rains occur—and subsequent pest damage where locusts destroyed crops were key precipitating events. Drought, given the prolonged absence of rain for nearly 2 years, is one factor in what is being labeled as the 2017 famine in Somalia. Crop failure and livestock deaths are occurring, and more than six million Somalians are facing increased prospects of malnutrition and related diseases. Thus, food security is a challenging and critically important topic given its complexity with regard to conceptual dimensions, its cross-scale attributes, and variability across spatial, temporal, and social gradients.

The paper proceeds as follows. The next section describes prior research that informs this study, in which a path diagram of the drivers of food insecurity across the four pillars of availability, access, stability, and utilization is developed. Next, the data and empirical models are discussed, followed by results and discussion of the analyses. The paper concludes with a discussion of avenues for future academic work.

2 Defining and explaining food insecurity

Consensus has emerged among scholars in recent decades regarding how to define food security in relation to the household. At the household level, food security is the ability of a household to secure enough food to ensure adequate dietary intake of socially acceptable foods for all its members. Efforts to explain food insecurity focus on how availability, access, utilization, and stability take shape across different scales ranging from the global to the individual and how this affects human populations and decision-making, especially at the household level.

Food security exists when every person has access to sufficient food to sustain a healthy and productive life, where malnutrition is absent, and where food originated from efficient, effective, [equitable] and low-cost food systems that are compatible with sustainable use of natural resources. (Quisumbing et al. 1995, p. 50)

The four key aspects of food security—availability, access, stability, and utilization—are described by the FAO (2008) and encompass the cross-scale relevance of daily struggles for subsistence related to adequate food along with system-level production and distribution challenges. Availability and access describe (1) the presence or existence of food, that is whether food in the area or particular place is physically available, and (2) physical and economic access to that food. Utilization refers to food preparation and feeding practices, food diversity and distribution in the household, and the biological ability to utilize the food consumed, which determines nutrition status and thus represents where physical health becomes a crucial component of food security. Although present in varying degrees depending on any number of social, economic, and political factors, the configuration and stability over time of the other three pillars in relation to one another can define a population as food insecure and/or be applied to the system in its entirety.

Research on food security is evident in numerous disciplinary fields in the social sciences including sociology, economics, geography, and political science as well as in the physical sciences including agronomy, ecology, and biogeochemistry. Increasingly, much of the food security work is transdisciplinary, incorporating insights from relevant fields as needed to address this critical issue with a shared objective: to improve human well-being. Given this broad disciplinary breadth, the unit of analysis of studies of food security ranges from individuals and households to communities, regions, countries, and groups of countries as defined by geographic boundaries (e.g., Sub-Saharan Africa) and/or geo-political/economic categories (e.g., less developed countries or LDCs). Extant work on food security chronicles not just the particular constellation of the pillars but also how these interconnected aspects play out over time, over space (e.g., across communities and/or countries), and in relation to defining vulnerable groups. Many programs run by international organizations aimed at reducing hunger and food insecurity are targeted at the national level to allow for tailored approaches with regard to how policies are constructed and implemented (Stamoulis and Zezza 2003). As a result, studies of food security are wide-ranging and varied, including regionally focused investigations, country-specific work, within-country research, examinations of households specifically, as well as multi and cross-scale approaches. Throughout, the crucial roles of contexts are highlighted.

In explaining food security, scholars have developed conceptual models that to varying degrees incorporate the pillars of food security. Building models is challenging given the uneven spatial distribution of these concepts and the identification of—to varying degrees—both proximate and underlying causes of hunger, undernutrition, malnutrition, and food insecurity. The seminal work of Sen (1981), for instance, outlined a clear shift in the puzzle of food security. How and why in the context of an increasing global food supply is food insecurity possible? Moving from the availability or supply-side argument, Sen posited that more attention should be paid to questions of access that illuminate demand-side dynamics and bring social, political, and economic factors squarely into focus as key drivers (Barrett 2010). Specifically, questions of access are demand-side topics that maintain a focus on adequacy of economic production and growth. More recent work builds on this, introducing notions of equity and justice with regard to distributional issues that also have cross-scale relevance. Briefly, harkening back to defining food insecurity, differences can be shown within a nation as well as within a household in terms of whether all societal or household members have access to sufficient food to meet their needs.

A central element in work on food security is the role of environmental conditions in facilitating agricultural production and economic growth. Ecological factors linked with temperature, rainfall, and soil conditions have clear roles in agricultural production, particularly in Africa where most agriculture is rain-fed. Given this fundamental reliance on natural system irregularity, populations are especially vulnerable to shifts in ecological conditions like climate variability. Food production as evidenced by agriculture and increased food supply is posited as crucial for development. Measured at the national scale and promoted as vital to improving societal well-being, increases in agricultural production through increased crop yields are touted as positive, and expected to reduce food insecurity with respect to food availability.

A long line of research in development argues that economic growth promotes societal and human well-being, at least since the inception of the growth consensus model (Rostow 1959; Kerr 1960). Within the field of development, promotion of the ‘benefits to growth’ model spills over into topics linked with economic growth like industrial and agricultural production. In effect, wealth accumulation reaches all social and economic categories in society—from rich to poor—and thus improves human welfare, although likely via different mechanisms related to the international stratification system including capital, aid, and investment as well as the intra-national income inequality distribution. The development potential of a nation can be hampered by poverty and poor health of the population, thus curtailing the potential for economic and social development and thus institutional transformation. This includes work on food security, particularly at the national or macro-comparative scale as incorporated in cross-national studies (Firebaugh and Beck 1994; Jenkins and Scanlan 2001).

Studies also examine economic and political determinants of food availability and access using a macro-comparative, cross-national approach to investigate whether or not an available food supply and by extension the broader food system holds the key to enhancing food security through reducing food insecurity (Jenkins and Scanlan 2001; Scanlan and Jenkins 2001; Brady et al. 2007; Jenkins et al. 2007). This work reveals that food availability does not necessarily result in food security in less developed countries (LDCs) (Jenkins and Scanlan 2001) and that economic gains do not consistently result in improved food, nutrition, and health (Brady et al. 2007). It is also the case that increases in food security, as measured by reductions in child hunger, is not uniformly found in LDCs but rather is confined to a subset of well-to-do LDCs (Scanlan and Jenkins 2001). This work also demonstrates the relevance of more than just food supply, such as key socio-political variables including gender stratification and military conflicts that increase the prevalence of child hunger in the developing world (Jenkins et al. 2007).

