extraction, and distribution necessary to make them available on the market. The existence of markets
allows agents to substitute the purchase for the effort of doing. This effort sets the price levels of
goods and services, whether they are market or non-market (Daniel, 2010).

In his analysis, Bastiat opts to deny the intertemporality of the allocation of natural resources. Such a conception fully legitimises agricultural expansion and highlights the denial of the harmful effects of the latter on biodiversity. In this context of infinite resources, agricultural expansion is likely to reduce biodiversity without any concern.

This conception of free natural resources is unrealistic because it considers the stock of natural
resources to be infinite. Additionally, the value lost to future generations as a result of the decline in
land stock is significant. Moreover, the increase in cultivable land immediately comes up against the
conservation and preservation of habitats and natural environments, which are by default singular.
Hence, the main idea of this work, which radically and fundamentally confronts this theory of the
timelessness of natural resources.

3.1.4.2. The Hotelling temporal theory of natural resources and agricultural land expansion

However, there is another view of the finiteness of natural resources that justifies the fact that the rate of depletion and exhaustion of these resources is lower than their rate of regeneration. Natural resources are limited, and both current and future generations must use them, according to Hotelling (1931). Structural expansion is a support ramp for present generations; it would be an obstacle to the preservation of natural resources. From the activities of the present generation, the problem of access to natural resources for future generations may arise. The author's work makes explicit this dynamic conception of the use of natural resources.

Nature provides resources, but, depending on the jurisdictions in force, they are appropriable. Then,
in response to the ‘conservationists," according to whom the price system does not allow for a rational
management of natural resources, Hotelling makes it possible to define, for the owner of a stock of
goods that diminishes as it is exploited, a rule for maximising the present value of his profits over a
finite time horizon. This rule can be formulated as follows:

The owner of a deposit has two options at any given time: either extract one more unit today, sell it, and invest his gain, or leave his deposit fallow, wait a certain amount of time, and later sell the extracted unit on the market.

The optimal rate of exploitation of the resource is given by the equalisation of the market price and
the marginal cost of production, to which is added a scarcity rent expressing the finiteness of the stock
and therefore the unavailability of the resource in the future. On this basis, the operator has an interest
in extracting as long as his marginal cost is not greater than his marginal revenue (quantity (Q) x
selling price (P));

The cost of the natural resource and the rent associated with it must rise at a rate equal to the interest
rate (or discount rate), making the preservation of the resource on the site equivalent to its extraction
and the investment of the sale proceeds with interest.

According to this rule, the total volume of a resource constitutes a rent for the owner that justifies a
price increase beyond the marginal costs. This scarcity rent therefore distinguishes the exploited
natural resource, whether renewable or not, from other goods or services, but in doing so, it is
assimilated into capital and implies its appropriation. In its absence (e.g., deep-sea fishing),
companies are not interested in the optimal management of a free, i.e., non-appropriable, resource
and will seek to exploit it at a frantic pace, possibly exhausting it.

In relation to agricultural land expansion, there are two possible scenarios. First, land expansion
occurs either consecutively or independently of natural resource extraction. In the first case,
agricultural land expansion does not cause the destruction of biodiversity or has mitigation effects.
This is because the scarcity rent exploited first has a higher value than the secondary agricultural
product for the owner. In the second case, the agricultural land increase causes the complete
destruction of natural habitats and species. The agricultural product is the primary product and has a
higher value than the natural resources or biodiversity, which is the secondary product.

In both cases, it is still possible that agricultural expansion poses serious conservation problems for
natural areas and species. While present or current generations may feel the first signs of natural
resource deprivation only slightly, future generations will feel the brunt of natural resource loss. This
study hypothesises the latter relationship in the context of sub-Saharan Africa.

**3.2. Expansion of agricultural land and biodiversity change: Empirical literature**

In this subsection, we consider studies that link, on the one hand, the expansion of land for agriculture
and biodiversity and, on the other hand, those relating how agricultural expansion destroys natural
habitats.

**3.2.1. Land expansion for agriculture affects species biodiversity**

In his latest publication, Barbier (2022) links agricultural land expansion and terrestrial biodiversity loss with two policy failures. The underpricing and underfunding of nature The underpricing of nature implies that when the price of natural areas is too low, it leads to agricultural expansion. Indeed, natural areas are converted to agriculture, forestry, and other land uses at a lower cost than protecting
or preserving habitats. This narrative study bequeaths a better place to Tan et al. (2022), who use a negative binomial regression model to confirm that agricultural land expansion increases threats to faunal biodiversity in South and Southeast Asian countries. However, both the important reserves of agricultural land and significant improvements in area protection raise questions about the land expansion and biodiversity loss nexus in Africa, which this study seeks to clarify.

Markuszewska (2013) finds that among the few positive aspects of the expansion of agriculturally fragmented land, there is increased biodiversity in the areas affected by this phenomenon. Bulte and Horan (2003) build a model where farmers have the option of either hunting for wildlife or growing crops. They later conclude that different patterns of conservation and agricultural expansion may emerge and that greater conservation may be consistent with higher incomes.

To understand the effects of cropland expansion on biodiversity loss, Phalan et al. (2013) analysed data on crop distribution and expansion in tropical countries. They find that areas of high potential for cultivation may be vulnerable to conversion in the future. These include some priority areas for biodiversity conservation in tropical countries that have previously been identified as having ‘low vulnerability,’ in particular in central Africa and northern Australia.

Zabel et al.'s (2019) purpose is to investigate the biodiversity consequences of attaining equal global production gains by 2030, either by cropland expansion or intensification. With the aid of a Computable General Equilibrium analysis, their results suggest that production gains will occur at the expense of biodiversity, predominantly in developing tropical regions, while Europe and North America benefit from lower world market prices without putting their own biodiversity at risk.

Ahrends et al. (2015) analyse the subsequent spread of land conversion to monoculture rubber plantations and its impact on biodiversity and livelihoods. They conclude that expansion into marginal areas creates a clear potential for loss-loss scenarios, such as the clearing of high-biodiversity-value land for a crop that is poorly adapted to local conditions and, by altering landscape function while not producing long-term sustainable yields, may ultimately also compromise livelihoods.

Kehoe et al. (2017) carry out research on the effects of expansion pathways and intensification on biodiversity using a cobb-douglas model. The authors find that 30% of species richness and 31% of species abundance are potentially lost because of agricultural expansion across the Amazon and Afrotropics. Only 21% of high-risk expansion areas in the Afrotropics overlap with protected areas (compared with 43% in the Neotropics). Areas at risk of biodiversity loss from intensification are found in India, Eastern Europe, and the Afromontane region (7% species richness, 13% abundance
loss). In a similar way, Dobrovolski et al. (2013) conclude that incorporating future agricultural expansion resulted in a lower representation of carnivore species, as the average proportion of the range represented reduced from 58% to 32%.

Medan et al. (2011) summarise for the first time the effects of agriculture expansion and intensification on animal diversity in the Pampas of Argentina. His systematic review suggests that birds and carnivores are more strongly affected than rodents and insects, but responses varied within groups: the geographic ranges or abundances of many native species were reduced, sometimes leading to regional extinction; other native species were unaffected (birds) or benefited (birds, rodents, and possibly generalist pollinators and crop-associated insect species); while novel species were introduced, thus increasing the species richness of most groups (26% of non-rodent mammals, 11.1% of rodents, 6.2% of birds, and 0.8% of pollinators).

Using a carbon model, Chaplin-Kramer et al. (2015) examine the impact of different spatial patterns of agricultural expansion on biodiversity. They find that for the same amount of land conversion, the declines in biodiversity and carbon storage can vary from two to fourfold. In addition, impacts increase most rapidly in the earliest stages of agricultural expansion, are more pronounced in scenarios where conversion occurs in forest interiors, and are most destructive when they occur in a fragmentary pattern rather than in a consolidated patch.

Perrings and Halkos (2015) assess the effects of agricultural expansion and intensification on threats to endemic species in sub-Saharan Africa. Using a country-level approach, they find that over longer time scales, agricultural intensification has offered conservation benefits in Sub-Saharan Africa. However, they found little evidence that intensification reduces threats to biodiversity on shorter time scales. On the other hand, the extensive growth of agriculture is associated with increasing threats to biodiversity at all time scales.

Foley et al. (2015) found that global croplands, pastures, plantations, and urban areas have expanded, accompanied by large increases in energy, water, and fertiliser consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases.

Evans and Potts (2015) develop an integrated econometric-ecological modelling framework that examines the impact of changing prices for agricultural commodities on grassland bird abundance in the United States. They use ecological models that predict changes in species abundance for twenty-
two grassland birds in response to land use change. They show that a relatively inelastic total cropland acreage responds to expected prices, mitigating some of the ecological impact arising from increased demand for agricultural commodities.

Kietzka et al. (2018) assessed the effects of local agricultural and urban land transformations on Odonata species richness and assemblage composition in three rivers. Land transformation significantly influenced Odonata assemblage composition but did not always significantly reduce species richness. Agricultural and urban local land use types reduced opportunities for some endemic species but provided for the persistence and establishment of widespread, generalist species, as indicated by great changes in DBI scores.

Ullah and Kim (2021) evaluate the conservation effects due to changes in the size of community-governed protected areas (PAs) with a synthetic control method and difference and difference. Their analysis concerns SSA countries from 2000-2016 and findings suggest that countries with community-governed PAs have reduced the International Union for Conservation of Nature (IUCN) Red List threat level by 17% for mammals. In addition, they also find stronger evidence of the effect of community-governed PAs on the IUCN threat level using the synthetic control method that allows them to match the ‘intervention countries’ with those countries that exhibit similar preintervention threat levels.

Delzeit et al. (2017) examined whether potential conversion to cropland would affect areas of high biodiversity value or conservation importance. Using a CGE model, they find that the expansion of cropland generally results in improved food security, not only in regions where crop production rises. On the other hand, the estimated cropland expansion could take place in many highly biodiverse regions. However, Sol (2019) shows analyses 557 regions of the globe with econometric estimation on that species extinction increases with population density and GDP per capita. The causal links and the author’s findings suggest that the conservation of nature requires degrowth, or at least a transition to a steady-state economy, because the growth of agricultural areas will hurt biodiversity.

The loss of biodiversity from biomass production is another central factor to be considered in the production of biofuels. Biomass production on previously ‘idle land’ (as opposed to crop substitution) may lead to biodiversity loss. Thus, the conversion of areas with high biodiversity, such as rainforests and peatlands, to the production of biomass results in the loss of habitat and ecosystem services, and most importantly, the capture of carbon. In addition, the use of pesticides and genetically modified crops may also endanger wildlife and biodiversity.
Changes in landscape due to such activities as agriculture, urban sprawl, and transportation infrastructure are generally recognised in the literature as major causes of the loss of biodiversity. Urban sprawl, for example, affects land change elsewhere through the transformation of urban-rural linkages. Given that urban lifestyles tend to raise consumption expectations and that 60% of the world's population will be urban by 2025, the rural-urban linkage, or the urban 'ecological footprint,' is critical to land change assessments.

To identify the priority threats to biodiversity in sub-Saharan Africa, Leisher et al. (2020) classified the direct threats to biodiversity. They find that the highest threats are annual and perennial crops (non-timber), despite the fact that within the sub-regions of sub-Saharan Africa there is considerable variation.

### 3.2.2. Land expansion and effects on natural habitats

Agricultural land expansion can have effects on many natural habits. As it expands rapidly through the conversion of forests, wetlands, and other natural habitats (Barbier, 2004a). Moreover, in all tropical regions of the world, the principal result of agricultural land expansion is deforestation (Barbier, 2004b).

Ngoma et al. (2021) revealed that cropland expansion by smallholders into forests represents about 60% of the estimated 250,000 ha of forests lost per year in Zambia. Similarly, Delacote (2007) tried to address the impact of the use of forest products as safety nets by poor agricultural households on deforestation and showed that the household is better off and deforests less when using the diversification strategy instead of the coping strategy.

Oliveira et al. (2013) use simulations and scenarios and find that expansion of agriculture in Amazonia may be a no-win scenario: in addition to reductions in carbon storage due to deforestation, total agriculture output may either increase much less than proportionally to the potential expansion in agricultural area or even decrease as a consequence of climate feedback from changes in land use.

Similarly, Fitzherbert et al. (2008) assess the contribution of palm oil to tropical deforestation and review its biodiversity. They use a map analysis and find that for both biodiversity and native tropical forests, oil palm plantations are a poor substitute. They support a few species of conservation importance and affect biodiversity in adjacent habitats through fragmentation, edge effects, and pollution.

Moraes et al. (2017) provide an analysis of the agricultural expansion and its impact on a protected area located in Brazil using map accuracy. They show that the landscape patterns were affected by economic cycles. The forest fragmentation process and the predominance of monoculture lands in the
buffer zone threaten the protected areas and can represent a barrier for these areas to provide effective
biodiversity conservation.