Expanding social and political rights and improving gender equality has been advanced as important for reducing child hunger and by extension food insecurity of vulnerable populations (Jenkins et al. 2007; FAO 2015a; UNDP 2017). Reductions in health and educational disparities between income groups, women and men, children and adults, rural and urban residents, and other social categories have the potential to reduce barriers to food accessibility as well as the stability and utilization of available resources. Strategies for poverty reduction that emphasize access to basic needs and increasing opportunity for livelihood gains have long been argued to result in promoting increased overall well-being such as education gains for girls that can with time result in gains in gender equality for women (Bouis and Hunt 1999; Brady et al. 2007).

Given this broad disciplinary breadth, the unit of analysis of studies of food security ranges from individuals and households to communities, regions, countries, and groups of countries as defined by particular discipline-relevant geographic boundaries (e.g., Sub-Saharan Africa) and/or geo-political/economic categories (e.g., less developed countries or LDCs). Some conceptual models include multiple paths of influence (Hammond and Dube 2012; Stamoulis and Zezza 2003). Given our focus on the nation-state as the unit of analysis and use of SEMLV, we draw from related substantive work investigating life expectancy and the likelihood of experiencing a disaster for the comparative, cross-national approach.

Substantively related cross-national, comparative work using SEMLV investigates life expectancy and disaster devastation (Austin and McKinney 2012, 2016). In the former, hunger is included in a model of health, political, and economic factors affecting life expectancy in developing countries, specifically LDCs, and in Sub-Saharan Africa (SSA) given the low life expectancy across the region and also in comparison globally with the rest of the world. In the latter, disaster vulnerability is examined in poor nations as the result of social, economic, political, and environmental factors associated with underdevelopment. Examining a path model of disaster vulnerability with 85 nations, Austin and McKinney (2016) demonstrate direct effects of health resources and gender equality and indirect effects of economic and political variables working through these two measures.

Extant work demonstrates that the factors shaping food security are complex and interrelated, encompassing social, economic, political, and environmental issues from poverty and inequality to social rights protections, health, and temperature and rainfall. Many empirical models tend to emphasize agricultural variables as central to understanding food security, often as stand-alone models without specific ties to social systems. We seek to integrate these disparate threads into an empirical model that accounts for complexities related to identifying primary and secondary factors affecting food security (Hammond and Dube 2012; Drimie and McLachlan 2013; Stamoulis and Zezza 2003). Prior work outlines the factors shaping food security to be direct and indirect but do not necessarily construct models that reflects the complexity. In addition, although work includes aspects of accessibility and availability, stability, and utilization, the latter is explicitly examined with less frequency in empirical models. In this study, we build a model of food security that specifies multiple paths from social, economic, political, and agricultural factors and tests these proposed relations in African countries, the West Africa region, and a group of five dryland West African countries over 55 years using structural equation modeling with latent variables (SEMLV).

3 Modeling food security

Drawing from prior research described in an earlier section, here we outline an empirical model that encompasses the four main pillars of food security: stability, availability, access, and utilization (FAO 2008, 2010; Hammond and Dube 2012; Stamoulis and Zezza 2003). Figure 1 shows our hypothesized model of food security and undernutrition using conventions from path analysis and SEMLV where boxes depict observed (manifest) variables and ovals show latent (unobserved) variables. Conceptual groupings that map onto the four pillars of food security are shown by boxes enclosed with dashed lines. Our model includes latent variables, created from the conceptual underpinnings in previous work, which are abstract constructs best modeled using multiple indicators that are conceptually relevant and intercorrelated. We describe each latent variable and the specific individual measures of which they are comprised in the data and methods section. This model shows that environmental, socio-political, and economic variables have direct and indirect effects on measures of availability, accessibility, utilization, and the final outcome variable: undernutrition.
Fig. 1

Conceptual path diagram. Boxes indicate observed variables, ovals indicate latent variables, dashed boxes indicate variables that represent the four pillars of food security

Moving from left to right in the path diagram in Fig. 1, our hypothesized model shows that we use three variables to incorporate social and environmental stability throughout the model. At the top of the model diagram, agricultural inputs are posited to affect agricultural production which then affects variability in the food supply and food aid, all of which affect the adequacy of dietary energy supply. These effects comprise the food supply or availability part of the model. At the bottom of the model are the utilization variables related to disease burden, prevention, and treatment. Also of importance is accessibility, measured with poverty, which is in the center of our path diagram and serves an important link between the stability, availability, and utilization components and their ultimate effect on our final outcome variable: undernutrition. We use a latent variable of undernutrition that encapsulates multiple measures of the nutritional status of the population as described in detail below. The nutritional status of the population, especially as it applies to vulnerable groups like children or the elderly, status has long been viewed as a measurable outcome of hunger and food insecurity (Anderson 1990; UNICEF 1990; Campbell 1991; Stamoulis and Zezza 2003; Bhattacharya et al. 2004; FAO 2015b).

To develop this model, we drew from past studies regarding what concepts were essential to include and how they were related to one another. Our model does not replicate a model from previous work but does share emphasis on the pillars of availability and stability and relies on prior conceptual work to also bring access and utilization into the framework (Hammond and Dube 2012; Stamoulis and Zezza 2003). Data coverage and availability, both spatially and temporally, were significant factors informing our decisions about how availability, access, stability, and utilization were operationalized and interconnected.

4 Data and methods

Structural equation modeling (SEM) has several benefits over the linear regression model because it can accommodate multiple relationships between multiple variables by simultaneously estimating multiple regression models. This allows one to model indirect, mediating and covarying relationships in addition to the direct relationships possible with simple regression models. Furthermore, the ability to include latent variables in an SEM [i.e., structural equation modeling with latent variables (SEMLV)] provides a way to include more abstract concepts, as well as additional error terms. Latent variables are constructed as measurement models and tested using confirmatory factor analysis (CFA). We use SEMLV to specify the relations between variables in our model of food insecurity in Africa, West Africa, and five dryland West African nations, according to the main conceptual categories or pillars as well as their specification as latent (unobserved) or manifest (observed) variables.

Figure 2 shows a pictorial representation of the latent variable water and sanitation infrastructure using conventions from SEMLV where boxes depict observed or indicator variables and ovals show latent variables. Error terms, here measurement errors, are represented by the arrows pointing to the boxes. The latent level of water and sanitation infrastructure in a nation consists of four critical measures: how much of the population has access to improved sanitation facilities and to an improved water source, and the percentages of the population in rural and urban areas not practicing open defecation. This measure builds on and extends previous work by specifying multiple measures linked together as depicted in the figure and including measurement error in each variable (Bollen 1989) as well as correlation between the errors of the open defecation measures. Further, this latent variable can be thoroughly examined by way of component and overall model fit that examine whether this model adequately captures the relations shown.
Fig. 2

Confirmatory factor analysis (CFA) of measurement model for water and sanitation infrastructure latent variable, with component and overall fit measures

As an initial step in model building and testing, CFA analyses or measurement models were completed for each of the latent variables. That is, we completed CFA analyses for each latent construct on its own, using the full sample of countries and years, to check the reliability of each observed variable in the measurement model, and overall fit. Detailed results for component and overall fit for each latent variable in the model in Fig. 1 are available upon request. Numerous measurement models were tested while selecting the specific measurement models used for the four latent variables in this model. Many latent variables were considered that we ultimately reduced to summed indexes (such as the poverty index, natural disaster index, and education index variables) or single observed variables (such as the tuberculosis rate variable), due to lack of data and/or poor measurement model fit.

Variables in the full empirical model include measures from social, economic, political, health, and agricultural domains. All variables were gathered from secondary data sources including the World Bank’s World DataBank (http://databank.worldbank.org/data/home.aspx), and the FAO’s FAOSTAT (http://www.fao.org/faostat/en/#home), unless otherwise noted. Variable transformations were completed as appropriate and as described in the following. We have 8 outcome (endogenous) variables in our full empirical model, of which 6 are observed and 2 are latent, and 12 predictor (exogenous) variables of which 10 are observed and 2 are latent.

4.1 Outcome variables

Undernutrition’ is a latent construct (or measurement model evaluated using confirmatory factor analysis (CFA), a technique in SEM) that includes three variables capturing critical dimensions of undernourishment: prevalence of undernourishment as percent of the population experiencing it, prevalence of undernourishment as a 3-year average of the percent of the population experiencing it, and share of dietary energy supply from starchy staples (i.e., cereals, roots and tubers) as a 3-year average of the percent of the diet as a measure of lack of dietary diversity (Hoddinott et al. 2002). Empirical checks provide information regarding the validity and reliability of the individual measures (e.g., standardized factor loadings ranging from 0.995 to 1 and unstandardized loadings from 0.892 to 1, all significant).

The Poverty Index variable comes from combining two measures of poverty: the percentage of the population living on less than $2 a day, and the poverty gap at $2 a day—the mean shortfall from $2 a day expressed as a percentage.

We include four measures of food availability: three measures of food from different sources and one of the adequacy of the food supply. Food production is the average value of food produced in standardized dollars per person expressed as a 3-year average. Food imports are shown as a percentage of merchandise imports. Food aid is in grams per capita per day and includes both cereal and non-cereal foods. Diet energy adequacy is the 3-year average of dietary energy supply adequacy expressed as a percentage (e.g., 100% = ‘adequate’).

We include two measures related to food utilization. Tuberculosis rate is the incidence of tuberculosis per 100,000 people; we use this variable as a proxy for the health of the population. Disease prevention and treatment is a latent variable with six indicators. Three indicators are immunization rates for: diphtheria, pertussis and tetanus (DPT) immunization (percentage of children ages 12–23 months), Hepatitis B (percentage of children 1-year old), and measles (percentage of children ages 12–23 months).1 The additional four indicators are: the percentage of newborns protected from tetanus by virtue of being born to mothers who had been vaccinated, the percentage of children under 6 months old who are exclusively breastfed, and the percentage of children under age 5 with diarrhea who are treated with oral rehydration salts. Fit statistics of this measurement model indicate very good to excellent fit of this latent construct (Bollen 1989; West et al. 2012). Overall model fit statistics are excellent—the Chi-square value is non-significant and values for the Incremental Fit Index (IFI), and the Comparative Fit Index (CFI), are 0.999. Values above 0.95 suggest very good to excellent fit for these measures (West et al. 2012). The root-mean-square error of approximation (RMSEA) is 0.015 (CI 0.000, 0.031). For the RMSEA, values closer to zero are desirable and below 0.05 suggests very good fit.

4.2 Predictor variables

We include three measures of stability, one environmental and two social. The natural disaster index is a sum of nine variables from the International Disaster Database from the Centre for Research on the Epidemiology of Disasters (CRED) (http://www.emdat.be/). These were nine dummy variables on whether or not there was a: drought, earthquake, extreme storm, temperature extreme, flood, insect infestation, landslide or dry mass movement, volcanic activity or a wildfire in the year. Social rights is a summed index of two variables: the scores of the degree of civil liberties and the degree of political rights from Freedom House (https://freedomhouse.org/reports), reverse coded so that high values indicate high degrees of rights and liberties. The Refugee population variable is the percentage of the country’s population that are refugees.

The latent construct ‘Gender Equality’ includes six variables. Four variables are related to education: the gender parity index (GPI) for gross enrollment in primary, secondary and tertiary schools,2 which is the ratio of girls to boys enrolled at each level of school expressed as a percentage, and the ratio of female to male literacy rates expressed as a percentage. The other two variables are the CPIA gender equality rating (rescaled to look like a percentage), and the percentage of teenagers (ages 15–19) that do not give birth (a transformed version of the adolescent fertility rate). The CFA results and fit statistics indicate a reasonable fit (Chi-square is significant,3 RMSEA = 0.069, IFI & CFI = 0.973).