Conversely, Zeng et al. (2018) found that large increases in cultivated areas have evolved from forests that vary in health and status, including primary and protected forests, or from recovering lands that were on a trajectory to become secondary forests in South Asia. Equally, they found that cropland expansion reduced species' natural habitats. Similarly, Ordway et al. (2017) draw the same conclusion while assessing the effects of domestic- and export-oriented agricultural expansion on deforestation in SSA. They claim that commodity crop expansion is increasing exposure, vulnerability, and pressures on natural habits and species diversity. Similarly, Gaveau et al. (2016) conclude that plantation industries have been the principal driver of deforestation in Malaysian Borneo for up to four decades. In contrast, their role in deforestation in Indonesian Borneo was less marked. Gibbs et al. (2010) examine whether new agricultural lands replace forests, degraded forests, or grasslands, which greatly affects the environmental consequences of expansion. Their results suggest that across the tropics, more than 55% of new agricultural land came at the expense of intact forests, and another 28% resulted from disturbed forests. In addition, Kløverpris (2009) indicates in his results that agricultural expansion on land suited for crop cultivation (cultivable land) typically affects forest biomes or potential grassland or steppe, whereas expansion on land suited for grazing but not for crop cultivation typically occurs on potential shrubland or a few other biomes depending on the region.

Other studies relate land expansion to other natural habits. In a recent study, Balima et al. (2020) used principal component analysis found that tropical West African savannah ecosystems protected areas hold higher species diversity than agricultural lands. Hence, land use pressures shrink plant species diversity in tree biomass. Working in marginal lands, Kraemer et al. (2015) mobilise land-use change simulations and find that cropland expansion during the Virgin Lands Campaign was significantly associated with favourable agro-environmental conditions. They concluded that the potential for cropland production in currently uncultivated areas is much lower than commonly believed, and further cropland expansion is only possible at the expense of marginal lands.

Laurance et al. (2014) assess how the required increases in food and biofuel production will affect tropical terrestrial and aquatic ecosystems and biodiversity. They find that the goals of a well-nourished global population and healthy ecosystems are inextricably linked, that the already-massive global footprint of agriculture is expanding rapidly, and that its impacts on terrestrial and aquatic ecosystems will be intense and increasingly pervasive, especially in Sub-Saharan Africa and South America. Tilman (1999) investigates the environmental costs of agricultural ecosystems that are
better at producing food and yielding more efficient yields. Findings suggested that the forecasted largest impacts would be on freshwater and marine ecosystems, which would be greatly eutrophied by high rates of nitrogen and phosphorus release from agricultural fields.

Similarly, Xin et al. (2021) characterise the temporal dynamics of estate crop expansion into natural forests in Indonesia. They conclude that the effectiveness of protected areas decreased over time and became insignificant. Moreover, estate crop expansion via land-cover and land-use change trajectory hopping would severely threaten biodiversity because it tends to occur in lowland forests, diminishing natural habitat area and increasing natural forest isolation.

There are inconclusive studies of the relationship between agricultural land expansion and biodiversity. Firstly, Minde et al. (2001) examine the potential that some of the macroeconomic policies under structural adjustment programmes (SAPs) have for increased agricultural land expansion. Although they indicate that land for agricultural production is obtained from the forests, the surveys did not link crops to this ‘new’ agricultural land. This means that agricultural land expansion was unable to cause biodiversity loss.

Secondly, Darkoh (2003) establishes that the relationships could be reciprocally beneficial. In all regions of Northern Africa, the West African Sahel, East Africa, the Horn, and Southern Africa, agriculture accelerates the loss of biodiversity because of attempts by farmers to increase crop and animal production to feed the increasing population and contribute to the growth of the national economies. However, they mention that agriculture and biodiversity can be complementary activities. If properly managed, agriculture should enhance and not be the enemy of biodiversity in the drylands of Africa.

Thirdly, Fenta et al. (2020) measure what loss in ecosystem services value (ESV) is caused by land degradation. They concluded that land cover change resulted in a net increase in the total ESV of US$125 billion, albeit with an ESV loss of US$60 billion per year from the conversion of evergreen forest and shrubland. Overall, cropland expansion accounted for about 60% of the increase in the total ESV, thereby outweighing the decrease in ESV due to the decline in natural vegetation.

Most of the studies reviewed here use map analysis and consider biodiversity from the viewpoint of natural habits to investigate how agricultural expansion causes biodiversity decline indirectly. This study diverges from theirs in that it explores the direct links while accounting for the number of species threatened and uses econometric regressions to find causation links.
# 4. Methodology of analysis of the effects of agricultural expansion on biodiversity loss

This section presents specifications regarding the study's empirical model and data sources are.

## 4.1. Nature, data sources and scope of the study

This essay provides empirical validation of the effects of agricultural land extension on biodiversity conservation. The data used are from secondary sources. To capture biodiversity, the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species (2016) database is used. Economic variables are drawn from the World Development Indicators (WDI, 2019). The latter reports agricultural land variables as well. Meanwhile, the variables on governance and institutions are from the World Governance Indicator (WGI, 2020), and data on protected areas comes from the World Database of Protected Areas (2020).

Due to data availability, the dataset mobilised by the study covers a first set of 12 countries and a second set of 31 countries for a 3-year period. The IUCN Red List of Endangered Species contains assessments of extinction threat levels for both plant and animal species based on population size, habitat range, and estimated extinction risk. This study uses the 2013-2, 2014-2, and 2015-2 versions that contain assessments categorised as either least concern, data deficient, vulnerable, endangered, near threatened, critically endangered, extinct, lower risk/conservation dependent, or extinct in the wild. In the majority, the assessments are carried out by the IUCN Species Survival Commission, and all assessments are reviewed by a member of the Red List Authority on the relevant taxonomic group (Sol, 2019).

The World Database of Protected Areas is the fruit of a joint initiative between the United Nations Environment Programme (UNEP), the World Conservation Monitoring Centre (UNEP-WCMC), the IUCN, and the World Commission on Protected Areas (WCPA). It is the largest database that gathers, compiles, and reports data on marine and terrestrial protected areas (PAs) in collaboration with various governmental and non-governmental organisations, academia, and industry. Its aim is to develop and maintain an accurate and freely available, up-to-date database on PA status around the world to be used as a global standard by all relevant stakeholders (Brook et al., 2004).

In the WDPA database, the governance of PAs has many sub-categories. These include PAs established by the government; areas established under regional and international conventions; privately owned conservation areas; and areas conserved by indigenous people and local communities. The IUCN has two further broad types of classifications: (1) management categories and (2) governance types. The protected area management categories help us classify PAs based on their primary management objectives (Dudley, 2008), while the governance types classify PAs according to who holds authority, responsibility, and accountability for them (Borrini et al., 2013). Currently, about 65% of the PAs in the WDPA have an IUCN management category, and 88% have
governance types (Deguignet et al., 2014). In this essay, the latter classification is used because it sufficiently reports the extent to which authority, responsibility, and accountability are devolved to the lower level in terms of resource use (Ullah and Kim, 2021).

### 4.1.1. Measures of biodiversity change

To measure biodiversity, the existing literature suggests many indicators. Roughly 100 different indicators are proposed for the Aichi meeting of the Convention on Biological Diversity (CBD) (Pereira et al. 2013). Recognising this complexity, Pereira et al. (2013) developed a framework of Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programmes worldwide. EBVs are designed to help prioritise indicators by seeking to define a minimum set of six<fnref n="8" /> broad essential classes to capture major dimensions of biodiversity change.

Despite the extensive work, the methodology employed by Pereira et al. (2013) was found to be complex. Subsequently, for simpler measures, Mace et al. (2018) suggested three key indicators to understand change in biodiversity, which are useful when considering trajectories to international biodiversity targets and would adequately capture key dimensions of biodiversity (essentially extinction risk, abundance, and composition):

(1) Conservation status: estimating near-future global losses of species (extinctions), which can be measured using the IUCN Red List Index (RLI).

(2) Population trends: trends in the abundance of wild species that can be measured using the Living Planet Index (LPI)).

(3) Biotic integrity (community composition): This can be measured using the Biodiversity Intactness Index (BII), which measures the fraction of naturally present terrestrial biodiversity that still remains.

#### 4.1.1.1. The IUCN Red List Index

The RLI is developed from experts' assessments of species using information on life-history traits, population and distribution, size and structure, and their change over time (IUCN, 2020). Red List assessors classify species into one of the eight categories (extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concerned, or data deficient). Over 100,000 species have now been assessed. The Red List Index uses this data to show trends in survival probability over time (Butchart et al., 2007). It is only available for five taxonomic groups that have had repeated assessments: birds, mammals, amphibians, corals, and cycads, although species

<footnote n="8">⁸ See Dasgupta Review (2021)</footnote>
assessments are being updated all the time. This is the measure used in this study to capture
biodiversity loss, focusing on birds and mammals' taxa.

The IUCN Red List is a globally recognised approach for assessing and monitoring the status of biodiversity. The scientific objectivity of the IUCN Red List is assessed through the categories and criteria developed in 1994 and revised in 2001. According to this criterion, there are nine categories: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern, data deficient, and not evaluated. Every surveyed species falls into one of these categories. So far, the IUCN has developed quantitative criteria for three categories: critically endangered, endangered, and vulnerable (Ullah and Kim, 2021). Species listed within each of these categories are believed to share a similar probability of extinction. Species falling into the categories of critically endangered or endangered and vulnerable are collectively described as 'threatened' and are generally used as a measure of threat to biodiversity. Consistent with IUCN classification, our measure of biodiversity loss in a country is the number of mammals and bird species known to be threatened from 2013 to 2015. These two taxonomic groups have been comprehensively assessed since 2000. Previous studies that have used this measure include country-level studies (Perrings and Halkos, 2015; Sol, 2019) and panel studies (Ullah and Kim, 2021).

4.1.1.2. The Living Planet Index

The Living Planet Index (LPI) is a measure of the state of the world's biological diversity based on population trends of vertebrate species from terrestrial, freshwater, and marine habitats. The LPI is based on trends for over 4,000 species and thousands of population time series collected from monitored sites around the world (Almond et al., 2020). Species groups are chosen based on data availability. The species population data that is collected goes into a global index as well as indexes for more specific biogeographic areas, referred to as realms, based upon distinct groupings of species. The extent to which this index can be disaggregated depends on the quantity and resolution of the data. For example, there is no LPI for countries like the United Kingdom. Due to the absence of such data for SSA countries, the index cannot be used in the study.

4.1.1.3. The Biodiversity Intactness Index

The Biodiversity Intactness Index (BII) represents the fraction of naturally present biodiversity still remaining in terrestrial ecological communities in a region (Scholes and Biggs, 2005). The BII aims at using taxonomically and geographically representative data and consequently includes considerable amounts of insect and plant data, unlike the LPI and the RLI. The BII considers the impacts of land use, land use intensity, and other pressures, such as human population density and proximity to roads, in a modelling framework (Purvis et al., 2018). It does not include climate change
impacts, fails to distinguish between plantations and natural forests, and does not account for the
delayed effects of land use change. The index, therefore, is not in line with the study's interest.

## 4.2. The baseline model and description of variables

To assess the effects of agricultural land extension on biodiversity, the study formulates and estimates a model of biodiversity threats. Assuming that people have preferences for agricultural and other goods and services, as well as the biodiversity that supports those services. Additionally, they implicitly weigh the benefits of agricultural growth against the costs of biodiversity loss.

Thus, economic agents make a liberal trade-off between reducing the abundance of weeds, rodents, pests, and other pathogens and increasing the abundance of other species of their choice. Following Perrings and Halkos (2015), we abstract from the complexity of the problem by assuming that the benefits of biodiversity can be separated into those that contribute directly to income and those that do not. The latter are constituted of cultural services, which reflect the totemic, religious, or cultural values attached to individual species, landscapes, or ecosystems.

Undoubtedly, many reasons explain why actual resource use may differ from socially optimal resource use, including ignorance or fundamental uncertainty about the consequences of particular actions, the displacement of the costs of actions in space or time, the structure of property rights, the ineffectiveness of mechanisms for the governance of resources in the public domain, externalities, and the public good nature of many ecosystem services (Perrings and Halkos, 2015). Nevertheless, as a first approximation, the assumption that decision-makers have some awareness of the factors to be balanced in growing the agricultural sector is reasonable.

To account for this, the following theoretical social utility function, $U_{it}$, is proposed:

$$U_{it} = F(Y_{it}(K_{it}, L_{it}, P_{it}, S_{it}), S) \qquad (\text{eq10})$$

Where $Y_{it}$ denotes the goods and services supporting consumption and investment, $K_{it}$ denotes nonagricultural assets (capital), $L_{it}$ denotes agricultural assets (land), $P_{it}$ denotes human resources (population), and $S = \sum_j S_j, j = 1, ..., n$ refers to the set of species in all countries that affects social utility (biodiversity).

That is, it is assumed that in a particular year, people derive both direct, $U_{it}(S_{it})$, and indirect, $U_{it}(Y_{it}(S_{it}))$, benefits from biodiversity.
For the optimum to occur in country i, the change in biodiversity associated with a change in other assets should balance the marginal utility of the goods and services generated by those assets when evaluated in terms of the marginal direct and indirect utility of biodiversity (Perrings and Halkos, 2015). In other words, if the growth of agricultural assets and other forms of capital causes a decline in biodiversity, then the value of the goods and services created in the process should be at least as great as the value of the lost biodiversity.

To match the model formulation with a panel dataset, the base-line model is expressed as follows:

$$lnTS_{it} = \alpha_0 + \alpha_1 \text{aland}_{it} + \beta_{it} X_{it} + \varepsilon_{it} \qquad (\text{eq11})$$

Where TS<sub>it</sub> is the measure of biodiversity threat captured by the threatened species, aland<sub>it</sub> is agricultural land in percentage of total area of country i in year t, and X<sub>it</sub> is the set of other control variables for country i in year t, which are presented in the variable justification subsection.