Water and Sanitation Infrastructure is a latent variable that includes 4 predictors: the percentage of the population with access to improved sanitation facilities, the percentage of the population with access to an improved water source, the percentage of the rural population not practicing open defecation, and the percentage of the urban population not practicing open defecation. Fit statistics of this measurement model indicate very good to excellent fit of this latent construct (Bollen 1989; West et al. 2012). The fit of this latent variable is very good (Chi-square is significant, RMSEA = 0.052, IFI & CFI = 0.996).

The Gross domestic product (GDP) per capita in constant 2005 US dollars is included to measure economic development. The age dependency ratio (ADR) is the ratio of individuals younger than 15 and older than 64 (dependents) to those between the ages of 15 and 64 years expressed as a percentage. The remaining three exogenous observed variables are related to agricultural productive capacity: the amount of agricultural land in 1000 sq. km, the tractor density, as the number of tractors per 100 sq. km of arable land, and the amount, in kilograms, of fertilizer used per hectare of arable land. Education is measured as the summed index of three variables: the gross enrolment ratio in primary, secondary and tertiary schools of both sexes combined expressed as a percentage. The gross enrolment ratio is the total enrollment of all ages compared to total population of expected age for the education level. Public health expenditures are the healthcare portion paid by government entities expressed as a percentage of total health expenditures.

4.3 Sample background

Using the above-mentioned model and variables, we focus on food insecurity in Africa where roughly one-fourth of the population goes hungry. Fourteen African countries have undernourishment rates of 25% or more of the population, another 7 have undernourishment rates of 15–24.9%, and 20 countries have failed to meet their Millennium Development Goal hunger target—which in the aggregate are higher rates than any other continent (FAO 2015a, b). Paarlberg (1999) suggests that a combination of historical, political, economic, ethnic and environmental factors contribute to Africa’s distinctiveness compared to developing countries in other parts of the world when it comes to efforts to improve food security. Not only does Africa seem to be unique from other global regions, but there is also likely to be substantial variation within global regions including within Africa (Barrett 2010).

In our analyses, we seek to further our understanding of the spatial distribution of vulnerable populations and the social, economic, political, and ecological contexts within which these individuals are embedded. Thus, we consider Africa as a continent,4 a regional sample of West African nations,5 and a five-country sample of nations within West Africa.6 Our approach creates multiple spatial comparisons to broaden our understanding of how food security takes shape across the African continent, across a region (West Africa), and then further zooms into five countries to provide a comprehensive look at how these dynamics play out at different spatial scales. Comparing across these three groupings, there is considerable variation in many indicators (see Table 1).
Table 1

Background on region sub-samples and African continent: 2005–2014 averages per country

 

Africa

West Africa

5 Countries

Years of independence

88.3a

58.2

53.6

Number of former British colonies

20

4

2

Number of landlocked countries

16

3

3

Land area (sq. km)

550,183.5

378,979.4

779,760.0

Percent of arable land

13.6

17.8

19.0

Percent of agricultural land

47.3

48.4

51.5

Total population

19,217,412.8

19,045,638.6

45,670,998.8

Rural population (% of total population)

58.8

57.4

65.6

Urban population (% of total)

41.2

42.6

34.4

Population density (people per sq. km of land area)

88.0

75.1

72.1

Age dependency ratio

80.8

87.0

93.4

Life expectancy at birth (years)

57.7

56.4

55.0

GDP per capita (constant 2005 US$)

1944.0

659.7

552.0

GINI index

43.7

39.7

38.7

Access to improved water source (% of population)

71.6

70.6

68.3

Access to improved sanitation facilities (% of population)

38.8

25.5

18.6

Total ecological footprint per capita

1.3

1.4

1.5

Cereal yield (kg per hectare)

1494.4

1217.6

1186.3

Prevalence of undernourishment (% of population)

20.9

16.2

11.2

Share of dietary energy supply derived from starchy staples (%) (3-year average)

23.6

17.4

12.5

Prevalence of food inadequacy (%) (3-year average)

11.3

3.1

1.7

Prevalence of stunting (% of children under 5)

35.2

33.9

37.3

Depth of the food deficit (kcal/person/day)

151.5

116.5

79.4

a This number includes Ethiopia, which was never colonized. If Ethiopia is left out, the average years of independence is 52.5 years

We highlight a few values from the table. First, the cereal yields are highest when a continental lens is used (close to 1500 kg per hectare averaged over countries and years) and lower across West Africa as a region and the group of five West African countries at 1218 and 1186 kg per hectare averaged as appropriate. Second, it should be noted that the prevalence of undernourishment as a percent of the population is highest across the African continent (approximately 21%) and lower in both West Africa as a region and the five West African countries at 16.2 and 11.2%, respectively. Third, the prevalence of food inadequacy is largest at the continent at 11.3% of the population, lower in the West Africa region at 3.1% and lower still for the group of five West African countries at 1.7% of the population. Fourth, the depth of the food deficit is the largest at the continental scale at 151 kcal per day compared with the West Africa region and group of five West African countries at 116 and 79 kcal per day, respectively. Although important, these values do not show the full range of variability across the continent nor do they show the within-region differences for individual nations.