Although there are no perfect measures of biodiversity change, dTS<sub>it</sub>, the most commonly used measure is the number of species designated as threatened in the IUCN Red List (IUCN 2014). Some potential difficulties with the Red List as a measure of biodiversity change are illustrated in the literature. It does not yet provide operational data on rates of change in the number of threatened species. Revisions in the basis on which estimates are made mean that estimates for different years are still not comparable. In addition, different levels of reporting effort mean that the series is likely to suffer from measurement error due to selection bias (although this is likely to affect the estimated model coefficients less than their standard errors) (Boakes et al., 2010; Butchart et al., 2007; Hilton-Taylor et al., 2009; Possingham et al., 2002; Rodrigues et al., 2006).

As a result, the study chose to employ two alternative proxies, each of which consisted of the natural log of the total number of endemic species in a country's taxonomic categories for both mammals and birds. The number of vulnerable species in both the taxonomic groupings of mammals and birds is used as a response variable for the robustness check.

### 4.2.1. Justification and expected relationships of variables with biodiversity change

The portion of the earth's surface covered by arable land, perennial crops, and perennial pastures is referred to as agricultural land. According to theoretical and empirical literature (Perrings and Halkos, 2015), the loss of biodiversity is anticipated to rise as a result of the development of agricultural land. This indicates that in all three models, a positive coefficient is anticipated.
Additionally, the area of land planted with cereal (measured in hectares; Lncland) is used to assess
the stability of the relationship between biodiversity and widespread agricultural growth. As a result,
the study anticipates a direct relationship between the two factors.

The intensive growth of agriculture is taken into account and measured by the agriculture value added per worker (agvapw) in 2010 in constant dollars. Consistent with Perrings and Halkos (2015), the intensive growth of agriculture should reduce biodiversity loss. There is then an expectation of a negative sign of the variable intensive growth.

The Gross Domestic Product per capita (lngdpcap) is used to evaluate the biodiversity effects of economic growth. Perrings and Halkos (2015) found that an increase in the annual return on all capital stocks in a country, or Gross National Income (GNI) per capita, raises threats to biodiversity. However, as Sol (2019) a negative relation between lngdpcap and risk of extinctions of species is hypothesised. i.e., more prosperous regions have more means for conservation or outsource their polluting activities. Demographic factors are measured using two variables: population density (popdens) and population growth (lnpop). Both variables are introduced in our models. According to the literature (Perrings and Halkos, 2015; Sol, 2019; Ullah and Kim, 2021), demographic factors are either unrelated or negatively related to biodiversity loss. The study agrees with that conclusion in the case of population density. However, because of the high poverty level in sub-Saharan Africa, an increase in population will pose threats to biodiversity.

Governance or institutional variables used in this essay include rule of law (rle) and the composite index of governance (Gov). The introduction of both variables is inspired by literature. According to previous studies, governance can have two different effects on biodiversity. In some cases, when all governance/institutional indicators are improved, it may reduce the risk of species extinction. On the contrary, improving a single indicator of governance, like the rule of law, can increase the risk of species extinction (Iritié et al., 2015).

Ullah and Kim (2021) reveal that countries with community-governed protected areas have reduced the IUCN Red List threat level by 17% for mammals. The study consecutively includes protected areas (pa) in the model, and its coefficient is expected to have a negative sign. This is to show that protected areas expressed as a proportion of total areas are efficient at reducing threat levels to biodiversity.

Finally, a dichotomous variable, "landlocked," is included in our model. This helps to assess if the openness of the sea reduces the risk of a threat to biodiversity. Landlocked countries are expected to have lower threats to biodiversity than sea-opening countries.
The variables included in the baseline model, as well as their expected signs, are summarised in Table 18 below.

<table><caption>Table 18: Variables and expected signs to threats to mammals</caption><thead><tr><td>Short names</td><td>Full names</td><td>Expected signs</td></tr></thead><tbody><tr><td>aland</td><td>Agricultural land</td><td>+</td></tr><tr><td>Incland</td><td>Cereal land (in log)</td><td>+</td></tr><tr><td>araland</td><td>Arable land</td><td>+</td></tr><tr><td>permland</td><td>Permanent land</td><td>+</td></tr><tr><td>agvapw</td><td>Agricultural</td><td>-</td></tr><tr><td>lngdpcap</td><td>Gross domestic product per capita</td><td>+</td></tr><tr><td>popdens</td><td>Population density</td><td>+</td></tr><tr><td>lnpop</td><td>Population growth</td><td>+</td></tr><tr><td>gov</td><td>Governance</td><td>-</td></tr><tr><td>rle</td><td>Rule of law</td><td>+</td></tr><tr><td>pa</td><td>Protected areas</td><td>-</td></tr><tr><td>1=landlocked</td><td>Landlocked countries (1 if yes, 0 if no)</td><td>-</td></tr></tbody></table>

Source: author

### 4.2.2. Estimation strategy

The Population – Averaged approach (PA) or Pooled Feasible General Least Square (PFGLS) is used to estimate the model. The database mobilised by the study covers a first set of 12 countries over a 3-year period and a second set of 31 countries over the same time span. This is an improvement, as Perrings and Halkos (2015) and Sol (2019) used only a single time period to measure how agricultural land expansion relates to biodiversity loss. They used generalised least squares (GLS) on country-level studies. The authors’ common reasons why their respective studies cover only one year are that the IUCN database is reassessed more than once within a year, and some species may be included in later versions but not in earlier versions. As a result, data on threatened species are not comparable from one year to another.

Contrarily to the previous study, Ullah and Kim (2021) carried out a panel study with available panel data exclusively delivered by the IUCN office. The latter study justifies the current one, which covers only the 2013–2015 period. During this time period, data are available and purged of some discrepancies. The estimation technique used to analyse this model is the population-averaged (PA) approach, which is the best fit for such short panels of data (Cameron and Trivedi, 2009).

One contribution of the current study is its methodological aspect. Indeed, it uses a population-averaged (PA) approach, which is also known as the Pooled Feasible General Least Square (PFGLS). Standard errors need to adjust for any error correlation, and given a model for error correlation, more
efficient FGLS estimation is possible (Cameron and Trivedi, 2009). As a matter of fact, this approach assumes that any individual effects are random and are averaged out. The estimators simply regress response variables ($Y_{it}$) on an intercept and the set of explanatory variables ($X_{it}$), using both between-section and within-time series variation in the data. Hence, the estimation technique brings into the analysis temporal information that has not been considered by recent literature.

Estimating a model with the population-average approach requires specifying restrictions on error correlations. Let $\rho_{ts} = Cor(U_{it}, U_{is})$, the error correlation over time for individual i, and note the restriction that $\rho_{ts}$, does not vary with i. The STATA corr options all set $\rho_{ts} = 1$ but differ in the model for $\rho_{ts}$ for $t \neq s$. Correlations structures used in the study include: (i) independent which means $\rho_{ts} = 0$ for $t \neq s$ which makes population-averaged approach equivalent to Pooled Ordinary Least Square estimators, and (ii) stationary at level (1) to use a moving average MA (g) process. Here $\rho_{ts} = \rho_{|t-s|}$ if $|t-s| \le g$, and, $\rho_{ts} = 0$ if $|t-s| > g$ is considered. Other error correlation restrictions include autoregressive, exchangeable, unstructured, and non-stationary. They are better explained in Cameron and Trivedi (2009).

The robustness analysis is made up of the following steps: First, the Pooled Feasible Generalised Least Square (PFGLS) for long panels is used to account for plausible heteroskedasticity and cross-sectional correlations in the panel. Then, we use alternative measures of agricultural land expansion, namely arable land and permanent land, to assess the contribution of those types of land expansion to biodiversity threats in Africa.
# 5. Results and discussion

The results section, presents the descriptive statistics of variables, a graphical illustration and the estimated results of our interest relationship.

## 5.1. Descriptive statistics of variables

To keep descriptive statistics light, table 19 only presents statistics of response variables as well as interest variables.

<table><caption>Table 19: Descriptive statistics</caption><thead><tr><th></th><th>Mammals<br>Endemic</th><th>Birds<br>Endemic</th><th>Mammals<br>Total</th><th>Birds<br>Total</th><th>Agr.<br>land</th><th>Cereal<br>Land</th><th>Arable<br>land</th><th>Permanent<br>land</th><th>Protected<br>area</th></tr></thead><tbody><tr><th>Count</th><td>143</td><td>143</td><td>107</td><td>107</td><td>134</td><td>134</td><td>134</td><td>134</td><td>140</td></tr><tr><th>Mean</th><td>3,83</td><td>2,64</td><td>17,50</td><td>16,91</td><td>48,36</td><td>13,31</td><td>39,83</td><td>39,81</td><td>49,01</td></tr><tr><th>Median</th><td>0</td><td>0</td><td>13</td><td>14</td><td>47,14</td><td>13,97</td><td>37,54</td><td>38,36</td><td>48,36</td></tr><tr><th>Min</th><td>0</td><td>0</td><td>2</td><td>5</td><td>3,37</td><td>5,87</td><td>8,85</td><td>3,87</td><td>0</td></tr><tr><th>Max</th><td>114</td><td>28</td><td>116</td><td>45</td><td>79,83</td><td>16,6</td><td>36,68</td><td>89,43</td><td>100</td></tr><tr><th>Sd</th><td>14,49</td><td>5,09</td><td>16,73</td><td>8,85</td><td>20,96</td><td>2,28</td><td>26,78</td><td>10,61</td><td>27,81</td></tr></tbody></table>

Source: Author

Descriptive statistics are presented in table 19 above. Information is given for both endemic species and non-endemic or total species threatened. For each endemic taxonomic group, there are great differences between the mean and the median. The median for mammals and birds is zero, while their mean stands at almost 4 and 3, respectively. Looking at the total number of threatened species, similar differences with endemic species are recorded. Means are greater than medians. An average of almost 18 against a median of 13 for mammals, and an average of 17 for birds compared to a median of 14. The results are in line with those of Perrings and Halkos (2015). A highly asymmetric distribution of data is suspected. The standard deviations show that mammals (endemic and total) distributions have higher levels of disparity than birds' distributions.

The red list index variable displays a lower median than the average. The mean risk of a threat to biodiversity is 0.87. In some countries, the lowest level of biodiversity conservation stands at 0.42, while the conservation level of biodiversity equals 0.99 in other countries. The small value of the standard deviation is 0.1. This may be due to the nature of the variable, which is a proportion.

In the sample, the average land expansion rate is 48.36%, which is very close to its median of 47.14%. The maximum expansion rate is 79.8%, compared to the minimum of 3.37%. The standard deviation is 20.96, which demonstrates a high disparity of distribution around its mean. The cereal land variable shares almost the same characteristics as cereal land.

The surface under protection in the sample has an average of 49,000 Km2. The median is almost equal to and displays 48,360 km2. However, there is a great dispersion around the mean, as the standard deviation is 27.81. Moreover, some countries have zero km2 of protected area in the sample,
while others dedicate up to 100,000 km² of land for biodiversity protection. These statistics are
slightly different from what the literature provides (Ullah and Kim, 2021).

**5.2. Graphical analysis agricultural land expansion and biodiversity loss**

In this section, a graphical analysis of the relationship between extensive agricultural growth and biodiversity loss is provided. This analysis is done in two steps: First, the presentation of the effects of the increase in agricultural land on endemic species, and second, the assessment of the impact of the increase in agricultural land on the total number of threatened species in the countries.

**5.2.1. Agricultural land and endemic species threats**

Figure 25 illustrates the relationship between endemic species and agricultural land expansion. The researcher analyses both mammals (figure 25a) and birds (figure 25b) taxonomic groups and finds positive relationships.

<figure><img src="image_27.png" /><figcaption>Figure 25: Agricultural land expansion and endemic species
Source: author</figcaption></figure>

From the first figure (25a), it appears that the expansion of agricultural land poses important threats to endemic mammals. The adjustment curve has a positive slope, showing that there is a direct relationship between agricultural land expansion and threats to endemic mammals. In other words, low percentages of land dedicated to agriculture are associated with low levels of threats to endemic mammals. As the land is converted for agricultural purposes, threats to endemic mammals also increase. Finally, there are high percentages of agricultural land coupled with a high number of threatened endemic mammals.
Looking at figure 25b, it is observed that a similar positive relationship between agricultural land and the number of endemic birds is threatened. Where land is moderately dedicated to agriculture, a small number of endemic bird species are threatened. However, threats increase as more land is used for agricultural activity. This confirms the literature on this topic, which explains that the extensive growth of agriculture causes a high risk of species extinction.

It is also important to note that the graphs show dissimilarity. While comparing the figures, evidence
shows that the slopes of the curves are different. The adjustment line for endemic mammals is sharper
than that for endemic birds. Expanding agricultural land will lead to a greater risk of loss for endemic
mammals than endemic birds. Therefore, increasing agricultural land is risky for both taxonomic
groups and for species occurring in one country only. But the threat is more dangerous for mammals
than for birds.

**5.2.2. Agricultural land and total species threats**

Figure 26 shows the relationship between arable land and three taxonomic groups: mammals (Fig. 26a) and birds (Fig. 26b). From the first sub-figure, we can discover a positive relationship between the variables.