4.4 Analysis

Table 2 presents the descriptive statistics for the variables used in the analysis. We investigate the empirical model outlined in Fig. 1, specifying that environmental, socio-political, and economic variables have direct and indirect effects on measures of availability, accessibility, utilization, and food insecurity or undernutrition. Our analyses use the four pillars of food security—accessibility, availability, utilization, and stability—as specified in the model shown in Fig. 1 and explained in detail in this section. Our sample is all 54 African countries over 55 years (1960–2014), which provides 2885 observations. We use structural equation modeling with latent variables (SEMLV) to estimate the model using AMOS software. We run three models: one unrestricted model that covers the entire African continent, and two that use a dummy variable to control for the West African region, and a five-country sub-sample of West African countries, respectively (see footnotes 4, 5, and 6).
Table 2

Descriptive statistics for model variables

 

Mean

SD

Range min

Range max

N

Exogenous observed variables

Fertilizer use

37.289

99.759

0.000

696.593

413

Tractor density

39.282

83.694

0.004

1170.000

1568

Agricultural land (1000 sq. km)

207.256

248.465

0.030

1366.980

2725

Natural disasters index

0.440

0.682

0.000

4.000

2885

Refugee population

0.738

1.437

0.000

15.744

1092

Age dependency ratio (ADR)

88.847

12.904

40.796

114.371

2885

GDP per capita

1368.800

2090.210

68.567

15,592.170

2429

Education index

116.340

53.236

11.432

276.293

1067

Public heath expenditures

46.835

16.965

3.091

94.567

1035

Social rights

49.290

9.729

16.670

75.000

387

West Africa

0.305

0.460

0.000

1.000

2885

5 Countries

0.095

0.294

0.000

1.000

2885

Exogenous latent variables

Water and sanitation infrastructure

    

Improved sanitation facilities

36.275

26.011

2.600

98.400

1284

Improved water source

66.451

18.868

13.200

99.900

1281

Rural people not practicing open defecation

59.868

28.334

0.000

100.000

1259

Urban people not practicing open defecation

90.347

12.010

32.400

100.000

1259

Gender equality

     

Female literacy ratio

69.877

20.972

21.907

129.672

185

Gender equality rating

53.630

8.757

33.330

75.000

388

Primary school enrollment GPI

82.454

18.258

31.913

150.735

1881

Secondary school enrollment GPI

71.307

28.103

7.857

160.186

1381

Tertiary school enrollment GPI

51.363

39.366

0.000

329.519

1052

Adolescent non-fertility rate

87.326

5.283

76.468

99.396

2885

Endogenous observed variables

Food production

151.171

60.032

58.000

346.000

1009

Food imports

19.020

8.327

0.474

62.416

1501

Total food aid

23.028

52.596

0.000

513.125

1429

Diet energy adequacy

108.000

15.640

69.000

152.000

1032

Poverty index

96.522

43.061

0.000

176.180

170

Tuberculosis rate

291.530

259.504

15.000

1354.000

1325

Endogenous latent variables

Undernutrition

Prevalence of undernourishment

25.216

15.443

5.000

76.800

1012

Share of dietary energy supply from cereals

27.294

14.678

5.000

76.800

941

Prevalence of undernourishment (3-year average)

33.613

16.756

5.000

83.500

1006

Disease prevention and treatment

    

DPT immunization

65.666

25.954

1.000

99.000

1702

Measles immunization

65.528

23.000

1.000

99.000

1694

Hep B immunization

81.193

17.224

10.000

99.000

576

Tetanus immunization

62.000

24.973

1.000

99.000

1574

Diarrhea treatment

29.414

20.408

1.000

93.600

354

Exclusive breastfeeding

31.612

19.304

1.200

88.400

164

5 Results and discussion

Figure 3 shows the generalized results of the Africa model from Fig. 1, while in Tables 3, 4, 5, 6 and 7 we present the main results7 of our models, focusing on the drivers of four key variables: diet energy adequacy, poverty index, tuberculosis rate, and undernutrition. In viewing Fig. 3, it is clear that undernutrition is driven by a complex set of factors. On the whole, we find many of the significant relationships between variables in our model to be as we would expect. Broadly speaking, agricultural resources increase food production, which in turn increases diet energy adequacy, which reduces undernutrition, while poverty increases undernutrition and better healthcare reduces it.
Fig. 3

Path diagram showing simplified model results for Africa model

5.1 Availability

Diet energy adequacy, the average dietary energy supply adequacy expressed as a percent, is the variable through which we hypothesize that the other food availability variables work. Food production, food imports and food aid are all food sources that would be expected to directly contribute to overall food availability. In addition, we expect that more socially and institutionally stable countries will have a more consistent and sufficient food supply and that natural disasters are likely to disrupt the food supply. While having enough food present in a country is not sufficient to ensuring food security for the population, it is a necessary component of food security. Table 3 shows the model results for diet energy adequacy.

As expected, food production is the most important driver of diet energy adequacy, as indicated by the standardized coefficients, followed by food imports and social rights. Interestingly, food aid does not have a significant effect on diet energy adequacy in any of our models. The lack of significance, and small effect size, of food aid could be due to the nature of food aid as an emergency measure—it is not a primary source of food at the national level and may be responding to the condition of low diet energy adequacy, suggesting a reciprocal relationship not fully captured in this model. The effect sizes are quite similar across our three models indicating that the drivers of food availability are consistent in West Africa and in our five-country sub-sample compared to the entire continent.
Table 3

Standardized and unstandardized maximum likelihood coefficients, and standard errors from SEMLV of undernutrition, predictors of dietary energy adequacy in Africa, with controls for West Africa region, and five-country sub-sample, 1960–2014 (n = 2885)

 

Africa

West Africaa

5 Countrya

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Food production

0.164***

0.611

0.163***

0.610

0.161***

0.603

 

(0.007)

 

(0.007)

 

(0.007)

 

Food imports

0.384***

0.200

0.254***

0.131

0.365***

0.190

 

(0.055)

 

(0.056)

 

(0.054)

 

Food aid

−0.007

−0.022

−0.007

−0.022

−0.004

−0.013

 

(0.008)

 

(0.008)

 

(0.008)

 

Social rights

0.331**

0.063

0.260*

0.050

0.269*

0.051

 

(0.127)

 

(0.124)

 

(0.126)

 

Natural disasters index

0.580

0.025

0.821

0.035

0.517

0.022

(0.571)

 

(0.558)

 

(0.567)

 

p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

a Regional dummy variable not shown

5.2 Accessibility

While the first prerequisite for food security is that there is food present in the country, the second requirement is that people can physically get to the food and are then able to acquire some of it. The accessibility of food is perhaps the most difficult aspect of food security to measure at the national level because many of its properties rely on individual-level capacity. We approximate accessibility in our model with an index measure of the poverty rate. As shown in Table 4, the poverty index in Africa is most significantly reduced by higher GDP per capita, and by greater social rights, and higher levels of the education index to a lesser degree. The ADR has the most influential positive effect on the poverty index, consistent with the expectation that children and the elderly are most vulnerable to poverty, followed by gender equality. The refugee population is not found to significantly influence the poverty rate.