<figure><img src="image_28.png" /><figcaption>Figure 26: Agricultural land expansion and total species
Source: author</figcaption></figure>

This was the case with endemic mammal species in the previous section. As land is progressively
transformed for agricultural purposes, the threat to mammals rises and becomes more and more
important. Hence, the greater the expansion of agricultural land, the greater the risk of mammal
species extinction (Fig. 26b). From the first sub-figure, we can discover a positive relationship between the variables. This was the case with endemic mammal species in the previous section. As land is progressively transformed for agricultural purposes, the threat to mammals rises and becomes more and more important. Hence, the greater the expansion of agricultural land, the greater the risk of mammal species' extinction. This should be justified by the fact that increasing land for agriculture leads to the natural habitat loss of mammals.

When looking at threatened bird species in the second sub-figure, a positive relationship with the agricultural land surface is also identified. As countries devote more of their available land to agriculture, bird species are endangered. The slope of the adjustment line shows a steady and strong direct relationship. Hence, expanding agricultural land will not only be risky to endemic species; it will certainly cause damage to overall bird species.

This section has permitted us to establish the relationship between the extensive growth of agriculture and biodiversity loss. The graphical analyses showed that no matter the taxonomic groups in this study, pressures from land expansion are perceptible. Evidence of such positive links exists irrespective of how they measure biodiversity, that is, both endemic and total species.

## 5.3. Econometric results

### 5.3.1. Baseline results

Table 20 displays two bunches of models of two taxonomic groups. Firstly, the estimated coefficients from both endemic mammals and birds' models (endemic models), and secondly estimated coefficients from total threatened mammals and birds' models (total models). The coefficients are produced by the population-averaged method of estimations. This method of estimation is indicated for short panels.

The general analysis of the table shows that all the coefficients together are statistically different from zero with high Chi squared values (409–585). This goodness-of-fits show that the estimations of the four models can be used for policy design and forecasting. Individual analysis of the variables goes in line with consistent results suggested by the global analysis. Indeed, most of the variables are significant at 10% threshold.

The variable of interest, agricultural land expansion, is significant in all four models. The expansion increases the risk of extinction of threatened species whether endemic or not. In endemic models (columns 1 and 2), a 1 percentage point increase in land devoted to agriculture causes a 5% increase in the risk of mammal extinction. A one percentage point increase in agricultural land causes a 0.9%
increase in the threat to bird species. The coefficients are significant at the 1% and 5% levels respectively.

When analysing the non-endemic models (columns 3 and 4), the same direct relationship between land expansion and the threat to biodiversity is noted. Indeed, the threat increases by 0.7% and 0.2% for mammals and birds respectively, following a 1% increase in land dedicated to agriculture. These results are significant at the 5% and 10% level respectively.

<table><caption>Table 20: Agricultural land and biodiversity loss</caption><thead><tr><th>Dependent variables</th><th>(1)<br>Endemic<br>Mammals</th><th>(2)<br>Endemic<br>Birds</th><th>(3)<br>Total<br>Mammals</th><th>(4)<br>Total<br>Birds</th></tr></thead><tbody><tr><td>Agricultural land</td><td>0.0543***<br>(0.0104)</td><td>0.00958*<br>(0.00455)</td><td>0.00670**<br>(0.00781)</td><td>0.00214*<br>(0.00256)</td></tr><tr><td>Cereal land (log)</td><td>1.069***<br>(0.174)</td><td>-1.132***<br>(0.0820)</td><td>0.218***<br>(0.0514)</td><td>-0.230***<br>(0.0272)</td></tr><tr><td>Intensive agriculture</td><td>-0.000389***<br>(0.000101)</td><td>-0.000549***<br>(6.32e-05)</td><td>-0.000184***<br>(6.89e-05)</td><td>-0.000158***<br>(4.07e-05)</td></tr><tr><td>GDP per cap (log)</td><td>-2.083***<br>(0.328)</td><td>-0.943***<br>(0.187)</td><td>0.00364<br>(0.172)</td><td>0.136*<br>(0.0764)</td></tr><tr><td>Population density</td><td>-0.0310***<br>(0.00532)</td><td>0.0370***<br>(0.00336)</td><td>0.00432***<br>(0.00165)</td><td>0.00310***<br>(0.000744)</td></tr><tr><td>Population growth (log)</td><td>2.369***<br>(0.558)</td><td>-3.269***<br>(0.362)</td><td>-0.313**<br>(0.128)</td><td>-0.196***<br>(0.0627)</td></tr><tr><td>Governance</td><td>-2.456***<br>(0.238)</td><td>0.437<br>(0.306)</td><td>-0.852***<br>(0.238)</td><td>-0.260**<br>(0.115)</td></tr><tr><td>Rule of law</td><td>9.317***<br>(0.739)</td><td>0.422<br>(0.813)</td><td>2.530***<br>(0.719)</td><td>0.999***<br>(0.335)</td></tr><tr><td>Protected areas</td><td>-0.0371***<br>(0.00712)</td><td>-0.0349***<br>(0.00389)</td><td>-0.000338<br>(0.00390)</td><td>-0.00401***<br>(0.00147)</td></tr><tr><td>1.landlocked<br>(1 if yes, 0 if no)</td><td>0.0817<br>(0.394)</td><td>-2.406***<br>(0.190)</td><td>-1.218***<br>(0.260)</td><td>-0.671***<br>(0.107)</td></tr><tr><td>Constant</td><td>38.87***<br>(3.848)</td><td>2.223<br>(3.243)</td><td>7.816***<br>(2.826)</td><td>1.740<br>(1.152)</td></tr><tr><td>Observations</td><td>36</td><td>35</td><td>93</td><td>93</td></tr><tr><td>Number of id<br>chi2</td><td>12</td><td>12</td><td>31</td><td>31</td></tr><tr><td></td><td>409.3***</td><td>585.1***</td><td>88.90***</td><td>132.4***</td></tr></tbody></table>

Standard errors in parentheses

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

Source: author

The explanation for this phenomenon is that the use of land for agricultural purposes causes the destruction of the species' natural ecosystems (mammals and birds). This results in the destruction of their natural habitats, which leads to overpopulation in other, more or less safe places. This facilitates the capture of species for game consumption or display as hunting trophies. These results converge with those in the literature (Perrings and Halkos, 2015; Sol, 2019).

The second variable, a cereal land, also increases the risk of mammal species not surviving. The coefficients are all significant at the 1% level. This result is constant whether the model is endemic
or non-endemic (columns 1 and 3). The analysis of the mammal models (columns 1 and 3) shows that a one-percent increase in cereal land leads to a 1.06% (exp(-1.07)-1) increase in the threat to endemic mammals and to a 0.21% increase in the risk of extinction of total mammals. This result is intuitive and expected. Cereal production is central to the food self-sufficiency goals of African countries. Africa needs to produce one billion tonnes of cereals per year to satisfy its population growth in 2050 (FAO, 2014). The finding is a reminder of the antagonism between achieving the objective of food security in Africa and the objective of biodiversity conservation. The finding converges with that of Sol (2019), who shows how agricultural expansion can accelerate the extinction of wildlife species.

In the case of birds (columns 2 and 4), it leads to a 1.13% decrease in the threat of extinction for endemics and a 0.23% reduction in total birds. This may result from the fact that cereal production is advantageous for birds and provides them with enough food to threaten their survival. The finding diverges from Perrings and Halkos (2015) and Tan et al. (2022).

The variable intensive agriculture measured by agricultural value added per worker (agvapw) maintains a negative relationship with the biodiversity proxies, although its effect is significant at the 1% level in all domains. Indeed, a small increase in the loss of endemic species' ability to capture biodiversity results from an increase of one dollar per worker. On the other hand, there is a negative and expected effect in columns 2-4. Indeed, intensive agriculture results in a very small decrease (less than 0.001%) in the loss of biodiversity and the disappearance of endangered species.

The explanation attributed to this result is that poverty and subsistence are intimately linked to threats to biodiversity. Increasing agricultural productivity and the incomes of producers, in turn, will generate a shift in their utility curve and reduce the amount of game hunted. In addition, the expansion of agricultural land will be slowed down, and fewer species will be threatened.

This result is similar to that obtained by Perrings and Halkos (2015), who show that agricultural intensification is associated with reduced biodiversity loss for all species in the long term. Moreover, it is shown that, in the short term, this relationship can be verified. In other words, agricultural productivity growth reduces biodiversity loss.

The economic growth variable (lngdpcap) is highly significant and negative in the endemic models (1-2) and positive in the non-endemic models (3-4). It is noted that a one-percent increase in per capita income results in a 2% reduction in the threat to endemic mammal species. Similarly, this increase reduces the threat to endemic bird species by 0.93%. The result is intuitive. Poverty justifies addressing the problems at the base of Maslow's pyramid. And the poorer a country is, the more it
will take care of its environment as a form of self-fulfilment. For Sol (2018), richer countries and
regions can generate enough resources to maintain their wildlife wealth.

The first demography variable, population density (lnpop), has a negative sign. Increasing it by one percent causes a decrease in biodiversity loss, according to estimates. Specifically, in the endemic models, an increase in the population growth rate causes a 2% increase in the extinction risk of mammalian species. However, it reduces the threat to birds by 3.2%.

This relationship is more consistent in non-endemic models. In this case, a one-percent increase in population growth rate will reduce the threat to mammals by 0.31% and the risk of extinction for the bird population by 0.19%.

The explanation given is the intrinsic composition of population growth. The threat to mammal and bird populations will tend to increase if rural population pressure dominates population growth. If, on the other hand, population pressure is greater in urban areas, there will be a reduction in the threat to endangered species.

Variable population density has a direct relationship with biodiversity loss. An increase in population density increases the risk of extinction of bird populations in the endemic model by 0.03% (column 2). The same increase in the number of people per square kilometre tends to increase the extinction of species in mammal and bird populations by 0.004% and 0.003%, respectively.

Countries with low population density exert less pressure on threatened animal populations. On the other hand, countries with high population density contribute to increasing pressure on threatened species. This is explained by the strong competition for food and subsistence that characterises high-density countries, all other things being equal. This relationship is consistent with the results observed in the literature. Indeed, Ullah and Kim (2021) find that human activity, captured by demographic variables, is positively related to proxies of biodiversity loss.

The analysis of our institutional variables allows us to establish and confirm two theoretical intuitions. When governance is captured by the composite index of all governance indicators, the variable (Gov) in all the models captures the beneficial effect on biodiversity loss. On the other hand, when the improvement in the quality of institutions by a single indicator is captured, the effect is perverse on the loss of biodiversity.

First, there is a negative relationship between overall governance improvement and biodiversity loss. Thus, when the overall index of institutional quality increases by one unit, it reduces the loss of endemic and non-endemic mammal species by 2.4% and 0.85%, respectively. These results are
significant at the 1% level. Similarly, increasing the composite index by one unit reduces the loss of bird species in the non-endemic model by 0.26%. This result is statistically significant at the 5% level.

Secondly, there is a positive and significant relationship between the improvement of an isolated indicator of institutional quality and the reduction of biodiversity. With a significance of 1%, the improvement of one unit of the rule of law variable increases the loss of endemic mammal species by 9.3%. Similarly, it exacerbates the loss of non-endemic species by 2.53% and 0.9% for mammals and birds, respectively. These results are all significant at the 1% level.

The two relationships identified are expected relationships. According to the literature (Iritié, 2015), improving governance globally and improving an isolated indicator have different impacts on biodiversity loss. When the state strives to build better institutions across the board, biodiversity conservation is more guaranteed. On the other hand, when a single indicator is taken into account, all other things being equal, it accentuates the deviations of human activity and increases the risk of the extinction of threatened species.

In addition, there is little criminalization of the illegal hunting of protected species. The strengthening of the law can have negative effects on biodiversity conservation. For example, studying the deterrent effect of penalties in Africa, Challender and MacMillan (2014) argued that while intensifying enforcement efforts was necessary, it was not sufficient to protect the most highly valued species. Rising prices of animal products and deepening poverty in areas of supply had both increased the incentive to hunt illegally. The study suggested that the only solution to the problem was to address poverty in the supply areas, building both the incentives and the capacity within local communities to conserve wildlife (Perrings and Kinzig, 2021).

The increase in protected areas (PA) has a positive relationship with the threat to endemic mammal species. At the 1% threshold, a one percentage point increase in the protected area increases the loss of endemic mammal species by 0.04%. However, increasing protected areas by one percentage point reduces the loss of endemic bird species by 0.03%. It also reduces the losses of non-endemic bird species by 0.004%. The coefficients are significant at the 1% level.

A possible explanation for this result is the lack of inclusion of local communities in the management of protected areas. For communities, mammals are financially important (Ullah and Kim, 2021). The establishment of protected areas that do not involve communities is dangerous for endemic mammal species. This is because protected areas are not a deterrent to the community's efforts to ensure the protection of endemic mammal species. This result for mammals is in contrast to that found by Ullah and Kim (2021) and is similar for birds.
The coefficient of the binary variable ‘landlocked’ introduced in the models captures the effects of being landlocked on biodiversity loss in Sub-Saharan Africa. It is found that being landlocked reduces the threat to bird species (endemic and non-endemic) and non-endemic mammal species. These coefficients are all significant at the 1% level. Such a result can certainly be explained by the fact that landlocked countries are slightly more difficult to access (by land or sea) than non-landlocked countries. They are therefore less attractive to hunters and traffickers of biodiversity.

The results obtained in this subsection highlight that there is a direct relationship between the expansion of agricultural land and biodiversity loss. Agricultural expansion is measured in terms of the ratio of arable land to the total area of the country, or the area of land allocated to cereal crops. The estimation technique used is the pooled population-averaged technique used for short panels. In the following subsection, the robustness of the relationship is analysed by varying the estimation technique.

### 5.3.2. Using an alternative estimation technique

The estimated coefficients of the relationships between the variables of interest using the methodology for long panels are hosted in table 21 page 161. In general, it is found that the variables have coefficients of the same statistical sign and significance as those obtained in the basic model of the previous subsection.

The relationship between the agricultural land expansion variable and the estimated biodiversity loss variable shows that agricultural expansion increases the risk of extinction for threatened species. The effect is more significant for endemic species and less so for non-endemic species.

The variables agricultural intensification (agvapw) and protected areas show signs consistent with the results obtained in the baseline model. Overall, they significantly reduced biodiversity loss in sub-Saharan Africa. The same is true for the proxies of institutional quality.

These estimates are robust to the presence of panel heteroscedasticity and the presence of a serial correlation of order one (1), which could potentially bias the results. The estimated Chi2 coefficients are significant at the 1% level. This means that the model is globally significant, as the variables taken together are different from zero.

After having analysed and validated the robustness of the agricultural expansion relationship and obtained the Feasible General Least Squares (FGLS) estimates, the robustness of the relationship using an alternative specification of the model is assessed.
<table><caption>Table 21: Agricultural land and biodiversity loss, robustness check</caption><thead><tr><th rowspan="2">Dependent variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Endemic Mammals</th><th>Endemic birds</th><th>Non Endemic Mammals</th><th>Non Endemic Birds</th></tr></thead><tbody><tr><td>Agricultural land</td><td>0.0362**<br>(0.0151)</td><td>0.0155***<br>(0.00441)</td><td>-0.00603**<br>(0.00247)</td><td>0.000567<br>(0.00149)</td></tr><tr><td>Cereal land (log)</td><td>0.709***<br>(0.224)</td><td>-1.131***<br>(0.0503)</td><td>0.191***<br>(0.0138)</td><td>-0.256***<br>(0.0152)</td></tr><tr><td>Intensive ag.</td><td>-0.000281*<br>(0.000158)</td><td>-0.000555***<br>(5.97e-05)</td><td>-0.000173***<br>(3.26e-05)</td><td>-0.000167***<br>(1.63e-05)</td></tr><tr><td>Gdp per cap (log)</td><td>-1.623***<br>(0.511)</td><td>-0.942***<br>(0.133)</td><td>0.233***<br>(0.0650)</td><td>0.119***<br>(0.0335)</td></tr><tr><td>Population density</td><td>-0.0244***<br>(0.00652)</td><td>0.0381***<br>(0.00259)</td><td>0.00416***<br>(0.000456)</td><td>0.00164***<br>(0.000595)</td></tr><tr><td>Pop. growth (log)</td><td>1.931***<br>(0.708)</td><td>-3.465***<br>(0.262)</td><td>-0.288***<br>(0.0447)</td><td>-0.112**<br>(0.0538)</td></tr><tr><td>Governance</td><td>-1.987***<br>(0.326)</td><td>0.412*<br>(0.226)</td><td>-0.368***<br>(0.0543)</td><td>-0.142***<br>(0.0195)</td></tr><tr><td>Rule of law</td><td>7.561***<br>(1.108)</td><td>0.480<br>(0.592)</td><td>0.925***<br>(0.148)</td><td>0.636***<br>(0.0339)</td></tr><tr><td>Protected areas</td><td>-0.0232**<br>(0.0102)</td><td>-0.0355***<br>(0.00286)</td><td>-0.00379**<br>(0.00159)</td><td>-0.00648***<br>(0.000931)</td></tr><tr><td>Trade</td><td></td><td></td><td>-0.00851***<br>(0.00101)</td><td>-0.00132**<br>(0.000626)</td></tr><tr><td>1.landlocked<br>(1 if yes, 0 if no)</td><td>-0.262<br>(0.467)</td><td>-2.181***<br>(0.164)</td><td>-0.943***<br>(0.0709)</td><td>-0.543***<br>(0.0685)</td></tr><tr><td>Constant</td><td>30.54***<br>(6.194)</td><td>2.969<br>(2.392)</td><td>3.433***<br>(0.626)</td><td>0.837**<br>(0.389)</td></tr><tr><td>chi2</td><td>203.3***</td><td>965.4***</td><td>618.9***</td><td>1691***</td></tr><tr><td>Observations</td><td>36</td><td>35</td><td>93</td><td>93</td></tr><tr><td>Number of id</td><td>12</td><td>12</td><td>31</td><td>31</td></tr><tr><td>Panel Correlation</td><td>Heteroskedastic</td><td>Heteroskedastic</td><td>Heteroskedastic</td><td>Heteroskedastic</td></tr><tr><td>Serial Correlation</td><td>AR1</td><td>AR1</td><td>AR1</td><td>PSAR1</td></tr></tbody></table>

Standard errors in parentheses

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

Source: author

### 5.3.3. Using alternative model specification and measures of agricultural land expansion.

The robustness of the relationship between agricultural expansion and biodiversity loss is analysed using another specification, and an alternative proxy for extensive agricultural growth is presented in table 22.

The new variables of interest are arable land and permanent land. It has been found that expansion accelerates biodiversity loss. It is found that an increase in permanent land has a significant impact on the increase in biodiversity loss for birds in general as well as for endemic bird and mammal species. These effects are significant at the 1% level. This result closely matches that of Ahrends et al. (2017), who show that the expansion of latex monoculture has a destructive effect on biodiversity
as it promotes the destruction of several animal and plant species in favour of one particular speculation.

<table><caption>Table 22: Agricultural expansion and biodiversity loss, alternative measure of land expansion</caption><thead><tr><th rowspan="2">Dependent variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th>Mammals Endemic</th><th>Birds Endemic</th><th>Mammals Non Endemic</th><th>Birds Non Endemic</th></tr></thead><tbody><tr><td>Arable land</td><td>0.561***<br>(0.108)</td><td>0.480<br>(0.592)</td><td>0.925***<br>(0.148)</td><td>0.636***<br>(0.0339)</td></tr><tr><td>Permanent land</td><td>0.886***<br>(0.194)</td><td>0.768***<br>(0.104)</td><td>0.185***<br>(0.0433)</td><td>0.211***<br>(0.0323)</td></tr><tr><td>Intensive agriculture</td><td>0.00291<br>(0.000219)</td><td>-0.000780***<br>(0.000145)</td><td>-0.000114***<br>(4.40e-05)</td><td>-0.000296***<br>(3.21e-05)</td></tr><tr><td>GDP per cap (log)</td><td>-3.516***<br>(0.515)</td><td>-0.508**<br>(0.223)</td><td>-0.0915<br>(0.0747)</td><td>0.175***<br>(0.0296)</td></tr><tr><td>Population density</td><td>-0.0106***<br>(0.00343)</td><td>0.0190***<br>(0.00290)</td><td>0.000325<br>(0.000769)</td><td>0.000785*<br>(0.000443)</td></tr><tr><td>Pop. growth (log)</td><td>-0.297<br>(0.517)</td><td>-1.847***<br>(0.319)</td><td>-0.100<br>(0.0697)</td><td>-0.0276<br>(0.0450)</td></tr><tr><td>Governance</td><td>-0.846**<br>(0.382)</td><td>0.572***<br>(0.101)</td><td>-0.0117<br>(0.0597)</td><td>-0.185***<br>(0.0285)</td></tr><tr><td>Rule of law</td><td>4.563***<br>(0.944)</td><td>-0.424*<br>(0.217)</td><td>0.159<br>(0.159)</td><td>0.615***<br>(0.0735)</td></tr><tr><td>Protected areas</td><td>-0.0249***<br>(0.00725)</td><td>-0.0219***<br>(0.00305)</td><td>-0.00136<br>(0.00160)</td><td>-0.00345***<br>(0.000984)</td></tr><tr><td>Trade</td><td>-0.0442***<br>(0.00871)</td><td>-0.00653*<br>(0.00370)</td><td>-0.00663***<br>(0.00156)</td><td>-0.00251***<br>(0.000638)</td></tr><tr><td>Landlocked countries<br>(I if yes, 0 if no)</td><td>-0.240<br>(0.296)</td><td>-1.772***<br>(0.251)</td><td>-0.779***<br>(0.0998)</td><td>-0.697***<br>(0.0745)</td></tr><tr><td>Constant</td><td>35.80***<br>(4.711)</td><td>10.34***<br>(2.422)</td><td>4.905***<br>(0.706)</td><td>4.044***<br>(0.325)</td></tr><tr><td>Chi2</td><td>494.6***</td><td>802.9***</td><td>209.6***</td><td>644.0***</td></tr><tr><td>Number of id</td><td>12</td><td>12</td><td>31</td><td>31</td></tr><tr><td>Observations</td><td>36</td><td>35</td><td>93</td><td>93</td></tr><tr><td>Panel Correlation</td><td>Heteroskedastic</td><td>Heteroskedastic</td><td>Heteroskedastic</td><td>Heteroskedastic</td></tr><tr><td>Serial Correlation</td><td>AR1</td><td>PSAR1</td><td>AR1</td><td>AR1</td></tr></tbody></table>

Standard errors in parentheses

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

Source: Author

Looking at the signs and significance of other coefficients, one notes that the relationships are for the most part consistent with those obtained in the baseline specification. The significance of the Chi2, taking into account the possible heteroscedasticity of the panel as well as the risk of serial correlation, makes it possible to attest to the quality of the adjustments produced and summarised in this table.

## 6. Conclusion

It is known that the majority of sub-Saharan countries have an abundance of land and have good reasons for seeking to expand their agricultural areas. Tropical forest ecosystems, which are the most species-rich environments, are being reduced rapidly because of agricultural land expansion (FAO, 2019), threatening therefore environmental dimension of agricultural sustainability. The trend of rapid agricultural land expansion in African tropical regions is of major concern to the problem of biodiversity loss because of the implications for the conversion of many species' natural habits.
Several studies have identified diverse sources of biodiversity loss, among which are population increase and economic growth (Sol, 2019), agricultural land expansion (Molotoks et al., 2020; Perrings and Halkos, 2015), institutional quality ('t Sas-Rolfes, 2017; Muchapondwa and Stage, 2015), and community protected areas (Ullah and Kim, 2021). Most of these use country-level methodologies or imagery observation. No previous study has neither used non-endemic species to test the relationship between land expansion and biodiversity nor used a short panel analysis, which provides more information than a country-level study. The aim is to fill this gap.

This essay examines the effects of agricultural land expansion on both endemic and non-endemic species of mammals and birds from 2013 to 2015 due to the availability of data. A set of 31 countries is included in the analysis. A Population-Average (PA) approach also known as Feasible General Least Square (FGLS) is used as an estimation technique to determine the parameters of the baseline equation. To evaluate the robustness of relationships, the approach adopted accounts for heteroskedasticity and serial correlation.

The main result of this essay suggests that agricultural land expansion is a significant factor accelerating biodiversity loss in sub-Saharan African countries. Independent of the taxonomy (mammals or birds) and irrespective of the model (endemic or non-endemic), using more land for agriculture purposes increases threats to biodiversity. This finding converges with previous studies (Perrings and Halkos, 2015; Sol, 2019; Ullah and Kim, 2021). The latter provide evidence that extensive agricultural growth is a key driver of species extinction. The most probable reason is that agriculture depletes the natural habitats of animals and places them in unsafe places.

Moreover, improving all the indicators of governance is more worthwhile for biodiversity conservation than making improvements to only one indicator—the rule of law in this case. Improvements in living standards reduce threats to biodiversity (Sol, 2019). Finally, creating or expanding protected areas is an efficient tool to reduce biodiversity loss in sub-Saharan Africa, according to Ullah and Kim (2021). All the relations are robust to other estimation techniques, model specification, and both heteroscedasticity and serial correlation. It is recommended to create farms with rich expansion and the use of organic farming methods.
# GENERAL CONCLUSION

The main objective of this thesis is to analyse the effects of agricultural land expansion on agricultural sustainability in sub-Saharan African countries. A threefold decomposition of this main objective is unfolded in three essays. In the first essay, the objective is to inquire into the effects of agricultural land expansion on food security. The second essay's objective is to analyse the effects of agricultural land expansion on the type of structural change. The third essay seeks to investigate the effects of agricultural expansion on biodiversity loss in sub-Saharan Africa.

Globally, the study covers the period 1990–2016 in 41 sub-Saharan African countries. However, the specific time coverage for the essays varies with respect to the availability of data. Hence, essay one covers the period 2001–2016, while essays 2 and 3 cover the periods 1990–2016 and 2013–2015, respectively. In addition, the methodologies used to evaluate the effects of agricultural land expansion on agricultural sustainability vary according to the objectives and the available data. Precisely, in essay number one, Panel Random effects (RE) for the baseline results are used, and the robustness of relationships is assessed with the Generalised Moment Method in Difference (D-GMM) and in System (S-GMM). The second essay uses a Pooled Multinomial Logit Model (POMLOGIT) and checks robustness with both Random Effects Multinomial Logit Model (REMLOGIT) and the Effects Multinomial Logit (FEMLOGIT). Finally, the third essay employs a Pooled Feasible Generalised Least Square (PFGLS) model for short panels to derive the parameters of the baseline model. The same methodology, but for long panels, helps check the robustness. The main results obtained are presented as follows:

## Main results

The expansion of agricultural land through increasing returns of scale leads to an increase in production, resulting in agricultural surplus and income. This surplus decreases the price of agricultural products and improves access to food in terms of both quantity and quality. Consequently, the prevalence of undernourishment is reduced. Moreover, the increase in production leads to a crowding-out effect, contributing to the release of labour from the agricultural sector to other sectors such as the processing of the agricultural surplus. As a result, an increase in investment by firms is observed, leading to the creation of new job opportunities. Additionally, the released producers from the agricultural sector are motivated to diversify their activities in the secondary sector, which ultimately leads to an increase in the likelihood of observing a more industrialised economic structure.