Of the significant effects, GDP, social rights, ADR and the education index are in the expected direction, while gender equality is not. A potential explanation for the effect direction of gender equality is that the measures we use in the latent variable, such as education participation, literacy, and adolescent non-fertility rate, simply do not function to improve economic status due to the presence of other forms of gender discrimination.

There are some notable cross-group differences for the drivers of the poverty index, particularly when controlling for West Africa, that show important spatial differences. In the West Africa model, social rights, and gender equality change sign and become non-significant, while the effect size of ADR is about half the size as in the Africa model, and the effect of the education index on the poverty index is modestly larger. The five-country sub-sample model is very similar to the Africa model, with the exception of the effect of the education index on the poverty index, which is not significant.
Table 4

Standardized and unstandardized maximum likelihood coefficients, and standard errors from SEMLV of undernutrition, predictors of poverty index in Africa, with controls for West Africa region, and five-country sub-sample, 1960–2014 (n = 2885)

 

Africa

West Africaa

5 Countrya

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Social rights

−1.934**

−0.125

0.417

0.026

−1.619**

−0.105

 

(0.616)

 

(0.622)

 

(0.609)

 

Refugee population

2.089

0.067

2.028

0.061

1.771

0.057

 

(1.296)

 

(1.201)

 

(1.298)

 

ADR

1.175***

0.319

0.658***

0.170

1.165***

0.319

 

(0.195)

 

(0.195)

 

(0.191)

 

GDP per capita

−0.011***

−0.510

−0.014***

−0.586

−0.012***

−0.529

 

(0.001)

 

(0.001)

 

(0.001)

 

Gender equality

0.424*

0.181

−0.013

−0.005

0.435*

0.184

 

(0.199)

 

(0.205)

 

(0.193)

 

Education index

−0.164*

−0.178

−0.206**

−0.211

−0.133

−0.144

 

(0.081)

 

(0.074)

 

(0.078)

 

p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

a Regional dummy variable not shown

5.3 Utilization

The ability to absorb and thereby utilize the nutrients that are consumed is an important element of nutritional security. Poor health can cause as well as be caused by poor nutrition. Table 5 shows the results of the drivers of tuberculosis rate, a proxy for overall population disease burden. In Africa water and sanitation infrastructure is the most important driver, as indicated by the size of the standardized coefficient, and it has an expected negative effect on tuberculosis rate. The ADR is the other predictor that has a negative and significant effect on the tuberculosis rate, while social rights, refugee population, and public health expenditures have a positive effect on the tuberculosis rate. There is also a significant and positive non-causal relationship between the tuberculosis rate and the poverty index. 8

Three variables have counterintuitive effects. The negative effect of ADR on tuberculosis rate is not as expected, but may be a result of the use of tuberculosis rate as a proxy for disease burden. We would expect having a larger proportion of young and old people in the population would increase the disease burden of the population as the young and old are more vulnerable to disease. However, if young and old people are less likely to contract tuberculosis specifically, then the negative effect of ADR is reasonable. The positive effects on tuberculosis rate by social rights and public health expenditures are also not as expected, but may be due to false assumptions of what effect these variables should have. For instance, we include social rights as a social stability measure, but it may be embedded within other neoliberal concepts and policies that ultimately decrease peoples’ health. The positive effect of public health expenditures on tuberculosis rate may be due to having unmeasured reciprocal effects—the government might be spending a larger proportional share of health care costs because there are higher tuberculosis rates, not because publicly funded healthcare somehow is less effective or contributes to the tuberculosis rate.

The West Africa results for tuberculosis rate are somewhat different in magnitude than for all of Africa, with greater effects of social rights, refugee population, ADR, and water and sanitation infrastructure, and a smaller effect of public health expenditures. The five-country sub-sample model is quite similar to all of Africa with the notable exceptions of refugee population, which is not significant, likely due to there being very low rates of refugees in these countries,9 and disease prevention and treatment having a significant negative effect on tuberculosis rate. These counterintuitive effects reinforce the importance of considering multiple levels of granularity and especially smaller-scale context effects.
Table 5

Standardized and unstandardized maximum likelihood coefficients, and standard errors from SEMLV of undernutrition, predictors of tuberculosis rate in Africa, with controls for West Africa region, and five-country sub-sample, 1960–2014 (n = 2885)

 

Africa

West Africaa

5 Countrya

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Social rights

11.440***

0.136

18.840***

0.224

14.631***

0.174

 

(2.651)

 

(2.541)

 

(2.605)

 

Refugee population

19.357***

0.114

23.725***

0.139

9.001

0.053

 

(5.408)

 

(5.044)

 

(5.389)

 

ADR

−2.328**

−0.116

−3.798***

−0.190

−2.394**

−0.120

 

(0.780)

 

(0.747)

 

(0.764)

 

Water and sanitation infrastructure

−3.274***

−0.265

−5.489***

−0.444

−3.884***

−0.314

(0.554)

 

(0.553)

 

(0.559)

 

Disease prevention and treatment

−0.758

−0.063

−0.660

−0.056

−1.405***

−0.118

(0.389)

 

(0.347)

 

(0.393)

 

Public health expenditures

2.834***

0.186

1.690***

0.111

2.865***

0.188

(0.512)

 

(0.488)

 

(0.499)

 

p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

a Regional dummy variable not shown

5.4 Undernutrition

Table 6 shows the direct effects on our key outcome variable, undernutrition, and Table 7 shows the total effects of all the model variables on undernutrition. Undernutrition in Africa is directly reduced (Table 6) by greater diet energy adequacy, most substantially, the ADR, disease prevention and treatment, and tuberculosis rate. As expected, poverty is found to increase undernutrition. Of these effects, the ADR and tuberculosis rate are not in the expected direction. The negative effect of ADR may be due to the working age population shielding the more vulnerable members of society through household food distribution strategies (Bhattacharya et al. 2004). The more surprising negative effect of tuberculosis rate on undernutrition could be the result of an unmodeled reciprocal relationship between undernutrition and tuberculosis rate as a proxy for disease burden, where undernutrition increases disease rate more than disease rate increases undernutrition.

The total effects on undernutrition in Table 7 compliment the results of Tables 3, 4, 5 and 6 and show the indirect effects that some variables have on undernutrition. Of particular interest are the relatively large indirect effects of fertilizer use, tractor density, and GDP per capita on undernutrition that were not visible in the direct effects presented in Table 6. Additionally, the total effect of ADR on undernutrition is smaller than the direct effect shown in Table 6 (approximately −0.04 vs. −0.1).

While the five direct drivers of undernutrition are quite consistent across spatial groups, several of the total effects on undernutrition vary more substantially, as shown in Table 7, although they are generally in a direction that confirms theoretical expectations. Looking across the models by variable we see that tractor density and refugee population have a larger effect in the five-country model than in the Africa or West Africa models, while the effect of agricultural land is positive, though very small, instead of negative. Gender equality has a small but negative total effect in the West Africa model, in contrast to the other two models, and the effect of water and sanitation infrastructure is much larger. We also see the total effect of social rights range from 0.003 to 0.131 across the models, similarly to GDP per capita, which ranges from −0.677 to −0.906. The total effect size of education and disease prevention and treatment also varies across the models.
Table 6

Standardized and unstandardized maximum likelihood coefficients, and standard errors from SEMLV of undernutrition, predictors of undernutrition in Africa, with controls for West Africa region, and five-country sub-sample, 1960–2014 (n = 2885)

 

Africa

West Africaa

5 Countrya

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Unstd coeffs (SE)

Std coeffs

Diet energy adequacy

−1.013***

−0.925

−0.958***

−0.898

−1.001***

−0.923

(0.012)

 

(0.011)

 

(0.012)

 

ADR

−0.192***

−0.141

−0.154***

−0.116

−0.186***

−0.137

 

(0.025)

 

(0.023)

 

(0.025)

 

Poverty index

0.092***

0.246

0.098***

0.286

0.095***

0.256

 

(0.008)

 

(0.007)

 

(0.008)

 

Disease prevention and treatment

−0.050***

−0.061

−0.069***

−0.087

−0.067***

−0.083

(0.010)

 

(0.009)

 

(0.010)

 

Tuberculosis rate

−0.008***

−0.117

−0.010***

−0.151

−0.009***

−0.127

 

(0.001)

 

(0.001)

 

(0.001)

 

p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

a Regional dummy variable not shown

Table 7

Standardized maximum likelihood coefficients from SEMLV of undernutrition, effects on undernutrition in Africa, with controls for West Africa region, and five-country sub-sample, 1960–2014 (n = 2885)

 

Africa

West Africa

5 Country

Fertilizer use

−0.511

−0.507

−0.613

Tractor density

0.642

0.623

0.852

Agricultural land

−0.062

−0.062

0.007

Food production

−0.570

−0.552

−0.559

Food imports

−0.181

−0.115

−0.173

Food aid

0.020

0.019

0.012

Diet energy adequacy

−0.925

−0.898

−0.923

Social rights

0.003

0.057

0.131

Natural disasters index

0.041

0.003

0.018

Refugee population

0.016

0.013

0.025

ADR

−0.048

−0.038

−0.040

GDP per capita

−0.677

−0.759

−0.906

Gender equality

0.045

−0.001

0.047

Education index

−0.044

−0.061

−0.037

Poverty index

0.247

0.287

0.256

Water and sanitation infrastructure

0.031

0.067

0.040

Disease prevention and treatment

−0.053

−0.079

−0.068

Tuberculosis rate

−0.117

−0.151

−0.127

Public health expenditures

−0.022

−0.017

−0.024

Region (West Africa)

 

−0.317

 

Region (5 Countries)

  

−0.241

5.5 Model discussion

In addition to finding important scale and context effects in the differences between our models, we make some observations on modeling food security and undernutrition that may prove useful to future modeling efforts. One observation is that the composition of the latent variables matters for their role in the overall model. By definition, the observed or indicator variables must have particular attributes regarding their substantive content and their empirical properties (e.g., directionality, number, and measurement scale), which requires careful selection and attention to detail. However, even if the predictors all contribute to the same latent concept, the latent variable may still not be measuring all the aspects of the concept that is desired. We potentially see this happening in the positive effect of gender equality on poverty in our model, where the type of gender equality we are capturing in the latent variable, which is largely focused on education, may not be the aspects of gender equality that are important to reducing poverty.

A second observation is that we need to pay more attention to how we incorporate the stability pillar into the model. The positive effect of social rights on TB rates suggests that we may need to include more stability measures that are more specifically selected for each outcome variable of interest, rather than trying to establish national stability with a few universal variables. If there are enough appropriate variables available, this will also be useful empirically for model identification and estimation.

A third observation is that we need to consider disentangling the potentially reciprocal effects between poverty, utilization/health, and undernutrition. There is an established conceptual connection between poverty, which contributes to household food insecurity and undernutrition in individuals, which can cause physical and cognitive limitations, which then makes it more difficult to break the cycle of poverty (FAO 2008). There are also predictors that have conceptually ambiguous causal directions at least at the national scale. For example, does disease prevention and treatment reduce the amount of disease or does the amount of disease drive the amount of disease prevention and treatment happening?

The SEMLV method can be used to test reciprocal or feedback relationships between two or more variables, but requires very careful model specification and sufficient data, which we could not do in the context of our current model beyond including the error correlation between the poverty index and the tuberculosis rate. A model designed just to test the empirical reciprocity between poverty, health status/utilization, and undernutrition would be an exciting extension of this research. Other avenues for future research using SEMLV include determining relevant ecological factors that can be added to the existing model, incorporating data on rural versus urban populations, comparing models for different African regions, and non-African regions, and integrating multiple spatial scales and time-lagged effects.