The summary of the results is presented in three subsections. First, the effects of land expansion for agriculture on food security, then the type of structural change, and lastly, the biodiversity change.
* **Effects of agricultural land expansion on food security**

The key findings of this essay suggest that the expansion of arable land available for agriculture reduces the proportion of undernourished people. The reason provided here is based on the assumption of increasing returns to scale. Primary arable land is generally more fertile than secondary land. The latter will require more inputs to carry out the agricultural activity and can generate a higher proportion of production. This agricultural product will help boost food availability and therefore reduce the proportion of undernourished people while holding other factors constant.

* **Effects of agricultural land expansion on the type of structural change**

The results show that the increase in agricultural land not only causes structural transformation but also increases the probability of countries having an industrial structure rather than a service structure. An increase in land for cultivation causes a rise in productivity, which not only releases a surplus of production but also releases the surplus labour in the agricultural sector, which causes a structural change. This creates more incentives for processing the additional agricultural yields and increases the likelihood of observing a more industrialised economic structure. Overall, this essay shows that land expansion for agriculture creates a ‘virtuous’ structural change. The more sub-Saharan countries use the land available for agriculture, the more likely they are to industrialise their economic structures.

* **Effects of agricultural land increases on biodiversity change**

The main result here suggests that agricultural land expansion is a significant factor accelerating biodiversity loss in sub-Saharan African countries. Independently from the taxonomy (mammals or birds) and irrespectively of the model (endemic or non-endemic), converting more land for agriculture purposes increases threats to biodiversity and the risk of extinction. This confirms the common belief and precedent findings that extensive agricultural growth is a key driver of species extinction.

Ultimately, the expansion of agricultural land can play a crucial role in improving access to food for local populations and promoting structural change. However, it is important that this expansion be managed sustainably and environmentally respectfully in order to minimise negative impacts on local ecosystems and ensure long-term food security and sustainable structural transformation.
## Recommendations

Civil society organisations, non-governmental organisations and policy makers should promote the creation of more protected areas that will both enhance biodiversity conservation and food and nutrition security.

Also, policy makers, international donors organisation and civil society organisations should increase investments efforts to expand the size of planted forests since the latter provides ecosystems services and socioeconomic gains.

Moreover, extension services, producers organisations, civil society organisations and policy makers should promote the adoption of agroforestry practices which can be incorporated in agricultural land expansion in order to provide socioeconomic and environmental services. Given that agroforestry will provide habitats for threatened species, raw materials needed for industrial type of structural change and at the same time provide food need to alleviate undernourishment. Investing efficiently in that sub-sector will help to reduce threats to natural habitats while increasing the productivity of agricultural output per unit of land and per agricultural worker (Giller, Delaune, et al., 2021; Giller, Hijbeek, et al., 2021; Pawlak and Kołodziejczak, 2020).

## First essay recommendation

States should improve the land tenure system and the functioning of land markets. It is necessary for African states to halt the acquisition of agricultural land and its exploitation by foreign firms. The acquisition of arable land by either foreign nationals or local populations is of undeniable importance for the food security of African countries. The gains that foreign countries, as secondary holders of land, are making only benefit themselves.

It is suggested that sub-Saharan states review and strengthen legislation around the retention of food production within the national territory and even impose the processing of a portion of the output produced locally. This will make it possible to retain, within national borders, agro-food products that are important for the fight against undernourishment.

The need for more intensive and diverse land use based on the domestication of indigenous trees to produce high-value products while increasing agroecosystem resilience. Approaches that include these three issues will transform smallholder farming in Africa into productive and sustainable enterprises and will greatly contribute to food security and environmental conservation, creating a win-win situation.
Expanding aquaculture and promoting organic agriculture (Schoonbeck et al., 2013), which prevents land degradation and even improves soil fertility (Kaihura et al., 1999; Niggli et al., 2007).

* **Second essay recommendations**

Land policies and investments should focus on improving the productivity of smallholder agriculture. This requires a more equitable distribution of land and means of subsistence in remote and land-abundant regions. By way of illustration, it has been proven that in specific areas, research results, their dissemination, access to markets, improvement of health and education services, and agricultural development improve livelihoods for the poor, increase employment opportunities, and even reduce environmental degradation. Thus, such targeted investment policies, consistent with the needs of small rural farmers, must be multiplied even in various parts of the remotest developing countries. The expected effect is that they would, in addition to reducing poverty and income inequality, spread more widely the economic and social gains that may occur as border areas develop.

The States must set up agricultural land expansion programmes or intervention mechanisms on land markets favourable to communitarisation, the securing of agricultural land, and the improvement of land tenure. They should generate some programmes to clear excessive farm fragmentation and manage it efficiently, as they did for the community forest. Hurdles posed by excessive fragmentation of landholdings need to be overcome. About 85 percent of Africa's farmers are smallholders with cultivation areas of less than 2 hectares. They are too small to be economically viable.

The state must increase the dissemination of agricultural research products. This is the case with mechanisation and the use of improved seeds, which are necessary to obtain economies of scale. Continue to support agricultural mechanisation, which is a solid support for agricultural expansion and a bridge to industrialization.

In addition, policies and investments should aim to improve small-scale agriculture, land distribution, and livelihoods in remote and land-rich areas and better support those who switch to more commercial agriculture.

* **Third essay recommendations**

African countries should promote agricultural intensification through the deployment of greenhouses and organic farming. Indeed, such practises have the double advantage of increasing yields fourfold and respecting endangered species. Expanding aquaculture and promoting organic agriculture (Schoonbeck et al., 2013), which prevents land degradation and even improves soil fertility (Kaihura et al., 1999; Niggli et al., 2007). It will be possible to solve the problems of food security, structural
transformation patterns, and respect for biodiversity. Similar processes are already being
implemented on the outskirts of Douala, Cameroon.

Creating and expanding new community protected areas to favour conservation of endangered animal species The management board of the protected areas should include the local population. This will reduce the risk of illegal hunting (poaching), which greatly increases the risk of the extinction of taxonomic groups.

Design context-specific law enforcement to fight against biodiversity loss. Localities where poverty is increasing and biodiversity is rich tend to defy established laws as the price of animals increases.

More importance should be given to the fight against poverty to encourage respect for biodiversity, especially in species-rich areas.

• Limitations of the study

This study suffers from methodological, empirical, and policy recommendation limits.

In general, our policy recommendations are not country-specific. Actually, the conclusions and policy recommendations we formulate require a regional-level policy and do not account for the specificities of each country. As a matter of fact, there are some important differences in the agricultural land expansion patterns within African countries. Consequently, it is important to extend this study at the country level to get further insights on the impact of these policies.

In the second essay, despite the robustness of the results, there is no way to test the goodness of fit of the model for the estimation of the random effects model (REMLOGIT) or for the fixed effect model (FEMLOGIT). On the other hand, the results clearly show that the increase in agricultural land created a movement of labour into the industrial sector. However, the study does not sufficiently identify the reasons why the structural change of landlocked countries differs from that of sea-opening countries. Further research needs to be done in this direction.

In the third essay, the main limitation is the availability of data. Although the study used an adequate methodology for a short panel analysis, he could have the strongest relationships with panel data from 1990 for all sub-Saharan African countries. However, panel data are very difficult to access from the IUCN. Relevant series are difficult to find, and we are still waiting for the IUCN to provide us with data..
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# Annexes

## Annex 1: Models of sustainability

<figure><img src="image_29.png" /><figcaption>Figure 27: The egg of sustainability</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Figure 28: The prism of sustainability</figcaption></figure>
## Annex 2 : Countries in the study

### Essay 1

Opened countries

Angola, Benin, Cameroon, Congo, Rep., Gabon, Gambia, The, Ghana, Kenya, Liberia, Madagascar, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Senegal, Sierra Leone, South Africa, Tanzania, Togo

Landlocked

Botswana, Burkina Faso, Chad, Ethiopia, Lesotho, Malawi, Malawi, Mali, Rwanda

### Essay 2

Opened countries

Angola, Benin, Cameroon, Comoros, Congo, Dem. Rep., Congo, Rep., Equatorial Guinea, Eritrea, Gabon, Gambia, The, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Senegal, Seychelles, Sierra Leone, South Africa, Tanzania, Togo

Landlocked

Botswana, Burkina Faso, Burundi, Central African Republic, Chad, Ethiopia, Lesotho, Malawi, Mali, Niger, Rwanda, Uganda, Zambia, Zimbabwe.

### Essay 3

Opened countries

Angola, Benin, Cameroon, Comoros, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Senegal, Tanzania, Togo

Landlocked

Botswana, Burundi, Chad, Ethiopia, Lesotho, Malawi, Mali, Niger, Rwanda, Uganda, Zambia, Zimbabwe
Annex 3: Variables used in the study

<table><caption>Table 23:Full description of variables</caption><thead><tr><th>Names</th><th>Labels</th><th>Source</th></tr></thead><tbody><tr><td>aland</td><td>Agricultural land</td><td></td></tr><tr><td>undnrsh</td><td>Prevalence of undernourishment</td><td></td></tr><tr><td>tsc*</td><td>Type of structural change (Inspired by Baymul and Sen, 2020)</td><td></td></tr><tr><td>pi*</td><td>Production Index (Based on Tankeu, 2020; Kinda and Badolo, 2019)</td><td></td></tr><tr><td>lnco2</td><td>CO2 emissions (kt)</td><td></td></tr><tr><td>lngdpcap</td><td>Gross domestic product per capita, constant (in log)</td><td></td></tr><tr><td>fpv*</td><td>Food Price vulnerability index (Based on Kinda and Badolo, 2019; Combes et al., 2014)</td><td></td></tr><tr><td>popg</td><td>Population growth</td><td></td></tr><tr><td>rurpop</td><td>Rural population (% of total population)</td><td></td></tr><tr><td>popdens</td><td>Population density (people per sq. km of land area)</td><td></td></tr><tr><td>indspill*</td><td>Spillover from agriculture to industry (Inspired by Baymul and Sen, 2020)</td><td></td></tr><tr><td>scespill*</td><td>Spillover from agriculture to service (Inspired by Baymul and Sen, 2020)</td><td></td></tr><tr><td>tb</td><td>Incidence of tuberculosis (per 100,000 people)</td><td>World<br>Development<br>indicators</td></tr><tr><td>phiv</td><td>Prevalence of HIV, total (% of population ages 15-49)</td><td></td></tr><tr><td>arlandcap</td><td>Agricultural land per capita</td><td></td></tr><tr><td>arable land</td><td>Arable land (% of land area)</td><td></td></tr><tr><td>cland*</td><td>Land under cereal production (% of land area)</td><td></td></tr><tr><td>for</td><td>Forest area (% of land area)</td><td></td></tr><tr><td>pland</td><td>Permanent cropland (% of land area)</td><td></td></tr><tr><td>trade</td><td>Trade (% of GDP)</td><td></td></tr><tr><td>minerent</td><td>Mineral rents (% of GDP)</td><td></td></tr><tr><td>Water total</td><td>Improved water source (% of population with access)</td><td></td></tr><tr><td>Sanitation total</td><td>Improved sanitation facilities (% of population with access)</td><td></td></tr><tr><td>Water rural</td><td>water source, rural (% of rural population with access)</td><td></td></tr><tr><td>Water urban</td><td>water source, urban (% of urban population with access)</td><td></td></tr><tr><td>Sanitation urban</td><td>sanitation facilities, urban (% of urban population with access)</td><td></td></tr><tr><td>Sanitation rural</td><td>sanitation facilities, rural (% of rural population with access)</td><td></td></tr><tr><td>gov*</td><td>Governance index (Inspired by Totouom et al, 2019)</td><td>World<br>Government<br>Indicator (WGI,<br>2020)</td></tr><tr><td>rle</td><td>Rule of law</td><td></td></tr><tr><td>mmt</td><td>Total Mammals species</td><td>International<br>Union for<br>Conservation of<br>Nature (IUCN,<br>2020)</td></tr><tr><td>mame</td><td>Mammals endemic species</td><td></td></tr><tr><td>bt</td><td>Total Birds species</td><td></td></tr><tr><td>be</td><td>Birds endemic species</td><td></td></tr><tr><td>pa</td><td>Terrestrial protected areas (% of total land area)</td><td>World<br>Database on<br>Protected Areas<br>(WDPA)</td></tr></tbody></table>

<footnote>*Variables constructed by the author</footnote>
## Annex 4: Supplementary Outcomes

<table><caption>Table 24: Principal components/correlation : governance index</caption><thead><tr><th>Factor</th><th>Eigenvalue</th><th>Difference</th><th>Proportion</th><th>Cumulative</th></tr></thead><tbody><tr><td>Factor1</td><td>4.78629</td><td>4.30563</td><td>0.7977</td><td>0.7977</td></tr><tr><td>Factor2</td><td>0.48067</td><td>0.13806</td><td>0.0801</td><td>0.8778</td></tr><tr><td>Factor3</td><td>0.34261</td><td>0.12274</td><td>0.0571</td><td>0.9349</td></tr><tr><td>Factor4</td><td>0.21987</td><td>0.11782</td><td>0.0366</td><td>0.9716</td></tr><tr><td>Factor5</td><td>0.10205</td><td>0.03354</td><td>0.0170</td><td>0.9886</td></tr><tr><td>Factor6</td><td>0.06851</td><td>.</td><td>0.0114</td><td>1.0000</td></tr></tbody></table>

<table><caption>Table 25: Principal component/correlation : production index</caption><thead><tr><th>Component</th><th>Eigenvalue</th><th>Difference</th><th>Proportion</th><th>Cumulative</th></tr></thead><tbody><tr><td>Comp1</td><td>1.50833</td><td>.580478</td><td>0.5028</td><td>0.5028</td></tr><tr><td>Comp2</td><td>.927854</td><td>.364041</td><td>0.3093</td><td>0.8121</td></tr><tr><td>Comp3</td><td>.563813</td><td>.</td><td>0.1879</td><td>1.0000</td></tr></tbody></table>

<table><caption>Table 26: Summary statistics essay 2</caption><thead><tr><th colspan="6">Sea coast Countries</th></tr><tr><th></th><th>count</th><th>mean</th><th>sd</th><th>min</th><th>Max</th></tr></thead><tbody><tr><td>aland</td><td>726</td><td>45.93724</td><td>20.7589</td><td>3.26087</td><td>80.92054</td></tr><tr><td>arlandcap</td><td>726</td><td>.2204696</td><td>.1057377</td><td>.0015439</td><td>.5563928</td></tr><tr><td>gdpcap</td><td>712</td><td>2618.945</td><td>3491.326</td><td>200.6313</td><td>20532.95</td></tr><tr><td>popg</td><td>697</td><td>.0257495</td><td>.0099637</td><td>-.0262866</td><td>.0790219</td></tr><tr><td>gov</td><td>486</td><td>5.375858</td><td>1.825777</td><td>1.063459</td><td>9.931457</td></tr><tr><td>pland</td><td>726</td><td>3.042974</td><td>5.295959</td><td>.0024293</td><td>29.554</td></tr><tr><td>trade</td><td>682</td><td>72.65652</td><td>37.56172</td><td>20.72252</td><td>311.3541</td></tr><tr><td colspan="6">Landlocked countries</td></tr><tr><td>aland</td><td>375</td><td>45.76765</td><td>19.68747</td><td>8.035571</td><td>82.67134</td></tr><tr><td>arlandcap</td><td>378</td><td>.3302578</td><td>.2644735</td><td>.0970429</td><td>1.515974</td></tr><tr><td>gdpcap</td><td>378</td><td>990.6976</td><td>1385.68</td><td>164.3366</td><td>7864.251</td></tr><tr><td>popg</td><td>364</td><td>.0243965</td><td>.0135885</td><td>-.0676622</td><td>.0811793</td></tr><tr><td>gov</td><td>252</td><td>5.380397</td><td>1.729467</td><td>2.347067</td><td>10</td></tr><tr><td>pland</td><td>375</td><td>2.77394</td><td>4.839821</td><td>.0017645</td><td>15.57632</td></tr><tr><td>trade</td><td>336</td><td>56.18993</td><td>26.25733</td><td>19.68416</td><td>161.8937</td></tr></tbody></table>

<figure><img src="image_31.png" /><figcaption>Figure 29: Evolution of agricultural land in Africa countries</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>Figure 30: Agricultural land and sectoral value added</figcaption></figure>

Supplementary estimation results

<table><caption>Table 27: Agricultural land and type of structural change - POMLOGIT</caption><thead><tr><th rowspan="2">VARIABLE<br>S</th><th colspan="2">pm1</th><th colspan="2">pm2</th><th colspan="2">pm3</th><th colspan="2">pm4</th><th colspan="2">pm5</th></tr><tr><th>Structurall<br>y<br>Developin<br>g</th><th>Structurall<br>y<br>Developed</th><th>Structurall<br>y<br>Developin<br>g</th><th>Structurall<br>y<br>Developed</th><th>Structurall<br>y<br>Developin<br>g</th><th>Structurall<br>y<br>Developed</th><th>Structurall<br>y<br>Developin<br>g</th><th>Structurall<br>y<br>Developed</th><th>Structurall<br>y<br>Developin<br>g</th><th>Structurall<br>y<br>Developed</th></tr></thead><tbody><tr><td>aland</td><td>1.027*<br>(0.0153)</td><td>1.178***<br>(0.0613)</td><td>1.050**<br>(0.0232)</td><td>1.180**<br>(0.0907)</td><td>1.077***<br>(0.0260)</td><td>1.282***<br>(0.0901)</td><td>1.089***<br>(0.0294)</td><td>1.312***<br>(0.0931)</td><td>1.088***<br>(0.0292)</td><td>1.319***<br>(0.0741)</td></tr><tr><td>arlandcap</td><td>1.939<br>(2.471)</td><td>0.000766<br>(0.00461)</td><td>3.314<br>(11.45)</td><td>0.968<br>(4.774)</td><td>2.167<br>(7.727)</td><td>8.109<br>(20.62)</td><td>0.360<br>(1.710)</td><td>8.999<br>(26.26)</td><td>0.320<br>(1.589)</td><td>15.09<br>(37.45)</td></tr><tr><td>aglXva</td><td>1.000<br>(0.000415)</td><td>0.996***<br>(0.00133)</td><td>0.999<br>(0.000545)</td><td>0.994**<br>(0.00242)</td><td>0.998**<br>(0.000854)</td><td>0.990***<br>(0.00248)</td><td>0.999<br>(0.000774)</td><td>0.988***<br>(0.00375)</td><td>0.999<br>(0.000796)</td><td>0.988***<br>(0.00300)</td></tr><tr><td>lngdpcap</td><td>3.797***<br>(1.716)</td><td>11.94***<br>(9.658)</td><td>3.160**<br>(1.459)</td><td>0.120<br>(0.174)</td><td>4.508***<br>(2.147)</td><td>0.0737**<br>(0.0947)</td><td>9.303***<br>(4.723)</td><td>0.0677<br>(0.111)</td><td>9.120***<br>(4.676)</td><td>0.0373***<br>(0.0342)</td></tr><tr><td>1.landl</td><td>0.527<br>(0.303)</td><td>0.804<br>(0.832)</td><td>0.226*<br>(0.184)</td><td>0.0968<br>(0.275)</td><td>0.216*<br>(0.176)</td><td>0.167<br>(0.381)</td><td>0.306<br>(0.254)</td><td>0.279<br>(0.637)</td><td>0.312<br>(0.262)</td><td>0.203<br>(0.340)</td></tr><tr><td>popg</td><td></td><td></td><td>3.73e-08<br>(1.39e-06)</td><td>0**<br>(0)</td><td>0<br>(0)</td><td>0***<br>(0)</td><td>0*<br>(0)</td><td>0***<br>(0)</td><td>0*<br>(0)</td><td>0***<br>(0)</td></tr><tr><td>gov</td><td></td><td></td><td>1.698***<br>(0.342)</td><td>8.633*<br>(10.28)</td><td>1.632**<br>(0.316)</td><td>13.61**<br>(16.83)</td><td>1.766***<br>(0.357)</td><td>14.67**<br>(16.47)</td><td>1.761***<br>(0.351)</td><td>19.07***<br>(12.90)</td></tr><tr><td>trade</td><td></td><td></td><td>1.008<br>(0.0116)</td><td>0.996<br>(0.0315)</td><td>1.006<br>(0.0109)</td><td>0.999<br>(0.0178)</td><td>1.005<br>(0.0110)</td><td>1.001<br>(0.0185)</td><td>1.005<br>(0.0109)</td><td>1.003<br>(0.0150)</td></tr><tr><td>popgXagva</td><td></td><td></td><td></td><td></td><td>8.974**<br>(9.790)</td><td>36,610***<br>(93,379)</td><td>9.924**<br>(10.88)</td><td>94,406***<br>(270,770)</td><td>9.808**<br>(10.77)</td><td>200,245**<br>(421,223)</td></tr><tr><td>pland</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.771**<br>(0.0830)</td><td>1.026<br>(0.199)</td><td>0.772**<br>(0.0820)</td><td>0.989<br>(0.294)</td></tr><tr><td>minerent</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.986<br>(0.0604)</td><td>1.155<br>(0.444)</td></tr><tr><td>Constant</td><td>8.37e-06***<br>(3.07e-05)</td><td>0***<br>(0)</td><td>6.48e-07***<br>(3.44e-06)</td><td>0.315<br>(2.638)</td><td>6.02e-08***<br>(3.20e-07)</td><td>0.0506<br>(0.398)</td><td>1.95e-10***<br>(1.16e-09)</td><td>0.0801<br>(0.641)</td><td>2.54e-10***<br>(1.55e-09)</td><td>1.144<br>(9.953)</td></tr><tr><td>Observations</td><td>1,034</td><td>1,034</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td></tr><tr><td>r2_p</td><td>0.416</td><td>0.416</td><td>0.530</td><td>0.530</td><td>0.548</td><td>0.548</td><td>0.577</td><td>0.577</td><td>0.578</td><td>0.578</td></tr><tr><td>N_clust</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td><td>41</td></tr><tr><td>chi2</td><td>111.8</td><td>111.8</td><td>557.2</td><td>557.2</td><td>563.5</td><td>563.5</td><td>674.0</td><td>674.0</td><td>1523</td><td>1523</td></tr></tbody></table>

Robust seeform in parentheses

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

Source : author
Scientific outcome 1

De : Ivonne Stephanie Lara Cortes

Envoyé le : mercredi 26 avril 2023 16:14

À : saubyphd@gmail.com

Cc : McNamara Fellowships program

Objet : RSMFP application results

Dear Saubaber Longang Gamo,

Congratulations! You have been longlisted for the **Robert S McNamara Fellowship Program**. You are being considered for a fellowship with the following team(s): **DIME - Agriculture and DIME - Finance and Private Sector**

The next step in the selection process is for you to complete a statistical software
assignment for: **R, Stata or Python (let candidate choose which one)**

The deadline for completion is Tuesday, May 2nd, 2023, at 1:00 pm Eastern Time.

We kindly ask you to read the following Pre-Test Instructions before beginning the
assignment. When you are ready to start your assignment, please visit the following link.

Cordially,
Robert S. McNamara Fellowships Program Secretariat
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Ministère de l'Éducation
UNIVERSITE des SCIENCES DE LA VIE
ION IONESCU de la BRAD" de IAȘI
"Iași University of Life Sciences"

USV 1842

Date 17.11.2022

N° de référence

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À l'attention de M. Master professionnel LONGANG GAMO Saubaber,
Doctorant auprès de l'Université de Dschang, Faculté de Sciences Economiques et de Gestion

Ville et pays de l'université d'origine : Dschang, Cameroun

Intitulé du projet de recherche : *Trois essais sur les effets de l'expansion des terres en Afrique subsaharienne*

ATTESTATION D'ACCUEIL

Votre dossier de pré-inscription pour une bourse doctorale « Eugen Ionescu » nous est bien parvenu.

Suite à l'analyse de ce dossier, nous avons le plaisir de vous annoncer que vous êtes accepté par notre université, au sein de l'École doctorale, Faculté de Horticulture, Laboratoire de Protection des Plantes, sous la direction scientifique de M. le Professeur univ. dr. Mihai TĂLMACIU, pour effectuer un stage de recherche qui se déroulera du 01 Mai au 31 Juillet 2023, pour une durée totale de 3 mois.

Il vous revient à présent de déposer votre dossier complet de candidature auprès de l'Agence Universitaire de la Francophonie, en vue de la sélection finale.

Les frais relatifs à votre séjour (matériels et expériences en laboratoire, assurance-maladie, logement, transport, éventuelles taxes pour l'obtention du permis de séjour etc.) ne sont pas pris en charge par l'université.

En vous souhaitant une bonne réussite dans vos projets, nous vous prions d'agréer nos salutations distinguées.

Responsable administratif,
Monsieur Prof. univ. dr. Gerard JITĂREANU
Recteur de l'Université,

Signature et cachet:

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Responsable scientifique,
Monsieur Prof. univ. dr. Mihai TALMACIU
Tuteur de doctorat, Responsable de Laboratoire de de Protection des Plantes

Signature:

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ANNUAL CONFERENCE ON AFRICAN POLITICAL ECONOMY2, ACAPE2 & ANNUAL
CONFERENCE ON REGIONAL INGEGRATION IN AFRICA12, ACRIA12.

THEME: POLICY OPTIONS FOR AFRICA'S SUSTAINABLE AND INCLUSIVE
GROWTH AND DEVELOPMENT.

ORGANIZED BY THE AFRICAN POLITICAL ECONOMY ASSOCIATION (APEA),
VIRTUALLY, OCTOBER 22-23, 2022.