Modifications to the models are possible, including additional paths not shown here or additional variables that may be of growing importance in this region for food security. We need to simultaneously grapple with goals of model parsimony and data quality, as is the case in any country-level analysis. Building multiple models across varying gradients remains an important goal of this research and model parsimony remains instrumental to achieve this. However, given the limitations of our data we decided to use a pooled sample for this analysis and will examine models for individual countries using an alternative but related strategy of model building, testing, and evaluation. Reducing the complexity of the model too much threatens its usefulness; the ability to model more of the observed complexity of food security is the reason we chose to use SEMLV rather than a classic regression model where we would be assured of getting model fit measures.

Our analysis examines food insecurity using country-level data, which represent one scale—the aggregate scale—that is important to chronicle in seeking to improve well-being worldwide. However, national-level processes can mask different patterns at the sub-national scale as our analysis shows. While studies using countries or regions as the unit of analysis are useful for identifying inadequacies in supply, studies also need to delve more deeply into within-country or intra-national challenges related to access and utilization (Barrett 2010).

6 Conclusion

The results of our model suggest that using undernutrition as a single, combined outcome of food insecurity, as represented by the four pillar concepts, is a useful practice. By using SEMLV, we are able to not only assess the direct predictors of undernutrition but also the drivers of the predictors of undernutrition. Based on standardized total effects (Table 7), we find that the most important overall drivers of national-level undernutrition in all three models are fertilizer use, food production, diet energy adequacy, GDP per capita, tractor density, and poverty. Looking across regional scales, we see that while the direct drivers of undernutrition and diet energy adequacy are quite consistent, the drivers of poverty and tuberculosis rate are not. This finding indicates important inter- and intra-regional heterogeneity that should be considered and accounted for in future research, as well as the potential need accommodate reciprocal effects among accessibility, utilization and undernutrition measures.

Economic growth, political stability and social rights and protections are vital to ensuring continued progress is made toward reducing food insecurity worldwide but especially in regions, countries, communities, and households where vulnerable populations are at high risk of food insecurity. Although some notable gains have been made in recent decades, food insecurity in Africa persists and remains a pressing challenge for scholars and practitioners (FAO 2015b). Moving forward, it is clear that addressing this intractable challenge requires transdisciplinary approaches and thinking across disciplinary lines. Critically, even while economic development efforts chronicle progress in well-being, these gains are not equally distributed across social categories and nontrivial populations remain vulnerable to shifts in food supply and access. Academics and stakeholders must continue to engage in meaningful dialogues in order to ensure that the emphasis is on both chronic undernutrition and disaster-related hunger.

Scholars are faced with many decisions related to model building that are important for analytical purposes and for subsequent work to use as a baseline for expansion, modification, and possible replication in future empirical work. Our analyses using SEMLV include latent variables and direct, indirect and total effects in a path model. There are likely reciprocal relations in the model, such as between TB rate, poverty and undernutrition, that, with more data and an alternate model specification, should be assessed in future studies using the same sample or perhaps another region or a different observational unit like the household instead of the country level.

Issues related to measurement are also of critical importance. This includes trade-offs related to data reduction strategies, issues of data quality, and the always relevant question of scale. Data reduction and variable selection criteria are necessary and incredibly difficult when the research team takes into account all possible variables that could be used to measure each of the food security pillars one by one—determining which indicator is the best requires developing protocols and best practices in terms of data coverage temporally and spatially. Issues of data quality go hand in hand with the aforementioned challenges pertaining specifically to data availability and data sources. Finally, issues of scale are of vital importance with regard to data distillation and in terms of model design and specification and model building and evaluation. Our results are based on national-level data which are best able to point to broad trends in the relations among stability, availability, access, and utilization in explaining food security. As an example, the effect of GDP on food insecurity is at an aggregate scale that makes it an imperfect proxy for measuring the income of individuals within a nation, region, or household. Macro-level analyses are not able to answer questions about how these relations play out within nations and, crucially, within households. Companion analyses that ask similar questions yet at different scales complement this work by zooming in on these interrelations on a finer level of granularity. Future scholarly work on food insecurity will continue to grapple with these challenges through combining analysis strategies and via collaboration across disciplinary boundaries.

Footnotes

  1. 1.

    We include theoretically based correlated errors between the three immunization rate variables.

  2. 2.

    We include theoretically based correlated errors between the three education GPI variables.

  3. 3.

    Give the large size of our sample a significant Chi-square test is not surprising.

  4. 4.

    Southern Region: Botswana, Lesotho, Namibia, South Africa, Swaziland; Eastern Region: Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, Seychelles, Somalia, South Sudan, Tanzania, Uganda, Zambia, Zimbabwe; Middle Region: Angola, Cameroon, Central African Republic, Chad, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Sao Tome and Principe; Northern Region: Algeria, Egypt, Libya, Morocco, Sudan, Tunisia; Western Region: Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo.

  5. 5.

    Benin, Burkina Faso, Cape Verde, Cote d'Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo.

  6. 6.

    Burkina Faso, Ghana, Mali, Niger, and Nigeria.

  7. 7.

    Detailed results are available upon request.

  8. 8.

    This non-causal relationship was included because of the suspected reciprocal relationship between health status and poverty that we were not able to otherwise include in this model.

  9. 9.

    The mean for these five countries ranges from 0.006 to 0.090%, while the Africa average is 0.738%.

Notes

Acknowledgements

Funding was provided by National Science Foundation (Grant No. SMA-1416730).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Riva C. H. Denny
    • 1
    Email author
  • Sandra T. Marquart-Pyatt
    • 1
  • Arika Ligmann-Zielinska
    • 2
  • Laura Schmitt Olabisi
    • 3
  • Louie Rivers
    • 4
  • Jing Du
    • 5
  • Lenis Saweda O. Liverpool-Tasie
    • 6
  1. 1.Department of SociologyMichigan State UniversityEast LansingUSA
  2. 2.Department of Geography, Environment, and Spatial SciencesMichigan State UniversityEast LansingUSA
  3. 3.Department of Community SustainabilityMichigan State UniversityEast LansingUSA
  4. 4.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighUSA
  5. 5.Department of Construction ScienceTexas A&M UniversityCollege StationUSA
  6. 6.Department of Agricultural, Food, and Resource EconomicsMichigan State UniversityEast LansingUSA

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