PARTNERS:

West African Institute for Financial and Economic Management (WAIFEM)

West African Monetary Institute (WAMI)

West African Monetary Agency (WAMA)

Foundation for Economic Research and Training (FERT)

SPONSORS:

<figure><img src="image_38.png" /></figure>

IU Hamilton Lugar School of Global and International Studies

SATURDAY, OCTOBER 22, 2022.

All Times are West Africa Time (UTC+1).

For instance, 2p.m Lagos (UTC+1) is 1 p.m. Dakar and 8 a.m. Atlanta.

2.00 p.m.-3. 00p.m: Opening and Keynote Address

Opening Addresses:-

- Baba Musa, Director General, WAIFEM

- Director General, West African Monetary Institute, WAMI

- Akpan H. Ekpo, University of Uyo, Chairman, Foundation for Economic Research and Training (FERT)

- Oladele Omosegbon, Indiana University, President, APEA

-

- Goodwill Messages: -

Director General, West African Monetary Agency, WAMA

Director, Country Economics Department, African Development Bank, AfDB

Director/CEO, African Economic Research Consortium, AERC

Keynote Address:

SESSION 1. GROWTH TRAJECTORY

3.05 pm-3.45 pm.
Presiding:

<table><tr><td>1.</td><td>POLICY STABILITY AND ECONOMIC GROWTH SUSTAINABILITY: EVIDENCE FROM WESTERN AND CENTRAL AFRICA ECONOMIES</td><td>Gbenga Peter Sanusi</td></tr><tr><td>2.</td><td>TOWARDS SUSTAINABLE GROWTH IN AFRICA: RE-ASSESSING THE DEBT-FRAGILITY-GROWTH NEXUS</td><td>Akpan H. Ekpo<br/>Obukohwo Oba Efayena</td></tr></table>

Discussants:

(1). Nwokolo C. Ifeoma

(2). Etayibtalnam Koudjom

SESSION 2. AGRICULTURE

3.50 – 5.10

Presiding:

<table><tr><td>1.</td><td>STRUCTURAL CHANGE EFFECTS OF AGRICULTURAL LAND EXPANSION IN AFRICA</td><td>LONGANG GAMO<br/>Saubaber<br/>NINGAYE Paul<br/>TANKEU MELI Yollande</td></tr><tr><td>2.</td><td>FOOD INSECURITY, HEALTH RISK AND CLIMATE CHANGE IN AFRICA: AN ENTREATY FOR AMBITIOUS ACTION AND INCLUSIVE DEVELOPMENT?</td><td>Nwokolo C. Ifeoma</td></tr><tr><td>3.</td><td>CLIMATE CHANGE ADAPTATION AND MAIZE PRODUCTIVITY: A GENDER-BASED ANALYSIS</td><td>Etayibtalnam Koudjom</td></tr><tr><td>4.</td><td>ANALYSEDU CONSENTEMENT A PAYER DES CACAOCULTEURS IVOIRIENS POUR L'AGROFORESTERIE : UNE APPROCHE PAR L'ECONOMIE COMPORTEMENTALE</td><td>KUNIBOUO K. K. Angelo<br/>DJEZOU W. Beaudelaire</td></tr></table>

Discussants:

(1). Gbenga P. Sanusi

(2). Iroume A. Bouebe

(3). Oluwatosin Adeniyi

(4). Hermann Hegueu Ndoya

SESSION 3. YOUTH AND EMPLOYMENT
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Sauby Longang <saubyphd@gmail.com>

# Email of acceptance for application within the Coimbra Group Short Stay Scholarship Programme at University of Cologne

1 message

Johannes Mueller <J.Mueller@verw.uni-koeln.de>

À : Saubaber <saubyphd@gmail.com>

24 mars 2023 à 14:58

Dear applicant,

Thanks for your interest in applying for a Coimbra Group Short Stay Scholarship at the University of Cologne. This is to confirm that University of Cologne is offering a place in the field of your studies. Prof. Dr. Peter Dannenberg is looking forward to accept a young researcher at his/her institute within the scheme of this scholarship and will work with the selected applicant from this round of bidding during his or her stay.

So, in your application please refer to Prof. Dr. Peter Dannenberg and attach a pdf-copy of this Email to your application.

Please note that this letter of acceptance does only refer to your application, which will be accepted as competing for the scholarship in question, provided that it arrives complete and in compliance with all requirements.
It does not affect by no means the independent decision of the selection committee and is only a prerequisite for the application. Please hand in your complete application, including this E-mail-letter, via the online platform as requested and explained at https://www.coimbra-group.eu/scholarships/
The deadline for application is 7th of April 2023.
The application must be done online at https://www.coimbra-group.eu/scholarships/sub-saharan-africa/ through the online registration form.

Please do abstain from contacting your possible supervisor directly at this stage. Your application will be reviewed by your supervisor in the second phase of the application process.

Kind regards

Johannes Müller

Dr. Johannes Müller
University of Cologne
International Office

Head of Dept. 94 - International Science
Visiting Scholars & Partner Universities
Albert's International Assistance

Visiting address: Universitätsstr. 22a, 50937 Köln
How to find us? click here!

Postal address:
Albertus Magnus Platz
D-50923 Köln
tel. +49 221 470 6898
fax. +49 221 470 4531
E-Mail: j.mueller@verw.uni-koeln.de
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UNIVERSITY
JOHANNESBURG

Second Young Scholars Conference on Structural
Change and Industrial Policy in Africa

21-22 June 2022

CERTIFICATE OF PRESENTATION

The Second Young Scholars conference on Structural Change and Industrial Policy in Africa was hosted by the DSi/NRF South African Research Chair in Industrial Development (SARCHI-ID) and the Young Scholars Initiative (YSI) on 21 and 22 June 2022. The conference was a virtual conference and covered a range of topics including: Trade and Regional Trade, Political Economy and Industrialisation, Industrial Policy, Sectoral Dynamics and Structural Change, Industrial Development and Gender, AIR and Digital Transformation, Structural Transformation, Global and Regional Value Chains, Technology and Upgrading.

The conference opened with a keynote address from Dr Adeyemi Dipeolu (Special Advisor to the President for Economic Matters in the Office of the Vice-President of the Federal Republic of Nigeria) and closed with a panel discussion between Dr Rob Davies (former Minister of Trade and Industry, South Africa), Dr Aloysius Ordu (Brookings) and Professor Magda Shaheen (American University in Cairo). Senior international scholars acted as discussants, providing feedback on presented papers.

This certificate certifies attendance and presentation of a paper by

LONGANG GAMO SAUBABER, University of Dschang

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DSI/NRF SARCHI Industrial Development
Prof Fiona Tregenna

24 June 2022

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Young Scholars Initiative
Dr Richard Itaman

24 June 2022

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Table of contents

<table><tr><td>Table de matières</td><td>iii</td></tr><tr><td>Dedication</td><td>iv</td></tr><tr><td>Acknowledgements</td><td>v</td></tr><tr><td>List of abbreviations</td><td>vi</td></tr><tr><td>List of tables</td><td>viii</td></tr><tr><td>List of figures</td><td>ix</td></tr><tr><td>Abstract</td><td>x</td></tr><tr><td>Résumé</td><td>xi</td></tr><tr><td>GENERAL INTRODUCTION</td><td>1</td></tr><tr><td>1.1. Context of the study</td><td>1</td></tr><tr><td>1.2. Problem statement and contributions</td><td>15</td></tr><tr><td>1.3. Objectives</td><td>18</td></tr><tr><td>1.4. Hypotheses</td><td>18</td></tr><tr><td>1.5. Interest of the study</td><td>19</td></tr><tr><td>1.6. Organisation of the thesis</td><td>19</td></tr><tr><td>ESSAY I FOOD SECURITY EFFECTS OF AGRICULTURAL LAND EXPANSION IN SUB-SAHARAN AFRICA</td><td>20</td></tr><tr><td>1. Introduction</td><td>21</td></tr><tr><td>2. Stylised facts on the state of food security in sub-Saharan Africa</td><td>22</td></tr><tr><td>2.1. Evolution of undernourishment in Sub-Saharan Africa</td><td>23</td></tr><tr><td>2.2. Prevalence of undernourishment in some countries</td><td>24</td></tr><tr><td>2.3. Prevalence of moderate or severe food insecurity in the population</td><td>24</td></tr><tr><td>3. Literature review of agricultural land expansion and food security nexus</td><td>26</td></tr><tr><td>3.1. Agricultural land expansion and food security: Theoretical background</td><td>26</td></tr><tr><td>3.1.1. Agricultural land expansion and food security: a productivity approach</td><td>26</td></tr><tr><td>3.1.1.1. The physiocracy and the surplus of agriculture</td><td>26</td></tr><tr><td>3.1.1.2. The classicalist returns of agricultural land expansion: the debate</td><td>27</td></tr><tr><td>3.1.2. Agricultural land expansion and food security: the ends approach</td><td>27</td></tr><tr><td>3.1.2.1. Debate over the ethics of industrial and biofuel production against food crop production</td><td>27</td></tr><tr><td>3.1.2.2. Agricultural land expansion and food security: Comparing large-scale monoculture versus small scale intercropping</td><td>28</td></tr><tr><td>3.1.2.3. Agricultural land expansion through grabbing and food security</td><td>29</td></tr><tr><td>3.1.3. Agricultural land expansion and food security: a demographic density approach</td><td>30</td></tr><tr><td>3.2. Agricultural land expansion and food security: Empirical review</td><td>31</td></tr><tr><td>3.2.1. Agricultural land expansion and food security</td><td>31</td></tr><tr><td>3.2.1.1. Expanding land for agriculture</td><td>31</td></tr><tr><td>3.2.1.2. Land expansion backed by large scale land acquisition</td><td>34</td></tr></table>
<table><tr><td>3.2.2.</td><td>Land and agricultural reforms, tenure, rights and food security</td><td>36</td></tr><tr><td>4.</td><td>Methodology of analysis of the effects agricultural expansion on food security</td><td>37</td></tr><tr><td>4.1.</td><td>Description of data and variables</td><td>37</td></tr><tr><td>4.1.1.</td><td>Nature, data sources and scope of the study</td><td>37</td></tr><tr><td>4.1.2.</td><td>Description and construction of variables</td><td>37</td></tr><tr><td>4.1.2.1.</td><td>Measures of food security</td><td>37</td></tr><tr><td>4.1.2.2.</td><td>Institutional quality index (Gov)</td><td>39</td></tr><tr><td>4.1.2.3.</td><td>Food price vulnerability index (FPV)</td><td>39</td></tr><tr><td>4.1.2.4.</td><td>Production index (PI)</td><td>40</td></tr><tr><td>4.1.2.5.</td><td>Building the variable labour spill overs</td><td>40</td></tr><tr><td>4.1.3.</td><td>Descriptive statistics of variables and graphical analysis</td><td>41</td></tr><tr><td>4.1.4.</td><td>Stationarity test</td><td>41</td></tr><tr><td>4.2.</td><td>Estimation strategy</td><td>42</td></tr><tr><td>4.2.1.</td><td>The baseline model</td><td>42</td></tr><tr><td>4.2.2.</td><td>Justification of variables in the model and expected signs</td><td>44</td></tr><tr><td>5.</td><td>Results and Discussion</td><td>47</td></tr><tr><td>5.1.</td><td>Descriptive statistics</td><td>47</td></tr><tr><td>5.1.1.</td><td>Stationarity Results</td><td>48</td></tr><tr><td>5.1.2.</td><td>Analysis of undernourishment trends in sub-Saharan Africa</td><td>49</td></tr><tr><td>5.1.2.1.</td><td>Undernourishment and agricultural land: Overall variation</td><td>49</td></tr><tr><td>5.1.2.2.</td><td>Undernourishment and land expansion: Within variation</td><td>50</td></tr><tr><td>5.2.</td><td>Results of the estimation of the econometric model</td><td>51</td></tr><tr><td>5.2.1.</td><td>Estimation of the model with fixed and random effects and Hausman test</td><td>51</td></tr><tr><td>5.2.2.</td><td>Estimation of the model parameters with Generalised Method of Moments</td><td>54</td></tr><tr><td>5.2.3.</td><td>Robustness check: averaged data and other agricultural land variables</td><td>60</td></tr><tr><td>5.2.3.1.</td><td>Effects of agricultural land expansion on undernourishment averaged year data</td><td>61</td></tr><tr><td>5.2.3.2.</td><td>Robustness check: alternative measures of agricultural land expansion</td><td>62</td></tr><tr><td>5.2.4.</td><td>Robustness check: alternative measures of food security</td><td>64</td></tr><tr><td>5.2.4.1.</td><td>Robustness check: total water and total sanitation</td><td>64</td></tr><tr><td>5.2.4.2.</td><td>Robustness check: Water and sanitation in urban or rural milieus</td><td>65</td></tr><tr><td>6.</td><td>Conclusion</td><td>66</td></tr><tr><td colspan="2">ESSAY II EFFECTS OF AGRICULTURAL LAND EXPANSION ON STRUCTURAL CHANGE IN SUB-SAHARAN AFRICA</td><td>68</td></tr><tr><td>1.</td><td>Introduction</td><td>69</td></tr><tr><td>2.</td><td>Stylised facts on the structural change process in sub-Saharan Africa: A Markov Chain Analysis</td><td>70</td></tr><tr><td>2.1.</td><td>Analysing the structural change process in Sub-Saharan Africa</td><td>70</td></tr><tr><td>2.2.</td><td>Analysing the structural change process in sea opened countries</td><td>71</td></tr></table>